diff --git a/tools/python/fluidtools/run.py b/tools/python/fluidtools/run.py
index e7ac827b998a049fd14b1cac1f84dcda6a0c84c3..fef7094e09f97e45f07a762eb6954842fc40c419 100644
--- a/tools/python/fluidtools/run.py
+++ b/tools/python/fluidtools/run.py
@@ -11,7 +11,8 @@ checked_model_path = "checked_model"
 feed_path = "feeds"
 output_path = "outputs"
 diff_threshold = 0.01
-is_lod = True
+is_lod = False
+mobile_model_path = ""
 
 np.set_printoptions(linewidth=150)
 
@@ -61,6 +62,10 @@ prog, feeds, fetches = load_model(model_path)
 
 # 强制要求所有张量的形状，在model和params中一致，并重新保存模型
 def resave_model(feed_kv):
+    if len(mobile_model_path) > 0:
+        pp_green("has set mobile_model_path, stop checking model & params", 1)
+        sh("cp {}/* {}".format(mobile_model_path, checked_model_path))
+        return
     ops = prog.current_block().ops
     vars = prog.current_block().vars
     # 强制所有var为可持久化
@@ -157,7 +162,7 @@ def load_feed_kv():
             cur_len = 0
             lod = [cur_len]
             for l in seq_lens:
-                cur_len += 1
+                cur_len += l
                 lod.append(cur_len)
             data = data.reshape(feed_shape)
             tensor.set(data, fluid.CPUPlace())
@@ -223,7 +228,7 @@ def save_all_op_output(feed_kv=None):
             var_name = name
             if "tmp" in name:
                 break
-        if "sequence_pool" in name:
+        if "sequence_pool" in var_name:
             continue
         try:
             data = get_var_data(var_name, feed_kv=feed_kv).flatten().tolist()
@@ -274,6 +279,10 @@ def check_mobile_results(args, fuse, mem_opt):
     error_index = None
     error_values1 = None
     error_values2 = None
+    checked_names = []
+    fetch_names = []
+    for fetch in fetches:
+        fetch_names.append(fetch.name)
     for index in op_cache:
         op_output_var_name, op = op_cache[index]
         if mem_opt:
@@ -299,16 +308,24 @@ def check_mobile_results(args, fuse, mem_opt):
                 if abs(v1 - v2) > diff_threshold:
                     error_index = index
                     break
+        checked_names.append(op_output_var_name)
         if error_index != None:
             error_values1 = values1
             error_values2 = values2
             break
+    for name in fetch_names:
+        if name not in checked_names:
+            error_index = -1
+            break
     if error_index == None:
         pp_green("outputs are all correct", 1)
+    elif error_index == -1:
+        pp_red("outputs are missing")
     else:
         error_values1 = np.array(error_values1)
         error_values2 = np.array(error_values2)
-        pp_red("{} op's output is not correct, op's type is {}".format(error_index, op_cache[error_index][1].type), 1)
+        # pp_red("mobile op is not correct, error occurs at {}th op, op's type is {}")
+        pp_red("corresponding fluid op is {}th op, op's type is {}".format(error_index, op_cache[error_index][1].type), 1)
         pp_red("fluid results are : ", 1)
         pp_red(str(error_values1).replace("\n", "\n" + "\t" * 1), 1)
         pp_red("paddle mobile results are : ", 1)
@@ -325,7 +342,9 @@ def main():
         feed_kv = load_feed_kv()
     pp_yellow(dot + dot + " checking fetch info")
     for fetch in fetches:
-        pp_tab("fetch var name : {}".format(fetch.name), 1)
+        fetch_name = fetch.name
+        fetch_shape = get_var_shape(fetch_name)
+        pp_tab("fetch var name : {}; fetch var shape : {}".format(fetch_name, fetch_shape), 1)
     # 预测
     pp_yellow(dot + dot + " checking inference")
     outputs = run_model(feed_kv=feed_kv)