105 lines
3.6 KiB
Python
105 lines
3.6 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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import os
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import platform
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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 pytorch_lightning import Trainer
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from tests.accelerators import ddp_model, DDPLauncher
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from tests.helpers.boring_model import BoringModel
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from tests.utilities.distributed import call_training_script
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CLI_ARGS = '--max_epochs 1 --gpus 2 --accelerator ddp'
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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_ddp_fit_only(tmpdir):
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# call the script
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call_training_script(ddp_model, CLI_ARGS, 'fit', tmpdir, timeout=120)
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# load the results of the script
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result_path = os.path.join(tmpdir, 'ddp.result')
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result = torch.load(result_path)
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# verify the file wrote the expected outputs
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assert result['status'] == 'complete'
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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_ddp_test_only(tmpdir):
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# call the script
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call_training_script(ddp_model, CLI_ARGS, 'test', tmpdir)
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# load the results of the script
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result_path = os.path.join(tmpdir, 'ddp.result')
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result = torch.load(result_path)
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# verify the file wrote the expected outputs
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assert result['status'] == 'complete'
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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_ddp_fit_test(tmpdir):
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# call the script
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call_training_script(ddp_model, CLI_ARGS, 'fit_test', tmpdir, timeout=20)
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# load the results of the script
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result_path = os.path.join(tmpdir, 'ddp.result')
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result = torch.load(result_path)
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# verify the file wrote the expected outputs
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assert result['status'] == 'complete'
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model_outs = result['result']
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for out in model_outs:
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assert out['test_acc'] > 0.7
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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@DDPLauncher.run(
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"--max_epochs [max_epochs] --gpus 2 --accelerator [accelerator]",
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max_epochs=["1"],
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accelerator=["ddp", "ddp_spawn"]
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)
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def test_cli_to_pass(tmpdir, args=None):
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"""
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This test verify we can call function using test_cli name
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"""
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return '1'
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@pytest.mark.skipif(platform.system() == "Windows", reason="Distributed training is not supported on Windows")
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@pytest.mark.skipif(torch.cuda.is_available(), reason="test doesn't requires GPU machine")
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def test_torch_distributed_backend_env_variables(tmpdir):
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"""
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This test set `undefined` as torch backend and should raise an `Backend.UNDEFINED` ValueError.
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"""
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_environ = {"PL_TORCH_DISTRIBUTED_BACKEND": "undefined", "CUDA_VISIBLE_DEVICES": "0,1", "WORLD_SIZE": "2"}
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with patch.dict(os.environ, _environ), \
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patch('torch.cuda.device_count', return_value=2):
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with pytest.raises(ValueError, match="Invalid backend: 'undefined'"):
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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="ddp",
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gpus=2,
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logger=False,
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)
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trainer.fit(model)
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