diff --git a/python/tvm/relay/frontend/tensorflow.py b/python/tvm/relay/frontend/tensorflow.py
index 47bd0cdaeec8..5778b25fa0f6 100644
--- a/python/tvm/relay/frontend/tensorflow.py
+++ b/python/tvm/relay/frontend/tensorflow.py
@@ -1331,10 +1331,7 @@ def _impl(inputs, attr, params, mod):
             except Exception:
                 output_shape = inputs[0]
 
-        fill_arg = _get_num_param(params, inputs.pop(1))
-        dtype = attr['T'].name
-        return _op.full(tvm.relay.const(fill_arg, dtype),
-                        output_shape, dtype)
+        return _op.full(inputs[1], output_shape, attr['T'].name)
     return _impl
 
 def _lrn():
diff --git a/tests/python/frontend/tensorflow/test_forward.py b/tests/python/frontend/tensorflow/test_forward.py
index 1a4294b52bfc..6d243c6c2216 100644
--- a/tests/python/frontend/tensorflow/test_forward.py
+++ b/tests/python/frontend/tensorflow/test_forward.py
@@ -107,7 +107,7 @@ def run_tvm_graph(graph_def, input_data, input_node, num_output=1,
     if target == "cuda":
         layout = cuda_layout
     target_host = None
-    shape_dict = {e: i.shape for e, i in zip(input_node, input_data)}
+    shape_dict = {e: i.shape if hasattr(i, "shape") else () for e, i in zip(input_node, input_data)}
     mod, params = relay.frontend.from_tensorflow(graph_def,
                                                  layout=layout,
                                                  shape=shape_dict,
@@ -135,7 +135,7 @@ def run_tvm_graph(graph_def, input_data, input_node, num_output=1,
         vm.init(tvm.cpu())
         inputs = {}
         for e, i in zip(input_node, input_data):
-            inputs[e] = i
+            inputs[e] = tvm.nd.array(i)
         result = vm.invoke("main", **inputs)
         return vmobj_to_list(result)
     else:
@@ -1938,12 +1938,24 @@ def _test_fill_from_tensor(in_shape):
         compare_tf_with_tvm(data, 'Placeholder:0', 'out1:0')
 
 
+def _test_fill_symbolic_inputs(in_shape_data, in_value_data, dtype):
+    with tf.Graph().as_default():
+        in_shape = tf.placeholder(shape=[in_shape_data.shape[0]], dtype=in_shape_data.dtype)
+        in_value = tf.placeholder(shape=(), dtype=dtype)
+        out = tf.fill(in_shape, in_value)
+        for mode in ['debug', 'vm']:
+            compare_tf_with_tvm([in_shape_data, in_value_data], [in_shape.name, in_value.name], out.name, mode=mode)
+
+
 def test_forward_fill():
     """ Resize Bilinear """
 
     _test_fill((32))
     _test_fill((6, 32, 64, 64))
     _test_fill_from_tensor((6, 32, 64, 64))
+    _test_fill_symbolic_inputs(np.array((2,)), np.int32(9), tf.int32)
+    _test_fill_symbolic_inputs(np.array((2, 3)), 9, tf.int64)
+    _test_fill_symbolic_inputs(np.array((2, 3, 4)), np.float32(9.0), tf.float32)
 
 #######################################################################
 # Crop to bounding box