def save_fn(): num_shards = len(self._single_device_savers) sharded_saves = [] sharded_prefixes = [] num_shards_tensor = constant_op.constant(num_shards, name="num_shards") last_device = None for shard, (device, saver) in enumerate( sorted(self._single_device_savers.items())): last_device = device with ops.device(saveable_object_util.set_cpu0(device)): shard_prefix = sharded_filename(tmp_checkpoint_prefix, shard, num_shards_tensor) sharded_prefixes.append(shard_prefix) with ops.device(device): # _SingleDeviceSaver will use the CPU device when necessary, but # initial read operations should be placed on the SaveableObject's # device. sharded_saves.append(saver.save(shard_prefix, options)) with ops.control_dependencies(sharded_saves): # Merge on the io_device if specified, otherwise co-locates the merge op # with the last device used. merge_device = ( options.experimental_io_device or saveable_object_util.set_cpu0(last_device)) with ops.device(merge_device): # V2 format write path consists of a metadata merge step. Once # merged, attempts to delete the temporary directory, # "<user-fed prefix>_temp". return gen_io_ops.merge_v2_checkpoints( sharded_prefixes, file_prefix, delete_old_dirs=True)
def _AddShardedSaveOps(self, variables, checkpoint_prefix, var_key_fn): """Adds per-device save ops to save `variables` to `checkpoint_prefix`.""" with self._var_graph.as_default(): per_device = collections.defaultdict(lambda: []) for var in variables: per_device[var.device].append(var) tmp_save_prefix = tf.strings.join( [checkpoint_prefix, "_temp/part"]) num_shards = tf.constant(len(per_device)) sharded_saves = [] sharded_prefixes = [] for shard, (device, var_list) in enumerate(per_device.items()): with self._var_graph.device(device): sharded_filename = gen_io_ops.sharded_filename( tmp_save_prefix, shard, num_shards) sharded_prefixes.append(sharded_filename) save_op = io_ops.save_v2( prefix=sharded_filename, tensor_names=[var_key_fn(v) for v in var_list], tensors=[v.read_value() for v in var_list], shape_and_slices=[""] * len(var_list)) sharded_saves.append(save_op) with tf.control_dependencies(sharded_saves): return gen_io_ops.merge_v2_checkpoints(sharded_prefixes, checkpoint_prefix, delete_old_dirs=True)
def save_fn(): saved_prefixes = [] # Save with the registered savers. These run before default savers due to # the API contract. for saver_name, (save_fn, _) in self._registered_savers.items(): maybe_saved_prefixes = save_fn(registered_paths[saver_name]) if maybe_saved_prefixes is not None: flattened_saved_prefixes = nest.flatten( maybe_saved_prefixes) if not all( tensor_util.is_tf_type(x) and x.dtype == dtypes.string for x in flattened_saved_prefixes): raise ValueError( "Registered saver must return a (maybe empty) list of " f"string type tensors. Got {maybe_saved_prefixes}." ) saved_prefixes.extend(flattened_saved_prefixes) # (Default saver) Save with single device savers. num_shards = len(self._single_device_savers) sharded_saves = [] num_shards_tensor = constant_op.constant(num_shards, name="num_shards") last_device = None for shard, (device, saver) in enumerate( sorted(self._single_device_savers.items())): last_device = device with ops.device(saveable_object_util.set_cpu0(device)): shard_prefix = sharded_filename(tmp_checkpoint_prefix, shard, num_shards_tensor) saved_prefixes.append(shard_prefix) with ops.device(device): # _SingleDeviceSaver will use the CPU device when necessary, but # initial read operations should be placed on the SaveableObject's # device. sharded_saves.append(saver.save(shard_prefix, options)) with ops.control_dependencies(sharded_saves): # Merge on the io_device if specified, otherwise co-locates the merge op # with the last device used. merge_device = (options.experimental_io_device or saveable_object_util.set_cpu0(last_device)) with ops.device(merge_device): # V2 format write path consists of a metadata merge step. Once # merged, attempts to delete the temporary directory, # "<user-fed prefix>_temp". return gen_io_ops.merge_v2_checkpoints( saved_prefixes, file_prefix, delete_old_dirs=True)
def save(self, file_prefix): """Save the saveable objects to a checkpoint with `file_prefix`. Args: file_prefix: A string or scalar string Tensor containing the prefix to save under. Returns: An `Operation`, or None when executing eagerly. """ # IMPLEMENTATION DETAILS: most clients should skip. # # Suffix for any well-formed "checkpoint_prefix", when sharded. # Transformations: # * Users pass in "save_path" in save() and restore(). Say "myckpt". # * checkpoint_prefix gets fed <save_path><sharded_suffix>. # # Example: # During runtime, a temporary directory is first created, which contains # files # # <train dir>/myckpt_temp/ # part-?????-of-?????{.index, .data-00000-of-00001} # # Before .save() finishes, they will be (hopefully, atomically) renamed to # # <train dir>/ # myckpt{.index, .data-?????-of-?????} # # Users only need to interact with the user-specified prefix, which is # "<train dir>/myckpt" in this case. Save() and Restore() work with the # prefix directly, instead of any physical pathname. (On failure and # subsequent restore, an outdated and orphaned temporary directory can be # safely removed.) sharded_suffix = "_temp_%s/part" % uuid.uuid4().hex with ops.device("cpu:0"): tmp_checkpoint_prefix = string_ops.string_join( [file_prefix, sharded_suffix]) num_shards = len(self._single_device_savers) sharded_saves = [] sharded_prefixes = [] num_shards_tensor = constant_op.constant(num_shards, name="num_shards") last_device = None for shard, (device, saver) in enumerate( sorted(self._single_device_savers.items())): last_device = device with ops.device(saveable_object_util.set_cpu0(device)): shard_prefix = sharded_filename(tmp_checkpoint_prefix, shard, num_shards_tensor) sharded_prefixes.append(shard_prefix) with ops.device(device): # _SingleDeviceSaver will use the CPU device when necessary, but initial # read operations should be placed on the SaveableObject's device. sharded_saves.append(saver.save(shard_prefix)) with ops.control_dependencies(sharded_saves): # Co-locates the merge step with the last device. with ops.device(saveable_object_util.set_cpu0(last_device)): # V2 format write path consists of a metadata merge step. Once merged, # attempts to delete the temporary directory, "<user-fed prefix>_temp". return gen_io_ops.merge_v2_checkpoints( sharded_prefixes, file_prefix, delete_old_dirs=True)
def save(self, file_prefix, options=None): """Save the saveable objects to a checkpoint with `file_prefix`. Args: file_prefix: A string or scalar string Tensor containing the prefix to save under. options: Optional `CheckpointOptions` object. Returns: An `Operation`, or None when executing eagerly. """ options = options or checkpoint_options.CheckpointOptions() for callback in self._before_save_callbacks: callback() # IMPLEMENTATION DETAILS: most clients should skip. # # Suffix for any well-formed "checkpoint_prefix", when sharded. # Transformations: # * Users pass in "save_path" in save() and restore(). Say "myckpt". # * checkpoint_prefix gets fed <save_path><sharded_suffix>. # # Example: # During runtime, a temporary directory is first created, which contains # files # # <train dir>/myckpt_temp/ # part-?????-of-?????{.index, .data-00000-of-00001} # # Before .save() finishes, they will be (hopefully, atomically) renamed to # # <train dir>/ # myckpt{.index, .data-?????-of-?????} # # Filesystems with eventual consistency (such as S3), don't need a # temporary location. Using a temporary directory in those cases might # cause situations where files are not available during copy. # # Users only need to interact with the user-specified prefix, which is # "<train dir>/myckpt" in this case. Save() and Restore() work with the # prefix directly, instead of any physical pathname. (On failure and # subsequent restore, an outdated and orphaned temporary directory can be # safely removed.) with ops.device("CPU"): sharded_suffix = array_ops.where( string_ops.regex_full_match(file_prefix, "^s3://.*"), constant_op.constant(".part"), constant_op.constant("_temp_%s/part" % uuid.uuid4().hex)) tmp_checkpoint_prefix = string_ops.string_join( [file_prefix, sharded_suffix]) num_shards = len(self._single_device_savers) sharded_saves = [] sharded_prefixes = [] num_shards_tensor = constant_op.constant(num_shards, name="num_shards") last_device = None for shard, (device, saver) in enumerate( sorted(self._single_device_savers.items())): last_device = device with ops.device(saveable_object_util.set_cpu0(device)): shard_prefix = sharded_filename(tmp_checkpoint_prefix, shard, num_shards_tensor) sharded_prefixes.append(shard_prefix) with ops.device(device): # _SingleDeviceSaver will use the CPU device when necessary, but initial # read operations should be placed on the SaveableObject's device. sharded_saves.append(saver.save(shard_prefix, options)) with ops.control_dependencies(sharded_saves): # Merge on the io_device if specified, otherwise co-locates the merge op # with the last device used. merge_device = (options.experimental_io_device or saveable_object_util.set_cpu0(last_device)) with ops.device(merge_device): # V2 format write path consists of a metadata merge step. Once merged, # attempts to delete the temporary directory, "<user-fed prefix>_temp". return gen_io_ops.merge_v2_checkpoints(sharded_prefixes, file_prefix, delete_old_dirs=True)