make python Parameter inherit from Tensor

mindspore/_extends/parse/__init__.py

@@ -21,12 +21,12 @@ from .parser import (Parser, create_obj_instance, generate_scope,
                      get_class_member_namespace_symbol, create_slice_obj,
                      get_dataclass_attributes, get_dataclass_methods, get_obj_id,
                      get_module_namespace, get_obj_type, get_object_key,
-                     get_default_input, get_parse_method_of_class, get_scope_name,
+                     get_parse_method_of_class, get_scope_name,
                      is_class_member, parse_cb, resolve_symbol)
 from .serialize import *
 
 __all__ = ['parse_cb', 'get_parse_method_of_class', 'get_bprop_method_of_class', 'resolve_symbol',
-           'get_object_key', 'get_default_input', 'get_class_instance_type', 'is_class_member',
+           'get_object_key', 'get_class_instance_type', 'is_class_member',
            'get_obj_type', 'get_obj_id', 'create_obj_instance', 'get_module_namespace',
            'get_class_member_namespace_symbol', 'get_obj_id', 'Parser', 'get_dataclass_attributes',
            'get_dataclass_methods', 'dump_obj', 'load_obj', 'get_dataclass_methods', 'get_scope_name',

mindspore/_extends/parse/parser.py

@@ -206,16 +206,6 @@ def get_object_key(obj):
     return obj_id, obj_key
 
 
-def get_default_input(obj):
-    if hasattr(obj, '__parameter__'):
-        return obj.default_input
-    if isinstance(obj, tuple):
-        convert = lambda x: x.default_input if hasattr(x, '__parameter__') else x
-        args = tuple(convert(x) for x in obj)
-        return args
-    return obj
-
-
 def is_class_member(node):
     """Check the attr is class member variable."""
     type_ = node.__class__.__name__

mindspore/ccsrc/backend/session/ascend_inference_session.cc

@@ -76,7 +76,7 @@ GraphId AscendInferenceSession::CompileGraph(NotNull<FuncGraphPtr> func_graph) {
     if (AnfAlgo::IsParameterWeight(pk_node)) {
       const auto &param_value = pk_node->default_param();
       MS_EXCEPTION_IF_NULL(param_value);
-      auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_value->value());
+      auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_value);
       MS_EXCEPTION_IF_NULL(tensor);
       if (!device_address->SyncHostToDevice(trans::GetRuntimePaddingShape(pk_node, 0),
                                             LongToSize(tensor->data().nbytes()), tensor->data_type(),

mindspore/ccsrc/backend/session/session_basic.cc

@@ -42,12 +42,12 @@
 
 namespace mindspore {
 namespace session {
-static std::shared_ptr<std::map<ParamValuePtr, ParameterPtr>> python_paras;
+static std::shared_ptr<std::map<ValuePtr, ParameterPtr>> python_paras;
 void ClearPythonParasMap() { python_paras = nullptr; }
 namespace {
 const int kSummaryGetItem = 2;
 
-ParamValuePtr GetParamDefaultValue(const AnfNodePtr &node) {
+ValuePtr GetParamDefaultValue(const AnfNodePtr &node) {
   if (node == nullptr) {
     return nullptr;
   }
@@ -209,8 +209,7 @@ ParameterPtr ConstructRunOpParameter(const std::shared_ptr<KernelGraph> &graph,
   auto param = graph->NewParameter();
   MS_EXCEPTION_IF_NULL(param);
   if (tensor_mask == kParameterWeightTensorMask) {
-    auto param_value_new = std::make_shared<ParamValue>();
-    param->set_default_param(param_value_new);
+    param->set_default_param(input_tensor);
   }
   // set the kernel info of parameter
   auto kernel_build_info_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
@@ -390,7 +389,7 @@ ParameterPtr SessionBasic::CreateNewParameterFromParameter(const AnfNodePtr &anf
   ParameterPtr new_parameter = nullptr;
   // if parameter's python parameter has been exist a backend parameter, reuse the exist parameter
   if (python_paras == nullptr) {
-    python_paras = std::make_shared<std::map<ParamValuePtr, ParameterPtr>>();
+    python_paras = std::make_shared<std::map<ValuePtr, ParameterPtr>>();
   }
   auto iter = python_paras->find(param_value);
   if (iter != python_paras->end()) {
@@ -667,7 +666,7 @@ ParameterPtr SessionBasic::CreateNewParameter(const AnfNodePtr &anf, KernelGraph
   auto param_value = GetParamDefaultValue(anf);
   ParameterPtr new_parameter = nullptr;
   if (python_paras == nullptr) {
-    python_paras = std::make_shared<std::map<ParamValuePtr, ParameterPtr>>();
+    python_paras = std::make_shared<std::map<ValuePtr, ParameterPtr>>();
   }
   auto iter = python_paras->find(param_value);
   if (iter != python_paras->end()) {

mindspore/ccsrc/debug/anf_ir_utils.cc

@@ -1670,7 +1670,7 @@ class IrParser {
 
         // load parameter default value from serialized file
         py::object default_obj = LoadObject(lexer_.GetTokenText());
-        auto param_value_new = py::cast<ParamValuePtr>(default_obj);
+        auto param_value_new = py::cast<tensor::TensorPtr>(default_obj);
         param->set_default_param(param_value_new);
 
         tok = lexer_.GetNextToken();

mindspore/ccsrc/debug/draw.cc

@@ -318,8 +318,9 @@ void BaseDigraph::FuncGraphParameters(const FuncGraphPtr &key) {
     buffer_ << parameter->ToString();
     auto param = parameter->cast<ParameterPtr>();
     if (param->has_default()) {
-      auto tensor = param->default_param()->value();
-      if (tensor) {
+      auto tensor_v = param->default_param();
+      if (tensor_v && tensor_v->isa<tensor::Tensor>()) {
+        auto tensor = tensor_v->cast<tensor::TensorPtr>();
         auto &shape = tensor->shape();
         std::ostringstream shape_str;
         std::copy(shape.begin(), shape.end(), std::ostream_iterator<int>(shape_str, ","));

mindspore/ccsrc/frontend/parallel/graph_util/node_info.cc

@@ -38,7 +38,12 @@ bool ParameterRequireGrad(const AnfNodePtr &node_ptr) {
   if (!para_ptr->has_default()) {
     return false;
   }
-  return para_ptr->default_param()->requires_grad();
+  auto obj = py::cast(para_ptr->default_param());
+  auto param_value = py::cast<ParamValuePtr>(obj.attr("_value"));
+  if (param_value == nullptr) {
+    return false;
+  }
+  return param_value->requires_grad();
 }
 }  // namespace parallel
 }  // namespace mindspore

mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc

@@ -41,6 +41,7 @@
 #include "frontend/parallel/context.h"
 #include "frontend/parallel/ops_info/tmp_identity_info.h"
 #include "frontend/parallel/ops_info/reshape_info.h"
+#include "frontend/parallel/graph_util/node_info.h"
 #include "frontend/parallel/step_parallel.h"
 #include "frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h"
 #include "pipeline/jit/parse/python_adapter.h"
@@ -122,12 +123,7 @@ std::vector<bool> ExtractInputParameterByNode(const CNodePtr &node) {
 
     if (input->isa<Parameter>()) {
       auto input_parameter = input->cast<ParameterPtr>();
-      if (input_parameter->has_default()) {
-        bool requires_grad = input_parameter->default_param()->requires_grad();
-        is_parameter.push_back(requires_grad);
-      } else {
-        is_parameter.push_back(false);
-      }
+      is_parameter.push_back(ParameterRequireGrad(input_parameter));
     } else if (input->isa<CNode>() || IsValueNode<tensor::Tensor>(input) || IsValueNode<RefKey>(input)) {
       is_parameter.push_back(false);
     }
@@ -798,12 +794,7 @@ void AugmentCostGraph(const std::vector<AnfNodePtr> &all_nodes) {
       std::vector<bool> is_parameter;
       auto casted_target_parameter = target_parameter->cast<ParameterPtr>();
       MS_EXCEPTION_IF_NULL(casted_target_parameter);
-      if (casted_target_parameter->has_default()) {
-        bool requires_grad = casted_target_parameter->default_param()->requires_grad();
-        is_parameter.push_back(requires_grad);
-      } else {
-        is_parameter.push_back(false);
-      }
+      is_parameter.push_back(ParameterRequireGrad(casted_target_parameter));
       if (tmp_identity_ptr->set_is_parameter(is_parameter) != SUCCESS) {
         MS_LOG(EXCEPTION) << "Setting parameter for TmpIdentityInfo failed";
       }

mindspore/ccsrc/frontend/parallel/step_parallel.cc

@@ -1295,11 +1295,8 @@ void CoverSliceShape(const FuncGraphPtr &root) {
   g_RefMap.clear();
 }
 
-bool ParameterIsCloned(const FuncGraphPtr &root, const AnfNodePtr &parameter_node) {
-  MS_EXCEPTION_IF_NULL(root);
+bool ParameterIsCloned(const AnfNodePtr &parameter_node) {
   MS_EXCEPTION_IF_NULL(parameter_node);
-  FuncGraphManagerPtr manager = root->manager();
-  MS_EXCEPTION_IF_NULL(manager);
   auto cloned_parameter = parameter_node->cast<ParameterPtr>();
   MS_EXCEPTION_IF_NULL(cloned_parameter);
 
@@ -1307,8 +1304,12 @@ bool ParameterIsCloned(const FuncGraphPtr &root, const AnfNodePtr &parameter_nod
   if (!cloned_parameter->has_default()) {
     return false;
   }
-
-  bool cloned = cloned_parameter->default_param()->cloned();
+  auto obj = py::cast(cloned_parameter->default_param());
+  auto param_value = py::cast<ParamValuePtr>(obj.attr("_value"));
+  if (param_value == nullptr) {
+    return false;
+  }
+  bool cloned = param_value->cloned();
   if (!cloned) {
     return false;
   }
@@ -1324,12 +1325,16 @@ void SetClonedTensorShapeForOptimizer(const FuncGraphPtr &root) {
     auto cloned_parameter = cloned_parameter_node->cast<ParameterPtr>();
     MS_EXCEPTION_IF_NULL(cloned_parameter);
 
-    if (!ParameterIsCloned(root, cloned_parameter_node)) {
+    if (!ParameterIsCloned(cloned_parameter_node)) {
+      continue;
+    }
+    auto obj = py::cast(cloned_parameter->default_param());
+    auto param_value = py::cast<ParamValuePtr>(obj.attr("_value"));
+    if (param_value == nullptr) {
       continue;
     }
-
     // get the cloned index
-    int32_t cloned_index = cloned_parameter->default_param()->cloned_index();
+    int32_t cloned_index = param_value->cloned_index();
 
     // find the be cloned parameter
     bool found_be_cloned_parameter = false;
@@ -1344,12 +1349,18 @@ void SetClonedTensorShapeForOptimizer(const FuncGraphPtr &root) {
       }
 
       const auto &param_value_cloned = be_cloned_parameter->default_param();
-      if (!param_value_cloned->be_cloned()) {
+
+      auto obj_in = py::cast(param_value_cloned);
+      auto param_value_in = py::cast<ParamValuePtr>(obj_in.attr("_value"));
+      if (param_value_in == nullptr) {
+        continue;
+      }
+      if (!param_value_in->be_cloned()) {
         continue;
       }
 
       // get the be cloned index
-      auto &be_cloned_index = param_value_cloned->be_cloned_index();
+      auto &be_cloned_index = param_value_in->be_cloned_index();
       if (std::find(be_cloned_index.begin(), be_cloned_index.end(), cloned_index) != be_cloned_index.end()) {
         found_be_cloned_parameter = true;
         cloned_from_parameter = be_cloned_parameter;
@@ -2103,10 +2114,7 @@ std::string NodeParameterName(const CNodePtr &node) {
     if (input->isa<Parameter>()) {
       auto input_parameter = input->cast<ParameterPtr>();
       if (input_parameter->has_default()) {
-        const auto &param_value = input_parameter->default_param();
-        if (param_value->requires_grad()) {
-          return param_value->name();
-        }
+        input_parameter->name();
       }
     }
   }

mindspore/ccsrc/pipeline/jit/action.cc

@@ -233,8 +233,7 @@ bool AbstractSpecializeAction(const ResourcePtr &res) {
   for (const auto &param : func_graph->parameters()) {
     auto param_node = std::static_pointer_cast<Parameter>(param);
     if (param_node->has_default()) {
-      const auto &param_value = param_node->default_param();
-      ValuePtr value = param_value->value();
+      ValuePtr value = param_node->default_param();
       constexpr bool broaden = true;
       AbstractBasePtr ptr = abstract::FromValue(value, broaden);
 

mindspore/ccsrc/pipeline/jit/init.cc

@@ -68,6 +68,8 @@ PYBIND11_MODULE(_c_expression, m) {
          py::arg("type") = py::str("onnx_ir"), "Get graph proto string by specifying ir type.")
     .def("compile", &ExecutorPy::Compile, py::arg("obj"), py::arg("args"), py::arg("phase") = py::str(""),
          py::arg("use_vm") = py::bool_(false), "Compile obj by executor.")
+    .def("updata_param_node_default_input", &ExecutorPy::UpdataParamNodeDefaultInput, py::arg("phase"),
+         py::arg("params"), "Fetch the inputs of Conv or Matmul for quant export.")
     .def("get_parameter_layout", &ExecutorPy::GetParameterLayout, py::arg("phase") = py::str("train"),
          "Get Parameter Tensor Layout Dictionary.")
     .def("get_strategy", &ExecutorPy::GetCNodeStrategy, py::arg("phase") = py::str("train"),

mindspore/ccsrc/pipeline/jit/parse/data_converter.cc

@@ -205,41 +205,6 @@ bool ConvertMetaFuncGraph(const py::object &obj, ValuePtr *const data, bool use_
   return true;
 }
 
-bool ConvertDataType(const py::object &obj, ValuePtr *const data) {
-  MS_LOG(DEBUG) << "Converting type object";
-  auto typeptr = obj.cast<TypePtr>();
-  if (typeptr == nullptr) {
-    MS_LOG(ERROR) << "Resolve TypePtr error, get ptr is null";
-    return false;
-  }
-  *data = typeptr;
-  return true;
-}
-
-bool ConvertMetaTensor(const py::object &obj, ValuePtr *const data) {
-  MS_LOG(DEBUG) << "Converting MetaTensor object.";
-
-  auto m_tensor = obj.cast<MetaTensorPtr>();
-  if (m_tensor == nullptr) {
-    MS_LOG(ERROR) << "Resolve MetaTensor error, get ptr is null.";
-    return false;
-  }
-  *data = m_tensor;
-  return true;
-}
-
-bool ConvertTensor(const py::object &obj, ValuePtr *const data) {
-  MS_LOG(DEBUG) << "Converting tensor object";
-
-  auto m_tensor = obj.cast<TensorPtr>();
-  if (m_tensor == nullptr) {
-    MS_LOG(ERROR) << "Resolve Tensor error, get ptr is null";
-    return false;
-  }
-  *data = m_tensor;
-  return true;
-}
-
 bool ConvertSlice(const py::object &obj, ValuePtr *const data) {
   MS_LOG(DEBUG) << "Converting slice object";
 
@@ -364,11 +329,11 @@ bool ConvertData(const py::object &obj, ValuePtr *const data, bool use_signature
   } else if (py::isinstance<MetaFuncGraph>(obj)) {
     ret = ConvertMetaFuncGraph(obj, &converted, use_signature);
   } else if (py::isinstance<Type>(obj)) {
-    ret = ConvertDataType(obj, &converted);
+    converted = obj.cast<TypePtr>();
   } else if (py::isinstance<Tensor>(obj)) {
-    ret = ConvertTensor(obj, &converted);
+    converted = obj.cast<TensorPtr>();
   } else if (py::isinstance<MetaTensor>(obj)) {
-    ret = ConvertMetaTensor(obj, &converted);
+    converted = obj.cast<MetaTensorPtr>();
   } else if (py::isinstance<EnvInstance>(obj)) {
     std::shared_ptr<EnvInstance> env = obj.cast<std::shared_ptr<EnvInstance>>();
     converted = env;

mindspore/ccsrc/pipeline/jit/parse/parse_base.h

@@ -85,7 +85,6 @@ const char PYTHON_PARSE_ANALYZE_SUPER[] = "analyze_super";
 
 const char PYTHON_PARSE_CLASS_SLICE[] = "create_slice_obj";
 const char PYTHON_PARSE_CLASS_ELLIPSIS[] = "create_ellipsis_obj";
-const char PYTHON_MOD_GET_DEFAULT_INPUT[] = "get_default_input";
 
 // define the common name
 const char NAMED_PRIMITIVE_LEN[] = "len";

mindspore/ccsrc/pipeline/jit/parse/resolve.cc

@@ -103,10 +103,9 @@ AnfNodePtr ResolveParameterObj(const FuncGraphPtr &func_graph, const py::object
   }
   if (para_node == nullptr) {
     auto node = top_graph->AddWeightParameter(param_name);
-    auto param_value = py::cast<ParamValuePtr>(python_adapter::GetPyObjAttr(obj, "_value"));
-    node->set_default_param(param_value);
+    auto value = py::cast<tensor::MetaTensorPtr>(obj);
+    node->set_default_param(value);
     // set_abstract for parameter
-    ValuePtr value = param_value->value();
     constexpr bool broaden = true;
     node->set_abstract(abstract::FromValue(value, broaden));
     para_node = node;

mindspore/ccsrc/pipeline/jit/pipeline.cc

@@ -719,7 +719,11 @@ void ProcessVmArgInner(const py::tuple &args, const ResourcePtr &res, VectorRef
       if (!param_ptr->has_default()) {
         MS_LOG(EXCEPTION) << "Parameter[" << i << "] has no default param";
       }
-      arg_list->push_back(param_ptr->default_param()->value());
+      if (!param_ptr->default_param()->isa<Tensor>()) {
+        MS_LOG(EXCEPTION) << "Parameter[" << param_ptr->ToString()
+                          << "] is not initialized, need to call `.init_data()`";
+      }
+      arg_list->push_back(param_ptr->default_param());
     }
   }
 }
@@ -782,6 +786,24 @@ FuncGraphPtr ExecutorPy::BuildGraph(const py::dict &init_params, const std::stri
 #endif
 }
 
+void ExecutorPy::UpdataParamNodeDefaultInput(const std::string &phase,
+                                             const std::unordered_map<std::string, tensor::TensorPtr> &params_value) {
+  FuncGraphPtr func_graph = info_[phase]->resource->func_graph();
+  MS_EXCEPTION_IF_NULL(func_graph);
+  MS_LOG(DEBUG) << "UpdataParamNodeDefaultInput for func graph(" << func_graph->ToString() << ") phase(" << phase
+                << ")!";
+  auto &params = func_graph->parameters();
+  for (const auto &param : params) {
+    MS_EXCEPTION_IF_NULL(param);
+    auto param_cast = param->cast<ParameterPtr>();
+    MS_EXCEPTION_IF_NULL(param_cast);
+    auto iter = params_value.find(param_cast->name());
+    if (iter != params_value.end()) {
+      param_cast->set_default_param(iter->second);
+    }
+  }
+}
+
 void ExecutorPy::RunInitGraph(const py::dict &init_params, const std::string &phase) {
 #if ENABLE_GE
   RunGEInitGraph(init_params, phase);

mindspore/ccsrc/pipeline/jit/pipeline.h

@@ -88,6 +88,8 @@ class ExecutorPy : public std::enable_shared_from_this<ExecutorPy> {
 
   FuncGraphPtr BuildGraph(const py::dict &init_params, const std::string &phase,
                           const py::object &broadcast_params = {});
+  void UpdataParamNodeDefaultInput(const std::string &phase,
+                                   const std::unordered_map<std::string, tensor::TensorPtr> &params);
   void RunInitGraph(const py::dict &init_params, const std::string &phase);
   py::dict GetParameterLayout(const std::string &phase);
   py::dict GetCNodeStrategy(const std::string &phase);

mindspore/ccsrc/pipeline/pynative/pynative_execute.cc

@@ -146,12 +146,6 @@ static std::string GetOpId(const OpExecInfoPtr &op_exec_info) {
   return id;
 }
 
-py::object GetTupleObj(const py::object &obj) {
-  py::module mod = parse::python_adapter::GetPyModule(parse::PYTHON_MOD_PARSE_MODULE);
-  py::object obj_tuple = parse::python_adapter::CallPyModFn(mod, parse::PYTHON_MOD_GET_DEFAULT_INPUT, obj);
-  return obj_tuple;
-}
-
 std::map<SignatureEnumDType, std::vector<size_t>> GetTypeIndex(const std::vector<SignatureEnumDType> &dtypes) {
   std::map<SignatureEnumDType, std::vector<size_t>> type_indexes;
   for (size_t i = 0; i < dtypes.size(); ++i) {
@@ -242,7 +236,7 @@ py::tuple ConvertInputs(const PrimitivePyPtr &prim, const py::list &args, py::tu
   py::tuple input_mask(args.size());
   for (size_t i = 0; i < args.size(); ++i) {
     input_mask[i] = py::hasattr(args[i], "__parameter__");
-    py_args[i] = GetTupleObj(args[i]);
+    py_args[i] = args[i];
   }
   auto signature = prim->signatures();
   std::vector<SignatureEnumDType> dtypes;
@@ -366,9 +360,6 @@ py::object RunOpInVM(const OpExecInfoPtr &op_exec_info, PynativeStatusCode *stat
     py::tuple result(op_inputs.size());
     for (size_t i = 0; i < op_inputs.size(); i++) {
       py::object input = op_inputs[i];
-      if (py::hasattr(input, "__parameter__")) {
-        input = py::getattr(input, "data");
-      }
       auto tensor = py::cast<tensor::TensorPtr>(input);
       auto new_tensor = std::make_shared<tensor::Tensor>(tensor->data_type(), tensor->shape(), tensor->data_ptr());
       new_tensor->set_device_address(tensor->device_address());
@@ -878,8 +869,7 @@ AnfNodePtr PynativeExecutor::GetInput(const py::object &obj, const py::object &o
     if (graph_info_map_[df_builder_].param_map.count(obj_id) == 0) {
       auto free_param = df_builder_->add_parameter();
       free_param->set_name(param_name);
-      auto free_param_new = py::cast<ParamValuePtr>(obj.attr("_value"));
-      free_param->set_default_param(free_param_new);
+      free_param->set_default_param(py::cast<tensor::TensorPtr>(obj));
       free_param->debug_info()->set_name(param_name);
       MS_LOG(DEBUG) << "Top graph set free parameter " << obj_id;
       graph_info_map_[df_builder_].param_map[obj_id] = free_param;
@@ -1074,8 +1064,7 @@ abstract::AbstractBasePtrList PynativeExecutor::GetArgsSpec(const py::args &args
   for (const auto &param : df_builder_->parameters()) {
     auto param_node = std::static_pointer_cast<Parameter>(param);
     if (param_node->has_default()) {
-      const auto &param_value = param_node->default_param();
-      ValuePtr value = param_value->value();
+      ValuePtr value = param_node->default_param();
       AbstractBasePtr ptr = abstract::FromValue(value, true);
       if (ptr == nullptr) {
         MS_LOG(EXCEPTION) << "Args convert error";

mindspore/ccsrc/transform/onnx/ir_exporter.cc

@@ -187,7 +187,7 @@ void IrExportBuilder::BuildParameters(const FuncGraphPtr &func_graph, onnx::Grap
     onnx::TensorProto *initializer_proto = graph_proto->add_initializer();
     initializer_proto->set_name(param_name);
     SetParamToTensorProto(param, initializer_proto);
-    auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param->default_param()->value());
+    auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param->default_param());
     if (tensor) {
       initializer_proto->set_raw_data(tensor->data_c(), tensor->data().nbytes());
     }

mindspore/ccsrc/transform/onnx/onnx_exporter.cc

@@ -449,7 +449,7 @@ void OnnxExporter::ExportParameters(const FuncGraphPtr &func_graph, onnx::GraphP
     initializer_proto->set_name(param_ptr->ToString());
     SetTensorProtoInfo(param_ptr, initializer_proto);
     // set value for initializer
-    auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_ptr->default_param()->value());
+    auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_ptr->default_param());
     if (tensor) {
       initializer_proto->set_raw_data(tensor->data_c(), tensor->data().nbytes());
     }

mindspore/ccsrc/utils/callbacks_ge.cc

@@ -52,7 +52,7 @@ bool GetParameterShape(const FuncGraphPtr &graph, const std::string &param_name,
     if (param_node->name() == param_name) {
       TensorPtr tensor;
       if (param_node->has_default()) {
-        tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_node->default_param()->value());
+        tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_node->default_param());
       }
       if (tensor == nullptr) {
         shape->push_back(ONE_SHAPE);

mindspore/ccsrc/utils/convert_utils.cc

@@ -448,7 +448,7 @@ bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr &output, const py::tuple
       if (!param->has_default()) {
         MS_LOG(EXCEPTION) << "Can not determine value of Parameter " << index << " (" << param->name() << ")";
       }
-      auto tensor = param->default_param()->value();
+      auto tensor = param->default_param();
       *ret_val = py::cast(tensor);
     }
     return true;

mindspore/ccsrc/utils/load_onnx/anf_model_parser.cc

@@ -124,10 +124,7 @@ bool MSANFModelParser::BuildParameterForFuncGraph(const ParameterPtr &node, cons
       MS_LOG(EXCEPTION) << "memcpy_s error, errorno" << ret;
     }
 
-    auto param_value = std::make_shared<ParamValue>();
-    MS_EXCEPTION_IF_NULL(param_value);
-    param_value->set_value(tensor_info);
-    node->set_default_param(param_value);
+    node->set_default_param(tensor_info);
   }
   anfnode_build_map_[value_proto.name()] = node;
   return true;

mindspore/ccsrc/utils/param_value_py.cc

@@ -24,22 +24,19 @@ REGISTER_PYBIND_DEFINE(ParamValue, ([](const py::module *m) {
                          (void)py::class_<ParamValue, ParamValuePtr>(*m, "ParamValue")
                            .def(py::init())
                            .def("clone", &ParamValue::Clone)
-                           .def_property("data", &ParamValue::value, &ParamValue::set_value)
                            .def_property("name", &ParamValue::name, &ParamValue::set_name)
                            .def_property("requires_grad", &ParamValue::requires_grad, &ParamValue::set_requires_grad)
                            .def_property("layerwise_parallel", &ParamValue::layerwise_parallel,
                                          &ParamValue::set_layerwise_parallel)
                            .def(py::pickle(
                              [](const ParamValue &p) {  // __getstate__
-                               return py::make_tuple(py::cast(p.value()), p.name(), p.requires_grad(),
-                                                     p.layerwise_parallel());
+                               return py::make_tuple(p.name(), p.requires_grad(), p.layerwise_parallel());
                              },
                              [](const py::tuple &t) {  // __setstate__
                                if (t.size() != 6) {
                                  std::runtime_error("Invalid state for ParamValue!");
                                }
                                ParamValuePtr p = std::make_shared<ParamValue>();
-                               p->set_value(t[0].cast<tensor::TensorPtr>());
                                p->set_name(t[1].cast<std::string>());
                                p->set_requires_grad(t[2].cast<bool>());
                                p->set_layerwise_parallel(t[3].cast<bool>());

mindspore/ccsrc/utils/tensor_py.cc

@@ -372,7 +372,7 @@ REGISTER_PYBIND_DEFINE(Tensor, ([](const py::module *m) {
                            .def(py::pickle(
                              [](const Tensor &t) {  // __getstate__
                                /* Return a tuple that fully encodes the state of the object */
-                               return py::make_tuple(TensorPy::AsNumpy(t));
+                               return py::make_tuple(TensorPy::SyncAsNumpy(t));
                              },
                              [](const py::tuple &t) {  // __setstate__
                                if (t.size() != 1) {

mindspore/common/api.py

@@ -255,7 +255,6 @@ def ms_function(fn=None, obj=None, input_signature=None):
             process_obj = obj
             if args and not isinstance(args[0], MsTensor) and hasattr(args[0], func.__name__):
                 process_obj = args[0]
-            args = (x.default_input if hasattr(x, 'default_input') else x for x in args)
             return _MindSporeFunction(func, input_signature, process_obj)(*args)
 
         return staging_specialize
@@ -354,28 +353,8 @@ class _Executor:
             raise RuntimeError("Failure to init and dataset subgraph!")
         return True
 
-    def _build_data_graph(self, obj, params, phase):
-        if params is None:
-            self._executor.build_data_graph(obj.parameters_dict(), phase, obj.parameters_broadcast_dict())
-        elif isinstance(params, OrderedDict):
-            self._executor.build_data_graph(params, phase)
-        else:
-            raise TypeError('Parameters need OrderedDict type, but got {}'.
-                            format(type(params)))
-
-    def _params_init_data(self, obj, params, auto_parallel_mode=False):
-        """Init parameters' data."""
-        if params is not None:
-            for key, param in params.items():
-                if not auto_parallel_mode:
-                    param.init_data()
-                elif key not in obj.parameter_layout_dict:
-                    logger.debug("Layout dict does not contain the key %s.", key)
-                    param.init_data(set_sliced=True)
-                else:
-                    layout = obj.parameter_layout_dict[key]
-                    param.init_data(layout, set_sliced=True)
-        obj.init_parameters_data(auto_parallel_mode=auto_parallel_mode)
+    def _build_data_graph(self, obj, phase):
+        self._executor.build_data_graph(obj.parameters_dict(), phase, obj.parameters_broadcast_dict())
 
     def _set_dataset_mode(self, args_list):
         """set dataset mode."""
@@ -386,7 +365,7 @@ class _Executor:
         else:
             _set_dataset_mode_config('normal')
 
-    def compile(self, obj, *args, phase='predict', params=None, do_convert=True, auto_parallel_mode=False):
+    def compile(self, obj, *args, phase='predict', do_convert=True, auto_parallel_mode=False):
         """
         Compiles graph.
 
@@ -394,7 +373,6 @@ class _Executor:
             obj (Function/Cell): The function or cell instance need compile.
             args (tuple): Function or cell input arguments.
             phase (str): The name of compile phase. Default: 'predict'.
-            params (OrderedDict): The parameters dictionary used for init data graph. Default: None.
             do_convert (bool): When set to True, convert ME graph to GE graph after compiling graph.
             auto_parallel_mode: When set to True, use auto parallel mode to compile graph.
 
@@ -435,10 +413,8 @@ class _Executor:
 
         if auto_parallel_mode:
             obj.parameter_layout_dict = self._executor.get_parameter_layout(phase)
-        self._params_init_data(obj, params, auto_parallel_mode)
-        if not enable_debug_runtime or enable_ge:
-            if auto_parallel_mode:
-                obj.load_parameter_slice(params)
+        replace = obj.init_parameters_data(auto_parallel_mode=auto_parallel_mode)
+        self._updata_param_node_default_input(phase, replace)
 
         # set parallel inputs in sink mode
         if auto_parallel_mode and (args and isinstance(args[0], Tensor) and args[0].virtual_flag):
@@ -446,16 +422,20 @@ class _Executor:
 
         # the following GE init process is not needed when use vm or ms backend
         if enable_ge:
-            self._build_data_graph(obj, params, phase)
+            self._build_data_graph(obj, phase)
 
             if "export" not in phase:
                 init_phase = "init_subgraph" + "." + str(obj.create_time)
                 _exec_init_graph(obj, init_phase)
         elif not enable_ge and "export" in phase:
-            self._build_data_graph(obj, params, phase)
+            self._build_data_graph(obj, phase)
 
         return phase, True
 
+    def _updata_param_node_default_input(self, phase, replace):
+        new_param = {x.name: replace[x] for x in replace if id(x) != id(replace[x])}
+        return self._executor.updata_param_node_default_input(phase, new_param)
+
     def _get_strategy(self, obj):
         real_phase = self.phase_prefix + obj.phase + '.' + str(obj.create_time)
         return self._executor.get_strategy(real_phase)

mindspore/common/parameter.py

@@ -14,7 +14,6 @@
 # ============================================================================
 
 """Parameter for cell."""
-import numbers
 from copy import copy
 from mindspore import context
 from .._c_expression import ParamValue
@@ -37,10 +36,17 @@ def _check_type(x):
     return True
 
 
-class Parameter:
+class Parameter(MetaTensor):
     """
     Parameter types of cell models.
 
+    After initialized `Parameter` is a subtype of `Tensor`.
+
+    In graph mode, if init `Parameter` by  a `Initializer`, the type of Parameter will be a `MetaTensor`
+    not a `Tensor`. `MetaTensor` only save the shape type info of a tensor with no memory usage. The shape
+    can be change while compile for auto-parallel. Call `init_data` will return a Tensor Parameter with
+    initialized data.
+
     Note:
         Each parameter of Cell is represented by Parameter class.
 
@@ -52,23 +58,85 @@ class Parameter:
         layerwise_parallel (bool): A kind of model parallel mode. When layerwise_parallel is true in paralle mode,
             broadcast and gradients communication would not be applied on parameters. Default: False.
     """
+    __base_type__ = {}
+
+    def __new__(cls, default_input, name, *args, **kwargs):
+        input_class, *class_init_args = Parameter._get_parameter_new_args(default_input)
+        new_type = Parameter._get_base_class(input_class)
+        obj = input_class.__new__(new_type)
+        input_class.__init__(obj, *class_init_args)
+        # it's better to make the Initializer a kind of metatensor.
+        obj.init_mode = None
+        if isinstance(default_input, Initializer):
+            obj.init_mode = default_input
+        return obj
+
+    def __reduce_ex__(self, _):
+        data = self
+        if self.init_mode is not None:
+            data = self.init_mode
+        else:
+            # cast to break deep infinit loop while deepcopy
+            data = Tensor(self)
+        return (
+            Parameter, (data, self.name, self.requires_grad, self.layerwise_parallel))
+
     def __init__(self, default_input, name, requires_grad=True, layerwise_parallel=False):
         self._value = ParamValue()
-        self.set_parameter_data(default_input)
         self.name = name
         self.requires_grad = requires_grad
         self.layerwise_parallel = layerwise_parallel
+        # this flag for tensor copy data.
+        self.init_flag = False
+        # this flag is for ge variable copy data.
         self._is_init = False
+        self._inited_param = None
         self._sliced = False
         self.is_param_ps = False
         self._cast_type = None
         self.init_in_server = False
-        if context.get_context("mode") == context.PYNATIVE_MODE:
-            self.init_data()
+
+    @staticmethod
+    def _get_base_class(input_class):
+        input_class_name = f'Parameter{input_class.__name__}'
+        if input_class_name in Parameter.__base_type__:
+            new_type = Parameter.__base_type__[input_class_name]
+        else:
+            new_type = type(input_class_name, (Parameter, input_class), {})
+            Parameter.__base_type__[input_class_name] = new_type
+        return new_type
+
+    @staticmethod
+    def _get_parameter_new_args(data):
+        """Set `default_input` of current `Parameter`."""
+        if isinstance(data, bool):
+            raise ValueError('Parameter data can not be `bool`')
+        if isinstance(data, Initializer):
+            if context.get_context("mode") == context.PYNATIVE_MODE:
+                # always init data while in pynative mode.
+                data = data.to_tensor()
+            else:
+                return (MetaTensor, data.dtype, data.shape)
+        if isinstance(data, Tensor):
+            # make a copy of Tensor to init the parameter
+            return (Tensor, data.asnumpy(),)
+        if isinstance(data, int):
+            return (Tensor, data, mstype.int32)
+        if isinstance(data, float):
+            return (Tensor, data, mstype.float32)
+        return (Tensor, data)
+
+    def __str__(self):
+        value_str = MetaTensor.__repr__(self)
+        if isinstance(self, Tensor):
+            value_str = Tensor.__repr__(self)
+        return f'Parameter (name={self._value.name}, value={value_str})'
 
     def __repr__(self):
-        format_str = 'Parameter (name={name})'
-        return format_str.format(name=self._value.name)
+        value_str = MetaTensor.__repr__(self)
+        if isinstance(self, Tensor):
+            value_str = Tensor.__repr__(self)
+        return f'Parameter (name={self._value.name}, value={value_str})'
 
     def __parameter__(self):
         """For parse check."""
@@ -77,6 +145,13 @@ class Parameter:
         self.is_param_ps = True
         self.init_in_server = init_in_server
 
+
+    @property
+    def inited_param(self):
+        """Get the new parameter after call the init_data."""
+        return self._inited_param
+
+
     @property
     def name(self):
         """Get the name of the parameter."""
@@ -157,15 +232,11 @@ class Parameter:
         x._value.name = prefix + '.' + self._value.name
         x.is_init = False
         if init != 'same':
-            shape = self.default_input.shape
-            dtype = self.default_input.dtype
-            if isinstance(init, (str, Initializer, numbers.Number)):
-                x.init_mode = initializer(init, shape=shape, dtype=dtype)
-                x.default_input = MetaTensor(dtype, shape)
-                if context.get_context("mode") == context.PYNATIVE_MODE:
-                    x.init_data()
-            else:
-                x.default_input = initializer(init, shape=shape, dtype=dtype)
+            shape = self.shape
+            dtype = self.dtype
+            x.default_input = initializer(init, shape=shape, dtype=dtype)
+            if context.get_context("mode") == context.PYNATIVE_MODE:
+                x.init_data()
         return x
 
     @property
@@ -195,50 +266,65 @@ class Parameter:
 
     @property
     def default_input(self):
-        return self._data
+        return self
 
     @default_input.setter
     def default_input(self, data):
-        self._data = data
-        self._value.data = data
-
-    def __add__(self, other):
-        return self.default_input + other
-
-    def __sub__(self, other):
-        return self.default_input - other
+        self.set_parameter_data(data)
 
-    def __mul__(self, other):
-        return self.default_input * other
+    def _update_tensor_data(self, data):
+        "Update the parameter by a Tensor."
+        if isinstance(self, Tensor):
+            # for Tensor same shape:
+            return self.assign_value(data)
+        # create a new tensor
+        return Parameter(data, self.name, self.requires_grad)
 
-    def __truediv__(self, other):
-        return self.default_input / other
+    def set_parameter_data(self, data, slice_shape=False):
+        """
+        Set `default_input` of current `Parameter`.
 
-    def __setitem__(self, index, value):
-        default_input = self.default_input
-        default_input[index] = value
-        return self
+        Args:
+            data (Union[Tensor, Initializer]): new data.
+            slice_shape (bool): If slice the Parameter. Default: False.
 
-    def set_parameter_data(self, data):
-        """Set `default_input` of current `Parameter`."""
-        if isinstance(data, bool):
-            raise ValueError('Parameter data can not be `bool`')
-        if isinstance(data, Tensor):
-            # make a copy of Tensor to init the parameter
-            data = Tensor(data.asnumpy())
-            data.init_flag = False
-        elif isinstance(data, Initializer):
-            self.init_mode = data
-            data = MetaTensor(self.init_mode.dtype, self.init_mode.shape)
-        elif isinstance(data, int):
-            data = Tensor(data, dtype=mstype.int32)
-        elif isinstance(data, float):
-            data = Tensor(data, dtype=mstype.float32)
+        Retruns:
+            Parameter, the parameter after set data.
+        """
+        if not isinstance(data, (MetaTensor, Initializer)):
+            raise ValueError(f"Parameter data must be `Initializer` or a kind of `MetaTensor` "
+                             f"(like `Tensor` or `MetaTensor`). But with type {type(data)}.")
+        # both not init.
+        is_incoming_tensor = isinstance(data, Tensor)
+        is_current_tensor = isinstance(self, Tensor)
+
+        if is_incoming_tensor and not is_current_tensor:
+            raise TypeError("Parameter is a `MetaTensor` and not initializered, `data` for `set_parameter_data`"
+                            "should be a Initializer. If you want to update it by Tensor, call method"
+                            "`init_parameters_data` of `Cell` to init and replace all the Parameter of"
+                            "network, then call this method.")
+        if tuple(self.shape) != tuple(data.shape):
+            # If Slice create Parameter shape can be change.
+            if slice_shape:
+                self._update_tensor_data(data)
+                self.sliced = True
+            else:
+                raise ValueError(f"Can not change the shape of Parameter which has been initialized."
+                                 f" Current shape is {self.shape}, and incoming is {data.shape}.")
+        if self.dtype != data.dtype:
+            raise ValueError(f"Can not change the Parameter dtype. Current dtype is {self.set_dtype}"
+                             f", and incoming is {data.dtype}. Use .set_dtype(xxx) to change the dtype.")
+        if isinstance(data, Initializer):
+            # The parameter has been initializered, directly update by the data
+            if is_current_tensor:
+                self._update_tensor_data(data.to_tensor())
+            else:
+                self.init_mode = data
+        elif is_incoming_tensor or is_current_tensor:
+            self._update_tensor_data(data)
         else:
-            data = Tensor(data)
-            data.init_flag = False
-
-        self.default_input = data
+            raise ValueError(f"Not support to update the Parameter by {data}")
+        return self
 
     def init_data(self, layout=None, set_sliced=False):
         """
@@ -252,31 +338,37 @@ class Parameter:
                 - slice_shape (list[int]): Shape of slice.
             set_sliced (bool): True if should set parameter sliced after init the data of initializer.
                 Default: False.
+
+        Returns:
+            Parameter, Parameter after init data.
         """
-        if isinstance(self.default_input, Tensor):
-            # skip if data already initialized.
-            return
+        if self.init_mode is None:
+            return self
+        if self.inited_param is not None:
+            return self.inited_param
         if layout is not None:
             if not isinstance(layout, list):
-                raise TypeError("The layout should be list! layout is {}."
-                                .format(layout))
+                raise TypeError("The layout should be list! layout is {}.".format(layout))
             if len(layout) < 3:
-                raise ValueError("The length of layout must be larger than 3! layout is {}."
-                                 .format(layout))
+                raise ValueError("The length of layout must be larger than 3! layout is {}.".format(layout))
             slice_index = int(_get_slice_index(layout[0], layout[1]))
             if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, Initializer)):
-                self.default_input = self.init_mode.to_tensor(0, [1])
+                data = self.init_mode.to_tensor(0, [1])
             else:
-                self.default_input = self.init_mode.to_tensor(slice_index, layout[2])
+                data = self.init_mode.to_tensor(slice_index, layout[2])
         else:
             if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, Initializer)):
-                self.default_input = self.init_mode.to_tensor(0, [1])
+                data = self.init_mode.to_tensor(0, [1])
             else:
-                self.default_input = self.init_mode.to_tensor()
+                data = self.init_mode.to_tensor()
 
-        self.init_mode = None
+        obj = self._update_tensor_data(data)
+        if id(obj) != id(self):
+            self._inited_param = obj
+        obj.init_mode = None
         if set_sliced:
-            self.sliced = True
+            obj.sliced = True
+        return obj
 
 
 class ParameterTuple(tuple):

mindspore/common/tensor.py

@@ -75,7 +75,7 @@ class Tensor(Tensor_):
         self._virtual_flag = False
 
     def __repr__(self):
-        return str(self.__str__())
+        return str(Tensor_.__str__(self))
 
     def __add__(self, other):
         out = tensor_operator_registry.get('__add__')(self, other)

mindspore/core/ir/anf.h

@@ -283,11 +283,11 @@ class Parameter : public ANode {
   std::string fullname_with_scope() override { return name(); };
 
   bool has_default() const { return has_default_; }
-  void set_default_param(ParamValuePtr param) {
+  void set_default_param(ValuePtr param) {
     default_param_ = param;
     has_default_ = true;
   }
-  ParamValuePtr default_param() const { return default_param_; }
+  ValuePtr default_param() const { return default_param_; }
 
   bool operator==(const AnfNode &other) const override {
     if (!other.isa<Parameter>()) {
@@ -303,7 +303,7 @@ class Parameter : public ANode {
  private:
   std::string name_;
   bool has_default_;
-  ParamValuePtr default_param_;
+  ValuePtr default_param_;
 };
 using ParameterPtr = std::shared_ptr<Parameter>;
 

mindspore/core/ir/param_value.h

@@ -33,9 +33,6 @@ class ParamValue {
 
   virtual ~ParamValue() = default;
 
-  tensor::MetaTensorPtr value() const { return value_; }
-  void set_value(const tensor::MetaTensorPtr &value) { value_ = value; }
-
   const std::string &name() const { return name_; }
   void set_name(const std::string &name) { name_ = name; }
 
@@ -72,7 +69,6 @@ class ParamValue {
   }
 
  private:
-  tensor::MetaTensorPtr value_;
   std::string name_{"Parameter"};
   bool requires_grad_{true};
   bool layerwise_parallel_{false};

mindspore/lite/src/param_value_lite.h

@@ -36,7 +36,7 @@ struct AnfQuantParam {
   int32_t numBits;
   AnfQuantParam() : scale(1.0), zeroPoint(0), min(0.0), max(0.0), narrowRange(false), numBits(8), inited(false) {}
 };
-class ParamValueLite : public ParamValue {
+class ParamValueLite : public Value {
  public:
   ParamValueLite() : tensor_addr_(nullptr), tensor_size_(0) {}
   virtual ~ParamValueLite() = default;
@@ -65,6 +65,10 @@ class ParamValueLite : public ParamValue {
     quant_params_.emplace_back(std::move(quant_param));
   }
 
+  bool operator==(const Value &other) const override {
+    this == &other;
+  }
+
  private:
   void *tensor_addr_;
   size_t tensor_size_;

mindspore/nn/cell.py

@@ -229,7 +229,6 @@ class Cell:
         for item in inputs:
             if isinstance(item, numpy.ndarray):
                 raise TypeError("cell inputs should not be numpy array.")
-        self.init_parameters_data()
         orign_grad = []
         if self.requires_grad is True:
             _pynative_exec.set_grad_flag(True)
@@ -350,19 +349,8 @@ class Cell:
             params (dict): The parameters dictionary used for init data graph.
         """
         if params is None:
-            for key in self.parameters_dict():
-                tensor = self.parameters_dict()[key].data
-                if key not in self.parameter_layout_dict:
-                    logger.info("layout dict does not contain the key %s", key)
-                    continue
-                if self.parameters_dict()[key].sliced:
-                    logger.debug("Param %s is already sliced.", key)
-                    continue
-                layout = self.parameter_layout_dict[key]
-                new_tensor = _load_tensor_by_layout(tensor, layout)
-                self.parameters_dict()[key].set_parameter_data(new_tensor)
-                self.parameters_dict()[key].sliced = True
-        elif isinstance(params, OrderedDict):
+            params = self.parameters_dict()
+        if isinstance(params, OrderedDict):
             for key in params:
                 tensor = params[key].data
                 if key not in self.parameter_layout_dict:
@@ -373,8 +361,7 @@ class Cell:
                     continue
                 layout = self.parameter_layout_dict[key]
                 new_tensor = _load_tensor_by_layout(tensor, layout)
-                params[key].set_parameter_data(new_tensor)
-                params[key].sliced = True
+                params[key].set_parameter_data(new_tensor, True)
         else:
             raise TypeError('Parameters need OrderedDict type, but got {}'.
                             format(type(params)))
@@ -545,17 +532,46 @@ class Cell:
         """
         raise NotImplementedError
 
-    def init_parameters_data(self, recurse=True, auto_parallel_mode=False):
-        """Init parameters' data."""
-        for param in self.get_parameters(expand=recurse):
-            if not auto_parallel_mode:
-                param.init_data()
-            elif param.name not in self.parameter_layout_dict:
-                logger.debug("Layout dict does not contain the key %s.", param.name)
-                param.init_data(set_sliced=True)
-            else:
-                layout = self.parameter_layout_dict[param.name]
-                param.init_data(layout, set_sliced=True)
+    def init_parameters_data(self, auto_parallel_mode=False):
+        """
+        Init all parameters' data and replace the original saved parameters in cell.
+
+        Args:
+            auto_parallel_mode (bool): If running in auto_parallel_mode.
+
+        Returns:
+            Dict[Parameter, Parameter], returns a dict of original parameter and replaced parameter.
+        """
+        replace = dict()
+        def _updata(param):
+            if param in replace:
+                return replace[param]
+            layout = None
+            set_sliced = False
+            if auto_parallel_mode:
+                set_sliced = True
+                if param.name not in self.parameter_layout_dict:
+                    logger.debug("Layout dict does not contain the key %s.", param.name)
+                else:
+                    layout = self.parameter_layout_dict[param.name]
+            new_p = param.init_data(layout, set_sliced=set_sliced)
+            replace[param] = new_p
+            return new_p
+        # replace all original usage.
+        cells = self.cells_and_names()
+        for _, cell in cells:
+            params = cell._params.items()
+            for param_name, param in params:
+                cell._params[param_name] = _updata(param)
+            cell_dict = cell.__dict__
+            for key in cell_dict:
+                if isinstance(cell_dict[key], ParameterTuple):
+                    param_tuple = cell_dict[key]
+                    new_param_tuple = []
+                    for param in param_tuple:
+                        new_param_tuple.append(_updata(param))
+                    cell.__dict__[key] = ParameterTuple(new_param_tuple)
+        return replace
 
     def parameters_dict(self, recurse=True):
         """
@@ -682,9 +698,10 @@ class Cell:
         for cell_name, cell in cells:
             params = cell._params.items()
             for par_name, par in params:
-                if par and par not in params_set:
+                if par.inited_param is not None:
+                    par = par.inited_param
+                if par is not None and par not in params_set:
                     params_set.add(par)
-
                     par_new_name = par_name
                     if cell_name:
                         par_new_name = cell_name + '.' + par_new_name

mindspore/nn/optim/optimizer.py

@@ -90,7 +90,7 @@ class Optimizer(Cell):
 
     def __init__(self, learning_rate, parameters, weight_decay=0.0, loss_scale=1.0):
         super(Optimizer, self).__init__(auto_prefix=False)
-        if parameters and not isinstance(parameters, list):
+        if parameters is not None and not isinstance(parameters, list):
             parameters = list(parameters)
 
         if not parameters:

mindspore/train/serialization.py

@@ -295,7 +295,6 @@ def load_param_into_net(net, parameter_dict):
                 logger.error("Failed to combine the net and the parameters.")
                 msg = ("Argument parameter_dict element should be a Parameter, but got {}.".format(type(new_param)))
                 raise TypeError(msg)
-            param.init_data()
             _update_param(param, new_param)
         else:
             param_not_load.append(param.name)
@@ -362,15 +361,13 @@ def _exec_save_checkpoint(train_network, ckpt_file_name, integrated_save=True, a
         integrated_save (bool): Whether to integrated save in automatic model parallel scene.
         async_save (bool): Whether asynchronous execute save checkpoint into file. Default: False.
     """
-
+    train_network.init_parameters_data()
     param_dict = {}
     for _, param in train_network.parameters_and_names():
         param_dict[param.name] = param
-
     param_list = []
     for (key, value) in param_dict.items():
         each_param = {"name": key}
-        value.init_data()
         if isinstance(value.data, Tensor):
             param_data = value.data
         else:

model_zoo/official/cv/mobilenetv2/src/mobilenetV2.py

@@ -263,6 +263,7 @@ class MobileNetV2(nn.Cell):
         Examples:
             >>> _initialize_weights()
         """
+        self.init_parameters_data()
         for _, m in self.cells_and_names():
             if isinstance(m, (nn.Conv2d, DepthwiseConv)):
                 n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels

model_zoo/official/cv/mobilenetv2_quant/src/mobilenetV2.py

@@ -196,6 +196,7 @@ class mobilenetV2(nn.Cell):
         self.head = nn.SequentialCell(head)
 
         # init weights
+        self.init_parameters_data()
         self._initialize_weights()
 
     def construct(self, x):
@@ -215,6 +216,7 @@ class mobilenetV2(nn.Cell):
         Examples:
             >>> _initialize_weights()
         """
+        self.init_parameters_data()
         for _, m in self.cells_and_names():
             if isinstance(m, nn.Conv2d):
                 n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels

model_zoo/official/cv/mobilenetv3/src/mobilenetV3.py

@@ -200,6 +200,7 @@ class ResUnit(nn.Cell):
         self.add = P.TensorAdd() if self.use_short_cut_conv else None
 
     def construct(self, x):
+        """construct"""
         if self.first_conv:
             out = self.expand(x)
         else:
@@ -289,6 +290,7 @@ class MobileNetV3(nn.Cell):
                                 kernel_size=1, has_bias=True, pad_mode='pad')
         self.squeeze = P.Squeeze(axis=(2, 3))
 
+        self.init_parameters_data()
         self._initialize_weights()
 
     def construct(self, x):

tests/st/networks/models/bert/test_bert_graph_kernel.py

@@ -171,9 +171,9 @@ def test_bert_tdt():
     netwithgrads.set_train(True)
     model = Model(netwithgrads)
     callback = ModelCallback()
+    netwithloss.init_parameters_data()
     params = netwithloss.trainable_params()
     for param in params:
-        param.init_data()
         value = param.default_input
         name = param.name
         if isinstance(value, Tensor):

tests/st/networks/models/bert/test_bert_tdt_lossscale.py

@@ -207,9 +207,9 @@ def test_bert_percision():
     netwithgrads.set_train(True)
     model = Model(netwithgrads)
     callback = ModelCallback()
+    netwithloss.init_parameters_data()
     params = netwithloss.trainable_params()
     for param in params:
-        param.init_data()
         value = param.default_input
         name = param.name
         if isinstance(value, Tensor):
@@ -279,9 +279,9 @@ def test_bert_performance():
     netwithgrads.set_train(True)
     model = Model(netwithgrads)
     callback = ModelCallback()
+    netwithloss.init_parameters_data()
     params = netwithloss.trainable_params()
     for param in params:
-        param.init_data()
         value = param.default_input
         name = param.name
         if isinstance(value, Tensor):

tests/st/networks/models/deeplabv3/test_deeplabv3.py

@@ -63,6 +63,7 @@ class LossCallBack(Callback):
                                                            str(cb_params.net_outputs)))
 
 def model_fine_tune(train_net, fix_weight_layer):
+    train_net.init_parameters_data()
     for para in train_net.trainable_params():
         para.set_parameter_data(Tensor(np.ones(para.data.shape).astype(np.float32) * 0.02))
         if fix_weight_layer in para.name:

tests/st/networks/models/resnet50/test_resnet50_imagenet.py

@@ -174,9 +174,14 @@ def train_process(q, device_id, epoch_size, device_num, enable_hccl):
                                   steps_per_epoch=step_size, lr_decay_mode=config.lr_decay_mode))
 
     # optimizer
-    decayed_params = list(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'bias' not in x.name,
-                                 net.trainable_params()))
-    no_decayed_params = [param for param in net.trainable_params() if param not in decayed_params]
+    decayed_params = []
+    no_decayed_params = []
+    for param in net.trainable_params():
+        if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
+            decayed_params.append(param)
+        else:
+            no_decayed_params.append(param)
+
     group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
                     {'params': no_decayed_params, 'weight_decay': 0.0},
                     {'order_params': net.trainable_params()}]

tests/ut/cpp/pre_activate/ascend/enhancer/insert_memcpy_async_for_hccl_op_test.cc

@@ -107,7 +107,6 @@ TEST_F(TestHWInsertMemcpyForHccl, test_cond2) {
   for (auto p : kg->parameters()) {
     auto param = p->cast<ParameterPtr>();
     EXPECT_NE(param, nullptr);
-    param->set_default_param(std::make_shared<ParamValue>());
   }
 
   auto optimizer = std::make_shared<opt::GraphOptimizer>();
@@ -157,7 +156,6 @@ TEST_F(TestHWInsertMemcpyForHccl, test_cond4) {
   for (auto p : kg->parameters()) {
     auto param = p->cast<ParameterPtr>();
     EXPECT_NE(param, nullptr);
-    param->set_default_param(std::make_shared<ParamValue>());
   }
 
   auto optimizer = std::make_shared<opt::GraphOptimizer>();
@@ -185,7 +183,6 @@ TEST_F(TestHWInsertMemcpyForHccl, test_cond5) {
   for (auto p : kg->parameters()) {
     auto param = p->cast<ParameterPtr>();
     EXPECT_NE(param, nullptr);
-    param->set_default_param(std::make_shared<ParamValue>());
   }
 
   auto optimizer = std::make_shared<opt::GraphOptimizer>();

tests/ut/cpp/session/anf_runtime_algorithm_test.cc

@@ -766,7 +766,7 @@ TEST_F(AnfRuntimeAlgorithmTest, IsParameterWeight) {
   auto kernel_graph = std::make_shared<KernelGraph>();
   auto parameter_node = kernel_graph->add_parameter();
   MS_EXCEPTION_IF_NULL(parameter_node);
-  auto param_value_new = std::make_shared<ParamValue>();
+  auto param_value_new = std::make_shared<tensor::Tensor>(int64_t(0), kInt32);
   parameter_node->set_default_param(param_value_new);
   EXPECT_TRUE(AnfAlgo::IsParameterWeight(parameter_node));
   EXPECT_THROW(AnfAlgo::IsParameterWeight(nullptr), std::runtime_error);

tests/ut/cpp/session/kernel_graph_test.cc

@@ -82,7 +82,7 @@ TEST_F(KernelGraphTest, NewParameter) {
   // test weight parameter node as input
   auto weight_parameter_node = anf_graph->add_parameter();
   MS_EXCEPTION_IF_NULL(weight_parameter_node);
-  auto param_value_new = std::make_shared<ParamValue>();
+  auto param_value_new = std::make_shared<tensor::Tensor>(kNumberTypeFloat32, shape);
   weight_parameter_node->set_default_param(param_value_new);
   weight_parameter_node->set_abstract(x_abstract);
   auto new_weight_parameter_node = kernel_graph->NewParameter(weight_parameter_node);

tests/ut/python/ir/test_tensor.py

@@ -225,7 +225,7 @@ def test_div():
 @non_graph_engine
 def test_parameter():
     x = Parameter(initializer(1, [1], ms.float32), name="beta1_power")
-    x.init_data()
+    x = x.init_data()
     z = x / 2
     print(z)
 

tests/ut/python/nn/test_parameter.py

@@ -139,14 +139,31 @@ def test_parameter_lazy_init():
     # Call init_data() without set default_input.
     para = Parameter(initializer('ones', [1, 2, 3], mstype.float32), 'test1')
     assert not isinstance(para.default_input, Tensor)
-    para.init_data()
+    para = para.init_data()
     assert isinstance(para.default_input, Tensor)
     assert np.array_equal(para.default_input.asnumpy(), np.ones((1, 2, 3)))
 
     # Call init_data() after default_input is set.
     para = Parameter(initializer('ones', [1, 2, 3], mstype.float32), 'test2')
     assert not isinstance(para.default_input, Tensor)
-    para.default_input = Tensor(np.zeros((1, 2, 3)))
-    assert np.array_equal(para.default_input.asnumpy(), np.zeros((1, 2, 3)))
-    para.init_data() # expect no effect.
+    # expect type error when not init
+    with pytest.raises(TypeError):
+        para.default_input = Tensor(np.zeros((1, 2, 3)))
+    # init then assign
+    para = para.init_data()
+    # check the type
+    with pytest.raises(ValueError):
+        para.default_input = Tensor(np.zeros((1, 2, 3)))
+    # check the shape
+    with pytest.raises(ValueError):
+        para.default_input = Tensor(np.zeros((1, 2)))
+    # expect change ok
+    para.default_input = Tensor(np.zeros((1, 2, 3)).astype(np.float32))
     assert np.array_equal(para.default_input.asnumpy(), np.zeros((1, 2, 3)))
+    para.default_input = initializer('ones', [1, 2, 3], mstype.float32)
+    assert isinstance(para.default_input, Tensor)
+    # same object and has inited
+    assert np.array_equal(para.default_input.asnumpy(), np.ones((1, 2, 3)))
+    # expect no effect.
+    para.init_data()
+    assert np.array_equal(para.default_input.asnumpy(), np.ones((1, 2, 3)))

tests/ut/python/train/quant/test_quant.py

@@ -69,8 +69,7 @@ def test_qat_lenet():
     net = qat.convert_quant_network(
         net, bn_fold=True, per_channel=[True, False], symmetric=[True, False])
     # should load the checkpoint. mock here
-    for param in net.get_parameters():
-        param.init_data()
+    net.init_parameters_data()
     qat.export(net, img, file_name="quant.pb")
 
 
@@ -80,8 +79,7 @@ def test_qat_mobile_per_channel_tf():
     img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
     network = qat.convert_quant_network(network, bn_fold=True, per_channel=[True, False], symmetric=[True, False])
     # should load the checkpoint. mock here
-    for param in network.get_parameters():
-        param.init_data()
+    network.init_parameters_data()
     qat.export(network, img, file_name="quant.pb")
 
 @pytest.mark.skip(reason="no `te.lang.cce` in ut env")
@@ -90,6 +88,5 @@ def test_qat_mobile_per_channel_ff():
     img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
     network = qat.convert_quant_network(network, bn_fold=True, per_channel=[False, False], symmetric=[True, False])
     # should load the checkpoint. mock here
-    for param in network.get_parameters():
-        param.init_data()
+    network.init_parameters_data()
     qat.export(network, img, file_name="quant.pb")