Introduction

tf.math.mod and tf.math.floordiv can run on a GPU when the TensorFlow build, hardware, and tensor dtypes support GPU kernels for those operations. The main debugging task is not the arithmetic itself, but verifying device placement and understanding when TensorFlow silently falls back to CPU.

Start by confirming that TensorFlow sees the GPU

Before testing individual ops, make sure the runtime can detect a usable GPU.

import tensorflow as tf

print(tf.config.list_physical_devices("GPU"))

If this prints an empty list, the issue is not mod or floordiv. It means the environment is not configured for GPU execution at all.

Place the operations on the GPU explicitly

You can request GPU placement with tf.device. If the op has a compatible GPU kernel, TensorFlow will place it there.

1import tensorflow as tf
2
3with tf.device("/GPU:0"):
4    x = tf.constant([10, 11, 12, 13], dtype=tf.int32)
5    y = tf.constant([3, 3, 3, 3], dtype=tf.int32)
6    remainder = tf.math.mod(x, y)
7    quotient = tf.math.floordiv(x, y)
8
9print(remainder)
10print(quotient)

Using the modern tf.math namespace is clearer than older aliases such as tf.mod.

Enable device placement logging when debugging

TensorFlow can log where each op is executed. This is the fastest way to tell whether the math is actually on the GPU.

1import tensorflow as tf
2
3tf.debugging.set_log_device_placement(True)
4
5with tf.device("/GPU:0"):
6    x = tf.constant([8, 9, 10], dtype=tf.int32)
7    y = tf.constant([2, 2, 2], dtype=tf.int32)
8    print(tf.math.mod(x, y))
9    print(tf.math.floordiv(x, y))

If the logs show CPU placement, either the requested device was unavailable or the runtime selected a CPU kernel for that combination.

Watch the dtype and kernel support

Kernel availability is dtype-specific. Some TensorFlow ops run on GPU only for certain numeric types. Integer operations are especially worth checking because not every integer kernel is equally optimized across environments.

If GPU execution is essential, test the exact dtypes used by your real model. Do not assume that support for float32 implies identical support for every integer variant.

1import tensorflow as tf
2
3for dtype in [tf.int32, tf.int64, tf.float32]:
4    with tf.device("/GPU:0"):
5        a = tf.constant([7, 8, 9], dtype=dtype)
6        b = tf.constant([2, 2, 2], dtype=dtype)
7        result = tf.math.floordiv(a, b)
8        print(dtype.name, result)

Fallback to CPU is not always a bug

If these operations are a tiny part of the overall graph, CPU fallback may be perfectly acceptable. Moving data between CPU and GPU can cost more than the arithmetic itself, especially for small tensors. The right question is not just "can it run on GPU," but "does GPU placement improve the full workload."

For large tensor pipelines that already live on the GPU, keeping placement consistent usually helps. For small preprocessing tasks, CPU execution may be fine.

Use modern TensorFlow names and verify behavior

Older code samples often use tf.mod and tf.floor_div. In current code, prefer tf.math.mod and tf.math.floordiv. The behavior is the same, but the namespace is clearer and aligns with current TensorFlow style.

Common Pitfalls

• Debugging tf.math.mod or tf.math.floordiv before confirming that TensorFlow can see any GPU at all.
• Assuming an explicit with tf.device("/GPU:0") block guarantees GPU execution for every dtype and op.
• Using old aliases and then struggling to compare behavior with newer TensorFlow documentation.
• Treating CPU fallback as failure even when the tensors are tiny and performance is unaffected.
• Forgetting to inspect device placement logs when trying to prove where the op executed.

Summary

• Confirm GPU visibility first with tf.config.list_physical_devices.
• Use tf.math.mod and tf.math.floordiv inside a GPU device context when appropriate.
• Turn on device placement logging to verify where the op actually ran.
• Test the real dtypes used in your workload because kernel support can differ.
• Judge success by end-to-end performance, not by GPU placement alone.