Network-Level Control of Precision (fp32 in training, fp16 in inference)
Hi @shshao-iceice ,

Could you please elaborate more on this Feature request. Right now we can use mixed_precision(to enable FP16) along with XLA.  > Hi @shshao-iceice ,
> 
> Could you please elaborate more on this Feature request. Right now we can use mixed_precision(to enable FP16) along with XLA.

I think `tf.keras.backend.set_floatx` and `mixed_precision` both do this to some extent, but I guess they only take effect on Keras layers? I hope there is a better option that can choose precision mode for the whole model. Hi @shshao-iceice ,

Both the APIs `tf.keras.backend.set_floatx`, `mixed_precision` can be used to enable FP16 for Model training.For numeric stability issues mixed precision, which is using a mix of float16 and float32 is preferred. Mixed precision policy once initiated applicable to all layers of model by default unless we set dtype explicitly at layer level. For Model training Tf recommends use of `mixed_precision` for better accuracy. AFAIK TensorRT also works for inference only but no role in model training.

For Model inference with lower precision TFlite is one way that can also be leveraged. Model built with XLA support can also be used for Model Inference with TensorRT right ? Correct me if i am wrong here. Thanks! > Hi @shshao-iceice ,
> 
> Both the APIs `tf.keras.backend.set_floatx`, `mixed_precision` can be used to enable FP16 for Model training.For numeric stability issues mixed precision, which is using a mix of float16 and float32 is preferred. Mixed precision policy once initiated applicable to all layers of model by default unless we set dtype explicitly at layer level. For Model training Tf recommends use of `mixed_precision` for better accuracy. AFAIK TensorRT also works for inference only but no role in model training.
> 
> For Model inference with lower precision TFlite is one way that can also be leveraged. Model built with XLA support can also be used for Model Inference with TensorRT right ? Correct me if i am wrong here. Thanks!

Yes, XLA is perfect for training, but we are exploring using it in inference and replacing TensorRT. From my understanding, TFlite is mainly built for edge devices so it probably doesn't fit here? Hi @shshao-iceice ,

Yes. TFlite is used for edge devices. So you requirement is that XLA should be usable in inference just like as TensorRT. AFAIK and based on the resource [here](https://developer.nvidia.com/tensorrt#:~:text=TensorRT%2C%20built%20on%20the%20NVIDIA,and%20others%20on%20NVIDIA%20GPUs.) , TensorRT also works on same concept of Fusion(Tensor/Layers) which also XLA's single most important optimization.

I am just curious to know whether XLA may give better performance than TensorRT. This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.
<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Feature Request

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

Yes

### Source

binary

### Tensorflow Version

tf 2.12

### Custom Code

No

### 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 Behaviour?

A very common use case is that we train and export the model in fp32, then use fp16 mode in inference. But it seems XLA doesn't support this?

It would be good if XLA can support [Network-Level Control of Precision](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#network-level-control) like TensorRT

### Standalone code to reproduce the issue

```shell
N/A
```


### Relevant log output

_No response_</details>