set value node & parameter's device dtype to the node connected with's device info

mindspore/ccsrc/device/ascend/kernel_select_ascend.cc

@@ -176,7 +176,7 @@ void SetTensorDeviceInfo(const kernel::KernelBuildInfo &selected_kernel_info, co
     if (AnfAlgo::GetOutputDeviceDataType(real_input_node, 0) == kTypeUnknown || is_ref) {
       std::vector<std::string> output_format = {selected_kernel_info.GetInputFormat(input_index)};
       builder->SetOutputsFormat(output_format);
-      std::vector<TypeId> output_type = {AnfAlgo::GetOutputInferDataType(real_input_node, 0)};
+      std::vector<TypeId> output_type = {AnfAlgo::GetInputDeviceDataType(kernel_node, input_index)};
       builder->SetOutputsDeviceType(output_type);
       AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), real_input_node.get());
     }

mindspore/ccsrc/pre_activate/ascend/ascend_helper.cc

@@ -298,7 +298,10 @@ CNodePtr InsertCastForInput(const FuncGraphPtr &func_graph, const CNodePtr &cnod
     auto cur_input = AnfAlgo::GetInputNode(cnode, input_index);
     auto kernel_with_index = AnfAlgo::VisitKernel(cur_input, 0);
     auto is_weight_boundary = [](const AnfNodePtr &node) -> bool {
-      if (node->isa<ValueNode>() || node->isa<Parameter>()) {
+      if (node->isa<ValueNode>()) {
+        return true;
+      }
+      if (node->isa<Parameter>() && AnfAlgo::IsParameterWeight(node->cast<ParameterPtr>())) {
         return true;
       }
       return false;