diff --git a/debug/accuracy_tools/api_accuracy_checker/common/utils.py b/debug/accuracy_tools/api_accuracy_checker/common/utils.py
index 24959a62950f03974aaf2822dc5cc83747475cfd..5a6e409aaeef0af881858a301a5db4eb0ee63585 100644
--- a/debug/accuracy_tools/api_accuracy_checker/common/utils.py
+++ b/debug/accuracy_tools/api_accuracy_checker/common/utils.py
@@ -211,6 +211,21 @@ def check_mode_valid(mode):
         raise CompareException(CompareException.INVALID_DUMP_MODE, msg)
 
 
+def check_object_type(check_object, allow_type):
+    """
+    Function Description:
+        Check if the object belongs to a certain data type
+    Parameter:
+        check_object: the object to be checked
+        allow_type: legal data type
+    Exception Description:
+        when invalid data throw exception
+    """
+    if not isinstance(check_object, allow_type):
+        print_error_log(f"{check_object} not of {allow_type} type")
+        raise CompareException(CompareException.INVALID_DATA_ERROR)
+
+
 def check_file_or_directory_path(path, isdir=False):
     """
     Function Description:
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 5976d90049e5f42e1ef0cb60e14037d5813e5c76..9c0e4e8ea6b8dcbcfbd163d706e1702bf86607ad 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
@@ -1 +1,195 @@
-# 用于解析落盘json，进行数据生成
\ No newline at end of file
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+# Copyright (C) 2022-2023. Huawei Technologies Co., Ltd. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+import os
+import torch
+import numpy as np
+
+from ..common.utils import check_file_or_directory_path, check_object_type, print_warn_log, print_error_log, \
+    CompareException
+
+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']
+
+
+def gen_data(info, need_grad):
+    """
+    Function Description:
+        Based on arg basic information, generate arg data
+    Parameter:
+        info: arg basic information. Dict
+        need_grad: set Tensor grad for backward
+    """
+    check_object_type(info, dict)
+    data_type = info.get('type')
+    data_path = info.get('datapath')
+    if data_type in TENSOR_DATA_LIST:
+        if data_path:
+            data = gen_real_tensor(data_path)
+        else:
+            data = gen_random_tensor(info)
+        if info.get('requires_grad') and need_grad:
+            data.requires_grad_(True)
+            data.retain_grad()
+    else:
+        data = info.get('value')
+        if info.get("type") == "slice":
+            data = slice(*data)
+    return data
+
+
+def gen_real_tensor(data_path):
+    """
+    Function Description:
+        Based on API data path, generate input parameters real data
+    Parameter:
+        data_path: API data path
+    """
+    data_path = os.path.realpath(data_path)
+    check_file_or_directory_path(data_path)
+    if not data_path.endswith('.npy'):
+        print_error_log(f"The file: {data_path} is not a numpy file.")
+        raise CompareException.INVALID_FILE_ERROR
+    data_np = np.load(data_path)
+    data = torch.from_numpy(data_np)
+    return data
+
+
+def gen_random_tensor(info):
+    """
+    Function Description:
+        Based on API MAX and MIN, generate input parameters random data
+    Parameter:
+        info: API data info
+    """
+    check_object_type(info, dict)
+    low, high = info.get('Min'), info.get('Max')
+    data_dtype = info.get('dtype')
+    shape = tuple(info.get('shape'))
+    if not isinstance(low, (int, float)) or not isinstance(high, (int, float)):
+        print_error_log(f'Data info Min: {low} , Max: {high}, info type must be int or float')
+        raise CompareException.INVALID_PARAM_ERROR
+    if data_dtype == "torch.bool":
+        data = gen_bool_tensor(low, high, shape)
+    else:
+        data = gen_common_tensor(low, high, shape, data_dtype)
+    return data
+
+
+def gen_common_tensor(low, high, shape, data_dtype):
+    """
+    Function Description:
+        Based on API basic information, generate int or float tensor
+    Parameter:
+        low: The minimum value in Tensor
+        high: The max value in Tensor
+        shape:The shape of Tensor
+        data_dtype: The data type of Tensor
+    """
+    if data_dtype in FLOAT_TYPE:
+        scale = high - low
+        rand01 = torch.rand(shape, dtype=eval(data_dtype))
+        tensor = rand01 * scale + low
+        tmp_tensor = tensor.reshape(-1)
+        tmp_tensor[0] = low
+        tmp_tensor[-1] = high
+    elif 'int' in data_dtype or 'long' in data_dtype:
+        low, high = int(low), int(high)
+        tensor = torch.randint(low, high + 1, shape, dtype=eval(data_dtype))
+    else:
+        print_error_log('Dtype is not supported: ' + data_dtype)
+        raise NotImplementedError()
+    tmp_tensor = tensor.reshape(-1)
+    tmp_tensor[0] = low
+    tmp_tensor[-1] = high
+    data = tmp_tensor.reshape(shape)
+    return data
+
+
+def gen_bool_tensor(low, high, shape):
+    """
+    Function Description:
+        Based on API basic information, generate bool tensor
+    Parameter:
+        low: The minimum value in Tensor
+        high: The max value in Tensor
+        shape:The shape of Tensor
+    """
+    low, high = int(low), int(high)
+    tensor = torch.randint(low, high + 1, shape)
+    data = torch.gt(tensor, 0)
+    return data
+
+
+def gen_args(args_info, need_grad=True):
+    """
+    Function Description:
+        Based on API basic information, generate input parameters: args, for API forward running
+    Parameter:
+        api_info: API basic information. List
+        need_grad: set Tensor grad for backward
+    """
+    check_object_type(args_info, list)
+    args_result = []
+    for arg in args_info:
+        if isinstance(arg, (list, tuple)):
+            data = gen_args(arg, need_grad)
+        elif isinstance(arg, dict):
+            data = gen_data(arg, need_grad)
+        else:
+            print_warn_log(f'Warning: {arg} is not supported')
+            raise NotImplementedError()
+        args_result.append(data)
+    return args_result
+
+
+def gen_kwargs(api_info):
+    """
+    Function Description:
+        Based on API basic information, generate input parameters: kwargs, for API forward running
+    Parameter:
+        api_info: API basic information. Dict
+    """
+    check_object_type(api_info, dict)
+    kwargs_params = api_info.get("kwargs")
+    for key, value in kwargs_params.items():
+        if value.get('type') in TENSOR_DATA_LIST:
+            kwargs_params[key] = gen_data(value, False)
+        else:
+            kwargs_params[key] = value.get('value')
+    return kwargs_params
+
+
+def gen_api_params(api_info, need_grad=True):
+    """
+    Function Description:
+        Based on API basic information, generate input parameters: args, kwargs, for API forward running
+    Parameter:
+        api_info: API basic information. Dict
+        need_grad: set grad for backward
+    """
+    check_object_type(api_info, dict)
+    kwargs_params = gen_kwargs(api_info)
+    if "inplace" in kwargs_params:
+        need_grad = False
+    if api_info.get("args"):
+        args_params = gen_args(api_info.get("args"), need_grad)
+    else:
+        print_warn_log(f'Warning: No args in {api_info} ')
+        raise NotImplementedError()
+    return args_params, kwargs_params