diff --git a/python/tvm/relax/frontend/torch/exported_program_translator.py b/python/tvm/relax/frontend/torch/exported_program_translator.py
index c05858fd887e..5e38d2ff6c16 100644
--- a/python/tvm/relax/frontend/torch/exported_program_translator.py
+++ b/python/tvm/relax/frontend/torch/exported_program_translator.py
@@ -447,24 +447,34 @@ def create_input_vars(
         """Create relax input vars."""
         parameters_buffers_constants = OrderedDict()
         user_inputs = OrderedDict()
+        torch_symbol_to_relax_var: Dict[str, tvm.tir.Var] = {}
+
         for spec in exported_program.graph_signature.input_specs:
             name_hint = spec.arg.name
             if spec.kind is torch.export.graph_signature.InputKind.CONSTANT_TENSOR:
-                shape = exported_program.tensor_constants[spec.target].shape
+                torch_shape = exported_program.tensor_constants[spec.target].shape
                 torch_dtype = exported_program.tensor_constants[spec.target].dtype
             elif spec.kind is torch.export.graph_signature.InputKind.USER_INPUT:
                 for node in exported_program.graph.find_nodes(op="placeholder", target=spec.target):
                     if node.name == name_hint and "tensor_meta" in node.meta:
-                        shape = node.meta["tensor_meta"].shape
+                        torch_shape = node.meta["tensor_meta"].shape
                         torch_dtype = node.meta["tensor_meta"].dtype
                         break
             else:
                 # PARAMETER or BUFFER
-                shape = exported_program.state_dict[spec.target].shape
+                torch_shape = exported_program.state_dict[spec.target].shape
                 torch_dtype = exported_program.state_dict[spec.target].dtype
 
+            # TODO(mshr-h): Support range constraints
+            relax_shape = [
+                torch_symbol_to_relax_var.setdefault(str(s), tvm.tir.SizeVar(str(s), "int64"))
+                if isinstance(s, torch.SymInt)
+                else s
+                for s in torch_shape
+            ]
             dtype = self._convert_data_type(torch_dtype)
-            relax_var = relax.Var(name_hint, relax.TensorStructInfo(shape, dtype))
+
+            relax_var = relax.Var(name_hint, relax.TensorStructInfo(relax_shape, dtype))
             if spec.kind is torch.export.graph_signature.InputKind.USER_INPUT:
                 user_inputs[name_hint] = relax_var
             else:
diff --git a/tests/python/relax/test_frontend_from_exported_program.py b/tests/python/relax/test_frontend_from_exported_program.py
index dd4ead9e593e..a3c939fcb64e 100644
--- a/tests/python/relax/test_frontend_from_exported_program.py
+++ b/tests/python/relax/test_frontend_from_exported_program.py
@@ -32,8 +32,8 @@
 torch_version = torch.__version__
 
 
-def verify_model(torch_model, example_args, binding, expected):
-    exported_program = export(torch_model, args=example_args)
+def verify_model(torch_model, example_args, binding, expected, dynamic_shapes=None):
+    exported_program = export(torch_model, args=example_args, dynamic_shapes=dynamic_shapes)
     mod = from_exported_program(exported_program)
 
     binding = {k: tvm.nd.array(v) for k, v in binding.items()}
@@ -3961,5 +3961,32 @@ def main(
     verify_model(Topk(), example_args, {}, Expected)
 
 
+def test_dynamic_shape():
+    class DynamicModel(torch.nn.Module):
+        def forward(self, x1, x2):
+            return torch.ops.aten.add.Tensor(x1, x2)
+
+    B = tvm.tir.SizeVar("BatchSize", dtype="int64")
+
+    @tvm.script.ir_module
+    class Expected:
+        @R.function
+        def main(
+            lhs: R.Tensor((B, 4), dtype="float32"),
+            rhs: R.Tensor((B, 4), dtype="float32"),
+        ) -> R.Tuple(R.Tensor((B, 4), dtype="float32")):
+            with R.dataflow():
+                lv: R.Tensor((B, 4), dtype="float32") = R.add(lhs, rhs)
+                gv: R.Tuple(R.Tensor((B, 4), dtype="float32")) = (lv,)
+                R.output(gv)
+            return gv
+
+    example_args = (torch.randn(2, 4), torch.randn(2, 4))
+    batch = torch.export.Dim("batch")
+    dynamic_shapes = {"x1": {0: batch}, "x2": {0: batch}}
+
+    verify_model(DynamicModel(), example_args, {}, Expected, dynamic_shapes=dynamic_shapes)
+
+
 if __name__ == "__main__":
     tvm.testing.main()