Issue type

Bug

Have you reproduced the bug with TensorFlow Nightly?

No

Source

source

TensorFlow version

2.19.0

Custom code

Yes

OS platform and distribution

Linux-6.6.113+-x86_64-with-glibc2.35 (Google Colab)

Mobile device

No response

Python version

3.12.13

Bazel version

No response

GCC/compiler version

No response

CUDA/cuDNN version

No GPU (CPU-only environment)

GPU model and memory

No response

Current behavior?

Using tf.where with differentiable branches results in a None gradient, even though the function is mathematically differentiable.

Observed Output

y: [1. 2. 9.]
grad: None

The gradient is not computed at all (None), leading to silent failure.

Standalone code to reproduce the issue

import tensorflow as tf

x = tf.Variable([-1.0, 2.0, -3.0])

with tf.GradientTape() as tape:
    y = tf.where(x > 0, x, x * x)

grad = tape.gradient(tf.reduce_sum(y), x)

print("y:", y.numpy())
print("grad:", grad)

Relevant log output

No error or warning is raised. Gradient is silently None.