316 lines
10 KiB
Python
316 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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import os
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from copy import deepcopy
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import pytest
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import torch
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from torch.utils.data import DataLoader
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import tests.helpers.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.tuner.tuning import Tuner
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from pytorch_lightning.utilities import AMPType
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.base import EvalModelTemplate
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from tests.helpers import BoringDataModule, BoringModel, RandomDataset
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from tests.helpers.datamodules import MNISTDataModule
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from tests.helpers.runif import RunIf
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class BatchSizeDataModule(BoringDataModule):
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def __init__(self, batch_size):
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super().__init__()
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if batch_size is not None:
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self.batch_size = batch_size
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def train_dataloader(self):
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return DataLoader(self.random_train, batch_size=getattr(self, "batch_size", 1))
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class BatchSizeModel(BoringModel):
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def __init__(self, batch_size):
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super().__init__()
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if batch_size is not None:
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self.batch_size = batch_size
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def train_dataloader(self):
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return DataLoader(RandomDataset(32, 64), batch_size=getattr(self, "batch_size", 1))
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@pytest.mark.parametrize(["model_bs", "dm_bs"], [
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(2, -1),
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(2, 2),
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(2, None),
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(None, 2),
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(16, 16),
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])
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def test_scale_batch_size_method_with_model_or_datamodule(tmpdir, model_bs, dm_bs):
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""" Test the tuner method `Tuner.scale_batch_size` with a datamodule. """
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=1,
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limit_val_batches=0,
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max_epochs=1,
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)
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tuner = Tuner(trainer)
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model = BatchSizeModel(model_bs)
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datamodule = BatchSizeDataModule(dm_bs) if dm_bs != -1 else None
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new_batch_size = tuner.scale_batch_size(model, mode="binsearch", init_val=4, max_trials=2, datamodule=datamodule)
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assert new_batch_size == 16
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if model_bs is not None:
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assert model.batch_size == new_batch_size
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if dm_bs == -1:
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# datamodule batch size takes precedence
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assert trainer.train_dataloader.loaders.batch_size == new_batch_size
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if dm_bs not in (-1, None):
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assert datamodule.batch_size == new_batch_size
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assert trainer.train_dataloader.loaders.batch_size == new_batch_size
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def test_model_reset_correctly(tmpdir):
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""" Check that model weights are correctly reset after scaling batch size. """
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tutils.reset_seed()
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model = EvalModelTemplate()
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# logger file to get meta
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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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)
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before_state_dict = deepcopy(model.state_dict())
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trainer.tuner.scale_batch_size(model, max_trials=5)
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after_state_dict = model.state_dict()
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for key in before_state_dict.keys():
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assert torch.all(torch.eq(before_state_dict[key], after_state_dict[key])), \
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'Model was not reset correctly after scaling batch size'
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def test_trainer_reset_correctly(tmpdir):
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""" Check that all trainer parameters are reset correctly after scaling batch size. """
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tutils.reset_seed()
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model = EvalModelTemplate()
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# logger file to get meta
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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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)
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changed_attributes = [
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'callbacks',
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'checkpoint_callback',
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'current_epoch',
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'limit_train_batches',
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'logger',
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'max_steps',
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'weights_summary',
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]
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expected = {ca: getattr(trainer, ca) for ca in changed_attributes}
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trainer.tuner.scale_batch_size(model, max_trials=5)
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actual = {ca: getattr(trainer, ca) for ca in changed_attributes}
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assert actual == expected
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@RunIf(min_gpus=1)
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@pytest.mark.parametrize('scale_arg', ['power', 'binsearch', True])
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def test_auto_scale_batch_size_trainer_arg(tmpdir, scale_arg):
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""" Test possible values for 'batch size auto scaling' Trainer argument. """
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tutils.reset_seed()
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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before_batch_size = hparams.get('batch_size')
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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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auto_scale_batch_size=scale_arg,
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gpus=1,
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)
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trainer.tune(model)
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after_batch_size = model.batch_size
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assert before_batch_size != after_batch_size, \
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'Batch size was not altered after running auto scaling of batch size'
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assert not os.path.exists(tmpdir / 'scale_batch_size_temp_model.ckpt')
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@RunIf(min_gpus=1)
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@pytest.mark.parametrize('use_hparams', [True, False])
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def test_auto_scale_batch_size_set_model_attribute(tmpdir, use_hparams):
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""" Test that new batch size gets written to the correct hyperparameter attribute. """
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tutils.reset_seed()
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hparams = EvalModelTemplate.get_default_hparams()
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before_batch_size = hparams.get('batch_size')
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class HparamsEvalModelTemplate(EvalModelTemplate):
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def dataloader(self, *args, **kwargs):
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# artificially set batch_size so we can get a dataloader
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# remove it immediately after, because we want only self.hparams.batch_size
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setattr(self, "batch_size", before_batch_size)
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dataloader = super().dataloader(*args, **kwargs)
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del self.batch_size
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return dataloader
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datamodule_model = MNISTDataModule(data_dir=tmpdir, batch_size=111) # this datamodule should get ignored!
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datamodule_fit = MNISTDataModule(data_dir=tmpdir, batch_size=before_batch_size)
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model_class = HparamsEvalModelTemplate if use_hparams else EvalModelTemplate
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model = model_class(**hparams)
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model.datamodule = datamodule_model # unused when another module gets passed to .tune() / .fit()
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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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auto_scale_batch_size=True,
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gpus=1,
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)
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trainer.tune(model, datamodule_fit)
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after_batch_size = model.hparams.batch_size if use_hparams else model.batch_size
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assert trainer.datamodule == datamodule_fit
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assert before_batch_size != after_batch_size
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assert after_batch_size <= len(trainer.train_dataloader.dataset)
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assert datamodule_fit.batch_size == after_batch_size
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# should be left unchanged, since it was not passed to .tune()
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assert datamodule_model.batch_size == 111
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def test_auto_scale_batch_size_duplicate_attribute_warning(tmpdir):
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""" Test for a warning when model.batch_size and model.hparams.batch_size both present. """
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class TestModel(BoringModel):
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def __init__(self, batch_size=1):
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super().__init__()
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# now we have model.batch_size and model.hparams.batch_size
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self.batch_size = 1
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self.save_hyperparameters()
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir, max_steps=1, max_epochs=1000, auto_scale_batch_size=True)
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expected_message = "Field `model.batch_size` and `model.hparams.batch_size` are mutually exclusive!"
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with pytest.warns(UserWarning, match=expected_message):
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trainer.tune(model)
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@pytest.mark.parametrize('scale_method', ['power', 'binsearch'])
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def test_call_to_trainer_method(tmpdir, scale_method):
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""" Test that calling the trainer method itself works. """
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tutils.reset_seed()
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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before_batch_size = hparams.get('batch_size')
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# logger file to get meta
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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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)
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after_batch_size = trainer.tuner.scale_batch_size(model, mode=scale_method, max_trials=5)
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model.batch_size = after_batch_size
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trainer.fit(model)
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assert before_batch_size != after_batch_size, \
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'Batch size was not altered after running auto scaling of batch size'
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def test_error_on_dataloader_passed_to_fit(tmpdir):
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"""Verify that when the auto scale batch size feature raises an error
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if a train dataloader is passed to fit """
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# only train passed to fit
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model = EvalModelTemplate()
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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=0.1,
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limit_train_batches=0.2,
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auto_scale_batch_size='power',
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)
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fit_options = dict(train_dataloader=model.dataloader(train=True))
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with pytest.raises(MisconfigurationException):
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trainer.tune(model, **fit_options)
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@RunIf(min_gpus=1, amp_native=True)
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def test_auto_scale_batch_size_with_amp(tmpdir):
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model = EvalModelTemplate()
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batch_size_before = model.batch_size
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_steps=1,
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auto_scale_batch_size=True,
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gpus=1,
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precision=16,
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)
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trainer.tune(model)
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batch_size_after = model.batch_size
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assert trainer.amp_backend == AMPType.NATIVE
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assert trainer.scaler is not None
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assert batch_size_after != batch_size_before
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def test_scale_batch_size_no_trials(tmpdir):
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"""Check the result is correct even when no trials are run"""
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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=1,
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auto_scale_batch_size='power',
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)
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model = BatchSizeModel(batch_size=2)
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result = trainer.tuner.scale_batch_size(model, max_trials=0)
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assert result == 2
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def test_scale_batch_size_fails_with_unavailable_mode(tmpdir):
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"""Check the tuning raises error when called with mode that does not exist."""
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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.batch_size = 2
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model = TestModel()
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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=1,
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auto_scale_batch_size='ThisModeDoesNotExist',
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)
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with pytest.raises(ValueError, match='could either be `power` or `binsearch`'):
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trainer.tune(model)
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with pytest.raises(ValueError, match='could either be `power` or `binsearch`'):
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trainer.tuner.scale_batch_size(model, mode='ThisModeDoesNotExist')
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