[Eager] Remove full reserved strategy (#42690)

* remove full reserved strategy

* fix inplace error

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -1156,28 +1156,20 @@ static std::string GenerateGradNodeCreationContent(
   for (const auto& iter : op_base_infos) {
     const std::map<std::string, std::string>& grad_ins_fwd_slotname_map =
         iter.GetGradInsFwdSlotnameMap();
-    const std::unordered_set<std::string>& no_need_buffer_ins =
-        iter.GetNoNeedBufferInputs();
     for (auto& kv : grad_ins_fwd_slotname_map) {
       const std::string& tensor_wrapper_name = kv.second;
-      std::string full_reserved = "false";
-      if (fwd_outputs_name_pos_map.find(tensor_wrapper_name) ==
-              fwd_outputs_name_pos_map.end() &&
-          !no_need_buffer_ins.count(tensor_wrapper_name)) {
-        full_reserved = "true";
-      }
       const char* SET_TENSOR_WRAPPER_TEMPLATE =
-          "      grad_node->SetTensorWrapper%s(%s, %s);\n";
+          "      grad_node->SetTensorWrapper%s(%s);\n";
       // Replace output directly with input in inplace op.
       if (!inplace_map.empty() && inplace_map.count(tensor_wrapper_name)) {
         auto inplace_input_name = inplace_map[tensor_wrapper_name];
         grad_node_creation_str += paddle::string::Sprintf(
             SET_TENSOR_WRAPPER_TEMPLATE, LegalizeVarName(tensor_wrapper_name),
-            LegalizeVarName(inplace_input_name), full_reserved);
+            LegalizeVarName(inplace_input_name));
       } else {
         grad_node_creation_str += paddle::string::Sprintf(
             SET_TENSOR_WRAPPER_TEMPLATE, LegalizeVarName(tensor_wrapper_name),
-            LegalizeVarName(tensor_wrapper_name), full_reserved);
+            LegalizeVarName(tensor_wrapper_name));
       }
     }
   }
@@ -2592,7 +2584,6 @@ static std::string GenerateGradNodeHeaderContents(
 
       std::string tensor_wrapper_arg_str;
       std::string tensor_wrapper_body_str;
-      std::string full_reserved_str = "full_reserved";
       std::string no_need_buffer_str = "false";
       if (no_need_buffer_ins.count(tensor_wrapper_name)) {
         no_need_buffer_str = "true";
@@ -2610,12 +2601,12 @@ static std::string GenerateGradNodeHeaderContents(
 
         const char* SET_TENSOR_WRAPPER_BODY_TEMPLATE =
             "for(const auto& eager_tensor : %s) {\n"
-            "          %s.emplace_back( egr::TensorWrapper(eager_tensor, %s "
-            "/*full_reserved*/, %s) );\n"
+            "          %s.emplace_back( egr::TensorWrapper(eager_tensor "
+            ", %s) );\n"
             "      }\n";
         tensor_wrapper_body_str = paddle::string::Sprintf(
             SET_TENSOR_WRAPPER_BODY_TEMPLATE, tensor_wrapper_name,
-            struct_tensor_wrapper_name, full_reserved_str, no_need_buffer_str);
+            struct_tensor_wrapper_name, no_need_buffer_str);
 
         const char* CLEAR_TENSOR_WRAPPER_TEMPLATE =
             "for (auto tw: %s)   {\n"
@@ -2636,22 +2627,20 @@ static std::string GenerateGradNodeHeaderContents(
             TENSOR_WRAPPER_MEMBER_TEMPLATE, struct_tensor_wrapper_name);
 
         const char* SET_TENSOR_WRAPPER_BODY_TEMPLATE =
-            "%s = egr::TensorWrapper(%s, %s /*full_reserved*/, %s);\n";
+            "%s = egr::TensorWrapper(%s, %s);\n";
         tensor_wrapper_body_str = paddle::string::Sprintf(
             SET_TENSOR_WRAPPER_BODY_TEMPLATE, struct_tensor_wrapper_name,
-            tensor_wrapper_name, full_reserved_str, no_need_buffer_str);
+            tensor_wrapper_name, no_need_buffer_str);
 
         const char* CLEAR_TENSOR_WRAPPER_TEMPLATE = "   %s.clear();\n";
         clear_tensor_wrappers_str += paddle::string::Sprintf(
             CLEAR_TENSOR_WRAPPER_TEMPLATE, struct_tensor_wrapper_name);
       }
-      std::string full_reserved_signature_str = "bool full_reserved";
       const char* SET_TENSOR_WRAPPER_TEMPLATE =
-          "   void SetTensorWrapper%s(%s, %s) {\n     %s\n   }\n";
+          "   void SetTensorWrapper%s(%s) {\n     %s\n   }\n";
       set_tensor_wrappers_str += paddle::string::Sprintf(
           SET_TENSOR_WRAPPER_TEMPLATE, tensor_wrapper_name,
-          tensor_wrapper_arg_str, full_reserved_signature_str,
-          tensor_wrapper_body_str);
+          tensor_wrapper_arg_str, tensor_wrapper_body_str);
     }
   }
   VLOG(6) << "Generated TensorWrapper";

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

@@ -55,8 +55,8 @@ def ParseArguments():
 ## Code Gen Templates ##
 ########################
 SET_PLAIN_TENSOR_WRAPPER_TEMPLATE = \
-"""  void SetTensorWrapper{}(const paddle::experimental::Tensor& {}, bool full_reserved) {{
-    {} = egr::TensorWrapper({}, full_reserved, {});
+"""  void SetTensorWrapper{}(const paddle::experimental::Tensor& {}) {{
+    {} = egr::TensorWrapper({}, {});
   }}
 """
 
@@ -69,9 +69,9 @@ CLEAR_TENSOR_WRAPPER_TEMPLATE = \
 """
 
 SET_VECTOR_TENSOR_WRAPPER_TEMPLATE = \
-"""  void SetTensorWrapper{}(const std::vector<paddle::experimental::Tensor>& {}, bool full_reserved) {{
+"""  void SetTensorWrapper{}(const std::vector<paddle::experimental::Tensor>& {}) {{
     for(const auto& eager_tensor : {}) {{
-      {}.emplace_back(egr::TensorWrapper(eager_tensor, full_reserved, {}));
+      {}.emplace_back(egr::TensorWrapper(eager_tensor, {}));
     }};
   }}
 """
@@ -676,9 +676,9 @@ class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
 
             if is_fwd_input:
                 if is_optional:
-                    set_tensor_wrappers = f"{indent}if({name}.get_ptr() != nullptr) grad_node->SetTensorWrapper{name}(*({name}.get_ptr()), true);"
+                    set_tensor_wrappers = f"{indent}if({name}.get_ptr() != nullptr) grad_node->SetTensorWrapper{name}(*({name}.get_ptr()));"
                 else:
-                    set_tensor_wrappers = f"{indent}grad_node->SetTensorWrapper{name}({name}, true);"
+                    set_tensor_wrappers = f"{indent}grad_node->SetTensorWrapper{name}({name});"
                 set_input_tensor_wrappers_list.append(set_tensor_wrappers)
             else:
                 if num_fwd_outputs > 1:
@@ -688,9 +688,9 @@ class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
                     fwd_output_pos = forward_outputs_position_map[name][1]
 
                 if is_optional:
-                    set_tensor_wrappers = f"{indent}if({name}.get_ptr() != nullptr) grad_node->SetTensorWrapper{name}(*({name}.get_ptr()), false);"
+                    set_tensor_wrappers = f"{indent}if({name}.get_ptr() != nullptr) grad_node->SetTensorWrapper{name}(*({name}.get_ptr()));"
                 else:
-                    set_tensor_wrappers = f"{indent}grad_node->SetTensorWrapper{name}({name}, false);"
+                    set_tensor_wrappers = f"{indent}grad_node->SetTensorWrapper{name}({name});"
                 set_output_tensor_wrappers_list.append(set_tensor_wrappers)
         set_input_tensor_wrappers_str = "\n".join(
             set_input_tensor_wrappers_list)

paddle/fluid/eager/tensor_wrapper.h

@@ -34,7 +34,6 @@ class TensorWrapper {
  public:
   TensorWrapper() = default;
   explicit TensorWrapper(const paddle::experimental::Tensor& tensor,
-                         bool full_reserved = false,
                          bool no_need_buffer = false) {
     // set inplace_version_snapshot_ according to tensor's current inplace
     // version.
@@ -46,32 +45,12 @@ class TensorWrapper {
     }
 
     /**
-     * Normally, we should fully reserved all non-output or non-leaf fwd tensor
-     * here. And for fwd output tensor, we should not reserve its autogradmeta,
-     * to avoid recursive depends on GradNodeBase
+     * Normally, we should only save data and part of autograd_meta of fwd
+     * tensor, and should not reserve its original grad_node,
+     * to avoid recursive and additional depends on GradNodeBase
      * **/
-    full_reserved_ = full_reserved;
+    auto* tensor_autograd_meta = EagerUtils::nullable_autograd_meta(tensor);
     no_need_buffer_ = no_need_buffer;
-    if (full_reserved_) {
-      VLOG(6) << "Fully reserved tensor: " << tensor.name();
-      intermidiate_tensor_ = tensor;
-      if (no_need_buffer_) {
-        if (phi::DenseTensor::classof(tensor.impl().get())) {
-          // Only Copy Meta
-          phi::DenseTensor* dense_tensor =
-              static_cast<phi::DenseTensor*>(tensor.impl().get());
-          auto tw_dense_tensor =
-              std::make_shared<phi::DenseTensor>(*dense_tensor);
-          tw_dense_tensor->clear();
-          intermidiate_tensor_.set_impl(tw_dense_tensor);
-        } else {
-          PADDLE_THROW(paddle::platform::errors::Fatal(
-              "Unrecognized tensor type for no_need_buffer feature"));
-        }
-      }
-      return;
-    }
-
     // shallow copy tensor_impl here
     if (no_need_buffer) {
       if (phi::DenseTensor::classof(tensor.impl().get())) {
@@ -89,10 +68,11 @@ class TensorWrapper {
       intermidiate_tensor_.set_impl(tensor.impl());
     }
 
+    if (VLOG_IS_ON(7)) {
       // TODO(jiabin): This may has server performance issue
       intermidiate_tensor_.set_name(tensor.name() + "@Saved");
+    }
 
-    auto* tensor_autograd_meta = EagerUtils::nullable_autograd_meta(tensor);
     if (tensor_autograd_meta) {
       auto autograd_meta =
           std::make_shared<AutogradMeta>(*tensor_autograd_meta);
@@ -112,10 +92,6 @@ class TensorWrapper {
 
     check_inplace_version();
 
-    // if it's full_reserved just return the full copy of tensor
-    if (full_reserved_) {
-      return intermidiate_tensor_;
-    } else {
     paddle::experimental::Tensor recovered_tensor = intermidiate_tensor_;
 
     std::shared_ptr<GradNodeBase> new_grad_node = weak_grad_node_.lock();
@@ -139,7 +115,6 @@ class TensorWrapper {
 
     return recovered_tensor;
   }
-  }
 
   void clear() { intermidiate_tensor_.reset(); }
 
@@ -179,7 +154,6 @@ class TensorWrapper {
   }
 
  private:
-  bool full_reserved_ = false;
   bool no_need_buffer_ = false;
   paddle::experimental::Tensor intermidiate_tensor_;
   std::weak_ptr<egr::GradNodeBase> weak_grad_node_;

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

@@ -40,9 +40,11 @@ TEST(TensorWrapper, Basic) {
   auto auto_grad0 = std::make_shared<egr::AutogradMeta>(edge0);
   et1.set_autograd_meta(auto_grad0);
   et1.set_name("et1");
-  auto tw0 = egr::TensorWrapper(et1, true);
+  auto tw0 = egr::TensorWrapper(et1);
   auto recover_et1 = tw0.recover();
-  CHECK_EQ(recover_et1.name(), std::string("et1"));
+  if (VLOG_IS_ON(7)) {
+    CHECK_EQ(recover_et1.name(), std::string("et1@saved"));
+  }
   CHECK_EQ(egr::EagerUtils::OutRankInfo(recover_et1).first,
            egr::EagerUtils::OutRankInfo(et1).first);
   CHECK_EQ(egr::EagerUtils::OutRankInfo(recover_et1).second,
@@ -68,13 +70,15 @@ TEST(TensorWrapper, Basic) {
   et2.set_autograd_meta(auto_grad1);
   auto tw1 = egr::TensorWrapper(et2, false);
   auto recover_et2 = tw1.recover();
+  if (VLOG_IS_ON(7)) {
     CHECK_EQ(recover_et2.name(), std::string("et2@Saved"));
+  }
   CHECK_EQ(egr::EagerUtils::OutRankInfo(recover_et2).first,
            egr::EagerUtils::OutRankInfo(et2).first);
   CHECK_EQ(egr::EagerUtils::OutRankInfo(recover_et2).second,
            egr::EagerUtils::OutRankInfo(et2).second);
   // Test Raw recover
   paddle::experimental::Tensor et3;
-  auto tw2 = egr::TensorWrapper(et3, true);
+  auto tw2 = egr::TensorWrapper(et3);
   CHECK(tw2.recover().initialized() == false);
 }