Unexpected slow training performance when using tf.function with TensorFlow 2.10 and TPU
I'm experiencing a significant slowdown in my model training when I wrap my training step in a `tf.function` decorator while using a TPU for acceleration in TensorFlow 2.10. When I execute the training step without `tf.function`, it runs much faster, but I need to use it for graph optimizations. Here's a minimal example of what I'm doing: ```python import tensorflow as tf # Example dataset and model (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data() train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255 model = tf.keras.Sequential([ tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)), tf.keras.layers.Flatten(), tf.keras.layers.Dense(10, activation='softmax') ]) optimizer = tf.keras.optimizers.Adam() @tf.function def train_step(images, labels): with tf.GradientTape() as tape: predictions = model(images, training=True) loss = tf.keras.losses.sparse_categorical_crossentropy(labels, predictions) gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) return loss for epoch in range(5): for step in range(0, len(train_images), 32): loss = train_step(train_images[step:step+32], train_labels[step:step+32]) print(f'Epoch {epoch}, Step {step}, Loss {loss.numpy()}') ``` When I run this code with `tf.function`, it takes roughly 30% longer per epoch compared to running the training step without the decorator. I've tried various configurations, including adjusting the batch size and playing with the TPU settings, but the slowdown persists. Is there a known issue with `tf.function` performance on TPUs in this version? Or are there any specific recommendations for optimizing `tf.function` usage that I might be overlooking? I appreciate any insights that would help me resolve this issue.