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

fastNLP/core/callbacks/fitlog_callback.py

@@ -44,7 +44,7 @@ class FitlogCallback(HasMonitorCallback):
         if get_global_rank() != 0:  # 如果不是 global rank 为 0 ，需要关闭 fitlog
             fitlog.debug()
         super().on_after_trainer_initialized(trainer, driver)
-        fitlog.add_other('launch_time', os.environ['FASTNLP_LAUNCH_TIME'])
+        fitlog.add_other(name='launch_time', value=os.environ['FASTNLP_LAUNCH_TIME'])
 
     def on_sanity_check_end(self, trainer, sanity_check_res):
         super(FitlogCallback, self).on_sanity_check_end(trainer, sanity_check_res)

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 = self.itemize_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,

fastNLP/core/metrics/accuracy.py

@@ -69,7 +69,7 @@ class Accuracy(Metric):
         elif pred.ndim == target.ndim + 1:
             pred = pred.argmax(axis=-1)
             if seq_len is None and target.ndim > 1:
-                logger.warn("You are not passing `seq_len` to exclude pad when calculate accuracy.")
+                logger.warning("You are not passing `seq_len` to exclude pad when calculate accuracy.")
 
         else:
             raise RuntimeError(f"when pred have size:{pred.shape}, target should have size: {pred.shape} or "

fastNLP/core/metrics/classify_f1_pre_rec_metric.py

@@ -156,7 +156,7 @@ class ClassifyFPreRecMetric(Metric):
         elif pred.ndim == target.ndim + 1:
             pred = pred.argmax(axis=-1)
             if seq_len is None and target.ndim > 1:
-                logger.warn("You are not passing `seq_len` to exclude pad when calculate accuracy.")
+                logger.warning("You are not passing `seq_len` to exclude pad when calculate accuracy.")
         else:
             raise RuntimeError(f"when pred have "
                                f"size:{pred.shape}, target should have size: {pred.shape} or "

fastNLP/core/metrics/span_f1_pre_rec_metric.py

@@ -39,7 +39,7 @@ def _check_tag_vocab_and_encoding_type(tag_vocab: Union[Vocabulary, dict], encod
                             f"encoding_type."
         tags = tags.replace(tag, '')  # 删除该值
     if tags:  # 如果不为空，说明出现了未使用的tag
-        logger.warn(f"Tag:{tags} in encoding type:{encoding_type} is not presented in your Vocabulary. Check your "
+        logger.warning(f"Tag:{tags} in encoding type:{encoding_type} is not presented in your Vocabulary. Check your "
                       "encoding_type.")
 
 

fastNLP/core/utils/utils.py

@@ -554,7 +554,7 @@ def deprecated(help_message: Optional[str] = None):
         def wrapper(*args, **kwargs):
             func_hash = hash(deprecated_function)
             if func_hash not in _emitted_deprecation_warnings:
-                logger.warn(warning_msg, category=FutureWarning, stacklevel=2)
+                logger.warning(warning_msg, category=FutureWarning, stacklevel=2)
                 _emitted_deprecation_warnings.add(func_hash)
             return deprecated_function(*args, **kwargs)
 

fastNLP/embeddings/torch/static_embedding.py

@@ -286,7 +286,7 @@ class StaticEmbedding(TokenEmbedding):
                     if word in vocab:
                         index = vocab.to_index(word)
                         if index in matrix:
-                            logger.warn(f"Word has more than one vector in embedding file. Set logger level to "
+                            logger.warning(f"Word has more than one vector in embedding file. Set logger level to "
                                           f"DEBUG for detail.")
                             logger.debug(f"Word:{word} occurs again in line:{idx}(starts from 0)")
                         matrix[index] = torch.from_numpy(np.fromstring(' '.join(nums), sep=' ', dtype=dtype, count=dim))
@@ -295,7 +295,7 @@ class StaticEmbedding(TokenEmbedding):
                         found_count += 1
                 except Exception as e:
                     if error == 'ignore':
-                        logger.warn("Error occurred at the {} line.".format(idx))
+                        logger.warning("Error occurred at the {} line.".format(idx))
                     else:
                         logger.error("Error occurred at the {} line.".format(idx))
                         raise e

fastNLP/io/embed_loader.py

@@ -91,7 +91,7 @@ class EmbedLoader:
                         hit_flags[index] = True
                 except Exception as e:
                     if error == 'ignore':
-                        logger.warn("Error occurred at the {} line.".format(idx))
+                        logger.warning("Error occurred at the {} line.".format(idx))
                     else:
                         logging.error("Error occurred at the {} line.".format(idx))
                         raise e
@@ -156,7 +156,7 @@ class EmbedLoader:
                         found_pad = True
                 except Exception as e:
                     if error == 'ignore':
-                        logger.warn("Error occurred at the {} line.".format(idx))
+                        logger.warning("Error occurred at the {} line.".format(idx))
                         pass
                     else:
                         logging.error("Error occurred at the {} line.".format(idx))

fastNLP/io/loader/classification.py

@@ -345,7 +345,7 @@ class SST2Loader(Loader):
         with open(path, 'r', encoding='utf-8') as f:
             f.readline()  # 跳过header
             if 'test' in os.path.split(path)[1]:
-                logger.warn("SST2's test file has no target.")
+                logger.warning("SST2's test file has no target.")
                 for line in f:
                     line = line.strip()
                     if line:

fastNLP/io/loader/matching.py

@@ -55,7 +55,7 @@ class MNLILoader(Loader):
         with open(path, 'r', encoding='utf-8') as f:
             f.readline()  # 跳过header
             if path.endswith("test_matched.tsv") or path.endswith('test_mismatched.tsv'):
-                logger.warn("MNLI's test file has no target.")
+                logger.warning("MNLI's test file has no target.")
                 for line in f:
                     line = line.strip()
                     if line:
@@ -227,7 +227,7 @@ class QNLILoader(JsonLoader):
         with open(path, 'r', encoding='utf-8') as f:
             f.readline()  # 跳过header
             if path.endswith("test.tsv"):
-                logger.warn("QNLI's test file has no target.")
+                logger.warning("QNLI's test file has no target.")
                 for line in f:
                     line = line.strip()
                     if line:
@@ -289,7 +289,7 @@ class RTELoader(Loader):
         with open(path, 'r', encoding='utf-8') as f:
             f.readline()  # 跳过header
             if path.endswith("test.tsv"):
-                logger.warn("RTE's test file has no target.")
+                logger.warning("RTE's test file has no target.")
                 for line in f:
                     line = line.strip()
                     if line:

fastNLP/io/pipe/matching.py

@@ -146,7 +146,7 @@ class MatchingBertPipe(Pipe):
             warn_msg = f"There are {len(target_vocab._no_create_word)} target labels" \
                        f" in {[name for name in data_bundle.datasets.keys() if 'train' not in name]} " \
                        f"data set but not in train data set!."
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
             print(warn_msg)
         
         has_target_datasets = [dataset for name, dataset in data_bundle.datasets.items() if
@@ -291,7 +291,7 @@ class MatchingPipe(Pipe):
             warn_msg = f"There are {len(target_vocab._no_create_word)} target labels" \
                        f" in {[name for name in data_bundle.datasets.keys() if 'train' not in name]} " \
                        f"data set but not in train data set!."
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
             print(warn_msg)
         
         has_target_datasets = [dataset for name, dataset in data_bundle.datasets.items() if

fastNLP/io/pipe/utils.py

@@ -138,7 +138,7 @@ def _indexize(data_bundle, input_field_names='words', target_field_names='target
                        f" in {[name for name in data_bundle.datasets.keys() if 'train' not in name]} " \
                        f"data set but not in train data set!.\n" \
                        f"These label(s) are {tgt_vocab._no_create_word}"
-            logger.warn(warn_msg)
+            logger.warning(warn_msg)
             # log.warning(warn_msg)
         tgt_vocab.index_dataset(*[ds for ds in data_bundle.datasets.values() if ds.has_field(target_field_name)], field_name=target_field_name)
         data_bundle.set_vocab(tgt_vocab, target_field_name)

fastNLP/modules/mix_modules/utils.py

@@ -112,7 +112,7 @@ def _jittor2torch(jittor_var: 'jittor.Var', device: Optional[Union[str, int]] =
     # 如果outputs有_grad键，可以实现求导
     no_gradient = not jittor_var.requires_grad if no_gradient is None else no_gradient
     if no_gradient == False:
-        logger.warn("The result tensor will not keep gradients due to differences between jittor and pytorch.")
+        logger.warning("The result tensor will not keep gradients due to differences between jittor and pytorch.")
     jittor_numpy = jittor_var.numpy()
     if not np.issubdtype(jittor_numpy.dtype, np.inexact):
         no_gradient = True

fastNLP/transformers/torch/configuration_utils.py

@@ -327,7 +327,7 @@ class PretrainedConfig:
 
         # Deal with gradient checkpointing
         if kwargs.get("gradient_checkpointing", False):
-            logger.warn(
+            logger.warning(
                 "Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 "
                 "Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the "
                 "`Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`."

fastNLP/transformers/torch/generation_beam_search.py

@@ -195,7 +195,7 @@ class BeamSearchScorer(BeamScorer):
             )
 
         if "max_length" in kwargs:
-            logger.warn(
+            logger.warning(
                 "Passing `max_length` to BeamSearchScorer is deprecated and has no effect."
                 "`max_length` should be passed directly to `beam_search(...)`, `beam_sample(...)`"
                 ",or `group_beam_search(...)`."

fastNLP/transformers/torch/generation_utils.py

@@ -872,7 +872,7 @@ class GenerationMixin:
             max_length = self.config.max_length
         elif max_length is not None and max_new_tokens is not None:
             # Both are set, this is odd, raise a warning
-            logger.warn(
+            logger.warning(
                 "Both `max_length` and `max_new_tokens` have been set but they serve the same purpose.", UserWarning
             )
 
@@ -1239,7 +1239,7 @@ class GenerationMixin:
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
         if max_length is not None:
-            logger.warn(
+            logger.warning(
                 "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                 UserWarning,
             )
@@ -1475,7 +1475,7 @@ class GenerationMixin:
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
         if max_length is not None:
-            logger.warn(
+            logger.warning(
                 "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                 UserWarning,
             )
@@ -1726,13 +1726,13 @@ class GenerationMixin:
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
         if max_length is not None:
-            logger.warn(
+            logger.warning(
                 "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                 UserWarning,
             )
             stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
         if len(stopping_criteria) == 0:
-            logger.warn("You don't have defined any stopping_criteria, this will likely loop forever", UserWarning)
+            logger.warning("You don't have defined any stopping_criteria, this will likely loop forever", UserWarning)
         pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
         eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
         output_scores = output_scores if output_scores is not None else self.config.output_scores
@@ -2030,7 +2030,7 @@ class GenerationMixin:
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
         if max_length is not None:
-            logger.warn(
+            logger.warning(
                 "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                 UserWarning,
             )
@@ -2325,7 +2325,7 @@ class GenerationMixin:
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
         if max_length is not None:
-            logger.warn(
+            logger.warning(
                 "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                 UserWarning,
             )

fastNLP/transformers/torch/models/auto/auto_factory.py

@@ -401,7 +401,7 @@ class _BaseAutoModelClass:
                     "the option `trust_remote_code=True` to remove this error."
                 )
             if kwargs.get("revision", None) is None:
-                logger.warn(
+                logger.warning(
                     "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure "
                     "no malicious code has been contributed in a newer revision."
                 )

fastNLP/transformers/torch/models/auto/configuration_auto.py

@@ -130,7 +130,7 @@ class _LazyLoadAllMappings(OrderedDict):
     def _initialize(self):
         if self._initialized:
             return
-        # logger.warn(
+        # logger.warning(
         #     "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP is deprecated and will be removed in v5 of Transformers. "
         #     "It does not contain all available model checkpoints, far from it. Checkout hf.co/models for that.",
         #     FutureWarning,

fastNLP/transformers/torch/models/auto/modeling_auto.py

@@ -306,7 +306,7 @@ AutoModelForSpeechSeq2Seq = auto_class_update(
 class AutoModelWithLMHead(_AutoModelWithLMHead):
     @classmethod
     def from_config(cls, config):
-        logger.warn(
+        logger.warning(
             "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
             "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and "
             "`AutoModelForSeq2SeqLM` for encoder-decoder models.",
@@ -316,7 +316,7 @@ class AutoModelWithLMHead(_AutoModelWithLMHead):
 
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
-        logger.warn(
+        logger.warning(
             "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
             "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and "
             "`AutoModelForSeq2SeqLM` for encoder-decoder models.",

fastNLP/transformers/torch/models/bart/modeling_bart.py

@@ -513,7 +513,7 @@ class BartPretrainedModel(PreTrainedModel):
 
 class PretrainedBartModel(BartPretrainedModel):
     def __init_subclass__(self):
-        logger.warn(
+        logger.warning(
             "The class `PretrainedBartModel` has been depreciated, please use `BartPretrainedModel` instead.",
             FutureWarning,
         )

fastNLP/transformers/torch/models/bert/modeling_bert.py

@@ -1374,7 +1374,7 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
         """
 
         if "next_sentence_label" in kwargs:
-            logger.warn(
+            logger.warning(
                 "The `next_sentence_label` argument is deprecated and will be removed in a future version, use `labels` instead.",
                 FutureWarning,
             )

fastNLP/transformers/torch/models/cpt/modeling_cpt.py

@@ -724,7 +724,7 @@ class CPTDecoder(CPTPretrainedModel):
             if getattr(self.config, "gradient_checkpointing", False) and self.training:
 
                 if use_cache:
-                    logger.warn(
+                    logger.warning(
                         "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
                         "`use_cache=False`..."
                     )

fastNLP/transformers/torch/tokenization_utils_base.py

@@ -312,7 +312,7 @@ class BatchEncoding(UserDict):
         """
         if not self._encodings:
             raise ValueError("words() is not available when using Python-based tokenizers")
-        logger.warn(
+        logger.warning(
             "`BatchEncoding.words()` property is deprecated and should be replaced with the identical, "
             "but more self-explanatory `BatchEncoding.word_ids()` property.",
             FutureWarning,
@@ -1601,7 +1601,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
                     f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is not "
                     "supported for this tokenizer. Use a model identifier or the path to a directory instead."
                 )
-            logger.warn(
+            logger.warning(
                 f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is deprecated and "
                 "won't be possible anymore in v5. Use a model identifier or the path to a directory instead.",
                 FutureWarning,
@@ -2163,7 +2163,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
         # Get padding strategy
         if padding is False and old_pad_to_max_length:
             if verbose:
-                logger.warn(
+                logger.warning(
                     "The `pad_to_max_length` argument is deprecated and will be removed in a future version, "
                     "use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or "
                     "use `padding='max_length'` to pad to a max length. In this case, you can give a specific "
@@ -2184,7 +2184,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
                             "To pad to max length, use `padding='max_length'`."
                         )
                     if old_pad_to_max_length is not False:
-                        logger.warn("Though `pad_to_max_length` = `True`, it is ignored because `padding`=`True`.")
+                        logger.warning("Though `pad_to_max_length` = `True`, it is ignored because `padding`=`True`.")
                 padding_strategy = PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
             elif not isinstance(padding, PaddingStrategy):
                 padding_strategy = PaddingStrategy(padding)
@@ -2196,7 +2196,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
         # Get truncation strategy
         if truncation is False and old_truncation_strategy != "do_not_truncate":
             if verbose:
-                logger.warn(
+                logger.warning(
                     "The `truncation_strategy` argument is deprecated and will be removed in a future version, "
                     "use `truncation=True` to truncate examples to a max length. You can give a specific "
                     "length with `max_length` (e.g. `max_length=45`) or leave max_length to None to truncate to the "
@@ -3352,7 +3352,7 @@ model_inputs["labels"] = labels["input_ids"]
 See the documentation of your specific tokenizer for more details on the specific arguments to the tokenizer of choice.
 For a more complete example, see the implementation of `prepare_seq2seq_batch`.
 """
-        logger.warn(formatted_warning, FutureWarning)
+        logger.warning(formatted_warning, FutureWarning)
         # mBART-specific kwargs that should be ignored by other models.
         kwargs.pop("src_lang", None)
         kwargs.pop("tgt_lang", None)