Inconsistency in XLA Compiled Model with `tf.add, tf.experimental.numpy.triu, tf.math.reduce_max` and Additional Transpose Output on GPU
Hi @GwiHwan-Go ,

The issue does not replicable in TF2.14v and TF2.15 GPU package not installable on colab now. Could you please confirm the behaviour with TF2.14v also.With Tf2.15 and CPU the code works fine. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b2f560f2a44359856157f0cbcb9efd06/62528.ipynb) for reference.

Thanks! Hi @SuryanarayanaY ,

We executed above code using Tf2.14v with same environment and encountered no errors. However, when we ran the refered behavior on Tf2.15v. @GwiHwan-Go ,

Thanks for clarification. Then it seems a regression issue.
### 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

cuda : 12.2 / cudnn 8.9.04

### GPU model and memory

Tesla V100S-PCIE-32GB

### Current behavior?

A combination of `tf.add`, `tf.experimental.numpy.triu`, and `tf.math.reduce_max`, with an additional transpose output results in inconsistent outputs under XLA compilation. Interestingly, this inconsistency is not triggered when altering the composition of these operations or changing the data type from `int8`.
This behavior is only seen on **gpu.**(Tesla V100)


### 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, inp1, inp2):
        # Forward pass logic using TensorFlow operations
        # inp1: [13, 1] : int8
        # inp2: [13, 60] : int8
        add = tf.add(inp2, inp1)
        triu = tf.experimental.numpy.triu(add, k=0)
        reduce_max = tf.math.reduce_max(triu, axis=1)
        return triu, reduce_max, 

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        # Forward pass logic using TensorFlow operations
        # inp1: [13, 1] : int8
        # inp2: [13, 60] : int8
        add = tf.add(inp2, inp1)
        transpose = tf.transpose(add, perm=[1, 0])
        triu = tf.experimental.numpy.triu(add, k=0)
        reduce_max = tf.math.reduce_max(triu, axis=1)
        return triu, reduce_max, transpose

inputs = [
tf.cast(tf.random.uniform(shape=[13, 1], minval=-128, maxval=128, dtype=tf.int32), tf.int8),
tf.cast(tf.random.uniform(shape=[13, 60], minval=-128, maxval=128, dtype=tf.int32), tf.int8),
]
model1 = Model1()
model2 = Model2()
device = "gpu"
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
2023-12-01 16:14:42.359231: I external/local_xla/xla/service/service.cc:168] XLA service 0x5621a8b5fae0 initialized for platform CUDA (this does not guarantee that XLA will be used).
=========compiled_output(version:2.15.0)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 1th
Mismatched elements: 12 / 13 (92.3%)
Max absolute difference: 126
Max relative difference: 18.
 x: array([119, 126, 126, 123, 127, 127, 120, 127, 127, 107, 127, 126, 109],
      dtype=int8)
 y: array([ -7, -10, 114, 114,  86, 125, 117, 120, 127,  86,  56, 114, 108],
      dtype=int8)
```
