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105 lines
3.7 KiB
105 lines
3.7 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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 unittest
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.incubate.fleet.base.role_maker as role_maker
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from paddle.fluid.incubate.fleet.collective import CollectiveOptimizer, fleet
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import os
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import sys
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from paddle.distributed.fleet.utils.fs import LocalFS, HDFSClient
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import paddle.fluid.incubate.checkpoint.auto_checkpoint as acp
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from paddle.fluid.incubate.checkpoint.checkpoint_saver import PaddleModel
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from paddle.fluid.framework import program_guard
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from paddle.fluid import unique_name
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import numpy as np
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from paddle.io import Dataset, BatchSampler, DataLoader
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from paddle.fluid.tests.unittests.auto_checkpoint_utils import AutoCheckpointBase, get_logger
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from paddle.fluid.tests.unittests.test_auto_checkpoint import AutoCheckPointACLBase
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paddle.enable_static()
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logger = get_logger()
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class AutoCheckpointTestMul(AutoCheckPointACLBase):
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def setUp(self):
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get_logger()
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logger.info("enter tests")
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self._old_environ = dict(os.environ)
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proc_env = {
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"PADDLE_RUNNING_ENV": "PADDLE_EDL_AUTO_CHECKPOINT",
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"PADDLE_TRAINER_ID": "0",
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"PADDLE_RUNNING_PLATFORM": "PADDLE_CLOUD",
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"PADDLE_JOB_ID": "test_job_auto_dist_multiple",
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"PADDLE_EDL_HDFS_HOME": "/usr/local/hadoop-2.7.7",
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"PADDLE_EDL_HDFS_NAME": "",
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"PADDLE_EDL_HDFS_UGI": "",
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"PADDLE_EDL_HDFS_CHECKPOINT_PATH": "auto_checkpoint_dist_multiple",
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"PADDLE_EDL_ONLY_FOR_CE_TEST": "1",
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"PADDLE_EDL_FS_CACHE": ".auto_checkpoint_test_dist_multiple",
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"PADDLE_EDL_SAVE_CHECKPOINT_INTER": "0"
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}
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os.environ.update(proc_env)
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def test_multiple(self):
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checker = acp._get_checker()
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fs = HDFSClient(checker.hdfs_home, None)
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fs.delete(checker.hdfs_checkpoint_path)
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self._reset_generator()
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logger.info("begin test_multiple")
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fs = LocalFS()
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save_dir = "./run_save_0"
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fs.delete(save_dir)
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exe, main_prog1, startup_prog1 = self._generate()
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_, main_prog2, startup_prog2 = self._generate()
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compiled1, data_loader1, optimizer1, loss1, image1, label1 = \
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self._init_env(exe, main_prog1, startup_prog1)
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compiled2, data_loader2, optimizer2, loss2, image2, label2 = \
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self._init_env(exe, main_prog2, startup_prog2)
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o = None
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epochs = []
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for i in acp.train_epoch_range(3, 0):
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for data in data_loader1():
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fetch = exe.run(compiled1, feed=data, fetch_list=[loss1])
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for data in data_loader2():
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fetch = exe.run(compiled2, feed=data, fetch_list=[loss2])
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o = acp._get_train_epoch_range()
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self.assertEqual(len(o._exe_status), 2)
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print(o._exe_status)
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epochs.append(i)
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o = acp._get_train_epoch_range()
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self.assertTrue(o == None, "now train epoch must not exits now")
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self.assertEqual(i, 2)
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self.assertEqual(epochs, [0, 1, 2])
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fs.delete(save_dir)
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logger.info("end test_multiple")
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if __name__ == '__main__':
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unittest.main()
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