XLA autocluster will treat `-0.0` as `0.0`
Any progress on this issue? I believe the autocluster should process `-0.0` as negative, otherwise, the sign of the whole computation is wrong Hi @YangChenyuan ,

I have replicated the reported behaviour and its observed with `multiplication` operation for value 0.0 (i.e float) only and for 0( i.e int) also there is no difference . For any other values the results are same and as intended.

I am bit curious personally as this is observed in only multiplication operation with `0.0` and the result here will be zero anyways and how it may affect the computation. This might be fixable but to prioritize it we may need a strong demo that demonstrate how this behaviour will affect the computations.

Thank you! Hi @SuryanarayanaY, thanks for your further exploration!

Here is one possible case that would be affected:
```py
class Model(tf.keras.Model):
    def __init__(self):
        super(Model, self).__init__()
        self.v3_weight = tf.Variable(123.45)

    def call(self, x1):
        x4 = (self.v3_weight * (- 0.0))
        x4 = 1 / x4
        return x4
m = Model()
x = tf.constant(702.89)
print(m(x)) # tf.Tensor(-inf, shape=(), dtype=float32)
```

```py
import os
os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=2 --tf_xla_cpu_global_jit'
class Model(tf.keras.Model):
    def __init__(self):
        super(Model, self).__init__()
        self.v3_weight = tf.Variable(123.45)

    @tf.function
    def call(self, x1):
        x4 = (self.v3_weight * (- 0.0))
        x4 = 1 / x4
        return x4
m = Model()
x = tf.constant(702.89)
print(m(x)) # tf.Tensor(inf, shape=(), dtype=float32)
```
If we divide the results 0, one of them will return `inf` and the other is `-inf`. Then if we do some check for their signs, the results will be much different.

Besides, if we use `@tf.function(jit_compile=True)`, the result is consistent with naive execution Hi @YangChenyuan ,

The above case can demonstrate the severity. Thanks for it. We will dig more to fix this. Thanks for your exploration and confirmation! @YangChenyuan ,

This also seems issue with tf.function only. I have replicated the reported behaviour with `jit_compile=True`  and `jit_compile=False` both. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/9849f49df56c21960083dad41b5db025/61939_r1.ipynb) for reference . Seems its a bug in tf.function. We will work on it and update you. @YangChenyuan  This is happening because we have a graph optimization that replaces `x * 0` with zero, regardless of what `x` is [here](https://github.com/tensorflow/tensorflow/blob/eb44a46d62a5312af79d69eaf56e26b1d4439236/tensorflow/core/grappler/optimizers/constant_folding.cc#L3073).

I'm not sure I'm convinced it's important to keep the -0.  What is your use-case? 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/61939">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/61939">No</a>

### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.15.0-dev20230921

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

XLA autocluster will treat `-0.0` as `0.0`. That said, it will automatically transfer `-0.0` to `0.0`

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

"""
Without Autocluster
"""
class Model(tf.keras.Model):
    def __init__(self):
        super(Model, self).__init__()
        self.v3_weight = tf.Variable(123.45)

    def call(self, x1):
        x4 = (self.v3_weight * (- 0.0))
        return x4
m = Model()
x = tf.constant(702.89)
print(m(x)) # tf.Tensor(-0.0, shape=(), dtype=float32)

"""
With Autocluster
"""
import os
os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=2 --tf_xla_cpu_global_jit'
class Model(tf.keras.Model):
    def __init__(self):
        super(Model, self).__init__()
        self.v3_weight = tf.Variable(123.45)
    
    @tf.function
    def call(self, x1):
        x4 = (self.v3_weight * (- 0.0))
        return x4
m = Model()
x = tf.constant(702.89)
print(m(x)) # tf.Tensor(0.0, shape=(), dtype=float32)
```


### Relevant log output

_No response_