optimize log (#46349)

paddle/fluid/eager/accumulation/accumulation_node.cc

@@ -31,12 +31,16 @@ static void CopyOrAddTensor(paddle::experimental::Tensor* tensor,
                             const paddle::experimental::Tensor& t,
                             bool is_fake_empty) {
   if (is_fake_empty) {
+    VLOG(3) << "Move Tensor ptr: " << t.impl();
     *tensor = t;
   } else {
     if (!tensor->defined() || !tensor->initialized()) {
       // Simply copy tensor->impl
+      VLOG(3) << "Move Tensor ptr: " << t.impl();
       *tensor = t;
     } else {
+      VLOG(3) << "Add Tensor ptr: " << t.impl()
+              << " with Tensor ptr: " << tensor->impl();
       // Accumulation
       if (LIKELY(t.is_dense_tensor())) {
         if (LIKELY(tensor->is_dense_tensor())) {

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

@@ -1414,13 +1414,13 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
         var_str = f"\n{indent}  std::string input_str = \"\";"
         var_str += f"\n{indent}  std::string output_str = \"\";"
         for name, (ttype, pos) in forward_inputs_position_map.items():
-            var_str += f"\n{indent}  const char* TENSOR_{name.upper()}_TEMPLATE = \"({name}, [%s]), \";"
+            var_str += f"\n{indent}  const char* TENSOR_{name.upper()}_TEMPLATE = \" \\n( {name} , [%s]), \";"
             var_str += f"\n{indent}  std::string input_{name}_str = paddle::string::Sprintf(TENSOR_{name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({name}));"
             var_str += f"\n{indent}  input_str += input_{name}_str; "
 
         before_log_str = BEFORE_LOG_PRINT_TEMPLATE.format(var_str)
         for name, (ttype, pos) in forward_outputs_position_map.items():
-            var_str += f"\n{indent}  const char* TENSOR_{name.upper()}_TEMPLATE = \"({name}, [%s]), \";"
+            var_str += f"\n{indent}  const char* TENSOR_{name.upper()}_TEMPLATE = \" \\n( {name} , [%s]), \";"
             var_str += f"\n{indent}  std::string output_{name}_str = paddle::string::Sprintf(TENSOR_{name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({name}));"
             var_str += f"\n{indent}  output_str += output_{name}_str; "
 
@@ -1930,14 +1930,14 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
         for name, (ttype, fwd_position,
                    grad_api_position) in backward_grad_inputs_map.items():
             new_name = self.TransformToNextGradName(name)
-            var_str += f"\n{indent}  const char* TENSOR_{new_name.upper()}_TEMPLATE = \"({new_name}, [%s]), \";"
+            var_str += f"\n{indent}  const char* TENSOR_{new_name.upper()}_TEMPLATE = \" \\n( {new_name} , [%s]), \";"
             var_str += f"\n{indent}  std::string input_{new_name}_str = paddle::string::Sprintf(TENSOR_{new_name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({new_name}));"
             var_str += f"\n{indent}  input_str += input_{new_name}_str; "
 
         for name, (backward_input_type, is_fwd_input,
                    grad_api_position), in backward_forward_inputs_map.items():
             new_name = self.TransformToNextGradName(name)
-            var_str += f"\n{indent}  const char* TENSOR_{new_name.upper()}_TEMPLATE = \"({new_name}, [%s]), \";"
+            var_str += f"\n{indent}  const char* TENSOR_{new_name.upper()}_TEMPLATE = \" \\n( {new_name} , [%s]), \";"
             var_str += f"\n{indent}  std::string input_{new_name}_str = paddle::string::Sprintf(TENSOR_{new_name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({new_name}));"
             var_str += f"\n{indent}  input_str += input_{new_name}_str; "
 
@@ -1946,7 +1946,7 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
         for name, (ttype, fwd_position,
                    grad_api_position) in backward_grad_outputs_map.items():
             new_name = self.TransformToNextGradName(name)
-            var_str += f"\n{indent}  const char* TENSOR_{new_name.upper()}_TEMPLATE = \"({new_name}, [%s]), \";"
+            var_str += f"\n{indent}  const char* TENSOR_{new_name.upper()}_TEMPLATE = \" \\n ( {new_name} , [%s]), \";"
             var_str += f"\n{indent}  std::string output_{new_name}_str = paddle::string::Sprintf(TENSOR_{new_name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({new_name}));"
             var_str += f"\n{indent}  output_str += output_{new_name}_str; "
 

paddle/fluid/eager/backward.cc

@@ -226,7 +226,6 @@ std::vector<paddle::experimental::Tensor> RunBackward(
   while (!queue.empty()) {
     GradNodeBase* node = queue.front();
     VLOG(3) << "Preparing GradNode:" << node->name() << " addr:" << node;
-    VLOG(4) << EagerUtils::GradNodeStr(*node);
     paddle::platform::RecordEvent node_record_event(
         std::string((*node).name()),
         paddle::platform::TracerEventType::Operator,
@@ -338,7 +337,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
           node_input_buffers_dict[next_node] = std::move(grad_tensor_holder);
         }
 
-        VLOG(3) << "Sum grad inputs for edge slot: " << edge_rank.first
+        VLOG(3) << "Sum or Move grad inputs for edge slot: " << edge_rank.first
                 << ", rank: " << edge_rank.second;
 
         node_input_buffers_dict[next_node]->add(edge_rank.first,

paddle/fluid/eager/general_grad.h

@@ -557,6 +557,9 @@ class GeneralGrad {
           } else {
             copied_next_node = orig_next_node->Copy();
             orig_to_copied_node_map_[orig_next_node.get()] = copied_next_node;
+            VLOG(3) << "Copied Node: " << orig_next_node->name()
+                    << " ptr: " << orig_next_node
+                    << " to ptr: " << copied_next_node;
             copied_grad_nodes_.push_back(copied_next_node);
           }