Looking for selective post training quantization for 8 bit weights and 16 bit activations
Hi @Hrayo712 

Mixed precision selective post training quantization is currently not supported in TFLite. However, the selective quantization is currently supported using [Quantization Debugger](https://www.tensorflow.org/lite/performance/quantization_debugger#selective_quantization), which accepts `denylisted_nodes` and `denylisted_op` options for skipping quantization for specific layers, or all instances of specific ops.

Thanks. Hi @pjpratik , thanks for the quick response!

I understand. However, when using selective quantization via the Quantization Debugger, denylisted_nodes are set to FP32 right ? or is there a way to set the denylisted nodes/ops to INT16 ? Hi @Hrayo712 

>when using selective quantization via the Quantization Debugger, denylisted_nodes are set to FP32 right ?

Yes, the quantization is skipped for those nodes keeping them in their default FP32.

>is there a way to set the denylisted nodes/ops to INT16 ?

Those are just denied ops for quantization. As per the documentation, we don't have an option to set it to INT16.

Can we consider this as feature request?

Thanks. Hi @pjpratik,

Yes. I think it would be a super valuable feature, to have mixed-precision TFLite models. Perhaps the easy way would be indeed to allow for denylisted ops to be able to be quantized to INT16.

Thanks team!
 Hi @abattery, can you please take a look? Thanks.
**System information**

TensorFlow version (you are using): TF 2.13.0
Are you willing to contribute it (Yes/No): No
Describe the feature and the current behavior/state.

Dear TF developers, I'm currently experimenting with PTQ using 8 bit weights and 16 bit activations (W8A16), and I've gotten great results. However, after some experimentation I have identified that only a certain part of my network requires the 16 bit activations. In other word, using 16 bit activations for the entire model is sub-optimal for my use-case.

Hence, I'm looking for a way to selectively quantize a part of my model to 8 bit weights and activations (W8A8), and the other part to W8A16.

In the current state, would this be possible somehow ?

**Who will benefit with this feature?**
Platforms that support mixed-precision execution of activations.

**Any Other info.**