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ops.py
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# 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.
# ==============================================================================
"""Classes and functions used to construct graphs."""
# pylint: disable=g-bad-name
import collections
from collections.abc import Callable, Iterator, Sequence
import contextlib
import copy
import enum
import re
import sys
import threading
import types
from typing import cast, TypeVar, Any, AnyStr, NoReturn, Optional, Pattern, Union, ContextManager
from absl import app
import numpy as np
from numpy import typing as npt
from google.protobuf import message
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import full_type_pb2
from tensorflow.core.framework import function_pb2
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import node_def_pb2
from tensorflow.core.framework import op_def_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.core.framework import versions_pb2
from tensorflow.core.protobuf import config_pb2
# pywrap_tensorflow must be imported first to avoid protobuf issues.
# (b/143110113)
# pylint: disable=invalid-import-order,g-bad-import-order,unused-import
from tensorflow.python import pywrap_tensorflow
from tensorflow.python import pywrap_tfe
# pylint: enable=invalid-import-order,g-bad-import-order,unused-import
from tensorflow.python import tf2
from tensorflow.python.client import pywrap_tf_session
from tensorflow.python.eager import context
from tensorflow.python.eager import core
from tensorflow.python.eager import monitoring
from tensorflow.python.eager import record
from tensorflow.python.framework import c_api_util
from tensorflow.python.framework import composite_tensor
from tensorflow.python.framework import device as pydev
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import op_callbacks
from tensorflow.python.framework import registry
from tensorflow.python.framework import stack
from tensorflow.python.framework import tensor as tensor_lib
from tensorflow.python.framework import tensor_conversion_registry
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.framework import traceable_stack
from tensorflow.python.framework import versions
from tensorflow.python.ops import control_flow_util
from tensorflow.python.ops import handle_data_util
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.profiler import trace as profiler_trace
from tensorflow.python.types import core as core_tf_types
from tensorflow.python.types import internal
from tensorflow.python.util import compat
from tensorflow.python.util import decorator_utils
from tensorflow.python.util import deprecation
from tensorflow.python.util import function_utils
from tensorflow.python.util import lock_util
from tensorflow.python.util import object_identity
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util import tf_stack
from tensorflow.python.util import traceback_utils
from tensorflow.python.util.compat import collections_abc
from tensorflow.python.util.deprecation import deprecated_args
from tensorflow.python.util.tf_export import kwarg_only
from tensorflow.python.util.tf_export import tf_export
_T = TypeVar("_T")
GraphType = TypeVar("GraphType", bound="Graph")
OpStatsType = TypeVar("OpStatsType", bound="OpStats")
OperationType = TypeVar("OperationType", bound="Operation")
EagerTensorType = TypeVar("EagerTensorType", bound="_EagerTensorBase")
# TODO(b/307794935): Remove after bug is fixed.
is_oss = True # Updated by copybara
# Temporary global switches determining if we should enable the work-in-progress
# calls to the C API. These will be removed once all functionality is supported.
_USE_C_API: bool = True
_USE_C_SHAPES: bool = True
_api_usage_gauge = monitoring.BoolGauge(
"/tensorflow/api/ops_eager_execution",
"Whether ops.enable_eager_execution() is called.")
_control_flow_api_gauge = monitoring.BoolGauge(
"/tensorflow/api/enable_control_flow_v2",
"Whether enable_control_flow_v2() is called.")
_tf_function_api_gauge = monitoring.BoolGauge(
"/tensorflow/api/tf_function",
"Whether tf.function() is used.")
# pylint: disable=protected-access
_DTYPES_INTERN_TABLE: dict[types_pb2.DataType, dtypes.DType] = (
dtypes._INTERN_TABLE)
# pylint: enable=protected-access
def tensor_id(tensor) -> Any:
"""Returns a unique identifier for this Tensor."""
return tensor._id # pylint: disable=protected-access
class _UserDeviceSpec(object):
"""Store user-specified device and provide computation of merged device."""
def __init__(self, device_name_or_function) -> None:
self._device_name_or_function = device_name_or_function
self.display_name = str(self._device_name_or_function)
self.function = device_name_or_function
self.raw_string = None
if isinstance(device_name_or_function, pydev.MergeDevice):
self.is_null_merge = device_name_or_function.is_null_merge
elif callable(device_name_or_function):
self.is_null_merge = False
dev_func = self._device_name_or_function
func_name = function_utils.get_func_name(dev_func)
func_code = function_utils.get_func_code(dev_func)
if func_code:
fname = func_code.co_filename
lineno = func_code.co_firstlineno
else:
fname = "unknown"
lineno = -1
self.display_name = "%s<%s, %d>" % (func_name, fname, lineno)
elif device_name_or_function is None:
# NOTE(taylorrobie): This MUST be False. None signals a break in the
# device stack, so `is_null_merge` must be False for such a case to
# allow callers to safely skip over null merges without missing a None.
self.is_null_merge = False
else:
self.raw_string = device_name_or_function
self.function = pydev.merge_device(device_name_or_function)
self.is_null_merge = self.function.is_null_merge
# We perform this check in __init__ because it is of non-trivial cost,
# and self.string_merge is typically called many times.
self.fast_string_merge = isinstance(self.function, pydev.MergeDevice)
def string_merge(self, node_def) -> str:
if self.fast_string_merge:
return self.function.shortcut_string_merge(node_def)
return compat.as_str(_device_string(self.function(node_def)))
class NullContextmanager(contextlib.AbstractContextManager[None]):
def __init__(self, *args, **kwargs) -> None:
pass
def __enter__(self) -> None:
pass
def __exit__(self, type_arg, value_arg, traceback_arg) -> bool:
return False # False values do not suppress exceptions
def _as_graph_element(obj):
"""Convert `obj` to a graph element if possible, otherwise return `None`.
Args:
obj: Object to convert.
Returns:
The result of `obj._as_graph_element()` if that method is available;
otherwise `None`.
"""
conv_fn = getattr(obj, "_as_graph_element", None)
if conv_fn and callable(conv_fn):
return conv_fn()
return None
# Deprecated - legacy purposes only.
def is_dense_tensor_like(t) -> bool:
return isinstance(t, core_tf_types.Tensor)
def uid() -> int:
"""A unique (within this program execution) integer."""
return pywrap_tfe.TFE_Py_UID()
def numpy_text(tensor, is_repr=False) -> str:
"""Human readable representation of a tensor's numpy value."""
if tensor.dtype.is_numpy_compatible:
# pylint: disable=protected-access
text = repr(tensor._numpy()) if is_repr else str(tensor._numpy())
# pylint: enable=protected-access
else:
text = "<unprintable>"
if "\n" in text:
text = "\n" + text
return text
def value_text(tensor, is_repr=False) -> AnyStr:
"""Either the NumPy value or a custom TensorFlow formatting of `tensor`.
Custom formatting is used for custom device tensors, e.g. parallel tensors
with multiple components on different devices.
Args:
tensor: The tensor to format.
is_repr: Controls the style/verbosity of formatting.
Returns:
The formatted tensor.
"""
# pylint: disable=protected-access # friend access
if tensor._prefer_custom_summarizer():
text = tensor._summarize_value()
# pylint: enable=protected-access
if is_repr:
text = "value=" + text
else:
text = numpy_text(tensor, is_repr=is_repr)
if is_repr:
text = "numpy=" + text
return text
@tf_export("__internal__.SymbolicTensor")
class SymbolicTensor(pywrap_tf_session.PyTensor, tensor_lib.Tensor):
"""A symbolic tensor from a graph or tf.function."""
def __new__(cls, op, value_index, dtype, unique_id=None) -> "SymbolicTensor":
if unique_id is None:
unique_id = uid()
return pywrap_tf_session.PyTensor.__new__(
SymbolicTensor, op, value_index, dtypes.as_dtype(dtype), unique_id
)
def __copy__(self) -> "SymbolicTensor":
cls = self.__class__
result = cls.__new__(cls, self.op, self.value_index, self.dtype, self._id)
result.__dict__.update(self.__dict__)
return result
def _create_graph_constant(
value, dtype, shape, name, verify_shape, allow_broadcast
) -> tensor_lib.Tensor:
"""Create a graph constant and invoke constant callbacks."""
g = get_default_graph()
tensor_value = attr_value_pb2.AttrValue()
tensor_value.tensor.CopyFrom(
tensor_util.make_tensor_proto(
value, dtype=dtype, shape=shape, verify_shape=verify_shape,
allow_broadcast=allow_broadcast))
dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
attrs = {"value": tensor_value, "dtype": dtype_value}
const_tensor = g._create_op_internal( # pylint: disable=protected-access
"Const", [], [dtype_value.type], attrs=attrs, name=name).outputs[0]
if op_callbacks.should_invoke_op_callbacks():
# TODO(b/147670703): Once the special-op creation code paths
# are unified. Remove this `if` block.
callback_outputs = op_callbacks.invoke_op_callbacks(
"Const", tuple(), attrs, (const_tensor,), op_name=name, graph=g)
if callback_outputs is not None:
[const_tensor] = callback_outputs
return const_tensor
class _EagerTensorBase(
tensor_lib.Tensor, internal.NativeObject, core_tf_types.Value):
"""Base class for EagerTensor."""
# __complex__, __int__, __float__ and __index__ may copy the tensor to CPU and
# only work for scalars; values are cast as per numpy.
def __complex__(self) -> complex:
return complex(self._numpy())
def __int__(self) -> int:
return int(self._numpy())
def __float__(self) -> float:
return float(self._numpy())
def __index__(self) -> int:
return cast(np.ndarray, self._numpy()).__index__()
def __bool__(self) -> bool:
return bool(self._numpy())
__nonzero__ = __bool__
def __format__(self, format_spec) -> str:
if self._prefer_custom_summarizer():
return self._summarize_value().__format__(format_spec)
elif self.dtype.is_numpy_compatible:
# Not numpy_text here, otherwise the __format__ behaves differently.
return self._numpy().__format__(format_spec)
else:
return "<unprintable>".__format__(format_spec) # pytype: disable=attribute-error
def __reduce__(self):
return convert_to_tensor, (self._numpy(),)
def __copy__(self: EagerTensorType) -> EagerTensorType:
# Eager Tensors are immutable so it's safe to return themselves as a copy.
return self
def __deepcopy__(self: EagerTensorType, memo) -> EagerTensorType:
# Eager Tensors are immutable so it's safe to return themselves as a copy.
del memo
return self
def __str__(self) -> str:
return "tf.Tensor(%s, shape=%s, dtype=%s)" % (
value_text(self, is_repr=False), self.shape, self.dtype.name)
def __repr__(self) -> str:
return "<tf.Tensor: shape=%s, dtype=%s, %s>" % (
self.shape, self.dtype.name, value_text(self, is_repr=True))
def __len__(self) -> int:
"""Returns the length of the first dimension in the Tensor."""
if not self.shape.ndims:
raise TypeError("Scalar tensor has no `len()`")
# pylint: disable=protected-access
try:
return self._shape_tuple()[0]
except core._NotOkStatusException as e:
raise core._status_to_exception(e) from None
def __array__(self, dtype=None) -> np.ndarray:
a = self._numpy()
if not dtype:
return cast(np.ndarray, a)
return np.array(a, dtype=dtype)
def __hash__(self) -> int:
# EagerTensors are never hashable.
raise TypeError("Tensor is unhashable. "
"Instead, use tensor.ref() as the key.")
def _numpy_internal(self) -> npt.ArrayLike:
raise NotImplementedError()
def _numpy(self) -> npt.ArrayLike:
try:
return self._numpy_internal()
except core._NotOkStatusException as e: # pylint: disable=protected-access
raise core._status_to_exception(e) from None # pylint: disable=protected-access
@property
def dtype(self) -> dtypes.DType:
# Note: using the intern table directly here as this is
# performance-sensitive in some models.
return dtypes._INTERN_TABLE[self._datatype_enum()] # pylint: disable=protected-access
def numpy(self) -> npt.ArrayLike:
"""Copy of the contents of this Tensor into a NumPy array or scalar.
Unlike NumPy arrays, Tensors are immutable, so this method has to copy
the contents to ensure safety. Use `memoryview` to get a readonly
view of the contents without doing a copy:
>>> t = tf.constant([42])
>>> np.asarray(memoryview(t))
array([42], dtype=int32)
Note that `memoryview` is only zero-copy for Tensors on CPU. If a Tensor
is on GPU, it will have to be transferred to CPU first in order for
`memoryview` to work.
Returns:
A NumPy array of the same shape and dtype or a NumPy scalar, if this
Tensor has rank 0.
Raises:
ValueError: If the dtype of this Tensor does not have a compatible
NumPy dtype.
"""
# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.
maybe_arr = self._numpy() # pylint: disable=protected-access
return maybe_arr.copy() if isinstance(maybe_arr, np.ndarray) else maybe_arr
@property
def backing_device(self):
"""Returns the name of the device holding this tensor's memory.
`.backing_device` is usually the same as `.device`, which returns
the device on which the kernel of the operation that produced this tensor
ran. However, some operations can produce tensors on a different device
(e.g., an operation that executes on the GPU but produces output tensors
in host memory).
"""
raise NotImplementedError()
def _datatype_enum(self) -> NoReturn:
raise NotImplementedError()
def _shape_tuple(self) -> NoReturn:
"""The shape of this Tensor, as a tuple.
This is more performant than tuple(shape().as_list()) as it avoids
two list and one object creation. Marked private for now as from an API
perspective, it would be better to have a single performant way of
getting a shape rather than exposing shape() and shape_tuple()
(and heaven forbid, shape_list() etc. as well!). Punting on that for now,
but ideally one would work things out and remove the need for this method.
Returns:
tuple with the shape.
"""
raise NotImplementedError()
def _rank(self) -> NoReturn:
"""Integer rank of this Tensor.
Unlike regular Tensors, the rank is always known for EagerTensors.
This is more performant than len(self._shape_tuple())
Returns:
Integer rank
"""
raise NotImplementedError()
def _num_elements(self) -> NoReturn:
"""Number of elements of this Tensor.
Unlike regular Tensors, the number of elements is always known for
EagerTensors.
This is more performant than tensor.shape.num_elements
Returns:
Long - num elements in the tensor
"""
raise NotImplementedError()
def _copy_to_device(self, device_name) -> NoReturn: # pylint: disable=redefined-outer-name
raise NotImplementedError()
@staticmethod
def _override_operator(name, func) -> None:
setattr(_EagerTensorBase, name, func)
def _copy_nograd(
self: EagerTensorType, ctx=None, device_name=None,
) -> EagerTensorType:
"""Copies tensor to dest device, but doesn't record the operation."""
# Creates a new tensor on the dest device.
if ctx is None:
ctx = context.context()
if device_name is None:
device_name = ctx.device_name
# pylint: disable=protected-access
try:
ctx.ensure_initialized()
new_tensor = self._copy_to_device(device_name)
except core._NotOkStatusException as e:
raise core._status_to_exception(e) from None
return new_tensor
def _copy(
self: EagerTensorType, ctx=None, device_name=None,
) -> EagerTensorType:
"""Copies tensor to dest device."""
new_tensor = self._copy_nograd(ctx, device_name)
# Record the copy on tape and define backprop copy as well.
if context.executing_eagerly():
self_device = self.device
def grad_fun(dresult):
return [
dresult._copy(device_name=self_device)
if hasattr(dresult, "_copy") else dresult
]
record.record_operation("_copy", [new_tensor], [self], grad_fun)
return new_tensor
# pylint: enable=protected-access
@property
def shape(self) -> tensor_shape.TensorShape:
if self._tensor_shape is None: # pylint: disable=access-member-before-definition
# pylint: disable=protected-access
try:
# `_tensor_shape` is declared and defined in the definition of
# `EagerTensor`, in C.
self._tensor_shape = tensor_shape.TensorShape(self._shape_tuple())
except core._NotOkStatusException as e:
raise core._status_to_exception(e) from None
return self._tensor_shape
def get_shape(self) -> tensor_shape.TensorShape:
"""Alias of Tensor.shape."""
return self.shape
def _shape_as_list(self) -> list[int]:
"""The shape of the tensor as a list."""
return list(self._shape_tuple())
@deprecation.deprecated(
None, "Use tf.identity with explicit device placement instead.")
def cpu(self: EagerTensorType) -> EagerTensorType:
"""A copy of this Tensor with contents backed by host memory."""
return self._copy(context.context(), "CPU:0")
@deprecation.deprecated(None, "Use tf.identity instead.")
def gpu(self: EagerTensorType, gpu_index=0) -> EagerTensorType:
"""A copy of this Tensor with contents backed by memory on the GPU.
Args:
gpu_index: Identifies which GPU to place the contents on the returned
Tensor in.
Returns:
A GPU-memory backed Tensor object initialized with the same contents
as this Tensor.
"""
return self._copy(context.context(), "GPU:" + str(gpu_index))
def set_shape(self, shape) -> None:
if not self.shape.is_compatible_with(shape):
raise ValueError(f"Tensor's shape {self.shape} is not compatible "
f"with supplied shape {shape}.")
# Methods not supported / implemented for Eager Tensors.
@property
def op(self) -> NoReturn:
raise AttributeError(
"Tensor.op is undefined when eager execution is enabled.")
@property
def graph(self) -> NoReturn:
raise AttributeError(
"Tensor.graph is undefined when eager execution is enabled.")
@property
def name(self) -> NoReturn:
raise AttributeError(
"Tensor.name is undefined when eager execution is enabled.")
@property
def value_index(self) -> NoReturn:
raise AttributeError(
"Tensor.value_index is undefined when eager execution is enabled.")
def consumers(self) -> NoReturn:
raise NotImplementedError(
"Tensor.consumers is undefined when eager execution is enabled.")
def _add_consumer(self, consumer) -> NoReturn:
raise NotImplementedError(
"_add_consumer not supported when eager execution is enabled.")
def _as_node_def_input(self) -> NoReturn:
raise NotImplementedError(
"_as_node_def_input not supported when eager execution is enabled.")
def _as_tf_output(self) -> NoReturn:
raise NotImplementedError(
"_as_tf_output not supported when eager execution is enabled.")
def eval(self, feed_dict=None, session=None) -> NoReturn:
raise NotImplementedError(
"eval is not supported when eager execution is enabled, "
"is .numpy() what you're looking for?")
def __tf_tensor__(
self, dtype: Optional[dtypes.DType] = None, name: Optional[str] = None
) -> tensor_lib.Tensor:
if not context.executing_eagerly():
graph = get_default_graph()
if not graph.building_function:
raise RuntimeError(
_add_error_prefix(
"Attempting to capture an EagerTensor without "
"building a function.",
name=name))
return graph.capture(self, name=name)
return super().__tf_tensor__(dtype, name)
def _capture_as_const(self, name) -> Optional[tensor_lib.Tensor]:
"""Capture the EagerTensor to a graph constant tensor."""
with control_dependencies(None):
constant_value = tensor_util.constant_value(self)
if constant_value is None:
# Some eager tensors, e.g. parallel tensors, are not convertible to
# a single constant. Return None in this case and the caller graph
# would create a placeholder instead.
return None
const_tensor = _create_graph_constant(
constant_value, dtype=self.dtype, shape=self.shape, name=name,
verify_shape=False, allow_broadcast=True)
return const_tensor
# This call creates an EagerTensor class, as a subclass of _EagerTensorBase, and
# registers it with the current module.
# It is exposed as an __internal__ api for now (b/171081052), though we
# expect it to be eventually covered by tf Tensor types and typing.
EagerTensor = tf_export("__internal__.EagerTensor", v1=[])(
pywrap_tfe.TFE_Py_InitEagerTensor(_EagerTensorBase))
def _add_error_prefix(msg: str, *, name: Optional[str] = None) -> str:
return msg if name is None else f"{name}: {msg}"
def pack_eager_tensors(tensors, ctx=None) -> EagerTensor:
"""Pack multiple `EagerTensor`s of the same dtype and shape.
Args:
tensors: a list of EagerTensors to pack.
ctx: context.context().
Returns:
A packed EagerTensor.
"""
if not isinstance(tensors, list):
raise TypeError(f"tensors must be a list, but got a {type(tensors)}")
if not tensors:
raise ValueError("Cannot pack an empty list of tensors.")
dtype = tensors[0].dtype
shape = tensors[0].shape
handle_data = tensors[0]._handle_data # pylint: disable=protected-access
is_resource = dtype == dtypes.resource
for i in range(len(tensors)):
t = tensors[i]
if not isinstance(t, EagerTensor):
raise TypeError(f"All tensors being packed must be EagerTensor. "
f"Found an item of type {type(t)}.")
if t.dtype != dtype:
raise ValueError(
f"All tensors being packed should have the same dtype {dtype}, "
f"but the {i}-th tensor is of dtype {t.dtype}")
if t.shape != shape:
raise ValueError(
f"All tensors being packed should have the same shape {shape}, "
f"but the {i}-th tensor is of shape {t.shape}")
# pylint: disable=protected-access
if is_resource and t._handle_data != handle_data:
raise ValueError(
f"All tensors being packed should have the same handle data "
f"{handle_data}, "
f"but the {i}-th tensor is of handle data {t._handle_data}")
# pylint: enable=protected-access
if ctx is None:
ctx = context.context()
# Propagate handle data for resource variables
packed_tensor = ctx.pack_eager_tensors(tensors)
if handle_data is not None:
packed_tensor._handle_data = handle_data # pylint: disable=protected-access
def grad_fun(_):
raise ValueError(
"Computing gradients through pack_eager_tensors is not supported.")
record.record_operation("pack_eager_tensors", [packed_tensor], tensors,
grad_fun)
return packed_tensor
@profiler_trace.trace_wrapper("convert_to_tensor")
def convert_to_tensor(
value,
dtype=None,
name=None,
as_ref=False,
preferred_dtype=None,
dtype_hint=None,
# TODO(b/268347915): Remove argument.
ctx=None, # pylint: disable=unused-argument
accepted_result_types=(tensor_lib.Tensor,),
) -> Union[EagerTensor, SymbolicTensor]:
"""Implementation of the public convert_to_tensor."""
# TODO(b/142518781): Fix all call-sites and remove redundant arg
preferred_dtype = preferred_dtype or dtype_hint
return tensor_conversion_registry.convert(
value, dtype, name, as_ref, preferred_dtype, accepted_result_types
)
internal_convert_to_tensor: Callable[
..., Union[EagerTensor, SymbolicTensor]] = convert_to_tensor
def internal_convert_n_to_tensor(
values,
dtype=None,
name=None,
as_ref=False,
preferred_dtype=None,
# TODO(b/268347915): Remove argument.
ctx=None) -> list[Union[EagerTensor, SymbolicTensor]]: # pylint: disable=unused-argument
"""Converts `values` to a list of `Tensor` objects.
Args:
values: A list of objects that can be consumed by `tf.convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor` objects.
name: (Optional.) A name prefix to used when a new `Tensor` is created, in
which case element `i` will be given the name `name + '_' + i`.
as_ref: True if the caller wants the results as ref tensors.
preferred_dtype: Optional element type for the returned tensors, used when
dtype is None. In some cases, a caller may not have a dtype in mind when
converting to a tensor, so preferred_dtype can be used as a soft
preference. If the conversion to `preferred_dtype` is not possible, this
argument has no effect.
ctx: Unused. Present for API backwards compatibility.
Returns:
A list of `Tensor` and/or `IndexedSlices` objects.
Raises:
TypeError: If no conversion function is registered for an element in
`values`.
RuntimeError: If a registered conversion function returns an invalid
value.
"""
if not isinstance(values, collections_abc.Sequence):
raise TypeError("values must be a sequence.")
ret = []
for i, value in enumerate(values):
n = None if name is None else "%s_%d" % (name, i)
ret.append(
convert_to_tensor(
value,
dtype=dtype,
name=n,
as_ref=as_ref,
preferred_dtype=preferred_dtype))
return ret
def convert_n_to_tensor(
values, dtype=None, name=None, preferred_dtype=None
) -> list[Union[EagerTensor, SymbolicTensor]]:
"""Converts `values` to a list of `Tensor` objects.
Args:
values: A list of objects that can be consumed by `tf.convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor` objects.
name: (Optional.) A name prefix to used when a new `Tensor` is created, in
which case element `i` will be given the name `name + '_' + i`.
preferred_dtype: Optional element type for the returned tensors, used when
dtype is None. In some cases, a caller may not have a dtype in mind when
converting to a tensor, so preferred_dtype can be used as a soft
preference. If the conversion to `preferred_dtype` is not possible, this
argument has no effect.
Returns:
A list of `Tensor` and/or `IndexedSlices` objects.
Raises:
TypeError: If no conversion function is registered for an element in
`values`.
RuntimeError: If a registered conversion function returns an invalid
value.
"""
return internal_convert_n_to_tensor(
values=values,
dtype=dtype,
name=name,
preferred_dtype=preferred_dtype,
as_ref=False)
def convert_to_tensor_or_composite(
value, dtype=None, name=None
) -> Union[EagerTensor, SymbolicTensor, composite_tensor.CompositeTensor]:
"""Converts the given object to a `Tensor` or `CompositeTensor`.
If `value` is a `CompositeTensor` it is returned unmodified. Otherwise, it
is converted to a `Tensor` using `convert_to_tensor()`.
Args:
value: A `CompositeTensor` or an object that can be consumed by
`convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor` or
`CompositeTensor`.
name: (Optional.) A name to use if a new `Tensor` is created.
Returns:
A `Tensor` or `CompositeTensor`, based on `value`.
Raises:
ValueError: If `dtype` does not match the element type of `value`.
"""
return internal_convert_to_tensor_or_composite(
value=value, dtype=dtype, name=name, as_ref=False)
def internal_convert_to_tensor_or_composite(
value, dtype=None,
name=None,
as_ref=False
) -> Union[EagerTensor, SymbolicTensor, composite_tensor.CompositeTensor]:
"""Converts the given object to a `Tensor` or `CompositeTensor`.
If `value` is a `CompositeTensor` it is returned unmodified. Otherwise, it
is converted to a `Tensor` using `convert_to_tensor()`.
Args:
value: A `CompositeTensor`, or an object that can be consumed by
`convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor` or
`CompositeTensor`.
name: (Optional.) A name to use if a new `Tensor` is created.
as_ref: True if the caller wants the results as ref tensors.
Returns:
A `Tensor` or `CompositeTensor`, based on `value`.
Raises:
ValueError: If `dtype` does not match the element type of `value`.
"""
if isinstance(value, composite_tensor.CompositeTensor):
value_dtype = getattr(value, "dtype", None)
if dtype and not dtypes.as_dtype(dtype).is_compatible_with(value_dtype):
raise ValueError(f"Tensor conversion dtype mismatch. "
f"Requested dtype is {dtypes.as_dtype(dtype).name}, "
f"Tensor has dtype {value.dtype.name}: {value!r}")
return value
else:
return convert_to_tensor(
value,
dtype=dtype,
name=name,
as_ref=as_ref,
accepted_result_types=(
tensor_lib.Tensor, composite_tensor.CompositeTensor))
def internal_convert_n_to_tensor_or_composite(
values,
dtype=None,
name=None,
as_ref=False
) -> list[Union[
EagerTensor, SymbolicTensor, composite_tensor.CompositeTensor, type(None)]]:
"""Converts `values` to a list of `Tensor` or `CompositeTensor` objects.
Any `CompositeTensor` objects in `values` are returned unmodified.
Args:
values: A list of `None`, `CompositeTensor`, or objects that can be consumed
by `convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor`s or
`CompositeTensor`s.
name: (Optional.) A name prefix to used when a new `Tensor` is created, in
which case element `i` will be given the name `name + '_' + i`.
as_ref: True if the caller wants the results as ref tensors.
Returns:
A list of `Tensor`, `CompositeTensor`, and/or `None` objects.
Raises:
TypeError: If no conversion function is registered for an element in
`values`.
RuntimeError: If a registered conversion function returns an invalid
value.
"""
if not isinstance(values, collections_abc.Sequence):
raise TypeError("values must be a sequence.")
ret = []
for i, value in enumerate(values):
if value is None:
ret.append(value)
else:
n = None if name is None else "%s_%d" % (name, i)
ret.append(
internal_convert_to_tensor_or_composite(
value, dtype=dtype, name=n, as_ref=as_ref))
return ret
def convert_n_to_tensor_or_composite(
values, dtype=None, name=None
) -> list[Union[
EagerTensor, SymbolicTensor, composite_tensor.CompositeTensor, type(None)]]:
"""Converts `values` to a list of `Output` or `CompositeTensor` objects.
Any `CompositeTensor` objects in `values` are returned unmodified.
Args:
values: A list of `None`, `CompositeTensor``, or objects that can be
consumed by `convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor`s or
`CompositeTensor`s.
name: (Optional.) A name prefix to used when a new `Tensor` is created, in
which case element `i` will be given the name `name + '_' + i`.
Returns:
A list of `Tensor` and/or `CompositeTensor` objects.
Raises:
TypeError: If no conversion function is registered for an element in
`values`.
RuntimeError: If a registered conversion function returns an invalid
value.
"""
return internal_convert_n_to_tensor_or_composite(
values=values, dtype=dtype, name=name, as_ref=False)
def _device_string(dev_spec) -> str:
if pydev.is_device_spec(dev_spec):
return dev_spec.to_string()
else:
return dev_spec
def _NodeDef(op_type, name, attrs=None) -> node_def_pb2.NodeDef:
"""Create a NodeDef proto.
Args:
op_type: Value for the "op" attribute of the NodeDef proto.
name: Value for the "name" attribute of the NodeDef proto.
attrs: Dictionary where the key is the attribute name (a string)
and the value is the respective "attr" attribute of the NodeDef proto (an
AttrValue).
Returns:
A node_def_pb2.NodeDef protocol buffer.
"""
node_def = node_def_pb2.NodeDef(op=compat.as_bytes(op_type),
name=compat.as_bytes(name))
if attrs:
for k, v in attrs.items():
node_def.attr[k].CopyFrom(v)
return node_def
# Copied from core/framework/node_def_util.cc
# TODO(mrry,josh11b): Consolidate this validation in C++ code.
_VALID_OP_NAME_REGEX: Pattern[str] = re.compile(
r"^[A-Za-z0-9.][A-Za-z0-9_.\\/>-]*$")
_VALID_SCOPE_NAME_REGEX: Pattern[str] = re.compile(
r"^[A-Za-z0-9_.\\/>-]*$")
@tf_export("__internal__.create_c_op", v1=[])
@traceback_utils.filter_traceback
def _create_c_op(graph,
node_def,
inputs,
control_inputs,
op_def=None,
extract_traceback=True) -> pywrap_tf_session.TF_Operation:
"""Creates a TF_Operation.
Args:
graph: a `Graph`.
node_def: `node_def_pb2.NodeDef` for the operation to create.
inputs: A flattened list of `Tensor`s. This function handles grouping
tensors into lists as per attributes in the `node_def`.
control_inputs: A list of `Operation`s to set as control dependencies.
op_def: Optional. `op_def_pb2.OpDef` for the operation to create. If not
specified, is looked up from the `graph` using `node_def.op`.
extract_traceback: if True, extract the current Python traceback to the
TF_Operation.
Returns:
A wrapped TF_Operation*.
"""
if op_def is None: