TensorFlow 2 version | View source on GitHub |
Creates a callable TensorFlow graph from a Python function.
tf.function(
func=None, input_signature=None, autograph=True,
experimental_autograph_options=None, experimental_relax_shapes=False,
experimental_compile=None
)
function
constructs a callable that executes a TensorFlow graph
(tf.Graph
) created by tracing the TensorFlow operations in func
.
This allows the TensorFlow runtime to apply optimizations and exploit
parallelism in the computation defined by func
.
Example Usage
def f(x, y):
return tf.reduce_mean(tf.multiply(x ** 2, 3) + y)
g = tf.function(f)
x = tf.constant([[2.0, 3.0]])
y = tf.constant([[3.0, -2.0]])
# `f` and `g` will return the same value, but `g` will be executed as a
# TensorFlow graph.
assert f(x, y).numpy() == g(x, y).numpy()
# Tensors and tf.Variables used by the Python function are captured in the
# graph.
@tf.function
def h():
return f(x, y)
assert (h().numpy() == f(x, y).numpy()).all()
# Data-dependent control flow is also captured in the graph. Supported
# control flow statements include `if`, `for`, `while`, `break`, `continue`,
# `return`.
@tf.function
def g(x):
if tf.reduce_sum(x) > 0:
return x * x
else:
return -x // 2
# print and TensorFlow side effects are supported, but exercise caution when
# using Python side effects like mutating objects, saving to files, etc.
l = []
@tf.function
def g(x):
for i in x:
print(i) # Works
tf.compat.v1.assign(v, i) # Works
tf.compat.v1.py_func(lambda i: l.append(i))(i) # Works
l.append(i) # Caution! Doesn't work.
Note that unlike other TensorFlow operations, we don't convert python
numerical inputs to tensors. Moreover, a new graph is generated for each
distinct python numerical value, for example calling g(2)
and g(3)
will
generate two new graphs (while only one is generated if you call
g(tf.constant(2))
and g(tf.constant(3))
). Therefore, python numerical
inputs should be restricted to arguments that will have few distinct values,
such as hyperparameters like the number of layers in a neural network. This
allows TensorFlow to optimize each variant of the neural network.
Referencing tf.Variable
s
The Python function func
may reference stateful objects (such as
tf.Variable
).
These are captured as implicit inputs to the callable returned by function
.
For example:
c = tf.Variable(0)
@tf.function
def f(x):
c.assign_add(1)
return x + tf.compat.v1.to_float(c)
assert int(c) == 0
assert f(1.0) == 2.0
assert int(c) == 1
assert f(1.0) == 3.0
assert int(c) == 2
function
can be applied to methods of an object. For example:
class Dense(object):
def __init__(self):
self.W = tf.Variable(tf.compat.v1.glorot_uniform_initializer()((10, 10)))
self.b = tf.Variable(tf.zeros(10))
@tf.function
def compute(self, x):
return tf.matmul(x, self.W) + self.b
d1 = Dense()
d2 = Dense()
x = tf.random.uniform((10, 10))
# d1 and d2 are using distinct variables
assert not (d1.compute(x).numpy() == d2.compute(x).numpy()).all()
Usage with tf.keras
The call
methods of a tf.keras.Model
subclass can be decorated with
function
in order to apply graph execution optimizations on it.
For example:
class MyModel(tf.keras.Model):
def __init__(self, keep_probability=0.2):
super(MyModel, self).__init__()
self.dense1 = tf.keras.layers.Dense(4)
self.dense2 = tf.keras.layers.Dense(5)
self.keep_probability = keep_probability
@tf.function
def call(self, inputs, training=True):
y = self.dense2(self.dense1(inputs))
if training:
return tf.nn.dropout(y, self.keep_probability)
else:
return y
model = MyModel()
model(x, training=True) # executes a graph, with dropout
model(x, training=False) # executes a graph, without dropout
Input Signatures
function
instantiates a separate graph for every unique set of input
shapes and datatypes. For example, the following code snippet will result
in three distinct graphs being traced, as each input has a different
shape.
@tf.function
def f(x): return tf.add(x, 1.)
scalar = tf.constant(1.0)
vector = tf.constant([1.0, 1.0])
matrix = tf.constant([[3.0]])
f(scalar)
f(vector)
f(matrix)
An "input signature" can be optionally provided to function
to control
the graphs traced. The input signature specifies the shape and type of each
Tensor
argument to the function using a tf.TensorSpec
object. For example,
the following code snippet ensures that a single graph is created where the
input Tensor
is required to be a floating point tensor with no restrictions
on shape.
@tf.function(input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)])
def f(x): return tf.add(x, 1.)
When an input_signature
is specified, the callable will convert the inputs
to the specified TensorSpecs.
Tracing and staging
When autograph
is True
, all Python control flow that depends on Tensor
values is staged into a TensorFlow graph. When autograph
is False
, the
function is traced and control flow is not allowed to depend on data.
Note that function
only stages TensorFlow operations, all Python code that
func
executes and does not depend on data will shape the construction of
the graph.
For example, consider the following:
import numpy as np
def add_noise():
return tf.eye(5) + np.random.randn(5, 5)
traced = tf.function(add_noise)
add_noise()
will return a different output every time it is invoked.
However, traced()
will return the same value every time it is called,
since a particular random value generated by the np.random.randn
call will
be inserted in the traced/staged TensorFlow graph as a constant. In this
particular example, replacing np.random.randn(5, 5)
with
tf.random.normal((5, 5))
will result in the same behavior for add_noise()
and traced()
.
Python Side-Effects
A corollary of the previous discussion on tracing is the following: If a
Python function func
has Python side-effects, then executing func
multiple
times may not be semantically equivalent to executing F = tf.function(func)
multiple times; this difference is due to the fact that function
only
captures the subgraph of TensorFlow operations that is constructed when func
is invoked to trace a graph.
The same is true if code with Python side effects is used inside control flow,
such as a loop. If your code uses side effects that are not intended to
control graph construction, wrap them inside tf.compat.v1.py_func
.
Retracing
A single tf.function object might need to map to multiple computation graphs under the hood. This should be visible only as performance (tracing graphs has a nonzero computational and memory cost) but should not affect the correctness of the program. A traced function should return the same result as it would when run eagerly, assuming no unintended Python side-effects.
Calling a tf.function
with tensor arguments of different dtypes should lead
to at least one computational graph per distinct set of dtypes. Alternatively,
always calling a tf.function
with tensor arguments of the same shapes and
dtypes and the same non-tensor arguments should not lead to additional
retracings of your function.
Other than that, TensorFlow reserves the right to retrace functions as many times as needed, to ensure that traced functions behave as they would when run eagerly and to provide the best end-to-end performance. For example, the behavior of how many traces TensorFlow will do when the function is repeatedly called with different python scalars as arguments is left undefined to allow for future optimizations.
To control the tracing behavior, use the following tools:
- different
tf.function
objects are guaranteed to not share traces; and - specifying a signature or using concrete function objects returned from get_concrete_function() guarantees that only one function graph will be built.
Args | |
---|---|
func
|
function to be compiled. If func is None, returns a decorator that
can be invoked with a single argument - func . The end result is
equivalent to providing all the arguments up front. In other words,
tf.function(input_signature=...)(func) is equivalent to
tf.function(func, input_signature=...) . The former can be used to
decorate Python functions, for example:
@tf.function(input_signature=...)
def foo(...): ...
|
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. This allows for dynamic control flow (Python if's, loops etc.)
in the traced graph. See https://www.tensorflow.org/guide/autograph for
more information.
|
experimental_autograph_options
|
Experimental knobs (in the form of a tuple of tensorflow.autograph.Feature values) to control behavior when autograph=True. |
experimental_relax_shapes
|
When true, argument shapes may be relaxed to avoid unecessary retracing. |
experimental_compile
|
If false, execute the function in a regular way. The function is optimized by some graph rewrite passes (some ops might be clustered into a single op) and interpreted by the standard TensorFlow executor, which dispatches op kernels one by one as they become executable. Set it to false when directly running a multi-device function on TPUs (e.g. two TPU cores, one TPU core and its host CPU). If True, the function is compiled directly by XLA (https://www.tensorflow.org/xla). XLA would fuse all the ops and emit more efficient code to run for some devices (e.g. TPU, XLA_GPU) and some use cases (e.g. dense tensor computation). It requires that the whole function is compilable by XLA (e.g. static tensor shape, a subset of operations, no string, compile-time constant input, etc). If None (default), compile the function with XLA when running on TPU and go through the regular function execution path when running on other devices. Note: TensorArrays on TPU don't work with standard TensorFlow executor. |
Returns | |
---|---|
If func is not None, returns a callable that will execute the compiled
function (and return zero or more tf.Tensor objects).
If func is None, returns a decorator that, when invoked with a single
func argument, returns a callable equivalent to the case above.
|
Raises | |
---|---|
TypeError
|
If input_signature is neither None nor a sequence of
TensorSpec objects.
|