[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):
     """