72 lines
2.3 KiB
Python
72 lines
2.3 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 unittest import mock
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import pytest
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import torch
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from pytorch_lightning import Trainer
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from pytorch_lightning.accelerators import GPUAccelerator
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from pytorch_lightning.accelerators.gpu import get_nvidia_gpu_stats
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from tests.helpers import BoringModel
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from tests.helpers.runif import RunIf
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@RunIf(min_gpus=1)
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def test_get_torch_gpu_stats(tmpdir):
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current_device = torch.device(f"cuda:{torch.cuda.current_device()}")
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gpu_stats = GPUAccelerator().get_device_stats(current_device)
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fields = ["allocated_bytes.all.freed", "inactive_split.all.peak", "reserved_bytes.large_pool.peak"]
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for f in fields:
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assert any(f in h for h in gpu_stats.keys())
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@RunIf(min_gpus=1)
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def test_get_nvidia_gpu_stats(tmpdir):
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current_device = torch.device(f"cuda:{torch.cuda.current_device()}")
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gpu_stats = get_nvidia_gpu_stats(current_device)
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fields = ["utilization.gpu", "memory.used", "memory.free", "utilization.memory"]
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for f in fields:
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assert any(f in h for h in gpu_stats.keys())
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@RunIf(min_gpus=1)
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@mock.patch("torch.cuda.set_device")
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def test_set_cuda_device(set_device_mock, tmpdir):
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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fast_dev_run=True,
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accelerator="gpu",
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devices=1,
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enable_checkpointing=False,
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enable_model_summary=False,
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enable_progress_bar=False,
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)
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trainer.fit(model)
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set_device_mock.assert_called_once()
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@RunIf(min_gpus=1)
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def test_gpu_availability():
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assert GPUAccelerator.is_available()
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@RunIf(min_gpus=1)
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def test_warning_if_gpus_not_used():
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with pytest.warns(UserWarning, match="GPU available but not used. Set `accelerator` and `devices`"):
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Trainer()
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