[Eager Grad] Support eager grad interface (#40170)

* [Eager] Support eager grad interface, draft version

* Support eager grad interface with allow_unused and multi startup_op

* Fix code format

* Fix allow_unused case, return PyNone if tensor not initialize

* Support output's stop_gradient related to create_graph

* Support grad exception case in eager mode, fix coverage CI

* Update ToPyObject, return PyNone if not initialize

* AccumulationNode add FLAGS_retain_grad_for_all_tensor

* Fix ci issue

* Fix CI issue

* fix, use core.eager.Tensor

* Add func SetBufferSlotRankZeros for GradTensorHolder

* Support retain_graph by using ClearTensorWrappers

* Support retain_graph by using ClearTensorWrappers

* Update retain_graph and no_grad_vars related test case

* Update code gen logic for ClearTensorWrappers

* Fix by override statement

* fix override func args

* Support retain_graph, update unit tests

* Updated ClearTensorWrappers logic

* fix grad python interface

* Use deep copy and update unit tests

* Polish code

* Polish code

* Fix CI issue, Deep copy only use when user set grad_tensors

* Fix CI, use Backward instead RunBackward

* Fix CI, Declare kernel explicitly in test file

* Polish, remove vector of TensorWrapper

* Refactor the logic of grad/backward, polish codes

* Update code after merge upstream develop

* Polish after merge upstream develop

* Update to adapt new GradNodeBase superclass

* Fix error introduced during conflict resolution

* Update purify potential_startup_nodes logic

* Fix errors

* Polish code

* Remove useless args for ToPyObject

* Remove useless TensorWrappersSet

* Fix code-format, re-install pre-commit

* Fix pre-process logic for potential_startup_ops

* Update unit tests, use eager mode

paddle/fluid/eager/accumulation/accumulation_node.cc

@@ -24,7 +24,7 @@
 #include "paddle/fluid/platform/errors.h"
 
 #include "glog/logging.h"
-
+DECLARE_bool(retain_grad_for_all_tensor);
 namespace egr {
 
 static void CopyOrAddTensor(paddle::experimental::Tensor* tensor,
@@ -39,8 +39,8 @@ static void CopyOrAddTensor(paddle::experimental::Tensor* tensor,
 }
 
 std::vector<std::vector<paddle::experimental::Tensor>> GradNodeAccumulation::
-operator()(
-    const std::vector<std::vector<paddle::experimental::Tensor>>& grads) {
+operator()(const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+           bool create_graph) {
   VLOG(3) << "Running Eager Backward Node: GradNodeAccumulation";
   PADDLE_ENFORCE(grads.size() == 1,
                  paddle::platform::errors::Fatal(
@@ -62,7 +62,7 @@ operator()(
     grad_out = grads[0][0];
   }
 
-  if (!weak_grad_.expired()) {
+  if (!weak_grad_.expired() && FLAGS_retain_grad_for_all_tensor) {
     auto grad = weak_grad_.lock();
     CopyOrAddTensor(grad.get(), grad_out);
   }

paddle/fluid/eager/accumulation/accumulation_node.h

@@ -35,8 +35,15 @@ class GradNodeAccumulation : public GradNodeBase {
 
   // Functor: perform backward computations
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads)
-      override;
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+      bool create_graph = false) override;
+
+  void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
+
+  bool IsTensorWrappersCleared() override {
+    VLOG(6) << "Do nothing here now";
+    return false;
+  }
 
   std::string name() { return "GradNodeAccumulation"; }
 

paddle/fluid/eager/api/generated/eager_generated/backwards/scale_node.cc

@@ -145,8 +145,8 @@ void GradNodeScale::SetTensorWrappers_X(
 void GradNodeScale::SetAttributes_scale(float scale) { scale_ = scale; }
 
 std::vector<std::vector<paddle::experimental::Tensor>> GradNodeScale::
-operator()(
-    const std::vector<std::vector<paddle::experimental::Tensor>>& grads) {
+operator()(const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+           bool create_graph) {
   // 1. Check Output Size
   PADDLE_ENFORCE(
       ((grads.size() == 1) && (grads[0].size() == 1)),

paddle/fluid/eager/api/generated/eager_generated/backwards/scale_node.h

@@ -39,8 +39,15 @@ class GradNodeScale : public GradNodeBase {
 
   // Functor: perform backward computations
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads)
-      override;
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+      bool create_graph = false) override;
+
+  void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
+
+  bool IsTensorWrappersCleared() override {
+    VLOG(6) << "Do nothing here now";
+    return false;
+  }
 
   void SetTensorWrappers_X(
       const std::vector<paddle::experimental::Tensor>& tensors);

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -2074,7 +2074,8 @@ static std::string GenerateGradNodeCCContents(
   const char* GRAD_FUNCTION_TEMPLATE =
       "std::vector<std::vector<paddle::experimental::Tensor>> "
       "GradNode%s::operator()(const "
-      "std::vector<std::vector<paddle::experimental::Tensor>>& grads) {\n%s\n}";
+      "std::vector<std::vector<paddle::experimental::Tensor>>& grads, "
+      "bool create_graph) {\n%s\n}";
   std::string grad_function_str = paddle::string::Sprintf(
       GRAD_FUNCTION_TEMPLATE, fwd_op_type, generated_grad_function_body);
 
@@ -2109,18 +2110,28 @@ static std::string GenerateGradNodeHeaderContents(
       "\n"
       "  virtual std::vector<std::vector<paddle::experimental::Tensor>> "
       "operator()(const "
-      "std::vector<std::vector<paddle::experimental::Tensor>>& grads) "
+      "std::vector<std::vector<paddle::experimental::Tensor>>& grads, const "
+      "bool create_graph = false) "
       "override;\n"
       "\n"
+      "  void ClearTensorWrappers() override { \n"
+      "%s\n"
+      "    is_tensor_wrappers_cleared = true;\n"
+      "  }\n"
       "  std::string name() override { return \" GradNode%s \"; } \n "
       "\n"
       "  // SetX, SetY, ...\n"
       "%s\n"
       "  // SetAttrMap\n"
       "%s\n"
+      "  bool IsTensorWrappersCleared() override { \n"
+      "    return is_tensor_wrappers_cleared;\n"
+      "  }\n"
       " private:\n"
       "   // TensorWrappers\n"
       "%s\n"
+      "   bool is_tensor_wrappers_cleared = false;\n"
+      "\n"
       "   // Attribute Map\n"
       "%s\n"
       "};";
@@ -2154,6 +2165,7 @@ static std::string GenerateGradNodeHeaderContents(
 
   std::string set_tensor_wrappers_str = "";
   std::string tensor_wrapper_members_str = "";
+  std::string clear_tensor_wrappers_str = "";
   for (const auto& iter : op_base_infos) {
     const std::map<std::string, std::string>& grad_ins_fwd_slotname_map =
         iter.GetGradInsFwdSlotnameMap();
@@ -2185,6 +2197,13 @@ static std::string GenerateGradNodeHeaderContents(
             SET_TENSOR_WRAPPER_BODY_TEMPLATE, tensor_wrapper_name,
             struct_tensor_wrapper_name);
 
+        const char* CLEAR_TENSOR_WRAPPER_TEMPLATE =
+            "for (auto tw: %s)   {\n"
+            "       tw.clear();\n"
+            "     }\n";
+        clear_tensor_wrappers_str += paddle::string::Sprintf(
+            CLEAR_TENSOR_WRAPPER_TEMPLATE, struct_tensor_wrapper_name);
+
       } else {
         const char* ATTR_TENSOR_WRAPPER_ARG_TEMPLATE =
             "const paddle::experimental::Tensor& %s";
@@ -2197,10 +2216,14 @@ static std::string GenerateGradNodeHeaderContents(
             TENSOR_WRAPPER_MEMBER_TEMPLATE, struct_tensor_wrapper_name);
 
         const char* SET_TENSOR_WRAPPER_BODY_TEMPLATE =
-            "%s = egr::TensorWrapper(%s, %s /*full_reserved*/);";
+            "%s = egr::TensorWrapper(%s, %s /*full_reserved*/);\n";
         tensor_wrapper_body_str = paddle::string::Sprintf(
             SET_TENSOR_WRAPPER_BODY_TEMPLATE, struct_tensor_wrapper_name,
             tensor_wrapper_name, full_reserved_str);
+
+        const char* CLEAR_TENSOR_WRAPPER_TEMPLATE = "   %s.clear();\n";
+        clear_tensor_wrappers_str += paddle::string::Sprintf(
+            CLEAR_TENSOR_WRAPPER_TEMPLATE, struct_tensor_wrapper_name);
       }
       std::string full_reserved_signature_str = "bool full_reserved";
       const char* SET_TENSOR_WRAPPER_TEMPLATE =
@@ -2215,8 +2238,8 @@ static std::string GenerateGradNodeHeaderContents(
 
   std::string grad_node_str = paddle::string::Sprintf(
       GRAD_NODE_TEMPLATE, op_type, op_type, op_type, op_type, op_type, op_type,
-      op_type, op_type, set_tensor_wrappers_str, set_attr_map_str,
-      tensor_wrapper_members_str, attr_members_str);
+      op_type, clear_tensor_wrappers_str, op_type, set_tensor_wrappers_str,
+      set_attr_map_str, tensor_wrapper_members_str, attr_members_str);
 
   return grad_node_str;
 }

paddle/fluid/eager/auto_code_generator/final_state_generator/eager_gen.py

@@ -478,6 +478,7 @@ def GenerateNodeDeclaration(fwd_api_name, backward_fwd_input_map,
     # SetTensorWrapper Methods & TensorWrapper Members
     set_tensor_wrapper_methods_str = ""
     tensor_wrapper_members_str = ""
+    clear_tensor_wrapper_str = ""
     for tname, (ttype, is_fwd_input, _) in backward_fwd_input_map.items():
         if tname in no_need_buffer_set:
             no_need_buffer = "true"
@@ -499,6 +500,13 @@ def GenerateNodeDeclaration(fwd_api_name, backward_fwd_input_map,
 """
             tensor_wrapper_members_str += PLAIN_TENSOR_MEMBER_TEMPLATE.format(
                 tensor_wrapper_name)
+
+            CLEAR_TENSOR_WRAPPERS_TEMPLATE = """
+   {}.clear();
+"""
+            clear_tensor_wrapper_str += CLEAR_TENSOR_WRAPPERS_TEMPLATE.format(
+                tensor_wrapper_name)
+
         else:
             assert IsVectorTensorType(ttype)
             SET_VECTOR_TENSOR_WRAPPER_TEMPLATE = """
@@ -516,6 +524,15 @@ def GenerateNodeDeclaration(fwd_api_name, backward_fwd_input_map,
 """
             tensor_wrapper_members_str += VECTOR_TENSOR_MEMBER_TEMPLATE.format(
                 tensor_wrapper_name)
+
+            CLEAR_TENSOR_WRAPPERS_TEMPLATE = """
+   for (auto tw: {}) {
+     tw.clear();
+   };
+"""
+            clear_tensor_wrapper_str += CLEAR_TENSOR_WRAPPERS_TEMPLATE.format(
+                tensor_wrapper_name)
+
     # End: SetTensorWrapper Methods & TensorWrapper Members
 
     # SetAttributes & Attribute Members
@@ -524,7 +541,7 @@ def GenerateNodeDeclaration(fwd_api_name, backward_fwd_input_map,
     for aname, atype, default_val, _ in backward_attrs_list:
         saved_attr_name = GetSavedName(aname)
         SET_ATTR_METHOD_TEMPLATE = """
-   void SetAttribute{}({} {}) {{     
+   void SetAttribute{}({} {}) {{
      {} = {};
    }}
 """
@@ -555,25 +572,37 @@ class {} : public egr::GradNodeBase {{
   ~{}() override = default;
 
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads) override;
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads, bool create_graph = false) override;
   std::string name() override {{ return \" {} \"; }}
+  
+  void ClearTensorWrappers() override {{
+      {}
+    is_tensor_wrappers_cleared = true;
+  }}
+  
   // SetTensorWrapperX, SetTensorWrapperY, ...
   {}
   // SetAttributes
   {}
+
+  bool IsTensorWrappersCleared() override {{
+      return is_tensor_wrappers_cleared;  
+  }}
  private:
   // TensorWrappers
   {}
 
+  bool is_tensor_wrappers_cleared = false;
+
   // Attributes
   {}
 }};
 """
     node_declaration_str = NODE_DECLARATION_TEMPLATE.format(
         grad_node_name, grad_node_name, grad_node_name, grad_node_name,
-        grad_node_name, set_tensor_wrapper_methods_str,
-        set_attribute_methods_str, tensor_wrapper_members_str,
-        attribute_members_str)
+        grad_node_name, clear_tensor_wrapper_str,
+        set_tensor_wrapper_methods_str, set_attribute_methods_str,
+        tensor_wrapper_members_str, attribute_members_str)
 
     return node_declaration_str
 
@@ -637,7 +666,7 @@ def GenerateNodeDefinition(fwd_api_name, bwd_api_name, backward_fwd_input_map,
         grad_api_namespace = f"paddle::experimental"
 
     FUNCTION_TEMPLATE = """
-std::vector<std::vector<paddle::experimental::Tensor>> {}::operator()(const std::vector<std::vector<paddle::experimental::Tensor>>& grads) {{
+std::vector<std::vector<paddle::experimental::Tensor>> {}::operator()(const std::vector<std::vector<paddle::experimental::Tensor>>& grads, bool create_graph) {{
     // Call grad_api function
     auto grad_api_returns = {}::{}({});
     {}

paddle/fluid/eager/backward.cc

@@ -39,12 +39,21 @@ std::unordered_map<GradNodeBase*, int> getInDegreeMap(
   // 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;
+      }
+      cnt += 1;
+    }
+
     if (visited.count(node)) {
       continue;
     }
@@ -76,23 +85,248 @@ std::unordered_map<GradNodeBase*, int> getInDegreeMap(
   return node_in_degree_map;
 }
 
-void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
-                 const std::vector<paddle::experimental::Tensor>& grad_tensors,
-                 bool retain_graph) {
-  paddle::platform::RecordEvent backward_record_event(
-      "backward", paddle::platform::TracerEventType::Operator, 1);
+// 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) {
+  // 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) {
+    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];
+      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);
+        }
+      }
+    } else {  // startup_ops have no precedding nodes
+      VLOG(6) << "Emplace _startup_ops";
+      _startup_ops.emplace(target_node);
+    }
+  }
+  // Purify potential_startup_nodes again, remove some
+  // 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) {
+      if (_startup_ops.count(node) == 0) {
+        VLOG(6) << "Set up potential_startup_nodes_to_be_erased";
+        potential_startup_nodes_to_be_erased.emplace(node);
+      }
+    }
+    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);
+      }
+    }
+  }
+}
+
+// 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;
+
+  VLOG(6) << "Runing In GetGraphInfoBetweenTargets";
+
+  // Calculate in_degree for each node
+  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);
+
+    // 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);
+
+    // 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;
+
+        // if node not in input_target_nodes,
+        // 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);
+        }
+
+        // Update in_degree
+        if (!node_in_degree_map.count(next_node))
+          node_in_degree_map[next_node] = 0;
+        node_in_degree_map[next_node]++;
 
+        // Record depending relationship
+        (*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);
+}
+
+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();
+
+      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;
+    }
+  }
+}
+
+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 {};
+
+  std::vector<paddle::experimental::Tensor> results;
+  results.reserve(inputs.size());
+
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    auto& input = inputs[i];
+    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()) {
+      // set StopGradient = !create_graph
+      AutogradMeta* tensor_auto_grad_meta =
+          EagerUtils::autograd_meta(&(iter->second));
+      tensor_auto_grad_meta->SetStopGradient(!create_graph);
+      results.emplace_back(iter->second);
+    } else {
+      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 "
+                            "allow_unused=True to get None result.",
+                            i));
+      results.emplace_back();
+    }
+  }
+  return results;
+}
+
+// Enforce GradNode has TensorWrappers as Input
+void EnforceGradNodeHasInput(GradNodeBase* node) {
+  VLOG(6) << "Running in EnforceGradNodeHasInput";
+  PADDLE_ENFORCE_NE(
+      node->IsTensorWrappersCleared(), true,
+      paddle::platform::errors::Fatal(
+          "The TensorWrappers of %s do not exist. This may be because:\n"
+          "You calculate backward twice for the same subgraph without "
+          "setting retain_graph=True. Please set retain_graph=True in the "
+          "first backward/grad call.\n",
+          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);
+    }
+  }
+}
+
+std::vector<paddle::experimental::Tensor> RunBackward(
+    const std::vector<paddle::experimental::Tensor>& tensors,  // output
+    const std::vector<paddle::experimental::Tensor>& grad_tensors,
+    bool retain_graph, bool create_graph = false,
+    const std::vector<paddle::experimental::Tensor>& inputs = {},
+    bool allow_unused = false,
+    const std::vector<paddle::experimental::Tensor>& no_grad_vars = {}) {
   VLOG(6) << "Start Backward";
   // *Gradient Hook should happen at node-level
   // *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);
+
   /* --- Initialization --- */
   // 1. Init queue with starting nodes
   // 2. Prepare initial input buffers
   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];
 
@@ -132,8 +366,17 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& 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";
-      node_input_buffers_dict[grad_node]->add(
-          input_info.first, input_info.second, grad_tensors[i]);
+
+      if (grad_tensors[i].is_initialized()) {
+        // Deep copy
+        paddle::experimental::Tensor tmp_tensor;
+        tmp_tensor.copy_(grad_tensors[i], true);
+        node_input_buffers_dict[grad_node]->add(input_info.first,
+                                                input_info.second, tmp_tensor);
+      } else {
+        node_input_buffers_dict[grad_node]->add(
+            input_info.first, input_info.second, grad_tensors[i]);
+      }
 
     } else {
       VLOG(6) << "Fill grad input tensor " << i << " with 1.0";
@@ -146,8 +389,9 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
           input_info.first, input_info.second, tensor, true /*fill_one=true*/);
     }
 
-    // Prepare queue
+    // Prepare queue, potential startup_nodes
     queue.push(grad_node);
+    potential_startup_nodes.emplace(grad_node);
   }
 
   VLOG(6) << "Update In degree Map for backward";
@@ -155,25 +399,74 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
   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);
+    }
+  }
+
+  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;
+      }
+    }
+  }
+
   /* --- Topological Visit --- */
   // 1. Pop queue
   // 2. Run node
+  //    |- Check and capture target result
   //    |- node(grads)
   //    |- Prepare for next node
   // 3. Update queue
   VLOG(6) << "Run Backward";
-  while (!queue.empty()) {
-    GradNodeBase* node = queue.front();
+  while (!ready_queue.empty()) {
+    GradNodeBase* node = ready_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),
@@ -184,10 +477,45 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
     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;
+    }
+
+    // no_grad_vars
+    if (no_grad_var_nodes_inputmeta_map.find(node) !=
+        no_grad_var_nodes_inputmeta_map.end()) {
+      VLOG(6) << "Change the input buffer[slot][rank] by Zeros";
+      auto rank_info = no_grad_var_nodes_inputmeta_map[node]->OutRankInfo();
+      node_input_buffer->SetBufferSlotRankZeros(rank_info.first,
+                                                rank_info.second);
+    }
+
+    VLOG(6) << "Running GradNode:" << node->name();
+
+    // check input
+    EnforceGradNodeHasInput(node);
+
     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());
+        (*node)(node_input_buffer->Buffers(), create_graph);
+
+    // retain_grad or not
+    if (!retain_graph) {
+      VLOG(6)
+          << "retain_graph is false, need to clear the TensorWrapper of nodes.";
+      node->ClearTensorWrappers();
+    }
+
     // TODO(jiabin): Should we erase it or find a more efficient way.
     node_input_buffers_dict.erase(node);
 
@@ -252,18 +580,44 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
 
         // Update queue
         node_in_degree_map[next_node]--;
+
         PADDLE_ENFORCE(
             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",
                 next_node->name()));
-        if (node_in_degree_map[next_node] == 0) {
-          queue.emplace(std::move(next_node));
+
+        bool is_potential_stop_node = potential_stop_nodes.count(next_node);
+
+        if (node_in_degree_map[next_node] == 0 && !is_potential_stop_node) {
+          ready_queue.emplace(std::move(next_node));
         }
       }
     }
   }
+
+  return GetResults(inputs, &results_map, allow_unused, create_graph);
 }
 
+void Backward(
+    const std::vector<paddle::experimental::Tensor>& tensors,  // output
+    const std::vector<paddle::experimental::Tensor>& grad_tensors,
+    bool retain_graph) {
+  VLOG(6) << "Run in Backward";
+  paddle::platform::RecordEvent backward_record_event(
+      "backward", paddle::platform::TracerEventType::Operator, 1);
+  RunBackward(tensors, grad_tensors, retain_graph);
+}
+
+std::vector<paddle::experimental::Tensor> Grad(
+    const std::vector<paddle::experimental::Tensor>& tensors,  // output
+    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";
+  return RunBackward(tensors, grad_tensors, retain_graph, create_graph, inputs,
+                     allow_unused, no_grad_vars);
+}
 }  // namespace egr

paddle/fluid/eager/backward.h

@@ -19,12 +19,20 @@
 
 namespace egr {
 
-// run_backward():
+// Backward():
 // tensors corresponds to those lived in the backward graph
 // each grad_tensors[i] keeps the value for its corresponding tensors[i]
-void RunBackward(const std::vector<paddle::experimental::Tensor> &tensors,
-                 const std::vector<paddle::experimental::Tensor> &grad_tensors,
-                 bool retain_graph = false);
+void Backward(const std::vector<paddle::experimental::Tensor>& tensors,
+              const std::vector<paddle::experimental::Tensor>& grad_tensors,
+              bool retain_graph = false);
+
+std::vector<paddle::experimental::Tensor> Grad(
+    const std::vector<paddle::experimental::Tensor>& tensors,
+    const std::vector<paddle::experimental::Tensor>& inputs,
+    const std::vector<paddle::experimental::Tensor>& grad_tensors = {},
+    bool retain_graph = false, bool create_graph = false,
+    bool only_inputs = false, bool allow_unused = false,
+    const std::vector<paddle::experimental::Tensor>& no_grad_vars = {});
 
 // Reserved for gradient()
 

paddle/fluid/eager/custom_operator/custom_operator_node.cc

@@ -20,8 +20,8 @@
 
 namespace egr {
 std::vector<std::vector<paddle::experimental::Tensor>> RunCustomOpNode::
-operator()(
-    const std::vector<std::vector<paddle::experimental::Tensor>>& grads) {
+operator()(const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+           bool create_graph) {
   paddle::CustomOpKernelContext ctx;
   auto grad_inputs_name = paddle::framework::OpMetaInfoHelper::GetInputs(
       egr::Controller::Instance().GetOpMetaInfoMap().at(op_type_)[1]);

paddle/fluid/eager/custom_operator/custom_operator_node.h

@@ -37,8 +37,8 @@ class RunCustomOpNode : public GradNodeBase {
 
   // Functor: perform backward computations
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads)
-      override;
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+      bool create_graph) override;
 
   std::string name() {
     return paddle::string::Sprintf("RunCustomOpNode: %s_grad", op_type_);
@@ -62,6 +62,12 @@ class RunCustomOpNode : public GradNodeBase {
     return res;
   }
 
+  void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
+  bool IsTensorWrappersCleared() override {
+    VLOG(6) << "Do nothing here now";
+    return false;
+  }
+
   void SetAttrs(const std::vector<paddle::any>& attr) { attrs_ = attr; }
 
  public:

paddle/fluid/eager/grad_node_info.h

@@ -95,8 +95,12 @@ class GradNodeBase {
    * is better choice to fit this format.
    * **/
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads) = 0;
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+      bool create_graph = false) = 0;
 
+  virtual void ClearTensorWrappers() = 0;
+
+  virtual bool IsTensorWrappersCleared() = 0;
   /**
    * AddEdges is designed to set input tensors' backward Node as current
    * node's Edges.

paddle/fluid/eager/grad_tensor_holder.cc

@@ -21,6 +21,11 @@
 
 namespace egr {
 
+void GradTensorHolder::SetBufferSlotRankZeros(size_t slot_id, size_t rank) {
+  buffer_[slot_id][rank] =
+      paddle::experimental::zeros_like(buffer_[slot_id][rank]);
+}
+
 void GradTensorHolder::add(size_t slot_id, size_t rank,
                            const paddle::experimental::Tensor& t,
                            bool fill_one) {

paddle/fluid/eager/grad_tensor_holder.h

@@ -56,6 +56,8 @@ class GradTensorHolder {
     return buffer_;
   }
 
+  void SetBufferSlotRankZeros(size_t slot_id, size_t rank);
+
  private:
   std::vector<std::vector<paddle::experimental::Tensor>> buffer_;
 };

paddle/fluid/eager/tensor_wrapper.h

@@ -98,6 +98,8 @@ class TensorWrapper {
     }
   }
 
+  void clear() { intermidiate_tensor_.reset(); }
+
  private:
   bool full_reserved_ = false;
   std::pair<size_t, size_t> out_rank_info_;

paddle/fluid/eager/tests/data_structure_tests/grad_node_test.h

@@ -32,8 +32,8 @@ class GradTestNode : public egr::GradNodeBase {
   GradTestNode() : GradNodeBase() { val_ = 1.0; }
   std::string name() override { return "GradTestNode"; }
   std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads)
-      override {
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+      bool create_graph = false) override {
     val_ = std::dynamic_pointer_cast<phi::DenseTensor>(grads[0][0].impl())
                ->data<float>()[0];
     phi::DenseTensorMeta meta =
@@ -49,6 +49,11 @@ class GradTestNode : public egr::GradNodeBase {
     std::vector<std::vector<paddle::experimental::Tensor>> res = {{et1}};
     return res;
   }
+  void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
+  bool IsTensorWrappersCleared() override {
+    VLOG(6) << "Do nothing here now";
+    return false;
+  }
   float val_;
 };
 }  // namespace eager_test

paddle/fluid/eager/tests/performance_tests/benchmark_utils.cc

@@ -58,7 +58,7 @@ void benchmark_eager_scale(const paddle::experimental::Tensor& tensor,
   }
 
   std::vector<paddle::experimental::Tensor> target_tensors = {input_tensor};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   if (accuracy_check) {
     // Examine Forward Grad (w.r.t max_num_runs = 10)
@@ -80,7 +80,7 @@ void benchmark_eager_matmul(const paddle::experimental::Tensor& X,
   }
 
   std::vector<paddle::experimental::Tensor> target_tensors = {input_tensor0};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   if (accuracy_check) {
     // Examine Forward Grad (w.r.t max_num_runs = 2)
@@ -106,7 +106,7 @@ void benchmark_eager_intermediate_matmul(const paddle::experimental::Tensor& X,
   }
 
   std::vector<paddle::experimental::Tensor> target_tensors = {input_tensor0};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   if (accuracy_check) {
     // Examine Forward Grad (w.r.t max_num_runs = 2)
@@ -137,7 +137,7 @@ void benchmark_eager_intermediate_mlp(
       reduce_sum_dygraph_function(input0, {{"reduce_all", true}});
 
   std::vector<paddle::experimental::Tensor> target_tensors = {Out};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   if (accuracy_check) {
     std::unordered_map<std::string, float> result =

paddle/fluid/eager/tests/task_tests/CMakeLists.txt

@@ -5,6 +5,7 @@ cc_test(test_egr_task_backward SRCS backward_test.cc DEPS ${eager_deps} ${fluid_
 cc_test(test_egr_task_hook SRCS hook_test.cc DEPS ${eager_deps} ${fluid_deps} eager_scale scale_node)
 cc_test(test_egr_task_cross_batch SRCS cross_batch_accumulation_test.cc DEPS ${eager_deps} ${fluid_deps} eager_scale scale_node)
 cc_test(test_egr_task_fwd_bwd_joint SRCS fwd_bwd_joint_test.cc DEPS ${eager_deps} ${fluid_deps} eager_scale scale_node)
+cc_test(test_egr_task_grad SRCS grad_test.cc DEPS ${eager_deps} ${fluid_deps} eager_scale scale_node)
 
 if(NOT ((NOT WITH_PYTHON) AND ON_INFER))
     cc_test(test_egr_task_hook_intermidiate SRCS hook_test_intermidiate.cc DEPS ${eager_deps} ${fluid_deps} ${generated_deps} dygraph_node)

paddle/fluid/eager/tests/task_tests/backward_test.cc

@@ -33,6 +33,7 @@
 #include "paddle/phi/core/kernel_registry.h"
 
 PD_DECLARE_KERNEL(full, CPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(copy, CPU, ALL_LAYOUT);
 
 namespace egr {
 
@@ -79,7 +80,7 @@ TEST(Backward, SingleNodeEmptyGrad) {
   }
   std::vector<paddle::experimental::Tensor> outs = {target_tensor};
   // Run Backward
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Check Output Value
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 5.0);
@@ -138,7 +139,7 @@ TEST(Backward, SingleNodeCustomGrad) {
   }
 
   // Run Backward
-  RunBackward(target_tensors, grad_tensors);
+  Backward(target_tensors, grad_tensors);
 
   // Check Output Value
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 50.0);
@@ -211,7 +212,7 @@ TEST(Backward, LinearNodes) {
   }
 
   // Use Empty Grad Tensor
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   // Check Output Value
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 50.0);
@@ -315,7 +316,7 @@ TEST(Backward, WithAccumulation) {
     node2_ptr->AddEdges(&res2, 0);
   }
 
-  RunBackward(target_tensors, grad_tensors);
+  Backward(target_tensors, grad_tensors);
 
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 2500.0);
 }

paddle/fluid/eager/tests/task_tests/cross_batch_accumulation_test.cc

@@ -71,12 +71,12 @@ TEST(CrossBatchAccumulation, SingleScaleNode) {
   std::vector<egr::AutogradMeta*> res = {meta};
   scale_node_ptr->AddEdges(&res, 0);
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(target_tensor, 1.0);
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 5.0);
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(target_tensor, 1.0);
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 10.0);

paddle/fluid/eager/tests/task_tests/fwd_bwd_joint_test.cc

@@ -86,7 +86,7 @@ TEST(FwdBwdJoint, SingleNode) {
 
   std::vector<paddle::experimental::Tensor> outs = {out};
   // 4. Run Backward
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   VLOG(7) << "Target Grad is: "
           << std::static_pointer_cast<phi::DenseTensor>(
@@ -137,7 +137,7 @@ TEST(FwdBwdJoint, LinearNodes) {
 
   std::vector<paddle::experimental::Tensor> outs = {out1};
   // 4. Run Backward
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 10.0);
@@ -203,7 +203,7 @@ TEST(FwdBwdJoint, BranchedNodes) {
 
   // 4. Run Backward
   std::vector<paddle::experimental::Tensor> outs = {out1, out2};
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 30.0);
@@ -260,7 +260,7 @@ TEST(FwdBwdJoint, GradientHook) {
 
   // 4. Run Backward
   std::vector<paddle::experimental::Tensor> outs = {out1, out2};
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   // leaf grad
@@ -318,13 +318,13 @@ TEST(FwdBwdJoint, CrossBatchAccumulation) {
 
   // 4. Run Backward
   std::vector<paddle::experimental::Tensor> outs = {out1, out2};
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 30.0);
 
   // Cross Batch Accumulation
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 60.0);
@@ -356,7 +356,7 @@ TEST(FwdBwdJoint, SingleNodeCUDA) {
 
   std::vector<paddle::experimental::Tensor> outs = {out};
   // 4. Run Backward
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 2.0);
@@ -412,7 +412,7 @@ TEST(FwdBwdJoint, BranchedNodesCUDA) {
   // TODO(jiabin): fix this with add functor
   // 4. Run Backward
   std::vector<paddle::experimental::Tensor> outs = {out1, out2};
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 30.0);

paddle/fluid/eager/tests/task_tests/generated_test.cc

@@ -57,7 +57,7 @@ TEST(Generated, Sigmoid) {
 
   std::vector<paddle::experimental::Tensor> target_tensors = {output_tensor};
   VLOG(6) << "Runing Backward";
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   VLOG(6) << "Finish Backward";
   eager_test::CompareGradTensorWithValue<float>(tensor, 0.25);
@@ -89,7 +89,7 @@ TEST(Generated, Matmul_v2) {
   eager_test::CompareTensorWithValue<float>(output_tensor, 96);
 
   std::vector<paddle::experimental::Tensor> target_tensors = {output_tensor};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(X, 2.0 * 20);
   eager_test::CompareGradTensorWithValue<float>(Y, 3.0 * 4);
@@ -120,7 +120,7 @@ TEST(Generated, ElementwiseAdd) {
   eager_test::CompareTensorWithValue<float>(output_tensor, 5);
 
   std::vector<paddle::experimental::Tensor> target_tensors = {output_tensor};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(X, 1.0);
   eager_test::CompareGradTensorWithValue<float>(Y, 1.0);

paddle/fluid/eager/tests/task_tests/grad_test.cc

+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <sstream>
+
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+
+#include "paddle/fluid/eager/accumulation/accumulation_node.h"
+#include "paddle/fluid/eager/api/generated/eager_generated/backwards/scale_node.h"
+#include "paddle/fluid/eager/api/utils/tensor_utils.h"
+#include "paddle/fluid/eager/autograd_meta.h"
+#include "paddle/fluid/eager/backward.h"
+#include "paddle/fluid/eager/grad_node_info.h"
+#include "paddle/fluid/eager/tests/test_utils.h"
+
+#include "paddle/fluid/eager/api/all.h"
+
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/core/tensor_meta.h"
+
+PD_DECLARE_KERNEL(full, CPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(copy, CPU, ALL_LAYOUT);
+namespace egr {
+
+TEST(Grad, SingleNodeEmptyGrad) {
+  // Prepare Device Contexts
+  eager_test::InitEnv(paddle::platform::CPUPlace());
+
+  // Prepare Inputs
+  paddle::framework::DDim ddim = phi::make_ddim({4, 16, 16, 32});
+
+  // Create Target Tensor (output)
+  paddle::experimental::Tensor output_tensor =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 1.0 /*value*/, false /*is_leaf*/);
+
+  // Create input tensor
+  const paddle::experimental::Tensor leaf_tensor =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 1.0 /*value*/, true /*is_leaf*/);
+
+  {
+    // Create Scale Node
+    auto node0_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node0_ptr->SetAttributes_scale(5.0 /*scale*/);
+
+    // Set grad in/out meta
+    node0_ptr->SetDefaultGradInOutMeta();
+
+    // Output_tensor set GradNode、OutRank、StopGradient propertis
+    AutogradMeta* auto_grad_meta = EagerUtils::autograd_meta(&output_tensor);
+    auto_grad_meta->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(node0_ptr));
+    auto_grad_meta->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta->SetStopGradient(false);
+
+    // Get autograd_meta from input tensor
+    AutogradMeta* auto_grad_meta1 =
+        EagerUtils::unsafe_autograd_meta(leaf_tensor);
+
+    // Connect Tensor and AccumulationNode via AutoGradMeta
+    auto acc_node_ptr =
+        std::make_shared<egr::GradNodeAccumulation>(auto_grad_meta1);
+
+    // input tensor set GradNode、OutRank、StopGradient propertis
+    auto_grad_meta1->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(acc_node_ptr));
+    auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta1->SetStopGradient(false);
+
+    // grad_node Add Edges
+    std::vector<egr::AutogradMeta*> res = {auto_grad_meta1};
+    node0_ptr->AddEdges(&res, 0);
+  }
+  std::vector<paddle::experimental::Tensor> outs = {output_tensor};
+
+  // Run Grad
+  auto result = Grad(outs, {leaf_tensor}, {});
+  // Check Output Value
+  eager_test::CompareTensorWithValue<float>(result[0], 5.0);
+}
+
+TEST(Grad, SingleNodeCustomGrad) {
+  // Prepare Device Contexts
+  eager_test::InitEnv(paddle::platform::CPUPlace());
+
+  // Prepare Inputs
+  std::vector<paddle::experimental::Tensor> target_tensors;
+  paddle::framework::DDim ddim = phi::make_ddim({4, 16, 16, 32});
+
+  // Create Target Tensor
+  paddle::experimental::Tensor tensor = egr_utils_api::CreateTensorWithValue(
+      ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+      phi::DataLayout::NCHW, 1.0 /*value*/, false /*is_leaf*/);
+  target_tensors.emplace_back(std::move(tensor));
+
+  std::vector<paddle::experimental::Tensor> grad_tensors;
+  // Create Grad Tensor
+  paddle::experimental::Tensor grad_tensor =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 10.0 /*value*/, false /*is_leaf*/);
+  grad_tensors.emplace_back(std::move(grad_tensor));
+
+  paddle::experimental::Tensor leaf_tensor =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 1.0 /*value*/, true /*is_leaf*/);
+
+  {
+    // Create Scale Node
+    auto node0_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node0_ptr->SetAttributes_scale(5.0 /*scale*/);
+
+    // Set grad in/out meta
+    node0_ptr->SetDefaultGradInOutMeta();
+
+    // Connect Tensor and Node via AutoGradMeta
+    AutogradMeta* auto_grad_meta =
+        EagerUtils::autograd_meta(&(target_tensors[0]));
+    auto_grad_meta->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(node0_ptr));
+    auto_grad_meta->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta->SetStopGradient(false);
+
+    AutogradMeta* auto_grad_meta1 = EagerUtils::autograd_meta(&leaf_tensor);
+    // Connect Tensor and AccumulationNode via AutoGradMeta
+    auto acc_node_ptr =
+        std::make_shared<egr::GradNodeAccumulation>(auto_grad_meta1);
+
+    auto_grad_meta1->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(acc_node_ptr));
+    auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta1->SetStopGradient(false);
+    std::vector<egr::AutogradMeta*> res = {auto_grad_meta1};
+    node0_ptr->AddEdges(&res, 0);
+  }
+
+  auto result = Grad(target_tensors, {leaf_tensor}, grad_tensors);
+
+  // Check Output Value
+  eager_test::CompareTensorWithValue<float>(result[0], 50.0);
+}
+
+/*
+Node1
+  |
+Node0
+  |
+ { } // empty grad tensor
+*/
+TEST(Grad, LinearNodes) {
+  // Prepare Device Contexts
+  eager_test::InitEnv(paddle::platform::CPUPlace());
+
+  // Prepare Target Tensor
+  std::vector<paddle::experimental::Tensor> target_tensors;
+  paddle::framework::DDim ddim = phi::make_ddim({4, 16, 16, 32});
+
+  // Create Target Tensor
+  paddle::experimental::Tensor tensor = egr_utils_api::CreateTensorWithValue(
+      ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+      phi::DataLayout::NCHW, 1.0 /*value*/, false /*is_leaf*/);
+  target_tensors.emplace_back(std::move(tensor));
+
+  paddle::experimental::Tensor leaf_tensor =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 1.0 /*value*/, true /*is_leaf*/);
+  {
+    // Create Node0
+    auto node0_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node0_ptr->SetAttributes_scale(5.0 /*scale*/);
+
+    // Set grad in/out meta for node0
+    node0_ptr->SetDefaultGradInOutMeta();
+
+    // Create Node1
+    auto node1_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node1_ptr->SetAttributes_scale(10.0 /*scale*/);
+
+    // Set grad in/out meta for node1
+    node1_ptr->SetDefaultGradInOutMeta();
+
+    // Connect Input Tensor and Node0 via AutoGradMeta
+    AutogradMeta* auto_grad_meta =
+        EagerUtils::autograd_meta(&(target_tensors[0]));
+    auto_grad_meta->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(node0_ptr));
+    auto_grad_meta->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta->SetStopGradient(false);
+    // Connect Node0 -> Node1 via Edge
+    auto meta0 = egr::AutogradMeta();
+    meta0.SetStopGradient(false);
+    meta0.SetSingleOutRankWithSlot(0, 0);
+    meta0.SetGradNode(node1_ptr);
+    std::vector<egr::AutogradMeta*> res0 = {&meta0};
+    node0_ptr->AddEdges(&res0, 0);
+
+    AutogradMeta* auto_grad_meta1 = EagerUtils::autograd_meta(&leaf_tensor);
+    // Connect Tensor and AccumulationNode via AutoGradMeta
+    auto acc_node_ptr =
+        std::make_shared<egr::GradNodeAccumulation>(auto_grad_meta1);
+
+    auto_grad_meta1->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(acc_node_ptr));
+    auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
+
+    auto_grad_meta1->SetStopGradient(false);
+    std::vector<egr::AutogradMeta*> res1 = {auto_grad_meta1};
+    node1_ptr->AddEdges(&res1, 0);
+  }
+
+  // Use Empty Grad Tensor
+  auto result = Grad(target_tensors, {leaf_tensor}, {});
+
+  // Check Output Value
+  eager_test::CompareTensorWithValue<float>(result[0], 50.0);
+}
+
+/*
+    Node2
+    |   |
+Node0   Node1
+  |      |
+ in0   in1
+*/
+TEST(Grad, WithAccumulation) {
+  // Prepare Device Contexts
+  eager_test::InitEnv(paddle::platform::CPUPlace());
+
+  // Prepare Inputs
+  paddle::framework::DDim ddim = phi::make_ddim({4, 16, 16, 32});
+
+  // Create Target Tensor
+  std::vector<paddle::experimental::Tensor> target_tensors;
+  paddle::experimental::Tensor tensor0 = egr_utils_api::CreateTensorWithValue(
+      ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+      phi::DataLayout::NCHW, 1.0 /*value*/, false /*is_leaf*/);
+  paddle::experimental::Tensor tensor1 = egr_utils_api::CreateTensorWithValue(
+      ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+      phi::DataLayout::NCHW, 1.0 /*value*/, false /*is_leaf*/);
+  target_tensors.emplace_back(std::move(tensor0));
+  target_tensors.emplace_back(std::move(tensor1));
+
+  // Create Grad Tensor
+  std::vector<paddle::experimental::Tensor> grad_tensors;
+  paddle::experimental::Tensor grad_tensor0 =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 5.0 /*value*/, false /*is_leaf*/);
+  paddle::experimental::Tensor grad_tensor1 =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 10.0 /*value*/, false /*is_leaf*/);
+  grad_tensors.emplace_back(std::move(grad_tensor0));
+  grad_tensors.emplace_back(std::move(grad_tensor1));
+
+  paddle::experimental::Tensor leaf_tensor;
+  {
+    // Create Node0
+    auto node0_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node0_ptr->SetAttributes_scale(5.0 /*scale*/);
+    node0_ptr->SetDefaultGradInOutMeta();
+
+    // Create Node1
+    auto node1_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node1_ptr->SetAttributes_scale(10.0 /*scale*/);
+    node1_ptr->SetDefaultGradInOutMeta();
+    // Create Node2
+    auto node2_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node2_ptr->SetAttributes_scale(20.0 /*scale*/);
+    node2_ptr->SetDefaultGradInOutMeta();
+    // Connect Inp0 and Node0 via AutoGradMeta
+    AutogradMeta* auto_grad_meta0 =
+        EagerUtils::autograd_meta(&(target_tensors[0]));
+    auto_grad_meta0->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(node0_ptr));
+    auto_grad_meta0->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta0->SetStopGradient(false);
+    // Connect Inp1 and Node1 via AutoGradMeta
+    AutogradMeta* auto_grad_meta1 =
+        EagerUtils::autograd_meta(&(target_tensors[1]));
+    auto_grad_meta1->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(node1_ptr));
+    auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta1->SetStopGradient(false);
+
+    // Connect Node0 -> Node2 via Edge
+    auto meta0 = egr::AutogradMeta();
+    meta0.SetStopGradient(false);
+    meta0.SetSingleOutRankWithSlot(0, 0);
+    meta0.SetGradNode(node2_ptr);
+    std::vector<egr::AutogradMeta*> res0 = {&meta0};
+    node0_ptr->AddEdges(&res0, 0);
+
+    // Connect Node1 -> Node2 via Edge
+    auto meta1 = egr::AutogradMeta();
+    meta1.SetStopGradient(false);
+    meta1.SetSingleOutRankWithSlot(0, 0);
+    meta1.SetGradNode(node2_ptr);
+    std::vector<egr::AutogradMeta*> res1 = {&meta1};
+    node1_ptr->AddEdges(&res1, 0);
+
+    AutogradMeta* auto_grad_meta2 = EagerUtils::autograd_meta(&leaf_tensor);
+    // Connect Tensor and AccumulationNode via AutoGradMeta
+    auto acc_node_ptr =
+        std::make_shared<egr::GradNodeAccumulation>(auto_grad_meta2);
+
+    auto_grad_meta2->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(acc_node_ptr));
+    auto_grad_meta2->SetSingleOutRankWithSlot(0, 0);
+
+    auto_grad_meta2->SetStopGradient(false);
+    std::vector<egr::AutogradMeta*> res2 = {auto_grad_meta2};
+    node2_ptr->AddEdges(&res2, 0);
+  }
+
+  auto result = Grad(target_tensors, {leaf_tensor}, grad_tensors);
+
+  eager_test::CompareTensorWithValue<float>(result[0], 2500.0);
+}
+
+}  // namespace egr

paddle/fluid/eager/tests/task_tests/hook_test.cc

@@ -132,7 +132,7 @@ TEST(RetainGrad, HookBeforeRetainGrad) {
         leaf_tensor);  // result: 4.0*5.0 + 3.0 = 23.0
   }
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(target_tensor, 4.0);
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 23.0);
@@ -199,7 +199,7 @@ TEST(RetainGrad, HookAfterRetainGrad) {
         leaf_tensor, std::make_shared<egr::CppTensorHook>(hook_function));
   }
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
   eager_test::CompareGradTensorWithValue<float>(target_tensor, 1.0);
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 23.0);
 }

paddle/fluid/eager/tests/task_tests/hook_test_intermidiate.cc

@@ -108,7 +108,7 @@ void test_sigmoid(bool is_remove_gradient_hook) {
   }
 
   VLOG(6) << "Runing Backward";
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
   VLOG(6) << "Finish Backward";
 
   eager_test::CompareGradTensorWithValue<float>(
@@ -166,7 +166,7 @@ void test_elementwiseAdd(bool is_remove_gradient_hook) {
     grad_node_tmp->RemoveGradientHook(hook_id);
   }
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(X, 1.0);
   eager_test::CompareGradTensorWithValue<float>(
@@ -224,7 +224,7 @@ void test_matmul(bool is_remove_gradient_hook) {
     grad_node_tmp->RemoveGradientHook(hook_id);
   }
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(X, 2.0 * 20);
   eager_test::CompareGradTensorWithValue<float>(

paddle/fluid/eager/to_static/run_program_op_node.h

@@ -370,8 +370,8 @@ class GradNodeRunProgram : public egr::GradNodeBase {
   ~GradNodeRunProgram() override = default;
   // Functor: perform backward computations
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>> &grads)
-      override {
+      const std::vector<std::vector<paddle::experimental::Tensor>> &grads,
+      bool create_graph) override {
     VLOG(3) << "Running Eager Backward Node: GradNodeRunProgram";
     PADDLE_ENFORCE_EQ(
         grads.size(), 1,
@@ -415,6 +415,12 @@ class GradNodeRunProgram : public egr::GradNodeBase {
     // return {x_grad, details::DereferenceTensors(params_grad_ptr)};
   }
 
+  void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
+  bool IsTensorWrappersCleared() override {
+    VLOG(6) << "Do nothing here now";
+    return false;
+  }
+
   // SetAttrMap
   void SetAttrMap(const paddle::framework::AttributeMap &attrs) {
     attrs_ = attrs;

paddle/fluid/pybind/eager_functions.cc

@@ -122,13 +122,33 @@ static PyObject* eager_api_run_backward(PyObject* self, PyObject* args,
   EAGER_TRY
   auto tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 0), 0);
   auto grad_tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 1), 1);
-  egr::RunBackward(tensors, grad_tensors,
-                   CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 2), 2));
+  egr::Backward(tensors, grad_tensors,
+                CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 2), 2));
   Py_INCREF(Py_None);
   return Py_None;
   EAGER_CATCH_AND_THROW_RETURN_NULL
 }
 
+static PyObject* eager_api_run_partial_grad(PyObject* self, PyObject* args,
+                                            PyObject* kwargs) {
+  EAGER_TRY
+  auto tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 0), 0);
+  auto inputs = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 1), 1);
+  auto grad_tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 2), 2);
+  auto retain_graph = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 3), 3);
+  auto create_graph = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 4), 4);
+  auto only_inputs = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 5), 5);
+  auto allow_unused = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 6), 6);
+  auto no_grad_vars = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 7), 7);
+
+  std::vector<paddle::experimental::Tensor> result =
+      egr::Grad(tensors, inputs, grad_tensors, retain_graph, create_graph,
+                only_inputs, allow_unused, no_grad_vars);
+  VLOG(1) << " in eager_api_run_partial_grad, after runing egr::Grad";
+  return ToPyObject(result, true /* return_py_none_if_not_initialize */);
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
 static PyObject* eager_api_tensor_copy(PyObject* self, PyObject* args,
                                        PyObject* kwargs) {
   EAGER_TRY
@@ -452,6 +472,9 @@ PyMethodDef variable_functions[] = {
      METH_VARARGS | METH_KEYWORDS, NULL},
     {"run_backward", (PyCFunction)(void (*)(void))eager_api_run_backward,
      METH_VARARGS | METH_KEYWORDS, NULL},
+    {"run_partial_grad",
+     (PyCFunction)(void (*)(void))eager_api_run_partial_grad,
+     METH_VARARGS | METH_KEYWORDS, NULL},
     {"_run_custom_op", (PyCFunction)(void (*)(void))eager_api_run_costum_op,
      METH_VARARGS | METH_KEYWORDS, NULL},
     {"tensor_copy", (PyCFunction)(void (*)(void))eager_api_tensor_copy,

paddle/fluid/pybind/eager_utils.cc

@@ -492,20 +492,26 @@ PyObject* ToPyObject(const std::vector<double>& value) {
   return result;
 }
 
-PyObject* ToPyObject(const std::vector<paddle::experimental::Tensor>& value) {
+PyObject* ToPyObject(const std::vector<paddle::experimental::Tensor>& value,
+                     bool return_py_none_if_not_initialize) {
   PyObject* result = PyList_New((Py_ssize_t)value.size());
 
   for (size_t i = 0; i < value.size(); i++) {
-    PyObject* obj = p_tensor_type->tp_alloc(p_tensor_type, 0);
-    if (obj) {
-      auto v = reinterpret_cast<TensorObject*>(obj);
-      new (&(v->tensor)) paddle::experimental::Tensor();
-      v->tensor = value[i];
+    if (!value[i].initialized() && return_py_none_if_not_initialize) {
+      Py_INCREF(Py_None);
+      PyList_SET_ITEM(result, static_cast<Py_ssize_t>(i), Py_None);
     } else {
-      PADDLE_THROW(platform::errors::Fatal(
-          "tp_alloc return null, can not new a PyObject."));
+      PyObject* obj = p_tensor_type->tp_alloc(p_tensor_type, 0);
+      if (obj) {
+        auto v = reinterpret_cast<TensorObject*>(obj);
+        new (&(v->tensor)) paddle::experimental::Tensor();
+        v->tensor = value[i];
+      } else {
+        PADDLE_THROW(platform::errors::Fatal(
+            "tp_alloc return null, can not new a PyObject."));
+      }
+      PyList_SET_ITEM(result, static_cast<Py_ssize_t>(i), obj);
     }
-    PyList_SET_ITEM(result, static_cast<Py_ssize_t>(i), obj);
   }
 
   return result;

paddle/fluid/pybind/eager_utils.h

@@ -68,7 +68,8 @@ PyObject* ToPyObject(const std::vector<int>& value);
 PyObject* ToPyObject(const std::vector<int64_t>& value);
 PyObject* ToPyObject(const std::vector<float>& value);
 PyObject* ToPyObject(const std::vector<double>& value);
-PyObject* ToPyObject(const std::vector<paddle::experimental::Tensor>& value);
+PyObject* ToPyObject(const std::vector<paddle::experimental::Tensor>& value,
+                     bool return_py_none_if_not_initialize = false);
 PyObject* ToPyObject(const platform::Place& value);
 PyObject* ToPyObject(const framework::LoDTensor* value);
 PyObject* ToPyObject(const paddle::framework::proto::VarType::Type& dtype);

python/paddle/fluid/dygraph/base.py

@@ -565,16 +565,25 @@ def grad(outputs,
         if isinstance(in_out_list, (list, tuple)):
             assert len(in_out_list) > 0, "{} cannot be empty".format(name)
             for each_var in in_out_list:
-                assert isinstance(
-                    each_var,
-                    core.VarBase), "Elements of {} must be Variable".format(
-                        name)
+                if core._in_eager_mode():
+                    assert isinstance(
+                        each_var, core.eager.
+                        Tensor), "Elements of {} must be Tensor".format(name)
+                else:
+                    assert isinstance(
+                        each_var,
+                        core.VarBase), "Elements of {} must be Variable".format(
+                            name)
             return in_out_list
         else:
-            assert isinstance(
-                in_out_list,
-                core.VarBase), "{} must be Variable or list of Variable".format(
-                    name)
+            if core._in_eager_mode():
+                assert isinstance(
+                    in_out_list, core.eager.
+                    Tensor), "{} must be Tensor or list of Tensor".format(name)
+            else:
+                assert isinstance(
+                    in_out_list, core.VarBase
+                ), "{} must be Variable or list of Variable".format(name)
             return [in_out_list]
 
     outputs = check_in_out(outputs, 'outputs')
@@ -586,9 +595,14 @@ def grad(outputs,
 
         for each_var in grad_outputs:
             if each_var is not None:
-                assert isinstance(
-                    each_var, core.VarBase
-                ), "grad_outputs must be None, a Variable or a list containing None or Variables"
+                if core._in_eager_mode():
+                    assert isinstance(
+                        each_var, core.eager.Tensor
+                    ), "grad_outputs must be None, a Variable or a list containing None or Variables"
+                else:
+                    assert isinstance(
+                        each_var, core.VarBase
+                    ), "grad_outputs must be None, a Variable or a list containing None or Variables"
     else:
         grad_outputs = []
 
@@ -600,14 +614,27 @@ def grad(outputs,
         no_grad_vars = []
     elif isinstance(no_grad_vars, core.VarBase):
         no_grad_vars = [no_grad_vars]
+    elif isinstance(no_grad_vars, core.eager.Tensor):
+        no_grad_vars = [no_grad_vars]
     elif isinstance(no_grad_vars, (list, tuple, set)):
         no_grad_vars = list(no_grad_vars)
         for var in no_grad_vars:
-            assert isinstance(
-                var, core.VarBase), "no_grad_vars can only contains Variable"
+            if core._in_eager_mode():
+                assert isinstance(
+                    var,
+                    core.eager.Tensor), "no_grad_vars can only contains Tensor"
+            else:
+                assert isinstance(
+                    var,
+                    core.VarBase), "no_grad_vars can only contains Variable"
     else:
-        raise AssertionError(
-            "no_grad_vars must be None, Variable or list/tuple/set of Variables")
+        if core._in_eager_mode():
+            raise AssertionError(
+                "no_grad_vars must be None, Tensor or list/tuple/set of Tensors")
+        else:
+            raise AssertionError(
+                "no_grad_vars must be None, Variable or list/tuple/set of Variables"
+            )
 
     assert isinstance(create_graph, bool), "create_graph must be True or False"
 
@@ -622,6 +649,11 @@ def grad(outputs,
     assert isinstance(only_inputs, bool), "only_inputs must be True or False"
     assert only_inputs, "only_inputs=False is not supported yet"
 
+    if core._in_eager_mode():
+        return core.eager.run_partial_grad(
+            outputs, inputs, grad_outputs, retain_graph, create_graph,
+            only_inputs, allow_unused, no_grad_vars)
+
     place = core.Place()
     place.set_place(framework._current_expected_place())
     return core.dygraph_partial_grad(inputs, outputs, grad_outputs,

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

@@ -52,7 +52,7 @@ class EagerScaleTestCase(unittest.TestCase):
             out_eager = core.eager.scale(data_eager, 1.0, 0.9, True, True)
             self.assertIsNone(data_eager.grad)
             out_eager.backward(grad_eager, False)
-            self.assertTrue(data_eager.grad._is_initialized())
+            self.assertIsNotNone(data_eager.grad)
             self.assertTrue(np.array_equal(data_eager.grad.numpy(), input_data))
 
     def test_retain_grad_and_run_backward_raises(self):

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

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -19,6 +19,9 @@ from paddle.vision.models import resnet50, resnet101
 import unittest
 from unittest import TestCase
 import numpy as np
+import paddle.compat as cpt
+from paddle.fluid.framework import _test_eager_guard
+import paddle.fluid.core as core
 
 
 def _dygraph_guard_(func):
@@ -40,6 +43,80 @@ def random_var(size, low=-1, high=1, dtype='float32'):
     return fluid.dygraph.to_variable(x_np)
 
 
+class TestEagerGrad(TestCase):
+    def func_simple_example_eager_grad(self):
+        np.random.seed(2021)
+        paddle.set_device('cpu')
+        np_x = np.random.random((3, 3))
+        np_y = 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)
+        out = paddle.matmul(x, y)
+        dx = fluid.dygraph.grad(out, x)
+
+        dout = np.ones_like(np_y)
+        expected_dx = np.matmul(dout, np.transpose(np_y))
+
+        # stop_gradient = !create_graph, create_graph default false
+        self.assertEqual(dx[0].stop_gradient, True)
+        self.assertTrue(np.allclose(dx[0].numpy(), expected_dx[0]))
+
+    def test_simple_example_eager_grad(self):
+        with _test_eager_guard():
+            self.func_simple_example_eager_grad()
+        self.func_simple_example_eager_grad()
+
+    def func_simple_example_eager_grad_allow_unused(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)
+
+        dx = fluid.dygraph.grad(out, [x, z], allow_unused=True)
+        dout = np.ones_like(np_y)
+        expected_dx = np.matmul(dout, np.transpose(np_y))
+        self.assertTrue(np.allclose(dx[0].numpy(), expected_dx[0]))
+        # stop_gradient = !create_graph, create_graph default false
+        self.assertEqual(dx[0].stop_gradient, True)
+        # x is unused input in the graph
+        self.assertEqual(dx[1], None)
+
+    def test_simple_example_eager_grad_allow_unused(self):
+        with _test_eager_guard():
+            self.func_simple_example_eager_grad_allow_unused()
+        self.func_simple_example_eager_grad_allow_unused()
+
+    def func_simple_example_eager_grad_not_allow_unused(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:
+            # allow_unused is false in default
+            dx = fluid.dygraph.grad(out, [x, z])
+        except ValueError as e:
+            error_msg = cpt.get_exception_message(e)
+            assert error_msg.find("allow_unused") > 0
+
+    def test_simple_example_eager_grad_not_allow_unused(self):
+        with _test_eager_guard():
+            self.func_simple_example_eager_grad_not_allow_unused()
+        self.func_simple_example_eager_grad_not_allow_unused()
+
+
 class TestDygraphDoubleGrad(TestCase):
     def setUp(self):
         self.sort_sum_gradient = False
@@ -64,7 +141,7 @@ class TestDygraphDoubleGrad(TestCase):
             allow_unused=allow_unused)
 
     @dygraph_guard
-    def test_exception(self):
+    def func_exception(self):
         with self.assertRaises(AssertionError):
             self.grad(None, None)
 
@@ -93,8 +170,13 @@ class TestDygraphDoubleGrad(TestCase):
         with self.assertRaises(AssertionError):
             self.grad([random_var(shape)], [random_var(shape)], no_grad_vars=1)
 
+    def test_exception(self):
+        with _test_eager_guard():
+            self.func_exception()
+        self.func_exception()
+
     @dygraph_guard
-    def test_simple_example(self):
+    def func_simple_example(self):
         x = random_var(self.shape)
         x.stop_gradient = False
         y = x + 1
@@ -123,8 +205,44 @@ class TestDygraphDoubleGrad(TestCase):
             self.assertNotEqual(grad_with_none_and_not_none.stop_gradient,
                                 create_graph)
 
+    def test_simple_example(self):
+        with _test_eager_guard():
+            self.func_simple_example()
+        self.func_simple_example()
+
     @dygraph_guard
-    def test_none_one_initial_gradient(self):
+    def func_example_no_grad_vars(self):
+        x = random_var(self.shape)
+        x_np = x.numpy()
+        numel = x_np.size
+        x.stop_gradient = False
+
+        y1 = fluid.layers.relu(x)
+        y2 = fluid.layers.relu(x)
+        z = y1 + y2
+        w = z * z
+
+        w_mean = fluid.layers.reduce_mean(w)
+        del y1, z, w
+
+        dx_actual, = self.grad(
+            [w_mean], [x], create_graph=True, no_grad_vars=[y2])
+
+        self.assertFalse(y2.stop_gradient)
+        self.assertFalse(dx_actual.stop_gradient)
+
+        dx_expected = (1.0 / float(numel) * (np.maximum(x_np, 0) + y2.numpy()) *
+                       (x_np > 0) * 2).astype('float32')
+
+        self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
+
+    def test_example_no_grad_vars(self):
+        with _test_eager_guard():
+            self.func_example_no_grad_vars()
+        self.func_example_no_grad_vars()
+
+    @dygraph_guard
+    def func_none_one_initial_gradient(self):
         numel = 1
         for s in self.shape:
             numel *= s
@@ -190,8 +308,13 @@ class TestDygraphDoubleGrad(TestCase):
                             np.array_equal(grad_z.numpy(),
                                            original_random_grad_z))
 
+    def test_none_one_initial_gradient(self):
+        with _test_eager_guard():
+            self.func_none_one_initial_gradient()
+        self.func_none_one_initial_gradient()
+
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_create_graph(self):
+    def func_example_with_gradient_accumulation_and_create_graph(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -214,25 +337,33 @@ class TestDygraphDoubleGrad(TestCase):
                        (x_np > 0) * 2).astype('float32')
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward(retain_graph=True)
-
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 / float(numel) *
-                           (x_np + dx_expected *
-                            (x_np > 0) * 2 / float(numel))).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
-
-        for i in range(5):
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
             loss.backward(retain_graph=True)
+
             x_grad_actual = x.gradient()
-            x_grad_expected = (i + 2) * (2.0 / float(numel) * (
+            x_grad_expected = (2.0 / float(numel) * (
                 x_np + dx_expected *
                 (x_np > 0) * 2 / float(numel))).astype('float32')
             self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
 
+            for i in range(5):
+                loss.backward(retain_graph=True)
+                x_grad_actual = x.gradient()
+                x_grad_expected = (i + 2) * (2.0 / float(numel) * (
+                    x_np + dx_expected *
+                    (x_np > 0) * 2 / float(numel))).astype('float32')
+                self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+
+    def test_example_with_gradient_accumulation_and_create_graph(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_create_graph()
+        self.func_example_with_gradient_accumulation_and_create_graph()
+
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_no_grad_vars(self):
+    def func_example_with_gradient_accumulation_and_no_grad_vars(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -256,17 +387,25 @@ class TestDygraphDoubleGrad(TestCase):
                        (x_np > 0) * 2).astype('float32')
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward()
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
+            loss.backward()
 
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 / float(numel) *
-                           (x_np + dx_expected *
-                            (x_np > 0) * 4 / float(numel))).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+            x_grad_actual = x.gradient()
+            x_grad_expected = (2.0 / float(numel) * (
+                x_np + dx_expected *
+                (x_np > 0) * 4 / float(numel))).astype('float32')
+            self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+
+    def test_example_with_gradient_accumulation_and_no_grad_vars(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_no_grad_vars()
+        self.func_example_with_gradient_accumulation_and_no_grad_vars()
 
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_not_create_graph(self):
+    def func_example_with_gradient_accumulation_and_not_create_graph(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -289,12 +428,20 @@ class TestDygraphDoubleGrad(TestCase):
 
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward()
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
+            loss.backward()
 
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 * x_np / float(numel)).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+            x_grad_actual = x.gradient()
+            x_grad_expected = (2.0 * x_np / float(numel)).astype('float32')
+            self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+
+    def test_example_with_gradient_accumulation_and_not_create_graph(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_not_create_graph()
+        self.func_example_with_gradient_accumulation_and_not_create_graph()
 
 
 class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):
@@ -304,7 +451,7 @@ class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):
 
 
 class TestDygraphDoubleGradVisitedUniq(TestCase):
-    def test_compare(self):
+    def func_compare(self):
         value = np.random.uniform(-0.5, 0.5, 100).reshape(10, 2,
                                                           5).astype("float32")
 
@@ -349,6 +496,11 @@ class TestDygraphDoubleGradVisitedUniq(TestCase):
 
         self.assertTrue(np.array_equal(grad_1, grad_2))
 
+    def test_compare(self):
+        with _test_eager_guard():
+            self.func_compare()
+        self.func_compare()
+
 
 class TestRaiseNoDoubleGradOp(TestCase):
     def raise_no_grad_op(self):

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

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -18,6 +18,8 @@ import unittest
 from unittest import TestCase
 import numpy as np
 import paddle
+from paddle.fluid.framework import _test_eager_guard
+import paddle.fluid.core as core
 
 
 def _dygraph_guard_(func):
@@ -62,7 +64,7 @@ class TestDygraphDoubleGrad(TestCase):
             allow_unused=allow_unused)
 
     @dygraph_guard
-    def test_exception(self):
+    def func_exception(self):
         with self.assertRaises(AssertionError):
             self.grad(None, None)
 
@@ -91,8 +93,13 @@ class TestDygraphDoubleGrad(TestCase):
         with self.assertRaises(AssertionError):
             self.grad([random_var(shape)], [random_var(shape)], no_grad_vars=1)
 
+    def test_exception(self):
+        with _test_eager_guard():
+            self.func_exception()
+        self.func_exception()
+
     @dygraph_guard
-    def test_simple_example(self):
+    def func_simple_example(self):
         x = random_var(self.shape)
         x.stop_gradient = False
         y = x + 1
@@ -121,8 +128,13 @@ class TestDygraphDoubleGrad(TestCase):
             self.assertNotEqual(grad_with_none_and_not_none.stop_gradient,
                                 create_graph)
 
+    def test_simple_example(self):
+        with _test_eager_guard():
+            self.func_simple_example()
+        self.func_simple_example()
+
     @dygraph_guard
-    def test_none_one_initial_gradient(self):
+    def func_none_one_initial_gradient(self):
         numel = 1
         for s in self.shape:
             numel *= s
@@ -188,8 +200,13 @@ class TestDygraphDoubleGrad(TestCase):
                             np.array_equal(grad_z.numpy(),
                                            original_random_grad_z))
 
+    def test_none_one_initial_gradient(self):
+        with _test_eager_guard():
+            self.func_none_one_initial_gradient()
+        self.func_none_one_initial_gradient()
+
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_create_graph(self):
+    def func_example_with_gradient_accumulation_and_create_graph(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -212,17 +229,25 @@ class TestDygraphDoubleGrad(TestCase):
                        (x_np > 0) * 2).astype('float32')
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward()
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
+            loss.backward()
 
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 / float(numel) *
-                           (x_np + dx_expected *
-                            (x_np > 0) * 2 / float(numel))).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+            x_grad_actual = x.gradient()
+            x_grad_expected = (2.0 / float(numel) * (
+                x_np + dx_expected *
+                (x_np > 0) * 2 / float(numel))).astype('float32')
+            self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+
+    def test_example_with_gradient_accumulation_and_create_graph(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_create_graph()
+        self.func_example_with_gradient_accumulation_and_create_graph()
 
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_no_grad_vars(self):
+    def func_example_with_gradient_accumulation_and_no_grad_vars(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -246,17 +271,25 @@ class TestDygraphDoubleGrad(TestCase):
                        (x_np > 0) * 2).astype('float32')
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward()
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
+            loss.backward()
+
+            x_grad_actual = x.gradient()
+            x_grad_expected = (2.0 / float(numel) * (
+                x_np + dx_expected *
+                (x_np > 0) * 4 / float(numel))).astype('float32')
+            self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
 
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 / float(numel) *
-                           (x_np + dx_expected *
-                            (x_np > 0) * 4 / float(numel))).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+    def test_example_with_gradient_accumulation_and_no_grad_vars(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_no_grad_vars()
+        self.func_example_with_gradient_accumulation_and_no_grad_vars()
 
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_not_create_graph(self):
+    def func_example_with_gradient_accumulation_and_not_create_graph(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -279,12 +312,20 @@ class TestDygraphDoubleGrad(TestCase):
 
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward()
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
+            loss.backward()
 
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 * x_np / float(numel)).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+            x_grad_actual = x.gradient()
+            x_grad_expected = (2.0 * x_np / float(numel)).astype('float32')
+            self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+
+    def test_example_with_gradient_accumulation_and_not_create_graph(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_not_create_graph()
+        self.func_example_with_gradient_accumulation_and_not_create_graph()
 
 
 class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):