forked from mindspore-Ecosystem/mindspore
101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import pytest
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from mindspore.ops import operations as P
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import mindspore.nn as nn
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from mindspore.train import Model
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from mindspore.common import set_seed
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import mindspore.dataset as ds
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from mindspore.train.callback import Callback
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from mindspore import log as logger
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set_seed(1)
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# pylint: disable=no-value-for-parameter
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def create_np_dataset(size, num_parallel_workers, python_multiprocessing):
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"""
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Create dataset for train or test
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"""
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def my_func(x):
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return x + 1 if x % 2 else x - 1
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data = ds.NumpySlicesDataset(list(range(1, size + 1)), shuffle=False)
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data = data.map(operations=my_func, num_parallel_workers=num_parallel_workers,
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python_multiprocessing=python_multiprocessing)
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return data
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def create_model():
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"""
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Define and return a simple model
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"""
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.print = P.Print()
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def construct(self, x):
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self.print(x)
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return x
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net = Net()
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model_ = Model(net)
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return model_
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class MyCallback(Callback):
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def __init__(self, dataset_size, reset_point):
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self.dataset_size = dataset_size
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self.reset_point = reset_point
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def epoch_end(self, run_context):
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cb_params = run_context.original_args()
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logger.info(f"Epoch #{cb_params.cur_epoch_num - 1} has ended")
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if cb_params.cur_epoch_num == self.reset_point:
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dataset = ds.engine.datasets._get_training_dataset() # pylint: disable=W0212
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dataset._reset(self.reset_point * self.dataset_size) # pylint: disable=W0212
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize("fast_recovery", (False, True))
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@pytest.mark.parametrize("num_parallel_workers", (1, 4))
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@pytest.mark.parametrize("python_multiprocessing", (False, True))
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@pytest.mark.forked
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def test_dataset_reset_sink(fast_recovery, num_parallel_workers, python_multiprocessing):
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"""
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Feature: Dataset recovery
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Description: Test Dataset recovery when GPU (and sink mode) is used.
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Expectation: Training completes successfully
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"""
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original_fast_recovery = ds.config.get_fast_recovery()
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ds.config.set_fast_recovery(fast_recovery)
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data = create_np_dataset(20, num_parallel_workers, python_multiprocessing)
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model = create_model()
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num_epochs = 3
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reset_point = 2 # 2nd epoch
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cb = MyCallback(dataset_size=data.get_dataset_size(), reset_point=reset_point)
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model.train(num_epochs, data, callbacks=[cb])
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ds.config.set_fast_recovery(original_fast_recovery)
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if __name__ == '__main__':
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test_dataset_reset_sink(fast_recovery=True, num_parallel_workers=4, python_multiprocessing=True)
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