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)