You don’t need to necessarily set the qconfig in this case to qnnpack. Please try using default_dynamic_qconfig instead and see if that helps to solve the issue. Also make sure if your build has qnnpack enabled.
I was able to get this example model to work with qnnpack backend.
class SingleLayerLinearDynamicModel(torch.nn.Module):
    def __init__(self):
        super(SingleLayerLinearDynamicModel, self).__init__()
        self.qconfig = default_qconfig
        self.fc1 = torch.nn.Linear(5, 5).to(dtype=torch.float)

    def forward(self, x):
        x = self.fc1(x)
        return x

torch.backends.quantized.engine = 'qnnpack'
base = SingleLayerLinearDynamicModel()
model = quantize_dynamic(base, dtype=torch.qint8)