Jerry-Ge

> > Request to enable a simple way to support fused quantized operators > > Not sure it this fits with the existing PT2 quant flow. Can you do such...

> @Jerry-Ge are you requesting this for partitioning purposes? You can add quantization_tag during annotation in the quantizer. For example if you add node.meta["quantization_tag"] = "my_q_add" on fp32 add then...

> > The support of running PyTorch models natively in quantized representation can really solve the precision issue between PyTorch and TOSA references > > FOr reference representation there are...

> Can we close this?+ thanks Digant for bringing this up. I haven't touched on this for a while. Let me go back to this and be back to you...

Hi Kris, Thanks for the issue! This is a known issue from Vela (https://pypi.org/project/ethos-u-vela/) The `TOSA->Vela` pass is experimental only currently. We have some fix internally but haven't upstreamed that...

> Hi @Jerry-Ge , > > Thanks for your reply. The executorch flow is better choice for us to deploy the pytorch model to run on our hardware chip with...

> > Hi @robell and @Jerry-Ge, > > I also have another question. > > Because the pytorch model is channel first and the tflite is channel last, and our...

> Hi @Jerry-Ge , > > I saw you add the mobilenet v2 example at the executorch. Would you write the readme about it after you reach some milestone? >...

> Hi @Jerry-Ge , > > I saw you add the mobilenet v2 example at the executorch. Would you write the readme about it after you reach some milestone? >...

The code change from this PR has already been merged in this PR: https://github.com/tensorflow/tensorflow/pull/87273. cc @leandron @mihaimaruseac @tom-arm We can close this PR now.