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Compiles a function into a callable TensorFlow graph. (deprecated arguments) (deprecated arguments) (deprecated arguments)
tf.function(
func=None,
input_signature=None,
autograph=True,
jit_compile=None,
reduce_retracing=False,
experimental_implements=None,
experimental_autograph_options=None,
experimental_attributes=None,
experimental_relax_shapes=None,
experimental_compile=None,
experimental_follow_type_hints=None
) > tf.types.experimental.PolymorphicFunction
tf.function
constructs a tf.types.experimental.PolymorphicFunction
that
executes a TensorFlow graph (tf.Graph
) created by tracecompiling the
TensorFlow operations in func
. More information on the topic can be found
in Introduction to Graphs and tf.function.
See Better Performance with tf.function for tips on performance and known limitations.
Example usage:
@tf.function
def f(x, y):
return x ** 2 + y
x = tf.constant([2, 3])
y = tf.constant([3, 2])
f(x, y)
<tf.Tensor: ... numpy=array([7, 7], ...)>
The tracecompilation allows nonTensorFlow operations to execute, but under
special conditions. In general, only TensorFlow operations are guaranteed to
run and create fresh results whenever the PolymorphicFunction
is called.
Features
func
may use datadependent Python control flow statements, including if
,
for
, while
break
, continue
and return
:
@tf.function
def f(x):
if tf.reduce_sum(x) > 0:
return x * x
else:
return x // 2
f(tf.constant(2))
<tf.Tensor: ... numpy=1>
func
's closure may include tf.Tensor
and tf.Variable
objects:
@tf.function
def f():
return x ** 2 + y
x = tf.constant([2, 3])
y = tf.Variable([3, 2])
f()
<tf.Tensor: ... numpy=array([7, 7], ...)>
func
may also use ops with side effects, such as tf.print
, tf.Variable
and others:
v = tf.Variable(1)
@tf.function
def f(x):
for i in tf.range(x):
v.assign_add(i)
f(3)
v
<tf.Variable ... numpy=4>
l = []
@tf.function
def f(x):
for i in x:
l.append(i + 1) # Caution! Will only happen once when tracing
f(tf.constant([1, 2, 3]))
l
[<tf.Tensor ...>]
Instead, use TensorFlow collections like tf.TensorArray
:
@tf.function
def f(x):
ta = tf.TensorArray(dtype=tf.int32, size=0, dynamic_size=True)
for i in range(len(x)):
ta = ta.write(i, x[i] + 1)
return ta.stack()
f(tf.constant([1, 2, 3]))
<tf.Tensor: ..., numpy=array([2, 3, 4], ...)>
tf.function
creates polymorphic callables
Internally, tf.types.experimental.PolymorphicFunction
may contain multiple
tf.types.experimental.ConcreteFunction
s, each specialized to arguments with
different data types or shapes, since TensorFlow can perform more
optimizations on graphs of specific shapes, dtypes and values of constant
arguments. tf.function
treats any pure Python values as opaque objects (best
thought of as compiletime constants), and builds a separate tf.Graph
for
each set of Python arguments that it encounters.
For more information, see the
tf.function guide
Executing a PolymorphicFunction
will select and execute the appropriate
ConcreteFunction
based on the argument types and values.
To obtain an individual ConcreteFunction
, use the
PolymorphicFunction.get_concrete_function
method. It can be called with the
same arguments as func
and returns a
tf.types.experimental.ConcreteFunction
. ConcreteFunction
s are backed by a
single tf.Graph
:
@tf.function
def f(x):
return x + 1
isinstance(f.get_concrete_function(1).graph, tf.Graph)
True
ConcreteFunction
s can be executed just like PolymorphicFunction
s, but their
input is resticted to the types to which they're specialized.
Retracing
ConcreteFunctions
are built (traced) on the fly, as the PolymorphicFunction
is
called with new TensorFlow types or shapes, or with new Python values as
arguments. When PolymorphicFunction
builds a new trace, it is said that func
is retraced. Retracing is a frequent performance concern for tf.function
as
it can be considerably slower than executing a graph that's already been
traced. It is ideal to minimize the amount of retracing in your code.
@tf.function
def f(x):
return tf.abs(x)
f1 = f.get_concrete_function(1)
f2 = f.get_concrete_function(2) # Slow  compiles new graph
f1 is f2
False
f1 = f.get_concrete_function(tf.constant(1))
f2 = f.get_concrete_function(tf.constant(2)) # Fast  reuses f1
f1 is f2
True
Python numerical arguments should only be used when they take few distinct values, such as hyperparameters like the number of layers in a neural network.
Input signatures
For Tensor arguments, PolymorphicFunction
creates a new ConcreteFunction
for
every unique set of input shapes and datatypes. The example below creates two
separate ConcreteFunction
s, each specialized to a different shape:
@tf.function
def f(x):
return x + 1
vector = tf.constant([1.0, 1.0])
matrix = tf.constant([[3.0]])
f.get_concrete_function(vector) is f.get_concrete_function(matrix)
False
An "input signature" can be optionally provided to tf.function
to control
this process. The input signature specifies the shape and type of each
Tensor argument to the function using a tf.TensorSpec
object. More general
shapes can be used. This ensures only one ConcreteFunction
is created, and
restricts the PolymorphicFunction
to the specified shapes and types. It is
an effective way to limit retracing when Tensors have dynamic shapes.
@tf.function(
input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)])
def f(x):
return x + 1
vector = tf.constant([1.0, 1.0])
matrix = tf.constant([[3.0]])
f.get_concrete_function(vector) is f.get_concrete_function(matrix)
True
Variables may only be created once
tf.function
only allows creating new tf.Variable
objects when it is called
for the first time:
class MyModule(tf.Module):
def __init__(self):
self.v = None
@tf.function
def __call__(self, x):
if self.v is None:
self.v = tf.Variable(tf.ones_like(x))
return self.v * x
In general, it is recommended to create tf.Variable
s outside of
tf.function
.
In simple cases, persisting state across tf.function
boundaries may be
implemented using a pure functional style in which state is represented by
tf.Tensor
s passed as arguments and returned as return values.
Contrast the two styles below:
state = tf.Variable(1)
@tf.function
def f(x):
state.assign_add(x)
f(tf.constant(2)) # Nonpure functional style
state
<tf.Variable ... numpy=3>
state = tf.constant(1)
@tf.function
def f(state, x):
state += x
return state
state = f(state, tf.constant(2)) # Pure functional style
state
<tf.Tensor: ... numpy=3>
Python operations execute only once per trace
func
may contain TensorFlow operations mixed with pure Python operations.
However, when the function is executed, only the TensorFlow operations will
run. The Python operations run only once, at trace time. If TensorFlow
operations depend on results from Python operations, those results will be
frozen into the graph.
@tf.function
def f(a, b):
print('this runs at trace time; a is', a, 'and b is', b)
return b
f(1, tf.constant(1))
this runs at trace time; a is 1 and b is Tensor("...", shape=(), dtype=int32)
<tf.Tensor: shape=(), dtype=int32, numpy=1>
f(1, tf.constant(2))
<tf.Tensor: shape=(), dtype=int32, numpy=2>
f(2, tf.constant(1))
this runs at trace time; a is 2 and b is Tensor("...", shape=(), dtype=int32)
<tf.Tensor: shape=(), dtype=int32, numpy=1>
f(2, tf.constant(2))
<tf.Tensor: shape=(), dtype=int32, numpy=2>
Args  

func

The function to be compiled. If func is None, tf.function returns
a decorator that can be invoked with a single argument  func . In other
words, tf.function(input_signature=...)(func) is equivalent to
tf.function(func, input_signature=...) . The former can be used as
decorator.

input_signature

A possibly nested sequence of tf.TensorSpec objects
specifying the shapes and dtypes of the Tensors that will be supplied to
this function. If None , a separate function is instantiated for each
inferred input signature. If input_signature is specified, every input to
func must be a Tensor , and func cannot accept **kwargs .

autograph

Whether autograph should be applied on func before tracing a
graph. Datadependent Python control flow statements require
autograph=True . For more information, see the
tf.function and AutoGraph guide.

jit_compile

If True , compiles the function using
XLA. XLA performs compiler optimizations,
such as fusion, and attempts to emit more efficient code. This may
drastically improve the performance. If set to True ,
the whole function needs to be compilable by XLA, or an
errors.InvalidArgumentError is thrown.
If None (default), compiles the function with XLA when running on TPU
and goes through the regular function execution path when running on
other devices.
If False , executes the function without XLA compilation. Set this value
to False when directly running a multidevice function on TPUs (e.g. two
TPU cores, one TPU core and its host CPU).
Not all functions are compilable, see a list of
sharp corners.

reduce_retracing

When True, tf.function attempts to reduce the
amount of retracing, for example by using more generic shapes. This
can be controlled for user objects by customizing their associated
tf.types.experimental.TraceType .

experimental_implements

If provided, contains a name of a "known" function
this implements. For example "mycompany.my_recurrent_cell".
This is stored as an attribute in inference function,
which can then be detected when processing serialized function.
See standardizing composite ops for details. For an example of utilizing this attribute see this example The code above automatically detects and substitutes function that implements "embedded_matmul" and allows TFLite to substitute its own implementations. For instance, a tensorflow user can use this attribute to mark that their function also implements embedded_matmul (perhaps more efficiently!)
by specifying it using this parameter:
@tf.function(experimental_implements="embedded_matmul")
This can either be specified as just the string name of the function or
a NameAttrList corresponding to a list of keyvalue attributes associated
with the function name. The name of the function will be in the 'name'
field of the NameAttrList. To define a formal TF op for this function
implements, try the experimental composite TF
project.

experimental_autograph_options

Optional tuple of
tf.autograph.experimental.Feature values.

experimental_attributes

Optional dictionary of attributes to include in the generated FunctionDefs. 
experimental_relax_shapes

Deprecated. Use reduce_retracing
instead.

experimental_compile

Deprecated alias to 'jit_compile'. 
experimental_follow_type_hints

Deprecated. Please use input_signature or reduce_retracing instead. 
Returns  

If func is not None, returns a tf.types.experimental.PolymorphicFunction .
If func is None, returns a decorator that, when invoked with a single
func argument, returns a tf.types.experimental.PolymorphicFunction .

Raises  

ValueError when attempting to use jit_compile=True , but XLA support is
not available.
