GPU-Specific Inconsistency in XLA Compiled Model with `tf.nn.atrous_conv2d, tf.cos, and tf.multiply`
Hi **@GwiHwan-Go** ,
I tried to reproduce above in colab, I downloaded the pickle file too. But this file is not opening could you provide proper link to me?

Thank you! Hi @Venkat6871 ,
My apologies for any inconvenience caused. I have prepared a [Colab notebook](https://colab.research.google.com/drive/1JihfqsRpAGTkgV2QGWV4HrSuOzhKuE4r?usp=sharing) to address this issue. However, as I don't have Colab Pro access, I couldn't test it on a V100 GPU. Could you please verify if the issue persists on a V100 GPU? Thank you
 Hi, 

Could you please provide the code in a simplified format without the need of pickle file, it would help us to debug faster. 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 
This issue can only be reproduced with the specific inputs.
### 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?


There are significant discrepancies in the model's output under XLA compilation when running on a GPU device, as indicated by the failed assertions. The differences are not minor, suggesting a deeper issue in TensorFlow's compiled execution path, specifically in the context of GPU-accelerated computation.
Before run below stand-alone code, please download [the pickle file](https://github.com/GwiHwan-Go/repo/raw/main/issues/pickles/ast_cos_mul.pickle) and replace YOUR_PICKLE_FILE_PATH with youre pickle file path.





### Standalone code to reproduce the issue

```python
import tensorflow as tf
import pickle
import os
import numpy as np

class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.p0 = tf.random.uniform(shape=[18, 14, 14, 4], dtype=tf.float32)

    @tf.function(jit_compile=True)
    def __call__(self, inp, inp1):
        astconv = tf.nn.atrous_conv2d(self.p0, inp1, rate=1, padding="VALID")
        _cos = tf.cos(astconv)
        mul = tf.multiply(_cos, astconv)
        return astconv, _cos, mul

model1 = Model1()
device = "gpu"
print(f'=========RUNNING WITH PICKLE FILES===========')
pickle_file_path = 'ast_cos_mul.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 :
        inputs = pickle.load(f)
    inputs = [tf.convert_to_tensor(arr) for arr in inputs]
    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 18:39:00.683604: I external/local_xla/xla/service/service.cc:168] XLA service 0x55a6d3345b50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-11-27 18:39:00.683633: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): Tesla V100S-PCIE-32GB, Compute Capability 7.0
2023-11-27 18:39:00.687057: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1701110341.197440  963817 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 2th # indicates that removing one of operators will not trigger the error
Mismatched elements: 40 / 13104 (0.305%)
Max absolute difference: 0.14808655
Max relative difference: 0.0466534
 x: array([[[[ 4.319121e+02,  5.756997e+01, -1.646362e+02, ...,
           1.929362e+02,  4.481723e+01,  5.383195e+02],
         [ 2.320642e+02, -6.552701e+01,  1.128223e+02, ...,...
 y: array([[[[ 4.319121e+02,  5.756836e+01, -1.646362e+02, ...,
           1.929362e+02,  4.479507e+01,  5.383195e+02],
         [ 2.320521e+02, -6.552701e+01,  1.128223e+02, ...,...
```
