增加RandomBatchSampler

fastNLP/core/collators/collator.py

@@ -65,12 +65,16 @@ def _get_backend() -> str:
         return catch_backend[0]
 
     # 方式 (2)
+    for backend in CHECK_BACKEND:
+        if backend in sys.modules:
+            logger.debug(f"sys.modules contains backend:{catch_backend[0]}.")
+            return backend
     for key, module in sys.modules.items():
         catch_backend = _check_module(module)
         if catch_backend:
             break
     if len(catch_backend):
-        logger.debug(f"Find a file named:{catch_backend[1]} from sys.modules contains backend:{catch_backend[0]}.")
+        logger.debug(f"Find a module file named:{catch_backend[1]} from sys.modules contains backend:{catch_backend[0]}.")
         return catch_backend[0]
 
     return 'numpy'
@@ -227,7 +231,7 @@ class Collator:
         设置可以 pad 的 field 默认 pad 为什么类型的 tensor
 
         :param backend: 对于可以 pad 的 field，使用哪种 tensor，支持 ['torch','jittor','paddle','numpy','raw', 'auto', None]，
-            若为 auto ，则在进行 pad 的时候会根据调用的环境决定其 backend 。
+            若为 auto ，则在进行 pad 的时候会自动根据调用的环境决定其 backend 。
         :return:
         """
         assert backend in SUPPORTED_BACKENDS

fastNLP/core/collators/padders/paddle_padder.py

@@ -74,7 +74,7 @@ def _get_dtype(ele_dtype, dtype, class_name):
         elif is_numpy_generic_class(ele_dtype):
             dtype = numpy_to_paddle_dtype_dict.get(ele_dtype)
         else:
-            dtype == ele_dtype
+            dtype = ele_dtype
 
     return dtype
 
@@ -174,6 +174,5 @@ def get_padded_paddle_tensor(batch_field, dtype=None, pad_val=0):
     """
     shapes = get_shape(batch_field)
     tensor = paddle.to_tensor(np.full(shape=shapes, fill_value=pad_val), dtype=dtype)
-    # tensor = paddle.full(shape=shapes, dtype=dtype, fill_value=pad_val)
     tensor = fill_tensor(batch_field, tensor, dtype=dtype)
     return tensor

fastNLP/core/controllers/trainer.py

@@ -363,7 +363,6 @@ class Trainer(TrainerEventTrigger):
             raise e
         finally:
             self.on_train_end()
-            self.driver.barrier()
 
     def _set_num_eval_batch_per_dl(self, num_eval_batch_per_dl):
         def _evaluate_fn(trainer: Trainer, evaluate_fn: Callable) -> None:
@@ -441,6 +440,7 @@ class Trainer(TrainerEventTrigger):
         """
         _own_callbacks: List = copy.deepcopy(self._custom_callbacks["all"])
         _own_callbacks.extend(self._custom_callbacks[None])
+        logger.debug(f"Get {len(_own_callbacks)} callback fns through Trainer.on().")
         self._custom_callbacks[None] = []
         if self.marker is not None:
             if len(self._custom_callbacks[self.marker]) == 0:

fastNLP/core/dataloaders/jittor_dataloader/fdl.py

@@ -14,7 +14,7 @@ else:
     from fastNLP.core.dataset import DataSet as Dataset
 from fastNLP.core.utils.jittor_utils import jittor_collate_wraps
 from fastNLP.core.collators import Collator
-from fastNLP.core.utils.utils import indice_collate_wrapper
+from fastNLP.core.dataloaders.utils import indice_collate_wrapper
 from fastNLP.core.dataset import DataSet as FDataSet
 
 
@@ -106,33 +106,33 @@ class JittorDataLoader:
             return len(self.dataset) // self.dataset.batch_size
         return (len(self.dataset) - 1) // self.dataset.batch_size + 1
 
-    def set_pad(self, field_name: Union[str, tuple], pad_val: Union[int, float, None] = 0, dtype=None, backend=None,
-                pad_fn: Callable = None) -> "JittorDataLoader":
+    def set_pad(self, field_name:Union[str, tuple], pad_val:Union[int, float, None]=0, dtype=None, backend=None,
+                pad_fn:Callable=None) -> Collator:
         """
-            如果需要对某个 field 的内容进行特殊的调整，请使用这个函数。
-
-            :param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的，则可以直接使用对应的
-                field 的 key 来表示，如果是 nested 的 dict，可以使用元组表示多层次的 key，例如 {'a': {'b': 1}} 中的使用 ('a', 'b');
-                如果 __getitem__ 返回的是 Sequence 类型的，则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没
-                有找到，则报错；如果 __getitem__ 返回的是就是整体内容，请使用 "_single" 。
-            :param pad_val: 这个 field 的默认 pad 值。如果设置为 None，则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的
-                field 进行 pad，所以如果对应 field 本身就不是可以 pad 的形式，可以不需要主动设置为 None 。
-            :param dtype: 对于需要 pad 的 field ，该 field 的数据 dtype 应该是什么。
-            :param backend: 可选[None, 'numpy', 'torch', 'paddle', 'jittor']，分别代表，输出为 list, numpy.ndarray, torch.Tensor,
-                paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ，该值只能为 None 或 numpy 。
-            :param pad_fn: 指定当前 field 的 pad 函数，传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的
-                batch 形式。 Collator 将自动 unbatch 数据，然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch
-                形式，输出将被直接作为结果输出。
-            :return: 返回 Collator 自身
+        如果需要对某个 field 的内容进行特殊的调整，请使用这个函数。
+
+        :param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的，则可以直接使用对应的
+            field 的 key 来表示，如果是 nested 的 dict，可以使用元组表示多层次的 key，例如 {'a': {'b': 1}} 中的使用 ('a', 'b');
+            如果 __getitem__ 返回的是 Sequence 类型的，则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没
+            有找到，则报错；如果 __getitem__ 返回的是就是整体内容，请使用 "_single" 。
+        :param pad_val: 这个 field 的默认 pad 值。如果设置为 None，则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的
+            field 进行 pad，所以如果对应 field 本身就不是可以 pad 的形式，可以不需要主动设置为 None 。如果 backend 为 None ，该值
+            无意义。
+        :param dtype: 对于需要 pad 的 field ，该 field 的数据 dtype 应该是什么。
+        :param backend: 可选['raw', 'numpy', 'torch', 'paddle', 'jittor', 'auto']，分别代表，输出为 list, numpy.ndarray,
+            torch.Tensor, paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ，该值无意义 。
+        :param pad_fn: 指定当前 field 的 pad 函数，传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的
+            batch 形式。 Collator 将自动 unbatch 数据，然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch
+            形式，输出将被直接作为结果输出。
+        :return: 返回 Collator 自身
         """
         if isinstance(self._collate_fn, Collator):
-            self._collate_fn.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn,
-                                     backend=backend)
-            return self
+            self._collate_fn.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn, backend=backend)
+            return self._collate_fn
         else:
-            raise ValueError(f"collate_fn is not fastnlp collator")
+            raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_pad() is allowed.")
 
-    def set_ignore(self, *field_names) -> "JittorDataLoader":
+    def set_ignore(self, *field_names) -> Collator:
         """
         如果有的内容不希望输出，可以在此处进行设置，被设置的 field 将在 batch 的输出中被忽略。
         Ex::
@@ -145,18 +145,17 @@ class JittorDataLoader:
         """
         if isinstance(self._collate_fn, Collator):
             self._collate_fn.set_ignore(*field_names)
-            return self
+            return self._collate_fn
         else:
-            raise ValueError(f"collate_fn is not fastnlp collator")
+            raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_ignore() is allowed.")
 
     def get_batch_indices(self) -> List[int]:
         """
-        获取当前数据的idx
+        获取当前 batch 的 idx
 
         :return:
         """
         return self.cur_batch_indices
 
-
 def prepare_jittor_dataloader():
     ...

fastNLP/core/dataloaders/paddle_dataloader/fdl.py

@@ -15,8 +15,9 @@ else:
     from fastNLP.core.utils.dummy_class import DummyClass as DataLoader
 
 from fastNLP.core.collators.collator import Collator
-from fastNLP.core.utils.utils import indice_collate_wrapper
+from fastNLP.core.dataloaders.utils import indice_collate_wrapper
 from fastNLP.core.dataset import DataSet as FDataSet
+from fastNLP.core.samplers import ReproducibleBatchSampler, RandomBatchSampler
 
 
 class _PaddleDataset(Dataset):
@@ -54,6 +55,10 @@ class PaddleDataLoader(DataLoader):
         if not isinstance(dataset, _PaddleDataset):
             dataset = _PaddleDataset(dataset)
 
+        if batch_sampler is None:
+            batch_sampler = RandomBatchSampler(dataset, batch_size=batch_size, shuffle=shuffle,
+                                               drop_last=drop_last)
+
         super(PaddleDataLoader, self).__init__(dataset=dataset, feed_list=feed_list, places=places,
                                                return_list=return_list, batch_sampler=batch_sampler,
                                                batch_size=batch_size, shuffle=shuffle, drop_last=drop_last,
@@ -66,8 +71,6 @@ class PaddleDataLoader(DataLoader):
                 if isinstance(dataset.dataset, FDataSet):
                     self._collate_fn = dataset.dataset.collator
                     self._collate_fn.set_backend(backend="paddle")
-                    # if collate_fn is not None:
-                    #     self._collate_fn.add_collator(collate_fn)
                 else:
                     self._collate_fn = Collator(backend="paddle")
 
@@ -94,33 +97,33 @@ class PaddleDataLoader(DataLoader):
             self.cur_batch_indices = indices
             yield data
 
-    def set_pad(self, field_name: Union[str, tuple], pad_val: Union[int, float, None] = 0, dtype=None, backend=None,
-                pad_fn: Callable = None) -> "PaddleDataLoader":
+    def set_pad(self, field_name:Union[str, tuple], pad_val:Union[int, float, None]=0, dtype=None, backend=None,
+                pad_fn:Callable=None) -> Collator:
         """
-            如果需要对某个 field 的内容进行特殊的调整，请使用这个函数。
-
-            :param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的，则可以直接使用对应的
-                field 的 key 来表示，如果是 nested 的 dict，可以使用元组表示多层次的 key，例如 {'a': {'b': 1}} 中的使用 ('a', 'b');
-                如果 __getitem__ 返回的是 Sequence 类型的，则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没
-                有找到，则报错；如果 __getitem__ 返回的是就是整体内容，请使用 "_single" 。
-            :param pad_val: 这个 field 的默认 pad 值。如果设置为 None，则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的
-                field 进行 pad，所以如果对应 field 本身就不是可以 pad 的形式，可以不需要主动设置为 None 。
-            :param dtype: 对于需要 pad 的 field ，该 field 的数据 dtype 应该是什么。
-            :param backend: 可选[None, 'numpy', 'torch', 'paddle', 'jittor']，分别代表，输出为 list, numpy.ndarray, torch.Tensor,
-                paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ，该值只能为 None 或 numpy 。
-            :param pad_fn: 指定当前 field 的 pad 函数，传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的
-                batch 形式。 Collator 将自动 unbatch 数据，然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch
-                形式，输出将被直接作为结果输出。
-            :return: 返回 Collator 自身
+        如果需要对某个 field 的内容进行特殊的调整，请使用这个函数。
+
+        :param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的，则可以直接使用对应的
+            field 的 key 来表示，如果是 nested 的 dict，可以使用元组表示多层次的 key，例如 {'a': {'b': 1}} 中的使用 ('a', 'b');
+            如果 __getitem__ 返回的是 Sequence 类型的，则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没
+            有找到，则报错；如果 __getitem__ 返回的是就是整体内容，请使用 "_single" 。
+        :param pad_val: 这个 field 的默认 pad 值。如果设置为 None，则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的
+            field 进行 pad，所以如果对应 field 本身就不是可以 pad 的形式，可以不需要主动设置为 None 。如果 backend 为 None ，该值
+            无意义。
+        :param dtype: 对于需要 pad 的 field ，该 field 的数据 dtype 应该是什么。
+        :param backend: 可选['raw', 'numpy', 'torch', 'paddle', 'jittor', 'auto']，分别代表，输出为 list, numpy.ndarray,
+            torch.Tensor, paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ，该值无意义 。
+        :param pad_fn: 指定当前 field 的 pad 函数，传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的
+            batch 形式。 Collator 将自动 unbatch 数据，然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch
+            形式，输出将被直接作为结果输出。
+        :return: 返回 Collator 自身
         """
         if isinstance(self._collate_fn, Collator):
-            self._collate_fn.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn,
-                                     backend=backend)
-            return self
+            self._collate_fn.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn, backend=backend)
+            return self._collate_fn
         else:
-            raise ValueError(f"collate_fn is not fastnlp collator")
+            raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_pad() is allowed.")
 
-    def set_ignore(self, *field_names) -> "PaddleDataLoader":
+    def set_ignore(self, *field_names) -> Collator:
         """
         如果有的内容不希望输出，可以在此处进行设置，被设置的 field 将在 batch 的输出中被忽略。
         Ex::
@@ -133,13 +136,13 @@ class PaddleDataLoader(DataLoader):
         """
         if isinstance(self._collate_fn, Collator):
             self._collate_fn.set_ignore(*field_names)
-            return self
+            return self._collate_fn
         else:
-            raise ValueError(f"collate_fn is not fastnlp collator")
+            raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_ignore() is allowed.")
 
     def get_batch_indices(self) -> List[int]:
         """
-        获取当前数据的idx
+        获取当前 batch 的 idx
 
         :return:
         """
@@ -147,7 +150,8 @@ class PaddleDataLoader(DataLoader):
 
 
 def prepare_paddle_dataloader(ds_or_db, feed_list=None, places=None,
-                              return_list: bool = True, batch_sampler=None,
+                              return_list: bool = True,
+                              batch_sampler: Union["Sampler[Sequence[int]]", ReproducibleBatchSampler] = None,
                               train_batch_size: int = 1, shuffle: bool = False,
                               drop_last: bool = False, collate_fn: Union[Callable, str, None] = None,
                               num_workers: int = 0, use_buffer_reader: bool = True,

fastNLP/core/dataloaders/torch_dataloader/fdl.py

@@ -3,14 +3,14 @@ __all__ = [
     'prepare_torch_dataloader'
 ]
 
-from typing import Optional, Callable, Sequence, List, Union, Tuple, Dict, Mapping
+from typing import Optional, Callable, Sequence, Union, Tuple, Dict, Mapping
 
 from fastNLP.core.dataset import DataSet
 from fastNLP.core.collators import Collator
-from fastNLP.core.utils.utils import indice_collate_wrapper
+from fastNLP.core.dataloaders.utils import indice_collate_wrapper
 from fastNLP.io.data_bundle import DataBundle
 from fastNLP.envs.imports import _NEED_IMPORT_TORCH
-from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, UnrepeatedSampler
+from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, UnrepeatedSampler, RandomSampler
 
 if _NEED_IMPORT_TORCH:
     from torch.utils.data import DataLoader, Sampler
@@ -76,6 +76,9 @@ class TorchDataLoader(DataLoader):
         if not isinstance(dataset, _FDataSet):
             dataset = _FDataSet(dataset)
 
+        if sampler is None and batch_sampler is None:
+            sampler = RandomSampler(dataset, shuffle=shuffle)
+
         super().__init__(dataset=dataset, batch_size=batch_size, shuffle=shuffle, sampler=sampler,
                          batch_sampler=batch_sampler, num_workers=num_workers, collate_fn=None,
                          pin_memory=pin_memory, drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
@@ -87,9 +90,6 @@ class TorchDataLoader(DataLoader):
                 if isinstance(dataset.dataset, DataSet):  # 使用了 fastnlp dataset
                     self._collate_fn = dataset.dataset.collator
                     self._collate_fn.set_backend(backend="torch")
-                    # if collate_fn is not None and collate_fn is not default_collate:
-                    #     # 防止ddp重新初始化时候将torch dataloader的默认collate加进来
-                    #     self._collate_fn.add_collator(collate_fn)
                 else:
                     self._collate_fn = Collator(backend="torch")
             else:
@@ -112,31 +112,32 @@ class TorchDataLoader(DataLoader):
             yield data
 
     def set_pad(self, field_name:Union[str, tuple], pad_val:Union[int, float, None]=0, dtype=None, backend=None,
-                pad_fn:Callable=None) -> "TorchDataLoader":
+                pad_fn:Callable=None) -> Collator:
         """
-            如果需要对某个 field 的内容进行特殊的调整，请使用这个函数。
+        如果需要对某个 field 的内容进行特殊的调整，请使用这个函数。
 
-            :param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的，则可以直接使用对应的
-                field 的 key 来表示，如果是 nested 的 dict，可以使用元组表示多层次的 key，例如 {'a': {'b': 1}} 中的使用 ('a', 'b');
-                如果 __getitem__ 返回的是 Sequence 类型的，则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没
-                有找到，则报错；如果 __getitem__ 返回的是就是整体内容，请使用 "_single" 。
-            :param pad_val: 这个 field 的默认 pad 值。如果设置为 None，则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的
-                field 进行 pad，所以如果对应 field 本身就不是可以 pad 的形式，可以不需要主动设置为 None 。
-            :param dtype: 对于需要 pad 的 field ，该 field 的数据 dtype 应该是什么。
-            :param backend: 可选[None, 'numpy', 'torch', 'paddle', 'jittor']，分别代表，输出为 list, numpy.ndarray, torch.Tensor,
-                paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ，该值只能为 None 或 numpy 。
-            :param pad_fn: 指定当前 field 的 pad 函数，传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的
-                batch 形式。 Collator 将自动 unbatch 数据，然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch
-                形式，输出将被直接作为结果输出。
-            :return: 返回 Collator 自身
+        :param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的，则可以直接使用对应的
+            field 的 key 来表示，如果是 nested 的 dict，可以使用元组表示多层次的 key，例如 {'a': {'b': 1}} 中的使用 ('a', 'b');
+            如果 __getitem__ 返回的是 Sequence 类型的，则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没
+            有找到，则报错；如果 __getitem__ 返回的是就是整体内容，请使用 "_single" 。
+        :param pad_val: 这个 field 的默认 pad 值。如果设置为 None，则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的
+            field 进行 pad，所以如果对应 field 本身就不是可以 pad 的形式，可以不需要主动设置为 None 。如果 backend 为 None ，该值
+            无意义。
+        :param dtype: 对于需要 pad 的 field ，该 field 的数据 dtype 应该是什么。
+        :param backend: 可选['raw', 'numpy', 'torch', 'paddle', 'jittor', 'auto']，分别代表，输出为 list, numpy.ndarray,
+            torch.Tensor, paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ，该值无意义 。
+        :param pad_fn: 指定当前 field 的 pad 函数，传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的
+            batch 形式。 Collator 将自动 unbatch 数据，然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch
+            形式，输出将被直接作为结果输出。
+        :return: 返回 Collator 自身
         """
         if isinstance(self._collate_fn, Collator):
             self._collate_fn.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn, backend=backend)
-            return self
+            return self._collate_fn
         else:
-            raise ValueError(f"collate_fn is not fastnlp collator")
+            raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_pad() is allowed.")
 
-    def set_ignore(self, *field_names) -> "TorchDataLoader":
+    def set_ignore(self, *field_names) -> Collator:
         """
         如果有的内容不希望输出，可以在此处进行设置，被设置的 field 将在 batch 的输出中被忽略。
         Ex::
@@ -149,24 +150,15 @@ class TorchDataLoader(DataLoader):
         """
         if isinstance(self._collate_fn, Collator):
             self._collate_fn.set_ignore(*field_names)
-            return self
+            return self._collate_fn
         else:
-            raise ValueError(f"collate_fn is not fastnlp collator")
-
-    def get_batch_indices(self) -> List[int]:
-        """
-        获取当前数据的idx
-
-        :return:
-        """
-        return self.cur_batch_indices
-
+            raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_ignore() is allowed.")
 
 
 def prepare_torch_dataloader(ds_or_db: Union[DataSet, DataBundle, Sequence[DataSet], Mapping[str, DataSet]],
                              batch_size: int = 1,
-                             shuffle: bool = False, sampler: Optional["Sampler[int]"] = None,
-                             batch_sampler: Optional["Sampler[Sequence[int]]"] = None,
+                             shuffle: bool = False, sampler: Union["Sampler[int]", ReproducibleSampler, UnrepeatedSampler] = None,
+                             batch_sampler: Union["Sampler[Sequence[int]]", ReproducibleBatchSampler] = None,
                              num_workers: int = 0, collate_fn: Union[str, Callable, None] = None,
                              pin_memory: bool = False, drop_last: bool = False,
                              timeout: float = 0, worker_init_fn: Optional[Callable] = None,

fastNLP/core/dataloaders/utils.py

@@ -0,0 +1,16 @@
+def indice_collate_wrapper(func):
+    """
+    其功能是封装一层collate_fn,将dataset取到的tuple数据分离开，将idx打包为indices。
+
+    :param func: 需要修饰的函数
+    :return:
+    """
+
+    def wrapper(tuple_data):
+        indice, ins_list = [], []
+        for idx, ins in tuple_data:
+            indice.append(idx)
+            ins_list.append(ins)
+        return indice, func(ins_list)
+
+    return wrapper

fastNLP/core/dataset/dataset.py

@@ -780,7 +780,7 @@ class DataSet:
         self.collator.set_ignore(*field_names)
 
     @property
-    def collator(self):
+    def collator(self) -> Collator:
         if self._collator is None:
             self._collator = Collator()
         return self._collator

fastNLP/core/drivers/paddle_driver/fleet.py

@@ -22,7 +22,7 @@ from fastNLP.core.utils import (
     rank_zero_rm
 )
 from fastNLP.core.samplers import (
-    RandomBatchSampler,
+    ReproduceBatchSampler,
     ReproducibleSampler,
     ReproducibleBatchSampler,
     RandomSampler,
@@ -485,7 +485,7 @@ class PaddleFleetDriver(PaddleDriver):
 
             return self.model, model.forward
 
-    def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleSampler, RandomBatchSampler]],
+    def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleSampler, ReproduceBatchSampler]],
                                   reproducible: bool = False):
         r"""
         根据输入的 dataloader 得到一个 支持分布式 （distributed） 与 可复现的 (reproducible) 的 dataloader。

fastNLP/core/drivers/paddle_driver/paddle_driver.py

@@ -22,7 +22,7 @@ from fastNLP.core.log import logger
 from fastNLP.core.samplers import (
     ReproducibleBatchSampler,
     ReproducibleSampler,
-    RandomBatchSampler,
+    ReproduceBatchSampler,
     RandomSampler,
 )
 
@@ -345,7 +345,7 @@ class PaddleDriver(Driver):
             raise RuntimeError("It is not allowed to use checkpoint retraining when you do not use our or "
                                "`ReproducibleSampler`.")
         else:
-            sampler = RandomBatchSampler(
+            sampler = ReproduceBatchSampler(
                 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
@@ -476,7 +476,7 @@ class PaddleDriver(Driver):
                     res.shuffle = True
                 else:
                     res.shuffle = False
-            # RandomBatchSampler 的情况
+            # ReproduceBatchSampler 的情况
             elif hasattr(dataloader.batch_sampler, "batch_sampler"):
                 batch_sampler = dataloader.batch_sampler.batch_sampler
                 res.sampler = batch_sampler.sampler

fastNLP/core/drivers/paddle_driver/single_device.py

@@ -14,7 +14,7 @@ from fastNLP.core.utils import (
 from fastNLP.core.utils.utils import _get_fun_msg
 from fastNLP.core.samplers import (
     ReproducibleBatchSampler,
-    RandomBatchSampler,
+    ReproduceBatchSampler,
     ReproducibleSampler,
     RandomSampler,
     re_instantiate_sampler,
@@ -177,7 +177,7 @@ class PaddleSingleDriver(PaddleDriver):
                 logger.debug("Replace paddle RandomSampler into fastNLP RandomSampler.")
                 return replace_sampler(dataloader, sampler)
             else:
-                batch_sampler = RandomBatchSampler(
+                batch_sampler = ReproduceBatchSampler(
                     batch_sampler=args.batch_sampler,
                     batch_size=args.batch_size,
                     drop_last=args.drop_last

fastNLP/core/drivers/torch_driver/single_device.py

@@ -15,7 +15,7 @@ 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.utils.utils import _get_fun_msg
-from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, re_instantiate_sampler, RandomBatchSampler
+from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, re_instantiate_sampler, ReproduceBatchSampler
 from fastNLP.core.samplers import RandomSampler
 from fastNLP.core.log import logger
 
@@ -113,7 +113,7 @@ class TorchSingleDriver(TorchDriver):
                 logger.debug("Replace torch RandomSampler into fastNLP RandomSampler.")
                 return replace_sampler(dataloader, sampler)
             else:
-                batch_sampler = RandomBatchSampler(
+                batch_sampler = ReproduceBatchSampler(
                     batch_sampler=args.batch_sampler,
                     batch_size=args.batch_size,
                     drop_last=args.drop_last

fastNLP/core/drivers/torch_driver/torch_driver.py

@@ -31,7 +31,7 @@ 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, ReproducibleSampler, RandomBatchSampler, RandomSampler
+from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, ReproduceBatchSampler, RandomSampler
 
 
 class TorchDriver(Driver):
@@ -293,7 +293,7 @@ class TorchDriver(Driver):
             raise RuntimeError("It is not allowed to use checkpoint retraining when you do not use our or "
                                "`ReproducibleSampler`.")
         else:
-            sampler = RandomBatchSampler(
+            sampler = ReproduceBatchSampler(
                 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
@@ -407,7 +407,7 @@ class TorchDriver(Driver):
                     res.shuffle = True
                 else:
                     res.shuffle = False
-            # RandomBatchSampler 的情况
+            # ReproduceBatchSampler 的情况
             elif hasattr(dataloader.batch_sampler, "batch_sampler"):
                 batch_sampler = dataloader.batch_sampler.batch_sampler
                 res.sampler = batch_sampler.sampler

fastNLP/core/samplers/__init__.py

@@ -14,9 +14,10 @@ __all__ = [
     "UnrepeatedSortedSampler",
     "UnrepeatedSequentialSampler",
 
-    "RandomBatchSampler",
+    "ReproduceBatchSampler",
     "BucketedBatchSampler",
     "ReproducibleBatchSampler",
+    "RandomBatchSampler",
 
     "re_instantiate_sampler"
 ]
@@ -26,5 +27,5 @@ from .mix_sampler import MixSampler, DopedSampler, MixSequentialSampler, Polling
 from .reproducible_sampler import ReproducibleSampler, RandomSampler, SequentialSampler, SortedSampler
 from .utils import re_instantiate_sampler
 from .conversion_utils import conversion_between_reproducible_and_unrepeated_sampler
-from .reproducible_batch_sampler import RandomBatchSampler, BucketedBatchSampler, ReproducibleBatchSampler
+from .reproducible_batch_sampler import ReproduceBatchSampler, BucketedBatchSampler, ReproducibleBatchSampler, RandomBatchSampler
 

fastNLP/core/samplers/reproducible_batch_sampler.py

@@ -1,5 +1,6 @@
 __all__ = [
     'BucketedBatchSampler',
+    "ReproduceBatchSampler",
     "RandomBatchSampler"
 ]
 
@@ -54,13 +55,13 @@ class ReproducibleBatchSampler:
         raise NotImplementedError("Each specific batch_sampler should implement its own `batch_idx_in_epoch` property.")
 
 
-class RandomBatchSampler(ReproducibleBatchSampler):
+class ReproduceBatchSampler(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。RandomBatchSampler 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。ReproduceBatchSampler 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。
@@ -143,7 +144,7 @@ class RandomBatchSampler(ReproducibleBatchSampler):
         self.need_reinitialize = False
 
     def set_distributed(self, num_replicas, rank, pad=True):
-        raise RuntimeError(f"RandomBatchSampler does not support to change to distributed training.")
+        raise RuntimeError(f"ReproduceBatchSampler does not support to change to distributed training.")
 
     def set_epoch(self, epoch):
         if hasattr(self.batch_sampler, "sampler") and hasattr(self.batch_sampler.sampler, 'set_epoch') and callable(self.batch_sampler.sampler.set_epoch):
@@ -158,6 +159,211 @@ class RandomBatchSampler(ReproducibleBatchSampler):
                    (len(self.index_list) - self.num_consumed_samples + self.batch_size - 1) // self.batch_size
 
 
+class RandomBatchSampler(ReproducibleBatchSampler):
+    def __init__(self, dataset, batch_size:int = 32, shuffle: bool = True,
+                 drop_last: bool = False, seed: int = 0, **kwargs):
+        """
+        随机分 batch 的 batch_sampler 。
+
+        :param dataset: 实现了 __len__ 方法的数据容器。
+        :param batch_size: 每个 batch 的大小
+        :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 drop_last: 如果最后一个 batch 的 sample 数量无法凑齐 batch_size 这么多，是否需要丢掉。
+        :param seed: 设置的随机数种子
+        :param kwargs: fastNLP 保留使用
+        """
+        super().__init__()
+
+        self.dataset = dataset
+
+        self.batch_size = batch_size
+        self.shuffle = shuffle
+        self.drop_last = drop_last
+        self.seed = seed
+
+        self.num_consumed_samples = kwargs.get("num_consumed_samples", 0)  # 总共迭代了多少数据了，包括多卡情况下的其它卡上的输出的数量
+
+        # 多卡的相关的参数
+        self.num_replicas = kwargs.get("num_replicas", 1)
+        self.rank = kwargs.get("rank", 0)
+        self.epoch = kwargs.get("epoch", -1)
+        self.pad = kwargs.get("pad", False)  # 该参数在单卡上不具有任何意义；
+
+        # 是否处于iteration之间，为True不允许调用 set_distributed()和load_state_dict()
+        self.during_iter = kwargs.get("during_iter", False)
+
+        # 以下变量为内部使用恢复状态的变量。
+        self.old_batch_size = kwargs.get('old_batch_size', self.batch_size)
+
+    def set_distributed(self, num_replicas, rank, pad=True):
+        assert self.during_iter is False, "Cannot set the sampler to be distributed when it is " \
+                                          "during an unfinished iteration."
+        assert num_replicas > 0 and isinstance(num_replicas, int)
+        assert isinstance(rank, int) and 0 <= rank < num_replicas
+        # 注意初始化该函数时，所有的状态都应当默认是一个 epoch 刚开始训练的状态；
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.pad = pad
+
+        return self
+
+    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 = list(range(len(self.dataset)))
+
+        if self.shuffle:
+            if self.num_consumed_samples > 0:  # 需要先按照原来的排序，删掉多余的
+                _batches = []
+                for _i in range(self.old_num_replicas):
+                    _indices = indices[_i:len(indices):self.old_num_replicas]
+                    __batches = self.batchify(_indices, self.old_batch_size, seed=self.seed + self.epoch)
+                    _batches.append(__batches)
+                batches = list(chain(*[_ for _ in zip(*_batches)]))
+                indices = list(chain(*batches))
+                indices = indices[self.num_consumed_samples:]
+            # 取出这个 rank ，
+            indices = indices[self.rank:len(indices):self.num_replicas]
+            batches = self.batchify(indices, self.batch_size, seed=self.seed + self.epoch)
+            batches = list(map(list, batches))
+        else:
+            indices = indices[self.num_consumed_samples:]
+            indices = indices[self.rank:len(indices):self.num_replicas]
+            _num_batches = len(indices) // self.batch_size
+            if _num_batches == 0:
+                batches = [indices]
+            else:
+                batches = list(map(list, np.array_split(indices[:_num_batches*self.batch_size], _num_batches)))
+                if len(indices)%self.batch_size!=0:
+                    batches.append(indices[_num_batches*self.batch_size:])
+
+        need_pad_num = (len(self.dataset)-self.num_consumed_samples) % self.num_replicas
+        if self.pad and need_pad_num !=0 and need_pad_num<=self.rank:
+            if len(batches) > 0:
+                if len(batches[-1])<self.batch_size:
+                    batches[-1].append(batches[-1][0])  # 这里可以保证这个bucket的长度没被破坏。
+                else:
+                    batches.append([batches[-1][0]])
+        elif self.pad is False and need_pad_num !=0 and need_pad_num>self.rank:
+            if len(batches):
+                batches[-1].pop(-1)
+            if len(batches[-1])==0:
+                batches.pop(-1)
+
+        assert sum(map(len, batches)) == self.num_left_samples
+
+        if self.drop_last and len(batches) >= 1 and len(batches[-1]) < self.batch_size:
+            batches = batches[:-1]
+
+        for batch in batches:
+            self.num_consumed_samples += self.num_replicas * len(batch)
+            yield list(map(int, batch))
+        self.during_iter = False
+        self.num_consumed_samples = 0
+        self.old_batch_size = self.batch_size
+        self.old_num_replicas = self.num_replicas
+        if self.epoch < 0:  # 防止用户没有修改epoch，导致每个epoch都一样了
+            self.epoch -= 1
+
+    def batchify(self, indices, batch_size, seed):
+        """
+        将 indices 分为 batches
+
+        :param sorted_indices: List[int]
+        :param batch_size: int
+        :param seed: int
+        :return:  List[List[int]]
+        """
+        # 实际的 bucket 大小
+        rng = np.random.default_rng(abs(seed))
+        rng.shuffle(indices)
+        num_samples = 0
+        batches = []
+        while num_samples<len(indices):
+            batches.append(indices[num_samples:num_samples+batch_size])
+            num_samples += batch_size
+        return batches
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch
+
+    @property
+    def batch_idx_in_epoch(self):
+        if self.drop_last:
+            return len(self.dataset) // self.num_replicas // self.batch_size - self.num_left_samples // self.batch_size
+        else:
+            return (len(self.dataset) // self.num_replicas + self.batch_size - 1) // self.batch_size - \
+                   (self.num_left_samples + self.batch_size - 1) // self.batch_size
+
+    @property
+    def total_size(self):
+        """
+        这个变量代表的含义是当前这个sampler会最终产生出的index数量（包括了其它rank的），因为replica和pad的原因，这个值可能等于、
+            大于或者小于len(dataset)
+
+        :return:
+        """
+        return self.num_consumed_samples + self.num_replicas*self.num_left_samples
+
+    @property
+    def num_left_samples(self):
+        """
+        返回当前 iteration 还有多少个 sample 结束，表示的是当前 rank 的还剩多少。
+
+        :return:
+        """
+        num_consumed_samples = self.num_consumed_samples
+        return math.ceil((len(self.dataset) - num_consumed_samples) / self.num_replicas) if \
+            self.pad else math.floor(((len(self.dataset) - num_consumed_samples) / self.num_replicas))
+
+    def __len__(self)->int:
+        """
+        返回当前 sampler 还会返回多少个 batch 的数据
+
+        :return:
+        """
+        num_sampler_per_rank = self.total_size//self.num_replicas
+        num_batches = num_sampler_per_rank//self.batch_size if self.drop_last else \
+            (num_sampler_per_rank+self.batch_size-1)//self.batch_size
+        return num_batches
+
+    def state_dict(self) -> Dict:
+        if self.old_batch_size != self.batch_size:
+            raise RuntimeError("BucketedBatchSampler does not support saving before last checkpoint states have been"
+                               " consumed. ")
+        states = {'seed': self.seed, 'epoch': self.epoch, 'num_consumed_samples': self.num_consumed_samples,
+                  'sampler_type': self.__class__.__name__, 'length': len(self.dataset), 'shuffle': self.shuffle,
+                  'batch_size': self.batch_size,
+                  'num_replicas': self.num_replicas}
+
+        return states
+
+    def load_state_dict(self, states: Dict):
+        # 如果 self.during_iter 是 True，那么 num_consumed_samples 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), "The number of samples is different between the checkpoint record " \
+                                            "and current dataset."
+        self.seed = states['seed']
+        self.epoch = states['epoch']
+        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
+        if self.shuffle != states['shuffle']:
+            logger.info(f"The shuffle from the checkpoint is {states['shuffle']}, while set as {self.shuffle}, "
+                        f"we use shuffle={states['shuffle']}")
+        self.shuffle = states["shuffle"]
+        self.old_batch_size = states['batch_size']
+        self.old_num_replicas = states['num_replicas']
+
+
 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

@@ -54,13 +54,12 @@ class RandomSampler(ReproducibleSampler):
     def __init__(self, dataset, shuffle: bool = True, seed: int = 0, **kwargs):
         """
 
-
         :param dataset: 实现了 __len__ 方法的数据容器
         :param shuffle: 是否在每次 iterate 的时候打乱顺序。
         :param seed: 随机数种子。
         :param kwargs: 用户不需要使用，fastNLP 内部使用
         """
-
+        super(RandomSampler, self).__init__()
         self.dataset = dataset
         self.shuffle = shuffle
         self.seed = seed

fastNLP/core/utils/__init__.py

@@ -21,7 +21,6 @@ __all__ = [
     'nullcontext',
     'pretty_table_printer',
     'Option',
-    'indice_collate_wrapper',
     'deprecated',
     'seq_len_to_mask',
     'rank_zero_rm',
@@ -37,6 +36,7 @@ from .torch_paddle_utils import torch_paddle_move_data_to_device
 from .torch_utils import torch_move_data_to_device
 from .utils import get_fn_arg_names, auto_param_call, check_user_specific_params, \
     dataclass_to_dict, match_and_substitute_params, apply_to_collection, nullcontext, pretty_table_printer, Option, \
-    indice_collate_wrapper, deprecated, seq_len_to_mask, rank_zero_rm, rank_zero_mkdir
+    deprecated, seq_len_to_mask, rank_zero_rm, rank_zero_mkdir
+from ..dataloaders.utils import indice_collate_wrapper
 
 

fastNLP/core/utils/utils.py

@@ -6,7 +6,7 @@ import warnings
 from dataclasses import is_dataclass
 from copy import deepcopy
 from collections import defaultdict, OrderedDict
-from typing import Callable, List, Any, Dict, AnyStr, Union, Mapping, Sequence, Optional
+from typing import Callable, List, Any, Dict, AnyStr, Union, Mapping, Sequence
 from typing import Tuple, Optional
 from time import sleep
 
@@ -35,7 +35,6 @@ __all__ = [
     'nullcontext',
     'pretty_table_printer',
     'Option',
-    'indice_collate_wrapper',
     'deprecated',
     'seq_len_to_mask',
     'rank_zero_rm',
@@ -513,24 +512,6 @@ class Option(dict):
         self.update(state)
 
 
-def indice_collate_wrapper(func):
-    """
-    其功能是封装一层collate_fn,将dataset取到的tuple数据分离开，将idx打包为indices。
-
-    :param func: 需要修饰的函数
-    :return:
-    """
-
-    def wrapper(tuple_data):
-        indice, ins_list = [], []
-        for idx, ins in tuple_data:
-            indice.append(idx)
-            ins_list.append(ins)
-        return indice, func(ins_list)
-
-    return wrapper
-
-
 _emitted_deprecation_warnings = set()
 
 

tests/core/drivers/paddle_driver/test_single_device.py

@@ -2,7 +2,7 @@ import pytest
 from pathlib import Path
 
 from fastNLP.core.drivers.paddle_driver.single_device import PaddleSingleDriver
-from fastNLP.core.samplers import RandomBatchSampler, RandomSampler
+from fastNLP.core.samplers import ReproduceBatchSampler, RandomSampler
 from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
 from tests.helpers.datasets.paddle_data import PaddleNormalDataset, PaddleRandomMaxDataset
 from tests.helpers.datasets.torch_data import TorchNormalDataset
@@ -278,7 +278,7 @@ class TestPaddleDriverFunctions:
         dataset = PaddleNormalDataset()
         dataloader = DataLoader(
             dataset,
-            batch_sampler=RandomBatchSampler(
+            batch_sampler=ReproduceBatchSampler(
                 BatchSampler(dataset, batch_size=batch_size, shuffle=shuffle),
                 batch_size, 
                 drop_last,
@@ -287,7 +287,7 @@ class TestPaddleDriverFunctions:
         res = PaddleSingleDriver.get_dataloader_args(dataloader)
 
         assert isinstance(res.dataset, PaddleNormalDataset)
-        assert isinstance(res.batch_sampler, RandomBatchSampler)
+        assert isinstance(res.batch_sampler, ReproduceBatchSampler)
         if shuffle:
             assert isinstance(res.sampler, paddle.io.RandomSampler)
         else:
@@ -387,7 +387,7 @@ class TestSetDistReproDataloader:
         """
         测试 set_dist_repro_dataloader 参数 `reproducible` 为 True 时的表现
         当dist为字符串时，此时应该返回新的 dataloader，且如果原 sampler 为 paddle.io.RandomSampler（shuffle=True），
-        只会替换 Sampler 为 RandomSampler；否则会替换 batch_sampler 为 RandomBatchSampler
+        只会替换 Sampler 为 RandomSampler；否则会替换 batch_sampler 为 ReproduceBatchSampler
         """
         dataloader = DataLoader(self.dataset, batch_size=2, shuffle=shuffle)
         replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist="dist", reproducible=True)
@@ -400,7 +400,7 @@ class TestSetDistReproDataloader:
             assert isinstance(replaced_loader.batch_sampler.sampler, RandomSampler)
         else:
             # 此时会替换 batch_sampler
-            assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler)
+            assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
             assert isinstance(replaced_loader.batch_sampler.batch_sampler, BatchSampler)
         assert replaced_loader.batch_sampler.batch_size == dataloader.batch_sampler.batch_size
         assert replaced_loader.drop_last == dataloader.drop_last
@@ -414,11 +414,11 @@ class TestSetDistReproDataloader:
         应该返回新的 dataloader，并将 batch_sampler 替换为 dist 对应的 Sampler
         """
         dataloader = DataLoader(self.dataset, batch_size=2, shuffle=not shuffle)
-        dist = RandomBatchSampler(BatchSampler(self.dataset, batch_size=4, shuffle=shuffle), 4, False)
+        dist = ReproduceBatchSampler(BatchSampler(self.dataset, batch_size=4, shuffle=shuffle), 4, False)
         replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist=dist, reproducible=False)
 
         assert not (replaced_loader is dataloader)
-        assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler)
+        assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
         assert replaced_loader.batch_sampler is dist
 
         self.check_set_dist_repro_dataloader(dataloader, replaced_loader, shuffle)
@@ -450,7 +450,7 @@ class TestSetDistReproDataloader:
         """
         dataloader = DataLoader(
             dataset=self.dataset,
-            batch_sampler=RandomBatchSampler(
+            batch_sampler=ReproduceBatchSampler(
                 BatchSampler(self.dataset, batch_size=4, shuffle=shuffle),
                 batch_size=4,
                 drop_last=False,
@@ -459,7 +459,7 @@ class TestSetDistReproDataloader:
         replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist="dist", reproducible=False)
 
         assert not (replaced_loader is dataloader)
-        assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler)
+        assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
         assert not (replaced_loader.batch_sampler is dataloader.batch_sampler)
         assert replaced_loader.batch_sampler.batch_size == dataloader.batch_sampler.batch_size
         assert replaced_loader.drop_last == dataloader.drop_last
@@ -500,20 +500,20 @@ class TestSetDistReproDataloader:
             if idx >= num_consumed_batches:
                 break
             already_seen_idx.update(batch)
-        if isinstance(replaced_loader.batch_sampler, RandomBatchSampler):
+        if isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler):
             sampler_states = replaced_loader.batch_sampler.state_dict()
         else:
             sampler_states = replaced_loader.batch_sampler.sampler.state_dict()
 
         # 重新加载，应该可以输出剩下的内容，且对于 PaddleNormalDataset 来说，排序后应该是一个 range
         left_idxes = set()
-        if isinstance(replaced_loader.batch_sampler, RandomBatchSampler):
+        if isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler):
             batch_size = replaced_loader.batch_sampler.batch_size
             sampler_states["num_consumed_samples"] = num_consumed_batches * batch_size
             # 重新改造 dataloader
             new_loader = DataLoader(
                 dataset=replaced_loader.dataset,
-                batch_sampler=RandomBatchSampler(
+                batch_sampler=ReproduceBatchSampler(
                     BatchSampler(replaced_loader.dataset, shuffle=shuffle, batch_size=batch_size),
                     batch_size=batch_size,
                     drop_last=False,
@@ -603,7 +603,7 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16):
         dataset = PaddleRandomMaxDataset(40, 10)
         dataloader = DataLoader(
             dataset=dataset,
-            batch_sampler=RandomBatchSampler(BatchSampler(dataset, batch_size=4), 4, False)
+            batch_sampler=ReproduceBatchSampler(BatchSampler(dataset, batch_size=4), 4, False)
         )
         driver1, driver2 = generate_random_driver(10, 10, fp16, "gpu"), generate_random_driver(10, 10, False, "gpu")
 
@@ -627,7 +627,7 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16):
         # 更改 batch_size
         dataloader = DataLoader(
             dataset=dataset,
-            batch_sampler=RandomBatchSampler(BatchSampler(dataset, batch_size=2, shuffle=True), 2, False)
+            batch_sampler=ReproduceBatchSampler(BatchSampler(dataset, batch_size=2, shuffle=True), 2, False)
         )
         load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True)
         replaced_loader = load_states.pop("dataloader")
@@ -637,7 +637,7 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16):
         # 2. 检查 batch_sampler 是否被正确地加载和替换
         assert not (replaced_loader is dataloader)
         assert replaced_loader.batch_sampler is dataloader.batch_sampler
-        assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler)
+        assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
         assert replaced_loader.batch_sampler.index_list == sampler_states["index_list"]
         assert replaced_loader.batch_sampler.num_consumed_samples == num_consumed_batches * 4
 

tests/core/drivers/paddle_driver/test_utils.py

@@ -6,7 +6,7 @@ from fastNLP.core.drivers.paddle_driver.utils import (
     replace_batch_sampler,
     replace_sampler,
 )
-from fastNLP.core.samplers import RandomBatchSampler, RandomSampler
+from fastNLP.core.samplers import ReproduceBatchSampler, RandomSampler
 from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
 if _NEED_IMPORT_PADDLE:
     import paddle
@@ -36,12 +36,12 @@ def test_get_device_from_visible_str(user_visible_devices, cuda_visible_devices,
 def test_replace_batch_sampler():
     dataset = PaddleNormalDataset(10)
     dataloader = DataLoader(dataset, batch_size=32)
-    batch_sampler = RandomBatchSampler(dataloader.batch_sampler, batch_size=16, drop_last=False)
+    batch_sampler = ReproduceBatchSampler(dataloader.batch_sampler, batch_size=16, drop_last=False)
 
     replaced_loader = replace_batch_sampler(dataloader, batch_sampler)
 
     assert not (replaced_loader is dataloader)
-    assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler)
+    assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
     assert isinstance(replaced_loader.dataset, PaddleNormalDataset)
     assert len(replaced_loader.dataset) == len(dataset)
     assert replaced_loader.batch_sampler.batch_size == 16

tests/core/drivers/torch_driver/test_single_device.py

@@ -2,7 +2,7 @@ import pytest
 from pathlib import Path
 
 from fastNLP.core.drivers.torch_driver.single_device import TorchSingleDriver
-from fastNLP.core.samplers import RandomBatchSampler, RandomSampler
+from fastNLP.core.samplers import ReproduceBatchSampler, RandomSampler
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
 from tests.helpers.datasets.torch_data import TorchNormalDataset, TorchArgMaxDataset
 from tests.helpers.datasets.paddle_data import PaddleNormalDataset
@@ -17,7 +17,7 @@ if _NEED_IMPORT_PADDLE:
 
 def dataloader_with_randombatchsampler(dataset, batch_size, shuffle, drop_last):
     """
-    建立一个 batch_sampler 为 RandomBatchSampler 的 dataloader
+    建立一个 batch_sampler 为 ReproduceBatchSampler 的 dataloader
     """
     if shuffle:
         sampler = torch.utils.data.RandomSampler(dataset)
@@ -25,7 +25,7 @@ def dataloader_with_randombatchsampler(dataset, batch_size, shuffle, drop_last):
         sampler = torch.utils.data.SequentialSampler(dataset)
     dataloader = DataLoader(
         dataset=dataset,
-        batch_sampler=RandomBatchSampler(
+        batch_sampler=ReproduceBatchSampler(
             BatchSampler(
                 sampler, batch_size=batch_size, drop_last=drop_last
             ),
@@ -306,7 +306,7 @@ class TestTorchDriverFunctions:
         res = TorchSingleDriver.get_dataloader_args(dataloader)
 
         assert isinstance(res.dataset, TorchNormalDataset)
-        assert isinstance(res.batch_sampler, RandomBatchSampler)
+        assert isinstance(res.batch_sampler, ReproduceBatchSampler)
         if shuffle:
             assert isinstance(res.sampler, torch.utils.data.RandomSampler)
         else:
@@ -401,7 +401,7 @@ class TestSetDistReproDataloader:
         """
         测试 set_dist_repro_dataloader 参数 `reproducible` 为 True 时的表现
         当dist为字符串时，此时应该返回新的 dataloader，且如果原 sampler 为 torch.utils.data.RandomSampler（shuffle=True），
-        只会替换 Sampler 为 RandomSampler；否则会替换 batch_sampler 为 RandomBatchSampler
+        只会替换 Sampler 为 RandomSampler；否则会替换 batch_sampler 为 ReproduceBatchSampler
         """
         dataloader = DataLoader(self.dataset, batch_size=2, shuffle=shuffle)
         replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist="dist", reproducible=True)
@@ -414,7 +414,7 @@ class TestSetDistReproDataloader:
             assert isinstance(replaced_loader.batch_sampler.sampler, RandomSampler)
         else:
             # 此时会替换 batch_sampler
-            assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler)
+            assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
             assert isinstance(replaced_loader.batch_sampler.batch_sampler, BatchSampler)
         assert replaced_loader.batch_sampler.batch_size == dataloader.batch_sampler.batch_size
         assert replaced_loader.drop_last == dataloader.drop_last
@@ -428,11 +428,11 @@ class TestSetDistReproDataloader:
         应该返回新的 dataloader，并将 batch_sampler 替换为 dist 对应的 Sampler
         """
         dataloader = DataLoader(self.dataset, batch_size=2, shuffle=shuffle)
-        dist = RandomBatchSampler(BatchSampler(self.dataset, batch_size=4, drop_last=False), 4, False)
+        dist = ReproduceBatchSampler(BatchSampler(self.dataset, batch_size=4, drop_last=False), 4, False)
         replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist=dist, reproducible=False)
 
         assert not (replaced_loader is dataloader)
-        assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler)
+        assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
         assert replaced_loader.batch_sampler is dist
 
         self.check_set_dist_repro_dataloader(dataloader, replaced_loader, shuffle)
@@ -466,7 +466,7 @@ class TestSetDistReproDataloader:
         replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist="dist", reproducible=False)
 
         assert not (replaced_loader is dataloader)
-        assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler)
+        assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
         assert not (replaced_loader.batch_sampler is dataloader.batch_sampler)
         assert replaced_loader.batch_sampler.batch_size == dataloader.batch_sampler.batch_size
         assert replaced_loader.drop_last == dataloader.drop_last
@@ -502,14 +502,14 @@ class TestSetDistReproDataloader:
             if idx >= num_consumed_batches:
                 break
             already_seen_idx.update(batch)
-        if isinstance(replaced_loader.batch_sampler, RandomBatchSampler):
+        if isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler):
             sampler_states = replaced_loader.batch_sampler.state_dict()
         else:
             sampler_states = replaced_loader.batch_sampler.sampler.state_dict()
 
         # 重新加载，应该可以输出剩下的内容，且对于 TorchNormalDataset 来说，排序后应该是一个 range
         left_idxes = set()
-        if isinstance(replaced_loader.batch_sampler, RandomBatchSampler):
+        if isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler):
             batch_size = replaced_loader.batch_sampler.batch_size
             sampler_states["num_consumed_samples"] = num_consumed_batches * batch_size
             # 重新改造 dataloader
@@ -613,7 +613,7 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16):
         # 2. 检查 batch_sampler 是否被正确地加载和替换
         assert not (replaced_loader is dataloader)
         assert replaced_loader.batch_sampler is dataloader.batch_sampler
-        assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler)
+        assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
         assert replaced_loader.batch_sampler.index_list == sampler_states["index_list"]
         assert replaced_loader.batch_sampler.num_consumed_samples == num_consumed_batches * 4
 

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，RandomBatchSampler
+    # dist_sampler 可以选择的有['dist', 'unrepeatdist', None]或者是ReproducibleSampler，ReproduceBatchSampler
     # reproducible 是 True 和 False
 
     # 需要 check 返回的 sampler 和 dataloader 都不同了

tests/core/drivers/torch_driver/test_utils.py

@@ -4,7 +4,7 @@ from fastNLP.core.drivers.torch_driver.utils import (
     replace_batch_sampler,
     replace_sampler,
 )
-from fastNLP.core.samplers import RandomBatchSampler, RandomSampler
+from fastNLP.core.samplers import ReproduceBatchSampler, RandomSampler
 from torch.utils.data import DataLoader, BatchSampler
 
 from tests.helpers.datasets.torch_data import TorchNormalDataset
@@ -14,12 +14,12 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset
 def test_replace_batch_sampler():
     dataset = TorchNormalDataset(10)
     dataloader = DataLoader(dataset, batch_size=32)
-    batch_sampler = RandomBatchSampler(dataloader.batch_sampler, batch_size=16, drop_last=False)
+    batch_sampler = ReproduceBatchSampler(dataloader.batch_sampler, batch_size=16, drop_last=False)
 
     replaced_loader = replace_batch_sampler(dataloader, batch_sampler)
 
     assert not (replaced_loader is dataloader)
-    assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler)
+    assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
     assert isinstance(replaced_loader.dataset, TorchNormalDataset)
     assert len(replaced_loader.dataset) == len(dataset)
     assert replaced_loader.batch_sampler.batch_size == 16

tests/core/samplers/test_reproducible_batch_sampler.py

@@ -5,7 +5,7 @@ import pytest
 from itertools import chain
 from copy import deepcopy
 
-from fastNLP.core.samplers import RandomBatchSampler, BucketedBatchSampler
+from fastNLP.core.samplers import ReproduceBatchSampler, BucketedBatchSampler, RandomBatchSampler
 from fastNLP.core.drivers.torch_driver.utils import replace_batch_sampler
 from tests.helpers.datasets.torch_data import TorchNormalDataset
 
@@ -19,7 +19,7 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset
 #         before_batch_size = 7
 #         dataset = TorchNormalDataset(num_of_data=100)
 #         dataloader = DataLoader(dataset, batch_size=before_batch_size)
-#         re_batchsampler = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
+#         re_batchsampler = ReproduceBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
 #         dataloader = replace_batch_sampler(dataloader, re_batchsampler)
 #
 #         forward_steps = 3
@@ -29,15 +29,15 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset
 #
 #         # 1. 保存状态
 #         _get_re_batchsampler = dataloader.batch_sampler
-#         assert isinstance(_get_re_batchsampler, RandomBatchSampler)
+#         assert isinstance(_get_re_batchsampler, ReproduceBatchSampler)
 #         state = _get_re_batchsampler.state_dict()
 #         assert state == {"index_list": array("I", list(range(100))), "num_consumed_samples": forward_steps*before_batch_size,
-#                          "sampler_type": "RandomBatchSampler"}
+#                          "sampler_type": "ReproduceBatchSampler"}
 #
 #         # 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 = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
+#         re_batchsampler = ReproduceBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
 #         re_batchsampler.load_state_dict(state)
 #         dataloader = replace_batch_sampler(dataloader, re_batchsampler)
 #
@@ -54,7 +54,7 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset
 #         # 改变 batch_size；
 #         after_batch_size = 3
 #         dataloader = DataLoader(dataset, batch_size=after_batch_size)
-#         re_batchsampler = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
+#         re_batchsampler = ReproduceBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
 #         re_batchsampler.load_state_dict(state)
 #         dataloader = replace_batch_sampler(dataloader, re_batchsampler)
 #
@@ -100,7 +100,7 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset
 #         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 = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
+#         re_batchsampler = ReproduceBatchSampler(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；
@@ -112,13 +112,13 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset
 #
 #         # 1. 保存状态
 #         _get_re_batchsampler = dataloader.batch_sampler
-#         assert isinstance(_get_re_batchsampler, RandomBatchSampler)
+#         assert isinstance(_get_re_batchsampler, ReproduceBatchSampler)
 #         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 = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
+#         re_batchsampler = ReproduceBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False)
 #         re_batchsampler.load_state_dict(state)
 #         dataloader = replace_batch_sampler(dataloader, re_batchsampler)
 #
@@ -511,3 +511,313 @@ class TestBucketedBatchSampler:
             already_seen_set.update(batch)
 
         assert len(already_seen_set)==len(dataset) if drop_last is False else len(already_seen_set)<=len(dataset)
+
+
+class TestRandomBatchSampler:
+    @pytest.mark.parametrize('shuffle', [True, False])
+    @pytest.mark.parametrize('drop_last', [True, False])
+    @pytest.mark.parametrize('num', [2, 7, 14, 15, 70, 71])
+    def test_single_num_batch(self, shuffle, drop_last, num):
+        # 数量不够不报错
+        for num in [2, 7, 14, 15, 70, 71]:
+            dataset = DatasetWithVaryLength(num_of_data=num)
+            before_batch_size = 7
+            re_batchsampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=before_batch_size,
+                                                   drop_last=drop_last,
+                                                   shuffle=shuffle)
+            count = len(list(iter(re_batchsampler)))
+            if drop_last:
+                assert count==num//before_batch_size, num
+            else:
+                assert count==(num+before_batch_size-1)//before_batch_size, num
+
+    @pytest.mark.parametrize('shuffle', [True, False])
+    @pytest.mark.parametrize('drop_last', [True, False])
+    def test_single(self, shuffle, drop_last):
+
+        before_batch_size = 7
+        num_batch_per_bucket = 4  # 那么任意 batch 内的长度差值不应该超过4
+
+        dataset = DatasetWithVaryLength(num_of_data=1000)
+        re_batchsampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=before_batch_size,
+                                               drop_last=drop_last,
+                                               shuffle=shuffle)
+        re_batchsampler.set_epoch(0)
+        forward_steps = 10
+        iterator = iter(re_batchsampler)
+        already_generate_indices = set()
+        for _ in range(forward_steps):
+            batch = next(iterator)
+            already_generate_indices.update(batch)
+
+        # 1. 保存状态
+        state = re_batchsampler.state_dict()
+
+        # 2. 断点重训，继续训练
+        re_batchsampler2 = RandomBatchSampler(dataset, length=dataset.data, batch_size=before_batch_size,
+                                                drop_last=drop_last,
+                                                shuffle=shuffle)
+        re_batchsampler2.load_state_dict(state)
+        re_batchsampler2.set_epoch(0)
+        new_already_generate_indices = set()
+        mask = np.ones(len(dataset), dtype=bool)
+        mask[list(already_generate_indices)] = 0
+        indices = np.arange(len(dataset))[mask]
+        max_diff = -1
+        for i in range(len(indices)-before_batch_size * num_batch_per_bucket):
+            max_diff = max(max_diff, indices[i+before_batch_size * num_batch_per_bucket]-indices[i])
+        for batch in re_batchsampler2:
+            for b in batch:
+                assert b not in already_generate_indices
+            new_already_generate_indices.update(batch)
+        if drop_last is False:
+            assert len(new_already_generate_indices.union(already_generate_indices))==len(dataset)
+
+        # 改变 batch_size；
+        after_batch_size = 3
+        re_batchsampler3 = RandomBatchSampler(dataset, length=dataset.data, batch_size=after_batch_size,
+                                                drop_last=drop_last,
+                                                shuffle=shuffle)
+        re_batchsampler3.load_state_dict(state)
+        re_batchsampler3.set_epoch(0)
+        count = 0
+
+        mask = np.ones(len(dataset), dtype=bool)
+        mask[list(already_generate_indices)] = 0
+        indices = np.arange(len(dataset))[mask]
+
+        for batch in re_batchsampler3:
+            for b in batch:
+                assert b not in already_generate_indices
+            already_generate_indices.update(batch)
+            count += 1
+            if count > 5:
+                break
+
+        # 再 save ，不允许再上个epoch没结束继续sample
+        after_batch_size = 5
+        with pytest.raises(RuntimeError):
+            state = re_batchsampler3.state_dict()
+
+        for batch in re_batchsampler3:  # consume all, 这样才能save
+            pass
+
+        already_generate_indices = set()
+        count = 0
+        for batch in re_batchsampler3:  # 重新开始
+            for b in batch:
+                assert b not in already_generate_indices
+            already_generate_indices.update(batch)
+            count += 1
+            if count > 5:
+                break
+
+        state = re_batchsampler3.state_dict()
+        # 这里的 drop_last 为 False，需要最终是所有 sample
+        re_batchsampler4 = RandomBatchSampler(dataset, length=dataset.data, batch_size=after_batch_size,
+                                                drop_last=False,
+                                                shuffle=shuffle)
+        re_batchsampler4.load_state_dict(state)
+        re_batchsampler4.set_epoch(0)
+
+        mask = np.ones(len(dataset), dtype=bool)
+        mask[list(already_generate_indices)] = 0
+        for batch in re_batchsampler4:
+            for b in batch:
+                assert b not in already_generate_indices
+            already_generate_indices.update(batch)
+
+        assert len(already_generate_indices) == len(dataset)
+
+    @pytest.mark.parametrize('shuffle', [True, False])
+    @pytest.mark.parametrize('drop_last', [True, False])
+    @pytest.mark.parametrize('pad', [True, False])
+    def test_multi(self, shuffle, drop_last, pad):
+        # def test_multi(self, shuffle=True, drop_last=False, pad=False):
+
+        # no shuffle
+        num_replica = 2
+        dataset = DatasetWithVaryLength(num_of_data=1000)
+        batch_size = 5
+        num_batch_per_bucket = 10
+        lengths = []
+        rank0_already_seen_indexes = None
+        max_diff = num_batch_per_bucket * batch_size * num_replica
+        for rank in range(num_replica):
+            sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size = batch_size,
+                                           shuffle = shuffle, drop_last=drop_last)
+            sampler.set_epoch(0)
+            sampler.set_distributed(num_replica, rank=rank, pad=pad)
+            lengths.append(len(sampler))
+            already_seen_indexes = set()
+            repeat_count = 0
+            for batch in sampler:
+                for b in batch:
+                    repeat_count += int(b in already_seen_indexes)
+                    if rank0_already_seen_indexes:  # 不能交叉出现
+                        assert b not in rank0_already_seen_indexes
+                already_seen_indexes.update(batch)
+            if rank0_already_seen_indexes is None:
+                rank0_already_seen_indexes = already_seen_indexes
+            if pad:  # 应该允许重复一次
+                assert repeat_count<=1
+            else:
+                assert repeat_count==0
+
+        assert len(set(lengths))==1, lengths  # 每个进程的batch数量一致
+
+        # 多进程的保存
+        already_seen_indexes = set()
+        for rank in range(num_replica):
+            sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size = batch_size,
+                                           shuffle = shuffle, drop_last=drop_last)
+            sampler.set_epoch(0)
+            sampler.set_distributed(num_replica, rank=rank, pad=pad)
+            lengths.append(len(sampler))
+            count = 0
+            for batch in sampler:
+                already_seen_indexes.update(batch)
+                if count>5:
+                    break
+                count += 1
+            state = sampler.state_dict()
+
+        # 切换成单机
+        new_batch_size = 6
+        num_batch_per_bucket = 3
+        new_sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=new_batch_size,
+                                           shuffle=shuffle, drop_last=drop_last)
+        new_sampler.load_state_dict(state)
+        repeat_count = 0
+        new_already_seen_indexes = set(list(already_seen_indexes))
+
+        mask = np.ones(len(dataset), dtype=bool)
+        mask[list(already_seen_indexes)] = 0
+        indices = np.arange(len(dataset))[mask]
+
+        for batch in new_sampler:
+            for b in batch:
+                repeat_count += int(b in new_already_seen_indexes)
+            new_already_seen_indexes.update(batch)
+        if pad:  # 应该允许重复一次
+            assert repeat_count <= 1
+        else:
+            assert repeat_count == 0
+        if drop_last is False:  # 如果没有drop应该相等
+            assert len(new_already_seen_indexes)==len(dataset)
+
+        # 测试替换卡的数量。
+        num_replica = 3
+        new_sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=new_batch_size,
+                                           shuffle=shuffle, drop_last=drop_last)
+        new_sampler.set_epoch(0)
+        new_sampler.load_state_dict(state)
+        new_sampler.set_distributed(num_replicas=num_replica, rank=1, pad=pad)
+        repeat_count = 0
+
+        mask = np.ones(len(dataset), dtype=bool)
+        mask[list(already_seen_indexes)] = 0
+        indices = np.arange(len(dataset))[mask]
+
+        for batch in new_sampler:
+            for b in batch:
+                repeat_count += int(b in already_seen_indexes)
+        if pad:  # 应该允许重复一次
+            assert repeat_count <= 1
+        else:
+            assert repeat_count == 0
+
+    @pytest.mark.parametrize('shuffle', [True, False])
+    @pytest.mark.parametrize('drop_last', [True, False])
+    @pytest.mark.parametrize('pad', [True, False])
+    @pytest.mark.parametrize('num_samples', [13, 100, 623, 1000])
+    @pytest.mark.parametrize('num_replicas', [2, 3])
+    def test_multi_same_bucket(self, shuffle, drop_last, pad, num_samples, num_replicas):
+        # def test_multi_same_bucket(self, shuffle=True, drop_last=True, pad=True, num_samples=623, num_replicas=2):
+        dataset = DatasetWithVaryLength(num_of_data=num_samples)
+        batch_size = 6
+        if num_replicas*batch_size > num_samples:
+            return
+        num_batch_per_bucket = 10
+        samplers = []
+        lengths = []
+        for i in range(num_replicas):
+            sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=batch_size,
+                                         shuffle=shuffle, drop_last=drop_last)
+            sampler.set_distributed(num_replicas, rank=i, pad=pad)
+            sampler.set_epoch(0)
+            samplers.append(sampler)
+            lengths.append(len(list(iter(sampler))))
+        assert len(set(lengths))==1
+
+    @pytest.mark.parametrize('shuffle', [True, False])
+    @pytest.mark.parametrize('drop_last', [True, False])
+    @pytest.mark.parametrize('pad', [True, False])
+    @pytest.mark.parametrize('num_samples', [13, 100, 623, 1000])
+    @pytest.mark.parametrize('num_replicas', [1, 2, 3])
+    def test_multi_save_load(self, shuffle, drop_last, pad, num_samples, num_replicas):
+        """
+        测试是否能够正确地恢复使用过的（forward）数据
+
+        :return:
+        """
+        batch_size = 6
+        dataset = DatasetWithVaryLength(num_of_data=num_samples)
+        samplers = []
+        num_consumed_samples_array = list(range(0, num_samples+num_replicas, num_replicas))
+        for i in range(num_replicas):
+            sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=batch_size,
+                                           shuffle=shuffle, drop_last=drop_last)
+
+            sampler.set_distributed(num_replicas=num_replicas, rank=i, pad=pad)
+            samplers.append(sampler)
+        count = 0
+        already_seen_sets = [set()]
+        already_seen_set = set()
+        for batchs in zip(*samplers):
+            batch = chain(*batchs)
+            already_seen_set.update(batch)
+            already_seen_sets.append(deepcopy(already_seen_set))
+            count += 1
+            if count > 3:
+                break
+        states = samplers[0].state_dict()
+        for i in range(len(already_seen_sets)):
+            states['num_consumed_samples'] = num_consumed_samples_array[i]
+            sampler = BucketedBatchSampler(dataset, length=dataset.data, batch_size=batch_size+1,
+                                           shuffle=shuffle, drop_last=drop_last)
+            sampler.set_epoch(0)
+            already_seen_set = deepcopy(already_seen_sets[i])
+            for batch in sampler:
+                already_seen_set.update(batch)
+            assert len(already_seen_set) == len(dataset) if drop_last is False else len(already_seen_set) <= len(
+                dataset)
+
+        # 测试保存之后再次保存
+        sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=batch_size + 1,
+                                       shuffle=shuffle,
+                                       drop_last=drop_last)
+        sampler.set_epoch(0)
+        states['num_consumed_samples'] = num_consumed_samples_array[2]
+        if len(already_seen_sets)<3:
+            return
+        already_seen_set = already_seen_sets[2]
+        count = 0
+        for batch in sampler:
+            already_seen_set.update(batch)
+            count += 1
+            if count > 6:
+                break
+
+        states = sampler.state_dict()
+        num_consumed_samples_array = list(range(len(dataset)))
+        states['num_consumed_samples'] = num_consumed_samples_array[count]
+        sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=batch_size//2,
+                                       shuffle=shuffle,
+                                       drop_last=drop_last)
+        sampler.load_state_dict(states)
+        sampler.set_epoch(0)
+        for batch in sampler:
+            already_seen_set.update(batch)
+
+        assert len(already_seen_set)==len(dataset) if drop_last is False else len(already_seen_set)<=len(dataset)