Source code for mindspore.train.callback._checkpoint

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# http://www.apache.org/licenses/LICENSE-2.0
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"""Checkpoint related classes and functions."""
from __future__ import absolute_import

import os
import stat
import time

import threading
import mindspore.context as context
from mindspore import log as logger
from mindspore import nn
from mindspore import _checkparam as Validator
from mindspore.train._utils import _make_directory
from mindspore.train.serialization import save_checkpoint, _save_graph
from mindspore.parallel._cell_wrapper import destroy_allgather_cell
from mindspore.parallel._recovery_context import _set_recovery_context, _get_recovery_context
from mindspore.train.callback._callback import Callback, set_cur_net
from mindspore.common.tensor import Tensor
from mindspore.common.parameter import Parameter

_cur_dir = os.getcwd()
SAVE_DIR = _cur_dir
_info_list = ["epoch_num", "step_num"]


def _chg_ckpt_file_name_if_same_exist(directory, prefix, exception=False):
    """Check if there is a file with the same name."""
    files = os.listdir(directory)
    suffix_num = 0
    pre_len = len(prefix)
    for filename in files:
        name_ext = os.path.splitext(filename)
        if exception and filename[-16:] != "_breakpoint.ckpt":
            continue
        if not exception and (name_ext[-1] != ".ckpt" or filename[-16:] == "_breakpoint.ckpt"):
            continue
        # find same prefix file
        if filename.find(prefix) == 0 and not filename[pre_len].isalpha():
            # add the max suffix + 1
            index = filename[pre_len:].find("-")
            if index == 0:
                suffix_num = max(suffix_num, 1)
            elif index != -1:
                num = filename[pre_len+1:pre_len+index]
                if num.isdigit():
                    suffix_num = max(suffix_num, int(num)+1)

    if suffix_num != 0:
        prefix = prefix + "_" + str(suffix_num)

    return prefix


[docs]class CheckpointConfig: """ The configuration of model checkpoint. Note: During the training process, if dataset is transmitted through the data channel, it is suggested to set 'save_checkpoint_steps' to an integer multiple of loop_size. Otherwise, the time to save the checkpoint may be biased. It is recommended to set only one save strategy and one keep strategy at the same time. If both `save_checkpoint_steps` and `save_checkpoint_seconds` are set, `save_checkpoint_seconds` will be invalid. If both `keep_checkpoint_max` and `keep_checkpoint_per_n_minutes` are set, `keep_checkpoint_per_n_minutes` will be invalid. Args: save_checkpoint_steps (int): Steps to save checkpoint. Default: 1. save_checkpoint_seconds (int): Seconds to save checkpoint. Can't be used with save_checkpoint_steps at the same time. Default: 0. keep_checkpoint_max (int): Maximum number of checkpoint files can be saved. Default: 5. keep_checkpoint_per_n_minutes (int): Save the checkpoint file every `keep_checkpoint_per_n_minutes` minutes. Can't be used with keep_checkpoint_max at the same time. Default: 0. integrated_save (bool): Whether to merge and save the split Tensor in the automatic parallel scenario. Integrated save function is only supported in automatic parallel scene, not supported in manual parallel. Default: True. async_save (bool): Whether asynchronous execution saves the checkpoint to a file. Default: False. saved_network (Cell): Network to be saved in checkpoint file. If the saved_network has no relation with the network in training, the initial value of saved_network will be saved. Default: None. append_info (list): The information save to checkpoint file. Support "epoch_num", "step_num" and dict. The key of dict must be str, the value of dict must be one of int, float, bool, Parameter or Tensor. Default: None enc_key (Union[None, bytes]): Byte type key used for encryption. If the value is None, the encryption is not required. Default: None. enc_mode (str): This parameter is valid only when enc_key is not set to None. Specifies the encryption mode, currently supports 'AES-GCM', 'AES-CBC' and 'SM4-CBC'. Default: 'AES-GCM'. exception_save (bool): Whether to save the current checkpoint when an exception occurs. Default: False. Raises: ValueError: If input parameter is not the correct type. Examples: >>> from mindspore import nn >>> from mindspore.train import Model, CheckpointConfig, ModelCheckpoint >>> >>> # Define the network structure of LeNet5. Refer to >>> # https://gitee.com/mindspore/docs/blob/r2.0/docs/mindspore/code/lenet.py >>> net = LeNet5() >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') >>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9) >>> model = Model(net, loss_fn=loss, optimizer=optim) >>> # Create the dataset taking MNIST as an example. Refer to >>> # https://gitee.com/mindspore/docs/blob/r2.0/docs/mindspore/code/mnist.py >>> dataset = create_dataset() >>> config = CheckpointConfig(saved_network=net) >>> ckpoint_cb = ModelCheckpoint(prefix='LeNet5', directory='./checkpoint', config=config) >>> model.train(10, dataset, callbacks=ckpoint_cb) """ def __init__(self, save_checkpoint_steps=1, save_checkpoint_seconds=0, keep_checkpoint_max=5, keep_checkpoint_per_n_minutes=0, integrated_save=True, async_save=False, saved_network=None, append_info=None, enc_key=None, enc_mode='AES-GCM', exception_save=False): if save_checkpoint_steps is not None: save_checkpoint_steps = Validator.check_non_negative_int(save_checkpoint_steps) if save_checkpoint_seconds is not None: save_checkpoint_seconds = Validator.check_non_negative_int(save_checkpoint_seconds) if keep_checkpoint_max is not None: keep_checkpoint_max = Validator.check_non_negative_int(keep_checkpoint_max) if keep_checkpoint_per_n_minutes is not None: keep_checkpoint_per_n_minutes = Validator.check_non_negative_int(keep_checkpoint_per_n_minutes) if saved_network is not None and not isinstance(saved_network, nn.Cell): raise TypeError(f"For 'CheckpointConfig', the type of 'saved_network' must be None or Cell, " f"but got {str(type(saved_network))}.") if not save_checkpoint_steps and not save_checkpoint_seconds and \ not keep_checkpoint_max and not keep_checkpoint_per_n_minutes: raise ValueError("For 'CheckpointConfig', the input arguments 'save_checkpoint_steps', " "'save_checkpoint_seconds', " "'keep_checkpoint_max' and 'keep_checkpoint_per_n_minutes' can't be all None or 0.") Validator.check_bool(exception_save) self.exception_save = exception_save self._save_checkpoint_steps = save_checkpoint_steps self._save_checkpoint_seconds = save_checkpoint_seconds if self._save_checkpoint_steps and self._save_checkpoint_steps > 0: self._save_checkpoint_seconds = None self._keep_checkpoint_max = keep_checkpoint_max self._keep_checkpoint_per_n_minutes = keep_checkpoint_per_n_minutes if self._keep_checkpoint_max and self._keep_checkpoint_max > 0: self._keep_checkpoint_per_n_minutes = None else: if not self._keep_checkpoint_per_n_minutes or self._keep_checkpoint_per_n_minutes == 0: self._keep_checkpoint_max = 1 self._integrated_save = Validator.check_bool(integrated_save) self._async_save = Validator.check_bool(async_save) self._saved_network = saved_network self._append_dict = self._handle_append_info(append_info) self._enc_key = Validator.check_isinstance('enc_key', enc_key, (type(None), bytes)) self._enc_mode = Validator.check_isinstance('enc_mode', enc_mode, str) @property def save_checkpoint_steps(self): """ Get the value of steps to save checkpoint. Returns: Int, steps to save checkpoint. """ return self._save_checkpoint_steps @property def save_checkpoint_seconds(self): """Get the value of _save_checkpoint_seconds. Returns: Int, seconds to save the checkpoint file. """ return self._save_checkpoint_seconds @property def keep_checkpoint_max(self): """ Get the value of maximum number of checkpoint files can be saved. Returns: Int, Maximum number of checkpoint files can be saved. """ return self._keep_checkpoint_max @property def keep_checkpoint_per_n_minutes(self): """ Get the value of save the checkpoint file every n minutes. Returns: Int, save the checkpoint file every n minutes. """ return self._keep_checkpoint_per_n_minutes @property def integrated_save(self): """ Get the value of whether to merge and save the split Tensor in the automatic parallel scenario. Returns: Bool, whether to merge and save the split Tensor in the automatic parallel scenario. """ return self._integrated_save @property def async_save(self): """ Get the value of whether asynchronous execution saves the checkpoint to a file. Returns: Bool, whether asynchronous execution saves the checkpoint to a file. """ return self._async_save @property def saved_network(self): """ Get the value of network to be saved in checkpoint file. Returns: Cell, network to be saved in checkpoint file. """ return self._saved_network @property def enc_key(self): """ Get the value of byte type key used for encryption. Returns: (None, bytes), byte type key used for encryption. """ return self._enc_key @property def enc_mode(self): """ Get the value of the encryption mode. Returns: str, encryption mode. """ return self._enc_mode @property def append_dict(self): """ Get the value of information dict saved to checkpoint file. Returns: Dict, the information saved to checkpoint file. """ return self._append_dict
[docs] def get_checkpoint_policy(self): """ Get the policy of checkpoint. Returns: Dict, the information of checkpoint policy. """ checkpoint_policy = {'save_checkpoint_steps': self.save_checkpoint_steps, 'save_checkpoint_seconds': self.save_checkpoint_seconds, 'keep_checkpoint_max': self.keep_checkpoint_max, 'keep_checkpoint_per_n_minutes': self.keep_checkpoint_per_n_minutes, 'saved_network': self.saved_network} return checkpoint_policy
@staticmethod def _handle_append_info(append_info): """Handle ckpt append info.""" if append_info is None or append_info == []: return None if not isinstance(append_info, list): raise TypeError(f"For 'CheckpointConfig', the type of 'append_info' must be list," f"but got {str(type(append_info))}.") handle_append_info = {} if "epoch_num" in append_info: handle_append_info["epoch_num"] = 0 if "step_num" in append_info: handle_append_info["step_num"] = 0 if "random_op" in append_info: handle_append_info["random_op"] = 0 dict_num = 0 for element in append_info: if not isinstance(element, str) and not isinstance(element, dict): raise TypeError(f"For 'CheckpointConfig', the type of 'append_info' element must be str or dict," f"but got {str(type(element))}.") if isinstance(element, str) and element not in _info_list: raise ValueError(f"For 'CheckpointConfig', the value of element in the argument 'append_info' " f"must be in {_info_list}, " f"but got {element}.") if isinstance(element, dict): dict_num += 1 if dict_num > 1: raise TypeError(f"For 'CheckpointConfig', the element of 'append_info' must has only one dict, " "but got {dict_num}") for key, value in element.items(): if isinstance(key, str) and isinstance(value, (int, float, bool, str, Parameter, Tensor)): handle_append_info[key] = value else: raise TypeError(f"For 'CheckpointConfig', the key type of the dict 'append_info' " f"must be string, the value type must be int or float or bool, " f"but got key type {type(key)}, value type {type(value)}") return handle_append_info
[docs]class ModelCheckpoint(Callback): """ The checkpoint callback class. It is called to combine with train process and save the model and network parameters after training. Note: In the distributed training scenario, please specify different directories for each training process to save the checkpoint file. Otherwise, the training may fail. If this callback is used in the `model` function, the checkpoint file will saved parameters of the optimizer by default. Args: prefix (str): The prefix name of checkpoint files. Default: 'CKP'. directory (str): The path of the folder which will be saved in the checkpoint file. By default, the file is saved in the current directory. Default: None. config (CheckpointConfig): Checkpoint strategy configuration. Default: None. Raises: ValueError: If `prefix` is not str or contains the '/' character. ValueError: If `directory` is not str. TypeError: If the config is not CheckpointConfig type. """ def __init__(self, prefix='CKP', directory=None, config=None): super(ModelCheckpoint, self).__init__() self._latest_ckpt_file_name = "" self._init_time = time.time() self._last_time = time.time() self._last_time_for_keep = time.time() self._last_triggered_step = 0 if not isinstance(prefix, str) or prefix.find('/') >= 0: raise ValueError("For 'ModelCheckpoint', the argument 'prefix' " "for checkpoint file name is invalid, it must be " "string and does not contain '/', but got {}.".format(prefix)) self._prefix = prefix self._exception_prefix = prefix if directory is not None: self._directory = _make_directory(directory) else: self._directory = _cur_dir if _get_recovery_context("enable_recovery"): _set_recovery_context(ckpt_path=self._directory) if config is None: self._config = CheckpointConfig() else: if not isinstance(config, CheckpointConfig): raise TypeError("For 'ModelCheckpoint', the type of argument 'config' should be " "'CheckpointConfig', " "but got {}.".format(type(config))) self._config = config # get existing checkpoint files self._manager = CheckpointManager() self._prefix = _chg_ckpt_file_name_if_same_exist(self._directory, self._prefix) self._append_dict = self._config.append_dict or {} self._append_epoch_num = self._append_dict.get("epoch_num") if "epoch_num" in self._append_dict else 0 self._append_step_num = self._append_dict.get("step_num") if "step_num" in self._append_dict else 0 self._graph_saved = False self._need_flush_from_cache = True
[docs] def step_end(self, run_context): """ Save the checkpoint at the end of step. Args: run_context (RunContext): Context of the train running. """ cb_params = run_context.original_args() # In disaster recovery scenario, the training process may be rolled back to the last step where # the ckpt was successfully saved, so the _last_triggered_step should be updated. if _get_recovery_context("enable_recovery") and cb_params.last_save_ckpt_step is not None: self._last_triggered_step = cb_params.last_save_ckpt_step cb_params.last_save_ckpt_step = None _make_directory(self._directory) # save graph (only once) if not self._graph_saved: graph_file_name = os.path.join(self._directory, self._prefix + '-graph.meta') if os.path.isfile(graph_file_name) and context.get_context("mode") == context.GRAPH_MODE: os.remove(graph_file_name) _save_graph(cb_params.train_network, graph_file_name) self._graph_saved = True thread_list = threading.enumerate() for thread in thread_list: if thread.getName() == "asyn_save_ckpt": thread.join() self._save_ckpt(cb_params)
[docs] def end(self, run_context): """ Save the last checkpoint after training finished. Args: run_context (RunContext): Context of the train running. """ cb_params = run_context.original_args() _to_save_last_ckpt = True self._save_ckpt(cb_params, _to_save_last_ckpt) thread_list = threading.enumerate() for thread in thread_list: if thread.getName() == "asyn_save_ckpt": thread.join() destroy_allgather_cell()
def _check_save_ckpt(self, cb_params, force_to_save): """Check whether save checkpoint files or not.""" if self._config.save_checkpoint_steps and self._config.save_checkpoint_steps > 0: if cb_params.cur_step_num >= self._last_triggered_step + self._config.save_checkpoint_steps \ or force_to_save is True: return True elif self._config.save_checkpoint_seconds and self._config.save_checkpoint_seconds > 0: self._cur_time = time.time() if (self._cur_time - self._last_time) > self._config.save_checkpoint_seconds or force_to_save: self._last_time = self._cur_time return True return False def _save_ckpt(self, cb_params, force_to_save=False): """Save checkpoint files.""" if cb_params.cur_step_num == self._last_triggered_step: return # if param is cache enable, flush data from cache to host before save_ckpt if self._need_flush_from_cache: self._flush_from_cache(cb_params) save_ckpt = self._check_save_ckpt(cb_params, force_to_save) step_num_in_epoch = int((cb_params.cur_step_num - 1) % cb_params.batch_num + 1) if save_ckpt: cur_ckpoint_file = self._prefix + "-" + str(cb_params.cur_epoch_num) + "_" \ + str(step_num_in_epoch) + ".ckpt" # update checkpoint file list. self._manager.update_ckpoint_filelist(self._directory, self._prefix) # keep checkpoint files number equal max number. if self._config.keep_checkpoint_max and 0 < self._config.keep_checkpoint_max <= self._manager.ckpoint_num: self._manager.remove_oldest_ckpoint_file() elif self._config.keep_checkpoint_per_n_minutes and self._config.keep_checkpoint_per_n_minutes > 0: self._cur_time_for_keep = time.time() if (self._cur_time_for_keep - self._last_time_for_keep) \ < self._config.keep_checkpoint_per_n_minutes * 60: self._manager.keep_one_ckpoint_per_minutes(self._config.keep_checkpoint_per_n_minutes, self._cur_time_for_keep) # generate the new checkpoint file and rename it. global SAVE_DIR SAVE_DIR = self._directory cur_file = os.path.join(self._directory, cur_ckpoint_file) self._last_time_for_keep = time.time() self._last_triggered_step = cb_params.cur_step_num if context.get_context("enable_ge"): set_cur_net(cb_params.train_network) cb_params.train_network.exec_checkpoint_graph() if "epoch_num" in self._append_dict: self._append_dict["epoch_num"] = self._append_epoch_num + cb_params.cur_epoch_num if "step_num" in self._append_dict: self._append_dict["step_num"] = self._append_step_num + cb_params.cur_step_num network = self._config.saved_network if self._config.saved_network is not None else cb_params.train_network save_checkpoint(network, cur_file, self._config.integrated_save, self._config.async_save, self._append_dict, self._config.enc_key, self._config.enc_mode) self._latest_ckpt_file_name = cur_file def _flush_from_cache(self, cb_params): """Flush cache data to host if tensor is cache enable.""" has_cache_params = False params = cb_params.train_network.get_parameters() for param in params: if param.cache_enable: has_cache_params = True Tensor(param).flush_from_cache() if not has_cache_params: self._need_flush_from_cache = False @property def latest_ckpt_file_name(self): """Return the latest checkpoint path and file name.""" return self._latest_ckpt_file_name
class CheckpointManager: """Manage checkpoint files according to train_config of checkpoint.""" def __init__(self): self._ckpoint_filelist = [] @property def ckpoint_filelist(self): """Get all the related checkpoint files managed here.""" return self._ckpoint_filelist @property def ckpoint_num(self): """Get the number of the related checkpoint files managed here.""" return len(self._ckpoint_filelist) def update_ckpoint_filelist(self, directory, prefix): """Update the checkpoint file list.""" self._ckpoint_filelist = [] files = os.listdir(directory) for filename in files: if os.path.splitext(filename)[-1] == ".ckpt" and filename.startswith(prefix + "-"): mid_name = filename[len(prefix):-5] flag = not (True in [char.isalpha() for char in mid_name]) if flag: self._ckpoint_filelist.append(os.path.join(directory, filename)) def remove_ckpoint_file(self, file_name): """Remove the specified checkpoint file from this checkpoint manager and also from the directory.""" try: os.chmod(file_name, stat.S_IWRITE) os.remove(file_name) self._ckpoint_filelist.remove(file_name) except OSError: logger.warning("OSError, failed to remove the older ckpt file %s.", file_name) except ValueError: logger.warning("ValueError, failed to remove the older ckpt file %s.", file_name) def remove_oldest_ckpoint_file(self): """Remove the oldest checkpoint file from this checkpoint manager and also from the directory.""" ckpoint_files = sorted(self._ckpoint_filelist, key=os.path.getmtime) self.remove_ckpoint_file(ckpoint_files[0]) def keep_one_ckpoint_per_minutes(self, minutes, cur_time): """Only keep the latest one ckpt file per minutes, remove other files generated in [last_time, cur_time].""" del_list = [] oldest_file = '' oldest_time = cur_time for ck_file in self._ckpoint_filelist: modify_time = os.path.getmtime(ck_file) if cur_time - modify_time < 60 * minutes: del_list.append(ck_file) if modify_time < oldest_time: oldest_time = modify_time oldest_file = ck_file for mv_file in del_list: if mv_file == oldest_file: continue self.remove_ckpoint_file(mv_file)