Output Discrepancies in TensorFlow Model with Extra Concat Output Node Under XLA Compilation
Hi @GwiHwan-Go  ,

I have replicated the reported behaviour with colab using TF v2.14, 2.15 and nightly. Please find the [gist](https://colab.research.google.com/gist/Venkat6871/1cefccbfed72119f66f57ea30674843f/62512_2-15_2-14-nightly-v.ipynb) here for reference.

Thank you! Hi, Could you please explain why `concated` was being returned in `Model2` where as `Model1` doesn't return this.  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. Hello @sachinprasadhs,
We were considering the importance of examining the intermediate values in the layer and thought it might be a crucial aspect to look into. Hi,

Returning the different outputs for both the results and then comparing will not yield same result.

The behavior is expected and it does not not look like a bug. 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. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62512">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62512">No</a>

### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.15.0

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04.3 LTS

### Mobile device

_No response_

### Python version

3.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

In TensorFlow 2.15.0, I have encountered an issue where adding an **extra concat output node** to a model results in discrepancies in the output when compiled with XLA. This inconsistency is not observed without XLA compilation.

### Additional Context

In our efforts to understand and locate the root cause of this issue, we have conducted several tests and experiments. Through this process, we've identified specific conditions under which the discrepancy does not occur:

1. Removing an Operator: If any one of the operators `(expand_dims, multiply, abs)` is removed from either model, the discrepancy under XLA compilation is no longer triggered.

2. Removing `concated` from Model2's Return: Omitting the concated tensor from the output of Model2 results in no discrepancy between the two models under XLA compilation.

3. Changing Input Tensor Data Type: Altering the data type of the input tensor to a type other than `int8` also prevents the occurrence of this discrepancy.


### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import numpy as np
class Model1(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        expanded = tf.expand_dims(inp, axis=0)
        multiplied = tf.multiply(inp, expanded)
        absed = tf.abs(multiplied)
        return absed,
class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        expanded = tf.expand_dims(inp, axis=0)
        multiplied = tf.multiply(expanded, inp)
        concated = tf.concat([multiplied, multiplied], axis=0)
        absed = tf.abs(multiplied)
        return absed, concated

inputs = [
tf.cast(tf.random.uniform(shape=[5, 5], minval=-128, maxval=128, dtype=tf.int32), tf.int8),
]
model1 = Model1()
model2 = Model2()
device = "cpu"
with tf.device(device):
    tf.config.run_functions_eagerly(True)
    out1 = model1(*inputs)
    out2 = model2(*inputs)
    print(f'=========eager_output(version:{tf.__version__})================')
    try :
        for i in range(min(len(out1),len(out2))):
            np.testing.assert_allclose(out1[i].numpy(), out2[i].numpy(), rtol=0.001, atol=0.001, err_msg=f'at checking {i}th')
        print("XLA_eager does not trigger assertion")
    except AssertionError as e:
        print("XLA_eager triggers assertion")
        print(e)
    tf.config.run_functions_eagerly(False)
    out1 = model1(*inputs)
    out2 = model2(*inputs)
    print(f'=========compiled_output(version:{tf.__version__})================')
    try :
        for i in range(min(len(out1),len(out2))):
            np.testing.assert_allclose(out1[i].numpy(), out2[i].numpy(), rtol=0.001, atol=0.001, err_msg=f'at checking {i}th')
        print("XLA_complie does not trigger assertion")
    except AssertionError as e:
        print("XLA_complie triggers assertion")
        print(e)
```


### Relevant log output

```shell
=========eager_output(version:2.15.0)================
XLA_eager does not trigger assertion
=========compiled_output(version:2.15.0)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 0th
Mismatched elements: 13 / 25 (52%)
Max absolute difference: 126
Max relative difference: 2.
 x: array([[[ -63,   16,  -23,  -15,   68],
        [ -92, -103,  -60,  100,    1],
        [   9,   16,   36,   33,   64],...
 y: array([[[ 63,  16,  23,  15,  68],
        [ 92, 103,  60, 100,   1],
        [  9,  16,  36,  33,  64],...
```
