qconfig = torch.ao.quantization.get_default_qat_qconfig("qnnpack")
>>> print(qconfig)
QConfig(activation=functools.partial(<class 'torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize'>, observer=<class 'torch.ao.quantization.observer.MovingAverageMinMaxObserver'>, quant_min=0, quant_max=255, reduce_range=False){}, weight=functools.partial(<class 'torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize'>, observer=<class 'torch.ao.quantization.observer.MovingAverageMinMaxObserver'>, quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){})
>>> qconfig = torch.ao.quantization.get_default_qconfig("qnnpack")
>>> print(qconfig)
QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=False){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){})
Can torch.ao.quantization.get_default_qconfig be used for QAT training,what is the difference between them?