diff --git a/docs/source/fastNLP.core.callbacks.fitlog_callback.rst b/docs/source/fastNLP.core.callbacks.fitlog_callback.rst new file mode 100644 index 00000000..020c3ff3 --- /dev/null +++ b/docs/source/fastNLP.core.callbacks.fitlog_callback.rst @@ -0,0 +1,7 @@ +fastNLP.core.callbacks.fitlog\_callback module +============================================== + +.. automodule:: fastNLP.core.callbacks.fitlog_callback + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/fastNLP.core.callbacks.rst b/docs/source/fastNLP.core.callbacks.rst index 0f3f93ac..89d85f52 100644 --- a/docs/source/fastNLP.core.callbacks.rst +++ b/docs/source/fastNLP.core.callbacks.rst @@ -25,6 +25,7 @@ Submodules fastNLP.core.callbacks.callback_manager fastNLP.core.callbacks.checkpoint_callback fastNLP.core.callbacks.early_stop_callback + fastNLP.core.callbacks.fitlog_callback fastNLP.core.callbacks.has_monitor_callback fastNLP.core.callbacks.load_best_model_callback fastNLP.core.callbacks.lr_scheduler_callback diff --git a/docs/source/fastNLP.modules.mix_modules.rst b/docs/source/fastNLP.modules.mix_modules.rst new file mode 100644 index 00000000..5351c55a --- /dev/null +++ b/docs/source/fastNLP.modules.mix_modules.rst @@ -0,0 +1,15 @@ +fastNLP.modules.mix\_modules package +==================================== + +.. automodule:: fastNLP.modules.mix_modules + :members: + :undoc-members: + :show-inheritance: + +Submodules +---------- + +.. toctree:: + :maxdepth: 4 + + fastNLP.modules.mix_modules.utils diff --git a/docs/source/fastNLP.modules.mix_modules.utils.rst b/docs/source/fastNLP.modules.mix_modules.utils.rst new file mode 100644 index 00000000..9dab336d --- /dev/null +++ b/docs/source/fastNLP.modules.mix_modules.utils.rst @@ -0,0 +1,7 @@ +fastNLP.modules.mix\_modules.utils module +========================================= + +.. automodule:: fastNLP.modules.mix_modules.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/fastNLP.modules.rst b/docs/source/fastNLP.modules.rst new file mode 100644 index 00000000..fa1d95de --- /dev/null +++ b/docs/source/fastNLP.modules.rst @@ -0,0 +1,15 @@ +fastNLP.modules package +======================= + +.. automodule:: fastNLP.modules + :members: + :undoc-members: + :show-inheritance: + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + fastNLP.modules.mix_modules diff --git a/docs/source/fastNLP.rst b/docs/source/fastNLP.rst index 726eb9c6..89c8e058 100644 --- a/docs/source/fastNLP.rst +++ b/docs/source/fastNLP.rst @@ -15,3 +15,4 @@ Subpackages fastNLP.core fastNLP.envs fastNLP.io + fastNLP.modules diff --git a/fastNLP/core/.DS_Store b/fastNLP/core/.DS_Store deleted file mode 100644 index 2a1e21a5..00000000 Binary files a/fastNLP/core/.DS_Store and /dev/null differ diff --git a/fastNLP/core/__init__.py b/fastNLP/core/__init__.py index 8800e1d6..343313a6 100644 --- a/fastNLP/core/__init__.py +++ b/fastNLP/core/__init__.py @@ -14,6 +14,7 @@ __all__ = [ "TorchGradClipCallback", "ResultsMonitor", 'HasMonitorCallback', + "FitlogCallback", # collators 'Collator', @@ -68,6 +69,7 @@ __all__ = [ # metrics "Metric", "Accuracy", + "TransformersAccuracy", 'SpanFPreRecMetric', 'ClassifyFPreRecMetric', diff --git a/fastNLP/core/callbacks/__init__.py b/fastNLP/core/callbacks/__init__.py index 9ba0d227..efd9280f 100644 --- a/fastNLP/core/callbacks/__init__.py +++ b/fastNLP/core/callbacks/__init__.py @@ -17,7 +17,9 @@ __all__ = [ "TorchGradClipCallback", "ResultsMonitor", - 'HasMonitorCallback' + 'HasMonitorCallback', + + "FitlogCallback" ] @@ -32,4 +34,5 @@ from .early_stop_callback import EarlyStopCallback from .torch_callbacks import * from .more_evaluate_callback import MoreEvaluateCallback from .has_monitor_callback import ResultsMonitor, HasMonitorCallback +from .fitlog_callback import FitlogCallback diff --git a/fastNLP/core/callbacks/callback_manager.py b/fastNLP/core/callbacks/callback_manager.py index 27770115..765a0346 100644 --- a/fastNLP/core/callbacks/callback_manager.py +++ b/fastNLP/core/callbacks/callback_manager.py @@ -25,7 +25,7 @@ def _transfer(func): for callback_fn in manager.callback_fns[func.__name__]: try: callback_fn(*arg, **kwargs) - except EarlyStopException as e: + except (EarlyStopException, KeyboardInterrupt) as e: raise e except BaseException as e: logger.error(f"The following callback_fn raise exception:{_get_fun_msg(callback_fn)}.") diff --git a/fastNLP/core/callbacks/checkpoint_callback.py b/fastNLP/core/callbacks/checkpoint_callback.py index c4ed8d47..625aea09 100644 --- a/fastNLP/core/callbacks/checkpoint_callback.py +++ b/fastNLP/core/callbacks/checkpoint_callback.py @@ -9,12 +9,13 @@ import sys from fastNLP.core.log import logger from .topk_saver import TopkSaver from .callback import Callback +from ..utils.exceptions import EarlyStopException class CheckpointCallback(Callback): def __init__(self, folder: Optional[Union[str, Path]] = None, every_n_epochs: Optional[int] = None, - every_n_batches: Optional[int] = None, last: bool = False, - on_exceptions: Optional[Union[BaseException, Sequence[BaseException]]] = None, topk: int = 0, + every_n_batches: Optional[int] = None, last: bool = False, topk: int = 0, + on_exceptions: Optional[Union[BaseException, Sequence[BaseException]]] = [EarlyStopException], monitor: Optional[Union[str, Callable]] = None, larger_better: bool = True, only_state_dict: bool = True, model_save_fn: Optional[Callable] = None, save_object: str = 'model', save_evaluate_results=True, **kwargs): @@ -33,16 +34,23 @@ class CheckpointCallback(Callback): 则 fastNLP 将 folder 绝对路径传递给该函数，fastNLP 在该 folder 下不进行模型保存。默认情况下，本 checkpoint 只保存了 model 的状态；如还需保存 Trainer 的状态以断点重训的话，请使用 ``save_object='trainer'`` 。 - :param monitor: 监控的 metric 值。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最长公共字符串算法 找到最匹配 - 的那个作为 monitor 。如果为 None，将尝试使用 Trainer 设置的 monitor 。也可以传入一个函数，接受参数为 evaluation 的结 - 果(字典类型)，返回一个 float 值作为 monitor 的结果，如果当前结果中没有相关的 monitor 值请返回 None 。 + :param monitor: 监控的 metric 值。 + + * 为 ``None`` + 将尝试使用 :class:`~fastNLP.Trainer` 中设置 `monitor` 值（如果有设置）。 + * 为 ``str`` + 尝试直接使用该名称从 ``evaluation`` 结果中寻找，如果在 ``evaluation`` 结果中没有找到完全一致的名称，将 + 使用 最长公共字符串算法 从 ``evaluation`` 结果中找到最匹配的那个作为 ``monitor`` 。 + * 为 ``Callable`` + 接受参数为 ``evaluation`` 的结果(字典类型)，返回一个 ``float`` 值作为 ``monitor`` 的结果，如果当前结果中没有相关 + 的 ``monitor`` 值请返回 ``None`` 。 :param folder: 保存的文件夹，fastNLP 将在该文件下以时间戳创建子文件夹，并在里面保存。因此不同次运行可以将被保存到不同的 时间戳文件夹中。如果为 None ，默认使用当前文件夹。 :param every_n_epochs: 多少个 epoch 保存一次。 :param every_n_batches: 多少个 batch 保存一次。 :param last: 如果为 True ，将在每次 epoch 运行结束都保存一次，会覆盖之前的保存。 :param topk: 保存 monitor 结果 topK 个。 - :param on_exceptions: 在出异常信息时，是否保存。传入需要捕获的异常的类。 + :param on_exceptions: 在出异常信息时，是否保存。传入需要捕获的异常的类。默认将捕获 EarlyStopException 。 :param larger_better: monitor 的值是否时越大越好。 :param only_state_dict: 保存模型时是否只保存 state_dict 。当 model_save_fn 不为 None 时，该参数无效。 :param model_save_fn: 个性化的保存函数，当触发保存操作时，就调用这个函数，这个函数应当接受一个文件夹作为参数，不返回任何东西。 diff --git a/fastNLP/core/callbacks/early_stop_callback.py b/fastNLP/core/callbacks/early_stop_callback.py index ad1c95cd..c706bf12 100644 --- a/fastNLP/core/callbacks/early_stop_callback.py +++ b/fastNLP/core/callbacks/early_stop_callback.py @@ -12,9 +12,16 @@ class EarlyStopCallback(HasMonitorCallback): def __init__(self, monitor:Union[str, Callable]=None, larger_better:bool=True, patience:int=10): """ - :param str monitor: 监控的 metric 值。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最长公共字符串算法 找到最匹配 - 的那个作为 monitor 。如果为 None，将尝试使用 Trainer 设置的 monitor 。也可以传入一个函数，接受参数为 evaluation 的结 - 果(字典类型)，返回一个 float 值作为 monitor 的结果。 + :param monitor: 监控的 metric 值。 + + * 为 ``None`` + 将尝试使用 :class:`~fastNLP.Trainer` 中设置 `monitor` 值（如果有设置）。 + * 为 ``str`` + 尝试直接使用该名称从 ``evaluation`` 结果中寻找，如果在 ``evaluation`` 结果中没有找到完全一致的名称，将 + 使用 最长公共字符串算法 从 ``evaluation`` 结果中找到最匹配的那个作为 ``monitor`` 。 + * 为 ``Callable`` + 接受参数为 ``evaluation`` 的结果(字典类型)，返回一个 ``float`` 值作为 ``monitor`` 的结果，如果当前结果中没有相关 + 的 ``monitor`` 值请返回 ``None`` 。 :param larger_better: monitor 的值是否是越大越好。 :param patience: 多少次 evaluate 不没有提升就停止。 """ diff --git a/fastNLP/core/callbacks/fitlog_callback.py b/fastNLP/core/callbacks/fitlog_callback.py new file mode 100644 index 00000000..867c9f68 --- /dev/null +++ b/fastNLP/core/callbacks/fitlog_callback.py @@ -0,0 +1,66 @@ +__all__ = [ + 'FitlogCallback' +] +from .has_monitor_callback import HasMonitorCallback +from ...envs import _module_available +from ...envs import get_global_rank +if _module_available('fitlog'): + import fitlog + + +class FitlogCallback(HasMonitorCallback): + """ + 自动记录 ``evaluation`` 结果到 ``fitlog`` 中。会自动记录每一次 ``evaluate`` 后的结果；同时会根据 + ``monitor`` 记录最好的结果。另外，会自动将非 ``rank 0`` 上的 ``fitlog`` 设置为 ``debug`` 状态。 + + :param monitor: 监控的 metric 值。 + + * 为 ``None`` + 将尝试使用 :class:`~fastNLP.Trainer` 中设置 `monitor` 值（如果有设置）。 + * 为 ``str`` + 尝试直接使用该名称从 ``evaluation`` 结果中寻找，如果在 ``evaluation`` 结果中没有找到完全一致的名称，将 + 使用 最长公共字符串算法 从 ``evaluation`` 结果中找到最匹配的那个作为 ``monitor`` 。 + * 为 ``Callable`` + 接受参数为 ``evaluation`` 的结果(字典类型)，返回一个 ``float`` 值作为 ``monitor`` 的结果，如果当前结果中没有相关 + 的 ``monitor`` 值请返回 ``None`` 。 + :param larger_better: 是否是越大越好。 + :param log_exception: 是否记录 ``exception`` 。 + :param log_loss_every: 多少个 ``batch`` 记录一次 loss 到 ``fitlog`` 中。 + """ + def __init__(self, monitor=None, larger_better: bool = True, log_exception:bool=True, log_loss_every:int=0): + assert _module_available('fitlog'), "fitlog is not installed." + + super().__init__(monitor=monitor, larger_better=larger_better) + self.log_exception = log_exception + self.log_loss_every = log_loss_every + self.avg_loss = 0 + + def on_after_trainer_initialized(self, trainer, driver): + if get_global_rank() != 0: # 如果不是 global rank 为 0 ，需要关闭 fitlog + fitlog.debug() + + def on_evaluate_end(self, trainer, results): + results = self.itemize_results(results) + fitlog.add_metric(results, step=trainer.global_forward_batches, epoch=trainer.cur_epoch_idx) + if self.is_better_results(results, keep_if_better=True): + results['step'] = trainer.global_forward_batches + results['epoch'] = trainer.cur_epoch_idx + fitlog.add_best_metric(results) + + def on_before_backward(self, trainer, outputs): + if self.log_loss_every > 0: + loss = trainer.extract_loss_from_outputs(outputs) + self.avg_loss += loss.item() + if trainer.global_forward_batches % self.log_loss_every == 0: + fitlog.add_loss(self.avg_loss / self.log_loss_every * trainer.accumulation_steps, name='loss', + step=trainer.global_forward_batches, + epoch=trainer.cur_epoch_idx) + self.avg_loss = 0 + + def on_train_end(self, trainer): + fitlog.finish() + + def on_exception(self, trainer, exception): + fitlog.finish(status=1) + if self.log_exception: + fitlog.add_other(repr(exception), name='except_info') diff --git a/fastNLP/core/callbacks/has_monitor_callback.py b/fastNLP/core/callbacks/has_monitor_callback.py index d934e24a..e5406d78 100644 --- a/fastNLP/core/callbacks/has_monitor_callback.py +++ b/fastNLP/core/callbacks/has_monitor_callback.py @@ -171,9 +171,16 @@ class HasMonitorCallback(ResultsMonitor, Callback): 该 callback 不直接进行使用，作为其它相关 callback 的父类使用，如果 callback 有使用 monitor 可以继承该函数里面实现了 （1）判断monitor合法性；（2）在需要时， 根据trainer的monitor设置自己的monitor名称。 - :param monitor: 监控的 metric 值。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最长公共字符串算法 找到最匹配 - 的那个作为 monitor 。如果为 None，将尝试使用 Trainer 设置的 monitor 。也可以传入一个函数，接受参数为 evaluation 的结 - 果(字典类型)，返回一个 float 值作为 monitor 的结果，如果当前结果中没有相关的 monitor 值请返回 None 。 + :param monitor: 监控的 metric 值。 + + * 为 ``None`` + 将尝试使用 :class:`~fastNLP.Trainer` 中设置 `monitor` 值（如果有设置）。 + * 为 ``str`` + 尝试直接使用该名称从 ``evaluation`` 结果中寻找，如果在 ``evaluation`` 结果中没有找到完全一致的名称，将 + 使用 最长公共字符串算法 从 ``evaluation`` 结果中找到最匹配的那个作为 ``monitor`` 。 + * 为 ``Callable`` + 接受参数为 ``evaluation`` 的结果(字典类型)，返回一个 ``float`` 值作为 ``monitor`` 的结果，如果当前结果中没有相关 + 的 ``monitor`` 值请返回 ``None`` 。 :param larger_better: monitor 是否时越大越好 :param must_have_monitor: 这个 callback 是否必须有 monitor 设置。如果设置为 True ，且没检测到设置 monitor 会报错。 """ diff --git a/fastNLP/core/callbacks/load_best_model_callback.py b/fastNLP/core/callbacks/load_best_model_callback.py index 55ef40ad..5083f5c3 100644 --- a/fastNLP/core/callbacks/load_best_model_callback.py +++ b/fastNLP/core/callbacks/load_best_model_callback.py @@ -22,9 +22,16 @@ class LoadBestModelCallback(HasMonitorCallback): 保存最佳的 monitor 值最佳的模型，并在训练结束的时候重新加载模型，默认会在加载之后删除权重文件。仅在训练正常结束的时候才能加载 最好的模型。 - :param str monitor: 监控的 metric 值。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最长公共字符串算法 找到最匹配 - 的那个作为 monitor 。如果为 None，将尝试使用 Trainer 设置的 monitor 。也可以传入一个函数，接受参数为 evaluation 的结 - 果(字典类型)，返回一个 float 值作为 monitor 的结果，如果当前结果中没有相关的 monitor 值请返回 None 。 + :param monitor: 监控的 metric 值。 + + * 为 ``None`` + 将尝试使用 :class:`~fastNLP.Trainer` 中设置 `monitor` 值（如果有设置）。 + * 为 ``str`` + 尝试直接使用该名称从 ``evaluation`` 结果中寻找，如果在 ``evaluation`` 结果中没有找到完全一致的名称，将 + 使用 最长公共字符串算法 从 ``evaluation`` 结果中找到最匹配的那个作为 ``monitor`` 。 + * 为 ``Callable`` + 接受参数为 ``evaluation`` 的结果(字典类型)，返回一个 ``float`` 值作为 ``monitor`` 的结果，如果当前结果中没有相关 + 的 ``monitor`` 值请返回 ``None`` 。 :param larger_better: 该 metric 值是否是越大越好。 :param save_folder: 保存的文件夹，如果为空，则保存在内存中。不为空，则保存一份权重到文件中，当为多机训练，且本值不为空时，请确保 不同的机器均可访问当该路径。当 model_save_fn 不为 None 时该值一定不能为空。 diff --git a/fastNLP/core/callbacks/progress_callback.py b/fastNLP/core/callbacks/progress_callback.py index 2ce177e2..24eda36e 100644 --- a/fastNLP/core/callbacks/progress_callback.py +++ b/fastNLP/core/callbacks/progress_callback.py @@ -45,10 +45,16 @@ class RichCallback(ProgressCallback): :param print_every: 多少个 batch 更新一次显示。 :param loss_round_ndigit: 显示的 loss 保留多少位有效数字 - :param monitor: 当检测到这个key的结果更好时，会打印出不同的颜色进行提示。监控的 metric 值。如果在 evaluation 结果中没有找到 - 完全一致的名称，将使用 最长公共字符串算法 找到最匹配的那个作为 monitor 。如果为 None，将尝试使用 Trainer 设置的 monitor - 。也可以传入一个函数，接受参数为 evaluation 的结果(字典类型)，返回一个 float 值作为 monitor 的结果，如果当前结果中没有 - 相关的 monitor 值请返回 None 。 + :param monitor: 监控的 metric 值。当检测到这个key的结果更好时，会打印出不同的颜色进行提示。 + + * 为 ``None`` + 将尝试使用 :class:`~fastNLP.Trainer` 中设置 `monitor` 值（如果有设置）。 + * 为 ``str`` + 尝试直接使用该名称从 ``evaluation`` 结果中寻找，如果在 ``evaluation`` 结果中没有找到完全一致的名称，将 + 使用 最长公共字符串算法 从 ``evaluation`` 结果中找到最匹配的那个作为 ``monitor`` 。 + * 为 ``Callable`` + 接受参数为 ``evaluation`` 的结果(字典类型)，返回一个 ``float`` 值作为 ``monitor`` 的结果，如果当前结果中没有相关 + 的 ``monitor`` 值请返回 ``None`` 。 :param larger_better: 是否是 monitor 的结果越大越好。 :param format_json: 是否格式化 json 再打印 """ @@ -140,10 +146,16 @@ class RawTextCallback(ProgressCallback): :param print_every: 多少个 batch 更新一次显示。 :param loss_round_ndigit: 显示的 loss 保留多少位有效数字 - :param monitor: 当检测到这个key的结果更好时，会打印出不同的颜色进行提示。监控的 metric 值。如果在 evaluation 结果中没有找到 - 完全一致的名称，将使用 最长公共字符串算法 找到最匹配的那个作为 monitor 。如果为 None，将尝试使用 Trainer 设置的 monitor - 。也可以传入一个函数，接受参数为 evaluation 的结果(字典类型)，返回一个 float 值作为 monitor 的结果，如果当前结果中没有 - 相关的 monitor 值请返回 None 。 + :param monitor: 监控的 metric 值。当检测到这个key的结果更好时，会打印出不同的颜色进行提示。 + + * 为 ``None`` + 将尝试使用 :class:`~fastNLP.Trainer` 中设置 `monitor` 值（如果有设置）。 + * 为 ``str`` + 尝试直接使用该名称从 ``evaluation`` 结果中寻找，如果在 ``evaluation`` 结果中没有找到完全一致的名称，将 + 使用 最长公共字符串算法 从 ``evaluation`` 结果中找到最匹配的那个作为 ``monitor`` 。 + * 为 ``Callable`` + 接受参数为 ``evaluation`` 的结果(字典类型)，返回一个 ``float`` 值作为 ``monitor`` 的结果，如果当前结果中没有相关 + 的 ``monitor`` 值请返回 ``None`` 。 :param larger_better: 是否是monitor的结果越大越好。 :param format_json: 是否format json再打印 """ diff --git a/fastNLP/core/controllers/evaluator.py b/fastNLP/core/controllers/evaluator.py index 9ad35ea8..8ac35ad2 100644 --- a/fastNLP/core/controllers/evaluator.py +++ b/fastNLP/core/controllers/evaluator.py @@ -7,7 +7,7 @@ __all__ = [ 'Evaluator' ] -from fastNLP.core.drivers import Driver +from fastNLP.core.drivers import Driver, TorchDriver from ..drivers.choose_driver import choose_driver from .loops import Loop, EvaluateBatchLoop from fastNLP.core.utils import auto_param_call, dataclass_to_dict, \ @@ -23,7 +23,7 @@ class Evaluator: driver: Driver _evaluate_batch_loop: Loop - def __init__(self, model, dataloaders, metrics: Optional[Union[Dict, Metric]] = None, + def __init__(self, model, dataloaders, metrics: Optional[Dict] = None, driver: Union[str, Driver] = 'torch', device: Optional[Union[int, List[int], str]] = None, evaluate_batch_step_fn: Optional[callable] = None, evaluate_fn: Optional[str] = None, input_mapping: Optional[Union[Callable, Dict]] = None, @@ -316,15 +316,15 @@ class _MetricsWrapper: raise TypeError("Parameter `metrics` can only be `Dict` type.") for metric_name, metric in metrics.items(): # 因为 torchmetrics 是一个 nn.Module，因此我们需要先将其移到对应的机器上； - if _is_torchmetrics_metric(metric): + if _is_torchmetrics_metric(metric) and isinstance(evaluator.driver, TorchDriver): # torchmetrics 是默认自动开启了多卡的 evaluator.driver.move_model_to_device(metric, evaluator.driver.data_device) elif isinstance(metric, Metric): # 如果数据是分布式的，但是不aggregate的话可能有问题 if evaluator._dist_sampler is not None and metric.aggregate_when_get_metric is False: - logger.warning_once( - "You have replace the sampler as distributed sampler when evaluation, but your " - f"metric {metric_name}:{metric.__class__.__name__}' `aggregate_when_get_metric` is False.") + logger.rank_zero_warning( + "You have replace the sampler as distributed sampler when evaluation, but your metric " + f"{metric_name}:{metric.__class__.__name__}'s `aggregate_when_get_metric` is False.", once=True) if metric.aggregate_when_get_metric is None: metric.aggregate_when_get_metric = evaluator._dist_sampler is not None @@ -388,5 +388,8 @@ class _MetricsWrapper: _results = metric.accumulate() else: raise RuntimeError(f"Not support `{type(metric)}` for now.") - results[metric_name] = _results + if _results is not None: + results[metric_name] = _results + else: + logger.warning_once(f"Metric:{metric_name} returns None when getting metric results.") return results diff --git a/fastNLP/core/controllers/loops/evaluate_batch_loop.py b/fastNLP/core/controllers/loops/evaluate_batch_loop.py index 80c234cd..c81379a1 100644 --- a/fastNLP/core/controllers/loops/evaluate_batch_loop.py +++ b/fastNLP/core/controllers/loops/evaluate_batch_loop.py @@ -27,19 +27,21 @@ class EvaluateBatchLoop(Loop): while True: try: batch = next(iterator) - batch = match_and_substitute_params(evaluator.input_mapping, batch) - batch = evaluator.move_data_to_device(batch) except StopIteration: break + try: + batch = match_and_substitute_params(evaluator.input_mapping, batch) + batch = evaluator.move_data_to_device(batch) + + self.batch_step_fn(evaluator, batch) + batch_idx += 1 + evaluator.update_progress_bar(batch_idx, evaluator.cur_dataloader_name) + except BaseException as e: if callable(getattr(dataloader, 'get_batch_indices', None)): indices = dataloader.get_batch_indices() logger.error(f"Exception happens when evaluating on samples: {indices}") raise e - - self.batch_step_fn(evaluator, batch) - batch_idx += 1 - evaluator.update_progress_bar(batch_idx, evaluator.cur_dataloader_name) # 获取metric结果。返回的dict内容示例为{'metric_name1': metric_results, 'metric_name2': metric_results, ...} results = evaluator.get_metric() return results diff --git a/fastNLP/core/controllers/loops/train_batch_loop.py b/fastNLP/core/controllers/loops/train_batch_loop.py index 989fb2ae..7bb9b653 100644 --- a/fastNLP/core/controllers/loops/train_batch_loop.py +++ b/fastNLP/core/controllers/loops/train_batch_loop.py @@ -19,30 +19,31 @@ class TrainBatchLoop(Loop): get_batch_indices = dataloader.get_batch_indices if callable(getattr(dataloader, 'get_batch_indices', None))\ else lambda *args, **kwargs: None dataloader = iter(dataloader) - indices = None while trainer.batch_idx_in_epoch<=trainer.num_batches_per_epoch: try: trainer.on_fetch_data_begin() batch = next(dataloader) indices = get_batch_indices() + except StopIteration: + break + + try: trainer.on_fetch_data_end() batch = match_and_substitute_params(trainer.input_mapping, batch) batch = trainer.move_data_to_device(batch) - except StopIteration: - break + + trainer.on_train_batch_begin(batch, indices) + with trainer.get_no_sync_context(): # 在多卡的时候可能需要关闭 sync + self.batch_step_fn(trainer, batch) + trainer.global_forward_batches += 1 + trainer.batch_idx_in_epoch += 1 + + trainer.check_batch_step_fn() + trainer.on_train_batch_end() except BaseException as e: - if indices and not isinstance(e, EarlyStopException): + if indices is not None and not isinstance(e, (EarlyStopException, KeyboardInterrupt)): logger.error(f"Exception happens when running on samples: {indices}") raise e - - trainer.on_train_batch_begin(batch, indices) - with trainer.get_no_sync_context(): # 在多卡的时候可能需要关闭 sync - self.batch_step_fn(trainer, batch) - trainer.global_forward_batches += 1 - trainer.batch_idx_in_epoch += 1 - - trainer.check_batch_step_fn() - trainer.on_train_batch_end() trainer.step_evaluate() trainer.batch_idx_in_epoch = 0 diff --git a/fastNLP/core/controllers/trainer.py b/fastNLP/core/controllers/trainer.py index 930af27b..d64a39fe 100644 --- a/fastNLP/core/controllers/trainer.py +++ b/fastNLP/core/controllers/trainer.py @@ -256,7 +256,6 @@ class Trainer(TrainerEventTrigger): :kwargs: * *torch_kwargs* -- 用于在指定 ``driver`` 为 'torch' 时设定具体 driver 实例的一些参数： - * ddp_kwargs -- 用于在使用 ``TorchDDPDriver`` 时指定 ``DistributedDataParallel`` 初始化时的参数；例如传入 {'find_unused_parameters': True} 来解决有参数不参与前向运算导致的报错等； * set_grad_to_none -- 是否在训练过程中在每一次 optimizer 更新后将 grad 置为 None； diff --git a/fastNLP/core/dataloaders/jittor_dataloader/fdl.py b/fastNLP/core/dataloaders/jittor_dataloader/fdl.py index 8ecd2d87..b76fd4c1 100644 --- a/fastNLP/core/dataloaders/jittor_dataloader/fdl.py +++ b/fastNLP/core/dataloaders/jittor_dataloader/fdl.py @@ -47,7 +47,7 @@ class JittorDataLoader: 提供给使用jittor框架的DataLoader函数，提供了auto_collate的功能， 支持实现了__getitem__和__len__的dataset """ - def __init__(self, dataset, batch_size: int = 16, shuffle: bool = False, + def __init__(self, dataset, batch_size: int = 16, shuffle: bool = True, 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") -> None: diff --git a/fastNLP/core/dataloaders/paddle_dataloader/fdl.py b/fastNLP/core/dataloaders/paddle_dataloader/fdl.py index 393324d4..4c2f2300 100644 --- a/fastNLP/core/dataloaders/paddle_dataloader/fdl.py +++ b/fastNLP/core/dataloaders/paddle_dataloader/fdl.py @@ -47,7 +47,7 @@ class PaddleDataLoader(DataLoader): def __init__(self, dataset, feed_list=None, places=None, return_list: bool = True, batch_sampler=None, - batch_size: int = 1, shuffle: bool = False, + batch_size: int = 1, shuffle: bool = True, drop_last: bool = False, collate_fn: Union[str, Callable, None] = 'auto', num_workers: int = 0, use_buffer_reader: bool = True, use_shared_memory: bool = True, timeout: int = 0, diff --git a/fastNLP/core/dataloaders/prepare_dataloader.py b/fastNLP/core/dataloaders/prepare_dataloader.py index 8a7e3d1e..33764c6f 100644 --- a/fastNLP/core/dataloaders/prepare_dataloader.py +++ b/fastNLP/core/dataloaders/prepare_dataloader.py @@ -14,7 +14,7 @@ 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 = True, drop_last: bool = False, collate_fn: Union[Callable, str, None] = 'auto', num_workers: int = 0, seed: int = 0, backend: str = 'auto'): """ diff --git a/fastNLP/core/dataloaders/torch_dataloader/fdl.py b/fastNLP/core/dataloaders/torch_dataloader/fdl.py index 456af44f..726abaae 100644 --- a/fastNLP/core/dataloaders/torch_dataloader/fdl.py +++ b/fastNLP/core/dataloaders/torch_dataloader/fdl.py @@ -179,7 +179,7 @@ class TorchDataLoader(DataLoader): def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping[str, DataSet]], batch_size: int = 1, - shuffle: bool = False, + shuffle: bool = True, sampler: Union["Sampler[int]", ReproducibleSampler, UnrepeatedSampler] = None, batch_sampler: Union["Sampler[Sequence[int]]", ReproducibleBatchSampler] = None, num_workers: int = 0, collate_fn: Union[str, Callable, None] = 'auto', @@ -236,8 +236,8 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping persistent_workers=persistent_workers, ) else: - dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=non_train_batch_size, - shuffle=shuffle, sampler=non_train_sampler, + dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=non_train_batch_size if non_train_batch_size else batch_size, + shuffle=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, drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn, diff --git a/fastNLP/core/dataloaders/utils.py b/fastNLP/core/dataloaders/utils.py index 39ce5983..495fb6d3 100644 --- a/fastNLP/core/dataloaders/utils.py +++ b/fastNLP/core/dataloaders/utils.py @@ -1,9 +1,10 @@ +from typing import Callable __all__ = [ "indice_collate_wrapper" ] -def indice_collate_wrapper(func): +def indice_collate_wrapper(func:Callable): """ 其功能是封装一层collate_fn,将dataset取到的tuple数据分离开，将idx打包为indices。 diff --git a/fastNLP/core/drivers/paddle_driver/initialize_paddle_driver.py b/fastNLP/core/drivers/paddle_driver/initialize_paddle_driver.py index 22098ff2..552fc622 100644 --- a/fastNLP/core/drivers/paddle_driver/initialize_paddle_driver.py +++ b/fastNLP/core/drivers/paddle_driver/initialize_paddle_driver.py @@ -40,8 +40,8 @@ def initialize_paddle_driver(driver: str, device: Optional[Union[str, int, List[ if user_visible_devices is None: raise RuntimeError("To run paddle distributed training, please set `FASTNLP_BACKEND` to 'paddle' before using FastNLP.") if device is not None: - logger.warning_once("Parameter `device` would be ignored when you are using `paddle.distributed.launch` to pull " - "up your script. And we will directly get the local device via environment variables.") + logger.rank_zero_warning("Parameter `device` would be ignored when you are using `paddle.distributed.launch` to pull " + "up your script. And we will directly get the local device via environment variables.", once=True) _visible_list = user_visible_devices.split(",") device = [ f"gpu:{_visible_list.index(g) }" for g in os.environ["CUDA_VISIBLE_DEVICES"].split(",")] # TODO 目前一个进程仅对应一个卡，所以暂时传入单个 diff --git a/fastNLP/core/drivers/torch_driver/initialize_torch_driver.py b/fastNLP/core/drivers/torch_driver/initialize_torch_driver.py index f9fac83f..723765d2 100644 --- a/fastNLP/core/drivers/torch_driver/initialize_torch_driver.py +++ b/fastNLP/core/drivers/torch_driver/initialize_torch_driver.py @@ -26,9 +26,9 @@ def initialize_torch_driver(driver: str, device: Optional[Union[str, "torch.devi # world_size 和 rank if FASTNLP_BACKEND_LAUNCH in os.environ: if device is not None: - logger.warning_once("Parameter `device` would be ignored when you are using `torch.distributed.run` to pull " + logger.rank_zero_warning("Parameter `device` would be ignored when you are using `torch.distributed.run` to pull " "up your script. And we will directly get the local device via " - "`os.environ['LOCAL_RANK']`.") + "`os.environ['LOCAL_RANK']`.", once=True) return TorchDDPDriver(model, torch.device(f"cuda:{os.environ['LOCAL_RANK']}"), True, **kwargs) if driver not in {"torch", "fairscale"}: diff --git a/fastNLP/core/drivers/torch_driver/single_device.py b/fastNLP/core/drivers/torch_driver/single_device.py index 6c125a73..8aa9a2d5 100644 --- a/fastNLP/core/drivers/torch_driver/single_device.py +++ b/fastNLP/core/drivers/torch_driver/single_device.py @@ -1,11 +1,13 @@ import os from typing import Dict, Union, Callable, Tuple, Optional from fastNLP.envs.imports import _NEED_IMPORT_TORCH + if _NEED_IMPORT_TORCH: import torch from torch.nn import DataParallel from torch.nn.parallel import DistributedDataParallel from torch.utils.data import RandomSampler as TorchRandomSampler + from torch.utils.data import SequentialSampler as TorchSequentialSampler __all__ = [ 'TorchSingleDriver' @@ -15,7 +17,8 @@ from .torch_driver import TorchDriver from fastNLP.core.drivers.torch_driver.utils import replace_sampler, replace_batch_sampler from fastNLP.core.utils import auto_param_call from fastNLP.core.utils.utils import _get_fun_msg -from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, re_instantiate_sampler, ReproduceBatchSampler +from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, re_instantiate_sampler, \ + ReproduceBatchSampler from fastNLP.core.samplers import RandomSampler from fastNLP.core.log import logger @@ -24,6 +27,7 @@ class TorchSingleDriver(TorchDriver): r""" 用于 cpu 和 单卡 gpu 运算； """ + def __init__(self, model, device: "torch.device", fp16: bool = False, **kwargs): if isinstance(model, DistributedDataParallel): raise ValueError("`DistributedDataParallel` is not supported in `TorchSingleDriver`") @@ -88,7 +92,8 @@ class TorchSingleDriver(TorchDriver): else: raise RuntimeError(f"There is no `{fn}` method in your {type(self.model)}.") - def set_dist_repro_dataloader(self, dataloader, dist: Union[str, ReproducibleBatchSampler, ReproducibleSampler]=None, + def set_dist_repro_dataloader(self, dataloader, + dist: Union[str, ReproducibleBatchSampler, ReproducibleSampler] = None, reproducible: bool = False): # 如果 dist 为 ReproducibleBatchSampler, ReproducibleIterator 说明是在断点重训时 driver.load 函数调用； @@ -108,17 +113,24 @@ class TorchSingleDriver(TorchDriver): if reproducible: if isinstance(args.sampler, TorchRandomSampler): - # 如果本来就是随机的，直接替换掉吧。 - sampler = RandomSampler(args.sampler.data_source) - logger.debug("Replace torch RandomSampler into fastNLP RandomSampler.") + if getattr(args.sampler, '_num_samples', None) is None \ + and getattr(args.sampler, 'replacements', False) is False \ + and getattr(args.sampler, 'generator', None) is None: + # 如果本来就是随机的，并且没有定制，直接替换掉吧。 + sampler = RandomSampler(args.sampler.data_source, shuffle=True) + logger.debug("Replace torch RandomSampler into fastNLP RandomSampler.") + return replace_sampler(dataloader, sampler) + elif isinstance(args.sampler, TorchSequentialSampler): + # 需要替换为不要 shuffle 的。 + sampler = RandomSampler(args.sampler.data_source, shuffle=False) + logger.debug("Replace torch SequentialSampler into fastNLP RandomSampler.") return replace_sampler(dataloader, sampler) - else: - batch_sampler = ReproduceBatchSampler( - batch_sampler=args.batch_sampler, - batch_size=args.batch_size, - drop_last=args.drop_last - ) - return replace_batch_sampler(dataloader, batch_sampler) + batch_sampler = ReproduceBatchSampler( + batch_sampler=args.batch_sampler, + batch_size=args.batch_size, + drop_last=args.drop_last + ) + return replace_batch_sampler(dataloader, batch_sampler) else: return dataloader @@ -138,9 +150,3 @@ class TorchSingleDriver(TorchDriver): def is_distributed(self): return False - - - - - - diff --git a/fastNLP/core/log/print.py b/fastNLP/core/log/print.py index f40d763e..827835aa 100644 --- a/fastNLP/core/log/print.py +++ b/fastNLP/core/log/print.py @@ -24,4 +24,4 @@ def print(*args, sep=' ', end='\n', file=None, flush=False): line = sep.join(map(str, args)) if logger.isEnabledFor(INFO): kwargs = logger._add_rank_info({}) - logger._log(INFO, line, args, **kwargs) + logger._log(INFO, line, None, **kwargs) diff --git a/fastNLP/core/metrics/__init__.py b/fastNLP/core/metrics/__init__.py index f7d60606..b7f572e8 100644 --- a/fastNLP/core/metrics/__init__.py +++ b/fastNLP/core/metrics/__init__.py @@ -1,11 +1,12 @@ __all__ = [ "Metric", "Accuracy", + "TransformersAccuracy", 'SpanFPreRecMetric', 'ClassifyFPreRecMetric', ] from .metric import Metric -from .accuracy import Accuracy +from .accuracy import Accuracy, TransformersAccuracy from .span_f1_pre_rec_metric import SpanFPreRecMetric from .classify_f1_pre_rec_metric import ClassifyFPreRecMetric diff --git a/fastNLP/core/metrics/accuracy.py b/fastNLP/core/metrics/accuracy.py index 0869d8c8..59990f95 100644 --- a/fastNLP/core/metrics/accuracy.py +++ b/fastNLP/core/metrics/accuracy.py @@ -1,5 +1,6 @@ __all__ = [ - 'Accuracy' + 'Accuracy', + "TransformersAccuracy" ] from typing import Union @@ -17,9 +18,9 @@ class Accuracy(Metric): """ 计算 准确率 的 metric 。 - :param str backend: 目前支持四种类型的backend, ['auto', 'torch', 'paddle', 'jittor']。其中 auto 表示根据实际调用 Metric.update() + :param backend: 目前支持四种类型的backend, ['auto', 'torch', 'paddle', 'jittor']。其中 auto 表示根据实际调用 Metric.update() 函数时传入的参数决定具体的 backend ，一般情况下直接使用 'auto' 即可。 - :param bool aggregate_when_get_metric: 在计算 metric 的时候是否自动将各个进程上的相同的 element 的数字聚合后再得到 metric， + :param aggregate_when_get_metric: 在计算 metric 的时候是否自动将各个进程上的相同的 element 的数字聚合后再得到 metric， 当 backend 不支持分布式时，该参数无意义。如果为 None ，将在 Evaluator 中根据 sampler 是否使用分布式进行自动设置。 """ super(Accuracy, self).__init__(backend=backend, aggregate_when_get_metric=aggregate_when_get_metric) @@ -39,11 +40,11 @@ class Accuracy(Metric): r""" update 函数将针对一个批次的预测结果做评价指标的累计 - :param torch.Tensor pred: 预测的tensor, tensor的形状可以是torch.Size([B,]), torch.Size([B, n_classes]), + :param pred: 预测的tensor, tensor的形状可以是torch.Size([B,]), torch.Size([B, n_classes]), torch.Size([B, max_len]), 或者torch.Size([B, max_len, n_classes]) - :param torch.Tensor target: 真实值的tensor, tensor的形状可以是Element's can be: torch.Size([B,]), + :param target: 真实值的tensor, tensor的形状可以是Element's can be: torch.Size([B,]), torch.Size([B,]), torch.Size([B, max_len]), 或者torch.Size([B, max_len]) - :param torch.Tensor seq_len: 序列长度标记, 标记的形状可以是None, None, torch.Size([B]), 或者torch.Size([B]). + :param seq_len: 序列长度标记, 标记的形状可以是None, None, torch.Size([B]), 或者torch.Size([B]). 如果mask也被传进来的话seq_len会被忽略. """ # 为了兼容不同框架，我们将输入变量全部转为numpy类型来进行计算。 @@ -79,3 +80,20 @@ class Accuracy(Metric): else: self.total += np.prod(list(pred.shape)).item() self.correct += (target == pred).sum().item() + + +class TransformersAccuracy(Accuracy): + """ + 适配 transformers 中相关模型的 Accuracy metric 。 + + """ + def update(self, logits, labels, attention_mask=None): + r""" + update 函数将针对一个批次的预测结果做评价指标的累计 + + :param logits: 形状为 ``[B, n_classes]`` 或 ``[B, max_len, n_classes]`` 。 + :param labels: 形状为 ``[B, ]`` 或 ``[B, max_len]`` + :param attention_mask: 序列长度标记。 + """ + seq_len = attention_mask.sum(dim=-1) + super().update(pred=logits, target=labels, seq_len=seq_len) \ No newline at end of file diff --git a/fastNLP/core/utils/paddle_utils.py b/fastNLP/core/utils/paddle_utils.py index c7bb9e79..d3764d4e 100644 --- a/fastNLP/core/utils/paddle_utils.py +++ b/fastNLP/core/utils/paddle_utils.py @@ -22,9 +22,9 @@ from .utils import apply_to_collection def _convert_data_device(device: Union[str, int]) -> str: """ - 用于转换 ``driver`` 的 ``data_device`` 的函数。如果用户设置了 ``FASTNLP_BACKEND=paddle``，那么 ``fastNLP`` 会将 + 用于转换 ``driver`` 的 ``data_device`` 的函数。如果用户设置了 ``FASTNLP_BACKEND=paddle``，那么 **fastNLP** 会将 可见的设备保存在 ``USER_CUDA_VISIBLE_DEVICES`` 中，并且将 ``CUDA_VISIBLE_DEVICES`` 设置为可见的第一张显卡；这是为 - 了顺利执行 ``paddle`` 的分布式训练而设置的。 + 了顺利执行 **paddle** 的分布式训练而设置的。 在这种情况下，单纯使用 ``driver.data_device`` 是无效的。比如在分布式训练中将设备设置为 ``[0,2,3]`` ，且用户设置了 ``CUDA_VISIBLE_DEVICES=3,4,5,6`` ，那么在 ``rank1``的进程中有:: @@ -127,7 +127,7 @@ def get_paddle_device_id(device: Union[str, int]) -> int: def paddle_move_data_to_device(batch: Any, device: Optional[Union[str, int]]) -> Any: r""" - 将 ``paddle`` 的数据集合传输到给定设备。只有 :class:`paddle.Tensor` 对象会被传输到设备中，其余保持不变。 + 将 **paddle** 的数据集合传输到给定设备。只有 :class:`paddle.Tensor` 对象会被传输到设备中，其余保持不变。 :param batch: 需要进行迁移的数据集合； :param device: 目标设备。可以是显卡设备的编号，或是``cpu``, ``gpu`` 或 ``gpu:x`` 格式的字符串；当这个参数 @@ -145,20 +145,20 @@ def paddle_move_data_to_device(batch: Any, device: Optional[Union[str, int]]) -> def is_in_paddle_dist() -> bool: """ - 判断是否处于 ``paddle`` 分布式的进程下，使用 ``PADDLE_RANK_IN_NODE`` 和 ``FLAGS_selected_gpus`` 判断。 + 判断是否处于 **paddle** 分布式的进程下，使用 ``PADDLE_RANK_IN_NODE`` 和 ``FLAGS_selected_gpus`` 判断。 """ return ('PADDLE_RANK_IN_NODE' in os.environ and 'FLAGS_selected_gpus' in os.environ) def is_in_fnlp_paddle_dist() -> bool: """ - 判断是否处于 ``fastNLP`` 拉起的 ``paddle`` 分布式进程中 + 判断是否处于 **fastNLP** 拉起的 **paddle** 分布式进程中 """ return FASTNLP_DISTRIBUTED_CHECK in os.environ def is_in_paddle_launch_dist() -> bool: """ - 判断是否处于 ``python -m paddle.distributed.launch`` 方法启动的 ``paddle`` 分布式进程中 + 判断是否处于 ``python -m paddle.distributed.launch`` 方法启动的 **paddle** 分布式进程中 """ return FASTNLP_BACKEND_LAUNCH in os.environ \ No newline at end of file diff --git a/fastNLP/core/utils/rich_progress.py b/fastNLP/core/utils/rich_progress.py index 02a30c26..53d4e281 100644 --- a/fastNLP/core/utils/rich_progress.py +++ b/fastNLP/core/utils/rich_progress.py @@ -1,5 +1,5 @@ """ -该文件用于为 ``fastNLP`` 提供一个统一的 ``progress bar`` 管理，通过共用一个``Task`` 对象， :class:`~fastNLP.core.Trainer` 中 +该文件用于为 **fastNLP** 提供一个统一的 ``progress bar`` 管理，通过共用一个``Task`` 对象， :class:`~fastNLP.core.Trainer` 中 的 ``progress bar`` 和 :class:`~fastNLP.core.Evaluator` 中的 ``progress bar`` 才能不冲突 """ import sys diff --git a/fastNLP/core/utils/torch_utils.py b/fastNLP/core/utils/torch_utils.py index 862ea20d..0cef2205 100644 --- a/fastNLP/core/utils/torch_utils.py +++ b/fastNLP/core/utils/torch_utils.py @@ -44,11 +44,11 @@ class TorchTransferableDataType(ABC): def torch_move_data_to_device(batch: Any, device: Optional[Union[str, "torch.device"]] = None, non_blocking: Optional[bool] = True) -> Any: r""" - 在 ``pytorch`` 中将数据集合 ``batch`` 传输到给定设备。任何定义方法 ``to(device)`` 的对象都将被移动并且集合中的所有其他对象将保持不变； + 在 **pytorch** 中将数据集合 ``batch`` 传输到给定设备。任何定义方法 ``to(device)`` 的对象都将被移动并且集合中的所有其他对象将保持不变； :param batch: 需要迁移的数据； :param device: 数据应当迁移到的设备；当该参数的值为 ``None`` 时则不执行任何操作； - :param non_blocking: ``pytorch`` 的数据迁移方法 ``to`` 的参数； + :param non_blocking: **pytorch** 的数据迁移方法 ``to`` 的参数； :return: 迁移到新设备上的数据集合； """ if device is None: diff --git a/fastNLP/core/utils/utils.py b/fastNLP/core/utils/utils.py index 00da9ac1..4d8bbb5e 100644 --- a/fastNLP/core/utils/utils.py +++ b/fastNLP/core/utils/utils.py @@ -55,7 +55,7 @@ def get_fn_arg_names(fn: Callable) -> List[str]: def auto_param_call(fn: Callable, *args, signature_fn: Optional[Callable] = None, mapping: Optional[Dict[AnyStr, AnyStr]] = None) -> Any: r""" - 该函数会根据输入函数的形参名从 ``*args`` （均为 ``dict`` 类型）中找到匹配的值进行调用，如果传入的数据与 ``fn`` 的形参不匹配，可以通过 + 该函数会根据输入函数的形参名从 ``*args`` （均为 **dict** 类型）中找到匹配的值进行调用，如果传入的数据与 ``fn`` 的形参不匹配，可以通过 ``mapping`` 参数进行转换。``mapping`` 参数中的一对 ``(key, value)`` 表示在 ``*args`` 中找到 ``key`` 对应的值，并将这个值传递给形参中名为 ``value`` 的参数。 @@ -259,21 +259,21 @@ def dataclass_to_dict(data: "dataclasses.dataclass") -> Dict: def match_and_substitute_params(mapping: Optional[Union[Callable, Dict]] = None, data: Optional[Any] = None) -> Any: r""" - 用来实现将输入的 ``batch`` 或者输出的 ``outputs`` 通过 ``mapping`` 将键值进行更换的功能； + 用来实现将输入的 **batch** 或者输出的 **outputs** 通过 ``mapping`` 将键值进行更换的功能； 该函数应用于 ``input_mapping`` 和 ``output_mapping``； * 对于 ``input_mapping``，该函数会在 :class:`~fastNLP.core.controllers.TrainBatchLoop` 中取完数据后立刻被调用； * 对于 ``output_mapping``，该函数会在 :class:`~fastNLP.core.Trainer` 的 :meth:`~fastNLP.core.Trainer.train_step` - 以及 :class:`~fastNLP.core.Evaluator` 的 :meth:`~fastNLP.core.Evaluator.train_step` 中得到结果后立刻被调用； + 以及 :class:`~fastNLP.core.Evaluator` 的 :meth:`~fastNLP.core.Evaluator.train_step` 中得到结果后立刻被调用； 转换的逻辑按优先级依次为： - 1. 如果 ``mapping`` 是一个函数，那么会直接返回 ``mapping(data)``； - 2. 如果 ``mapping`` 是一个 ``Dict``，那么 ``data`` 的类型只能为以下三种： ``[Dict, dataclass, Sequence]``； + 1. 如果 ``mapping`` 是一个函数，那么会直接返回 **mapping(data)**； + 2. 如果 ``mapping`` 是一个 **Dict**，那么 ``data`` 的类型只能为以下三种： ``[Dict, dataclass, Sequence]``； - * 如果 ``data`` 是 ``Dict``，那么该函数会将 ``data`` 的 ``key`` 替换为 ``mapping[key]``； - * 如果 ``data`` 是 ``dataclass``，那么该函数会先使用 :func:`dataclasses.asdict` 函数将其转换为 ``Dict``，然后进行转换； - * 如果 ``data`` 是 ``Sequence``，那么该函数会先将其转换成一个对应的字典:: + * 如果 ``data`` 是 **Dict**，那么该函数会将 ``data`` 的 ``key`` 替换为 **mapping[key]**； + * 如果 ``data`` 是 **dataclass**，那么该函数会先使用 :func:`dataclasses.asdict` 函数将其转换为 **Dict**，然后进行转换； + * 如果 ``data`` 是 **Sequence**，那么该函数会先将其转换成一个对应的字典:: { "_0": list[0], @@ -281,7 +281,7 @@ def match_and_substitute_params(mapping: Optional[Union[Callable, Dict]] = None, ... } - 然后使用 ``mapping`` 对这个 ``Dict`` 进行转换，如果没有匹配上 ``mapping`` 中的 ``key`` 则保持 ``\'\_number\'`` 这个形式。 + 然后使用 ``mapping`` 对这个字典进行转换，如果没有匹配上 ``mapping`` 中的 ``key`` 则保持 ``'_number'`` 这个形式。 :param mapping: 用于转换的字典或者函数；当 ``mapping`` 是函数时，返回值必须为字典类型； :param data: 需要被转换的对象； @@ -459,7 +459,7 @@ def _is_iterable(value): def pretty_table_printer(dataset_or_ins) -> PrettyTable: r""" - 用于在 ``fastNLP`` 中展示数据的函数:: + 用于在 **fastNLP** 中展示数据的函数:: >>> ins = Instance(field_1=[1, 1, 1], field_2=[2, 2, 2], field_3=["a", "b", "c"]) +-----------+-----------+-----------------+ diff --git a/fastNLP/io/data_bundle.py b/fastNLP/io/data_bundle.py index a3c15a28..df194df2 100644 --- a/fastNLP/io/data_bundle.py +++ b/fastNLP/io/data_bundle.py @@ -249,7 +249,7 @@ class DataBundle: return self def apply_field_more(self, func: Callable, field_name: str, num_proc: int = 0, modify_fields=True, - ignore_miss_dataset=True, progress_desc: str = '', show_progress_bar: bool = True): + ignore_miss_dataset=True, show_progress_bar: bool = True, progress_desc: str = ''): r""" 对 :class:`~fastNLP.io.DataBundle` 中所有的 dataset 使用 :meth:`~fastNLP.DataSet.apply_field_more` 方法 @@ -263,8 +263,8 @@ class DataBundle: :param num_proc: 进程的数量。请注意，由于python语言的特性，多少进程就会导致多少倍内存的增长。 :param bool ignore_miss_dataset: 当某个field名称在某个dataset不存在时，如果为True，则直接忽略该DataSet; 如果为False，则报错 - :param show_progress_bar: 是否显示tqdm进度条 - :param progress_desc: 当show_progress_barm为True时，可以显示当前tqdm正在处理的名称 + :param show_progress_bar: 是否显示进度条 + :param progress_desc: 当 ``show_progress_bar`` 为 ``True`` 时，可以显示 ``progress`` 的名称。 :return Dict[str:Dict[str:Field]]: 返回一个字典套字典，第一层的 key 是 dataset 的名字，第二层的 key 是 field 的名字 diff --git a/fastNLP/modules/mix_modules/utils.py b/fastNLP/modules/mix_modules/utils.py index e69de29b..b19a5d53 100644 --- a/fastNLP/modules/mix_modules/utils.py +++ b/fastNLP/modules/mix_modules/utils.py @@ -0,0 +1,242 @@ +import warnings +from typing import Any, Optional, Union + +import numpy as np + +from fastNLP.core.utils import paddle_to, apply_to_collection +from fastNLP.core.log import logger +from fastNLP.envs.imports import _NEED_IMPORT_JITTOR, _NEED_IMPORT_TORCH, _NEED_IMPORT_PADDLE + +if _NEED_IMPORT_PADDLE: + import paddle + +if _NEED_IMPORT_JITTOR: + import jittor + +if _NEED_IMPORT_TORCH: + import torch + +__all__ = [ + "paddle2torch", + "torch2paddle", + "jittor2torch", + "torch2jittor", +] + +def _paddle2torch(paddle_tensor: 'paddle.Tensor', device: Optional[Union[str, int]] = None, no_gradient: bool = None) -> 'torch.Tensor': + """ + 将 :class:`paddle.Tensor` 转换为 :class:`torch.Tensor` ，并且能够保留梯度进行反向传播 + + :param paddle_tensor: 要转换的 **paddle** 张量； + :param device: 是否将转换后的张量迁移到特定设备上，为 ``None``时，和输入的张量相同； + :param no_gradient: 是否保留原张量的梯度。为 ``None`` 时，新的张量与输入张量保持一致； + 为 ``True`` 时，全部不保留梯度；为 ``False`` 时，全部保留梯度； + :return: 转换后的 **torch** 张量； + """ + no_gradient = paddle_tensor.stop_gradient if no_gradient is None else no_gradient + paddle_numpy = paddle_tensor.numpy() + if not np.issubdtype(paddle_numpy.dtype, np.inexact): + no_gradient = True + + if device is None: + if paddle_tensor.place.is_gpu_place(): + # paddlepaddle有两种Place，对应不同的device id获取方式 + if hasattr(paddle_tensor.place, "gpu_device_id"): + # paddle.fluid.core_avx.Place + # 在gpu环境下创建张量的话，张量的place是这一类型 + device = f"cuda:{paddle_tensor.place.gpu_device_id()}" + else: + # paddle.CUDAPlace + device = f"cuda:{paddle_tensor.place.get_device_id()}" + else: + # TODO: 可能需要支持xpu等设备 + device = "cpu" + + if not no_gradient: + # 保持梯度，并保持反向传播 + # torch.tensor会保留numpy数组的类型 + torch_tensor = torch.tensor(paddle_numpy, requires_grad=True, device=device) + hook = torch_tensor.register_hook( + lambda grad: paddle.autograd.backward(paddle_tensor, paddle.to_tensor(grad.cpu().numpy())) + ) + else: + # 不保留梯度 + torch_tensor = torch.tensor(paddle_numpy, requires_grad=False, device=device) + + return torch_tensor + + +def _torch2paddle(torch_tensor: 'torch.Tensor', device: str = None, no_gradient: bool = None) -> 'paddle.Tensor': + """ + 将 :class:`torch.Tensor` 转换为 :class:`paddle.Tensor`，并且能够保留梯度进行反向传播。 + + :param torch_tensor: 要转换的 **torch** 张量； + :param device: 是否将转换后的张量迁移到特定设备上，输入为 ``None`` 时，和输入的张量相同； + :param no_gradient: 是否保留原张量的梯度。为 ``None`` 时，新的张量与输入张量保持一致； + 为 ``True`` 时，全部不保留梯度；为 ``False`` 时，全部保留梯度； + :return: 转换后的 **paddle** 张量； + """ + no_gradient = not torch_tensor.requires_grad if no_gradient is None else no_gradient + if device is None: + if torch_tensor.is_cuda: + device = f"gpu:{torch_tensor.device.index}" + else: + device = "cpu" + + if not no_gradient: + # 保持梯度并保持反向传播 + # paddle的stop_gradient和torch的requires_grad表现是相反的 + paddle_tensor = paddle.to_tensor(torch_tensor.detach().numpy(), stop_gradient=False) + hook = paddle_tensor.register_hook( + lambda grad: torch.autograd.backward(torch_tensor, torch.tensor(grad.numpy())) + ) + else: + paddle_tensor = paddle.to_tensor(torch_tensor.detach().numpy(), stop_gradient=True) + + paddle_tensor = paddle_to(paddle_tensor, device) + + return paddle_tensor + + +def _jittor2torch(jittor_var: 'jittor.Var', device: Optional[Union[str, int]] = None, no_gradient: bool = None) -> 'torch.Tensor': + """ + 将 :class:`jittor.Var` 转换为 :class:`torch.Tensor` 。 + + :param jittor_var: 要转换的 **jittor** 变量； + :param device: 是否将转换后的张量迁移到特定设备上，输入为 ``None`` 时，根据 ``jittor.flags.use_cuda`` 决定； + :param no_gradient: 是否保留原张量的梯度。为``None``时，新的张量与输入张量保持一致； + 为 ``True`` 时，全部不保留梯度；为 ``False`` 时，全部保留梯度； + :return: 转换后的 **torch** 张量； + """ + # TODO: warning：无法保留梯度 + # jittor的grad可以通过callback进行传递 + # 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: + warnings.warn("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 + + if device is None: + # jittor的设备分配是自动的 + # 根据use_cuda判断 + if jittor.flags.use_cuda: + device = "cuda:0" + else: + device = "cpu" + + torch_tensor = torch.tensor(jittor_numpy, requires_grad=not no_gradient, device=device) + + return torch_tensor + + +def _torch2jittor(torch_tensor: 'torch.Tensor', no_gradient: bool = None) -> 'jittor.Var': + """ + 将 :class:`torch.Tensor` 转换为 :class:`jittor.Var` 。 + + :param torch_tensor: 要转换的 **torch** 张量； + :param no_gradient: 是否保留原张量的梯度。为``None``时，新的张量与输入张量保持一致； + 为 ``True`` 时，全部不保留梯度；为 ``False`` 时，全部保留梯度； + :return: 转换后的 **jittor** 变量； + """ + no_gradient = not torch_tensor.requires_grad if no_gradient is None else no_gradient + + if not no_gradient: + # 保持梯度并保持反向传播 + jittor_var = jittor.Var(torch_tensor.detach().numpy()) + jittor_var.requires_grad = True + hook = jittor_var.register_hook( + lambda grad: torch.autograd.backward(torch_tensor, torch.tensor(grad.numpy())) + ) + else: + jittor_var = jittor.Var(torch_tensor.detach().numpy()) + jittor_var.requires_grad = False + + return jittor_var + + +def torch2paddle(batch: Any, device: str = None, no_gradient: bool = None) -> Any: + """ + 递归地将输入中包含的 :class:`torch.Tensor` 转换为 :class:`paddle.Tensor` 。 + + :param batch: 包含 :class:`torch.Tensor` 类型的数据集合 + :param device: 是否将转换后的张量迁移到特定设备上。为 ``None`` 时，和输入保持一致； + :param no_gradient: 是否保留原张量的梯度。为 ``None`` 时，新的张量与输入张量保持一致； + 为 ``True`` 时，不保留梯度；为 ``False`` 时，保留梯度； + :return: 转换后的数据； + """ + + return apply_to_collection( + batch, + dtype=torch.Tensor, + function=_torch2paddle, + device=device, + no_gradient=no_gradient, + ) + + +def paddle2torch(batch: Any, device: str = None, no_gradient: bool = None) -> Any: + """ + 递归地将输入中包含的 :class:`paddle.Tensor` 转换为 :class:`torch.Tensor` 。 + + :param batch: 包含 :class:`paddle.Tensor` 类型的数据集合； + :param device: 是否将转换后的张量迁移到特定设备上。为 ``None``时，和输入保持一致； + :param no_gradient: 是否保留原张量的梯度。为 ``None`` 时，新的张量与输入张量保持一致； + 为 ``True`` 时，不保留梯度；为 ``False`` 时，保留梯度； + :return: 转换后的数据； + """ + + return apply_to_collection( + batch, + dtype=paddle.Tensor, + function=_paddle2torch, + device=device, + no_gradient=no_gradient, + ) + + +def jittor2torch(batch: Any, device: str = None, no_gradient: bool = None) -> Any: + """ + 递归地将输入中包含的 :class:`jittor.Var` 转换为 :class:`torch.Tensor` 。 + + .. note:: + + 注意，由于 **pytorch** 和 **jittor** 之间的差异，从 :class:`jittor.Var` 转换 + 至 :class:`torch.Tensor` 的过程中无法保留原张量的梯度。 + + :param batch: 包含 :class:`jittor.Var` 类型的数据集合； + :param device: 是否将转换后的张量迁移到特定设备上。为 ``None``时，和输入保持一致； + :param no_gradient: 是否保留原张量的梯度，在这个函数中该参数无效。 + :return: 转换后的数据； + """ + + return apply_to_collection( + batch, + dtype=jittor.Var, + function=_jittor2torch, + device=device, + no_gradient=no_gradient, + ) + + +def torch2jittor(batch: Any, no_gradient: bool = None) -> Any: + """ + 递归地将输入中包含的 :class:`torch.Tensor` 转换为 :class:`jittor.Var` 。 + + .. note:: + + **jittor** 会自动为创建的变量分配设备。 + + :param batch: 包含 :class:`torch.Tensor` 类型的数据集合； + :param no_gradient: 是否保留原张量的梯度。为 ``None`` 时，新的张量与输入张量保持一致； + 为 ``True`` 时，不保留梯度；为 ``False`` 时，保留梯度； + :return: 转换后的数据； + """ + + return apply_to_collection( + batch, + dtype=torch.Tensor, + function=_torch2jittor, + no_gradient=no_gradient, + ) \ No newline at end of file diff --git a/fastNLP/transformers/torch/configuration_utils.py b/fastNLP/transformers/torch/configuration_utils.py index 9c17f336..948d9873 100644 --- a/fastNLP/transformers/torch/configuration_utils.py +++ b/fastNLP/transformers/torch/configuration_utils.py @@ -314,7 +314,7 @@ class PretrainedConfig: # TPU arguments if kwargs.pop("xla_device", None) is not None: - logger.warning( + logger.rank_zero_warning( "The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can " "safely remove it from your `config.json` file." ) @@ -474,7 +474,7 @@ class PretrainedConfig: """ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: - logger.warn( + logger.rank_zero_warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) @@ -564,9 +564,9 @@ class PretrainedConfig: raise EnvironmentError(msg) if resolved_config_file == config_file: - logger.info(f"loading configuration file {config_file}") + logger.debug(f"loading configuration file {config_file}") else: - logger.info(f"loading configuration file {config_file} from cache at {resolved_config_file}") + logger.debug(f"loading configuration file {config_file} from cache at {resolved_config_file}") return config_dict, kwargs @@ -603,7 +603,7 @@ class PretrainedConfig: for key in to_remove: kwargs.pop(key, None) - logger.info(f"Model config {config}") + logger.debug(f"Model config {config}") if return_unused_kwargs: return config, kwargs else: diff --git a/fastNLP/transformers/torch/file_utils.py b/fastNLP/transformers/torch/file_utils.py index 2b606b33..60f95fdd 100644 --- a/fastNLP/transformers/torch/file_utils.py +++ b/fastNLP/transformers/torch/file_utils.py @@ -17,7 +17,7 @@ from enum import Enum from functools import partial from hashlib import sha256 from pathlib import Path -from typing import Any, BinaryIO, Dict, Optional, Tuple, Union +from typing import Any, BinaryIO, Dict, Optional, Tuple, Union, List from urllib.parse import urlparse from uuid import uuid4 from zipfile import ZipFile, is_zipfile @@ -750,6 +750,78 @@ def get_from_cache( return cache_path +def get_list_of_files( + path_or_repo: Union[str, os.PathLike], + revision: Optional[str] = None, + use_auth_token: Optional[Union[bool, str]] = None, + local_files_only: bool = False, +) -> List[str]: + """ + Gets the list of files inside :obj:`path_or_repo`. + + Args: + path_or_repo (:obj:`str` or :obj:`os.PathLike`): + Can be either the id of a repo on huggingface.co or a path to a `directory`. + revision (:obj:`str`, `optional`, defaults to :obj:`"main"`): + The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a + git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any + identifier allowed by git. + use_auth_token (:obj:`str` or `bool`, `optional`): + The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token + generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`). + local_files_only (:obj:`bool`, `optional`, defaults to :obj:`False`): + Whether or not to only rely on local files and not to attempt to download any files. + + Returns: + :obj:`List[str]`: The list of files available in :obj:`path_or_repo`. + """ + path_or_repo = str(path_or_repo) + # If path_or_repo is a folder, we just return what is inside (subdirectories included). + if os.path.isdir(path_or_repo): + list_of_files = [] + for path, dir_names, file_names in os.walk(path_or_repo): + list_of_files.extend([os.path.join(path, f) for f in file_names]) + return list_of_files + + # Can't grab the files if we are on offline mode. + if is_offline_mode() or local_files_only: + return [] + + # Otherwise we grab the token and use the model_info method. + if isinstance(use_auth_token, str): + token = use_auth_token + elif use_auth_token is True: + # token = HfFolder.get_token() + path_token = os.path.expanduser("~/.huggingface/token") + try: + with open(path_token, "r") as f: + token = f.read() + except FileNotFoundError: + token = None + else: + token = None + # model_info = HfApi(endpoint=HUGGINGFACE_CO_RESOLVE_ENDPOINT).model_info( + # path_or_repo, revision=revision, token=token + # ) + endpoint=HUGGINGFACE_CO_RESOLVE_ENDPOINT + path = ( + f"{HUGGINGFACE_CO_RESOLVE_ENDPOINT}/api/models/{path_or_repo}" + if revision is None + else f"{HUGGINGFACE_CO_RESOLVE_ENDPOINT}/api/models/{path_or_repo}/revision/{revision}" + ) + headers = {"authorization": f"Bearer {token}"} if token is not None else None + status_query_param = None + r = requests.get( + path, headers=headers, timeout=None, params=status_query_param + ) + r.raise_for_status() + d = r.json() + siblings = d.get("siblings", None) + rfilenames = ( + [x["rfilename"] for x in siblings] if siblings is not None else None + ) + return rfilenames + def is_torch_fx_available(): return _TORCH_GREATER_EQUAL_1_8 and _compare_version("torch", operator.lt, "1.9.0") diff --git a/fastNLP/transformers/torch/generation_stopping_criteria.py b/fastNLP/transformers/torch/generation_stopping_criteria.py index 179bf7c1..da2bcf9b 100644 --- a/fastNLP/transformers/torch/generation_stopping_criteria.py +++ b/fastNLP/transformers/torch/generation_stopping_criteria.py @@ -122,7 +122,7 @@ def validate_stopping_criteria(stopping_criteria: StoppingCriteriaList, max_leng stopping_max_length = stopping_criteria.max_length new_stopping_criteria = deepcopy(stopping_criteria) if stopping_max_length is not None and stopping_max_length != max_length: - logger.warn("You set different `max_length` for stopping criteria and `max_length` parameter", UserWarning) + logger.rank_zero_warning("You set different `max_length` for stopping criteria and `max_length` parameter", UserWarning) elif stopping_max_length is None: new_stopping_criteria.append(MaxLengthCriteria(max_length=max_length)) return new_stopping_criteria diff --git a/fastNLP/transformers/torch/generation_utils.py b/fastNLP/transformers/torch/generation_utils.py index cfc2108c..0e6fe5c7 100644 --- a/fastNLP/transformers/torch/generation_utils.py +++ b/fastNLP/transformers/torch/generation_utils.py @@ -429,7 +429,7 @@ class GenerationMixin: def _get_pad_token_id(self, pad_token_id: int = None, eos_token_id: int = None) -> int: if pad_token_id is None and eos_token_id is not None: - logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.") + logger.rank_zero_warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.") pad_token_id = eos_token_id return pad_token_id @@ -912,7 +912,7 @@ class GenerationMixin: # special case if pad_token_id is not defined if pad_token_id is None and eos_token_id is not None: - logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.") + logger.rank_zero_warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.") pad_token_id = eos_token_id # Storing encoder_input_ids for logits_processor that could use them diff --git a/fastNLP/transformers/torch/modeling_utils.py b/fastNLP/transformers/torch/modeling_utils.py index d1d5c2f3..74f370b6 100644 --- a/fastNLP/transformers/torch/modeling_utils.py +++ b/fastNLP/transformers/torch/modeling_utils.py @@ -352,7 +352,7 @@ class ModuleUtilsMixin: if token_inputs: return sum([token_input.numel() for token_input in token_inputs]) else: - logger.warn( + logger.rank_zero_warning( "Could not estimate the number of tokens of the input, floating-point operations will not be computed" ) return 0 @@ -646,7 +646,7 @@ class PreTrainedModel(Module, ModuleUtilsMixin, GenerationMixin): # tie weights recursively tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights) if len(uninitialized_encoder_weights) > 0: - logger.warning( + logger.rank_zero_warning( f"The following encoder weights were not tied to the decoder {uninitialized_encoder_weights}" ) @@ -1260,9 +1260,9 @@ class PreTrainedModel(Module, ModuleUtilsMixin, GenerationMixin): raise EnvironmentError(msg) if resolved_archive_file == archive_file: - logger.info(f"loading weights file {archive_file}") + logger.debug(f"loading weights file {archive_file}") else: - logger.info(f"loading weights file {archive_file} from cache at {resolved_archive_file}") + logger.debug(f"loading weights file {archive_file} from cache at {resolved_archive_file}") else: resolved_archive_file = None @@ -1486,7 +1486,7 @@ class PreTrainedModel(Module, ModuleUtilsMixin, GenerationMixin): raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}") if len(unexpected_keys) > 0: - logger.warning( + logger.rank_zero_warning( f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when " f"initializing {model.__class__.__name__}: {unexpected_keys}\n" f"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task " diff --git a/fastNLP/transformers/torch/models/bart/configuration_bart.py b/fastNLP/transformers/torch/models/bart/configuration_bart.py index 3b52bc81..9465326b 100644 --- a/fastNLP/transformers/torch/models/bart/configuration_bart.py +++ b/fastNLP/transformers/torch/models/bart/configuration_bart.py @@ -171,7 +171,7 @@ class BartConfig(PretrainedConfig): # ensure backward compatibility for BART CNN models if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): self.forced_bos_token_id = self.bos_token_id - logger.warn( + logger.rank_zero_warning( f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions." "The config can simply be saved and uploaded again to be fixed." ) diff --git a/fastNLP/transformers/torch/tokenization_utils_base.py b/fastNLP/transformers/torch/tokenization_utils_base.py index aebf4bb6..8ed5a2e2 100644 --- a/fastNLP/transformers/torch/tokenization_utils_base.py +++ b/fastNLP/transformers/torch/tokenization_utils_base.py @@ -44,6 +44,8 @@ from .file_utils import ( cached_path, is_offline_mode, is_remote_url, + get_list_of_files, + hf_bucket_url, is_tokenizers_available, to_py_obj, ) @@ -100,7 +102,7 @@ TOKENIZER_CONFIG_FILE = "tokenizer_config.json" # Fast tokenizers (provided by HuggingFace tokenizer's library) can be saved in a single file FULL_TOKENIZER_FILE = "tokenizer.json" - +_re_tokenizer_file = re.compile(r"tokenizer\.(.*)\.json") class TruncationStrategy(ExplicitEnum): """ @@ -1607,8 +1609,41 @@ class PreTrainedTokenizerBase(SpecialTokensMixin): file_id = list(cls.vocab_files_names.keys())[0] vocab_files[file_id] = pretrained_model_name_or_path else: - raise RuntimeError("At this point pretrained_model_name_or_path is either a directory or a model identifier name, ", - "which is not supported in fastNLP now.") + # raise RuntimeError("At this point pretrained_model_name_or_path is either a directory or a model identifier name, ", + # "which is not supported in fastNLP now.") + # At this point pretrained_model_name_or_path is either a directory or a model identifier name + fast_tokenizer_file = get_fast_tokenizer_file( + pretrained_model_name_or_path, + revision=revision, + use_auth_token=use_auth_token, + local_files_only=local_files_only, + ) + additional_files_names = { + "added_tokens_file": ADDED_TOKENS_FILE, + "special_tokens_map_file": SPECIAL_TOKENS_MAP_FILE, + "tokenizer_config_file": TOKENIZER_CONFIG_FILE, + "tokenizer_file": fast_tokenizer_file, + } + # Look for the tokenizer files + for file_id, file_name in {**cls.vocab_files_names, **additional_files_names}.items(): + if os.path.isdir(pretrained_model_name_or_path): + if subfolder is not None: + full_file_name = os.path.join(pretrained_model_name_or_path, subfolder, file_name) + else: + full_file_name = os.path.join(pretrained_model_name_or_path, file_name) + if not os.path.exists(full_file_name): + logger.info(f"Didn't find file {full_file_name}. We won't load it.") + full_file_name = None + else: + full_file_name = hf_bucket_url( + pretrained_model_name_or_path, + filename=file_name, + subfolder=subfolder, + revision=revision, + mirror=None, + ) + + vocab_files[file_id] = full_file_name # Get files from url, cache, or disk depending on the case resolved_vocab_files = {} @@ -1665,9 +1700,9 @@ class PreTrainedTokenizerBase(SpecialTokensMixin): continue if file_path == resolved_vocab_files[file_id]: - logger.info(f"loading file {file_path}") + logger.debug(f"loading file {file_path}") else: - logger.info(f"loading file {file_path} from cache at {resolved_vocab_files[file_id]}") + logger.debug(f"loading file {file_path} from cache at {resolved_vocab_files[file_id]}") return cls._from_pretrained( resolved_vocab_files, @@ -3349,3 +3384,52 @@ For a more complete example, see the implementation of `prepare_seq2seq_batch`. ) model_inputs["labels"] = labels["input_ids"] return model_inputs + +def get_fast_tokenizer_file( + path_or_repo: Union[str, os.PathLike], + revision: Optional[str] = None, + use_auth_token: Optional[Union[bool, str]] = None, + local_files_only: bool = False, +) -> str: + """ + Get the tokenizer file to use for this version of transformers. + + Args: + path_or_repo (:obj:`str` or :obj:`os.PathLike`): + Can be either the id of a repo on huggingface.co or a path to a `directory`. + revision(:obj:`str`, `optional`, defaults to :obj:`"main"`): + The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a + git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any + identifier allowed by git. + use_auth_token (:obj:`str` or `bool`, `optional`): + The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token + generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`). + local_files_only (:obj:`bool`, `optional`, defaults to :obj:`False`): + Whether or not to only rely on local files and not to attempt to download any files. + + Returns: + :obj:`str`: The tokenizer file to use. + """ + # Inspect all files from the repo/folder. + all_files = get_list_of_files( + path_or_repo, revision=revision, use_auth_token=use_auth_token, local_files_only=local_files_only + ) + tokenizer_files_map = {} + for file_name in all_files: + search = _re_tokenizer_file.search(file_name) + if search is not None: + v = search.groups()[0] + tokenizer_files_map[v] = file_name + available_versions = sorted(tokenizer_files_map.keys()) + + # Defaults to FULL_TOKENIZER_FILE and then try to look at some newer versions. + tokenizer_file = FULL_TOKENIZER_FILE + transformers_version = version.parse(__version__) + for v in available_versions: + if version.parse(v) <= transformers_version: + tokenizer_file = tokenizer_files_map[v] + else: + # No point going further since the versions are sorted. + break + + return tokenizer_file \ No newline at end of file diff --git a/tests/core/log/test_print.py b/tests/core/log/test_print.py new file mode 100644 index 00000000..8b5b3fa1 --- /dev/null +++ b/tests/core/log/test_print.py @@ -0,0 +1,8 @@ +from fastNLP import print + + +def test_print(): + print("a") + print([1, 2, 3]) + print([1,2,3], [4,5,6], 'a') + print(print)