Repository: tensorflow
Issue: Large inconsistencies in tf.signal.stft's and tf.signal.inverse_stft's results with @tf.function decorator for certain inputs
Labels: stat:awaiting tensorflower,type:bug,comp:ops,comp:autograph,TF 2.12

<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

Yes

### Source

source

### Tensorflow Version

2.14.0-dev20230509

### 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

N/A

### GPU model and memory

_No response_

### Current Behaviour?

`tf.signal.stft` and `tf.signal.inverse_stft` has large inconsistencies in their results with or without @tf.function for some inputs. This issue seems to be unrelated to precision errors, as previously discussed under issues (#57960 and #57961), given that the inconsistencies can reach very high values, such as 7.530909102308483e+252+6.2143661415679e-310j. I open this issue because the behavior still exists in the latest nightly version of tensorfow.

Further investigation finds that it is because the results are different during each run and thus the inconsistencies are different, where sometimes the discrepancies are extremely large, while at other times they are relatively small. It appears that the inconsistencies are non-deterministic, which indicates a potential issue with the underlying implementation.

I rerun the reproduction code several times and record the large inconsistencies below in the log file.

The reproduction colab links are here:
For tf.signal.stft, https://colab.research.google.com/drive/1WleKXby71iZXOL12r8nIN8B_jd2wJQks?usp=sharing.
For tf.signal.inverse_stft, https://colab.research.google.com/drive/1MhNfkZgltqQqHw8kKG2zQi8ivwvKfRoj?usp=sharing.


### Standalone code to reproduce the issue

```shell
# for tf.signal.stft
import tensorflow as tf
import numpy as np

print(tf.__version__)

input = {'fft_length': 46, 'frame_step': 19, 'frame_length': 0, 'signals': np.array([[[[-8.75314539e+307, -4.03838038e+307,  8.23775798e+307, -1.32627219e+307,  1.19815521e+307,  4.57117750e+307],
                                                                              [-4.74761327e+307, -4.71580522e+307, -5.88832102e+307, -6.48759076e+307, -4.36028464e+307, -4.77775171e+307],
                                                                              [ 1.20113701e+307, -7.60106094e+307,  7.22716917e+307, 2.17687950e+307, -5.25271143e+306,  5.41182394e+307]]]])}

output1 = tf.signal.stft(**input)

@tf.function
def fun_wrapper(x):
    return tf.signal.stft(**x)

output2 = fun_wrapper(input)

print(np.allclose(output1, output2))
print(np.max(np.subtract(output1, output2)))

# for tf.signal.inverse_stft
import tensorflow as tf
import numpy as np

print(tf.__version__)

input = {'frame_step': 29343, 'frame_length': 61, 'stfts': np.array([[]], dtype=np.complex64)}

output1 = tf.signal.inverse_stft(**input)

@tf.function
def fun_wrapper(x):
    return tf.signal.inverse_stft(**x)

output2 = fun_wrapper(input)

print(np.allclose(output1, output2))
print(np.max(np.subtract(output1, output2)))
```


### Relevant log output

```shell
### for tf.signal.stft
False
(1.2623837153272947e+180+2.19373012209e-312j)

False
(6.443468248812391e+278-3.2e-322j)

False
(2.347922071768121e+228+1.74e-321j)

### for tf.signal.inverse_stft
False
1.4412957e+32

False
7.529253e+23

False
7800730000.0
```
</details>