diff --git a/python/tvm/relay/frontend/tensorflow.py b/python/tvm/relay/frontend/tensorflow.py
index 52a501c4cd33a..e24765529542f 100644
--- a/python/tvm/relay/frontend/tensorflow.py
+++ b/python/tvm/relay/frontend/tensorflow.py
@@ -34,7 +34,7 @@
 from .. import function as _function
 from .. import op as _op
 from ..ty import Any
-from ..expr_functor import ExprVisitor
+from ..expr_functor import ExprMutator, ExprVisitor
 from .common import AttrCvt, get_relay_op
 from .common import infer_type as _infer_type
 from .common import infer_shape as _infer_shape
@@ -2416,6 +2416,26 @@ def _in_while_loop(control_flow_node_map, op_name):
     return op_name in control_flow_node_map and \
             "LoopCond" in control_flow_node_map[op_name]
 
+class RewriteSubgraph(ExprMutator):
+    """
+    A helper class to rewrite expr in while loop function to variable
+    Parameters
+    ----------
+    rewrite_map : Dict[expr, expr]
+        A dictionay contains a set of expr to var mapping.
+    """
+    def __init__(self, rewrite_map):
+        ExprMutator.__init__(self)
+        self.rewrite_map = rewrite_map
+
+    def visit(self, expr):
+        if expr in self.rewrite_map:
+            return self.rewrite_map[expr]
+        return super().visit(expr)
+
+def rewrite_subgraph(expr, rewrites):
+    return RewriteSubgraph(rewrites).visit(expr)
+
 class Branch:
     """A class contains the components that are used to build up a Relay if
     node.
@@ -2576,18 +2596,39 @@ def _while_loop(self):
         """An internal API to create a Relay recursive call for a matched TF
         `while_loop` construct.
         """
+        bind_map = {}
         wl = tvm.relay.var('while_loop')
         sb = tvm.relay.scope_builder.ScopeBuilder()
-        cond = tvm.relay.op.min(self.cond)
 
         lv_list = []
         expr_list = []
         extra_vars = []
 
-        for lv in self.loop_vars:
+        for i, lv in enumerate(self.loop_vars):
+            if self._loop_name not in self._lvar2expr:
+                self._lvar2expr[self._loop_name] = {}
+
+            # Handle the case when loop var is not properly lifted.
+            # This can happen when loop var node name is set accidentally
+            # beginning with loop name.
+            if lv not in self._lvar2expr[self._loop_name]:
+                var_name = "{}_loop_var_{}".format(self._loop_name, i)
+                var_type = _infer_type(lv, self._mod).checked_type
+                loop_var = tvm.relay.var(var_name, type_annotation=var_type)
+                self._lvar2expr[self._loop_name][loop_var] = lv
+                bind_map[lv] = loop_var
+                self.loop_vars[i] = loop_var
+                lv = loop_var
+
             lv_list.append(lv)
             expr_list.append(self._lvar2expr[self._loop_name][lv])
 
+        if bind_map:
+            self.cond = rewrite_subgraph(self.cond, bind_map)
+            self.body = [rewrite_subgraph(b, bind_map) for b in self.body]
+
+        cond = tvm.relay.op.min(self.cond)
+
         for lv, exp in self._lvar2expr[self._loop_name].items():
             if lv not in self.loop_vars:
                 var_checker = VarChecker(lv)
@@ -3211,8 +3252,8 @@ def _licm_construct(self, loop_name, node_name):
             Converted relay expression or loop var.
         """
         actual_expr = self._backtrack_construct(node_name)
-        tn = node_name.split(':').split("^")[-1]
-        node_name = tn[0]
+        tn = node_name.split(':')
+        node_name = tn[0].split("^")[-1]
         cloop_name = find_parent_loop_name(node_name, self._while_loop_name_set)
 
         if loop_name in self._while_loop_name_set and not cloop_name.startswith(loop_name):
diff --git a/tests/python/frontend/tensorflow/test_control_flow.py b/tests/python/frontend/tensorflow/test_control_flow.py
index f00018de299f9..3ec04bf384909 100644
--- a/tests/python/frontend/tensorflow/test_control_flow.py
+++ b/tests/python/frontend/tensorflow/test_control_flow.py
@@ -46,7 +46,7 @@ def check_equal(graph, tf_out, input_map=None):
 def test_vanilla_loop():
     graph = tf.Graph()
     with graph.as_default():
-        i = tf.constant(0)
+        i = tf.constant(0, name="while/constant")
 
         def c(i): return tf.less(i, 10)