107 lines
3.7 KiB
Python
107 lines
3.7 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 pytest
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import torch
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from tests.backends import ddp_model
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from tests.backends import DDPLauncher
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from tests.utilities.distributed import call_training_script
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@pytest.mark.parametrize('cli_args', [
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pytest.param('--max_epochs 1 --gpus 2 --accelerator ddp'),
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])
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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, cli_args):
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# call the script
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std, err = 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.parametrize('cli_args', [
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pytest.param('--max_epochs 1 --gpus 2 --accelerator ddp'),
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])
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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, cli_args):
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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.parametrize('cli_args', [
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pytest.param('--max_epochs 1 --gpus 2 --accelerator ddp'),
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])
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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, cli_args):
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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'] > 0.90
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# START: test_cli ddp test
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@pytest.mark.skipif(os.getenv("PL_IN_LAUNCHER", '0') == '1', reason="test runs only in DDPLauncher")
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def internal_test_cli(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(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_cli(tmpdir):
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DDPLauncher.run_from_cmd_line("--max_epochs 1 --gpus 2 --accelerator ddp", internal_test_cli, 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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assert str(result['result']) == '1'
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# END: test_cli ddp test
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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@DDPLauncher.run("--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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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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