Content Overview

In TensorFlow 2, eager execution is turned on by default. The user interface is intuitive and flexible (running one-off operations is much easier and faster), but this can come at the expense of performance and deployability.

You can use tf.function to make graphs out of your programs. It is a transformation tool that creates Python-independent dataflow graphs out of your Python code. This will help you create performant and portable models, and it is required to use SavedModel.

This guide will help you conceptualize how tf.function works under the hood, so you can use it effectively.

The main takeaways and recommendations are:

Setup

import tensorflow as tf

2024-08-15 02:57:28.958444: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-08-15 02:57:28.979712: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-08-15 02:57:28.986177: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

Define a helper function to demonstrate the kinds of errors you might encounter:

import traceback
import contextlib

# Some helper code to demonstrate the kinds of errors you might encounter.
@contextlib.contextmanager
def assert_raises(error_class):
  try:
    yield
  except error_class as e:
    print('Caught expected exception \n  {}:'.format(error_class))
    traceback.print_exc(limit=2)
  except Exception as e:
    raise e
  else:
    raise Exception('Expected {} to be raised but no error was raised!'.format(
        error_class))

Basics

Usage

A tf.function that you define (for example by applying the @tf.function decorator) is just like a core TensorFlow operation: You can execute it eagerly; you can compute gradients; and so on.

@tf.function  # The decorator converts `add` into a `PolymorphicFunction`.
def add(a, b):
  return a + b

add(tf.ones([2, 2]), tf.ones([2, 2]))  #  [[2., 2.], [2., 2.]]

tf.ones([2, 2]), tf.ones([2, 2]))  #  [[2., 2.], [2., 2.]]

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1723690651.607368  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690651.611235  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690651.614398  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690651.618234  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690651.629890  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690651.633433  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690651.636337  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690651.639748  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690651.643233  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690651.646588  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690651.649526  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690651.652949  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.865955  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.868101  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.870112  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.872121  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.874165  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.876153  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.878068  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.879960  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.881883  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.883841  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.885768  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.887660  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.926250  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.928321  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.930298  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.932288  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.934241  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.936253  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.938172  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.940080  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.942041  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.944593  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.946947  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690652.949245  167534 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[2., 2.],
       [2., 2.]], dtype=float32)>

v = tf.Variable(1.0)
with tf.GradientTape() as tape:
  result = add(v, 1.0)
tape.gradient(result, v)

<tf.Tensor: shape=(), dtype=float32, numpy=1.0>

You can use tf.functions inside other tf.functions.

@tf.function
def dense_layer(x, w, b):
  return add(tf.matmul(x, w), b)

dense_layer(tf.ones([3, 2]), tf.ones([2, 2]), tf.ones([2]))

<tf.Tensor: shape=(3, 2), dtype=float32, numpy=
array([[3., 3.],
       [3., 3.],
       [3., 3.]], dtype=float32)>

tf.functions can be faster than eager code, especially for graphs with many small ops. But for graphs with a few expensive ops (like convolutions), you may not see much speedup.

import timeit
conv_layer = tf.keras.layers.Conv2D(100, 3)

@tf.function
def conv_fn(image):
  return conv_layer(image)

image = tf.zeros([1, 200, 200, 100])
# Warm up
conv_layer(image); conv_fn(image)
print("Eager conv:", timeit.timeit(lambda: conv_layer(image), number=10))
print("Function conv:", timeit.timeit(lambda: conv_fn(image), number=10))
print("Note how there's not much difference in performance for convolutions")

W0000 00:00:1723690654.228267  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.285525  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.290477  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.295072  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.299820  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.304580  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.322737  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.327483  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.332646  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.337747  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.343046  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.347480  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.361780  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.370325  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.381185  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723690654.405763  167534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
Eager conv: 0.011224052000216034
Function conv: 0.005400947000453016
Note how there's not much difference in performance for convolutions

Tracing

This section exposes how tf.function works under the hood, including implementation details which may change in the future. However, once you understand why and when tracing happens, it's much easier to use tf.function effectively!

What is "tracing"?

A tf.function runs your program in a TensorFlow Graph. However, a tf.Graph cannot represent all the things that you'd write in an eager TensorFlow program. For instance, Python supports polymorphism, but tf.Graph requires its inputs to have a specified data type and dimension. Or you may perform side tasks like reading command-line arguments, raising an error, or working with a more complex Python object; none of these things can run in a tf.Graph.

tf.function bridges this gap by separating your code in two stages:

  1. In the first stage, referred to as "tracing", tf.function creates a new tf.Graph. Python code runs normally, but all TensorFlow operations (like adding two Tensors) are deferred: they are captured by the tf.Graph and not run.
  2. In the second stage, a tf.Graph which contains everything that was deferred in the first stage is run. This stage is much faster than the tracing stage.

Depending on its inputs, tf.function will not always run the first stage when it is called. See "Rules of tracing" below to get a better sense of how it makes that determination. Skipping the first stage and only executing the second stage is what gives you TensorFlow's high performance.

When tf.function does decide to trace, the tracing stage is immediately followed by the second stage, so calling the tf.function both creates and runs the tf.Graph. Later you will see how you can run only the tracing stage with get_concrete_function.

When you pass arguments of different types into a tf.function, both stages are run:

@tf.function
def double(a):
  print("Tracing with", a)
  return a + a

print(double(tf.constant(1)))
print()
print(double(tf.constant(1.1)))
print()
print(double(tf.constant("a")))
print()

Tracing with Tensor("a:0", shape=(), dtype=int32)
tf.Tensor(2, shape=(), dtype=int32)

Tracing with Tensor("a:0", shape=(), dtype=float32)
tf.Tensor(2.2, shape=(), dtype=float32)

Tracing with Tensor("a:0", shape=(), dtype=string)
tf.Tensor(b'aa', shape=(), dtype=string)

Note that if you repeatedly call a tf.function with the same argument type, TensorFlow will skip the tracing stage and reuse a previously traced graph, as the generated graph would be identical.

# This doesn't print 'Tracing with ...'
print(double(tf.constant("b")))

tf.Tensor(b'bb', shape=(), dtype=string)

You can use pretty_printed_concrete_signatures() to see all of the available traces:

print(double.pretty_printed_concrete_signatures())

Input Parameters:
  a (POSITIONAL_OR_KEYWORD): TensorSpec(shape=(), dtype=tf.int32, name=None)
Output Type:
  TensorSpec(shape=(), dtype=tf.int32, name=None)
Captures:
  None

Input Parameters:
  a (POSITIONAL_OR_KEYWORD): TensorSpec(shape=(), dtype=tf.float32, name=None)
Output Type:
  TensorSpec(shape=(), dtype=tf.float32, name=None)
Captures:
  None

So far, you've seen that tf.function creates a cached, dynamic dispatch layer over TensorFlow's graph tracing logic. To be more specific about the terminology:

Rules of tracing

When called, a tf.function first evaluates the type of each input argument using the tf.types.experimental.TraceType of each argument. This is used to construct a tf.types.experimental.FunctionType describing the signature of the desired ConcreteFunction. We compare this FunctionType to the FunctionTypes of existing ConcreteFunctions. If a matching ConcreteFunction is found, the call is dispatched to it. If no match is found, a new ConcreteFunction is traced for the desired FunctionType.

If multiple matches are found, the most specific signature is chosen. Matching is done by subtyping, much like normal function calls in C++ or Java, for instance. For example, TensorShape([1, 2]) is a subtype of TensorShape([None, None]) and so a call to the tf.function with TensorShape([1, 2]) can be dispatched to the ConcreteFunction produced with TensorShape([None, None]) but if a ConcreteFunction with TensorShape([1, None]) also exists then it will be prioritized since it is more specific.

The TraceType is determined from input arguments as follows:

Note: TraceType is based on the tf.function input parameters so changes to global and free variables alone will not create a new trace. See this section for recommended practices when dealing with Python global and free variables.

Controlling retracing

Retracing, which is when your tf.function creates more than one trace, helps ensure that TensorFlow generates correct graphs for each set of inputs. However, tracing is an expensive operation! If your tf.function retraces a new graph for every call, you'll find that your code executes more slowly than if you didn't use tf.function.

To control the tracing behavior, you can use the following techniques:

Pass a fixed input_signature to tf.function

This forces tf.function to constrain itself to only one tf.types.experimental.FunctionType composed of the types enumerated by the input_signature. Calls that cannot be dispatched to this FunctionType will throw an error.

@tf.function(input_signature=(tf.TensorSpec(shape=[None], dtype=tf.int32),))
def next_collatz(x):
  print("Tracing with", x)
  return tf.where(x % 2 == 0, x // 2, 3 * x + 1)

print(next_collatz(tf.constant([1, 2])))
# You specified a 1-D tensor in the input signature, so this should fail.
with assert_raises(TypeError):
  next_collatz(tf.constant([[1, 2], [3, 4]]))

# You specified an int32 dtype in the input signature, so this should fail.
with assert_raises(TypeError):
  next_collatz(tf.constant([1.0, 2.0]))

Tracing with Tensor("x:0", shape=(None,), dtype=int32)
tf.Tensor([4 1], shape=(2,), dtype=int32)
Caught expected exception 
  <class 'TypeError'>:
Caught expected exception 
  <class 'TypeError'>:
Traceback (most recent call last):
  File "/tmpfs/tmp/ipykernel_167534/3551158538.py", line 8, in assert_raises
    yield
  File "/tmpfs/tmp/ipykernel_167534/3657259638.py", line 9, in <module>
    next_collatz(tf.constant([[1, 2], [3, 4]]))
TypeError: Binding inputs to tf.function failed due to `Can not cast TensorSpec(shape=(2, 2), dtype=tf.int32, name=None) to TensorSpec(shape=(None,), dtype=tf.int32, name=None)`. Received args: (<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[1, 2],
       [3, 4]], dtype=int32)>,) and kwargs: {} for signature: (x: TensorSpec(shape=(None,), dtype=tf.int32, name=None)).
Traceback (most recent call last):
  File "/tmpfs/tmp/ipykernel_167534/3551158538.py", line 8, in assert_raises
    yield
  File "/tmpfs/tmp/ipykernel_167534/3657259638.py", line 13, in <module>
    next_collatz(tf.constant([1.0, 2.0]))
TypeError: Binding inputs to tf.function failed due to `Can not cast TensorSpec(shape=(2,), dtype=tf.float32, name=None) to TensorSpec(shape=(None,), dtype=tf.int32, name=None)`. Received args: (<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>,) and kwargs: {} for signature: (x: TensorSpec(shape=(None,), dtype=tf.int32, name=None)).

Use unknown dimensions for flexibility

Since TensorFlow matches tensors based on their shape, using a None dimension as a wildcard will allow tf.functions to reuse traces for variably-sized input. Variably-sized input can occur if you have sequences of different length, or images of different sizes for each batch.

@tf.function(input_signature=(tf.TensorSpec(shape=[None], dtype=tf.int32),))
def g(x):
  print('Tracing with', x)
  return x

# No retrace!
print(g(tf.constant([1, 2, 3])))
print(g(tf.constant([1, 2, 3, 4, 5])))

Tracing with Tensor("x:0", shape=(None,), dtype=int32)
tf.Tensor([1 2 3], shape=(3,), dtype=int32)
tf.Tensor([1 2 3 4 5], shape=(5,), dtype=int32)

Use reduce_retracing for automatic flexibility

When reduce_retracing is enabled, tf.function automatically identifies supertypes of the input types it is observing and chooses to trace more generalized graphs automatically. It is less efficient than setting the input_signature directly but useful when many types need to be supported.

@tf.function(reduce_retracing=True)
def g(x):
  print('Tracing with', x)
  return x

# Traces once.
print(g(tf.constant([1, 2, 3])))

# Traces again, but more generalized this time.
print(g(tf.constant([1, 2, 3, 4, 5])))

# No more tracing!
print(g(tf.constant([1, 2, 3, 4, 5, 6, 7])))
print(g(tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9])))

Tracing with Tensor("x:0", shape=(3,), dtype=int32)
tf.Tensor([1 2 3], shape=(3,), dtype=int32)
Tracing with Tensor("x:0", shape=(None,), dtype=int32)
tf.Tensor([1 2 3 4 5], shape=(5,), dtype=int32)
tf.Tensor([1 2 3 4 5 6 7], shape=(7,), dtype=int32)
tf.Tensor([1 2 3 4 5 6 7 8 9], shape=(9,), dtype=int32)

Pass tensors instead of python literals

Often, Python arguments are used to control hyperparameters and graph constructions - for example, num_layers=10 or training=True or nonlinearity='relu'. So, if the Python argument changes, it makes sense that you'd have to retrace the graph.

However, it's possible that a Python argument is not being used to control graph construction. In these cases, a change in the Python value can trigger needless retracing. Take, for example, this training loop, which AutoGraph will dynamically unroll. Despite the multiple traces, the generated graph is actually identical, so retracing is unnecessary.

def train_one_step():
  pass

@tf.function
def train(num_steps):
  print("Tracing with num_steps = ", num_steps)
  tf.print("Executing with num_steps = ", num_steps)
  for _ in tf.range(num_steps):
    train_one_step()

print("Retracing occurs for different Python arguments.")
train(num_steps=10)
train(num_steps=20)

print()
print("Traces are reused for Tensor arguments.")
train(num_steps=tf.constant(10))
train(num_steps=tf.constant(20))

Retracing occurs for different Python arguments.
Tracing with num_steps =  10
Executing with num_steps =  10
Tracing with num_steps =  20
Executing with num_steps =  20

Traces are reused for Tensor arguments.
Tracing with num_steps =  Tensor("num_steps:0", shape=(), dtype=int32)
Executing with num_steps =  10
Executing with num_steps =  20

If you need to force retracing, create a new tf.function. Separate tf.function objects are guaranteed not to share traces.

def f():
  print('Tracing!')
  tf.print('Executing')

tf.function(f)()
tf.function(f)()

Tracing!
Executing
Tracing!
Executing

Use the tracing protocol

Where possible, you should prefer converting the Python type into a tf.experimental.ExtensionType instead. Moreover, the TraceType of an ExtensionType is the tf.TypeSpec associated with it. Therefore, if needed, you can simply override the default tf.TypeSpec to take control of an ExtensionType's Tracing Protocol.

Otherwise, for direct control over when tf.function should retrace in regards to a particular Python type, you can implement the Tracing Protocol for it yourself.

@tf.function
def get_mixed_flavor(fruit_a, fruit_b):
  return fruit_a.flavor + fruit_b.flavor

class Fruit:
  flavor = tf.constant([0, 0])

class Apple(Fruit):
  flavor = tf.constant([1, 2])

class Mango(Fruit):
  flavor = tf.constant([3, 4])

# As described in the above rules, a generic TraceType for `Apple` and `Mango`
# is generated (and a corresponding ConcreteFunction is traced) but it fails to
# match the second function call since the first pair of Apple() and Mango()
# have gone out out of scope by then and deleted.
get_mixed_flavor(Apple(), Mango()) # Traces a new concrete function
get_mixed_flavor(Apple(), Mango()) # Traces a new concrete function again

# However, each subclass of the `Fruit` class has a fixed flavor, and you
# can reuse an existing traced concrete function if it was the same
# subclass. Avoiding such unnecessary tracing of concrete functions
# can have significant performance benefits.

class FruitTraceType(tf.types.experimental.TraceType):
  def __init__(self, fruit):
    self.fruit_type = type(fruit)
    self.fruit_value = fruit

  def is_subtype_of(self, other):
      # True if self subtypes `other` and `other`'s type matches FruitTraceType.
      return (type(other) is FruitTraceType and
              self.fruit_type is other.fruit_type)

  def most_specific_common_supertype(self, others):
      # `self` is the specific common supertype if all input types match it.
      return self if all(self == other for other in others) else None

  def placeholder_value(self, placeholder_context=None):
      # Use the fruit itself instead of the type for correct tracing.
      return self.fruit_value

  def __eq__(self, other):
    return type(other) is FruitTraceType and self.fruit_type == other.fruit_type

  def __hash__(self):
    return hash(self.fruit_type)

class FruitWithTraceType:

  def __tf_tracing_type__(self, context):
    return FruitTraceType(self)

class AppleWithTraceType(FruitWithTraceType):
  flavor = tf.constant([1, 2])

class MangoWithTraceType(FruitWithTraceType):
  flavor = tf.constant([3, 4])

# Now if you try calling it again:
get_mixed_flavor(AppleWithTraceType(), MangoWithTraceType()) # Traces a new concrete function
get_mixed_flavor(AppleWithTraceType(), MangoWithTraceType()) # Re-uses the traced concrete function

<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 6], dtype=int32)>

Obtaining concrete functions

Every time a function is traced, a new concrete function is created. You can directly obtain a concrete function, by using get_concrete_function.

print("Obtaining concrete trace")
double_strings = double.get_concrete_function(tf.constant("a"))
print("Executing traced function")
print(double_strings(tf.constant("a")))
print(double_strings(a=tf.constant("b")))

Obtaining concrete trace
Executing traced function
tf.Tensor(b'aa', shape=(), dtype=string)
tf.Tensor(b'bb', shape=(), dtype=string)

# You can also call get_concrete_function on an InputSpec
double_strings_from_inputspec = double.get_concrete_function(tf.TensorSpec(shape=[], dtype=tf.string))
print(double_strings_from_inputspec(tf.constant("c")))

tf.Tensor(b'cc', shape=(), dtype=string)

Printing a ConcreteFunction displays a summary of its input arguments (with types) and its output type.

print(double_strings)

ConcreteFunction Input Parameters:
  a (POSITIONAL_OR_KEYWORD): TensorSpec(shape=(), dtype=tf.string, name=None)
Output Type:
  TensorSpec(shape=(), dtype=tf.string, name=None)
Captures:
  None

You can also directly retrieve a concrete function's signature.

print(double_strings.function_type)

(a: TensorSpec(shape=(), dtype=tf.string, name=None)) -> TensorSpec(shape=(), dtype=tf.string, name=None)

Using a concrete trace with incompatible types will throw an error

with assert_raises(tf.errors.InvalidArgumentError):
  double_strings(tf.constant(1))

Caught expected exception 
  <class 'tensorflow.python.framework.errors_impl.InvalidArgumentError'>:
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/function_type_utils.py", line 442, in bind_function_inputs
    bound_arguments = function_type.bind_with_defaults(
  File "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/core/function/polymorphism/function_type.py", line 277, in bind_with_defaults
    with_default_args[arg_name] = constraint.cast(
TypeError: Can not cast TensorSpec(shape=(), dtype=tf.int32, name=None) to TensorSpec(shape=(), dtype=tf.string, name=None)

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py", line 1179, in _call_impl
    return self._call_with_structured_signature(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py", line 1259, in _call_with_structured_signature
    function_type_utils.canonicalize_function_inputs(
TypeError: Binding inputs to tf.function failed due to `Can not cast TensorSpec(shape=(), dtype=tf.int32, name=None) to TensorSpec(shape=(), dtype=tf.string, name=None)`. Received args: (<tf.Tensor: shape=(), dtype=int32, numpy=1>,) and kwargs: {} for signature: (a: TensorSpec(shape=(), dtype=tf.string, name=None)) -> TensorSpec(shape=(), dtype=tf.string, name=None).

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/tmpfs/tmp/ipykernel_167534/3551158538.py", line 8, in assert_raises
    yield
  File "/tmpfs/tmp/ipykernel_167534/3196284684.py", line 2, in <module>
    double_strings(tf.constant(1))
tensorflow.python.framework.errors_impl.InvalidArgumentError: cannot compute __inference_double_189 as input #0(zero-based) was expected to be a string tensor but is a int32 tensor [Op:__inference_double_189]

You may notice that Python arguments are given special treatment in a concrete function's input signature. Prior to TensorFlow 2.3, Python arguments were simply removed from the concrete function's signature. Starting with TensorFlow 2.3, Python arguments remain in the signature, but are constrained to take the value set during tracing.

@tf.function
def pow(a, b):
  return a ** b

square = pow.get_concrete_function(a=tf.TensorSpec(None, tf.float32), b=2)
print(square)

ConcreteFunction Input Parameters:
  a (POSITIONAL_OR_KEYWORD): TensorSpec(shape=<unknown>, dtype=tf.float32, name=None)
  b (POSITIONAL_OR_KEYWORD): Literal[2]
Output Type:
  TensorSpec(shape=<unknown>, dtype=tf.float32, name=None)
Captures:
  None

assert square(tf.constant(10.0)) == 100

with assert_raises(TypeError):
  square(tf.constant(10.0), b=3)

Caught expected exception 
  <class 'TypeError'>:
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/function_type_utils.py", line 442, in bind_function_inputs
    bound_arguments = function_type.bind_with_defaults(
  File "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/core/function/polymorphism/function_type.py", line 277, in bind_with_defaults
    with_default_args[arg_name] = constraint.cast(
ValueError: Can not cast 3 to Literal[2]

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py", line 1179, in _call_impl
    return self._call_with_structured_signature(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py", line 1259, in _call_with_structured_signature
    function_type_utils.canonicalize_function_inputs(
TypeError: Binding inputs to tf.function failed due to `Can not cast 3 to Literal[2]`. Received args: (<tf.Tensor: shape=(), dtype=float32, numpy=10.0>,) and kwargs: {'b': 3} for signature: (a: TensorSpec(shape=<unknown>, dtype=tf.float32, name=None), b: Literal[2]) -> TensorSpec(shape=<unknown>, dtype=tf.float32, name=None).

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py", line 1182, in _call_impl
    return self._call_with_flat_signature(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py", line 1233, in _call_with_flat_signature
    raise TypeError(f"{self._flat_signature_summary()} got unexpected "
TypeError: pow(a) got unexpected keyword arguments: b.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/tmpfs/tmp/ipykernel_167534/3551158538.py", line 8, in assert_raises
    yield
  File "/tmpfs/tmp/ipykernel_167534/2310937119.py", line 4, in <module>
    square(tf.constant(10.0), b=3)
TypeError: Binding inputs to tf.function failed due to `Can not cast 3 to Literal[2]`. Received args: (<tf.Tensor: shape=(), dtype=float32, numpy=10.0>,) and kwargs: {'b': 3} for signature: (a: TensorSpec(shape=<unknown>, dtype=tf.float32, name=None), b: Literal[2]) -> TensorSpec(shape=<unknown>, dtype=tf.float32, name=None).
Fallback to flat signature also failed due to: pow(a) got unexpected keyword arguments: b.

Obtaining graphs

Although retrieving the actual tf.Graph object is not something you'll normally need to do, you can obtain it easily from any concrete function.

graph = double_strings.graph
for node in graph.as_graph_def().node:
  print(f'{node.input} -> {node.name}')

[] -> a
['a', 'a'] -> add
['add'] -> Identity

In reality, tf.Graphs are not directly callable. We actually use an tf.types.experimental.AtomicFunction to perform the computations described by the tf.Graph. You can access the AtomicFunction describing the traced tf.Graph and call it directly instead of the ConcreteFunction:

atomic_fn = double_strings.inference_fn
atomic_fn(tf.constant("a"))

<tf.Tensor: shape=(), dtype=string, numpy=b'aa'>

This has the advantage of having lower Python overhead for high-performance scenarios. But it should only be used for forward inference (no gradient support), and captured tensor values (if any) would need to be explicitly supplied.

Debugging

In general, debugging code is easier in eager mode than inside tf.function. You should ensure that your code executes error-free in eager mode before decorating with tf.function. To assist in the debugging process, you can call tf.config.run_functions_eagerly(True) to globally disable and reenable tf.function.

When tracking down issues that only appear within tf.function, here are some tips:

Originally published on the TensorFlow website, this article appears here under a new headline and is licensed under CC BY 4.0. Code samples shared under the Apache 2.0 License.