diff --git a/debug/accuracy_tools/api_accuracy_checker/dump/api_info.py b/debug/accuracy_tools/api_accuracy_checker/dump/api_info.py
index adb0c4b0f34215b93c2bcbcce1d8bbd4bdac877b..12d59820c6fb61821a05fc644577bce0dd839a35 100644
--- a/debug/accuracy_tools/api_accuracy_checker/dump/api_info.py
+++ b/debug/accuracy_tools/api_accuracy_checker/dump/api_info.py
@@ -2,6 +2,7 @@
 import os
 import inspect
 import torch
+import numpy as np
 from api_accuracy_checker.common.config import msCheckerConfig
 from api_accuracy_checker.common.utils import print_error_log, write_pt, create_directory, DumpException
 from ptdbg_ascend.src.python.ptdbg_ascend.common.utils import check_path_before_create
@@ -92,6 +93,10 @@ class APIInfo:
                 else:
                     out_dict[key] = self.analyze_element(value)
             return out_dict
+        
+        converted_numpy, numpy_type = self._convert_numpy_to_builtin(element)
+        if converted_numpy is not element:
+            return self._analyze_numpy(converted_numpy, numpy_type)
 
         if isinstance(element, torch.Tensor):
             return self._analyze_tensor(element)
@@ -135,6 +140,29 @@ class APIInfo:
             single_arg.update({'type': get_type_name(str(type(arg)))})
             single_arg.update({'value': arg})
         return single_arg
+    
+    def _analyze_numpy(self, value, numpy_type):
+        single_arg = {}
+        if self.is_save_data:
+            self.args_num += 1
+        single_arg.update({'type': numpy_type})
+        single_arg.update({'value': value})
+        return single_arg
+    
+    def _convert_numpy_to_builtin(self, arg):
+        type_mapping = {
+            np.integer: int,
+            np.floating: float,
+            np.bool_: bool,
+            np.complexfloating: complex,
+            np.str_: str,
+            np.bytes_: bytes,
+            np.unicode_: str
+        }
+        for numpy_type, builtin_type in type_mapping.items():
+            if isinstance(arg, numpy_type):
+                return builtin_type(arg), get_type_name(str(type(arg)))
+        return arg, ''
 
 
 class ForwardAPIInfo(APIInfo):
diff --git a/debug/accuracy_tools/api_accuracy_checker/run_ut/data_generate.py b/debug/accuracy_tools/api_accuracy_checker/run_ut/data_generate.py
index 5765f980d2850e9caee6147230e8fbd48aec46be..21bc23cfb86d79d99e9b42fb3d35e7a173e8de4a 100644
--- a/debug/accuracy_tools/api_accuracy_checker/run_ut/data_generate.py
+++ b/debug/accuracy_tools/api_accuracy_checker/run_ut/data_generate.py
@@ -17,7 +17,7 @@
 
 import os
 import torch
-import numpy as np
+import numpy
 
 from api_accuracy_checker.common.utils import Const, check_file_or_directory_path, check_object_type, print_warn_log, print_error_log, \
     CompareException
@@ -26,6 +26,9 @@ TORCH_TYPE = ["torch.device", "torch.dtype"]
 TENSOR_DATA_LIST = ["torch.Tensor", "torch.nn.parameter.Parameter"]
 FLOAT_TYPE = ['torch.float32', 'torch.float', 'torch.float64', 'torch.double', 'torch.float16',
               'torch.half', 'torch.bfloat16']
+NUMPY_TYPE = ["numpy.int8", "numpy.int16", "numpy.int32", "numpy.int64", "numpy.uint8", "numpy.uint16", "numpy.uint32",
+              "numpy.uint64", "numpy.float16", "numpy.float32", "numpy.float64", "numpy.float128", "numpy.complex64", 
+              "numpy.complex128", "numpy.complex256", "numpy.bool_", "numpy.string_", "numpy.bytes_", "numpy.unicode_"]
 
 
 def gen_data(info, need_grad, convert_type):
@@ -50,6 +53,14 @@ def gen_data(info, need_grad, convert_type):
             temp_data = data * 1
             data = temp_data.type_as(data)
             data.retain_grad()
+    elif data_type.startswith("numpy"):
+        if data_type not in NUMPY_TYPE:
+            raise Exception("{} is not supported now".format(data_type))
+        data = info.get("value")
+        try:
+            data = eval(data_type)(data)
+        except Exception as err:
+            print_error_log("Failed to convert the type to numpy: %s" % str(err))
     else:
         data = info.get('value')
         if info.get("type") == "slice":
@@ -73,7 +84,7 @@ def gen_real_tensor(data_path, convert_type):
     if data_path.endswith('.pt'):
         data = torch.load(data_path)
     else:
-        data_np = np.load(data_path)
+        data_np = numpy.load(data_path)
         data = torch.from_numpy(data_np)
     if convert_type:
         ori_dtype = Const.CONVERT.get(convert_type)[0]
@@ -193,7 +204,7 @@ def gen_kwargs(api_info, convert_type=None):
     for key, value in kwargs_params.items():
         if isinstance(value, (list, tuple)):
             kwargs_params[key] = gen_list_kwargs(value, convert_type)
-        elif value.get('type') in TENSOR_DATA_LIST:
+        elif value.get('type') in TENSOR_DATA_LIST or value.get('type').startswith("numpy"):
             kwargs_params[key] = gen_data(value, False, convert_type)
         elif value.get('type') in TORCH_TYPE:
             gen_torch_kwargs(kwargs_params, key, value)