Using small vector for slot and merge edge into grad_slot_meta (#42350)

paddle/fluid/distributed/collective/reducer.cc

@@ -447,10 +447,12 @@ void EagerReducer::TraverseBackwardGraph(const std::vector<Tensor> &outputs) {
   while (!queue.empty()) {
     egr::GradNodeBase *node = queue.front();
     queue.pop();
-    const std::vector<std::vector<egr::Edge>> &edges = node->GetEdges();
-    for (size_t i = 0; i < edges.size(); i++) {
-      for (size_t j = 0; j < edges[i].size(); j++) {
-        const egr::Edge &edge = edges[i][j];
+    const paddle::small_vector<std::vector<egr::GradSlotMeta>,
+                               egr::kSlotSmallVectorSize> &metas =
+        node->OutputMeta();
+    for (size_t i = 0; i < metas.size(); i++) {
+      for (size_t j = 0; j < metas[i].size(); j++) {
+        const egr::Edge &edge = metas[i][j].GetEdge();
         auto next_node_shared = edge.GetMutableGradNode();
         if (!next_node_shared || !next_node_shared.get()) {
           continue;

paddle/fluid/eager/accumulation/accumulation_node.cc

@@ -38,10 +38,13 @@ static void CopyOrAddTensor(paddle::experimental::Tensor* tensor,
   }
 }
 
-std::vector<std::vector<paddle::experimental::Tensor>> GradNodeAccumulation::
-operator()(
-    std::vector<std::vector<paddle::experimental::Tensor>>& grads,  // NOLINT
-    bool create_graph, bool is_new_grad) {
+paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                     kSlotSmallVectorSize>
+GradNodeAccumulation::operator()(
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         kSlotSmallVectorSize>& grads,  // NOLINT
+    bool create_graph,
+    bool is_new_grad) {
   VLOG(3) << "Running Eager Backward Node: GradNodeAccumulation";
   PADDLE_ENFORCE(grads.size() == 1,
                  paddle::platform::errors::Fatal(
@@ -56,8 +59,9 @@ operator()(
   // Apply Gradient Hooks
   paddle::experimental::Tensor grad_out;
   if (GradientHooksRegistered()) {
-    std::vector<std::vector<paddle::experimental::Tensor>> hooked_grads =
-        ApplyGradientHooks(grads);
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         kSlotSmallVectorSize>
+        hooked_grads = ApplyGradientHooks(grads);
     grad_out = hooked_grads[0][0];
   } else {
     grad_out = grads[0][0];

paddle/fluid/eager/accumulation/accumulation_node.h

@@ -37,9 +37,12 @@ class GradNodeAccumulation : public GradNodeBase {
   }
 
   // Functor: perform backward computations
-  virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      std::vector<std::vector<paddle::experimental::Tensor>>& grads,  // NOLINT
-      bool create_graph = false, bool is_new_grad = false) override;
+  virtual paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                               kSlotSmallVectorSize>
+  operator()(paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                                  kSlotSmallVectorSize>& grads,  // NOLINT
+             bool create_graph = false,
+             bool is_new_grad = false) override;
 
   void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
 

paddle/fluid/eager/amp_utils.h

@@ -21,8 +21,8 @@ namespace egr {
 
 static inline paddle::experimental::DataType GetPromoteType(
     const std::string& op_name,
-    const std::vector<std::vector<paddle::experimental::Tensor>>&
-        amp_tensors_vector,
+    const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                               kSlotSmallVectorSize>& amp_tensors_vector,
     const paddle::experimental::DataType& amp_dtype) {
   auto dst_type = amp_dtype;
   if (egr::Controller::Instance().GetCurrentTracer()->GetAmpDtype() ==
@@ -86,8 +86,8 @@ static inline paddle::experimental::DataType GetPromoteType(
 
 inline paddle::experimental::DataType GetAmpDestDtype(
     const std::string& op_name,
-    const std::vector<std::vector<paddle::experimental::Tensor>>&
-        amp_tensors_vector) {
+    const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                               kSlotSmallVectorSize>& amp_tensors_vector) {
   auto amp_dtype =
       egr::Controller::Instance().GetCurrentTracer()->GetAmpDtype();
   auto amp_level = egr::Controller::Instance().GetAMPLevel();

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

@@ -144,11 +144,15 @@ void GradNodeScale::SetTensorWrappers_X(
 
 void GradNodeScale::SetAttributes_scale(float scale) { scale_ = scale; }
 
-std::vector<std::vector<paddle::experimental::Tensor>> GradNodeScale::
-operator()(
-    std::vector<std::vector<paddle::experimental::Tensor>>& grads,  // NOLINT
-    bool create_graph, bool is_new_grad) {
+paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                     kSlotSmallVectorSize>
+GradNodeScale::operator()(
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         kSlotSmallVectorSize>& grads,  // NOLINT
+    bool create_graph,
+    bool is_new_grad) {
   // 1. Check Output Size
+  VLOG(6) << "grad size is: " << grads.size();
   PADDLE_ENFORCE(
       ((grads.size() == 1) && (grads[0].size() == 1)),
       paddle::platform::errors::Fatal(
@@ -156,15 +160,18 @@ operator()(
           "However received: %d",
           "This indicates an issue with Eager Dygraph Backward logic",
           grads.size()));
-  std::vector<std::vector<paddle::experimental::Tensor>> outs;
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+      outs;
   // 2. Create needed out parttern
   paddle::experimental::Tensor out;
   // Apply Gradient Hooks
   if (GradientHooksRegistered()) {
     // TODO(jiabin): Shall we apply hook slot by slot here or accept
     // vector<vector<phi::tensor>> to apply all hooks?
-    std::vector<std::vector<paddle::experimental::Tensor>> hooked_grads =
-        ApplyGradientHooks(grads);
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         kSlotSmallVectorSize>
+        hooked_grads = ApplyGradientHooks(grads);
     ScaleAPI(/* slot by slot set */ hooked_grads[0][0], scale_, 0.0 /* bias */,
              true /* bias_after_scale */, &out);
   } else {

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

@@ -38,9 +38,12 @@ class GradNodeScale : public GradNodeBase {
   ~GradNodeScale() override = default;
 
   // Functor: perform backward computations
-  virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      std::vector<std::vector<paddle::experimental::Tensor>>& grads,  // NOLINT
-      bool create_graph = false, bool is_new_grad = false) override;
+  virtual paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                               kSlotSmallVectorSize>
+  operator()(paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                                  kSlotSmallVectorSize>& grads,  // NOLINT
+             bool create_graph = false,
+             bool is_new_grad = false) override;
 
   void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
 
@@ -48,7 +51,7 @@ class GradNodeScale : public GradNodeBase {
       const std::vector<paddle::experimental::Tensor>& tensors);
 
   void SetAttributes_scale(float scale);
-  std::string name() override { return ""; }
+  std::string name() override { return "scale node"; }
   // Members: define fwd input tensors
   // For Scale there is no fwd input tensor needed
 

paddle/fluid/eager/api/generated/eager_generated/forwards/scale.cc

@@ -79,9 +79,6 @@ paddle::experimental::Tensor scale(const paddle::experimental::Tensor& x,
     // Pass Attributes to GradNode
     scale_node->SetAttributes_scale(scale);
 
-    // Set Next Edges
-    scale_node->AddEdges(p_autograd_in, /*slot id*/ 0);
-
     // Set TensorWrappers
     scale_node->SetTensorWrappers_X({x});
 

paddle/fluid/eager/api/utils/global_utils.h

@@ -19,8 +19,9 @@
 #include <memory>
 #include "paddle/fluid/imperative/tracer.h"
 #include "paddle/phi/api/ext/op_meta_info.h"
+#include "paddle/utils/small_vector.h"
 namespace egr {
-
+constexpr size_t kSlotSmallVectorSize = 15U;
 class UniqueNameGenerator {
  public:
   explicit UniqueNameGenerator(std::string prefix = "") : prefix_(prefix) {}

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -1187,11 +1187,6 @@ static std::string GenerateGradNodeCreationContent(
       grad_node_creation_str += paddle::string::Sprintf(
           SET_GRAD_OUT_META_TEMPLATE, input_name, input_position);
 
-      const char* ADD_EDGES_TEMPLATE =
-          "      if(%s) grad_node->AddEdges(%s, %d);\n";
-      grad_node_creation_str +=
-          paddle::string::Sprintf(ADD_EDGES_TEMPLATE, input_autograd_name,
-                                  input_autograd_name, input_position);
     } else {
       compute_require_grad_args += ", &" + input_autograd_name;
       size_t input_position = fwd_inputs_name_pos_map.at(input_name);
@@ -1200,10 +1195,6 @@ static std::string GenerateGradNodeCreationContent(
           "      grad_node->SetGradOutMeta(%s, %d);\n";
       grad_node_creation_str += paddle::string::Sprintf(
           SET_GRAD_OUT_META_TEMPLATE, input_name, input_position);
-
-      const char* ADD_EDGES_TEMPLATE = "      grad_node->AddEdges(&%s, %d);\n";
-      grad_node_creation_str += paddle::string::Sprintf(
-          ADD_EDGES_TEMPLATE, input_autograd_name, input_position);
     }
   }
 
@@ -1649,7 +1640,8 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
     std::string amp_logic_str = "";
     if (in_vars.size() != 0) {
       const char* AMP_TENSORS_VECTOR_TEMPLATE =
-          "    std::vector<std::vector<paddle::experimental::Tensor>> "
+          "    paddle::small_vector<std::vector<paddle::experimental::Tensor>, "
+          "egr::kSlotSmallVectorSize> "
           "amp_tensors_vector = { "
           "%s };\n";
       std::string amp_tensors_vector = paddle::string::Sprintf(
@@ -2428,9 +2420,11 @@ static std::string GenerateGradNodeCCContents(
   }
 
   const char* BWD_RETURN_TEMPLATE =
-      "  std::vector<std::vector<paddle::experimental::Tensor>> hooked_grads = "
+      "  paddle::small_vector<std::vector<paddle::experimental::Tensor>, "
+      "egr::kSlotSmallVectorSize> hooked_grads = "
       "GradNode%s::ApplyGradientHooks(grads);\n"
-      "  std::vector<std::vector<paddle::experimental::Tensor>> outputs(%d);\n"
+      "  paddle::small_vector<std::vector<paddle::experimental::Tensor>, "
+      "egr::kSlotSmallVectorSize> outputs(%d);\n"
       "  %s\n"
       "  if(NeedComplexToRealConversion()) "
       "HandleComplexGradToRealGrad(&outputs);\n"
@@ -2441,9 +2435,11 @@ static std::string GenerateGradNodeCCContents(
 
   // [Generation] Get Full Grad Function
   const char* GRAD_FUNCTION_TEMPLATE =
-      "std::vector<std::vector<paddle::experimental::Tensor>> "
+      "paddle::small_vector<std::vector<paddle::experimental::Tensor>, "
+      "egr::kSlotSmallVectorSize> "
       "GradNode%s::operator()("
-      "std::vector<std::vector<paddle::experimental::Tensor>>& grads, bool "
+      "paddle::small_vector<std::vector<paddle::experimental::Tensor>, "
+      "egr::kSlotSmallVectorSize>& grads, bool "
       "create_graph, bool is_new_grad) {\n"
       "%s"
       "%s"
@@ -2487,9 +2483,12 @@ static std::string GenerateGradNodeHeaderContents(
       "Construct GradNode%s \"; }\n"
       "  ~GradNode%s() override { VLOG(6) << \" Destruct GradNode%s \"; }\n"
       "\n"
-      "  virtual std::vector<std::vector<paddle::experimental::Tensor>> "
+      "  virtual "
+      "paddle::small_vector<std::vector<paddle::experimental::Tensor>, "
+      "egr::kSlotSmallVectorSize> "
       "operator()("
-      "std::vector<std::vector<paddle::experimental::Tensor>>& grads, bool "
+      "paddle::small_vector<std::vector<paddle::experimental::Tensor>, "
+      "egr::kSlotSmallVectorSize>& grads, bool "
       "create_graph = false, bool is_new_grad = false) "
       "override;\n"
       "\n"

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

@@ -118,8 +118,8 @@ class {} : public egr::GradNodeBase {{
       egr::GradNodeBase(bwd_in_slot_num, bwd_out_slot_num) {{}}
   ~{}() override = default;
 
-  virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      std::vector<std::vector<paddle::experimental::Tensor>>& grads, bool create_graph = false, bool is_new_grad = false) override;
+  virtual paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallVectorSize> operator()(
+      paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallVectorSize>& grads, bool create_graph = false, bool is_new_grad = false) override;
   std::string name() override {{ return \"{}\"; }}
   
   void ClearTensorWrappers() override {{
@@ -149,7 +149,7 @@ class {} : public egr::GradNodeBase {{
 
 GRAD_FUNCTION_TEMPLATE = \
 """
-std::vector<std::vector<paddle::experimental::Tensor>> {}::operator()(std::vector<std::vector<paddle::experimental::Tensor>>& grads, bool create_graph, bool is_new_grad) {{
+paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallVectorSize> {}::operator()(paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallVectorSize>& grads, bool create_graph, bool is_new_grad) {{
     // Fill Zero For GradIn Tensors
 {}
 
@@ -239,7 +239,6 @@ FORWARD_BODY_TEMPLATE = \
       // Set TensorWrappers for Forward Inputs
 {}
       // SetGradOutMeta & SetEdges
-{}
 {}
       // SetOutRank & SetHistory & SetGradInMeta & RetainGrad
 {}
@@ -356,7 +355,7 @@ AMP_LOGIC_TEMPLATE = \
     if (egr::Controller::Instance().GetAMPLevel() != paddle::imperative::AmpLevel::O0) {{
         VLOG(5) << "Check and Prepare For AMP";
         {}
-        std::vector<std::vector<paddle::experimental::Tensor>> amp_tensors_vector = {};
+        paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallVectorSize> amp_tensors_vector = {};
         {}
         {}
         {}
@@ -769,15 +768,11 @@ class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
             is_optional = (name in self.optional_inputs)
             if is_optional:
                 set_grad_out_meta = f"{indent}if({name}.get_ptr() != nullptr) grad_node->SetGradOutMeta(*({name}.get_ptr()), {pos});"
-                set_edges = f"{indent}if({name}.get_ptr() != nullptr)  grad_node->AddEdges({input_autograd_meta_name}, {pos});"
             else:
                 set_grad_out_meta = f"{indent}grad_node->SetGradOutMeta({name}, {pos});"
-                set_edges = f"{indent}grad_node->AddEdges({input_autograd_meta_name}, {pos});"
 
             set_grad_out_meta_list.append(set_grad_out_meta)
-            set_edges_list.append(set_edges)
         set_grad_out_meta_str = "\n".join(set_grad_out_meta_list)
-        set_edges_str = "\n".join(set_edges_list)
 
         # SetOutRank & SetHistory & SetGradInMeta
         set_out_rank_list = []
@@ -808,7 +803,7 @@ class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
         self.node_creation_str = FORWARD_BODY_TEMPLATE.format(
             node_creation_event_str, pass_stop_gradient_args_str,
             node_construction_str, set_attributes_str,
-            set_input_tensor_wrappers_str, set_grad_out_meta_str, set_edges_str,
+            set_input_tensor_wrappers_str, set_grad_out_meta_str,
             set_out_rank_str, set_history_str, set_grad_in_meta_str,
             set_retain_grad_str, set_output_tensor_wrappers_str)
 
@@ -1454,7 +1449,7 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
 
         # Construct grad_api returns
         slot_num_bwd_outputs = len(self.forward_inputs_position_map.keys())
-        returns_str = f"{indent}std::vector<std::vector<paddle::experimental::Tensor>> returns({slot_num_bwd_outputs});\n"
+        returns_str = f"{indent}paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallVectorSize> returns({slot_num_bwd_outputs});\n"
         for name, (ttype, fwd_position,
                    grad_api_position) in backward_grad_outputs_map.items():
             transformed_tensor_name = self.TransformToNextGradName(name)

paddle/fluid/eager/backward.cc

@@ -169,9 +169,12 @@ class GeneralGrad {
           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) {
+      const paddle::small_vector<std::vector<GradSlotMeta>,
+                                 kSlotSmallVectorSize>& metas =
+          node->OutputMeta();
+      for (const auto& meta_list : metas) {
+        for (const GradSlotMeta& meta : meta_list) {
+          const auto& edge = meta.GetEdge();
           GradNodeBase* next_node = edge.GetMutableGradNode().get();
 
           // Next node could be nullptr if it is leaf tensor with no
@@ -381,13 +384,15 @@ class GeneralGrad {
               "unable to find copied target for certain grad node."));
       GradNodeBase* copied_node = orig_to_copied_node_mapping_[orig_node].get();
 
-      const std::vector<std::vector<Edge>>& orig_edges = orig_node->GetEdges();
-      std::vector<std::vector<Edge>>& copied_edges =
-          copied_node->GetMutableEdges();
-      for (size_t i = 0; i < orig_edges.size(); i++) {
-        for (size_t j = 0; j < orig_edges[i].size(); j++) {
-          const Edge& orig_edge = orig_edges[i][j];
-          Edge& copied_edge = copied_edges[i][j];
+      const paddle::small_vector<std::vector<GradSlotMeta>,
+                                 kSlotSmallVectorSize>& orig_meta =
+          orig_node->OutputMeta();
+      paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>&
+          copied_edges = copied_node->MutableOutputMeta();
+      for (size_t i = 0; i < orig_meta.size(); i++) {
+        for (size_t j = 0; j < orig_meta[i].size(); j++) {
+          const Edge& orig_edge = orig_meta[i][j].GetEdge();
+          Edge& copied_edge = copied_edges[i][j].GetMutableEdge();
 
           std::shared_ptr<GradNodeBase> orig_next_node =
               orig_edge.GetMutableGradNode();
@@ -468,9 +473,11 @@ std::unordered_map<GradNodeBase*, int> getInDegreeMap(
             "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) {
+    const paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>&
+        metas = node->OutputMeta();
+    for (const auto& meta_list : metas) {
+      for (const GradSlotMeta& meta : meta_list) {
+        const auto& edge = meta.GetEdge();
         GradNodeBase* next_node = edge.GetMutableGradNode().get();
         // Next node could be nullptr if it is leaf tensor with no
         // AccumulationNode attached
@@ -689,8 +696,10 @@ std::vector<paddle::experimental::Tensor> RunBackward(
 
     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, is_general_grad);
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         kSlotSmallVectorSize>
+        grad_output_tensors = (*node)(node_input_buffer->Buffers(),
+                                      create_graph, is_general_grad);
 
     // retain_grad or not
     if (!retain_graph) {
@@ -704,17 +713,18 @@ std::vector<paddle::experimental::Tensor> RunBackward(
     node_input_buffers_dict.erase(node);
 
     // Prepare GradTensorHolder for next node
-    const std::vector<std::vector<Edge>>& edges = node->GetEdges();
-    PADDLE_ENFORCE(edges.size() == grad_output_tensors.size() || edges.empty(),
+    const paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>&
+        metas = node->OutputMeta();
+    PADDLE_ENFORCE(metas.size() == grad_output_tensors.size() || metas.empty(),
                    paddle::platform::errors::Fatal(
                        "Number of edges should be either empty ( for leaf node "
                        ") or the same as number of output grad tensors, but we "
                        "got edges size is: %d, grad_output size is: %d",
-                       edges.size(), grad_output_tensors.size()));
+                       metas.size(), grad_output_tensors.size()));
 
-    for (size_t i = 0; i < edges.size(); i++) {
-      for (size_t j = 0; j < edges[i].size(); j++) {
-        const Edge& edge = edges[i][j];
+    for (size_t i = 0; i < metas.size(); i++) {
+      for (size_t j = 0; j < metas[i].size(); j++) {
+        const Edge& edge = metas[i][j].GetEdge();
         if (!edge.IsInitialized()) {
           continue;
         }

paddle/fluid/eager/custom_operator/custom_operator_node.cc

@@ -19,10 +19,12 @@
 #include "paddle/phi/core/dense_tensor.h"
 
 namespace egr {
-std::vector<std::vector<paddle::experimental::Tensor>> RunCustomOpNode::
-operator()(
-    std::vector<std::vector<paddle::experimental::Tensor>>& grads,  // NOLINT
-    bool create_graph, bool is_new_grad) {
+paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                     kSlotSmallVectorSize>
+RunCustomOpNode::operator()(
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         kSlotSmallVectorSize>& grads,
+    bool create_graph, bool is_new_grad) {  // NOLINT
   paddle::CustomOpKernelContext ctx;
   auto grad_inputs_name = paddle::framework::OpMetaInfoHelper::GetInputs(
       egr::Controller::Instance().GetOpMetaInfoMap().at(op_type_)[1]);
@@ -31,8 +33,9 @@ operator()(
   auto map = egr::Controller::Instance().GetCustomEdgesSlotMap().at(op_type_);
   auto kernel_map = egr::Controller::Instance().GetOpMetaInfoMap();
 
-  std::vector<std::vector<paddle::experimental::Tensor>> tmp_ins(
-      grad_inputs_name.size());
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+      tmp_ins(grad_inputs_name.size());
   VLOG(7) << " Prepare Backward inputs of grads with size: " << grads.size()
           << ", whose grad_inputs_name size is: " << grad_inputs_name.size();
   for (size_t i = 0; i < grads.size(); i++) {
@@ -58,17 +61,19 @@ operator()(
   }
   VLOG(6) << "Prepare Grad attrs";
   ctx.EmplaceBackAttrs(attrs_);
-  std::vector<std::vector<paddle::experimental::Tensor>> outs(
-      GetEdges().size());
-  std::vector<std::vector<paddle::experimental::Tensor>> tmp_outs(
-      grad_outputs_names.size());
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+      outs(OutputMeta().size());
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+      tmp_outs(grad_outputs_names.size());
   VLOG(6) << "Prepare Grad outputs for size: " << grad_outputs_names.size();
-  for (size_t i = 0; i < GetEdges().size(); i++) {
+  for (size_t i = 0; i < OutputMeta().size(); i++) {
     if (map[0].find(i) != map[0].end()) {
       VLOG(7) << "Insert grad outputs: " << i
-              << " with size: " << GetEdges()[i].size()
+              << " with size: " << OutputMeta()[i].size()
               << " to tmp_outputs: " << map[0][i];
-      for (size_t j = 0; j < GetEdges()[i].size(); j++) {
+      for (size_t j = 0; j < OutputMeta()[i].size(); j++) {
         outs[i].emplace_back(/* init it incase of copy nullptr of shared_ptr */
                              std::make_shared<phi::DenseTensor>(
                                  phi::DataType::UNDEFINED),

paddle/fluid/eager/custom_operator/custom_operator_node.h

@@ -36,10 +36,13 @@ class RunCustomOpNode : public GradNodeBase {
   }
 
   // Functor: perform backward computations
-  virtual std::vector<std::vector<paddle::experimental::Tensor>>
+  virtual paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                               kSlotSmallVectorSize>
   operator()(  // NOLINT
-      std::vector<std::vector<paddle::experimental::Tensor>>& grads,  // NOLINT
-      bool create_graph = false, bool is_new_grad = false)            // NOLINT
+      paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                           kSlotSmallVectorSize>& grads,  // NOLINT
+      bool create_graph = false,
+      bool is_new_grad = false)  // NOLINT
       override;
 
   std::string name() {

paddle/fluid/eager/grad_node_info.cc

@@ -40,70 +40,20 @@ GradNodeBase::GradNodeBase(size_t bwd_in_slot_num, size_t bwd_out_slot_num) {
   VLOG(6) << "Construct GradNodeBase";
   bwd_in_meta_.resize(bwd_in_slot_num);
   bwd_out_meta_.resize(bwd_out_slot_num);
-  adj_edges_.resize(bwd_out_slot_num);
 }
 
-void GradNodeBase::AddEdges(std::vector<AutogradMeta*>* metas, size_t slot_id) {
-  PADDLE_ENFORCE_LT(
-      slot_id, adj_edges_.size(),
-      paddle::platform::errors::InvalidArgument(
-          "Given slot id is out of range of adj_edges outter size, "
-          "adj_edges is designed to has the same size of grad "
-          "inputs's slot num."));
-
-  for (size_t i = 0; i < metas->size(); i++) {
-    const auto& meta = (*metas)[i];
-    // adj_edges has as same rank as fwd inputs, and record it's output rank
-    // from
-    // its pre-ops
-    if (meta && !meta->StopGradient()) {
-      auto node = meta->GetMutableGradNode();
-      if (!node || !node.get()) {
-        meta->SetGradNode(std::make_shared<egr::GradNodeAccumulation>(meta));
-      }
-      VLOG(6) << "Add Edges for slot: " << slot_id << ", the Edge is from "
-              << this->name() << " (addr: " << this << ") "
-              << " to " << meta->GetMutableGradNode()->name()
-              << " (addr: " << meta->GetMutableGradNode().get() << ")";
-
-      adj_edges_[slot_id].emplace_back(meta->GetMutableGradNode(),
-                                       meta->OutRankInfo());
-    } else {
-      adj_edges_[slot_id].emplace_back();
-    }
-  }
-}
-
-void GradNodeBase::AddEdges(AutogradMeta* meta, size_t slot_id) {
-  PADDLE_ENFORCE_LT(
-      slot_id, adj_edges_.size(),
-      paddle::platform::errors::InvalidArgument(
-          "Given slot id is out of range of adj_edges outter size, "
-          "adj_edges is designed to has the same size of grad "
-          "inputs's slot num."));
-
-  if (meta && !meta->StopGradient()) {
-    auto node = meta->GetMutableGradNode();
-    if (!node || !node.get()) {
-      meta->SetGradNode(std::make_shared<egr::GradNodeAccumulation>(meta));
-    }
-    VLOG(6) << "Add Edges for slot: " << slot_id << ", the Edge is from "
-            << this->name() << " (addr: " << this << ") "
-            << " to " << meta->GetMutableGradNode()->name()
-            << " (addr: " << meta->GetMutableGradNode().get() << ")";
-
-    adj_edges_[slot_id].emplace_back(meta->GetMutableGradNode(),
-                                     meta->OutRankInfo());
-  } else {
-    adj_edges_[slot_id].emplace_back();
-  }
+const paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>&
+GradNodeBase::InputMeta() const {
+  return bwd_in_meta_;
 }
 
-const std::vector<std::vector<GradSlotMeta>>& GradNodeBase::InputMeta() const {
-  return bwd_in_meta_;
+const paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>&
+GradNodeBase::OutputMeta() const {
+  return bwd_out_meta_;
 }
 
-const std::vector<std::vector<GradSlotMeta>>& GradNodeBase::OutputMeta() const {
+paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>&
+GradNodeBase::MutableOutputMeta() {
   return bwd_out_meta_;
 }
 
@@ -123,7 +73,9 @@ void GradNodeBase::SetGradInMeta(const paddle::experimental::Tensor& fwd_out,
   }
 
   auto& meta = metas[0];
+  if (fwd_out_meta && fwd_out_meta->StopGradient()) {
     meta.SetStopGradient(fwd_out_meta->StopGradient());
+  }
 
   if (!fwd_out.initialized()) {
     VLOG(6)
@@ -153,8 +105,8 @@ void GradNodeBase::SetGradInMeta(const paddle::experimental::Tensor& fwd_out,
   meta.SetTensorMeta(dense_tensor->meta());
   meta.SetPlace(fwd_out.place());
 
-  if (paddle::framework::IsComplexType(
-          paddle::framework::TransToProtoVarType(dense_tensor->type()))) {
+  if (dense_tensor->type() == paddle::experimental::DataType::COMPLEX64 ||
+      dense_tensor->type() == paddle::experimental::DataType::COMPLEX128) {
     need_complex_to_real_ = true;
   }
 }
@@ -186,7 +138,7 @@ void GradNodeBase::SetGradInMeta(
                                 "Bwd_in_meta should only be called while "
                                 "autograd_meta is not null. If you got this "
                                 "error, it indicates bugs in framework."));
-    if (fwd_out_meta->StopGradient()) {
+    if (fwd_out_meta && fwd_out_meta->StopGradient()) {
       // Set Stop Gradient only when its true or non-initialized autograd_meta,
       // since all default value is false.
       meta.SetStopGradient(fwd_out_meta->StopGradient());
@@ -212,8 +164,8 @@ void GradNodeBase::SetGradInMeta(
       meta.SetTensorMeta(dense_tensor->meta());
       meta.SetPlace(fwd_out_tensor.place());
 
-      if (paddle::framework::IsComplexType(
-              paddle::framework::TransToProtoVarType(dense_tensor->type()))) {
+      if (dense_tensor->type() == paddle::experimental::DataType::COMPLEX64 ||
+          dense_tensor->type() == paddle::experimental::DataType::COMPLEX128) {
         need_complex_to_real_ = true;
       }
     } else {
@@ -238,12 +190,24 @@ void GradNodeBase::SetGradOutMeta(const paddle::experimental::Tensor& fwd_in,
     metas.resize(1);
   }
   auto& meta = metas[0];
+  // Set Stop_gradient
   if (fwd_in_meta) {
     meta.SetStopGradient(fwd_in_meta->StopGradient());
-  } else {
-    meta.SetStopGradient(true);
   }
+  // Set Adj Edges
+  if (fwd_in_meta && !fwd_in_meta->StopGradient()) {
+    auto node = fwd_in_meta->GetMutableGradNode();
+    if (!node || !node.get()) {
+      fwd_in_meta->SetGradNode(
+          std::make_shared<egr::GradNodeAccumulation>(fwd_in_meta));
+    }
+    VLOG(6) << "Add Edges for slot: " << slot_rank << ", the Edge is from "
+            << this->name() << " (addr: " << this << ") "
+            << " to " << fwd_in_meta->GetMutableGradNode()->name()
+            << " (addr: " << fwd_in_meta->GetMutableGradNode().get() << ")";
 
+    meta.SetEdge(fwd_in_meta->GetMutableGradNode(), fwd_in_meta->OutRankInfo());
+  }
   // Record TensorMeta
   if (fwd_in.impl() && fwd_in.impl().get()) {
     if (phi::DenseTensor::classof(fwd_in.impl().get())) {
@@ -282,30 +246,43 @@ void GradNodeBase::SetGradOutMeta(
     const auto& fwd_in_tensor = fwd_in[i];
     auto& meta = metas[i];
     auto* fwd_in_meta = egr::EagerUtils::nullable_autograd_meta(fwd_in_tensor);
+    // Set Stop_gradient
     if (fwd_in_meta) {
-      // Set Stop Gradient only when its true or non-initialized autograd_meta,
-      // since all default value is false.
       meta.SetStopGradient(fwd_in_meta->StopGradient());
     }
+    // Set Adj Edges
+    if (fwd_in_meta && !fwd_in_meta->StopGradient()) {
+      auto node = fwd_in_meta->GetMutableGradNode();
+      if (!node || !node.get()) {
+        fwd_in_meta->SetGradNode(
+            std::make_shared<egr::GradNodeAccumulation>(fwd_in_meta));
+      }
+      VLOG(6) << "Add Edges for slot: " << slot_rank << ", the Edge is from "
+              << this->name() << " (addr: " << this << ") "
+              << " to " << fwd_in_meta->GetMutableGradNode()->name()
+              << " (addr: " << fwd_in_meta->GetMutableGradNode().get() << ")";
 
+      meta.SetEdge(fwd_in_meta->GetMutableGradNode(),
+                   fwd_in_meta->OutRankInfo());
+    }
     // Record TensorMeta
     if (fwd_in_tensor.impl() && fwd_in_tensor.impl().get()) {
       if (phi::DenseTensor::classof(fwd_in_tensor.impl().get())) {
         // Only Copy Meta
         phi::DenseTensor* dense_tensor =
             static_cast<phi::DenseTensor*>(fwd_in_tensor.impl().get());
-
         PADDLE_ENFORCE_NE(dense_tensor->meta().dtype, phi::DataType::UNDEFINED,
                           paddle::platform::errors::Fatal(
-                              "Attempting to copy DenseTensorMeta with "
-                              "phi::DataType::UNDEFINED,"
+                              "Attempting to copy DenseTensorMeta "
+                              "with phi::DataType::UNDEFINED,"
                               "which is illegal."));
         meta.SetTensorMeta(dense_tensor->meta());
         meta.SetPlace(fwd_in_tensor.place());
       }
     } else {
-      VLOG(6) << "Unable to initialize the DenseTensorMeta of GradSlotMeta "
-                 "with non-DenseTensor argument.";
+      VLOG(6)
+          << "Unable to initialize the DenseTensorMeta of GradSlotMeta with "
+             "non-DenseTensor argument.";
     }
   }
 }
@@ -328,18 +305,14 @@ int64_t GradNodeBase::RegisterGradientHook(
   return next_hook_id_++;
 }
 
-const std::vector<std::vector<Edge>>& GradNodeBase::GetEdges() const {
-  return adj_edges_;
-}
-
-std::vector<std::vector<Edge>>& GradNodeBase::GetMutableEdges() {
-  return adj_edges_;
-}
-
-std::vector<std::vector<paddle::experimental::Tensor>>
+paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                     kSlotSmallVectorSize>
 GradNodeBase::ApplyGradientHooks(
-    const std::vector<std::vector<paddle::experimental::Tensor>>& tensors) {
-  std::vector<std::vector<paddle::experimental::Tensor>> outs(tensors.size());
+    const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                               kSlotSmallVectorSize>& tensors) {
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+      outs(tensors.size());
   for (auto& hook_pair : gradient_hooks_) {
     size_t slot_id = std::get<0>(hook_pair.second);
     size_t rank = std::get<1>(hook_pair.second);
@@ -386,7 +359,8 @@ GradNodeBase::ApplyGradientHooks(
 }
 
 void GradNodeBase::HandleComplexGradToRealGrad(
-    std::vector<std::vector<paddle::experimental::Tensor>>* out_grads) {
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         kSlotSmallVectorSize>* out_grads) {
   for (size_t slot_id = 0; slot_id < out_grads->size(); slot_id++) {
     const std::vector<paddle::experimental::Tensor>& slot_out_grads =
         (*out_grads)[slot_id];

paddle/fluid/eager/grad_node_info.h

@@ -16,6 +16,7 @@
 
 #include <memory>
 
+#include "paddle/fluid/eager/api/utils/global_utils.h"
 #include "paddle/fluid/eager/eager_tensor.h"
 #include "paddle/fluid/eager/hooks.h"
 #include "paddle/phi/api/all.h"
@@ -46,9 +47,8 @@ namespace egr {
  * indicate which
  * input of grad this edge belong).
  * */
-class Edge;
 class AutogradMeta;
-
+class GradNodeBase;
 /**
  * GradSlotMeta is used to Record Forward Tensor info to backward, since paddle
  * has lots of operators
@@ -56,6 +56,70 @@ class AutogradMeta;
  * So, we need a meta info
  * to record it's needs.
  * **/
+class Edge {
+ public:
+  // Default constructor for Edges in order to construct it for AutogradMeta
+  Edge() : in_slot_id_(0), in_rank_(0), grad_node_(nullptr) {}
+
+  // In real use cases we should create Edge from grad node and input rank which
+  // indicate which edge it is.
+  // Since we have slot design in operators we will have to locate an edge with
+  // slot
+  // and rank.
+  Edge(const std::shared_ptr<GradNodeBase>& grad_node, size_t in_slot_id,
+       size_t in_rank)
+      : in_slot_id_(in_slot_id), in_rank_(in_rank), grad_node_(grad_node) {}
+
+  Edge(const std::shared_ptr<GradNodeBase>& grad_node,
+       const std::pair</* slot_id */ size_t, /* rank */ size_t>& rank_info)
+      : in_slot_id_(rank_info.first),
+        in_rank_(rank_info.second),
+        grad_node_(grad_node) {}
+
+  GradNodeBase* GetGradNode() const { return grad_node_.get(); }
+
+  std::shared_ptr<GradNodeBase> GetMutableGradNode() const {
+    return grad_node_;
+  }
+
+  void SetGradNode(const std::shared_ptr<GradNodeBase>& node) {
+    VLOG(6) << "Reseting Edge's Grad Node";
+    grad_node_ = node;
+  }
+
+  std::pair<size_t, size_t> GetEdgeRankInfo() const {
+    return std::make_pair(in_slot_id_, in_rank_);
+  }
+
+  void SetEdgeRankInfo(size_t slot_id, size_t in_rank) {
+    in_slot_id_ = slot_id;
+    in_rank_ = in_rank;
+  }
+
+  void SetEdgeRankInfo(
+      const std::pair</* slot_id */ size_t, /* rank */ size_t>& edge_rank) {
+    in_slot_id_ = edge_rank.first;
+    in_rank_ = edge_rank.second;
+  }
+
+  // Currently we use grad_node_ to identify if a edge is initialized.
+  bool IsInitialized() const {
+    if (!grad_node_) {
+      return false;
+    } else {
+      if (!(grad_node_.get())) {
+        return false;
+      } else {
+        return true;
+      }
+    }
+  }
+
+ private:
+  size_t in_slot_id_;
+  size_t in_rank_;
+  std::shared_ptr<GradNodeBase> grad_node_{nullptr};
+};
 class GradSlotMeta {
  public:
   GradSlotMeta() = default;
@@ -81,10 +145,21 @@ class GradSlotMeta {
   void SetPlace(const phi::Place& place) { place_ = place; }
   const phi::Place& GetPlace() const { return place_; }
 
+  void SetEdge(const Edge& edge) { adj_edge_ = edge; }
+  void SetEdge(
+      const std::shared_ptr<GradNodeBase>& grad_node,
+      const std::pair</* slot_id */ size_t, /* rank */ size_t>& rank_info) {
+    adj_edge_.SetGradNode(grad_node);
+    adj_edge_.SetEdgeRankInfo(rank_info);
+  }
+  Edge& GetMutableEdge() { return adj_edge_; }
+  const Edge& GetEdge() const { return adj_edge_; }
+
  private:
   bool stop_gradient_{false};
   phi::Place place_;
   std::shared_ptr<phi::DenseTensorMeta> meta_ = nullptr;
+  Edge adj_edge_;
 };
 
 class GradNodeBase {
@@ -107,9 +182,12 @@ class GradNodeBase {
    * so, vector of vector
    * is better choice to fit this format.
    * **/
-  virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      std::vector<std::vector<paddle::experimental::Tensor>>& grads,  // NOLINT
-      bool create_graph = false, bool is_new_grad = false) = 0;
+  virtual paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                               kSlotSmallVectorSize>
+  operator()(paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                                  kSlotSmallVectorSize>& grads,  // NOLINT
+             bool create_graph = false,
+             bool is_new_grad = false) = 0;
 
   virtual void ClearTensorWrappers() = 0;
 
@@ -118,17 +196,6 @@ class GradNodeBase {
        * **/
   virtual std::shared_ptr<GradNodeBase> Copy() const = 0;
 
-  /**
-   * AddEdges is designed to set input tensors' backward Node as current
-   * node's Edges.
-   * This method should be call in forward code and for double backward depends
-   * computation.
-   *
-   * This one is called slot by slot
-   * **/
-  void AddEdges(std::vector<AutogradMeta*>* metas, size_t slot_id);
-  void AddEdges(AutogradMeta* meta, size_t slot_id);
-
   // adj_edges were moved inside OutputMeta(), so no available direct access
   // from GradNodeBase.
   // To access Edges, get GradSlotMeta by calling OutputMeta(), then use
@@ -136,10 +203,15 @@ class GradNodeBase {
 
   /**
    * Get Input Meta of current Grad node**/
-  const std::vector<std::vector<GradSlotMeta>>& InputMeta() const;
+  const paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>&
+  InputMeta() const;
   /**
    * Get Output Meta of current Grad node**/
-  const std::vector<std::vector<GradSlotMeta>>& OutputMeta() const;
+  const paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>&
+  OutputMeta() const;
+
+  paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>&
+  MutableOutputMeta();
   /**
    * Set bwd ins and outs info with forward vars
    * **/
@@ -180,23 +252,22 @@ class GradNodeBase {
    * **/
   inline bool GradientHooksRegistered() { return !gradient_hooks_.empty(); }
 
-  std::vector<std::vector<paddle::experimental::Tensor>> ApplyGradientHooks(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& tensors);
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+  ApplyGradientHooks(
+      const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                                 kSlotSmallVectorSize>& tensors);
 
   /**
     * Handle Complex - Real Type Promotion
     * **/
   void HandleComplexGradToRealGrad(
-      std::vector<std::vector<paddle::experimental::Tensor>>* out_grads);
+      paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                           kSlotSmallVectorSize>* out_grads);
   bool NeedComplexToRealConversion() { return need_complex_to_real_; }
 
   virtual std::string name() { return "GradNodeBase"; }
 
-  /**
-       * GetEdges is designed to get all edges of current node**/
-  const std::vector<std::vector<Edge>>& GetEdges() const;
-  std::vector<std::vector<Edge>>& GetMutableEdges();
-
   /**
        * The following interfaces are designed for no_need_buffer
        * **/
@@ -207,18 +278,13 @@ class GradNodeBase {
   }
 
  private:
-  // TODO(zhanlve): Merge adj_edges_ into GradOutMeta
-  // Edges recorded the backward related node info, which indicate all edges
-  // linked
-  // by this Grad Node.
-  // Why we need vector<vector<Edge>>: Edges is as same rank as bwd output.
-  std::vector<std::vector<Edge>> adj_edges_;
-
   // bwd_out_meta_ is used to record Grad output info for backward
-  std::vector<std::vector<GradSlotMeta>> bwd_out_meta_;
+  paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>
+      bwd_out_meta_;
 
   // bwd_in_meta_ used to record Grad input info for backward
-  std::vector<std::vector<GradSlotMeta>> bwd_in_meta_;
+  paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>
+      bwd_in_meta_;
   // Gradient Hooks
   // Customer may register a list of hooks which will be called in order during
   // backward
@@ -235,71 +301,6 @@ class GradNodeBase {
   bool is_tensor_wrappers_cleared_ = false;
 };
 
-class Edge {
- public:
-  // Default constructor for Edges in order to construct it for AutogradMeta
-  Edge() : in_slot_id_(0), in_rank_(0), grad_node_(nullptr) {}
-
-  // In real use cases we should create Edge from grad node and input rank which
-  // indicate which edge it is.
-  // Since we have slot design in operators we will have to locate an edge with
-  // slot
-  // and rank.
-  Edge(const std::shared_ptr<GradNodeBase>& grad_node, size_t in_slot_id,
-       size_t in_rank)
-      : in_slot_id_(in_slot_id), in_rank_(in_rank), grad_node_(grad_node) {}
-
-  Edge(const std::shared_ptr<GradNodeBase>& grad_node,
-       const std::pair</* slot_id */ size_t, /* rank */ size_t>& rank_info)
-      : in_slot_id_(rank_info.first),
-        in_rank_(rank_info.second),
-        grad_node_(grad_node) {}
-
-  GradNodeBase* GetGradNode() const { return grad_node_.get(); }
-
-  std::shared_ptr<GradNodeBase> GetMutableGradNode() const {
-    return grad_node_;
-  }
-
-  void SetGradNode(const std::shared_ptr<GradNodeBase>& node) {
-    VLOG(6) << "Reseting Edge's Grad Node";
-    grad_node_ = node;
-  }
-
-  std::pair<size_t, size_t> GetEdgeRankInfo() const {
-    return std::make_pair(in_slot_id_, in_rank_);
-  }
-
-  void SetEdgeRankInfo(size_t slot_id, size_t in_rank) {
-    in_slot_id_ = slot_id;
-    in_rank_ = in_rank;
-  }
-
-  void SetEdgeRankInfo(
-      const std::pair</* slot_id */ size_t, /* rank */ size_t>& edge_rank) {
-    in_slot_id_ = edge_rank.first;
-    in_rank_ = edge_rank.second;
-  }
-
-  // Currently we use grad_node_ to identify if a edge is initialized.
-  bool IsInitialized() const {
-    if (!grad_node_) {
-      return false;
-    } else {
-      if (!(grad_node_.get())) {
-        return false;
-      } else {
-        return true;
-      }
-    }
-  }
-
- private:
-  size_t in_slot_id_;
-  size_t in_rank_;
-  std::shared_ptr<GradNodeBase> grad_node_{nullptr};
-};
-
 inline void CheckTensor(const paddle::experimental::Tensor& pre,
                         const paddle::experimental::Tensor& post) {
   if (!pre.initialized() && post.initialized()) {

paddle/fluid/eager/grad_tensor_holder.h

@@ -27,7 +27,8 @@ namespace egr {
 class GradTensorHolder {
  public:
   explicit GradTensorHolder(
-      const std::vector<std::vector<GradSlotMeta>>& metas) {
+      const paddle::small_vector<std::vector<GradSlotMeta>,
+                                 kSlotSmallVectorSize>& metas) {
     VLOG(7) << "Init GradTensorHolder with meta size: " << metas.size();
     buffer_.resize(metas.size());
     for (size_t i = 0; i < buffer_.size(); i++) {
@@ -39,7 +40,8 @@ class GradTensorHolder {
   GradTensorHolder(const GradTensorHolder& other) = default;
 
   explicit GradTensorHolder(
-      std::vector<std::vector<paddle::experimental::Tensor>>&& inputs)
+      paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                           kSlotSmallVectorSize>&& inputs)
       : buffer_(std::move(inputs)) {}
 
   GradTensorHolder& operator=(const GradTensorHolder& other) = default;
@@ -56,14 +58,18 @@ class GradTensorHolder {
     return buffer_[pos];
   }
 
-  std::vector<std::vector<paddle::experimental::Tensor>>& Buffers() {
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>&
+  Buffers() {
     return buffer_;
   }
 
   void SetBufferSlotRankZeros(size_t slot_id, size_t rank);
 
  private:
-  std::vector<std::vector<paddle::experimental::Tensor>> buffer_;
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+      buffer_;
 };
 
 }  // namespace egr

paddle/fluid/eager/pylayer/py_layer_node.cc

@@ -29,14 +29,18 @@
 #include "pybind11/pytypes.h"
 
 namespace egr {
-std::vector<std::vector<paddle::experimental::Tensor>> GradNodePyLayer::
-operator()(
-    std::vector<std::vector<paddle::experimental::Tensor>>& grads,  // NOLINT
-    bool create_graph, bool is_new_grad) {
+paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                     kSlotSmallVectorSize>
+GradNodePyLayer::operator()(
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         kSlotSmallVectorSize>& grads,  // NOLINT
+    bool create_graph,
+    bool is_new_grad) {
   VLOG(3) << "Running Eager Backward Node: " << name();
 
-  std::vector<std::vector<paddle::experimental::Tensor>> hooked_grads =
-      GradNodePyLayer::ApplyGradientHooks(grads);
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+      hooked_grads = GradNodePyLayer::ApplyGradientHooks(grads);
 
   paddle::pybind::PyLayerObject* ctx =
       reinterpret_cast<paddle::pybind::PyLayerObject*>(ctx_);
@@ -124,7 +128,9 @@ operator()(
         ctx->forward_input_tensor_is_duplicable.size(), outputs_size));
   }
 
-  std::vector<std::vector<paddle::experimental::Tensor>> grad_out;
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+      grad_out;
   grad_out.reserve(ctx->forward_input_tensor_is_duplicable.size());
   for (size_t i = 0; i < ctx->forward_input_tensor_is_duplicable.size(); i++) {
     if (i < outputs_size) {

paddle/fluid/eager/pylayer/py_layer_node.h

@@ -34,9 +34,12 @@ class GradNodePyLayer : public GradNodeBase {
 
   ~GradNodePyLayer() override { Py_DECREF(ctx_); };
 
-  virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      std::vector<std::vector<paddle::experimental::Tensor>>& grads,  // NOLINT
-      bool create_graph = false, bool is_new_grad = false) override;
+  virtual paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                               kSlotSmallVectorSize>
+  operator()(paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                                  kSlotSmallVectorSize>& grads,  // NOLINT
+             bool create_graph = false,
+             bool is_new_grad = false) override;
 
   void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
 

paddle/fluid/eager/tensor_wrapper.h

@@ -88,7 +88,7 @@ class TensorWrapper {
     } else {
       intermidiate_tensor_.set_impl(tensor.impl());
     }
-
+    // TODO(jiabin): This may has server performance issue
     intermidiate_tensor_.set_name(tensor.name() + "@Saved");
 
     auto* tensor_autograd_meta = EagerUtils::nullable_autograd_meta(tensor);

paddle/fluid/eager/tests/data_structure_tests/accumulation_node_test.cc

@@ -80,14 +80,18 @@ TEST(AccumulationNode, Tensor) {
   grad_meta->SetStopGradient(false);
 
   // operator()
-  std::vector<std::vector<paddle::experimental::Tensor>> et0_vec = {{et0}};
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+      et0_vec = {{et0}};
   paddle::experimental::Tensor ret_et0 = node->operator()(et0_vec)[0][0];
   auto* ret_et0_ptr =
       std::dynamic_pointer_cast<phi::DenseTensor>(ret_et0.impl())
           ->data<paddle::platform::float16>();
   CHECK_EQ(ret_et0_ptr[0], paddle::platform::float16(10.0f));
 
-  std::vector<std::vector<paddle::experimental::Tensor>> et1_vec = {{et1}};
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+      et1_vec = {{et1}};
   paddle::experimental::Tensor ret_et1 = node->operator()(et1_vec)[0][0];
 
   auto* ret_et1_ptr =

paddle/fluid/eager/tests/data_structure_tests/grad_node_info_test.cc

@@ -34,7 +34,9 @@ void TestGradNodeBase(bool is_remove_gradient_hook) {
   auto grad_test_node0 = std::make_shared<eager_test::GradTestNode>(
       /* val */ 5.0, /* in_num */ 2, /* out_num */ 2);
   auto grad_test_node1 = std::make_shared<eager_test::GradTestNode>();
-  std::vector<std::vector<paddle::experimental::Tensor>> grads;
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       egr::kSlotSmallVectorSize>
+      grads;
   phi::DenseTensorMeta meta =
       phi::DenseTensorMeta(phi::DataType::FLOAT32, phi::make_ddim({1, 1}));
   std::shared_ptr<phi::DenseTensor> dt = std::make_shared<phi::DenseTensor>(
@@ -51,28 +53,9 @@ void TestGradNodeBase(bool is_remove_gradient_hook) {
   CHECK_EQ(std::dynamic_pointer_cast<phi::DenseTensor>(res[0][0].impl())
                ->data<float>()[0],
            6.0f);
-  VLOG(6) << "Test Add Edges";
-  egr::Edge tmp_edge0(grad_test_node1, 1, 2);
-  auto auto_grad0 = std::make_shared<egr::AutogradMeta>(tmp_edge0);
-  auto_grad0->SetStopGradient(false);
-
   egr::Edge tmp_edge1(grad_test_node1, 3, 4);
   auto auto_grad1 = std::make_shared<egr::AutogradMeta>(tmp_edge1);
   et1.set_autograd_meta(auto_grad1);
-  auto_grad1->SetStopGradient(false);
-  grad_test_node0->AddEdges(auto_grad0.get(), 0);
-
-  CHECK_EQ(grad_test_node0->GetEdges()[0][0].GetEdgeRankInfo().first,
-           size_t(1));
-  CHECK_EQ(grad_test_node0->GetEdges()[0][0].GetEdgeRankInfo().second,
-           size_t(2));
-  std::vector<egr::AutogradMeta*> metas = {auto_grad1.get()};
-
-  grad_test_node0->AddEdges(&metas, 1);
-  CHECK_EQ(grad_test_node0->GetEdges()[1][0].GetEdgeRankInfo().first,
-           size_t(3));
-  CHECK_EQ(grad_test_node0->GetEdges()[1][0].GetEdgeRankInfo().second,
-           size_t(4));
 
   VLOG(6) << "Test Set Meta and Get Meta";
   auto_grad1->SetStopGradient(true);

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

@@ -31,9 +31,12 @@ class GradTestNode : public egr::GradNodeBase {
       : GradNodeBase(in_num, out_num), val_(val) {}
   GradTestNode() : GradNodeBase() { val_ = 1.0; }
   std::string name() override { return "GradTestNode"; }
-  std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      std::vector<std::vector<paddle::experimental::Tensor>>& grads,  // NOLINT
-      bool create_graph = false, bool is_new_grad = false) override {
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       egr::kSlotSmallVectorSize>
+  operator()(paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                                  egr::kSlotSmallVectorSize>& grads,  // NOLINT
+             bool create_graph = false,
+             bool is_new_grad = false) override {
     val_ = std::dynamic_pointer_cast<phi::DenseTensor>(grads[0][0].impl())
                ->data<float>()[0];
     phi::DenseTensorMeta meta =
@@ -46,7 +49,9 @@ class GradTestNode : public egr::GradNodeBase {
     auto* dt_ptr = dt->mutable_data<float>(paddle::platform::CPUPlace());
     dt_ptr[0] = 6.0f;
     paddle::experimental::Tensor et1(dt);
-    std::vector<std::vector<paddle::experimental::Tensor>> res = {{et1}};
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         egr::kSlotSmallVectorSize>
+        res = {{et1}};
     return res;
   }
   void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }

paddle/fluid/eager/tests/data_structure_tests/grad_tensor_holder_test.cc

@@ -45,7 +45,9 @@ TEST(GradTensorHolder, Constructor) {
       meta);
   paddle::experimental::Tensor et = paddle::experimental::Tensor(dt);
 
-  std::vector<std::vector<paddle::experimental::Tensor>> inputs;
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       kSlotSmallVectorSize>
+      inputs;
   inputs.push_back({et});
 
   GradTensorHolder grad_tensor_holder4 = GradTensorHolder(std::move(inputs));

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

@@ -76,8 +76,7 @@ TEST(Backward, SingleNodeEmptyGrad) {
     auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
     auto_grad_meta1->SetStopGradient(false);
 
-    std::vector<egr::AutogradMeta*> res = {auto_grad_meta1};
-    node0_ptr->AddEdges(&res, 0);
+    node0_ptr->SetGradOutMeta({leaf_tensor}, 0);
   }
   std::vector<paddle::experimental::Tensor> outs = {target_tensor};
   // Run Backward
@@ -135,8 +134,7 @@ TEST(Backward, SingleNodeCustomGrad) {
         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);
+    node0_ptr->SetGradOutMeta({leaf_tensor}, 0);
   }
 
   // Run Backward
@@ -191,12 +189,12 @@ TEST(Backward, LinearNodes) {
     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);
+    auto tmp_tensor = paddle::experimental::Tensor();
+    auto* meta0 = EagerUtils::autograd_meta(&tmp_tensor);
+    meta0->SetStopGradient(false);
+    meta0->SetSingleOutRankWithSlot(0, 0);
+    meta0->SetGradNode(node1_ptr);
+    node0_ptr->SetGradOutMeta(tmp_tensor, 0);
 
     AutogradMeta* auto_grad_meta1 = EagerUtils::autograd_meta(&leaf_tensor);
     // Connect Tensor and AccumulationNode via AutoGradMeta
@@ -208,8 +206,7 @@ TEST(Backward, LinearNodes) {
     auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
 
     auto_grad_meta1->SetStopGradient(false);
-    std::vector<egr::AutogradMeta*> res1 = {auto_grad_meta1};
-    node1_ptr->AddEdges(&res1, 0);
+    node1_ptr->SetGradOutMeta(leaf_tensor, 0);
   }
 
   // Use Empty Grad Tensor
@@ -288,20 +285,20 @@ TEST(Backward, WithAccumulation) {
     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);
+    auto tmp_tensor0 = paddle::experimental::Tensor();
+    auto* meta0 = EagerUtils::autograd_meta(&tmp_tensor0);
+    meta0->SetStopGradient(false);
+    meta0->SetSingleOutRankWithSlot(0, 0);
+    meta0->SetGradNode(node2_ptr);
+    node0_ptr->SetGradOutMeta(tmp_tensor0, 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);
+    auto tmp_tensor1 = paddle::experimental::Tensor();
+    auto* meta1 = EagerUtils::autograd_meta(&tmp_tensor1);
+    meta1->SetStopGradient(false);
+    meta1->SetSingleOutRankWithSlot(0, 0);
+    meta1->SetGradNode(node2_ptr);
+    node1_ptr->SetGradOutMeta(tmp_tensor1, 0);
 
     AutogradMeta* auto_grad_meta2 = EagerUtils::autograd_meta(&leaf_tensor);
     // Connect Tensor and AccumulationNode via AutoGradMeta
@@ -314,7 +311,7 @@ TEST(Backward, WithAccumulation) {
 
     auto_grad_meta2->SetStopGradient(false);
     std::vector<egr::AutogradMeta*> res2 = {auto_grad_meta2};
-    node2_ptr->AddEdges(&res2, 0);
+    node2_ptr->SetGradOutMeta(leaf_tensor, 0);
   }
 
   Backward(target_tensors, grad_tensors);

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

@@ -69,7 +69,7 @@ TEST(CrossBatchAccumulation, SingleScaleNode) {
   meta->SetSingleOutRankWithSlot(0, 0);
   meta->SetGradNode(acc_node_ptr);
   std::vector<egr::AutogradMeta*> res = {meta};
-  scale_node_ptr->AddEdges(&res, 0);
+  scale_node_ptr->SetGradOutMeta(leaf_tensor, 0);
 
   Backward(target_tensors, {});
 

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

@@ -251,10 +251,11 @@ TEST(EagerUtils, GetGradAccumulationNode) {
 }
 
 TEST(EagerUtils, FillZeroForEmptyGradInputs) {
-  std::vector<std::vector<paddle::experimental::Tensor>> grads = {
-      std::vector<paddle::experimental::Tensor>(1)};
-  std::vector<std::vector<GradSlotMeta>> slot_metas = {
-      std::vector<GradSlotMeta>(1)};
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       egr::kSlotSmallVectorSize>
+      grads = {std::vector<paddle::experimental::Tensor>(1)};
+  paddle::small_vector<std::vector<GradSlotMeta>, egr::kSlotSmallVectorSize>
+      slot_metas = {std::vector<GradSlotMeta>(1)};
 
   phi::DenseTensorMeta tensor_meta;
   tensor_meta.dtype = paddle::experimental::DataType::FLOAT32;

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

@@ -137,12 +137,16 @@ TEST(Forward, LinearNodes) {
 
     // 2. TensorWrapper: No TensorWrapper for ScaleNode
     // 3. NextEdges: Node 1 -> Node 0
-    const std::vector<std::vector<Edge>>& node1_edges = grad_node1->GetEdges();
-    const auto& node1_edge = node1_edges[0];
-
-    CHECK_EQ(static_cast<int>(node1_edge[0].GetEdgeRankInfo().first), 0);
-    CHECK_EQ(static_cast<int>(node1_edge[0].GetEdgeRankInfo().second), 0);
-    CHECK_EQ(node1_edge[0].GetGradNode(), grad_node0);
+    const paddle::small_vector<std::vector<GradSlotMeta>,
+                               egr::kSlotSmallVectorSize>& node1_metas =
+        grad_node1->OutputMeta();
+    const auto& node1_meta = node1_metas[0];
+
+    CHECK_EQ(static_cast<int>(node1_meta[0].GetEdge().GetEdgeRankInfo().first),
+             0);
+    CHECK_EQ(static_cast<int>(node1_meta[0].GetEdge().GetEdgeRankInfo().second),
+             0);
+    CHECK_EQ(node1_meta[0].GetEdge().GetGradNode(), grad_node0);
   }
 }
 
@@ -232,16 +236,19 @@ TEST(Forward, BranchedNodes) {
     // 2. TensorWrapper: No TensorWrapper for ScaleNode
     // 3. NextEdges
     // Node 1 -> Node 0
-    const std::vector<std::vector<Edge>>& node1_edges = grad_node1->GetEdges();
-    const Edge& node1_edge = node1_edges[0][0];
+    const paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>&
+        node1_metas = grad_node1->OutputMeta();
+    const Edge& node1_edge = node1_metas[0][0].GetEdge();
 
     CHECK_EQ(static_cast<int>(node1_edge.GetEdgeRankInfo().first), 0);
     CHECK_EQ(static_cast<int>(node1_edge.GetEdgeRankInfo().second), 0);
     CHECK_EQ(node1_edge.GetGradNode(), grad_node0);
 
     // Node 2 -> Node 0
-    const std::vector<std::vector<Edge>>& node2_edges = grad_node2->GetEdges();
-    const Edge& node2_edge = node2_edges[0][0];
+    const paddle::small_vector<std::vector<egr::GradSlotMeta>,
+                               egr::kSlotSmallVectorSize>& node2_metas =
+        grad_node2->OutputMeta();
+    const Edge& node2_edge = node2_metas[0][0].GetEdge();
 
     CHECK_EQ(static_cast<int>(node2_edge.GetEdgeRankInfo().first), 0);
     CHECK_EQ(static_cast<int>(node2_edge.GetEdgeRankInfo().second), 0);

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

@@ -87,7 +87,7 @@ TEST(Grad, SingleNodeEmptyGrad) {
 
     // grad_node Add Edges
     std::vector<egr::AutogradMeta*> res = {auto_grad_meta1};
-    node0_ptr->AddEdges(&res, 0);
+    node0_ptr->SetGradOutMeta(leaf_tensor, 0);
   }
   std::vector<paddle::experimental::Tensor> outs = {output_tensor};
 
@@ -150,7 +150,7 @@ TEST(Grad, SingleNodeCustomGrad) {
     auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
     auto_grad_meta1->SetStopGradient(false);
     std::vector<egr::AutogradMeta*> res = {auto_grad_meta1};
-    node0_ptr->AddEdges(&res, 0);
+    node0_ptr->SetGradOutMeta(leaf_tensor, 0);
   }
 
   auto result = Grad(target_tensors, {leaf_tensor}, grad_tensors);
@@ -207,12 +207,12 @@ TEST(Grad, LinearNodes) {
     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);
+    auto tmp_tensor = paddle::experimental::Tensor();
+    auto* meta0 = EagerUtils::autograd_meta(&tmp_tensor);
+    meta0->SetStopGradient(false);
+    meta0->SetSingleOutRankWithSlot(0, 0);
+    meta0->SetGradNode(node1_ptr);
+    node0_ptr->SetGradOutMeta(tmp_tensor, 0);
 
     AutogradMeta* auto_grad_meta1 = EagerUtils::autograd_meta(&leaf_tensor);
     // Connect Tensor and AccumulationNode via AutoGradMeta
@@ -224,8 +224,7 @@ TEST(Grad, LinearNodes) {
     auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
 
     auto_grad_meta1->SetStopGradient(false);
-    std::vector<egr::AutogradMeta*> res1 = {auto_grad_meta1};
-    node1_ptr->AddEdges(&res1, 0);
+    node1_ptr->SetGradOutMeta(leaf_tensor, 0);
   }
 
   // Use Empty Grad Tensor
@@ -304,20 +303,20 @@ TEST(Grad, WithAccumulation) {
     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);
+    auto tmp_tensor0 = paddle::experimental::Tensor();
+    auto* meta0 = EagerUtils::autograd_meta(&tmp_tensor0);
+    meta0->SetStopGradient(false);
+    meta0->SetSingleOutRankWithSlot(0, 0);
+    meta0->SetGradNode(node2_ptr);
+    node0_ptr->SetGradOutMeta(tmp_tensor0, 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);
+    auto tmp_tensor1 = paddle::experimental::Tensor();
+    auto meta1 = EagerUtils::autograd_meta(&tmp_tensor1);
+    meta1->SetStopGradient(false);
+    meta1->SetSingleOutRankWithSlot(0, 0);
+    meta1->SetGradNode(node2_ptr);
+    node1_ptr->SetGradOutMeta(tmp_tensor1, 0);
 
     AutogradMeta* auto_grad_meta2 = EagerUtils::autograd_meta(&leaf_tensor);
     // Connect Tensor and AccumulationNode via AutoGradMeta
@@ -329,8 +328,7 @@ TEST(Grad, WithAccumulation) {
     auto_grad_meta2->SetSingleOutRankWithSlot(0, 0);
 
     auto_grad_meta2->SetStopGradient(false);
-    std::vector<egr::AutogradMeta*> res2 = {auto_grad_meta2};
-    node2_ptr->AddEdges(&res2, 0);
+    node2_ptr->SetGradOutMeta(leaf_tensor, 0);
   }
 
   auto result = Grad(target_tensors, {leaf_tensor}, grad_tensors);

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

@@ -110,21 +110,20 @@ TEST(RetainGrad, HookBeforeRetainGrad) {
   paddle::experimental::Tensor leaf_tensor = paddle::experimental::Tensor();
   {
     // AccumulationNode Hook: +3
+    auto tmp_tensor0 = paddle::experimental::Tensor();
+    auto auto_grad_meta = EagerUtils::autograd_meta(&tmp_tensor0);
 
-    auto auto_grad_meta = std::make_shared<AutogradMeta>();
-
-    auto acc_node_ptr =
-        std::make_shared<GradNodeAccumulation>(auto_grad_meta.get());
+    auto acc_node_ptr = std::make_shared<GradNodeAccumulation>(auto_grad_meta);
 
     auto_grad_meta->SetStopGradient(false);
     auto_grad_meta->SetGradNode(acc_node_ptr);
     auto_grad_meta->SetSingleOutRankWithSlot(0, 0);
-    std::vector<egr::AutogradMeta*> res = {auto_grad_meta.get()};
-    scale_node_ptr->AddEdges(&res, 0);
+    std::vector<egr::AutogradMeta*> res = {auto_grad_meta};
+    scale_node_ptr->SetGradOutMeta(tmp_tensor0, 0);
 
     leaf_tensor.set_autograd_meta(
         std::dynamic_pointer_cast<paddle::experimental::AbstractAutogradMeta>(
-            auto_grad_meta));
+            tmp_tensor0.mutable_autograd_meta()));
 
     egr_utils_api::RegisterGradientHookForTensor(
         leaf_tensor, std::make_shared<egr::CppTensorHook>(hook_function));
@@ -181,19 +180,17 @@ TEST(RetainGrad, HookAfterRetainGrad) {
   paddle::experimental::Tensor leaf_tensor = paddle::experimental::Tensor();
   {
     // AccumulationNode Hook: +3
-
-    auto auto_grad_meta = std::make_shared<AutogradMeta>();
-    auto acc_node_ptr =
-        std::make_shared<GradNodeAccumulation>(auto_grad_meta.get());
+    auto tmp_tensor0 = paddle::experimental::Tensor();
+    auto auto_grad_meta = EagerUtils::autograd_meta(&tmp_tensor0);
+    auto acc_node_ptr = std::make_shared<GradNodeAccumulation>(auto_grad_meta);
     auto_grad_meta->SetGradNode(acc_node_ptr);
     auto_grad_meta->SetStopGradient(false);
-    std::vector<egr::AutogradMeta*> res = {auto_grad_meta.get()};
-    scale_node_ptr->AddEdges(&res, 0);
+    scale_node_ptr->SetGradOutMeta(tmp_tensor0, 0);
 
     auto_grad_meta->SetSingleOutRankWithSlot(0, 0);
     leaf_tensor.set_autograd_meta(
         std::dynamic_pointer_cast<paddle::experimental::AbstractAutogradMeta>(
-            auto_grad_meta));
+            tmp_tensor0.mutable_autograd_meta()));
 
     egr_utils_api::RegisterGradientHookForTensor(
         leaf_tensor, std::make_shared<egr::CppTensorHook>(hook_function));

paddle/fluid/eager/to_static/run_program_op_func.h

@@ -69,9 +69,6 @@ inline void run_program_dygraph_function(
     grad_node->SetGradOutMeta(params, /*slot id*/ 1);
 
     grad_node->SetGradInMeta(deref_out, 0);
-    // Set Next Edges
-    grad_node->AddEdges(&p_autograd_x, /*slot id*/ 0);
-    grad_node->AddEdges(&p_autograd_params, /*slot id*/ 1);
 
     egr::EagerUtils::SetOutRankWithSlot(&p_autograd_outs, 0);
 

paddle/fluid/eager/to_static/run_program_op_node.h

@@ -364,12 +364,16 @@ class GradNodeRunProgram : public egr::GradNodeBase {
 
   ~GradNodeRunProgram() override = default;
   // Functor: perform backward computations
-  virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      std::vector<std::vector<paddle::experimental::Tensor>> &grads,  // NOLINT
-      bool create_graph, bool is_new_grad) override {
+  virtual paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                               egr::kSlotSmallVectorSize>
+  operator()(paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                                  egr::kSlotSmallVectorSize> &grads,  // NOLINT
+             bool create_graph,
+             bool is_new_grad) override {
     VLOG(3) << "Running Eager Backward Node: GradNodeRunProgram";
-    std::vector<std::vector<paddle::experimental::Tensor>> hooked_grads =
-        GradNodeRunProgram::ApplyGradientHooks(grads);
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         egr::kSlotSmallVectorSize>
+        hooked_grads = GradNodeRunProgram::ApplyGradientHooks(grads);
     PADDLE_ENFORCE_EQ(hooked_grads.size(), 1,
                       paddle::platform::errors::InvalidArgument(
                           "The hooked_grads.size() of RunProgramGradOp should "

paddle/fluid/eager/utils.cc

@@ -441,8 +441,10 @@ std::shared_ptr<egr::GradNodeBase> EagerUtils::GetGradAccumulationNode(
 }
 
 void EagerUtils::FillZeroForEmptyGradInputs(
-    std::vector<std::vector<paddle::experimental::Tensor>>* in_grads,
-    const std::vector<std::vector<GradSlotMeta>>& grad_in_metas) {
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         kSlotSmallVectorSize>* in_grads,
+    const paddle::small_vector<std::vector<GradSlotMeta>, kSlotSmallVectorSize>&
+        grad_in_metas) {
   for (size_t i = 0; i < in_grads->size(); i++) {
     for (size_t j = 0; j < (*in_grads)[i].size(); j++) {
       paddle::experimental::Tensor& grad = (*in_grads)[i][j];

paddle/fluid/eager/utils.h

@@ -234,8 +234,10 @@ class EagerUtils {
     * Fill Zero
     * **/
   static void FillZeroForEmptyGradInputs(
-      std::vector<std::vector<paddle::experimental::Tensor>>* out_grads,
-      const std::vector<std::vector<GradSlotMeta>>& grad_out_metas);
+      paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                           kSlotSmallVectorSize>* out_grads,
+      const paddle::small_vector<std::vector<GradSlotMeta>,
+                                 kSlotSmallVectorSize>& grad_out_metas);
 };
 
 }  // namespace egr

paddle/fluid/pybind/eager_functions.cc

@@ -406,12 +406,9 @@ static PyObject* eager_api_run_costum_op(PyObject* self, PyObject* args,
 
       if (slot_map[0].find(i) != slot_map[0].end()) {
         grad_node->SetGradOutMeta(in_tensors, slot_map[0][i]);
-        grad_node->AddEdges(&ins_auto_grad_metas[i], slot_map[0][i]);
       } else {
         grad_node->SetGradOutMeta(in_tensors,
                                   ins_auto_grad_metas.size() - 1 - no_grad_cnt);
-        grad_node->AddEdges(&ins_auto_grad_metas[i],
-                            ins_auto_grad_metas.size() - 1 - no_grad_cnt);
         no_grad_cnt++;
       }
     }

paddle/fluid/pybind/eager_py_layer.cc

@@ -346,10 +346,8 @@ PyObject* pylayer_method_apply(PyObject* cls, PyObject* args,
         for (auto t : inputs_tensor[i]) {
           grad_node->SetGradOutMeta(*t, i);
         }
-        grad_node->AddEdges(&inputs_autograd_meta[i], i);
       } else {
         grad_node->SetGradOutMeta(*inputs_tensor[i][0], i);
-        grad_node->AddEdges(inputs_autograd_meta[i][0], i);
       }
     }