The tutorial here 4 provides an example of per-channel quantization training.
In my case I need to perform per-tensor quantization since the downstream mobile-device inference library (e.g. TNN 1) does not support per-channel quantized models.
I think the problem here is how to setup a per-tensor quantization around:
model.qconfig = torch.quantization.get_default_qat_qconfig("fbgemm")

Currently this part is not extensively documented and I cannot find many resources.
So could someone give an example configuration for per-tensor quantization?