xref: /aosp_15_r20/external/tensorflow/tensorflow/python/checkpoint/functional_saver.py (revision b6fb3261f9314811a0f4371741dbb8839866f948)

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Saves and restore variables inside traced @tf.functions."""

from tensorflow.core.protobuf import saver_pb2
from tensorflow.python.checkpoint import checkpoint_options
from tensorflow.python.eager import context
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_spec
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_io_ops
from tensorflow.python.ops import io_ops
from tensorflow.python.ops import string_ops
from tensorflow.python.saved_model import registration
from tensorflow.python.training.saving import saveable_object
from tensorflow.python.training.saving import saveable_object_util
from tensorflow.python.util import nest


class _SingleDeviceSaver(object):
  """Saves and restores checkpoints from the current device."""

  __slots__ = ["_tensor_slice_dict"]

  def __init__(self, tensor_slice_dict):
    """Specify a list of `SaveableObject`s to save and restore.

    Args:
      tensor_slice_dict: A dict mapping checkpoint key -> slice_spec -> tensor.
    """
    self._tensor_slice_dict = tensor_slice_dict

  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()
    tensor_names = []
    tensors = []
    slice_specs = []
    for checkpoint_key, tensor_slices in self._tensor_slice_dict.items():
      for slice_spec, tensor in tensor_slices.items():
        if isinstance(tensor, saveable_object.SaveSpec):
          tensor_value = tensor.tensor
          # A tensor value of `None` indicates that this SaveableObject gets
          # recorded in the object graph, but that no value is saved in the
          # checkpoint.
          if tensor_value is not None:
            tensor_names.append(tensor.name)
            tensors.append(tensor_value)
            slice_specs.append(tensor.slice_spec)
        else:
          tensor_names.append(checkpoint_key)
          tensors.append(tensor)
          slice_specs.append(slice_spec)
    save_device = options.experimental_io_device or "cpu:0"
    with ops.device(save_device):
      return io_ops.save_v2(file_prefix, tensor_names, slice_specs, tensors)

  def restore(self, file_prefix, options=None):
    """Restore the saveable objects from a checkpoint with `file_prefix`.

    Args:
      file_prefix: A string or scalar string Tensor containing the prefix for
        files to read from.
      options: Optional `CheckpointOptions` object.

    Returns:
      A restored tensor dict (maps checkpoint_key -> slice_spec -> tensor).
    """
    options = options or checkpoint_options.CheckpointOptions()
    tensor_names = []
    tensor_dtypes = []
    slice_specs = []

    for checkpoint_key, tensor_slices in self._tensor_slice_dict.items():
      for slice_spec, tensor in tensor_slices.items():
        tensor_dtypes.append(tensor.dtype)
        if isinstance(tensor, saveable_object.SaveSpec):
          slice_specs.append(tensor.slice_spec)
          tensor_names.append(tensor.name)
        else:
          slice_specs.append(slice_spec)
          tensor_names.append(checkpoint_key)

    restore_device = options.experimental_io_device or "cpu:0"
    with ops.device(restore_device):
      restored_tensors = io_ops.restore_v2(
          file_prefix, tensor_names, slice_specs, tensor_dtypes)

    restored_tensor_dict = {}
    for checkpoint_key, tensor_slices in self._tensor_slice_dict.items():
      for slice_spec in tensor_slices:
        restored_tensor = restored_tensors.pop(0)
        restored_tensor_dict.setdefault(checkpoint_key, {})[slice_spec] = (
            restored_tensor)
    return restored_tensor_dict


def sharded_filename(filename_tensor, shard, num_shards):
  """Append sharding information to a filename.

  Args:
    filename_tensor: A string tensor.
    shard: Integer.  The shard for the filename.
    num_shards: An int Tensor for the number of shards.

  Returns:
    A string tensor.
  """
  return gen_io_ops.sharded_filename(filename_tensor, shard, num_shards)


def registered_saver_filename(filename_tensor, saver_name):
  return string_ops.string_join(
      [filename_tensor, constant_op.constant(f"-{saver_name}")])


def _get_mapped_registered_save_fn(fn, trackables, call_with_mapped_captures):
  """Converts the function to a python or tf.function with a single file arg."""

  def save_fn(file_prefix):
    return fn(trackables=trackables, file_prefix=file_prefix)
  if call_with_mapped_captures is None:
    return save_fn
  else:
    tf_fn = def_function.function(save_fn, autograph=False)
    concrete = tf_fn.get_concrete_function(
        file_prefix=tensor_spec.TensorSpec(shape=(), dtype=dtypes.string))

    def save_fn_with_replaced_captures(file_prefix):
      return call_with_mapped_captures(concrete, [file_prefix])

    return save_fn_with_replaced_captures


def _get_mapped_registered_restore_fn(fn, trackables,
                                      call_with_mapped_captures):
  """Converts the function to a python or tf.function with a single file arg."""

  def restore_fn(merged_prefix):
    return fn(trackables=trackables, merged_prefix=merged_prefix)
  if call_with_mapped_captures is None:
    return restore_fn
  else:
    tf_fn = def_function.function(restore_fn, autograph=False)
    concrete = tf_fn.get_concrete_function(
        merged_prefix=tensor_spec.TensorSpec(shape=(), dtype=dtypes.string))

    def restore_fn_with_replaced_captures(merged_prefix):
      return call_with_mapped_captures(concrete, [merged_prefix])

    return restore_fn_with_replaced_captures


class MultiDeviceSaver(object):
  """Saves checkpoints directly from multiple devices.

  Note that this is a low-level utility which stores Tensors in the keys
  specified by `SaveableObject`s. Higher-level utilities for object-based
  checkpointing are built on top of it.
  """

  def __init__(self,
               saveable_objects,
               registered_savers=None,
               call_with_mapped_captures=None):
    """Specify a list of `SaveableObject`s to save and restore.

    Args:
      saveable_objects: A list of `SaveableObject`s.
        Objects extending `SaveableObject` will be saved and restored.
      registered_savers: A dictionary mapping `registration.RegisteredSaver`
        namedtuples to a dictionary of named Trackables. The keys of the
        Trackable dictionary are string names that uniquely identify the
        Trackable in the checkpoint.
      call_with_mapped_captures: TODO
    """
    saveable_objects = list(saveable_objects)

    # Keep these two data structures so that we can map restored tensors to
    # the Trackable restore functions.
    self._keys_to_restore_fn = {}
    self._restore_fn_to_keys = {}

    # Extract serialized tensors and separate by device.
    tensors_by_device = {}  # device -> checkpoint key -> (slice_spec ->) tensor
    for saveable in saveable_objects:
      tensor_dict = saveable_object_util.saveable_object_to_tensor_dict(
          [saveable])
      restore_fn = saveable_object_util.saveable_object_to_restore_fn(
          [saveable])

      # Divide tensor_dict by device.
      for checkpoint_key, maybe_tensor in tensor_dict.items():
        if not isinstance(maybe_tensor, dict):
          # Make sure that maybe_tensor is structured as {slice_spec -> tensor}.
          maybe_tensor = {"": maybe_tensor}

        for slice_spec, tensor in maybe_tensor.items():
          if (checkpoint_key, slice_spec) in self._keys_to_restore_fn:
            raise ValueError(
                "Recieved multiple tensors with the same checkpoint key and "
                "slice spec. This is invalid because one will overwrite the "
                "other in the checkpoint. This indicates a bug in the "
                "Checkpoint key-generation.")
          self._keys_to_restore_fn[(checkpoint_key, slice_spec)] = restore_fn
          self._restore_fn_to_keys.setdefault(restore_fn, []).append(
              (checkpoint_key, slice_spec))

          host_device = saveable_object_util.set_cpu0(tensor.device)
          (tensors_by_device
           .setdefault(host_device, {})
           .setdefault(checkpoint_key, {})[slice_spec]) = tensor
    self._single_device_savers = {
        device: _SingleDeviceSaver(tensor_slice_dict)
        for device, tensor_slice_dict in tensors_by_device.items()}

    self._registered_savers = {}
    if registered_savers:
      for registered_name, trackables in registered_savers.items():
        save_fn = _get_mapped_registered_save_fn(
            registration.get_save_function(registered_name),
            trackables, call_with_mapped_captures)
        restore_fn = _get_mapped_registered_restore_fn(
            registration.get_restore_function(registered_name),
            trackables, call_with_mapped_captures)
        self._registered_savers[registered_name] = (save_fn, restore_fn)

  def to_proto(self):
    """Serializes to a SaverDef referencing the current graph."""
    filename_tensor = array_ops.placeholder(
        shape=[], dtype=dtypes.string, name="saver_filename")
    save_tensor = self._traced_save(filename_tensor)
    restore_op = self._traced_restore(filename_tensor).op
    return saver_pb2.SaverDef(
        filename_tensor_name=filename_tensor.name,
        save_tensor_name=save_tensor.name,
        restore_op_name=restore_op.name,
        version=saver_pb2.SaverDef.V2)

  @def_function.function(
      input_signature=(tensor_spec.TensorSpec(shape=(), dtype=dtypes.string),),
      autograph=False)
  def _traced_save(self, file_prefix):
    save_op = self.save(file_prefix)
    with ops.device("cpu:0"):
      with ops.control_dependencies([save_op]):
        return array_ops.identity(file_prefix)

  @def_function.function(
      input_signature=(tensor_spec.TensorSpec(shape=(), dtype=dtypes.string),),
      autograph=False)
  def _traced_restore(self, file_prefix):
    restore_ops = self.restore(file_prefix)
    with ops.device("cpu:0"):
      with ops.control_dependencies(restore_ops.values()):
        return array_ops.identity(file_prefix)

  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()

    # 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/part"))
      tmp_checkpoint_prefix = string_ops.string_join(
          [file_prefix, sharded_suffix])
      registered_paths = {
          saver_name: registered_saver_filename(file_prefix, saver_name)
          for saver_name in self._registered_savers
      }

    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)

    # Since this will causes a function re-trace on each save, limit this to the
    # cases where it is needed: eager and when there are multiple tasks/single
    # device savers. Note that the retrace is needed to ensure we pickup the
    # latest values of options like experimental_io_device.
    if context.executing_eagerly() and len(self._single_device_savers) > 1:
      # Explicitly place the identity op on the first device.
      @def_function.function(jit_compile=False)
      def tf_function_save():
        save_fn()
      tf_function_save()
    else:
      return save_fn()

  def restore(self, file_prefix, options=None):
    """Restore the saveable objects from a checkpoint with `file_prefix`.

    Args:
      file_prefix: A string or scalar string Tensor containing the prefix for
        files to read from.
      options: Optional `CheckpointOptions` object.

    Returns:
      When not run eagerly or when saving on a single device, returns a
      dictionary mapping from SaveableObject names to restore operations;
      otherwise, returns an empty dict.
    """
    options = options or checkpoint_options.CheckpointOptions()

    def restore_fn():
      restore_fn_inputs = {}
      restore_fn_input_count = {
          fn: len(keys) for fn, keys in self._restore_fn_to_keys.items()}

      restore_ops = {}
      # Sort by device name to avoid propagating non-deterministic dictionary
      # ordering in some Python versions.
      for device, saver in sorted(self._single_device_savers.items()):
        with ops.device(device):
          # Load values from checkpoint
          restored_tensor_dict = saver.restore(file_prefix, options)

          # Map restored tensors to the corresponding restore_fn, and see if all
          # inputs have all been loaded. Call `restore_fn` if that is the case.
          for checkpoint_key, slice_and_tensor in restored_tensor_dict.items():
            for slice_spec, tensor in slice_and_tensor.items():
              restore_fn = self._keys_to_restore_fn[(checkpoint_key,
                                                     slice_spec)]
              (restore_fn_inputs
               .setdefault(restore_fn, {})
               .setdefault(checkpoint_key, {})[slice_spec]) = tensor
              restore_fn_input_count[restore_fn] -= 1

              if restore_fn_input_count[restore_fn] == 0:
                ret = restore_fn(restore_fn_inputs[restore_fn])
                if isinstance(ret, dict):
                  restore_ops.update(ret)
      # Run registered restore methods after the default restore ops.
      for _, (_, restore_fn) in self._registered_savers.items():
        restore_fn(file_prefix)
      return restore_ops

    restore_device = options.experimental_io_device or "cpu:0"

    # Since this will causes a function re-trace on each restore, limit this to
    # cases where it is needed: eager and when there are multiple tasks/single
    # device savers. Note that the retrace is needed to ensure we pickup the
    # latest values of options like experimental_io_device.
    if context.executing_eagerly() and (len(self._single_device_savers) > 1 or
                                        options.experimental_io_device):
      @def_function.function(jit_compile=False)
      def tf_function_restore():
        restore_fn()
        return {}

      with ops.device(restore_device):
        restore_ops = tf_function_restore()
    else:
      restore_ops = restore_fn()

    return restore_ops