From 8467cb6e416d641a5129b61b1930a5bf47f9682e Mon Sep 17 00:00:00 2001 From: yhcc Date: Sun, 19 Jun 2022 17:16:39 +0800 Subject: [PATCH] fix bug and make shuffle automatic --- .../core/callbacks/load_best_model_callback.py | 18 ++++++++++-------- fastNLP/core/callbacks/progress_callback.py | 12 +++++------- .../core/dataloaders/jittor_dataloader/fdl.py | 15 ++++++++------- .../core/dataloaders/paddle_dataloader/fdl.py | 15 +++++++++------ fastNLP/core/dataloaders/prepare_dataloader.py | 5 +++-- .../core/dataloaders/torch_dataloader/fdl.py | 15 ++++++++------- 6 files changed, 43 insertions(+), 37 deletions(-) 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..d1295682 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 = 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,