325 lines
13 KiB
Python
325 lines
13 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import namedtuple
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from unittest.mock import patch
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import pytest
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import torch
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from torchtext.data import Batch, Dataset, Example, Field, LabelField
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import tests.helpers.pipelines as tpipes
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import tests.helpers.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.utilities import device_parser
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers import BoringModel
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PRETEND_N_OF_GPUS = 16
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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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tutils.set_random_master_port()
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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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limit_train_batches=0.2,
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limit_val_batches=0.2,
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gpus=2,
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)
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, min_acc=0.20)
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-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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limit_train_batches=0.1,
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limit_val_batches=0.1,
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gpus=gpus
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)
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model = BoringModel()
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tpipes.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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def is_available():
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return True
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monkeypatch.setattr(torch.cuda, 'is_available', is_available)
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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, accelerator=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, accelerator=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, accelerator=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, accelerator=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, accelerator=distributed_backend)
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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 device_parser.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((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 device_parser.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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])
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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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device_parser.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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device_parser.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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device_parser.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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device_parser.parse_gpu_ids(gpus)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_single_gpu_batch_parse():
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trainer = Trainer(gpus=1)
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# non-transferrable types
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primitive_objects = [None, {}, [], 1.0, "x", [None, 2], {"x": (1, 2), "y": None}]
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for batch in primitive_objects:
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data = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
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assert data == batch
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# batch is just a tensor
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batch = torch.rand(2, 3)
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batch = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
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assert batch.device.index == 0 and batch.type() == 'torch.cuda.FloatTensor'
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# tensor list
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batch = [torch.rand(2, 3), torch.rand(2, 3)]
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batch = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
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assert batch[0].device.index == 0 and batch[0].type() == 'torch.cuda.FloatTensor'
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assert batch[1].device.index == 0 and batch[1].type() == 'torch.cuda.FloatTensor'
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# tensor list of lists
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batch = [[torch.rand(2, 3), torch.rand(2, 3)]]
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batch = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
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assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
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assert batch[0][1].device.index == 0 and batch[0][1].type() == 'torch.cuda.FloatTensor'
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# tensor dict
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batch = [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)}]
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batch = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
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assert batch[0]['a'].device.index == 0 and batch[0]['a'].type() == 'torch.cuda.FloatTensor'
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assert batch[0]['b'].device.index == 0 and batch[0]['b'].type() == 'torch.cuda.FloatTensor'
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# tuple of tensor list and list of tensor dict
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batch = ([torch.rand(2, 3) for _ in range(2)], [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)} for _ in range(2)])
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batch = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
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assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
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assert batch[1][0]['a'].device.index == 0
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assert batch[1][0]['a'].type() == 'torch.cuda.FloatTensor'
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assert batch[1][0]['b'].device.index == 0
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assert batch[1][0]['b'].type() == 'torch.cuda.FloatTensor'
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# namedtuple of tensor
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BatchType = namedtuple('BatchType', ['a', 'b'])
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batch = [BatchType(a=torch.rand(2, 3), b=torch.rand(2, 3)) for _ in range(2)]
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batch = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
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assert batch[0].a.device.index == 0
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assert batch[0].a.type() == 'torch.cuda.FloatTensor'
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# non-Tensor that has `.to()` defined
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class CustomBatchType:
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def __init__(self):
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self.a = torch.rand(2, 2)
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def to(self, *args, **kwargs):
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self.a = self.a.to(*args, **kwargs)
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return self
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batch = trainer.accelerator_backend.batch_to_device(CustomBatchType(), torch.device('cuda:0'))
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assert batch.a.type() == 'torch.cuda.FloatTensor'
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# torchtext.data.Batch
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samples = [{
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'text': 'PyTorch Lightning is awesome!',
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'label': 0
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}, {
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'text': 'Please make it work with torchtext',
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'label': 1
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}]
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text_field = Field()
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label_field = LabelField()
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fields = {'text': ('text', text_field), 'label': ('label', label_field)}
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examples = [Example.fromdict(sample, fields) for sample in samples]
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dataset = Dataset(examples=examples, fields=fields.values())
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# Batch runs field.process() that numericalizes tokens, but it requires to build dictionary first
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text_field.build_vocab(dataset)
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label_field.build_vocab(dataset)
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batch = Batch(data=examples, dataset=dataset)
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batch = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
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assert batch.text.type() == 'torch.cuda.LongTensor'
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assert batch.label.type() == 'torch.cuda.LongTensor'
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_non_blocking():
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""" Tests that non_blocking=True only gets passed on torch.Tensor.to, but not on other objects. """
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trainer = Trainer()
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batch = torch.zeros(2, 3)
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with patch.object(batch, 'to', wraps=batch.to) as mocked:
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batch = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
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mocked.assert_called_with(torch.device('cuda', 0), non_blocking=True)
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class BatchObject(object):
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def to(self, *args, **kwargs):
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pass
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batch = BatchObject()
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with patch.object(batch, 'to', wraps=batch.to) as mocked:
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batch = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
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mocked.assert_called_with(torch.device('cuda', 0))
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