lightning/tests/models/test_tpu.py

# 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.helpers.pipelines as tpipes
import tests.helpers.utils as tutils
from pytorch_lightning import Trainer
from pytorch_lightning.accelerators import TPUAccelerator
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.plugins import TPUSpawnPlugin
from pytorch_lightning.trainer.states import TrainerState
from pytorch_lightning.utilities import _TPU_AVAILABLE
from pytorch_lightning.utilities.distributed import ReduceOp
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.helpers import BoringModel, RandomDataset
from tests.helpers.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 = RandomDataset(32, 2000)


# 8 cores needs a big dataset
def _serial_train_loader():
    return DataLoader(_LARGER_DATASET, batch_size=32)


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)


@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."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        progress_bar_refresh_rate=0,
        max_epochs=2,
        tpu_cores=1,
        limit_train_batches=4,
        limit_val_batches=4,
    )

    model = BoringModel()
    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."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        progress_bar_refresh_rate=0,
        max_epochs=2,
        tpu_cores=[tpu_core],
        limit_train_batches=4,
        limit_val_batches=4,
    )

    model = BoringModel()
    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."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        progress_bar_refresh_rate=0,
        max_epochs=1,
        tpu_cores=8,
        limit_train_batches=4,
        limit_val_batches=4,
    )

    # 8 cores needs a big dataset
    model = SerialLoaderBoringModel()
    tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False, min_acc=0.05)


@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."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        precision=16,
        progress_bar_refresh_rate=0,
        max_epochs=2,
        tpu_cores=1,
        limit_train_batches=8,
        limit_val_batches=2,
    )

    model = BoringModel()
    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."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        precision=16,
        progress_bar_refresh_rate=0,
        max_epochs=2,
        tpu_cores=[tpu_core],
        limit_train_batches=4,
        limit_val_batches=2,
    )

    model = BoringModel()
    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."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        precision=16,
        progress_bar_refresh_rate=0,
        max_epochs=1,
        tpu_cores=8,
        limit_train_batches=4,
        limit_val_batches=4,
    )

    # 8 cores needs a big dataset
    model = SerialLoaderBoringModel()
    tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False, min_acc=0.05)


@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"""

    # todo: Test on 8 cores - hanging.

    class CustomBoringModel(BoringModel):

        def validation_step(self, *args, **kwargs):
            out = super().validation_step(*args, **kwargs)
            self.log('val_loss', out['x'])
            return out

    tutils.reset_seed()
    model = CustomBoringModel()
    trainer = Trainer(
        callbacks=[EarlyStopping(monitor='val_loss')],
        default_root_dir=tmpdir,
        progress_bar_refresh_rate=0,
        max_epochs=2,
        limit_train_batches=2,
        limit_val_batches=2,
        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."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        progress_bar_refresh_rate=0,
        max_epochs=4,
        tpu_cores=1,
        limit_train_batches=4,
        limit_val_batches=4,
        gradient_clip_val=0.5,
    )

    model = BoringModel()
    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"""

    tutils.reset_seed()
    model = BoringModel()

    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).accelerator_connector.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)
        assert isinstance(trainer.accelerator_backend, TPUAccelerator)
        assert isinstance(trainer.training_type_plugin, TPUSpawnPlugin)
        obj = ("ver_0.5", "logger_name", rank)
        result = trainer.training_type_plugin.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.accelerator_connector.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)


@pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")
@pl_multi_process_test
def test_tpu_reduce():
    """Test tpu spawn reduce operation """

    def test_reduce(rank):
        trainer = Trainer(tpu_cores=8)
        # faster this way
        reduce_ops = ["mean", "AVG", "undefined", "sum", ReduceOp.SUM, ReduceOp.MAX]
        for reduce_op in reduce_ops:
            if reduce_op == "undefined" or reduce_op == ReduceOp.MAX:
                with pytest.raises(MisconfigurationException, match="TPUSpawn TrainingTypePlugin only support"):
                    result = trainer.training_type_plugin.reduce(1, reduce_op)
            else:
                result = trainer.training_type_plugin.reduce(1, reduce_op)
            if isinstance(reduce_op, str) and reduce_op.lower() in ("mean", "avg"):
                assert result.item() == 1
            else:
                assert result.item() == 8

    xmp.spawn(test_reduce, nprocs=8, start_method='fork')