diff --git a/fastNLP/core/controllers/trainer.py b/fastNLP/core/controllers/trainer.py
index 9d49641b..182cd9f5 100644
--- a/fastNLP/core/controllers/trainer.py
+++ b/fastNLP/core/controllers/trainer.py
@@ -282,32 +282,41 @@ class Trainer(TrainerEventTrigger):
 
         :kwargs:
             * *torch_kwargs* -- 用于在指定 ``driver`` 为 'torch' 时设定具体 driver 实例的一些参数：
+                
                 * ddp_kwargs -- 用于在使用 ``TorchDDPDriver`` 时指定 ``DistributedDataParallel`` 初始化时的参数；例如传入
-                 {'find_unused_parameters': True} 来解决有参数不参与前向运算导致的报错等；
+                {'find_unused_parameters': True} 来解决有参数不参与前向运算导致的报错等；
                 * set_grad_to_none -- 是否在训练过程中在每一次 optimizer 更新后将 grad 置为 None；
                 * torch_non_blocking -- 表示用于 pytorch 的 tensor 的 to 方法的参数 non_blocking；
+            * *paddle_kwargs* -- 用于在指定 ``driver`` 为 'paddle' 时设定具体 driver 实例的一些参数：
+                
+                * fleet_kwargs -- 用于在使用 ``PaddleFleetDriver`` 时指定 ``DataParallel`` 和 ``fleet`` 初始化时的参数，包括：
+                    
+                    * is_collective -- 是否使用 paddle 集群式的分布式训练方法，目前仅支持为 True 的情况；
+                    * role_maker -- 初始化 ``fleet`` 分布式训练 API 时使用的 ``RoleMaker``
+                    * 其它用于初始化 ``DataParallel`` 的参数；
             * *data_device* -- 一个具体的 driver 实例中，有 ``model_device`` 和 ``data_device``，前者表示模型所在的设备，后者表示
-             当 ``model_device`` 为 None 时应当将数据迁移到哪个设备；
+            当 ``model_device`` 为 None 时应当将数据迁移到哪个设备；
 
-             .. note::
+                .. note::
 
                 注意您在绝大部分情况下不会用到该参数！
 
                 1. 当 driver 实例的 ``model_device`` 不为 None 时，该参数无效；
                 2. 对于 pytorch，仅当用户自己通过 ``python -m torch.distributed.launch`` 并且自己初始化 ``init_process_group`` 时，
                 driver 实例的 ``model_device`` 才会为 None；
+                3. 对于 paddle，该参数无效；
 
             * *use_dist_sampler* -- 表示是否使用分布式的 ``sampler``。在多卡时，分布式 ``sampler`` 将自动决定每张卡上读取的 sample ，使得一个 epoch
-             内所有卡的 sample 加起来为一整个数据集的 sample。默认会根据 driver 是否为分布式进行设置。
+            内所有卡的 sample 加起来为一整个数据集的 sample。默认会根据 driver 是否为分布式进行设置。
             * *evaluate_use_dist_sampler* -- 表示在 ``Evaluator`` 中在使用分布式的时候是否将 dataloader 的 ``sampler`` 替换为分布式的 ``sampler``；默认为 ``True``；
             * *output_from_new_proc* -- 应当为一个字符串，表示在多进程的 driver 中其它进程的输出流应当被做如何处理；其值应当为以下之一：
-             ["all", "ignore", "only_error"]；当该参数的值不是以上值时，该值应当表示一个文件夹的名字，我们会将其他 rank 的输出流重定向到
-             log 文件中，然后将 log 文件保存在通过该参数值设定的文件夹中；默认为 "only_error"；
+            ["all", "ignore", "only_error"]；当该参数的值不是以上值时，该值应当表示一个文件夹的名字，我们会将其他 rank 的输出流重定向到
+            log 文件中，然后将 log 文件保存在通过该参数值设定的文件夹中；默认为 "only_error"；
 
-             注意该参数仅当使用分布式的 ``driver`` 时才有效，例如 ``TorchDDPDriver``；
+                注意该参数仅当使用分布式的 ``driver`` 时才有效，例如 ``TorchDDPDriver``；
             * *progress_bar* -- 以哪种方式显示 progress ，目前支持[None, 'raw', 'rich', 'auto'] 或者 RichCallback, RawTextCallback对象，
-             默认为 auto , auto 表示如果检测到当前 terminal 为交互型则使用 RichCallback，否则使用 RawTextCallback对象。如果
-             需要定制 progress bar 的参数，例如打印频率等，可以传入 RichCallback, RawTextCallback 对象。
+            默认为 auto , auto 表示如果检测到当前 terminal 为交互型则使用 RichCallback，否则使用 RawTextCallback对象。如果
+            需要定制 progress bar 的参数，例如打印频率等，可以传入 RichCallback, RawTextCallback 对象。
             * *train_input_mapping* -- 与 input_mapping 一致，但是只用于 ``Trainer`` 中。与 input_mapping 互斥。
             * *train_output_mapping* -- 与 output_mapping 一致，但是只用于 ``Trainer`` 中。与 output_mapping 互斥。
             * *evaluate_input_mapping* -- 与 input_mapping 一致，但是只用于 ``Evaluator`` 中。与 input_mapping 互斥。
diff --git a/fastNLP/core/dataloaders/paddle_dataloader/fdl.py b/fastNLP/core/dataloaders/paddle_dataloader/fdl.py
index 342a6c19..393324d4 100644
--- a/fastNLP/core/dataloaders/paddle_dataloader/fdl.py
+++ b/fastNLP/core/dataloaders/paddle_dataloader/fdl.py
@@ -164,7 +164,7 @@ class PaddleDataLoader(DataLoader):
         """
         获取当前 ``batch`` 中每条数据对应的索引。
 
-        :return: 当前 ``batch`` 数据的索引
+        :return: 当前 ``batch`` 数据的索引；
         """
         return self.cur_batch_indices
 
diff --git a/fastNLP/core/dataloaders/torch_dataloader/fdl.py b/fastNLP/core/dataloaders/torch_dataloader/fdl.py
index 1f737467..456af44f 100644
--- a/fastNLP/core/dataloaders/torch_dataloader/fdl.py
+++ b/fastNLP/core/dataloaders/torch_dataloader/fdl.py
@@ -172,7 +172,7 @@ class TorchDataLoader(DataLoader):
         """
         获取当前 ``batch`` 中每条数据对应的索引。
 
-        :return: 当前 ``batch`` 数据的索引
+        :return: 当前 ``batch`` 数据的索引；
         """
         return self.cur_batch_indices
 
diff --git a/fastNLP/core/dataset/dataset.py b/fastNLP/core/dataset/dataset.py
index 6b908c6a..c592984f 100644
--- a/fastNLP/core/dataset/dataset.py
+++ b/fastNLP/core/dataset/dataset.py
@@ -400,16 +400,22 @@ class DataSet:
                     new_field_name: str = None, num_proc: int = 0,
                     progress_desc: str = None, show_progress_bar: bool = True):
         r"""
-        将 :class:`~DataSet` 每个 ``instance`` 中为 ``field_name`` 的 ``field`` 传给函数 ``func``，并获取函数的返回值。
-
-        :param field_name: 传入 ``func`` 的 ``field`` 名称。
-        :param func: 一个函数，其输入是 ``instance`` 中名为 ``field_name`` 的 ``field`` 的内容。
-        :param new_field_name: 将 ``func`` 返回的内容放入到 ``new_field_name`` 对应的 ``field`` 中，如果名称与已有的 ``field`` 相同
-            则进行覆盖。如果为 ``None`` 则不会覆盖和创建 ``field`` 。
-        :param num_proc: 使用进程的数量。请注意，由于 ``python`` 语言的特性，使用了多少进程就会导致多少倍内存的增长。
-        :param progress_desc: 进度条的描述字符，默认为 ``Main``。
-        :param show_progress_bar: 是否展示进度条；默认为展示。
-        :return: 从函数 ``func`` 中得到的返回值。
+        将 :class:`~DataSet` 每个 ``instance`` 中为 ``field_name`` 的 ``field`` 传给函数 ``func``，并写入到 ``new_field_name``
+        中。
+
+        :param field_name: 传入 ``func`` 的 ``field`` 名称；
+        :param func: 对指定 ``field`` 进行处理的函数，注意其输入应为 ``instance`` 中名为 ``field_name`` 的 ``field`` 的内容；
+        :param new_field_name: 函数执行结果写入的 ``field`` 名称。该函数会将 ``func`` 返回的内容放入到 ``new_field_name`` 对
+            应的 ``field`` 中，注意如果名称与已有的 ``field`` 相同则会进行覆盖。如果为 ``None`` 则不会覆盖和创建 ``field`` ；
+        :param num_proc: 使用进程的数量。
+        
+            .. note::
+            
+                由于 ``python`` 语言的特性，设置该参数后会导致相应倍数的内存增长，这可能会对您程序的执行带来一定的影响。
+
+        :param progress_desc: 进度条的描述字符，默认为 ``Main``；
+        :param show_progress_bar: 是否在处理过程中展示进度条；
+        :return: 从函数 ``func`` 中得到的返回值；
         """
         assert len(self) != 0, "Null DataSet cannot use apply_field()."
         if not self.has_field(field_name=field_name):
diff --git a/fastNLP/core/drivers/choose_driver.py b/fastNLP/core/drivers/choose_driver.py
index 294bfe28..4be1e502 100644
--- a/fastNLP/core/drivers/choose_driver.py
+++ b/fastNLP/core/drivers/choose_driver.py
@@ -23,9 +23,9 @@ def choose_driver(model, driver: Union[str, Driver], device: Optional[Union[int,
     elif driver in {"jittor"}:
         from fastNLP.core.drivers.jittor_driver.initialize_jittor_driver import initialize_jittor_driver
         return initialize_jittor_driver(driver, device, model, **kwargs)
-    elif driver in {"paddle", "fleet"}:
+    elif driver in {"paddle"}:
         from fastNLP.core.drivers.paddle_driver.initialize_paddle_driver import initialize_paddle_driver
         return initialize_paddle_driver(driver, device, model, **kwargs)
     else:
         raise ValueError("Parameter `driver` can only be one of these values: ['torch', 'fairscale', "
-                         "'jittor', 'paddle', 'fleet'].")
\ No newline at end of file
+                         "'jittor', 'paddle'].")
\ No newline at end of file
diff --git a/fastNLP/core/drivers/jittor_driver/initialize_jittor_driver.py b/fastNLP/core/drivers/jittor_driver/initialize_jittor_driver.py
index e2d8aadb..4b1fcba7 100644
--- a/fastNLP/core/drivers/jittor_driver/initialize_jittor_driver.py
+++ b/fastNLP/core/drivers/jittor_driver/initialize_jittor_driver.py
@@ -7,18 +7,22 @@ from fastNLP.envs.imports import _NEED_IMPORT_JITTOR
 if _NEED_IMPORT_JITTOR:
     import jittor
 
+__all__ = []
+
 def initialize_jittor_driver(driver: str, device: Union[str, int, List[int]], model: jittor.Module, **kwargs) -> JittorDriver:
     r"""
-    用来根据参数 `driver` 和 `device` 来确定并且初始化一个具体的 `Driver` 实例然后返回回去；
-    在这个函数中，我们会根据用户设置的device来确定JittorDriver的mode。
+    用来根据参数 ``device`` 来确定并且初始化一个具体的 ``Driver`` 实例然后返回回去。
+
+    .. todo::
+
+        创建多卡的 driver
 
-    :param driver: 该参数的值应为以下之一：["jittor"]；
-    :param device: jittor运行的设备
+    :param driver: 该参数的值应为以下之一：``["jittor"]``；
+    :param device: ``jittor`` 运行的设备；
     :param model: 训练或者评测的具体的模型；
     :param kwargs: 
 
-    :return: 返回一个元组，元组的第一个值是具体的基于 jittor 的 `Driver` 实例，元组的第二个值是该 driver 的名字（用于检测一个脚本中
-     先后 driver 的次序的正确问题）；
+    :return: :class:`~fastNLP.core.JittorSingleDriver` 或 :class:`~fastNLP.core.JittorMPIDriver` 实例；
     """
 
     if driver not in {"jittor"}:
diff --git a/fastNLP/core/drivers/jittor_driver/jittor_driver.py b/fastNLP/core/drivers/jittor_driver/jittor_driver.py
index b751354d..7efff348 100644
--- a/fastNLP/core/drivers/jittor_driver/jittor_driver.py
+++ b/fastNLP/core/drivers/jittor_driver/jittor_driver.py
@@ -24,7 +24,17 @@ if _NEED_IMPORT_JITTOR:
 
 class JittorDriver(Driver):
     r"""
-    Jittor 框架的 Driver
+    ``Jittor`` 框架的 ``Driver``
+
+    .. note::
+
+        这是一个正在开发中的功能，敬请期待。
+
+    .. todo::
+
+        实现 fp16 的设置，且支持 cpu 和 gpu 的切换；
+        实现用于断点重训的 save 和 load 函数；
+
     """
 
     def __init__(self, model, fp16: bool = False, **kwargs):
diff --git a/fastNLP/core/drivers/jittor_driver/mpi.py b/fastNLP/core/drivers/jittor_driver/mpi.py
index bb52f67d..bfa49e68 100644
--- a/fastNLP/core/drivers/jittor_driver/mpi.py
+++ b/fastNLP/core/drivers/jittor_driver/mpi.py
@@ -13,6 +13,14 @@ __all__ = [
 ]
 
 class JittorMPIDriver(JittorDriver):
+    """
+    执行 ``Jittor`` 框架下分布式训练的 ``Driver``。
+
+    .. note::
+
+        这是一个正在开发中的功能，敬请期待。
+
+    """
     def __init__(
         self,
         model,
diff --git a/fastNLP/core/drivers/jittor_driver/single_device.py b/fastNLP/core/drivers/jittor_driver/single_device.py
index ab1e8595..be704e69 100644
--- a/fastNLP/core/drivers/jittor_driver/single_device.py
+++ b/fastNLP/core/drivers/jittor_driver/single_device.py
@@ -16,8 +16,17 @@ __all__ = [
 
 class JittorSingleDriver(JittorDriver):
     r"""
-    用于 cpu 和 单卡 gpu 运算
-    TODO: jittor 的 fp16
+    ``Jittor`` 框架下用于 ``cpu`` 和单卡 ``gpu`` 运算的 ``Driver``。
+
+    .. note::
+
+        这是一个正在开发中的功能，敬请期待。
+
+    .. todo::
+
+        支持 cpu 和 gpu 的切换；
+        实现断点重训中替换 dataloader 的 set_dist_repro_dataloader 函数
+
     """
 
     def __init__(self, model, device=None, fp16: bool = False, **kwargs):
@@ -30,11 +39,6 @@ class JittorSingleDriver(JittorDriver):
         self.world_size = 1
 
     def step(self):
-        """
-        jittor optimizers 的step函数可以传入参数loss
-        此时会同时进行 zero_grad 和 backward
-        为了统一，这里暂不使用这样的方式
-        """
         for optimizer in self.optimizers:
             optimizer.step()
 
diff --git a/fastNLP/core/drivers/jittor_driver/utils.py b/fastNLP/core/drivers/jittor_driver/utils.py
index f8ddbbe1..43be9ac3 100644
--- a/fastNLP/core/drivers/jittor_driver/utils.py
+++ b/fastNLP/core/drivers/jittor_driver/utils.py
@@ -5,10 +5,11 @@ from fastNLP.envs.imports import _NEED_IMPORT_JITTOR
 if _NEED_IMPORT_JITTOR:
     import jittor
 
+__all__ = []
+
 class DummyGradScaler:
     """
     用于仿造的GradScaler对象，防止重复写大量的if判断
-
     """
     def __init__(self, *args, **kwargs):
         pass
diff --git a/fastNLP/core/drivers/paddle_driver/fleet.py b/fastNLP/core/drivers/paddle_driver/fleet.py
index e5b2a06f..03dc3375 100644
--- a/fastNLP/core/drivers/paddle_driver/fleet.py
+++ b/fastNLP/core/drivers/paddle_driver/fleet.py
@@ -1,8 +1,6 @@
 import os
 from typing import List, Union, Optional, Dict, Tuple, Callable
 
-from fastNLP.core.utils.paddle_utils import get_device_from_visible
-
 from .paddle_driver import PaddleDriver
 from .fleet_launcher import FleetLauncher
 from .utils import (
@@ -19,7 +17,9 @@ from fastNLP.core.utils import (
     check_user_specific_params,
     is_in_paddle_dist,
     is_in_paddle_dist,
+    get_paddle_device_id,
 )
+from fastNLP.core.utils.paddle_utils import _convert_data_device
 from fastNLP.envs.distributed import rank_zero_rm
 from fastNLP.core.samplers import (
     ReproduceBatchSampler,
@@ -31,7 +31,12 @@ from fastNLP.core.samplers import (
     re_instantiate_sampler,
     conversion_between_reproducible_and_unrepeated_sampler,
 )
-from fastNLP.envs.env import FASTNLP_DISTRIBUTED_CHECK, FASTNLP_GLOBAL_SEED, FASTNLP_NO_SYNC
+from fastNLP.envs.env import (
+    FASTNLP_DISTRIBUTED_CHECK,
+    FASTNLP_GLOBAL_SEED,
+    FASTNLP_NO_SYNC,
+    USER_CUDA_VISIBLE_DEVICES,
+)
 from fastNLP.core.log import logger
 
 if _NEED_IMPORT_PADDLE:
@@ -51,7 +56,7 @@ class PaddleFleetDriver(PaddleDriver):
     def __init__(
             self, 
             model, 
-            parallel_device: Optional[Union[List[int], int]],
+            parallel_device: Optional[Union[List[str], str]],
             is_pull_by_paddle_run: bool = False,
             fp16: bool = False,
             **kwargs
@@ -185,6 +190,8 @@ class PaddleFleetDriver(PaddleDriver):
             不管是什么情况，`PaddleFleetDriver` 在 `setup` 函数的最后，都会将所有进程的 pid 主动记录下来，这样当一个进程出现 exception 后，
              driver 的 on_exception 函数就会被 trainer 调用，其会调用 os.kill 指令将其它进程 kill 掉；
         """
+        if USER_CUDA_VISIBLE_DEVICES not in os.environ:
+            raise RuntimeError("To run paddle distributed training, please set `FASTNLP_BACKEND` to 'paddle' before using FastNLP.")
         super(PaddleFleetDriver, self).__init__(model, fp16=fp16, **kwargs)
 
         # 如果不是通过 launch 启动，要求用户必须传入 parallel_device
@@ -213,25 +220,6 @@ class PaddleFleetDriver(PaddleDriver):
                     "you initialize the paddle distribued process out of our control.")
 
             self.outside_fleet = True
-            # 用户只有将模型上传到对应机器上后才能用 DataParallel 包裹，因此如果用户在外面初始化了 Fleet，那么在 PaddleFleetDriver 中
-            # 我们就直接将 model_device 置为 None；
-            self._model_device = None
-
-        # 当参数 `device` 为 None 时并且该参数不为 None，表示将对应的数据移到指定的机器上；
-        self._data_device = kwargs.get("data_device", None)
-        if self._data_device is not None:
-            if isinstance(self._data_device, int):
-                if self._data_device < 0:
-                    raise ValueError("Parameter `data_device` can not be smaller than 0.")
-                _could_use_device_num = paddle.device.cuda.device_count()
-                if self._data_device >= _could_use_device_num:
-                    raise ValueError("The gpu device that parameter `device` specifies is not existed.")
-                self._data_device = f"gpu:{self._data_device}"
-            elif not isinstance(self._data_device, str):
-                raise ValueError("Parameter `device` is wrong type, please check our documentation for the right use.")
-            if self.outside_fleet and paddle.device.get_device() != self._data_device:
-                logger.warning("`Parameter data_device` is not equal to paddle.deivce.get_device(), "
-                                "please keep them equal to avoid some potential bugs.")
 
         self.world_size = None
         self.global_rank = 0
@@ -304,7 +292,8 @@ class PaddleFleetDriver(PaddleDriver):
             else:
                 # 已经设置过一次，保证参数必须是一样的
                 pre_gpus = os.environ[FASTNLP_DISTRIBUTED_CHECK]
-                pre_gpus = [int (x) for x in pre_gpus.split(",")]
+                pre_gpus = [int(x) for x in pre_gpus.split(",")]
+                cur_gpus = [get_paddle_device_id(g) for g in self.parallel_device]
                 if sorted(pre_gpus) != sorted(self.parallel_device):
                     raise RuntimeError("Notice you are using `PaddleFleetDriver` after one instantiated `PaddleFleetDriver`, it is not"
                                     "allowed that your second `PaddleFleetDriver` has a new setting of parameters `parallel_device`.")
@@ -410,8 +399,6 @@ class PaddleFleetDriver(PaddleDriver):
 
     @property
     def data_device(self):
-        if self.outside_fleet:
-            return self._data_device
         return self.model_device
 
     def model_call(self, batch, fn: Callable, signature_fn: Optional[Callable]) -> Dict:
@@ -565,7 +552,7 @@ class PaddleFleetDriver(PaddleDriver):
 
     def broadcast_object(self, obj, src:int=0, group=None, **kwargs):
         # 因为设置了CUDA_VISIBLE_DEVICES，可能会引起错误
-        device = get_device_from_visible(self.data_device)
+        device = _convert_data_device(self.data_device)
         return fastnlp_paddle_broadcast_object(obj, src, device=device, group=group)
 
     def all_gather(self, obj, group=None) -> List:
diff --git a/fastNLP/core/drivers/paddle_driver/fleet_launcher.py b/fastNLP/core/drivers/paddle_driver/fleet_launcher.py
index ca341db5..b53680cc 100644
--- a/fastNLP/core/drivers/paddle_driver/fleet_launcher.py
+++ b/fastNLP/core/drivers/paddle_driver/fleet_launcher.py
@@ -11,11 +11,14 @@ from fastNLP.envs.env import (
     FASTNLP_LOG_LEVEL,
     FASTNLP_GLOBAL_SEED,
 )
+from fastNLP.core.utils import get_paddle_device_id
 from .utils import (
     find_free_ports,
     reset_seed,
 )
 
+__all__ = []
+
 # 记录各个进程信息
 class SubTrainer(object):
     """
@@ -34,11 +37,11 @@ class FleetLauncher:
     """
     def __init__(
         self,
-        devices: List[int],
+        devices: List[str],
         output_from_new_proc: str = "only_error"
     ):
 
-        self.devices = devices
+        self.devices = [ get_paddle_device_id(g) for g in devices]
         self.output_from_new_proc = output_from_new_proc
 
         self.setup()
diff --git a/fastNLP/core/drivers/paddle_driver/initialize_paddle_driver.py b/fastNLP/core/drivers/paddle_driver/initialize_paddle_driver.py
index 60e8afc0..22098ff2 100644
--- a/fastNLP/core/drivers/paddle_driver/initialize_paddle_driver.py
+++ b/fastNLP/core/drivers/paddle_driver/initialize_paddle_driver.py
@@ -7,50 +7,58 @@ from .single_device import PaddleSingleDriver
 from .fleet import PaddleFleetDriver
 
 from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
-from fastNLP.core.utils import is_in_paddle_launch_dist
+from fastNLP.envs.env import USER_CUDA_VISIBLE_DEVICES
+from fastNLP.core.utils import is_in_paddle_launch_dist, get_paddle_gpu_str
 from fastNLP.core.log import logger
 
 if _NEED_IMPORT_PADDLE:
     import paddle
 
+__all__ = []
+
 def initialize_paddle_driver(driver: str, device: Optional[Union[str, int, List[int]]],
                             model: "paddle.nn.Layer", **kwargs) -> PaddleDriver:
     r"""
-    用来根据参数 `driver` 和 `device` 来确定并且初始化一个具体的 `Driver` 实例然后返回回去；
-    1、如果检测到当前进程为用户通过 `python -m paddle.distributed.launch xxx.py` 方式拉起的，则将
-    设备自动设置为用户指定的设备（由于我们在引入 fastNLP 进行了特殊的设置，因此可以通过 `CUDA_VISIBLE_DEVICES` 获取）
-    2、如果检测到输入的 `driver` 是 `paddle` 但 `device` 包含了多个设备，那么我们会给出警告并且自动返回多卡的 Driver
-    3、如果检测到输入的 `driver` 是 `fleet` 但 `device` 仅有一个设备，那么我们会给出警告但仍旧返回多卡的 Driver
+    用来根据参数 ``device`` 来确定并且初始化一个具体的 ``Driver`` 实例。
+
+    1. 如果检测到当前进程为用户通过 ``python -m paddle.distributed.launch xxx.py`` 方式拉起的，则将
+    设备自动设置为用户指定的设备（由于我们要求分布式训练必须进行 ``backend`` 的设置，因此可以通过 ``CUDA_VISIBLE_DEVICES`` 获取）
+    
+    2. 如果 ``device`` 包含了多个设备，则返回一个 :class:`~fastNLP.core.PaddleFleetDriver` 实例，否则返回
+    单卡的 :class:`~fastNLP.core.PaddleSingleDriver` 实例
 
-    :param driver: 使用的 ``driver`` 类型，在这个函数中仅支持 ``paddle``
-    :param device: 该参数的格式与 `Trainer` 对参数 `device` 的要求一致；
+    :param driver: 使用的 ``driver`` 类型，在这个函数中仅支持 ``paddle``；
+    :param device: 该参数的格式与 ``Trainer`` 对参数 ``device`` 的要求一致；
     :param model: 训练或者评测的具体的模型；
 
-    :return: 返回构造的 `Driver` 实例。
+    :return: 一个 :class:`~fastNLP.core.PaddleSingleDriver` 或 :class:`~fastNLP.core.PaddleFleetDriver` 实例；
     """
     if driver != "paddle":
         raise ValueError("When initialize PaddleDriver, parameter `driver` must be 'paddle'.")
+    user_visible_devices = os.getenv(USER_CUDA_VISIBLE_DEVICES)
     if is_in_paddle_launch_dist():
+        if user_visible_devices is None:
+            raise RuntimeError("To run paddle distributed training, please set `FASTNLP_BACKEND` to 'paddle' before using FastNLP.")
         if device is not None:
             logger.warning_once("Parameter `device` would be ignored when you are using `paddle.distributed.launch` to pull "
-                           "up your script. And we will directly get the local device via "
-                           "and `os.environ['CUDA_VISIBLE_DEVICES']``.")
-        device = [int(g) for g in os.environ["CUDA_VISIBLE_DEVICES"].split(",")]
-        # TODO 目前一个进程仅对应一个卡，所以暂时传入一个 int
+                           "up your script. And we will directly get the local device via environment variables.")
+        _visible_list = user_visible_devices.split(",")
+        device = [ f"gpu:{_visible_list.index(g) }" for g in os.environ["CUDA_VISIBLE_DEVICES"].split(",")]
+        # TODO 目前一个进程仅对应一个卡，所以暂时传入单个
         return PaddleFleetDriver(model, device[0], True, **kwargs)
 
-    user_visible_devices = os.getenv("USER_CUDA_VISIBLE_DEVICES")
     if user_visible_devices is None:
-        raise RuntimeError("`USER_CUDA_VISIBLE_DEVICES` cannot be None, please check if you have set "
-                            "`FASTNLP_BACKEND` to 'paddle' before using FastNLP.")
-    _could_use_device_num = len(user_visible_devices.split(","))
+        _could_use_device_num = paddle.device.cuda.device_count()
+    else:
+        _could_use_device_num = len(user_visible_devices.split(","))
+
     if isinstance(device, int):
         if device < 0 and device != -1:
             raise ValueError("Parameter `device` can only be '-1' when it is smaller than 0.")
         if device >= _could_use_device_num:
             raise ValueError("The gpu device that parameter `device` specifies is not existed.")
         if device == -1:
-            device = list(range(_could_use_device_num))
+            device = [ get_paddle_gpu_str(g) for g in range(_could_use_device_num)]
     elif isinstance(device, Sequence) and not isinstance(device, str):
         device = list(set(device))
         for each in device:
@@ -61,8 +69,10 @@ def initialize_paddle_driver(driver: str, device: Optional[Union[str, int, List[
             elif each >= _could_use_device_num:
                 raise ValueError("When parameter `device` is 'Sequence' type, the value in it should not be bigger than"
                                  " the available gpu number.")
+        device = [get_paddle_gpu_str(g) for g in device]
     elif device is not None and not isinstance(device, str):
         raise ValueError("Parameter `device` is wrong type, please check our documentation for the right use.")
+    
     if isinstance(device, List):
         return PaddleFleetDriver(model, device, **kwargs)
     else:
diff --git a/fastNLP/core/drivers/paddle_driver/paddle_driver.py b/fastNLP/core/drivers/paddle_driver/paddle_driver.py
index 00b0da4e..74c7b7a8 100644
--- a/fastNLP/core/drivers/paddle_driver/paddle_driver.py
+++ b/fastNLP/core/drivers/paddle_driver/paddle_driver.py
@@ -7,10 +7,13 @@ from dataclasses import dataclass
 
 import numpy as np
 
+from fastNLP.envs.env import USER_CUDA_VISIBLE_DEVICES
+
 from .utils import _build_fp16_env, optimizer_state_to_device, DummyGradScaler
 from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
 from fastNLP.core.drivers.driver import Driver
-from fastNLP.core.utils import apply_to_collection, paddle_move_data_to_device, get_device_from_visible
+from fastNLP.core.utils import apply_to_collection, paddle_move_data_to_device
+from fastNLP.core.utils.paddle_utils import _convert_data_device
 from fastNLP.envs import (
     FASTNLP_SEED_WORKERS,
     FASTNLP_MODEL_FILENAME,
@@ -369,7 +372,7 @@ class PaddleDriver(Driver):
 
         :return: 将移动到指定机器上的 batch 对象返回；
         """
-        device = get_device_from_visible(self.data_device)
+        device = _convert_data_device(self.data_device)
         return paddle_move_data_to_device(batch, device)
 
     @staticmethod
diff --git a/fastNLP/core/drivers/paddle_driver/single_device.py b/fastNLP/core/drivers/paddle_driver/single_device.py
index 69b58954..c0957dbf 100644
--- a/fastNLP/core/drivers/paddle_driver/single_device.py
+++ b/fastNLP/core/drivers/paddle_driver/single_device.py
@@ -8,10 +8,10 @@ from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
 from fastNLP.envs.env import USER_CUDA_VISIBLE_DEVICES
 from fastNLP.core.utils import (
     auto_param_call,
-    get_device_from_visible,
     get_paddle_gpu_str,
     get_paddle_device_id,
 )
+from fastNLP.core.utils.paddle_utils import _convert_data_device
 from fastNLP.core.utils.utils import _get_fun_msg
 from fastNLP.core.samplers import (
     ReproducibleBatchSampler,
@@ -40,9 +40,6 @@ class PaddleSingleDriver(PaddleDriver):
             raise ValueError("`paddle.DataParallel` is not supported in `PaddleSingleDriver`")
 
         cuda_visible_devices = os.getenv(USER_CUDA_VISIBLE_DEVICES)
-        if cuda_visible_devices is None:
-            raise RuntimeError("`USER_CUDA_VISIBLE_DEVICES` cannot be None, please check if you have set "
-                            "`FASTNLP_BACKEND` to 'paddle' before using FastNLP.")
         if cuda_visible_devices == "":
             device = "cpu"
             logger.info("You have set `CUDA_VISIBLE_DEVICES` to '' in system environment variable, and we are gonna to"
@@ -54,11 +51,9 @@ class PaddleSingleDriver(PaddleDriver):
             raise ValueError("Parameter `device` can not be None in `PaddleSingleDriver`.")
 
         if device != "cpu":
-            if isinstance(device, int):
-                device_id = device
-            else:
-                device_id = get_paddle_device_id(device)
-            os.environ["CUDA_VISIBLE_DEVICES"] = cuda_visible_devices.split(",")[device_id]
+            device_id = get_paddle_device_id(device)
+            if cuda_visible_devices is not None:
+                os.environ["CUDA_VISIBLE_DEVICES"] = cuda_visible_devices.split(",")[device_id]
         self.model_device = get_paddle_gpu_str(device)
 
         self.local_rank = 0
@@ -69,7 +64,8 @@ class PaddleSingleDriver(PaddleDriver):
         r"""
         该函数用来初始化训练环境，用于设置当前训练的设备，并将模型迁移到对应设备上。
         """
-        device = get_device_from_visible(self.model_device, output_type=str)
+        device = _convert_data_device(self.data_device)
+
         paddle.device.set_device(device)
         with contextlib.redirect_stdout(None):
             self.model.to(device)
diff --git a/fastNLP/core/drivers/torch_driver/initialize_torch_driver.py b/fastNLP/core/drivers/torch_driver/initialize_torch_driver.py
index 025744bb..f9fac83f 100644
--- a/fastNLP/core/drivers/torch_driver/initialize_torch_driver.py
+++ b/fastNLP/core/drivers/torch_driver/initialize_torch_driver.py
@@ -10,19 +10,18 @@ from .ddp import TorchDDPDriver
 from fastNLP.core.log import logger
 from fastNLP.envs import FASTNLP_BACKEND_LAUNCH
 
+__all__ = []
 
 def initialize_torch_driver(driver: str, device: Optional[Union[str, "torch.device", int, List[int]]],
                             model: "torch.nn.Module", **kwargs) -> TorchDriver:
     r"""
-    用来根据参数 `driver` 和 `device` 来确定并且初始化一个具体的 `Driver` 实例然后返回回去；
-    注意如果输入的 `device` 如果和 `driver` 对应不上就直接报错；
+    用来根据参数 ``driver` 和 ``device`` 来确定并且初始化一个具体的 ``Driver`` 实例然后返回回去；
 
-    :param driver: 该参数的值应为以下之一：["torch", "torch_ddp", "fairscale"]；
-    :param device: 该参数的格式与 `Trainer` 对参数 `device` 的要求一致；
+    :param driver: 该参数的值应为以下之一：``["torch", "fairscale"]``；
+    :param device: 该参数的格式与 ``Trainer`` 对参数 ``device`` 的要求一致；
     :param model: 训练或者评测的具体的模型；
 
-    :return: 返回一个元组，元组的第一个值是具体的基于 pytorch 的 `Driver` 实例，元组的第二个值是该 driver 的名字（用于检测一个脚本中
-     先后 driver 的次序的正确问题）；
+    :return: 返回一个 :class:`~fastNLP.core.TorchSingleDriver` 或 :class:`~fastNLP.core.TorchDDPDriver` 实例；
     """
     # world_size 和 rank
     if FASTNLP_BACKEND_LAUNCH in os.environ:
diff --git a/fastNLP/core/metrics/backend/paddle_backend/backend.py b/fastNLP/core/metrics/backend/paddle_backend/backend.py
index aa57bbc2..b8ea9cb0 100644
--- a/fastNLP/core/metrics/backend/paddle_backend/backend.py
+++ b/fastNLP/core/metrics/backend/paddle_backend/backend.py
@@ -1,12 +1,14 @@
+import os
 from typing import List, Any
 
 import numpy as np
 
 from fastNLP.core.metrics.backend import Backend
-from fastNLP.core.utils.paddle_utils import paddle_to, get_device_from_visible
+from fastNLP.core.utils.paddle_utils import paddle_to, _convert_data_device
 from fastNLP.core.metrics.utils import AggregateMethodError
 from fastNLP.core.drivers.paddle_driver.dist_utils import fastnlp_paddle_all_gather
 from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
+from fastNLP.envs.env import USER_CUDA_VISIBLE_DEVICES
 
 if _NEED_IMPORT_PADDLE:
     import paddle
@@ -79,7 +81,7 @@ class PaddleBackend(Backend):
             raise ValueError(f"tensor: {tensor} can not convert to ndarray!")
 
     def move_tensor_to_device(self, tensor, device):
-        device = get_device_from_visible(device)
+        device = _convert_data_device(device)
         return paddle_to(tensor, device)
 
     def all_gather_object(self, obj, group=None) -> List:
diff --git a/fastNLP/core/utils/__init__.py b/fastNLP/core/utils/__init__.py
index aca01344..6c65c8a5 100644
--- a/fastNLP/core/utils/__init__.py
+++ b/fastNLP/core/utils/__init__.py
@@ -2,7 +2,6 @@ __all__ = [
     'cache_results',
     'is_jittor_dataset',
     'jittor_collate_wraps',
-    'get_device_from_visible',
     'paddle_to',
     'paddle_move_data_to_device',
     'get_paddle_device_id',
@@ -28,7 +27,7 @@ __all__ = [
 
 from .cache_results import cache_results
 from .jittor_utils import is_jittor_dataset, jittor_collate_wraps
-from .paddle_utils import get_device_from_visible, paddle_to, paddle_move_data_to_device, get_paddle_device_id, get_paddle_gpu_str, is_in_paddle_dist, \
+from .paddle_utils import paddle_to, paddle_move_data_to_device, get_paddle_device_id, get_paddle_gpu_str, is_in_paddle_dist, \
     is_in_fnlp_paddle_dist, is_in_paddle_launch_dist
 from .rich_progress import f_rich_progress
 from .torch_utils import torch_move_data_to_device
diff --git a/fastNLP/core/utils/jittor_utils.py b/fastNLP/core/utils/jittor_utils.py
index 89686cff..08b3b7a8 100644
--- a/fastNLP/core/utils/jittor_utils.py
+++ b/fastNLP/core/utils/jittor_utils.py
@@ -15,6 +15,12 @@ from fastNLP.core.dataset import Instance
 
 
 def is_jittor_dataset(dataset) -> bool:
+    """
+    判断传入的 ``dataset`` 是否是 :class:`jittor.dataset.Dataset` 类型
+
+    :param dataset: 数据集；
+    :return: 当前 ``dataset`` 是否为 ``jittor`` 的数据集类型；
+    """
     try:
         if isinstance(dataset, jt.dataset.Dataset):
             return True
@@ -26,7 +32,8 @@ def is_jittor_dataset(dataset) -> bool:
 
 def jittor_collate_wraps(func, auto_collator: Callable):
     """
-    对jittor的collate_fn进行wrap封装, 如果数据集为mapping类型，那么采用auto_collator，否则还是采用jittor自带的collate_batch
+    对 ``jittor`` 的 ``collate_fn`` 进行 ``wrap`` 封装,。如果数据集为 ``mapping`` 类型，那么采用 ``auto_collator`` ，否则
+    还是采用 ``jittor`` 的 ``collate_batch``。
 
     :param func:
     :param auto_collator:
diff --git a/fastNLP/core/utils/paddle_utils.py b/fastNLP/core/utils/paddle_utils.py
index db68879f..c7bb9e79 100644
--- a/fastNLP/core/utils/paddle_utils.py
+++ b/fastNLP/core/utils/paddle_utils.py
@@ -1,5 +1,4 @@
 __all__ = [
-    "get_device_from_visible",
     "paddle_to",
     "paddle_move_data_to_device",
     "get_paddle_gpu_str",
@@ -21,73 +20,90 @@ if _NEED_IMPORT_PADDLE:
 
 from .utils import apply_to_collection
 
-def get_device_from_visible(device: Union[str, int], output_type=int):
+def _convert_data_device(device: Union[str, int]) -> str:
     """
-    在有 CUDA_VISIBLE_DEVICES 的情况下，获取对应的设备。
-    如 CUDA_VISIBLE_DEVICES=2,3 ，device=3 ，则返回1。
+    用于转换 ``driver`` 的 ``data_device`` 的函数。如果用户设置了 ``FASTNLP_BACKEND=paddle``，那么 ``fastNLP`` 会将
+    可见的设备保存在 ``USER_CUDA_VISIBLE_DEVICES`` 中，并且将 ``CUDA_VISIBLE_DEVICES`` 设置为可见的第一张显卡；这是为
+    了顺利执行 ``paddle`` 的分布式训练而设置的。
+    
+    在这种情况下，单纯使用 ``driver.data_device`` 是无效的。比如在分布式训练中将设备设置为 ``[0,2,3]`` ，且用户设置了
+    ``CUDA_VISIBLE_DEVICES=3,4,5,6`` ，那么在 ``rank1``的进程中有::
 
-    :param device: 未转化的设备名
-    :param output_type: 返回值的类型
-    :return: 转化后的设备id
-    """
-    if output_type not in [int, str]:
-        raise ValueError("Parameter `output_type` should be one of these types: [int, str]")
-    if device == "cpu":
-        return device
-    cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
-    user_visible_devices = os.getenv(USER_CUDA_VISIBLE_DEVICES)
-    if user_visible_devices is None:
-        raise RuntimeError("`USER_CUDA_VISIBLE_DEVICES` cannot be None, please check if you have set "
-                            "`FASTNLP_BACKEND` to 'paddle' before using FastNLP.")
-    idx = get_paddle_device_id(device)
-    # 利用 USER_CUDA_VISIBLDE_DEVICES 获取用户期望的设备
-    if user_visible_devices is None:
-        raise RuntimeError("This situation cannot happen, please report a bug to us.")
-    idx = user_visible_devices.split(",")[idx]
-
-    cuda_visible_devices_list = cuda_visible_devices.split(',')
-    if idx not in cuda_visible_devices_list:
-        raise ValueError(f"Can't find your devices {idx} in CUDA_VISIBLE_DEVICES[{cuda_visible_devices}]. ")
-    res = cuda_visible_devices_list.index(idx)
-    if output_type == int:
-        return res
-    else:
-        return f"gpu:{res}"
+        os.environ["CUDA_VISIBLE_DEVICES"] = "5"
+        os.environ["USER_CUDA_VISIBLE_DEVICES"] = "3,4,5,6"
+        driver.data_device = "gpu:2" # 为了向用户正确地反映他们设置的设备减少歧义，因此这里没有设置为 "gpu:5"
+
+    此时我们便需要通过这个函数将 ``data_device`` 转换为 ``gpu:0``。具体过程便是通过索引 **2** 在 ``USER_CUDA_VISIBLE_DEVICES`` 中
+    找到设备 **5**，然后在 ``CUDA_VISIBLE_DEVICES`` 中找到设备 **5** 的索引 **0** 返回。
+
+    .. note::
 
-def paddle_to(data, device: Union[str, int]):
+        在分布式单进程仅支持单卡的情况下中，这个函数实际等同于直接转换为 ``gpu:0`` 返回。
+
+    :param device: 未转化的设备；
+    :return: 转化后的设备，格式为 ``gpu:x``；
+    """
+    try:
+        user_visible_devices = os.getenv(USER_CUDA_VISIBLE_DEVICES)
+        if device == "cpu" or user_visible_devices is None:
+            # 传入的是 CPU，或者没有设置 USER_CUDA_VISIBLE_DEVICES
+            # 此时不需要进行转换
+            return get_paddle_gpu_str(device)
+
+        idx = get_paddle_device_id(device)
+        idx = user_visible_devices.split(",")[idx]
+        # 此时 CUDA_VISIBLE_DEVICES 一定不是 None
+        cuda_visible_devices_list = os.getenv("CUDA_VISIBLE_DEVICES").split(',')
+        return f"gpu:{cuda_visible_devices_list.index(idx)}"
+    except Exception as e:
+        raise ValueError(f"Can't convert device {device} when USER_CUDA_VISIBLE_DEVICES={user_visible_devices} "
+                        "and CUDA_VISIBLE_DEVICES={cuda_visible_devices}. If this situation happens, please report this bug to us.")
+
+def paddle_to(data: "paddle.Tensor", device: Union[str, int]) -> "paddle.Tensor":
     """
-    将 `data` 迁移到指定的 `device` 上
+    将 ``data`` 迁移到指定的 ``device`` 上。``paddle.Tensor`` 没有类似 ``torch.Tensor`` 的 ``to`` 函数，该函数
+    只是集成了 :func:`paddle.Tensor.cpu` 和 :func:`paddle.Tensor.cuda` 两个函数。
 
-    :param data: 要迁移的张量
-    :param device: 目标设备，可以是 `str` 或 `int`
-    :return: 迁移后的张量
+    :param data: 要迁移的张量；
+    :param device: 目标设备，可以是 ``str`` 或 ``int`` 类型；
+    :return: 迁移后的张量；
     """
 
     if device == "cpu":
         return data.cpu()
     else:
-        # device = get_device_from_visible(device, output_type=int)
         return data.cuda(get_paddle_device_id(device))
 
-
-def get_paddle_gpu_str(device: Union[str, int]):
+def get_paddle_gpu_str(device: Union[str, int]) -> str:
     """
-    获得 `gpu:x` 类型的设备名
+    获得 ``gpu:x`` 格式的设备名::
 
-    :param device: 设备编号或设备名
-    :return: 返回对应的 `gpu:x` 格式的设备名
+        >>> get_paddle_gpu_str(1)
+        'gpu:1'
+        >>> get_paddle_gpu_str("cuda:1")
+        'gpu:1'
+
+    :param device: 设备编号或设备名；
+    :return: 返回对应的 ``gpu:x`` 格式的设备名；
     """
     if isinstance(device, str):
         return device.replace("cuda", "gpu")
     return f"gpu:{device}"
 
 
-def get_paddle_device_id(device: Union[str, int]):
+def get_paddle_device_id(device: Union[str, int]) -> int:
     """
-    获得 gpu 的设备id
+    获得 ``device`` 的设备编号::
+
+        >>> get_paddle_device_id("gpu:1")
+        1
+        >>> get_paddle_device_id("gpu")
+        0
+
+    请注意不要向这个函数中传入 ``cpu``。
 
-    :param: device: 设备编号或设备名
-    :return: 设备对应的编号
+    :param: device: 设备编号或设备名；
+    :return: 设备对应的编号；
     """
     if isinstance(device, int):
         return device
@@ -109,21 +125,17 @@ def get_paddle_device_id(device: Union[str, int]):
 
     return device_id
 
-def paddle_move_data_to_device(batch: Any, device: Optional[str] = None,
-                              data_device: Optional[str] = None) -> Any:
+def paddle_move_data_to_device(batch: Any, device: Optional[Union[str, int]]) -> Any:
     r"""
-    将数据集合传输到给定设备。只有paddle.Tensor对象会被传输到设备中，其余保持不变
+    将 ``paddle`` 的数据集合传输到给定设备。只有 :class:`paddle.Tensor` 对象会被传输到设备中，其余保持不变。
 
-    :param batch:
-    :param device: `cpu`, `gpu` or `gpu:x`
-    :param data_device:
-    :return: 相同的集合，但所有包含的张量都驻留在新设备上；
+    :param batch: 需要进行迁移的数据集合；
+    :param device: 目标设备。可以是显卡设备的编号，或是``cpu``, ``gpu`` 或 ``gpu:x`` 格式的字符串；当这个参数
+        为 `None`` 时，不会执行任何操作。
+    :return: 迁移到新设备上的数据集合；
     """
     if device is None:
-        if data_device is not None:
-            device = data_device
-        else:
-            return batch
+        return batch
 
     def batch_to(data: Any) -> Any:
         return paddle_to(data, device)
@@ -131,22 +143,22 @@ def paddle_move_data_to_device(batch: Any, device: Optional[str] = None,
     return apply_to_collection(batch, dtype=paddle.Tensor, function=batch_to)
 
 
-def is_in_paddle_dist():
+def is_in_paddle_dist() -> bool:
     """
-    判断是否处于分布式的进程下，使用 global_rank 和 selected_gpus 判断
+    判断是否处于 ``paddle`` 分布式的进程下，使用 ``PADDLE_RANK_IN_NODE`` 和 ``FLAGS_selected_gpus`` 判断。
     """
     return ('PADDLE_RANK_IN_NODE' in os.environ and 'FLAGS_selected_gpus' in os.environ)
 
 
-def is_in_fnlp_paddle_dist():
+def is_in_fnlp_paddle_dist() -> bool:
     """
-    判断是否处于 FastNLP 拉起的分布式进程中
+    判断是否处于 ``fastNLP`` 拉起的 ``paddle`` 分布式进程中
     """
     return FASTNLP_DISTRIBUTED_CHECK in os.environ
 
 
-def is_in_paddle_launch_dist():
+def is_in_paddle_launch_dist() -> bool:
     """
-    判断是否处于 launch 启动的分布式进程中
+    判断是否处于 ``python -m paddle.distributed.launch`` 方法启动的 ``paddle`` 分布式进程中
     """
     return FASTNLP_BACKEND_LAUNCH in os.environ
\ No newline at end of file
diff --git a/fastNLP/core/utils/torch_utils.py b/fastNLP/core/utils/torch_utils.py
index 72f1058f..862ea20d 100644
--- a/fastNLP/core/utils/torch_utils.py
+++ b/fastNLP/core/utils/torch_utils.py
@@ -44,12 +44,12 @@ class TorchTransferableDataType(ABC):
 def torch_move_data_to_device(batch: Any, device: Optional[Union[str, "torch.device"]] = None,
                               non_blocking: Optional[bool] = True) -> Any:
     r"""
-    将数据集合传输到给定设备。任何定义方法 “to(device)” 的对象都将被移动并且集合中的所有其他对象将保持不变；
+    在 ``pytorch`` 中将数据集合 ``batch`` 传输到给定设备。任何定义方法 ``to(device)`` 的对象都将被移动并且集合中的所有其他对象将保持不变；
 
-    :param batch: 应当迁移的数据；
-    :param device: 数据应当迁移到的设备；当该参数的值为 None 时，表示迁移数据的操作由用户自己完成，我们不需要经管；
-    :param non_blocking: pytorch 的迁移数据方法 `to` 的参数；
-    :return: 相同的集合，但所有包含的张量都驻留在新设备上；
+    :param batch: 需要迁移的数据；
+    :param device: 数据应当迁移到的设备；当该参数的值为 ``None`` 时则不执行任何操作；
+    :param non_blocking: ``pytorch`` 的数据迁移方法 ``to`` 的参数；
+    :return: 迁移到新设备上的数据集合；
     """
     if device is None:
         return batch
diff --git a/fastNLP/core/utils/utils.py b/fastNLP/core/utils/utils.py
index ec7a8b47..00da9ac1 100644
--- a/fastNLP/core/utils/utils.py
+++ b/fastNLP/core/utils/utils.py
@@ -38,10 +38,16 @@ __all__ = [
 
 def get_fn_arg_names(fn: Callable) -> List[str]:
     r"""
-    返回一个函数所有参数的名字
+    该函数可以返回一个函数所有参数的名字::
 
-    :param fn: 需要查询的函数
-    :return: 一个列表，其中的元素是函数 ``fn`` 参数的字符串名字
+        >>> def function(a, b=1):
+        ...     return a
+        ...
+        >>> get_fn_arg_names(function)
+        ['a', 'b']
+
+    :param fn: 需要查询的函数；
+    :return: 包含函数 ``fn`` 参数名的列表；
     """
     return list(inspect.signature(fn).parameters)
 
@@ -49,7 +55,7 @@ def get_fn_arg_names(fn: Callable) -> List[str]:
 def auto_param_call(fn: Callable, *args, signature_fn: Optional[Callable] = None,
                     mapping: Optional[Dict[AnyStr, AnyStr]] = None) -> Any:
     r"""
-    该函数会根据输入函数的形参名从 ``*args`` （因此都需要是 ``dict`` 类型）中找到匹配的值进行调用，如果传入的数据与 ``fn`` 的形参不匹配，可以通过
+    该函数会根据输入函数的形参名从 ``*args`` （均为 ``dict`` 类型）中找到匹配的值进行调用，如果传入的数据与 ``fn`` 的形参不匹配，可以通过
     ``mapping`` 参数进行转换。``mapping`` 参数中的一对 ``(key, value)`` 表示在 ``*args`` 中找到 ``key`` 对应的值，并将这个值传递给形参中名为
     ``value`` 的参数。
 
@@ -161,13 +167,13 @@ def _get_keys(args:List[Dict]) -> List[List[str]]:
 
 def _get_fun_msg(fn, with_fp=True)->str:
     """
-    获取函数的基本信息，帮助报错。
-    ex:
-        print(_get_fun_msg(_get_fun_msg))
-        # `_get_fun_msg(fn) -> str`(In file:/Users/hnyan/Desktop/projects/fastNLP/fastNLP/fastNLP/core/utils/utils.py)
+    获取函数的基本信息，帮助报错::
+
+        >>>> print(_get_fun_msg(_get_fun_msg))
+        `_get_fun_msg(fn) -> str`(In file:/Users/hnyan/Desktop/projects/fastNLP/fastNLP/fastNLP/core/utils/utils.py)
 
     :param callable fn:
-    :param with_fp: 是否包含函数所在的文件信息。
+    :param with_fp: 是否包含函数所在的文件信息；
     :return:
     """
     if isinstance(fn, functools.partial):
@@ -224,7 +230,7 @@ def _check_valid_parameters_number(fn, expected_params:List[str], fn_name=None):
 def check_user_specific_params(user_params: Dict, fn: Callable):
     """
     该函数使用用户的输入来对指定函数的参数进行赋值，主要用于一些用户无法直接调用函数的情况；
-    该函数主要的作用在于帮助检查用户对使用函数 ``fn`` 的参数输入是否有误；
+    主要作用在于帮助检查用户对使用函数 ``fn`` 的参数输入是否有误；
 
     :param user_params: 用户指定的参数的值，应当是一个字典，其中 ``key`` 表示每一个参数的名字，
         ``value`` 为每一个参数的值；
@@ -241,7 +247,7 @@ def check_user_specific_params(user_params: Dict, fn: Callable):
 
 def dataclass_to_dict(data: "dataclasses.dataclass") -> Dict:
     """
-    将传入的 `dataclass` 实例转换为字典。
+    将传入的 ``dataclass`` 实例转换为字典。
     """
     if not is_dataclass(data):
         raise TypeError(f"Parameter `data` can only be `dataclass` type instead of {type(data)}.")
@@ -253,12 +259,12 @@ def dataclass_to_dict(data: "dataclasses.dataclass") -> Dict:
 
 def match_and_substitute_params(mapping: Optional[Union[Callable, Dict]] = None, data: Optional[Any] = None) -> Any:
     r"""
-    用来实现将输入的 ``batch``，或者输出的 ``outputs``，通过 ``mapping`` 将键值进行更换的功能；
+    用来实现将输入的 ``batch`` 或者输出的 ``outputs`` 通过 ``mapping`` 将键值进行更换的功能；
     该函数应用于 ``input_mapping`` 和 ``output_mapping``；
 
-    对于 ``input_mapping``，该函数会在 :class:`~fastNLP.core.controllers.TrainBatchLoop` 中取完数据后立刻被调用；
-    对于 ``output_mapping``，该函数会在 :class:`~fastNLP.core.Trainer` 的 :meth:`~fastNLP.core.Trainer.train_step`
-    以及 :class:`~fastNLP.core.Evaluator` 的 :meth:`~fastNLP.core.Evaluator.train_step` 中得到结果后立刻被调用；
+    * 对于 ``input_mapping``，该函数会在 :class:`~fastNLP.core.controllers.TrainBatchLoop` 中取完数据后立刻被调用；
+    * 对于 ``output_mapping``，该函数会在 :class:`~fastNLP.core.Trainer` 的 :meth:`~fastNLP.core.Trainer.train_step`
+     以及 :class:`~fastNLP.core.Evaluator` 的 :meth:`~fastNLP.core.Evaluator.train_step` 中得到结果后立刻被调用；
 
     转换的逻辑按优先级依次为：
 
@@ -277,9 +283,9 @@ def match_and_substitute_params(mapping: Optional[Union[Callable, Dict]] = None,
 
           然后使用 ``mapping`` 对这个 ``Dict`` 进行转换，如果没有匹配上 ``mapping`` 中的 ``key`` 则保持 ``\'\_number\'`` 这个形式。
 
-    :param mapping: 用于转换的字典或者函数；``mapping`` 是函数时，返回值必须为字典类型。
+    :param mapping: 用于转换的字典或者函数；当 ``mapping`` 是函数时，返回值必须为字典类型；
     :param data: 需要被转换的对象；
-    :return: 返回转换好的结果；
+    :return: 返回转换后的结果；
     """
     if mapping is None:
         return data
@@ -331,19 +337,19 @@ def apply_to_collection(
         **kwargs: Any,
 ) -> Any:
     """
-    使用函数 ``function`` 递归地在 ``data`` 中的元素执行，但是仅在满足元素为 ``dtype`` 时执行。
+    递归地对 ``data`` 中的元素执行函数 ``function``，且仅在满足元素为 ``dtype`` 时执行。
 
     该函数参考了 `pytorch-lightning <https://github.com/PyTorchLightning/pytorch-lightning>`_ 的实现
 
-    :param data: 需要进行处理的数据集合或数据
-    :param dtype: 数据的类型，函数 ``function`` 只会被应用于 ``data`` 中类型为 ``dtype`` 的数据
-    :param function: 对数据进行处理的函数
-    :param args: ``function`` 所需要的其它参数
+    :param data: 需要进行处理的数据集合或数据；
+    :param dtype: 数据的类型，函数 ``function`` 只会被应用于 ``data`` 中类型为 ``dtype`` 的数据；
+    :param function: 对数据进行处理的函数；
+    :param args: ``function`` 所需要的其它参数；
     :param wrong_dtype: ``function`` 一定不会生效的数据类型。如果数据既是 ``wrong_dtype`` 类型又是 ``dtype`` 类型
-        那么也不会生效。
-    :param include_none: 是否包含执行结果为 ``None`` 的数据，默认为 ``True``。
-    :param kwargs: ``function`` 所需要的其它参数
-    :return: 经过 ``function`` 处理后的数据集合
+        那么也不会生效；
+    :param include_none: 是否包含执行结果为 ``None`` 的数据，默认为 ``True``；
+    :param kwargs: ``function`` 所需要的其它参数；
+    :return: 经过 ``function`` 处理后的数据集合；
     """
     # Breaking condition
     if isinstance(data, dtype) and (wrong_dtype is None or not isinstance(data, wrong_dtype)):
@@ -411,20 +417,20 @@ def apply_to_collection(
 @contextmanager
 def nullcontext():
     r"""
-    实现一个什么都不做的上下文环境
+    实现一个什么都不做的上下文环境。
     """
     yield
 
 
 def sub_column(string: str, c: int, c_size: int, title: str) -> str:
     r"""
-    对传入的字符串进行截断，方便在命令行中显示
+    对传入的字符串进行截断，方便在命令行中显示。
 
-    :param string: 要被截断的字符串
-    :param c: 命令行列数
-    :param c_size: :class:`~fastNLP.core.Instance` 或 :class:`fastNLP.core.DataSet` 的 ``field`` 数目
-    :param title: 列名
-    :return: 对一个过长的列进行截断的结果
+    :param string: 要被截断的字符串；
+    :param c: 命令行列数；
+    :param c_size: :class:`~fastNLP.core.Instance` 或 :class:`fastNLP.core.DataSet` 的 ``field`` 数目；
+    :param title: 列名；
+    :return: 对一个过长的列进行截断的结果；
     """
     avg = max(int(c / c_size / 2), len(title))
     string = str(string)
@@ -453,7 +459,7 @@ def _is_iterable(value):
 
 def pretty_table_printer(dataset_or_ins) -> PrettyTable:
     r"""
-    在 ``fastNLP`` 中展示数据的函数::
+    用于在 ``fastNLP`` 中展示数据的函数::
 
         >>> ins = Instance(field_1=[1, 1, 1], field_2=[2, 2, 2], field_3=["a", "b", "c"])
         +-----------+-----------+-----------------+
@@ -462,8 +468,8 @@ def pretty_table_printer(dataset_or_ins) -> PrettyTable:
         | [1, 1, 1] | [2, 2, 2] | ['a', 'b', 'c'] |
         +-----------+-----------+-----------------+
 
-    :param dataset_or_ins: 要展示的 :class:`~fastNLP.core.DataSet` 或者 :class:`~fastNLP.core.Instance`
-    :return: 根据 ``terminal`` 大小进行自动截断的数据表格
+    :param dataset_or_ins: 要展示的 :class:`~fastNLP.core.DataSet` 或者 :class:`~fastNLP.core.Instance` 实例；
+    :return: 根据命令行大小进行自动截断的数据表格；
     """
     x = PrettyTable()
     try:
@@ -529,7 +535,7 @@ def deprecated(help_message: Optional[str] = None):
     """
     标记当前功能已经过时的装饰器。
 
-    :param help_message: 一段指引信息，告知用户如何将代码切换为当前版本提倡的用法。
+    :param help_message: 一段指引信息，告知用户如何将代码切换为当前版本提倡的用法；
     """
 
     def decorator(deprecated_function: Callable):
@@ -578,10 +584,10 @@ def seq_len_to_mask(seq_len, max_len: Optional[int]):
         >>>print(mask.size())
         torch.Size([14, 100])
 
-    :param seq_len: 大小为是 ``(B,)`` 的长度序列
-    :param int max_len: 将长度 ``pad`` 到 ``max_len``。默认情况（为 ``None``）使用的是 ``seq_len`` 中最长的长度。
+    :param seq_len: 大小为 ``(B,)`` 的长度序列；
+    :param int max_len: 将长度补齐或截断到 ``max_len``。默认情况（为 ``None``）使用的是 ``seq_len`` 中最长的长度；
         但在 :class:`torch.nn.DataParallel` 等分布式的场景下可能不同卡的 ``seq_len`` 会有区别，所以需要传入
-        一个 ``max_len`` 使得 ``mask`` 的长度 ``pad`` 到该长度。
+        一个 ``max_len`` 使得 ``mask`` 的补齐或截断到该长度。
     :return: 大小为 ``(B, max_len)`` 的 ``mask``， 元素类型为 ``bool`` 或 ``uint8``
     """
     if isinstance(seq_len, np.ndarray):
diff --git a/fastNLP/envs/set_backend.py b/fastNLP/envs/set_backend.py
index b75a9610..d925d282 100644
--- a/fastNLP/envs/set_backend.py
+++ b/fastNLP/envs/set_backend.py
@@ -51,23 +51,33 @@ def _set_backend():
         assert _module_available(backend), f"You must have {backend} available to use {backend} backend."
         assert 'paddle' not in sys.modules, "You have to use `set_backend()` before `import paddle`."
         user_visible_devices = os.getenv(USER_CUDA_VISIBLE_DEVICES)
+        cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
         if 'PADDLE_RANK_IN_NODE' in os.environ and 'FLAGS_selected_gpus' in os.environ:
             # 在分布式子进程下，根据 USER_VISIBLE_DEVICES 得到进程真正占有的设备
             selected_gpus = os.environ['FLAGS_selected_gpus'].split(',')
             if user_visible_devices is not None:
-                # 用户通过 CUDA_VISIBLE_DEVICES 启动了分布式训练
+                # 用户使用 fastNLP 启动了分布式训练
                 # 此时经过 set_backend，用户的设置会保存在 USER_CUDA_VISIBLE_DEVICES 中
-                # 我们需要从中找到真正使用的设备编号
+                # 我们需要从中转换为用户找到真正使用的设备编号
                 user_visible_devices = user_visible_devices.split(",")
-                selected_gpus = ",".join([user_visible_devices[int(i)] for i in selected_gpus])
+                selected_gpus = [user_visible_devices[int(i)] for i in selected_gpus]
+            # 没有找到 USER_CUDA_VISIBLE_DEVICES，说明用户是直接用 launch 启动的
+            elif cuda_visible_devices:
+                # 用户设置了可见设备，需要进行转换
+                # 如 CUDA_VISIBLE_DEVICES = 0,2,3 --gpus=0,2,3
+                # 在 rank1 中此时 selected_gpus = ['1']，需要转换为设备 2
+                os.environ[USER_CUDA_VISIBLE_DEVICES] = cuda_visible_devices
+                cuda_visible_devices = cuda_visible_devices.split(",")
+                selected_gpus = [cuda_visible_devices[int(i)] for i in selected_gpus]
             else:
-                # 没有找到 USER_CUDA_VISIBLE_DEVICES，则将之设置为所有的设备
+                # 用户没有设置可见设备，则赋值成所有的设备
                 os.environ[USER_CUDA_VISIBLE_DEVICES] = ",".join(map(str, list(
                     range(get_gpu_count())
                 )))
             os.environ['CUDA_VISIBLE_DEVICES'] = ",".join(selected_gpus)
             os.environ['FLAGS_selected_gpus'] = ",".join([str(g) for g in range(len(selected_gpus))])
             os.environ['FLAGS_selected_accelerators'] = ",".join([str(g) for g in range(len(selected_gpus))])
+        
         elif 'CUDA_VISIBLE_DEVICES' in os.environ:
             # 主进程中，用户设置了 CUDA_VISIBLE_DEVICES
             # 将用户设置的 CUDA_VISIBLE_DEVICES hack 掉
@@ -91,6 +101,11 @@ def _set_backend():
     elif backend == 'torch':
         assert _module_available(backend), f"You must have {backend} available to use {backend} backend."
 
+    if 'PADDLE_RANK_IN_NODE' in os.environ and 'FLAGS_selected_gpus' in os.environ \
+        and "USER_CUDA_VISIBLE_DEVICES" not in os.environ:
+        # 当用户没有设置 backend 并且使用 launch 启动了多卡，应该提醒用户进行设置
+        raise RuntimeError("To run paddle distributed training, please set `FASTNLP_BACKEND` to 'paddle' before using FastNLP.")
+
 
 def set_env(global_seed=None):
     """
diff --git a/fastNLP/modules/__init__.py b/fastNLP/modules/__init__.py
new file mode 100644
index 00000000..db7c9436
--- /dev/null
+++ b/fastNLP/modules/__init__.py
@@ -0,0 +1,9 @@
+__all__ = [
+    # "MixModule",
+    "torch2paddle",
+    "paddle2torch",
+    "torch2jittor",
+    "jittor2torch",
+]
+
+from .mix_modules import torch2paddle, paddle2torch, torch2jittor, jittor2torch
\ No newline at end of file
diff --git a/fastNLP/modules/mix_modules/__init__.py b/fastNLP/modules/mix_modules/__init__.py
new file mode 100644
index 00000000..bd8b4e8f
--- /dev/null
+++ b/fastNLP/modules/mix_modules/__init__.py
@@ -0,0 +1,10 @@
+__all__ = [
+    # "MixModule",
+    "torch2paddle",
+    "paddle2torch",
+    "torch2jittor",
+    "jittor2torch",
+]
+
+# from .mix_module import MixModule
+from .utils import *
\ No newline at end of file
diff --git a/fastNLP/modules/mix_modules/utils.py b/fastNLP/modules/mix_modules/utils.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/core/controllers/_test_trainer_fleet.py b/tests/core/controllers/_test_trainer_fleet.py
index 1a01bb5d..dd87f348 100644
--- a/tests/core/controllers/_test_trainer_fleet.py
+++ b/tests/core/controllers/_test_trainer_fleet.py
@@ -1,7 +1,15 @@
 """
-这个文件测试用户以python -m paddle.distributed.launch 启动的情况
-看看有没有用pytest执行的机会
-FASTNLP_BACKEND=paddle python -m paddle.distributed.launch --gpus=0,2,3 _test_trainer_fleet.py
+这个文件测试多卡情况下使用 paddle 的情况::
+
+    >>> # 测试用 python -m paddle.distributed.launch 启动
+    >>> FASTNLP_BACKEND=paddle python -m paddle.distributed.launch --gpus=0,2,3 _test_trainer_fleet.py
+    >>> # 测试在限制 GPU 的情况下用 python -m paddle.distributed.launch 启动
+    >>> CUDA_VISIBLE_DEVICES=0,2,3 FASTNLP_BACKEND=paddle python -m paddle.distributed.launch --gpus=0,2,3 _test_trainer_fleet.py
+    >>> # 测试直接使用多卡
+    >>> FASTNLP_BACKEND=paddle python _test_trainer_fleet.py
+    >>> # 测试在限制 GPU 的情况下直接使用多卡
+    >>> CUDA_VISIBLE_DEVICES=3,4,5,6 FASTNLP_BACKEND=paddle python _test_trainer_fleet.py
+
 """
 import os
 import sys
@@ -71,14 +79,13 @@ def test_trainer_fleet(
 
         n_epochs=n_epochs,
         callbacks=callbacks,
-        output_from_new_proc="logs",
+        # output_from_new_proc="logs",
     )
     trainer.run()
 
 if __name__ == "__main__":
     driver = "paddle"
-    device = [0,2,3]
-    # driver = "paddle"
+    device = [0,1,3]
     # device = 2
     callbacks = [
         # RecordMetricCallback(monitor="acc#acc", metric_threshold=0.0, larger_better=True), 
diff --git a/tests/core/controllers/_test_trainer_fleet_outside.py b/tests/core/controllers/_test_trainer_fleet_outside.py
index 1ab2e624..963276db 100644
--- a/tests/core/controllers/_test_trainer_fleet_outside.py
+++ b/tests/core/controllers/_test_trainer_fleet_outside.py
@@ -1,7 +1,11 @@
 """
-这个文件测试用户以python -m paddle.distributed.launch 启动的情况
-并且自己初始化了 fleet
-FASTNLP_BACKEND=paddle python -m paddle.distributed.launch --gpus=0,2,3 _test_trainer_fleet_outside.py
+这个文件测试用户自己初始化分布式环境后使用 paddle 的情况:
+
+    >>> # 测试用 python -m paddle.distributed.launch 启动
+    >>> FASTNLP_BACKEND=paddle python -m paddle.distributed.launch --gpus=0,2,3 _test_trainer_fleet_outside.py
+    >>> # 测试在限制 GPU 的情况下用 python -m paddle.distributed.launch 启动
+    >>> CUDA_VISIBLE_DEVICES=0,2,3 FASTNLP_BACKEND=paddle python -m paddle.distributed.launch --gpus=0,2,3 _test_trainer_fleet_outside.py
+
 """
 import os
 import sys
@@ -77,14 +81,13 @@ def test_trainer_fleet(
 
         n_epochs=n_epochs,
         callbacks=callbacks,
-        output_from_new_proc="logs",
-        data_device=f"gpu:{os.environ['CUDA_VISIBLE_DEVICES']}"
+        # output_from_new_proc="logs",
     )
     trainer.run()
 
 if __name__ == "__main__":
     driver = "paddle"
-    device = [0,2,3]
+    device = [0,1,3]
     callbacks = [
         # RecordMetricCallback(monitor="acc#acc", metric_threshold=0.0, larger_better=True), 
         RichCallback(5),
diff --git a/tests/core/controllers/_test_trainer_jittor.py b/tests/core/controllers/_test_trainer_jittor.py
new file mode 100644
index 00000000..bc4b05f0
--- /dev/null
+++ b/tests/core/controllers/_test_trainer_jittor.py
@@ -0,0 +1,237 @@
+import os
+import sys
+import time
+# os.environ["cuda_archs"] = "61"
+# os.environ["FAS"]
+os.environ["log_silent"] = "1"
+sys.path.append("../../../")
+
+from datasets import load_dataset
+from datasets import DatasetDict
+import jittor as jt
+from jittor import nn, Module
+from jittor.dataset import Dataset
+jt.flags.use_cuda = True
+
+from fastNLP.core.controllers.trainer import Trainer
+from fastNLP.core.metrics.accuracy import Accuracy
+from fastNLP.core.vocabulary import Vocabulary
+from fastNLP.core.callbacks.progress_callback import RichCallback
+from fastNLP.core.callbacks.callback import Callback
+from fastNLP.core.dataloaders.jittor_dataloader.fdl import JittorDataLoader
+
+class TextClassificationDataset(Dataset):
+    def __init__(self, dataset):
+        super(TextClassificationDataset, self).__init__()
+        self.dataset = dataset
+        self.set_attrs(total_len=len(dataset))
+
+    def __getitem__(self, idx):
+        return {"x": self.dataset["input_ids"][idx], "y": self.dataset["label"][idx]}
+
+
+class LSTM(Module):
+    
+    def __init__(self, num_of_words, hidden_size, features):
+
+        self.embedding = nn.Embedding(num_of_words, features)
+        self.lstm = nn.LSTM(features, hidden_size, batch_first=True)
+        self.layer = nn.Linear(hidden_size, 2)
+        self.softmax = nn.Softmax(dim=1)
+        self.loss_fn = nn.CrossEntropyLoss()
+        
+        self.hidden_size = hidden_size
+        self.features = features
+
+    def init_hidden(self, x):
+        # batch_first
+        batch_size = x.shape[0]
+        h0 = jt.randn(1, batch_size, hidden_size)
+        c0 = jt.randn(1, batch_size, hidden_size)
+
+        return h0, c0
+
+    def execute(self, input_ids):
+
+        output = self.embedding(input_ids)
+        # TODO 去除padding
+        output, (h, c) = self.lstm(output, self.init_hidden(output))
+        # len, batch, hidden_size
+        output = self.layer(output[-1])
+
+        return output
+
+    def train_step(self, x, y):
+        x = self(x)
+        outputs = self.loss_fn(x, y)
+        return {"loss": outputs}
+
+    def evaluate_step(self, x, y):
+        x = self(x)
+        return {"pred": x, "target": y.reshape((-1,))}
+
+
+class PrintWhileTrainingCallBack(Callback):
+    """
+    通过该Callback实现训练过程中loss的输出
+    """
+
+    def __init__(self, print_every_epoch, print_every_batch):
+        self.print_every_epoch = print_every_epoch
+        self.print_every_batch = print_every_batch
+
+        self.loss = 0
+        self.start = 0
+        self.epoch_start = 0
+
+    def on_train_begin(self, trainer):
+        """
+        在训练开始前输出信息
+        """
+        print("Start training. Total {} epochs and {} batches in each epoch.".format(
+            trainer.n_epochs, trainer.num_batches_per_epoch
+        ))
+        self.start = time.time()
+
+    def on_before_backward(self, trainer, outputs):
+        """
+        每次反向传播前统计loss，用于计算平均值
+        """
+        loss = trainer.extract_loss_from_outputs(outputs)
+        loss = trainer.driver.tensor_to_numeric(loss)
+        self.loss += loss
+
+    def on_train_epoch_begin(self, trainer):
+        self.epoch_start = time.time()
+
+    def on_train_epoch_end(self, trainer):
+        """
+        在每经过一定epoch或最后一个epoch时输出当前epoch的平均loss和使用时间
+        """
+        if trainer.cur_epoch_idx % self.print_every_epoch == 0 \
+            or trainer.cur_epoch_idx == trainer.n_epochs:
+            print("Epoch: {} Loss: {} Current epoch training time: {}s".format(
+                trainer.cur_epoch_idx, self.loss / trainer.num_batches_per_epoch, time.time() - self.epoch_start
+            ))
+        # 将loss清零
+        self.loss = 0
+    
+    def on_train_batch_end(self, trainer):
+        """
+        在每经过一定batch或最后一个batch时输出当前epoch截止目前的平均loss
+        """
+        if trainer.batch_idx_in_epoch % self.print_every_batch == 0 \
+            or trainer.batch_idx_in_epoch == trainer.num_batches_per_epoch:
+            print("\tBatch: {} Loss: {}".format(
+                trainer.batch_idx_in_epoch, self.loss / trainer.batch_idx_in_epoch
+            ))
+
+    def on_train_end(self, trainer):
+        print("Total training time: {}s".format(time.time() - self.start))
+
+    
+def process_data(ds: DatasetDict, vocabulary: Vocabulary, max_len=256) -> DatasetDict:
+    # 分词
+    ds = ds.map(lambda x: {"input_ids": text_to_id(vocabulary, x["text"], max_len)})
+    ds.set_format(type="numpy", columns=ds.column_names)
+    return ds
+
+def set_vocabulary(vocab, dataset):
+
+    for data in dataset:
+        vocab.update(data["text"].split())
+    return vocab
+
+def text_to_id(vocab, text: str, max_len):
+    text = text.split()
+    # to index
+    ids = [vocab.to_index(word) for word in text]
+    # padding
+    ids += [vocab.padding_idx] * (max_len - len(text))
+    return ids[:max_len]
+
+def get_dataset(name, max_len, train_format="", test_format=""):
+
+    # datasets
+    train_dataset = load_dataset(name, split="train" + train_format).shuffle(seed=123)
+    test_dataset = load_dataset(name, split="test" + test_format).shuffle(seed=321)
+    split = train_dataset.train_test_split(test_size=0.2, seed=123)
+    train_dataset = split["train"]
+    val_dataset = split["test"]
+
+    vocab = Vocabulary()
+    vocab = set_vocabulary(vocab, train_dataset)
+    vocab = set_vocabulary(vocab, val_dataset)
+
+    train_dataset = process_data(train_dataset, vocab, max_len)
+    val_dataset = process_data(val_dataset, vocab, max_len)
+    test_dataset = process_data(test_dataset, vocab, max_len)
+
+    return TextClassificationDataset(train_dataset), TextClassificationDataset(val_dataset), \
+            TextClassificationDataset(test_dataset), vocab
+
+if __name__ == "__main__":
+
+    # 训练参数
+    max_len = 20
+    epochs = 40
+    lr = 1
+    batch_size = 64
+
+    features = 100
+    hidden_size = 128
+
+    # 获取数据集
+    # imdb.py SetFit/sst2
+    train_data, val_data, test_data, vocab = get_dataset("SetFit/sst2", max_len, "", "")
+    # 使用dataloader
+    train_dataloader = JittorDataLoader(
+        dataset=train_data,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=4,
+    )
+    val_dataloader = JittorDataLoader(
+        dataset=val_data,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=4,
+    )
+    test_dataloader = JittorDataLoader(
+        dataset=test_data,
+        batch_size=1,
+        shuffle=False,
+    )
+
+    # 初始化模型
+    model = LSTM(len(vocab), hidden_size, features)
+
+    # 优化器
+    # 也可以是多个优化器的list
+    optimizer = nn.SGD(model.parameters(), lr)
+
+    # Metrics
+    metrics = {"acc": Accuracy()}
+
+    # callbacks
+    callbacks = [
+        PrintWhileTrainingCallBack(print_every_epoch=1, print_every_batch=10),
+        # RichCallback(), # print_every参数默认为1，即每一个batch更新一次进度条
+    ]
+
+    trainer = Trainer(
+        model=model,
+        driver="jittor",
+        device=[0,1,2,3,4],
+        optimizers=optimizer,
+        train_dataloader=train_dataloader,
+        validate_dataloaders=val_dataloader,
+        validate_every=-1,
+        input_mapping=None,
+        output_mapping=None,
+        metrics=metrics,
+        n_epochs=epochs,
+        callbacks=callbacks,
+        # progress_bar="raw"
+    )
+    trainer.run()
\ No newline at end of file
diff --git a/tests/core/controllers/imdb.py b/tests/core/controllers/imdb.py
new file mode 100644
index 00000000..cdf59047
--- /dev/null
+++ b/tests/core/controllers/imdb.py
@@ -0,0 +1,110 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""IMDB movie reviews dataset."""
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+_DESCRIPTION = """\
+Large Movie Review Dataset.
+This is a dataset for binary sentiment classification containing substantially \
+more data than previous benchmark datasets. We provide a set of 25,000 highly \
+polar movie reviews for training, and 25,000 for testing. There is additional \
+unlabeled data for use as well.\
+"""
+
+_CITATION = """\
+@InProceedings{maas-EtAl:2011:ACL-HLT2011,
+  author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
+  title     = {Learning Word Vectors for Sentiment Analysis},
+  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
+  month     = {June},
+  year      = {2011},
+  address   = {Portland, Oregon, USA},
+  publisher = {Association for Computational Linguistics},
+  pages     = {142--150},
+  url       = {http://www.aclweb.org/anthology/P11-1015}
+}
+"""
+
+_DOWNLOAD_URL = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
+
+
+class IMDBReviewsConfig(datasets.BuilderConfig):
+    """BuilderConfig for IMDBReviews."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for IMDBReviews.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(IMDBReviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+
+
+class Imdb(datasets.GeneratorBasedBuilder):
+    """IMDB movie reviews dataset."""
+
+    BUILDER_CONFIGS = [
+        IMDBReviewsConfig(
+            name="plain_text",
+            description="Plain text",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["neg", "pos"])}
+            ),
+            supervised_keys=None,
+            homepage="http://ai.stanford.edu/~amaas/data/sentiment/",
+            citation=_CITATION,
+            task_templates=[TextClassification(text_column="text", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        archive = dl_manager.download(_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("unsupervised"),
+                gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train", "labeled": False},
+            ),
+        ]
+
+    def _generate_examples(self, files, split, labeled=True):
+        """Generate aclImdb examples."""
+        # For labeled examples, extract the label from the path.
+        if labeled:
+            label_mapping = {"pos": 1, "neg": 0}
+            for path, f in files:
+                if path.startswith(f"aclImdb/{split}"):
+                    label = label_mapping.get(path.split("/")[2])
+                    if label is not None:
+                        yield path, {"text": f.read().decode("utf-8"), "label": label}
+        else:
+            for path, f in files:
+                if path.startswith(f"aclImdb/{split}"):
+                    if path.split("/")[2] == "unsup":
+                        yield path, {"text": f.read().decode("utf-8"), "label": -1}
\ No newline at end of file
diff --git a/tests/core/controllers/test_trainer_paddle.py b/tests/core/controllers/test_trainer_paddle.py
index d7bfaeaf..7945e2c6 100644
--- a/tests/core/controllers/test_trainer_paddle.py
+++ b/tests/core/controllers/test_trainer_paddle.py
@@ -1,3 +1,5 @@
+import os
+from typing import List
 import pytest
 from dataclasses import dataclass
 
@@ -5,6 +7,7 @@ from fastNLP.core.controllers.trainer import Trainer
 from fastNLP.core.metrics.accuracy import Accuracy
 from fastNLP.core.callbacks.progress_callback import RichCallback
 from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
+from fastNLP.envs.env import USER_CUDA_VISIBLE_DEVICES
 
 if _NEED_IMPORT_PADDLE:
     from paddle.optimizer import Adam
@@ -34,6 +37,8 @@ def test_trainer_paddle(
         callbacks,
         n_epochs=2,
 ):
+    if isinstance(device, List) and USER_CUDA_VISIBLE_DEVICES not in os.environ:
+        pytest.skip("Skip test fleet if FASTNLP_BACKEND is not set to paddle.")
     model = PaddleNormalModel_Classification_1(
         num_labels=TrainPaddleConfig.num_labels,
         feature_dimension=TrainPaddleConfig.feature_dimension
diff --git a/tests/core/utils/test_paddle_utils.py b/tests/core/utils/test_paddle_utils.py
index d86d215f..c5daac63 100644
--- a/tests/core/utils/test_paddle_utils.py
+++ b/tests/core/utils/test_paddle_utils.py
@@ -2,37 +2,42 @@ import os
 
 import pytest
 
-from fastNLP.core.utils.paddle_utils import get_device_from_visible, paddle_to, paddle_move_data_to_device
+from fastNLP.core.utils.paddle_utils import _convert_data_device, paddle_to, paddle_move_data_to_device
 from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
 if _NEED_IMPORT_PADDLE:
     import paddle
+
 @pytest.mark.parametrize(
-    ("user_visible_devices, cuda_visible_devices, device, output_type, correct"),
+    ("user_visible_devices, cuda_visible_devices, device, correct"),
     (
-        ("0,1,2,3,4,5,6,7", "0", "cpu", str, "cpu"),
-        ("0,1,2,3,4,5,6,7", "0", "cpu", int, "cpu"),
-        ("0,1,2,3,4,5,6,7", "3,4,5", "gpu:4", int, 1),
-        ("0,1,2,3,4,5,6,7", "3,4,5", "gpu:5", str, "gpu:2"),
-        ("3,4,5,6", "3,5", 0, int, 0),
-        ("3,6,7,8", "6,7,8", "gpu:2", str, "gpu:1"),
+        (None, None, 1, "gpu:1"),
+        (None, "2,4,5,6", 2, "gpu:2"),
+        (None, "3,4,5", 1, "gpu:1"),
+        ("0,1,2,3,4,5,6,7", "0", "cpu", "cpu"),
+        ("3,4,5,6,7", "0", "cpu", "cpu"),
+        ("0,1,2,3,4,5,6,7", "3,4,5", "gpu:4", "gpu:1"),
+        ("0,1,2,3,4,5,6,7", "3,4,5", "gpu:5", "gpu:2"),
+        ("3,4,5,6", "3,5", 0, "gpu:0"),
+        ("3,6,7,8", "6,7,8", "gpu:2", "gpu:1"),
     )
 )
-@pytest.mark.paddle
-def test_get_device_from_visible(user_visible_devices, cuda_visible_devices, device, output_type, correct):
+def test_convert_data_device(user_visible_devices, cuda_visible_devices, device, correct):
     _cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
     _user_visible_devices = os.getenv("USER_CUDA_VISIBLE_DEVICES")
-    os.environ["CUDA_VISIBLE_DEVICES"] = cuda_visible_devices
-    os.environ["USER_CUDA_VISIBLE_DEVICES"] = user_visible_devices
-    res = get_device_from_visible(device, output_type)
+    if cuda_visible_devices is not None:
+        os.environ["CUDA_VISIBLE_DEVICES"] = cuda_visible_devices
+    if user_visible_devices is not None:
+        os.environ["USER_CUDA_VISIBLE_DEVICES"] = user_visible_devices
+    res = _convert_data_device(device)
     assert res == correct
 
     # 还原环境变量
     if _cuda_visible_devices is None:
-        del os.environ["CUDA_VISIBLE_DEVICES"]
+        os.environ.pop("CUDA_VISIBLE_DEVICES", None)
     else:
         os.environ["CUDA_VISIBLE_DEVICES"] = _cuda_visible_devices
     if _user_visible_devices is None:
-        del os.environ["USER_CUDA_VISIBLE_DEVICES"]
+        os.environ.pop("USER_CUDA_VISIBLE_DEVICES", None)
     else:
         os.environ["USER_CUDA_VISIBLE_DEVICES"] = _user_visible_devices
 
diff --git a/tests/modules/__init__.py b/tests/modules/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/modules/mix_modules/__init__.py b/tests/modules/mix_modules/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/modules/mix_modules/test_utils.py b/tests/modules/mix_modules/test_utils.py
new file mode 100644
index 00000000..890a714a
--- /dev/null
+++ b/tests/modules/mix_modules/test_utils.py
@@ -0,0 +1,442 @@
+import pytest
+
+from fastNLP.envs.imports import _NEED_IMPORT_JITTOR, _NEED_IMPORT_PADDLE, _NEED_IMPORT_TORCH
+from fastNLP.modules.mix_modules.utils import (
+    paddle2torch,
+    torch2paddle,
+    jittor2torch,
+    torch2jittor,
+)
+
+if _NEED_IMPORT_TORCH:
+    import torch
+
+if _NEED_IMPORT_PADDLE:
+    import paddle
+
+if _NEED_IMPORT_JITTOR:
+    import jittor
+
+
+############################################################################
+#
+# 测试paddle到torch的转换
+#
+############################################################################
+
+@pytest.mark.torchpaddle
+class TestPaddle2Torch:
+
+    def check_torch_tensor(self, tensor, device, requires_grad):
+        """
+        检查张量设备和梯度情况的工具函数
+        """
+
+        assert isinstance(tensor, torch.Tensor)
+        assert tensor.device == torch.device(device)
+        assert tensor.requires_grad == requires_grad
+
+    def test_gradient(self):
+        """
+        测试张量转换后的反向传播是否正确
+        """
+
+        x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0, 5.0], stop_gradient=False)
+        y = paddle2torch(x)
+        z = 3 * (y ** 2)
+        z.sum().backward()
+        assert y.grad.tolist() == [6, 12, 18, 24, 30]
+
+    def test_tensor_transfer(self):
+        """
+        测试单个张量的设备和梯度转换是否正确
+        """
+
+        paddle_tensor = paddle.rand((3, 4, 5)).cpu()
+        res = paddle2torch(paddle_tensor)
+        self.check_torch_tensor(res, "cpu", not paddle_tensor.stop_gradient)
+
+        res = paddle2torch(paddle_tensor, target_device="cuda:2", no_gradient=None)
+        self.check_torch_tensor(res, "cuda:2", not paddle_tensor.stop_gradient)
+
+        res = paddle2torch(paddle_tensor, target_device="cuda:1", no_gradient=True)
+        self.check_torch_tensor(res, "cuda:1", False)
+
+        res = paddle2torch(paddle_tensor, target_device="cuda:1", no_gradient=False)
+        self.check_torch_tensor(res, "cuda:1", True)
+
+    def test_list_transfer(self):
+        """
+        测试张量列表的转换
+        """
+
+        paddle_list = [paddle.rand((6, 4, 2)).cuda(1) for i in range(10)]
+        res = paddle2torch(paddle_list)
+        assert isinstance(res, list)
+        for t in res:
+            self.check_torch_tensor(t, "cuda:1", False)
+
+        res = paddle2torch(paddle_list, target_device="cpu", no_gradient=False)
+        assert isinstance(res, list)
+        for t in res:
+            self.check_torch_tensor(t, "cpu", True)
+
+    def test_tensor_tuple_transfer(self):
+        """
+        测试张量元组的转换
+        """
+
+        paddle_list = [paddle.rand((6, 4, 2)).cuda(1) for i in range(10)]
+        paddle_tuple = tuple(paddle_list)
+        res = paddle2torch(paddle_tuple)
+        assert isinstance(res, tuple)
+        for t in res:
+            self.check_torch_tensor(t, "cuda:1", False)
+
+    def test_dict_transfer(self):
+        """
+        测试包含复杂结构的字典的转换
+        """
+
+        paddle_dict = {
+            "tensor": paddle.rand((3, 4)).cuda(0),
+            "list": [paddle.rand((6, 4, 2)).cuda(0) for i in range(10)],
+            "dict":{
+                "list": [paddle.rand((6, 4, 2)).cuda(0) for i in range(10)],
+                "tensor": paddle.rand((3, 4)).cuda(0)
+            },
+            "int": 2,
+            "string": "test string"
+        }
+        res = paddle2torch(paddle_dict)
+        assert isinstance(res, dict)
+        self.check_torch_tensor(res["tensor"], "cuda:0", False)
+        assert isinstance(res["list"], list)
+        for t in res["list"]:
+            self.check_torch_tensor(t, "cuda:0", False)
+        assert isinstance(res["int"], int)
+        assert isinstance(res["string"], str)
+        assert isinstance(res["dict"], dict)
+        assert isinstance(res["dict"]["list"], list)
+        for t in res["dict"]["list"]:
+            self.check_torch_tensor(t, "cuda:0", False)
+        self.check_torch_tensor(res["dict"]["tensor"], "cuda:0", False)
+
+
+############################################################################
+#
+# 测试torch到paddle的转换
+#
+############################################################################
+
+@pytest.mark.torchpaddle
+class TestTorch2Paddle:
+
+    def check_paddle_tensor(self, tensor, device, stop_gradient):
+        """
+        检查得到的paddle张量设备和梯度情况的工具函数
+        """
+
+        assert isinstance(tensor, paddle.Tensor)
+        if device == "cpu":
+            assert tensor.place.is_cpu_place()
+        elif device.startswith("gpu"):
+            paddle_device = paddle.device._convert_to_place(device)
+            assert tensor.place.is_gpu_place()
+            if hasattr(tensor.place, "gpu_device_id"):
+                # paddle中，有两种Place
+                # paddle.fluid.core.Place是创建Tensor时使用的类型
+                # 有函数gpu_device_id获取设备
+                assert tensor.place.gpu_device_id() == paddle_device.get_device_id()
+            else:
+                # 通过_convert_to_place得到的是paddle.CUDAPlace
+                # 通过get_device_id获取设备
+                assert tensor.place.get_device_id() == paddle_device.get_device_id()
+        else:
+            raise NotImplementedError
+        assert tensor.stop_gradient == stop_gradient
+
+    def test_gradient(self):
+        """
+        测试转换后梯度的反向传播
+        """
+
+        x = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0], requires_grad=True)
+        y = torch2paddle(x)
+        z = 3 * (y ** 2)
+        z.sum().backward()
+        assert y.grad.tolist() == [6, 12, 18, 24, 30]
+
+    def test_tensor_transfer(self):
+        """
+        测试单个张量的转换
+        """
+
+        torch_tensor = torch.rand((3, 4, 5))
+        res = torch2paddle(torch_tensor)
+        self.check_paddle_tensor(res, "cpu", True)
+
+        res = torch2paddle(torch_tensor, target_device="gpu:2", no_gradient=None)
+        self.check_paddle_tensor(res, "gpu:2", True)
+
+        res = torch2paddle(torch_tensor, target_device="gpu:2", no_gradient=True)
+        self.check_paddle_tensor(res, "gpu:2", True)
+
+        res = torch2paddle(torch_tensor, target_device="gpu:2", no_gradient=False)
+        self.check_paddle_tensor(res, "gpu:2", False)
+
+    def test_tensor_list_transfer(self):
+        """
+        测试张量列表的转换
+        """
+
+        torch_list = [torch.rand(6, 4, 2) for i in range(10)]
+        res = torch2paddle(torch_list)
+        assert isinstance(res, list)
+        for t in res:
+            self.check_paddle_tensor(t, "cpu", True)
+
+        res = torch2paddle(torch_list, target_device="gpu:1", no_gradient=False)
+        assert isinstance(res, list)
+        for t in res:
+            self.check_paddle_tensor(t, "gpu:1", False)
+
+    def test_tensor_tuple_transfer(self):
+        """
+        测试张量元组的转换
+        """
+        
+        torch_list = [torch.rand(6, 4, 2) for i in range(10)]
+        torch_tuple = tuple(torch_list)
+        res = torch2paddle(torch_tuple, target_device="cpu")
+        assert isinstance(res, tuple)
+        for t in res:
+            self.check_paddle_tensor(t, "cpu", True)
+
+    def test_dict_transfer(self):
+        """
+        测试复杂的字典结构的转换
+        """
+
+        torch_dict = {
+            "tensor": torch.rand((3, 4)),
+            "list": [torch.rand(6, 4, 2) for i in range(10)],
+            "dict":{
+                "list": [torch.rand(6, 4, 2) for i in range(10)],
+                "tensor": torch.rand((3, 4))
+            },
+            "int": 2,
+            "string": "test string"
+        }
+        res = torch2paddle(torch_dict)
+        assert isinstance(res, dict)
+        self.check_paddle_tensor(res["tensor"], "cpu", True)
+        assert isinstance(res["list"], list)
+        for t in res["list"]:
+            self.check_paddle_tensor(t, "cpu", True)
+        assert isinstance(res["int"], int)
+        assert isinstance(res["string"], str)
+        assert isinstance(res["dict"], dict)
+        assert isinstance(res["dict"]["list"], list)
+        for t in res["dict"]["list"]:
+            self.check_paddle_tensor(t, "cpu", True)
+        self.check_paddle_tensor(res["dict"]["tensor"], "cpu", True)
+
+
+############################################################################
+#
+# 测试jittor到torch的转换
+#
+############################################################################
+
+class TestJittor2Torch:
+
+    def check_torch_tensor(self, tensor, device, requires_grad):
+        """
+        检查得到的torch张量的工具函数
+        """
+
+        assert isinstance(tensor, torch.Tensor)
+        if device == "cpu":
+            assert not tensor.is_cuda
+        else:
+            assert tensor.device == torch.device(device)
+        assert tensor.requires_grad == requires_grad
+
+    def test_var_transfer(self):
+        """
+        测试单个Jittor Var的转换
+        """
+
+        jittor_var = jittor.rand((3, 4, 5))
+        res = jittor2torch(jittor_var)
+        self.check_torch_tensor(res, "cpu", True)
+
+        res = jittor2torch(jittor_var, target_device="cuda:2", no_gradient=None)
+        self.check_torch_tensor(res, "cuda:2", True)
+
+        res = jittor2torch(jittor_var, target_device="cuda:2", no_gradient=True)
+        self.check_torch_tensor(res, "cuda:2", False)
+
+        res = jittor2torch(jittor_var, target_device="cuda:2", no_gradient=False)
+        self.check_torch_tensor(res, "cuda:2", True)
+
+    def test_var_list_transfer(self):
+        """
+        测试Jittor列表的转换
+        """
+
+        jittor_list = [jittor.rand((6, 4, 2)) for i in range(10)]
+        res = jittor2torch(jittor_list)
+        assert isinstance(res, list)
+        for t in res:
+            self.check_torch_tensor(t, "cpu", True)
+
+        res = jittor2torch(jittor_list, target_device="cuda:1", no_gradient=False)
+        assert isinstance(res, list)
+        for t in res:
+            self.check_torch_tensor(t, "cuda:1", True)
+
+    def test_var_tuple_transfer(self):
+        """
+        测试Jittor变量元组的转换
+        """
+
+        jittor_list = [jittor.rand((6, 4, 2)) for i in range(10)]
+        jittor_tuple = tuple(jittor_list)
+        res = jittor2torch(jittor_tuple, target_device="cpu")
+        assert isinstance(res, tuple)
+        for t in res:
+            self.check_torch_tensor(t, "cpu", True)
+
+    def test_dict_transfer(self):
+        """
+        测试字典结构的转换
+        """
+
+        jittor_dict = {
+            "tensor": jittor.rand((3, 4)),
+            "list": [jittor.rand(6, 4, 2) for i in range(10)],
+            "dict":{
+                "list": [jittor.rand(6, 4, 2) for i in range(10)],
+                "tensor": jittor.rand((3, 4))
+            },
+            "int": 2,
+            "string": "test string"
+        }
+        res = jittor2torch(jittor_dict)
+        assert isinstance(res, dict)
+        self.check_torch_tensor(res["tensor"], "cpu", True)
+        assert isinstance(res["list"], list)
+        for t in res["list"]:
+            self.check_torch_tensor(t, "cpu", True)
+        assert isinstance(res["int"], int)
+        assert isinstance(res["string"], str)
+        assert isinstance(res["dict"], dict)
+        assert isinstance(res["dict"]["list"], list)
+        for t in res["dict"]["list"]:
+            self.check_torch_tensor(t, "cpu", True)
+        self.check_torch_tensor(res["dict"]["tensor"], "cpu", True)
+
+
+############################################################################
+#
+# 测试torch到jittor的转换
+#
+############################################################################
+
+class TestTorch2Jittor:
+
+    def check_jittor_var(self, var, requires_grad):
+        """
+        检查得到的Jittor Var梯度情况的工具函数
+        """
+
+        assert isinstance(var, jittor.Var)
+        assert var.requires_grad == requires_grad
+
+    def test_gradient(self):
+        """
+        测试反向传播的梯度
+        """
+
+        x = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0], requires_grad=True)
+        y = torch2jittor(x)
+        z = 3 * (y ** 2)
+        grad = jittor.grad(z, y)
+        assert grad.tolist() == [6.0, 12.0, 18.0, 24.0, 30.0]
+
+    def test_tensor_transfer(self):
+        """
+        测试单个张量转换为Jittor
+        """
+
+        torch_tensor = torch.rand((3, 4, 5))
+        res = torch2jittor(torch_tensor)
+        self.check_jittor_var(res, False)
+
+        res = torch2jittor(torch_tensor, no_gradient=None)
+        self.check_jittor_var(res, False)
+
+        res = torch2jittor(torch_tensor, no_gradient=True)
+        self.check_jittor_var(res, False)
+
+        res = torch2jittor(torch_tensor, no_gradient=False)
+        self.check_jittor_var(res, True)
+
+    def test_tensor_list_transfer(self):
+        """
+        测试张量列表的转换
+        """
+
+        torch_list = [torch.rand((6, 4, 2)) for i in range(10)]
+        res = torch2jittor(torch_list)
+        assert isinstance(res, list)
+        for t in res:
+            self.check_jittor_var(t, False)
+
+        res = torch2jittor(torch_list, no_gradient=False)
+        assert isinstance(res, list)
+        for t in res:
+            self.check_jittor_var(t, True)
+
+    def test_tensor_tuple_transfer(self):
+        """
+        测试张量元组的转换
+        """
+
+        torch_list = [torch.rand((6, 4, 2)) for i in range(10)]
+        torch_tuple = tuple(torch_list)
+        res = torch2jittor(torch_tuple)
+        assert isinstance(res, tuple)
+        for t in res:
+            self.check_jittor_var(t, False)
+
+    def test_dict_transfer(self):
+        """
+        测试字典结构的转换
+        """
+
+        torch_dict = {
+            "tensor": torch.rand((3, 4)),
+            "list": [torch.rand(6, 4, 2) for i in range(10)],
+            "dict":{
+                "list": [torch.rand(6, 4, 2) for i in range(10)],
+                "tensor": torch.rand((3, 4))
+            },
+            "int": 2,
+            "string": "test string"
+        }
+        res = torch2jittor(torch_dict)
+        assert isinstance(res, dict)
+        self.check_jittor_var(res["tensor"], False)
+        assert isinstance(res["list"], list)
+        for t in res["list"]:
+            self.check_jittor_var(t, False)
+        assert isinstance(res["int"], int)
+        assert isinstance(res["string"], str)
+        assert isinstance(res["dict"], dict)
+        assert isinstance(res["dict"]["list"], list)
+        for t in res["dict"]["list"]:
+            self.check_jittor_var(t, False)
+        self.check_jittor_var(res["dict"]["tensor"], False)