386 lines
12 KiB
Python
386 lines
12 KiB
Python
# Copyright The PyTorch Lightning team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import os
|
|
from argparse import ArgumentParser
|
|
from functools import partial
|
|
from unittest import mock
|
|
|
|
import pytest
|
|
import torch
|
|
from torch.utils.data import DataLoader
|
|
|
|
import tests_pytorch.helpers.pipelines as tpipes
|
|
from pytorch_lightning import Trainer
|
|
from pytorch_lightning.accelerators import TPUAccelerator
|
|
from pytorch_lightning.callbacks import EarlyStopping
|
|
from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
|
|
from pytorch_lightning.strategies import TPUSpawnStrategy
|
|
from pytorch_lightning.strategies.launchers.xla import _XLALauncher
|
|
from pytorch_lightning.trainer.connectors.logger_connector.result import _Sync
|
|
from pytorch_lightning.utilities.exceptions import MisconfigurationException
|
|
from tests_pytorch.helpers.runif import RunIf
|
|
|
|
|
|
class SerialLoaderBoringModel(BoringModel):
|
|
def train_dataloader(self):
|
|
return DataLoader(RandomDataset(32, 2000), batch_size=32)
|
|
|
|
def val_dataloader(self):
|
|
return DataLoader(RandomDataset(32, 2000), batch_size=32)
|
|
|
|
|
|
@RunIf(tpu=True, standalone=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_model_tpu_devices_1(tmpdir):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
enable_progress_bar=False,
|
|
max_epochs=2,
|
|
accelerator="tpu",
|
|
devices=1,
|
|
limit_train_batches=4,
|
|
limit_val_batches=4,
|
|
)
|
|
|
|
model = BoringModel()
|
|
tpipes.run_model_test(trainer_options, model, with_hpc=False)
|
|
|
|
|
|
@pytest.mark.parametrize("tpu_core", [1, 5])
|
|
@RunIf(tpu=True, standalone=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_model_tpu_index(tmpdir, tpu_core):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
enable_progress_bar=False,
|
|
max_epochs=2,
|
|
accelerator="tpu",
|
|
devices=[tpu_core],
|
|
limit_train_batches=4,
|
|
limit_val_batches=4,
|
|
)
|
|
|
|
model = BoringModel()
|
|
tpipes.run_model_test(trainer_options, model, with_hpc=False)
|
|
import torch_xla
|
|
|
|
assert torch_xla._XLAC._xla_get_default_device() == f"xla:{tpu_core}"
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_model_tpu_devices_8(tmpdir):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
enable_progress_bar=False,
|
|
max_epochs=1,
|
|
accelerator="tpu",
|
|
devices=8,
|
|
limit_train_batches=4,
|
|
limit_val_batches=4,
|
|
)
|
|
|
|
# 8 cores needs a big dataset
|
|
model = SerialLoaderBoringModel()
|
|
tpipes.run_model_test(trainer_options, model, with_hpc=False, min_acc=0.05)
|
|
|
|
|
|
@RunIf(tpu=True, standalone=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_model_16bit_tpu_devices_1(tmpdir):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
precision=16,
|
|
enable_progress_bar=False,
|
|
max_epochs=2,
|
|
accelerator="tpu",
|
|
devices=1,
|
|
limit_train_batches=8,
|
|
limit_val_batches=2,
|
|
)
|
|
|
|
model = BoringModel()
|
|
tpipes.run_model_test(trainer_options, model)
|
|
|
|
|
|
@pytest.mark.parametrize("tpu_core", [1, 5])
|
|
@RunIf(tpu=True, standalone=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_model_16bit_tpu_index(tmpdir, tpu_core):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
precision=16,
|
|
enable_progress_bar=False,
|
|
max_epochs=2,
|
|
accelerator="tpu",
|
|
devices=[tpu_core],
|
|
limit_train_batches=4,
|
|
limit_val_batches=2,
|
|
)
|
|
|
|
model = BoringModel()
|
|
tpipes.run_model_test(trainer_options, model)
|
|
import torch_xla
|
|
|
|
assert torch_xla._XLAC._xla_get_default_device() == f"xla:{tpu_core}"
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_model_16bit_tpu_devices_8(tmpdir):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
precision=16,
|
|
enable_progress_bar=False,
|
|
max_epochs=1,
|
|
accelerator="tpu",
|
|
devices=8,
|
|
limit_train_batches=4,
|
|
limit_val_batches=4,
|
|
)
|
|
|
|
# 8 cores needs a big dataset
|
|
model = SerialLoaderBoringModel()
|
|
tpipes.run_model_test(trainer_options, model, with_hpc=False, min_acc=0.05)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_model_tpu_early_stop(tmpdir):
|
|
"""Test if single TPU core training works."""
|
|
|
|
class CustomBoringModel(BoringModel):
|
|
def validation_step(self, *args, **kwargs):
|
|
out = super().validation_step(*args, **kwargs)
|
|
self.log("val_loss", out["x"])
|
|
return out
|
|
|
|
model = CustomBoringModel()
|
|
trainer = Trainer(
|
|
callbacks=[EarlyStopping(monitor="val_loss")],
|
|
default_root_dir=tmpdir,
|
|
enable_progress_bar=False,
|
|
max_epochs=2,
|
|
limit_train_batches=2,
|
|
limit_val_batches=2,
|
|
accelerator="tpu",
|
|
devices=8,
|
|
)
|
|
trainer.fit(model)
|
|
trainer.test(dataloaders=DataLoader(RandomDataset(32, 2000), batch_size=32))
|
|
|
|
|
|
@RunIf(tpu=True, standalone=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_tpu_grad_norm(tmpdir):
|
|
"""Test if grad_norm works on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
enable_progress_bar=False,
|
|
max_epochs=4,
|
|
accelerator="tpu",
|
|
devices=1,
|
|
limit_train_batches=0.4,
|
|
limit_val_batches=0.4,
|
|
gradient_clip_val=0.5,
|
|
)
|
|
|
|
model = BoringModel()
|
|
tpipes.run_model_test(trainer_options, model, with_hpc=False)
|
|
|
|
|
|
@RunIf(tpu=True, standalone=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_tpu_clip_grad_by_value(tmpdir):
|
|
"""Test if clip_gradients by value works on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
enable_progress_bar=False,
|
|
max_epochs=4,
|
|
accelerator="tpu",
|
|
devices=1,
|
|
limit_train_batches=10,
|
|
limit_val_batches=10,
|
|
gradient_clip_val=0.5,
|
|
gradient_clip_algorithm="value",
|
|
)
|
|
|
|
model = BoringModel()
|
|
tpipes.run_model_test(trainer_options, model, with_hpc=False)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_dataloaders_passed_to_fit(tmpdir):
|
|
"""Test if dataloaders passed to trainer works on TPU."""
|
|
model = BoringModel()
|
|
|
|
trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, accelerator="tpu", devices=8)
|
|
trainer.fit(model, train_dataloaders=model.train_dataloader(), val_dataloaders=model.val_dataloader())
|
|
|
|
|
|
@pytest.mark.parametrize("tpu_cores", [[1, 8], "9, ", [9], [0], 2, 10])
|
|
def test_tpu_misconfiguration(tpu_cores, tpu_available):
|
|
with pytest.raises(TypeError, match="`tpu_cores` can only be"):
|
|
Trainer(accelerator="tpu", devices=tpu_cores)
|
|
|
|
|
|
@pytest.mark.skipif(TPUAccelerator.is_available(), reason="test requires missing TPU")
|
|
def test_exception_when_no_tpu_found(xla_available):
|
|
"""Test if exception is thrown when xla devices are not available."""
|
|
with pytest.raises(MisconfigurationException, match="TPUAccelerator` can not run on your system"):
|
|
Trainer(accelerator="tpu", devices=8)
|
|
|
|
|
|
@pytest.mark.parametrize("devices", [1, 8, [1]])
|
|
@RunIf(tpu=True, standalone=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_accelerator_set_when_using_tpu(devices):
|
|
"""Test if the accelerator is set to `tpu` when devices is not None."""
|
|
assert isinstance(Trainer(accelerator="tpu", devices=devices).accelerator, TPUAccelerator)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
["cli_args", "expected"],
|
|
[("--tpu_cores=8", {"tpu_cores": 8}), ("--tpu_cores=1,", {"tpu_cores": "1,"})],
|
|
)
|
|
@RunIf(tpu=True, standalone=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_tpu_cores_with_argparse(cli_args, expected):
|
|
"""Test passing tpu_cores in command line."""
|
|
cli_args = cli_args.split(" ") if cli_args else []
|
|
with mock.patch("argparse._sys.argv", ["any.py"] + cli_args):
|
|
parser = ArgumentParser(add_help=False)
|
|
parser = Trainer.add_argparse_args(parent_parser=parser)
|
|
args = Trainer.parse_argparser(parser)
|
|
|
|
for k, v in expected.items():
|
|
assert getattr(args, k) == v
|
|
with pytest.deprecated_call(match=r"is deprecated in v1.7 and will be removed in v2.0."):
|
|
assert Trainer.from_argparse_args(args)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_if_test_works_with_checkpoint_false(tmpdir):
|
|
"""Ensure that model trains properly when `enable_checkpointing` is set to False."""
|
|
|
|
# Train a model on TPU
|
|
model = BoringModel()
|
|
trainer = Trainer(
|
|
max_epochs=1,
|
|
accelerator="tpu",
|
|
devices=8,
|
|
default_root_dir=tmpdir,
|
|
fast_dev_run=True,
|
|
enable_checkpointing=False,
|
|
)
|
|
trainer.fit(model)
|
|
assert trainer.state.finished, f"Training failed with {trainer.state}"
|
|
|
|
|
|
def wrap_launch_function(fn, strategy, *args, **kwargs):
|
|
# the launcher does not manage this automatically. explanation available in:
|
|
# https://github.com/Lightning-AI/lightning/pull/14926#discussion_r982976718
|
|
strategy.setup_environment()
|
|
return fn(*args, **kwargs)
|
|
|
|
|
|
def xla_launch(fn):
|
|
# TODO: the accelerator should be optional to just launch processes, but this requires lazy initialization
|
|
accelerator = TPUAccelerator()
|
|
strategy = TPUSpawnStrategy(accelerator=accelerator, parallel_devices=list(range(8)))
|
|
launcher = _XLALauncher(strategy=strategy)
|
|
wrapped = partial(wrap_launch_function, fn, strategy)
|
|
return launcher.launch(wrapped, strategy)
|
|
|
|
|
|
def tpu_sync_dist_fn(strategy):
|
|
sync = _Sync(strategy.reduce, _should=True, _op=torch.distributed.ReduceOp.SUM)
|
|
value = torch.tensor([1.0])
|
|
value = sync(value)
|
|
assert value.item() == 8
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_tpu_sync_dist():
|
|
"""Test tpu spawn sync dist operation."""
|
|
xla_launch(tpu_sync_dist_fn)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_tpu_debug_mode(tmpdir):
|
|
"""Test if debug mode works on TPU."""
|
|
|
|
class DebugModel(BoringModel):
|
|
def on_train_start(self):
|
|
assert os.environ.get("PT_XLA_DEBUG") == str(1), "PT_XLA_DEBUG was not set in environment variables"
|
|
|
|
def teardown(self, stage):
|
|
assert "PT_XLA_DEBUG" not in os.environ
|
|
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
enable_progress_bar=False,
|
|
max_epochs=4,
|
|
accelerator="tpu",
|
|
devices=8,
|
|
limit_train_batches=0.4,
|
|
limit_val_batches=0.4,
|
|
strategy=TPUSpawnStrategy(debug=True),
|
|
)
|
|
|
|
model = DebugModel()
|
|
tpipes.run_model_test(trainer_options, model, with_hpc=False)
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
|
|
def test_tpu_host_world_size(tmpdir):
|
|
"""Test Host World size env setup on TPU."""
|
|
|
|
class DebugModel(BoringModel):
|
|
def on_train_start(self):
|
|
assert os.environ.get("XRT_HOST_WORLD_SIZE") == str(1)
|
|
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
enable_progress_bar=False,
|
|
max_epochs=4,
|
|
accelerator="tpu",
|
|
devices=8,
|
|
limit_train_batches=0.4,
|
|
limit_val_batches=0.4,
|
|
)
|
|
|
|
model = DebugModel()
|
|
assert "XRT_HOST_WORLD_SIZE" not in os.environ
|
|
tpipes.run_model_test(trainer_options, model, with_hpc=False)
|
|
assert "XRT_HOST_WORLD_SIZE" not in os.environ
|
|
|
|
|
|
@RunIf(tpu=True)
|
|
def test_device_type_when_tpu_strategy_passed(tmpdir):
|
|
trainer = Trainer(default_root_dir=tmpdir, strategy=TPUSpawnStrategy(), accelerator="tpu", devices=8)
|
|
assert isinstance(trainer.strategy, TPUSpawnStrategy)
|
|
assert isinstance(trainer.accelerator, TPUAccelerator)
|