Discrepancy in TensorFlow XLA Compiled Models Due to Different Multiplication Orders with `tf.abs` on GPU
Here is another piece of code that triggers the same error. I hope this assists you in identifying the root cause of the bug
```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__()

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

class Model2(tf.keras.Model):
    def __init__(self):
        super().__init__()

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

inputs = [
tf.cast(tf.random.uniform(shape=[11], minval=-128, maxval=128, dtype=tf.int32), tf.int16),
tf.cast(tf.random.uniform(shape=[], minval=-128, maxval=128, dtype=tf.int32), tf.int16),
]
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)
```
and log output : 
```
=========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: 1 / 1 (100%)
Max absolute difference: 1736
Max relative difference: 0.05442006
 x: array(-31900, dtype=int16)
 y: array(31900, dtype=int16)
``` @tilakrayal Hey, have you reproduced this issue?
### 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 an issue where two mathematically equivalent models yield different results when compiled with XLA. The discrepancy appears to be tied to the order of multiplication operations. This issue is critical as it questions the consistency and reliability of TensorFlow's computation, especially in a compiled environment.

The models yield **dramatically different** results under XLA compilation, with discrepancies that are far from trivial. 

### Standalone code to reproduce the issue

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

class Model1(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2, inp3):
        # inp3*(inp2*inp1)*(inp3*(inp2*inp1))
        mul3 = tf.multiply(tf.multiply(inp3, tf.multiply(inp1, inp2)), tf.multiply(inp3, tf.multiply(inp1, inp2)))
        _abs = tf.abs(mul3)
        return mul3, _abs

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2, inp3):
        # inp3 * ((inp1*inp2)*(inp3*(inp1*inp2)))
        mul3 = tf.multiply(inp3, tf.multiply(tf.multiply(inp1, inp2), tf.multiply(inp3, tf.multiply(inp1, inp2))))
        _abs = tf.abs(mul3)
        return mul3, _abs

with tf.device(tf.config.list_logical_devices('GPU')[0].name):
    inputs = [
    tf.random.uniform(shape=[3], minval=-100, maxval=100, dtype=tf.int32),
    tf.random.uniform(shape=[], minval=-100, maxval=100, dtype=tf.int32),
    tf.random.uniform(shape=[3], minval=-100, maxval=100, dtype=tf.int32),
    ]
    model1 = Model1()
    model2 = Model2()
    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
2023-11-27 08:25:14.134072: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-11-27 08:25:14.134128: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-11-27 08:25:14.135901: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-11-27 08:25:14.142938: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-11-27 08:25:14.955654: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-11-27 08:25:25.751708: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1123 MB memory:  -> device: 0, name: Tesla V100S-PCIE-32GB, pci bus id: 0000:01:00.0, compute capability: 7.0
2023-11-27 08:25:25.754126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 15774 MB memory:  -> device: 1, name: Tesla V100S-PCIE-32GB, pci bus id: 0000:24:00.0, compute capability: 7.0
2023-11-27 08:25:25.755764: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 16176 MB memory:  -> device: 2, name: Tesla V100S-PCIE-32GB, pci bus id: 0000:41:00.0, compute capability: 7.0
2023-11-27 08:25:25.757607: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 17872 MB memory:  -> device: 3, name: Tesla V100S-PCIE-32GB, pci bus id: 0000:61:00.0, compute capability: 7.0
2023-11-27 08:25:25.759556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:4 with 18494 MB memory:  -> device: 4, name: Tesla V100S-PCIE-32GB, pci bus id: 0000:81:00.0, compute capability: 7.0
2023-11-27 08:25:25.761281: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:5 with 19166 MB memory:  -> device: 5, name: Tesla V100S-PCIE-32GB, pci bus id: 0000:a1:00.0, compute capability: 7.0
2023-11-27 08:25:25.763252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:6 with 20272 MB memory:  -> device: 6, name: Tesla V100S-PCIE-32GB, pci bus id: 0000:c1:00.0, compute capability: 7.0
2023-11-27 08:25:25.765351: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:7 with 20720 MB memory:  -> device: 7, name: Tesla V100S-PCIE-32GB, pci bus id: 0000:e1:00.0, compute capability: 7.0
=========eager_output(version:2.15.0)================
XLA_eager does not trigger assertion
2023-11-27 08:25:26.220882: I external/local_xla/xla/service/service.cc:168] XLA service 0x555a544232d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-11-27 08:25:26.220929: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): Tesla V100S-PCIE-32GB, Compute Capability 7.0
rvice.cc:176]   
2023-11-27 08:25:26.226472: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2023-11-27 08:25:26.908888: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1701073527.016479  336904 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 error happens after tf.abs
Mismatched elements: 1 / 3 (33.3%)
Max absolute difference: 1964494976
Max relative difference: 1.68592003
 x: array([-1165236160,   395214400,     1192464], dtype=int32)
 y: array([1165236160,  395214400,    1192464], dtype=int32)
```
