should use .data in quantization wrapper (#4113)

nni/compression/pytorch/compressor.py

@@ -529,13 +529,13 @@ class QuantizerModuleWrapper(torch.nn.Module):
             else:
                 self.module.register_parameter('old_weight', torch.nn.Parameter(self.module.weight))
                 delattr(self.module, 'weight')
-                self.module.register_buffer('weight', self.module.old_weight)
+                self.module.register_buffer('weight', self.module.old_weight.data)
 
                 # for batch normalization folding
                 if self.bn_module is not None:
                     if _check_bias(self.module):
                         self.module.register_parameter('old_bias', torch.nn.Parameter(self.module.bias))
-                        init_tensor = self.module.old_bias
+                        init_tensor = self.module.old_bias.data
                     else:
                         init_tensor = torch.zeros_like(self.bn_module.weight)
                     delattr(self.module, 'bias')

test/ut/sdk/test_compressor_torch.py

@@ -305,6 +305,33 @@ class CompressorTestCase(TestCase):
         self.assertTrue(calibration_config is not None)
         self.assertTrue(len(calibration_config) == 4)
 
+    def test_torch_quantizer_weight_type(self):
+        quantizer_list = [
+            torch_quantizer.QAT_Quantizer,
+            torch_quantizer.LsqQuantizer,
+            torch_quantizer.ObserverQuantizer,
+            torch_quantizer.NaiveQuantizer,
+            torch_quantizer.DoReFaQuantizer]
+        for quantizer_type in quantizer_list:
+            model = TorchModel().eval()
+            config_list = [{
+                'quant_types': ['weight'],
+                'quant_bits': 8,
+                'op_types': ['Conv2d', 'Linear']
+            }]
+
+            optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
+            dummy = torch.randn(1, 1, 28, 28)
+            if quantizer_type == torch_quantizer.QAT_Quantizer:
+                quantizer_type(model, config_list, optimizer, dummy_input=dummy)
+            else:
+                quantizer_type(model, config_list, optimizer)
+
+            self.assertFalse(isinstance(model.conv1.module.weight, torch.nn.Parameter))
+            self.assertFalse(isinstance(model.conv2.module.weight, torch.nn.Parameter))
+            self.assertFalse(isinstance(model.fc1.module.weight, torch.nn.Parameter))
+            self.assertFalse(isinstance(model.fc2.module.weight, torch.nn.Parameter))
+
     def test_torch_QAT_quantizer(self):
         model = TorchModel()
         config_list = [{