!1320 [MindConverter] Fix shared weight for mixture between tensor and non-tensor

From: @moran3
Reviewed-by: @liuchongming74,@ouwenchang
Signed-off-by: @ouwenchang

mindinsight/mindconverter/graph_based_converter/generator/generator.py

@@ -30,13 +30,15 @@ from mindinsight.mindconverter.graph_based_converter.common.outputs import BaseO
 from mindinsight.mindconverter.graph_based_converter.common.yapf_config import mindspore_yapf_config
 from mindinsight.mindconverter.graph_based_converter.common.name_mgr import GlobalVarNameMgr
 from mindinsight.mindconverter.graph_based_converter.constant import NEW_LINE, SECOND_LEVEL_INDENT, \
-    FIRST_LEVEL_INDENT, get_imported_module, SEPARATOR_BTW_NAME_AND_ID, WeightType, LINK_IN_WEIGHT_NAME
+    FIRST_LEVEL_INDENT, get_imported_module, SEPARATOR_BTW_NAME_AND_ID, WeightType, LINK_IN_WEIGHT_NAME, \
+    ExchangeMessageKeywords
 from mindinsight.mindconverter.graph_based_converter.report_generator import ReportGenerator
 from mindinsight.mindconverter.graph_based_converter.common.utils import replace_string_in_list
 from mindinsight.mindconverter.graph_based_converter.generator.matcher import MatcherLauncher
 from mindinsight.mindconverter.graph_based_converter.generator.shared_weights import SharedWeightHelper
 from mindinsight.mindconverter.graph_based_converter.constant import CHECKPOINT_SEGMENT_SIZE
 
+
 class CodeStruct:
     """
     Define the Code template for each module generated in the final output.
@@ -112,7 +114,7 @@ class CodeStruct:
                 init_lines += init_str
                 cons_lines += cons_str
 
-            else: # is ModuleStruct
+            else:  # is ModuleStruct
                 # check if this instance generated CodeStruct
                 if GlobalContext().code_structs.get(struct.pattern_id) is None:
                     CodeStruct(struct, repeated_submodules)
@@ -126,7 +128,7 @@ class CodeStruct:
         self.new_line = f"{FIRST_LEVEL_INDENT}def __init__({', '.join(module_def_args)}):"
         self.new_line = f"{SECOND_LEVEL_INDENT}super({class_name}, self).__init__()"
 
-        #add shared weights declaration in init code part
+        # add shared weights declaration in init code part
         if md_struct.identifier == []:
             passthrough_w_declaration = SharedWeightHelper.public_module_shared_weight_statement_generation(md_struct)
             for s in passthrough_w_declaration:
@@ -259,14 +261,34 @@ class Generator:
     @staticmethod
     def _set_translated_args_for_unshared_weights(t_param_postfix, nd_struct_list):
         """Set the weight with given param postfix to args translation."""
+
+        args_name = ExchangeMessageKeywords.VariableScope.value.ARGS.value
+        parameters_name = ExchangeMessageKeywords.VariableScope.value.PARAMETERS_DECLARED.value
+        trainable_params_name = ExchangeMessageKeywords.VariableScope.value.TRAINABLE_PARAMS.value
+
+        has_non_tensor = not np.alltrue(
+            [bool(nd_struct.fragment.default_var.get(parameters_name)) for _, nd_struct in nd_struct_list])
         for _, nd_struct in nd_struct_list:
-            nparr = nd_struct.fragment.default_var["trainable_params"].get(t_param_postfix).get('data')
-            nd_struct.fragment.default_var["args"][f"{t_param_postfix}_shape"] = nparr.shape
-            nd_struct.fragment.default_var["args"][f"{t_param_postfix}_dtype"] = nparr.dtype
-            init_tensor_template = f"Parameter(Tensor(np.random.uniform(0, 1, "\
-                                    f"{{{t_param_postfix}_shape}}).astype(np.{{{t_param_postfix}_dtype}})), "\
-                                    f"name=None)"
-            nd_struct.fragment.default_var["parameters"][t_param_postfix] = init_tensor_template
+            if has_non_tensor:
+                parameters = nd_struct.fragment.default_var.get(parameters_name)
+                if parameters:
+                    nparr = nd_struct.fragment.default_var[trainable_params_name].get(t_param_postfix).get('data')
+                    init_tensor_template = nd_struct.fragment.fragment.create_parameter(nparr.shape, nparr.dtype)
+                    nd_struct.fragment.default_var[args_name][f"{t_param_postfix}"] = init_tensor_template
+                    nd_struct.fragment.default_var[parameters_name][t_param_postfix] = f"{{var_0}}_{t_param_postfix}"
+                else:
+                    value_name = f"{t_param_postfix}_value"
+                    init_tensor_template = f"{nd_struct.fragment.default_var[args_name][value_name]}"
+                    nd_struct.fragment.default_var[args_name][f"{t_param_postfix}"] = init_tensor_template
+                    nd_struct.fragment.default_var[parameters_name] = {t_param_postfix: f"{{var_0}}_{t_param_postfix}"}
+                    del nd_struct.fragment.default_var[args_name][value_name]
+            else:
+                nparr = nd_struct.fragment.default_var[trainable_params_name].get(t_param_postfix).get('data')
+                nd_struct.fragment.default_var[args_name][f"{t_param_postfix}_shape"] = nparr.shape
+                nd_struct.fragment.default_var[args_name][f"{t_param_postfix}_dtype"] = nparr.dtype
+                init_tensor_template = nd_struct.fragment.fragment.create_parameter(f"{{{t_param_postfix}_shape}}",
+                                                                                    f"{{{t_param_postfix}_dtype}}")
+                nd_struct.fragment.default_var[parameters_name][t_param_postfix] = init_tensor_template
 
     def _get_same_trainable_params_onnx_name_from_repeated_nodes(self,
                                                                  t_param_postfix,
@@ -279,8 +301,8 @@ class Generator:
         for (_, nd_struct) in nd_struct_list[1:]:
             compared_t_param_data_dict = nd_struct.fragment.default_var["trainable_params"].get(t_param_postfix)
             if not compared_t_param_data_dict:
-                raise ValueError(f"Inconsistent trainable params detected for node "\
-                                    f"{nd_struct.topo_idx} with base node {base_nd_struct.topo_idx}")
+                raise ValueError(f"Inconsistent trainable params detected for node " \
+                                 f"{nd_struct.topo_idx} with base node {base_nd_struct.topo_idx}")
             compared_t_name = compared_t_param_data_dict.get('onnx_name')
             t_onnx_names.append(compared_t_name)
         return t_onnx_names
@@ -301,7 +323,7 @@ class Generator:
             if base_nd_struct.fragment.default_var.get("parameters"):
                 # set only if has parameters as it requires rewritten.
                 for (t_param_postfix, t_param_data_dict) in \
-                    base_nd_struct.fragment.default_var["trainable_params"].items():
+                        base_nd_struct.fragment.default_var["trainable_params"].items():
                     if not isinstance(t_param_data_dict.get('data'), np.ndarray):
                         continue
                     Generator._set_translated_args_for_unshared_weights(t_param_postfix, nd_struct_list)
@@ -332,7 +354,6 @@ class Generator:
                     continue
                 Generator._set_translated_args_for_unshared_weights(t_param_postfix, nd_struct_list)
 
-
     def _list_formal_parameters_in_a_module(self, module_filter_return):
         """
         Find all formal args / params from nodes in a module.