diff --git a/python/tvm/autotvm/tuner/xgboost_cost_model.py b/python/tvm/autotvm/tuner/xgboost_cost_model.py
index 99972ee3d74ea..637891854aee0 100644
--- a/python/tvm/autotvm/tuner/xgboost_cost_model.py
+++ b/python/tvm/autotvm/tuner/xgboost_cost_model.py
@@ -360,98 +360,78 @@ def _extract_popen_initializer(space, target, task):
 
 def _extract_itervar_feature_index(args):
     """extract iteration var feature for an index in extract_space"""
-    try:
-        config = _extract_space.get(args)
-        with _extract_target:
-            sch, fargs = _extract_task.instantiate(config)
+    config = _extract_space.get(args)
+    with _extract_target:
+        sch, fargs = _extract_task.instantiate(config)
 
-        fea = feature.get_itervar_feature_flatten(sch, fargs, take_log=True)
-        fea = np.concatenate((fea, list(config.get_other_option().values())))
-        return fea
-    except Exception:  # pylint: disable=broad-except
-        return None
+    fea = feature.get_itervar_feature_flatten(sch, fargs, take_log=True)
+    fea = np.concatenate((fea, list(config.get_other_option().values())))
+    return fea
 
 
 def _extract_itervar_feature_log(arg):
     """extract iteration var feature for log items"""
-    try:
-        inp, res = arg
-        config = inp.config
-        with inp.target:
-            sch, args = inp.task.instantiate(config)
-        fea = feature.get_itervar_feature_flatten(sch, args, take_log=True)
-        x = np.concatenate((fea, list(config.get_other_option().values())))
-
-        if res.error_no == 0:
-            y = inp.task.flop / np.mean(res.costs)
-        else:
-            y = 0.0
-        return x, y
-    except Exception:  # pylint: disable=broad-except
-        return None
+    inp, res = arg
+    config = inp.config
+    with inp.target:
+        sch, args = inp.task.instantiate(config)
+    fea = feature.get_itervar_feature_flatten(sch, args, take_log=True)
+    x = np.concatenate((fea, list(config.get_other_option().values())))
+
+    if res.error_no == 0:
+        y = inp.task.flop / np.mean(res.costs)
+    else:
+        y = 0.0
+    return x, y
 
 
 def _extract_knob_feature_index(args):
     """extract knob feature for an index in extract_space"""
-    try:
-
-        config = _extract_space.get(args)
+    config = _extract_space.get(args)
 
-        return config.get_flatten_feature()
-    except Exception:  # pylint: disable=broad-except
-        return None
+    return config.get_flatten_feature()
 
 
 def _extract_knob_feature_log(arg):
     """extract knob feature for log items"""
-    try:
-        inp, res = arg
-        config = inp.config
-        x = config.get_flatten_feature()
-
-        if res.error_no == 0:
-            with inp.target:  # necessary, for calculating flops of this task
-                inp.task.instantiate(config)
-            y = inp.task.flop / np.mean(res.costs)
-        else:
-            y = 0.0
-        return x, y
-    except Exception:  # pylint: disable=broad-except
-        return None
+    inp, res = arg
+    config = inp.config
+    x = config.get_flatten_feature()
+
+    if res.error_no == 0:
+        with inp.target:  # necessary, for calculating flops of this task
+            inp.task.instantiate(config)
+        y = inp.task.flop / np.mean(res.costs)
+    else:
+        y = 0.0
+    return x, y
 
 
 def _extract_curve_feature_index(args):
     """extract sampled curve feature for an index in extract_space"""
-    try:
+    config = _extract_space.get(args)
+    with _extract_target:
+        sch, fargs = _extract_task.instantiate(config)
 
-        config = _extract_space.get(args)
-        with _extract_target:
-            sch, fargs = _extract_task.instantiate(config)
-
-        fea = feature.get_buffer_curve_sample_flatten(sch, fargs, sample_n=20)
-        fea = np.concatenate((fea, list(config.get_other_option().values())))
-        return np.array(fea)
-    except Exception:  # pylint: disable=broad-except
-        return None
+    fea = feature.get_buffer_curve_sample_flatten(sch, fargs, sample_n=20)
+    fea = np.concatenate((fea, list(config.get_other_option().values())))
+    return np.array(fea)
 
 
 def _extract_curve_feature_log(arg):
     """extract sampled curve feature for log items"""
-    try:
-        inp, res = arg
-        config = inp.config
-        with inp.target:
-            sch, args = inp.task.instantiate(config)
-        fea = feature.get_buffer_curve_sample_flatten(sch, args, sample_n=20)
-        x = np.concatenate((fea, list(config.get_other_option().values())))
-
-        if res.error_no == 0:
-            y = inp.task.flop / np.mean(res.costs)
-        else:
-            y = 0.0
-        return x, y
-    except Exception:  # pylint: disable=broad-except
-        return None
+    inp, res = arg
+    config = inp.config
+    with inp.target:
+        sch, args = inp.task.instantiate(config)
+    fea = feature.get_buffer_curve_sample_flatten(sch, args, sample_n=20)
+    x = np.concatenate((fea, list(config.get_other_option().values())))
+
+    if res.error_no == 0:
+        y = inp.task.flop / np.mean(res.costs)
+    else:
+        y = 0.0
+    return x, y
 
 
 def custom_callback(