232 lines
8.3 KiB
Python
232 lines
8.3 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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import logging
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from unittest.mock import Mock
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import pytest
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import torch
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from pytorch_lightning import seed_everything, Trainer
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from pytorch_lightning.demos.boring_classes import BoringModel
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from pytorch_lightning.loops import FitLoop
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def test_outputs_format(tmpdir):
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"""Tests that outputs objects passed to model hooks and methods are consistent and in the correct format."""
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class HookedModel(BoringModel):
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def training_step(self, batch, batch_idx):
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output = super().training_step(batch, batch_idx)
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self.log("foo", 123)
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output["foo"] = 123
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return output
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@staticmethod
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def _check_output(output):
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assert "loss" in output
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assert "foo" in output
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assert output["foo"] == 123
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def on_train_batch_end(self, outputs, batch, batch_idx):
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HookedModel._check_output(outputs)
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super().on_train_batch_end(outputs, batch, batch_idx)
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def training_epoch_end(self, outputs):
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assert len(outputs) == 2
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[HookedModel._check_output(output) for output in outputs]
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super().training_epoch_end(outputs)
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model = HookedModel()
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# fit model
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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limit_val_batches=1,
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limit_train_batches=2,
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limit_test_batches=1,
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enable_progress_bar=False,
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enable_model_summary=False,
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)
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trainer.fit(model)
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@pytest.mark.parametrize("seed_once", (True, False))
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def test_training_starts_with_seed(tmpdir, seed_once):
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"""Test the behavior of seed_everything on subsequent Trainer runs in combination with different settings of
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num_sanity_val_steps (which must not affect the random state)."""
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class SeededModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.seen_batches = []
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def training_step(self, batch, batch_idx):
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self.seen_batches.append(batch.view(-1))
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return super().training_step(batch, batch_idx)
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def run_training(**trainer_kwargs):
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model = SeededModel()
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trainer = Trainer(**trainer_kwargs)
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trainer.fit(model)
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return torch.cat(model.seen_batches)
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if seed_once:
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seed_everything(123)
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sequence0 = run_training(default_root_dir=tmpdir, max_steps=2, num_sanity_val_steps=0)
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sequence1 = run_training(default_root_dir=tmpdir, max_steps=2, num_sanity_val_steps=2)
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assert not torch.allclose(sequence0, sequence1)
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else:
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seed_everything(123)
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sequence0 = run_training(default_root_dir=tmpdir, max_steps=2, num_sanity_val_steps=0)
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seed_everything(123)
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sequence1 = run_training(default_root_dir=tmpdir, max_steps=2, num_sanity_val_steps=2)
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assert torch.allclose(sequence0, sequence1)
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@pytest.mark.parametrize(["max_epochs", "batch_idx_"], [(2, 5), (3, 8), (4, 12)])
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def test_on_train_batch_start_return_minus_one(max_epochs, batch_idx_, tmpdir):
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class CurrentModel(BoringModel):
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def on_train_batch_start(self, batch, batch_idx):
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if batch_idx == batch_idx_:
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return -1
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model = CurrentModel()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=max_epochs, limit_train_batches=10)
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trainer.fit(model)
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if batch_idx_ > trainer.num_training_batches - 1:
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assert trainer.fit_loop.batch_idx == trainer.num_training_batches - 1
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assert trainer.global_step == trainer.num_training_batches * max_epochs
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else:
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assert trainer.fit_loop.batch_idx == batch_idx_
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assert trainer.global_step == batch_idx_ * max_epochs
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def test_should_stop_mid_epoch(tmpdir):
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"""Test that training correctly stops mid epoch and that validation is still called at the right time."""
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.validation_called_at = None
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def training_step(self, batch, batch_idx):
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if batch_idx == 4:
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self.trainer.should_stop = True
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return super().training_step(batch, batch_idx)
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def validation_step(self, *args):
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self.validation_called_at = (self.trainer.current_epoch, self.trainer.global_step)
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return super().validation_step(*args)
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, limit_train_batches=10, limit_val_batches=1)
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trainer.fit(model)
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# even though we stopped mid epoch, the fit loop finished normally and the current epoch was increased
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assert trainer.current_epoch == 1
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assert trainer.global_step == 5
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assert model.validation_called_at == (0, 5)
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def test_fit_loop_done_log_messages(caplog):
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fit_loop = FitLoop(max_epochs=1)
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trainer = Mock(spec=Trainer)
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fit_loop.trainer = trainer
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trainer.should_stop = False
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trainer.num_training_batches = 5
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assert not fit_loop.done
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assert not caplog.messages
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trainer.num_training_batches = 0
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assert fit_loop.done
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assert "No training batches" in caplog.text
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caplog.clear()
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trainer.num_training_batches = 5
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epoch_loop = Mock()
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epoch_loop.global_step = 10
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fit_loop.connect(epoch_loop=epoch_loop)
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fit_loop.max_steps = 10
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assert fit_loop.done
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assert "max_steps=10` reached" in caplog.text
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caplog.clear()
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fit_loop.max_steps = 20
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fit_loop.epoch_progress.current.processed = 3
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fit_loop.max_epochs = 3
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trainer.should_stop = True
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assert fit_loop.done
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assert "max_epochs=3` reached" in caplog.text
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caplog.clear()
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fit_loop.max_epochs = 5
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fit_loop.epoch_loop.min_steps = 0
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with caplog.at_level(level=logging.DEBUG, logger="pytorch_lightning.utilities.rank_zero"):
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assert fit_loop.done
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assert "should_stop` was set" in caplog.text
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fit_loop.epoch_loop.min_steps = 100
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assert not fit_loop.done
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def test_warning_valid_train_step_end(tmpdir):
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class ValidTrainStepEndModel(BoringModel):
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def training_step(self, batch, batch_idx):
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output = self(batch)
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return {"output": output}
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def training_step_end(self, outputs):
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loss = self.loss(outputs["output"])
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return loss
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# No error is raised
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model = ValidTrainStepEndModel()
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1)
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trainer.fit(model)
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@pytest.mark.parametrize(
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"min_epochs, min_steps, current_epoch, early_stop, fit_loop_done, raise_debug_msg",
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[
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(4, None, 100, True, True, False),
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(4, None, 3, False, False, False),
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(4, 10, 3, False, False, False),
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(None, 10, 4, True, True, True),
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(4, None, 4, True, True, True),
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(4, 10, 4, True, True, True),
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],
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)
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def test_should_stop_early_stopping_conditions_met(
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caplog, min_epochs, min_steps, current_epoch, early_stop, fit_loop_done, raise_debug_msg
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):
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"""Test that checks that debug message is logged when users sets `should_stop` and min conditions are met."""
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trainer = Trainer(min_epochs=min_epochs, min_steps=min_steps, limit_val_batches=0, max_epochs=100)
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trainer.num_training_batches = 10
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trainer.should_stop = True
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trainer.fit_loop.epoch_loop.optimizer_loop.optim_progress.optimizer.step.total.completed = (
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current_epoch * trainer.num_training_batches
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)
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trainer.fit_loop.epoch_loop.batch_progress.current.ready = 10
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trainer.fit_loop.epoch_progress.current.processed = current_epoch
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message = "`Trainer.fit` stopped: `trainer.should_stop` was set."
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with caplog.at_level(level=logging.DEBUG, logger="pytorch_lightning.utilities.rank_zero"):
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assert trainer.fit_loop.done is fit_loop_done
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assert (message in caplog.text) is raise_debug_msg
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assert trainer.fit_loop._should_stop_early is early_stop
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