Output Discrepancy in Mathematically Equivalent Models with`tf.abs` Under XLA Compilation
Hi @GwiHwan-Go ,

I have replicated the reported behaviour with jit_compile=True. With jit_compile=False the results are same. Attaching [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/a48c48f1fffa79b939676d94ff2e5a0a/62510.ipynb) for reference. Needs to check for this behaviour. It seems the problem is with tf.abs Op with XLA which is not converting -ve values to positive for Model1.

If we remove this operation then it works as intended and results are same with `jit` also.Refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/d51757db95de060f6fa8439fef61d0be/62510_r1.ipynb).
### 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

_No response_

### GPU model and memory

_No response_

### Current behavior?

In TensorFlow 2.15.0, two models involving `tf.abs` and presenting mathematically equivalent operations are producing different results when compiled with XLA. This inconsistency is not observed in eager execution mode.

Both models essentially compute the square of the sum of inp1 and inp2. In Model1, this is done directly by squaring the sum (inp1 + inp2)^2. In Model2, it is achieved by distributing the multiplication across the sum, inp1*(inp1 + inp2) + inp2*(inp1 + inp2), which simplifies to the same result. The inclusion of tf.abs should not affect the mathematical equivalence of these operations.


### Standalone code to reproduce the issue

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

class Model1(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        # (inp1 + inp2)^2
        added = tf.add(inp1, inp2)
        fin_out = tf.multiply(added, added)
        absed = tf.abs(fin_out)
        return fin_out, absed

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        # inp1*(inp1 + inp2) + inp2*(inp1 + inp2)
        added = tf.add(inp1, inp2)
        v5_0 = tf.multiply(inp2, added)
        v6_0 = tf.multiply(inp1, added)
        fin_out = tf.add(v6_0, v5_0)
        absed = tf.abs(fin_out)
        return fin_out, absed

inputs = [
    tf.cast(tf.random.uniform(shape=[], minval=-128, maxval=128, dtype=tf.int32), tf.int8),
    tf.cast(tf.random.uniform(shape=[10, 10, 10], minval=-128, maxval=128, dtype=tf.int32), tf.int8),
]
model1 = Model1()
model2 = Model2()
device = "cpu"
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
I0000 00:00:1701344148.577671 2752804 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
2023-11-30 11:35:48.578399: E external/local_xla/xla/stream_executor/stream_executor_internal.h:177] SetPriority unimplemented for this stream.
=========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: 445 / 1000 (44.5%)
Max absolute difference: 126
Max relative difference: 2.
 x: array([[[  36,   81,  -92,  -31,   64, -111,   41,  -23,   36,   64],
        [   9,   25,  113, -103,   49,   17,   64,  -71, -112,  -71],
        [-124,  -28, -119,    0, -112,    1,    9,  -60,  -63,   65],...
 y: array([[[ 36,  81,  92,  31,  64, 111,  41,  23,  36,  64],
        [  9,  25, 113, 103,  49,  17,  64,  71, 112,  71],
        [124,  28, 119,   0, 112,   1,   9,  60,  63,  65],...
```
