Output mismatch between direct pass and looped pass through Dense layer
@gergely-soti I have tried to replicate the issue and faced [this](https://colab.research.google.com/gist/sushreebarsa/743b11062cfcd70da181a08c7163fb4d/61390.ipynb) different result as output. Could you please take a look and confirm the result as the issue reported ?
Thank you! @sushreebarsa Thank you for checking the issue. I tried the provided Colab link, and initially, the problem was resolved. However, when I doubled the second dimension of the input tensor to (2, 16384, 42, 379), the problem reappeared.

FYI: I used an RTX A6000 in my initial post Hi @gergely-soti ,

The difference in outputs are related to precision errors on GPU. [tf.equal()](https://www.tensorflow.org/api_docs/python/tf/math/equal) is very specific to precision and there is no tolerance in it. Where as in [tf.debugging.assert_near()](https://www.tensorflow.org/api_docs/python/tf/debugging/assert_near) we have tolerance parameters` rtol()` and `mtol()` which made difference.

Suppose if i pass `tf.debugging.assert_near(y1, y2,rtol=1.1920929e-05, atol=1.1920929e-05)` then there is no assertion error in Colab.Where as `tf.debugging.assert_near(y1, y2,rtol=1.1920929e-06, atol=1.1920929e-06)` raises `InvalidArgumentError`. Please refer to attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/ce33b6d8b16597b455f6c4878a13f7d4/61394_gpu.ipynb).

Each GPU will have its own precision values and hence the outputs seems different but only to certain precision.

Thanks! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. Hi @SuryanarayanaY,
I appreciate the clarification regarding the precision-related issues. While I understand that precision discrepancies can occur across different GPUs, I find it a bit surprising that the results differ even when the computations are performed on the same machine. Nevertheless, I understand the nature of these variations and will take them into account. Thank you for your assistance and explanation.
### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

binary

### TensorFlow version

2.11.0

### Custom code

No

### OS platform and distribution

Linux Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.8

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

12.0 

### GPU model and memory

_No response_

### Current behavior?

When passing a large 3D tensor hrough a Dense layer using two different methods, the outputs are not always equal. Specifically, when the input tensor is relatively small, e.g., (2, 128, 42, 128), the outputs are equal, but when the input tensor is larger, e.g., (2, 8192, 42, 379), the outputs differ.
The tf.debugging.assert_near(y1, y2) statement does not trigger any assertion errors.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

# create a random tensor
x = tf.random.uniform(shape=(2, 8192, 42, 379))

# create a dense layer
layer = tf.keras.layers.Dense(128)

# pass the input through the layer to get output
y1 = layer(x)

# pass the input using a for loop over the 3d dimension
y2 = tf.stack([layer(x[:, :, i, :]) for i in range(x.shape[2])], axis=2)

# check if the outputs are the same
print(tf.reduce_all(tf.equal(y1, y2)))
tf.debugging.assert_near(y1, y2)
```


### Relevant log output

_No response_