diff --git a/paddleslim/prune/auto_pruner.py b/paddleslim/prune/auto_pruner.py
index 5dbdb6d4aa064fc6d5534f0ea02fefe19e580899..b144251a0a9a294094f7101f30958486abcf0543 100644
--- a/paddleslim/prune/auto_pruner.py
+++ b/paddleslim/prune/auto_pruner.py
@@ -106,7 +106,7 @@ class AutoPruner(object):
                 self, _program, self._params, self._pruned_flops,
                 self._pruned_latency)
         init_tokens = self._ratios2tokens(self._init_ratios)
-
+        _logger.info("range table: {}".format(self._range_table))
         controller = SAController(self._range_table, self._reduce_rate,
                                   self._init_temperature, self._max_try_number,
                                   init_tokens, self._constrain_func)
@@ -143,10 +143,10 @@ class AutoPruner(object):
     def _get_range_table(self, min_ratios, max_ratios):
         assert isinstance(min_ratios, list) or isinstance(min_ratios, float)
         assert isinstance(max_ratios, list) or isinstance(max_ratios, float)
-        min_ratios = min_ratios if isinstance(min_ratios,
-                                              list) else [min_ratios]
-        max_ratios = max_ratios if isinstance(max_ratios,
-                                              list) else [max_ratios]
+        min_ratios = min_ratios if isinstance(
+            min_ratios, list) else [min_ratios] * len(self._params)
+        max_ratios = max_ratios if isinstance(
+            max_ratios, list) else [max_ratios] * len(self._params)
         min_tokens = self._ratios2tokens(min_ratios)
         max_tokens = self._ratios2tokens(max_ratios)
         return (min_tokens, max_tokens)
@@ -163,7 +163,7 @@ class AutoPruner(object):
         return flops(pruned_program) < self._base_flops * (
             1 - self._pruned_flops)
 
-    def prune(self, program):
+    def prune(self, program, eval_program=None):
         """
         Prune program with latest tokens generated by controller.
         Args:
@@ -178,10 +178,21 @@ class AutoPruner(object):
             self._params,
             self._current_ratios,
             place=self._place,
+            only_graph=False,
             param_backup=self._param_backup)
+        pruned_val_program = None
+        if eval_program is not None:
+            pruned_val_program = self._pruner.prune(
+                program,
+                self._scope,
+                self._params,
+                self._current_ratios,
+                place=self._place,
+                only_graph=True)
+
         _logger.info("AutoPruner - pruned ratios: {}".format(
             self._current_ratios))
-        return pruned_program
+        return pruned_program, pruned_val_program
 
     def reward(self, score):
         """