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diff --git a/‎paddle/fluid/framework/new_executor/interpreter/interpreter_util.cc
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-2Lines changed: 0 additions & 2 deletions b/‎paddle/fluid/framework/new_executor/interpreter/interpreter_util.cc
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diff --git a/‎paddle/fluid/framework/prune_test.cc
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-77Lines changed: 0 additions & 77 deletions b/‎paddle/fluid/framework/prune_test.cc
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diff --git a/‎paddle/fluid/inference/analysis/ir_passes/dlnne_subgraph_pass.cc
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-1Lines changed: 0 additions & 1 deletion b/‎paddle/fluid/inference/analysis/ir_passes/dlnne_subgraph_pass.cc
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diff --git a/‎paddle/fluid/operators/rnn_memory_helper_op.cc
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-184Lines changed: 0 additions & 184 deletions b/‎paddle/fluid/operators/rnn_memory_helper_op.cc
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diff --git a/‎paddle/fluid/operators/unity_build_rule.cmake
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diff --git a/‎python/paddle/base/backward.py
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+2-11Lines changed: 2 additions & 11 deletions b/‎python/paddle/base/backward.py
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+2-11Lines changed: 2 additions & 11 deletions
diff --git a/‎python/paddle/base/framework.py
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diff --git a/‎test/ir/inference/program_config.py
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@@ -620,8 +620,6 @@ void BuildOpFuncList(const platform::Place& place,
         "pylayer",
         "pylayer_grad"
         "recurrent_grad",
-        "rnn_memory_helper",
-        "rnn_memory_helper_grad",
         "while",
         "while_grad"};
     bool allow_var_not_in_program = ops_with_var_not_in_program.count(op_type);
 
@@ -198,80 +198,3 @@ TEST(Prune, multi_target) {
   f::Prune(*pdesc, feed_var_names, &pruned);
   EXPECT_EQ(pruned.blocks(0).ops_size(), 3);
 }
-
-TEST(Prune, recurrrent_op) {
-  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", {"y"}}},
-        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 = {"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);
-}
-
-// 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);
-}
@@ -39,7 +39,6 @@ void analysis::DlnneSubgraphPass::InferShapeForDlnneMainGraph() const {
       "fetch",
       "recurrent",
       "go",
-      "rnn_memory_helper_grad",
       "conditional_block",
       "while",
       "send",
 
@@ -223,7 +223,6 @@ register_unity_group(
   reverse_op.cc)
 register_unity_group(
   cc
-  rnn_memory_helper_op.cc
   roi_align_op.cc
   roll_op.cc
   run_program_op.cc
 
@@ -1671,17 +1671,8 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
                     or var in parent_op_vars
                 ]
                 if not existing_grad_var_ins:
-                    '''
-                    FIXME(paddle-dev, zengjinle): rnn_memory_helper_grad is used
-                    in recurrent op. The input of this op does not even exist in
-                    the program! Therefore, any dependency analysis would not
-                    work to this op! If I do not add the following code, this op
-                    would be pruned, and the calculation result would be wrong.
-                    Maybe we should re-design this op later...
-                    '''
-                    if op_desc.type() not in ['rnn_memory_helper_grad']:
-                        ops_to_remove.append(op_idx)
-                        continue
+                    ops_to_remove.append(op_idx)
+                    continue
 
         # sum may create invalid variable, here to deal with it.
         if op_desc.type() == 'sum':
 
@@ -2879,7 +2879,6 @@ class Operator:
         'fetch',
         'recurrent',
         'go',
-        'rnn_memory_helper_grad',
         'conditional_block',
         'pylayer',
         'while',
 
@@ -113,7 +113,6 @@ def __repr__(self):
     'fetch',
     'recurrent',
     'go',
-    'rnn_memory_helper_grad',
     'conditional_block',
     'static_pylayer',
     'while',