Merge remote-tracking branch 'refs/remotes/origin/dev0.8.0' into dev0.8.0

fastNLP/core/callbacks/__init__.py

@@ -10,7 +10,8 @@ __all__ = [
     'ProgressCallback',
     'RichCallback',
     "LRSchedCallback",
-    'LoadBestModelCallback'
+    'LoadBestModelCallback',
+    "EarlyStopCallback"
 ]
 
 
@@ -21,4 +22,5 @@ from .checkpoint_callback import ModelCheckpointCallback, TrainerCheckpointCallb
 from .progress_callback import choose_progress_callback, ProgressCallback, RichCallback
 from .lr_scheduler_callback import LRSchedCallback
 from .load_best_model_callback import LoadBestModelCallback
+from .early_stop_callback import EarlyStopCallback
 

fastNLP/core/callbacks/callback.py

@@ -1,11 +1,15 @@
-from typing import Union, Callable, Dict, Optional
+from typing import Union, Callable, Dict, Optional, Any
+from abc import ABC
 
 __all__ = [
     'Callback',
 ]
 
 from .callback_events import Events, EventsList, Filter
+from .utils import _get_monitor_value
 from fastNLP.core.callbacks.callback_events import _SingleEventState
+from fastNLP.core.log import logger
+from fastNLP.core.utils import apply_to_collection
 
 
 class Callback:
@@ -150,4 +154,82 @@ class _CallbackWrapper(Callback):
         return self.fn.__name__
 
 
+class CanItemDataType(ABC):
+    """
+    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 HasMonitorCallback(Callback):
+    def __init__(self, monitor, larger_better, must_have_monitor=False):
+        self.set_monitor(monitor, larger_better)
+        self.must_have_moinitor = must_have_monitor
+
+    def set_monitor(self, monitor, larger_better):
+        self.monitor = str(monitor) if monitor is not None else None
+        self.larger_better = bool(larger_better)
+        if larger_better:
+            self.monitor_value = float('-inf')
+        else:
+            self.monitor_value = float('inf')
+        self._real_monitor = self.monitor
+
+    def on_after_trainer_initialized(self, trainer, driver):
+        """
+        如果本身的 monitor 没有设置，则根据 Trainer 中的 monitor 设置 monitor 。
+        同时对于必须要有 monitor 设置的 callback ，该函数会进行检查。
+
+        :param trainer:
+        :param driver:
+        :return:
+        """
+        if self.monitor is None and trainer.monitor is not None:
+            self.set_monitor(monitor=trainer.monitor, larger_better=trainer.larger_better)
+        if self.must_have_moinitor and self.monitor is None:
+            raise RuntimeError(f"No `monitor` is set for {self.__class__.__name__}. "
+                               f"You can set it in the initialization or through Trainer.")
+
+    def get_monitor_value(self, results:Dict)->float:
+        """
+        获取 monitor 的值，如果 monitor 没有直接找到，会尝试使用匹配的方式寻找，并把匹配到的设置到 self._real_monitor 属性上。
 
+        :param results:
+        :return:
+        """
+        if len(results)==0:
+            return 0
+        # 保证所有的 tensor 都被转换为了 python 特定的类型
+        results = apply_to_collection(results, dtype=CanItemDataType, function=lambda x: x.item())
+        use_monitor, monitor_value = _get_monitor_value(monitor=self.monitor,
+                                                        real_monitor=self._real_monitor,
+                                                        res=results)
+        if self._real_monitor != use_monitor:  # 发生了替换需要打印
+            logger.warning(
+                f"We can not find `{self.monitor}` in the evaluation result (with keys as {list(results.keys())}), "
+                f"we use the `{use_monitor}` as the monitor for {self.__class__.__name__}.")
+        self._real_monitor = use_monitor
+        return monitor_value
+
+    def is_better_monitor_value(self, monitor_value: float, keep_if_better=True):
+        """
+        检测 monitor_value 是否是更好的
+
+        :param monitor_value:
+        :param keep_if_better: 如果传入的 monitor_value 值更好，则将其保存下来。
+        :return:
+        """
+        better = False
+        if (self.larger_better and monitor_value > self.monitor_value) or \
+            (not self.larger_better and monitor_value < self.monitor_value):
+            better = True
+            if keep_if_better:
+                self.monitor_value = monitor_value
+        return better

fastNLP/core/callbacks/checkpoint_callback.py

@@ -5,12 +5,12 @@ __all__ = [
 import os
 from typing import Union, Optional, Callable, Dict, Sequence, Any, Mapping
 from pathlib import Path
-from abc import ABC
 import sys
+from copy import deepcopy
 
 
 import fastNLP
-from .callback import Callback, Filter
+from .callback import Callback, HasMonitorCallback
 from fastNLP.core.callbacks.utils import _get_monitor_value
 from fastNLP.core.log import logger
 from fastNLP.envs import FASTNLP_LAUNCH_TIME
@@ -18,22 +18,7 @@ from fastNLP.core.utils import synchronize_safe_rm, synchronize_mkdir
 from fastNLP.core.utils import apply_to_collection
 
 
-class CanItemDataType(ABC):
-    """
-    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):
+class CheckpointCallback(HasMonitorCallback):
     def __init__(
             self,
             monitor,
@@ -48,12 +33,8 @@ class CheckpointCallback(Callback):
             model_save_fn: Optional[Callable] = None,
             **kwargs,
     ):
-        if monitor is None and save_topk is not None:
-            raise ValueError("Parameter `monitor` must be set when you want to use 'save_topk'.")
-
-        if monitor is not None and not isinstance(monitor, str):
-            raise ValueError("Parameter `monitor` should be of 'str' type.")
-
+        super().__init__(monitor=monitor, larger_better=larger_better,
+                         must_have_monitor=save_topk is not None)
         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.")
@@ -91,13 +72,12 @@ class CheckpointCallback(Callback):
                                     "`BaseException` type.")
         else:
             save_on_exception = []
-        self.monitor = monitor
+
         self.save_folder = Path(save_folder)
         self.save_every_n_epochs = save_every_n_epochs
         self.save_every_n_batches = save_every_n_batches
         self.save_last = save_last
         self.save_topk = save_topk
-        self.larger_better = larger_better
         self.only_state_dict = only_state_dict
         self.model_save_fn = model_save_fn
         self.save_on_exception = save_on_exception
@@ -107,20 +87,22 @@ 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；
-        self._real_monitor = self.monitor
-
         # 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.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.timestamp_path)
 
-    def on_validate_end(self, trainer, validate_res):
-        self._save_topk(trainer, validate_res)
+    def on_after_trainer_initialized(self, trainer, driver):
+        if self.save_topk is not None:
+            super().on_after_trainer_initialized(trainer, driver)
+        if self.save_topk is not None and trainer.evaluator is None:
+            logger.warning("You set `save_topk`, but `validate_dataloaders` is not set in Trainer.")
+
+    def on_validate_end(self, trainer, results):
+        if len(results) == 0:
+            return
+        self._save_topk(trainer, results)
 
     def on_train_epoch_end(self, trainer: "fastNLP.Trainer"):
         if trainer.cur_epoch_idx % self.save_every_n_epochs == 0:
@@ -143,7 +125,7 @@ class CheckpointCallback(Callback):
 
     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)
+        self.get_monitor_value(results=sanity_check_res)
 
     def on_save_checkpoint(self, trainer) -> Dict:
         """
@@ -154,8 +136,7 @@ class CheckpointCallback(Callback):
 
         states = {}
         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['_topk_model'] = deepcopy(self._topk_model)
         states['save_topk'] = 0 if self.save_topk is None else self.save_topk
         states['_real_monitor'] = self._real_monitor
         return states
@@ -176,30 +157,30 @@ class CheckpointCallback(Callback):
             self._topk_model.update(self._topk_model)
         self._real_monitor = states["real_monitor"]
 
-    def _save_topk(self, trainer: "fastNLP.Trainer", validate_res: Dict):
+    def _save_topk(self, trainer: "fastNLP.Trainer", results: 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:
+        :param results:
         :return:
         """
         if self.save_topk is not None:
-            _metric_value = self._get_validate_metric(validate_res)
+            monitor_value = self.get_monitor_value(results=results)
             folder_name = f"{self.folder_prefix}-epoch_{trainer.cur_epoch_idx}-batch_{trainer.global_forward_batches}" \
-                         f"-{self._real_monitor}_{_metric_value}"
+                         f"-{self._real_monitor}_{monitor_value}"
 
             _should_save = False
             if self._topn < self.save_topk:
-                self._topk_model[folder_name] = _metric_value
+                self._topk_model[folder_name] = monitor_value
                 self._topn += 1
                 _should_save = True
             else:
                 _least_valuable_model = (min if self.larger_better else max)(self._topk_model,
                                                                              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[folder_name] = _metric_value
+                if (self.larger_better and monitor_value > self._topk_model[_least_valuable_model]) or \
+                        (self.larger_better is False and monitor_value < self._topk_model[_least_valuable_model]):
+                    self._topk_model[folder_name] = monitor_value
                     _should_save = True
                     self._topk_model.pop(_least_valuable_model)
                     synchronize_safe_rm(self.timestamp_path.joinpath(_least_valuable_model))
@@ -235,7 +216,11 @@ class CheckpointCallback(Callback):
         :return:
         """
         use_monitor, value = _get_monitor_value(monitor=self.monitor, real_monitor=self._real_monitor, res=res)
+        if self._real_monitor != use_monitor:
+            logger.warning(f"We can not find `{self._real_monitor}` in the evaluation result (with keys as {list(res.keys())}), "
+                           f"we use the `{use_monitor}` as the monitor for {self.__class__.__name__}.")
         self._real_monitor = use_monitor
+
         return value
 
     @property
@@ -263,7 +248,7 @@ class ModelCheckpointCallback(CheckpointCallback):
     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 。
+        的那个作为 monitor 。如果为 None 将尝试从 Trainer 中获取该值。
     :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。
@@ -310,7 +295,7 @@ class TrainerCheckpointCallback(CheckpointCallback):
     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 。
+        的那个作为 monitor 。如果为 None 将尝试从 Trainer 中获取该值。
     :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。

fastNLP/core/callbacks/early_stop_callback.py

@@ -0,0 +1,61 @@
+__all__ = [
+    'EarlyStopCallback'
+]
+
+from typing import Dict
+
+from .callback import HasMonitorCallback
+from fastNLP.core.utils.exceptions import EarlyStopException
+
+
+class EarlyStopCallback(HasMonitorCallback):
+    def __init__(self, monitor:str=None, larger_better:bool=True, patience:int=10):
+        """
+
+        :param str monitor: 监控的 metric 值。如果为 None，将尝试使用 Trainer 设置的 monitor 。
+        :param larger_better: monitor 的值是否是越大越好。
+        :param patience: 多少次 validate 不没有提升就停止。
+        """
+        super(EarlyStopCallback, self).__init__(monitor=monitor, larger_better=larger_better, must_have_monitor=True)
+        self.wait = 0
+        self.patience = patience
+
+    def on_validate_end(self, trainer, results):
+        if len(results)==0:
+            return
+        monitor_value = self.get_monitor_value(results)
+        if self.is_better_monitor_value(monitor_value, keep_if_better=True):
+            self.wait = 0
+        else:
+            self.wait += 1
+
+    def on_fetch_data_begin(self, trainer):
+        # 当是 step validate 的时候，下一步执行的就是这个， 所以在这里检查。
+        if self.wait >= self.patience:
+            raise EarlyStopException(f"After {self.wait} validations, no improvement for "
+                                 f"metric `{self._real_monitor}`")
+
+    def on_train_epoch_begin(self, trainer):
+        # 当是 epoch validate 的时候，下一步执行的就是这个， 所以在这里检查。
+        if self.wait >= self.patience:
+            raise EarlyStopException(f"After {self.wait} validations, no improvement for "
+                                     f"metric `{self._real_monitor}`(best value: {self.monitor_value})")
+
+    def on_save_checkpoint(self, trainer) -> Dict:
+        states = {
+            'patience': self.patience,
+            'wait': self.wait,
+            'monitor': self.monitor,
+            'monitor_value': self.monitor_value
+        }
+        return states
+
+    def on_load_checkpoint(self, trainer, states):
+        self.patience = states['patience']
+        self.wait = states['wait']
+        self.monitor = states['monitor']
+        self.monitor_value = float(states['monitor_value'])
+
+    def callback_name(self):
+        return f'EarlyStopCallback#monitor-{self.monitor}#patience-{self.patience}'
+

fastNLP/core/callbacks/load_best_model_callback.py

@@ -4,8 +4,7 @@ __all__ = [
 
 import os
 from typing import Optional, Callable
-from .callback import Callback
-from .utils import _get_monitor_value
+from .callback import HasMonitorCallback
 from io import BytesIO
 import shutil
 
@@ -14,15 +13,15 @@ from fastNLP.core.log import logger
 from fastNLP.envs import all_rank_call
 
 
-class LoadBestModelCallback(Callback):
-    def __init__(self, monitor:str, larger_better:bool = True, only_state_dict:bool = True,
+class LoadBestModelCallback(HasMonitorCallback):
+    def __init__(self, monitor:str=None, larger_better:bool = True, only_state_dict:bool = True,
                  save_folder:Optional[str] = None, model_save_fn:Optional[Callable] = None,
                  model_load_fn:Optional[Callable] = None,
                  delete_after_train:bool = True):
         """
         保存最佳的 monitor 值最佳的模型，并在训练结束的时候重新加载模型。仅在训练正常结束的时候才能加载最好的模型。
 
-        :param str monitor: 监控的 metric 值。
+        :param str monitor: 监控的 metric 值。如果为 None，将尝试使用 Trainer 设置的 monitor 。
         :param larger_better: 该 metric 值是否是越大越好。
         :param save_folder: 保存的文件夹，如果为空，则保存在内存中。不为空，则保存一份权重到文件中，当为多机训练，且本值不为空时，请确保
             不同的机器均可访问当该路径。当 model_save_fn 不为 None 时该值一定不能为空。
@@ -33,6 +32,7 @@ class LoadBestModelCallback(Callback):
             请在函数内完成对模型的加载。
         :param delete_after_train: 在训练结束后是否删掉模型。
         """
+        super().__init__(monitor=monitor, larger_better=larger_better, must_have_monitor=True)
         if model_load_fn is not None:
             assert callable(model_load_fn), "`model_load_fn` must be a callable object."
             assert model_save_fn is not None, "`model_load_fn` and `model_save_fn` must be passed at the same time."
@@ -56,15 +56,11 @@ class LoadBestModelCallback(Callback):
             self.real_save_folder = None
             self.buffer = BytesIO()
 
-        self.monitor = monitor
-        self.larger_better = larger_better
         self.save_folder = save_folder
         self.only_state_dict = only_state_dict
         self.model_save_fn = model_save_fn
         self.model_load_fn = model_load_fn
         self.delete_after_after = delete_after_train
-        self._real_monitor = None
-        self.monitor_value = float('-inf') if larger_better else float('inf')
 
     def on_after_trainer_initialized(self, trainer, driver):
         if self.save_folder is not None and driver.is_distributed() and int(os.environ.get(FASTNLP_BACKEND_LAUNCH, 0))==1:
@@ -76,13 +72,16 @@ class LoadBestModelCallback(Callback):
                 raise RuntimeError(f"Currently {driver.__class__.__name__} does not support using `save_folder` to "
                                    f"save best model when launch using script.")
 
+        super().on_after_trainer_initialized(trainer, driver)
+
+    def on_sanity_check_end(self, trainer, sanity_check_res):
+        self.get_monitor_value(sanity_check_res)
+
     def on_validate_end(self, trainer, results):
-        self._real_monitor, monitor_value = _get_monitor_value(monitor=self.monitor,
-                                                               real_monitor=self._real_monitor,
-                                                               res=results)
-        if (monitor_value < self.monitor_value and self.larger_better is False) or \
-            (monitor_value > self.monitor_value and self.larger_better):
-            self.monitor_value = monitor_value
+        if len(results)==0:
+            return
+        monitor_value = self.get_monitor_value(results)
+        if self.is_better_monitor_value(monitor_value, keep_if_better=True):
             if self.real_save_folder:
                 trainer.save_model(folder=self.real_save_folder, only_state_dict=self.only_state_dict,
                                    model_save_fn=self.model_save_fn)

fastNLP/core/callbacks/progress_callback.py

@@ -8,7 +8,7 @@ __all__ = [
     'RichCallback'
 ]
 
-from .callback import Callback
+from .callback import HasMonitorCallback
 from fastNLP.core.callbacks.utils import _get_monitor_value
 from fastNLP.core.utils import f_rich_progress
 from fastNLP.core.log import logger
@@ -28,15 +28,13 @@ def choose_progress_callback(progress_bar:str):
         return None
 
 
-class ProgressCallback(Callback):
+class ProgressCallback(HasMonitorCallback):
     def on_train_end(self, trainer):
         f_rich_progress.stop()
 
     def on_sanity_check_end(self, trainer, sanity_check_res):
         if len(sanity_check_res) and getattr(self, 'monitor', None) is not None:
-            self._real_monitor, monitor_value = _get_monitor_value(monitor=self.monitor,
-                                                                   real_monitor=self._real_monitor,
-                                                                   res=sanity_check_res)
+            self.get_monitor_value(sanity_check_res)
 
 
 class RichCallback(ProgressCallback):
@@ -46,28 +44,22 @@ class RichCallback(ProgressCallback):
 
         :param print_every: 多少个 batch 更新一次显示。
         :param loss_round_ndigit: 显示的 loss 保留多少位有效数字
-        :param monitor: 当检测到这个key的结果更好时，会打印出不同的颜色进行提示。
+        :param monitor: 当检测到这个key的结果更好时，会打印出不同的颜色进行提示。如果为 None ，会尝试使用 trainer 中设置的 monitor 。
         :param larger_better: 是否是monitor的结果越大越好。
         :param format_json: 是否format json再打印
         """
-        super().__init__()
+        super().__init__(monitor=monitor, larger_better=larger_better, must_have_monitor=False)
         self.print_every = print_every
         self.progress_bar = f_rich_progress
         self.task2id = {}
         self.loss = 0
         self.loss_round_ndigit = loss_round_ndigit
-        self.monitor = monitor
-        self.larger_better = larger_better
-        if larger_better:
-            self.monitor_value = float('-inf')
-        else:
-            self.monitor_value = float('inf')
-        self._real_monitor = monitor
         self.format_json = format_json
 
     def on_after_trainer_initialized(self, trainer, driver):
         if not self.progress_bar.disable:
             self.progress_bar.set_disable(flag=trainer.driver.get_local_rank() != 0)
+        super(RichCallback, self).on_after_trainer_initialized(trainer, driver)
 
     def on_train_begin(self, trainer):
         self.task2id['epoch'] = self.progress_bar.add_task(description='Epoch:0', total=trainer.n_epochs,
@@ -109,16 +101,12 @@ class RichCallback(ProgressCallback):
         text_style = ''
         characters = '-'
         if self.monitor is not None:
-            self._real_monitor, monitor_value = _get_monitor_value(monitor=self.monitor,
-                                                                    real_monitor=self._real_monitor,
-                                                                    res=results)
-            if (self.larger_better and monitor_value > self.monitor_value) or \
-                    (not self.larger_better and monitor_value < self.monitor_value):
+            monitor_value = self.get_monitor_value(results)
+            if self.is_better_monitor_value(monitor_value, keep_if_better=True):
                 if abs(self.monitor_value) != float('inf'):
                     rule_style = 'spring_green3'
                     text_style = '[bold]'
                     characters = '+'
-                self.monitor_value = monitor_value
         self.progress_bar.print()
         self.progress_bar.console.rule(text_style+f"Eval. results on Epoch:{trainer.cur_epoch_idx}, "
                                                   f"Batch:{trainer.batch_idx_in_epoch}",
@@ -151,18 +139,12 @@ class RawTextCallback(ProgressCallback):
         :param larger_better: 是否是monitor的结果越大越好。
         :param format_json: 是否format json再打印
         """
-        super().__init__()
+        super().__init__(monitor=monitor, larger_better=larger_better, must_have_monitor=False)
         self.print_every = print_every
         self.task2id = {}
         self.loss = 0
         self.loss_round_ndigit = loss_round_ndigit
-        self.monitor = monitor
-        self.larger_better = larger_better
-        if larger_better:
-            self.monitor_value = float('-inf')
-        else:
-            self.monitor_value = float('inf')
-        self._real_monitor = monitor
+        self.set_monitor(monitor, larger_better)
         self.format_json = format_json
         self.num_signs = 10
 
@@ -189,14 +171,10 @@ class RawTextCallback(ProgressCallback):
         base_text = f'Eval. results on Epoch:{trainer.cur_epoch_idx}, Batch:{trainer.batch_idx_in_epoch}'
         text = ''
         if self.monitor is not None:
-            self._real_monitor, monitor_value = _get_monitor_value(monitor=self.monitor,
-                                                                   real_monitor=self._real_monitor,
-                                                                   res=results)
-            if (self.larger_better and monitor_value > self.monitor_value) or \
-                    (not self.larger_better and monitor_value < self.monitor_value):
+            monitor_value = self.get_monitor_value(results)
+            if self.is_better_monitor_value(monitor_value, keep_if_better=True):
                 if abs(self.monitor_value) != float('inf'):
                     text = '+'*self.num_signs + base_text + '+'*self.num_signs
-                self.monitor_value = monitor_value
         if len(text) == 0:
             text = '-'*self.num_signs + base_text + '-'*self.num_signs
 

fastNLP/core/callbacks/utils.py

@@ -19,23 +19,31 @@ def _get_monitor_value(monitor: str, real_monitor: Optional[str], res: dict) ->(
     if monitor in res:
         return monitor, res[monitor]
 
+    if real_monitor in res:
+        return real_monitor, res[real_monitor]
+
     pairs = []
     for idx, (key, value) in enumerate(res.items()):
-        match = SequenceMatcher(None, key, monitor).find_longest_match(0, len(key), 0, len(monitor))
-        pairs.append((key, value, match.size, idx))
+        match_size = _match_length(monitor, key)
+        pairs.append((key, value, match_size, idx))
 
     pairs.sort(key=lambda pair: (pair[2], -pair[3]), reverse=True)
     key, value, match_size = pairs[0][:3]
 
-    if real_monitor is not None and real_monitor in res and real_monitor != key:
-        # 如果 real_monitor 比新找的更长就继续用之前的。
-        match = SequenceMatcher(None, real_monitor, monitor).find_longest_match(0, len(real_monitor), 0, len(monitor))
-        if match.size > match_size:
-            return real_monitor, res[real_monitor]
+    return key, value
+
 
-        logger.warning(f"We can not find `{monitor}` in the evaluation result (with keys as {list(res.keys())}), "
-                       f"we use the `{key}` as the monitor.")
-    real_monitor = key
-    return real_monitor, value
+def _match_length(a:str, b:str)->int:
+    """
+    需要把长度短的放在前面
+
+    :param a:
+    :param b:
+    :return:
+    """
+    short = a if len(a) < len(b) else b
+    long = a if len(a)>=len(b) else b
+    match = SequenceMatcher(None, short, long).find_longest_match(0, len(short), 0, len(long))
+    return match.size
 
 

fastNLP/core/controllers/evaluator.py

@@ -219,6 +219,7 @@ class Evaluator:
     def remove_progress_bar(self, dataloader_name):
         if self.progress_bar == 'rich' and hasattr(self, '_rich_task_id'):
             f_rich_progress.destroy_task(self._rich_task_id)
+            f_rich_progress.refresh()  # 使得最终的bar可以消失
             delattr(self, '_rich_task_id')
         elif self.progress_bar == 'raw':
             desc = 'Evaluation ends'
@@ -229,6 +230,7 @@ class Evaluator:
     def finally_progress_bar(self):
         if self.progress_bar == 'rich' and hasattr(self, '_rich_task_id'):
             f_rich_progress.destroy_task(self._rich_task_id)
+            f_rich_progress.refresh()
             delattr(self, '_rich_task_id')
 
     @property

fastNLP/core/controllers/trainer.py

@@ -23,9 +23,9 @@ 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.log import logger
 from fastNLP.envs import FASTNLP_MODEL_FILENAME
+from fastNLP.core.utils.exceptions import EarlyStopException
 
 
 class Trainer(TrainerEventTrigger):
@@ -50,6 +50,8 @@ class Trainer(TrainerEventTrigger):
             output_mapping: Optional[Union[Callable, Dict]] = None,
             accumulation_steps: int = 1,
             fp16: bool = False,
+            monitor: str = None,
+            larger_better: bool = True,
             marker: Optional[str] = None,
             **kwargs
     ):
@@ -103,6 +105,10 @@ class Trainer(TrainerEventTrigger):
          如果 batch 是一个 `dataclass`，那么我们会先将该 dataclass 转换为一个 Dict，然后再进行上述转换
         :param accumulation_steps: 梯度累积的步数，表示每隔几个 batch 优化器迭代一次；默认为 1；
         :param fp16: 是否开启混合精度训练；默认为 False；
+        :param monitor: 当存在 validate_dataloaders 时，默认的 monitor metric 的名字。传入的 callback 如果有 monitor 参数且没有
+            在 callback 初始化设定的，将采取这个值。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 larger_better: monitor 的值是否是越大越好。
         :param marker: 用于标记一个 Trainer 实例，从而在用户调用 `Trainer.on` 函数时，标记该 callback 函数属于哪一个具体的 'trainer' 实例；默认为 None；
         :param kwargs: 一些其它的可能需要的参数；
             torch_non_blocking: 表示用于 pytorch 的 tensor 的 to 方法的参数 non_blocking；
@@ -211,6 +217,8 @@ class Trainer(TrainerEventTrigger):
         self.evaluator = None
         self.epoch_validate = lambda *args, **kwargs: ...
         self.step_validate = lambda *args, **kwargs: ...
+        self.monitor = monitor
+        self.larger_better = larger_better
         if metrics is not None and validate_dataloaders is not None:
             if not callable(validate_every) and (not isinstance(validate_every, int) or validate_every == 0):
                 raise ValueError("Parameter 'validate_every' should be set to 'int' type and either < 0 or > 0.")
@@ -240,6 +248,7 @@ class Trainer(TrainerEventTrigger):
             else:
                 # validate_every > 0
                 self._step_validate_filter = Filter(every=validate_every)
+
         self.metrics = metrics
         self.validate_every = validate_every
 
@@ -321,6 +330,10 @@ class Trainer(TrainerEventTrigger):
                 self.driver.barrier()
             self.on_train_end()
             self.driver.barrier()
+
+        except EarlyStopException as e:
+            logger.info(f"Catch early stop exception: {e.msg}.")
+            self.on_exception(e)
         except KeyboardInterrupt as e:
             self.driver.on_exception()
             self.on_exception(e)
@@ -610,7 +623,7 @@ 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/drivers/driver.py

@@ -49,13 +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, ReproducibleBatchSampler 时，是断点重训加载时 driver.load 函数在调用；
+        注意当 dist 为 ReproducibleSampler, ReproducibleBatchSampler 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 ReproducibleBatchSampler 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:

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 ReproducibleBatchSampler, 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, ReproducibleBatchSampler, 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):
             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

@@ -19,7 +19,7 @@ from fastNLP.core.utils import (
     paddle_move_data_to_device,
     is_in_paddle_dist,
 )
-from fastNLP.core.samplers import ReproducibleIterator, RandomSampler, UnrepeatedSampler
+from fastNLP.core.samplers import ReproducibleSampler, RandomSampler, UnrepeatedRandomSampler
 from fastNLP.envs.env import FASTNLP_DISTRIBUTED_CHECK, USER_CUDA_VISIBLE_DEVICES
 from fastNLP.core.log import logger
 
@@ -312,13 +312,13 @@ 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]],
                                   reproducible: bool = False, sampler_or_batch_sampler=None):
         
         # 暂时不支持iterableDataset
         assert dataloader.dataset_kind != _DatasetKind.ITER, \
                     "FastNLP does not support `IteratorDataset` now."
-        if isinstance(dist, ReproducibleIterator):
+        if isinstance(dist, ReproducibleSampler):
             dataloader.batch_sampler.sampler = dist
             return dataloader
 
@@ -340,7 +340,7 @@ class PaddleFleetDriver(PaddleDriver):
         # trainer
         elif dist == "dist":
             # 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(dataloader.batch_sampler.sampler, ReproducibleIterator):
+            if isinstance(dataloader.batch_sampler.sampler, ReproducibleSampler):
                 dataloader.batch_sampler.sampler.set_distributed(
                     num_replicas=self.world_size,
                     rank=self.global_rank,
@@ -362,7 +362,7 @@ class PaddleFleetDriver(PaddleDriver):
                 return dataloader
         # evaluator
         elif dist == "unrepeatdist":
-            sampler = UnrepeatedSampler(
+            sampler = UnrepeatedRandomSampler(
                 dataset=dataloader.dataset,
                 shuffle=shuffle,
                 seed=int(os.environ.get("FASTNLP_SEED", 0))

fastNLP/core/drivers/paddle_driver/single_device.py

@@ -10,7 +10,7 @@ from fastNLP.core.utils import (
     get_paddle_device_id,
     paddle_move_data_to_device,
 )
-from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleIterator
+from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler
 from fastNLP.core.log import logger
 
 if _NEED_IMPORT_PADDLE:
@@ -139,7 +139,7 @@ class PaddleSingleDriver(PaddleDriver):
         """
         return paddle_move_data_to_device(batch, "gpu:0")
 
-    def set_dist_repro_dataloader(self, dataloader, dist: Union[str, ReproducibleBatchSampler, ReproducibleIterator],
+    def set_dist_repro_dataloader(self, dataloader, dist: Union[str, ReproducibleBatchSampler, ReproducibleSampler],
                                   reproducible: bool = False, sampler_or_batch_sampler=None):
         # 暂时不支持IteratorDataset
         assert dataloader.dataset_kind != _DatasetKind.ITER, \
@@ -147,12 +147,12 @@ class PaddleSingleDriver(PaddleDriver):
         if isinstance(dist, ReproducibleBatchSampler):
             dataloader.batch_sampler = dist
             return dataloader
-        if isinstance(dist, ReproducibleIterator):
+        if isinstance(dist, ReproducibleSampler):
             dataloader.batch_sampler.sampler = dist
             return dataloader            
 
         if reproducible:
-            if isinstance(dataloader.batch_sampler.sampler, ReproducibleIterator):
+            if isinstance(dataloader.batch_sampler.sampler, ReproducibleSampler):
                 return dataloader
             elif isinstance(dataloader.batch_sampler, ReproducibleBatchSampler):
                 return dataloader

fastNLP/core/drivers/torch_driver/ddp.py

@@ -28,11 +28,11 @@ from fastNLP.core.drivers.torch_driver.utils import (
 )
 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, UnrepeatedSampler, ReproducibleBatchSampler
+from fastNLP.core.samplers import ReproducibleSampler, RandomSampler, UnrepeatedSequentialSampler, ReproducibleBatchSampler, \
+    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):
@@ -446,13 +446,23 @@ 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, ReproducibleBatchSampler]]=None,
+    def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleSampler, ReproducibleBatchSampler]]=None,
                                   reproducible: bool = False):
-        # 如果 dist 为 ReproducibleBatchSampler, ReproducibleIterator 说明是在断点重训时 driver.load 函数调用；
+        # 如果 dist 为 ReproducibleBatchSampler, ReproducibleSampler 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, ReproducibleBatchSampler):
+            dist.set_distributed(
+                num_replicas=self.world_size,
+                rank=self.global_rank,
+                pad=True
+            )
             return replace_batch_sampler(dataloader, dist)
-        if isinstance(dist, ReproducibleIterator):
+        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；
@@ -465,7 +475,7 @@ class TorchDDPDriver(TorchDriver):
                 if isinstance(dist, ReproducibleBatchSampler):
                     dist = re_instantiate_sampler(dist)
                     return replace_batch_sampler(dataloader, dist)
-                if isinstance(dist, ReproducibleIterator):
+                if isinstance(dist, ReproducibleSampler):
                     dist = re_instantiate_sampler(dist)
                     return replace_sampler(dataloader, dist)
                 return dataloader
@@ -481,7 +491,7 @@ class TorchDDPDriver(TorchDriver):
                     pad=True
                 )
                 return replace_batch_sampler(dataloader, batch_sampler)
-            elif isinstance(args.sampler, ReproducibleIterator):
+            elif isinstance(args.sampler, ReproducibleSampler):
                 sampler = re_instantiate_sampler(args.sampler)
                 sampler.set_distributed(
                     num_replicas=self.world_size,
@@ -503,14 +513,15 @@ class TorchDDPDriver(TorchDriver):
                 return replace_sampler(dataloader, sampler)
         # evaluator
         elif dist == "unrepeatdist":
-            # todo @yh，补充 unrepeatdist 相关内容；
             args = self.get_dataloader_args(dataloader)
-
-            # todo 判断 batch_sampler；
-            sampler = UnrepeatedSampler(
-                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
@@ -588,7 +599,7 @@ class TorchDDPDriver(TorchDriver):
         :param group:
         :return:
         """
-        return fastnlp_torch_all_gather(obj, device=self.data_device, group=group)
+        return fastnlp_torch_all_gather(obj, group=group)
 
 
 def find_free_network_port() -> str:

fastNLP/core/drivers/torch_driver/dist_utils.py

@@ -1,11 +1,8 @@
 import io
 import pickle
-from typing import Mapping
 _pickler = pickle.Pickler
 _unpickler = pickle.Unpickler
-from abc import ABC
-from typing import Any, Union, List
-import numpy as np
+from typing import Any, List
 from fastNLP.envs.imports import _TORCH_GREATER_EQUAL_1_8
 
 
@@ -13,103 +10,25 @@ from fastNLP.envs.imports import _NEED_IMPORT_TORCH
 if _NEED_IMPORT_TORCH:
     import torch
     from torch import distributed as dist
+    try:
+        from torch._C._distributed_c10d import ProcessGroupMPI
+    except ImportError:
+        _MPI_AVAILABLE = False
+
+    try:
+        from torch._C._distributed_c10d import ProcessGroupNCCL
+    except ImportError:
+        _NCCL_AVAILABLE = False
+
+    try:
+        from torch._C._distributed_c10d import ProcessGroupGloo
+        from torch._C._distributed_c10d import _ProcessGroupWrapper
+    except ImportError:
+        _GLOO_AVAILABLE = False
 
 from fastNLP.core.utils import apply_to_collection
 
 
-
-def all_gather_object(object_list, obj, group=None):
-    """
-    Gathers picklable objects from the whole group into a list. Similar to
-    :func:`all_gather`, but Python objects can be passed in. Note that the object
-    must be picklable in order to be gathered.
-
-    Args:
-        object_list (list[Any]): Output list. It should be correctly sized as the
-            size of the group for this collective and will contain the output.
-        object (Any): Pickable Python object to be broadcast from current process.
-        group (ProcessGroup, optional): The process group to work on. If None,
-            the default process group will be used. Default is ``None``.
-
-    Returns:
-        None. If the calling rank is part of this group, the output of the
-        collective will be populated into the input ``object_list``. If the
-        calling rank is not part of the group, the passed in ``object_list`` will
-        be unmodified.
-
-    .. note:: Note that this API differs slightly from the :func:`all_gather`
-        collective since it does not provide an ``async_op`` handle and thus
-        will be a blocking call.
-
-    .. note:: For NCCL-based processed groups, internal tensor representations
-        of objects must be moved to the GPU device before communication takes
-        place. In this case, the device used is given by
-        ``torch.cuda.current_device()`` and it is the user's responsiblity to
-        ensure that this is set so that each rank has an individual GPU, via
-        ``torch.cuda.set_device()``.
-
-    .. warning::
-        :func:`all_gather_object` uses ``pickle`` module implicitly, which is
-        known to be insecure. It is possible to construct malicious pickle data
-        which will execute arbitrary code during unpickling. Only call this
-        function with data you trust.
-
-    Example::
-        >>> # Note: Process group initialization omitted on each rank.
-        >>> import torch.distributed as dist
-        >>> # Assumes world_size of 3.
-        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
-        >>> output = [None for _ in gather_objects]
-        >>> dist.all_gather_object(output, gather_objects[dist.get_rank()])
-        >>> output
-        ['foo', 12, {1: 2}]
-    """
-    if dist.distributed_c10d._rank_not_in_group(group):
-        return
-
-    input_tensor, local_size = _object_to_tensor(obj)
-    current_device = torch.device("cpu")
-    if dist.is_nccl_available() and isinstance(
-            group or dist.distributed_c10d._get_default_group(), dist.ProcessGroupNCCL
-    ):
-        # See note about using torch.cuda.current_device() here in docstring.
-        # We cannot simply use my_rank since rank == device is not necessarily
-        # true.
-        current_device = torch.device("cuda", torch.cuda.current_device())
-        input_tensor = input_tensor.to(current_device)
-        local_size = local_size.to(current_device)
-    # Gather all local sizes. This is so that we can find the max size, and index
-    # until the correct size when deserializing the tensors.
-    group_size = dist.get_world_size(group=group)
-    object_sizes_tensor = torch.zeros(
-        group_size, dtype=torch.long, device=current_device
-    )
-    object_size_list = [
-        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
-    ]
-    # Allgather tensor sizes
-    dist.all_gather(object_size_list, local_size, group=group)
-    max_object_size = int(max(object_size_list).item())  # type: ignore[type-var]
-    # Resize tensor to max size across all ranks.
-    input_tensor.resize_(max_object_size)
-    coalesced_output_tensor = torch.empty(
-        max_object_size * group_size, dtype=torch.uint8, device=current_device
-    )
-    # Output tensors are nonoverlapping views of coalesced_output_tensor
-    output_tensors = [
-        coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
-        for i in range(group_size)
-    ]
-    dist.all_gather(output_tensors, input_tensor, group=group)
-    # Deserialize outputs back to object.
-    for i, tensor in enumerate(output_tensors):
-        tensor = tensor.type(torch.uint8)
-        if tensor.device != torch.device("cpu"):
-            tensor = tensor.cpu()
-        tensor_size = object_size_list[i]
-        object_list[i] = _tensor_to_object(tensor, tensor_size)
-
-
 def _validate_output_list_for_rank(my_rank, dst, gather_list):
     if dst == my_rank:
         if not gather_list:
@@ -123,8 +42,10 @@ def _validate_output_list_for_rank(my_rank, dst, gather_list):
         )
 
 
-def gather_object(obj, object_gather_list=None, dst=0, group=None):
+def fastnlp_torch_gather_object(obj, object_gather_list=None, dst=0, group=None):
     """
+    从其它 rank gather 东西到 dst rank 。
+
     Gathers picklable objects from the whole group in a single process.
     Similar to :func:`gather`, but Python objects can be passed in. Note that the
     object must be picklable in order to be gathered.
@@ -176,6 +97,8 @@ def gather_object(obj, object_gather_list=None, dst=0, group=None):
     # Ensure object_gather_list is specified appopriately.
     my_rank = dist.get_rank()
     _validate_output_list_for_rank(my_rank, dst, object_gather_list)
+    # 防止 unpickle 的时候出现在了发送的 gpu 上。
+    obj = apply_to_collection(obj, torch.Tensor, _to_device, device=torch.device('cpu'))
     input_tensor, local_size = _object_to_tensor(obj)
     group_backend = dist.get_backend(group)
     current_device = torch.device("cpu")
@@ -266,113 +189,11 @@ def send_recv_object(obj, src, cur_rank, device, group=None, tag=0):
     return _tensor_to_object(tensor.cpu(), size)
 
 
-def _all_gather(obj, **kwargs):
-    group = kwargs.get('group', None)
-    if isinstance(obj, torch.Tensor):
-        gathered_tensor = [torch.zeros_like(obj) for _ in
-                           range(torch.distributed.get_world_size(group=group))]
-
-        torch.distributed.all_gather(gathered_tensor, obj, group=group)
-
-        return gathered_tensor
-
-    elif isinstance(obj, tuple) and isinstance(obj[1], torch.Tensor):
-        tensor, size = obj
-        # 首先需要同步 size 吧？
-        group_size = dist.get_world_size(group=group)
-        object_sizes_tensor = torch.zeros(
-            group_size, dtype=torch.long, device=tensor.device
-        )
-        object_size_list = [
-            object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
-        ]
-        dist.all_gather(object_size_list, size, group=group)
-        max_object_size = int(max(object_size_list).item())  # type: ignore[type-var]
-        # Resize tensor to max size across all ranks.
-        tensor.resize_(max_object_size)
-        coalesced_output_tensor = torch.empty(
-            max_object_size * group_size, dtype=torch.uint8, device=tensor.device
-        )
-
-        # Output tensors are nonoverlapping views of coalesced_output_tensor
-        output_tensors = [
-            coalesced_output_tensor[max_object_size * i: max_object_size * (i + 1)]
-            for i in range(group_size)
-        ]
-        dist.all_gather(output_tensors, tensor, group=group)
-        object_list = []
-        for i, tensor in enumerate(output_tensors):
-            tensor = tensor.type(torch.uint8)
-            tensor_size = object_size_list[i]
-            object_list.append(_tensor_to_object(tensor, tensor_size))
-        return object_list
-    elif isinstance(obj, tuple) and len(obj) == 2:
-        obj, _type = obj
-        gathered_tensor = [torch.zeros_like(obj) for _ in
-                           range(torch.distributed.get_world_size(group=group))]
-
-        torch.distributed.all_gather(gathered_tensor, obj, group=group)
-
-        if _type == np.ndarray:
-            gathered_tensor = [t.detach().cpu().numpy() for t in gathered_tensor]
-        else:
-            gathered_tensor = [_type(t.item()) for t in gathered_tensor]
-
-        return gathered_tensor
-    else:
-        raise RuntimeError("Unsupported types to implement all_gather.")
-
-
-class CanTransferDataType(ABC):
-    """
-    检测可以进行传输的对象。
-
-    """
-
-    @classmethod
-    def __subclasshook__(cls, subclass: Any) -> Union[bool, Any]:
-        if cls is CanTransferDataType:
-            if issubclass(subclass, Mapping):
-                return False
-            if subclass in (torch.Tensor, tuple, list, str, int, float, bool, np.ndarray):
-                return True
-            return False
-        return NotImplemented
-
-
-def _tensorize(obj, device=None):
-    if isinstance(obj, torch.Tensor):
-        return obj
-    if isinstance(obj, bool):
-        return torch.tensor(obj, dtype=torch.uint8, device=device), bool
-    if isinstance(obj, float):
-        return torch.tensor(obj, dtype=torch.float, device=device), float
-    if isinstance(obj, int):
-        return torch.tensor(obj, dtype=torch.int, device=device), int
-    if isinstance(obj, np.ndarray):
-        return torch.from_numpy(obj), np.ndarray
-    return _object_to_tensor(obj, device)
-
-
 def _to_device(tensor, device):
     return tensor.contiguous().to(device)
 
 
-def convert_to_tensors(data: Any, device=None) -> Any:
-    data = apply_to_collection(data, CanTransferDataType, _tensorize)
-    def _move_to_device_and_make_contiguous(t: Union[torch.Tensor, tuple], device: Union[str, torch.device]):
-        if isinstance(t, tuple):
-            if isinstance(t[1], torch.Tensor): # 说明是 object 转的
-                return t[0].to(device).contiguous(), t[1].to(device)
-            else:  # 说明第二个元素是type，见 to_dtype_tensor 函数
-                return t[0].to(device).contiguous(), t[1]
-        return t.to(device).contiguous()
-
-    data = apply_to_collection(data, (torch.Tensor, tuple), _move_to_device_and_make_contiguous, device=device)
-    return data
-
-
-def fastnlp_torch_all_gather(obj:Any, device=None, group=None)->List:
+def fastnlp_torch_all_gather(obj: Any, device=None, group=None) ->List:
     """
     实现任何类型的数据都使用该接口可以进行 all_gather 操作。对于非 tensor 类型的数据，通过 pickle 序列化再反序列化的方式进行传输。
 
@@ -390,36 +211,28 @@ def fastnlp_torch_all_gather(obj:Any, device=None, group=None)->List:
             {'a': 1, 'b':[1, 2], 'c':{'d': 2}}
         ]
 
-    :param obj: 任意结构的数据，所有的 value 都会变成 list ，其长度为 world_size ，依次为每个 rank 上的对象值
-    :param device: 当前 rank 使用的 device 是哪个。为 None 的话默认使用 torch.cuda.current_device() 获取。
+    :param obj: 任意结构的数据，如果为 tensor ，需要保证每个显卡上的 tensor 的形状是一样的。如果传入的是非 tensor 对象都将直接进行
+        序列化之后进行传输。
+    :param device: 当前该参数无意义。
     :param group:
     :return: 返回的结果是 [obj0, obj1, ...]，其中 obj_i 即为第 i 个 rank 上的 obj 。
     """
     # # 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:
+    if isinstance(obj, torch.Tensor):
+        objs = [torch.zeros_like(obj) for _ in range(dist.get_world_size(group))]
+        dist.all_gather(objs, obj, group=group)
+    else:
         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
-    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)
-
-    objs = []
-
-    def _get_obj_on_idx(obj, idx):
-        return obj[idx]
-
-    for i in range(dist.get_world_size(group)):
-        objs.append(apply_to_collection(data, dtype=list, function=_get_obj_on_idx, idx=i))
-
+        # 防止 unpickle 的时候弄到发送的 gpu 上了
+        obj = apply_to_collection(obj, torch.Tensor, _to_device, device=torch.device('cpu'))
+        if _TORCH_GREATER_EQUAL_1_8:
+            dist.all_gather_object(objs, obj, group=group)
+        else:
+            objs = all_gather_object(objs, obj, group=group)
     return objs
 
 
-def fastnlp_torch_broadcast_object(obj, src, device, group=None):
+def fastnlp_torch_broadcast_object(obj, src, device=None, group=None):
     """
     将 src 上的 obj 对象广播到其它 rank 上。
 
@@ -430,10 +243,9 @@ def fastnlp_torch_broadcast_object(obj, src, device, group=None):
     :return:
     """
     cur_rank = dist.get_rank(group)
-    # if cur_rank == src:
-    #     # 如果有 tensor 全部移动到 cpu 上，方便 pickle
-    #     obj = apply_to_collection(obj, torch.Tensor, _to_device, device=torch.device('cpu'))
-
+    if cur_rank == src:
+        # 如果有 tensor 全部移动到 cpu 上，方便 pickle , 不然 unpickle 的时候可能会 pickle 到发送过来的卡那里
+        obj = apply_to_collection(obj, torch.Tensor, _to_device, device=torch.device('cpu'))
     if _TORCH_GREATER_EQUAL_1_8:
         if cur_rank!=src:
             get_obj = [None]
@@ -442,6 +254,8 @@ def fastnlp_torch_broadcast_object(obj, src, device, group=None):
         else:
             dist.broadcast_object_list([obj], src=src, group=group)
             return obj
+    if device is None:
+        device = torch.cuda.current_device()
 
     if cur_rank == src:
         tensor, size = _object_to_tensor(obj, device=device)
@@ -460,3 +274,107 @@ def fastnlp_torch_broadcast_object(obj, src, device, group=None):
     return _tensor_to_object(tensor, tensor_size=size.item())
 
 
+def _check_for_nccl_backend(group):
+    pg = group or dist.distributed_c10d._get_default_group()
+    # It is not expected for PG to be wrapped many times, but support it just
+    # in case
+    while isinstance(pg, _ProcessGroupWrapper):
+        pg = pg.wrapped_pg
+
+    return (
+            dist.is_nccl_available() and
+            isinstance(pg, dist.ProcessGroupNCCL)
+    )
+
+
+def all_gather_object(object_list, obj, group=None):
+    """
+    复制 pytorch 的代码，使得可以版本兼容低版本的 pytorch 。
+
+    Gathers picklable objects from the whole group into a list. Similar to
+    :func:`all_gather`, but Python objects can be passed in. Note that the object
+    must be picklable in order to be gathered.
+
+    Args:
+        object_list (list[Any]): Output list. It should be correctly sized as the
+            size of the group for this collective and will contain the output.
+        object (Any): Pickable Python object to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+
+    Returns:
+        None. If the calling rank is part of this group, the output of the
+        collective will be populated into the input ``object_list``. If the
+        calling rank is not part of the group, the passed in ``object_list`` will
+        be unmodified.
+
+    .. note:: Note that this API differs slightly from the :func:`all_gather`
+        collective since it does not provide an ``async_op`` handle and thus
+        will be a blocking call.
+
+    .. note:: For NCCL-based processed groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsiblity to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        :func:`all_gather_object` uses ``pickle`` module implicitly, which is
+        known to be insecure. It is possible to construct malicious pickle data
+        which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    Example::
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes world_size of 3.
+        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> output = [None for _ in gather_objects]
+        >>> dist.all_gather_object(output, gather_objects[dist.get_rank()])
+        >>> output
+        ['foo', 12, {1: 2}]
+    """
+    if dist._rank_not_in_group(group):
+        return
+
+    input_tensor, local_size = _object_to_tensor(obj)
+    current_device = torch.device("cpu")
+    is_nccl_backend = _check_for_nccl_backend(group)
+    if is_nccl_backend:
+        # See note about using torch.cuda.current_device() here in docstring.
+        # We cannot simply use my_rank since rank == device is not necessarily
+        # true.
+        current_device = torch.device("cuda", torch.cuda.current_device())
+        input_tensor = input_tensor.to(current_device)
+        local_size = local_size.to(current_device)
+    # Gather all local sizes. This is so that we can find the max size, and index
+    # until the correct size when deserializing the tensors.
+    group_size = dist.get_world_size(group=group)
+    object_sizes_tensor = torch.zeros(
+        group_size, dtype=torch.long, device=current_device
+    )
+    object_size_list = [
+        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
+    ]
+    # Allgather tensor sizes
+    dist.all_gather(object_size_list, local_size, group=group)
+    max_object_size = int(max(object_size_list).item())  # type: ignore[type-var]
+    # Resize tensor to max size across all ranks.
+    input_tensor.resize_(max_object_size)
+    coalesced_output_tensor = torch.empty(
+        max_object_size * group_size, dtype=torch.uint8, device=current_device
+    )
+    # Output tensors are nonoverlapping views of coalesced_output_tensor
+    output_tensors = [
+        coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
+        for i in range(group_size)
+    ]
+    dist.all_gather(output_tensors, input_tensor, group=group)
+    # Deserialize outputs back to object.
+    for i, tensor in enumerate(output_tensors):
+        tensor = tensor.type(torch.uint8)
+        if tensor.device != torch.device("cpu"):
+            tensor = tensor.cpu()
+        tensor_size = object_size_list[i]
+        object_list[i] = _tensor_to_object(tensor, tensor_size)

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 ReproducibleBatchSampler, ReproducibleSampler, re_instantiate_sampler
 from fastNLP.core.log import logger
-from fastNLP.core.samplers import re_instantiate_sampler
 
 
 class TorchSingleDriver(TorchDriver):
@@ -130,13 +129,13 @@ 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, ReproducibleBatchSampler, ReproducibleSampler]=None,
                                   reproducible: bool = False):
 
         # 如果 dist 为 ReproducibleBatchSampler, ReproducibleIterator 说明是在断点重训时 driver.load 函数调用；
         if isinstance(dist, ReproducibleBatchSampler):
             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；
@@ -144,7 +143,7 @@ class TorchSingleDriver(TorchDriver):
         if isinstance(args.batch_sampler, ReproducibleBatchSampler):
             batch_sampler = re_instantiate_sampler(args.batch_sampler)
             return replace_batch_sampler(dataloader, batch_sampler)
-        elif isinstance(args.sampler, ReproducibleIterator):
+        elif isinstance(args.sampler, ReproducibleSampler):
             sampler = re_instantiate_sampler(args.sampler)
             return replace_sampler(dataloader, sampler)
 

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, ReproducibleIterator
+from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler
 
 
 class TorchDriver(Driver):
@@ -182,8 +182,8 @@ 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 `ReproducibleBatchSampler`；
         dataloader_args = self.get_dataloader_args(dataloader)
         if isinstance(dataloader_args.batch_sampler, ReproducibleBatchSampler):
             sampler = dataloader_args.batch_sampler
@@ -247,11 +247,10 @@ class TorchDriver(Driver):
         dataloader_args = self.get_dataloader_args(dataloader)
         if isinstance(dataloader_args.batch_sampler, ReproducibleBatchSampler):
             sampler = dataloader_args.batch_sampler
-        elif isinstance(dataloader_args.sampler, ReproducibleIterator):
+        elif isinstance(dataloader_args.sampler, ReproducibleSampler):
             sampler = dataloader_args.sampler
         elif self.is_distributed():
-            raise RuntimeError("It is not allowed to use checkpoint retraining when you do not use our "
-                               "`ReproducibleBatchSampler` or `ReproducibleIterator`.")
+            raise RuntimeError("It is not allowed to use checkpoint retraining when you do not use our or `ReproducibleSampler`.")
         else:
             sampler = ReproducibleBatchSampler(
                 batch_sampler=dataloader_args.batch_sampler if dataloader_args.batch_sampler is not None else dataloader_args.sampler,
@@ -291,7 +290,7 @@ 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/samplers/__init__.py

@@ -9,18 +9,28 @@ __all__ = [
     'MixSequentialSampler',
     'PollingSampler',
 
-    'ReproducibleIterator',
+    'ReproducibleSampler',
     'RandomSampler',
-
-    're_instantiate_sampler',
+    "SequentialSampler",
+    "SortedSampler",
 
     'UnrepeatedSampler',
-    "UnrepeatedSortedSampler"
+    'UnrepeatedRandomSampler',
+    "UnrepeatedSortedSampler",
+    "UnrepeatedSequentialSampler",
+
+    "RandomBatchSampler",
+    "BucketedBatchSampler",
+    "ReproducibleBatchSampler",
+
+    "re_instantiate_sampler",
+    "conversion_between_reproducible_and_unrepeated_sampler"
 ]
 
 from .sampler import BucketSampler, SortedSampler, ConstTokenNumSampler, ConstantTokenNumSampler
-from .unrepeated_sampler import UnrepeatedSampler, UnrepeatedSortedSampler
+from .unrepeated_sampler import UnrepeatedSampler, UnrepeatedRandomSampler, UnrepeatedSortedSampler, UnrepeatedSequentialSampler
 from .mix_sampler import MixSampler, DopedSampler, MixSequentialSampler, PollingSampler
-from .reproducible_sampler import ReproducibleIterator, RandomSampler, re_instantiate_sampler
-from .reproducible_batch_sampler import ReproducibleBatchSampler, BucketedBatchSampler
+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, ReproducibleBatchSampler
 

fastNLP/core/samplers/reproducible_batch_sampler.py

@@ -1,6 +1,6 @@
 __all__ = [
     'BucketedBatchSampler',
-    "ReproducibleBatchSampler"
+    "RandomBatchSampler"
 ]
 
 import math
@@ -16,7 +16,10 @@ from fastNLP.core.log import logger
 from abc import abstractmethod
 
 
-class ReproducibleBatchIterator:
+class ReproducibleBatchSampler:
+    def __init__(self, **kwargs):
+        pass
+
     @abstractmethod
     def set_distributed(self, num_replicas, rank, pad=True):
         raise NotImplementedError("Each specific batch_sampler should implement its own `set_distributed` method.")
@@ -41,19 +44,25 @@ class ReproducibleBatchIterator:
     def set_epoch(self, epoch):
         pass
 
+    @property
+    def batch_idx_in_epoch(self):
+        raise NotImplementedError("Each specific batch_sampler should implement its own `batch_idx_in_epoch` property.")
 
-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。
         :param kwargs: fastNLP 内部使用。
         """
+        super().__init__()
+
         self.batch_sampler = batch_sampler
         self.batch_size = batch_size
         self.drop_last = drop_last
@@ -138,7 +147,7 @@ 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

@@ -1,24 +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。
 
     """
@@ -46,7 +43,7 @@ class ReproducibleIterator:
         pass
 
 
-class RandomSampler(ReproducibleIterator):
+class RandomSampler(ReproducibleSampler):
     def __init__(self, dataset, shuffle: bool = True, seed: int = 0, **kwargs):
         """
 
@@ -156,8 +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']
@@ -214,9 +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/unrepeated_sampler.py

@@ -1,6 +1,8 @@
 __all__ = [
+    'UnrepeatedSampler',
     'UnrepeatedSortedSampler',
-    'UnrepeatedSampler'
+    'UnrepeatedRandomSampler',
+    "UnrepeatedSequentialSampler"
 ]
 
 from typing import List, Union
@@ -10,13 +12,21 @@ 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:
-        :param shuffle:
-        :param seed:
+        :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
@@ -33,8 +43,8 @@ class UnrepeatedSampler:
         :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
+        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()
@@ -83,8 +93,8 @@ class UnrepeatedSampler:
         return self
 
 
-class UnrepeatedSortedSampler(UnrepeatedSampler):
-    def __init__(self, dataset, length:Union[str, List], seed: int = 0):
+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。
@@ -92,11 +102,9 @@ class UnrepeatedSortedSampler(UnrepeatedSampler):
         :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 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
         """
-        super().__init__(dataset=dataset, shuffle=False, seed=seed)
+        super().__init__(dataset=dataset, shuffle=False, seed=0, **kwargs)
         if isinstance(dataset, DataSet):
             length = dataset.get_field(length)
             if not isinstance(length[0], int):
@@ -107,8 +115,29 @@ class UnrepeatedSortedSampler(UnrepeatedSampler):
 
         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
+        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/exceptions.py

@@ -0,0 +1,10 @@
+
+class EarlyStopException(BaseException):
+    r"""
+    用于EarlyStop时从Trainer训练循环中跳出。
+
+    """
+
+    def __init__(self, msg):
+        super(EarlyStopException, self).__init__(msg)
+        self.msg = msg

fastNLP/core/utils/rich_progress.py

@@ -94,9 +94,6 @@ class FRichProgress(Progress, metaclass=Singleton):
         self.print = self.console.print
         self.log = self.console.log
 
-        # start new
-        self.start()
-        self.console.show_cursor(show=True)
         return self
 
     def set_transient(self, transient: bool = True):
@@ -154,6 +151,7 @@ class FRichProgress(Progress, metaclass=Singleton):
         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(
         "[progress.description]{task.description}",

tests/core/callbacks/test_utils.py

@@ -12,32 +12,27 @@ def test_get_monitor_value():
     with Capturing() as output:
         monitor, value = _get_monitor_value(monitor='f1', real_monitor=None, res=res)
     assert monitor == 'f1' and value==0.2
-    assert 'We can not find' not in output[0]
 
     # 测试可以匹配，且选择更靠前的
     res = {'acc#f1': 0.2, 'acc#rec': 0.3, 'add#f':0.4}
     with Capturing() as output:
         monitor, value = _get_monitor_value(monitor='f1', real_monitor=None, res=res)
     assert monitor=='acc#f1' and value==0.2
-    assert 'We can not find' in output[0]
 
     # 测试monitor匹配不上，使用real_monitor
     res = {'acc#f1': 0.2, 'acc#rec': 0.3, 'add#f':0.4}
     with Capturing() as output:
-        monitor, value = _get_monitor_value(monitor='acc#f', real_monitor='acc#rec', res=res)
+        monitor, value = _get_monitor_value(monitor='acc', real_monitor='acc#rec', res=res)
     assert monitor=='acc#rec' and value==0.3
-    assert 'We can not find' not in output[0]
 
     # 测试monitor/real_monitor匹配不上, 重新选择
     res = {'acc#f1': 0.2, 'acc#rec': 0.3, 'add#f':0.4}
     with Capturing() as output:
         monitor, value = _get_monitor_value(monitor='acc#f', real_monitor='acc#r', res=res)
     assert monitor=='acc#f1' and value==0.2
-    assert 'We can not find' in output[0]
 
     # 测试partial的位置
     res = {"acc#acc": 0.52, "loss#loss": 2}
     with Capturing() as output:
         monitor, value = _get_monitor_value(monitor='-loss', real_monitor=None, res=res)
     assert monitor=='loss#loss' and value==2
-    assert 'We can not find' in output[0]

tests/core/drivers/paddle_driver/test_single_device.py

@@ -10,7 +10,7 @@ from paddle.io import DataLoader, BatchSampler
 
 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 fastNLP.core.samplers import RandomBatchSampler
 from tests.helpers.models.paddle_model import PaddleNormalModel_Classification
 from tests.helpers.datasets.paddle_data import PaddleDataset_MNIST, PaddleRandomDataset
 from fastNLP.core import synchronize_safe_rm
@@ -153,7 +153,7 @@ class TestSingleDeviceFunction:
 
     @pytest.mark.parametrize(
         "dist_sampler",
-        ["dist", ReproducibleBatchSampler(BatchSampler(PaddleDataset_MNIST("train")), 32, False), RandomSampler(PaddleDataset_MNIST("train"))]
+        ["dist", RandomBatchSampler(BatchSampler(PaddleDataset_MNIST("train")), 32, False), RandomSampler(PaddleDataset_MNIST("train"))]
     )
     @pytest.mark.parametrize(
         "reproducible",

tests/core/drivers/torch_driver/test_dist_utils.py

@@ -7,38 +7,10 @@ import numpy as np
 # print(isinstance((1,), tuple))
 # exit()
 
-from fastNLP.core.drivers.torch_driver.dist_utils import fastnlp_torch_all_gather, convert_to_tensors, fastnlp_torch_broadcast_object
+from fastNLP.core.drivers.torch_driver.dist_utils import fastnlp_torch_all_gather, fastnlp_torch_broadcast_object
 from tests.helpers.utils import re_run_current_cmd_for_torch, magic_argv_env_context
 
 
-
-def test_convert_to_tensors():
-    local_rank = 0
-    obj = {
-        'tensor': torch.full(size=(2,), fill_value=local_rank),
-        'numpy': np.full(shape=(1,), fill_value=local_rank),
-        'bool': local_rank % 2 == 0,
-        'float': local_rank + 0.1,
-        'int': local_rank,
-        'dict': {
-            'rank': local_rank
-        },
-        'list': [local_rank] * 2,
-        'str': 'xxx'
-    }
-    data = convert_to_tensors(obj)
-    assert len(data) == len(obj)
-    assert (data['tensor'] == obj['tensor']).sum() == 2
-    for name in ['list', 'str']:
-        assert len(data[name])==2 and isinstance(data[name][0], torch.Tensor) and \
-               isinstance(data[name][1], torch.Tensor) and data[name][1].ndim==1
-
-    for name in ['numpy', 'bool', 'float', 'int']:
-        assert isinstance(data[name][0], torch.Tensor) and data[name][0].numel()==1
-
-    assert isinstance(data['dict']['rank'][0], torch.Tensor) and data[name][0].numel() == 1
-
-
 @magic_argv_env_context
 def test_fastnlp_torch_all_gather():
     os.environ['MASTER_ADDR'] = '127.0.0.1'
@@ -66,7 +38,7 @@ def test_fastnlp_torch_all_gather():
         'tensors': [torch.full(size=(2,), fill_value=local_rank).cuda(),
                     torch.full(size=(2,), fill_value=local_rank).cuda()]
     }
-    data = fastnlp_torch_all_gather(obj, device=torch.cuda.current_device())
+    data = fastnlp_torch_all_gather(obj)
     world_size = int(os.environ['WORLD_SIZE'])
     assert len(data) == world_size
     for i in range(world_size):
@@ -81,10 +53,12 @@ def test_fastnlp_torch_all_gather():
         assert data[i]['tensors'][0][0] == i
 
     for obj in [1, True, 'xxx']:
-        data = fastnlp_torch_all_gather(obj, device=torch.cuda.current_device())
+        data = fastnlp_torch_all_gather(obj)
         assert len(data)==world_size
         assert data[0]==data[1]
 
+    dist.destroy_process_group()
+
 @magic_argv_env_context
 def test_fastnlp_torch_broadcast_object():
     os.environ['MASTER_ADDR'] = '127.0.0.1'
@@ -130,3 +104,4 @@ def test_fastnlp_torch_broadcast_object():
     for obj in [int(os.environ['LOCAL_RANK']), bool(os.environ['LOCAL_RANK']=='1'), os.environ['LOCAL_RANK']]:
         data = fastnlp_torch_broadcast_object(obj, src=0, device=torch.cuda.current_device())
         assert int(data)==0
+    dist.destroy_process_group()

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/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)
 

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

@@ -2,7 +2,7 @@ from itertools import chain
 
 import pytest
 
-from fastNLP.core.samplers import UnrepeatedSampler, UnrepeatedSortedSampler
+from fastNLP.core.samplers import UnrepeatedRandomSampler, UnrepeatedSortedSampler, UnrepeatedSequentialSampler
 
 
 class DatasetWithVaryLength:
@@ -21,7 +21,7 @@ class TestUnrepeatedSampler:
     def test_single(self, shuffle):
         num_of_data = 100
         data = DatasetWithVaryLength(num_of_data)
-        sampler = UnrepeatedSampler(data, shuffle)
+        sampler = UnrepeatedRandomSampler(data, shuffle)
         indexes = set(sampler)
         assert indexes==set(range(num_of_data))
 
@@ -32,17 +32,18 @@ class TestUnrepeatedSampler:
         data = DatasetWithVaryLength(num_of_data=num_of_data)
         samplers = []
         for i in range(num_replica):
-            sampler = UnrepeatedSampler(dataset=data, shuffle=shuffle)
+            sampler = UnrepeatedRandomSampler(dataset=data, shuffle=shuffle)
             sampler.set_distributed(num_replica, rank=i)
             samplers.append(sampler)
 
-        indexes = set(chain(*samplers))
+        indexes = list(chain(*samplers))
+        assert len(indexes) == num_of_data
+        indexes = set(indexes)
         assert indexes==set(range(num_of_data))
 
 
 class TestUnrepeatedSortedSampler:
-    @pytest.mark.parametrize('shuffle', [True, False])
-    def test_single(self, shuffle):
+    def test_single(self):
         num_of_data = 100
         data = DatasetWithVaryLength(num_of_data)
         sampler = UnrepeatedSortedSampler(data, length=data.data)
@@ -51,8 +52,7 @@ class TestUnrepeatedSortedSampler:
 
     @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):
+    def test_multi(self, num_replica, num_of_data):
         data = DatasetWithVaryLength(num_of_data=num_of_data)
         samplers = []
         for i in range(num_replica):
@@ -60,5 +60,45 @@ class TestUnrepeatedSortedSampler:
             sampler.set_distributed(num_replica, rank=i)
             samplers.append(sampler)
 
-        indexes = set(chain(*samplers))
+        # 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))