tensorflow/python/framework/op_def_library.py
# 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.
# ==============================================================================
"""Class to hold a library of OpDefs and use it to create Brain operations."""
from google.protobuf import text_format
from tensorflow.core.config import flags
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import tensor_pb2
from tensorflow.core.framework import tensor_shape_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import op_callbacks
from tensorflow.python.framework import op_def_library_pybind
from tensorflow.python.framework import op_def_registry
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import _pywrap_utils
from tensorflow.python.util import compat
from tensorflow.python.util import tf_contextlib
def _Attr(op_def, name):
for attr in op_def.attr:
if attr.name == name:
return attr
raise TypeError(f"Inconsistent OpDef for '{op_def.name}', missing attr "
f"'{name}'")
def _AttrValue(attr_protos, name, op_type_name):
if name in attr_protos:
return attr_protos[name]
raise TypeError(f"Inconsistent OpDef for '{op_type_name}', missing attr "
f"'{name}' from '{attr_protos}'.")
def _SatisfiesTypeConstraint(dtype, attr_def, param_name):
if attr_def.HasField("allowed_values"):
allowed_list = attr_def.allowed_values.list.type
allowed_values = ", ".join(dtypes.as_dtype(x).name for x in allowed_list)
if dtype not in allowed_list:
raise TypeError(
f"Value passed to parameter '{param_name}' has DataType "
f"{dtypes.as_dtype(dtype).name} not in list of allowed values: "
f"{allowed_values}")
def _SatisfiesLengthConstraint(length, attr_def, param_name, op_type_name):
if attr_def.has_minimum and length < attr_def.minimum:
raise ValueError(f"Attr '{param_name}' of '{op_type_name}' Op passed list "
f"of length {length} less than minimum "
f"{attr_def.minimum}.")
def _SatisfiesAllowedStringsConstraint(value, attr_def, arg_name, op_type_name):
if value not in attr_def.allowed_values.list.s:
allowed_values = '", "'.join(
map(compat.as_text, attr_def.allowed_values.list.s))
raise ValueError(f"Attr '{arg_name}' of '{op_type_name}' Op passed string "
f"'{compat.as_text(value)}' not in: \"{allowed_values}\".")
def _SatisfiesIntMinimumConstraint(value, attr_def, arg_name, op_type_name):
if value < attr_def.minimum:
raise ValueError(f"Attr '{arg_name}' of '{op_type_name}' Op passed {value} "
f"less than minimum {attr_def.minimum}.")
def _IsListParameter(arg):
if arg.number_attr:
return True
elif arg.type_list_attr:
return True
return False
def _NumTypeFields(arg):
num = 0
if arg.type != types_pb2.DT_INVALID: num += 1
if arg.type_attr: num += 1
if arg.type_list_attr: num += 1
return num
def _IsListValue(v):
return isinstance(v, (list, tuple))
def _Flatten(l):
"""Converts [1, 2, [3, 4], [5]] to [1, 2, 3, 4, 5]."""
# [1, 2, [3, 4], [5]] -> [[1], [2], [3, 4], [5]]
l_of_l = [x if _IsListValue(x) else [x] for x in l]
# [[1], [2], [3, 4], [5]] -> [1, 2, 3, 4, 5]
return [item for sublist in l_of_l for item in sublist]
def _Restructure(l, structure):
"""Returns the elements of list l structured according to the given structure.
A structure is represented by a list whose elements are either
`None` or a non-negative integer. `None` corresponds to a single
element in the output list, and an integer N corresponds to a nested
list of length N.
The function returns a data structure whose shape is given by
`structure`, and whose elements are taken from `l`. If `structure`
is a singleton, the function returns the single data structure
implied by the 0th element of `structure`. For example:
_Restructure(["foo", "bar", "baz", "qux"], [None, 2, None])
-> ["foo", ["bar", "baz"], "qux"]
_Restructure(["foo"], [None]) -> "foo"
_Restructure(["foo"], [1]) -> ["foo"]
_Restructure([], [0]) -> []
Args:
l: A list.
structure: A list whose elements are either `None` or a non-negative
integer.
Returns:
The elements of `l`, restructured according to `structure`. If
`structure` is a list of length 1, this function returns the
single data structure implied by `structure[0]`.
"""
result = []
current_index = 0
for element in structure:
if element is None:
result.append(l[current_index])
current_index += 1
else:
result.append(l[current_index:current_index+element])
current_index += element
if len(result) == 1:
return result[0]
else:
return tuple(result)
def _MakeFloat(v, arg_name):
if not isinstance(v, compat.real_types):
raise TypeError(f"Expected float for argument '{arg_name}' not {repr(v)}.")
return float(v)
def _MakeInt(v, arg_name):
if isinstance(v, str):
raise TypeError(f"Expected int for argument '{arg_name}' not {repr(v)}.")
try:
return int(v)
except (ValueError, TypeError):
raise TypeError(f"Expected int for argument '{arg_name}' not {repr(v)}.")
def _MakeStr(v, arg_name):
if not isinstance(v, compat.bytes_or_text_types):
raise TypeError(f"Expected string for argument '{arg_name}' not {repr(v)}.")
return compat.as_bytes(v) # Convert unicode strings to bytes.
def _MakeBool(v, arg_name):
if not isinstance(v, bool):
raise TypeError(f"Expected bool for argument '{arg_name}' not {repr(v)}.")
return v
def _MakeType(v, arg_name):
try:
v = dtypes.as_dtype(v).base_dtype
except TypeError:
raise TypeError(f"Expected DataType for argument '{arg_name}' not "
f"{repr(v)}.")
return v.as_datatype_enum
def _MakeShape(v, arg_name):
"""Convert v into a TensorShapeProto."""
# Args:
# v: A TensorShapeProto, a list of ints, or a tensor_shape.TensorShape.
# arg_name: String, for error messages.
# Returns:
# A TensorShapeProto.
if isinstance(v, tensor_shape_pb2.TensorShapeProto):
for d in v.dim:
if d.name:
logging.warning("Warning: TensorShapeProto with a named dimension: %s",
str(v))
break
return v
try:
return tensor_shape.as_shape(v).as_proto()
except TypeError as e:
raise TypeError(f"Error converting {repr(v)} (arg name = {arg_name}) to a "
f"TensorShape: {e}")
except ValueError as e:
raise TypeError(f"Error converting {repr(v)} (arg name = {arg_name}) to a "
f"TensorShape: {e}")
def _MakeTensor(v, arg_name):
"""Ensure v is a TensorProto."""
if isinstance(v, tensor_pb2.TensorProto):
return v
raise TypeError(
f"Don't know how to convert {repr(v)} to a TensorProto for argument "
f"'{arg_name}'")
def _MakeFunc(v, arg_name):
"""Ensure v is a func."""
if isinstance(v, attr_value_pb2.NameAttrList):
return v
if isinstance(v, compat.bytes_or_text_types):
fn_attr = attr_value_pb2.NameAttrList(name=v)
elif hasattr(v, "add_to_graph"):
v.add_to_graph(ops.get_default_graph())
if hasattr(v, "_as_name_attr_list"):
fn_attr = v._as_name_attr_list # pylint: disable=protected-access
else:
fn_attr = attr_value_pb2.NameAttrList(name=v.name)
else:
raise TypeError(f"Don't know how to convert {repr(v)} to a func for "
f"argument {arg_name}")
return fn_attr
# pylint: disable=g-doc-return-or-yield
@tf_contextlib.contextmanager
def _MaybeColocateWith(inputs):
"""A context manager for (maybe) colocating with a list of input tensors.
Args:
inputs: A list of `Tensor` or `Operation` objects.
Returns:
A context manager.
"""
if not inputs:
yield
else:
# NOTE(mrry): The `ops.colocate_with()` function accepts only a single
# op or tensor, so we create one context manager per element in the list.
with ops.colocate_with(inputs[0]), _MaybeColocateWith(inputs[1:]):
yield
# pylint: enable=g-doc-return-or-yield
def apply_op(op_type_name, name=None, **keywords): # pylint: disable=invalid-name
"""Add a node invoking a registered Op to a graph.
Example usage:
# input1 and input2 can be Tensors or anything ops.convert_to_tensor()
# will convert to a Tensor.
op_def_library.apply_op("op", input1=input1, input2=input2)
# Can specify a node name.
op_def_library.apply_op("op", input1=input1, name="node_name")
# Must use keyword arguments, with the names specified in the OpDef.
op_def_library.apply_op("op", input_name=input, attr_name=attr)
All attrs must either be inferred from an input or specified.
(If inferred, the attr must not be specified.) If an attr has a default
value specified in the Op's OpDef, then you may pass None as the value
of that attr to get the default.
Args:
op_type_name: string. Must match the name field of a registered Op.
name: string. Optional name of the created op.
**keywords: input Tensor and attr arguments specified by name, and optional
parameters to pass when constructing the Operation.
Returns:
The Tensor(s) representing the output of the operation, or the Operation
itself if there are no outputs.
Raises:
RuntimeError: On some errors.
TypeError: On some errors.
ValueError: On some errors.
"""
output_structure, is_stateful, op, outputs = _apply_op_helper(
op_type_name, name, **keywords)
if output_structure:
res = _Restructure(ops.convert_n_to_tensor(outputs), output_structure)
if isinstance(res, list) and not res and is_stateful:
return op
else:
return res
else:
return op
# This is temporary Python/C++ code duplication until all of it can be ported
# over to C++.
# LINT.IfChange
def _ExtractAttrProto(op_type_name, op_def, attrs, attr_protos):
"""Extracts `attr_protos`. For use in _apply_op_helper."""
for attr_def in op_def.attr:
key = attr_def.name
value = attrs[key]
if attr_def.HasField("default_value") and value is None:
attr_value = attr_value_pb2.AttrValue()
attr_value.CopyFrom(attr_def.default_value)
attr_protos[key] = attr_value
continue
attr_value = value_to_attr_value(value, attr_def.type, key)
if attr_def.type.startswith("list("):
_SatisfiesLengthConstraint(len(value), attr_def, key, op_type_name)
if attr_def.HasField("allowed_values"):
if attr_def.type == "string":
_SatisfiesAllowedStringsConstraint(attr_value.s, attr_def, key,
op_type_name)
elif attr_def.type == "list(string)":
for value in attr_value.list.s:
_SatisfiesAllowedStringsConstraint(value, attr_def, key, op_type_name)
if attr_def.has_minimum and attr_def.type == "int":
_SatisfiesIntMinimumConstraint(attr_value.i, attr_def, key, op_type_name)
if attr_def.type == "type":
_SatisfiesTypeConstraint(attr_value.type, attr_def, key)
if attr_def.type == "list(type)":
for value in attr_value.list.type:
_SatisfiesTypeConstraint(value, attr_def, key)
attr_protos[key] = attr_value
def _ExtractOutputStructure(op_type_name, op_def, attr_protos,
output_structure):
"""Extracts `output_structure`. For use in _apply_op_helper."""
for arg in op_def.output_arg:
if arg.number_attr:
n = _AttrValue(attr_protos, arg.number_attr, op_type_name).i
output_structure.append(n)
elif arg.type_attr:
t = _AttrValue(attr_protos, arg.type_attr, op_type_name)
output_structure.append(None)
elif arg.type_list_attr:
t = _AttrValue(attr_protos, arg.type_list_attr, op_type_name)
output_structure.append(len(t.list.type))
else:
output_structure.append(None)
def _CanExtractAttrsFastPath(op_def, keywords):
"""Check if the fast path for _apply_op_helper is applicable."""
# Check if all inputs are already tf.Tensor
for input_arg in op_def.input_arg:
value = keywords.get(input_arg.name, None)
if not isinstance(value, tensor.Tensor):
return False
# Check that attrs are not `func` or `list(func)` type.
for attr_def in op_def.attr:
if attr_def.type == "func" or attr_def.type == "list(func)":
return False
return True
def _CheckOpDeprecation(op_type_name, op_def, producer):
"""Checks if the op is deprecated."""
deprecation_version = op_def.deprecation.version
if deprecation_version and producer >= deprecation_version:
raise NotImplementedError(
f"Op {op_type_name} is not available in GraphDef version {producer}. "
f"It has been removed in version {deprecation_version}. "
f"{op_def.deprecation.explanation}.")
def _ExtractDefaultTypesAndAllowedTypes(op_def, default_type_attr_map,
allowed_list_attr_map):
"""Extracts the `default_type_attr_map` and `allowed_list_attr_map`."""
# TODO(b/31302892): Currently the defaults don't work in the right
# way if you have two inputs, one of whose type resolution depends
# on the other. Handling this will require restructuring this code
# significantly.
for attr_def in op_def.attr:
if attr_def.type != "type":
continue
key = attr_def.name
if attr_def.HasField("default_value"):
default_type_attr_map[key] = dtypes.as_dtype(
attr_def.default_value.type)
if attr_def.HasField("allowed_values"):
allowed_list_attr_map[key] = attr_def.allowed_values.list.type
def _ExtractInputsAndAttrs(op_type_name, op_def, allowed_list_attr_map,
keywords, default_type_attr_map, attrs, inputs,
input_types):
"""Extracts `attrs`, `inputs`, and `input_types` in _apply_op_helper."""
inferred_from = {}
for input_arg in op_def.input_arg:
input_name = input_arg.name
if input_name in keywords:
values = keywords.pop(input_name)
elif input_name + "_" in keywords:
# Handle the case where the name is a keyword or built-in
# for Python so we use the name + _ instead.
input_name += "_"
values = keywords.pop(input_name)
else:
raise TypeError(f"No argument for input {input_name} found in {op_def}")
# Goals:
# * Convert values to Tensors if it contains constants.
# * Verify that values is a list if that matches the input_arg's
# type.
# * If the input_arg's type is determined by attrs, either set
# those attrs and validate those attr values are legal (if
# they have not yet been set) or validate the input matches
# the type indicated by the attrs (if they have already been
# inferred via an earlier input).
# * If the input_arg has an explicit type, make sure the input
# conforms.
if _IsListParameter(input_arg):
if not _IsListValue(values):
raise TypeError(
f"Expected list for '{input_name}' argument to '{op_type_name}' "
f"Op, not {values}.")
# In cases where we expect all elements of the list to have the
# same dtype, try to cast non-Tensor elements to that type.
dtype = None
default_dtype = None
if input_arg.type != types_pb2.DT_INVALID:
dtype = input_arg.type
elif input_arg.number_attr:
if input_arg.type_attr in attrs:
dtype = attrs[input_arg.type_attr]
else:
for t in values:
if isinstance(t, tensor.Tensor):
dtype = t.dtype
break
# dtype still not found, prefer using the default dtype
# from the attr.
if dtype is None and input_arg.type_attr in default_type_attr_map:
default_dtype = default_type_attr_map[input_arg.type_attr]
try:
if not input_arg.is_ref and dtype:
dtype = dtypes.as_dtype(dtype).base_dtype
values = ops.internal_convert_n_to_tensor(
values,
name=input_arg.name,
dtype=dtype if dtype else None,
preferred_dtype=default_dtype,
as_ref=input_arg.is_ref)
all_types = set(v.dtype.base_dtype for v in values)
if input_arg.number_attr and len(all_types) > 1:
# All types should match.
raise TypeError(f"Not all types matched for {input_arg.name} for "
f"{op_type_name}. Got {all_types}")
except (TypeError, ValueError):
# What types does the conversion function think values have?
observed_types = []
for value in values:
try:
converted_value = ops.convert_to_tensor(
value, as_ref=input_arg.is_ref)
observed_types.append(converted_value.dtype.base_dtype.name)
except (TypeError, ValueError):
observed_types.append("<NOT CONVERTIBLE TO TENSOR>")
observed = ", ".join(observed_types)
prefix = ("Tensors in list passed to '%s' of '%s' Op have types [%s]" %
(input_name, op_type_name, observed))
if input_arg.number_attr:
if input_arg.type != types_pb2.DT_INVALID:
raise TypeError(f"{prefix} that do not match expected type "
f"{dtype.name}.")
elif input_arg.type_attr in attrs:
raise TypeError(f"{prefix} that do not match type {dtype.name} "
"inferred from earlier arguments.")
else:
raise TypeError(f"{prefix} that don't all match.")
else:
raise TypeError(f"{prefix} that are invalid. Tensors: {values}")
types = [x.dtype for x in values]
inputs.extend(values)
else:
# In cases where we have an expected type, try to convert non-Tensor
# arguments to that type.
dtype = None
default_dtype = None
allowed_list = None
if input_arg.type != types_pb2.DT_INVALID:
dtype = input_arg.type
elif input_arg.type_attr in attrs:
dtype = attrs[input_arg.type_attr]
elif input_arg.type_attr in default_type_attr_map:
# The dtype could not be inferred solely from the inputs,
# so we prefer the attr's default, so code that adds a new attr
# with a default is backwards compatible.
default_dtype = default_type_attr_map[input_arg.type_attr]
allowed_list = allowed_list_attr_map.get(input_arg.type_attr)
try:
# First see if we can get a valid dtype with the default conversion
# and see if it matches an allowed dtypes. Some ops like ConcatV2 may
# not list allowed dtypes, in which case we should skip this.
if dtype is None and allowed_list:
inferred = None
try:
inferred = ops.convert_to_tensor(
values, name=input_arg.name, as_ref=input_arg.is_ref)
except TypeError as err:
# When converting a python object such as a list of Dimensions, we
# need a dtype to be specified, thus tensor conversion may throw
# an exception which we will ignore and try again below.
pass
# If we did not match an allowed dtype, try again with the default
# dtype. This could be because we have an empty tensor and thus we
# picked the wrong type.
if inferred is not None and inferred.dtype in allowed_list:
values = inferred
else:
values = ops.convert_to_tensor(
values,
name=input_arg.name,
as_ref=input_arg.is_ref,
preferred_dtype=default_dtype)
else:
values = ops.convert_to_tensor(
values,
name=input_arg.name,
dtype=dtype,
as_ref=input_arg.is_ref,
preferred_dtype=default_dtype)
except TypeError as err:
if dtype is None:
raise err
else:
raise TypeError(
f"Expected {dtypes.as_dtype(dtype).name} passed to parameter "
f"'{input_arg.name}' of op '{op_type_name}', got "
f"{repr(values)} of type '{type(values).__name__}' instead. "
f"Error: {err}")
except ValueError:
# What type does convert_to_tensor think it has?
try:
observed = ops.convert_to_tensor(
values, as_ref=input_arg.is_ref).dtype.name
except ValueError as err:
raise ValueError(
f"Tried to convert '{input_name}' to a tensor and failed. "
f"Error: {err}")
prefix = ("Input '%s' of '%s' Op has type %s that does not match" %
(input_name, op_type_name, observed))
if input_arg.type != types_pb2.DT_INVALID:
raise TypeError(f"{prefix} expected type of "
f"{dtypes.as_dtype(input_arg.type).name}.")
else:
# Update the maps with the default, if needed.
k = input_arg.type_attr
if k in default_type_attr_map:
if k not in attrs:
attrs[k] = default_type_attr_map[k]
if k not in inferred_from:
inferred_from[k] = "Default in OpDef"
raise TypeError(
f"{prefix} type "
f"{dtypes.as_dtype(attrs[input_arg.type_attr]).name} of "
f"argument '{inferred_from[input_arg.type_attr]}'.")
types = [values.dtype]
inputs.append(values)
base_types = [x.base_dtype for x in types]
if input_arg.number_attr:
# <number-attr> * <type> or <number-attr> * <type-attr>
if input_arg.number_attr in attrs:
if len(values) != attrs[input_arg.number_attr]:
raise ValueError(
f"List argument '{input_name}' to '{op_type_name}' Op with "
f"length {len(values)} must match length "
f"{attrs[input_arg.number_attr]} of argument "
f"'{inferred_from[input_arg.number_attr]}'.")
else:
attrs[input_arg.number_attr] = len(values)
inferred_from[input_arg.number_attr] = input_name
num_attr = _Attr(op_def, input_arg.number_attr)
if num_attr.has_minimum and len(values) < num_attr.minimum:
raise ValueError(
f"List argument '{input_name}' to '{op_type_name}' Op with "
f"length {len(values)} shorter than minimum length "
f"{num_attr.minimum}.")
# All tensors must have the same base type.
if any(bt != base_types[0] for bt in base_types):
raise TypeError(
f"All tensors passed to '{input_name}' of '{op_type_name}' Op "
f"must have the same type. Got {base_types} instead.")
if input_arg.type != types_pb2.DT_INVALID:
# <number-attr> * <type> case
if base_types and base_types[0] != input_arg.type:
assert False, "Unreachable"
elif input_arg.type_attr in attrs:
# <number-attr> * <type-attr> case, where <type-attr> already
# has an inferred value.
if base_types and base_types[0] != attrs[input_arg.type_attr]:
assert False, "Unreachable"
else:
# <number-attr> * <type-attr> case, where we are now setting
# the <type-attr> based on this input
if not base_types:
# If it's in default_type_attr_map, then wait to set it
# (in "process remaining attrs", below).
if input_arg.type_attr not in default_type_attr_map:
raise TypeError(
"Don't know how to infer type variable from empty input "
f"list passed to input '{input_name}' of '{op_type_name}' "
"Op.")
else:
attrs[input_arg.type_attr] = base_types[0]
inferred_from[input_arg.type_attr] = input_name
type_attr = _Attr(op_def, input_arg.type_attr)
_SatisfiesTypeConstraint(
base_types[0], type_attr, param_name=input_name)
elif input_arg.type_attr:
# <type-attr>
attr_value = base_types[0]
if input_arg.type_attr in attrs:
if attrs[input_arg.type_attr] != attr_value:
raise TypeError(
f"Input '{input_name}' of '{op_type_name}' Op has type "
f"{dtypes.as_dtype(attr_value).name} that does not match type "
f"{dtypes.as_dtype(attrs[input_arg.type_attr]).name} of "
f"argument '{inferred_from[input_arg.type_attr]}'.")
else:
for base_type in base_types:
_SatisfiesTypeConstraint(
base_type,
_Attr(op_def, input_arg.type_attr),
param_name=input_name)
attrs[input_arg.type_attr] = attr_value
inferred_from[input_arg.type_attr] = input_name
elif input_arg.type_list_attr:
# <type-list-attr>
attr_value = base_types
if input_arg.type_list_attr in attrs:
if attrs[input_arg.type_list_attr] != attr_value:
actual_types = ", ".join(dtypes.as_dtype(x).name for x in attr_value)
expected_types = ", ".join(
dtypes.as_dtype(x).name for x in attrs[input_arg.type_list_attr])
raise TypeError(
f"Input '{input_name}' of '{op_type_name}' Op has type list of "
f"{actual_types} that does not match type list {expected_types}"
f" of argument '{inferred_from[input_arg.type_list_attr]}'.")
else:
for base_type in base_types:
_SatisfiesTypeConstraint(
base_type,
_Attr(op_def, input_arg.type_list_attr),
param_name=input_name)
attrs[input_arg.type_list_attr] = attr_value
inferred_from[input_arg.type_list_attr] = input_name
else:
# single Tensor with specified type
if base_types[0] != input_arg.type:
assert False, "Unreachable"
if input_arg.is_ref:
if not all(x._is_ref_dtype for x in types): # pylint: disable=protected-access
raise TypeError(
f"'{op_type_name}' Op requires that input '{input_name}' be a "
"mutable tensor (e.g.: a tf.Variable)")
input_types.extend(types)
else:
input_types.extend(base_types)
def _ExtractRemainingAttrs(op_type_name, op_def, keywords,
default_type_attr_map, attrs):
"""Extracts the remaining attributes into `attrs` in _apply_op_helper."""
for attr in op_def.attr:
# Skip attrs that have already had their values inferred
if attr.name in attrs:
if attr.name in keywords:
raise TypeError(
f"Should not specify value for inferred attr '{attr.name}' for "
f"{op_type_name}.")
continue
if attr.name in keywords:
attrs[attr.name] = keywords.pop(attr.name)
elif attr.name + "_" in keywords:
# Attrs whose names match Python keywords have an extra '_'
# appended, so we must check for that as well.
attrs[attr.name] = keywords.pop(attr.name + "_")
elif attr.name in default_type_attr_map:
attrs[attr.name] = default_type_attr_map[attr.name]
else:
raise TypeError(f"No argument found for attr {attr.name} for "
f"{op_type_name}")
def _GetOpDef(op_type_name, keywords):
"""Returns the OpDef, Graph and Producer. For use in _apply_op_helper."""
op_def = op_def_registry.get(op_type_name)
if op_def is None:
raise RuntimeError(f"Unrecognized Op name {op_type_name}")
# Determine the graph context.
try:
# Need to flatten all the arguments into a list.
# pylint: disable=protected-access
g = ops._get_graph_from_inputs(_Flatten(keywords.values()))
producer = g.graph_def_versions.producer
# pylint: enable=protected-access
except AssertionError as e:
raise RuntimeError(
f"Cannot determine graph for Op '{op_type_name}' due to: {e.message}")
return op_def, g, producer
def _CheckAllInputsUsed(op_type_name, keywords):
"""Ensures all inputs passed into _apply_op_helper were used."""
if keywords:
all_keywords = ", ".join(sorted(keywords.keys()))
raise TypeError(f"{op_type_name} got unexpected keyword arguments: "
f"{all_keywords}.")
def _apply_op_helper(op_type_name, name=None, **keywords): # pylint: disable=invalid-name
"""Implementation of apply_op that returns output_structure, op."""
op_def, g, producer = _GetOpDef(op_type_name, keywords)
name = name if name else op_type_name
attrs, attr_protos = {}, {}
default_type_attr_map, allowed_list_attr_map = {}, {}
inputs, input_types, output_structure = [], [], []
fallback = True
if (_CanExtractAttrsFastPath(op_def, keywords) and
flags.config().graph_building_optimization.value()):
fallback = False
attr_protos, inputs, input_types, output_structure = (
op_def_library_pybind.process_inputs(op_type_name, producer, keywords))
if fallback:
_CheckOpDeprecation(op_type_name, op_def, producer)
_ExtractDefaultTypesAndAllowedTypes(op_def, default_type_attr_map,
allowed_list_attr_map)
# Requires that op_def has passed validation (using the C++
# ValidateOpDef() from ../framework/op_def_util.h).
with g.as_default(), ops.name_scope(name) as scope:
if fallback:
_ExtractInputsAndAttrs(op_type_name, op_def, allowed_list_attr_map,
keywords, default_type_attr_map, attrs, inputs,
input_types)
_ExtractRemainingAttrs(op_type_name, op_def, keywords,
default_type_attr_map, attrs)
_ExtractAttrProto(op_type_name, op_def, attrs, attr_protos)
del attrs # attrs is no longer authoritative, use attr_protos instead
_ExtractOutputStructure(op_type_name, op_def, attr_protos,
output_structure)
_CheckAllInputsUsed(op_type_name, keywords)
# NOTE(mrry): We add an explicit colocation constraint between
# the newly created op and any of its reference-typed inputs.
must_colocate_inputs = [val for arg, val in zip(op_def.input_arg, inputs)
if arg.is_ref]
with _MaybeColocateWith(must_colocate_inputs):
# Add Op to graph
# pylint: disable=protected-access
op = g._create_op_internal(op_type_name, inputs, dtypes=None,
name=scope, input_types=input_types,
attrs=attr_protos, op_def=op_def)
# `outputs` is returned as a separate return value so that the output
# tensors can the `op` per se can be decoupled so that the
# `op_callbacks` can function properly. See framework/op_callbacks.py
# for more details.
outputs = op.outputs
# Conditionally invoke tfdbg v2's op callback(s).
if op_callbacks.should_invoke_op_callbacks():
callback_outputs = op_callbacks.invoke_op_callbacks(
op.node_def.op, tuple(op.inputs), attr_protos, tuple(outputs),
op_name=op.name, graph=g)
if callback_outputs is not None:
outputs = callback_outputs
return output_structure, op_def.is_stateful, op, outputs
def value_to_attr_value(value, attr_type, arg_name): # pylint: disable=invalid-name
"""Encodes a Python value as an `AttrValue` proto message.
Args:
value: The value to convert.
attr_type: The value type (string) -- see the AttrValue proto definition for
valid strings.
arg_name: Argument name (for error messages).
Returns:
An AttrValue proto message that encodes `value`.
"""
attr_value = attr_value_pb2.AttrValue()
if attr_type.startswith("list("):
if not _IsListValue(value):
raise TypeError(f"Expected list for attr {arg_name}, obtained "
f"{type(value).__name__} instead.")
if attr_type == "string":
attr_value.s = _MakeStr(value, arg_name)
elif attr_type == "list(string)":
attr_value.list.s.extend([_MakeStr(x, arg_name) for x in value])
elif attr_type == "int":
attr_value.i = _MakeInt(value, arg_name)
elif attr_type == "list(int)":
attr_value.list.i.extend([_MakeInt(x, arg_name) for x in value])
elif attr_type == "float":
attr_value.f = _MakeFloat(value, arg_name)
elif attr_type == "list(float)":
attr_value.list.f.extend([_MakeFloat(x, arg_name) for x in value])
elif attr_type == "bool":
attr_value.b = _MakeBool(value, arg_name)
elif attr_type == "list(bool)":
attr_value.list.b.extend([_MakeBool(x, arg_name) for x in value])
elif attr_type == "type":
attr_value.type = _MakeType(value, arg_name)
elif attr_type == "list(type)":
attr_value.list.type.extend([_MakeType(x, arg_name) for x in value])
elif attr_type == "shape":
attr_value.shape.CopyFrom(_MakeShape(value, arg_name))
elif attr_type == "list(shape)":
attr_value.list.shape.extend([_MakeShape(x, arg_name) for x in value])
elif attr_type == "tensor":
attr_value.tensor.CopyFrom(_MakeTensor(value, arg_name))
elif attr_type == "list(tensor)":
attr_value.list.tensor.extend([_MakeTensor(x, arg_name) for x in value])
elif attr_type == "func":
attr_value.func.CopyFrom(_MakeFunc(value, arg_name))
elif attr_type == "list(func)":
attr_value.list.func.extend([_MakeFunc(x, arg_name) for x in value])
else:
raise TypeError(f"Unrecognized Attr type {attr_type} for {arg_name}.")
return attr_value
# LINT.ThenChange(//tensorflow/python/framework/op_def_library_pybind.cc)
# The following symbols are used by op_def_util.cc.
_pywrap_utils.RegisterPyObject("tf.dtypes.DType", dtypes.DType)
_pywrap_utils.RegisterPyObject("tf.dtypes.as_dtype", dtypes.as_dtype)
_pywrap_utils.RegisterPyObject("tf.TensorShape", tensor_shape.TensorShape)
_pywrap_utils.RegisterPyObject("tf.as_shape", tensor_shape.as_shape)
_pywrap_utils.RegisterPyObject("tf.TensorProto", tensor_pb2.TensorProto)
_pywrap_utils.RegisterPyObject("text_format.Parse", text_format.Parse)
_pywrap_utils.RegisterPyObject("tf.convert_to_tensor", ops.convert_to_tensor)