Inconsistency in TensorFlow's Handling of Associative Operations: `(a+b)*c - (a+b)*c vs. - (a*c + b*c) + a*c + b*c`
@tilakrayal Hey, I was wondering, have you replicated this issue? @sachinprasadhs,
I was able to reproduce the issue on tensorflow v2.14, v2.15 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/4bf126a7a0bec88ef2dbc07deeb60828/untitled.ipynb).
### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.16.0-dev20231122

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04.3

### Mobile device

_No response_

### Python version

3.10.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

This bug is also seen on nightly version of tf(`2.16.0-dev20231122`).
I have two TensorFlow models (`Model1` and `Model2`) that are semantically the same but produce different outputs when executed. This discrepancy might indicate an underlying issue in TensorFlow's execution or optimization paths, particularly with XLA compilation.

```python
class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()
        # Tensor objects (with comments for shapes)
        self.p0 = tf.Variable(params[0]) # [1] float32
        self.p1 = tf.Variable(params[1]) # [] float32

        # Layers or other Keras model objects

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [52, 33, 4] : float32
        v9_0 = tf.add(self.p1, self.p0)
        v5_0 = tf.multiply(v9_0, inp)
        v0_0 = tf.negative(v5_0)
        v11_0 = tf.add(v5_0, v0_0)
        return v11_0
```

```python
class Model2(tf.keras.Model):
    def __init__(self):
        super().__init__()
        # Tensor objects (with comments for shapes)
        self.p0 = tf.Variable(params[0]) # [1] float32
        self.p1 = tf.Variable(params[1]) # [] float32

        # Layers or other Keras model objects

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [52, 33, 4] : float32
        v10_0 = tf.multiply(self.p0, inp)
        v12_0 = tf.multiply(self.p1, inp)
        v13_0 = tf.add(v12_0, v10_0)
        v17_0 = tf.negative(v13_0)
        v38_0 = tf.add(v17_0, v10_0)
        v39_0 = tf.add(v38_0, v12_0)
        return v39_0
```

Given that Model1 and Model2 are semantically the same (i.e.,` model1 = (p0+p1)*inp - (p0+p1)*inp` and `model2 = - (p0*inp + p1*inp) + p0*inp + p1*inp`), I would expect them to produce identical outputs for the same input.

However, the models are yielding different results, which is unexpected and suggests a potential issue in TensorFlow's computation or optimization mechanisms.

### Standalone code to reproduce the issue

```shell
from typing import Dict
import tensorflow as tf
import os
import numpy as np
params = [
tf.random.uniform(shape=[1], dtype=tf.float32),
tf.random.uniform(shape=[], dtype=tf.float32),
]
class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()
        # Tensor objects (with comments for shapes)
        self.p0 = tf.Variable(params[0]) # [1] float32
        self.p1 = tf.Variable(params[1]) # [] float32

        # Layers or other Keras model objects

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [52, 33, 4] : float32
        v9_0 = tf.add(self.p1, self.p0)
        v5_0 = tf.multiply(v9_0, inp)
        v0_0 = tf.negative(v5_0)
        v11_0 = tf.add(v5_0, v0_0)
        return v11_0

class Model2(tf.keras.Model):
    def __init__(self):
        super().__init__()
        # Tensor objects (with comments for shapes)
        self.p0 = tf.Variable(params[0]) # [1] float32
        self.p1 = tf.Variable(params[1]) # [] float32

        # Layers or other Keras model objects

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [52, 33, 4] : float32
        v10_0 = tf.multiply(self.p0, inp)
        v12_0 = tf.multiply(self.p1, inp)
        v13_0 = tf.add(v12_0, v10_0)
        v17_0 = tf.negative(v13_0)
        v38_0 = tf.add(v17_0, v10_0)
        v39_0 = tf.add(v38_0, v12_0)
        return v39_0

inputs = [
tf.random.uniform(shape=[10,10,10], dtype=tf.float32),
]

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=0.01, 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=0.01, 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
2023-11-23 17:14:24.344078: 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.
2023-11-23 17:14:25.779158: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-11-23 17:14:28.121908: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5558 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:02:00.0, compute capability: 7.5
2023-11-23 17:14:28.122693: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 4932 MB memory:  -> device: 1, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:04:00.0, compute capability: 7.5
2023-11-23 17:14:28.123338: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 4080 MB memory:  -> device: 2, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:83:00.0, compute capability: 7.5
2023-11-23 17:14:28.123938: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 6062 MB memory:  -> device: 3, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:84:00.0, compute capability: 7.5
=========eager_output(version:2.16.0-dev20231122)================
XLA_eager triggers assertion

Not equal to tolerance rtol=0.01, atol=0
at checking 0th
Mismatched elements: 66 / 100 (66%)
Max absolute difference: 5.9604645e-08
Max relative difference: 1.
 x: array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],...
 y: array([[ 5.960464e-08, -2.980232e-08,  0.000000e+00, -1.490116e-08,
        -2.980232e-08,  5.960464e-08,  0.000000e+00,  5.960464e-08,
         7.450581e-09, -2.980232e-08],...
2023-11-23 17:14:28.641380: I external/local_xla/xla/service/service.cc:144] XLA service 0x950adf0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-11-23 17:14:28.641420: I external/local_xla/xla/service/service.cc:152]   StreamExecutor device (0): NVIDIA GeForce RTX 2070, Compute Capability 7.5
2023-11-23 17:14:28.641432: I external/local_xla/xla/service/service.cc:152]   StreamExecutor device (1): NVIDIA GeForce RTX 2070, Compute Capability 7.5
2023-11-23 17:14:28.641444: I external/local_xla/xla/service/service.cc:152]   StreamExecutor device (2): NVIDIA GeForce RTX 2070, Compute Capability 7.5
2023-11-23 17:14:28.641455: I external/local_xla/xla/service/service.cc:152]   StreamExecutor device (3): NVIDIA GeForce RTX 2070, Compute Capability 7.5
2023-11-23 17:14:28.679907: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:456] Loaded cuDNN version 8904
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1700730868.755509 2277729 device_compiler.h:187] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
=========compiled_output(version:2.16.0-dev20231122)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.01, atol=0
at checking 0th
Mismatched elements: 66 / 100 (66%)
Max absolute difference: 5.9604645e-08
Max relative difference: 1.
 x: array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],...
 y: array([[ 5.960464e-08, -2.980232e-08,  0.000000e+00, -1.490116e-08,
        -2.980232e-08,  5.960464e-08,  0.000000e+00,  5.960464e-08,
         7.450581e-09, -2.980232e-08],...
```
