442 lines
14 KiB
Python
442 lines
14 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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import torch
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from torch.utils.data import DataLoader
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import tests_pytorch.helpers.pipelines as tpipes
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import tests_pytorch.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.demos.boring_classes import BoringModel, RandomDataset
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from pytorch_lightning.strategies import TPUSpawnStrategy
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from pytorch_lightning.strategies.launchers.xla import _save_spawn
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from pytorch_lightning.trainer.connectors.logger_connector.result import _Sync
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from pytorch_lightning.utilities import _TPU_AVAILABLE
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from pytorch_lightning.utilities.distributed import ReduceOp
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests_pytorch.helpers.runif import RunIf
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if _TPU_AVAILABLE:
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import torch_xla
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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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@RunIf(tpu=True, standalone=True)
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def test_model_tpu_devices_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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enable_progress_bar=False,
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max_epochs=2,
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accelerator="tpu",
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devices=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, with_hpc=False)
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@pytest.mark.parametrize("tpu_core", [1, 5])
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@RunIf(tpu=True, standalone=True)
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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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enable_progress_bar=False,
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max_epochs=2,
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accelerator="tpu",
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devices=[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, with_hpc=False)
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assert torch_xla._XLAC._xla_get_default_device() == f"xla:{tpu_core}"
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@RunIf(tpu=True)
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def test_model_tpu_devices_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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enable_progress_bar=False,
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max_epochs=1,
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accelerator="tpu",
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devices=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, with_hpc=False, min_acc=0.05)
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@RunIf(tpu=True, standalone=True)
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def test_model_16bit_tpu_devices_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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enable_progress_bar=False,
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max_epochs=2,
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accelerator="tpu",
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devices=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)
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@pytest.mark.parametrize("tpu_core", [1, 5])
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@RunIf(tpu=True, standalone=True)
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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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enable_progress_bar=False,
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max_epochs=2,
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accelerator="tpu",
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devices=[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)
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assert torch_xla._XLAC._xla_get_default_device() == f"xla:{tpu_core}"
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@RunIf(tpu=True)
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def test_model_16bit_tpu_devices_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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enable_progress_bar=False,
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max_epochs=1,
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accelerator="tpu",
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devices=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, with_hpc=False, min_acc=0.05)
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@RunIf(tpu=True)
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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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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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enable_progress_bar=False,
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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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accelerator="tpu",
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devices=8,
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)
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trainer.fit(model)
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trainer.test(dataloaders=DataLoader(RandomDataset(32, 2000), batch_size=32))
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@RunIf(tpu=True, standalone=True)
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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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enable_progress_bar=False,
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max_epochs=4,
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accelerator="tpu",
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devices=1,
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limit_train_batches=0.4,
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limit_val_batches=0.4,
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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, with_hpc=False)
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@RunIf(tpu=True, standalone=True)
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def test_tpu_clip_grad_by_value(tmpdir):
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"""Test if clip_gradients by value 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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enable_progress_bar=False,
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max_epochs=4,
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accelerator="tpu",
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devices=1,
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limit_train_batches=10,
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limit_val_batches=10,
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gradient_clip_val=0.5,
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gradient_clip_algorithm="value",
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)
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, with_hpc=False)
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@RunIf(tpu=True)
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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, accelerator="tpu", devices=8)
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trainer.fit(model, train_dataloaders=model.train_dataloader(), val_dataloaders=model.val_dataloader())
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@RunIf(tpu=True)
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@pytest.mark.parametrize("tpu_cores", [[1, 8], "9, ", [9], [0], 2, 10])
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def test_tpu_misconfiguration(tpu_cores):
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with pytest.raises(MisconfigurationException, match="`tpu_cores` can only be"):
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Trainer(accelerator="tpu", devices=tpu_cores)
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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():
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"""Test if exception is thrown when xla devices are not available."""
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with pytest.raises(MisconfigurationException, match="TPUAccelerator can not run on your system"):
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Trainer(accelerator="tpu", devices=8)
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@pytest.mark.parametrize("tpu_cores", [1, 8, [1]])
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@RunIf(tpu=True, standalone=True)
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def test_accelerator_set_when_using_tpu(tpu_cores):
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"""Test if the accelerator is set to `tpu` when tpu_cores is not None."""
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assert isinstance(Trainer(accelerator="tpu", devices=tpu_cores).accelerator, TPUAccelerator)
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@RunIf(tpu=True)
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def test_broadcast_on_tpu():
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"""Checks if an object from the main process is broadcasted to other processes correctly."""
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def test_broadcast(rank):
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trainer = Trainer(accelerator="tpu", devices=8)
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assert isinstance(trainer.accelerator, TPUAccelerator)
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assert isinstance(trainer.strategy, TPUSpawnStrategy)
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trainer.strategy._launched = True
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obj = ("ver_0.5", "logger_name", rank)
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result = trainer.strategy.broadcast(obj)
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assert result == ("ver_0.5", "logger_name", 0)
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_save_spawn(test_broadcast, nprocs=8, start_method="fork")
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@pytest.mark.parametrize(
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["cli_args", "expected"],
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[("--tpu_cores=8", {"tpu_cores": 8}), ("--tpu_cores=1,", {"tpu_cores": "1,"})],
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)
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@RunIf(tpu=True, standalone=True)
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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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with pytest.deprecated_call(match=r"is deprecated in v1.7 and will be removed in v2.0."):
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assert Trainer.from_argparse_args(args)
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@RunIf(tpu=True)
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def test_tpu_reduce():
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"""Test tpu spawn reduce operation."""
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def test_reduce(rank):
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trainer = Trainer(accelerator="tpu", devices=8)
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trainer.strategy._launched = True
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with pytest.raises(ValueError, match="TPUSpawnStrategy only supports"):
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trainer.strategy.reduce(1, reduce_op="undefined")
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with pytest.raises(ValueError, match="TPUSpawnStrategy only supports"):
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trainer.strategy.reduce(1, reduce_op=ReduceOp.MAX)
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# it is faster to loop over here than to parameterize the test
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for reduce_op in ("mean", "AVG", "sum", ReduceOp.SUM):
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result = trainer.strategy.reduce(1, reduce_op=reduce_op)
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if isinstance(reduce_op, str) and reduce_op.lower() in ("mean", "avg"):
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assert result.item() == 1
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else:
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assert result.item() == 8
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_save_spawn(test_reduce, nprocs=8, start_method="fork")
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@RunIf(tpu=True, standalone=True)
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@pytest.mark.parametrize("clip_val", [10])
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@mock.patch("torch.nn.utils.clip_grad_norm_")
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def test_tpu_precision_16_clip_gradients(mock_clip_grad_norm, clip_val, tmpdir):
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"""Ensure that clip gradients is only called if the value is greater than 0.
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TODO: Fix (test fails with parametrize)
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"""
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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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enable_progress_bar=False,
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max_epochs=1,
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accelerator="tpu",
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devices=1,
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precision=16,
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limit_train_batches=4,
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limit_val_batches=4,
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gradient_clip_val=clip_val,
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)
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, with_hpc=False)
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if clip_val > 0:
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mock_clip_grad_norm.assert_called()
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else:
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mock_clip_grad_norm.assert_not_called()
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@RunIf(tpu=True)
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def test_if_test_works_with_checkpoint_false(tmpdir):
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"""Ensure that model trains properly when `enable_checkpointing` is set to False."""
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# Train a model on TPU
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model = BoringModel()
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trainer = Trainer(
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max_epochs=1,
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accelerator="tpu",
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devices=8,
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default_root_dir=tmpdir,
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fast_dev_run=True,
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enable_checkpointing=False,
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)
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trainer.fit(model)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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@RunIf(tpu=True)
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def test_tpu_sync_dist():
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"""Test tpu spawn sync dist operation."""
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def test_sync_dist(rank):
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trainer = Trainer(accelerator="tpu", devices=8)
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trainer.strategy._launched = True
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sync = _Sync(trainer.strategy.reduce, _should=True, _op=torch.distributed.ReduceOp.SUM)
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value = torch.tensor([1.0])
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value = sync(value)
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assert value.item() == 8
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_save_spawn(test_sync_dist, nprocs=8, start_method="fork")
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@RunIf(tpu=True)
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def test_tpu_debug_mode(tmpdir):
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"""Test if debug mode works on TPU."""
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class DebugModel(BoringModel):
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def on_train_start(self):
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assert os.environ.get("PT_XLA_DEBUG") == str(1), "PT_XLA_DEBUG was not set in environment variables"
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def teardown(self, stage):
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assert "PT_XLA_DEBUG" not in os.environ
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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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enable_progress_bar=False,
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max_epochs=4,
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accelerator="tpu",
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devices=8,
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limit_train_batches=0.4,
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limit_val_batches=0.4,
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strategy=TPUSpawnStrategy(debug=True),
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)
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model = DebugModel()
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tpipes.run_model_test(trainer_options, model, with_hpc=False)
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@RunIf(tpu=True)
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def test_tpu_host_world_size(tmpdir):
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"""Test Host World size env setup on TPU."""
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class DebugModel(BoringModel):
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def on_train_start(self):
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assert os.environ.get("XRT_HOST_WORLD_SIZE") == str(1)
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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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enable_progress_bar=False,
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max_epochs=4,
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accelerator="tpu",
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devices=8,
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limit_train_batches=0.4,
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limit_val_batches=0.4,
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)
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model = DebugModel()
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assert "XRT_HOST_WORLD_SIZE" not in os.environ
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tpipes.run_model_test(trainer_options, model, with_hpc=False)
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assert "XRT_HOST_WORLD_SIZE" not in os.environ
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@RunIf(tpu=True)
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def test_device_type_when_tpu_strategy_passed(tmpdir):
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trainer = Trainer(default_root_dir=tmpdir, strategy=TPUSpawnStrategy(), accelerator="tpu", devices=8)
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assert isinstance(trainer.strategy, TPUSpawnStrategy)
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assert isinstance(trainer.accelerator, TPUAccelerator)
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