1# Copyright 2019 The TensorFlow Authors. All Rights Reserved. 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================== 15"""Training state management.""" 16 17import os 18 19from tensorflow.python.framework import constant_op 20from tensorflow.python.framework import dtypes 21from tensorflow.python.framework import errors 22from tensorflow.python.keras import backend 23from tensorflow.python.keras.distribute import distributed_file_utils 24from tensorflow.python.keras.utils import mode_keys 25from tensorflow.python.lib.io import file_io 26from tensorflow.python.ops import variables 27from tensorflow.python.training import checkpoint_management 28from tensorflow.python.training.tracking import util as trackable_util 29 30# Constant for `tf.keras.Model` attribute to store the epoch at which the most 31# recently saved checkpoint was saved. 32CKPT_SAVED_EPOCH = '_ckpt_saved_epoch' 33 34CKPT_SAVED_EPOCH_UNUSED_VALUE = -1 35 36 37class WorkerTrainingState(object): 38 """Training state management class. 39 40 This class provides apis for backing up and restoring the training state. 41 This allows model and epoch information to be saved periodically and restore 42 for fault-tolerance, also known as preemption-recovery purpose. 43 """ 44 45 def __init__(self, model, checkpoint_dir): 46 self._model = model 47 48 # The epoch at which the checkpoint is saved. Used for fault-tolerance. 49 # GPU device only has int64 dtype registered VarHandleOp. 50 self._ckpt_saved_epoch = variables.Variable( 51 initial_value=constant_op.constant( 52 CKPT_SAVED_EPOCH_UNUSED_VALUE, dtype=dtypes.int64), 53 name='ckpt_saved_epoch') 54 55 # Variable initialization. 56 backend.set_value(self._ckpt_saved_epoch, CKPT_SAVED_EPOCH_UNUSED_VALUE) 57 58 # _ckpt_saved_epoch gets tracked and is included in the checkpoint file 59 # when backing up. 60 checkpoint = trackable_util.Checkpoint( 61 model=self._model, ckpt_saved_epoch=self._ckpt_saved_epoch) 62 63 # If this is single-worker training, checkpoint_dir are the same for 64 # write_checkpoint_manager and read_checkpoint_manager. 65 # 66 # If this is multi-worker training, and this worker should not 67 # save checkpoint, we replace the write_checkpoint_manager's checkpoint_dir 68 # with a temp filepath, so it writes to a file that will be removed at the 69 # end of back_up() call. This is necessary because the SyncOnReadVariable 70 # needs to be synced across all the workers in order to be read, and all 71 # workers need to perform `save()`. 72 # But all workers should restore from the same checkpoint_dir as passed in 73 # read_checkpoint_manager. 74 self.read_checkpoint_manager = checkpoint_management.CheckpointManager( 75 checkpoint, 76 directory=os.path.join(checkpoint_dir, 'chief'), 77 max_to_keep=1) 78 write_checkpoint_dir = distributed_file_utils.write_dirpath( 79 checkpoint_dir, self._model.distribute_strategy) 80 if self._model.distribute_strategy.extended.should_checkpoint: 81 self.write_checkpoint_manager = self.read_checkpoint_manager 82 else: 83 self.write_checkpoint_manager = checkpoint_management.CheckpointManager( 84 checkpoint, directory=write_checkpoint_dir, max_to_keep=1) 85 86 def back_up(self, epoch): 87 """Back up the current state of training into a checkpoint file. 88 89 Args: 90 epoch: The current epoch information to be saved. 91 """ 92 backend.set_value(self._ckpt_saved_epoch, epoch) 93 # Save the model plus CKPT_SAVED_EPOCH variable. 94 if self.write_checkpoint_manager.save(): 95 distributed_file_utils.remove_temp_dirpath( 96 self.write_checkpoint_manager.directory, 97 self._model.distribute_strategy) 98 99 def restore(self): 100 """Restore the training state from the backed up checkpoint file. 101 102 Returns: 103 True if the training state is successfully restored. False if the training 104 state doesn't need to be restored, or error occurred so it can't. 105 """ 106 self.read_checkpoint_manager.restore_or_initialize() 107 108 def delete_backup(self): 109 """Delete the backup directories. 110 111 Delete the backup directories which should not exist after `fit()` 112 successfully finishes. 113 """ 114 if self.write_checkpoint_manager is self.read_checkpoint_manager: 115 try: 116 file_io.delete_recursively_v2(self.write_checkpoint_manager.directory) 117 except errors.NotFoundError: 118 pass 119 120 def maybe_load_initial_epoch_from_ckpt(self, initial_epoch, mode): 121 """Maybe load initial epoch from ckpt considering possible worker recovery. 122 123 When `_ckpt_saved_epoch` attribute exists and is not 124 `CKPT_SAVED_EPOCH_UNUSED_VALUE`, this is under multi-worker training setting 125 and indicates the worker is recovering from previous failure. In this case, 126 infer `initial_epoch` from `self._ckpt_saved_epoch` to continue previous 127 unfinished training from certain epoch. 128 129 Args: 130 initial_epoch: The original initial_epoch user passes in in `fit()`. 131 mode: The mode for running `model.fit()`. 132 133 Returns: 134 If the training is recovering from previous failure under multi-worker 135 training setting, return the epoch the training is supposed to continue 136 at. Otherwise, return the `initial_epoch` the user passes in. 137 """ 138 139 epoch = backend.eval(self._ckpt_saved_epoch) 140 if mode == mode_keys.ModeKeys.TRAIN and epoch >= 0: 141 # The most recently saved epoch is one epoch prior to the epoch it 142 # failed at, so return the value of 'self._ckpt_saved_epoch' plus one. 143 return epoch + 1 144 return initial_epoch 145