Inconsistent Outputs from `tf.nn.astrous_conv2d + tf.round` with XLA compiled model on GPU
@sachinprasadhs Hey! Have you reproduced this issue? Hi, Could you please simplify the code without pickle file to debug in detail about the issue. 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, @sachinprasadhs 
Here is the colab notebook [link](https://colab.research.google.com/drive/15fYjGKA8XmBns1GgzR7MXglAJci6PQ_1?usp=sharing), Please note that the bug can only be reproduced when using a Tesla V100 GPU.
### 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?

I've identified a issue in TensorFlow 2.15.0 where the combination of `tf.nn.atrous_conv2d` and `tf.round` in a XLA compiled model produces different outputs compared to the eager execution mode.  This error is only seen on **gpu**

To reproduce this, please download first [pickle file](https://github.com/GwiHwan-Go/repo/raw/main/issues/pickles/ast_round.pickle) and replace `YOUR_PICKLE_FILE_PATH` with your pickle file path.

### Standalone code to reproduce the issue

```python
from typing import Dict
import tensorflow as tf
import pickle
import os
import numpy as np

class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()
        # Tensor objects (with comments for shapes)
        self.p1 = tf.Variable(tf.random.uniform(shape=[2, 34, 35, 25], dtype=tf.float32)) # [2, 34, 35, 25] float32

        # Layers or other Keras model objects

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [14, 2, 25, 53] : float32
        astconv = tf.nn.atrous_conv2d(self.p1, inp, rate=2, padding="VALID")
        round_ast = tf.round(astconv)
        return astconv, round_ast 

inputs = [
tf.random.uniform(shape=[14, 2, 25, 53], dtype=tf.float32),
]
model1 = Model1()
device = "gpu"
pickle_file_path = "ast_round.pickle" #YOUR_PICKLE_FILE_PATH
if not os.path.exists(pickle_file_path) :
    print(f'Pickle file not exist')
else :
    with open(pickle_file_path, 'rb') as f :
        oracle = pickle.load(f)
    inputs = [tf.convert_to_tensor(arr) for arr in oracle.values()]
    with tf.device(device):
        tf.config.run_functions_eagerly(True)
        out1 = model1(*inputs)
        out2 = model1(*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 = model1(*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
2023-11-27 16:28:59.925669: I external/local_xla/xla/service/service.cc:168] XLA service 0x55968b3b66f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-11-27 16:28:59.925702: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): Tesla V100S-PCIE-32GB, Compute Capability 7.0
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1701102540.412886  749930 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
=========compiled_output(version:2.15.0)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 1th # indicates that astrous_conv2d alone will not result in different behavior.
Mismatched elements: 2 / 27984 (0.00715%)
Max absolute difference: 1.
Max relative difference: 0.00327869
 x: array([[[[ 1120.,  -118.,   834., ...,  -443.,  -693.,  -699.],
         [ 1132.,   717.,  1710., ..., -1091., -1453.,  -906.],
         [ 1380.,   757.,  1576., ...,  -918.,  -495.,  -634.],...
 y: array([[[[ 1120.,  -118.,   834., ...,  -443.,  -693.,  -699.],
         [ 1132.,   717.,  1710., ..., -1091., -1453.,  -906.],
         [ 1380.,   757.,  1576., ...,  -918.,  -495.,  -634.],...
```
