242 lines
8.8 KiB
Python
242 lines
8.8 KiB
Python
import os
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import pytest
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import torch
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import tests.base.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.core import memory
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from pytorch_lightning.trainer.distrib_parts import parse_gpu_ids, determine_root_gpu_device
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.base import EvalModelTemplate
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PRETEND_N_OF_GPUS = 16
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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@pytest.mark.parametrize('gpus', [1, [0], [1]])
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def test_single_gpu_model(tmpdir, gpus):
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"""Make sure single GPU works (DP mode)."""
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trainer_options = dict(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=1,
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train_percent_check=0.1,
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val_percent_check=0.1,
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gpus=gpus
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)
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model = EvalModelTemplate()
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tutils.run_model_test(trainer_options, model)
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@pytest.mark.spawn
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@pytest.mark.parametrize("backend", ['dp', 'ddp', 'ddp2'])
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_multi_gpu_model(tmpdir, backend):
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"""Make sure DDP works."""
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tutils.set_random_master_port()
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trainer_options = dict(
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default_root_dir=tmpdir,
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max_epochs=1,
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train_percent_check=0.4,
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val_percent_check=0.2,
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gpus=[0, 1],
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distributed_backend=backend,
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)
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model = EvalModelTemplate()
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# tutils.run_model_test(trainer_options, model)
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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assert result
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# test memory helper functions
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memory.get_memory_profile('min_max')
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@pytest.mark.spawn
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
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"""Make sure DDP works with dataloaders passed to fit()"""
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tutils.set_random_master_port()
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trainer_options = dict(default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=1,
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train_percent_check=0.1,
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val_percent_check=0.1,
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gpus=[0, 1],
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distributed_backend='ddp')
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model = EvalModelTemplate()
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fit_options = dict(train_dataloader=model.train_dataloader(),
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val_dataloaders=model.val_dataloader())
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model, **fit_options)
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assert result == 1, "DDP doesn't work with dataloaders passed to fit()."
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@pytest.mark.spawn
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_multi_gpu_none_backend(tmpdir):
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"""Make sure when using multiple GPUs the user can't use `distributed_backend = None`."""
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trainer_options = dict(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=1,
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train_percent_check=0.1,
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val_percent_check=0.1,
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gpus='-1'
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)
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model = EvalModelTemplate()
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with pytest.warns(UserWarning):
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tutils.run_model_test(trainer_options, model)
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@pytest.fixture
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def mocked_device_count(monkeypatch):
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def device_count():
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return PRETEND_N_OF_GPUS
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monkeypatch.setattr(torch.cuda, 'device_count', device_count)
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@pytest.fixture
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def mocked_device_count_0(monkeypatch):
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def device_count():
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return 0
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monkeypatch.setattr(torch.cuda, 'device_count', device_count)
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(["gpus", "expected_num_gpus", "distributed_backend"], [
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pytest.param(None, 0, None, id="None - expect 0 gpu to use."),
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pytest.param(0, 0, None, id="Oth gpu, expect 1 gpu to use."),
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pytest.param(1, 1, None, id="1st gpu, expect 1 gpu to use."),
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pytest.param(-1, PRETEND_N_OF_GPUS, "ddp", id="-1 - use all gpus"),
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pytest.param('-1', PRETEND_N_OF_GPUS, "ddp", id="'-1' - use all gpus"),
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pytest.param(3, 3, "ddp", id="3rd gpu - 1 gpu to use (backend:ddp)")
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])
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def test_trainer_gpu_parse(mocked_device_count, gpus, expected_num_gpus, distributed_backend):
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assert Trainer(gpus=gpus, distributed_backend=distributed_backend).num_gpus == expected_num_gpus
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(["gpus", "expected_num_gpus", "distributed_backend"], [
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pytest.param(None, 0, None, id="None - expect 0 gpu to use."),
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pytest.param(None, 0, "ddp", id="None - expect 0 gpu to use."),
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])
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def test_trainer_num_gpu_0(mocked_device_count_0, gpus, expected_num_gpus, distributed_backend):
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assert Trainer(gpus=gpus, distributed_backend=distributed_backend).num_gpus == expected_num_gpus
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(['gpus', 'expected_root_gpu', "distributed_backend"], [
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pytest.param(None, None, "ddp", id="None is None"),
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pytest.param(0, None, "ddp", id="O gpus, expect gpu root device to be None."),
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pytest.param(1, 0, "ddp", id="1 gpu, expect gpu root device to be 0."),
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pytest.param(-1, 0, "ddp", id="-1 - use all gpus, expect gpu root device to be 0."),
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pytest.param('-1', 0, "ddp", id="'-1' - use all gpus, expect gpu root device to be 0."),
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pytest.param(3, 0, "ddp", id="3 gpus, expect gpu root device to be 0.(backend:ddp)")
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])
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def test_root_gpu_property(mocked_device_count, gpus, expected_root_gpu, distributed_backend):
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assert Trainer(gpus=gpus, distributed_backend=distributed_backend).root_gpu == expected_root_gpu
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(['gpus', 'expected_root_gpu', "distributed_backend"], [
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pytest.param(None, None, None, id="None is None"),
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pytest.param(None, None, "ddp", id="None is None"),
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pytest.param(0, None, "ddp", id="None is None"),
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])
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def test_root_gpu_property_0_passing(mocked_device_count_0, gpus, expected_root_gpu, distributed_backend):
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assert Trainer(gpus=gpus, distributed_backend=distributed_backend).root_gpu == expected_root_gpu
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# Asking for a gpu when non are available will result in a MisconfigurationException
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(['gpus', 'expected_root_gpu', "distributed_backend"], [
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pytest.param(1, None, "ddp"),
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pytest.param(3, None, "ddp"),
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pytest.param(3, None, "ddp"),
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pytest.param([1, 2], None, "ddp"),
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pytest.param([0, 1], None, "ddp"),
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pytest.param(-1, None, "ddp"),
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pytest.param('-1', None, "ddp")
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])
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def test_root_gpu_property_0_raising(mocked_device_count_0, gpus, expected_root_gpu, distributed_backend):
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with pytest.raises(MisconfigurationException):
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Trainer(gpus=gpus, distributed_backend=distributed_backend).root_gpu
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(['gpus', 'expected_root_gpu'], [
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pytest.param(None, None, id="No gpus, expect gpu root device to be None"),
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pytest.param([0], 0, id="Oth gpu, expect gpu root device to be 0."),
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pytest.param([1], 1, id="1st gpu, expect gpu root device to be 1."),
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pytest.param([3], 3, id="3rd gpu, expect gpu root device to be 3."),
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pytest.param([1, 2], 1, id="[1, 2] gpus, expect gpu root device to be 1."),
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])
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def test_determine_root_gpu_device(gpus, expected_root_gpu):
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assert determine_root_gpu_device(gpus) == expected_root_gpu
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(['gpus', 'expected_gpu_ids'], [
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pytest.param(None, None),
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pytest.param(0, None),
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pytest.param(1, [0]),
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pytest.param(3, [0, 1, 2]),
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pytest.param(-1, list(range(PRETEND_N_OF_GPUS)), id="-1 - use all gpus"),
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pytest.param([0], [0]),
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pytest.param([1, 3], [1, 3]),
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pytest.param('0', [0]),
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pytest.param('3', [3]),
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pytest.param('1, 3', [1, 3]),
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pytest.param('2,', [2]),
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pytest.param('-1', list(range(PRETEND_N_OF_GPUS)), id="'-1' - use all gpus"),
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])
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def test_parse_gpu_ids(mocked_device_count, gpus, expected_gpu_ids):
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assert parse_gpu_ids(gpus) == expected_gpu_ids
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(['gpus'], [
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pytest.param(0.1),
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pytest.param(-2),
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pytest.param(False),
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pytest.param([]),
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pytest.param([-1]),
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pytest.param([None]),
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pytest.param(['0']),
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pytest.param((0, 1)),
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])
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def test_parse_gpu_fail_on_unsupported_inputs(mocked_device_count, gpus):
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with pytest.raises(MisconfigurationException):
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parse_gpu_ids(gpus)
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize("gpus", [[1, 2, 19], -1, '-1'])
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def test_parse_gpu_fail_on_non_existent_id(mocked_device_count_0, gpus):
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with pytest.raises(MisconfigurationException):
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parse_gpu_ids(gpus)
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@pytest.mark.gpus_param_tests
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def test_parse_gpu_fail_on_non_existent_id_2(mocked_device_count):
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with pytest.raises(MisconfigurationException):
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parse_gpu_ids([1, 2, 19])
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize("gpus", [-1, '-1'])
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def test_parse_gpu_returns_None_when_no_devices_are_available(mocked_device_count_0, gpus):
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with pytest.raises(MisconfigurationException):
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parse_gpu_ids(gpus)
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