diff --git a/fastNLP/core/callbacks/fitlog_callback.py b/fastNLP/core/callbacks/fitlog_callback.py index 35662539..19a8b476 100644 --- a/fastNLP/core/callbacks/fitlog_callback.py +++ b/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) diff --git a/fastNLP/core/callbacks/load_best_model_callback.py b/fastNLP/core/callbacks/load_best_model_callback.py index ec6579a6..b0fa83c4 100644 --- a/fastNLP/core/callbacks/load_best_model_callback.py +++ b/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() diff --git a/fastNLP/core/callbacks/progress_callback.py b/fastNLP/core/callbacks/progress_callback.py index 2f1d2b17..c172a9a7 100644 --- a/fastNLP/core/callbacks/progress_callback.py +++ b/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(): diff --git a/fastNLP/core/dataloaders/jittor_dataloader/fdl.py b/fastNLP/core/dataloaders/jittor_dataloader/fdl.py index 83555f6e..4631ba7b 100644 --- a/fastNLP/core/dataloaders/jittor_dataloader/fdl.py +++ b/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) diff --git a/fastNLP/core/dataloaders/paddle_dataloader/fdl.py b/fastNLP/core/dataloaders/paddle_dataloader/fdl.py index c84c1aaf..8999322b 100644 --- a/fastNLP/core/dataloaders/paddle_dataloader/fdl.py +++ b/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) diff --git a/fastNLP/core/dataloaders/prepare_dataloader.py b/fastNLP/core/dataloaders/prepare_dataloader.py index 5f469f2b..9cda2bd3 100644 --- a/fastNLP/core/dataloaders/prepare_dataloader.py +++ b/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： diff --git a/fastNLP/core/dataloaders/torch_dataloader/fdl.py b/fastNLP/core/dataloaders/torch_dataloader/fdl.py index 2a119260..9b0ab8d3 100644 --- a/fastNLP/core/dataloaders/torch_dataloader/fdl.py +++ b/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, diff --git a/fastNLP/core/metrics/accuracy.py b/fastNLP/core/metrics/accuracy.py index 47d5e114..fbd826bd 100644 --- a/fastNLP/core/metrics/accuracy.py +++ b/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 " diff --git a/fastNLP/core/metrics/classify_f1_pre_rec_metric.py b/fastNLP/core/metrics/classify_f1_pre_rec_metric.py index daf325c0..39565f40 100644 --- a/fastNLP/core/metrics/classify_f1_pre_rec_metric.py +++ b/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 " diff --git a/fastNLP/core/metrics/span_f1_pre_rec_metric.py b/fastNLP/core/metrics/span_f1_pre_rec_metric.py index 9a0b1d9d..07a6cd56 100644 --- a/fastNLP/core/metrics/span_f1_pre_rec_metric.py +++ b/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.") diff --git a/fastNLP/core/utils/utils.py b/fastNLP/core/utils/utils.py index 33a7ee7e..ec0c87b0 100644 --- a/fastNLP/core/utils/utils.py +++ b/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) diff --git a/fastNLP/embeddings/torch/static_embedding.py b/fastNLP/embeddings/torch/static_embedding.py index de2b231a..cc15c214 100644 --- a/fastNLP/embeddings/torch/static_embedding.py +++ b/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 diff --git a/fastNLP/io/embed_loader.py b/fastNLP/io/embed_loader.py index 9080ff28..df82643b 100644 --- a/fastNLP/io/embed_loader.py +++ b/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)) diff --git a/fastNLP/io/loader/classification.py b/fastNLP/io/loader/classification.py index 4416376f..2ae0b163 100644 --- a/fastNLP/io/loader/classification.py +++ b/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: diff --git a/fastNLP/io/loader/matching.py b/fastNLP/io/loader/matching.py index 5595b798..08387df9 100644 --- a/fastNLP/io/loader/matching.py +++ b/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: diff --git a/fastNLP/io/pipe/matching.py b/fastNLP/io/pipe/matching.py index a89f2f2b..baebdbaa 100644 --- a/fastNLP/io/pipe/matching.py +++ b/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 diff --git a/fastNLP/io/pipe/utils.py b/fastNLP/io/pipe/utils.py index aa28af08..05dd3cf4 100644 --- a/fastNLP/io/pipe/utils.py +++ b/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) diff --git a/fastNLP/modules/mix_modules/utils.py b/fastNLP/modules/mix_modules/utils.py index 21d0f05c..04dab056 100644 --- a/fastNLP/modules/mix_modules/utils.py +++ b/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 diff --git a/fastNLP/transformers/torch/configuration_utils.py b/fastNLP/transformers/torch/configuration_utils.py index 948d9873..26a80377 100644 --- a/fastNLP/transformers/torch/configuration_utils.py +++ b/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`." diff --git a/fastNLP/transformers/torch/generation_beam_search.py b/fastNLP/transformers/torch/generation_beam_search.py index 117d9a38..1c3fc592 100644 --- a/fastNLP/transformers/torch/generation_beam_search.py +++ b/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(...)`." diff --git a/fastNLP/transformers/torch/generation_utils.py b/fastNLP/transformers/torch/generation_utils.py index 0e6fe5c7..29828c15 100644 --- a/fastNLP/transformers/torch/generation_utils.py +++ b/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, ) diff --git a/fastNLP/transformers/torch/models/auto/auto_factory.py b/fastNLP/transformers/torch/models/auto/auto_factory.py index 9eb8ec69..d0969a5b 100644 --- a/fastNLP/transformers/torch/models/auto/auto_factory.py +++ b/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." ) diff --git a/fastNLP/transformers/torch/models/auto/configuration_auto.py b/fastNLP/transformers/torch/models/auto/configuration_auto.py index 45d3c071..1289071d 100644 --- a/fastNLP/transformers/torch/models/auto/configuration_auto.py +++ b/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, diff --git a/fastNLP/transformers/torch/models/auto/modeling_auto.py b/fastNLP/transformers/torch/models/auto/modeling_auto.py index aace27a2..dbf4b610 100644 --- a/fastNLP/transformers/torch/models/auto/modeling_auto.py +++ b/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.", diff --git a/fastNLP/transformers/torch/models/bart/modeling_bart.py b/fastNLP/transformers/torch/models/bart/modeling_bart.py index 7219f49a..377afa41 100644 --- a/fastNLP/transformers/torch/models/bart/modeling_bart.py +++ b/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, ) diff --git a/fastNLP/transformers/torch/models/bert/modeling_bert.py b/fastNLP/transformers/torch/models/bert/modeling_bert.py index b95da0df..79f1c459 100644 --- a/fastNLP/transformers/torch/models/bert/modeling_bert.py +++ b/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, ) diff --git a/fastNLP/transformers/torch/models/cpt/modeling_cpt.py b/fastNLP/transformers/torch/models/cpt/modeling_cpt.py index 2910cc26..df7d477b 100644 --- a/fastNLP/transformers/torch/models/cpt/modeling_cpt.py +++ b/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`..." ) diff --git a/fastNLP/transformers/torch/tokenization_utils_base.py b/fastNLP/transformers/torch/tokenization_utils_base.py index 3a033c96..a04dbaf1 100644 --- a/fastNLP/transformers/torch/tokenization_utils_base.py +++ b/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)