[DoubleGrad] Enabled test_imperative_triple_grad test cases under eag…

…er_mode (PaddlePaddle#41612)

* [DoubleGrad] Enabled double grad test cases in eager_mode for test_imperative_double_grad

* Fixed elementwise issue

* Addressed CI failures

* [DoubleGrad] Enabled test_imperative_triple_grad test cases under eager_mode

* Fixed minor issues

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -2011,8 +2011,7 @@ static std::string GenerateSingleOpBase(
           "egr::EagerUtils::TrySyncToVars(egr::EagerUtils::"
           "RecoverTensorWrapper("
           "&"
-          "this->%s, "
-          "nullptr)) },";
+          "this->%s)) },";
       ins_contents_str +=
           paddle::string::Sprintf(GRAD_INS_FWD_CONTENT_TEMPLATE,
                                   grad_input_name, struct_fwd_input_name);
@@ -2058,15 +2057,15 @@ static std::string GenerateSingleOpBase(
           const char* DISPENSABLE_GRAD_INS_FWD_CONTENT_TEMPLATE =
               "  if(this->%s.size() > 0) %s[\"%s\"] = "
               "egr::EagerUtils::TrySyncToVars(egr::EagerUtils::"
-              "RecoverTensorWrapper(&this->%s, nullptr));\n";
+              "RecoverTensorWrapper(&this->%s));\n";
           generated_grad_function_body += paddle::string::Sprintf(
               DISPENSABLE_GRAD_INS_FWD_CONTENT_TEMPLATE, struct_fwd_input_name,
               ins_name, grad_input_name, struct_fwd_input_name);
         } else {
           const char* DISPENSABLE_GRAD_INS_FWD_CONTENT_TEMPLATE =
-              "  auto %s = egr::EagerUtils::RecoverTensorWrapper(&this->%s, "
-              "nullptr);\n  if(%s.initialized()) %s[\"%s\"] = "
-              "egr::EagerUtils::TrySyncToVars(%s);\n";
+              "  auto %s = egr::EagerUtils::RecoverTensorWrapper(&this->%s);\n"
+              "  if(%s.initialized()) %s[\"%s\"] = "
+              "     egr::EagerUtils::TrySyncToVars(%s);\n";
           generated_grad_function_body += paddle::string::Sprintf(
               DISPENSABLE_GRAD_INS_FWD_CONTENT_TEMPLATE, grad_input_name,
               struct_fwd_input_name, grad_input_name, ins_name, grad_input_name,

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

@@ -23,7 +23,8 @@
 ########################
 ops_to_fill_zero_for_empty_grads = set([
     "split_grad", "rnn_grad", "matmul_double_grad", "matmul_triple_grad",
-    "sigmoid_triple_grad, add_double_grad"
+    "sigmoid_double_grad", "sigmoid_triple_grad", "add_double_grad",
+    "add_triple_grad"
 ])
 
 # For API dispatch used at python-level

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

@@ -236,7 +236,7 @@ class {} : public egr::GradNodeBase {{
 {}
       // SetAttributes
 {}
-      // SetTensorWrappers
+      // Set TensorWrappers for Forward Inputs
 {}
       // SetGradOutMeta & SetEdges
 {}
@@ -245,6 +245,8 @@ class {} : public egr::GradNodeBase {{
 {}
 {}
 {}
+{}
+      // Set TensorWrappers for Forward Outputs
 {}
     }}
 """
@@ -720,7 +722,8 @@ def GenerateNodeCreationCodes(self):
         set_attributes_str = "\n".join(set_attributes_list)
 
         # SetTensorWrappers
-        set_tensor_wrappers_list = []
+        set_input_tensor_wrappers_list = []
+        set_output_tensor_wrappers_list = []
         num_fwd_outputs = len(forward_outputs_position_map.keys())
         for name, (atype, is_fwd_input,
                    pos) in backward_forward_inputs_map.items():
@@ -732,6 +735,7 @@ def GenerateNodeCreationCodes(self):
                     set_tensor_wrappers = f"{indent}if({name}.get_ptr() != nullptr) grad_node->SetTensorWrapper{name}(*({name}.get_ptr()), true);"
                 else:
                     set_tensor_wrappers = f"{indent}grad_node->SetTensorWrapper{name}({name}, {need_input_data});"
+                set_input_tensor_wrappers_list.append(set_tensor_wrappers)
             else:
                 if num_fwd_outputs > 1:
                     # Aligned with forward output position
@@ -743,8 +747,11 @@ def GenerateNodeCreationCodes(self):
                     set_tensor_wrappers = f"{indent}if({name}.get_ptr() != nullptr) grad_node->SetTensorWrapper{name}(*({name}.get_ptr()), false);"
                 else:
                     set_tensor_wrappers = f"{indent}grad_node->SetTensorWrapper{name}({name}, false);"
-            set_tensor_wrappers_list.append(set_tensor_wrappers)
-        set_tensor_wrappers_str = "\n".join(set_tensor_wrappers_list)
+                set_output_tensor_wrappers_list.append(set_tensor_wrappers)
+        set_input_tensor_wrappers_str = "\n".join(
+            set_input_tensor_wrappers_list)
+        set_output_tensor_wrappers_str = "\n".join(
+            set_output_tensor_wrappers_list)
 
         # SetGradOutMeta & SetEdges
         set_grad_out_meta_list = []
@@ -801,9 +808,10 @@ def GenerateNodeCreationCodes(self):
 
         self.node_creation_str = FORWARD_BODY_TEMPLATE.format(
             node_creation_event_str, pass_stop_gradient_args_str,
-            node_construction_str, set_attributes_str, set_tensor_wrappers_str,
-            set_grad_out_meta_str, set_edges_str, set_out_rank_str,
-            set_history_str, set_grad_in_meta_str, set_retain_grad_str)
+            node_construction_str, set_attributes_str,
+            set_input_tensor_wrappers_str, set_grad_out_meta_str, set_edges_str,
+            set_out_rank_str, set_history_str, set_grad_in_meta_str,
+            set_retain_grad_str, set_output_tensor_wrappers_str)
 
     def run(self):
         # Basic Validation Check
@@ -1296,7 +1304,7 @@ def GenerateNodeDefinition(self, grad_node_creation_str):
             transformed_tensor_name = self.TransformToNextGradName(name)
 
             is_optional = (name in self.optional_inputs)
-            tensor_wrapper_recover_str = f"{indent}auto {transformed_tensor_name} = egr::EagerUtils::RecoverTensorWrapper(&this->{tensor_wrapper_name}, this->shared_from_this());"
+            tensor_wrapper_recover_str = f"{indent}auto {transformed_tensor_name} = egr::EagerUtils::RecoverTensorWrapper(&this->{tensor_wrapper_name});"
             if is_optional:
                 tensor_wrapper_recover_str += "\n" + CREATE_RECOVER_OPTIONAL_TENSOR_TEMPLATE.format(
                     transformed_tensor_name, transformed_tensor_name,

paddle/fluid/eager/backward.cc

@@ -731,16 +731,6 @@ std::vector<paddle::experimental::Tensor> RunBackward(
           continue;
         }
 
-        auto* next_node = next_node_shared.get();
-        if (!node_input_buffers_dict.count(next_node)) {
-          const auto& input_meta = next_node->InputMeta();
-          auto grad_tensor_holder =
-              std::make_unique<GradTensorHolder>(input_meta);
-          VLOG(6) << "Construct GradTensorHolder for grad node: "
-                  << next_node->name();
-          node_input_buffers_dict[next_node] = std::move(grad_tensor_holder);
-        }
-
         PADDLE_ENFORCE_LT(
             j, grad_output_tensors[i].size(),
             paddle::platform::errors::Fatal(
@@ -760,8 +750,19 @@ std::vector<paddle::experimental::Tensor> RunBackward(
                 << ", rank: " << j
                 << " 's name is: " << grad_output_tensor.name();
 
+        auto* next_node = next_node_shared.get();
+        if (!node_input_buffers_dict.count(next_node)) {
+          const auto& input_meta = next_node->InputMeta();
+          auto grad_tensor_holder =
+              std::make_unique<GradTensorHolder>(input_meta);
+          VLOG(6) << "Construct GradTensorHolder for grad node: "
+                  << next_node->name();
+          node_input_buffers_dict[next_node] = std::move(grad_tensor_holder);
+        }
+
         VLOG(6) << "Sum grad inputs for edge slot: " << edge_rank.first
                 << ", rank: " << edge_rank.second;
+
         node_input_buffers_dict[next_node]->add(
             edge_rank.first, edge_rank.second, grad_output_tensor);
 

paddle/fluid/eager/custom_operator/custom_operator_node.h

@@ -59,7 +59,7 @@ class RunCustomOpNode : public GradNodeBase {
       std::vector<egr::TensorWrapper>* fwd_var) {
     std::vector<paddle::experimental::Tensor> res;
     for (size_t i = 0; i < fwd_var->size(); i++) {
-      res.emplace_back(fwd_var->at(i).recover(nullptr));
+      res.emplace_back(fwd_var->at(i).recover());
     }
     return res;
   }

paddle/fluid/eager/grad_node_info.cc

@@ -61,6 +61,10 @@ void GradNodeBase::AddEdges(std::vector<AutogradMeta*>* metas, size_t slot_id) {
       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());
@@ -84,7 +88,9 @@ void GradNodeBase::AddEdges(AutogradMeta* meta, size_t slot_id) {
       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();
+            << this->name() << " (addr: " << this << ") "
+            << " to " << meta->GetMutableGradNode()->name()
+            << " (addr: " << meta->GetMutableGradNode().get() << ")";
 
     adj_edges_[slot_id].emplace_back(meta->GetMutableGradNode(),
                                      meta->OutRankInfo());

paddle/fluid/eager/grad_tensor_holder.cc

@@ -110,6 +110,7 @@ void GradTensorHolder::add(size_t slot_id, size_t rank,
                           "got tensor: %s is empty please check you network "
                           "and make sure it creates grads.",
                           t.name()));
+
     if (t.is_dense_tensor()) {
       if (buffer_tensor.is_dense_tensor()) {
         buffer_tensor = add_final_state_dygraph_function(t, buffer_tensor);

paddle/fluid/eager/tensor_wrapper.h

@@ -77,16 +77,17 @@ class TensorWrapper {
 
     intermidiate_tensor_.set_name(tensor.name() + "@Saved");
 
-    // If an output is marked "intermedaite", we won't create
-    // autograd_meta for it.
-    // In that case, simply skip OutRankInfo Copy
-    if (EagerUtils::nullable_autograd_meta(tensor)) {
-      out_rank_info_ = EagerUtils::OutRankInfo(tensor);
+    auto* tensor_autograd_meta = EagerUtils::nullable_autograd_meta(tensor);
+    if (tensor_autograd_meta) {
+      auto autograd_meta = std::make_shared<AutogradMeta>(
+          Edge(nullptr, EagerUtils::OutRankInfo(tensor)));
+      autograd_meta->SetStopGradient(tensor_autograd_meta->StopGradient());
+      intermidiate_tensor_.set_autograd_meta(autograd_meta);
+      weak_grad_node_ = tensor_autograd_meta->GetMutableGradNode();
     }
   }
 
-  paddle::experimental::Tensor recover(
-      const std::shared_ptr<GradNodeBase>& grad_node) {
+  paddle::experimental::Tensor recover() {
     VLOG(6) << "Recover tensor: " << intermidiate_tensor_.name()
             << " for wrapper";
     if (!intermidiate_tensor_.defined()) {
@@ -99,9 +100,20 @@ class TensorWrapper {
     // if it's full_reserved just return the full copy of tensor
     paddle::experimental::Tensor recovered_tensor = intermidiate_tensor_;
     if (!full_reserved_) {
-      std::shared_ptr<GradNodeBase> new_grad_node = grad_node;
-      auto p_ab_autograd_meta =
-          std::make_shared<AutogradMeta>(Edge(new_grad_node, out_rank_info_));
+      std::shared_ptr<GradNodeBase> new_grad_node = weak_grad_node_.lock();
+      if (new_grad_node) {
+        VLOG(3) << "Recovered TensorWrapper with GradNode "
+                << new_grad_node->name() << " addr: " << new_grad_node.get();
+      } else {
+        VLOG(3) << "Recovered TensorWrapper with Empth GradNode";
+      }
+      auto* intermediate_autograd_meta =
+          EagerUtils::unsafe_autograd_meta(intermidiate_tensor_);
+      auto p_ab_autograd_meta = std::make_shared<AutogradMeta>(
+          Edge(new_grad_node, intermediate_autograd_meta->OutRankInfo()));
+      p_ab_autograd_meta->SetStopGradient(
+          intermediate_autograd_meta->StopGradient());
+
       recovered_tensor.set_autograd_meta(
           std::static_pointer_cast<paddle::experimental::AbstractAutogradMeta>(
               p_ab_autograd_meta));
@@ -149,8 +161,8 @@ class TensorWrapper {
  private:
   bool full_reserved_ = false;
   bool no_need_buffer_ = false;
-  std::pair<size_t, size_t> out_rank_info_;
   paddle::experimental::Tensor intermidiate_tensor_;
+  std::weak_ptr<egr::GradNodeBase> weak_grad_node_;
   uint32_t inplace_version_snapshot_ = 0;
 };
 }  // namespace egr

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

@@ -41,7 +41,7 @@ TEST(TensorWrapper, Basic) {
   et1.set_autograd_meta(auto_grad0);
   et1.set_name("et1");
   auto tw0 = egr::TensorWrapper(et1, true);
-  auto recover_et1 = tw0.recover(std::make_shared<eager_test::GradTestNode>());
+  auto recover_et1 = tw0.recover();
   CHECK_EQ(recover_et1.name(), std::string("et1"));
   CHECK_EQ(egr::EagerUtils::OutRankInfo(recover_et1).first,
            egr::EagerUtils::OutRankInfo(et1).first);
@@ -67,7 +67,7 @@ TEST(TensorWrapper, Basic) {
   auto auto_grad1 = std::make_shared<egr::AutogradMeta>(edge1);
   et2.set_autograd_meta(auto_grad1);
   auto tw1 = egr::TensorWrapper(et2, false);
-  auto recover_et2 = tw1.recover(grad_test_node1);
+  auto recover_et2 = tw1.recover();
   CHECK_EQ(recover_et2.name(), std::string("et2@Saved"));
   CHECK_EQ(egr::EagerUtils::OutRankInfo(recover_et2).first,
            egr::EagerUtils::OutRankInfo(et2).first);
@@ -76,7 +76,5 @@ TEST(TensorWrapper, Basic) {
   // Test Raw recover
   paddle::experimental::Tensor et3;
   auto tw2 = egr::TensorWrapper(et3, true);
-  CHECK(
-      tw2.recover(std::make_shared<eager_test::GradTestNode>()).initialized() ==
-      false);
+  CHECK(tw2.recover().initialized() == false);
 }

paddle/fluid/eager/utils.cc

@@ -360,16 +360,15 @@ void EagerUtils::Output2Result(
 }
 
 paddle::experimental::Tensor EagerUtils::RecoverTensorWrapper(
-    TensorWrapper* tw, const std::shared_ptr<GradNodeBase>& grad_node) {
-  return tw->recover(grad_node);
+    TensorWrapper* tw) {
+  return tw->recover();
 }
 
 std::vector<paddle::experimental::Tensor> EagerUtils::RecoverTensorWrapper(
-    std::vector<TensorWrapper>* tw,
-    const std::shared_ptr<GradNodeBase>& grad_node) {
+    std::vector<TensorWrapper>* tw) {
   std::vector<paddle::experimental::Tensor> ret;
   for (auto& t : *tw) {
-    ret.emplace_back(t.recover(grad_node));
+    ret.emplace_back(t.recover());
   }
   return ret;
 }

paddle/fluid/eager/utils.h

@@ -174,11 +174,9 @@ class EagerUtils {
       const std::shared_ptr<EagerVariable>& view_output_var);
 
   // TensorWrapper Utils
-  static paddle::experimental::Tensor RecoverTensorWrapper(
-      TensorWrapper* tw, const std::shared_ptr<GradNodeBase>& grad_node);
+  static paddle::experimental::Tensor RecoverTensorWrapper(TensorWrapper* tw);
   static std::vector<paddle::experimental::Tensor> RecoverTensorWrapper(
-      std::vector<TensorWrapper>* tw,
-      const std::shared_ptr<GradNodeBase>& grad_node);
+      std::vector<TensorWrapper>* tw);
 
   // Intermidate needed remove this once we don't need legacy
   // Inner Method

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

@@ -209,7 +209,9 @@ def func_example_with_gradient_and_create_graph(self):
         self.assertTrue(np.allclose(dddx_grad_actual, dddx_expected))
 
     def test_all_cases(self):
-        if _in_legacy_dygraph():
+        self.func_exception()
+        self.func_example_with_gradient_and_create_graph()
+        with _test_eager_guard():
             self.func_exception()
             self.func_example_with_gradient_and_create_graph()
 
@@ -296,7 +298,8 @@ def func_example_with_gradient_and_create_graph(self):
         self.assertTrue(np.allclose(dddx_grad_actual, dddx_expected))
 
     def test_all_cases(self):
-        if _in_legacy_dygraph():
+        self.func_example_with_gradient_and_create_graph()
+        with _test_eager_guard():
             self.func_example_with_gradient_and_create_graph()
 
 

python/paddle/utils/code_gen/backward.yaml

@@ -1411,7 +1411,7 @@
     func : GeneralTernaryGradInferMeta
     param : [out, fwd_grad_out, grad_grad_x]
   kernel :
-    func : sigmoid_double_grad
+    func : sigmoid_triple_grad
 
 - backward_api : silu_grad
   forward : silu (Tensor x) -> Tensor(out)