need help about both model weight and activation quantization with only a float32 mlmodel

[AndreaChiChengdu]

from the issue "https://developer.apple.com/forums/thread/740518 how do we use the computational power of A17 Pro Neural Engine?"

I learn that if i want to inference my mlmodel on my ipad pro with m4 soc int8 38T ane high performance, i have to use the coreml torch api to quantize both weight and activation during training time quantization with int8 datatype.

my question is:
I only have a fp32 mlmodel without torch code or model, what can i do?
by the way, if just only weight int8 quantization, M4 ane will use fp16 to compute or int8?
thanks for your help～

[jakesabathia2]

• If you don't have the torch model, you will not be able to do training-aware quantization, instead, you will be only able to run the post-training-quantization through the ct.optimize.coreml API.
• The weight only quantization will result in a model in size of int8,
  but at the runtime, the compute precision is still fp16.