lightning/tests/models/test_amp.py

164 lines
4.3 KiB
Python

import os
import pytest
import torch
import tests.base.utils as tutils
from pytorch_lightning import Trainer
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.base import (
LightningTestModel,
)
@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
def test_amp_single_gpu(tmpdir):
"""Make sure DDP + AMP work."""
tutils.reset_seed()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
gpus=1,
distributed_backend='ddp',
precision=16
)
tutils.run_model_test(trainer_options, model)
@pytest.mark.spawn
@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
def test_no_amp_single_gpu(tmpdir):
"""Make sure DDP + AMP work."""
tutils.reset_seed()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
gpus=1,
distributed_backend='dp',
precision=16
)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_amp_gpu_ddp(tmpdir):
"""Make sure DDP + AMP work."""
tutils.reset_seed()
tutils.set_random_master_port()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
gpus=2,
distributed_backend='ddp',
precision=16
)
tutils.run_model_test(trainer_options, model)
@pytest.mark.spawn
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_amp_gpu_ddp_slurm_managed(tmpdir):
"""Make sure DDP + AMP work."""
tutils.reset_seed()
# simulate setting slurm flags
tutils.set_random_master_port()
os.environ['SLURM_LOCALID'] = str(0)
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
trainer_options = dict(
max_epochs=1,
gpus=[0],
distributed_backend='ddp',
precision=16
)
# exp file to get meta
logger = tutils.get_default_testtube_logger(tmpdir, False)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# add these to the trainer options
trainer_options['checkpoint_callback'] = checkpoint
trainer_options['logger'] = logger
# fit model
trainer = Trainer(**trainer_options)
trainer.is_slurm_managing_tasks = True
result = trainer.fit(model)
# correct result and ok accuracy
assert result == 1, 'amp + ddp model failed to complete'
# test root model address
assert trainer.resolve_root_node_address('abc') == 'abc'
assert trainer.resolve_root_node_address('abc[23]') == 'abc23'
assert trainer.resolve_root_node_address('abc[23-24]') == 'abc23'
assert trainer.resolve_root_node_address('abc[23-24, 45-40, 40]') == 'abc23'
def test_cpu_model_with_amp(tmpdir):
"""Make sure model trains on CPU."""
tutils.reset_seed()
trainer_options = dict(
default_save_path=tmpdir,
progress_bar_refresh_rate=0,
logger=tutils.get_default_testtube_logger(tmpdir),
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.4,
precision=16
)
model, hparams = tutils.get_default_model()
with pytest.raises((MisconfigurationException, ModuleNotFoundError)):
tutils.run_model_test(trainer_options, model, on_gpu=False)
@pytest.mark.spawn
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_amp_gpu_dp(tmpdir):
"""Make sure DP + AMP work."""
tutils.reset_seed()
model, hparams = tutils.get_default_model()
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
gpus='0, 1', # test init with gpu string
distributed_backend='dp',
precision=16
)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1
if __name__ == '__main__':
pytest.main([__file__])