diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index 4d1efdc9fe65..2f1f1152a4c2 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -662,6 +662,7 @@ def cast(self, dtype):
             The new data type.
         """
         self._dtype = dtype
+        self._var = None  # Clear Symbol Variable as it caches the dtype
         if self._data is None:
             return
         with autograd.pause():
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index a934fbe12e58..71ae41c2cfa6 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -259,7 +259,8 @@ def test_hybrid_sequential_unique_internals():
 
 
 @with_seed()
-def test_symbol_block(tmpdir):
+@pytest.mark.parametrize('compute_before_cast', [True, False])
+def test_symbol_block(tmpdir, compute_before_cast):
     model = nn.HybridSequential()
     model.add(nn.Dense(128, activation='tanh'))
     model.add(nn.Dropout(0.5))
@@ -309,6 +310,10 @@ def hybrid_forward(self, F, x):
     ctx = mx.cpu(0)
 
     net_fp32 = mx.gluon.model_zoo.vision.resnet34_v2(pretrained=True, ctx=ctx, root=tmp)
+    if compute_before_cast:
+        # Compute before casting to catch bugs where symbol dtype isn't casted correctly GH-18843
+        net_fp32.initialize()
+        net_fp32(mx.nd.zeros((1,3,224,224), ctx=ctx))
     net_fp32.cast('float64')
     net_fp32.hybridize()
     data = mx.nd.zeros((1,3,224,224), dtype='float64', ctx=ctx)