307 lines
10 KiB
Python
307 lines
10 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 unittest import mock
|
|
|
|
import pytest
|
|
from torch.utils.data import DataLoader
|
|
|
|
import tests.base.develop_pipelines as tpipes
|
|
from pytorch_lightning import Trainer
|
|
from pytorch_lightning.accelerators import TPUAccelerator
|
|
from pytorch_lightning.callbacks import EarlyStopping
|
|
from pytorch_lightning.trainer.states import TrainerState
|
|
from pytorch_lightning.utilities import _TPU_AVAILABLE
|
|
from pytorch_lightning.utilities.exceptions import MisconfigurationException
|
|
from tests.base import EvalModelTemplate
|
|
from tests.base.datasets import TrialMNIST
|
|
from tests.base.develop_utils import pl_multi_process_test
|
|
|
|
if _TPU_AVAILABLE:
|
|
import torch_xla
|
|
import torch_xla.distributed.xla_multiprocessing as xmp
|
|
SERIAL_EXEC = xmp.MpSerialExecutor()
|
|
|
|
|
|
_LARGER_DATASET = TrialMNIST(download=True, num_samples=2000, digits=(0, 1, 2, 5, 8))
|
|
|
|
|
|
# 8 cores needs a big dataset
|
|
def _serial_train_loader():
|
|
return DataLoader(_LARGER_DATASET, batch_size=32)
|
|
|
|
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
def test_model_tpu_cores_1(tmpdir):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
progress_bar_refresh_rate=0,
|
|
max_epochs=1,
|
|
tpu_cores=1,
|
|
limit_train_batches=0.4,
|
|
limit_val_batches=0.4,
|
|
)
|
|
|
|
model = EvalModelTemplate()
|
|
tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False)
|
|
|
|
|
|
@pytest.mark.parametrize('tpu_core', [1, 5])
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
def test_model_tpu_index(tmpdir, tpu_core):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
progress_bar_refresh_rate=0,
|
|
max_epochs=1,
|
|
tpu_cores=[tpu_core],
|
|
limit_train_batches=0.4,
|
|
limit_val_batches=0.4,
|
|
)
|
|
|
|
model = EvalModelTemplate()
|
|
tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False)
|
|
assert torch_xla._XLAC._xla_get_default_device() == f'xla:{tpu_core}'
|
|
|
|
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
def test_model_tpu_cores_8(tmpdir):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
progress_bar_refresh_rate=0,
|
|
max_epochs=1,
|
|
tpu_cores=8,
|
|
limit_train_batches=0.4,
|
|
limit_val_batches=0.4,
|
|
)
|
|
|
|
model = EvalModelTemplate()
|
|
# 8 cores needs a big dataset
|
|
model.train_dataloader = _serial_train_loader
|
|
model.val_dataloader = _serial_train_loader
|
|
|
|
tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False)
|
|
|
|
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
def test_model_16bit_tpu_cores_1(tmpdir):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
precision=16,
|
|
progress_bar_refresh_rate=0,
|
|
max_epochs=1,
|
|
tpu_cores=1,
|
|
limit_train_batches=0.4,
|
|
limit_val_batches=0.4,
|
|
)
|
|
|
|
model = EvalModelTemplate()
|
|
tpipes.run_model_test(trainer_options, model, on_gpu=False)
|
|
assert os.environ.get('XLA_USE_BF16') == str(1), "XLA_USE_BF16 was not set in environment variables"
|
|
|
|
|
|
@pytest.mark.parametrize('tpu_core', [1, 5])
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
def test_model_16bit_tpu_index(tmpdir, tpu_core):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
precision=16,
|
|
progress_bar_refresh_rate=0,
|
|
max_epochs=1,
|
|
tpu_cores=[tpu_core],
|
|
limit_train_batches=0.4,
|
|
limit_val_batches=0.2,
|
|
)
|
|
|
|
model = EvalModelTemplate()
|
|
tpipes.run_model_test(trainer_options, model, on_gpu=False)
|
|
assert torch_xla._XLAC._xla_get_default_device() == f'xla:{tpu_core}'
|
|
assert os.environ.get('XLA_USE_BF16') == str(1), "XLA_USE_BF16 was not set in environment variables"
|
|
|
|
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
def test_model_16bit_tpu_cores_8(tmpdir):
|
|
"""Make sure model trains on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
precision=16,
|
|
progress_bar_refresh_rate=0,
|
|
max_epochs=1,
|
|
tpu_cores=8,
|
|
limit_train_batches=0.4,
|
|
limit_val_batches=0.4,
|
|
)
|
|
|
|
model = EvalModelTemplate()
|
|
# 8 cores needs a big dataset
|
|
model.train_dataloader = _serial_train_loader
|
|
model.val_dataloader = _serial_train_loader
|
|
|
|
tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False)
|
|
|
|
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
def test_model_tpu_early_stop(tmpdir):
|
|
"""Test if single TPU core training works"""
|
|
model = EvalModelTemplate()
|
|
trainer = Trainer(
|
|
callbacks=[EarlyStopping()],
|
|
default_root_dir=tmpdir,
|
|
progress_bar_refresh_rate=0,
|
|
max_epochs=50,
|
|
limit_train_batches=10,
|
|
limit_val_batches=10,
|
|
tpu_cores=1,
|
|
)
|
|
trainer.fit(model)
|
|
|
|
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
def test_tpu_grad_norm(tmpdir):
|
|
"""Test if grad_norm works on TPU."""
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
progress_bar_refresh_rate=0,
|
|
max_epochs=1,
|
|
tpu_cores=1,
|
|
limit_train_batches=0.4,
|
|
limit_val_batches=0.4,
|
|
gradient_clip_val=0.1,
|
|
)
|
|
|
|
model = EvalModelTemplate()
|
|
tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False)
|
|
|
|
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
def test_dataloaders_passed_to_fit(tmpdir):
|
|
"""Test if dataloaders passed to trainer works on TPU"""
|
|
|
|
model = EvalModelTemplate()
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=1,
|
|
tpu_cores=8
|
|
)
|
|
trainer.fit(model, train_dataloader=model.train_dataloader(), val_dataloaders=model.val_dataloader())
|
|
assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
['tpu_cores', 'expected_tpu_id'],
|
|
[pytest.param(1, None), pytest.param(8, None), pytest.param([1], 1), pytest.param([8], 8)],
|
|
)
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires missing TPU")
|
|
def test_tpu_id_to_be_as_expected(tpu_cores, expected_tpu_id):
|
|
"""Test if trainer.tpu_id is set as expected"""
|
|
assert Trainer(tpu_cores=tpu_cores).tpu_id == expected_tpu_id
|
|
|
|
|
|
def test_tpu_misconfiguration():
|
|
"""Test if trainer.tpu_id is set as expected"""
|
|
with pytest.raises(MisconfigurationException, match="`tpu_cores` can only be"):
|
|
Trainer(tpu_cores=[1, 8])
|
|
|
|
|
|
@pytest.mark.skipif(_TPU_AVAILABLE, reason="test requires missing TPU")
|
|
def test_exception_when_no_tpu_found(tmpdir):
|
|
"""Test if exception is thrown when xla devices are not available"""
|
|
|
|
with pytest.raises(MisconfigurationException, match='No TPU devices were found.'):
|
|
Trainer(tpu_cores=8)
|
|
|
|
|
|
@pytest.mark.parametrize('tpu_cores', [1, 8, [1]])
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
def test_distributed_backend_set_when_using_tpu(tmpdir, tpu_cores):
|
|
"""Test if distributed_backend is set to `tpu` when tpu_cores is not None"""
|
|
assert Trainer(tpu_cores=tpu_cores).distributed_backend == "tpu"
|
|
|
|
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
def test_broadcast_on_tpu():
|
|
""" Checks if an object from the master process is broadcasted to other processes correctly"""
|
|
def test_broadcast(rank):
|
|
trainer = Trainer(tpu_cores=8)
|
|
backend = TPUAccelerator(trainer)
|
|
obj = ("ver_0.5", "logger_name", rank)
|
|
result = backend.broadcast(obj)
|
|
assert result == ("ver_0.5", "logger_name", 0)
|
|
|
|
xmp.spawn(test_broadcast, nprocs=8, start_method='fork')
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
["tpu_cores", "expected_tpu_id", "error_expected"],
|
|
[
|
|
pytest.param(1, None, False),
|
|
pytest.param(8, None, False),
|
|
pytest.param([1], 1, False),
|
|
pytest.param([8], 8, False),
|
|
pytest.param("1,", 1, False),
|
|
pytest.param("1", None, False),
|
|
pytest.param("9, ", 9, True),
|
|
pytest.param([9], 9, True),
|
|
pytest.param([0], 0, True),
|
|
pytest.param(2, None, True),
|
|
pytest.param(10, None, True),
|
|
],
|
|
)
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
def test_tpu_choice(tmpdir, tpu_cores, expected_tpu_id, error_expected):
|
|
if error_expected:
|
|
with pytest.raises(MisconfigurationException, match=r".*tpu_cores` can only be 1, 8 or [<1-8>]*"):
|
|
Trainer(default_root_dir=tmpdir, tpu_cores=tpu_cores)
|
|
else:
|
|
trainer = Trainer(default_root_dir=tmpdir, tpu_cores=tpu_cores)
|
|
assert trainer.tpu_id == expected_tpu_id
|
|
|
|
|
|
@pytest.mark.parametrize(['cli_args', 'expected'], [
|
|
pytest.param('--tpu_cores=8',
|
|
{'tpu_cores': 8}),
|
|
pytest.param("--tpu_cores=1,",
|
|
{'tpu_cores': '1,'})
|
|
])
|
|
@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
|
|
@pl_multi_process_test
|
|
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
|
|
assert Trainer.from_argparse_args(args)
|