diff --git a/python/mnm/utils/tuner.py b/python/mnm/utils/tuner.py
index b0489906..f9d1096b 100644
--- a/python/mnm/utils/tuner.py
+++ b/python/mnm/utils/tuner.py
@@ -84,19 +84,18 @@ def extract_tuning_tasks(mod_or_executor, args, device, *, fuse_level=0, pass_se
 
     tasks = []
     weights = []
-    for (func_name, wkl_key), weight in env_tracing_task.wkl_key_to_weight.items():
+    for wkl_key, (weight, func_names) in env_tracing_task.wkl_key_to_weight.items():
         tasks.append(
             auto_scheduler.SearchTask(
                 workload_key=wkl_key,
                 target=tvm_target,
-                target_host=None,
                 hardware_params=None,
                 # When auto scheduler is used in end to end network, try to apply layout rewrite
                 # to improve the overall performance
                 layout_rewrite_option=compute_dag.LayoutRewriteOption.get_target_default(
                     tvm_target, True
                 ),
-                desc=func_name,
+                desc=",".join(func_names),
             )
         )
         weights.append(weight)
@@ -120,14 +119,24 @@ def tune_tasks(tasks, weights, log_file, n_trials):
     """
     measure_device = auto_scheduler.LocalRPCMeasureContext(repeat=1, min_repeat_ms=400, timeout=10)
 
+    # FIXME(comaniac): Remove this custom objective function after
+    # https://github.com/apache/tvm/pull/8984
+    def weighted_sum(costs):
+        score = sum(c * w for c, w in zip(costs, weights))
+        if not hasattr(score, "value"):
+            return tvm.tir.expr.FloatImm("float32", score)
+        return score
+
     if os.path.exists(log_file):
-        tuner = auto_scheduler.TaskScheduler(tasks, weights, load_log_file=log_file)
+        tuner = auto_scheduler.TaskScheduler(tasks, weights, load_log_file=log_file,
+                                             objective_func=weighted_sum)
     else:
-        tuner = auto_scheduler.TaskScheduler(tasks, weights)
+        tuner = auto_scheduler.TaskScheduler(tasks, weights, objective_func=weighted_sum)
 
     if callable(n_trials):
         n_trials = n_trials(len(tasks))
 
+    print("Start tuning for maximal %d trials" % n_trials)
     tune_option = auto_scheduler.TuningOptions(
         num_measure_trials=n_trials,
         runner=measure_device.runner,