Supported Complex2Real Conversion for Eager Dygraph (#39878)

* Supported Complex2Real Conversion for Eager Dygraph

* Supported Complex2Real Conversion for Eager Dygraph

* Enabled complex type promotion test for matmul_v2

* Fix CI issues

* Merged adj_edges_ with GradSlotMeta

* Fixed monir issue

* Adjusted num runs

* Recovered Eager performance tests configurations

* Recovered Eager performance tests configurations

* Adjusted performance tests configurations

* Fixed Minor Issues with performance tests

* Moved out Edge from GradSlotMeta

* Fixed issues from merge

* Fixed typo

* Addressed review comments

* Fixed minor issues

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

@@ -86,9 +86,9 @@ paddle::experimental::Tensor scale(const paddle::experimental::Tensor& x,
     scale_node->SetTensorWrappers_X({x});
 
     // Set Grad out rank as same as fwd input and set stop gradient to bwd
-    scale_node->SetGradOutMeta(p_autograd_in, /*slot id*/ 0);
+    scale_node->SetGradOutMeta(x, /*slot id*/ 0);
     // Set Grad out rank as same as fwd input and set stop gradient to bwd
-    scale_node->SetGradInMeta(p_autograd_out, /*slot id*/ 0);
+    scale_node->SetGradInMeta(out, /*slot id*/ 0);
 
     // Set History for output set current Grad Node for
     EagerUtils::SetHistory(p_autograd_out, scale_node);

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -1117,7 +1117,7 @@ static std::string GenerateGradNodeCreationContent(
       const char* SET_GRAD_OUT_META_TEMPLATE =
           "    grad_node->SetGradOutMeta(%s, %d);\n";
       grad_node_creation_str += paddle::string::Sprintf(
-          SET_GRAD_OUT_META_TEMPLATE, input_autograd_name, input_position);
+          SET_GRAD_OUT_META_TEMPLATE, input_name, input_position);
 
       const char* ADD_EDGES_TEMPLATE =
           "    if(%s) grad_node->AddEdges(%s, %d);\n";
@@ -1129,9 +1129,9 @@ static std::string GenerateGradNodeCreationContent(
       size_t input_position = fwd_inputs_name_pos_map.at(input_name);
 
       const char* SET_GRAD_OUT_META_TEMPLATE =
-          "    grad_node->SetGradOutMeta(&%s, %d);\n";
+          "    grad_node->SetGradOutMeta(%s, %d);\n";
       grad_node_creation_str += paddle::string::Sprintf(
-          SET_GRAD_OUT_META_TEMPLATE, input_autograd_name, input_position);
+          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(
@@ -1165,9 +1165,9 @@ static std::string GenerateGradNodeCreationContent(
             paddle::string::Sprintf(SET_HISTORY_TEMPLATE, output_autograd_name);
       }
       const char* SET_GRAD_IN_META_TEMPLATE =
-          "    grad_node->SetGradInMeta(&%s, %d);\n";
+          "    grad_node->SetGradInMeta(%s, %d);\n";
       grad_node_creation_str += paddle::string::Sprintf(
-          SET_GRAD_IN_META_TEMPLATE, output_autograd_name, output_position);
+          SET_GRAD_IN_META_TEMPLATE, output_name, output_position);
 
     } else {
       pass_stop_gradient_args += ", " + output_autograd_name;
@@ -1186,7 +1186,7 @@ static std::string GenerateGradNodeCreationContent(
       const char* SET_GRAD_IN_META_TEMPLATE =
           "    grad_node->SetGradInMeta(%s, %d);\n";
       grad_node_creation_str += paddle::string::Sprintf(
-          SET_GRAD_IN_META_TEMPLATE, output_autograd_name, output_position);
+          SET_GRAD_IN_META_TEMPLATE, output_name, output_position);
     }
 
     // Intermediate Tensor does not require CheckAndRetainGrad
@@ -1834,7 +1834,7 @@ static std::string GenerateSingleOpBase(
             !is_op_base_per_duplicable_input) {
           const char* GRAD_OUTS_CONTENT_TEMPLATE =
               "{ \"%s\", egr::EagerUtils::CreateVars( "
-              "this->OutputMeta()[%d].Size() ) },";
+              "this->OutputMeta()[%d].size() ) },";
           outs_contents_str += paddle::string::Sprintf(
               GRAD_OUTS_CONTENT_TEMPLATE, grad_output_name, fwd_input_position);
         } else {
@@ -2053,7 +2053,7 @@ static std::string GenerateGradNodeCCContents(
 
   if (is_op_base_per_duplicable_input) {
     const char* OP_BASE_PER_DUP_INPUT_TEMPLATE =
-        "  for(int i = 0; i < this->OutputMeta()[0].Size(); i++) {\n"
+        "  for(size_t i = 0; i < this->OutputMeta()[0].size(); i++) {\n"
         "    %s\n"
         "  }\n";
     generated_grad_function_body = paddle::string::Sprintf(
@@ -2065,6 +2065,8 @@ static std::string GenerateGradNodeCCContents(
       "GradNode%s::ApplyGradientHooks(grads);\n"
       "  std::vector<std::vector<paddle::experimental::Tensor>> outputs(%d);\n"
       "  %s\n"
+      "  if(NeedComplexToRealConversion()) "
+      "HandleComplexGradToRealGrad(&outputs);\n"
       "  return outputs;\n";
   generated_grad_function_body =
       paddle::string::Sprintf(BWD_RETURN_TEMPLATE, fwd_op_type, in_vars.size(),

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

@@ -656,6 +656,7 @@ def GenerateNodeDefinition(fwd_api_name, bwd_api_name, backward_fwd_input_map,
         else:
             # Rearrange output order accordingly
             returns_str += f"returns[{fwd_position}] =  grad_api_returns[{grad_api_position}];\n"
+    returns_str += f"if(NeedComplexToRealConversion()) HandleComplexGradToRealGrad(&returns);\n"
     returns_str += f"return returns;\n"
 
     grad_node_name = GetGradNodeName(fwd_api_name)
@@ -783,7 +784,7 @@ def GenerateNodeCreationCodes(
     set_edges_list = []
     for name, (_, pos) in forward_inputs_position_map.items():
         input_autograd_meta_name = GetAutoGradMetaName(name)
-        set_grad_out_meta = f"        grad_node->SetGradOutMeta({input_autograd_meta_name}, {pos});"
+        set_grad_out_meta = f"        grad_node->SetGradOutMeta({name}, {pos});"
         set_edges = f"        grad_node->AddEdges({input_autograd_meta_name}, {pos});"
         set_grad_out_meta_list.append(set_grad_out_meta)
         set_edges_list.append(set_edges)
@@ -800,17 +801,18 @@ def GenerateNodeCreationCodes(
         output_autograd_meta_name = GetAutoGradMetaName(name)
         set_out_rank = f"        egr::EagerUtils::SetOutRankWithSlot({output_autograd_meta_name}, {pos});"
         set_history = f"        egr::EagerUtils::SetHistory({output_autograd_meta_name}, grad_node);"
-        set_grad_in_meta = f"        grad_node->SetGradInMeta({output_autograd_meta_name}, {pos});"
-
-        set_out_rank_list.append(set_out_rank)
-        set_history_list.append(set_history)
-        set_grad_in_meta_list.append(set_grad_in_meta)
-
         if num_outputs == 1:
             set_retain_grad = f"        egr::EagerUtils::CheckAndRetainGrad(api_result);"
+            set_grad_in_meta = f"        grad_node->SetGradInMeta(api_result, {pos});"
         else:
             set_retain_grad = f"        egr::EagerUtils::CheckAndRetainGrad(api_result[{pos}]);"
+            set_grad_in_meta = f"        grad_node->SetGradInMeta(api_result[{pos}], {pos});"
+
+        set_out_rank_list.append(set_out_rank)
+        set_history_list.append(set_history)
+        set_grad_in_meta_list.append(set_grad_in_meta)
         set_retain_grad_list.append(set_retain_grad)
+
     set_out_rank_str = "\n".join(set_out_rank_list)
     set_history_str = "\n".join(set_history_list)
     set_grad_in_meta_str = "\n".join(set_grad_in_meta_list)

paddle/fluid/eager/backward.cc

@@ -517,11 +517,11 @@ std::vector<paddle::experimental::Tensor> RunBackward(
     }
 
     // TODO(jiabin): Should we erase it or find a more efficient way.
+
     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(),
                    paddle::platform::errors::Fatal(
                        "Number of edges should be either empty ( for leaf node "
@@ -532,6 +532,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
     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];
+
         auto edge_rank = edge.GetEdgeRankInfo();
         // Since we make edge has as same rank as bwd outputs, we indexing them
         // with
@@ -545,6 +546,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
             grad_output_tensors[i].empty()) {
           continue;
         }
+
         PADDLE_ENFORCE_LT(
             j, grad_output_tensors[i].size(),
             paddle::platform::errors::Fatal(

paddle/fluid/eager/grad_node_info.cc

@@ -15,10 +15,16 @@
 #include "paddle/fluid/eager/grad_node_info.h"
 #include "paddle/fluid/eager/accumulation/accumulation_node.h"
 #include "paddle/fluid/eager/autograd_meta.h"
+#include "paddle/fluid/eager/utils.h"
+
 #include "paddle/phi/common/data_type.h"
 #include "paddle/phi/core/dense_tensor.h"
 
+#include "paddle/fluid/framework/convert_utils.h"
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/data_type_transform.h"
 #include "paddle/fluid/framework/var_type.h"
+
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/errors.h"
 
@@ -33,7 +39,6 @@ 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 has the same num as backward outputs
   adj_edges_.resize(bwd_out_slot_num);
 }
 
@@ -44,24 +49,20 @@ void GradNodeBase::AddEdges(std::vector<AutogradMeta*>* metas, size_t slot_id) {
           "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 (const auto& meta : *metas) {
+
+  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()) {
-        VLOG(6) << "Add Edges for slot: " << slot_id
-                << " which is: " << meta->GetMutableGradNode()->name();
-        adj_edges_[slot_id].emplace_back(meta->GetMutableGradNode(),
-                                         meta->OutRankInfo());
-      } else {
+      if (!node || !node.get()) {
         meta->SetGradNode(std::make_shared<egr::GradNodeAccumulation>(meta));
-        VLOG(6) << "Add Edges for slot: " << slot_id
-                << " which is: " << meta->GetMutableGradNode()->name();
-        adj_edges_[slot_id].emplace_back(meta->GetMutableGradNode(),
-                                         meta->OutRankInfo());
       }
+
+      adj_edges_[slot_id].emplace_back(meta->GetMutableGradNode(),
+                                       meta->OutRankInfo());
     }
   }
 }
@@ -73,130 +74,205 @@ void GradNodeBase::AddEdges(AutogradMeta* meta, size_t slot_id) {
           "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()) {
-      VLOG(6) << "Add Edges for slot: " << slot_id << ", the Edge is from "
-              << this->name() << " to " << meta->GetMutableGradNode()->name();
-      adj_edges_[slot_id].emplace_back(meta->GetMutableGradNode(),
-                                       meta->OutRankInfo());
-    } else {
+    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() << " to " << meta->GetMutableGradNode()->name();
-      adj_edges_[slot_id].emplace_back(meta->GetMutableGradNode(),
-                                       meta->OutRankInfo());
     }
+    VLOG(6) << "Add Edges for slot: " << slot_id << ", the Edge is from "
+            << this->name() << " to " << meta->GetMutableGradNode()->name();
+
+    adj_edges_[slot_id].emplace_back(meta->GetMutableGradNode(),
+                                     meta->OutRankInfo());
   }
 }
 
-const std::vector<GradSlotMeta>& GradNodeBase::InputMeta() const {
+const std::vector<std::vector<GradSlotMeta>>& GradNodeBase::InputMeta() const {
   return bwd_in_meta_;
 }
 
-const std::vector<GradSlotMeta>& GradNodeBase::OutputMeta() const {
+const std::vector<std::vector<GradSlotMeta>>& GradNodeBase::OutputMeta() const {
   return bwd_out_meta_;
 }
 
-void GradNodeBase::SetGradInMeta(std::vector<AutogradMeta*>* fwd_out,
+void GradNodeBase::SetGradInMeta(const paddle::experimental::Tensor& fwd_out,
                                  size_t slot_rank) {
-  size_t slot_size = fwd_out->size();
+  auto* fwd_out_meta = egr::EagerUtils::nullable_autograd_meta(fwd_out);
   PADDLE_ENFORCE_LE(
       slot_rank, (bwd_in_meta_.size() - 1),
       paddle::platform::errors::InvalidArgument(
           "Slot Rank should less equal than bwd_in_meta_ size, since "
           "bwd_in_meta_ is designed to hold as same num as backward "
           "inputs."));
-  auto& meta = bwd_in_meta_.at(slot_rank);
-  PADDLE_ENFORCE_EQ(meta.IsInitialized(), false,
-                    paddle::platform::errors::PreconditionNotMet(
-                        "Bwd_in_meta should only be init once, addition "
-                        "initialization for it is forbidden. If you got this "
-                        "error, it indicates bugs in framework."));
-  // Init stop gradient vector before use to avoid push back
-  meta.Init(slot_size);
-  for (size_t i = 0; i < slot_size; i++) {
-    PADDLE_ENFORCE_NOT_NULL((*fwd_out)[i],
-                            paddle::platform::errors::PreconditionNotMet(
-                                "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)[i]->StopGradient()) {
-      // Set Stop Gradient only when its true or non-initialized autograd_meta,
-      // since all default value is false.
-      meta.SetStopGradient(i, (*fwd_out)[i]->StopGradient());
+  auto& metas = bwd_in_meta_.at(slot_rank);
+  if (metas.size() == 0) {
+    metas.resize(1);
+  }
+
+  auto& meta = metas[0];
+  meta.SetStopGradient(fwd_out_meta->StopGradient());
+
+  // Record TensorMeta
+  if (phi::DenseTensor::classof(fwd_out.impl().get())) {
+    // Only Copy Meta
+    phi::DenseTensor* dense_tensor =
+        static_cast<phi::DenseTensor*>(fwd_out.impl().get());
+
+    PADDLE_ENFORCE_NE(
+        dense_tensor->meta().dtype, phi::DataType::UNDEFINED,
+        paddle::platform::errors::Fatal(
+            "Attempting to copy DenseTensorMeta with phi::DataType::UNDEFINED,"
+            "which is illegal."));
+    meta.SetTensorMeta(dense_tensor->meta());
+
+    if (paddle::framework::IsComplexType(
+            paddle::framework::TransToProtoVarType(dense_tensor->type()))) {
+      need_complex_to_real_ = true;
     }
+  } else {
+    VLOG(6) << "Unable to initialize the DenseTensorMeta of GradSlotMeta with "
+               "non-DenseTensor argument.";
   }
 }
 
-void GradNodeBase::SetGradInMeta(AutogradMeta* fwd_out, size_t slot_rank) {
+void GradNodeBase::SetGradInMeta(
+    const std::vector<paddle::experimental::Tensor>& fwd_out,
+    size_t slot_rank) {
+  size_t slot_size = fwd_out.size();
   PADDLE_ENFORCE_LE(
       slot_rank, (bwd_in_meta_.size() - 1),
       paddle::platform::errors::InvalidArgument(
           "Slot Rank should less equal than bwd_in_meta_ size, since "
           "bwd_in_meta_ is designed to hold as same num as backward "
           "inputs."));
-  auto& meta = bwd_in_meta_.at(slot_rank);
-  PADDLE_ENFORCE_EQ(meta.IsInitialized(), false,
-                    paddle::platform::errors::PreconditionNotMet(
-                        "Bwd_in_meta should only be init once, Additional "
-                        "initialization for it is forbidden. If you got this "
-                        "error, it indicates bugs in framework."));
+  auto& metas = bwd_in_meta_.at(slot_rank);
   // Init stop gradient vector before use to avoid push back
-  VLOG(7) << "Init bwd_in_meta_ with slot rank: " << slot_rank;
-  meta.Init(1);
-  meta.SetStopGradient(0, fwd_out->StopGradient());
+  if (metas.size() < slot_size) {
+    VLOG(7) << "Init bwd_in_meta_ with slot rank: " << slot_rank;
+    metas.resize(slot_size);
+  }
+  for (size_t i = 0; i < slot_size; i++) {
+    auto& meta = metas[i];
+    const auto& fwd_out_tensor = fwd_out[i];
+    auto* fwd_out_meta =
+        egr::EagerUtils::nullable_autograd_meta(fwd_out_tensor);
+    PADDLE_ENFORCE_NOT_NULL(fwd_out_meta,
+                            paddle::platform::errors::PreconditionNotMet(
+                                "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()) {
+      // Set Stop Gradient only when its true or non-initialized autograd_meta,
+      // since all default value is false.
+      meta.SetStopGradient(fwd_out_meta->StopGradient());
+    }
+
+    // Record TensorMeta
+    if (phi::DenseTensor::classof(fwd_out_tensor.impl().get())) {
+      // Only Copy Meta
+      phi::DenseTensor* dense_tensor =
+          static_cast<phi::DenseTensor*>(fwd_out_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,"
+                                          "which is illegal."));
+      meta.SetTensorMeta(dense_tensor->meta());
+      if (paddle::framework::IsComplexType(
+              paddle::framework::TransToProtoVarType(dense_tensor->type()))) {
+        need_complex_to_real_ = true;
+      }
+    } else {
+      VLOG(6) << "Unable to initialize the DenseTensorMeta of GradSlotMeta "
+                 "with non-DenseTensor argument.";
+    }
+  }
 }
 
-void GradNodeBase::SetGradOutMeta(std::vector<AutogradMeta*>* fwd_in,
+void GradNodeBase::SetGradOutMeta(const paddle::experimental::Tensor& fwd_in,
                                   size_t slot_rank) {
-  size_t slot_size = fwd_in->size();
+  auto* fwd_in_meta = egr::EagerUtils::nullable_autograd_meta(fwd_in);
   PADDLE_ENFORCE_LE(
-      slot_rank, (bwd_out_meta_.size() - 1),
+      (slot_rank + 1), bwd_out_meta_.size(),
       paddle::platform::errors::InvalidArgument(
           "Slot Rank should less equal than bwd_out_meta_ size, "
           "since bwd_out_meta_ is designed to hold as same num as "
           "backward outputs."));
-  auto& meta = bwd_out_meta_.at(slot_rank);
-  PADDLE_ENFORCE_EQ(meta.IsInitialized(), false,
-                    paddle::platform::errors::PreconditionNotMet(
-                        "Bwd_out_meta should only be init once. Additional "
-                        "initialization for it is forbidden. If you got this "
-                        "error, it indicates bugs in framework."));
+  auto& metas = bwd_out_meta_.at(slot_rank);
   // Init stop gradient vector before use to avoid push back
-  meta.Init(slot_size);
-  for (size_t i = 0; i < slot_size; i++) {
-    if (!(*fwd_in)[i]) {
-      meta.SetStopGradient(i, true);
-      continue;
-    }
-    if ((*fwd_in)[i]->StopGradient()) {
-      // Set Stop Gradient only when its true or non-initialized autograd_meta,
-      // since all default value is false.
-      meta.SetStopGradient(i, (*fwd_in)[i]->StopGradient());
+  if (metas.size() == 0) {
+    metas.resize(1);
+  }
+  auto& meta = metas[0];
+  if (fwd_in_meta) {
+    meta.SetStopGradient(fwd_in_meta->StopGradient());
+  } else {
+    meta.SetStopGradient(true);
+  }
+
+  // Record TensorMeta
+  if (fwd_in.impl() && fwd_in.impl().get()) {
+    if (phi::DenseTensor::classof(fwd_in.impl().get())) {
+      // Only Copy Meta
+      phi::DenseTensor* dense_tensor =
+          static_cast<phi::DenseTensor*>(fwd_in.impl().get());
+      PADDLE_ENFORCE_NE(
+          dense_tensor->meta().dtype, phi::DataType::UNDEFINED,
+          paddle::platform::errors::Fatal("Attempting to copy DenseTensorMeta "
+                                          "with phi::DataType::UNDEFINED,"
+                                          "which is illegal."));
+      meta.SetTensorMeta(dense_tensor->meta());
     }
+  } else {
+    VLOG(6) << "Unable to initialize the DenseTensorMeta of GradSlotMeta with "
+               "non-DenseTensor argument.";
   }
 }
 
-void GradNodeBase::SetGradOutMeta(AutogradMeta* fwd_in, size_t slot_rank) {
+void GradNodeBase::SetGradOutMeta(
+    const std::vector<paddle::experimental::Tensor>& fwd_in, size_t slot_rank) {
+  size_t slot_size = fwd_in.size();
   PADDLE_ENFORCE_LE(
-      (slot_rank + 1), bwd_out_meta_.size(),
+      slot_rank, (bwd_out_meta_.size() - 1),
       paddle::platform::errors::InvalidArgument(
           "Slot Rank should less equal than bwd_out_meta_ size, "
           "since bwd_out_meta_ is designed to hold as same num as "
           "backward outputs."));
-  auto& meta = bwd_out_meta_.at(slot_rank);
-  PADDLE_ENFORCE_EQ(meta.IsInitialized(), false,
-                    paddle::platform::errors::PreconditionNotMet(
-                        "Bwd_out_meta should only be init once. Additional "
-                        "initialization for it is forbidden. If you got this "
-                        "error, it indicates bugs in framework."));
+  auto& metas = bwd_out_meta_.at(slot_rank);
   // Init stop gradient vector before use to avoid push back
-  meta.Init(1);
-  if (fwd_in) {
-    meta.SetStopGradient(0, fwd_in->StopGradient());
-  } else {
-    meta.SetStopGradient(0, true);
+  if (metas.size() < slot_size) {
+    metas.resize(slot_size);
+  }
+  for (size_t i = 0; i < slot_size; i++) {
+    const auto& fwd_in_tensor = fwd_in[i];
+    auto& meta = metas[i];
+    auto* fwd_in_meta = egr::EagerUtils::nullable_autograd_meta(fwd_in_tensor);
+    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());
+    }
+
+    // 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,"
+                              "which is illegal."));
+        meta.SetTensorMeta(dense_tensor->meta());
+      }
+    } else {
+      VLOG(6) << "Unable to initialize the DenseTensorMeta of GradSlotMeta "
+                 "with non-DenseTensor argument.";
+    }
   }
 }
 
@@ -207,12 +283,8 @@ void GradNodeBase::SetDefaultGradInOutMeta() {
                      "meta setter, other size of inputs and outputs should "
                      "create with Setter and Getters"));
   // Default stop_gradient is false and slot id is 0, slot size is 1;
-  bwd_out_meta_[0].Init(1);
-  bwd_in_meta_[0].Init(1);
-}
-
-const std::vector<std::vector<Edge>>& GradNodeBase::GetEdges() const {
-  return adj_edges_;
+  bwd_out_meta_[0].resize(1);
+  bwd_in_meta_[0].resize(1);
 }
 
 int64_t GradNodeBase::RegisterGradientHook(
@@ -222,6 +294,10 @@ int64_t GradNodeBase::RegisterGradientHook(
   return next_hook_id_++;
 }
 
+const std::vector<std::vector<Edge>>& GradNodeBase::GetEdges() const {
+  return adj_edges_;
+}
+
 std::vector<std::vector<paddle::experimental::Tensor>>
 GradNodeBase::ApplyGradientHooks(
     const std::vector<std::vector<paddle::experimental::Tensor>>& tensors) {
@@ -270,4 +346,45 @@ GradNodeBase::ApplyGradientHooks(
   return outs;
 }
 
+void GradNodeBase::HandleComplexGradToRealGrad(
+    std::vector<std::vector<paddle::experimental::Tensor>>* 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];
+    for (size_t rank_id = 0; rank_id < slot_out_grads.size(); rank_id++) {
+      const GradSlotMeta& slot_meta = bwd_out_meta_[slot_id][rank_id];
+
+      PADDLE_ENFORCE(
+          slot_meta.HasTensorMeta() > 0,
+          paddle::platform::errors::Fatal(
+              "We require TensorMeta in GradInputMeta() to obtain forward data "
+              "types."
+              "However, no TensorMeta is detected in bwd_out_meta_."));
+
+      auto fwd_data_type = paddle::framework::TransToProtoVarType(
+          slot_meta.GetTensorMeta().dtype);
+      const paddle::experimental::Tensor& grad = slot_out_grads[rank_id];
+
+      if (paddle::framework::IsComplexType(fwd_data_type)) continue;
+
+      // Only Handle Complex To Real for DenseTensor for now
+      if (phi::DenseTensor::classof(grad.impl().get())) {
+        phi::DenseTensor* grad_dense_tensor =
+            static_cast<phi::DenseTensor*>(grad.impl().get());
+
+        auto curr_data_type =
+            paddle::framework::TransToProtoVarType(grad_dense_tensor->type());
+        if (!paddle::framework::IsComplexType(curr_data_type)) continue;
+
+        // Convert Complex GradOut to Real
+        auto out = std::make_shared<phi::DenseTensor>();
+        paddle::framework::TransComplexToReal(fwd_data_type, curr_data_type,
+                                              *grad_dense_tensor, out.get());
+
+        (*out_grads)[slot_id][rank_id].set_impl(out);
+      }
+    }
+  }
+}
+
 }  // namespace egr

paddle/fluid/eager/grad_node_info.h

@@ -57,21 +57,28 @@ class AutogradMeta;
 class GradSlotMeta {
  public:
   GradSlotMeta() = default;
-  void Init(size_t size) {
-    size_ = static_cast<int>(size);
-    stop_gradient_.resize(size, false);
+  bool IsStopGradient() const { return stop_gradient_; }
+  void SetStopGradient(bool stop_gradient = true) {
+    stop_gradient_ = stop_gradient;
   }
 
-  bool IsInitialized() const { return size_ != -1; }
-  bool IsStopGradient(size_t rank) const { return stop_gradient_[rank]; }
-  int Size() const { return size_; }
-  void SetStopGradient(size_t rank, bool stop_gradient = true) {
-    stop_gradient_.at(rank) = stop_gradient;
+  void SetTensorMeta(const phi::DenseTensorMeta& meta) {
+    meta_ = std::make_shared<phi::DenseTensorMeta>(meta);
+  }
+  bool HasTensorMeta() const { return meta_ && meta_.get(); }
+  const phi::DenseTensorMeta& GetTensorMeta() const {
+    if (!HasTensorMeta()) {
+      PADDLE_THROW(paddle::platform::errors::Fatal(
+          "meta_ of GradSlotMeta has not been initialized yet."
+          "You're expected to check Edge availability with HasTensorMeta()"
+          "before calling GetTensorMeta() interface."));
+    }
+    return *meta_.get();
   }
 
  private:
-  int size_{-1};
-  std::vector<bool> stop_gradient_{false};
+  bool stop_gradient_{false};
+  std::shared_ptr<phi::DenseTensorMeta> meta_ = nullptr;
 };
 
 class GradNodeBase {
@@ -112,25 +119,30 @@ class GradNodeBase {
   void AddEdges(std::vector<AutogradMeta*>* metas, size_t slot_id);
   void AddEdges(AutogradMeta* meta, size_t slot_id);
 
-  /**
-   * GetEdges is designed to get all edges of current node**/
-  const std::vector<std::vector<Edge>>& GetEdges() const;
+  // adj_edges were moved inside OutputMeta(), so no available direct access
+  // from GradNodeBase.
+  // To access Edges, get GradSlotMeta by calling OutputMeta(), then use
+  // slot_meta.GetEdge()
 
   /**
    * Get Input Meta of current Grad node**/
-  const std::vector<GradSlotMeta>& InputMeta() const;
+  const std::vector<std::vector<GradSlotMeta>>& InputMeta() const;
   /**
    * Get Output Meta of current Grad node**/
-  const std::vector<GradSlotMeta>& OutputMeta() const;
+  const std::vector<std::vector<GradSlotMeta>>& OutputMeta() const;
   /**
    * Set bwd ins and outs info with forward vars
    * **/
 
-  void SetGradInMeta(std::vector<AutogradMeta*>* fwd_out, size_t slot_rank);
-  void SetGradInMeta(AutogradMeta* fwd_out, size_t slot_rank);
+  void SetGradInMeta(const std::vector<paddle::experimental::Tensor>& fwd_out,
+                     size_t slot_rank);
+  void SetGradInMeta(const paddle::experimental::Tensor& fwd_out,
+                     size_t slot_rank);
 
-  void SetGradOutMeta(std::vector<AutogradMeta*>* fwd_in, size_t slot_rank);
-  void SetGradOutMeta(AutogradMeta* fwd_in, size_t slot_rank);
+  void SetGradOutMeta(const std::vector<paddle::experimental::Tensor>& fwd_in,
+                      size_t slot_rank);
+  void SetGradOutMeta(const paddle::experimental::Tensor& fwd_in,
+                      size_t slot_rank);
 
   /**
    * Default setters for Grad in/out meta this should be used for same special
@@ -162,11 +174,21 @@ class GradNodeBase {
   std::vector<std::vector<paddle::experimental::Tensor>> ApplyGradientHooks(
       const std::vector<std::vector<paddle::experimental::Tensor>>& tensors);
 
+  /**
+    * Handle Complex - Real Type Promotion
+    * **/
+  void HandleComplexGradToRealGrad(
+      std::vector<std::vector<paddle::experimental::Tensor>>* out_grads);
+  bool NeedComplexToRealConversion() { return need_complex_to_real_; }
+
   virtual std::string name() { return "GradNodeBase"; }
 
- private:
-  // TODO(jiabin): Use SmallVector instead after merge PR from develop
+  /**
+       * GetEdges is designed to get all edges of current node**/
+  const std::vector<std::vector<Edge>>& GetEdges() const;
 
+ private:
+  // TODO(zhanlve): Merge adj_edges_ into GradOutMeta
   // Edges recorded the backward related node info, which indicate all edges
   // linked
   // by this Grad Node.
@@ -174,10 +196,10 @@ class GradNodeBase {
   std::vector<std::vector<Edge>> adj_edges_;
 
   // bwd_out_meta_ is used to record Grad output info for backward
-  std::vector<GradSlotMeta> bwd_out_meta_;
+  std::vector<std::vector<GradSlotMeta>> bwd_out_meta_;
 
   // bwd_in_meta_ used to record Grad input info for backward
-  std::vector<GradSlotMeta> bwd_in_meta_;
+  std::vector<std::vector<GradSlotMeta>> bwd_in_meta_;
   // Gradient Hooks
   // Customer may register a list of hooks which will be called in order during
   // backward
@@ -188,6 +210,8 @@ class GradNodeBase {
                         /* hook */ std::shared_ptr<TensorHook>>>
       gradient_hooks_;
 
+  // We handle complex to real conversion only if any complex GradIn is involved
+  bool need_complex_to_real_ = false;
   int64_t next_hook_id_{0};
 };
 

paddle/fluid/eager/grad_tensor_holder.h

@@ -26,12 +26,13 @@ namespace egr {
  * GradTensorHolder should have as same format as forward output **/
 class GradTensorHolder {
  public:
-  explicit GradTensorHolder(const std::vector<GradSlotMeta>& meta) {
-    VLOG(7) << "Init GradTensorHolder with meta size: " << meta.size();
-    buffer_.resize(meta.size());
+  explicit GradTensorHolder(
+      const std::vector<std::vector<GradSlotMeta>>& metas) {
+    VLOG(7) << "Init GradTensorHolder with meta size: " << metas.size();
+    buffer_.resize(metas.size());
     for (size_t i = 0; i < buffer_.size(); i++) {
-      VLOG(7) << "Init GradTensorHolder with meta rank: " << meta[i].Size();
-      buffer_[i].resize(meta[i].Size());
+      VLOG(7) << "Init GradTensorHolder with meta rank: " << metas[i].size();
+      buffer_[i].resize(metas[i].size());
     }
   }
 

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

@@ -11,6 +11,7 @@
 // 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 "glog/logging.h"
 #include "gtest/gtest.h"
 
@@ -23,14 +24,9 @@
 
 TEST(GradNodeInfo, GradSlotMeta) {
   auto grad_slot = egr::GradSlotMeta();
-  CHECK(grad_slot.IsInitialized() == false);
-  VLOG(6) << "Init GradSlotMeta";
-  grad_slot.Init(2);
-  CHECK(grad_slot.IsInitialized() == true);
   VLOG(6) << "Set SetStopGradient";
-  grad_slot.SetStopGradient(0);
-  CHECK(grad_slot.IsStopGradient(0) == true);
-  CHECK_EQ(grad_slot.Size(), 2);
+  grad_slot.SetStopGradient();
+  CHECK(grad_slot.IsStopGradient() == true);
 }
 
 void TestGradNodeBase(bool is_remove_gradient_hook) {
@@ -56,18 +52,22 @@ void TestGradNodeBase(bool is_remove_gradient_hook) {
                ->data<float>()[0],
            6.0f);
   VLOG(6) << "Test Add Edges";
-  egr::Edge edge0(grad_test_node1, 1, 2);
-  auto auto_grad0 = std::make_shared<egr::AutogradMeta>(edge0);
+  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 edge1(grad_test_node1, 3, 4);
-  auto auto_grad1 = std::make_shared<egr::AutogradMeta>(edge1);
+
+  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));
@@ -76,22 +76,30 @@ void TestGradNodeBase(bool is_remove_gradient_hook) {
 
   VLOG(6) << "Test Set Meta and Get Meta";
   auto_grad1->SetStopGradient(true);
-  grad_test_node0->SetGradInMeta(&metas, 0);
-  grad_test_node0->SetGradInMeta(auto_grad1.get(), 1);
-  grad_test_node0->SetGradOutMeta(&metas, 0);
-  grad_test_node0->SetGradOutMeta(auto_grad1.get(), 1);
-  CHECK_EQ(grad_test_node0->InputMeta()[0].Size(), 1);
-  CHECK_EQ(grad_test_node0->InputMeta()[1].Size(), 1);
-  CHECK(grad_test_node0->OutputMeta()[0].IsStopGradient(0));
-  CHECK(grad_test_node0->OutputMeta()[1].IsStopGradient(0));
+  grad_test_node0->SetGradInMeta(et1, 0);
+  grad_test_node0->SetGradInMeta({et1}, 1);
+  grad_test_node0->SetGradOutMeta(et1, 0);
+  grad_test_node0->SetGradOutMeta({et1}, 1);
+  CHECK_EQ(grad_test_node0->InputMeta()[0].size(), size_t(1));
+  CHECK_EQ(grad_test_node0->InputMeta()[1].size(), size_t(1));
+  CHECK_EQ(grad_test_node0->InputMeta()[0][0].GetTensorMeta().dtype,
+           meta.dtype);
+  CHECK_EQ(grad_test_node0->InputMeta()[1][0].GetTensorMeta().dtype,
+           meta.dtype);
+  CHECK(grad_test_node0->OutputMeta()[0][0].IsStopGradient());
+  CHECK(grad_test_node0->OutputMeta()[1][0].IsStopGradient());
+  CHECK_EQ(grad_test_node0->OutputMeta()[0][0].GetTensorMeta().dtype,
+           meta.dtype);
+  CHECK_EQ(grad_test_node0->OutputMeta()[1][0].GetTensorMeta().dtype,
+           meta.dtype);
 
   VLOG(6) << "Test Default Set Meta and Get Meta";
   auto grad_test_node2 = std::make_shared<eager_test::GradTestNode>(
       /* val */ 5.0, /* in_num */ 1, /* out_num */ 1);
   grad_test_node2->SetDefaultGradInOutMeta();
-  CHECK(grad_test_node2->OutputMeta()[0].IsInitialized());
-  CHECK(grad_test_node2->OutputMeta()[0].IsStopGradient(0) == false);
-  CHECK_EQ(grad_test_node2->OutputMeta()[0].Size(), 1);
+  CHECK_GT(grad_test_node2->OutputMeta()[0].size(), size_t(0));
+  CHECK(grad_test_node2->OutputMeta()[0][0].IsStopGradient() == false);
+  CHECK_EQ(grad_test_node2->OutputMeta()[0].size(), size_t(1));
 
   VLOG(6) << "Test Gradient Hook";
   auto gradient_hook = [](
@@ -135,7 +143,17 @@ TEST(GradNodeInfo, GradNodeBase) {
 }
 
 TEST(GradNodeInfo, Edge) {
+  phi::DenseTensorMeta meta =
+      phi::DenseTensorMeta(phi::DataType::FLOAT32, phi::make_ddim({1, 1}));
+  std::shared_ptr<phi::DenseTensor> dt = std::make_shared<phi::DenseTensor>(
+      std::make_unique<paddle::experimental::DefaultAllocator>(
+          paddle::platform::CPUPlace())
+          .get(),
+      meta);
+  paddle::experimental::Tensor et1(dt);
+
   auto grad_test_node0 = std::make_shared<eager_test::GradTestNode>(5, 2, 2);
+  auto auto_grad1 = std::make_shared<egr::AutogradMeta>();
   VLOG(6) << "Test Construct Edge";
   egr::Edge edge0 = egr::Edge();
   CHECK(edge0.IsInitialized() == false);
@@ -145,13 +163,12 @@ TEST(GradNodeInfo, Edge) {
       egr::Edge(grad_test_node0, std::make_pair(size_t(1), size_t(0)));
   VLOG(6) << "Test Set Edge's Grad Node";
   auto* grad_node = edge1.GetGradNode();
+  et1.set_autograd_meta(auto_grad1);
+  grad_node->SetGradInMeta(et1, 0);
+
   CHECK_EQ(grad_node->InputMeta().size(), size_t(2));
-  auto mt_grad_node = edge1.GetMutableGradNode();
-  auto auto_grad1 = std::make_shared<egr::AutogradMeta>();
   std::vector<egr::AutogradMeta*> metas = {auto_grad1.get()};
-  // Uninitialized AutogradMeta indicates
-  mt_grad_node->SetGradInMeta(&metas, 0);
-  CHECK(grad_node->InputMeta()[0].IsStopGradient(0) == true);
+  CHECK(grad_node->InputMeta()[0][0].IsStopGradient() == true);
   VLOG(6) << "Test Get/Set Edge Rank Info";
   CHECK_EQ(edge2.GetEdgeRankInfo().first, size_t(1));
   CHECK_EQ(edge2.GetEdgeRankInfo().second, size_t(0));

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

@@ -30,8 +30,7 @@ PD_DECLARE_KERNEL(full_like, CPU, ALL_LAYOUT);
 using namespace egr;  // NOLINT
 
 TEST(GradTensorHolder, Constructor) {
-  GradSlotMeta slot_meta;
-  slot_meta.Init(1);
+  std::vector<GradSlotMeta> slot_meta(1);
   GradTensorHolder grad_tensor_holder = GradTensorHolder({slot_meta});
   GradTensorHolder grad_tensor_holder2 = GradTensorHolder(grad_tensor_holder);
 
@@ -72,8 +71,7 @@ TEST(GradTensorHolder, Interfaces) {
   paddle::experimental::Tensor et1 = paddle::experimental::Tensor(dt1);
 
   // Constructor empty GradTensorHolder
-  GradSlotMeta slot_meta;
-  slot_meta.Init(1);
+  std::vector<GradSlotMeta> slot_meta(1);
   GradTensorHolder grad_tensor_holder =
       GradTensorHolder({slot_meta, slot_meta});
 
@@ -138,8 +136,7 @@ TEST(GradTensorHolder, SelectedRowsMergeAdd) {
   paddle::experimental::Tensor t2(sr2);
 
   // Constructor empty GradTensorHolder
-  GradSlotMeta slot_meta;
-  slot_meta.Init(1);
+  std::vector<GradSlotMeta> slot_meta(1);
   GradTensorHolder grad_tensor_holder =
       GradTensorHolder({slot_meta, slot_meta});
 

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

@@ -37,7 +37,7 @@
 #include "paddle/fluid/imperative/tracer.h"
 #include "paddle/fluid/memory/memcpy.h"
 
-static size_t max_num_benchmark_runs = 5000;
+static size_t max_num_benchmark_runs = 4000;
 
 namespace egr {
 

paddle/fluid/eager/to_static/run_program_op_func.h

@@ -66,10 +66,10 @@ inline void run_program_dygraph_function(
     grad_node->SetStepScope(step_scope);
 
     // Set Grad out rank as same as fwd input and set stop gradient to bwd
-    grad_node->SetGradOutMeta(&p_autograd_x, /*slot id*/ 0);
-    grad_node->SetGradOutMeta(&p_autograd_params, /*slot id*/ 1);
+    grad_node->SetGradOutMeta(x, /*slot id*/ 0);
+    grad_node->SetGradOutMeta(params, /*slot id*/ 1);
 
-    grad_node->SetGradInMeta(&p_autograd_outs, 0);
+    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);

paddle/fluid/pybind/eager_functions.cc

@@ -375,6 +375,7 @@ static PyObject* eager_api_run_costum_op(PyObject* self, PyObject* args,
   ins_auto_grad_metas.resize(ctx.InputRange().size());
   VLOG(7) << "We got slot num of outs is: " << ctx.OutputRange().size();
   outs_auto_grad_metas.resize(ctx.OutputRange().size());
+
   for (size_t i = 0; i < ctx.InputRange().size(); i++) {
     ins_auto_grad_metas[i] =
         egr::EagerUtils::nullable_autograd_meta(ctx.InputsBetween(
@@ -404,11 +405,15 @@ static PyObject* eager_api_run_costum_op(PyObject* self, PyObject* args,
     // Prepare Grad outputs
     size_t no_grad_cnt = 0;
     for (size_t i = 0; i < ins_auto_grad_metas.size(); i++) {
+      const std::vector<paddle::experimental::Tensor>& in_tensors =
+          ctx.InputsBetween(ctx.InputRangeAt(i).first,
+                            ctx.InputRangeAt(i).second);
+
       if (slot_map[0].find(i) != slot_map[0].end()) {
-        grad_node->SetGradOutMeta(&ins_auto_grad_metas[i], slot_map[0][i]);
+        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(&ins_auto_grad_metas[i],
+        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);
@@ -417,11 +422,14 @@ static PyObject* eager_api_run_costum_op(PyObject* self, PyObject* args,
     }
     // Prepare Grad inputs with grad of fwd outputs
     for (size_t i = 0; i < outs_auto_grad_metas.size(); i++) {
+      const std::vector<paddle::experimental::Tensor>& out_tensors =
+          ctx.OutputsBetweeen(ctx.OutputRangeAt(i).first,
+                              ctx.OutputRangeAt(i).second);
+
       egr::EagerUtils::SetOutRankWithSlot(&(outs_auto_grad_metas[i]), i);
       egr::EagerUtils::SetHistory(&(outs_auto_grad_metas[i]), grad_node);
-      grad_node->SetGradInMeta(&(outs_auto_grad_metas[i]), i);
-      egr::EagerUtils::CheckAndRetainGrad(ctx.OutputsBetweeen(
-          ctx.OutputRangeAt(i).first, ctx.OutputRangeAt(i).second));
+      grad_node->SetGradInMeta(out_tensors, i);
+      egr::EagerUtils::CheckAndRetainGrad(out_tensors);
     }
 
     // Prepare Grad inputs with fwd outputs

python/paddle/fluid/tests/unittests/test_matmul_v2_op.py

@@ -542,7 +542,7 @@ class TestComplexMatMulOp(OpTest):
             'Out',
             user_defined_grads=[self.grad_x, self.grad_y],
             user_defined_grad_outputs=[self.grad_out],
-            check_eager=False)
+            check_eager=True)
 
     def test_check_grad_ingore_x(self):
         self.check_grad(
@@ -560,7 +560,7 @@ class TestComplexMatMulOp(OpTest):
             no_grad_set=set('Y'),
             user_defined_grads=[self.grad_x],
             user_defined_grad_outputs=[self.grad_out],
-            check_eager=False)
+            check_eager=True)
 
 
 class TestComplexMatMulOpBroadcast(OpTest):