470 lines
14 KiB
Python
470 lines
14 KiB
Python
import math
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import warnings
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import pytest
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import torch
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import tests.base.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import (
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EarlyStopping,
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)
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from tests.base import (
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TestModelBase,
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LightTrainDataloader,
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LightningTestModel,
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LightTestMixin,
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LightValidationMixin
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)
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def test_early_stopping_cpu_model(tmpdir):
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"""Test each of the trainer options."""
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tutils.reset_seed()
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stopping = EarlyStopping(monitor='val_loss', min_delta=0.1)
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trainer_options = dict(
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default_save_path=tmpdir,
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early_stop_callback=stopping,
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gradient_clip_val=1.0,
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overfit_pct=0.20,
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track_grad_norm=2,
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show_progress_bar=True,
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logger=tutils.get_default_testtube_logger(tmpdir),
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train_percent_check=0.1,
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val_percent_check=0.1,
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)
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model, hparams = tutils.get_default_model()
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tutils.run_model_test(trainer_options, model, on_gpu=False)
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# test freeze on cpu
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model.freeze()
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model.unfreeze()
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def test_lbfgs_cpu_model(tmpdir):
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"""Test each of the trainer options."""
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tutils.reset_seed()
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trainer_options = dict(
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default_save_path=tmpdir,
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max_epochs=2,
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show_progress_bar=False,
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weights_summary='top',
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train_percent_check=1.0,
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val_percent_check=0.2,
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)
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model, hparams = tutils.get_default_model(lbfgs=True)
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tutils.run_model_test_no_loggers(trainer_options, model, min_acc=0.30)
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def test_default_logger_callbacks_cpu_model(tmpdir):
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"""Test each of the trainer options."""
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tutils.reset_seed()
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trainer_options = dict(
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default_save_path=tmpdir,
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max_epochs=1,
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gradient_clip_val=1.0,
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overfit_pct=0.20,
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show_progress_bar=False,
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train_percent_check=0.01,
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val_percent_check=0.01,
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)
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model, hparams = tutils.get_default_model()
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tutils.run_model_test_no_loggers(trainer_options, model)
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# test freeze on cpu
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model.freeze()
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model.unfreeze()
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def test_running_test_after_fitting(tmpdir):
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"""Verify test() on fitted model."""
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tutils.reset_seed()
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hparams = tutils.get_default_hparams()
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model = LightningTestModel(hparams)
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# logger file to get meta
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logger = tutils.get_default_testtube_logger(tmpdir, False)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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trainer_options = dict(
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default_save_path=tmpdir,
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show_progress_bar=False,
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max_epochs=8,
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train_percent_check=0.4,
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val_percent_check=0.2,
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test_percent_check=0.2,
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checkpoint_callback=checkpoint,
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logger=logger
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)
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# fit model
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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assert result == 1, 'training failed to complete'
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trainer.test()
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# test we have good test accuracy
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tutils.assert_ok_model_acc(trainer, thr=0.35)
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def test_running_test_without_val(tmpdir):
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"""Verify `test()` works on a model with no `val_loader`."""
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tutils.reset_seed()
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class CurrentTestModel(LightTrainDataloader, LightTestMixin, TestModelBase):
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pass
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hparams = tutils.get_default_hparams()
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model = CurrentTestModel(hparams)
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# logger file to get meta
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logger = tutils.get_default_testtube_logger(tmpdir, False)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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trainer_options = dict(
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show_progress_bar=False,
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max_epochs=1,
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train_percent_check=0.4,
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val_percent_check=0.2,
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test_percent_check=0.2,
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checkpoint_callback=checkpoint,
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logger=logger,
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early_stop_callback=False
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)
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# fit model
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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assert result == 1, 'training failed to complete'
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trainer.test()
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# test we have good test accuracy
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tutils.assert_ok_model_acc(trainer)
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def test_disabled_validation():
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"""Verify that `val_percent_check=0` disables the validation loop unless `fast_dev_run=True`."""
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tutils.reset_seed()
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class CurrentModel(LightTrainDataloader, LightValidationMixin, TestModelBase):
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validation_step_invoked = False
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validation_end_invoked = False
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def validation_step(self, *args, **kwargs):
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self.validation_step_invoked = True
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return super().validation_step(*args, **kwargs)
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def validation_end(self, *args, **kwargs):
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self.validation_end_invoked = True
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return super().validation_end(*args, **kwargs)
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hparams = tutils.get_default_hparams()
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model = CurrentModel(hparams)
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trainer_options = dict(
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show_progress_bar=False,
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max_epochs=2,
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train_percent_check=0.4,
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val_percent_check=0.0,
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fast_dev_run=False,
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)
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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# check that val_percent_check=0 turns off validation
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assert result == 1, 'training failed to complete'
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assert trainer.current_epoch == 1
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assert not model.validation_step_invoked, '`validation_step` should not run when `val_percent_check=0`'
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assert not model.validation_end_invoked, '`validation_end` should not run when `val_percent_check=0`'
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# check that val_percent_check has no influence when fast_dev_run is turned on
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model = CurrentModel(hparams)
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trainer_options.update(fast_dev_run=True)
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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assert result == 1, 'training failed to complete'
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assert trainer.current_epoch == 0
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assert model.validation_step_invoked, 'did not run `validation_step` with `fast_dev_run=True`'
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assert model.validation_end_invoked, 'did not run `validation_end` with `fast_dev_run=True`'
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def test_single_gpu_batch_parse():
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tutils.reset_seed()
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if not tutils.can_run_gpu_test():
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return
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trainer = Trainer()
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# batch is just a tensor
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batch = torch.rand(2, 3)
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batch = trainer.transfer_batch_to_gpu(batch, 0)
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assert batch.device.index == 0 and batch.type() == 'torch.cuda.FloatTensor'
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# tensor list
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batch = [torch.rand(2, 3), torch.rand(2, 3)]
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batch = trainer.transfer_batch_to_gpu(batch, 0)
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assert batch[0].device.index == 0 and batch[0].type() == 'torch.cuda.FloatTensor'
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assert batch[1].device.index == 0 and batch[1].type() == 'torch.cuda.FloatTensor'
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# tensor list of lists
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batch = [[torch.rand(2, 3), torch.rand(2, 3)]]
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batch = trainer.transfer_batch_to_gpu(batch, 0)
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assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
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assert batch[0][1].device.index == 0 and batch[0][1].type() == 'torch.cuda.FloatTensor'
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# tensor dict
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batch = [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)}]
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batch = trainer.transfer_batch_to_gpu(batch, 0)
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assert batch[0]['a'].device.index == 0 and batch[0]['a'].type() == 'torch.cuda.FloatTensor'
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assert batch[0]['b'].device.index == 0 and batch[0]['b'].type() == 'torch.cuda.FloatTensor'
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# tuple of tensor list and list of tensor dict
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batch = ([torch.rand(2, 3) for _ in range(2)],
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[{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)} for _ in range(2)])
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batch = trainer.transfer_batch_to_gpu(batch, 0)
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assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
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assert batch[1][0]['a'].device.index == 0
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assert batch[1][0]['a'].type() == 'torch.cuda.FloatTensor'
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assert batch[1][0]['b'].device.index == 0
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assert batch[1][0]['b'].type() == 'torch.cuda.FloatTensor'
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def test_simple_cpu(tmpdir):
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"""Verify continue training session on CPU."""
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tutils.reset_seed()
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hparams = tutils.get_default_hparams()
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model = LightningTestModel(hparams)
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# logger file to get meta
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trainer_options = dict(
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default_save_path=tmpdir,
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max_epochs=1,
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val_percent_check=0.1,
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train_percent_check=0.1,
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)
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# fit model
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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# traning complete
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assert result == 1, 'amp + ddp model failed to complete'
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def test_cpu_model(tmpdir):
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"""Make sure model trains on CPU."""
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tutils.reset_seed()
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trainer_options = dict(
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default_save_path=tmpdir,
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show_progress_bar=False,
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logger=tutils.get_default_testtube_logger(tmpdir),
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max_epochs=1,
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train_percent_check=0.4,
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val_percent_check=0.4
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)
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model, hparams = tutils.get_default_model()
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tutils.run_model_test(trainer_options, model, on_gpu=False)
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def test_all_features_cpu_model(tmpdir):
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"""Test each of the trainer options."""
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tutils.reset_seed()
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trainer_options = dict(
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default_save_path=tmpdir,
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gradient_clip_val=1.0,
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overfit_pct=0.20,
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track_grad_norm=2,
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show_progress_bar=False,
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logger=tutils.get_default_testtube_logger(tmpdir),
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accumulate_grad_batches=2,
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max_epochs=1,
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train_percent_check=0.4,
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val_percent_check=0.4
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)
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model, hparams = tutils.get_default_model()
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tutils.run_model_test(trainer_options, model, on_gpu=False)
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def test_tbptt_cpu_model(tmpdir):
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"""Test truncated back propagation through time works."""
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tutils.reset_seed()
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truncated_bptt_steps = 2
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sequence_size = 30
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batch_size = 30
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x_seq = torch.rand(batch_size, sequence_size, 1)
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y_seq_list = torch.rand(batch_size, sequence_size, 1).tolist()
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class MockSeq2SeqDataset(torch.utils.data.Dataset):
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def __getitem__(self, i):
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return x_seq, y_seq_list
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def __len__(self):
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return 1
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class BpttTestModel(LightTrainDataloader, TestModelBase):
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def __init__(self, hparams):
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super().__init__(hparams)
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self.test_hidden = None
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def training_step(self, batch, batch_idx, hiddens):
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assert hiddens == self.test_hidden, "Hidden state not persistent between tbptt steps"
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self.test_hidden = torch.rand(1)
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x_tensor, y_list = batch
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assert x_tensor.shape[1] == truncated_bptt_steps, "tbptt split Tensor failed"
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y_tensor = torch.tensor(y_list, dtype=x_tensor.dtype)
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assert y_tensor.shape[1] == truncated_bptt_steps, "tbptt split list failed"
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pred = self(x_tensor.view(batch_size, truncated_bptt_steps))
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loss_val = torch.nn.functional.mse_loss(
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pred, y_tensor.view(batch_size, truncated_bptt_steps))
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return {
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'loss': loss_val,
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'hiddens': self.test_hidden,
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}
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def train_dataloader(self):
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return torch.utils.data.DataLoader(
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dataset=MockSeq2SeqDataset(),
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batch_size=batch_size,
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shuffle=False,
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sampler=None,
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)
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trainer_options = dict(
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default_save_path=tmpdir,
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max_epochs=1,
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truncated_bptt_steps=truncated_bptt_steps,
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val_percent_check=0,
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weights_summary=None,
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early_stop_callback=False
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)
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hparams = tutils.get_default_hparams()
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hparams.batch_size = batch_size
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hparams.in_features = truncated_bptt_steps
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hparams.hidden_dim = truncated_bptt_steps
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hparams.out_features = truncated_bptt_steps
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model = BpttTestModel(hparams)
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# fit model
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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assert result == 1, 'training failed to complete'
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def test_single_gpu_model(tmpdir):
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"""Make sure single GPU works (DP mode)."""
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tutils.reset_seed()
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if not torch.cuda.is_available():
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warnings.warn('test_single_gpu_model cannot run.'
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' Rerun on a GPU node to run this test')
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return
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model, hparams = tutils.get_default_model()
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trainer_options = dict(
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default_save_path=tmpdir,
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show_progress_bar=False,
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max_epochs=1,
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train_percent_check=0.1,
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val_percent_check=0.1,
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gpus=1
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)
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tutils.run_model_test(trainer_options, model)
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def test_nan_loss_detection(tmpdir):
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test_step = 8
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class InfLossModel(LightTrainDataloader, TestModelBase):
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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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if batch_idx == test_step:
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if isinstance(output, dict):
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output['loss'] *= torch.tensor(math.inf) # make loss infinite
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else:
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output /= 0
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return output
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hparams = tutils.get_default_hparams()
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model = InfLossModel(hparams)
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# fit model
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trainer = Trainer(
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default_save_path=tmpdir,
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max_steps=(test_step + 1),
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)
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with pytest.raises(ValueError, match=r'.*The loss returned in `training_step` is nan or inf.*'):
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trainer.fit(model)
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assert trainer.global_step == test_step
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for param in model.parameters():
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assert torch.isfinite(param).all()
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def test_nan_params_detection(tmpdir):
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test_step = 8
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class NanParamModel(LightTrainDataloader, TestModelBase):
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def on_after_backward(self):
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if self.global_step == test_step:
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# simulate parameter that became nan
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torch.nn.init.constant_(self.c_d1.bias, math.nan)
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hparams = tutils.get_default_hparams()
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model = NanParamModel(hparams)
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trainer = Trainer(
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default_save_path=tmpdir,
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max_steps=(test_step + 1),
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)
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with pytest.raises(ValueError, match=r'.*Detected nan and/or inf values in `c_d1.bias`.*'):
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trainer.fit(model)
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assert trainer.global_step == test_step
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# after aborting the training loop, model still has nan-valued params
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params = torch.cat([param.view(-1) for param in model.parameters()])
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assert not torch.isfinite(params).all()
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# if __name__ == '__main__':
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# pytest.main([__file__])
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