TF 2.18 & Keras: Real-World Performance Review

I finally bit the bullet last week. After ignoring the notification icons for two months, I upgraded our main training pipeline to TensorFlow 2.18. The release dropped back in December, but honestly? I usually wait for the “.1” patch before touching anything in production. I’ve been burned too many times by “revolutionary” updates that just break my CUDA drivers.

But the changelog promised 15% speed gains thanks to better XLA (Accelerated Linear Algebra) integration, and we’re burning way too much cash on GPU compute right now. So, I cloned the repo, bumped the requirements file, and held my breath.

Spoiler: It didn’t break. And the speedup is actually real. Mostly.

The XLA “Free Lunch”

Let’s start with the performance claims. Usually, when a framework promises “up to X% faster,” that means “in one specific, highly optimized scenario that you will never replicate.” But I tested this on a standard BERT fine-tuning task we run for sentiment analysis. Nothing fancy, just a few million parameters. On the old setup (TF 2.16), we were averaging about 142ms per step on an RTX 4090. After the update? 124ms per step.

That’s roughly a 12-13% improvement out of the box. I didn’t change the model architecture. I didn’t rewrite the training loop. I just updated the library. The XLA compiler seems much more aggressive now about fusing operations without needing explicit @tf.function(jit_compile=True) decorators everywhere, although adding them explicitly still squeezes out a bit more juice.

And if you’re running massive jobs on Google Cloud or AWS, that 13% adds up fast. It’s the difference between a training run finishing before dinner or waiting until the next morning.

Keras Integration: Less Boilerplate, Finally

The “better Keras integration” point in the release notes was vague, but in practice, it means the API for custom layers feels less like a hack. I’ve always found writing custom layers in TF/Keras to be a bit verbose compared to PyTorch. But in 2.18, they’ve cleaned up the internal dispatching.

Here’s a quick custom layer I threw together to test the new serialization support. It’s a simple Gated Linear Unit, but notice how clean the config handling is now:

import tensorflow as tf
from tensorflow import keras

class SimpleGLU(keras.layers.Layer):
    def __init__(self, units=32, **kwargs):
        super().__init__(**kwargs)
        self.units = units

    def build(self, input_shape):
        self.dense_act = keras.layers.Dense(self.units, activation='sigmoid')
        self.dense_lin = keras.layers.Dense(self.units)
        super().build(input_shape)

    def call(self, inputs):
        return self.dense_act(inputs) * self.dense_lin(inputs)

    def get_config(self):
        config = super().get_config()
        config.update({"units": self.units})
        return config

# Testing serialization
layer = SimpleGLU(64)
input_tensor = tf.random.normal((1, 128))
out = layer(input_tensor)
print(f"Output shape: {out.shape}")

It just works. I didn’t run into the dreaded NotImplementedError during saving that used to plague my custom models in 2.14.

The Edge Case Nightmare

But it’s not all sunshine. While the server-side training is great, the “improved mobile/edge support” gave me a headache on Tuesday. I tried to convert a model for a mobile demo using TFLite, and the documentation says the converter handles dynamic shapes better now. In my experience? It handles them by crashing.

I was getting a cryptic SegFault when converting a model that used a specific combination of Conv2D and BatchNormalization. The fix? It turns out the new quantization scheme in 2.18 is aggressive by default. I had to explicitly disable the new experimental_new_quantizer flag (which defaults to True now) to get the conversion to finish without segfaulting.

# If you're getting segfaults on conversion in 2.18, try this:
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.experimental_new_quantizer = False # The magic fix
tflite_model = converter.convert()

So, if you’re doing heavy edge deployment, maybe test this on a non-critical branch first. The speed is there, but the tooling feels a little raw on the edges (pun intended).

Should You Upgrade?

But here’s my take: If you are paying for compute by the hour, upgrade. The XLA improvements alone are worth the hassle. A 10-15% reduction in training time is direct money in your pocket. If you have a massive legacy codebase full of custom TFLite conversion scripts? Maybe wait for 2.18.1 or 2.18.2. The mobile support feels like it needs a bit more baking time.

For me, I’m keeping it. The training speedup is just too good to give up, even if I have to hack around the converter bugs.