192 lines
8.8 KiB
Python
192 lines
8.8 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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"""Test deprecated functionality which will be removed in vX.Y.Z"""
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import sys
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import pytest
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import torch
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from pytorch_lightning import Trainer
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from pytorch_lightning.overrides.data_parallel import (
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LightningDataParallel,
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LightningDistributedDataParallel,
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LightningParallelModule,
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)
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from pytorch_lightning.overrides.distributed import LightningDistributedModule
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from pytorch_lightning.plugins import DDPSpawnPlugin
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from pytorch_lightning.plugins.environments import TorchElasticEnvironment
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from tests.deprecated_api import _soft_unimport_module
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from tests.helpers import BoringModel
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def test_v1_4_0_deprecated_imports():
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_soft_unimport_module('pytorch_lightning.utilities.argparse_utils')
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with pytest.deprecated_call(match='will be removed in v1.4'):
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from pytorch_lightning.utilities.argparse_utils import from_argparse_args # noqa: F811 F401
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_soft_unimport_module('pytorch_lightning.utilities.model_utils')
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with pytest.deprecated_call(match='will be removed in v1.4'):
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from pytorch_lightning.utilities.model_utils import is_overridden # noqa: F811 F401
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_soft_unimport_module('pytorch_lightning.utilities.warning_utils')
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with pytest.deprecated_call(match='will be removed in v1.4'):
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from pytorch_lightning.utilities.warning_utils import WarningCache # noqa: F811 F401
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_soft_unimport_module('pytorch_lightning.utilities.xla_device_utils')
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with pytest.deprecated_call(match='will be removed in v1.4'):
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from pytorch_lightning.utilities.xla_device_utils import XLADeviceUtils # noqa: F811 F401
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def test_v1_4_0_deprecated_trainer_device_distrib():
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"""Test that Trainer attributes works fine."""
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trainer = Trainer()
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trainer.accelerator_connector._distrib_type = None
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trainer.accelerator_connector._device_type = None
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.on_cpu = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.on_cpu
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.on_gpu = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.on_gpu
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.on_tpu = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.on_tpu
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trainer.accelerator_connector._device_type = None
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.use_tpu = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.use_tpu
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.use_dp = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.use_dp
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.use_ddp = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.use_ddp
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.use_ddp2 = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.use_ddp2
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.use_horovod = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.use_horovod
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def test_v1_4_0_deprecated_metrics():
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from pytorch_lightning.metrics.functional.classification import stat_scores_multiple_classes
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with pytest.deprecated_call(match='will be removed in v1.4'):
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stat_scores_multiple_classes(pred=torch.tensor([0, 1]), target=torch.tensor([0, 1]))
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from pytorch_lightning.metrics.functional.classification import iou
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with pytest.deprecated_call(match='will be removed in v1.4'):
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iou(torch.randint(0, 2, (10, 3, 3)), torch.randint(0, 2, (10, 3, 3)))
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from pytorch_lightning.metrics.functional.classification import recall
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with pytest.deprecated_call(match='will be removed in v1.4'):
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recall(torch.randint(0, 2, (10, 3, 3)), torch.randint(0, 2, (10, 3, 3)))
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from pytorch_lightning.metrics.functional.classification import precision
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with pytest.deprecated_call(match='will be removed in v1.4'):
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precision(torch.randint(0, 2, (10, 3, 3)), torch.randint(0, 2, (10, 3, 3)))
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from pytorch_lightning.metrics.functional.classification import precision_recall
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with pytest.deprecated_call(match='will be removed in v1.4'):
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precision_recall(torch.randint(0, 2, (10, 3, 3)), torch.randint(0, 2, (10, 3, 3)))
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# Testing deprecation of class_reduction arg in the *new* precision
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from pytorch_lightning.metrics.functional import precision
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with pytest.deprecated_call(match='will be removed in v1.4'):
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precision(torch.randint(0, 2, (10, )), torch.randint(0, 2, (10, )), class_reduction='micro')
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# Testing deprecation of class_reduction arg in the *new* recall
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from pytorch_lightning.metrics.functional import recall
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with pytest.deprecated_call(match='will be removed in v1.4'):
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recall(torch.randint(0, 2, (10, )), torch.randint(0, 2, (10, )), class_reduction='micro')
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from pytorch_lightning.metrics.functional.classification import auc
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with pytest.deprecated_call(match='will be removed in v1.4'):
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auc(torch.rand(10, ).sort().values, torch.rand(10, ))
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from pytorch_lightning.metrics.functional.classification import auroc
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with pytest.deprecated_call(match='will be removed in v1.4'):
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auroc(torch.rand(10, ), torch.randint(0, 2, (10, )))
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from pytorch_lightning.metrics.functional.classification import multiclass_auroc
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with pytest.deprecated_call(match='will be removed in v1.4'):
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multiclass_auroc(torch.rand(20, 5).softmax(dim=-1), torch.randint(0, 5, (20, )), num_classes=5)
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from pytorch_lightning.metrics.functional.classification import auc_decorator
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with pytest.deprecated_call(match='will be removed in v1.4'):
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auc_decorator()
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from pytorch_lightning.metrics.functional.classification import multiclass_auc_decorator
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with pytest.deprecated_call(match='will be removed in v1.4'):
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multiclass_auc_decorator()
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class CustomDDPPlugin(DDPSpawnPlugin):
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def configure_ddp(self):
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# old, deprecated implementation
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with pytest.deprecated_call(
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match='`LightningDistributedDataParallel` is deprecated since v1.2 and will be removed in v1.4.'
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):
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self._model = LightningDistributedDataParallel(
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module=self.lightning_module,
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device_ids=self.determine_ddp_device_ids(),
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**self._ddp_kwargs,
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)
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assert isinstance(self.model, torch.nn.parallel.DistributedDataParallel)
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assert isinstance(self.model.module, LightningDistributedModule)
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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@pytest.mark.skipif(sys.platform == "win32", reason="DDP not available on windows")
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def test_v1_4_0_deprecated_lightning_distributed_data_parallel(tmpdir):
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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fast_dev_run=True,
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gpus=2,
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accelerator="ddp_spawn",
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plugins=[
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CustomDDPPlugin(
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parallel_devices=[torch.device("cuda", 0), torch.device("cuda", 1)],
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cluster_environment=TorchElasticEnvironment(),
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)
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]
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)
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trainer.fit(model)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_v1_4_0_deprecated_lightning_data_parallel():
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model = BoringModel()
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with pytest.deprecated_call(match="`LightningDataParallel` is deprecated since v1.2 and will be removed in v1.4."):
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dp_model = LightningDataParallel(model, device_ids=[0])
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assert isinstance(dp_model, torch.nn.DataParallel)
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assert isinstance(dp_model.module, LightningParallelModule)
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