Tensors and operations make up the fundamental units of TensorFlow programs. However, to optimize the execution efficiency and deploy TensorFlow models on various platforms, you need to compile these operations into efficient graphs. The tf.function decorator serves precisely this purpose, transforming Python functions into TensorFlow computation graphs. Here, we will explore the advantages of using tf.function and demonstrate now you can utilize it in your projects.
Understanding tf.function
The tf.function decorator allows your TensorFlow code to run both eagerly (step-by-step) and as a graph (fast execution). Compiling functions into graphs provides faster execution because TensorFlow can execute operations in parallel and optimize them.
Let us review an example function that adds two numbers:
def add_numbers(a, b):
return a + b
When we wrap this function with tf.function, it becomes part of the computation graph.
import tensorflow as tf
@tf.function
def add_numbers_compiled(a, b):
return a + b
Using tf.function
With tf.function, TensorFlow starts recording operations to the computation graph the moment the function is called. Let's execute the above function:
x = tf.constant([1, 2, 3])
y = tf.constant([4, 5, 6])
# Eager execution
print("Eager execution result: ", add_numbers(x, y).numpy())
# Graph execution with tf.function
result = add_numbers_compiled(x, y)
print("Graph execution result: ", result.numpy())
Output:
Eager execution result: [5 7 9]
Graph execution result: [5 7 9]Benefits of Graphs
Using graphs via tf.function comes with several benefits:
- Performance Improvements: TensorFlow can optimize graphs with opportunities like operation fusing. This can lead to significant performance gains.
- Portability: Graphs can be exported and used with the TensorFlow ecosystem, such as deploying on mobile devices or web servers.
- Tooling: TensorBoard support for graph execution to visualize your code’s behavior over time.
Tracing and Retracing
It's worth mentioning that tf.function traces (converts Python to a graph) the function based on input signatures. Avoid variability in trace if the inputs are static. If inputs vary in structure or type, TensorFlow retraces to handle new signatures. This can affect performance if overdone.
@tf.function
def squaring_fn(x):
return x * x
# Traced once for each unique input shape and dtype.
print(squaring_fn(tf.constant(3))) # Traced here.
print(squaring_fn(tf.constant([1, 2, 3]))) # Different shape, retraced.
Create explicit signatures and use argument-based configurations to control tracing.
Debugging with tf.function
Debugging inside tf.function can sometimes be challenging. With graph execution, Python’s control statements and print statements function differently, because they will become part of the graph. Use tf.print instead of an ordinary print statement to get outputs during graph execution:
@tf.function
def add_and_print(a, b):
result = a + b
tf.print("The result is:", result)
return result
add_and_print(tf.constant(5), tf.constant(7))
Conclusion
Utilizing TensorFlow's tf.function enables scalable, high-performance model training and inference by efficiently compiling and optimizing computation graphs. By leveraging its capabilities, developers can write TensorFlow code that runs efficiently across various platforms while ensuring scalability and flexibility in model deployment.
