[Eager grad] Refactor partial grad logic (#40693)

* Refactor partial_grad/backward logic

* Add DuplicateCheck and polish code

* Refactor partial_grad/backward more clearly

* Refactor GeneralGrad by SingleInstance

paddle/fluid/eager/backward.cc

@@ -29,90 +29,83 @@
 
 namespace egr {
 
-std::unordered_map<GradNodeBase*, int> getInDegreeMap(
-    const std::queue<GradNodeBase*>& init_queue) {
-  // Calculate in_degree for each node
-  // We can completely remove this pass, if in_degree were set during forward
-  // pass
-  std::unordered_map<GradNodeBase*, int> node_in_degree_map;
-
-  // Copy nodes
-  std::queue<GradNodeBase*> queue = init_queue;
-  std::unordered_set<GradNodeBase*> visited;
-  size_t potential_startup_ops_cnt = queue.size();
-  size_t cnt = 0;
-
-  // Visit each node exactly once in any order
-  while (!queue.empty()) {
-    GradNodeBase* node = queue.front();
-    queue.pop();
-
-    if (cnt < potential_startup_ops_cnt) {
-      if (!node_in_degree_map.count(node)) {
-        node_in_degree_map[node] = 0;
+/*
+* GeneralGrad is Helpper class to implement custom grad operation between
+* outputs and inputs.
+*
+* **/
+class GeneralGrad {
+ public:
+  static GeneralGrad& Instance() { return *general_grad_; }
+
+  // Get inputs's / no_grad_vars's GradNodes and InputMeta Info
+  void GetTargetNodesInfo(
+      const std::vector<paddle::experimental::Tensor>& inputs,
+      bool is_no_grad_vars) {
+    std::string msg = is_no_grad_vars ? "no_grad_vars" : "inputs";
+    VLOG(6) << "Running in GetTargetNodesInfo.";
+    if (!inputs.empty()) {
+      VLOG(6) << msg << " are not empty.";
+      size_t num_inputs = inputs.size();
+      for (size_t i = 0; i < num_inputs; i++) {
+        AutogradMeta* auto_grad_meta =
+            EagerUtils::unsafe_autograd_meta(inputs[i]);
+        auto target_node = auto_grad_meta->GetMutableGradNode().get();
+        PADDLE_ENFORCE_NOT_NULL(target_node,
+                                paddle::platform::errors::Fatal(
+                                    "There is no grad op for %s:[%d] or it's"
+                                    "stop_gradient=True.",
+                                    msg, i));
+        if (is_no_grad_vars) {
+          (no_grad_var_nodes_inputmeta_map)[target_node] = auto_grad_meta;
+        } else {  // normal input
+          (input_target_nodes_inputmeta_map)[target_node] = auto_grad_meta;
+        }
       }
-      cnt += 1;
     }
-
-    if (visited.count(node)) {
-      continue;
   }
-    visited.insert(node);
-
-    PADDLE_ENFORCE_NOT_NULL(
-        node,
-        paddle::platform::errors::Fatal(
-            "We got null node when we traverse the backward graph, and this "
-            "should not happened please check your code and contact us."));
-    // Find and append next nodes
-    const std::vector<std::vector<Edge>>& edges = node->GetEdges();
-    for (const auto& edge_list : edges) {
-      for (const Edge& edge : edge_list) {
-        GradNodeBase* next_node = edge.GetMutableGradNode().get();
-        // Next node could be nullptr if it is leaf tensor with no
-        // AccumulationNode attached
-        // Or it could also originated from dispensable inputs
-        if (!next_node) continue;
 
-        // Update in_degree
-        if (!node_in_degree_map.count(next_node))
-          node_in_degree_map[next_node] = 0;
-        node_in_degree_map[next_node]++;
-        queue.push(next_node);
+  // Purify potential_startup_nodes, remove nodes those are the same as
+  // input_target_nodes
+  void PurifyPotentialStartUpNodes() {
+    VLOG(6) << "Running in PurifyPotentialStartUpNodes";
+    if (input_target_nodes_inputmeta_map.empty()) return;
+    std::unordered_set<GradNodeBase*> potential_startup_nodes_to_be_erased;
+    for (auto startup_op : potential_startup_nodes) {
+      auto iter = input_target_nodes_inputmeta_map.find(startup_op);
+      if (iter != input_target_nodes_inputmeta_map.end()) {
+        potential_startup_nodes_to_be_erased.emplace(iter->first);
+      }
+    }
+    if (!potential_startup_nodes_to_be_erased.empty()) {
+      for (auto nodes : potential_startup_nodes_to_be_erased) {
+        potential_startup_nodes.erase(nodes);
       }
     }
   }
-  return node_in_degree_map;
-}
 
-// Remove some nodes those doesn't need to be
-// stored in potential_stop_nodes、potential_startup_nodes
-void UpdateGraphInfo(
-    std::unordered_map<GradNodeBase*, AutogradMeta*>*
-        target_nodes_inputmeta_map,
-    std::unordered_map<GradNodeBase*, std::unordered_set<GradNodeBase*>>*
-        depending_nodes,
-    std::unordered_set<GradNodeBase*>* potential_stop_nodes,
-    std::unordered_set<GradNodeBase*>* potential_startup_nodes) {
+  // Remove some nodes those doesn't need to be
+  // stored in potential_stop_nodes、potential_startup_nodes
+  void UpdateGraphInfo() {
     // Updated potential_sotp_nodes by depending_nodes,
     // make sure the path from root to target_node is ok
     std::unordered_set<GradNodeBase*> _startup_ops;
     VLOG(6) << "Running in UpdateGraphInfo";
     std::queue<GradNodeBase*> queue;
-  for (auto& target_nodes_inputmeta_pair : *target_nodes_inputmeta_map) {
+    for (auto& target_nodes_inputmeta_pair : input_target_nodes_inputmeta_map) {
       queue.emplace(target_nodes_inputmeta_pair.first);
     }
 
     while (!queue.empty()) {
       auto* target_node = queue.front();
       queue.pop();
-    if (!(*depending_nodes)[target_node].empty()) {
-      auto precedding_nodes = (*depending_nodes)[target_node];
+      if (!(depending_nodes)[target_node].empty()) {
+        auto precedding_nodes = (depending_nodes)[target_node];
         for (auto pre_nodes : precedding_nodes) {
           queue.emplace(pre_nodes);
-        if (potential_stop_nodes->find(pre_nodes) !=
-            potential_stop_nodes->end()) {
-          potential_stop_nodes->erase(pre_nodes);
+          if (potential_stop_nodes.find(pre_nodes) !=
+              potential_stop_nodes.end()) {
+            potential_stop_nodes.erase(pre_nodes);
           }
         }
       } else {  // startup_ops have no precedding nodes
@@ -124,7 +117,7 @@ void UpdateGraphInfo(
     // potential startup_nodes that unreach to input target nodes
     if (!_startup_ops.empty()) {
       std::unordered_set<GradNodeBase*> potential_startup_nodes_to_be_erased;
-    for (auto node : *potential_startup_nodes) {
+      for (auto node : potential_startup_nodes) {
         if (_startup_ops.count(node) == 0) {
           VLOG(6) << "Set up potential_startup_nodes_to_be_erased";
           potential_startup_nodes_to_be_erased.emplace(node);
@@ -133,25 +126,15 @@ void UpdateGraphInfo(
       if (!potential_startup_nodes_to_be_erased.empty()) {
         for (auto node : potential_startup_nodes_to_be_erased) {
           VLOG(6) << "Erase nodes in potential_startup_nodes_to_be_erased";
-        potential_startup_nodes->erase(node);
+          potential_startup_nodes.erase(node);
+        }
       }
     }
   }
-}
-
-// Get Graph Info Betweent input target gradnode and outputs，
-// record depending_nodes、 potential_stop_nodes、potential_startup_nodes
-void GetGraphInfoBetweenTargets(
-    const std::queue<GradNodeBase*>& init_queue,
-    std::unordered_map<GradNodeBase*, AutogradMeta*>*
-        input_target_nodes_inputmeta_map,
-    std::unordered_map</*child node*/ GradNodeBase*,
-                       /*father nodes*/ std::unordered_set<GradNodeBase*>>*
-        depending_nodes,
-    std::unordered_set<GradNodeBase*>* potential_stop_nodes,
-    std::unordered_set<GradNodeBase*>* potential_startup_nodes) {
-  if (input_target_nodes_inputmeta_map->empty()) return;
 
+  // Get Graph Info Betweent input target GradNode and outputs，
+  // record depending_nodes、potential_stop_nodes、potential_startup_nodes
+  void GetGraphInfoBetweenTargets(const std::queue<GradNodeBase*>& init_queue) {
     VLOG(6) << "Runing In GetGraphInfoBetweenTargets";
 
     // Calculate in_degree for each node
@@ -174,7 +157,7 @@ void GetGraphInfoBetweenTargets(
       // Check node is target_nodes or not, if node is not target_node,
       // all the next_node will be marked in potential_stop_nodes
       bool is_potential_stop_nodes =
-        input_target_nodes_inputmeta_map->count(node);
+          input_target_nodes_inputmeta_map.count(node);
 
       // Find and append next nodes
       const std::vector<std::vector<Edge>>& edges = node->GetEdges();
@@ -191,7 +174,7 @@ void GetGraphInfoBetweenTargets(
           // all the next_nodes of current node will be inserted to
           // potential_stop_node
           if (is_potential_stop_nodes) {
-          potential_stop_nodes->emplace(next_node);
+            potential_stop_nodes.emplace(next_node);
           }
 
           // Update in_degree
@@ -200,42 +183,62 @@ void GetGraphInfoBetweenTargets(
           node_in_degree_map[next_node]++;
 
           // Record depending relationship
-        (*depending_nodes)[next_node].emplace(node);
+          (depending_nodes)[next_node].emplace(node);
           queue.push(next_node);
         }
       }
     }
-  // Update Graph Info, remove some stop_node in potential_stop_nodes
-  UpdateGraphInfo(input_target_nodes_inputmeta_map, depending_nodes,
-                  potential_stop_nodes, potential_startup_nodes);
-}
+    // Update Graph Info, remove some nodes in
+    // potential_stop_nodes、potential_startup_nodes、
+    UpdateGraphInfo();
+  }
 
-void GetTargetNodesInfo(const std::vector<paddle::experimental::Tensor>& inputs,
-                        std::unordered_map<GradNodeBase*, AutogradMeta*>*
-                            target_nodes_inputmeta_map) {
-  VLOG(6) << "Running in GetTargetNodesInfo";
-  if (!inputs.empty()) {
-    VLOG(6) << "Inputs are not empty";
-    size_t num_inputs = inputs.size();
-    for (size_t i = 0; i < num_inputs; i++) {
-      AutogradMeta* auto_grad_meta =
-          EagerUtils::unsafe_autograd_meta(inputs[i]);
-      auto target_node = auto_grad_meta->GetMutableGradNode().get();
+  void ModifyReadyQueue(std::queue<GradNodeBase*>* queue) {
+    std::queue<GradNodeBase*> tmp_queue;
+    for (auto nodes : potential_startup_nodes) {
+      tmp_queue.emplace(nodes);
+    }
+    tmp_queue.swap(*queue);
+  }
 
-      PADDLE_ENFORCE_NOT_NULL(target_node,
-                              paddle::platform::errors::Fatal(
-                                  "There is no grad op for input:%d or it's"
-                                  "stop_gradient=True",
-                                  i));
-      (*target_nodes_inputmeta_map)[target_node] = auto_grad_meta;
+  // Set result for input target grad_var when potential_startup_nodes is empty
+  void SetResultForInputTargetVar(
+      const std::unordered_map<GradNodeBase*,
+                               std::unique_ptr<GradTensorHolder>>&
+          node_input_buffers_dict) {
+    if (potential_startup_nodes.size() == 0) {
+      for (auto input_target_node : *GetInPutTargetNodesInputMetaMap()) {
+        // out rank_info of forward op
+        auto rank_info = input_target_node.second->OutRankInfo();
+        auto iter = node_input_buffers_dict.find(input_target_node.first);
+        if (iter != node_input_buffers_dict.end()) {
+          auto& target_result =
+              (iter->second)->Buffers()[rank_info.first][rank_info.second];
+          // save the target result
+          results_map[input_target_node.first] = target_result;
+        }
+      }
     }
   }
-}
 
-std::vector<paddle::experimental::Tensor> GetResults(
+  // Set input target grad_var from node_input_buffer by inputmeta
+  void SetResultForInputTargetVar(GradTensorHolder input_buffers,
+                                  GradNodeBase* node) {
+    auto iter = GetInPutTargetNodesInputMetaMap()->find(node);
+    if (iter != GetInPutTargetNodesInputMetaMap()->end()) {
+      VLOG(6) << "Get target result by by inputmeta";
+      // out rank_info of forward op
+      auto rank_info = (iter->second)->OutRankInfo();
+      // rank_info is a pair, first means slot_id, second means rank.
+      auto& target_result =
+          input_buffers.Buffers()[rank_info.first][rank_info.second];
+      // save the target result
+      results_map[node] = target_result;
+    }
+  }
+
+  std::vector<paddle::experimental::Tensor> GetResults(
       const std::vector<paddle::experimental::Tensor>& inputs,
-    std::unordered_map<GradNodeBase*, paddle::experimental::Tensor>*
-        results_map,
       bool allow_unused, bool create_graph) {
     VLOG(6) << "Running in GetResults";
     if (inputs.empty()) return {};
@@ -248,8 +251,8 @@ std::vector<paddle::experimental::Tensor> GetResults(
       AutogradMeta* auto_grad_meta = EagerUtils::unsafe_autograd_meta(input);
       auto target_node = auto_grad_meta->GetMutableGradNode().get();
 
-    auto iter = results_map->find(target_node);
-    if (iter != results_map->end()) {
+      auto iter = results_map.find(target_node);
+      if (iter != results_map.end()) {
         // set StopGradient = !create_graph
         AutogradMeta* tensor_auto_grad_meta =
             EagerUtils::autograd_meta(&(iter->second));
@@ -259,13 +262,137 @@ std::vector<paddle::experimental::Tensor> GetResults(
         PADDLE_ENFORCE_EQ(allow_unused, true,
                           paddle::platform::errors::InvalidArgument(
                               "The %d-th input does not appear in the backward "
-                            "graph. Please check the input variable or set "
+                              "graph. Please check the input tensor or set "
                               "allow_unused=True to get None result.",
                               i));
         results.emplace_back();
       }
     }
+    Clear();
     return results;
+  }
+
+  void PreparedForGeneralGrad(
+      const std::vector<paddle::experimental::Tensor>& inputs,
+      const std::vector<paddle::experimental::Tensor>& no_grad_vars,
+      std::queue<GradNodeBase*>* queue,
+      const std::unordered_map<GradNodeBase*,
+                               std::unique_ptr<GradTensorHolder>>&
+          node_input_buffers_dict) {
+    // Get no_grad_vars's GradNodes and InputMeta Info
+    GetTargetNodesInfo(no_grad_vars, true /* is_no_grad_vars */);
+    // Get inputs's GradNodes and InputMeta Info
+    GetTargetNodesInfo(inputs, false /* is_no_grad_vars */);
+    // Purify potential_startup_ops, remove those nodes that are the same as
+    // input_target_nodes
+    PurifyPotentialStartUpNodes();
+    // Get Graph Info Betweent input target gradnode and outputs
+    // Record the depending_nodes and
+    // potential_stop_nodes、potential_startup_nodes
+    GetGraphInfoBetweenTargets(*queue);
+    // Reset queue. Queue is empty only when
+    // 1.input equals to output. 2.input can not reach to output.
+    ModifyReadyQueue(queue);
+    // Set result for input target grad_var when queue is empty
+    if (queue->empty()) SetResultForInputTargetVar(node_input_buffers_dict);
+  }
+
+  bool IsPotentialStopNodes(GradNodeBase* node) {
+    return potential_stop_nodes.count(node);
+  }
+
+  std::unordered_map<GradNodeBase*, AutogradMeta*>*
+  GetNoGradVarNodesInputMetaMap() {
+    return &no_grad_var_nodes_inputmeta_map;
+  }
+
+  std::unordered_map<GradNodeBase*, AutogradMeta*>*
+  GetInPutTargetNodesInputMetaMap() {
+    return &input_target_nodes_inputmeta_map;
+  }
+
+  std::unordered_set<GradNodeBase*>* GetPotentialStopNodes() {
+    return &potential_stop_nodes;
+  }
+
+  std::unordered_set<GradNodeBase*>* GetPotentialStartupNodes() {
+    return &potential_startup_nodes;
+  }
+
+  void Clear() {
+    no_grad_var_nodes_inputmeta_map.clear();
+    input_target_nodes_inputmeta_map.clear();
+    potential_startup_nodes.clear();
+    potential_stop_nodes.clear();
+    depending_nodes.clear();
+    results_map.clear();
+  }
+
+ private:
+  GeneralGrad() = default;
+  static GeneralGrad* general_grad_;
+  // no_grad_vars's GradNode and GradNode's InputMeta.
+  std::unordered_map<GradNodeBase*, AutogradMeta* /* InputMeta */>
+      no_grad_var_nodes_inputmeta_map;
+  // inputs's GradNode and GradNode's InputMeta.
+  std::unordered_map<GradNodeBase*, AutogradMeta* /* InputMeta */>
+      input_target_nodes_inputmeta_map;
+  // Record all the potential startup_nodes, will be changed.
+  std::unordered_set<GradNodeBase*> potential_startup_nodes;
+  // Record all the potential stop nodes, will be changed.
+  std::unordered_set<GradNodeBase*> potential_stop_nodes;
+  std::unordered_map<GradNodeBase* /* next node */,
+                     std::unordered_set<GradNodeBase*> /* pre nodes */>
+      depending_nodes;
+  std::unordered_map<GradNodeBase*, paddle::experimental::Tensor> results_map;
+  DISABLE_COPY_AND_ASSIGN(GeneralGrad);
+};
+
+std::unordered_map<GradNodeBase*, int> getInDegreeMap(
+    const std::queue<GradNodeBase*>& init_queue) {
+  // Calculate in_degree for each node
+  // We can completely remove this pass, if in_degree were set during forward
+  // pass
+  std::unordered_map<GradNodeBase*, int> node_in_degree_map;
+
+  // Copy nodes
+  std::queue<GradNodeBase*> queue = init_queue;
+  std::unordered_set<GradNodeBase*> visited;
+
+  // Visit each node exactly once in any order
+  while (!queue.empty()) {
+    GradNodeBase* node = queue.front();
+    queue.pop();
+
+    if (visited.count(node)) {
+      continue;
+    }
+    visited.insert(node);
+
+    PADDLE_ENFORCE_NOT_NULL(
+        node,
+        paddle::platform::errors::Fatal(
+            "We got null node when we traverse the backward graph, and this "
+            "should not happened please check your code and contact us."));
+    // Find and append next nodes
+    const std::vector<std::vector<Edge>>& edges = node->GetEdges();
+    for (const auto& edge_list : edges) {
+      for (const Edge& edge : edge_list) {
+        GradNodeBase* next_node = edge.GetMutableGradNode().get();
+        // Next node could be nullptr if it is leaf tensor with no
+        // AccumulationNode attached
+        // Or it could also originated from dispensable inputs
+        if (!next_node) continue;
+
+        // Update in_degree
+        if (!node_in_degree_map.count(next_node))
+          node_in_degree_map[next_node] = 0;
+        node_in_degree_map[next_node]++;
+        queue.push(next_node);
+      }
+    }
+  }
+  return node_in_degree_map;
 }
 
 // Enforce GradNode has TensorWrappers as Input
@@ -281,28 +408,23 @@ void EnforceGradNodeHasInput(GradNodeBase* node) {
           node->name()));
 }
 
-// Purify potential_startup_nodes, remove nodes those are the same as
-// input_target_nodes
-void PurifyPotentialStartUpNodes(
-    std::unordered_set<GradNodeBase*>* potential_startup_nodes,
-    std::unordered_map<GradNodeBase*, AutogradMeta* /* InputMeta */>*
-        input_target_nodes_inputmeta_map) {
-  VLOG(6) << "Running in PurifyPotentialStartUpNodes";
-  if (input_target_nodes_inputmeta_map->empty()) return;
-  std::unordered_set<GradNodeBase*> potential_startup_nodes_to_be_erased;
-  for (auto startup_op : *potential_startup_nodes) {
-    auto iter = input_target_nodes_inputmeta_map->find(startup_op);
-    if (iter != input_target_nodes_inputmeta_map->end()) {
-      potential_startup_nodes_to_be_erased.emplace(iter->first);
-    }
-  }
-  if (!potential_startup_nodes_to_be_erased.empty()) {
-    for (auto nodes : potential_startup_nodes_to_be_erased) {
-      potential_startup_nodes->erase(nodes);
-    }
+void DuplicateCheck(const std::vector<paddle::experimental::Tensor>& inputs,
+                    bool is_input) {
+  std::unordered_set<AutogradMeta*> visisted_ins;
+  std::string msg = is_input ? "inputs" : "outputs";
+  for (auto in : inputs) {
+    AutogradMeta* auto_grad_meta = EagerUtils::unsafe_autograd_meta(in);
+    PADDLE_ENFORCE_EQ(
+        visisted_ins.count(auto_grad_meta), 0,
+        paddle::platform::errors::AlreadyExists(
+            "%s contain duplicate tensor %s, please check %s carefully.", msg,
+            in.name(), msg));
+    visisted_ins.insert(auto_grad_meta);
   }
 }
 
+GeneralGrad* GeneralGrad::general_grad_ = new GeneralGrad();
+
 std::vector<paddle::experimental::Tensor> RunBackward(
     const std::vector<paddle::experimental::Tensor>& tensors,  // output
     const std::vector<paddle::experimental::Tensor>& grad_tensors,
@@ -315,10 +437,8 @@ std::vector<paddle::experimental::Tensor> RunBackward(
   // *Inplace version check should perform at node-level
   // *Cross-batch accumulation happens at forward pass
 
-  std::unordered_map<GradNodeBase*, AutogradMeta*>
-      no_grad_var_nodes_inputmeta_map;
-  // Get no_grad_vars's GradNodes and InputMeta Info
-  GetTargetNodesInfo(no_grad_vars, &no_grad_var_nodes_inputmeta_map);
+  // GeneralGrad
+  bool is_general_grad = !inputs.empty();
 
   /* --- Initialization --- */
   // 1. Init queue with starting nodes
@@ -326,7 +446,6 @@ std::vector<paddle::experimental::Tensor> RunBackward(
   std::queue<GradNodeBase*> queue;
   std::unordered_map<GradNodeBase*, std::unique_ptr<GradTensorHolder>>
       node_input_buffers_dict;
-  std::unordered_set<GradNodeBase*> potential_startup_nodes;
   for (size_t i = 0; i < tensors.size(); i++) {
     const paddle::experimental::Tensor& tensor = tensors[i];
 
@@ -363,7 +482,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
           paddle::platform::errors::Fatal(
               "Detected size mismatch between tensors and grad_tensors"
               "grad_tensors should either have "
-              "size = 0 or same size as tensors"));
+              "size = 0 or same size as tensors."));
       // Feed given tensor if it's provided
       VLOG(6) << "Fill grad input tensor " << i << "with give grad tensor";
 
@@ -391,7 +510,9 @@ std::vector<paddle::experimental::Tensor> RunBackward(
 
     // Prepare queue, potential startup_nodes
     queue.push(grad_node);
-    potential_startup_nodes.emplace(grad_node);
+    if (is_general_grad) {
+      GeneralGrad::Instance().GetPotentialStartupNodes()->emplace(grad_node);
+    }
   }
 
   VLOG(6) << "Update In degree Map for backward";
@@ -399,56 +520,13 @@ std::vector<paddle::experimental::Tensor> RunBackward(
   std::unordered_map<GradNodeBase*, int> node_in_degree_map =
       getInDegreeMap(queue);
 
-  // Get input's GradNodes and InputMeta Info
-  std::unordered_map<GradNodeBase*, AutogradMeta* /* InputMeta */>
-      input_target_nodes_inputmeta_map;
-  GetTargetNodesInfo(inputs, &input_target_nodes_inputmeta_map);
-
-  // Purify potential_startup_ops, remove those nodes that are the same as
-  // input_target_nodes
-  PurifyPotentialStartUpNodes(&potential_startup_nodes,
-                              &input_target_nodes_inputmeta_map);
-
-  // Get Graph Info Betweent input target gradnode and outputs
-  // Record the depending_nodes and potential_stop_nodes
-  std::unordered_map<GradNodeBase* /* child node */,
-                     std::unordered_set<GradNodeBase*> /* father node */>
-      depending_nodes;
-  std::unordered_set<GradNodeBase*> potential_stop_nodes;
-  // std::unordered_set<GradNodeBase*> startup_ops;
-
-  GetGraphInfoBetweenTargets(queue, &input_target_nodes_inputmeta_map,
-                             &depending_nodes, &potential_stop_nodes,
-                             &potential_startup_nodes);
-
-  // ready_queue store all startup nodes
-  std::queue<GradNodeBase*> ready_queue;
-  // startup op's indegree should be 0
-  for (auto node : potential_startup_nodes) {
-    if (node_in_degree_map[node] == 0) {
-      ready_queue.emplace(node);
-    }
+  if (is_general_grad) {
+    // Prepare several vital preprocess for GeneralGrad
+    GeneralGrad::Instance().PreparedForGeneralGrad(inputs, no_grad_vars, &queue,
+                                                   node_input_buffers_dict);
   }
 
-  VLOG(1) << " startup_ops' size is :" << ready_queue.size();
-
-  std::unordered_map<GradNodeBase*, paddle::experimental::Tensor> results_map;
-
-  // read_queue is empty only when 1.input equals to output. 2.input can not
-  // reach to output.
-  if (ready_queue.size() == 0) {
-    for (auto input_target_node : input_target_nodes_inputmeta_map) {
-      // out rank_info of forward op
-      auto rank_info = input_target_node.second->OutRankInfo();
-      if (node_input_buffers_dict[input_target_node.first]) {
-        auto& target_result =
-            node_input_buffers_dict[input_target_node.first]
-                ->Buffers()[rank_info.first][rank_info.second];
-        // save the target result
-        results_map[input_target_node.first] = target_result;
-      }
-    }
-  }
+  VLOG(6) << " startup_ops' size is :" << queue.size();
 
   /* --- Topological Visit --- */
   // 1. Pop queue
@@ -458,53 +536,55 @@ std::vector<paddle::experimental::Tensor> RunBackward(
   //    |- Prepare for next node
   // 3. Update queue
   VLOG(6) << "Run Backward";
-  while (!ready_queue.empty()) {
-    GradNodeBase* node = ready_queue.front();
+  while (!queue.empty()) {
+    GradNodeBase* node = queue.front();
     VLOG(6) << "Running GradNode:" << node->name();
-    ready_queue.pop();
 
     paddle::platform::RecordEvent node_record_event(
         std::string(typeid(*node).name()) + " grad_node",
         paddle::platform::TracerEventType::Operator, 1);
 
+    if (queue.size() > 1 && node_in_degree_map[node] != 0) {
+      queue.pop();
+      continue;
+    }
+    queue.pop();
+
     // Run node: This is where Hook happens
     PADDLE_ENFORCE(
         node_input_buffers_dict.count(node),
         paddle::platform::errors::Fatal(
             "Unable to find next node in the GradTensorHolder \n"
-            "Trying to run Node without configuring its GradTensorHolder"));
+            "Trying to run Node without configuring its GradTensorHolder."));
 
     std::unique_ptr<GradTensorHolder> node_input_buffer =
         std::move(node_input_buffers_dict[node]);
 
-    // get target grad_var from node_input_buffer by inputmeta
-    if (input_target_nodes_inputmeta_map.find(node) !=
-        input_target_nodes_inputmeta_map.end()) {
-      VLOG(6) << "Get target result by by inputmeta";
-      // out rank_info of forward op
-      auto rank_info = input_target_nodes_inputmeta_map[node]->OutRankInfo();
-      // rank_info is a pair, first means slot_id, second means rank.
-      auto& target_result =
-          node_input_buffer->Buffers()[rank_info.first][rank_info.second];
-      // save the target result
-      results_map[node] = target_result;
+    // Set input target grad_var from node_input_buffer by inputmeta
+    if (!inputs.empty() && is_general_grad) {
+      GeneralGrad::Instance().SetResultForInputTargetVar(*node_input_buffer,
+                                                         node);
     }
 
     // no_grad_vars
-    if (no_grad_var_nodes_inputmeta_map.find(node) !=
-        no_grad_var_nodes_inputmeta_map.end()) {
+    if (!no_grad_vars.empty() && is_general_grad) {
+      auto iter =
+          GeneralGrad::Instance().GetNoGradVarNodesInputMetaMap()->find(node);
+      if (iter !=
+          GeneralGrad::Instance().GetNoGradVarNodesInputMetaMap()->end()) {
         VLOG(6) << "Change the input buffer[slot][rank] by Zeros";
-      auto rank_info = no_grad_var_nodes_inputmeta_map[node]->OutRankInfo();
+        auto rank_info = (iter->second)->OutRankInfo();
         node_input_buffer->SetBufferSlotRankZeros(rank_info.first,
                                                   rank_info.second);
       }
+    }
 
     VLOG(6) << "Running GradNode:" << node->name();
 
-    // check input
+    // Check input
     EnforceGradNodeHasInput(node);
 
-    VLOG(6) << "Run Backward Kernel with GradTensorHolder";
+    VLOG(6) << "Run Backward Kernel with GradTensorHolder.";
     // Run Pre Backward Node and get outputs
     std::vector<std::vector<paddle::experimental::Tensor>> grad_output_tensors =
         (*node)(node_input_buffer->Buffers(), create_graph);
@@ -587,23 +667,29 @@ std::vector<paddle::experimental::Tensor> RunBackward(
             node_in_degree_map[next_node] >= 0,
             paddle::platform::errors::Fatal(
                 "Detected in-degree value smaller than zero. For Node: %s"
-                "Node's in-degree cannot be negative",
+                "Node's in-degree cannot be negative.",
                 next_node->name()));
 
-        bool is_potential_stop_node = potential_stop_nodes.count(next_node);
-
+        if (is_general_grad) {
+          bool is_potential_stop_node =
+              GeneralGrad::Instance().GetPotentialStopNodes()->count(next_node);
           if (node_in_degree_map[next_node] == 0 && !is_potential_stop_node) {
-          ready_queue.emplace(std::move(next_node));
+            queue.emplace(std::move(next_node));
+          }
+        } else {
+          if (node_in_degree_map[next_node] == 0) {
+            queue.emplace(std::move(next_node));
           }
         }
       }
     }
-
-  return GetResults(inputs, &results_map, allow_unused, create_graph);
+  }
+  if (!is_general_grad) return {};
+  return GeneralGrad::Instance().GetResults(inputs, allow_unused, create_graph);
 }
 
 void Backward(
-    const std::vector<paddle::experimental::Tensor>& tensors,  // output
+    const std::vector<paddle::experimental::Tensor>& tensors,  // outputs
     const std::vector<paddle::experimental::Tensor>& grad_tensors,
     bool retain_graph) {
   VLOG(6) << "Run in Backward";
@@ -613,12 +699,16 @@ void Backward(
 }
 
 std::vector<paddle::experimental::Tensor> Grad(
-    const std::vector<paddle::experimental::Tensor>& tensors,  // output
+    const std::vector<paddle::experimental::Tensor>& tensors,  // outputs
     const std::vector<paddle::experimental::Tensor>& inputs,
     const std::vector<paddle::experimental::Tensor>& grad_tensors,
     bool retain_graph, bool create_graph, bool only_inputs, bool allow_unused,
     const std::vector<paddle::experimental::Tensor>& no_grad_vars) {
   VLOG(6) << "Run in Grad";
+
+  DuplicateCheck(inputs, true /* is_input */);
+  DuplicateCheck(tensors, false /* is_input */);
+
   return RunBackward(tensors, grad_tensors, retain_graph, create_graph, inputs,
                      allow_unused, no_grad_vars);
 }

python/paddle/fluid/tests/unittests/test_imperative_double_grad.py

@@ -116,6 +116,54 @@ class TestEagerGrad(TestCase):
             self.func_simple_example_eager_grad_not_allow_unused()
         self.func_simple_example_eager_grad_not_allow_unused()
 
+    def func_simple_example_eager_grad_duplicate_input(self):
+        np.random.seed(2021)
+        paddle.set_device('cpu')
+        np_x = np.random.random((3, 3))
+        np_y = np.random.random((3, 1))
+        np_z = np.random.random((3, 1))
+        x = paddle.to_tensor(np_x, dtype="float64", stop_gradient=False)
+        y = paddle.to_tensor(np_y, dtype="float64", stop_gradient=False)
+        z = paddle.to_tensor(np_z, dtype="float64", stop_gradient=False)
+        out_z = paddle.nn.functional.sigmoid(z)
+        out = paddle.matmul(x, y)
+
+        try:
+            # duplicate input will arise RuntimeError errors
+            dx = fluid.dygraph.grad(out, [x, x])
+        except RuntimeError as e:
+            error_msg = cpt.get_exception_message(e)
+            assert error_msg.find("duplicate") > 0
+
+    def test_simple_example_eager_grad_duplicate_input(self):
+        with _test_eager_guard():
+            self.func_simple_example_eager_grad_duplicate_input()
+        self.func_simple_example_eager_grad_duplicate_input()
+
+    def func_simple_example_eager_grad_duplicate_output(self):
+        np.random.seed(2021)
+        paddle.set_device('cpu')
+        np_x = np.random.random((3, 3))
+        np_y = np.random.random((3, 1))
+        np_z = np.random.random((3, 1))
+        x = paddle.to_tensor(np_x, dtype="float64", stop_gradient=False)
+        y = paddle.to_tensor(np_y, dtype="float64", stop_gradient=False)
+        z = paddle.to_tensor(np_z, dtype="float64", stop_gradient=False)
+        out_z = paddle.nn.functional.sigmoid(z)
+        out = paddle.matmul(x, y)
+
+        try:
+            # duplicate output will arise RuntimeError errors
+            dx = fluid.dygraph.grad([out, out], [x])
+        except RuntimeError as e:
+            error_msg = cpt.get_exception_message(e)
+            assert error_msg.find("duplicate") > 0
+
+    def test_simple_example_eager_grad_duplicate_output(self):
+        with _test_eager_guard():
+            self.func_simple_example_eager_grad_duplicate_output()
+        self.func_simple_example_eager_grad_duplicate_output()
+
 
 class TestDygraphDoubleGrad(TestCase):
     def setUp(self):