fix conflict

README.md

@@ -6,4 +6,133 @@
 ![Hex.pm](https://img.shields.io/hexpm/l/plug.svg)
 [![Documentation Status](https://readthedocs.org/projects/fastnlp/badge/?version=latest)](http://fastnlp.readthedocs.io/?badge=latest)
 
-dev0.8.0正在开发中
+fastNLP是一款轻量级的自然语言处理（NLP）工具包，目标是快速实现NLP任务以及构建复杂模型。
+
+fastNLP具有如下的特性：
+
+- 统一的Tabular式数据容器，简化数据预处理过程；
+- 内置多种数据集的Loader和Pipe，省去预处理代码;
+- 各种方便的NLP工具，例如Embedding加载（包括ELMo和BERT）、中间数据cache等;
+- 部分[数据集与预训练模型](https://docs.qq.com/sheet/DVnpkTnF6VW9UeXdh?c=A1A0A0)的自动下载；
+- 提供多种神经网络组件以及复现模型（涵盖中文分词、命名实体识别、句法分析、文本分类、文本匹配、指代消解、摘要等任务）;
+- Trainer提供多种内置Callback函数，方便实验记录、异常捕获等。
+
+## 安装指南
+
+fastNLP 依赖以下包:
+
++ numpy>=1.14.2
++ torch>=1.0.0
++ tqdm>=4.28.1
++ nltk>=3.4.1
++ requests
++ spacy
++ prettytable>=0.7.2
+
+其中torch的安装可能与操作系统及 CUDA 的版本相关，请参见 [PyTorch 官网](https://pytorch.org/) 。 
+在依赖包安装完成后，您可以在命令行执行如下指令完成安装
+
+```shell
+pip install fastNLP
+python -m spacy download en
+```
+
+
+## fastNLP教程
+中文[文档](https://fastnlp.readthedocs.io/)、[教程](https://fastnlp.readthedocs.io/zh/latest/user/tutorials.html)
+
+### 快速入门
+
+- [0. 快速入门](https://fastnlp.readthedocs.io/zh/latest/user/quickstart.html)
+
+### 详细使用教程
+
+- [1. 使用DataSet预处理文本](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_1_data_preprocess.html)
+- [2. 使用Vocabulary转换文本与index](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_2_vocabulary.html)
+- [3. 使用Embedding模块将文本转成向量](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_3_embedding.html)
+- [4. 使用Loader和Pipe加载并处理数据集](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_4_load_dataset.html)
+- [5. 动手实现一个文本分类器I-使用Trainer和Tester快速训练和测试](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_5_loss_optimizer.html)
+- [6. 动手实现一个文本分类器II-使用DataSetIter实现自定义训练过程](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_6_datasetiter.html)
+- [7. 使用Metric快速评测你的模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_7_metrics.html)
+- [8. 使用Modules和Models快速搭建自定义模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_8_modules_models.html)
+- [9. 快速实现序列标注模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_9_seq_labeling.html)
+- [10. 使用Callback自定义你的训练过程](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_10_callback.html)
+
+### 扩展教程
+
+- [Extend-1. BertEmbedding的各种用法](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_1_bert_embedding.html)
+- [Extend-2. 分布式训练简介](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_2_dist.html)
+- [Extend-3. 使用fitlog 辅助 fastNLP 进行科研](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_3_fitlog.html)
+
+
+## 内置组件
+
+大部分用于的 NLP 任务神经网络都可以看做由词嵌入（embeddings）和两种模块：编码器（encoder）、解码器（decoder）组成。
+
+以文本分类任务为例，下图展示了一个BiLSTM+Attention实现文本分类器的模型流程图：
+
+
+![](./docs/source/figures/text_classification.png)
+
+fastNLP 在 embeddings 模块中内置了几种不同的embedding：静态embedding（GloVe、word2vec）、上下文相关embedding
+（ELMo、BERT）、字符embedding（基于CNN或者LSTM的CharEmbedding）
+
+与此同时，fastNLP 在 modules 模块中内置了两种模块的诸多组件，可以帮助用户快速搭建自己所需的网络。 两种模块的功能和常见组件如下:
+
+<table>
+<tr>
+    <td><b> 类型 </b></td>
+    <td><b> 功能 </b></td>
+    <td><b> 例子 </b></td>
+</tr>
+<tr>
+    <td> encoder </td>
+    <td> 将输入编码为具有具有表示能力的向量 </td>
+    <td> Embedding, RNN, CNN, Transformer, ...
+</tr>
+<tr>
+    <td> decoder </td>
+    <td> 将具有某种表示意义的向量解码为需要的输出形式 </td>
+    <td> MLP, CRF, ... </td>
+</tr>
+</table>
+
+
+## 项目结构
+
+<div align=center><img width="450" height="350" src="./docs/source/figures/workflow.png"/></div>
+
+
+
+fastNLP的大致工作流程如上图所示，而项目结构如下：
+
+<table>
+<tr>
+    <td><b> fastNLP </b></td>
+    <td> 开源的自然语言处理库 </td>
+</tr>
+<tr>
+    <td><b> fastNLP.core </b></td>
+    <td> 实现了核心功能，包括数据处理组件、训练器、测试器等 </td>
+</tr>
+<tr>
+    <td><b> fastNLP.models </b></td>
+    <td> 实现了一些完整的神经网络模型 </td>
+</tr>
+<tr>
+    <td><b> fastNLP.modules </b></td>
+    <td> 实现了用于搭建神经网络模型的诸多组件 </td>
+</tr>
+<tr>
+    <td><b> fastNLP.embeddings </b></td>
+    <td> 实现了将序列index转为向量序列的功能，包括读取预训练embedding等 </td>
+</tr>
+<tr>
+    <td><b> fastNLP.io </b></td>
+    <td> 实现了读写功能，包括数据读入与预处理，模型读写，数据与模型自动下载等 </td>
+</tr>
+</table>
+
+<hr>
+
+*In memory of @FengZiYjun.  May his soul rest in peace. We will miss you very very much!*

fastNLP/core/callbacks/__init__.py

@@ -4,7 +4,8 @@ __all__ = [
     'EventsList',
     'Filter',
     'CallbackManager',
-    'CheckpointCallback',
+    'ModelCheckpointCallback',
+    'TrainerCheckpointCallback',
     'choose_progress_callback',
     'ProgressCallback',
     'RichCallback',
@@ -16,7 +17,7 @@ __all__ = [
 from .callback import Callback
 from .callback_events import EventsList, Events, Filter
 from .callback_manager import CallbackManager
-from .checkpoint_callback import CheckpointCallback
+from .checkpoint_callback import ModelCheckpointCallback, TrainerCheckpointCallback
 from .progress_callback import choose_progress_callback, ProgressCallback, RichCallback
 from .lr_scheduler_callback import LRSchedCallback
 from .load_best_model_callback import LoadBestModelCallback

fastNLP/core/callbacks/callback_manager.py

@@ -8,7 +8,7 @@ __all__ = [
 
 from .callback_events import Events
 from .callback import Callback
-from .checkpoint_callback import CheckpointCallback
+from .checkpoint_callback import TrainerCheckpointCallback
 from .progress_callback import ProgressCallback, choose_progress_callback
 from fastNLP.core.log import logger
 
@@ -98,7 +98,7 @@ class CallbackManager:
         :return:
         """
         for each_callback in self.class_callbacks:
-            if isinstance(each_callback, CheckpointCallback) and each_callback.is_trainer_checkpoint:
+            if isinstance(each_callback, TrainerCheckpointCallback):
                 self._has_trainer_checkpoint = True
             self.dissect_one_callback(each_callback)
 
@@ -210,7 +210,7 @@ class CallbackManager:
                 each_callback.on_load_checkpoint(trainer, None)
 
     @property
-    def has_trainer_chechpoint(self) -> bool:
+    def has_trainer_checkpoint(self) -> bool:
         return self._has_trainer_checkpoint
 
     @_transfer

fastNLP/core/callbacks/checkpoint_callback.py

@@ -1,12 +1,13 @@
+__all__ = [
+    'ModelCheckpointCallback',
+    'TrainerCheckpointCallback'
+]
 import os
-from typing import Union, Optional, Callable, Dict, Sequence
+from typing import Union, Optional, Callable, Dict, Sequence, Any, Mapping
 from pathlib import Path
-from functools import partial
-from time import sleep
+from abc import ABC
+import sys
 
-__all__ = [
-    'CheckpointCallback'
-]
 
 import fastNLP
 from .callback import Callback, Filter
@@ -14,35 +15,37 @@ from fastNLP.core.callbacks.utils import _get_monitor_value
 from fastNLP.core.log import logger
 from fastNLP.envs import FASTNLP_LAUNCH_TIME
 from fastNLP.core.utils import synchronize_safe_rm, synchronize_mkdir
+from fastNLP.core.utils import apply_to_collection
 
 
-class CheckpointCallback(Callback):
+class CanItemDataType(ABC):
     """
-    1. 因为只有 'Trainer' 才有 callback，因此评测 metric 实际上就是 validate 时干的事情；
-    2. 默认 'save_last' 为 True，即 model_checkpoint 的默认逻辑是在每一个 epoch 下保存最后的一个模型，模型名字为 last.pth.tar；
-    3. 理论上一个 model_checkpoint 的实例只会负责一个 monitor 的监视，如果用户在训练过程中指定了多个 monitor 的监视，例如 "acc1",
-    "acc2", ... 那么我们会为用户创建多个 model_checkpoint 的实例；
-    4. 理论上，在实际保存的过程中，topk 模式和 固定频率保存的模式是完全独立的，我们确实应当采取一些措施至少保证两者的名字不一样；
+    检测可以进行传输的对象。
+
     """
 
+    @classmethod
+    def __subclasshook__(cls, subclass: Any) -> Union[bool, Any]:
+        if cls is CanItemDataType:
+            item = getattr(subclass, 'item', None)
+            return callable(item)
+        return NotImplemented
+
+
+
+class CheckpointCallback(Callback):
     def __init__(
             self,
             monitor,
-            is_trainer_checkpoint: Optional[bool] = False,
-
             save_folder: Optional[Union[str, Path]] = None,
-
             save_every_n_epochs: Optional[int] = None,
-            save_every_n_global_batches: Optional[int] = None,
+            save_every_n_batches: Optional[int] = None,
             save_last: bool = True,
             save_topk: Optional[int] = None,
             save_on_exception: Optional[Union[BaseException, Sequence[BaseException]]] = None,
-
             larger_better: bool = True,
             only_state_dict: bool = True,
-
             model_save_fn: Optional[Callable] = None,
-
             **kwargs,
     ):
         if monitor is None and save_topk is not None:
@@ -51,9 +54,6 @@ class CheckpointCallback(Callback):
         if monitor is not None and not isinstance(monitor, str):
             raise ValueError("Parameter `monitor` should be of 'str' type.")
 
-        if not isinstance(is_trainer_checkpoint, bool):
-            raise TypeError("Parameter 'is_trainer_checkpoint' can only be `bool` type.")
-
         if save_folder is None:
             logger.warning(
                 "Parameter `path` is None, and we will use the current work directory to find and load your model.")
@@ -67,15 +67,15 @@ class CheckpointCallback(Callback):
             if not isinstance(save_every_n_epochs, int) or save_every_n_epochs < 1:
                 raise ValueError("parameter save_after_epoch_num should be an int and greater than or equal to 1.")
 
-            # 突然发现有一个骚操作在于 'Filter' 内部记载的状态值例如 'num_called' 是这个类全局的，而每次调用 __call__ 中输入的
-            # 函数却是及时传入的，也就是说，我们可以保证 'Filter' 的正常控制频率的逻辑，然后每一次运行的函数都不一样；
-            self._filter_every_n_epochs = Filter(every=save_every_n_epochs)
+        else:
+            save_every_n_epochs = sys.maxsize  # 使得没有数字可以整除
 
-        if save_every_n_global_batches is not None:
-            if not isinstance(save_every_n_global_batches, int) or save_every_n_global_batches < 1:
+        if save_every_n_batches is not None:
+            if not isinstance(save_every_n_batches, int) or save_every_n_batches < 1:
                 raise ValueError(
-                    "parameter save_every_n_global_batches should be an int and greater than or equal to 1.")
-            self._filter_every_n_global_batches = Filter(every=save_every_n_global_batches)
+                    "parameter save_every_n_batches should be an int and greater than or equal to 1.")
+        else:
+            save_every_n_batches = sys.maxsize  # 使得没有数字可以整除
 
         if save_topk is not None:
             if not isinstance(save_topk, int) or save_topk < 1:
@@ -89,12 +89,12 @@ class CheckpointCallback(Callback):
                 if not issubclass(exception, BaseException):
                     raise TypeError("Each exception in parameter `save_on_exception` can only be "
                                     "`BaseException` type.")
-
+        else:
+            save_on_exception = []
         self.monitor = monitor
-        self.is_trainer_checkpoint = is_trainer_checkpoint
         self.save_folder = Path(save_folder)
         self.save_every_n_epochs = save_every_n_epochs
-        self.save_every_n_global_batches = save_every_n_global_batches
+        self.save_every_n_batches = save_every_n_batches
         self.save_last = save_last
         self.save_topk = save_topk
         self.larger_better = larger_better
@@ -107,7 +107,7 @@ class CheckpointCallback(Callback):
         self._topk_model = {}
         self._topn = 0  # 表示目前已经保存了几个最好的模型；
 
-        # 因为我们在 `_get_validate_metric` 函数中，当在返回的 `validate_res` 字典中找不到 `monitor` 时，是使用模糊匹配找到的第一个
+        # 因为我们在 `_get_validate_metric` 函数中，当在返回的 `validate_res` 字典中找不到 `monitor` 时，是使用匹配找到的
         #  key 对应的 value 当做结果；但是这样存在的一个问题在于如果用户传入的 metric 返回的 sub_metric 的名字可能会混淆，并且其在下一次
         #  训练的代码中修改了这些 sub_metric 返回的顺序，那么就会导致模糊匹配拿到的 key 和 value 与之前的不是同一个，这显然不是合理的行为；
         # 因此我们通过该变量来表示我们通过模糊匹配拿到的 key；
@@ -115,76 +115,83 @@ class CheckpointCallback(Callback):
 
         # 注意这里应当保证只有进程 0 在执行这个操作，因为当用户使用 python -m torch.distributed.launch 来拉起进程的时候，
         #  FASTNLP_LAUNCH_TIME 在每一个进程上的值是不一样的；
-        self.log_filepath = self.save_folder.joinpath(os.environ[FASTNLP_LAUNCH_TIME])
+        self.timestamp_path = self.save_folder.joinpath(os.environ[FASTNLP_LAUNCH_TIME])
         # 我们只需要保证这个创建文件夹的操作只在进程 0 上进行即可；因为后续的实际的保存操作，其它进程实际并不会去执行；
-        synchronize_mkdir(self.log_filepath)
+        synchronize_mkdir(self.timestamp_path)
 
     def on_validate_end(self, trainer, validate_res):
         self._save_topk(trainer, validate_res)
 
     def on_train_epoch_end(self, trainer: "fastNLP.Trainer"):
-        self._save_every_n_epochs(trainer)
-        self._save_last(trainer)
+        if trainer.cur_epoch_idx % self.save_every_n_epochs == 0:
+            folder_name = f'{self.folder_prefix}-epoch_{trainer.cur_epoch_idx}'
+            self.save(trainer, folder_name=folder_name)
+        if self.save_last:
+            folder_name = f'{self.folder_prefix}-last'
+            self.save(trainer, folder_name=folder_name)
 
     def on_train_batch_end(self, trainer):
-        self._save_every_n_global_batches(trainer)
+        if trainer.global_forward_batches % self.save_every_n_batches == 0:
+            folder_name = f'{self.folder_prefix}-epoch_{trainer.cur_epoch_idx}-batch_{trainer.global_forward_batches}'
+            self.save(trainer, folder_name=folder_name)
 
     def on_exception(self, trainer, exception: BaseException):
-        if self.save_on_exception is not None and exception.__class__ in self.save_on_exception:
-            folder = self._get_checkpoint_real_save_folder(trainer=trainer, topk=False, metric=None)
-            folder = folder + f"_{exception.__class__.__name__}"
-            self._save_fn(trainer=trainer, topk=False, metric=None, substitute_folder=folder)
+        if exception.__class__ in self.save_on_exception:
+            folder_name = f'{self.folder_prefix}-epoch_{trainer.cur_epoch_idx}-batch_{trainer.global_forward_batches}-' \
+                     f'exception_{exception.__class__.__name__}'
+            self.save(trainer=trainer, folder_name=folder_name)
 
     def on_sanity_check_end(self, trainer, sanity_check_res):
+        # 主要核对一下 monitor 是否存在。
         self._get_validate_metric(sanity_check_res)
 
     def on_save_checkpoint(self, trainer) -> Dict:
         """
-        我们需要保存 CheckpointCallback 内部的几个 filter 的状态；
+        保存 timestamp_path 使得之后可以继续训练并保存到该文件夹。
+        topk_model的状态
+        _real_monitor的值
         """
+
         states = {}
-        if self.save_every_n_epochs is not None:
-            states["_filter_every_n_epochs"] = self._filter_every_n_epochs.state_dict()
-        if self.save_every_n_global_batches is not None:
-            states["_filter_every_n_global_batches"] = self._filter_every_n_global_batches.state_dict()
-        states["real_monitor"] = self._real_monitor
+        states['timestamp_path'] = str(self.timestamp_path.absolute())
+        states['_topk_model'] = apply_to_collection(self._topk_model, dtype=CanItemDataType,
+                                                    function=lambda x:x.item())
+        states['save_topk'] = 0 if self.save_topk is None else self.save_topk
+        states['_real_monitor'] = self._real_monitor
         return states
 
     def on_load_checkpoint(self, trainer, states: Optional[Dict]):
-        if self.save_every_n_epochs is not None:
-            self._filter_every_n_epochs.load_state_dict(states["_filter_every_n_epochs"])
-        if self.save_every_n_global_batches is not None:
-            self._filter_every_n_global_batches.load_state_dict(states["_filter_every_n_global_batches"])
+        timestamp_path = states['timestamp_path']
+        if not os.path.exists(timestamp_path):
+            logger.info(f"The resuming save folder {timestamp_path} is not exists, will checkpoint save to "
+                        f" {self.timestamp_path.absolute()}.")
+        else:
+            logger.info(f"Resume to save in path: {timestamp_path}.")
+            self.timestamp_path = Path(timestamp_path)
+            _topk_model = states['_topk_model']
+            save_topk = None if int(states['save_topk']) == 0 else int(states['save_topk'])
+            if save_topk is not None and self.save_topk is not None:
+                assert self.save_topk == save_topk, f"The checkpoint set save_topk={save_topk}, while this callback set it " \
+                                                    f"as {save_topk}."
+            self._topk_model.update(self._topk_model)
         self._real_monitor = states["real_monitor"]
 
-    def _save_every_n_epochs(self, trainer: "fastNLP.Trainer"):
-        if self.save_every_n_epochs is not None:
-            if self.is_trainer_checkpoint:
-                _fn_every_n_epochs = trainer.save
-            else:
-                _fn_every_n_epochs = trainer.save_model
-            _fn_every_n_epochs = partial(self._save_fn, trainer, False, None, _fn_every_n_epochs, None)
-            _fn_every_n_epochs = self._filter_every_n_epochs(_fn_every_n_epochs)
-            _fn_every_n_epochs()
-
-    def _save_every_n_global_batches(self, trainer: "fastNLP.Trainer"):
-        if self.save_every_n_global_batches is not None:
-            if self.is_trainer_checkpoint:
-                _fn_every_n_global_batches = trainer.save
-            else:
-                _fn_every_n_global_batches = trainer.save_model
-            _fn_every_n_global_batches = partial(self._save_fn, trainer, False, None, _fn_every_n_global_batches, None)
-            _fn_every_n_global_batches = self._filter_every_n_global_batches(_fn_every_n_global_batches)
-            _fn_every_n_global_batches()
-
     def _save_topk(self, trainer: "fastNLP.Trainer", validate_res: Dict):
+        """
+        根据validate_res决定保存哪些model的函数。会自动移除掉不满足topk的文件夹。
+
+        :param trainer:
+        :param validate_res:
+        :return:
+        """
         if self.save_topk is not None:
             _metric_value = self._get_validate_metric(validate_res)
-            _saved_name = self._get_checkpoint_real_save_folder(trainer=trainer, topk=True, metric=_metric_value)
+            folder_name = f"{self.folder_prefix}-epoch_{trainer.cur_epoch_idx}-batch_{trainer.global_forward_batches}" \
+                         f"-{self._real_monitor}_{_metric_value}"
 
             _should_save = False
             if self._topn < self.save_topk:
-                self._topk_model[_saved_name] = _metric_value
+                self._topk_model[folder_name] = _metric_value
                 self._topn += 1
                 _should_save = True
             else:
@@ -192,39 +199,27 @@ class CheckpointCallback(Callback):
                                                                              key=lambda x: self._topk_model[x])
                 if (self.larger_better and _metric_value > self._topk_model[_least_valuable_model]) or \
                         (self.larger_better is False and _metric_value < self._topk_model[_least_valuable_model]):
-                    self._topk_model[_saved_name] = _metric_value
+                    self._topk_model[folder_name] = _metric_value
                     _should_save = True
                     self._topk_model.pop(_least_valuable_model)
-                    synchronize_safe_rm(self.log_filepath.joinpath(_least_valuable_model))
+                    synchronize_safe_rm(self.timestamp_path.joinpath(_least_valuable_model))
 
                 assert len(self._topk_model) == self.save_topk == self._topn
 
             if _should_save:
-                self._save_fn(trainer=trainer, topk=True, metric=_metric_value, substitute_folder=_saved_name)
+                self.save(trainer, folder_name=folder_name)
 
-    def _save_last(self, trainer: "fastNLP.Trainer"):
-        if self.save_last:
-            self._save_fn(trainer=trainer, topk=False, metric=None, substitute_folder="last")
-
-    def _save_fn(self, trainer, topk: bool = False, metric: Optional[Union[int, float]] = None,
-                 substitute_fn: Optional[Callable] = None, substitute_folder: Optional[str] = None):
-        # 首先根据当前的 epoch 和 batch 在 parent_path/FASTNLP_LAUNCH_TIME 下创建子文件夹 epoch-batch-monitor 或者
-        #  epoch-batch-monitor-monitor_value；
-        if substitute_folder is None:
-            folder = self.log_filepath.joinpath(self._get_checkpoint_real_save_folder(trainer, topk, metric))
-        else:
-            folder = self.log_filepath.joinpath(substitute_folder)
+    def save(self, trainer, folder_name):
+        """
+        执行保存的函数，将数据保存在 save_folder/timestamp/folder_name 下。
 
+        :param trainer:
+        :param folder_name:
+        :return:
+        """
+        folder = self.timestamp_path.joinpath(folder_name)
         synchronize_mkdir(folder)
-
-        # 然后再调用 trainer 的 save_model（用于保存模型）或者 save（用于断点重训）函数；
-        if substitute_fn is not None:
-            _fn = substitute_fn
-        else:
-            if self.is_trainer_checkpoint:
-                _fn = trainer.save
-            else:
-                _fn = trainer.save_model
+        _fn = getattr(trainer, self.save_fn_name)
         _fn(
             folder=folder,
             only_state_dict=self.only_state_dict,
@@ -243,18 +238,95 @@ class CheckpointCallback(Callback):
         self._real_monitor = use_monitor
         return value
 
-    def _get_checkpoint_real_save_folder(self, trainer: "fastNLP.Trainer", topk: bool = False,
-                                         metric: Optional[Union[int, float]] = None) -> str:
+    @property
+    def folder_prefix(self):
+        raise NotImplementedError("The `folder_prefix` is not specified")
+
+    @property
+    def save_fn_name(self):
+        raise NotImplementedError("The `save_fn_name` is not specified.")
+
+
+class ModelCheckpointCallback(CheckpointCallback):
+    """
+    保存模型 checkpoint 的  callback ，其保存的文件目录以及文件名命名规则如下
+
+    - save_folder/
+        - YYYY-mm-dd-HH_MM_SS_fffff/  # 自动根据当前脚本的启动时间创建的
+            - model-epoch_{epoch_idx}/  # 满足 save_every_n_epochs 条件保存的模型
+            - model-epoch_{epoch_idx}-batch_{global_batch_idx}/  # 满足 save_every_n_batches 保存的模型
+            - model-last/  # 最后一个 epoch 的保存
+            - model-epoch_{epoch_idx}-batch_{global_batch_idx}-exception_{exception_type}/  # exception时保存。
+            - model-epoch_{epoch_idx}-batch_{global_batch_idx}-{monitor}_{monitor_value}/  # 满足topk条件存储文件名
+
+    model_save_fn 为 None ，则以上每个 folder 中，将生成 fastnlp_model.pkl.tar 文件。
+    若 model_save_fn 不为 None，则 fastNLP 将 folder 绝对路径传递给该函数，fastNLP 不在该 folder 下创建任何文件。
+
+    :param monitor: 监控的 metric 的名称。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最短公共字符串算法 找到最匹配
+        的那个作为 monitor 。
+    :param save_folder: 保存的文件夹，fastNLP 将在该文件下以时间戳创建子文件夹，并在里面保存。因此不同次运行可以将被保存到不同的
+        时间戳文件夹中。如果为 None ，默认使用当前文件夹。
+    :param save_every_n_epochs: 多少个 epoch 保存一次。
+    :param save_every_n_batches: 多少个 batch 保存一次。
+    :param save_last: 如果为 True ，将在每次 epoch 运行结束都保存一次，会覆盖之前的保存。
+    :param save_topk: 保存 monitor 结果 topK 个。
+    :param save_on_exception: 在出异常信息时，是否保存。传入需要捕获的异常的类。
+    :param larger_better: monitor 的值是否时越大越好。
+    :param only_state_dict: 保存模型时是否只保存 state_dict 。当 model_save_fn 不为 None 时，该参数无效。
+    :param model_save_fn: 个性化的保存函数，当触发保存操作时，就调用这个函数，这个函数应当接受一个文件夹作为参数，不返回任何东西。
+        如果传入了 model_save_fn 函数，fastNLP 将不再进行模型相关的保存。在多卡场景下，我们只在 rank 0 上会运行该函数。
+    :param kwargs:
+    """
+    @property
+    def save_fn_name(self):
+        return 'save_model'
+
+    @property
+    def callback_name(self):
         """
-        获取当前保存模型的真正地名字；
-        metric 参数仅当 mode 为 'topk' 时起作用；
+        通过该值决定两个 CheckpointCallback 实例是否可以共用断点重训的状态；
+        :return:
         """
-        cur_epoch_idx = trainer.cur_epoch_idx
-        global_forward_batches = trainer.global_forward_batches
-        _other = ""
-        if topk:
-            _other = f"_{metric}"
-        return f"epoch_{cur_epoch_idx}-global_batch_{global_forward_batches}-{self._real_monitor}{_other}"
+        return f"model_checkpoint#monitor-{self.monitor}#topK-{self.save_topk}#only_state_dict-{self.only_state_dict}"
+
+    @property
+    def folder_prefix(self):
+        return 'model'
+
+
+class TrainerCheckpointCallback(CheckpointCallback):
+    """
+    保存 Trainer checkpoint 的  callback ，其保存的文件目录以及文件名命名规则如下
+
+    - save_folder/
+        - YYYY-mm-dd-HH_MM_SS_fffff/  # 自动根据当前脚本的启动时间创建的
+            - trainer-epoch_{epoch_idx}/  # 满足 save_every_n_epochs 条件保存的模型
+            - trainer-epoch_{epoch_idx}-batch_{global_batch_idx}/  # 满足 save_every_n_batches 保存的模型
+            - trainer-last/  # 最后一个 epoch 的保存
+            - trainer-epoch_{epoch_idx}-batch_{global_batch_idx}-exception_{exception_type}/  # exception时保存。
+            - trainer-epoch_{epoch_idx}-batch_{global_batch_idx}-{monitor}_{monitor_value}/  # 满足topk条件存储文件名
+
+    model_save_fn 为 None ，则以上每个 folder 中，将生成两个文件：fastnlp_trainer.pkl.tar 以及 fastnlp_model.pkl.tar 。
+    若 model_save_fn 不为 None，则 fastNLP 只会在每个 folder 下生成 fastnlp_trainer.pkl.tar 文件。
+
+    :param monitor: 监控的 metric 的名称。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最短公共字符串算法 找到最匹配
+        的那个作为 monitor 。
+    :param save_folder: 保存的文件夹，fastNLP 将在该文件下以时间戳创建子文件夹，并在里面保存。因此不同次运行可以将被保存到不同的
+        时间戳文件夹中。如果为 None ，默认使用当前文件夹。
+    :param save_every_n_epochs: 多少个 epoch 保存一次。
+    :param save_every_n_batches: 多少个 batch 保存一次。
+    :param save_last: 如果为 True ，将在每次 epoch 运行结束都保存一次，会覆盖之前的保存。
+    :param save_topk: 保存 monitor 结果 topK 个。
+    :param save_on_exception: 在出异常信息时，是否保存。
+    :param larger_better: monitor 的值是否时越大越好。
+    :param only_state_dict: 保存模型时是否只保存 state_dict 。当 model_save_fn 不为 None 时，该参数无意义。
+    :param model_save_fn: 个性化的保存函数，当触发保存操作时，就调用这个函数，这个函数应当接受一个文件夹作为参数，不返回任何东西。
+        如果传入了 model_save_fn 函数，fastNLP 将不再进行模型相关的保存。在多卡场景下，我们只在 rank 0 上会运行该函数。
+    :param kwargs:
+    """
+    @property
+    def save_fn_name(self):
+        return 'save'
 
     @property
     def callback_name(self):
@@ -262,6 +334,8 @@ class CheckpointCallback(Callback):
         通过该值决定两个 CheckpointCallback 实例是否可以共用断点重训的状态；
         :return:
         """
-        return f"monitor-{self.monitor}#trainer_checkpoint-{self.is_trainer_checkpoint}#only_state_dict-{self.only_state_dict}"
-
+        return f"trainer_checkpoint#monitor-{self.monitor}#topK-{self.save_topk}#only_state_dict-{self.only_state_dict}"
 
+    @property
+    def folder_prefix(self):
+        return 'trainer'

fastNLP/core/callbacks/load_best_model_callback.py

@@ -31,7 +31,7 @@ class LoadBestModelCallback(Callback):
             请在函数内完成对模型的保存。
         :param model_load_fn: 加载 model 的函数，与 model_save_fn 必须同时不为空。本函数的输入为一个已经创建好的文件夹，没有输出，
             请在函数内完成对模型的加载。
-        :param delete_after_train: 在加载了最佳模型之后是否删掉模型。
+        :param delete_after_train: 在训练结束后是否删掉模型。
         """
         if model_load_fn is not None:
             assert callable(model_load_fn), "`model_load_fn` must be a callable object."

fastNLP/core/controllers/evaluator.py

@@ -133,17 +133,18 @@ class Evaluator:
 
         self.driver.barrier()
 
-    def run(self, num_eval_batch_per_dl: int = -1) -> Dict:
+    def run(self, num_eval_batch_per_dl: int = -1, **kwargs) -> Dict:
         """
         返回一个字典类型的数据，其中key为metric的名字，value为对应metric的结果。
-        如果存在多个metric，一个dataloader的情况，key的命名规则是
-            metric_indicator_name#metric_name
-        如果存在多个数据集，一个metric的情况，key的命名规则是
-            metric_indicator_name#dataloader_name (其中 # 是默认的 separator ，可以通过 Evaluator 初始化参数修改)。
-        如果存在多个metric，多个dataloader的情况，key的命名规则是
-            metric_indicator_name#metric_name#dataloader_name
-        :param num_eval_batch_per_dl: 每个 dataloader 测试多少个 batch 的数据，-1 为测试所有数据。
+            如果存在多个metric，一个dataloader的情况，key的命名规则是
+                metric_indicator_name#metric_name
+            如果存在多个数据集，一个metric的情况，key的命名规则是
+                metric_indicator_name#metric_name#dataloader_name (其中 # 是默认的 separator ，可以通过 Evaluator 初始化参数修改)。
+            如果存在多个metric，多个dataloader的情况，key的命名规则是
+                metric_indicator_name#metric_name#dataloader_name
+            其中 metric_indicator_name 可能不存在。
 
+        :param num_eval_batch_per_dl: 每个 dataloader 测试多少个 batch 的数据，-1 为测试所有数据。
         :return:
         """
         assert isinstance(num_eval_batch_per_dl, int), "num_eval_batch_per_dl must be of int type."
@@ -157,7 +158,6 @@ class Evaluator:
             assert self.driver.has_test_dataloaders()
 
         metric_results = {}
-
         self.reset()
         evaluate_context = self.driver.get_evaluate_context()
         self.driver.set_model_mode(mode='eval' if self.model_use_eval_mode else 'train')

fastNLP/core/controllers/trainer.py

@@ -23,7 +23,7 @@ from fastNLP.core.drivers import Driver
 from fastNLP.core.drivers.utils import choose_driver
 from fastNLP.core.utils import check_fn_not_empty_params, get_fn_arg_names, match_and_substitute_params, nullcontext
 from fastNLP.envs import rank_zero_call
-from fastNLP.core.samplers import ReproducibleIterator, ReproducibleBatchSampler
+from fastNLP.core.samplers import ReproducibleSampler, RandomBatchSampler
 from fastNLP.core.log import logger
 from fastNLP.envs import FASTNLP_MODEL_FILENAME
 
@@ -251,7 +251,7 @@ class Trainer(TrainerEventTrigger):
         self.driver.set_deterministic_dataloader(self.dataloader)
 
         self.dataloader = self.driver.set_dist_repro_dataloader(dataloader=self.train_dataloader, dist=_dist_sampler,
-                                                                reproducible=self.callback_manager.has_trainer_chechpoint)
+                                                                reproducible=self.callback_manager.has_trainer_checkpoint)
 
         self.set_grad_to_none = kwargs.get("set_grad_to_none", True)
         self.on_after_trainer_initialized(self.driver)
@@ -291,6 +291,7 @@ class Trainer(TrainerEventTrigger):
                 raise FileNotFoundError("You are using `resume_from`, but we can not find your specific file.")
 
         if self.evaluator is not None and num_eval_sanity_batch > 0:
+            logger.info(f"Running evaluator sanity check for {num_eval_sanity_batch} batches.")
             self.on_sanity_check_begin()
             sanity_check_res = self.evaluator.run(num_eval_batch_per_dl=num_eval_sanity_batch)
             self.on_sanity_check_end(sanity_check_res)
@@ -509,7 +510,7 @@ class Trainer(TrainerEventTrigger):
 
         :param folder: 保存模型的地址；
         :param only_state_dict: 是否只保存模型的 `state_dict`；
-        :param save_fn: 用户自己定制的用来替换该保存函数本身保存逻辑的函数；
+        :param model_save_fn: 用户自己定制的用来替换该保存函数本身保存逻辑的函数；
         :param kwargs: 一些 driver 的保存模型的函数的参数另有其它；
         """
 
@@ -534,7 +535,16 @@ class Trainer(TrainerEventTrigger):
 
     def load_model(self, folder: Union[str, Path, BinaryIO, io.BytesIO], only_state_dict: bool = False,
                    model_load_fn: Optional[Callable] = None, **kwargs):
+        """
+        加载模型
 
+        :param folder: 读取 model 的文件夹，默认会尝试读取该文件夹下的 fastnlp_model.pkl.tar 文件。在 model_load_fn 不为空时，
+            直接将该 folder 传递到 model_load_fn 中。
+        :param only_state_dict: 要读取的文件中是否仅包含模型权重。在 model_load_fn 不为 None 时，该参数无意义。
+        :param model_load_fn: callable 的函数，接受一个 folder 作为参数，不返回任何内容。
+        :param kwargs:
+        :return:
+        """
         self.on_load_model()
         self.driver.barrier()
         if not isinstance(folder, (io.BytesIO, BinaryIO)):
@@ -555,7 +565,13 @@ class Trainer(TrainerEventTrigger):
 
     def save(self, folder: Union[str, Path], only_state_dict: bool = True, model_save_fn: Optional[Callable] = None, **kwargs):
         r"""
-        用于断点重训的保存函数;
+        用于断点重训 Trainer 的保存函数;
+
+        :param folder:
+        :param only_state_dict:
+        :param model_save_fn:
+        :param kwargs:
+        :return:
         """
         self.driver.barrier()
 
@@ -594,7 +610,7 @@ class Trainer(TrainerEventTrigger):
         r"""
         用于断点重训的加载函数；
         注意在 fastNLP 中断点重训的保存和加载逻辑是分开的，因此可能存在一种情况：用户只希望加载一个断点重训的状态，而在之后不再进行断点重训的
-         保存；在这种情况下，dataloader 的 sampler 就不一定会被替换成我们的 ReproducibleIterator；
+         保存；在这种情况下，dataloader 的 sampler 就不一定会被替换成我们的 ReproducibleSampler；
 
         注意我们目前不支持单卡到多卡的断点重训；
 

fastNLP/core/dataloaders/torch_dataloader/fdl.py

@@ -24,6 +24,7 @@ class _FDataSet:
     对Dataset的封装，主要是修改dataset的__getitem__函数，增加返回下标idx，值得注意的是dataset需要实现__getattribute__函数才能在_FDataset
     中调用dataset的方法
     """
+
     def __init__(self, dataset) -> None:
         self.dataset = dataset
 
@@ -45,6 +46,7 @@ class TorchDataLoader(DataLoader):
     提供给使用pytorch框架的DataLoader函数，若是配套使用FastNLP的dataset则可以自动使用AutoCollate函数对数据进行自动padding操作，用户也可以通过
     提供的方法调节设置collate_fn的若干参数。
     """
+
     def __init__(self, dataset, batch_size: int = 1,
                  shuffle: bool = False, sampler: Optional["Sampler[int]"] = None,
                  batch_sampler: Optional["Sampler[Sequence[int]]"] = None,
@@ -175,17 +177,17 @@ class TorchDataLoader(DataLoader):
 
 
 def prepare_torch_dataloader(ds_or_db: Union[DataSet, DataBundle, Sequence[DataSet], Mapping[str, DataSet]],
-                       batch_size: int = 1,
-                       shuffle: bool = False, sampler: Optional["Sampler[int]"] = None,
-                       batch_sampler: Optional["Sampler[Sequence[int]]"] = None,
-                       num_workers: int = 0, collate_fn: Optional[Callable] = None,
-                       pin_memory: bool = False, drop_last: bool = False,
-                       timeout: float = 0, worker_init_fn: Optional[Callable] = None,
-                       multiprocessing_context=None, generator=None, prefetch_factor: int = 2,
-                       persistent_workers: bool = False, non_train_sampler: Optional["Sampler[int]"] = None,
-                       non_train_batch_size: int = 16, as_numpy: bool = False,
-                       input_fields: Union[List, str] = None)\
-                       -> Union[TorchDataLoader, Dict[str, TorchDataLoader], Sequence[TorchDataLoader]]:
+                             batch_size: int = 1,
+                             shuffle: bool = False, sampler: Optional["Sampler[int]"] = None,
+                             batch_sampler: Optional["Sampler[Sequence[int]]"] = None,
+                             num_workers: int = 0, collate_fn: Optional[Callable] = None,
+                             pin_memory: bool = False, drop_last: bool = False,
+                             timeout: float = 0, worker_init_fn: Optional[Callable] = None,
+                             multiprocessing_context=None, generator=None, prefetch_factor: int = 2,
+                             persistent_workers: bool = False, non_train_sampler: Optional["Sampler[int]"] = None,
+                             non_train_batch_size: int = 16, as_numpy: bool = False,
+                             input_fields: Union[List, str, None] = None) \
+        -> Union[TorchDataLoader, Dict[str, TorchDataLoader], Sequence[TorchDataLoader]]:
     """
     传入dataset或者data_bundle后，将其处理返回相对应的FdataLoader实例化对象
 
@@ -221,7 +223,8 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, DataBundle, Sequence[DataS
                              multiprocessing_context=multiprocessing_context, generator=generator,
                              prefetch_factor=prefetch_factor, persistent_workers=persistent_workers,
                              as_numpy=as_numpy)
-        dl.set_input(*input_fields)
+        if input_fields:
+            dl.set_input(*input_fields)
         return dl
 
     elif isinstance(ds_or_db, DataBundle):
@@ -233,17 +236,21 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, DataBundle, Sequence[DataS
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                                                   drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
                                                   multiprocessing_context=multiprocessing_context, generator=generator,
-                                                  prefetch_factor=prefetch_factor, persistent_workers=persistent_workers,
+                                                  prefetch_factor=prefetch_factor,
+                                                  persistent_workers=persistent_workers,
                                                   as_numpy=as_numpy)
             else:
                 dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=non_train_batch_size,
-                                                  shuffle=shuffle, sampler=non_train_sampler, batch_sampler=batch_sampler,
+                                                  shuffle=shuffle, sampler=non_train_sampler,
+                                                  batch_sampler=batch_sampler,
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                                                   drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
                                                   multiprocessing_context=multiprocessing_context, generator=generator,
-                                                  prefetch_factor=prefetch_factor, persistent_workers=persistent_workers,
+                                                  prefetch_factor=prefetch_factor,
+                                                  persistent_workers=persistent_workers,
                                                   as_numpy=as_numpy)
-            dl_bundle[name].set_input(*input_fields)
+            if input_fields:
+                dl_bundle[name].set_input(*input_fields)
         return dl_bundle
 
     elif isinstance(ds_or_db, Sequence):
@@ -269,8 +276,9 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, DataBundle, Sequence[DataS
                                     prefetch_factor=prefetch_factor, persistent_workers=persistent_workers,
                                     as_numpy=as_numpy)
                 )
-        for dl in dl_bundle:
-            dl.set_input(*input_fields)
+        if input_fields:
+            for dl in dl_bundle:
+                dl.set_input(*input_fields)
         return dl_bundle
 
     elif isinstance(ds_or_db, Mapping):
@@ -282,18 +290,22 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, DataBundle, Sequence[DataS
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                                                   drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
                                                   multiprocessing_context=multiprocessing_context, generator=generator,
-                                                  prefetch_factor=prefetch_factor, persistent_workers=persistent_workers,
+                                                  prefetch_factor=prefetch_factor,
+                                                  persistent_workers=persistent_workers,
                                                   as_numpy=as_numpy)
             else:
                 dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=non_train_batch_size,
-                                                  shuffle=shuffle, sampler=non_train_sampler, batch_sampler=batch_sampler,
+                                                  shuffle=shuffle, sampler=non_train_sampler,
+                                                  batch_sampler=batch_sampler,
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                                                   drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
                                                   multiprocessing_context=multiprocessing_context, generator=generator,
-                                                  prefetch_factor=prefetch_factor, persistent_workers=persistent_workers,
+                                                  prefetch_factor=prefetch_factor,
+                                                  persistent_workers=persistent_workers,
                                                   as_numpy=as_numpy)
 
-            dl_bundle[name].set_input(*input_fields)
+            if input_fields:
+                dl_bundle[name].set_input(*input_fields)
 
         return dl_bundle
     else:

fastNLP/core/dataset/dataset.py

@@ -8,9 +8,8 @@ __all__ = [
 
 import _pickle as pickle
 from copy import deepcopy
-from typing import Optional, List, Callable, Union, Dict, Any
+from typing import Optional, List, Callable, Union, Dict, Any, Mapping
 from functools import partial
-import warnings
 
 import numpy as np
 from threading import Thread
@@ -197,6 +196,20 @@ class DataSet:
         else:
             raise KeyError("Unrecognized type {} for idx in __getitem__ method".format(type(idx)))
 
+    def __setitem__(self, key, value):
+        assert isinstance(key, int) and key<len(self)
+        assert isinstance(value, Instance) or isinstance(value, Mapping)
+        ins_keys = set(value.keys())
+        ds_keys = set(self.get_field_names())
+
+        if len(ins_keys - ds_keys) != 0:
+            raise KeyError(f"The following keys are not found in the Dataset:{list(ins_keys - ds_keys)}.")
+        if len(ds_keys - ins_keys) != 0:
+            raise KeyError(f"The following keys are not found in the Instance:{list(ds_keys - ins_keys)}.")
+
+        for field_name, field in self.field_arrays.items():
+            field[key] = value[field_name]
+
     def __getattribute__(self, item):
         return object.__getattribute__(self, item)
 
@@ -813,6 +826,3 @@ class DataSet:
         self.collate_fns.set_input(*field_names)
 
 
-class IterableDataset:
-    pass
-

fastNLP/core/dataset/field.py

@@ -46,9 +46,6 @@ class FieldArray:
 
     def __setitem__(self, idx: int, val: Any):
         assert isinstance(idx, int)
-        if idx == -1:
-            idx = len(self) - 1
-        assert 0 <= idx < len(self), f"0<= idx <{len(self)}, but idx is {idx}"
         self.content[idx] = val
 
     def get(self, indices: Union[int, List[int]]):
@@ -79,7 +76,7 @@ class FieldArray:
 
     def split(self, sep: str = None, inplace: bool = True):
         r"""
-        依次对自身的元素使用.split()方法，应该只有当本field的元素为str时，该方法才有用。将返回值
+        依次对自身的元素使用.split()方法，应该只有当本field的元素为str时，该方法才有用。
 
         :param sep: 分割符，如果为None则直接调用str.split()。
         :param inplace: 如果为True，则将新生成值替换本field。否则返回list。

fastNLP/core/drivers/driver.py

@@ -6,6 +6,7 @@ from abc import ABC, abstractmethod
 from datetime import datetime
 from pathlib import Path
 from io import BytesIO
+import json
 
 __all__ = [
     'Driver'
@@ -48,10 +49,13 @@ class Driver(ABC):
             不同 gpu 上出现重复；为 'unrepeatdist' 时，表示该 dataloader 应该保证所有 gpu 上迭代出来的数据合并起来应该刚好等于原始的
             数据，允许不同 gpu 上 batch 的数量不一致。其中 trainer 中 kwargs 的参数 `use_dist_sampler` 为 True 时，该值为 "dist"；
             否则为 None ，evaluator 中的 kwargs 的参数 `use_dist_sampler` 为 True 时，该值为 "unrepeatdist"，否则为 None；
+        注意当 dist 为 ReproducibleIterator, RandomBatchSampler 时，是断点重训加载时 driver.load 函数在调用；
+        当 dist 为 str 或者 None 时，是 trainer 在初始化时调用该函数；
+
         :param reproducible: 如果为 False ，不要做任何考虑；如果为 True ，需要保证返回的 dataloader 可以保存当前的迭代状态，使得
             可以可以加载。
         :return: 应当返回一个被替换 sampler 后的新的 dataloader 对象 (注意此处一定需要返回一个新的 dataloader 对象) ；此外，
-            如果传入的 dataloader 中是 ReproducibleIterator 或者 ReproducibleBatchSampler 需要重新初始化一个放入返回的
+            如果传入的 dataloader 中是 ReproducibleSampler 或者 RandomBatchSampler 需要重新初始化一个放入返回的
             dataloader 中。如果 dist 为空，且 reproducible 为 False，可直接返回原对象。
         """
         if dist is None and reproducible is False:
@@ -68,9 +72,12 @@ class Driver(ABC):
     def set_sampler_epoch(self, dataloader, cur_epoch_idx):
         r"""
         对于分布式的 sampler，例如 torch 的 DistributedSampler，其需要在每一个 epoch 前设置随机数种子，来保证每一个进程上的 shuffle 是一样的；
+            dataloader 中可能真正发挥作用的是 batch_sampler 也可能是 sampler。
 
+        :param dataloader: 需要设置 epoch 的 dataloader 。
         :param cur_epoch_idx: 当前是第几个 epoch；
         """
+
     @abstractmethod
     def train_step(self, batch):
         """
@@ -444,13 +451,14 @@ class Driver(ABC):
 
             exc_type, exc_value, exc_traceback_obj = sys.exc_info()
             _write_exc_info = {
-                'exc_type': exc_type,
-                'exc_value': exc_value,
-                'time': str(datetime.now().strftime('%Y-%m-%d-%H:%M:%S')),
-                'global_rank': getattr(self, "global_rank", None),
-                'rank': self.get_local_rank(),
+                'exc_type': str(exc_type.__name__),
+                'exc_value': str(exc_value),
+                'exc_time': str(datetime.now().strftime('%Y-%m-%d-%H:%M:%S')),
+                'exc_global_rank': getattr(self, "global_rank", None),
+                'exc_local_rank': self.get_local_rank(),
             }
-            sys.stderr.write(str(_write_exc_info)+"\n")
+            sys.stderr.write("\nException info:\n")
+            sys.stderr.write(json.dumps(_write_exc_info, indent=2)+"\n")
 
             sys.stderr.write(f"Start to stop these pids:{self._pids}, please wait several seconds.\n")
             for pid in self._pids:

fastNLP/core/drivers/jittor_driver/mpi.py

@@ -3,7 +3,7 @@ from typing import Optional, Union
 
 from .jittor_driver import JittorDriver
 from fastNLP.envs.imports import _NEED_IMPORT_JITTOR
-from fastNLP.core.samplers import ReproducibleIterator
+from fastNLP.core.samplers import ReproducibleSampler
 
 if _NEED_IMPORT_JITTOR:
     import jittor
@@ -70,7 +70,7 @@ class JittorMPIDriver(JittorDriver):
     def test_step(self, batch):
         return self._test_step(batch)
 
-    def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleIterator]],
+    def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleSampler]],
                                   reproducible: bool = False, sampler_or_batch_sampler=None):
         pass
 

fastNLP/core/drivers/jittor_driver/single_device.py

@@ -3,7 +3,7 @@ from typing import Dict, Union
 from .jittor_driver import JittorDriver
 from fastNLP.core.utils import auto_param_call
 from fastNLP.envs.imports import _NEED_IMPORT_JITTOR
-from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleIterator
+from fastNLP.core.samplers import RandomBatchSampler, ReproducibleSampler
 
 if _NEED_IMPORT_JITTOR:
     import jittor
@@ -99,25 +99,25 @@ class JittorSingleDriver(JittorDriver):
     def is_distributed(self):
         return False
 
-    def set_dist_repro_dataloader(self, dataloader, dist: Union[str, ReproducibleBatchSampler, ReproducibleIterator],
+    def set_dist_repro_dataloader(self, dataloader, dist: Union[str, RandomBatchSampler, ReproducibleSampler],
                                   reproducible: bool = False, sampler_or_batch_sampler=None):
         # reproducible 的相关功能暂时没有实现
-        if isinstance(dist, ReproducibleBatchSampler):
+        if isinstance(dist, RandomBatchSampler):
             raise NotImplementedError
             dataloader.batch_sampler = dist_sample
-        if isinstance(dist, ReproducibleIterator):
+        if isinstance(dist, ReproducibleSampler):
             raise NotImplementedError  
             dataloader.batch_sampler.sampler = dist
 
         if reproducible:
             raise NotImplementedError
-            if isinstance(dataloader.batch_sampler.sampler, ReproducibleIterator):
+            if isinstance(dataloader.batch_sampler.sampler, ReproducibleSampler):
                 return dataloader
-            elif isinstance(dataloader.batch_sampler, ReproducibleBatchSampler):
+            elif isinstance(dataloader.batch_sampler, RandomBatchSampler):
                 return dataloader
             else:
                 # TODO
-                batch_sampler = ReproducibleBatchSampler(
+                batch_sampler = RandomBatchSampler(
                     batch_sampler=dataloader.batch_sampler,
                     batch_size=dataloader.batch_sampler.batch_size,
                     drop_last=dataloader.drop_last

fastNLP/core/drivers/paddle_driver/fleet.py

@@ -21,6 +21,8 @@ from fastNLP.core.utils import (
     is_in_paddle_dist,
 )
 from fastNLP.core.samplers import (
+    RandomBatchSampler,
+    ReproducibleSampler,
     ReproducibleIterator,
     RandomSampler,
     UnrepeatedDistributedSampler,
@@ -318,7 +320,7 @@ class PaddleFleetDriver(PaddleDriver):
     def test_step(self, batch):
         return self._test_step(batch)
 
-    def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleIterator]],
+    def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleSampler, RandomBatchSampler]],
                                   reproducible: bool = False, sampler_or_batch_sampler=None):
         # 暂时不支持iterableDataset
         assert dataloader.dataset_kind != _DatasetKind.ITER, \

fastNLP/core/drivers/paddle_driver/single_device.py

@@ -11,7 +11,11 @@ from fastNLP.core.utils import (
     get_paddle_device_id,
     paddle_move_data_to_device,
 )
-from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleIterator, re_instantiate_sampler
+from fastNLP.core.samplers import (
+    ReproducibleBatchSampler,
+    ReproducibleIterator,
+    re_instantiate_sampler,
+)
 from fastNLP.core.log import logger
 
 if _NEED_IMPORT_PADDLE:

fastNLP/core/drivers/torch_driver/ddp.py

@@ -23,15 +23,16 @@ from fastNLP.core.drivers.torch_driver.utils import (
     ForwardState,
     _MODE_PARAMETER,
     reset_seed,
-    replace_sampler
+    replace_sampler,
+    replace_batch_sampler
 )
 from fastNLP.core.drivers.utils import distributed_open_proc
 from fastNLP.core.utils import auto_param_call, check_user_specific_params
-from fastNLP.core.samplers import ReproducibleIterator, RandomSampler, UnrepeatedDistributedSampler
+from fastNLP.core.samplers import ReproducibleSampler, RandomSampler, UnrepeatedSequentialSampler, RandomBatchSampler, \
+    re_instantiate_sampler, UnrepeatedSampler, conversion_between_reproducible_and_unrepeated_sampler
 from fastNLP.envs import FASTNLP_DISTRIBUTED_CHECK, FASTNLP_GLOBAL_RANK, FASTNLP_GLOBAL_SEED
 from fastNLP.core.log import logger
 from fastNLP.core.drivers.torch_driver.dist_utils import fastnlp_torch_all_gather, fastnlp_torch_broadcast_object
-from fastNLP.core.samplers import re_instantiate_sampler
 
 
 class TorchDDPDriver(TorchDriver):
@@ -445,25 +446,52 @@ class TorchDDPDriver(TorchDriver):
         # return self.model(batch, **{_MODE_PARAMETER: ForwardState.TEST})
         return self._test_step(batch)
 
-    def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleIterator]],
-                                  reproducible: bool = False, sampler_or_batch_sampler=None):
-        if isinstance(dist, ReproducibleIterator):
-            # 注意这里不需要调用 dist_sampler.set_distributed；因为如果用户使用的是 TorchDDPDriver，那么其在 Trainer 初始化的时候就已经调用了该函数；
-            dist = re_instantiate_sampler(dist)
+    def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleSampler, RandomBatchSampler]]=None,
+                                  reproducible: bool = False):
+        # 如果 dist 为 RandomBatchSampler, ReproducibleIterator 说明是在断点重训时 driver.load 函数调用；
+        # 注意这里不需要调用 dist_sampler.set_distributed；因为如果用户使用的是 TorchDDPDriver，那么其在 Trainer 初始化的时候就已经调用了该函数；
+        if isinstance(dist, RandomBatchSampler):
+            dist.set_distributed(
+                num_replicas=self.world_size,
+                rank=self.global_rank,
+                pad=True
+            )
+            return replace_batch_sampler(dataloader, dist)
+        if isinstance(dist, ReproducibleSampler):
+            dist.set_distributed(
+                num_replicas=self.world_size,
+                rank=self.global_rank,
+                pad=True
+            )
             return replace_sampler(dataloader, dist)
 
+        # 如果 dist 为 str 或者 None，说明是在 trainer 初试化时调用；
         # trainer, evaluator
         if dist is None:
             if reproducible:
                 raise RuntimeError("It is not allowed to use checkpoint retraining when you initialize ddp out of our "
                                    "control.")
             else:
+                if isinstance(dist, RandomBatchSampler):
+                    dist = re_instantiate_sampler(dist)
+                    return replace_batch_sampler(dataloader, dist)
+                if isinstance(dist, ReproducibleSampler):
+                    dist = re_instantiate_sampler(dist)
+                    return replace_sampler(dataloader, dist)
                 return dataloader
         # trainer
         elif dist == "dist":
             args = self.get_dataloader_args(dataloader)
             # 如果用户的 trainer.use_dist_sampler 为 True，那么此时其是否进行断点重训，不影响这里的行为；
-            if isinstance(args.sampler, ReproducibleIterator):
+            if isinstance(args.batch_sampler, RandomBatchSampler):
+                batch_sampler = re_instantiate_sampler(args.batch_sampler)
+                batch_sampler.set_distributed(
+                    num_replicas=self.world_size,
+                    rank=self.global_rank,
+                    pad=True
+                )
+                return replace_batch_sampler(dataloader, batch_sampler)
+            elif isinstance(args.sampler, ReproducibleSampler):
                 sampler = re_instantiate_sampler(args.sampler)
                 sampler.set_distributed(
                     num_replicas=self.world_size,
@@ -477,21 +505,23 @@ class TorchDDPDriver(TorchDriver):
                     shuffle=args.shuffle,
                     seed=int(os.environ.get(FASTNLP_GLOBAL_SEED, 0))
                 )
-                # todo 这个你写个todo吧，有两个角度；第一个是dataloader即使检测到sampler是我们reproducible，也不能直接set_distributeds; 第二个如果是单卡的，也需要替换sampler乃至切换sampler的状态，方式之前多卡，现在切换成单卡运行
                 sampler.set_distributed(
                     num_replicas=self.world_size,
                     rank=self.global_rank,
                     pad=True
                 )
                 return replace_sampler(dataloader, sampler)
-
         # evaluator
         elif dist == "unrepeatdist":
             args = self.get_dataloader_args(dataloader)
-            sampler = UnrepeatedDistributedSampler(
-                dataset=args.dataset,
-                shuffle=args.shuffle,
-            )
+            if isinstance(args.sampler, ReproducibleSampler):
+                sampler = conversion_between_reproducible_and_unrepeated_sampler(args.sampler)
+            elif not isinstance(args.sampler, UnrepeatedSampler):
+                sampler = UnrepeatedSequentialSampler(
+                    dataset=args.dataset
+                )
+            else:
+                sampler = re_instantiate_sampler(args.sampler)
             sampler.set_distributed(
                 num_replicas=self.world_size,
                 rank=self.global_rank

fastNLP/core/drivers/torch_driver/dist_utils.py

@@ -397,12 +397,13 @@ def fastnlp_torch_all_gather(obj:Any, device=None, group=None)->List:
     """
     # # 首先将所有的都移动到cpu上并且连续，防止有 pickle 出问题
     # obj = apply_to_collection(obj, torch.Tensor, _to_device, device=torch.device('cpu'))
+    if device is None:
+        device = torch.cuda.current_device()
     if _TORCH_GREATER_EQUAL_1_8:
         objs = [None for _ in range(dist.get_world_size(group))]
         dist.all_gather_object(objs, obj)
+        objs = apply_to_collection(objs, torch.Tensor, _to_device, device=device)  # 保证如果有tensor的话，所有tensor都在当前卡上
         return objs
-    if device is None:
-        device = torch.cuda.current_device()
     group = group if group is not None else torch.distributed.group.WORLD
     data = convert_to_tensors(obj, device=device)
     data = apply_to_collection(data, (torch.Tensor, tuple), _all_gather, group=group)

fastNLP/core/drivers/torch_driver/initialize_torch_driver.py

@@ -27,7 +27,7 @@ def initialize_torch_driver(driver: str, device: Optional[Union[str, torch.devic
     # world_size 和 rank
     if FASTNLP_BACKEND_LAUNCH in os.environ:
         if device is not None:
-            logger.warning("Parameter `device` would be ignored when you are using `torch.distributed.run` to pull "
+            logger.info("Parameter `device` would be ignored when you are using `torch.distributed.run` to pull "
                            "up your script. And we will directly get the local device via "
                            "`os.environ['LOCAL_RANK']`.")
         return TorchDDPDriver(model, torch.device(f"cuda:{os.environ['LOCAL_RANK']}"), True, **kwargs)
@@ -39,11 +39,14 @@ def initialize_torch_driver(driver: str, device: Optional[Union[str, torch.devic
     if isinstance(device, str):
         device = torch.device(device)
     elif 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:
+        if device < 0:
+            if device != -1:
+                raise ValueError("Parameter `device` can only be '-1' when it is smaller than 0.")
+            device = [torch.device(f"cuda:{w}") for w in range(_could_use_device_num)]
+        elif device >= _could_use_device_num:
             raise ValueError("The gpu device that parameter `device` specifies is not existed.")
-        device = torch.device(f"cuda:{device}")
+        else:
+            device = torch.device(f"cuda:{device}")
     elif isinstance(device, Sequence):
         device = list(set(device))
         for each in device:
@@ -62,7 +65,7 @@ def initialize_torch_driver(driver: str, device: Optional[Union[str, torch.devic
         if not isinstance(device, List):
             return TorchSingleDriver(model, device, **kwargs)
         else:
-            logger.warning("Notice you are using `torch` driver but your chosen `device` are multi gpus, we will use "
+            logger.info("Notice you are using `torch` driver but your chosen `device` are multi gpus, we will use "
                            "`TorchDDPDriver` by default. But if you mean using `TorchDDPDriver`, you should choose parameter"
                            "`driver` as `TorchDDPDriver`.")
             return TorchDDPDriver(model, device, **kwargs)

fastNLP/core/drivers/torch_driver/single_device.py

@@ -13,9 +13,8 @@ __all__ = [
 from .torch_driver import TorchDriver
 from fastNLP.core.drivers.torch_driver.utils import replace_sampler, replace_batch_sampler
 from fastNLP.core.utils import auto_param_call
-from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleIterator
+from fastNLP.core.samplers import RandomBatchSampler, ReproducibleSampler, re_instantiate_sampler
 from fastNLP.core.log import logger
-from fastNLP.core.samplers import re_instantiate_sampler
 
 
 class TorchSingleDriver(TorchDriver):
@@ -130,25 +129,31 @@ class TorchSingleDriver(TorchDriver):
         else:
             return self._test_step(batch)
 
-    def set_dist_repro_dataloader(self, dataloader, dist: Union[str, ReproducibleBatchSampler, ReproducibleIterator]=None,
+    def set_dist_repro_dataloader(self, dataloader, dist: Union[str, RandomBatchSampler, ReproducibleSampler]=None,
                                   reproducible: bool = False):
-        if isinstance(dist, ReproducibleBatchSampler):
+
+        # 如果 dist 为 RandomBatchSampler, ReproducibleIterator 说明是在断点重训时 driver.load 函数调用；
+        if isinstance(dist, RandomBatchSampler):
             return replace_batch_sampler(dataloader, dist)
-        elif isinstance(dist, ReproducibleIterator):
+        elif isinstance(dist, ReproducibleSampler):
             return replace_sampler(dataloader, dist)
 
+        # 如果 dist 为 str 或者 None，说明是在 trainer 初试化时调用；
+        args = self.get_dataloader_args(dataloader)
+        if isinstance(args.batch_sampler, RandomBatchSampler):
+            batch_sampler = re_instantiate_sampler(args.batch_sampler)
+            return replace_batch_sampler(dataloader, batch_sampler)
+        elif isinstance(args.sampler, ReproducibleSampler):
+            sampler = re_instantiate_sampler(args.sampler)
+            return replace_sampler(dataloader, sampler)
+
         if reproducible:
-            args = self.get_dataloader_args(dataloader)
-            if isinstance(args.sampler, ReproducibleIterator):
-                sampler = re_instantiate_sampler(args.sampler)
-                return replace_sampler(dataloader, sampler)
-            else:
-                batch_sampler = ReproducibleBatchSampler(
-                    batch_sampler=args.batch_sampler,
-                    batch_size=args.batch_size,
-                    drop_last=args.drop_last
-                )
-                return replace_batch_sampler(dataloader, batch_sampler)
+            batch_sampler = RandomBatchSampler(
+                batch_sampler=args.batch_sampler,
+                batch_size=args.batch_size,
+                drop_last=args.drop_last
+            )
+            return replace_batch_sampler(dataloader, batch_sampler)
         else:
             return dataloader
 

fastNLP/core/drivers/torch_driver/torch_driver.py

@@ -30,7 +30,7 @@ from fastNLP.core.utils import apply_to_collection, torch_move_data_to_device
 from fastNLP.envs import  rank_zero_call
 from fastNLP.envs import FASTNLP_SEED_WORKERS, FASTNLP_GLOBAL_RANK, FASTNLP_MODEL_FILENAME, FASTNLP_CHECKPOINT_FILENAME
 from fastNLP.core.log import logger
-from fastNLP.core.samplers import ReproducibleBatchSampler
+from fastNLP.core.samplers import RandomBatchSampler, ReproducibleIterator
 
 
 class TorchDriver(Driver):
@@ -143,8 +143,6 @@ class TorchDriver(Driver):
 
         :param filepath: 保存到哪个文件夹；
         :param only_state_dict: 是否只保存权重；
-        :param model_save_fn:
-
         :return:
         """
         model = self.unwrap_model()
@@ -184,10 +182,10 @@ class TorchDriver(Driver):
         #  trainer.dataloader 来改变 dataloader 的状态，从而适配训练或者评测环境；
 
         # 1. sampler 的状态，因为我们支持 resume training，即精确恢复到具体的一个 batch；
-        # 首先 pytorch 的 DataLoader 一定会有 sampler；另一方面，我们在断点重训的时候一定会在 `replace_sampler` 中将 dataloader 的
-        #  sampler 替换为 `ReproducibleIterator`；否则就是在单卡情况下将 batch_sampler 替换为 `ReproducibleBatchSampler`；
+        # 首先 pytorch 的 DataLoader 一定会有 sampler；另一方面，我们在断点重训的时候一定会在 `set_` 中将 dataloader 的
+        #  sampler 替换为 `ReproducibleSampler`；否则就是在单卡情况下将 batch_sampler 替换为 `RandomBatchSampler`；
         dataloader_args = self.get_dataloader_args(dataloader)
-        if isinstance(dataloader_args.batch_sampler, ReproducibleBatchSampler):
+        if isinstance(dataloader_args.batch_sampler, RandomBatchSampler):
             sampler = dataloader_args.batch_sampler
         elif dataloader_args.sampler:
             sampler = dataloader_args.sampler
@@ -247,25 +245,25 @@ class TorchDriver(Driver):
 
         # 3. 恢复 sampler 的状态；
         dataloader_args = self.get_dataloader_args(dataloader)
-
-        sampler = dataloader_args.sampler
-        if not (hasattr(sampler, 'load_state_dict') and callable(sampler.load_state_dict)):
-            # 说明这里需要使用 ReproduceSampler 来弄一下了
-            if self.is_distributed():
-                raise RuntimeError(
-                    "It is not allowed to use single device checkpoint retraining before but ddp now.")
-            sampler = ReproducibleBatchSampler(
-                batch_sampler=sampler,
+        if isinstance(dataloader_args.batch_sampler, RandomBatchSampler):
+            sampler = dataloader_args.batch_sampler
+        elif isinstance(dataloader_args.sampler, ReproducibleIterator):
+            sampler = dataloader_args.sampler
+        elif self.is_distributed():
+            raise RuntimeError("It is not allowed to use checkpoint retraining when you do not use our "
+                               "`RandomBatchSampler` or `ReproducibleIterator`.")
+        else:
+            sampler = RandomBatchSampler(
+                batch_sampler=dataloader_args.batch_sampler if dataloader_args.batch_sampler is not None else dataloader_args.sampler,
                 batch_size=dataloader_args.batch_size,
                 drop_last=dataloader_args.drop_last
             )
         sampler.load_state_dict(states['sampler_states'])
-
         states["dataloader"] = self.set_dist_repro_dataloader(dataloader, sampler)
 
         # 4. 修改 trainer_state.batch_idx_in_epoch
         # sampler 是类似 RandomSampler 的sampler，不是 batch_sampler；
-        if not isinstance(sampler, ReproducibleBatchSampler):
+        if not isinstance(sampler, RandomBatchSampler):
             if dataloader_args.drop_last:
                 batch_idx_in_epoch = len(
                     sampler) // dataloader_args.batch_size - sampler.num_left_samples // dataloader_args.batch_size
@@ -293,7 +291,7 @@ class TorchDriver(Driver):
 
     @staticmethod
     def worker_init_function(worker_id: int, rank: Optional[int] = None) -> None:  # pragma: no cover
-        """The worker_init_fn that Lightning automatically adds to your dataloader if you previously set set the seed
+        """The worker_init_fn that Lightning automatically adds to your dataloader if you previously set the seed
         with ``seed_everything(seed, workers=True)``.
 
         See also the PyTorch documentation on

fastNLP/core/metrics/backend/torch_backend/backend.py

@@ -33,7 +33,7 @@ class TorchBackend(Backend):
             if dist.is_initialized():
                 if method is None:
                     raise AggregateMethodError(should_have_aggregate_method=True)
-                tensor = self._gather_all(tensor)
+                tensor = fastnlp_torch_all_gather(tensor)
                 if isinstance(tensor[0], torch.Tensor):
                     tensor = torch.stack(tensor)
                 # 第一步, aggregate结果
@@ -68,59 +68,6 @@ class TorchBackend(Backend):
     def get_scalar(self, tensor) -> float:
         return tensor.item()
 
-    @staticmethod
-    def _gather_all(result, group: Optional[Any] = None) -> List:
-        """Function to gather all tensors from several ddp processes onto a list that is broadcasted to all processes.
-        Works on tensors that have the same number of dimensions, but where each dimension may differ. In this case
-        tensors are padded, gathered and then trimmed to secure equal workload for all processes.
-
-        Args:
-            result: the value to sync
-            group: the process group to gather results from. Defaults to all processes (world)
-
-        Return:
-            gathered_result: list with size equal to the process group where
-                gathered_result[i] corresponds to result tensor from process i
-        """
-
-        if group is None:
-            group = dist.group.WORLD
-
-        # convert tensors to contiguous format
-        result = result.contiguous()
-
-        world_size = dist.get_world_size(group)
-        dist.barrier(group=group)
-
-        # if the tensor is scalar, things are easy
-        if result.ndim == 0:
-            return _simple_gather_all_tensors(result, group, world_size)
-
-        # 1. Gather sizes of all tensors
-        local_size = torch.tensor(result.shape, device=result.device)
-        local_sizes = [torch.zeros_like(local_size) for _ in range(world_size)]
-        dist.all_gather(local_sizes, local_size, group=group)
-        max_size = torch.stack(local_sizes).max(dim=0).values
-        all_sizes_equal = all(all(ls == max_size) for ls in local_sizes)
-
-        # 2. If shapes are all the same, then do a simple gather:
-        if all_sizes_equal:
-            return _simple_gather_all_tensors(result, group, world_size)
-
-        # 3. If not, we need to pad each local tensor to maximum size, gather and then truncate
-        pad_dims = []
-        pad_by = (max_size - local_size).detach().cpu()
-        for val in reversed(pad_by):
-            pad_dims.append(0)
-            pad_dims.append(val.item())
-        result_padded = torch.nn.functional.pad(result, pad_dims)
-        gathered_result = [torch.zeros_like(result_padded) for _ in range(world_size)]
-        dist.all_gather(gathered_result, result_padded, group)
-        for idx, item_size in enumerate(local_sizes):
-            slice_param = [slice(dim_size) for dim_size in item_size]
-            gathered_result[idx] = gathered_result[idx][slice_param]
-        return gathered_result
-
     def tensor2numpy(self, tensor) -> np.array:
         """
         将对应的tensor转为numpy对象

fastNLP/core/metrics/element.py

@@ -11,12 +11,12 @@ from fastNLP.envs.env import FASTNLP_GLOBAL_RANK
 
 
 class Element:
-    def __init__(self, value: float, aggregate_method, backend: Backend, name=None):
+    def __init__(self, name, value: float, aggregate_method, backend: Backend):
+        self.name = name
         self.init_value = value
         self.aggregate_method = aggregate_method
-        self.name = name
         if backend == 'auto':
-            raise RuntimeError("You have to specify the backend.")
+            raise RuntimeError(f"You have to specify the backend for Element:{self.name}.")
         elif isinstance(backend, AutoBackend):
             self.backend = backend
         else:
@@ -34,20 +34,16 @@ class Element:
         自动aggregate对应的元素
 
         """
+        self._check_value_initialized()
         try:
             self._value = self.backend.aggregate(self._value, self.aggregate_method)
         except AggregateMethodError as e:
             msg = 'If you see this message, please report a bug.'
             if self.name and e.should_have_aggregate_method:
                 msg = f"Element:{self.name} has no specified `aggregate_method`."
-            elif e.should_have_aggregate_method:
-                msg = "Element has no specified `aggregate_method`."
             elif self.name and not e.should_have_aggregate_method:
                 msg = f"Element:{self.name}'s backend:{self.backend.__class__.__name__} does not support " \
                       f'aggregate_method:{self.aggregate_method}.'
-            elif not e.should_have_aggregate_method:
-                msg = f"Element's backend:{self.backend.__class__.__name__} does not support " \
-                      f'aggregate_method:{self.aggregate_method}.'
             if e.only_warn:
                 if int(os.environ.get(FASTNLP_GLOBAL_RANK, 0)) == 0:
                     logger.warning(msg)
@@ -74,6 +70,7 @@ class Element:
         return self._value
 
     def get_scalar(self) -> float:
+        self._check_value_initialized()
         return self.backend.get_scalar(self._value)
 
     def fill_value(self, value):
@@ -95,7 +92,7 @@ class Element:
 
     def _check_value_when_call(self):
         if self.value is None:
-            prefix = f'Element:`{self.name}`' if self.name else 'Element'
+            prefix = f'Element:`{self.name}`'
             raise RuntimeError(prefix + " is not initialized. Please either specify backend when creating this "
                                         "element, or use it after it being used by the `Metric.compute()` method.")
 
@@ -273,9 +270,10 @@ class Element:
         """
         try:
             if self._value is None:
-                prefix = f'Element:`{self.name}`' if self.name else 'Element'
+                prefix = f'Element:`{self.name}`'
                 raise RuntimeError(prefix + " is not initialized. Please either specify backend when creating this "
                                             "element, or use it after it being used by the `Metric.compute()` method.")
             return getattr(self._value, item)
         except AttributeError as e:
+            logger.error(f"Element:{self.name} has no `{item}` attribute.")
             raise e

fastNLP/core/metrics/metric.py

@@ -35,7 +35,7 @@ class Metric:
     def elements(self) -> dict:
         return self._elements
 
-    def register_element(self, name=None, value: float = 0, aggregate_method=None, backend='auto') -> Element:
+    def register_element(self, name, value: float = 0, aggregate_method=None, backend='auto') -> Element:
         """
         注册一个 element 对象，注册之后便可以通过在 Metric 中直接通过 self.{name} 进行调用，可以认为该对象即为对应 backend 的
             tensor 直接进行加减乘除计算即可。
@@ -57,11 +57,9 @@ class Metric:
         else:
             backend = AutoBackend(backend)
 
-        # 当name为None，默认为变量取得变量名
-        if name is None:
-            name = f'ele_var_{len(self._elements)}'
+        assert name is not None and name not in self.elements
 
-        element = Element(value=value, aggregate_method=aggregate_method, backend=backend, name=name)
+        element = Element(name=name, value=value, aggregate_method=aggregate_method, backend=backend)
         self.elements[name] = element
         setattr(self, name, element)
         return element

fastNLP/core/metrics/span_f1_pre_rec_metric.py

@@ -216,9 +216,26 @@ def _compute_f_pre_rec(beta_square, tp, fn, fp):
 
 class SpanFPreRecMetric(Metric):
 
-    def __init__(self, backend: Union[str, Backend, None] = 'auto', tag_vocab: Vocabulary = None,
-                 encoding_type: str = None, ignore_labels: List[str] = None, only_gross: bool = True, f_type='micro',
-                 beta=1, aggregate_when_get_metric: bool = True,) -> None:
+    def __init__(self, tag_vocab: Vocabulary, encoding_type: str = None, ignore_labels: List[str] = None,
+                 only_gross: bool = True, f_type='micro',
+                 beta=1, backend: Union[str, Backend, None] = 'auto', aggregate_when_get_metric: bool = True,) -> None:
+        r"""
+
+        :param tag_vocab: 标签的 :class:`~fastNLP.Vocabulary` 。支持的标签为"B"(没有label)；或"B-xxx"(xxx为某种label，比如POS中的NN)，
+            在解码时，会将相同xxx的认为是同一个label，比如['B-NN', 'E-NN']会被合并为一个'NN'.
+        :param str pred: 用该key在evaluate()时从传入dict中取出prediction数据。 为None，则使用 `pred` 取数据
+        :param str target: 用该key在evaluate()时从传入dict中取出target数据。 为None，则使用 `target` 取数据
+        :param str seq_len: 用该key在evaluate()时从传入dict中取出sequence length数据。为None，则使用 `seq_len` 取数据。
+        :param str encoding_type: 目前支持bio, bmes, bmeso, bioes。默认为None，通过tag_vocab自动判断.
+        :param list ignore_labels: str 组成的list. 这个list中的class不会被用于计算。例如在POS tagging时传入['NN']，则不会计算'NN'个label
+        :param bool only_gross: 是否只计算总的f1, precision, recall的值；如果为False，不仅返回总的f1, pre, rec, 还会返回每个label的f1, pre, rec
+        :param str f_type: `micro` 或 `macro` . `micro` :通过先计算总体的TP，FN和FP的数量，再计算f, precision, recall; `macro` : 分布计算每个类别的f, precision, recall，然后做平均（各类别f的权重相同）
+        :param float beta: f_beta分数， :math:`f_{beta} = \frac{(1 + {beta}^{2})*(pre*rec)}{({beta}^{2}*pre + rec)}` . 常用为 `beta=0.5, 1, 2` 若为0.5则精确率的权重高于召回率；若为1，则两者平等；若为2，则召回率权重高于精确率。
+        :param str backend: 目前支持四种类型的backend, ['auto', 'torch', 'paddle', 'jittor']。其中 auto 表示根据实际调用 Metric.update()
+            函数时传入的参数决定具体的 backend ，一般情况下直接使用 'auto' 即可。
+        :param bool aggregate_when_get_metric: 在计算 metric 的时候是否自动将各个进程上的相同的 element 的数字聚合后再得到metric，
+            当 backend 不支持分布式时，该参数无意义。
+        """
         super(SpanFPreRecMetric, self).__init__(backend=backend, aggregate_when_get_metric=aggregate_when_get_metric)
         if f_type not in ('micro', 'macro'):
             raise ValueError("f_type only supports `micro` or `macro`', got {}.".format(f_type))
@@ -249,16 +266,25 @@ class SpanFPreRecMetric(Metric):
         self.only_gross = only_gross
         self.tag_vocab = tag_vocab
 
-        self._true_positives = defaultdict(partial(self.register_element, aggregate_method='sum', name=None))
-        self._false_positives = defaultdict(partial(self.register_element, aggregate_method='sum', name=None))
-        self._false_negatives = defaultdict(partial(self.register_element, aggregate_method='sum', name=None))
+        self._true_positives = {}
+        self._false_positives = {}
+        self._false_negatives = {}
+        for word, _ in tag_vocab:
+            word = word.lower()
+            if word != 'o':
+                word = word[2:]
+            if word in self._true_positives:
+                continue
+            self._true_positives[word] = self.register_element(name=f'tp_{word}', aggregate_method='sum', backend=backend)
+            self._false_negatives[word] = self.register_element(name=f'fn_{word}', aggregate_method='sum', backend=backend)
+            self._false_positives[word] = self.register_element(name=f'fp_{word}', aggregate_method='sum', backend=backend)
 
     def get_metric(self) -> dict:
         evaluate_result = {}
         if not self.only_gross or self.f_type == 'macro':
             tags = set(self._false_negatives.keys())
-            tags.update(set(self._false_positives.keys()))
-            tags.update(set(self._true_positives.keys()))
+            tags.update(self._false_positives.keys())
+            tags.update(self._true_positives.keys())
             f_sum = 0
             pre_sum = 0
             rec_sum = 0
@@ -266,6 +292,9 @@ class SpanFPreRecMetric(Metric):
                 tp = self._true_positives[tag].get_scalar()
                 fn = self._false_negatives[tag].get_scalar()
                 fp = self._false_positives[tag].get_scalar()
+                if tp == fn == fp == 0:
+                    continue
+
                 f, pre, rec = _compute_f_pre_rec(self.beta_square, tp, fn, fp)
                 f_sum += f
                 pre_sum += pre
@@ -284,10 +313,17 @@ class SpanFPreRecMetric(Metric):
                 evaluate_result['rec'] = rec_sum / len(tags)
 
         if self.f_type == 'micro':
+            tp, fn, fp = [], [], []
+            for val in self._true_positives.values():
+                tp.append(val.get_scalar())
+            for val in self._false_negatives.values():
+                fn.append(val.get_scalar())
+            for val in self._false_positives.values():
+                fp.append(val.get_scalar())
             f, pre, rec = _compute_f_pre_rec(self.beta_square,
-                                             sum(val.get_scalar() for val in self._true_positives.values()),
-                                             sum(val.get_scalar() for val in self._false_negatives.values()),
-                                             sum(val.get_scalar() for val in self._false_positives.values()))
+                                             sum(tp),
+                                             sum(fn),
+                                             sum(fp))
             evaluate_result['f'] = f
             evaluate_result['pre'] = pre
             evaluate_result['rec'] = rec

fastNLP/core/samplers/__init__.py

@@ -3,19 +3,30 @@ __all__ = [
     'SortedSampler',
     'ConstTokenNumSampler',
     'ConstantTokenNumSampler',
-    'UnrepeatedDistributedSampler',
+
     'MixSampler',
-    'InnerSampler',
     'DopedSampler',
     'MixSequentialSampler',
     'PollingSampler',
-    'ReproducibleIterator',
+
+    'ReproducibleSampler',
     'RandomSampler',
-    're_instantiate_sampler'
+    "SequentialSampler",
+    "SortedSampler",
+
+    'UnrepeatedSampler',
+    'UnrepeatedRandomSampler',
+    "UnrepeatedSortedSampler",
+    "UnrepeatedSequentialSampler",
+
+    "re_instantiate_sampler",
+    "conversion_between_reproducible_and_unrepeated_sampler"
 ]
 
-from .sampler import BucketSampler, SortedSampler, ConstTokenNumSampler, ConstantTokenNumSampler, UnrepeatedDistributedSampler
-from .mix_sampler import MixSampler, InnerSampler, DopedSampler, MixSequentialSampler, PollingSampler
-from .reproducible_sampler import ReproducibleIterator, RandomSampler, re_instantiate_sampler
-from .reproducible_batch_sampler import ReproducibleBatchSampler, BucketedBatchSampler
+from .sampler import BucketSampler, SortedSampler, ConstTokenNumSampler, ConstantTokenNumSampler
+from .unrepeated_sampler import UnrepeatedSampler, UnrepeatedRandomSampler, UnrepeatedSortedSampler, UnrepeatedSequentialSampler
+from .mix_sampler import MixSampler, DopedSampler, MixSequentialSampler, PollingSampler
+from .reproducible_sampler import ReproducibleSampler, RandomSampler, SequentialSampler, SortedSampler
+from .utils import re_instantiate_sampler, conversion_between_reproducible_and_unrepeated_sampler
+from .reproducible_batch_sampler import RandomBatchSampler, BucketedBatchSampler
 

fastNLP/core/samplers/mix_sampler.py

@@ -4,7 +4,6 @@ from typing import Union, List, Iterable, Dict
 
 __all__ = [
     'MixSampler',
-    'InnerSampler',
     'DopedSampler',
     'MixSequentialSampler',
     'PollingSampler'

fastNLP/core/samplers/reproducible_batch_sampler.py

@@ -1,6 +1,6 @@
 __all__ = [
     'BucketedBatchSampler',
-    "ReproducibleBatchSampler"
+    "RandomBatchSampler"
 ]
 
 import math
@@ -16,7 +16,7 @@ from fastNLP.core.log import logger
 from abc import abstractmethod
 
 
-class ReproducibleBatchIterator:
+class ReproducibleBatchSampler:
     @abstractmethod
     def set_distributed(self, num_replicas, rank, pad=True):
         raise NotImplementedError("Each specific batch_sampler should implement its own `set_distributed` method.")
@@ -42,13 +42,13 @@ class ReproducibleBatchIterator:
         pass
 
 
-class ReproducibleBatchSampler(ReproducibleBatchIterator):
+class RandomBatchSampler(ReproducibleBatchSampler):
     # 这两个参数的值应当交给 driver 的 get_dataloader_args 函数去拿；
     def __init__(self, batch_sampler, batch_size: int, drop_last: bool, **kwargs):
         """
         可以使得 batch_sampler 对象状态恢复的 wrapper 。
 
-        :param batch_sampler: 可迭代出 数字 或 数字列表 的可迭代对象。ReproducibleBatchSampler 将首先遍历一边该对象，然后将迭代
+        :param batch_sampler: 可迭代出 数字 或 数字列表 的可迭代对象。RandomBatchSampler 将首先遍历一边该对象，然后将迭代
             出来的序号暂存起来，使用时按照 batch_size 的 batch 大小吐出序号列表。
         :param batch_size: 每个 batch 的大小是多少。
         :param drop_last: 如果最后一个 batch 无法构成 batch_size 那么多个 sample ，是否丢掉。
@@ -138,7 +138,7 @@ class ReproducibleBatchSampler(ReproducibleBatchIterator):
                    (len(self.index_list) - self.data_idx + self.batch_size - 1) // self.batch_size
 
 
-class BucketedBatchSampler(ReproducibleBatchIterator):
+class BucketedBatchSampler(ReproducibleBatchSampler):
     def __init__(self, dataset, length: Union[List[int], str], batch_size:int = 32, num_batch_per_bucket:int = 10,
                  shuffle: bool = True, drop_last: bool = False, seed: int = 0, **kwargs):
         """

fastNLP/core/samplers/reproducible_sampler.py

@@ -1,25 +1,21 @@
-from typing import Dict, List
+from typing import Dict, List, Union
 import math
 import numpy as np
 
 from fastNLP.core.log import logger
+from fastNLP.core.dataset import DataSet
 
 __all__ = [
-    'ReproducibleIterator',
+    'ReproducibleSampler',
     'RandomSampler',
-    're_instantiate_sampler'
+    "SortedSampler",
+    "SequentialSampler"
 ]
 
 
-def re_instantiate_sampler(sampler):
-    all_attributes = vars(sampler)
-    return type(sampler)(**all_attributes)
-
-
-
-class ReproducibleIterator:
+class ReproducibleSampler:
     """
-    注意所有继承 `ReproducibleIterator` 的类的  `__init__` 方法中都需要加入参数 `**kwargs`，用来使我们再断点重训时重新实例化这个 sampler
+    注意所有继承 `ReproducibleSampler` 的类的  `__init__` 方法中都需要加入参数 `**kwargs`，用来使我们再断点重训时重新实例化这个 sampler
      或者 batch_sampler；注意，所有在 init 中初始化的变量，都不能含有 _ 下横线作为开头；所有不在 init 中设置的变量都必须以下横线开头。
 
     """
@@ -47,7 +43,7 @@ class ReproducibleIterator:
         pass
 
 
-class RandomSampler(ReproducibleIterator):
+class RandomSampler(ReproducibleSampler):
     def __init__(self, dataset, shuffle: bool = True, seed: int = 0, **kwargs):
         """
 
@@ -157,8 +153,8 @@ class RandomSampler(ReproducibleIterator):
                                                                   f"we cannot use {self.__class__.__name__} to load it."
 
         length = states['length']
-        assert length == len(self.dataset), "The number of samples is different between the checkpoint record " \
-                                            "and current dataset."
+        assert length == len(self.dataset), f"The number of samples is different between the checkpoint record({length}) " \
+                                            f"and current dataset({len(self.dataset)})."
         self.seed = states['seed']
         self.epoch = states['epoch']
         self.num_consumed_samples = states['num_consumed_samples']
@@ -215,9 +211,132 @@ class RandomSampler(ReproducibleIterator):
             self.pad else math.floor(((len(self.dataset) - num_consumed_samples) / self.num_replicas))
 
 
+class SequentialSampler(RandomSampler):
+    def __init__(self, dataset, dist_mode:str='interval', **kwargs):
+        """
+        按照顺序读取 dataset 。在多卡情况下，间隔读取，例如，在两卡情况下，卡0取 [0,2,4,..], 卡1取 [1,3,5...]。
+
+        :param dataset: 实现了 __len__ 方法的数据容器。
+        :param kwargs:
+        """
+        super().__init__(dataset=dataset, shuffle=False, seed=0, **kwargs)
+
+    def __iter__(self):
+        if self.during_iter:  # 如果发现_during_iter为True，说明之前的还没结束，只有强制重新初始化了
+            self.num_consumed_samples = 0
+        self.during_iter = True
+        indices = self.generate_indices()
+
+        if self.pad:
+            # add extra samples to make it evenly divisible
+            padding_size = self.total_size - len(indices)
+            if padding_size <= len(indices):
+                indices += indices[:padding_size]
+            else:
+                indices += (indices * math.ceil(padding_size / len(indices)))[:padding_size]
+        else:
+            # remove tail of data to make it evenly divisible.
+            indices = indices[:self.total_size]
+
+        assert len(indices) == self.total_size
+
+        # subsample
+        indices = indices[self.num_consumed_samples:]
+        indices = indices[self.rank:len(indices):self.num_replicas]
+        assert len(indices) == self.num_left_samples
+
+        for index in indices:
+            self.num_consumed_samples += self.num_replicas
+            yield index
+        self.during_iter = False
+        self.num_consumed_samples = 0
+
+    def generate_indices(self) -> List[int]:
+        """
+        生成随机序列
+
+        :return:
+        """
+        return list(range(len(self.dataset)))
+
+    def state_dict(self) -> Dict:
+        states = {
+            'num_consumed_samples': self.num_consumed_samples,  # 注意该值是计算所有 rank 上训练的所有数据；
+            'sampler_type': self.__class__.__name__,
+            'length': len(self.dataset),
+        }
+        return states
+
+    def load_state_dict(self, states: Dict):
+        # 如果 self.during_iter 是 True，那么 data_idx 一定是 0；
+        assert self.during_iter is False, "Cannot call load_state_dict() when it is " \
+                                          "during an unfinished iteration."
+
+        assert states['sampler_type'] == self.__class__.__name__, f"The sampler type in checkpoint is {states['sampler_type']}," \
+                                                                  f"we cannot use {self.__class__.__name__} to load it."
+
+        length = states['length']
+        assert length == len(self.dataset), f"The number of samples is different between the checkpoint record({length}) " \
+                                            f"and current dataset({len(self.dataset)})."
+        self.num_consumed_samples = states['num_consumed_samples']
+        if self.num_consumed_samples >= length:  # 如果保存的时候已经到达了最后一个sample了，则直接将结果重置为0
+            self.num_consumed_samples = 0
+
+
+class SortedSampler(SequentialSampler):
+    def __init__(self, dataset, length:Union[str, List], **kwargs):
+        """
+        将 dataset 中的数据根据 length 从长到短进行迭代。在多卡情况下，由于padding 最后一个 sample 可能是最长的那个 sample。
+
+        :param dataset: 实现了 __len__ 方法的数据容器。
+        :param length: 如果为 List，应当与 dataset 有一样的长度，表示 dataset 中每个元素的数量；仅当传入的 dataset 为 fastNLP 的
+            DataSet 时支持传入 str，会将该str理解为 dataset 的 field 名称，若 field 中的元素为 int，则认为该值是 sample 的长度。
+        :param seed: 设置的随机数种子
+        :param kwargs: fastNLP 保留使用
+        """
+        super().__init__(dataset=dataset, **kwargs)
+        if isinstance(dataset, DataSet):
+            length = dataset.get_field(length)
+            if not isinstance(length[0], int):
+                length = list(map(len, length))
+        else:
+            assert len(length) == len(dataset), "When the dataset is not fastNLP.DataSet, " \
+                                                "the length parameter can only be List[int]"
+
+        assert len(length) == len(dataset), "The length of `data` and `length` should be equal."
+
+        self.length = np.array(length, dtype=int)  # 按照长到短排列的序号。
+        self.sorted_indices = np.argsort(self.length)[::-1].tolist()  # 按长度从高到低排序的
+
+    def generate_indices(self) -> List[int]:
+        return self.sorted_indices
 
+    def __iter__(self):
+        if self.during_iter:  # 如果发现_during_iter为True，说明之前的还没结束，只有强制重新初始化了
+            self.num_consumed_samples = 0
+        self.during_iter = True
+        indices = self.generate_indices()
 
+        if self.pad:
+            padding_size = self.total_size - len(indices)
+            if padding_size <= len(indices):
+                indices += indices[:padding_size]
+            else:
+                indices += (indices * math.ceil(padding_size / len(indices)))[:padding_size]
+        else:
+            # remove tail of data to make it evenly divisible.
+            indices = indices[:self.total_size]
 
+        assert len(indices) == self.total_size
 
+        # subsample
+        indices = indices[self.num_consumed_samples:]
+        indices = indices[self.rank:len(indices):self.num_replicas]
+        assert len(indices) == self.num_left_samples
 
+        for index in indices:
+            self.num_consumed_samples += self.num_replicas
+            yield index
+        self.during_iter = False
+        self.num_consumed_samples = 0
 

fastNLP/core/samplers/sampler.py

@@ -7,7 +7,6 @@ __all__ = [
     "SortedSampler",
     'ConstTokenNumSampler',
     "ConstantTokenNumSampler",
-    "UnrepeatedDistributedSampler",
 ]
 
 from itertools import chain
@@ -18,7 +17,7 @@ import numpy as np
 from fastNLP.envs.imports import _NEED_IMPORT_TORCH
 
 if _NEED_IMPORT_TORCH:
-    from torch.utils.data import SequentialSampler, Sampler, RandomSampler
+    from torch.utils.data import Sampler
 else:
     from fastNLP.core.utils.dummy_class import DummyClass as Sampler
 
@@ -727,87 +726,3 @@ def k_means_bucketing(lengths, buckets):
         if buckets[bucket_id] is None or lengths[idx] <= buckets[bucket_id]:
             bucket_data[bucket_id].append(idx)
     return bucket_data
-
-
-class UnrepeatedDistributedSampler:
-    def __init__(self, dataset, shuffle: bool = False, seed: int = 0):
-        """
-        考虑在多卡evaluate的场景下，不能重复sample。
-
-        :param dataset:
-        :param shuffle:
-        :param seed:
-        """
-        self.dataset = dataset
-        self.shuffle = shuffle
-        self.seed = seed
-
-        # 多卡的相关的参数
-        self.num_replicas = 1
-        self.rank = 0
-        self.epoch = -1
-
-    def __len__(self):
-        """
-        返回 sampler 一次完整的迭代过程会产生多少个index。多卡的情况下，只考虑当前rank；
-        :return:
-        """
-        num_common = len(self.dataset)//self.num_replicas
-        self.num_samples = num_common + int(self.rank < (len(self.dataset)-num_common*self.num_replicas))
-        return self.num_samples
-
-    def __iter__(self):
-        r"""
-        当前使用num_consumed_samples做法会在交替使用的时候遇到问题；
-        Example:
-            >>> sampler = RandomSampler()
-            >>> iter1 = iter(sampler)
-            >>> iter2 = iter(sampler)
-            >>> next(iter1)
-            >>> next(iter2)  # 当前num_consumed_samples的数量会发生变化
-        """
-
-        indices = self.generate_indices()
-
-        # subsample
-        indices = indices[self.rank:len(indices):self.num_replicas]
-        assert len(indices) == len(self)
-
-        for index in indices:
-            yield index
-
-    def generate_indices(self) -> List[int]:
-        """
-        生成随机序列
-
-        :return:
-        """
-        if self.shuffle:
-            indices = list(range(len(self.dataset)))
-            seed = self.seed + self.epoch
-            rng = np.random.default_rng(abs(seed))
-            rng.shuffle(indices)
-            if self.epoch < 0:  # 防止用户忘记调用 set_epoch，至少这样可以保证每次epoch出来的index顺序不同。
-                self.epoch -= 1
-        else:
-            indices = list(range(len(self.dataset)))
-        return indices
-
-    def set_epoch(self, epoch: int) -> None:
-        self.epoch = epoch
-
-    def set_distributed(self, num_replicas, rank):
-        """
-        该方法本质上等同于 ddp 情形下的没有完成的初始化，应当在初始化该 sampler 本身后立即被调用；
-
-        :param num_replicas:
-        :param rank:
-        :return:
-        """
-        assert num_replicas>0 and isinstance(num_replicas, int)
-        assert isinstance(rank, int) and 0<=rank<num_replicas
-        # 注意初始化该函数时，所有的状态都应当默认是一个 epoch 刚开始训练的状态；
-        self.num_replicas = num_replicas
-        self.rank = rank
-
-        return self

fastNLP/core/samplers/unrepeated_sampler.py

@@ -0,0 +1,143 @@
+__all__ = [
+    'UnrepeatedSampler',
+    'UnrepeatedSortedSampler',
+    'UnrepeatedRandomSampler',
+    "UnrepeatedSequentialSampler"
+]
+
+from typing import List, Union
+from fastNLP.core.dataset import DataSet
+
+import numpy as np
+
+
+class UnrepeatedSampler:
+    """
+    在多卡场景下保证 indice 不重复的 sampler
+    """
+    pass
+
+
+class UnrepeatedRandomSampler(UnrepeatedSampler):
+    def __init__(self, dataset, shuffle: bool = False, seed: int = 0, **kwargs):
+        """
+        考虑在多卡evaluate的场景下，不能重复sample。
+
+        :param dataset: 实现了 __len__ 方法的数据容器。
+        :param shuffle: 如果为 True，将不进行 shuffle，实际上数据会以从长到短的方式输出。
+        :param seed: 设置的随机数种子
+        :param kwargs: fastNLP 保留使用
+        """
+        self.dataset = dataset
+        self.shuffle = shuffle
+        self.seed = seed
+
+        # 多卡的相关的参数
+        self.num_replicas = kwargs.get('num_replicas', 1)
+        self.rank = kwargs.get('rank', 0)
+        self.epoch = kwargs.get('epoch', -1)
+
+    def __len__(self):
+        """
+        返回 sampler 一次完整的迭代过程会产生多少个index。多卡的情况下，只考虑当前rank；
+        :return:
+        """
+        num_common = len(self.dataset)//self.num_replicas
+        num_samples = num_common + int(self.rank < (len(self.dataset)-num_common*self.num_replicas))
+        return num_samples
+
+    def __iter__(self):
+        indices = self.generate_indices()
+
+        # subsample
+        indices = indices[self.rank:len(indices):self.num_replicas]
+        assert len(indices) == len(self)
+
+        for index in indices:
+            yield index
+
+    def generate_indices(self) -> List[int]:
+        """
+        生成随机序列
+
+        :return:
+        """
+        if self.shuffle:
+            indices = list(range(len(self.dataset)))
+            seed = self.seed + self.epoch
+            rng = np.random.default_rng(abs(seed))
+            rng.shuffle(indices)
+            if self.epoch < 0:  # 防止用户忘记调用 set_epoch，至少这样可以保证每次epoch出来的index顺序不同。
+                self.epoch -= 1
+        else:
+            indices = list(range(len(self.dataset)))
+        return indices
+
+    def set_epoch(self, epoch: int) -> None:
+        self.epoch = epoch
+
+    def set_distributed(self, num_replicas, rank):
+        """
+        该方法本质上等同于 ddp 情形下的没有完成的初始化，应当在初始化该 sampler 本身后立即被调用；
+
+        :param num_replicas:
+        :param rank:
+        :return:
+        """
+        assert num_replicas>0 and isinstance(num_replicas, int)
+        assert isinstance(rank, int) and 0<=rank<num_replicas
+        # 注意初始化该函数时，所有的状态都应当默认是一个 epoch 刚开始训练的状态；
+        self.num_replicas = num_replicas
+        self.rank = rank
+
+        return self
+
+
+class UnrepeatedSortedSampler(UnrepeatedRandomSampler):
+    def __init__(self, dataset, length:Union[str, List], **kwargs):
+        """
+        将 dataset 中的数据根据 length 从长到短进行迭代，并且保证在多卡场景下数据不重复。本 sampler 可能导致各个机器上的
+            batch 数量不完全一致。
+
+        :param dataset: 实现了 __len__ 方法的数据容器。
+        :param length: 如果为 List，应当与 dataset 有一样的长度，表示 dataset 中每个元素的数量；仅当传入的 dataset 为 fastNLP 的
+            DataSet 时支持传入 str，会将该str理解为 dataset 的 field 名称，若 field 中的元素为 int，则认为该值是 sample 的长度。
+        :param kwargs: fastNLP 保留使用
+        """
+        super().__init__(dataset=dataset, shuffle=False, seed=0, **kwargs)
+        if isinstance(dataset, DataSet):
+            length = dataset.get_field(length)
+            if not isinstance(length[0], int):
+                length = list(map(len, length))
+        else:
+            assert len(length) == len(dataset), "When the dataset is not fastNLP.DataSet, " \
+                                                "the length parameter can only be List[int]"
+
+        assert len(length) == len(dataset), "The length of `data` and `length` should be equal."
+
+        length = np.array(length, dtype=int)  # 按照长到短排列的序号。
+        self.sorted_indices = np.argsort(length)[::-1].tolist()  # 按长度从高到低排序的
+
+    def generate_indices(self) -> List[int]:
+        return self.sorted_indices
+
+
+class UnrepeatedSequentialSampler(UnrepeatedRandomSampler):
+    def __init__(self, dataset, **kwargs):
+        """
+        按照顺序读取 dataset。在多卡情况下，间隔读取，例如，在两卡情况下，卡0取 [0,2,4,..], 卡1取 [1,3,5...]。
+
+        :param dataset: 实现了 __len__ 方法的数据容器。
+        :param kwargs:
+        """
+        super(UnrepeatedSequentialSampler, self).__init__(dataset, shuffle=False, seed=0, **kwargs)
+
+    def __iter__(self):
+        indices = self.generate_indices()
+        indices = indices[self.rank:len(indices):self.num_replicas]
+        for index in indices:
+            yield index
+
+    def generate_indices(self) -> List[int]:
+        return list(range(len(self.dataset)))
+

fastNLP/core/samplers/utils.py

@@ -0,0 +1,42 @@
+__all__ = [
+    're_instantiate_sampler',
+    'conversion_between_reproducible_and_unrepeated_sampler'
+]
+
+from fastNLP.core.samplers.unrepeated_sampler import *
+from fastNLP.core.samplers.reproducible_sampler import *
+
+
+def conversion_between_reproducible_and_unrepeated_sampler(sampler):
+    """
+    将 sampler 替换成其对应的 reproducible 版本或 unrepeated 版本。如果输入是 UnrepeatedSampler 但是没找到对应的
+        ReproducibleSampler，
+
+    :param sampler:
+    :return:
+    """
+    assert isinstance(sampler, UnrepeatedSampler) or isinstance(sampler, ReproducibleSampler), \
+        "The sampler must be UnrepeatedSampler or ReproducibleSampler"
+    if isinstance(sampler, UnrepeatedSampler):
+        if isinstance(sampler, UnrepeatedRandomSampler):
+            return re_instantiate_sampler(sampler, new_sampler_class=RandomSampler)
+        elif isinstance(sampler, UnrepeatedSequentialSampler):
+            return re_instantiate_sampler(sampler, new_sampler_class=SequentialSampler)
+        elif isinstance(sampler, UnrepeatedSortedSampler):
+            return re_instantiate_sampler(sampler, new_sampler_class=SortedSampler)
+        raise TypeError(f"{sampler.__class__} has no unrepeated version.")
+    else:
+        if isinstance(sampler, RandomSampler):
+            return re_instantiate_sampler(sampler, new_sampler_class=UnrepeatedRandomSampler)
+        elif isinstance(sampler, SequentialSampler):
+            return re_instantiate_sampler(sampler, new_sampler_class=UnrepeatedSequentialSampler)
+        elif isinstance(sampler, SortedSampler):
+            return re_instantiate_sampler(sampler, new_sampler_class=UnrepeatedSortedSampler)
+        raise TypeError(f"{sampler.__class__} has no reproducible version.")
+
+
+def re_instantiate_sampler(sampler, new_sampler_class=None):
+    all_attributes = vars(sampler)
+    if new_sampler_class is not None:
+        return new_sampler_class(**all_attributes)
+    return type(sampler)(**all_attributes)

fastNLP/core/utils/rich_progress.py

@@ -96,6 +96,7 @@ class FRichProgress(Progress, metaclass=Singleton):
 
         # start new
         self.start()
+        self.console.show_cursor(show=True)
         return self
 
     def set_transient(self, transient: bool = True):
@@ -149,6 +150,9 @@ class FRichProgress(Progress, metaclass=Singleton):
             super().stop_task(task_id)
             super().remove_task(task_id)
 
+    def start(self) -> None:
+        super().start()
+        self.console.show_cursor(show=True)
 
 if (sys.stdin and sys.stdin.isatty()) and get_global_rank() == 0:
     f_rich_progress = FRichProgress().new_progess(
@@ -161,7 +165,7 @@ if (sys.stdin and sys.stdin.isatty()) and get_global_rank() == 0:
         TextColumn("{task.fields[post_desc]}", justify="right"),
         transient=True,
         disable=False,
-        speed_estimate_period=10
+        speed_estimate_period=1
     )
 else:
     f_rich_progress = DummyFRichProgress()

fastNLP/core/utils/utils.py

@@ -44,6 +44,9 @@ __all__ = [
 ]
 
 
+
+
+
 def get_fn_arg_names(fn: Callable) -> List[str]:
     r"""
     返回一个函数的所有参数的名字；

fastNLP/envs/set_backend.py

@@ -153,7 +153,7 @@ def seed_jittor_global_seed(global_seed):
     pass
 
 
-def dump_fastnlp_backend(default:bool = False):
+def dump_fastnlp_backend(default:bool = False, backend=None):
     """
     将 fastNLP 的设置写入到 ~/.fastNLP/envs/ 文件夹下，
         若 default 为 True，则保存的文件为 ~/.fastNLP/envs/default.json 。
@@ -165,6 +165,7 @@ def dump_fastnlp_backend(default:bool = False):
     会保存的环境变量为 FASTNLP_BACKEND 。
 
     :param default:
+    :param backend: 保存使用的 backend 为哪个值，允许的值有 ['torch', 'paddle', 'jittor']。如果为 None ，则使用环境变量中的值。
     :return:
     """
     if int(os.environ.get(FASTNLP_GLOBAL_RANK, 0)) == 0:
@@ -179,10 +180,16 @@ def dump_fastnlp_backend(default:bool = False):
         os.makedirs(os.path.dirname(env_path), exist_ok=True)
 
         envs = {}
-        if FASTNLP_BACKEND in os.environ:
-            envs[FASTNLP_BACKEND] = os.environ[FASTNLP_BACKEND]
+        assert backend in SUPPORT_BACKENDS, f"fastNLP only supports {SUPPORT_BACKENDS} right now."
+        if backend is None:
+            if FASTNLP_BACKEND in os.environ:
+                envs[FASTNLP_BACKEND] = os.environ[FASTNLP_BACKEND]
+        else:
+            envs[FASTNLP_BACKEND] = backend
         if len(envs):
             with open(env_path, 'w', encoding='utf8') as f:
                 json.dump(fp=f, obj=envs)
 
             print(f"Writing the default fastNLP backend:{envs[FASTNLP_BACKEND]} to {env_path}.")
+        else:
+            raise RuntimeError("No backend specified.")

fastNLP/envs/set_env_on_import.py

@@ -47,7 +47,8 @@ def set_env_on_import_paddle():
 # TODO jittor may need set this
 def set_env_on_import_jittor():
     # todo 需要设置 FASTNLP_GLOBAL_RANK 和 FASTNLP_BACKEND_LAUNCH
-    pass
+    if 'log_silent' not in os.environ:
+        os.environ['log_silent'] = '1'
 
 
 def set_env_on_import():
@@ -63,7 +64,7 @@ def set_env_on_import():
 
     # fastNLP 内部使用的一些变量
     if FASTNLP_LAUNCH_TIME not in os.environ:
-        cur_time = f"{datetime.datetime.now().strftime('%Y-%m-%d-%H_%M_%S_%M_%f')}"
+        cur_time = f"{datetime.datetime.now().strftime('%Y-%m-%d-%H_%M_%S_%f')}"
         os.environ[FASTNLP_LAUNCH_TIME] = cur_time
 
     # 设置对应的值

tests/core/callbacks/test_checkpoint_callback_torch.py

@@ -8,7 +8,7 @@ import torch.distributed as dist
 from pathlib import Path
 import re
 
-from fastNLP.core.callbacks.checkpoint_callback import CheckpointCallback
+from fastNLP.core.callbacks.checkpoint_callback import ModelCheckpointCallback, TrainerCheckpointCallback
 from fastNLP.core.controllers.trainer import Trainer
 from fastNLP.envs import FASTNLP_MODEL_FILENAME, FASTNLP_CHECKPOINT_FILENAME, FASTNLP_LAUNCH_TIME, FASTNLP_DISTRIBUTED_CHECK
 
@@ -80,16 +80,23 @@ def test_model_checkpoint_callback_1(
     version,
     only_state_dict
 ):
+# def test_model_checkpoint_callback_1(
+#         model_and_optimizers: TrainerParameters,
+#         driver='torch_ddp',
+#         device=[0, 1],
+#         version=1,
+#         only_state_dict=True
+# ):
     path = Path.cwd().joinpath(f"test_model_checkpoint")
     path.mkdir(exist_ok=True, parents=True)
 
     if version == 0:
         callbacks = [
-            CheckpointCallback(
+            ModelCheckpointCallback(
                 monitor="acc",
                 save_folder=path,
                 save_every_n_epochs=1,
-                save_every_n_global_batches=123,  # 避免和 epoch 的保存重复；
+                save_every_n_batches=123,  # 避免和 epoch 的保存重复；
                 save_topk=None,
                 save_last=False,
                 save_on_exception=None,
@@ -98,11 +105,11 @@ def test_model_checkpoint_callback_1(
         ]
     elif version == 1:
         callbacks = [
-            CheckpointCallback(
+            ModelCheckpointCallback(
                 monitor="acc",
                 save_folder=path,
                 save_every_n_epochs=3,
-                save_every_n_global_batches=None,
+                save_every_n_batches=None,
                 save_topk=2,
                 save_last=True,
                 save_on_exception=None,
@@ -121,7 +128,6 @@ def test_model_checkpoint_callback_1(
             input_mapping=model_and_optimizers.input_mapping,
             output_mapping=model_and_optimizers.output_mapping,
             metrics=model_and_optimizers.metrics,
-
             n_epochs=10,
             callbacks=callbacks,
             output_from_new_proc="all"
@@ -134,31 +140,31 @@ def test_model_checkpoint_callback_1(
         if version == 0:
 
             if driver == "torch":
-                assert "epoch_10-global_batch_250-acc" in all_saved_model_paths
-                assert "epoch_4-global_batch_123-acc" in all_saved_model_paths
+                assert "model-epoch_10" in all_saved_model_paths
+                assert "model-epoch_4-batch_123" in all_saved_model_paths
 
-                epoch_save_path = all_saved_model_paths["epoch_10-global_batch_250-acc"]
-                step_save_path = all_saved_model_paths["epoch_4-global_batch_123-acc"]
+                epoch_save_path = all_saved_model_paths["model-epoch_10"]
+                step_save_path = all_saved_model_paths["model-epoch_4-batch_123"]
 
                 assert len(all_saved_model_paths) == 12
             # ddp 下的文件名不同，因为同样的数据，ddp 用了更少的步数跑完；
             else:
-                assert "epoch_6-global_batch_78-acc" in all_saved_model_paths
-                assert "epoch_9-global_batch_123-acc" in all_saved_model_paths
+                assert "model-epoch_6" in all_saved_model_paths
+                assert "model-epoch_9-batch_123" in all_saved_model_paths
 
-                epoch_save_path = all_saved_model_paths["epoch_6-global_batch_78-acc"]
-                step_save_path = all_saved_model_paths["epoch_9-global_batch_123-acc"]
+                epoch_save_path = all_saved_model_paths["model-epoch_6"]
+                step_save_path = all_saved_model_paths["model-epoch_9-batch_123"]
 
                 assert len(all_saved_model_paths) == 11
             all_state_dicts = [epoch_save_path, step_save_path]
 
         elif version == 1:
 
-            pattern = re.compile("epoch_[0-9]+-global_batch_[0-9]+-[a-z|A-Z]+_[0-9]*.?[0-9]*")
+            pattern = re.compile("model-epoch_[0-9]+-batch_[0-9]+-[a-zA-Z#]+_[0-9]*.?[0-9]*")
 
             if driver == "torch":
-                assert "epoch_9-global_batch_225-acc" in all_saved_model_paths
-                assert "last" in all_saved_model_paths
+                assert "model-epoch_9" in all_saved_model_paths
+                assert "model-last" in all_saved_model_paths
                 aLL_topk_folders = []
                 for each_folder_name in all_saved_model_paths:
                     each_folder_name = pattern.findall(each_folder_name)
@@ -166,15 +172,15 @@ def test_model_checkpoint_callback_1(
                         aLL_topk_folders.append(each_folder_name[0])
                 assert len(aLL_topk_folders) == 2
 
-                epoch_save_path = all_saved_model_paths["epoch_9-global_batch_225-acc"]
-                last_save_path = all_saved_model_paths["last"]
+                epoch_save_path = all_saved_model_paths["model-epoch_9"]
+                last_save_path = all_saved_model_paths["model-last"]
                 topk_save_path = all_saved_model_paths[aLL_topk_folders[0]]
 
                 assert len(all_saved_model_paths) == 6
             # ddp 下的文件名不同，因为同样的数据，ddp 用了更少的步数跑完；
             else:
-                assert "epoch_9-global_batch_117-acc" in all_saved_model_paths
-                assert "last" in all_saved_model_paths
+                assert "model-epoch_9" in all_saved_model_paths
+                assert "model-last" in all_saved_model_paths
 
                 aLL_topk_folders = []
                 for each_folder_name in all_saved_model_paths:
@@ -183,8 +189,8 @@ def test_model_checkpoint_callback_1(
                         aLL_topk_folders.append(each_folder_name[0])
                 assert len(aLL_topk_folders) == 2
 
-                epoch_save_path = all_saved_model_paths["epoch_9-global_batch_117-acc"]
-                last_save_path = all_saved_model_paths["last"]
+                epoch_save_path = all_saved_model_paths["model-epoch_9"]
+                last_save_path = all_saved_model_paths["model-last"]
                 topk_save_path = all_saved_model_paths[aLL_topk_folders[0]]
 
                 assert len(all_saved_model_paths) == 6
@@ -212,7 +218,7 @@ def test_model_checkpoint_callback_1(
 
     finally:
         synchronize_safe_rm(path)
-        # pass
+        pass
 
     if dist.is_initialized():
         dist.destroy_process_group()
@@ -238,11 +244,11 @@ def test_model_checkpoint_callback_2(
             raise NotImplementedError
 
     callbacks = [
-        CheckpointCallback(
+        ModelCheckpointCallback(
             monitor="acc1",
             save_folder=path,
             save_every_n_epochs=None,
-            save_every_n_global_batches=None,
+            save_every_n_batches=None,
             save_topk=None,
             save_last=False,
             save_on_exception=NotImplementedError,
@@ -279,12 +285,12 @@ def test_model_checkpoint_callback_2(
         all_saved_model_paths = {w.name: w for w in path.joinpath(os.environ[FASTNLP_LAUNCH_TIME]).iterdir()}
 
         if driver == "torch":
-            assert "epoch_4-global_batch_100-acc_NotImplementedError" in all_saved_model_paths
-            exception_model_path = all_saved_model_paths["epoch_4-global_batch_100-acc_NotImplementedError"]
+            assert "model-epoch_4-batch_100-exception_NotImplementedError" in all_saved_model_paths
+            exception_model_path = all_saved_model_paths["model-epoch_4-batch_100-exception_NotImplementedError"]
         # ddp 下的文件名不同，因为同样的数据，ddp 用了更少的步数跑完；
         else:
-            assert "epoch_4-global_batch_52-acc_NotImplementedError" in all_saved_model_paths
-            exception_model_path = all_saved_model_paths["epoch_4-global_batch_52-acc_NotImplementedError"]
+            assert "model-epoch_4-batch_52-exception_NotImplementedError" in all_saved_model_paths
+            exception_model_path = all_saved_model_paths["model-epoch_4-batch_52-exception_NotImplementedError"]
 
         assert len(all_saved_model_paths) == 1
         all_state_dicts = [exception_model_path]
@@ -332,12 +338,11 @@ def test_trainer_checkpoint_callback_1(
 
     if version == 0:
         callbacks = [
-            CheckpointCallback(
+            TrainerCheckpointCallback(
                 monitor="acc",
-                is_trainer_checkpoint=True,
                 save_folder=path,
                 save_every_n_epochs=7,
-                save_every_n_global_batches=123,  # 避免和 epoch 的保存重复；
+                save_every_n_batches=123,  # 避免和 epoch 的保存重复；
                 save_topk=None,
                 save_last=False,
                 save_on_exception=None,
@@ -346,12 +351,11 @@ def test_trainer_checkpoint_callback_1(
         ]
     elif version == 1:
         callbacks = [
-            CheckpointCallback(
+            TrainerCheckpointCallback(
                 monitor="acc",
-                is_trainer_checkpoint=True,
                 save_folder=path,
                 save_every_n_epochs=None,
-                save_every_n_global_batches=None,
+                save_every_n_batches=None,
                 save_topk=2,
                 save_last=True,
                 save_on_exception=None,
@@ -383,31 +387,31 @@ def test_trainer_checkpoint_callback_1(
         if version == 0:
 
             if driver == "torch":
-                assert "epoch_7-global_batch_175-acc" in all_saved_model_paths
-                assert "epoch_4-global_batch_123-acc" in all_saved_model_paths
+                assert "trainer-epoch_7" in all_saved_model_paths
+                assert "trainer-epoch_4-batch_123" in all_saved_model_paths
 
-                epoch_save_path = all_saved_model_paths["epoch_7-global_batch_175-acc"]
-                step_save_path = all_saved_model_paths["epoch_4-global_batch_123-acc"]
+                epoch_save_path = all_saved_model_paths["trainer-epoch_7"]
+                step_save_path = all_saved_model_paths["trainer-epoch_4-batch_123"]
 
                 assert len(all_saved_model_paths) == 3
             # ddp 下的文件名不同，因为同样的数据，ddp 用了更少的步数跑完；
             else:
-                assert "epoch_7-global_batch_91-acc" in all_saved_model_paths
-                assert "epoch_9-global_batch_123-acc" in all_saved_model_paths
+                assert "trainer-epoch_7" in all_saved_model_paths
+                assert "trainer-epoch_9-batch_123" in all_saved_model_paths
 
-                epoch_save_path = all_saved_model_paths["epoch_7-global_batch_91-acc"]
-                step_save_path = all_saved_model_paths["epoch_9-global_batch_123-acc"]
+                epoch_save_path = all_saved_model_paths["trainer-epoch_7"]
+                step_save_path = all_saved_model_paths["trainer-epoch_9-batch_123"]
 
                 assert len(all_saved_model_paths) == 2
             all_state_dicts = [epoch_save_path, step_save_path]
 
         elif version == 1:
 
-            pattern = re.compile("epoch_[0-9]+-global_batch_[0-9]+-[a-z|A-Z]+_[0-9]*.?[0-9]*")
+            pattern = re.compile("trainer-epoch_[0-9]+-batch_[0-9]+-[a-zA-Z#]+_[0-9]*.?[0-9]*")
 
             # all_saved_model_paths = {w.name: w for w in path.joinpath(os.environ[FASTNLP_LAUNCH_TIME]).iterdir()}
             if driver == "torch":
-                assert "last" in all_saved_model_paths
+                assert "trainer-last" in all_saved_model_paths
                 aLL_topk_folders = []
                 for each_folder_name in all_saved_model_paths:
                     each_folder_name = pattern.findall(each_folder_name)
@@ -415,13 +419,13 @@ def test_trainer_checkpoint_callback_1(
                         aLL_topk_folders.append(each_folder_name[0])
                 assert len(aLL_topk_folders) == 2
 
-                last_save_path = all_saved_model_paths["last"]
+                last_save_path = all_saved_model_paths["trainer-last"]
                 topk_save_path = all_saved_model_paths[aLL_topk_folders[0]]
 
                 assert len(all_saved_model_paths) == 3
             # ddp 下的文件名不同，因为同样的数据，ddp 用了更少的步数跑完；
             else:
-                assert "last" in all_saved_model_paths
+                assert "trainer-last" in all_saved_model_paths
 
                 aLL_topk_folders = []
                 for each_folder_name in all_saved_model_paths:
@@ -430,7 +434,7 @@ def test_trainer_checkpoint_callback_1(
                         aLL_topk_folders.append(each_folder_name[0])
                 assert len(aLL_topk_folders) == 2
 
-                last_save_path = all_saved_model_paths["last"]
+                last_save_path = all_saved_model_paths["trainer-last"]
                 topk_save_path = all_saved_model_paths[aLL_topk_folders[0]]
 
                 assert len(all_saved_model_paths) == 3
@@ -474,10 +478,11 @@ def test_trainer_checkpoint_callback_2(
     device,
     version
 ):
+    pytest.skip("Skip transformers test for now.")
     path = Path.cwd().joinpath(f"test_model_checkpoint")
     path.mkdir(exist_ok=True, parents=True)
 
-    import transformers
+    import transformers  # 版本4.16.2
     import torch
     from torchmetrics import Accuracy
     from transformers import AutoModelForSequenceClassification
@@ -587,12 +592,11 @@ def test_trainer_checkpoint_callback_2(
 
     if version == 0:
         callbacks = [
-            CheckpointCallback(
+            TrainerCheckpointCallback(
                 monitor="acc",
-                is_trainer_checkpoint=True,
                 save_folder=path,
                 save_every_n_epochs=None,
-                save_every_n_global_batches=50,
+                save_every_n_batches=50,
                 save_topk=None,
                 save_last=False,
                 save_on_exception=None,
@@ -601,12 +605,11 @@ def test_trainer_checkpoint_callback_2(
         ]
     elif version == 1:
         callbacks = [
-            CheckpointCallback(
+            TrainerCheckpointCallback(
                 monitor="acc",
-                is_trainer_checkpoint=True,
                 save_folder=path,
                 save_every_n_epochs=None,
-                save_every_n_global_batches=None,
+                save_every_n_batches=None,
                 save_topk=1,
                 save_last=True,
                 save_on_exception=None,
@@ -638,27 +641,27 @@ def test_trainer_checkpoint_callback_2(
         if version == 0:
 
             if driver == "torch":
-                assert "epoch_1-global_batch_200-acc" in all_saved_model_paths
+                assert "trainer-epoch_1-batch_200" in all_saved_model_paths
 
-                epoch_save_path = all_saved_model_paths["epoch_1-global_batch_200-acc"]
+                epoch_save_path = all_saved_model_paths["trainer-epoch_1-batch_200"]
 
                 assert len(all_saved_model_paths) == 4
             # ddp 下的文件名不同，因为同样的数据，ddp 用了更少的步数跑完；
             else:
-                assert "epoch_1-global_batch_100-acc" in all_saved_model_paths
+                assert "trainer-epoch_1-batch_100" in all_saved_model_paths
 
-                epoch_save_path = all_saved_model_paths["epoch_1-global_batch_100-acc"]
+                epoch_save_path = all_saved_model_paths["trainer-epoch_1-batch_100"]
 
                 assert len(all_saved_model_paths) == 2
             all_state_dicts = [epoch_save_path]
 
         elif version == 1:
 
-            pattern = re.compile("epoch_[0-9]+-global_batch_[0-9]+-[a-z|A-Z]+_[0-9]*.?[0-9]*")
+            pattern = re.compile("trainer-epoch_[0-9]+-batch_[0-9]+-[a-zA-Z#]+_[0-9]*.?[0-9]*")
 
             # all_saved_model_paths = {w.name: w for w in path.joinpath(os.environ[FASTNLP_LAUNCH_TIME]).iterdir()}
             if driver == "torch":
-                assert "last" in all_saved_model_paths
+                assert "trainer-last" in all_saved_model_paths
                 aLL_topk_folders = []
                 for each_folder_name in all_saved_model_paths:
                     each_folder_name = pattern.findall(each_folder_name)
@@ -666,13 +669,13 @@ def test_trainer_checkpoint_callback_2(
                         aLL_topk_folders.append(each_folder_name[0])
                 assert len(aLL_topk_folders) == 1
 
-                last_save_path = all_saved_model_paths["last"]
+                last_save_path = all_saved_model_paths["trainer-last"]
                 topk_save_path = all_saved_model_paths[aLL_topk_folders[0]]
 
                 assert len(all_saved_model_paths) == 2
             # ddp 下的文件名不同，因为同样的数据，ddp 用了更少的步数跑完；
             else:
-                assert "last" in all_saved_model_paths
+                assert "trainer-last" in all_saved_model_paths
 
                 aLL_topk_folders = []
                 for each_folder_name in all_saved_model_paths:
@@ -681,7 +684,7 @@ def test_trainer_checkpoint_callback_2(
                         aLL_topk_folders.append(each_folder_name[0])
                 assert len(aLL_topk_folders) == 1
 
-                last_save_path = all_saved_model_paths["last"]
+                last_save_path = all_saved_model_paths["trainer-last"]
                 topk_save_path = all_saved_model_paths[aLL_topk_folders[0]]
 
                 assert len(all_saved_model_paths) == 2

tests/core/dataloaders/paddle_dataloader/test_fdl.py

@@ -1,4 +1,4 @@
-import unittest
+import pytest
 
 from fastNLP.core.dataloaders.paddle_dataloader.fdl import PaddleDataLoader
 from fastNLP.core.dataset import DataSet
@@ -17,7 +17,7 @@ class RandomDataset(Dataset):
         return 10
 
 
-class TestPaddle(unittest.TestCase):
+class TestPaddle:
 
     def test_init(self):
         # ds = DataSet({'x': [[1, 2], [2, 3, 4], [1]] * 10, 'y': [0, 1, 1] * 10})

tests/core/dataloaders/torch_dataloader/test_fdl.py

@@ -1,25 +1,25 @@
-import unittest
+import pytest
 
-from fastNLP.core.dataloaders.torch_dataloader import FDataLoader, prepare_dataloader
+from fastNLP.core.dataloaders.torch_dataloader import TorchDataLoader, prepare_torch_dataloader
 from fastNLP.core.dataset import DataSet
 from fastNLP.io.data_bundle import DataBundle
 
 
-class TestFdl(unittest.TestCase):
+class TestFdl:
 
     def test_init_v1(self):
         ds = DataSet({"x": [[1, 2], [2, 3, 4], [4, 5, 6, 7]] * 10, "y": [1, 0, 1] * 10})
-        fdl = FDataLoader(ds, batch_size=3, shuffle=True, drop_last=True)
+        fdl = TorchDataLoader(ds, batch_size=3, shuffle=True, drop_last=True)
         # for batch in fdl:
         #     print(batch)
-        fdl1 = FDataLoader(ds, batch_size=3, shuffle=True, drop_last=True, as_numpy=True)
+        fdl1 = TorchDataLoader(ds, batch_size=3, shuffle=True, drop_last=True, as_numpy=True)
         # for batch in fdl1:
         #     print(batch)
 
     def test_set_padding(self):
         ds = DataSet({"x": [[1, 2], [2, 3, 4], [4, 5, 6, 7]] * 10, "y": [1, 0, 1] * 10})
         ds.set_pad_val("x", val=-1)
-        fdl = FDataLoader(ds, batch_size=3)
+        fdl = TorchDataLoader(ds, batch_size=3)
         fdl.set_input("x", "y")
         for batch in fdl:
             print(batch)
@@ -36,7 +36,7 @@ class TestFdl(unittest.TestCase):
                 _dict["Y"].append(ins['y'])
             return _dict
 
-        fdl = FDataLoader(ds, batch_size=3, as_numpy=True)
+        fdl = TorchDataLoader(ds, batch_size=3, as_numpy=True)
         fdl.set_input("x", "y")
         fdl.add_collator(collate_fn)
         for batch in fdl:
@@ -44,7 +44,7 @@ class TestFdl(unittest.TestCase):
 
     def test_get_batch_indices(self):
         ds = DataSet({"x": [[1, 2], [2, 3, 4], [4, 5, 6, 7]] * 10, "y": [1, 0, 1] * 10})
-        fdl = FDataLoader(ds, batch_size=3, shuffle=True)
+        fdl = TorchDataLoader(ds, batch_size=3, shuffle=True)
         fdl.set_input("y", "x")
         for batch in fdl:
             print(fdl.get_batch_indices())
@@ -67,30 +67,30 @@ class TestFdl(unittest.TestCase):
                 return object.__getattribute__(self, item)
 
         dataset = _DataSet()
-        dl = FDataLoader(dataset, batch_size=2, shuffle=True)
+        dl = TorchDataLoader(dataset, batch_size=2, shuffle=True)
         # dl.set_inputs('data', 'labels')
         # dl.set_pad_val('labels', val=None)
         for batch in dl:
             print(batch)
             print(dl.get_batch_indices())
 
-    def test_prepare_dataloader(self):
+    def test_prepare_torch_dataloader(self):
         ds = DataSet({"x": [[1, 2], [2, 3, 4], [4, 5, 6, 7]] * 10, "y": [1, 0, 1] * 10})
-        dl = prepare_dataloader(ds, batch_size=8, shuffle=True, num_workers=2)
-        assert isinstance(dl, FDataLoader)
+        dl = prepare_torch_dataloader(ds, batch_size=8, shuffle=True, num_workers=2)
+        assert isinstance(dl, TorchDataLoader)
 
         ds1 = DataSet({"x": [[1, 2], [2, 3, 4], [4, 5, 6, 7]] * 10, "y": [1, 0, 1] * 10})
         dbl = DataBundle(datasets={'train': ds, 'val': ds1})
-        dl_bundle = prepare_dataloader(dbl)
-        assert isinstance(dl_bundle['train'], FDataLoader)
-        assert isinstance(dl_bundle['val'], FDataLoader)
+        dl_bundle = prepare_torch_dataloader(dbl)
+        assert isinstance(dl_bundle['train'], TorchDataLoader)
+        assert isinstance(dl_bundle['val'], TorchDataLoader)
 
         ds_dict = {'train_1': ds, 'val': ds1}
-        dl_dict = prepare_dataloader(ds_dict)
-        assert isinstance(dl_dict['train_1'], FDataLoader)
-        assert isinstance(dl_dict['val'], FDataLoader)
+        dl_dict = prepare_torch_dataloader(ds_dict)
+        assert isinstance(dl_dict['train_1'], TorchDataLoader)
+        assert isinstance(dl_dict['val'], TorchDataLoader)
 
         sequence = [ds, ds1]
-        seq_ds = prepare_dataloader(sequence)
-        assert isinstance(seq_ds[0], FDataLoader)
-        assert isinstance(seq_ds[1], FDataLoader)
+        seq_ds = prepare_torch_dataloader(sequence)
+        assert isinstance(seq_ds[0], TorchDataLoader)
+        assert isinstance(seq_ds[1], TorchDataLoader)

tests/core/dataset/test_dataset.py

@@ -1,12 +1,12 @@
 import os
-import unittest
+import pytest
 
 import numpy as np
 
 from fastNLP.core.dataset import DataSet, FieldArray, Instance, ApplyResultException
 
 
-class TestDataSetInit(unittest.TestCase):
+class TestDataSetInit:
     """初始化DataSet的办法有以下几种：
     1) 用dict:
         1.1) 二维list  DataSet({"x": [[1, 2], [3, 4]]})
@@ -24,46 +24,46 @@ class TestDataSetInit(unittest.TestCase):
     def test_init_v1(self):
         # 一维list
         ds = DataSet([Instance(x=[1, 2, 3, 4], y=[5, 6])] * 40)
-        self.assertTrue("x" in ds.field_arrays and "y" in ds.field_arrays)
-        self.assertEqual(ds.field_arrays["x"].content, [[1, 2, 3, 4], ] * 40)
-        self.assertEqual(ds.field_arrays["y"].content, [[5, 6], ] * 40)
+        assert ("x" in ds.field_arrays and "y" in ds.field_arrays) == True
+        assert ds.field_arrays["x"].content == [[1, 2, 3, 4], ] * 40
+        assert ds.field_arrays["y"].content == [[5, 6], ] * 40
 
     def test_init_v2(self):
         # 用dict
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
-        self.assertTrue("x" in ds.field_arrays and "y" in ds.field_arrays)
-        self.assertEqual(ds.field_arrays["x"].content, [[1, 2, 3, 4], ] * 40)
-        self.assertEqual(ds.field_arrays["y"].content, [[5, 6], ] * 40)
+        assert ("x" in ds.field_arrays and "y" in ds.field_arrays) == True
+        assert ds.field_arrays["x"].content == [[1, 2, 3, 4], ] * 40
+        assert ds.field_arrays["y"].content == [[5, 6], ] * 40
 
     def test_init_assert(self):
-        with self.assertRaises(AssertionError):
+        with pytest.raises(AssertionError):
             _ = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 100})
-        with self.assertRaises(AssertionError):
+        with pytest.raises(AssertionError):
             _ = DataSet([[1, 2, 3, 4]] * 10)
-        with self.assertRaises(ValueError):
+        with pytest.raises(ValueError):
             _ = DataSet(0.00001)
 
 
-class TestDataSetMethods(unittest.TestCase):
+class TestDataSetMethods:
     def test_append(self):
         dd = DataSet()
         for _ in range(3):
             dd.append(Instance(x=[1, 2, 3, 4], y=[5, 6]))
-        self.assertEqual(len(dd), 3)
-        self.assertEqual(dd.field_arrays["x"].content, [[1, 2, 3, 4]] * 3)
-        self.assertEqual(dd.field_arrays["y"].content, [[5, 6]] * 3)
+        assert len(dd) == 3
+        assert dd.field_arrays["x"].content == [[1, 2, 3, 4]] * 3
+        assert dd.field_arrays["y"].content == [[5, 6]] * 3
 
     def test_add_field(self):
         dd = DataSet()
         dd.add_field("x", [[1, 2, 3]] * 10)
         dd.add_field("y", [[1, 2, 3, 4]] * 10)
         dd.add_field("z", [[5, 6]] * 10)
-        self.assertEqual(len(dd), 10)
-        self.assertEqual(dd.field_arrays["x"].content, [[1, 2, 3]] * 10)
-        self.assertEqual(dd.field_arrays["y"].content, [[1, 2, 3, 4]] * 10)
-        self.assertEqual(dd.field_arrays["z"].content, [[5, 6]] * 10)
+        assert len(dd) == 10
+        assert dd.field_arrays["x"].content == [[1, 2, 3]] * 10
+        assert dd.field_arrays["y"].content == [[1, 2, 3, 4]] * 10
+        assert dd.field_arrays["z"].content == [[5, 6]] * 10
 
-        with self.assertRaises(RuntimeError):
+        with pytest.raises(RuntimeError):
             dd.add_field("??", [[1, 2]] * 40)
 
     def test_delete_field(self):
@@ -71,8 +71,8 @@ class TestDataSetMethods(unittest.TestCase):
         dd.add_field("x", [[1, 2, 3]] * 10)
         dd.add_field("y", [[1, 2, 3, 4]] * 10)
         dd.delete_field("x")
-        self.assertFalse("x" in dd.field_arrays)
-        self.assertTrue("y" in dd.field_arrays)
+        assert ("x" in dd.field_arrays) == False
+        assert "y" in dd.field_arrays
 
     def test_delete_instance(self):
         dd = DataSet()
@@ -80,99 +80,113 @@ class TestDataSetMethods(unittest.TestCase):
         dd.add_field("x", [[1, 2, 3]] * old_length)
         dd.add_field("y", [[1, 2, 3, 4]] * old_length)
         dd.delete_instance(0)
-        self.assertEqual(len(dd), old_length - 1)
+        assert len(dd) == old_length - 1
         dd.delete_instance(0)
-        self.assertEqual(len(dd), old_length - 2)
+        assert len(dd) == old_length - 2
 
     def test_getitem(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
         ins_1, ins_0 = ds[0], ds[1]
-        self.assertTrue(isinstance(ins_1, Instance) and isinstance(ins_0, Instance))
-        self.assertEqual(ins_1["x"], [1, 2, 3, 4])
-        self.assertEqual(ins_1["y"], [5, 6])
-        self.assertEqual(ins_0["x"], [1, 2, 3, 4])
-        self.assertEqual(ins_0["y"], [5, 6])
+        assert isinstance(ins_1, Instance) and isinstance(ins_0, Instance) == True
+        assert ins_1["x"] == [1, 2, 3, 4]
+        assert ins_1["y"] == [5, 6]
+        assert ins_0["x"] == [1, 2, 3, 4]
+        assert ins_0["y"] == [5, 6]
 
         sub_ds = ds[:10]
-        self.assertTrue(isinstance(sub_ds, DataSet))
-        self.assertEqual(len(sub_ds), 10)
+        assert isinstance(sub_ds, DataSet) == True
+        assert len(sub_ds) == 10
 
         sub_ds_1 = ds[[10, 0, 2, 3]]
-        self.assertTrue(isinstance(sub_ds_1, DataSet))
-        self.assertEqual(len(sub_ds_1), 4)
+        assert isinstance(sub_ds_1, DataSet) == True
+        assert len(sub_ds_1) == 4
 
         field_array = ds['x']
-        self.assertTrue(isinstance(field_array, FieldArray))
-        self.assertEqual(len(field_array), 40)
+        assert isinstance(field_array, FieldArray) == True
+        assert len(field_array) == 40
+
+    def test_setitem(self):
+        ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
+        ds.add_field('i', list(range(len(ds))))
+        assert ds.get_field('i').content == list(range(len(ds)))
+        import random
+        random.shuffle(ds)
+        import numpy as np
+        np.random.shuffle(ds)
+        assert ds.get_field('i').content != list(range(len(ds)))
+
+        ins1 = ds[1]
+        ds[2] = ds[1]
+        assert ds[2]['x'] == ins1['x'] and ds[2]['y'] == ins1['y']
 
     def test_get_item_error(self):
-        with self.assertRaises(RuntimeError):
+        with pytest.raises(RuntimeError):
             ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})
             _ = ds[40:]
 
-        with self.assertRaises(KeyError):
+        with pytest.raises(KeyError):
             ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})
             _ = ds["kom"]
 
     def test_len_(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
-        self.assertEqual(len(ds), 40)
+        assert len(ds) == 40
 
         ds = DataSet()
-        self.assertEqual(len(ds), 0)
+        assert len(ds) == 0
 
     def test_add_fieldarray(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
-        ds.add_fieldarray('z', FieldArray('z', [[7, 8]]*40))
-        self.assertEqual(ds['z'].content, [[7, 8]]*40)
+        ds.add_fieldarray('z', FieldArray('z', [[7, 8]] * 40))
+        assert ds['z'].content == [[7, 8]] * 40
 
-        with self.assertRaises(RuntimeError):
-            ds.add_fieldarray('z', FieldArray('z', [[7, 8]]*10))
+        with pytest.raises(RuntimeError):
+            ds.add_fieldarray('z', FieldArray('z', [[7, 8]] * 10))
 
-        with self.assertRaises(TypeError):
+        with pytest.raises(TypeError):
             ds.add_fieldarray('z', [1, 2, 4])
 
     def test_copy_field(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
         ds.copy_field('x', 'z')
-        self.assertEqual(ds['x'].content, ds['z'].content)
+        assert ds['x'].content == ds['z'].content
 
     def test_has_field(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
-        self.assertTrue(ds.has_field('x'))
-        self.assertFalse(ds.has_field('z'))
+        assert ds.has_field('x') == True
+        assert ds.has_field('z') == False
 
     def test_get_field(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
-        with self.assertRaises(KeyError):
+        with pytest.raises(KeyError):
             ds.get_field('z')
         x_array = ds.get_field('x')
-        self.assertEqual(x_array.content, [[1, 2, 3, 4]] * 40)
+        assert x_array.content == [[1, 2, 3, 4]] * 40
 
     def test_get_all_fields(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
         field_arrays = ds.get_all_fields()
-        self.assertEqual(field_arrays["x"], [[1, 2, 3, 4]] * 40)
-        self.assertEqual(field_arrays['y'], [[5, 6]] * 40)
+        assert field_arrays["x"].content == [[1, 2, 3, 4]] * 40
+        assert field_arrays['y'].content == [[5, 6]] * 40
 
     def test_get_field_names(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
         field_names = ds.get_field_names()
-        self.assertTrue('x' in field_names)
-        self.assertTrue('y' in field_names)
+        assert 'x' in field_names
+        assert 'y' in field_names
 
     def test_apply(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 4000, "y": [[5, 6]] * 4000})
         ds.apply(lambda ins: ins["x"][::-1], new_field_name="rx", progress_desc='rx')
-        self.assertTrue("rx" in ds.field_arrays)
-        self.assertEqual(ds.field_arrays["rx"].content[0], [4, 3, 2, 1])
+        assert ("rx" in ds.field_arrays) == True
+        assert ds.field_arrays["rx"].content[0] == [4, 3, 2, 1]
 
         ds.apply(lambda ins: len(ins["y"]), new_field_name="y", show_progress_bar=False)
-        self.assertEqual(ds.field_arrays["y"].content[0], 2)
+        assert ds.field_arrays["y"].content[0] == 2
 
         res = ds.apply(lambda ins: len(ins["x"]), num_proc=0, progress_desc="len")
-        self.assertTrue(isinstance(res, list) and len(res) > 0)
-        self.assertTrue(res[0], 4)
+        assert (isinstance(res, list) and len(res) > 0) == True
+        assert res[0] == 4
 
         ds.apply(lambda ins: (len(ins["x"]), "hahaha"), new_field_name="k")
         # expect no exception raised
@@ -192,6 +206,7 @@ class TestDataSetMethods(unittest.TestCase):
 
         def modify_inplace(instance):
             instance['words'] = 1
+
         ds.apply(modify_inplace)
         # with self.assertRaises(TypeError):
         #     ds.apply(modify_inplace)
@@ -216,48 +231,48 @@ class TestDataSetMethods(unittest.TestCase):
 
         T.apply_more(func_1)
         # print(T['c'][0, 1, 2])
-        self.assertEqual(list(T["c"].content), [2, 4, 6])
-        self.assertEqual(list(T["d"].content), [1, 4, 9])
+        assert list(T["c"].content) == [2, 4, 6]
+        assert list(T["d"].content) == [1, 4, 9]
 
         res = T.apply_field_more(func_2, "a", modify_fields=False)
-        self.assertEqual(list(T["c"].content), [2, 4, 6])
-        self.assertEqual(list(T["d"].content), [1, 4, 9])
-        self.assertEqual(list(res["c"]), [3, 6, 9])
-        self.assertEqual(list(res["d"]), [1, 8, 27])
+        assert list(T["c"].content) == [2, 4, 6]
+        assert list(T["d"].content) == [1, 4, 9]
+        assert list(res["c"]) == [3, 6, 9]
+        assert list(res["d"]) == [1, 8, 27]
 
-        with self.assertRaises(ApplyResultException) as e:
+        with pytest.raises(ApplyResultException) as e:
             T.apply_more(func_err_1)
             print(e)
 
-        with self.assertRaises(ApplyResultException) as e:
+        with pytest.raises(ApplyResultException) as e:
             T.apply_field_more(func_err_2, "a")
             print(e)
 
     def test_drop(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6], [7, 8, 9, 0]] * 20})
         ds.drop(lambda ins: len(ins["y"]) < 3, inplace=True)
-        self.assertEqual(len(ds), 20)
+        assert len(ds) == 20
 
     def test_contains(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
-        self.assertTrue("x" in ds)
-        self.assertTrue("y" in ds)
-        self.assertFalse("z" in ds)
+        assert ("x" in ds) == True
+        assert ("y" in ds) == True
+        assert ("z" in ds) == False
 
     def test_rename_field(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})
         ds.rename_field("x", "xx")
-        self.assertTrue("xx" in ds)
-        self.assertFalse("x" in ds)
+        assert ("xx" in ds) == True
+        assert ("x" in ds) == False
 
-        with self.assertRaises(KeyError):
+        with pytest.raises(KeyError):
             ds.rename_field("yyy", "oo")
 
     def test_split(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})
         d1, d2 = ds.split(0.1)
-        self.assertEqual(len(d1), len(ds)*0.9)
-        self.assertEqual(len(d2), len(ds)*0.1)
+        assert len(d2) == (len(ds) * 0.9)
+        assert len(d1) == (len(ds) * 0.1)
 
     def test_add_field_v2(self):
         ds = DataSet({"x": [3, 4]})
@@ -268,14 +283,14 @@ class TestDataSetMethods(unittest.TestCase):
     def test_save_load(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})
         ds.save("./my_ds.pkl")
-        self.assertTrue(os.path.exists("./my_ds.pkl"))
+        assert os.path.exists("./my_ds.pkl") == True
 
         ds_1 = DataSet.load("./my_ds.pkl")
         os.remove("my_ds.pkl")
 
     def test_add_null(self):
         ds = DataSet()
-        with self.assertRaises(RuntimeError) as RE:
+        with pytest.raises(RuntimeError) as RE:
             ds.add_field('test', [])
 
     def test_concat(self):
@@ -287,16 +302,16 @@ class TestDataSetMethods(unittest.TestCase):
         ds2 = DataSet({"x": [[4, 3, 2, 1] for _ in range(10)], "y": [[6, 5] for _ in range(10)]})
         ds3 = ds1.concat(ds2)
 
-        self.assertEqual(len(ds3), 20)
+        assert len(ds3) == 20
 
-        self.assertListEqual(ds1[9]['x'], [1, 2, 3, 4])
-        self.assertListEqual(ds1[10]['x'], [4, 3, 2, 1])
+        assert ds1[9]['x'] == [1, 2, 3, 4]
+        assert ds1[10]['x'] == [4, 3, 2, 1]
 
         ds2[0]['x'][0] = 100
-        self.assertEqual(ds3[10]['x'][0], 4)  # 不改变copy后的field了
+        assert ds3[10]['x'][0] == 4  # 不改变copy后的field了
 
         ds3[10]['x'][0] = -100
-        self.assertEqual(ds2[0]['x'][0], 100)  # 不改变copy前的field了
+        assert ds2[0]['x'][0] == 100  # 不改变copy前的field了
 
         # 测试inplace
         ds1 = DataSet({"x": [[1, 2, 3, 4] for i in range(10)], "y": [[5, 6] for i in range(10)]})
@@ -304,19 +319,19 @@ class TestDataSetMethods(unittest.TestCase):
         ds3 = ds1.concat(ds2, inplace=True)
 
         ds2[0]['x'][0] = 100
-        self.assertEqual(ds3[10]['x'][0], 4)  # 不改变copy后的field了
+        assert ds3[10]['x'][0] == 4  # 不改变copy后的field了
 
         ds3[10]['x'][0] = -100
-        self.assertEqual(ds2[0]['x'][0], 100)  # 不改变copy前的field了
+        assert ds2[0]['x'][0] == 100  # 不改变copy前的field了
 
         ds3[0]['x'][0] = 100
-        self.assertEqual(ds1[0]['x'][0], 100)  # 改变copy前的field了
+        assert ds1[0]['x'][0] == 100  # 改变copy前的field了
 
         # 测试mapping
         ds1 = DataSet({"x": [[1, 2, 3, 4] for i in range(10)], "y": [[5, 6] for i in range(10)]})
         ds2 = DataSet({"X": [[4, 3, 2, 1] for i in range(10)], "Y": [[6, 5] for i in range(10)]})
         ds3 = ds1.concat(ds2, field_mapping={'X': 'x', 'Y': 'y'})
-        self.assertEqual(len(ds3), 20)
+        assert len(ds3) == 20
 
         # 测试忽略掉多余的
         ds1 = DataSet({"x": [[1, 2, 3, 4] for i in range(10)], "y": [[5, 6] for i in range(10)]})
@@ -326,7 +341,7 @@ class TestDataSetMethods(unittest.TestCase):
         # 测试报错
         ds1 = DataSet({"x": [[1, 2, 3, 4] for i in range(10)], "y": [[5, 6] for i in range(10)]})
         ds2 = DataSet({"X": [[4, 3, 2, 1] for i in range(10)]})
-        with self.assertRaises(RuntimeError):
+        with pytest.raises(RuntimeError):
             ds3 = ds1.concat(ds2, field_mapping={'X': 'x'})
 
     def test_instance_field_disappear_bug(self):
@@ -334,7 +349,7 @@ class TestDataSetMethods(unittest.TestCase):
         data.copy_field(field_name='raw_chars', new_field_name='chars')
         _data = data[:1]
         for field_name in ['raw_chars', 'target', 'chars']:
-            self.assertTrue(_data.has_field(field_name))
+            assert _data.has_field(field_name) == True
 
     def test_from_pandas(self):
         import pandas as pd
@@ -342,8 +357,8 @@ class TestDataSetMethods(unittest.TestCase):
         df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
         ds = DataSet.from_pandas(df)
         print(ds)
-        self.assertEqual(ds['x'].content, [1, 2, 3])
-        self.assertEqual(ds['y'].content, [4, 5, 6])
+        assert ds['x'].content == [1, 2, 3]
+        assert ds['y'].content == [4, 5, 6]
 
     def test_to_pandas(self):
         ds = DataSet({'x': [1, 2, 3], 'y': [4, 5, 6]})
@@ -352,7 +367,7 @@ class TestDataSetMethods(unittest.TestCase):
     def test_to_csv(self):
         ds = DataSet({'x': [1, 2, 3], 'y': [4, 5, 6]})
         ds.to_csv("1.csv")
-        self.assertTrue(os.path.exists("1.csv"))
+        assert os.path.exists("1.csv") == True
         os.remove("1.csv")
 
     def test_add_collate_fn(self):
@@ -360,27 +375,26 @@ class TestDataSetMethods(unittest.TestCase):
 
         def collate_fn(item):
             return item
-        ds.add_collate_fn(collate_fn)
 
-        self.assertEqual(len(ds.collate_fns.collators), 2)
+        ds.add_collate_fn(collate_fn)
 
     def test_get_collator(self):
         from typing import Callable
         ds = DataSet({'x': [1, 2, 3], 'y': [4, 5, 6]})
         collate_fn = ds.get_collator()
-        self.assertEqual(isinstance(collate_fn, Callable), True)
+        assert isinstance(collate_fn, Callable) == True
 
     def test_add_seq_len(self):
-        ds = DataSet({'x': [[1, 2], [2, 3 , 4], [3]], 'y': [4, 5, 6]})
+        ds = DataSet({'x': [[1, 2], [2, 3, 4], [3]], 'y': [4, 5, 6]})
         ds.add_seq_len('x')
         print(ds)
 
     def test_set_target(self):
-        ds = DataSet({'x': [[1, 2], [2, 3 , 4], [3]], 'y': [4, 5, 6]})
+        ds = DataSet({'x': [[1, 2], [2, 3, 4], [3]], 'y': [4, 5, 6]})
         ds.set_target('x')
 
 
-class TestFieldArrayInit(unittest.TestCase):
+class TestFieldArrayInit:
     """
     1） 如果DataSet使用dict初始化，那么在add_field中会构造FieldArray：
             1.1) 二维list  DataSet({"x": [[1, 2], [3, 4]]})
@@ -428,7 +442,6 @@ class TestFieldArrayInit(unittest.TestCase):
         # list of array
         fa = FieldArray("x", [np.array([[1, 2], [3, 4]]), np.array([[1, 2], [3, 4]])])
 
-
     def test_init_v8(self):
         # 二维list
         val = np.array([[1, 2], [3, 4]])
@@ -436,78 +449,78 @@ class TestFieldArrayInit(unittest.TestCase):
         fa.append(val)
 
 
-class TestFieldArray(unittest.TestCase):
+class TestFieldArray:
     def test_main(self):
         fa = FieldArray("x", [1, 2, 3, 4, 5])
-        self.assertEqual(len(fa), 5)
+        assert len(fa) == 5
         fa.append(6)
-        self.assertEqual(len(fa), 6)
+        assert len(fa) == 6
 
-        self.assertEqual(fa[-1], 6)
-        self.assertEqual(fa[0], 1)
+        assert fa[-1] == 6
+        assert fa[0] == 1
         fa[-1] = 60
-        self.assertEqual(fa[-1], 60)
+        assert fa[-1] == 60
 
-        self.assertEqual(fa.get(0), 1)
-        self.assertTrue(isinstance(fa.get([0, 1, 2]), np.ndarray))
-        self.assertListEqual(list(fa.get([0, 1, 2])), [1, 2, 3])
+        assert fa.get(0) == 1
+        assert isinstance(fa.get([0, 1, 2]), np.ndarray) == True
+        assert list(fa.get([0, 1, 2])) == [1, 2, 3]
 
     def test_getitem_v1(self):
         fa = FieldArray("y", [[1.1, 2.2, 3.3, 4.4, 5.5], [1.0, 2.0, 3.0, 4.0, 5.0]])
-        self.assertEqual(fa[0], [1.1, 2.2, 3.3, 4.4, 5.5])
+        assert fa[0] == [1.1, 2.2, 3.3, 4.4, 5.5]
         ans = fa[[0, 1]]
-        self.assertTrue(isinstance(ans, np.ndarray))
-        self.assertTrue(isinstance(ans[0], np.ndarray))
-        self.assertEqual(ans[0].tolist(), [1.1, 2.2, 3.3, 4.4, 5.5])
-        self.assertEqual(ans[1].tolist(), [1, 2, 3, 4, 5])
-        self.assertEqual(ans.dtype, np.float64)
+        assert isinstance(ans, np.ndarray) == True
+        assert isinstance(ans[0], np.ndarray) == True
+        assert ans[0].tolist() == [1.1, 2.2, 3.3, 4.4, 5.5]
+        assert ans[1].tolist() == [1, 2, 3, 4, 5]
+        assert ans.dtype == np.float64
 
     def test_getitem_v2(self):
         x = np.random.rand(10, 5)
         fa = FieldArray("my_field", x)
         indices = [0, 1, 3, 4, 6]
         for a, b in zip(fa[indices], x[indices]):
-            self.assertListEqual(a.tolist(), b.tolist())
+            assert a.tolist() == b.tolist()
 
     def test_append(self):
         fa = FieldArray("y", [[1.1, 2.2, 3.3, 4.4, 5.5], [1.0, 2.0, 3.0, 4.0, 5.0]])
         fa.append([1.2, 2.3, 3.4, 4.5, 5.6])
-        self.assertEqual(len(fa), 3)
-        self.assertEqual(fa[2], [1.2, 2.3, 3.4, 4.5, 5.6])
+        assert len(fa) == 3
+        assert fa[2] == [1.2, 2.3, 3.4, 4.5, 5.6]
 
     def test_pop(self):
         fa = FieldArray("y", [[1.1, 2.2, 3.3, 4.4, 5.5], [1.0, 2.0, 3.0, 4.0, 5.0]])
         fa.pop(0)
-        self.assertEqual(len(fa), 1)
-        self.assertEqual(fa[0], [1.0, 2.0, 3.0, 4.0, 5.0])
+        assert len(fa) == 1
+        assert fa[0] == [1.0, 2.0, 3.0, 4.0, 5.0]
         fa[0] = [1.1, 2.2, 3.3, 4.4, 5.5]
-        self.assertEqual(fa[0], [1.1, 2.2, 3.3, 4.4, 5.5])
+        assert fa[0] == [1.1, 2.2, 3.3, 4.4, 5.5]
 
 
-class TestCase(unittest.TestCase):
+class TestCase:
 
     def test_init(self):
         fields = {"x": [1, 2, 3], "y": [4, 5, 6]}
         ins = Instance(x=[1, 2, 3], y=[4, 5, 6])
-        self.assertTrue(isinstance(ins.fields, dict))
-        self.assertEqual(ins.fields, fields)
+        assert isinstance(ins.fields, dict) == True
+        assert ins.fields == fields
 
         ins = Instance(**fields)
-        self.assertEqual(ins.fields, fields)
+        assert ins.fields == fields
 
     def test_add_field(self):
         fields = {"x": [1, 2, 3], "y": [4, 5, 6]}
         ins = Instance(**fields)
         ins.add_field("z", [1, 1, 1])
         fields.update({"z": [1, 1, 1]})
-        self.assertEqual(ins.fields, fields)
+        assert ins.fields == fields
 
     def test_get_item(self):
         fields = {"x": [1, 2, 3], "y": [4, 5, 6], "z": [1, 1, 1]}
         ins = Instance(**fields)
-        self.assertEqual(ins["x"], [1, 2, 3])
-        self.assertEqual(ins["y"], [4, 5, 6])
-        self.assertEqual(ins["z"], [1, 1, 1])
+        assert ins["x"] == [1, 2, 3]
+        assert ins["y"] == [4, 5, 6]
+        assert ins["z"] == [1, 1, 1]
 
     def test_repr(self):
         fields = {"x": [1, 2, 3], "y": [4, 5, 6], "z": [1, 1, 1]}

tests/core/drivers/paddle_driver/test_single_device.py

@@ -6,7 +6,7 @@ from fastNLP.core.drivers.paddle_driver.single_device import PaddleSingleDriver
 from fastNLP.core.samplers.reproducible_sampler import RandomSampler
 from fastNLP.core.samplers import ReproducibleBatchSampler
 from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
-from tests.helpers.datasets.paddle_data import PaddleRandomMaxDataset
+from tests.helpers.datasets.paddle_data import PaddleNormalDataset, PaddleRandomMaxDataset
 from tests.helpers.datasets.torch_data import TorchNormalDataset
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
 from fastNLP.core import synchronize_safe_rm

tests/core/drivers/torch_driver/test_torch_replace_sampler.py

@@ -30,7 +30,7 @@ class SequenceDataSet:
 
 
 def check_replace_sampler(driver):
-    # dist_sampler 可以选择的有['dist', 'unrepeatdist', None]或者是ReproducibleSampler，ReproducibleBatchSampler
+    # dist_sampler 可以选择的有['dist', 'unrepeatdist', None]或者是ReproducibleSampler，RandomBatchSampler
     # reproducible 是 True 和 False
 
     # 需要 check 返回的 sampler 和 dataloader 都不同了

tests/core/metrics/test_accuracy_torch.py

@@ -118,7 +118,6 @@ class TestAccuracy:
     def test_v1(self, is_ddp: bool, dataset: DataSet, metric_class: Type['Metric'],
                 metric_kwargs: Dict[str, Any]) -> None:
         global pool
-        print(pool)
         if is_ddp:
             if sys.platform == "win32":
                 pytest.skip("DDP not supported on windows")

tests/core/metrics/test_f1_rec_acc_torch.py → tests/core/metrics/test_span_f1_rec_acc_torch.py

@@ -1,5 +1,5 @@
 import pytest
-import unittest
+
 from collections import Counter
 import os, sys
 import copy
@@ -14,6 +14,7 @@ from torch.multiprocessing import Pool, set_start_method
 from fastNLP.core.vocabulary import Vocabulary
 from fastNLP.core.metrics import SpanFPreRecMetric
 from fastNLP.core.dataset import DataSet
+
 set_start_method("spawn", force=True)
 
 
@@ -45,7 +46,6 @@ def setup_ddp(rank: int, world_size: int, master_port: int) -> None:
 
     os.environ["MASTER_ADDR"] = "localhost"
     os.environ["MASTER_PORT"] = str(master_port)
-    print(torch.cuda.device_count())
     if torch.distributed.is_available() and sys.platform not in ("win32", "cygwin"):
         torch.distributed.init_process_group("gloo", rank=rank, world_size=world_size)
 
@@ -64,15 +64,15 @@ def find_free_network_port() -> int:
     return port
 
 
-@pytest.fixture(scope='class', autouse=True)
-def pre_process():
-    global pool
-    pool = Pool(processes=NUM_PROCESSES)
-    master_port = find_free_network_port()
-    pool.starmap(setup_ddp, [(rank, NUM_PROCESSES, master_port) for rank in range(NUM_PROCESSES)])
-    yield
-    pool.close()
-    pool.join()
+# @pytest.fixture(scope='class', autouse=True)
+# def pre_process():
+#     global pool
+#     pool = Pool(processes=NUM_PROCESSES)
+#     master_port = find_free_network_port()
+#     pool.starmap(setup_ddp, [(rank, NUM_PROCESSES, master_port) for rank in range(NUM_PROCESSES)])
+#     yield
+#     pool.close()
+#     pool.join()
 
 
 def _test(local_rank: int,
@@ -87,18 +87,19 @@ def _test(local_rank: int,
     # dataset 也类似（每个进程有自己的一个）
     dataset = copy.deepcopy(dataset)
     metric.to(device)
-    print(os.environ.get("MASTER_PORT", "xx"))
     # 把数据拆到每个 GPU 上，有点模仿 DistributedSampler 的感觉，但这里数据单位是一个 batch（即每个 i 取了一个 batch 到自己的 GPU 上）
     for i in range(local_rank, len(dataset), world_size):
         pred, tg, seq_len = dataset[i]['pred'].to(device), dataset[i]['tg'].to(device), dataset[i]['seq_len']
+        print(tg, seq_len)
         metric.update(pred, tg, seq_len)
 
     my_result = metric.get_metric()
+    print(my_result)
+    print(sklearn_metric)
     assert my_result == sklearn_metric
 
 
-class SpanFPreRecMetricTest(unittest.TestCase):
-    global pool
+class TestSpanFPreRecMetric:
 
     def test_case1(self):
         from fastNLP.core.metrics.span_f1_pre_rec_metric import _bmes_tag_to_spans
@@ -135,38 +136,36 @@ class SpanFPreRecMetricTest(unittest.TestCase):
         fastnlp_bio_vocab = Vocabulary(unknown=None, padding=None)
         fastnlp_bio_vocab.word_count = Counter(_generate_tags('BIO', number_labels))
         fastnlp_bio_metric = SpanFPreRecMetric(tag_vocab=fastnlp_bio_vocab, only_gross=False)
-        bio_sequence = torch.FloatTensor([[[-0.4424, -0.4579, -0.7376, 1.8129, 0.1316, 1.6566, -1.2169,
-                                            -0.3782, 0.8240],
-                                           [-1.2348, -0.1876, -0.1462, -0.4834, -0.6692, -0.9735, 1.1563,
+        bio_sequence = torch.FloatTensor([[[-0.4424, -0.4579, -0.7376,  1.8129,  0.1316,  1.6566, -1.2169,
+                                            -0.3782,  0.8240],
+                                           [-1.2348, -0.1876, -0.1462, -0.4834, -0.6692, -0.9735,  1.1563,
                                             -0.3562, -1.4116],
-                                           [1.6550, -0.9555, 0.3782, -1.3160, -1.5835, -0.3443, -1.7858,
-                                            2.0023, 0.7075],
-                                           [-0.3772, -0.5447, -1.5631, 1.1614, 1.4598, -1.2764, 0.5186,
+                                           [ 1.6550, -0.9555,  0.3782, -1.3160, -1.5835, -0.3443, -1.7858,
+                                             2.0023,  0.7075],
+                                           [-0.3772, -0.5447, -1.5631,  1.1614,  1.4598, -1.2764,  0.5186,
                                             0.3832, -0.1540],
-                                           [-0.1011, 0.0600, 1.1090, -0.3545, 0.1284, 1.1484, -1.0120,
+                                           [-0.1011,  0.0600,  1.1090, -0.3545,  0.1284,  1.1484, -1.0120,
                                             -1.3508, -0.9513],
-                                           [1.8948, 0.8627, -2.1359, 1.3740, -0.7499, 1.5019, 0.6919,
-                                            -0.0842, -0.4294]],
+                                           [ 1.8948,  0.8627, -2.1359,  1.3740, -0.7499,  1.5019,  0.6919,
+                                             -0.0842, -0.4294]],
 
-                                          [[-0.2802, 0.6941, -0.4788, -0.3845, 1.7752, 1.2950, -1.9490,
+                                          [[-0.2802,  0.6941, -0.4788, -0.3845,  1.7752,  1.2950, -1.9490,
                                             -1.4138, -0.8853],
-                                           [-1.3752, -0.5457, -0.5305, 0.4018, 0.2934, 0.7931, 2.3845,
-                                            -1.0726, 0.0364],
-                                           [0.3621, 0.2609, 0.1269, -0.5950, 0.7212, 0.5959, 1.6264,
-                                            -0.8836, -0.9320],
-                                           [0.2003, -1.0758, -1.1560, -0.6472, -1.7549, 0.1264, 0.6044,
-                                            -1.6857, 1.1571],
-                                           [1.4277, -0.4915, 0.4496, 2.2027, 0.0730, -3.1792, -0.5125,
-                                            -0.5837, 1.0184],
-                                           [1.9495, 1.7145, -0.2143, -0.1230, -0.2205, 0.8250, 0.4943,
-                                            -0.9025, 0.0864]]])
-        bio_target = torch.LongTensor([[3, 6, 0, 8, 2, 4],
-                                       [4, 1, 7, 0, 4, 7]])
+                                           [-1.3752, -0.5457, -0.5305,  0.4018,  0.2934,  0.7931,  2.3845,
+                                            -1.0726,  0.0364],
+                                           [ 0.3621,  0.2609,  0.1269, -0.5950,  0.7212,  0.5959,  1.6264,
+                                             -0.8836, -0.9320],
+                                           [ 0.2003, -1.0758, -1.1560, -0.6472, -1.7549,  0.1264,  0.6044,
+                                             -1.6857,  1.1571],
+                                           [ 1.4277, -0.4915,  0.4496,  2.2027,  0.0730, -3.1792, -0.5125,
+                                             -0.5837,  1.0184],
+                                           [ 1.9495,  1.7145, -0.2143, -0.1230, -0.2205,  0.8250,  0.4943,
+                                             -0.9025,  0.0864]]])
+        bio_target = torch.LongTensor([[3, 6, 0, 8, 2, 4], [4, 1, 7, 0, 4, 7]])
         fastnlp_bio_metric.update(bio_sequence, bio_target, [6, 6])
         expect_bio_res = {'pre-1': 0.333333, 'rec-1': 0.333333, 'f-1': 0.333333, 'pre-2': 0.5, 'rec-2': 0.5,
                           'f-2': 0.5, 'pre-0': 0.0, 'rec-0': 0.0, 'f-0': 0.0, 'pre-3': 0.0, 'rec-3': 0.0,
                           'f-3': 0.0, 'pre': 0.222222, 'rec': 0.181818, 'f': 0.2}
-
         assert expect_bio_res == fastnlp_bio_metric.get_metric()
         # print(fastnlp_bio_metric.get_metric())
 
@@ -253,7 +252,7 @@ class SpanFPreRecMetricTest(unittest.TestCase):
         # print(expected_metric)
         metric_value = metric.get_metric()
         for key, value in expected_metric.items():
-            self.assertAlmostEqual(value, metric_value[key], places=5)
+            np.allclose(value, metric_value[key])
 
     def test_auto_encoding_type_infer(self):
         #  检查是否可以自动check encode的类型
@@ -270,9 +269,8 @@ class SpanFPreRecMetricTest(unittest.TestCase):
                         vocab.add_word('o')
             vocabs[encoding_type] = vocab
         for e in ['bio', 'bioes', 'bmeso']:
-            with self.subTest(e=e):
-                metric = SpanFPreRecMetric(tag_vocab=vocabs[e])
-                assert metric.encoding_type == e
+            metric = SpanFPreRecMetric(tag_vocab=vocabs[e])
+            assert metric.encoding_type == e
 
         bmes_vocab = _generate_tags('bmes')
         vocab = Vocabulary()
@@ -285,7 +283,7 @@ class SpanFPreRecMetricTest(unittest.TestCase):
         vocab = Vocabulary()
         for i in range(10):
             vocab.add_word(str(i))
-        with self.assertRaises(Exception):
+        with pytest.raises(Exception):
             metric = SpanFPreRecMetric(vocab)
 
     def test_encoding_type(self):
@@ -304,65 +302,72 @@ class SpanFPreRecMetricTest(unittest.TestCase):
                         vocab.add_word('o')
             vocabs[encoding_type] = vocab
         for e1, e2 in product(['bio', 'bioes', 'bmeso'], ['bio', 'bioes', 'bmeso']):
-            with self.subTest(e1=e1, e2=e2):
-                if e1 == e2:
+            if e1 == e2:
+                metric = SpanFPreRecMetric(tag_vocab=vocabs[e1], encoding_type=e2)
+            else:
+                s2 = set(e2)
+                s2.update(set(e1))
+                if s2 == set(e2):
+                    continue
+                with pytest.raises(AssertionError):
                     metric = SpanFPreRecMetric(tag_vocab=vocabs[e1], encoding_type=e2)
-                else:
-                    s2 = set(e2)
-                    s2.update(set(e1))
-                    if s2 == set(e2):
-                        continue
-                    with self.assertRaises(AssertionError):
-                        metric = SpanFPreRecMetric(tag_vocab=vocabs[e1], encoding_type=e2)
         for encoding_type in ['bio', 'bioes', 'bmeso']:
-            with self.assertRaises(AssertionError):
+            with pytest.raises(AssertionError):
                 metric = SpanFPreRecMetric(tag_vocab=vocabs[encoding_type], encoding_type='bmes')
 
-        with self.assertWarns(Warning):
+        with pytest.warns(Warning):
             vocab = Vocabulary(unknown=None, padding=None).add_word_lst(list('bmes'))
             metric = SpanFPreRecMetric(tag_vocab=vocab, encoding_type='bmeso')
             vocab = Vocabulary().add_word_lst(list('bmes'))
             metric = SpanFPreRecMetric(tag_vocab=vocab, encoding_type='bmeso')
 
     def test_case5(self):
-        global pool
-        # pool = Pool(NUM_PROCESSES)
-        # master_port = find_free_network_port()
-        # pool.starmap(setup_ddp, [(rank, NUM_PROCESSES, master_port) for rank in range(NUM_PROCESSES)])
+        # global pool
+        pool = Pool(NUM_PROCESSES)
+        master_port = find_free_network_port()
+        pool.starmap(setup_ddp, [(rank, NUM_PROCESSES, master_port) for rank in range(NUM_PROCESSES)])
         number_labels = 4
         # bio tag
         fastnlp_bio_vocab = Vocabulary(unknown=None, padding=None)
         fastnlp_bio_vocab.word_count = Counter(_generate_tags('BIO', number_labels))
         # fastnlp_bio_metric = SpanFPreRecMetric(tag_vocab=fastnlp_bio_vocab, only_gross=False)
-        dataset = DataSet({'pred': [torch.FloatTensor(
-            [[[-0.4424, -0.4579, -0.7376, 1.8129, 0.1316, 1.6566, -1.2169,
-               -0.3782, 0.8240],
-              [-1.2348, -0.1876, -0.1462, -0.4834, -0.6692, -0.9735, 1.1563,
-               -0.3562, -1.4116],
-              [1.6550, -0.9555, 0.3782, -1.3160, -1.5835, -0.3443, -1.7858,
-               2.0023, 0.7075],
-              [-0.3772, -0.5447, -1.5631, 1.1614, 1.4598, -1.2764, 0.5186,
-               0.3832, -0.1540],
-              [-0.1011, 0.0600, 1.1090, -0.3545, 0.1284, 1.1484, -1.0120,
-               -1.3508, -0.9513],
-              [1.8948, 0.8627, -2.1359, 1.3740, -0.7499, 1.5019, 0.6919,
-               -0.0842, -0.4294]],
-
-             [[-0.2802, 0.6941, -0.4788, -0.3845, 1.7752, 1.2950, -1.9490,
-               -1.4138, -0.8853],
-              [-1.3752, -0.5457, -0.5305, 0.4018, 0.2934, 0.7931, 2.3845,
-               -1.0726, 0.0364],
-              [0.3621, 0.2609, 0.1269, -0.5950, 0.7212, 0.5959, 1.6264,
-               -0.8836, -0.9320],
-              [0.2003, -1.0758, -1.1560, -0.6472, -1.7549, 0.1264, 0.6044,
-               -1.6857, 1.1571],
-              [1.4277, -0.4915, 0.4496, 2.2027, 0.0730, -3.1792, -0.5125,
-               -0.5837, 1.0184],
-              [1.9495, 1.7145, -0.2143, -0.1230, -0.2205, 0.8250, 0.4943,
-               -0.9025, 0.0864]]])] * 100,
-                           'tg': [torch.LongTensor([[3, 6, 0, 8, 2, 4],
-                                                    [4, 1, 7, 0, 4, 7]])] * 100,
-                          'seq_len': [[6, 6]] * 100})
+        dataset = DataSet({'pred': [
+            torch.FloatTensor([[[-0.4424, -0.4579, -0.7376, 1.8129, 0.1316, 1.6566, -1.2169,
+                                 -0.3782, 0.8240],
+                                [-1.2348, -0.1876, -0.1462, -0.4834, -0.6692, -0.9735, 1.1563,
+                                 -0.3562, -1.4116],
+                                [1.6550, -0.9555, 0.3782, -1.3160, -1.5835, -0.3443, -1.7858,
+                                 2.0023, 0.7075],
+                                [-0.3772, -0.5447, -1.5631, 1.1614, 1.4598, -1.2764, 0.5186,
+                                 0.3832, -0.1540],
+                                [-0.1011, 0.0600, 1.1090, -0.3545, 0.1284, 1.1484, -1.0120,
+                                 -1.3508, -0.9513],
+                                [1.8948, 0.8627, -2.1359, 1.3740, -0.7499, 1.5019, 0.6919,
+                                 -0.0842, -0.4294]]
+
+                               ]),
+            torch.FloatTensor([
+                [[-0.2802, 0.6941, -0.4788, -0.3845, 1.7752, 1.2950, -1.9490,
+                  -1.4138, -0.8853],
+                 [-1.3752, -0.5457, -0.5305, 0.4018, 0.2934, 0.7931, 2.3845,
+                  -1.0726, 0.0364],
+                 [0.3621, 0.2609, 0.1269, -0.5950, 0.7212, 0.5959, 1.6264,
+                  -0.8836, -0.9320],
+                 [0.2003, -1.0758, -1.1560, -0.6472, -1.7549, 0.1264, 0.6044,
+                  -1.6857, 1.1571],
+                 [1.4277, -0.4915, 0.4496, 2.2027, 0.0730, -3.1792, -0.5125,
+                  -0.5837, 1.0184],
+                 [1.9495, 1.7145, -0.2143, -0.1230, -0.2205, 0.8250, 0.4943,
+                  -0.9025, 0.0864]]
+            ])
+                                   ],
+                           'tg': [
+            torch.LongTensor([[3, 6, 0, 8, 2, 4]]),
+            torch.LongTensor([[4, 1, 7, 0, 4, 7]])
+                                 ],
+                           'seq_len': [
+            [6], [6]
+                                      ]})
         metric_kwargs = {
             'tag_vocab': fastnlp_bio_vocab,
             'only_gross': False,
@@ -372,7 +377,6 @@ class SpanFPreRecMetricTest(unittest.TestCase):
                           'f-2': 0.5, 'pre-0': 0.0, 'rec-0': 0.0, 'f-0': 0.0, 'pre-3': 0.0, 'rec-3': 0.0,
                           'f-3': 0.0, 'pre': 0.222222, 'rec': 0.181818, 'f': 0.2}
         processes = NUM_PROCESSES
-        print(torch.cuda.device_count())
 
         pool.starmap(
             partial(
@@ -384,3 +388,5 @@ class SpanFPreRecMetricTest(unittest.TestCase):
             ),
             [(rank, processes, torch.device(f'cuda:{rank}')) for rank in range(processes)]
         )
+        pool.close()
+        pool.join()

tests/core/samplers/test_reproducible_batch_sampler.py

@@ -4,7 +4,7 @@ import numpy as np
 import pytest
 from itertools import chain
 
-from fastNLP.core.samplers import ReproducibleBatchSampler, BucketedBatchSampler
+from fastNLP.core.samplers import RandomBatchSampler, BucketedBatchSampler
 from fastNLP.core.drivers.torch_driver.utils import replace_batch_sampler
 from tests.helpers.datasets.torch_data import TorchNormalDataset
 
@@ -18,7 +18,7 @@ class TestReproducibleBatchSampler:
         before_batch_size = 7
         dataset = TorchNormalDataset(num_of_data=100)
         dataloader = DataLoader(dataset, batch_size=before_batch_size)
-        re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
+        re_batchsampler = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
         dataloader = replace_batch_sampler(dataloader, re_batchsampler)
 
         forward_steps = 3
@@ -28,15 +28,15 @@ class TestReproducibleBatchSampler:
 
         # 1. 保存状态
         _get_re_batchsampler = dataloader.batch_sampler
-        assert isinstance(_get_re_batchsampler, ReproducibleBatchSampler)
+        assert isinstance(_get_re_batchsampler, RandomBatchSampler)
         state = _get_re_batchsampler.state_dict()
         assert state == {"index_list": array("I", list(range(100))), "data_idx": forward_steps*before_batch_size,
-                         "sampler_type": "ReproducibleBatchSampler"}
+                         "sampler_type": "RandomBatchSampler"}
 
         # 2. 断点重训，重新生成一个 dataloader；
         # 不改变 batch_size；
         dataloader = DataLoader(dataset, batch_size=before_batch_size)
-        re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
+        re_batchsampler = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
         re_batchsampler.load_state_dict(state)
         dataloader = replace_batch_sampler(dataloader, re_batchsampler)
 
@@ -53,7 +53,7 @@ class TestReproducibleBatchSampler:
         # 改变 batch_size；
         after_batch_size = 3
         dataloader = DataLoader(dataset, batch_size=after_batch_size)
-        re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
+        re_batchsampler = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
         re_batchsampler.load_state_dict(state)
         dataloader = replace_batch_sampler(dataloader, re_batchsampler)
 
@@ -99,7 +99,7 @@ class TestReproducibleBatchSampler:
         dataset = TorchNormalDataset(num_of_data=100)
         # 开启 shuffle，来检验断点重训后的第二轮的 index list 是不是重新生成的；
         dataloader = DataLoader(dataset, batch_size=before_batch_size, shuffle=True)
-        re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
+        re_batchsampler = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
         dataloader = replace_batch_sampler(dataloader, re_batchsampler)
 
         # 将一轮的所有数据保存下来，看是否恢复的是正确的；
@@ -111,13 +111,13 @@ class TestReproducibleBatchSampler:
 
         # 1. 保存状态
         _get_re_batchsampler = dataloader.batch_sampler
-        assert isinstance(_get_re_batchsampler, ReproducibleBatchSampler)
+        assert isinstance(_get_re_batchsampler, RandomBatchSampler)
         state = _get_re_batchsampler.state_dict()
 
         # 2. 断点重训，重新生成一个 dataloader；
         # 不改变 batch_size；
         dataloader = DataLoader(dataset, batch_size=before_batch_size, shuffle=True)
-        re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
+        re_batchsampler = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
         re_batchsampler.load_state_dict(state)
         dataloader = replace_batch_sampler(dataloader, re_batchsampler)
 
@@ -416,7 +416,6 @@ class TestBucketedBatchSampler:
     @pytest.mark.parametrize('num_replica', [2, 3])
     def test_multi_same_bucket(self, shuffle, drop_last, pad, num_samples, num_replica):
     # def test_multi_same_bucket(self, shuffle=True, drop_last=True, pad=True, num_samples=623, num_replica=2):
-        # TODO 两个 rank 上的长度是要在同一个bucket的
         dataset = DatasetWithVaryLength(num_of_data=num_samples)
         batch_size = 6
         if num_replica*batch_size > num_samples:

tests/core/samplers/test_reproducible_sampler.py

@@ -1,18 +1,14 @@
-import unittest
-
-from itertools import product
 import numpy as np
+import pytest
 
 from functools import partial
-from array import array
+from itertools import chain
 
-from fastNLP.core.samplers.reproducible_sampler import RandomSampler
-from fastNLP.core.drivers.torch_driver.utils import replace_batch_sampler
+from fastNLP.core.samplers.reproducible_sampler import RandomSampler, SortedSampler, SequentialSampler
 from tests.helpers.datasets.torch_data import TorchNormalDataset
 
 
-
-class TestRandomSamplerYh(unittest.TestCase):
+class TestRandomSamplerYh:
     def test_init(self):
         # 测试能否正确初始化
         dataset = TorchNormalDataset(num_of_data=100)
@@ -24,7 +20,7 @@ class TestRandomSamplerYh(unittest.TestCase):
         dataset = TorchNormalDataset(num_of_data=100)
         sampler = RandomSampler(dataset)
         for i in sampler:
-            with self.assertRaises(AssertionError):
+            with pytest.raises(AssertionError):
                 sampler.set_distributed(1, 0)
             break
 
@@ -37,39 +33,39 @@ class TestRandomSamplerYh(unittest.TestCase):
         dataset = TorchNormalDataset(num_of_data=100)
         sampler = RandomSampler(dataset, shuffle=False)
         sampler.set_distributed(num_replicas=2, rank=0, pad=False)
-        self.assertEqual(len(sampler), 50)
+        assert len(sampler)==50
         count = 0
         for i in sampler:
-            self.assertEqual(i%2, 0)
+            assert i%2==0
             count += 1
-        self.assertEqual(count, 50)
+        assert count == 50
 
         sampler.set_distributed(num_replicas=2, rank=1, pad=False)
-        self.assertEqual(len(sampler), 50)
+        assert len(sampler)==50
         count = 0
         for i in sampler:
-            self.assertEqual(i%2, 1)
+            assert i%2==1
             count += 1
-        self.assertEqual(count, 50)
+        assert count==50
 
         dataset = TorchNormalDataset(num_of_data=101)
         sampler = RandomSampler(dataset, shuffle=False)
         sampler.set_distributed(num_replicas=2, rank=0, pad=True)
-        self.assertEqual(len(sampler), 51)
+        assert len(sampler)==51
         count = 0
         for i in sampler:
-            self.assertEqual(i%2, 0)
+            assert i%2==0
             count += 1
-        self.assertEqual(count, 51)
+        assert count == 51
 
         sampler.set_distributed(num_replicas=2, rank=1, pad=True)
-        self.assertEqual(len(sampler), 51)
+        assert len(sampler) == 51
         count = 0
         for i in sampler:
             if i!=0:
-                self.assertEqual(i%2, 1)
+                assert i%2==1
             count += 1
-        self.assertEqual(count, 51)
+        assert count == 51
 
     def test_state_dict_check_length(self):
         dataset = TorchNormalDataset(num_of_data=100)
@@ -77,7 +73,7 @@ class TestRandomSamplerYh(unittest.TestCase):
         states = sampler.state_dict()
 
         new_ds = TorchNormalDataset(num_of_data=10)
-        with self.assertRaises(AssertionError):
+        with pytest.raises(AssertionError):
             new_sampler = RandomSampler(new_ds)
             new_sampler.load_state_dict(states)
 
@@ -85,99 +81,107 @@ class TestRandomSamplerYh(unittest.TestCase):
         new_sampler = RandomSampler(new_ds)
         new_sampler.load_state_dict(states)
 
-    def test_state_dict(self):
+    @pytest.mark.parametrize('pad', [True, False])
+    @pytest.mark.parametrize('pre_shuffle', [True, False])
+    @pytest.mark.parametrize('post_shuffle', [True, False])
+    @pytest.mark.parametrize('num_consumed_samples', [0]+np.random.randint(1, 100, size=3).tolist())
+    def test_state_dict(self, pad, pre_shuffle, post_shuffle, num_consumed_samples):
         num_samples = 100
         dataset = TorchNormalDataset(num_of_data=num_samples)
         # 测试使用 前后shuffle不一致的load操作
-        lst = [0]+np.random.randint(1, num_samples, size=3).tolist()
-        for pre_shuffle, post_shuffle, num_consumed_samples in product([True, False], [True, False],
-                                                                       lst):
-            with self.subTest(pre_shuffle=pre_shuffle, post_shuffle=post_shuffle, num_consumed_samples=num_consumed_samples):
-                sampler = RandomSampler(dataset, shuffle=pre_shuffle)
-                sampler.set_epoch(0)
-                already_numbers = set()
-                if num_consumed_samples>0:
-                    for i, j in enumerate(sampler, start=1):
-                        already_numbers.add(j)
-                        if i == num_consumed_samples:
-                            break
-                self.assertEqual(len(already_numbers), num_consumed_samples)
-
-                states = sampler.state_dict()
-
-                new_sampler = RandomSampler(dataset, shuffle=post_shuffle)
-                new_sampler.load_state_dict(states)
-                new_sampler.set_epoch(0)
-                for i in new_sampler:
-                    self.assertNotIn(i, already_numbers)
-
-                # 测试切换成多卡也没有问题
-                other_rank_number = set()
-                for rank in range(3):
-                    new_sampler = RandomSampler(dataset, shuffle=post_shuffle)
-                    new_sampler.load_state_dict(states)
-                    new_sampler.set_distributed(num_replicas=3, rank=rank, pad=False)
-                    new_sampler.set_epoch(0)
-                    count = 0
-                    for i in new_sampler:
-                        self.assertNotIn(i, other_rank_number)
-                        other_rank_number.add(i)
-                        self.assertNotIn(i, already_numbers)
-                        count += 1
-
-    def test_state_dict_2(self):
+        sampler = RandomSampler(dataset, shuffle=pre_shuffle)
+        sampler.set_epoch(0)
+        already_numbers = set()
+        if num_consumed_samples>0:
+            for i, j in enumerate(sampler, start=1):
+                already_numbers.add(j)
+                if i == num_consumed_samples:
+                    break
+        assert len(already_numbers) == num_consumed_samples
+
+        states = sampler.state_dict()
+
+        new_sampler = RandomSampler(dataset, shuffle=post_shuffle)
+        new_sampler.load_state_dict(states)
+        new_sampler.set_epoch(0)
+        for i in new_sampler:
+            assert i not in already_numbers
+
+        # 测试切换成多卡也没有问题
+        other_rank_number = set()
+        for rank in range(3):
+            new_sampler = RandomSampler(dataset, shuffle=post_shuffle)
+            new_sampler.load_state_dict(states)
+            new_sampler.set_distributed(num_replicas=3, rank=rank, pad=pad)
+            new_sampler.set_epoch(0)
+            count = 0
+            seen = 0
+            seen_in_other_rank = 0
+            for i in new_sampler:
+                seen_in_other_rank += int(i in other_rank_number)
+                other_rank_number.add(i)
+                seen += int(i in already_numbers)
+                count += 1
+            assert seen <= 1 if pad else seen == 0
+            assert seen_in_other_rank<=1  # 因为pad可能重复
+
+    @pytest.mark.parametrize('pad', [True, False])
+    @pytest.mark.parametrize('pre_shuffle', [True, False])
+    @pytest.mark.parametrize('post_shuffle', [True, False])
+    @pytest.mark.parametrize('num_consumed_samples', [0]+np.random.randint(1, 100//2, size=3).tolist())
+    def test_state_dict_2(self, pad, pre_shuffle, post_shuffle, num_consumed_samples):
         # 测试一下从多卡切换到单卡，或者切换到不同卡数量的多卡
         num_samples = 100
         dataset = TorchNormalDataset(num_of_data=num_samples)
         # 测试使用 前后shuffle不一致的load操作
-        lst = [0]+np.random.randint(1, num_samples//2, size=3).tolist()
         # lst = [30]
-        for pre_shuffle, post_shuffle, num_consumed_samples in product([True, False], [True, False],
-                                                                       lst):
-            with self.subTest(pre_shuffle=pre_shuffle, post_shuffle=post_shuffle, num_consumed_samples=num_consumed_samples):
-                already_numbers = set()
-                sampler = RandomSampler(dataset, shuffle=pre_shuffle, seed=0)
-                sampler.set_distributed(num_replicas=2, rank=0)
-                sampler.set_epoch(0)
-                if num_consumed_samples>0:
-                    for i, j in enumerate(sampler, start=1):
-                        already_numbers.add(j)
-                        if i == num_consumed_samples:
-                            break
-                sampler = RandomSampler(dataset, shuffle=pre_shuffle, seed=0)
-                sampler.set_epoch(0)
-                sampler.set_distributed(num_replicas=2, rank=1)
-                if num_consumed_samples>0:
-                    for i, j in enumerate(sampler, start=1):
-                        already_numbers.add(j)
-                        if i == num_consumed_samples:
-                            break
-                self.assertEqual(len(already_numbers), num_consumed_samples*2)
-
-                states = sampler.state_dict()
-
-                new_sampler = RandomSampler(dataset, shuffle=post_shuffle)
-                new_sampler.load_state_dict(states)
-                new_sampler.set_epoch(0)
-                for i in new_sampler:
-                    self.assertNotIn(i, already_numbers)
-
-                # 测试切换成多卡也没有问题
-                other_rank_number = set()
-                for rank in range(3):
-                    new_sampler = RandomSampler(dataset, shuffle=post_shuffle)
-                    new_sampler.load_state_dict(states)
-                    new_sampler.set_epoch(0)
-                    new_sampler.set_distributed(num_replicas=3, rank=rank, pad=False)
-                    count = 0
-                    for i in new_sampler:
-                        self.assertNotIn(i, other_rank_number)
-                        other_rank_number.add(i)
-                        self.assertNotIn(i, already_numbers)
-                        count += 1
-
-
-class TestRandomSampler(unittest.TestCase):
+        already_numbers = set()
+        sampler = RandomSampler(dataset, shuffle=pre_shuffle, seed=0)
+        sampler.set_distributed(num_replicas=2, rank=0)
+        sampler.set_epoch(0)
+        if num_consumed_samples>0:
+            for i, j in enumerate(sampler, start=1):
+                already_numbers.add(j)
+                if i == num_consumed_samples:
+                    break
+        sampler = RandomSampler(dataset, shuffle=pre_shuffle, seed=0)
+        sampler.set_epoch(0)
+        sampler.set_distributed(num_replicas=2, rank=1)
+        if num_consumed_samples>0:
+            for i, j in enumerate(sampler, start=1):
+                already_numbers.add(j)
+                if i == num_consumed_samples:
+                    break
+        assert len(already_numbers) == num_consumed_samples*2
+
+        states = sampler.state_dict()
+
+        new_sampler = RandomSampler(dataset, shuffle=post_shuffle)
+        new_sampler.load_state_dict(states)
+        new_sampler.set_epoch(0)
+        for i in new_sampler:
+            assert i not in already_numbers
+
+        # 测试切换成多卡也没有问题
+        other_rank_number = set()
+        for rank in range(3):
+            new_sampler = RandomSampler(dataset, shuffle=post_shuffle)
+            new_sampler.load_state_dict(states)
+            new_sampler.set_epoch(0)
+            new_sampler.set_distributed(num_replicas=3, rank=rank, pad=pad)
+            count = 0
+            seen = 0
+            seen_in_other_rank = 0
+            for i in new_sampler:
+                seen_in_other_rank += int(i in other_rank_number)
+                other_rank_number.add(i)
+                seen += int(i in already_numbers)
+                count += 1
+            assert seen <= 1 if pad else seen == 0
+            assert seen_in_other_rank<=1  # 因为pad可能重复
+
+
+class TestRandomSampler:
     # 测试单卡；
     def test_seed_work_when_shuffle_is_true(self):
         data_length = 100
@@ -360,4 +364,324 @@ class TestRandomSampler(unittest.TestCase):
         ...
 
 
+class DatasetWithVaryLength:
+    def __init__(self, num_of_data=100, reverse=False):
+        self.data = np.arange(num_of_data)
+        if reverse:
+            self.data = self.data[::-1]
+
+    def __getitem__(self, item):
+        return self.data[item]
+
+    def __len__(self):
+        return len(self.data)
+
+
+class TestSortedSampler:
+    def test_single(self):
+        num_of_data = 100
+        data = DatasetWithVaryLength(num_of_data)
+        sampler = SortedSampler(data, length=data.data)
+        indexes = list(sampler)
+        assert indexes==list(range(num_of_data-1, -1, -1))
+
+    @pytest.mark.parametrize('pad', [True, False])
+    @pytest.mark.parametrize('num_replica', [2, 3])
+    @pytest.mark.parametrize('num_of_data', [2, 3, 4, 100])
+    def test_multi(self, pad, num_replica, num_of_data):
+        data = DatasetWithVaryLength(num_of_data=num_of_data)
+        samplers = []
+        for i in range(num_replica):
+            sampler = SortedSampler(dataset=data, length=data.data)
+            sampler.set_distributed(num_replica, rank=i, pad=pad)
+            samplers.append(sampler)
+
+        # 保证顺序是没乱的
+        already_seen_index = set()
+        for sampler in samplers:
+            larger_count = 0  # 这里为 0 就可以，因为最后补充的index一定是比较大的数。
+            prev_index = float('inf')
+            cur_set = set()
+            seen_in_other_rank = 0
+            for index in sampler:
+                seen_in_other_rank += int(index in already_seen_index)  # 不同的卡不交叉
+                cur_set.add(index)
+                larger_count += int(index <= prev_index)
+                prev_index = index
+            assert larger_count+1 >= len(sampler)  # 除了最后一个可能乱掉，其它都必须要保持这个顺序
+            assert seen_in_other_rank <= 1 if pad else seen_in_other_rank == 0
+            already_seen_index.update(cur_set)
+
+        indexes = list(chain(*samplers))
+        indexes = set(indexes)
+        if pad:
+            assert indexes == set(range(num_of_data))
+        else:
+            assert len(indexes) <= num_of_data
+
+    @pytest.mark.parametrize('pad', [True, False])
+    @pytest.mark.parametrize('num_consumed_samples', [0]+np.random.randint(1, 100, size=3).tolist())
+    def test_state_dict(self, pad, num_consumed_samples):
+        num_samples = 100
+        dataset = DatasetWithVaryLength(num_of_data=num_samples)
+        # 测试使用 前后shuffle不一致的load操作
+        sampler = SortedSampler(dataset, length=dataset.data)
+        sampler.set_epoch(0)
+        already_numbers = set()
+        if num_consumed_samples>0:
+            for i, j in enumerate(sampler, start=1):
+                if already_numbers:
+                    assert j<max(already_numbers)
+                already_numbers.add(j)
+                if i == num_consumed_samples:
+                    break
+        assert len(already_numbers) == num_consumed_samples
+
+        states = sampler.state_dict()
+
+        new_sampler = SortedSampler(dataset, length=dataset.data)
+        new_sampler.load_state_dict(states)
+        new_sampler.set_epoch(0)
+        for i in new_sampler:
+            if already_numbers:
+                assert i < max(already_numbers)
+            assert i not in already_numbers
+
+        # 测试切换成多卡也没有问题
+        other_rank_number = set()
+        for rank in range(3):
+            new_sampler = SortedSampler(dataset, length=dataset.data)
+            new_sampler.load_state_dict(states)
+            new_sampler.set_distributed(num_replicas=3, rank=rank, pad=pad)
+            new_sampler.set_epoch(0)
+            count = 0
+            seen = 0
+            seen_in_other_rank = 0
+            smaller = 0
+            for i in new_sampler:
+                if already_numbers:
+                    smaller += int(i >= max(already_numbers))
+                seen_in_other_rank += int(i in other_rank_number)
+                other_rank_number.add(i)
+                seen += int(i in already_numbers)
+                count += 1
+            assert seen <= 1 if pad else seen == 0
+            assert seen_in_other_rank<=1  # 因为pad可能重复
+            assert smaller<=1 if pad else smaller==0
+
+    @pytest.mark.parametrize('pad', [True, False])
+    @pytest.mark.parametrize('num_consumed_samples', [0]+np.random.randint(1, 100//2, size=3).tolist())
+    def test_state_dict_2(self, pad, num_consumed_samples):
+        # 测试一下从多卡切换到单卡，或者切换到不同卡数量的多卡
+        num_samples = 100
+        dataset = DatasetWithVaryLength(num_of_data=num_samples)
+        # 测试使用 前后shuffle不一致的load操作
+        # lst = [30]
+        already_numbers = set()
+        sampler = SortedSampler(dataset, length=dataset.data)
+        sampler.set_distributed(num_replicas=2, rank=0)
+        sampler.set_epoch(0)
+        if num_consumed_samples>0:
+            for i, j in enumerate(sampler, start=1):
+                if already_numbers:
+                    assert j<=max(already_numbers)
+                already_numbers.add(j)
+                if i == num_consumed_samples:
+                    break
+        sampler = SortedSampler(dataset, length=dataset.data)
+        sampler.set_epoch(0)
+        sampler.set_distributed(num_replicas=2, rank=1)
+        if num_consumed_samples>0:
+            for i, j in enumerate(sampler, start=1):
+                already_numbers.add(j)
+                if i == num_consumed_samples:
+                    break
+        assert len(already_numbers) == num_consumed_samples*2
+
+        states = sampler.state_dict()
+
+        new_sampler = SortedSampler(dataset, length=dataset.data)
+        new_sampler.load_state_dict(states)
+        new_sampler.set_epoch(0)
+        for i in new_sampler:
+            if already_numbers:
+                assert i < max(already_numbers)
+            assert i not in already_numbers
+
+        # 测试切换成多卡也没有问题
+        other_rank_number = set()
+        for rank in range(3):
+            new_sampler = SortedSampler(dataset, length=dataset.data)
+            new_sampler.load_state_dict(states)
+            new_sampler.set_epoch(0)
+            new_sampler.set_distributed(num_replicas=3, rank=rank, pad=pad)
+            count = 0
+            seen = 0
+            seen_in_other_rank = 0
+            smaller = 0
+            for i in new_sampler:
+                if already_numbers:
+                    smaller += int(i>=max(already_numbers))
+                seen_in_other_rank += int(i in other_rank_number)
+                other_rank_number.add(i)
+                seen += int(i in already_numbers)
+                count += 1
+            assert seen <= 1 if pad else seen == 0
+            assert seen_in_other_rank<=1  # 因为pad可能重复
+            assert smaller <= 1 if pad else smaller == 0
+
+
+class TestSequentialSampler:
+    def test_single(self):
+        num_of_data = 100
+        data = DatasetWithVaryLength(num_of_data)
+        sampler = SequentialSampler(data)
+        indexes = list(sampler)
+        assert indexes==list(range(num_of_data))
+
+    @pytest.mark.parametrize('pad', [True, False])
+    @pytest.mark.parametrize('num_replica', [2, 3])
+    @pytest.mark.parametrize('num_of_data', [2, 3, 4, 100])
+    def test_multi(self, pad, num_replica, num_of_data):
+        data = DatasetWithVaryLength(num_of_data=num_of_data)
+        samplers = []
+        for i in range(num_replica):
+            sampler = SequentialSampler(dataset=data)
+            sampler.set_distributed(num_replica, rank=i, pad=pad)
+            samplers.append(sampler)
+
+        # 保证顺序是没乱的
+        already_seen_index = set()
+        for idx, sampler in enumerate(samplers):
+            larger_count = 1
+            prev_index = float('inf')
+            cur_set = set()
+            seen_in_other_rank = 0
+            for index in sampler:
+                seen_in_other_rank += int(index in already_seen_index)  # 不同的卡不交叉
+                cur_set.add(index)
+                larger_count += int(index >= prev_index)
+                prev_index = index
+            assert larger_count+1 >= len(sampler)  # 除了最后一个可能乱掉，其它都必须要保持这个顺序
+            assert seen_in_other_rank <= idx if pad else seen_in_other_rank == 0
+            already_seen_index.update(cur_set)
+
+        indexes = list(chain(*samplers))
+        indexes = set(indexes)
+        if pad:
+            assert indexes == set(range(num_of_data))
+        else:
+            assert len(indexes) <= num_of_data
+
+    @pytest.mark.parametrize('pad', [True, False])
+    @pytest.mark.parametrize('num_consumed_samples', [0]+np.random.randint(1, 100, size=3).tolist())
+    def test_state_dict(self, pad, num_consumed_samples):
+        num_samples = 100
+        dataset = DatasetWithVaryLength(num_of_data=num_samples)
+        # 测试使用 前后shuffle不一致的load操作
+        sampler = SequentialSampler(dataset=dataset)
+        sampler.set_epoch(0)
+        already_numbers = set()
+        if num_consumed_samples>0:
+            for i, j in enumerate(sampler, start=1):
+                if already_numbers:
+                    assert j>max(already_numbers)
+                already_numbers.add(j)
+                if i == num_consumed_samples:
+                    break
+        assert len(already_numbers) == num_consumed_samples
+
+        states = sampler.state_dict()
+
+        new_sampler = SequentialSampler(dataset=dataset)
+        new_sampler.load_state_dict(states)
+        new_sampler.set_epoch(0)
+        for i in new_sampler:
+            if already_numbers:
+                assert i > max(already_numbers)
+            assert i not in already_numbers
+
+        # 测试切换成多卡也没有问题
+        other_rank_number = set()
+        for rank in range(3):
+            new_sampler = SequentialSampler(dataset=dataset)
+            new_sampler.load_state_dict(states)
+            new_sampler.set_distributed(num_replicas=3, rank=rank, pad=pad)
+            new_sampler.set_epoch(0)
+            count = 0
+            seen = 0
+            seen_in_other_rank = 0
+            smaller = 0
+            for i in new_sampler:
+                if already_numbers:
+                    smaller += int(i <= max(already_numbers))
+                seen_in_other_rank += int(i in other_rank_number)
+                other_rank_number.add(i)
+                seen += int(i in already_numbers)
+                count += 1
+            assert seen <= 1 if pad else seen == 0
+            assert seen_in_other_rank<=rank  # 因为pad可能重复
+            assert smaller<=1 if pad else smaller==0
+
+    @pytest.mark.parametrize('pad', [True, False])
+    @pytest.mark.parametrize('num_consumed_samples', [0]+np.random.randint(1, 100//2, size=3).tolist())
+    def test_state_dict_2(self, pad, num_consumed_samples):
+        # 测试一下从多卡切换到单卡，或者切换到不同卡数量的多卡
+        num_samples = 100
+        dataset = DatasetWithVaryLength(num_of_data=num_samples)
+        # 测试使用 前后shuffle不一致的load操作
+        # lst = [30]
+        already_numbers = set()
+        sampler = SequentialSampler(dataset=dataset)
+        sampler.set_distributed(num_replicas=2, rank=0)
+        sampler.set_epoch(0)
+        if num_consumed_samples>0:
+            for i, j in enumerate(sampler, start=1):
+                if already_numbers:
+                    assert j>max(already_numbers)
+                already_numbers.add(j)
+                if i == num_consumed_samples:
+                    break
+        sampler = SequentialSampler(dataset=dataset)
+        sampler.set_epoch(0)
+        sampler.set_distributed(num_replicas=2, rank=1)
+        if num_consumed_samples>0:
+            for i, j in enumerate(sampler, start=1):
+                already_numbers.add(j)
+                if i == num_consumed_samples:
+                    break
+        assert len(already_numbers) == num_consumed_samples*2
+
+        states = sampler.state_dict()
+
+        new_sampler = SequentialSampler(dataset=dataset)
+        new_sampler.load_state_dict(states)
+        new_sampler.set_epoch(0)
+        for i in new_sampler:
+            if already_numbers:
+                assert i > max(already_numbers)
+            assert i not in already_numbers
+
+        # 测试切换成多卡也没有问题
+        other_rank_number = set()
+        for rank in range(3):
+            new_sampler = SequentialSampler(dataset=dataset)
+            new_sampler.load_state_dict(states)
+            new_sampler.set_epoch(0)
+            new_sampler.set_distributed(num_replicas=3, rank=rank, pad=pad)
+            count = 0
+            seen = 0
+            seen_in_other_rank = 0
+            smaller = 0
+            for i in new_sampler:
+                if already_numbers:
+                    smaller += int(i<max(already_numbers))
+                seen_in_other_rank += int(i in other_rank_number)
+                other_rank_number.add(i)
+                seen += int(i in already_numbers)
+                count += 1
+            assert seen <= 1 if pad else seen == 0
+            assert seen_in_other_rank<=1  # 因为pad可能重复
+            assert smaller <= rank if pad else smaller == 0
+
 

tests/core/samplers/test_unrepeated_sampler.py

@@ -0,0 +1,104 @@
+from itertools import chain
+
+import pytest
+
+from fastNLP.core.samplers import UnrepeatedRandomSampler, UnrepeatedSortedSampler, UnrepeatedSequentialSampler
+
+
+class DatasetWithVaryLength:
+    def __init__(self, num_of_data=100):
+        self.data = list(range(num_of_data))
+
+    def __getitem__(self, item):
+        return self.data[item]
+
+    def __len__(self):
+        return len(self.data)
+
+
+class TestUnrepeatedSampler:
+    @pytest.mark.parametrize('shuffle', [True, False])
+    def test_single(self, shuffle):
+        num_of_data = 100
+        data = DatasetWithVaryLength(num_of_data)
+        sampler = UnrepeatedRandomSampler(data, shuffle)
+        indexes = set(sampler)
+        assert indexes==set(range(num_of_data))
+
+    @pytest.mark.parametrize('num_replica', [2, 3])
+    @pytest.mark.parametrize('num_of_data', [2, 3, 4, 100])
+    @pytest.mark.parametrize('shuffle', [False, True])
+    def test_multi(self, num_replica, num_of_data, shuffle):
+        data = DatasetWithVaryLength(num_of_data=num_of_data)
+        samplers = []
+        for i in range(num_replica):
+            sampler = UnrepeatedRandomSampler(dataset=data, shuffle=shuffle)
+            sampler.set_distributed(num_replica, rank=i)
+            samplers.append(sampler)
+
+        indexes = list(chain(*samplers))
+        assert len(indexes) == num_of_data
+        indexes = set(indexes)
+        assert indexes==set(range(num_of_data))
+
+
+class TestUnrepeatedSortedSampler:
+    def test_single(self):
+        num_of_data = 100
+        data = DatasetWithVaryLength(num_of_data)
+        sampler = UnrepeatedSortedSampler(data, length=data.data)
+        indexes = list(sampler)
+        assert indexes==list(range(num_of_data-1, -1, -1))
+
+    @pytest.mark.parametrize('num_replica', [2, 3])
+    @pytest.mark.parametrize('num_of_data', [2, 3, 4, 100])
+    def test_multi(self, num_replica, num_of_data):
+        data = DatasetWithVaryLength(num_of_data=num_of_data)
+        samplers = []
+        for i in range(num_replica):
+            sampler = UnrepeatedSortedSampler(dataset=data, length=data.data)
+            sampler.set_distributed(num_replica, rank=i)
+            samplers.append(sampler)
+
+        # 保证顺序是没乱的
+        for sampler in samplers:
+            prev_index = float('inf')
+            for index in sampler:
+                assert index <= prev_index
+                prev_index = index
+
+        indexes = list(chain(*samplers))
+        assert len(indexes) == num_of_data  # 不同卡之间没有交叉
+        indexes = set(indexes)
+        assert indexes==set(range(num_of_data))
+
+
+class TestUnrepeatedSequentialSampler:
+    def test_single(self):
+        num_of_data = 100
+        data = DatasetWithVaryLength(num_of_data)
+        sampler = UnrepeatedSequentialSampler(data, length=data.data)
+        indexes = list(sampler)
+        assert indexes==list(range(num_of_data))
+
+    @pytest.mark.parametrize('num_replica', [2, 3])
+    @pytest.mark.parametrize('num_of_data', [2, 3, 4, 100])
+    def test_multi(self, num_replica, num_of_data):
+        data = DatasetWithVaryLength(num_of_data=num_of_data)
+        samplers = []
+        for i in range(num_replica):
+            sampler = UnrepeatedSequentialSampler(dataset=data, length=data.data)
+            sampler.set_distributed(num_replica, rank=i)
+            samplers.append(sampler)
+
+        # 保证顺序是没乱的
+        for sampler in samplers:
+            prev_index = float('-inf')
+            for index in sampler:
+                assert index>=prev_index
+                prev_index = index
+
+        indexes = list(chain(*samplers))
+        assert len(indexes) == num_of_data
+        indexes = set(indexes)
+        assert indexes == set(range(num_of_data))