Output Inconsistency in XLA Compiled Model with `tf.floor, tf.reverse` and Additional Concat Nodes on GPU
@GwiHwan-Go I wasn't able to replicate this error reported, please have a look at this gists of [cpu](https://colab.research.google.com/gist/sushreebarsa/7c1bae96f5faebbe9d805474a9a9d16e/untitled915.ipynb#scrollTo=Shbx4IHeqP3Y) and [gpu](https://colab.research.google.com/gist/sushreebarsa/61ec1deddc1c2322753598e2bc468e63/62548.ipynb).
Thank you! Hi @sushreebarsa , have you tried this with V100 GPU? I can reproduce this issue with V100 GPU. @GwiHwan-Go Inside the call function you are generating the value for tensor, which in each calls generates different random values because XLA currently ignores TF seeds to random operations which makes the output different for obvious reason. Please refer [known](https://www.tensorflow.org/xla/known_issues#random_number_generation_ignores_tf_seed) issues from XLA section.
Thank you! 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/62548">Yes</a>
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### 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

3.10.0

### 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 significant output inconsistency occurs under XLA compilation in a TensorFlow model that uses `tf.transpose`, `tf.floor`, `tf.reverse`, and `tf.add`, followed by returning additional concatenation nodes. This behavior is only seen on **GPU.**

### Troubleshooting and Findings

   - Removing any one of the operations (`tf.floor`, `tf.reverse`, or `tf.add`) eliminates the inconsistency. This suggests that the XLA's optimization strategies for these operations might be contributing to the issue.
   - Additionally, swapping the order of operands in `tf.add` also prevents the inconsistency, indicating that operand order affects the XLA compilation outcome.


### Standalone code to reproduce the issue

```shell
from typing import Dict
import tensorflow as tf
import pickle
import os
import numpy as np
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        # Currently, memory growth needs to be the same across GPUs
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
    except RuntimeError as e:
        # Memory growth must be set before GPUs have been initialized
        print(e)
params = [
]
class Model1(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        trans = tf.transpose(inp, perm=[1, 0])
        flo = tf.floor(trans)
        rev = tf.reverse(trans, axis=[0])
        add = tf.add(rev, flo)
        return add,

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        concat = tf.concat([inp, inp], axis=0)
        trans = tf.transpose(inp, perm=[1, 0])
        flo = tf.floor(trans)
        rev = tf.reverse(trans, axis=[0])
        add = tf.add(flo, rev)
        return add, concat, #v5_0,

inputs = [
tf.random.uniform(shape=[20, 20], dtype=tf.float64),
]
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
=========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: 398 / 400 (99.5%)
Max absolute difference: 0.95656053
Max relative difference: 297.73353716
 x: array([[0.385214, 0.821024, 0.628158, 0.563975, 0.91364 , 0.01333 ,
        0.314812, 0.788177, 0.219594, 0.532986, 0.839771, 0.163734,
        0.785233, 0.788404, 0.97439 , 0.469803, 0.412193, 0.702105,...
 y: array([[0.850826, 0.587126, 0.269039, 0.623998, 0.800557, 0.788701,
        0.483439, 0.970967, 0.663002, 0.772746, 0.634461, 0.091355,
        0.399323, 0.13452 , 0.229845, 0.657769, 0.668356, 0.510788,...
```
