330 lines
11 KiB
Python
330 lines
11 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 argparse import ArgumentParser
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from unittest import mock
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import pytest
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from torch.utils.data import DataLoader
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import tests.helpers.pipelines as tpipes
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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.accelerators import TPUAccelerator
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from pytorch_lightning.callbacks import EarlyStopping
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from pytorch_lightning.plugins import TPUSpawnPlugin
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from pytorch_lightning.trainer.states import TrainerState
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from pytorch_lightning.utilities import _TPU_AVAILABLE
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers import BoringModel, RandomDataset
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from tests.helpers.utils import pl_multi_process_test
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if _TPU_AVAILABLE:
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import torch_xla
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import torch_xla.distributed.xla_multiprocessing as xmp
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SERIAL_EXEC = xmp.MpSerialExecutor()
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_LARGER_DATASET = RandomDataset(32, 2000)
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# 8 cores needs a big dataset
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def _serial_train_loader():
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return DataLoader(_LARGER_DATASET, batch_size=32)
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class SerialLoaderBoringModel(BoringModel):
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def train_dataloader(self):
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return DataLoader(RandomDataset(32, 2000), batch_size=32)
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def val_dataloader(self):
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return DataLoader(RandomDataset(32, 2000), batch_size=32)
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_model_tpu_cores_1(tmpdir):
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"""Make sure model trains on TPU."""
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tutils.reset_seed()
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trainer_options = dict(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=2,
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tpu_cores=1,
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limit_train_batches=4,
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limit_val_batches=4,
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)
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False)
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@pytest.mark.parametrize('tpu_core', [1, 5])
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_model_tpu_index(tmpdir, tpu_core):
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"""Make sure model trains on TPU."""
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tutils.reset_seed()
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trainer_options = dict(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=2,
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tpu_cores=[tpu_core],
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limit_train_batches=4,
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limit_val_batches=4,
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)
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False)
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assert torch_xla._XLAC._xla_get_default_device() == f'xla:{tpu_core}'
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_model_tpu_cores_8(tmpdir):
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"""Make sure model trains on TPU."""
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tutils.reset_seed()
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trainer_options = dict(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=1,
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tpu_cores=8,
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limit_train_batches=4,
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limit_val_batches=4,
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)
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# 8 cores needs a big dataset
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model = SerialLoaderBoringModel()
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tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False, min_acc=0.05)
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_model_16bit_tpu_cores_1(tmpdir):
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"""Make sure model trains on TPU."""
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tutils.reset_seed()
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trainer_options = dict(
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default_root_dir=tmpdir,
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precision=16,
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progress_bar_refresh_rate=0,
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max_epochs=2,
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tpu_cores=1,
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limit_train_batches=8,
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limit_val_batches=2,
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)
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, on_gpu=False)
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assert os.environ.get('XLA_USE_BF16') == str(1), "XLA_USE_BF16 was not set in environment variables"
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@pytest.mark.parametrize('tpu_core', [1, 5])
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_model_16bit_tpu_index(tmpdir, tpu_core):
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"""Make sure model trains on TPU."""
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tutils.reset_seed()
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trainer_options = dict(
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default_root_dir=tmpdir,
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precision=16,
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progress_bar_refresh_rate=0,
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max_epochs=2,
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tpu_cores=[tpu_core],
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limit_train_batches=4,
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limit_val_batches=2,
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)
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, on_gpu=False)
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assert torch_xla._XLAC._xla_get_default_device() == f'xla:{tpu_core}'
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assert os.environ.get('XLA_USE_BF16') == str(1), "XLA_USE_BF16 was not set in environment variables"
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_model_16bit_tpu_cores_8(tmpdir):
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"""Make sure model trains on TPU."""
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tutils.reset_seed()
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trainer_options = dict(
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default_root_dir=tmpdir,
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precision=16,
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progress_bar_refresh_rate=0,
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max_epochs=1,
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tpu_cores=8,
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limit_train_batches=4,
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limit_val_batches=4,
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)
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# 8 cores needs a big dataset
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model = SerialLoaderBoringModel()
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tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False, min_acc=0.05)
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_model_tpu_early_stop(tmpdir):
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"""Test if single TPU core training works"""
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# todo: Test on 8 cores - hanging.
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class CustomBoringModel(BoringModel):
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def validation_step(self, *args, **kwargs):
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out = super().validation_step(*args, **kwargs)
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self.log('val_loss', out['x'])
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return out
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tutils.reset_seed()
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model = CustomBoringModel()
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trainer = Trainer(
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callbacks=[EarlyStopping(monitor='val_loss')],
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=2,
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limit_train_batches=2,
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limit_val_batches=2,
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tpu_cores=[1],
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)
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trainer.fit(model)
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_tpu_grad_norm(tmpdir):
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"""Test if grad_norm works on TPU."""
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tutils.reset_seed()
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trainer_options = dict(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=4,
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tpu_cores=1,
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limit_train_batches=4,
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limit_val_batches=4,
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gradient_clip_val=0.5,
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)
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False)
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_dataloaders_passed_to_fit(tmpdir):
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"""Test if dataloaders passed to trainer works on TPU"""
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tutils.reset_seed()
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model = BoringModel()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, tpu_cores=8)
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trainer.fit(model, train_dataloader=model.train_dataloader(), val_dataloaders=model.val_dataloader())
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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@pytest.mark.parametrize(
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['tpu_cores', 'expected_tpu_id'],
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[pytest.param(1, None), pytest.param(8, None),
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pytest.param([1], 1), pytest.param([8], 8)],
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)
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires missing TPU")
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def test_tpu_id_to_be_as_expected(tpu_cores, expected_tpu_id):
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"""Test if trainer.tpu_id is set as expected"""
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assert Trainer(tpu_cores=tpu_cores).accelerator_connector.tpu_id == expected_tpu_id
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def test_tpu_misconfiguration():
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"""Test if trainer.tpu_id is set as expected"""
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with pytest.raises(MisconfigurationException, match="`tpu_cores` can only be"):
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Trainer(tpu_cores=[1, 8])
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@pytest.mark.skipif(_TPU_AVAILABLE, reason="test requires missing TPU")
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def test_exception_when_no_tpu_found(tmpdir):
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"""Test if exception is thrown when xla devices are not available"""
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with pytest.raises(MisconfigurationException, match='No TPU devices were found.'):
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Trainer(tpu_cores=8)
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@pytest.mark.parametrize('tpu_cores', [1, 8, [1]])
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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def test_distributed_backend_set_when_using_tpu(tmpdir, tpu_cores):
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"""Test if distributed_backend is set to `tpu` when tpu_cores is not None"""
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assert Trainer(tpu_cores=tpu_cores).distributed_backend == "tpu"
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pytest.mark.skipif(
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not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1', reason="test should be run outside of pytest"
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)
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@pl_multi_process_test
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def test_broadcast_on_tpu():
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""" Checks if an object from the master process is broadcasted to other processes correctly"""
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def test_broadcast(rank):
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trainer = Trainer(tpu_cores=8)
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assert isinstance(trainer.accelerator_backend, TPUAccelerator)
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assert isinstance(trainer.training_type_plugin, TPUSpawnPlugin)
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obj = ("ver_0.5", "logger_name", rank)
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result = trainer.training_type_plugin.broadcast(obj)
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assert result == ("ver_0.5", "logger_name", 0)
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xmp.spawn(test_broadcast, nprocs=8, start_method='fork')
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@pytest.mark.parametrize(
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["tpu_cores", "expected_tpu_id", "error_expected"],
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[
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pytest.param(1, None, False),
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pytest.param(8, None, False),
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pytest.param([1], 1, False),
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pytest.param([8], 8, False),
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pytest.param("1,", 1, False),
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pytest.param("1", None, False),
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pytest.param("9, ", 9, True),
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pytest.param([9], 9, True),
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pytest.param([0], 0, True),
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pytest.param(2, None, True),
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pytest.param(10, None, True),
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],
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)
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_tpu_choice(tmpdir, tpu_cores, expected_tpu_id, error_expected):
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if error_expected:
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with pytest.raises(MisconfigurationException, match=r".*tpu_cores` can only be 1, 8 or [<1-8>]*"):
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Trainer(default_root_dir=tmpdir, tpu_cores=tpu_cores)
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else:
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trainer = Trainer(default_root_dir=tmpdir, tpu_cores=tpu_cores)
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assert trainer.accelerator_connector.tpu_id == expected_tpu_id
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@pytest.mark.parametrize(
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['cli_args', 'expected'],
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[pytest.param('--tpu_cores=8', {'tpu_cores': 8}),
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pytest.param("--tpu_cores=1,", {'tpu_cores': '1,'})]
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)
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@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
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@pl_multi_process_test
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def test_tpu_cores_with_argparse(cli_args, expected):
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"""Test passing tpu_cores in command line"""
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cli_args = cli_args.split(' ') if cli_args else []
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with mock.patch("argparse._sys.argv", ["any.py"] + cli_args):
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parser = ArgumentParser(add_help=False)
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parser = Trainer.add_argparse_args(parent_parser=parser)
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args = Trainer.parse_argparser(parser)
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for k, v in expected.items():
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assert getattr(args, k) == v
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assert Trainer.from_argparse_args(args)
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