lightning/tests/models/test_amp.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 unittest import mock

import pytest
import torch
from torch import optim
from torch.utils.data import DataLoader

import tests.helpers.utils as tutils
from pytorch_lightning import Trainer
from pytorch_lightning.plugins.environments import SLURMEnvironment
from pytorch_lightning.trainer.states import TrainerState
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.helpers import BoringModel, RandomDataset
from tests.helpers.runif import RunIf


class AMPTestModel(BoringModel):

    def _step(self, batch, batch_idx):
        assert torch.is_autocast_enabled()
        output = self(batch)
        assert output.dtype == torch.float16
        loss = self.loss(batch, output)
        return loss

    def training_step(self, batch, batch_idx):
        output = self._step(batch, batch_idx)
        return {"loss": output}

    def validation_step(self, batch, batch_idx):
        output = self._step(batch, batch_idx)
        return {"x": output}

    def test_step(self, batch, batch_idx):
        output = self._step(batch, batch_idx)
        return {"y": output}

    def predict(self, batch, batch_idx, dataloader_idx=None):
        assert torch.is_autocast_enabled()
        output = self(batch)
        assert output.dtype == torch.float16
        return output


@pytest.mark.skip(reason='dp + amp not supported currently')  # TODO
@RunIf(min_gpus=1)
def test_amp_single_gpu_dp(tmpdir):
    """Make sure DP/DDP + AMP work."""
    tutils.reset_seed()

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=1,
        gpus=1,
        accelerator='dp',
        precision=16,
    )

    model = AMPTestModel()
    # tutils.run_model_test(trainer_options, model)
    trainer.fit(model)
    trainer.test(model)
    trainer.predict(model, DataLoader(RandomDataset(32, 64)))

    assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"


@RunIf(min_gpus=1)
def test_amp_single_gpu_ddp_spawn(tmpdir):
    """Make sure DP/DDP + AMP work."""
    tutils.reset_seed()
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=1,
        gpus=1,
        accelerator='ddp_spawn',
        precision=16,
    )

    model = AMPTestModel()
    # tutils.run_model_test(trainer_options, model)
    trainer.fit(model)
    trainer.test(model)
    trainer.predict(model, DataLoader(RandomDataset(32, 64)))
    assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"


@pytest.mark.skip(reason='dp + amp not supported currently')  # TODO
@RunIf(min_gpus=1)
def test_amp_multi_gpu_dp(tmpdir):
    """Make sure DP/DDP + AMP work."""
    tutils.reset_seed()

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=1,
        gpus=2,
        accelerator='dp',
        precision=16,
    )

    model = AMPTestModel()
    # tutils.run_model_test(trainer_options, model)
    trainer.fit(model)

    assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"


@RunIf(min_gpus=2)
def test_amp_multi_gpu_ddp_spawn(tmpdir):
    """Make sure DP/DDP + AMP work."""
    tutils.reset_seed()
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=1,
        gpus=2,
        accelerator='ddp_spawn',
        precision=16,
    )

    model = AMPTestModel()
    # tutils.run_model_test(trainer_options, model)
    trainer.fit(model)
    trainer.test(model)
    trainer.predict(model, DataLoader(RandomDataset(32, 64)))
    assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"


@RunIf(min_gpus=2)
@mock.patch.dict(
    os.environ, {
        "SLURM_NTASKS": "1",
        "SLURM_JOB_NAME": "SOME_NAME",
        "SLURM_NODEID": "0",
        "LOCAL_RANK": "0",
        "SLURM_LOCALID": "0",
        "SLURM_PROCID": "0",
    }
)
def test_amp_gpu_ddp_slurm_managed(tmpdir):
    """Make sure DDP + AMP work."""
    # simulate setting slurm flags
    tutils.set_random_master_port()

    model = AMPTestModel()

    # exp file to get meta
    logger = tutils.get_default_logger(tmpdir)

    # exp file to get weights
    checkpoint = tutils.init_checkpoint_callback(logger)

    # fit model
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=1,
        gpus=[0],
        accelerator='ddp_spawn',
        precision=16,
        callbacks=[checkpoint],
        logger=logger,
    )
    trainer.fit(model)

    # correct result and ok accuracy
    assert trainer.state == TrainerState.FINISHED, 'amp + ddp model failed to complete'

    # test root model address
    assert isinstance(trainer.training_type_plugin.cluster_environment, SLURMEnvironment)
    assert trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc') == 'abc'
    assert trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc[23]') == 'abc23'
    assert trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc[23-24]') == 'abc23'
    generated = trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc[23-24, 45-40, 40]')
    assert generated == 'abc23'


@pytest.mark.skipif(torch.cuda.is_available(), reason="test is restricted only on CPU")
def test_cpu_model_with_amp(tmpdir):
    """Make sure model trains on CPU."""
    with pytest.raises(MisconfigurationException, match="AMP is only available on GPU"):
        Trainer(precision=16)


@mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})
def test_amp_without_apex(tmpdir):
    """Check that even with apex amp type without requesting precision=16 the amp backend is void."""
    model = BoringModel()

    trainer = Trainer(
        default_root_dir=tmpdir,
        amp_backend='native',
    )
    assert trainer.amp_backend is None

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=1,
        amp_backend='apex',
    )
    assert trainer.amp_backend is None
    trainer.fit(model)
    assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
    assert trainer.dev_debugger.count_events('AMP') == 0


@mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})
@RunIf(min_gpus=1, amp_apex=True)
def test_amp_with_apex(tmpdir):
    """Check calling apex scaling in training."""

    class CustomModel(BoringModel):

        def training_step(self, batch, batch_idx, optimizer_idx):
            return super().training_step(batch, batch_idx)

        def configure_optimizers(self):
            optimizer1 = optim.Adam(self.parameters(), lr=0.01)
            optimizer2 = optim.SGD(self.parameters(), lr=0.01)
            lr_scheduler1 = optim.lr_scheduler.StepLR(optimizer1, 1, gamma=0.1)
            lr_scheduler2 = optim.lr_scheduler.StepLR(optimizer2, 1, gamma=0.1)
            return [optimizer1, optimizer2], [lr_scheduler1, lr_scheduler2]

    model = CustomModel()
    model.training_epoch_end = None

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_steps=5,
        precision=16,
        amp_backend='apex',
        gpus=1,
    )
    assert str(trainer.amp_backend) == "AMPType.APEX"
    trainer.fit(model)
    assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
    assert trainer.dev_debugger.count_events('AMP') == 10

    assert isinstance(trainer.lr_schedulers[0]['scheduler'].optimizer, optim.Adam)
    assert isinstance(trainer.lr_schedulers[1]['scheduler'].optimizer, optim.SGD)