540 lines
18 KiB
Python
540 lines
18 KiB
Python
# Copyright The Lightning AI 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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import math
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import os
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from copy import deepcopy
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from typing import Any
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from unittest import mock
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import pytest
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import torch
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from lightning_utilities.test.warning import no_warning_call
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from lightning.pytorch import seed_everything, Trainer
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from lightning.pytorch.callbacks.lr_finder import LearningRateFinder
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from lightning.pytorch.demos.boring_classes import BoringModel
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from lightning.pytorch.tuner.lr_finder import _LRFinder
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from lightning.pytorch.tuner.tuning import Tuner
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from lightning.pytorch.utilities.exceptions import MisconfigurationException
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from lightning.pytorch.utilities.types import STEP_OUTPUT
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from tests_pytorch.helpers.datamodules import ClassifDataModule
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from tests_pytorch.helpers.runif import RunIf
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from tests_pytorch.helpers.simple_models import ClassificationModel
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from tests_pytorch.helpers.utils import getattr_recursive
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def test_error_with_multiple_optimizers(tmpdir):
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"""Check that error is thrown when more than 1 optimizer is passed."""
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class CustomBoringModel(BoringModel):
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def __init__(self, lr):
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super().__init__()
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self.save_hyperparameters()
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self.automatic_optimization = False
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def configure_optimizers(self):
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optimizer1 = torch.optim.SGD(self.parameters(), lr=self.hparams.lr)
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optimizer2 = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
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return [optimizer1, optimizer2]
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model = CustomBoringModel(lr=1e-2)
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
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tuner = Tuner(trainer)
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with pytest.raises(MisconfigurationException, match="only works with single optimizer"):
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tuner.lr_find(model)
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def test_model_reset_correctly(tmpdir):
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"""Check that model weights are correctly reset after _lr_find()"""
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model = BoringModel()
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model.lr = 0.1
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# logger file to get meta
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
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tuner = Tuner(trainer)
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before_state_dict = deepcopy(model.state_dict())
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tuner.lr_find(model, num_training=5)
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after_state_dict = model.state_dict()
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for key in before_state_dict:
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assert torch.all(
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torch.eq(before_state_dict[key], after_state_dict[key])
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), "Model was not reset correctly after learning rate finder"
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assert not any(f for f in os.listdir(tmpdir) if f.startswith(".lr_find"))
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def test_trainer_reset_correctly(tmpdir):
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"""Check that all trainer parameters are reset correctly after lr_find()"""
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model = BoringModel()
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model.lr = 0.1
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# logger file to get meta
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
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tuner = Tuner(trainer)
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changed_attributes = [
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"accumulate_grad_batches",
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"callbacks",
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"checkpoint_callback",
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"current_epoch",
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"loggers",
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"global_step",
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"max_steps",
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"fit_loop.max_steps",
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"strategy.setup_optimizers",
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"should_stop",
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]
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expected = {ca: getattr_recursive(trainer, ca) for ca in changed_attributes}
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with no_warning_call(UserWarning, match="Please add the following callbacks"):
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tuner.lr_find(model, num_training=5)
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actual = {ca: getattr_recursive(trainer, ca) for ca in changed_attributes}
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assert actual == expected
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assert model.trainer == trainer
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@pytest.mark.parametrize("use_hparams", [False, True])
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def test_tuner_lr_find(tmpdir, use_hparams):
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"""Test that lr_find updates the learning rate attribute."""
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seed_everything(1)
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class CustomBoringModel(BoringModel):
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def __init__(self, lr):
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super().__init__()
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self.save_hyperparameters()
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self.lr = lr
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def configure_optimizers(self):
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return torch.optim.SGD(self.parameters(), lr=self.hparams.lr if use_hparams else self.lr)
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before_lr = 1e-2
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model = CustomBoringModel(lr=before_lr)
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=2)
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tuner = Tuner(trainer)
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tuner.lr_find(model, update_attr=True)
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after_lr = model.hparams.lr if use_hparams else model.lr
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assert after_lr is not None
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assert before_lr != after_lr, "Learning rate was not altered after running learning rate finder"
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@pytest.mark.parametrize("use_hparams", [False, True])
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def test_trainer_arg_str(tmpdir, use_hparams):
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"""Test that setting trainer arg to string works."""
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seed_everything(1)
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class CustomBoringModel(BoringModel):
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def __init__(self, my_fancy_lr):
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super().__init__()
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self.save_hyperparameters()
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self.my_fancy_lr = my_fancy_lr
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def configure_optimizers(self):
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return torch.optim.SGD(self.parameters(), lr=self.hparams.my_fancy_lr if use_hparams else self.my_fancy_lr)
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before_lr = 1e-2
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model = CustomBoringModel(my_fancy_lr=before_lr)
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=2)
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tuner = Tuner(trainer)
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tuner.lr_find(model, update_attr=True, attr_name="my_fancy_lr")
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after_lr = model.hparams.my_fancy_lr if use_hparams else model.my_fancy_lr
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assert after_lr is not None
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assert before_lr != after_lr, "Learning rate was not altered after running learning rate finder"
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@pytest.mark.parametrize("opt", ["Adam", "Adagrad"])
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def test_call_to_trainer_method(tmpdir, opt):
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"""Test that directly calling the trainer method works."""
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seed_everything(1)
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class CustomBoringModel(BoringModel):
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def __init__(self, lr):
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super().__init__()
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self.save_hyperparameters()
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def configure_optimizers(self):
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return (
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torch.optim.Adagrad(self.parameters(), lr=self.hparams.lr)
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if opt == "Adagrad"
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else torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
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)
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before_lr = 1e-2
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model = CustomBoringModel(1e-2)
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=2)
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tuner = Tuner(trainer)
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lr_finder = tuner.lr_find(model, mode="linear")
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after_lr = lr_finder.suggestion()
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assert after_lr is not None
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model.hparams.lr = after_lr
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tuner.lr_find(model, update_attr=True)
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assert after_lr is not None
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assert before_lr != after_lr, "Learning rate was not altered after running learning rate finder"
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@RunIf(sklearn=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_datamodule_parameter(tmpdir):
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"""Test that the datamodule parameter works."""
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seed_everything(1)
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dm = ClassifDataModule()
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model = ClassificationModel(lr=1e-3)
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before_lr = model.lr
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# logger file to get meta
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=2)
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tuner = Tuner(trainer)
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lr_finder = tuner.lr_find(model, datamodule=dm)
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after_lr = lr_finder.suggestion()
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model.lr = after_lr
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assert after_lr is not None
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assert before_lr != after_lr, "Learning rate was not altered after running learning rate finder"
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def test_accumulation_and_early_stopping(tmpdir):
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"""Test that early stopping of learning rate finder works, and that accumulation also works for this feature."""
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seed_everything(1)
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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.lr = 1e-3
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir, accumulate_grad_batches=2)
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tuner = Tuner(trainer)
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lr_finder = tuner.lr_find(model, early_stop_threshold=None)
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assert lr_finder.suggestion() != 1e-3
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assert len(lr_finder.results["lr"]) == len(lr_finder.results["loss"]) == 100
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assert lr_finder._total_batch_idx == 199
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def test_suggestion_parameters_work(tmpdir):
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"""Test that default skipping does not alter results in basic case."""
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seed_everything(1)
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class CustomBoringModel(BoringModel):
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def __init__(self, lr):
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super().__init__()
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self.lr = lr
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def configure_optimizers(self):
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return torch.optim.SGD(self.parameters(), lr=self.lr)
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# logger file to get meta
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model = CustomBoringModel(lr=1e-2)
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=3)
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tuner = Tuner(trainer)
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lr_finder = tuner.lr_find(model)
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lr1 = lr_finder.suggestion(skip_begin=10) # default
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lr2 = lr_finder.suggestion(skip_begin=70) # way too high, should have an impact
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assert lr1 is not None
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assert lr2 is not None
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assert lr1 != lr2, "Skipping parameter did not influence learning rate"
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def test_suggestion_with_non_finite_values(tmpdir):
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"""Test that non-finite values does not alter results."""
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seed_everything(1)
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class CustomBoringModel(BoringModel):
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def __init__(self, lr):
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super().__init__()
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self.lr = lr
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def configure_optimizers(self):
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return torch.optim.SGD(self.parameters(), lr=self.lr)
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model = CustomBoringModel(lr=1e-2)
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=3)
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tuner = Tuner(trainer)
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lr_finder = tuner.lr_find(model)
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before_lr = lr_finder.suggestion()
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lr_finder.results["loss"][-1] = float("nan")
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after_lr = lr_finder.suggestion()
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assert before_lr is not None
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assert after_lr is not None
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assert before_lr == after_lr, "Learning rate was altered because of non-finite loss values"
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def test_lr_finder_fails_fast_on_bad_config(tmpdir):
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"""Test that tune fails if the model does not have a lr BEFORE running lr find."""
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trainer = Trainer(default_root_dir=tmpdir, max_steps=2)
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tuner = Tuner(trainer)
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with pytest.raises(AttributeError, match="should have one of these fields"):
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tuner.lr_find(BoringModel(), update_attr=True)
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def test_lr_candidates_between_min_and_max(tmpdir):
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"""Test that learning rate candidates are between min_lr and max_lr."""
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seed_everything(1)
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class TestModel(BoringModel):
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def __init__(self, learning_rate=0.1):
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super().__init__()
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self.save_hyperparameters()
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir)
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lr_min = 1e-8
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lr_max = 1.0
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tuner = Tuner(trainer)
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lr_finder = tuner.lr_find(model, max_lr=lr_min, min_lr=lr_max, num_training=3)
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lr_candidates = lr_finder.results["lr"]
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assert all(lr_min <= lr <= lr_max for lr in lr_candidates)
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def test_lr_finder_ends_before_num_training(tmpdir):
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"""Tests learning rate finder ends before `num_training` steps."""
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class TestModel(BoringModel):
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def __init__(self, learning_rate=0.1):
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super().__init__()
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self.save_hyperparameters()
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def on_before_optimizer_step(self, optimizer):
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assert self.global_step < num_training
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir)
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tuner = Tuner(trainer)
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num_training = 3
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tuner.lr_find(model=model, num_training=num_training)
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def test_multiple_lr_find_calls_gives_same_results(tmpdir):
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"""Tests that lr_finder gives same results if called multiple times."""
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seed_everything(1)
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model = BoringModel()
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model.lr = 0.1
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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limit_train_batches=10,
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limit_val_batches=2,
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enable_progress_bar=False,
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enable_model_summary=False,
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enable_checkpointing=False,
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)
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tuner = Tuner(trainer)
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all_res = [tuner.lr_find(model).results for _ in range(3)]
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assert all(
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all_res[0][k] == curr_lr_finder[k] and len(curr_lr_finder[k]) > 10
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for curr_lr_finder in all_res[1:]
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for k in all_res[0]
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)
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@pytest.mark.parametrize(
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("skip_begin", "skip_end", "losses", "expected_error"),
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[
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(0, 0, [], True),
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(10, 1, [], True),
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(0, 2, [0, 1, 2], True),
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(0, 1, [0, 1, 2], False),
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(1, 1, [0, 1, 2], True),
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(1, 1, [0, 1, 2, 3], False),
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(0, 1, [float("nan"), float("nan"), 0, float("inf"), 1, 2, 3, float("inf"), 2, float("nan"), 1], False),
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(4, 1, [float("nan"), float("nan"), 0, float("inf"), 1, 2, 3, float("inf"), 2, float("nan"), 1], False),
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],
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)
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def test_suggestion_not_enough_finite_points(losses, skip_begin, skip_end, expected_error, caplog):
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"""Tests the error handling when not enough finite points are available to make a suggestion."""
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caplog.clear()
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lr_finder = _LRFinder(
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mode="exponential",
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lr_min=1e-8,
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lr_max=1,
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num_training=100,
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)
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lrs = list(torch.arange(len(losses)))
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lr_finder.results = {
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"lr": lrs,
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"loss": losses,
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}
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with caplog.at_level(logging.ERROR, logger="root.tuner.lr_finder"):
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lr = lr_finder.suggestion(skip_begin=skip_begin, skip_end=skip_end)
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if expected_error:
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assert lr is None
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assert "Failed to compute suggestion for learning rate" in caplog.text
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else:
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assert lr is not None
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def test_lr_attribute_when_suggestion_invalid(tmpdir):
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"""Tests learning rate finder ends before `num_training` steps."""
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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.learning_rate = 0.123
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir)
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tuner = Tuner(trainer)
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lr_finder = tuner.lr_find(model=model, update_attr=True, num_training=1) # force insufficient data points
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assert lr_finder.suggestion() is None
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assert model.learning_rate == 0.123 # must remain unchanged because suggestion is not possible
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def test_lr_finder_callback_restarting(tmpdir):
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"""Test that `LearningRateFinder` does not set restarting=True when loading checkpoint."""
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num_lr_steps = 100
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class MyBoringModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.learning_rate = 0.123
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def on_train_batch_start(self, batch, batch_idx):
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if getattr(self, "_expected_max_steps", None) is not None:
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assert self.trainer.fit_loop.max_steps == self._expected_max_steps
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def configure_optimizers(self):
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return torch.optim.SGD(self.parameters(), lr=self.learning_rate)
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class CustomLearningRateFinder(LearningRateFinder):
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milestones = (1,)
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def lr_find(self, trainer, pl_module) -> None:
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pl_module._expected_max_steps = trainer.global_step + self._num_training_steps
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super().lr_find(trainer, pl_module)
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pl_module._expected_max_steps = None
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assert not trainer.fit_loop.restarting
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def on_train_epoch_start(self, trainer, pl_module):
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if trainer.current_epoch in self.milestones or trainer.current_epoch == 0:
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self.lr_find(trainer, pl_module)
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model = MyBoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=3,
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callbacks=[
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CustomLearningRateFinder(early_stop_threshold=None, update_attr=True, num_training_steps=num_lr_steps)
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],
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limit_train_batches=10,
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limit_val_batches=0,
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limit_test_batches=0,
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num_sanity_val_steps=0,
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enable_model_summary=False,
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)
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trainer.fit(model)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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@RunIf(standalone=True)
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def test_lr_finder_with_ddp(tmpdir):
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seed_everything(7)
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init_lr = 1e-4
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dm = ClassifDataModule()
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model = ClassificationModel(lr=init_lr)
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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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strategy="ddp",
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devices=2,
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accelerator="cpu",
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)
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tuner = Tuner(trainer)
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tuner.lr_find(model, datamodule=dm, update_attr=True, num_training=20)
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lr = trainer.lightning_module.lr
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lr = trainer.strategy.broadcast(lr)
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assert trainer.lightning_module.lr == lr
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assert lr != init_lr
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|
|
|
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def test_lr_finder_callback_val_batches(tmpdir):
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"""Test that `LearningRateFinder` does not limit the number of val batches during training."""
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|
|
|
class CustomBoringModel(BoringModel):
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|
def __init__(self, lr):
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|
super().__init__()
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|
self.lr = lr
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|
|
|
def configure_optimizers(self):
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|
return torch.optim.SGD(self.parameters(), lr=self.lr)
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|
|
|
num_lr_tuner_training_steps = 5
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|
model = CustomBoringModel(0.1)
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|
trainer = Trainer(
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|
default_root_dir=tmpdir,
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|
num_sanity_val_steps=0,
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|
max_epochs=1,
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|
enable_model_summary=False,
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|
callbacks=[LearningRateFinder(num_training_steps=num_lr_tuner_training_steps)],
|
|
)
|
|
trainer.fit(model)
|
|
|
|
assert trainer.num_val_batches[0] == len(trainer.val_dataloaders)
|
|
assert trainer.num_val_batches[0] != num_lr_tuner_training_steps
|
|
|
|
|
|
def test_lr_finder_training_step_none_output(tmpdir):
|
|
# add some nans into the skipped steps (first 10) but also into the steps used to compute the lr
|
|
none_steps = [5, 12, 17]
|
|
|
|
class CustomBoringModel(BoringModel):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.lr = 0.123
|
|
|
|
def training_step(self, batch: Any, batch_idx: int) -> STEP_OUTPUT:
|
|
if self.trainer.global_step in none_steps:
|
|
return None
|
|
|
|
return super().training_step(batch, batch_idx)
|
|
|
|
seed_everything(1)
|
|
model = CustomBoringModel()
|
|
|
|
trainer = Trainer(default_root_dir=tmpdir)
|
|
|
|
tuner = Tuner(trainer)
|
|
# restrict number of steps for faster test execution
|
|
# and disable early stopping to easily check expected number of lrs and losses
|
|
lr_finder = tuner.lr_find(model=model, update_attr=True, num_training=20, early_stop_threshold=None)
|
|
assert len(lr_finder.results["lr"]) == len(lr_finder.results["loss"]) == 20
|
|
assert torch.isnan(torch.tensor(lr_finder.results["loss"])[none_steps]).all()
|
|
|
|
suggested_lr = lr_finder.suggestion()
|
|
assert math.isfinite(suggested_lr)
|
|
assert math.isclose(model.lr, suggested_lr)
|