How do you use `tf.function` to improve performance?

Q: How do you use `tf.function` to improve performance?

Learn the answer to "How do you use `tf.function` to improve performance?" with detailed explanations, code examples, and best practices on DeployU.

The Scenario

You are an ML engineer at a gaming company. You have written a custom training loop in TensorFlow 2.x to train a reinforcement learning agent. However, the training is very slow. Each training step is taking more than 100ms, which is not fast enough for your use case.

You have used the TensorFlow Profiler to analyze the performance of your training loop, and you have identified that the forward and backward passes of the model are the bottleneck.

The Challenge

Explain how you would use the tf.function decorator to optimize the performance of your custom training loop. What are some of the common pitfalls you would need to avoid, and how would you measure the performance improvement?

Wrong Approach

A junior engineer might not be aware of `tf.function` and might try to optimize the code by hand. They might not understand the difference between eager execution and graph execution, and they might not know how to use the TensorFlow Profiler to measure the performance improvement.

Addresses symptoms, not root cause

Right Approach

A senior engineer would know that `tf.function` is the key to writing high-performance code in TensorFlow 2.x. They would be able to explain how to use it to optimize a custom training loop, and they would know how to use the TensorFlow Profiler to measure the performance improvement. They would also be aware of the common pitfalls to avoid when using `tf.function`.

Step 1: Benchmark the Baseline

The first step is to benchmark the performance of the existing training loop. We can use the tf.profiler to do this.

import tensorflow as tf

# ... (define your model, optimizer, etc.) ...

# Start the profiler
tf.profiler.experimental.start("logs")

for i in range(num_steps):
    # ... your training step ...

# Stop the profiler
tf.profiler.experimental.stop()

We can then use TensorBoard to visualize the profiler data and identify the bottlenecks.

Step 2: Apply the `tf.function` Decorator

The next step is to apply the tf.function decorator to our training step function.

@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images, training=True)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

Step 3: Re-benchmark and Compare

After applying the tf.function decorator, we need to re-benchmark the performance of the training loop and compare it to the baseline.

Code Version	Average Step Time (ms)
Regular Python function	120
`@tf.function`	25

As you can see, using tf.function can lead to a significant performance improvement.

Step 4: Avoid Common Pitfalls

Here are some common pitfalls to avoid when using tf.function:

Side effects: Side effects like printing to the console or appending to a list will only happen once, when the function is traced.
Python control flow: If you use Python control flow that depends on the values of tensors, tf.function will have to re-trace the function every time the condition changes. It’s better to use TensorFlow’s control flow operations, like tf.cond and tf.while_loop.
Creating new variables: You should not create new tf.Variable objects inside a decorated function.

By being aware of these pitfalls, you can use tf.function effectively to write high-performance TensorFlow code.

Systematic, production-ready debugging

Practice Question

You have a function decorated with `tf.function` that contains a Python `if` statement that depends on the value of a tensor. What will happen?

Questions