Inconsistency in Output of `tf.nn.conv2d + tf.cos` Combination in XLA Compiled Models on GPU
We found another code that triggers same bug, which **does not require** specific input conditions. Below is the standalone code responsible for this 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)

    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        # Forward pass logic using TensorFlow operations

        nnconv = tf.nn.conv2d(inp2, inp1, strides=1, padding="VALID", dilations=(2, 1))
        _convcos = tf.cos(nnconv)
        return nnconv, _convcos

inputs = [
tf.random.uniform(shape=[2, 120, 120, 2], dtype=tf.float32),
tf.random.uniform(shape=[1, 3, 140, 120], dtype=tf.float32),
]
model1 = Model1()
device = "gpu"
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)
``` @SuryanarayanaY Hello, Have you reproduced this issue? Hi @GwiHwan-Go ,

I have tested with Tf2.14v and GPU runtime and the difference is related to precision only which happens due to XLA internal fusions and conversions. If I change atol to 0.01 from 0.001 no assertion error will be raises.

Also I have printed `tf.reduce_sum(out1).numpy()` and `tf.reduce_sum(out2).numpy()` which is printing `303656.34` and `303656.3` respectively. This indicates the results are same but only precision differences with XLA which is expected.

Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b4854039d081d04d1c63ae112ff6de25/62478_r1-2-14.ipynb).

Could you please check the same with TF2.15v also and confirm the results are only minor precision differences or not.

Thanks! Thank you for your response! As it is not an bug, I'm closing the issue. 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/62478">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62478">No</a>

### 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 critical issue in TensorFlow 2.15.0 where the combination of `tf.nn.conv2d` and `tf.cos` in an XLA compiled model produces **significantly different outputs** compared to the eager execution mode. This inconsistency is particularly concerning given that the data type used is `float32`, which is a standard in many applications. This issue only occurs with **certain input data with gpu device**

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


### Standalone code to reproduce the issue

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

class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()

    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        conv2 = tf.nn.conv2d(inp1, inp2, strides=1, padding="VALID", dilations=(1, 3))
        _cos = tf.cos(conv2)
        return conv2, _cos

model1 = Model1()
device = "gpu"
# Path to the pickle file relative to the script directory
pickle_file_path = 'conv_cos_bug_inputs.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]
    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
2023-11-26 16:40:44.610101: I external/local_xla/xla/service/service.cc:168] XLA service 0x559e2c9bb480 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-11-26 16:40:44.610131: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): Tesla V100S-PCIE-32GB, Compute Capability 7.0
=========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: 4113 / 10000 (41.1%)
Max absolute difference: 0.03115243
Max relative difference: 129.51477
 x: array([[[[ 0.87046 , -0.997616,  0.527893, ..., -0.624568, -0.543016,
          -0.689372],
         [-0.174798, -0.671232,  0.72486 , ...,  0.273734,  0.056437,...
 y: array([[[[ 0.87142 , -0.997339,  0.534512, ..., -0.624568, -0.536439,
          -0.686538],
         [-0.186324, -0.671232,  0.727545, ...,  0.273734,  0.056437,...
```
