[Eager] Fix edvr starganv2 (#43471)

* fix starganv2

* fix starganv2 stop_gradient end error

* fix edvr_starganv2

* fix mul kernel to fix optional ddx

* fix typo

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -1152,7 +1152,8 @@ static std::string GenerateGradNodeCreationContent(
   size_t bwd_in_slot_num = out_vars.size();
   size_t bwd_out_slot_num = in_vars.size();
   const char* GRAD_OP_NODE_TEMPLATE =
-      "      auto grad_node = std::shared_ptr<GradNode%s>(new GradNode%s(%d, "
+      "      auto grad_node = std::shared_ptr<%sGradNodeCompat>(new "
+      "%sGradNodeCompat(%d, "
       "%d));\n";
   grad_node_creation_str += "    // Create GradOpNode\n";
   grad_node_creation_str +=
@@ -2080,10 +2081,8 @@ static std::string GenerateSingleOpBase(
   generated_grad_function_body +=
       "  paddle::small_vector<std::vector<paddle::experimental::Tensor>, "
       "egr::kSlotSmallVectorSize> " +
-      hooked_grads +
-      " = "
-      "GradNode" +
-      fwd_op_type + "::ApplyGradientHooks(grads);\n";
+      hooked_grads + " = " + fwd_op_type +
+      "GradNodeCompat::ApplyGradientHooks(grads);\n";
 
   // [Generation] Get Ins Map
   std::unordered_set<std::string> dispensable_input_name_set;
@@ -2547,7 +2546,7 @@ static std::string GenerateGradNodeCCContents(
   */
 
   const char* EAGER_LOG_TEMPLATE =
-      "  VLOG(3) << \"Running Eager Backward Node: GradNode%s\";\n";
+      "  VLOG(3) << \"Running Eager Backward Node: %sGradNodeCompat\";\n";
   std::string generated_grad_function_body =
       paddle::string::Sprintf(EAGER_LOG_TEMPLATE, fwd_op_type);
 
@@ -2616,7 +2615,7 @@ static std::string GenerateGradNodeCCContents(
   const char* GRAD_FUNCTION_TEMPLATE =
       "paddle::small_vector<std::vector<paddle::experimental::Tensor>, "
       "egr::kSlotSmallVectorSize> "
-      "GradNode%s::operator()("
+      "%sGradNodeCompat::operator()("
       "paddle::small_vector<std::vector<paddle::experimental::Tensor>, "
       "egr::kSlotSmallVectorSize>& grads, bool "
       "create_graph, bool is_new_grad) {\n"
@@ -2645,14 +2644,15 @@ static std::string GenerateGradNodeHeaderContents(
   VLOG(6) << "Generating Grad Node Header";
 
   const char* GRAD_NODE_TEMPLATE =
-      "class GradNode%s : public egr::GradNodeBase {\n"
+      "class %sGradNodeCompat : public egr::GradNodeBase {\n"
       " public:\n"
-      "  GradNode%s() : egr::GradNodeBase() { VLOG(7) << \" Construct "
-      "GradNode%s \"; }\n"
-      "  GradNode%s(size_t bwd_in_slot_num, size_t bwd_out_slot_num) : "
+      "  %sGradNodeCompat() : egr::GradNodeBase() { VLOG(7) << \" Construct "
+      "%sGradNodeCompat \"; }\n"
+      "  %sGradNodeCompat(size_t bwd_in_slot_num, size_t bwd_out_slot_num) : "
       "egr::GradNodeBase(bwd_in_slot_num, bwd_out_slot_num) { VLOG(7) << \" "
-      "Construct GradNode%s \"; }\n"
-      "  ~GradNode%s() override { VLOG(6) << \" Destruct GradNode%s \"; }\n"
+      "Construct %sGradNodeCompat \"; }\n"
+      "  ~%sGradNodeCompat() override { VLOG(6) << \" Destruct "
+      "%sGradNodeCompat \"; }\n"
       "\n"
       "  virtual "
       "paddle::small_vector<std::vector<paddle::experimental::Tensor>, "
@@ -2667,11 +2667,11 @@ static std::string GenerateGradNodeHeaderContents(
       "%s\n"
       "    SetIsTensorWrappersCleared(true);\n"
       "  }\n"
-      "  std::string name() override { return \"GradNode%sMid\"; } \n "
+      "  std::string name() override { return \"%sGradNodeCompat\"; } \n "
       "\n"
       "std::shared_ptr<GradNodeBase> Copy() const override {{\n "
-      "    auto copied_node = std::shared_ptr<GradNode%s>(new "
-      "GradNode%s(*this));\n "
+      "    auto copied_node = std::shared_ptr<%sGradNodeCompat>(new "
+      "%sGradNodeCompat(*this));\n "
       "    return copied_node;\n "
       "}}\n "
       "\n"

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

 # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# 
+#
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
-# 
+#
 #     http://www.apache.org/licenses/LICENSE-2.0
-# 
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -147,7 +147,18 @@ def RemoveConstAndReference(string):
 
 
 def GetGradNodeName(string):
-    return f"GradNode{string}Final"
+
+    def str2Hump(text):
+        arr = filter(None, text.split('_'))
+        res = ''
+        for i in arr:
+            res = res + i[0].upper() + i[1:]
+        return res
+
+    string = str2Hump(string)
+    if string.rfind("Grad") == (len(string) - 4):
+        string = string[:-4]
+    return f"{string}GradNodeFinal"
 
 
 def GetDygraphForwardFunctionName(string):
@@ -335,6 +346,7 @@ def ParseYamlInplaceInfo(string):
 ###  Generator Base  ###
 ########################
 class FunctionGeneratorBase:
+
     def __init__(self, forward_api_contents, namespace):
         self.forward_api_contents = forward_api_contents
         self.namespace = namespace
@@ -357,7 +369,7 @@ class FunctionGeneratorBase:
         # Special Op Attributes
         self.optional_inputs = []  #[name, ...]
         self.no_need_buffers = []  #[name, ...]
-        self.intermediate_outputs = []  #[name, ...]    
+        self.intermediate_outputs = []  #[name, ...]
         self.forward_inplace_map = {}  #{name : name, ...}
 
     def ParseForwardInplaceInfo(self):
@@ -423,8 +435,9 @@ class FunctionGeneratorBase:
             input_type = forward_input[1]
             input_pos = forward_input[2]
 
-            self.forward_inputs_position_map[
-                input_name] = [input_type, input_pos]
+            self.forward_inputs_position_map[input_name] = [
+                input_type, input_pos
+            ]
 
         for i in range(len(forward_returns_list)):
             forward_return = forward_returns_list[i]
@@ -432,11 +445,13 @@ class FunctionGeneratorBase:
             return_type = forward_return[1]
             return_pos = forward_return[2]
 
-            self.forward_outputs_position_map[
-                return_name] = [return_type, return_pos]
+            self.forward_outputs_position_map[return_name] = [
+                return_type, return_pos
+            ]
 
 
 class GeneratorBase:
+
     def __init__(self, api_yaml_path):
         self.namespace = ""
         self.api_yaml_path = api_yaml_path

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

@@ -411,6 +411,7 @@ def GenerateCoreOpInfoDefinition():
 ## Generator Class ##
 #####################
 class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
+
     def __init__(self, forward_api_contents, grad_api_contents, namespace):
         self.forward_api_contents = forward_api_contents
         # Members from Parent:
@@ -532,8 +533,8 @@ class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
             max_input_position = max(max_input_position, pos)
 
         for _, _, _, pos in forward_attrs_list:
-            assert pos > max_input_position, AssertMessage(pos,
-                                                           max_input_position)
+            assert pos > max_input_position, AssertMessage(
+                pos, max_input_position)
 
     def BackwardValidationCheck(self):
         backward_forward_inputs_map = self.backward_forward_inputs_map
@@ -678,7 +679,7 @@ class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
         # Node Construction
         num_backward_inputs = len(forward_outputs_position_map.keys())
         num_backward_outputs = len(forward_inputs_position_map.keys())
-        grad_node_name = GetGradNodeName(forward_api_name)
+        grad_node_name = GetGradNodeName(self.backward_api_name)
 
         # Helper
         indent = GetIndent(2)
@@ -845,6 +846,7 @@ class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
 
 
 class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
+
     def __init__(self, forward_api_contents, grad_api_contents, namespace):
         DygraphFunctionGeneratorBase.__init__(self, forward_api_contents,
                                               grad_api_contents, namespace)
@@ -947,12 +949,12 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
         if is_inplaced and len(forward_outputs_position_map) == 1:
             api_out_type = "auto&"
         forward_call_str = f"{indent}{api_out_type} api_result = paddle::experimental::{namespace}{function_name}({inputs_call_args_str});"
-        num_outputs = len(forward_outputs_position_map.keys()) - len(
-            intermediate_outputs)
+        num_outputs = len(
+            forward_outputs_position_map.keys()) - len(intermediate_outputs)
 
         # Check Nan and Inf
-        check_nan_inf_str = CHECK_NAN_AND_INF_TEMPLATE.format(function_name,
-                                                              "api_result")
+        check_nan_inf_str = CHECK_NAN_AND_INF_TEMPLATE.format(
+            function_name, "api_result")
 
         # Get Outputs
         get_outputs_str = ""
@@ -1007,8 +1009,8 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
                 if pos == corresponding_pos:
                     has_corresponding_grad_output = True
             if has_corresponding_grad_output or (
-                    name in forward_inplace_map and
-                    forward_api_name not in inplace_check_blacklist):
+                    name in forward_inplace_map
+                    and forward_api_name not in inplace_check_blacklist):
                 input_autograd_meta_name = GetAutoGradMetaName(name)
                 if IsPlainTensorType(ttype):
                     input_autograd_meta = f"{indent}egr::AutogradMeta* {input_autograd_meta_name} = egr::EagerUtils::nullable_autograd_meta({name});"
@@ -1116,17 +1118,20 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
         forward_outputs_position_map = self.forward_outputs_position_map
         forward_attrs_list = self.forward_attrs_list
 
-        num_args = len(forward_inputs_position_map.keys()) + len(
-            forward_attrs_list)
+        num_args = len(
+            forward_inputs_position_map.keys()) + len(forward_attrs_list)
         num_returns = len(forward_outputs_position_map.keys())
 
         final_state_fwd_api_name = "final_state_" + forward_api_name
-        core_ops_returns_info[
-            final_state_fwd_api_name] = ["" for i in range(num_returns)]
-        core_ops_args_info[
-            final_state_fwd_api_name] = ["" for i in range(num_args)]
-        core_ops_args_type_info[
-            final_state_fwd_api_name] = ["" for i in range(num_args)]
+        core_ops_returns_info[final_state_fwd_api_name] = [
+            "" for i in range(num_returns)
+        ]
+        core_ops_args_info[final_state_fwd_api_name] = [
+            "" for i in range(num_args)
+        ]
+        core_ops_args_type_info[final_state_fwd_api_name] = [
+            "" for i in range(num_args)
+        ]
 
         for name, (ttype, pos) in forward_inputs_position_map.items():
             core_ops_args_info[final_state_fwd_api_name][pos] = name
@@ -1159,6 +1164,7 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
 
 
 class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
+
     def __init__(self,
                  forward_api_contents,
                  grad_api_contents,
@@ -1167,7 +1173,7 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
         DygraphFunctionGeneratorBase.__init__(self, forward_api_contents,
                                               grad_api_contents, namespace)
 
-        # Record name mapping from forward_api_name to grad_api_names
+        # Record name mapping from forward_var_name to grad_var_names
         self.to_next_grad_name_mapping = {}  # {name : name}
 
         # Generated Results
@@ -1281,7 +1287,7 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
                 attribute_members_str += ATTRIBUTE_MEMBER_TEMPLATE.format(
                     RemoveConstAndReference(atype), saved_attr_name)
 
-        grad_node_name = GetGradNodeName(forward_op_name)
+        grad_node_name = GetGradNodeName(self.backward_api_name)
         self.node_declaration_str = NODE_DECLARATION_TEMPLATE.format(
             grad_node_name, grad_node_name, grad_node_name, grad_node_name,
             grad_node_name, clear_tensor_wrapper_str, grad_node_name,
@@ -1447,8 +1453,8 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
 {indent}{grad_api_namespace}{backward_api_name}({grad_api_args_str});"""
 
         # Check Nan and Inf
-        check_nan_inf_str = CHECK_NAN_AND_INF_TEMPLATE.format(backward_api_name,
-                                                              "returns")
+        check_nan_inf_str = CHECK_NAN_AND_INF_TEMPLATE.format(
+            backward_api_name, "returns")
 
         # Prepare for Node Creation if Necessary
         inputs_autograd_meta_str = ""
@@ -1533,7 +1539,7 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
         returns_str = f"{indent}if(NeedComplexToRealConversion()) HandleComplexGradToRealGrad(&returns);\n"
         returns_str += f"{indent}return returns;\n"
 
-        grad_node_name = GetGradNodeName(forward_api_name)
+        grad_node_name = GetGradNodeName(self.backward_api_name)
 
         self.node_definition_str = GRAD_FUNCTION_TEMPLATE.format(
             grad_node_name, fill_zero_str, get_grad_in_args_str, grad_node_name,
@@ -1560,6 +1566,7 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
 
 
 class DygraphForwardAndNodesGenerator(GeneratorBase):
+
     def __init__(self, api_yaml_path, backward_yaml_path):
         # Parent members:
         # self.namespace
@@ -1617,9 +1624,10 @@ class DygraphForwardAndNodesGenerator(GeneratorBase):
                 next_grad_api_contents = self.GetBackwardAPIContents(
                     backward_api_contents)
 
-                node_generator = DygraphNodeGenerator(
-                    forward_api_contents, backward_api_contents, namespace,
-                    next_grad_api_contents)
+                node_generator = DygraphNodeGenerator(forward_api_contents,
+                                                      backward_api_contents,
+                                                      namespace,
+                                                      next_grad_api_contents)
                 node_generator.run()
                 self.node_declaration_str += node_generator.node_declaration_str + "\n"
                 self.node_definition_str += node_generator.node_definition_str + "\n"

paddle/fluid/eager/backward.cc

@@ -536,7 +536,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
     const std::vector<paddle::experimental::Tensor>& inputs = {},
     bool allow_unused = false,
     const std::vector<paddle::experimental::Tensor>& no_grad_vars = {}) {
-  VLOG(6) << "Start Backward";
+  VLOG(3) << "Start Backward";
 
   // *Gradient Hook should happen at node-level
   // *Inplace version check should perform at node-level
@@ -634,7 +634,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
     GeneralGrad::Instance().ReconstructBackwardGraph(orig_queue);
   }
 
-  VLOG(6) << "Update In degree Map for backward";
+  VLOG(3) << "Update In degree Map for backward";
   // 3. Compute in_degree for each node
   std::unordered_map<GradNodeBase*, int> node_in_degree_map =
       getInDegreeMap(queue);
@@ -654,7 +654,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
   //    |- node(grads)
   //    |- Prepare for next node
   // 3. Update queue
-  VLOG(6) << "Run Backward";
+  VLOG(3) << "Run Backward";
   while (!queue.empty()) {
     GradNodeBase* node = queue.front();
     VLOG(6) << "Running GradNode:" << node->name();
@@ -739,7 +739,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
         // Since we make edge has as same rank as bwd outputs, we indexing them
         // with the same rank(i, j)
         auto next_node_shared = edge.GetMutableGradNode();
-
+        VLOG(3) << "Found pending node: " << next_node_shared->name();
         // Next node could be nullptr if it is leaf tensor with no
         // AccumulationNode attached
         // Or it could also originated from dispensable inputs
@@ -826,7 +826,7 @@ void Backward(
     const std::vector<paddle::experimental::Tensor>& tensors,  // outputs
     const std::vector<paddle::experimental::Tensor>& grad_tensors,
     bool retain_graph) {
-  VLOG(6) << "Run in Backward";
+  VLOG(3) << "Run in Backward";
   paddle::platform::RecordEvent backward_record_event(
       "backward", paddle::platform::TracerEventType::Operator, 1);
   RunBackward(tensors, grad_tensors, retain_graph);
@@ -839,7 +839,7 @@ std::vector<paddle::experimental::Tensor> Grad(
     const std::vector<paddle::experimental::Tensor>& grad_tensors,
     bool retain_graph, bool create_graph, bool only_inputs, bool allow_unused,
     const std::vector<paddle::experimental::Tensor>& no_grad_vars) {
-  VLOG(6) << "Run in Grad";
+  VLOG(3) << "Run in Grad";
 
   DuplicateCheck(inputs, true /* is_input */);
   DuplicateCheck(tensors, false /* is_input */);

paddle/fluid/eager/grad_node_info.cc

@@ -225,7 +225,7 @@ void GradNodeBase::SetGradOutMeta(const paddle::experimental::Tensor& fwd_in,
       fwd_in_meta->SetGradNode(
           std::make_shared<egr::GradNodeAccumulation>(fwd_in_meta));
     }
-    VLOG(6) << "Add Edges for slot: " << slot_rank << ", the Edge is from "
+    VLOG(3) << "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() << ")";
@@ -281,7 +281,7 @@ void GradNodeBase::SetGradOutMeta(
         fwd_in_meta->SetGradNode(
             std::make_shared<egr::GradNodeAccumulation>(fwd_in_meta));
       }
-      VLOG(6) << "Add Edges for slot: " << slot_rank << ", the Edge is from "
+      VLOG(3) << "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() << ")";

paddle/fluid/eager/grad_tensor_holder.cc

@@ -68,6 +68,8 @@ void GradTensorHolder::CopyValueFromTensor(
       // Fill 1.0, use full to support complex, one_like don't support it.
       buffer_[slot_id][rank] =
           paddle::experimental::full(t.shape(), 1, t.dtype(), t.place());
+      egr::EagerUtils::autograd_meta(&(buffer_[slot_id][rank]))
+          ->SetStopGradient(false);
     }
   }
 }
@@ -75,8 +77,6 @@ void GradTensorHolder::CopyValueFromTensor(
 void GradTensorHolder::add(size_t slot_id, size_t rank,
                            const paddle::experimental::Tensor& t,
                            bool create_graph) {
-  // TODO(jiabin): We need to deal with empty input_buffer with slot size not
-  // empty;
   PADDLE_ENFORCE(slot_id < buffer_.size(),
                  paddle::platform::errors::Fatal(
                      "Invalid slot_id for GradTensorHolder::add() "

paddle/fluid/imperative/partial_grad_engine.cc

@@ -1085,7 +1085,7 @@ void PartialGradEngine::Clear() {
 void PartialGradEngine::Execute() {
   PADDLE_ENFORCE_NOT_NULL(task_, platform::errors::PermissionDenied(
                                      "PartialGradEngine has been destructed"));
-  VLOG(10) << "Starts to execute PartialGradEngine";
+  VLOG(3) << "Starts to execute PartialGradEngine";
   results_ = task_->Run();
   Clear();
 }

paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h

@@ -442,8 +442,14 @@ void MultiplyDoubleGradKernel(const Context& dev_ctx,
   // (5) dx = dout * ddy
   if (ddout) {
     auto& place = *dev_ctx.eigen_device();
-    // size(ddout) > size(ddx), ddout can't use memory of ddx using inplace
-    if (ddout->numel() > ddx.get_ptr()->numel()) {
+    // size(ddout) > size(ddx) or we don't have ddx, ddout can't use memory of
+    // ddx using inplace
+
+    bool without_ddx = (ddx.get_ptr() == nullptr);
+    if (!without_ddx) {
+      without_ddx = (ddout->numel() > ddx.get_ptr()->numel());
+    }
+    if (without_ddx) {
       phi::funcs::ElemwiseGradCompute<Context, T, MulGradDX<T>, MulGradDY<T>>(
           dev_ctx,
           ddx_safe,