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280 lines
8.9 KiB
280 lines
8.9 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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paddle.enable_static()
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logger = get_logger()
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class AutoCheckPointACLBase(AutoCheckpointBase):
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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",
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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",
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"PADDLE_EDL_ONLY_FOR_CE_TEST": "1",
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"PADDLE_EDL_FS_CACHE": ".auto_checkpoint_test",
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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 tearDown(self):
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os.environ.clear()
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os.environ.update(self._old_environ)
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file_name = os.path.basename(__file__)
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base_name = os.path.splitext(file_name)[0]
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print("runnng name:", base_name)
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def _run_normal(self):
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exe, main_prog, startup_prog = self._generate()
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save_dir = "./run_save_model"
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fs = LocalFS()
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fs.delete(save_dir)
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logger.info("begin _run_normal")
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compiled, data_loader, optimizer, loss, image, label = self._init_env(
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exe, main_prog, startup_prog)
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for i in range(3):
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self.assertEqual(acp._get_train_epoch_range(), None)
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self.assertEqual(acp.g_acp_type, None)
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for data in data_loader():
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self.assertEqual(acp.g_acp_type, None)
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self.assertEqual(acp._get_train_epoch_range(), None)
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fetch = exe.run(compiled, feed=data, fetch_list=[loss])
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self.assertEqual(acp.g_acp_type, None)
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self.assertEqual(acp._get_train_epoch_range(), None)
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m1 = PaddleModel(exe, compiled)
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m1.serialize(save_dir)
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m2 = PaddleModel(exe, compiled)
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m2.deserialize(save_dir)
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logger.info("end _run_normal")
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fs.delete(save_dir)
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def _not_use_train(self):
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logger.info("begin _not_use_train")
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exe, main_prog, startup_prog = self._generate()
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compiled, data_loader, optimizer, loss, image, label = \
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self._init_env(exe, main_prog, startup_prog)
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epochs = []
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for i in acp.train_epoch_range(3, 0):
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epochs.append(i)
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for data in data_loader():
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fetch = exe.run(compiled, feed=data, fetch_list=[loss])
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self.assertEqual(epochs, [0, 1, 2])
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logger.info("end _not_use_train")
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def _run_save_0(self, break_epoch_no=None):
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logger.info("begin _run_save_0")
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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_prog, startup_prog = self._generate()
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compiled, data_loader, optimizer, loss, image, label = \
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self._init_env(exe, main_prog, startup_prog)
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o = None
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i = 0
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name = None
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for i in acp.train_epoch_range(3, 0):
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o = acp._get_train_epoch_range()
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name = o.name
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for data in data_loader():
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fetch = exe.run(compiled, feed=data, fetch_list=[loss])
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self.assertEqual(len(o._exe_status), 1)
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if break_epoch_no is not None:
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if i == break_epoch_no:
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break
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o = acp._get_train_epoch_range()
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assert o == None, "now train epoch must not exits now"
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if break_epoch_no is None:
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self.assertEqual(i, 2)
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else:
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self.assertEqual(i, break_epoch_no)
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fs.delete(save_dir)
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logger.info("end _run_save_0")
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def _run_load_0(self, break_epoch_no=None):
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logger.info("begin _run_load_0")
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exe, main_prog, startup_prog = self._generate()
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fs = LocalFS()
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save_dir = "./run_load_0"
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fs.delete(save_dir)
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compiled, data_loader, optimizer, loss, image, label = self._init_env(
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exe, main_prog, startup_prog)
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o = None
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i = 0
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check = False
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epochs = []
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for i in acp.train_epoch_range(3, 0):
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epochs.append(i)
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for data in data_loader():
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fetch = exe.run(compiled, feed=data, fetch_list=[loss])
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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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if break_epoch_no is not None:
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if break_epoch_no == 0:
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self.assertEqual(epochs, [0, 1, 2])
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elif break_epoch_no == 1:
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self.assertEqual(epochs, [1, 2])
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elif break_epoch_no == 2:
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self.assertEqual(epochs, [2])
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else:
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self.assertEqual(epochs, [2])
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fs.delete(save_dir)
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logger.info("begin _run_load_0")
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def _test_corner_epoch_no(self, break_epoch_no):
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logger.info("begin test_corener_epoch_no")
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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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self._run_save_0(break_epoch_no=break_epoch_no)
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self._reset_generator()
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self._run_load_0(break_epoch_no=break_epoch_no)
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fs.delete(checker.hdfs_checkpoint_path)
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logger.info("end test_corener_epoch_no")
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class AutoCheckpointTest(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_0",
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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_0",
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"PADDLE_EDL_ONLY_FOR_CE_TEST": "1",
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"PADDLE_EDL_FS_CACHE": ".auto_checkpoint_test_0",
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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_normal(self):
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logger.info("begin test_normal")
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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._clear_envs()
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self._reset_generator()
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self._run_normal()
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self._readd_envs()
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logger.info("end test_normal")
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def test_basic(self):
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logger.info("begin test_basic")
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checker = acp._get_checker()
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self.assertEqual(checker.run_env, "PADDLE_EDL_AUTO_CHECKPOINT")
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self.assertEqual(checker.platform, "PADDLE_CLOUD")
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self.assertEqual(checker.save_checkpoint_inter, 0)
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print(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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self._run_save_0()
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self._reset_generator()
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self._run_load_0()
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logger.info("end test_basic")
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def test_not_use(self):
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logger.info("begin test_not_use")
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self._clear_envs()
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self._reset_generator()
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self._not_use_train()
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self._readd_envs()
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logger.info("end test_not_use")
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def test_checker(self):
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os.environ.pop("PADDLE_JOB_ID", None)
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try:
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checker = AutoCheckpointChecker()
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self.assertFalse(True)
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except Exception as e:
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pass
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os.environ["PADDLE_JOB_ID"] = "test_job_auto_1"
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if __name__ == '__main__':
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unittest.main()
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