Enabling XLA in tensorflow 2.16 causes memory leaks
@NBCBM 
Please do not post low-quality LLM-generated text. Hi **@Di-Is** ,
I tried to run your code on Colab using TF v2.16 and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/ecfc2fbc57f632a152665274bab565d9/64170_2-16-1-v.ipynb) here for reference.

Thank you! @Venkat6871
Thank you for your reply!
I am using tensorflow compiled for Nvidia GPU(tensorflow[and-cuda] 2.16.1).
It seems there is also a difference in the Python version you confirmed. I observed memory leaks in [Google Colab with T4 GPU](https://colab.research.google.com/gist/Di-Is/bcc17f37f8c88639317da9b503d25beb/tf216_memory_leak.ipynb). @Venkat6871, do you have any progress on this issue.

I'm also affected because of this issue, I tried will all tf versions from 2.11 up to 2.16 and it seems like this happens since tf 2.12. It seems this was not happening for tf 2.11

thank you I face the exact problem. Using Ubuntu 20.04, NVIDIA RTX3060 & python=3.11

@Venkat6871, do you have any progress on this issue? This document from NVIDIA on tweaking environment variables for XLA memory could help. We're yet to test this out though and will try to keep this thread updated on any findings: 
https://docs.nvidia.com/deeplearning/frameworks/tensorflow-user-guide/index.html#xla-best-practices

Btw, the formation of clusters / Operation fusing / compilation using XLA increases the memory for each different shape of input that the framework comes across. If we keep the shape constant, we've found that after X amount of time, the memory growth stabilizes even with TF 2.15.  I'm also encountering the exact same problem using Ubuntu 20.04 NVIDIA GTX1070 and python 3.10.  Would be great to get this fixed since the memory leakage can get pretty astronomical if left unchecked.  I've had it grow up to 12gB until it crashed my kernel. Hi @Di-Is ,

I have replicated the reported memory leak with XLA. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/a63cfe84f71920b5364878c338f22990/64170_tf216_memory_leak.ipynb) for reference. 
### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tensorflow 2.16.1
(tensorflow[and-cuda] 2.16.1)

### Custom code

Yes

### OS platform and distribution

Linux Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

12.3/8.9.7

### GPU model and memory

RTX3060Ti、RTX3060

### Current behavior?

Executing the tf.keras.Model.fit method with XLA enabled will cause a memory leak.
Note that XLA seems to be enabled by default since Tensorflow 2.16.1.
Setting tf.keras.Model.jit_compile to False disables XLA and eliminates the memory leak.

I think that updating to tensorflow 2.16 will cause memory leaks in almost all existing programs.
I think you need to fix this problem as soon as possible or alert people about it in documents such as Release Note or README.

### Standalone code to reproduce the issue

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


def call(epochs: int):
    x = np.arange(-1, 1, 0.01)
    y = 0.8 * x + 0.2
    model = tf.keras.Sequential([tf.keras.layers.Dense(1, activation=None)])
    model.compile("sgd", "mse")
    # model.jit_compile = False
    model.build(input_shape=(0,1))
    model.fit(x, y, epochs=epochs)
    del model

epoch_num = 10000
for i in range(epoch_num):
    call(1)
    tf.keras.backend.clear_session()
    gc.collect()
```


### Relevant log output

_No response_