Inconsistency in XLA compiled Models with Sliced Matrix Multiplication and Additional Transpose Output on GPU
@GwiHwan-Go I didn't observe the error reported in both [gpu](https://colab.research.google.com/gist/sushreebarsa/d91675515a38b3c2ddd7b333b6549812/62527-cpu.ipynb#scrollTo=YlqadxaeNgnH) and [cpu](https://colab.research.google.com/gist/sushreebarsa/c8d3a720a012fd4c2091c9c195d97a54/62527-cpu.ipynb). Please find the attached gists and confirm the same.
Thank you! Hi, @sushreebarsa have you tried this 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?
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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

### 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?

In TensorFlow 2.15.0, I've encountered a inconsistency under XLA compilation involving sliced matrix multiplication combined with an extra transposed output. This discrepancy is not observed **on CPU** or when the additional **transpose output is removed.**

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import numpy as np

class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()
        # Tensor objects (with comments for shapes)
    @tf.function(jit_compile=True)
    def __call__(self, inp, inp2):
        concat = tf.concat([inp, inp2], axis=1)
        sliced = concat[:, -17:17:4]
        matmul = tf.matmul(sliced, sliced)
        return matmul,

class Model2(tf.keras.Model):
    def __init__(self):
        super().__init__()
    @tf.function(jit_compile=True)
    def __call__(self, inp, inp2):
        concat = tf.concat([inp, inp2], axis=1)
        transposed = tf.transpose(concat, perm=[1, 0])
        sliced = concat[:, -17:17:4]
        matmul = tf.matmul(sliced, sliced)
        return matmul, transposed,

inputs = [
tf.random.uniform(shape=[5, 1], dtype=tf.float16),
tf.random.uniform(shape=[5, 16], dtype=tf.float16),

]
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 15:39:59.507327: I external/local_xla/xla/service/service.cc:168] XLA service 0x5629c95d1e50 initialized for platform CUDA (this does not guarantee that XLA w
=========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: 25 / 25 (100%)
Max absolute difference: 1.549
Max relative difference: 0.904
 x: array([[2.295 , 2.365 , 1.842 , 2.246 , 2.33  ],
       [1.873 , 1.657 , 1.174 , 1.865 , 1.745 ],
       [1.298 , 1.384 , 1.203 , 1.486 , 1.062 ],...
 y: array([[2.408 , 2.37  , 2.574 , 2.504 , 2.104 ],
       [0.984 , 1.614 , 1.528 , 1.3955, 1.48  ],
       [1.441 , 1.555 , 1.679 , 1.263 , 0.9844],...
```
