!1137 Fix the weights shape error of matmul and biasadd

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

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/mat_mul_mapper.py

@@ -14,8 +14,6 @@
 # ==============================================================================
 """Mapper module."""
 from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
-from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting, Tensor, get_dtype
-from mindinsight.mindconverter.graph_based_converter.common.utils import reset_init_or_construct
 from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
 
 
@@ -34,18 +32,6 @@ class MatMulMapper(ONNXToMindSporeMapper):
     def _convert_trained_weights(**kwargs):
         return dict()
 
-    @staticmethod
-    def _convert_settings(**kwargs):
-        weights = kwargs.get("weights")
-        if not weights:
-            return Setting()
-        tensor, ref = None, ""
-        for t_name, t_value in weights.items():
-            tensor = t_value
-            ref = t_name
-        return Setting(op_extra_tensor=Tensor(shape=tensor.shape,
-                                              dtype=get_dtype(tensor), reference=ref))
-
     @staticmethod
     def _generate_snippet_template(**kwargs):
         template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
@@ -53,6 +39,8 @@ class MatMulMapper(ONNXToMindSporeMapper):
         op = kwargs.get("operation")
         args = kwargs.get("converted_params")
         weights = kwargs.get("weights")
+        if not op:
+            raise ValueError("Can not get MindSpore operation name.")
         if not weights:
             return template, exchange_msg, outputs_list, outputs_mapping
 
@@ -61,14 +49,31 @@ class MatMulMapper(ONNXToMindSporeMapper):
 
         variable_slot = "var_0"
         init_template = f"self.{{{variable_slot}}} = {op}({', '.join(['%s={%s}' % (p, p) for p in args])})"
+        args["weight_shape"] = tensor.shape
+        args["weight_dtype"] = tensor.dtype
         init_tensor = f"self.{{{variable_slot}}}_w = " \
-                      f"Tensor(np.random.uniform(0, 1, {tensor.shape}).astype(np.{tensor.dtype}))"
+                      f"Tensor(np.random.uniform(0, 1, {{weight_shape}}).astype(np.{{weight_dtype}}))"
         construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
                              f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}," \
                              f"self.{{{variable_slot}}}_w)"
-        template = reset_init_or_construct(template, variable_slot, [init_template, init_tensor],
-                                           TemplateKeywords.INIT.value)
-        template = reset_init_or_construct(template, variable_slot, [construct_template],
-                                           TemplateKeywords.CONSTRUCT.value)
-
+        template = {
+            variable_slot: {
+                TemplateKeywords.INIT.value: [init_template, init_tensor],
+                TemplateKeywords.CONSTRUCT.value: [construct_template]
+            }
+        }
+        exchange_msg = {
+            variable_slot: {
+                ExchangeMessageKeywords.VariableScope.value.OPERATION.value: op,
+                ExchangeMessageKeywords.VariableScope.value.VARIABLE_NAME.value: None,
+                ExchangeMessageKeywords.VariableScope.value.OUTPUT_TYPE.value:
+                    ExchangeMessageKeywords.VariableScope.value.TSR_TYPE.value,
+                ExchangeMessageKeywords.VariableScope.value.INPUTS.value: [],
+                ExchangeMessageKeywords.VariableScope.value.ARGS.value: args,
+                ExchangeMessageKeywords.VariableScope.value.WEIGHTS.value: weights,
+                ExchangeMessageKeywords.VariableScope.value.TRAINABLE_PARAMS.value: {}
+            }
+        }
+        outputs_list = [f"opt_{{{variable_slot}}}"]
+        outputs_mapping = ((0, 0),)
         return template, exchange_msg, outputs_list, outputs_mapping

mindinsight/mindconverter/graph_based_converter/mapper/impl/ops/add_mapper.py

@@ -13,10 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from mindinsight.mindconverter.graph_based_converter.common.utils import reset_init_or_construct
 from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
 from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
-from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting, Tensor, get_dtype
 
 
 class AddMapper(ONNXToMindSporeMapper):
@@ -34,18 +32,6 @@ class AddMapper(ONNXToMindSporeMapper):
     def _convert_trained_weights(**kwargs):
         return dict()
 
-    @staticmethod
-    def _convert_settings(**kwargs):
-        weights = kwargs.get("weights")
-        if not weights:
-            return Setting()
-        tensor, ref = None, ""
-        for t_name, t_value in weights.items():
-            tensor = t_value
-            ref = t_name
-        return Setting(op_extra_tensor=Tensor(shape=tensor.shape,
-                                              dtype=get_dtype(tensor), reference=ref))
-
     @staticmethod
     def _generate_snippet_template(**kwargs):
         template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
@@ -53,6 +39,8 @@ class AddMapper(ONNXToMindSporeMapper):
         op = kwargs.get("operation")
         args = kwargs.get("converted_params")
         weights = kwargs.get("weights")
+        if not op:
+            raise ValueError("Can not get MindSpore operation name.")
         if not weights:
             return template, exchange_msg, outputs_list, outputs_mapping
 
@@ -61,14 +49,31 @@ class AddMapper(ONNXToMindSporeMapper):
 
         variable_slot = "var_0"
         init_template = f"self.{{{variable_slot}}} = {op}({', '.join(['%s={%s}' % (p, p) for p in args])})"
+        args["bias_shape"] = tensor.shape
+        args["bias_dtype"] = tensor.dtype
         init_tensor = f"self.{{{variable_slot}}}_bias = " \
-                      f"Tensor(np.random.uniform(0, 1, {tensor.shape}).astype(np.{tensor.dtype}))"
+                      f"Tensor(np.random.uniform(0, 1, {{bias_shape}}).astype(np.{{bias_dtype}}))"
         construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
                              f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}," \
                              f"self.{{{variable_slot}}}_bias)"
-        template = reset_init_or_construct(template, variable_slot, [init_template, init_tensor],
-                                           TemplateKeywords.INIT.value)
-        template = reset_init_or_construct(template, variable_slot, [construct_template],
-                                           TemplateKeywords.CONSTRUCT.value)
-
+        template = {
+            variable_slot: {
+                TemplateKeywords.INIT.value: [init_template, init_tensor],
+                TemplateKeywords.CONSTRUCT.value: [construct_template]
+            }
+        }
+        exchange_msg = {
+            variable_slot: {
+                ExchangeMessageKeywords.VariableScope.value.OPERATION.value: op,
+                ExchangeMessageKeywords.VariableScope.value.VARIABLE_NAME.value: None,
+                ExchangeMessageKeywords.VariableScope.value.OUTPUT_TYPE.value:
+                    ExchangeMessageKeywords.VariableScope.value.TSR_TYPE.value,
+                ExchangeMessageKeywords.VariableScope.value.INPUTS.value: [],
+                ExchangeMessageKeywords.VariableScope.value.ARGS.value: args,
+                ExchangeMessageKeywords.VariableScope.value.WEIGHTS.value: weights,
+                ExchangeMessageKeywords.VariableScope.value.TRAINABLE_PARAMS.value: {}
+            }
+        }
+        outputs_list = [f"opt_{{{variable_slot}}}"]
+        outputs_mapping = ((0, 0),)
         return template, exchange_msg, outputs_list, outputs_mapping