diff --git a/paddle/fluid/framework/prune.cc b/paddle/fluid/framework/prune.cc
index f5e20e041e9e44437907f384c7cadae007a9f510..cc0240017de9fa1b74c4460471e1b320294e85ad 100644
--- a/paddle/fluid/framework/prune.cc
+++ b/paddle/fluid/framework/prune.cc
@@ -210,6 +210,23 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
       should_run.push_back(true);
     } else {
       should_run.push_back(false);
+      // If the output of an op modifies feed vars, the op should not clip.
+      // For example, in the transformer structure, the third parameter returned
+      // by beam_search op is generally assigned to a feed var. Cutting the
+      // assign op will cause an error.
+      if (parent_block_id != -1) {
+        bool flag = false;
+        for (auto& var : op_desc.outputs()) {
+          for (auto& argu : var.arguments()) {
+            if (feed_var_names.count(argu)) {
+              flag = true;
+            }
+          }
+        }
+        if (flag) {
+          should_run.back() = true;
+        }
+      }
     }
   }
 
diff --git a/paddle/fluid/framework/prune_test.cc b/paddle/fluid/framework/prune_test.cc
index eb5c241a8372a460483c70e38f962168b1cdbbc0..12fa0c61f8121d475a0cf2aa78e4bb995a01b132 100644
--- a/paddle/fluid/framework/prune_test.cc
+++ b/paddle/fluid/framework/prune_test.cc
@@ -185,3 +185,34 @@ TEST(Prune, recurrrent_op) {
   EXPECT_EQ(pruned.blocks(0).ops_size(), 2);
   EXPECT_EQ(pruned.blocks(1).ops_size(), 1);
 }
+
+// If the output of an op modifies feed vars, the op should not clip.
+TEST(Prune, recurrrent_op_2) {
+  f::ProgramDesc program;
+  f::BlockDesc *block = program.MutableBlock(0);
+  f::BlockDesc *sub_block = program.AppendBlock(*block);
+  AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}},
+        f::AttributeMap{}, block);
+
+  std::vector<std::string> state_var_name(1, "y");
+  AddOp("recurrent", {{"input", {"b", "c"}}}, {{"output", {"b1, c1"}}},
+        {{"ex_states", state_var_name},
+         {"states", state_var_name},
+         {"sub_block", sub_block}},
+        block);
+
+  EXPECT_TRUE(sub_block != nullptr);
+  AddOp("rnn_memory_helper", {{"input", {"x"}}}, {{"output", {"a"}}},
+        f::AttributeMap{}, sub_block);
+
+  f::proto::ProgramDesc *pdesc = program.Proto();
+  pdesc->mutable_blocks(0)->mutable_ops(1)->set_is_target(true);
+
+  f::proto::ProgramDesc pruned;
+  std::set<std::string> feed_var_names = {"x", "a"};
+
+  f::Prune(*pdesc, feed_var_names, &pruned);
+  EXPECT_EQ(pruned.blocks_size(), 2);
+  EXPECT_EQ(pruned.blocks(0).ops_size(), 2);
+  EXPECT_EQ(pruned.blocks(1).ops_size(), 1);
+}
diff --git a/python/paddle/fluid/layers/rnn.py b/python/paddle/fluid/layers/rnn.py
index f3606dae19e57a6c9845e2782e4552099b29e652..b5526383d6c510703402526e19d88d6c3b28bd8b 100644
--- a/python/paddle/fluid/layers/rnn.py
+++ b/python/paddle/fluid/layers/rnn.py
@@ -3040,7 +3040,8 @@ def beam_search_decode(ids, scores, beam_size, end_id, name=None):
                              'beam_search_encode')
     helper = LayerHelper('beam_search_decode', **locals())
     sentence_ids = helper.create_variable_for_type_inference(dtype=ids.dtype)
-    sentence_scores = helper.create_variable_for_type_inference(dtype=ids.dtype)
+    sentence_scores = helper.create_variable_for_type_inference(
+        dtype=scores.dtype)
 
     helper.append_op(
         type="beam_search_decode",