increase the level of some log (#47990)

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -592,7 +592,7 @@ static bool CheckOpProto(proto::OpProto* op_proto) {
   }
 
   // Only handle matmul_v2 for now
-  VLOG(1) << "------ Analyzing Op ------: " << op_type;
+  VLOG(3) << "------ Analyzing Op ------: " << op_type;
 
   return true;
 }

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

@@ -115,7 +115,7 @@ NOAMP_DYGRAPH_FUNCTION_TEMPLATE = "decltype({}({})) out = {}({});"
 FUNCTION_SET_DEVICE_TEMPLATE = """{}    if (paddle::platform::is_gpu_place(place)) {{
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
       phi::backends::gpu::SetDeviceId(place.device);
-      VLOG(1) <<"CurrentDeviceId: " << phi::backends::gpu::GetCurrentDeviceId() << " from " << (int)place.device;
+      VLOG(4) <<"CurrentDeviceId: " << phi::backends::gpu::GetCurrentDeviceId() << " from " << (int)place.device;
 #else
       PADDLE_THROW(paddle::platform::errors::PreconditionNotMet(
         "PaddlePaddle should compile with GPU if use CUDAPlace."));
@@ -124,7 +124,7 @@ FUNCTION_SET_DEVICE_TEMPLATE = """{}    if (paddle::platform::is_gpu_place(place
     if (paddle::platform::is_custom_place(place)) {{
 #if defined(PADDLE_WITH_CUSTOM_DEVICE)
       phi::DeviceManager::SetDevice(place);
-      VLOG(1) <<"CurrentDeviceId: " << phi::DeviceManager::GetDevice(place.GetDeviceType()) << " from " << (int)place.device;
+      VLOG(4) <<"CurrentDeviceId: " << phi::DeviceManager::GetDevice(place.GetDeviceType()) << " from " << (int)place.device;
 #else
       PADDLE_THROW(paddle::platform::errors::PreconditionNotMet(
         "PaddlePaddle should compile with CUSTOM_DEVICE if use CustomPlace."));

paddle/fluid/eager/to_static/run_program_op_func.h

@@ -32,7 +32,7 @@ static void clear_no_grad_edges(
   for (size_t i = 0; i < params.size(); ++i) {
     auto p_grad_name = paddle::framework::GradVarName(params[i].name());
     if (!block_desc->HasVar(p_grad_name)) {
-      VLOG(1) << "clear edge of " << p_grad_name;
+      VLOG(3) << "clear edge of " << p_grad_name;
       grad_node->MutableOutputMeta()[slot_id][i].GetMutableEdge().Clear();
     }
   }
@@ -48,7 +48,7 @@ static void clear_no_grad_edges_with_partial_block(
     auto p_grad_name = paddle::framework::GradVarName(params[i].name());
     if (!forward_block_desc->HasVar(p_grad_name) &&
         !backward_block_desc->HasVar(p_grad_name)) {
-      VLOG(1) << "clear edge of " << p_grad_name;
+      VLOG(3) << "clear edge of " << p_grad_name;
       grad_node->MutableOutputMeta()[slot_id][i].GetMutableEdge().Clear();
     }
   }

paddle/fluid/framework/attribute_checker.h

@@ -249,7 +249,7 @@ class TypedAttrChecker {
                             "doesn't support phi::DenseTensor type.",
                             attr_name_));
 
-      VLOG(1) << "Found Attribute " << attr_name_ << " with type(Variable).";
+      VLOG(3) << "Found Attribute " << attr_name_ << " with type(Variable).";
       var_info_checker_(it->second);
       return;
     }

paddle/fluid/imperative/tracer.cc

@@ -223,7 +223,7 @@ void Tracer::TraceOpImpl(const std::string& type,
   platform::RecordEvent op_type_record_event(
       type, platform::TracerEventType::Operator, 1);
   platform::ScopedFlushDenormal flush;
-  VLOG(1) << "Trace Op: " << type;
+  VLOG(4) << "Trace Op: " << type;
   if (FLAGS_use_mkldnn) {
     // if both lists are empty all ops are enabled (default for
     // FLAGS_use_mkldnn=1)

paddle/fluid/pybind/eager_functions.cc

@@ -161,7 +161,7 @@ static PyObject* eager_api_run_partial_grad(PyObject* self,
                        only_inputs,
                        allow_unused,
                        no_grad_vars);
-    VLOG(1) << " in eager_api_run_partial_grad, after runing egr::Grad";
+    VLOG(4) << " in eager_api_run_partial_grad, after runing egr::Grad";
   }
   return ToPyObject(result, true /* return_py_none_if_not_initialize */);
   EAGER_CATCH_AND_THROW_RETURN_NULL

paddle/fluid/pybind/eager_math_op_patch.cc

@@ -349,7 +349,7 @@ static PyObject* tensor__rsub__method(TensorObject* self,
       1);
 
   EAGER_TRY
-  VLOG(1) << "Running Eager tensor__rsub__method";
+  VLOG(4) << "Running Eager tensor__rsub__method";
 
   // Set Device ID
   auto place = egr::Controller::Instance().GetExpectedPlace();
@@ -771,7 +771,7 @@ static PyObject* tensor__gt__method(TensorObject* self,
       1);
 
   EAGER_TRY
-  VLOG(1) << "Running Eager tensor__gt__method";
+  VLOG(4) << "Running Eager tensor__gt__method";
 
   // Set Device ID
   auto place = egr::Controller::Instance().GetExpectedPlace();
@@ -857,7 +857,7 @@ static PyObject* tensor__ge__method(TensorObject* self,
       1);
 
   EAGER_TRY
-  VLOG(1) << "Running Eager tensor__ge__method";
+  VLOG(4) << "Running Eager tensor__ge__method";
 
   // Set Device ID
   auto place = egr::Controller::Instance().GetExpectedPlace();
@@ -1134,7 +1134,7 @@ static PyObject* tensor__lt__method(TensorObject* self,
       1);
 
   EAGER_TRY
-  VLOG(1) << "Running Eager tensor__lt__method";
+  VLOG(4) << "Running Eager tensor__lt__method";
 
   // Set Device ID
   auto place = egr::Controller::Instance().GetExpectedPlace();
@@ -1220,7 +1220,7 @@ static PyObject* tensor__le__method(TensorObject* self,
       1);
 
   EAGER_TRY
-  VLOG(1) << "Running Eager tensor__le__method";
+  VLOG(4) << "Running Eager tensor__le__method";
 
   // Set Device ID
   auto place = egr::Controller::Instance().GetExpectedPlace();

paddle/fluid/pybind/eager_method.cc

@@ -800,7 +800,7 @@ static PyObject* tensor_method__get_tensor_from_selected_rows(
 
   auto* dense_tensor =
       static_cast<phi::DenseTensor*>(selected_rows->mutable_value());
-  VLOG(1) << "dense_tensor: " << dense_tensor->IsInitialized();
+  VLOG(4) << "dense_tensor: " << dense_tensor->IsInitialized();
 
   auto t = paddle::experimental::Tensor(
       egr::Controller::Instance().GenerateUniqueName());

paddle/fluid/pybind/eager_utils.cc

@@ -1243,7 +1243,7 @@ paddle::experimental::Scalar CastNumpy2Scalar(PyObject* obj,
                                               ssize_t arg_pos) {
   PyTypeObject* type = obj->ob_type;
   auto type_name = std::string(type->tp_name);
-  VLOG(1) << "type_name: " << type_name;
+  VLOG(4) << "type_name: " << type_name;
   if (type_name == "numpy.ndarray" && PySequence_Check(obj)) {
     PyObject* item = nullptr;
     item = PySequence_GetItem(obj, 0);
@@ -1296,7 +1296,7 @@ paddle::experimental::Scalar CastPyArg2Scalar(PyObject* obj,
   // obj could be: int, float, bool, paddle.Tensor
   PyTypeObject* type = obj->ob_type;
   auto type_name = std::string(type->tp_name);
-  VLOG(1) << "type_name: " << type_name;
+  VLOG(4) << "type_name: " << type_name;
   if (PyBool_Check(obj)) {
     bool value = CastPyArg2Boolean(obj, op_type, arg_pos);
     return paddle::experimental::Scalar(value);
@@ -1348,7 +1348,7 @@ std::vector<phi::Scalar> CastPyArg2ScalarArray(PyObject* obj,
 
   PyTypeObject* type = obj->ob_type;
   auto type_name = std::string(type->tp_name);
-  VLOG(1) << "type_name: " << type_name;
+  VLOG(4) << "type_name: " << type_name;
   if (PyList_Check(obj)) {
     Py_ssize_t len = PyList_Size(obj);
     PyObject* item = nullptr;

paddle/phi/infermeta/binary.cc

@@ -195,10 +195,10 @@ void BincountInferMeta(const MetaTensor& x,
                                    "But the dimension of Input(X) is [%d]",
                                    input_dim.size()));
 
-  VLOG(1) << "####### CHECK weights";
+  VLOG(4) << "####### CHECK weights";
   if (weights) {
     auto weights_dim = weights.dims();
-    VLOG(1) << "##### weights_dim " << weights_dim;
+    VLOG(4) << "##### weights_dim " << weights_dim;
     PADDLE_ENFORCE_EQ(weights_dim.size(),
                       1,
                       phi::errors::InvalidArgument(

paddle/phi/infermeta/unary.cc

@@ -3732,7 +3732,7 @@ void StridedSliceRawInferMeta(const MetaTensor& x,
     }
     out_dims = phi::make_ddim(new_out_shape);
   }
-  VLOG(1) << "out_dims: " << out_dims;
+  VLOG(4) << "out_dims: " << out_dims;
   out->set_dims(out_dims);
   out->share_lod(x);
   out->set_dtype(x.dtype());