tensorflow--tensorflow/tensorflow/python/saved_model/function_serialization.py

# Copyright 2018 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.
# ==============================================================================
"""Tools for serializing `Function`s."""

from tensorflow.core.function.polymorphism import function_type as function_type_lib
from tensorflow.core.protobuf import saved_object_graph_pb2
from tensorflow.python.eager import function as defun
from tensorflow.python.eager import wrap_function as wrap_function_lib
from tensorflow.python.eager.polymorphic_function import function_type_utils
from tensorflow.python.framework import func_graph as func_graph_module
from tensorflow.python.saved_model import nested_structure_coder
from tensorflow.python.util import nest


def _serialize_function_spec(function_spec):
  """Serialize a FunctionSpec object into its proto representation."""
  if (
      function_spec.fullargspec.args
      and function_spec.fullargspec.args[0] == "self"
  ):
    raise TypeError(
        "Can not serialize tf.function with unbound 'self' parameter."
    )

  proto = saved_object_graph_pb2.FunctionSpec()

  # Intentionally skip encoding annotations of a function because function
  # annotations are mainly for optional type checking during development
  # and does not affect runtime behavior.
  # https://www.python.org/dev/peps/pep-3107/
  # https://docs.python.org/3/library/inspect.html#inspect.getfullargspec
  proto.fullargspec.CopyFrom(
      nested_structure_coder.encode_structure(
          function_spec.fullargspec._replace(annotations={})))

  proto.is_method = False
  proto.input_signature.CopyFrom(
      nested_structure_coder.encode_structure(function_spec.input_signature))

  # See `tf.function` and the JitCompile proto for details.
  proto.jit_compile = {
      None: saved_object_graph_pb2.FunctionSpec.JitCompile.DEFAULT,
      True: saved_object_graph_pb2.FunctionSpec.JitCompile.ON,
      False: saved_object_graph_pb2.FunctionSpec.JitCompile.OFF,
  }.get(function_spec.jit_compile)

  return proto


def serialize_concrete_function(concrete_function, node_ids):
  """Build a SavedConcreteFunction."""
  bound_inputs = []
  try:
    for capture in concrete_function.captured_inputs:
      bound_inputs.append(node_ids[capture])
  except KeyError:
    raise KeyError(
        f"Failed to add concrete function '{concrete_function.name}' to object-"
        f"based SavedModel as it captures tensor {capture!r} which is unsupported"
        " or not reachable from root. "
        "One reason could be that a stateful object or a variable that the "
        "function depends on is not assigned to an attribute of the serialized "
        "trackable object (see SaveTest.test_captures_unreachable_variable).")
  concrete_function_proto = saved_object_graph_pb2.SavedConcreteFunction()
  structured_outputs = func_graph_module.convert_structure_to_signature(
      concrete_function.structured_outputs)
  concrete_function_proto.canonicalized_input_signature.CopyFrom(
      nested_structure_coder.encode_structure(
          concrete_function.structured_input_signature))
  concrete_function_proto.output_signature.CopyFrom(
      nested_structure_coder.encode_structure(structured_outputs))
  concrete_function_proto.bound_inputs.extend(bound_inputs)
  return concrete_function_proto


# TODO(b/203440205): Support FunctionType directly.
def get_preinitialized_function_spec(concrete_function):
  """Generates an unconstrained FunctionSpec from FunctionType."""
  # TODO(b/203440205): SavedModel does not support FunctionType on its own
  # without a FuncGraph signature.
  # WrappedFunctions are not supposed to have FunctionSpecs.
  if concrete_function.structured_input_signature is None or isinstance(
      concrete_function, wrap_function_lib.WrappedFunction
  ):
    return None

  function_type = concrete_function.function_type
  if function_type is None:
    return None

  unconstrained_type = function_type_lib.FunctionType(
      [
          function_type_lib.Parameter(p.name, p.kind, p.optional, None)
          for p in function_type.parameters.values()
      ]
  )
  default_values = {
      p.default for p in function_type.parameters.values() if p.optional
  }
  return function_type_utils.FunctionSpec(
      unconstrained_type,
      default_values,
      False,
      name=concrete_function.name,
  )


def serialize_bare_concrete_function(concrete_function):
  """Build a SavedBareConcreteFunction."""
  # pylint: disable=protected-access
  proto = saved_object_graph_pb2.SavedBareConcreteFunction(
      concrete_function_name=concrete_function.name,
      allowed_positional_arguments=concrete_function._num_positional_args,
      argument_keywords=concrete_function._arg_keywords)
  function_spec = get_preinitialized_function_spec(concrete_function)
  if function_spec is not None:
    proto.function_spec.CopyFrom(_serialize_function_spec(function_spec))
  return proto
  # pylint: enable=protected-access


def serialize_function(function, concrete_functions):
  """Build a SavedFunction proto."""
  proto = saved_object_graph_pb2.SavedFunction()

  function_spec_proto = _serialize_function_spec(function.function_spec)
  proto.function_spec.CopyFrom(function_spec_proto)
  for concrete_function in concrete_functions:
    proto.concrete_functions.append(concrete_function.name)
  return proto


def wrap_cached_variables(concrete_function):
  """Wraps the concrete function if it uses cached read tensors.

  This function creates a new concrete function that captures variables
  instead of the cached read tensors.

  Args:
    concrete_function: A Concrete function that maybe captures cached read
      tensors.

  Returns:
    A concrete function that wraps the original concrete function, which
    captures variables instead. If the original function did not capture any
    cached values, then the function is not wrapped and the original object is
    returned.
  """
  outer_graph = func_graph_module.FuncGraph(
      "{}_no_cache".format(concrete_function.graph.name))
  mapped_captures = None
  remapped_captures = {}

  # Update the external captures to use read tensors generated in the outer
  # graph.
  with outer_graph.as_default():
    for capture, placeholder in concrete_function.graph.captures:
      cached_variable = getattr(capture, "_cached_variable", None)
      if cached_variable is None:
        continue
      cached_variable = cached_variable()
      new_cached_value = cached_variable.read_value()
      key = id(capture)
      external = concrete_function.graph.function_captures.by_val_external[key]
      internal = concrete_function.graph.function_captures.by_val_internal[key]
      remapped_captures[key] = [external, internal]
      concrete_function.graph.function_captures.add_or_replace(
          key=key,
          external=new_cached_value,
          internal=placeholder,
          is_by_ref=False)
      mapped_captures = True

  if not mapped_captures:
    return concrete_function

  inner_concrete = defun.ConcreteFunction.from_func_graph(
      concrete_function.graph, concrete_function.function_type, {}
  )

  def wrap_function(*args):
    return inner_concrete._call_flat(list(args), inner_concrete.captured_inputs)  # pylint:disable=protected-access

  args = nest.flatten(concrete_function.structured_input_signature,
                      expand_composites=True)
  func_graph_module.func_graph_from_py_func(
      None, wrap_function, args=tuple(args), kwargs={},
      func_graph=outer_graph)

  # Create concrete function, and copy the attributes necessary to serialize
  # the function.
  # pylint: disable=protected-access
  fn = defun.ConcreteFunction.from_func_graph(
      outer_graph, concrete_function.function_type, {}
  )
  fn._arg_keywords = concrete_function._arg_keywords
  fn._num_positional_args = concrete_function._num_positional_args
  # pylint: enable=protected-access

  # Return the captures to their original values
  for key, capture in remapped_captures.items():
    external, internal = capture
    concrete_function.graph._function_captures.add_or_replace(  # pylint: disable=protected-access
        key=key,
        external=external,
        internal=internal,
        is_by_ref=False)
  return fn