[CPU-PSLIB] Add consistency insepection of use_var_list and data_generator data (#34988)

python/paddle/fluid/dataset.py

@@ -321,6 +321,71 @@ class DatasetBase(object):
     def _dynamic_adjust_after_train(self):
         pass
 
+    def check_use_var_with_data_generator(self, var_list, data_generator_class,
+                                          test_file):
+        """
+         Var consistency insepection of use_var_list and data_generator data.
+
+        Examples:
+            .. code-block:: python
+
+              # required: skiptest
+              import paddle.fluid as fluid
+              from dataset_generator import CTRDataset
+              dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+              generator_class = CTRDataset()
+              dataset.check_use_var_with_data_generator([data, label], generator_class, "data/part-00000")
+
+        Args:
+            var_list(list): variable list
+            data_generator_class(class): data_generator class
+            test_file(str): local test file path
+        """
+
+        f = open(test_file, "r")
+        var_len = len(var_list)
+
+        while True:
+            line = f.readline()
+            if line:
+                line_iter = data_generator_class.generate_sample(line)
+                for user_parsed_line in line_iter():
+                    data_gen_len = len(user_parsed_line)
+                    if var_len != data_gen_len:
+                        raise ValueError(
+                            "var length mismatch error: var_list = %s vs data_generator = %s"
+                            % (var_len, data_gen_len))
+
+                    for i, ele in enumerate(user_parsed_line):
+                        if len(ele[1]) == 0:
+                            raise ValueError(
+                                "var length error: var %s's length in data_generator is 0"
+                                % ele[0])
+
+                        if var_list[
+                                i].dtype == core.VarDesc.VarType.FP32 and not all(
+                                    isinstance(ele, float) for ele in ele[1]):
+                            raise TypeError(
+                                "var dtype mismatch error: var name = %s, var type in var_list = %s, while var in data_generator contains non-float value, which is %s \n"
+                                "Please check if order of var_list and data_generator are aligned. \n"
+                                "Please check if var's type in data_generator is correct."
+                                % (ele[0], "float", ele[1]))
+
+                        if (var_list[i].dtype == core.VarDesc.VarType.INT64 or
+                                var_list[i].dtype == core.VarDesc.VarType.INT32
+                            ) and not all(
+                                isinstance(ele, int) for ele in ele[1]):
+                            raise TypeError(
+                                "var dtype mismatch error: var name = %s, var type in var_list = %s, while var in data_generator contains non-int value, which is %s \n"
+                                "Please check if order of var_list and data_generator are aligned. \n"
+                                "Please check if var's type in data_generator is correct."
+                                % (ele[0], "int", ele[1]))
+
+            else:
+                break
+
+        f.close()
+
 
 class InMemoryDataset(DatasetBase):
     """

python/paddle/fluid/tests/unittests/test_dataset_consistency_inspection.py

+#   Copyright (c) 2018 PaddlePaddle Authors. 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.
+"""
+TestCases for Dataset consistency insepection of use_var_list and data_generator.
+"""
+
+from __future__ import print_function
+import paddle
+import paddle.fluid as fluid
+import paddle.compat as cpt
+import paddle.fluid.core as core
+import numpy as np
+import random
+import math
+import os
+import shutil
+import unittest
+import paddle.fluid.incubate.data_generator as dg
+
+#paddle.enable_static()
+# fluid.disable_dygraph()
+fluid.disable_dygraph()
+url_schema_len = 5
+query_schema = [
+    'Q_query_basic', 'Q_query_phrase', 'Q_quq', 'Q_timelevel',
+    'Q_context_title_basic1', 'Q_context_title_basic2',
+    'Q_context_title_basic3', 'Q_context_title_basic4',
+    'Q_context_title_basic5', 'Q_context_title_phrase1',
+    'Q_context_title_phrase2', 'Q_context_title_phrase3',
+    'Q_context_title_phrase4', 'Q_context_title_phrase5', 'Q_context_site1',
+    'Q_context_site2', 'Q_context_site3', 'Q_context_site4', 'Q_context_site5'
+]
+
+
+class CTRDataset(dg.MultiSlotDataGenerator):
+    def __init__(self, mode):
+        self.test = mode
+
+    def generate_sample(self, line):
+        def reader():
+            ins = line.strip().split(';')
+            label_pos_num = int(ins[1].split(' ')[0])
+            label_neg_num = int(ins[1].split(' ')[1])
+
+            #query fea parse
+            bias = 2
+            query_len = 0
+            sparse_query_feature = []
+            for index in range(len(query_schema)):
+                pos = index + bias
+                sparse_query_feature.append(
+                    [int(x) for x in ins[pos].split(' ')])
+                if index == 0:
+                    query_len = len(ins[pos].split(' '))
+                    query_len = 1.0 / (1 + pow(2.7182818, 3 - 1.0 * query_len))
+
+            #positive url fea parse
+            bias = 2 + len(query_schema)
+            pos_url_feas = []
+            pos_click_feas = []
+            pos_context_feas = []
+            for k in range(label_pos_num):
+                pos_url_fea = []
+                pos = 0
+                for index in range(url_schema_len - 1):
+                    pos = bias + k * (url_schema_len) + index
+                    pos_url_fea.append([int(x) for x in ins[pos].split(' ')])
+                #click info
+                if (ins[pos + 1] == ''):
+                    continue
+                item = ins[pos + 1].split(' ')
+                if len(item) != 17:
+                    continue
+                stat_fea = [[max(float(item[i]), 0.0)] for i in range(len(item)) \
+                            if not (i == 5 or i == 9 or i == 13 or i == 14 or i ==15 or i ==16)]
+                pos_url_feas.append(pos_url_fea)
+                pos_click_feas.append(stat_fea)
+
+                query_serach = float(item[5])
+                if query_serach > 0.0:
+                    query_serach = min(math.log(query_serach), 10.0) / 10.0
+                pos_context_fea = [[query_serach], [query_len]]
+                pos_context_feas.append(pos_context_fea)
+
+            #negative url fea parse
+            bias = 2 + len(query_schema) + label_pos_num * (url_schema_len)
+            neg_url_feas = []
+            neg_click_feas = []
+            neg_context_feas = []
+            for k in range(label_neg_num):
+                neg_url_fea = []
+                pos = 0
+                for index in range(url_schema_len - 1):
+                    pos = bias + k * (url_schema_len) + index
+                    neg_url_fea.append([int(x) for x in ins[pos].split(' ')])
+                if (ins[pos + 1] == ''):
+                    continue
+                item = ins[pos + 1].split(' ')
+                #zdf_tmp
+                if len(item) != 17:
+                    continue
+                    #print ins[pos + 1]
+                stat_fea = [[max(float(item[i]), 0.0)] for i in range(len(item)) \
+                            if not (i == 5 or i == 9 or i == 13 or i == 14 or i == 15 or i == 16)]
+                neg_click_feas.append(stat_fea)
+                neg_url_feas.append(neg_url_fea)
+
+                query_serach = float(item[5])
+                if query_serach > 0.0:
+                    query_serach = min(math.log(query_serach), 10.0) / 10.0
+                neg_context_fea = [[query_serach], [query_len]]
+                neg_context_feas.append(neg_context_fea)
+
+            #make train data
+            if self.test == 1:
+                for p in range(len(pos_url_feas)):
+                    # feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2]
+                    feature_name = ["click"]
+                    for i in range(1, 54):
+                        feature_name.append(str(i))
+                    pos_url_fea = pos_url_feas[p]
+                    pos_click_fea = pos_click_feas[p]
+                    pos_context_fea = pos_context_feas[p]
+                    yield zip(feature_name, [[1]] + sparse_query_feature +
+                              pos_url_fea + pos_click_fea + pos_context_fea +
+                              pos_url_fea + pos_click_fea + pos_context_fea)
+                for n in range(len(neg_url_feas)):
+                    feature_name = ["click"]
+                    for i in range(1, 54):
+                        feature_name.append(str(i))
+                    neg_url_fea = neg_url_feas[n]
+                    neg_click_fea = neg_click_feas[n]
+                    neg_context_fea = neg_context_feas[n]
+                    yield zip(feature_name, [[0]] + sparse_query_feature +
+                              neg_url_fea + neg_click_fea + neg_context_fea +
+                              neg_url_fea + neg_click_fea + neg_context_fea)
+            elif self.test == 0:
+                for p in range(len(pos_url_feas)):
+                    #feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2] + url_schema[4:] + click_info_schema[11:] + context_schema[2:]
+                    feature_name = ["click"]
+                    for i in range(1, 54):
+                        feature_name.append(str(i))
+                    #print("#######")
+                    #print(feature_name)
+                    #print("#######")
+                    pos_url_fea = pos_url_feas[p]
+                    pos_click_fea = pos_click_feas[p]
+                    pos_context_fea = pos_context_feas[p]
+                    for n in range(len(neg_url_feas)):
+                        # prob = get_rand()
+                        # if prob < sample_rate:
+                        neg_url_fea = neg_url_feas[n]
+                        neg_click_fea = neg_click_feas[n]
+                        neg_context_fea = neg_context_feas[n]
+                        #print("q:", query_feas)
+                        #print("pos:", pos_url_fea)
+                        #print("neg:", neg_url_fea)
+                        # yield zip(feature_name[:3], sparse_query_feature[:3])
+                        yield list(zip(feature_name, [[1]] + sparse_query_feature + pos_url_fea + pos_click_fea + pos_context_fea + \
+                            neg_url_fea + neg_click_fea + neg_context_fea))
+            elif self.test == 2:
+                for p in range(len(pos_url_feas)):
+                    #feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2] + url_schema[4:] + click_info_schema[11:] + context_schema[2:]
+                    feature_name = ["click"]
+                    for i in range(1, 54):
+                        feature_name.append(str(i))
+                    #print("#######")
+                    #print(feature_name)
+                    #print("#######")
+                    pos_url_fea = pos_url_feas[p]
+                    pos_click_fea = pos_click_feas[p]
+                    pos_context_fea = pos_context_feas[p]
+                    for n in range(len(neg_url_feas)):
+                        # prob = get_rand()
+                        # if prob < sample_rate:
+                        neg_url_fea = neg_url_feas[n]
+                        neg_click_fea = neg_click_feas[n]
+                        neg_context_fea = neg_context_feas[n]
+                        #print("q:", query_feas)
+                        #print("pos:", pos_url_fea)
+                        #print("neg:", neg_url_fea)
+                        # yield zip(feature_name[:3], sparse_query_feature[:3])
+                        yield list(zip(feature_name, [[1], [2]] + sparse_query_feature + pos_url_fea + pos_click_fea + pos_context_fea + \
+                            neg_url_fea + neg_click_fea + neg_context_fea))
+            elif self.test == 3:
+                for p in range(len(pos_url_feas)):
+                    #feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2] + url_schema[4:] + click_info_schema[11:] + context_schema[2:]
+                    feature_name = ["click"]
+                    for i in range(1, 54):
+                        feature_name.append(str(i))
+                    #print("#######")
+                    #print(feature_name)
+                    #print("#######")
+                    pos_url_fea = pos_url_feas[p]
+                    pos_click_fea = pos_click_feas[p]
+                    pos_context_fea = pos_context_feas[p]
+                    for n in range(len(neg_url_feas)):
+                        # prob = get_rand()
+                        # if prob < sample_rate:
+                        neg_url_fea = neg_url_feas[n]
+                        neg_click_fea = neg_click_feas[n]
+                        neg_context_fea = neg_context_feas[n]
+                        #print("q:", query_feas)
+                        #print("pos:", pos_url_fea)
+                        #print("neg:", neg_url_fea)
+                        # yield zip(feature_name[:3], sparse_query_feature[:3])
+                        yield list(zip(feature_name, [[1], [2.0]] + sparse_query_feature + pos_url_fea + pos_click_fea + pos_context_fea + \
+                            neg_url_fea + neg_click_fea + neg_context_fea))
+            elif self.test == 4:
+                for p in range(len(pos_url_feas)):
+                    #feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2] + url_schema[4:] + click_info_schema[11:] + context_schema[2:]
+                    feature_name = ["click"]
+                    for i in range(1, 54):
+                        feature_name.append(str(i))
+                    #print("#######")
+                    #print(feature_name)
+                    #print("#######")
+                    pos_url_fea = pos_url_feas[p]
+                    pos_click_fea = pos_click_feas[p]
+                    pos_context_fea = pos_context_feas[p]
+                    for n in range(len(neg_url_feas)):
+                        # prob = get_rand()
+                        # if prob < sample_rate:
+                        neg_url_fea = neg_url_feas[n]
+                        neg_click_fea = neg_click_feas[n]
+                        neg_context_fea = neg_context_feas[n]
+                        #print("q:", query_feas)
+                        #print("pos:", pos_url_fea)
+                        #print("neg:", neg_url_fea)
+                        # yield zip(feature_name[:3], sparse_query_feature[:3])
+                        yield list(zip(feature_name, [[], [2.0]] + sparse_query_feature + pos_url_fea + pos_click_fea + pos_context_fea + \
+                            neg_url_fea + neg_click_fea + neg_context_fea))
+            elif self.test == 5:
+                for p in range(len(pos_url_feas)):
+                    #feature_name = ["click"] + query_schema + url_schema[:4] + click_info_schema[:11] + context_schema[:2] + url_schema[4:] + click_info_schema[11:] + context_schema[2:]
+                    feature_name = ["click"]
+                    for i in range(1, 54):
+                        feature_name.append(str(i))
+                    #print("#######")
+                    #print(feature_name)
+                    #print("#######")
+                    pos_url_fea = pos_url_feas[p]
+                    pos_click_fea = pos_click_feas[p]
+                    pos_context_fea = pos_context_feas[p]
+                    for n in range(len(neg_url_feas)):
+                        # prob = get_rand()
+                        # if prob < sample_rate:
+                        neg_url_fea = neg_url_feas[n]
+                        neg_click_fea = neg_click_feas[n]
+                        neg_context_fea = neg_context_feas[n]
+                        #print("q:", query_feas)
+                        #print("pos:", pos_url_fea)
+                        #print("neg:", neg_url_fea)
+                        # yield zip(feature_name[:3], sparse_query_feature[:3])
+                        yield list(zip(feature_name, sparse_query_feature + pos_url_fea + pos_click_fea + pos_context_fea + \
+                            neg_url_fea + neg_click_fea + neg_context_fea))
+
+        return reader
+
+
+class TestDataset(unittest.TestCase):
+    """  TestCases for Dataset. """
+
+    def setUp(self):
+        pass
+        # use_data_loader = False
+        # epoch_num = 10
+        # drop_last = False
+
+    def test_var_consistency_insepection(self):
+        """
+        Testcase for InMemoryDataset of consistency insepection of use_var_list and data_generator.
+        """
+        with open("test_run_with_dump_a.txt", "w") as f:
+            # data = "\n"
+            # data += "\n"
+            data = "2 1;1 9;20002001 20001240 20001860 20003611 20000723;20002001 20001240 20001860 20003611 20000723;0;40000001;20002001 20001240 20001860 20003611 20000157 20000723 20000070 20002616 20000157 20000005;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20002616 20000157 20000005;20002001 20001240 20001860 20003611 20000723 20000070 20002001 20001240 20001860 20003611 20012788 20000157;20002001 20001240 20001860 20003611 20000623 20000251 20000157 20000723 20000070 20000001 20000057;20002640 20004695 20000157 20000723 20000070 20002001 20001240 20001860 20003611;20002001 20001240 20001860 20003611 20000157 20000723 20000070 20003519 20000005;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20003519 20000005;20002001 20001240 20001860 20003611 20000723 20000070 20002001 20001240 20001860 20003611 20131464;20002001 20001240 20001860 20003611 20018820 20000157 20000723 20000070 20000001 20000057;20002640 20034154 20000723 20000070 20002001 20001240 20001860 20003611;10000200;10000200;10063938;10000008;10000177;20002001 20001240 20001860 20003611 20010833 20000210 20000500 20000401 20000251 20012198 20001023 20000157;20002001 20001240 20001860 20003611 20012396 20000500 20002513 20012198 20001023 20000157;10000123;30000004;0.623 0.233 0.290 0.208 0.354 49.000 0.000 0.000 0.000 -1.000 0.569 0.679 0.733 53 17 2 0;20002001 20001240 20001860 20003611 20000723;20002001 20001240 20001860 20003611 20000723;10000047;30000004;0.067 0.000 0.161 0.005 0.000 49.000 0.000 0.000 0.000 -1.000 0.000 0.378 0.043 0 6 0 0;20002001 20001240 20001860 20003611 20000157 20000723 20000070 20002616 20000157 20000005;20002001 20001240 20001860 20003611 20000157 20000723 20000070 20003519 20000005;10000200;30000001;0.407 0.111 0.196 0.095 0.181 49.000 0.000 0.000 0.000 -1.000 0.306 0.538 0.355 48 8 0 0;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20002616 20000157 20000005;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20003519 20000005;10000200;30000001;0.226 0.029 0.149 0.031 0.074 49.000 0.000 0.000 0.000 -1.000 0.220 0.531 0.286 26 6 0 0;20002001 20001240 20001860 20003611 20000723 20000070 20002001 20001240 20001860 20003611 20012788 20000157;20002001 20001240 20001860 20003611 20000723 20000070 20002001 20001240 20001860 20003611 20131464;10063938;30000001;0.250 0.019 0.138 0.012 0.027 49.000 0.000 0.000 0.000 -1.000 0.370 0.449 0.327 7 2 0 0;20002001 20001240 20001860 20003611 20000723;20002001 20001240 20001860 20003611 20000723;10000003;30000002;0.056 0.000 0.139 0.003 0.000 49.000 0.000 0.000 0.000 -1.000 0.000 0.346 0.059 15 3 0 0;20002001 20001240 20001860 20003611 20000623 20000251 20000157 20000723 20000070 20000001 20000057;20002001 20001240 20001860 20003611 20018820 20000157 20000723 20000070 20000001 20000057;10000008;30000001;0.166 0.004 0.127 0.001 0.004 49.000 0.000 0.000 0.000 -1.000 0.103 0.417 0.394 10 3 0 0;20002640 20004695 20000157 20000723 20000070 20002001 20001240 20001860 20003611;20002640 20034154 20000723 20000070 20002001 20001240 20001860 20003611;10000177;30000001;0.094 0.008 0.157 0.012 0.059 49.000 0.000 0.000 0.000 -1.000 0.051 0.382 0.142 21 0 0 0;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20000157;20002001 20001240 20001860 20003611 20000157 20001776 20000070 20000157;10000134;30000001;0.220 0.016 0.181 0.037 0.098 49.000 0.000 0.000 0.000 -1.000 0.192 0.453 0.199 17 1 0 0;20002001 20001240 20001860 20003611 20002640 20004695 20000157 20000723 20000070 20002001 20001240 20001860 20003611;20002001 20001240 20001860 20003611 20002640 20034154 20000723 20000070 20002001 20001240 20001860 20003611;10000638;30000001;0.000 0.000 0.000 0.000 0.000 49.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 0 0 0;\n"
+            data += "2 1;1 11;20000025 20000404;20001923;20000002 20000157 20000028 20004205 20000500 20028809 20000571 20000007 20027523 20004940 20000651 20000043 20000051 20000520 20015398 20000066 20004720 20000070 20001648;40000001;20000025 20000404 20000571 20004940 20000001 20000017;20000025 20000404 20000029 20000500 20001408 20000404 20000001 20000017;0;0;0;20001923 20011130 20000027;20001923 20000029 20000500 20001408 20000404 20000027;0;0;0;10000005;10000005;0;0;0;20003316 20000392 20001979 20000474 20000025 20000194 20000025 20000404 20000019 20000109;20016528 20024913 20004748 20001923 20000019 20000109;10000015;30000002;0.572 0.043 0.401 0.352 0.562 32859.000 0.005 0.060 0.362 -1.000 0.448 0.673 0.222 16316 991 89 0;20000025 20000404 20000571 20004940 20000001 20000017;20001923 20011130 20000027;10000005;30000001;0.495 0.024 0.344 0.285 0.379 32859.000 0.002 0.050 0.362 -1.000 0.423 0.764 0.254 19929 896 72 0;20000202 20000026 20001314 20004289 20000025 20000404 20000451 20000089 20000007;20000202 20000026 20014094 20001314 20004289 20001923 20000451 20000089 20000007;10000035;30000003;0.133 0.006 0.162 0.042 0.174 32859.000 0.003 0.037 0.362 -1.000 0.363 0.542 0.122 14763 664 53 0;20000202 20000026 20001314 20004289 20000025 20000404;20000202 20000026 20014094 20001314 20004289 20001923;10000021;30000001;0.058 0.004 0.133 0.017 0.120 32859.000 0.000 0.006 0.362 -1.000 0.168 0.437 0.041 -1 -1 -1 -1;20000025 20000404 20000018 20012461 20001699 20000446 20000174 20000062 20000133 20003172 20000240 20007877 20067375 20000111 20000164 20001410 20000204 20016958;20001923 20000018 20012461 20001699 20007717 20000062 20000133 20003172 20000240 20007877 20067375 20000111 20000164 20001410 20000204 20016958;10000002;30000001;0.017 0.000 0.099 0.004 0.072 32859.000 0.000 0.009 0.362 -1.000 0.058 0.393 0.025 -1 -1 -1 -1;20000025 20000404;20001923;10000133;30000005;0.004 0.000 0.122 0.000 0.000 32859.000 0.000 0.000 0.362 -1.000 0.000 0.413 0.020 0 444 35 0;20000025 20000404;20001923;10005297;30000004;0.028 0.000 0.138 0.002 0.000 32859.000 0.000 0.000 0.362 -1.000 0.000 0.343 0.024 0 600 48 0;20000025 20000404;20001923;10000060;30000005;0.107 0.000 0.110 0.027 0.077 32859.000 0.000 0.005 0.362 -1.000 0.095 0.398 0.062 1338 491 39 0;20002960 20005534 20000043 20000025 20000404 20000025 20000007;20002960 20005534 20000043 20001923 20000025 20000007;10000020;30000003;0.041 0.000 0.122 0.012 0.101 32859.000 0.001 0.025 0.362 -1.000 0.302 0.541 0.065 9896 402 35 0;20000025 20000404 20000259 20000228 20000235 20000142;20001923 20000259 20000264 20000142;10000024;30000003;0.072 0.002 0.156 0.026 0.141 32859.000 0.002 0.032 0.362 -1.000 0.386 0.569 0.103 9896 364 35 0;20000025 20000404 20000029 20000500 20001408 20000404 20000001 20000017;20001923 20000029 20000500 20001408 20000404 20000027;10000005;30000001;0.328 0.006 0.179 0.125 0.181 32859.000 0.003 0.058 0.362 -1.000 0.300 0.445 0.141 9896 402 32 0;20000025 20000404;20001923;10012839;30000002;0.012 0.000 0.108 0.002 0.048 32859.000 0.000 0.000 0.362 -1.000 0.021 0.225 0.016 2207 120 12 0;\n"
+            # data += ""
+            f.write(data)
+
+        slot_data = []
+        label = fluid.layers.data(
+            name="click",
+            shape=[-1, 1],
+            dtype="int64",
+            lod_level=0,
+            append_batch_size=False)
+        slot_data.append(label)
+
+        # sprase_query_feat_names
+        len_sparse_query = 19
+        for feat_name in range(1, len_sparse_query + 1):
+            slot_data.append(
+                fluid.layers.data(
+                    name=str(feat_name), shape=[1], dtype='int64', lod_level=1))
+
+        # sparse_url_feat_names 
+        for feat_name in range(len_sparse_query + 1, len_sparse_query + 5):
+            slot_data.append(
+                fluid.layers.data(
+                    name=str(feat_name), shape=[1], dtype='int64', lod_level=1))
+
+        # dense_feat_names
+        for feat_name in range(len_sparse_query + 5, len_sparse_query + 16):
+            slot_data.append(
+                fluid.layers.data(
+                    name=str(feat_name), shape=[1], dtype='float32'))
+
+        # context_feat_namess
+        for feat_name in range(len_sparse_query + 16, len_sparse_query + 18):
+            slot_data.append(
+                fluid.layers.data(
+                    name=str(feat_name), shape=[1], dtype='float32'))
+
+        # neg sparse_url_feat_names 
+        for feat_name in range(len_sparse_query + 18, len_sparse_query + 22):
+            slot_data.append(
+                fluid.layers.data(
+                    name=str(feat_name), shape=[1], dtype='int64', lod_level=1))
+
+        # neg dense_feat_names
+        for feat_name in range(len_sparse_query + 22, len_sparse_query + 33):
+            slot_data.append(
+                fluid.layers.data(
+                    name=str(feat_name), shape=[1], dtype='float32'))
+
+        # neg context_feat_namess
+        for feat_name in range(len_sparse_query + 33, len_sparse_query + 35):
+            slot_data.append(
+                fluid.layers.data(
+                    name=str(feat_name), shape=[1], dtype='float32'))
+
+        dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+
+        print("========================================")
+        generator_class = CTRDataset(mode=0)
+        try:
+            dataset.check_use_var_with_data_generator(
+                slot_data, generator_class, "test_run_with_dump_a.txt")
+            print("case 1: check passed!")
+        except Exception as e:
+            print("warning: catch expected error")
+            print(e)
+        print("========================================")
+        print("\n")
+
+        print("========================================")
+        generator_class = CTRDataset(mode=2)
+        try:
+            dataset.check_use_var_with_data_generator(
+                slot_data, generator_class, "test_run_with_dump_a.txt")
+        except Exception as e:
+            print("warning: case 2 catch expected error")
+            print(e)
+        print("========================================")
+        print("\n")
+
+        print("========================================")
+        generator_class = CTRDataset(mode=3)
+        try:
+            dataset.check_use_var_with_data_generator(
+                slot_data, generator_class, "test_run_with_dump_a.txt")
+        except Exception as e:
+            print("warning: case 3 catch expected error")
+            print(e)
+        print("========================================")
+        print("\n")
+
+        print("========================================")
+        generator_class = CTRDataset(mode=4)
+        try:
+            dataset.check_use_var_with_data_generator(
+                slot_data, generator_class, "test_run_with_dump_a.txt")
+        except Exception as e:
+            print("warning: case 4 catch expected error")
+            print(e)
+        print("========================================")
+        print("\n")
+
+        print("========================================")
+        generator_class = CTRDataset(mode=5)
+        try:
+            dataset.check_use_var_with_data_generator(
+                slot_data, generator_class, "test_run_with_dump_a.txt")
+        except Exception as e:
+            print("warning: case 5 catch expected error")
+            print(e)
+        print("========================================")
+
+        os.remove("./test_run_with_dump_a.txt")
+
+
+if __name__ == '__main__':
+    unittest.main()