diff --git a/test/quantization/test_quantize.py b/test/quantization/test_quantize.py
index 1198466b83a0..71391a9ddee7 100644
--- a/test/quantization/test_quantize.py
+++ b/test/quantization/test_quantize.py
@@ -89,6 +89,7 @@
 import numpy as np
 
 class TestPostTrainingStatic(QuantizationTestCase):
+
     def test_single_layer(self):
         r"""Quantize SingleLayerLinearModel which has one Linear module, make sure it is swapped
         to nnq.Linear which is the quantized version of the module
@@ -537,6 +538,29 @@ def test_quantized_embedding(self):
         self.assertTrue('QuantizedLinear' in str(model))
         self.checkQuantizedLinear(model.fc)
 
+    @skipIfNoFBGEMM
+    def test_embedding_linear_dynamic(self):
+        class EmbeddingWithLinearDynamic(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.emb = torch.nn.Embedding(num_embeddings=10, embedding_dim=12)
+                self.fc = torch.nn.Linear(5, 5)
+
+            def forward(self, indices, linear_in):
+                return self.emb(indices), self.fc(linear_in)
+
+        model = EmbeddingWithLinearDynamic()
+        qconfig_dict = {'fc' : default_dynamic_qconfig}
+        model = EmbeddingWithLinear()
+        quantize_dynamic(model, qconfig_dict, inplace=True)
+
+        model.emb.qconfig = float_qparams_weight_only_qconfig
+        prepare(model, inplace=True)
+        convert(model, inplace=True)
+        self.assertTrue('QuantizedEmbedding' in str(model))
+        self.assertTrue('DynamicQuantizedLinear' in str(model))
+
+
     @skipIfNoFBGEMM
     def test_dequant_stub(self):
         m = QuantStubModel().eval()
diff --git a/torch/nn/quantized/modules/embedding_ops.py b/torch/nn/quantized/modules/embedding_ops.py
index 523994b364c8..b12591a6d1de 100644
--- a/torch/nn/quantized/modules/embedding_ops.py
+++ b/torch/nn/quantized/modules/embedding_ops.py
@@ -97,16 +97,16 @@ def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: Optiona
         if _weight is None:
             scales = torch.ones(num_embeddings, dtype=torch.float)
             zero_points = torch.zeros(num_embeddings, dtype=torch.float)
-            self.qweight = torch._empty_per_channel_affine_quantized([num_embeddings, embedding_dim],
-                                                                     scales=scales, zero_points=zero_points,
-                                                                     axis=0, dtype=torch.quint8)
+            qweight = torch._empty_per_channel_affine_quantized([num_embeddings, embedding_dim],
+                                                                scales=scales, zero_points=zero_points,
+                                                                axis=0, dtype=torch.quint8)
         else:
             assert list(_weight.shape) == [num_embeddings, embedding_dim], \
                 'Shape of weight does not match num_embeddings and embedding_dim'
-            self.qweight = _weight
+            qweight = _weight
 
         self._packed_params = EmbeddingPackedParams(num_embeddings, embedding_dim, dtype)
-        self._packed_params.set_weight(self.qweight)
+        self._packed_params.set_weight(qweight)
 
     def forward(self, indices: Tensor) -> Tensor:
         return torch.ops.quantized.embedding_byte(self._packed_params._packed_weight, indices)
@@ -119,7 +119,7 @@ def __repr__(self):
 
     def extra_repr(self):
         extra_repr_str = 'num_embeddings={}, embedding_dim={}, dtype={}, qscheme={}'.format(
-            self.num_embeddings, self.embedding_dim, self._packed_params.dtype, self.qweight.qscheme()
+            self.num_embeddings, self.embedding_dim, self._packed_params.dtype, self.weight().qscheme()
         )
 
         return extra_repr_str