fix bug and make shuffle automatic

fastNLP/core/callbacks/load_best_model_callback.py

@@ -105,14 +105,16 @@ class LoadBestModelCallback(HasMonitorCallback):
 
     def on_train_end(self, trainer):
         if abs(self.monitor_value) != float('inf'):  # 如果是 inf 说明从来没有运行过。
-            if self.real_save_folder:
-                logger.info(f"Loading best model from {self.real_save_folder} with {self._real_monitor}: {self.monitor_value}...")
-                trainer.load_model(folder=self.real_save_folder, only_state_dict=self.only_state_dict,
-                                   model_load_fn=self.model_load_fn)
-            else:
-                logger.info(f"Loading best model from buffer with {self._real_monitor}: {self.monitor_value}...")
-                self.buffer.seek(0)
-                trainer.load_model(folder=self.buffer, only_state_dict=self.only_state_dict)
+            # 如果是分布式且报错了，就不要加载了，防止barrier的问题
+            if not (trainer.driver.is_distributed() and self.encounter_exception):
+                if self.real_save_folder:
+                    logger.info(f"Loading best model from {self.real_save_folder} with {self._real_monitor}: {self.monitor_value}...")
+                    trainer.load_model(folder=self.real_save_folder, only_state_dict=self.only_state_dict,
+                                       model_load_fn=self.model_load_fn)
+                else:
+                    logger.info(f"Loading best model from buffer with {self._real_monitor}: {self.monitor_value}...")
+                    self.buffer.seek(0)
+                    trainer.load_model(folder=self.buffer, only_state_dict=self.only_state_dict)
             if self.delete_after_after:
                 if not self.encounter_exception:  # 防止出现死锁。
                     trainer.driver.barrier()

fastNLP/core/callbacks/progress_callback.py

@@ -22,9 +22,10 @@ class ProgressCallback(HasMonitorCallback):
         self.best_monitor_step = -1
         self.best_results = None
 
-    def record_better_monitor(self, trainer):
+    def record_better_monitor(self, trainer, results):
         self.best_monitor_step = trainer.global_forward_batches
         self.best_monitor_epoch = trainer.cur_epoch_idx
+        self.best_results = results
 
     def on_train_end(self, trainer):
         if self.best_monitor_epoch != -1:
@@ -138,7 +139,7 @@ class RichCallback(ProgressCallback):
         characters = '-'
         if self.monitor is not None:
             if self.is_better_results(results, keep_if_better=True):
-                self.record_better_monitor(trainer)
+                self.record_better_monitor(trainer, results)
                 if abs(self.monitor_value) != float('inf'):
                     rule_style = 'spring_green3'
                     text_style = '[bold]'
@@ -154,7 +155,6 @@ class RichCallback(ProgressCallback):
             self.progress_bar.console.print_json(results)
         else:
             self.progress_bar.print(results)
-        self.best_results = results
 
     def clear_tasks(self):
         for key, taskid in self.task2id.items():
@@ -222,7 +222,7 @@ class RawTextCallback(ProgressCallback):
         text = ''
         if self.monitor is not None:
             if self.is_better_results(results, keep_if_better=True):
-                self.record_better_monitor(trainer)
+                self.record_better_monitor(trainer, results)
                 if abs(self.monitor_value) != float('inf'):
                     text = '+'*self.num_signs + base_text + '+'*self.num_signs
         if len(text) == 0:
@@ -234,7 +234,6 @@ class RawTextCallback(ProgressCallback):
         if self.format_json:
             results = json.dumps(results)
         logger.info(results)
-        self.best_results = results
 
     @property
     def name(self):  # progress bar的名称
@@ -311,7 +310,7 @@ class TqdmCallback(ProgressCallback):
         text = ''
         if self.monitor is not None:
             if self.is_better_results(results, keep_if_better=True):
-                self.record_better_monitor(trainer)
+                self.record_better_monitor(trainer, results)
                 if abs(self.monitor_value) != float('inf'):
                     text = '+'*self.num_signs + base_text + '+'*self.num_signs
         if len(text) == 0:
@@ -323,7 +322,6 @@ class TqdmCallback(ProgressCallback):
         if self.format_json:
             results = json.dumps(results)
         logger.info(results)
-        self.best_results = results
 
     def clear_tasks(self):
         for key, taskid in self.task2id.items():

fastNLP/core/dataloaders/jittor_dataloader/fdl.py

@@ -200,7 +200,7 @@ class JittorDataLoader:
         return self.cur_batch_indices
 
 
-def prepare_jittor_dataloader(ds_or_db, batch_size: int = 16, shuffle: bool = False,
+def prepare_jittor_dataloader(ds_or_db, batch_size: int = 16, shuffle: bool = None,
                               drop_last: bool = False, num_workers: int = 0, buffer_size: int = 512 * 1024 * 1024,
                               stop_grad: bool = True, keep_numpy_array: bool = False, endless: bool = False,
                               collate_fn: Union[None, str, Callable] = "auto",
@@ -230,7 +230,8 @@ def prepare_jittor_dataloader(ds_or_db, batch_size: int = 16, shuffle: bool = Fa
     :param non_train_batch_size: 如果传入的 ``ds_or_db`` 为 :class:`Dict` 或 :class:`~fastNLP.io.DataBundle` 对象，可以通过改参数
         设置名称不为 `train` 的其他 ``dataset`` 的 ``batch_size``。 默认为 ``16``。
     :param batch_size: 批次大小，默认为 ``16`` 且当 batch_sampler 为 None 有效。
-    :param shuffle: 是否打乱数据集， 默认为 ``False``。
+    :param shuffle: 是否打乱数据集， 默认为 ``None``, 如果传入的 ``ds_or_db`` 可以判断出哪个是 'train' 则设置其 shuffle 为 True ，
+        其它的为 False 。
     :param drop_last: 当 ``drop_last=True`` 时，:class:`JittorDataLoader` 会扔掉最后一个长度小于 ``batch_size`` 的 batch 数据;
         若 ``drop_last=False`` , 则会返回该 batch 数据。 默认为 ``False`` 。
     :param num_workers: 当 ``num_workers > 0`` 时, :class:`JittorDataLoader` 会开启 num_workers 个子进程来处理数据， 可以加快
@@ -258,7 +259,7 @@ def prepare_jittor_dataloader(ds_or_db, batch_size: int = 16, shuffle: bool = Fa
         dl_bundle = {}
         for name, ds in ds_or_db.iter_datasets():
             if 'train' in name:
-                dl_bundle[name] = JittorDataLoader(ds, batch_size=batch_size, shuffle=shuffle,
+                dl_bundle[name] = JittorDataLoader(ds, batch_size=batch_size, shuffle=True if shuffle is None else shuffle,
                                                    drop_last=drop_last, num_workers=num_workers,
                                                    buffer_size=buffer_size,
                                                    stop_grad=stop_grad, keep_numpy_array=keep_numpy_array,
@@ -267,7 +268,7 @@ def prepare_jittor_dataloader(ds_or_db, batch_size: int = 16, shuffle: bool = Fa
             else:
                 dl_bundle[name] = JittorDataLoader(ds,
                                                    batch_size=non_train_batch_size if non_train_batch_size else batch_size,
-                                                   shuffle=shuffle,
+                                                   shuffle=False if shuffle is None else shuffle,
                                                    drop_last=drop_last, num_workers=num_workers,
                                                    buffer_size=buffer_size,
                                                    stop_grad=stop_grad, keep_numpy_array=keep_numpy_array,
@@ -279,14 +280,14 @@ def prepare_jittor_dataloader(ds_or_db, batch_size: int = 16, shuffle: bool = Fa
         ds_dict = {}
         for name, ds in ds_or_db.items():
             if 'train' in name:
-                dl = JittorDataLoader(ds, batch_size=batch_size, shuffle=shuffle,
+                dl = JittorDataLoader(ds, batch_size=batch_size, shuffle=True if shuffle is None else shuffle,
                                       drop_last=drop_last, num_workers=num_workers, buffer_size=buffer_size,
                                       stop_grad=stop_grad, keep_numpy_array=keep_numpy_array, endless=endless,
                                       collate_fn=collate_fn)
             else:
                 dl = JittorDataLoader(ds,
                                       batch_size=non_train_batch_size if non_train_batch_size else batch_size,
-                                      shuffle=shuffle,
+                                      shuffle=False if shuffle is None else shuffle,
                                       drop_last=drop_last, num_workers=num_workers,
                                       buffer_size=buffer_size,
                                       stop_grad=stop_grad, keep_numpy_array=keep_numpy_array,
@@ -296,7 +297,7 @@ def prepare_jittor_dataloader(ds_or_db, batch_size: int = 16, shuffle: bool = Fa
         return ds_dict
 
     elif isinstance(ds_or_db, HasLenGetitemType):
-        dl = JittorDataLoader(ds_or_db, batch_size=batch_size, shuffle=shuffle,
+        dl = JittorDataLoader(ds_or_db, batch_size=batch_size, shuffle=False if shuffle is None else shuffle,
                               drop_last=drop_last, num_workers=num_workers, buffer_size=buffer_size,
                               stop_grad=stop_grad, keep_numpy_array=keep_numpy_array, endless=endless,
                               collate_fn=collate_fn)

fastNLP/core/dataloaders/paddle_dataloader/fdl.py

@@ -293,7 +293,8 @@ def prepare_paddle_dataloader(ds_or_db, feed_list=None, places=None,
     :param batch_sampler: 实现了 __len__() 和 __iter__() 的实例化对象，，其__iter__() 方法每次都会返回一个 List 对象， List中的值为
      dataset 的下标 index ；默认为 None，当其不为 None 时，bacth_size, shuffle 参数均失效。
     :param batch_size: 批次大小，默认为 ``16`` 且当 batch_sampler 为 None 有效。
-    :param shuffle: 是否将数据打乱，若``shuffle=True``则会将dataset打乱；若否则什么也不做。
+    :param shuffle: 是否打乱数据集， 默认为 ``None``, 如果传入的 ``ds_or_db`` 可以判断出哪个是 'train' 则设置其 shuffle 为 True ，
+        其它的为 False 。
     :param drop_last: 当 ``drop_last=True`` 时，``PaddleDataLoader`` 会扔掉最后一个长度小于 ``batch_size`` 的 batch 数据;
     若 ``drop_last=False`` , 则会返回该 batch 数据。 默认为 ``False`` 。
     :param collate_fn: 用于从 dataset 取到的一个 batch 数据进行打包处理的 Callable 函数，其值应该为以下三个: ``[None, "auto", Callable]``.
@@ -326,7 +327,7 @@ def prepare_paddle_dataloader(ds_or_db, feed_list=None, places=None,
                 dl_bundle[name] = PaddleDataLoader(ds, feed_list=feed_list, places=places,
                                                    return_list=return_list,
                                                    batch_sampler=batch_sampler, batch_size=batch_size,
-                                                   shuffle=shuffle,
+                                                   shuffle=True if shuffle is None else shuffle,
                                                    drop_last=drop_last, collate_fn=collate_fn, num_workers=num_workers,
                                                    use_shared_memory=use_shared_memory,
                                                    use_buffer_reader=use_buffer_reader,
@@ -337,7 +338,7 @@ def prepare_paddle_dataloader(ds_or_db, feed_list=None, places=None,
                                                    return_list=return_list,
                                                    batch_sampler=batch_sampler,
                                                    batch_size=non_train_batch_size if non_train_batch_size else batch_size,
-                                                   shuffle=shuffle,
+                                                   shuffle=False if shuffle is None else shuffle,
                                                    drop_last=drop_last, collate_fn=collate_fn, num_workers=num_workers,
                                                    use_shared_memory=use_shared_memory,
                                                    use_buffer_reader=use_buffer_reader,
@@ -350,7 +351,8 @@ def prepare_paddle_dataloader(ds_or_db, feed_list=None, places=None,
         for name, ds in ds_or_db.items():
             if 'train' in name:
                 dl = PaddleDataLoader(ds, feed_list=feed_list, places=places, return_list=return_list,
-                                      batch_sampler=batch_sampler, batch_size=batch_size, shuffle=shuffle,
+                                      batch_sampler=batch_sampler, batch_size=batch_size,
+                                      shuffle=False if shuffle is None else shuffle,
                                       drop_last=drop_last, collate_fn=collate_fn, num_workers=num_workers,
                                       use_shared_memory=use_shared_memory, use_buffer_reader=use_buffer_reader,
                                       timeout=timeout, worker_init_fn=worker_init_fn,
@@ -359,7 +361,7 @@ def prepare_paddle_dataloader(ds_or_db, feed_list=None, places=None,
                 dl = PaddleDataLoader(ds, feed_list=feed_list, places=places, return_list=return_list,
                                       batch_sampler=batch_sampler,
                                       batch_size=non_train_batch_size if non_train_batch_size else batch_size,
-                                      shuffle=shuffle,
+                                      shuffle=False if shuffle is None else shuffle,
                                       drop_last=drop_last, collate_fn=collate_fn, num_workers=num_workers,
                                       use_shared_memory=use_shared_memory, use_buffer_reader=use_buffer_reader,
                                       timeout=timeout, worker_init_fn=worker_init_fn,
@@ -369,7 +371,8 @@ def prepare_paddle_dataloader(ds_or_db, feed_list=None, places=None,
 
     elif isinstance(ds_or_db, HasLenGetitemType):
         dl = PaddleDataLoader(ds_or_db, feed_list=feed_list, places=places, return_list=return_list,
-                              batch_sampler=batch_sampler, batch_size=batch_size, shuffle=shuffle,
+                              batch_sampler=batch_sampler, batch_size=batch_size,
+                              shuffle=False if shuffle is None else shuffle,
                               drop_last=drop_last, collate_fn=collate_fn, num_workers=num_workers,
                               use_shared_memory=use_shared_memory, use_buffer_reader=use_buffer_reader,
                               timeout=timeout, worker_init_fn=worker_init_fn, persistent_workers=persistent_workers)

fastNLP/core/dataloaders/prepare_dataloader.py

@@ -13,7 +13,7 @@ from ...envs import FASTNLP_BACKEND, SUPPORT_BACKENDS
 from ..log import logger
 
 
-def prepare_dataloader(dataset, batch_size: int = 16, shuffle: bool = False, drop_last: bool = False,
+def prepare_dataloader(dataset, batch_size: int = 16, shuffle: bool = None, drop_last: bool = False,
              collate_fn: Union[Callable, str, None] = 'auto', num_workers: int = 0,
              backend: str = 'auto'):
     """
@@ -28,7 +28,8 @@ def prepare_dataloader(dataset, batch_size: int = 16, shuffle: bool = False, dro
         * 为字典型 或 :class:`~fastNLP.io.DataBundle` 数据时，返回 `Dict` 类型的数据。
 
     :param batch_size: 批次大小。
-    :param shuffle: 是否打乱数据集。
+    :param shuffle: 是否打乱数据集， 默认为 ``None``, 如果传入的 ``ds_or_db`` 可以判断出哪个是 'train' 则设置其 shuffle 为 True ，
+        其它的为 False 。
     :param drop_last: 当最后一个 batch 不足 batch_size 数量的是否，是否丢弃。
     :param collate_fn: 用于处理一个 batch 的函数，一般包括 padding 和转为 tensor。有以下三种取值：
 

fastNLP/core/dataloaders/torch_dataloader/fdl.py

@@ -218,7 +218,7 @@ class TorchDataLoader(DataLoader):
 
 def prepare_torch_dataloader(ds_or_db,
                              batch_size: int = 16,
-                             shuffle: bool = False,
+                             shuffle: bool = None,
                              sampler: Union["Sampler[int]", ReproducibleSampler, UnrepeatedSampler] = None,
                              batch_sampler: Union["Sampler[Sequence[int]]", ReproducibleBatchSampler] = None,
                              num_workers: int = 0, collate_fn: Union[Callable, str, None] = 'auto',
@@ -252,7 +252,8 @@ def prepare_torch_dataloader(ds_or_db,
 
     :param batch_size: 批次大小，默认为 ``16`` 且当 batch_sampler 为 None 有效。
     :param non_train_batch_size: 非 'train' 数据集的 ``TorchDataLoader`` 批次大小，默认为 ``16`` 且当 batch_sampler 为 None 有效。
-    :param shuffle: 是否打乱数据集， 默认为 ``False``。
+    :param shuffle: 是否打乱数据集， 默认为 ``None``, 如果传入的 ``ds_or_db`` 可以判断出哪个是 'train' 则设置其 shuffle 为 True ，
+        其它的为 False 。
     :param sampler: 实现了 __len__() 和 __iter__() 的实例化对象，其 __iter__() 方法每次都会返回 dataset 的一个下标 index ，
         默认为None， 当其不为 None 时， shuffle 参数无效。
     :param non_train_sampler: 非 'train' 数据集的的实现了 __len__() 和 __iter__() 的实例化对象，其 __iter__() 方法每次都会返回 dataset 的一个下标 index ，
@@ -290,7 +291,7 @@ def prepare_torch_dataloader(ds_or_db,
         for name, ds in ds_or_db.iter_datasets():
             if 'train' in name:
                 dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=batch_size,
-                                                  shuffle=shuffle, sampler=sampler, batch_sampler=batch_sampler,
+                                                  shuffle=True if shuffle is None else shuffle, sampler=sampler, batch_sampler=batch_sampler,
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                                                   drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
                                                   multiprocessing_context=multiprocessing_context, generator=generator,
@@ -300,7 +301,7 @@ def prepare_torch_dataloader(ds_or_db,
             else:
                 dl_bundle[name] = TorchDataLoader(dataset=ds,
                                                   batch_size=non_train_batch_size if non_train_batch_size else batch_size,
-                                                  shuffle=shuffle,
+                                                  shuffle=False if shuffle is None else shuffle,
                                                   sampler=non_train_sampler if non_train_sampler else sampler,
                                                   batch_sampler=batch_sampler,
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
@@ -316,7 +317,7 @@ def prepare_torch_dataloader(ds_or_db,
         for name, ds in ds_or_db.items():
             if 'train' in name:
                 dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=batch_size,
-                                                  shuffle=shuffle, sampler=sampler, batch_sampler=batch_sampler,
+                                                  shuffle=True if shuffle is None else shuffle, sampler=sampler, batch_sampler=batch_sampler,
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                                                   drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
                                                   multiprocessing_context=multiprocessing_context, generator=generator,
@@ -326,7 +327,7 @@ def prepare_torch_dataloader(ds_or_db,
             else:
                 dl_bundle[name] = TorchDataLoader(dataset=ds,
                                                   batch_size=non_train_batch_size if non_train_batch_size else batch_size,
-                                                  shuffle=shuffle,
+                                                  shuffle=False if shuffle is None else shuffle,
                                                   sampler=non_train_sampler if non_train_sampler else sampler,
                                                   batch_sampler=batch_sampler,
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
@@ -340,7 +341,7 @@ def prepare_torch_dataloader(ds_or_db,
 
     elif isinstance(ds_or_db, HasLenGetitemType):
         dl = TorchDataLoader(dataset=ds_or_db, batch_size=batch_size,
-                             shuffle=shuffle, sampler=sampler, batch_sampler=batch_sampler,
+                             shuffle=False if shuffle is None else shuffle, sampler=sampler, batch_sampler=batch_sampler,
                              num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                              drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
                              multiprocessing_context=multiprocessing_context, generator=generator,