新增SortedSampler和SequentialSampler

fastNLP/core/controllers/trainer.py

@@ -23,7 +23,7 @@ from fastNLP.core.drivers import Driver
 from fastNLP.core.drivers.utils import choose_driver
 from fastNLP.core.utils import check_fn_not_empty_params, get_fn_arg_names, match_and_substitute_params, nullcontext
 from fastNLP.envs import rank_zero_call
-from fastNLP.core.samplers import ReproducibleIterator, ReproducibleBatchSampler
+from fastNLP.core.samplers import ReproducibleSampler, RandomBatchSampler
 from fastNLP.core.log import logger
 from fastNLP.envs import FASTNLP_MODEL_FILENAME
 
@@ -610,7 +610,7 @@ class Trainer(TrainerEventTrigger):
         r"""
         用于断点重训的加载函数；
         注意在 fastNLP 中断点重训的保存和加载逻辑是分开的，因此可能存在一种情况：用户只希望加载一个断点重训的状态，而在之后不再进行断点重训的
-         保存；在这种情况下，dataloader 的 sampler 就不一定会被替换成我们的 ReproducibleIterator；
+         保存；在这种情况下，dataloader 的 sampler 就不一定会被替换成我们的 ReproducibleSampler；
 
         注意我们目前不支持单卡到多卡的断点重训；
 

fastNLP/core/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 ReproducibleIterator, RandomBatchSampler 时，是断点重训加载时 driver.load 函数在调用；
         当 dist 为 str 或者 None 时，是 trainer 在初始化时调用该函数；
 
         :param reproducible: 如果为 False ，不要做任何考虑；如果为 True ，需要保证返回的 dataloader 可以保存当前的迭代状态，使得
             可以可以加载。
         :return: 应当返回一个被替换 sampler 后的新的 dataloader 对象 (注意此处一定需要返回一个新的 dataloader 对象) ；此外，
-            如果传入的 dataloader 中是 ReproducibleIterator 或者 ReproducibleBatchSampler 需要重新初始化一个放入返回的
+            如果传入的 dataloader 中是 ReproducibleSampler 或者 RandomBatchSampler 需要重新初始化一个放入返回的
             dataloader 中。如果 dist 为空，且 reproducible 为 False，可直接返回原对象。
         """
         if dist is None and reproducible is False:

fastNLP/core/drivers/jittor_driver/mpi.py

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

fastNLP/core/drivers/jittor_driver/single_device.py

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

fastNLP/core/drivers/paddle_driver/fleet.py

@@ -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 RandomBatchSampler, ReproducibleSampler
 from fastNLP.core.log import logger
 
 if _NEED_IMPORT_PADDLE:
@@ -139,26 +139,26 @@ 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, RandomBatchSampler, ReproducibleSampler],
                                   reproducible: bool = False, sampler_or_batch_sampler=None):
         # 暂时不支持IteratorDataset
         assert dataloader.dataset_kind != _DatasetKind.ITER, \
                 "FastNLP does not support `IteratorDataset` now."
-        if isinstance(dist, ReproducibleBatchSampler):
+        if isinstance(dist, RandomBatchSampler):
             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):
+            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/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, RandomBatchSampler, \
+    re_instantiate_sampler, UnrepeatedSampler, conversion_between_reproducible_and_unrepeated_sampler
 from fastNLP.envs import FASTNLP_DISTRIBUTED_CHECK, FASTNLP_GLOBAL_RANK, FASTNLP_GLOBAL_SEED
 from fastNLP.core.log import logger
 from fastNLP.core.drivers.torch_driver.dist_utils import fastnlp_torch_all_gather, fastnlp_torch_broadcast_object
-from fastNLP.core.samplers import re_instantiate_sampler
 
 
 class TorchDDPDriver(TorchDriver):
@@ -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, RandomBatchSampler]]=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 RandomBatchSampler, ReproducibleIterator 说明是在断点重训时 driver.load 函数调用；
         # 注意这里不需要调用 dist_sampler.set_distributed；因为如果用户使用的是 TorchDDPDriver，那么其在 Trainer 初始化的时候就已经调用了该函数；
-        if isinstance(dist, ReproducibleBatchSampler):
+        if isinstance(dist, RandomBatchSampler):
+            dist.set_distributed(
+                num_replicas=self.world_size,
+                rank=self.global_rank,
+                pad=True
+            )
             return replace_batch_sampler(dataloader, dist)
-        if isinstance(dist, 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；
@@ -462,10 +472,10 @@ class TorchDDPDriver(TorchDriver):
                 raise RuntimeError("It is not allowed to use checkpoint retraining when you initialize ddp out of our "
                                    "control.")
             else:
-                if isinstance(dist, ReproducibleBatchSampler):
+                if isinstance(dist, RandomBatchSampler):
                     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
@@ -473,7 +483,7 @@ class TorchDDPDriver(TorchDriver):
         elif dist == "dist":
             args = self.get_dataloader_args(dataloader)
             # 如果用户的 trainer.use_dist_sampler 为 True，那么此时其是否进行断点重训，不影响这里的行为；
-            if isinstance(args.batch_sampler, ReproducibleBatchSampler):
+            if isinstance(args.batch_sampler, RandomBatchSampler):
                 batch_sampler = re_instantiate_sampler(args.batch_sampler)
                 batch_sampler.set_distributed(
                     num_replicas=self.world_size,
@@ -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

fastNLP/core/drivers/torch_driver/single_device.py

@@ -13,9 +13,8 @@ __all__ = [
 from .torch_driver import TorchDriver
 from fastNLP.core.drivers.torch_driver.utils import replace_sampler, replace_batch_sampler
 from fastNLP.core.utils import auto_param_call
-from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleIterator
+from fastNLP.core.samplers import RandomBatchSampler, ReproducibleSampler, re_instantiate_sampler
 from fastNLP.core.log import logger
-from fastNLP.core.samplers import re_instantiate_sampler
 
 
 class TorchSingleDriver(TorchDriver):
@@ -130,26 +129,26 @@ class TorchSingleDriver(TorchDriver):
         else:
             return self._test_step(batch)
 
-    def set_dist_repro_dataloader(self, dataloader, dist: Union[str, ReproducibleBatchSampler, ReproducibleIterator]=None,
+    def set_dist_repro_dataloader(self, dataloader, dist: Union[str, RandomBatchSampler, ReproducibleSampler]=None,
                                   reproducible: bool = False):
 
-        # 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):
+        # 如果 dist 为 RandomBatchSampler, ReproducibleIterator 说明是在断点重训时 driver.load 函数调用；
+        if isinstance(dist, RandomBatchSampler):
             return replace_batch_sampler(dataloader, dist)
-        elif isinstance(dist, ReproducibleIterator):
+        elif isinstance(dist, ReproducibleSampler):
             return replace_sampler(dataloader, dist)
 
         # 如果 dist 为 str 或者 None，说明是在 trainer 初试化时调用；
         args = self.get_dataloader_args(dataloader)
-        if isinstance(args.batch_sampler, ReproducibleBatchSampler):
+        if isinstance(args.batch_sampler, RandomBatchSampler):
             batch_sampler = re_instantiate_sampler(args.batch_sampler)
             return replace_batch_sampler(dataloader, batch_sampler)
-        elif isinstance(args.sampler, ReproducibleIterator):
+        elif isinstance(args.sampler, ReproducibleSampler):
             sampler = re_instantiate_sampler(args.sampler)
             return replace_sampler(dataloader, sampler)
 
         if reproducible:
-            batch_sampler = ReproducibleBatchSampler(
+            batch_sampler = RandomBatchSampler(
                 batch_sampler=args.batch_sampler,
                 batch_size=args.batch_size,
                 drop_last=args.drop_last

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 RandomBatchSampler, ReproducibleIterator
 
 
 class TorchDriver(Driver):
@@ -182,10 +182,10 @@ class TorchDriver(Driver):
         #  trainer.dataloader 来改变 dataloader 的状态，从而适配训练或者评测环境；
 
         # 1. sampler 的状态，因为我们支持 resume training，即精确恢复到具体的一个 batch；
-        # 首先 pytorch 的 DataLoader 一定会有 sampler；另一方面，我们在断点重训的时候一定会在 `replace_sampler` 中将 dataloader 的
-        #  sampler 替换为 `ReproducibleIterator`；否则就是在单卡情况下将 batch_sampler 替换为 `ReproducibleBatchSampler`；
+        # 首先 pytorch 的 DataLoader 一定会有 sampler；另一方面，我们在断点重训的时候一定会在 `set_` 中将 dataloader 的
+        #  sampler 替换为 `ReproducibleSampler`；否则就是在单卡情况下将 batch_sampler 替换为 `RandomBatchSampler`；
         dataloader_args = self.get_dataloader_args(dataloader)
-        if isinstance(dataloader_args.batch_sampler, ReproducibleBatchSampler):
+        if isinstance(dataloader_args.batch_sampler, RandomBatchSampler):
             sampler = dataloader_args.batch_sampler
         elif dataloader_args.sampler:
             sampler = dataloader_args.sampler
@@ -245,15 +245,15 @@ class TorchDriver(Driver):
 
         # 3. 恢复 sampler 的状态；
         dataloader_args = self.get_dataloader_args(dataloader)
-        if isinstance(dataloader_args.batch_sampler, ReproducibleBatchSampler):
+        if isinstance(dataloader_args.batch_sampler, RandomBatchSampler):
             sampler = dataloader_args.batch_sampler
         elif isinstance(dataloader_args.sampler, ReproducibleIterator):
             sampler = dataloader_args.sampler
         elif self.is_distributed():
             raise RuntimeError("It is not allowed to use checkpoint retraining when you do not use our "
-                               "`ReproducibleBatchSampler` or `ReproducibleIterator`.")
+                               "`RandomBatchSampler` or `ReproducibleIterator`.")
         else:
-            sampler = ReproducibleBatchSampler(
+            sampler = RandomBatchSampler(
                 batch_sampler=dataloader_args.batch_sampler if dataloader_args.batch_sampler is not None else dataloader_args.sampler,
                 batch_size=dataloader_args.batch_size,
                 drop_last=dataloader_args.drop_last
@@ -263,7 +263,7 @@ class TorchDriver(Driver):
 
         # 4. 修改 trainer_state.batch_idx_in_epoch
         # sampler 是类似 RandomSampler 的sampler，不是 batch_sampler；
-        if not isinstance(sampler, ReproducibleBatchSampler):
+        if not isinstance(sampler, RandomBatchSampler):
             if dataloader_args.drop_last:
                 batch_idx_in_epoch = len(
                     sampler) // dataloader_args.batch_size - sampler.num_left_samples // dataloader_args.batch_size
@@ -291,7 +291,7 @@ class TorchDriver(Driver):
 
     @staticmethod
     def worker_init_function(worker_id: int, rank: Optional[int] = None) -> None:  # pragma: no cover
-        """The worker_init_fn that Lightning automatically adds to your dataloader if you previously set set the seed
+        """The worker_init_fn that Lightning automatically adds to your dataloader if you previously set the seed
         with ``seed_everything(seed, workers=True)``.
 
         See also the PyTorch documentation on

fastNLP/core/samplers/__init__.py

@@ -9,18 +9,24 @@ __all__ = [
     'MixSequentialSampler',
     'PollingSampler',
 
-    'ReproducibleIterator',
+    'ReproducibleSampler',
     'RandomSampler',
-
-    're_instantiate_sampler',
+    "SequentialSampler",
+    "SortedSampler",
 
     'UnrepeatedSampler',
-    "UnrepeatedSortedSampler"
+    'UnrepeatedRandomSampler',
+    "UnrepeatedSortedSampler",
+    "UnrepeatedSequentialSampler",
+
+    "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
 

fastNLP/core/samplers/reproducible_batch_sampler.py

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

fastNLP/core/samplers/reproducible_sampler.py

@@ -1,24 +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)

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_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))