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constant_op.py
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/
constant_op.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.
# ==============================================================================
"""Operations that generate constants.
See the [constants guide](https://tensorflow.org/api_guides/python/constant_op).
"""
# Must be separate from array_ops to avoid a cyclic dependency.
from typing import Union
import numpy as np
from tensorflow.core.framework import types_pb2
from tensorflow.core.protobuf import struct_pb2
from tensorflow.python.eager import context
from tensorflow.python.eager import execute
# Import constant_tensor_conversion.py to register tensor conversion functions
# for builtins. These functions were previously in this file, but were
# refactored out so they can be registered at TF import time without importing
# all of constant_op.py.
from tensorflow.python.framework import constant_tensor_conversion # pylint: disable=unused-import
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
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.profiler import trace
from tensorflow.python.saved_model import nested_structure_coder
from tensorflow.python.util.tf_export import tf_export
def _eager_reshape(tensor, shape, ctx):
"""Eager-only version of Reshape op; requires tensor is an eager Tensor."""
attr_t = tensor._datatype_enum() # pylint: disable=protected-access
attr_tshape, (shape,) = execute.args_to_matching_eager(
[shape], ctx, [dtypes.int32, dtypes.int64], dtypes.int32)
inputs_flat = [tensor, shape]
attrs = ("T", attr_t, "Tshape", attr_tshape)
[result] = execute.execute(
b"Reshape", 1, inputs=inputs_flat, attrs=attrs, ctx=ctx)
return result
def _eager_fill(dims, value, ctx):
"""Eager-only version of Fill op; requires value is an eager Tensor."""
attr_t = value.dtype.as_datatype_enum
dims = convert_to_eager_tensor(dims, ctx, dtypes.int32)
inputs_flat = [dims, value]
attrs = ("T", attr_t, "index_type", types_pb2.DT_INT32)
[result] = execute.execute(
b"Fill", 1, inputs=inputs_flat, attrs=attrs, ctx=ctx)
return result
def _eager_identity(tensor, ctx):
"""Eager-only version of Identity op; requires tensor is an eager Tensor."""
attrs = ("T", tensor.dtype.as_datatype_enum)
[result] = execute.execute(
b"Identity", 1, inputs=[tensor], attrs=attrs, ctx=ctx)
return result
def convert_to_eager_tensor(value, ctx, dtype=None) -> ops._EagerTensorBase:
"""Converts the given `value` to an `EagerTensor`.
Note that this function could return cached copies of created constants for
performance reasons.
Args:
value: value to convert to EagerTensor.
ctx: value of context.context().
dtype: optional desired dtype of the converted EagerTensor.
Returns:
EagerTensor created from value.
Raises:
TypeError: if `dtype` is not compatible with the type of t.
"""
if isinstance(value, np.ndarray):
# Make a copy explicitly because the EagerTensor might share the underlying
# memory with the input array. Without this copy, users will be able to
# modify the EagerTensor after its creation by changing the input array.
value = value.copy()
if isinstance(value, ops.EagerTensor):
if dtype is not None and value.dtype != dtype:
raise TypeError(f"Expected tensor {value} with dtype {dtype!r}, but got "
f"dtype {value.dtype!r}.")
return value
if dtype is not None:
try:
dtype = dtype.as_datatype_enum
except AttributeError:
dtype = dtypes.as_dtype(dtype).as_datatype_enum
ctx.ensure_initialized()
return ops.EagerTensor(value, ctx.device_name, dtype)
@tf_export(v1=["constant"])
def constant_v1(
value, dtype=None, shape=None, name="Const", verify_shape=False
) -> Union[ops.Operation, ops._EagerTensorBase]:
"""Creates a constant tensor.
The resulting tensor is populated with values of type `dtype`, as
specified by arguments `value` and (optionally) `shape` (see examples
below).
The argument `value` can be a constant value, or a list of values of type
`dtype`. If `value` is a list, then the length of the list must be less
than or equal to the number of elements implied by the `shape` argument (if
specified). In the case where the list length is less than the number of
elements specified by `shape`, the last element in the list will be used
to fill the remaining entries.
The argument `shape` is optional. If present, it specifies the dimensions of
the resulting tensor. If not present, the shape of `value` is used.
If the argument `dtype` is not specified, then the type is inferred from
the type of `value`.
For example:
```python
# Constant 1-D Tensor populated with value list.
tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]
# Constant 2-D tensor populated with scalar value -1.
tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.]
[-1. -1. -1.]]
```
`tf.constant` differs from `tf.fill` in a few ways:
* `tf.constant` supports arbitrary constants, not just uniform scalar
Tensors like `tf.fill`.
* `tf.constant` creates a `Const` node in the computation graph with the
exact value at graph construction time. On the other hand, `tf.fill`
creates an Op in the graph that is expanded at runtime.
* Because `tf.constant` only embeds constant values in the graph, it does
not support dynamic shapes based on other runtime Tensors, whereas
`tf.fill` does.
Args:
value: A constant value (or list) of output type `dtype`.
dtype: The type of the elements of the resulting tensor.
shape: Optional dimensions of resulting tensor.
name: Optional name for the tensor.
verify_shape: Boolean that enables verification of a shape of values.
Returns:
A Constant Tensor.
Raises:
TypeError: if shape is incorrectly specified or unsupported.
"""
return _constant_impl(value, dtype, shape, name, verify_shape=verify_shape,
allow_broadcast=False)
@tf_export("constant", v1=[])
def constant(
value, dtype=None, shape=None, name="Const"
) -> Union[ops.Operation, ops._EagerTensorBase]:
"""Creates a constant tensor from a tensor-like object.
Note: All eager `tf.Tensor` values are immutable (in contrast to
`tf.Variable`). There is nothing especially _constant_ about the value
returned from `tf.constant`. This function is not fundamentally different from
`tf.convert_to_tensor`. The name `tf.constant` comes from the `value` being
embedded in a `Const` node in the `tf.Graph`. `tf.constant` is useful
for asserting that the value can be embedded that way.
If the argument `dtype` is not specified, then the type is inferred from
the type of `value`.
>>> # Constant 1-D Tensor from a python list.
>>> tf.constant([1, 2, 3, 4, 5, 6])
<tf.Tensor: shape=(6,), dtype=int32,
numpy=array([1, 2, 3, 4, 5, 6], dtype=int32)>
>>> # Or a numpy array
>>> a = np.array([[1, 2, 3], [4, 5, 6]])
>>> tf.constant(a)
<tf.Tensor: shape=(2, 3), dtype=int64, numpy=
array([[1, 2, 3],
[4, 5, 6]])>
If `dtype` is specified, the resulting tensor values are cast to the requested
`dtype`.
>>> tf.constant([1, 2, 3, 4, 5, 6], dtype=tf.float64)
<tf.Tensor: shape=(6,), dtype=float64,
numpy=array([1., 2., 3., 4., 5., 6.])>
If `shape` is set, the `value` is reshaped to match. Scalars are expanded to
fill the `shape`:
>>> tf.constant(0, shape=(2, 3))
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[0, 0, 0],
[0, 0, 0]], dtype=int32)>
>>> tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[1, 2, 3],
[4, 5, 6]], dtype=int32)>
`tf.constant` has no effect if an eager Tensor is passed as the `value`, it
even transmits gradients:
>>> v = tf.Variable([0.0])
>>> with tf.GradientTape() as g:
... loss = tf.constant(v + v)
>>> g.gradient(loss, v).numpy()
array([2.], dtype=float32)
But, since `tf.constant` embeds the value in the `tf.Graph` this fails for
symbolic tensors:
>>> with tf.compat.v1.Graph().as_default():
... i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32)
... t = tf.constant(i)
Traceback (most recent call last):
...
TypeError: ...
`tf.constant` will create tensors on the current device. Inputs which are
already tensors maintain their placements unchanged.
Related Ops:
* `tf.convert_to_tensor` is similar but:
* It has no `shape` argument.
* Symbolic tensors are allowed to pass through.
>>> with tf.compat.v1.Graph().as_default():
... i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32)
... t = tf.convert_to_tensor(i)
* `tf.fill`: differs in a few ways:
* `tf.constant` supports arbitrary constants, not just uniform scalar
Tensors like `tf.fill`.
* `tf.fill` creates an Op in the graph that is expanded at runtime, so it
can efficiently represent large tensors.
* Since `tf.fill` does not embed the value, it can produce dynamically
sized outputs.
Args:
value: A constant value (or list) of output type `dtype`.
dtype: The type of the elements of the resulting tensor.
shape: Optional dimensions of resulting tensor.
name: Optional name for the tensor.
Returns:
A Constant Tensor.
Raises:
TypeError: if shape is incorrectly specified or unsupported.
ValueError: if called on a symbolic tensor.
"""
return _constant_impl(value, dtype, shape, name, verify_shape=False,
allow_broadcast=True)
def _constant_impl(
value, dtype, shape, name, verify_shape, allow_broadcast
) -> Union[ops.Operation, ops._EagerTensorBase]:
"""Implementation of constant."""
ctx = context.context()
if ctx.executing_eagerly():
if trace.enabled:
with trace.Trace("tf.constant"):
return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
const_tensor = ops._create_graph_constant( # pylint: disable=protected-access
value, dtype, shape, name, verify_shape, allow_broadcast
)
return const_tensor
def _constant_eager_impl(
ctx, value, dtype, shape, verify_shape
) -> ops._EagerTensorBase:
"""Creates a constant on the current device."""
t = convert_to_eager_tensor(value, ctx, dtype)
if shape is None:
return t
shape = tensor_shape.as_shape(shape)
if shape == t.shape:
return t
if verify_shape:
raise TypeError(f"Expected Tensor {t} (converted from {value}) with shape "
f"{tuple(shape)}, but got shape {tuple(t.shape)}.")
num_t = t.shape.num_elements()
# TODO(josh11b): Implement shape -> eager tensor conversion.
if num_t == shape.num_elements():
return _eager_reshape(t, shape.as_list(), ctx)
if num_t == 1:
if t.dtype == dtypes.bool:
# We don't have a Fill kernel for bool dtype on GPU. So we first run
# Fill on CPU and then copy to GPU if needed.
with ops.device("/device:CPU:0"):
x = _eager_fill(shape.as_list(), _eager_identity(t, ctx), ctx)
return _eager_identity(x, ctx)
else:
return _eager_fill(shape.as_list(), t, ctx)
raise TypeError("Eager execution of tf.constant with unsupported shape. "
f"Tensor {t} (converted from {value}) has {num_t:d} "
f"elements, but got `shape` {shape} with "
f"{shape.num_elements()} elements).")
def is_constant(tensor_or_op):
if isinstance(tensor_or_op, tensor_lib.Tensor):
op = tensor_or_op.op
else:
op = tensor_or_op
return op.type == "Const"
def _tensor_shape_tensor_conversion_function(s,
dtype=None,
name=None,
as_ref=False):
"""Function to convert TensorShape to Tensor."""
_ = as_ref
if not s.is_fully_defined():
raise ValueError(
f"Cannot convert a partially known TensorShape {s} to a Tensor.")
s_list = s.as_list()
int64_value = 0
for dim in s_list:
if dim >= 2**31:
int64_value = dim
break
if dtype is not None:
if dtype not in (dtypes.int32, dtypes.int64):
raise TypeError(f"Cannot convert TensorShape {s} to dtype {dtype}. "
"Allowed dtypes are tf.int32 and tf.int64.")
if dtype == dtypes.int32 and int64_value:
raise ValueError(f"Cannot convert TensorShape {s} to dtype int32; "
f"a dimension is too large. Consider using tf.int64.")
else:
dtype = dtypes.int64 if int64_value else dtypes.int32
if name is None:
name = "shape_as_tensor"
return constant(s_list, dtype=dtype, name=name)
tensor_conversion_registry.register_tensor_conversion_function(
tensor_shape.TensorShape, _tensor_shape_tensor_conversion_function, 100)
def _dimension_tensor_conversion_function(d,
dtype=None,
name=None,
as_ref=False):
"""Function to convert Dimension to Tensor."""
_ = as_ref
if d.value is None:
raise ValueError(f"Cannot convert unknown Dimension {d} to a Tensor.")
if dtype is not None:
if dtype not in (dtypes.int32, dtypes.int64):
raise TypeError(f"Cannot convert Dimension {d} to dtype {dtype}. "
"Allowed dtypes are tf.int32 and tf.int64.")
else:
dtype = dtypes.int32
if name is None:
name = "shape_as_tensor"
return constant(d.value, dtype=dtype, name=name)
tensor_conversion_registry.register_tensor_conversion_function(
tensor_shape.Dimension, _dimension_tensor_conversion_function, 100)
class _ConstantTensorCodec:
"""Codec for Tensor."""
def can_encode(self, pyobj):
return isinstance(pyobj, tensor_lib.Tensor)
def do_encode(self, tensor_value, encode_fn):
"""Returns an encoded `TensorProto` for the given `tf.Tensor`."""
del encode_fn
encoded_tensor = struct_pb2.StructuredValue()
if isinstance(tensor_value, ops.EagerTensor):
encoded_tensor.tensor_value.CopyFrom(
tensor_util.make_tensor_proto(tensor_value.numpy())
)
else:
if tensor_value.op.type == "Const":
encoded_tensor.tensor_value.CopyFrom(tensor_value.op.get_attr("value"))
else:
raise nested_structure_coder.NotEncodableError(
f"No encoder for object {str(tensor_value)} of type"
f" {type(tensor_value)}."
)
return encoded_tensor
def can_decode(self, value):
return value.HasField("tensor_value")
def do_decode(self, value, decode_fn):
"""Returns the `tf.Tensor` encoded by the proto `value`."""
del decode_fn
tensor_proto = value.tensor_value
tensor = constant(tensor_util.MakeNdarray(tensor_proto))
return tensor
nested_structure_coder.register_codec(_ConstantTensorCodec())
class _NumpyCodec:
"""Codec for Numpy."""
def can_encode(self, pyobj):
return isinstance(pyobj, np.ndarray)
def do_encode(self, numpy_value, encode_fn):
"""Returns an encoded `TensorProto` for `np.ndarray`."""
del encode_fn
encoded_numpy = struct_pb2.StructuredValue()
encoded_numpy.numpy_value.CopyFrom(
tensor_util.make_tensor_proto(numpy_value)
)
return encoded_numpy
def can_decode(self, value):
return value.HasField("numpy_value")
def do_decode(self, value, decode_fn):
"""Returns the `np.ndarray` encoded by the proto `value`."""
del decode_fn
tensor_proto = value.numpy_value
numpy = tensor_util.MakeNdarray(tensor_proto)
return numpy
nested_structure_coder.register_codec(_NumpyCodec())