I couldn’t solve  this problem 3,  so I moved it here. Sorry about that.
I followed tutorials/quantization  4 and tried to PTQ MobileNetV2 from torchvision.
However, when I tried to predict with the quantized model, I got the following error and could not run it.
How can I solve this problem?
Error
Traceback (most recent call last):
  File "ptq_imagenet_pth.py", line 184, in <module>
    res = model_static_quantized(x.clone().detach().to(device, dtype=torch.float))

....

RuntimeError: Could not run 'aten::add.Tensor' with arguments from the 'QuantizedCPU' backend. 'aten::add.Tensor' is only available for these backends: [CPU, CUDA, MkldnnCPU, SparseCPU, SparseCUDA, Meta, BackendSelect, Named, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, Autocast, Batched, VmapMode].

CPU: registered at /pytorch/build/aten/src/ATen/CPUType.cpp:2127 [kernel]
CUDA: registered at /pytorch/build/aten/src/ATen/CUDAType.cpp:2983 [kernel]
MkldnnCPU: registered at /pytorch/build/aten/src/ATen/MkldnnCPUType.cpp:144 [kernel]
SparseCPU: registered at /pytorch/build/aten/src/ATen/SparseCPUType.cpp:239 [kernel]
SparseCUDA: registered at /pytorch/build/aten/src/ATen/SparseCUDAType.cpp:320 [kernel]
Meta: registered at /pytorch/aten/src/ATen/native/BinaryOps.cpp:1049 [kernel]
BackendSelect: fallthrough registered at /pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Named: fallthrough registered at /pytorch/aten/src/ATen/core/NamedRegistrations.cpp:11 [kernel]
AutogradOther: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel]
AutogradCPU: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel]
AutogradCUDA: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel]
AutogradXLA: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel]
AutogradPrivateUse1: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel]
AutogradPrivateUse2: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel]
AutogradPrivateUse3: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:8078 [autograd kernel]
Tracer: registered at /pytorch/torch/csrc/autograd/generated/TraceType_2.cpp:9654 [kernel]
Autocast: fallthrough registered at /pytorch/aten/src/ATen/autocast_mode.cpp:254 [backend fallback]
Batched: registered at /pytorch/aten/src/ATen/BatchingRegistrations.cpp:515 [kernel]
VmapMode: fallthrough registered at /pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]

Script
class QuantizedMobileNetV2(nn.Module):
    def __init__(self, model_fp32):
        super(QuantizedMobileNetV2, self).__init__()
        self.quant = torch.quantization.QuantStub()
        self.dequant = torch.quantization.DeQuantStub()
        self.model_fp32 = model_fp32

    def forward(self, x):
        x = self.quant(x)
        x = self.model_fp32(x)
        x = self.dequant(x)
        return x


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = torchvision.models.mobilenet_v2(pretrained=True)

model.eval()
model = QuantizedMobileNetV2(model_fp32=model)
backend = "fbgemm"
model.qconfig = torch.quantization.get_default_qconfig(backend)
torch.backends.quantized.engine = backend
model_static_quantized = torch.quantization.prepare(model, inplace=False)
model_static_quantized = torch.quantization.convert(model_static_quantized, inplace=False)
model_static_quantized = model_static_quantized.to(device)
#x is input tensor whose shape is (100, 3, 224, 224) 
res = model_static_quantized(x.clone().detach().to(device, dtype=torch.float))

I got same error when I used QuantWrapper().
Environment

Ubuntu: 18.0
CUDA: 11.0
Python: 3.6.10
PyTorch: 1.7.1
torchvision: 0.8.2