fix conflict

fastNLP/core/callbacks/callback.py

@@ -390,4 +390,23 @@ class HasMonitorCallback(Callback):
         if (self.larger_better and monitor_value1 > monitor_value2) or \
                 (not self.larger_better and monitor_value1 < monitor_value2):
             better = True
-        return better
+        return better
+
+    @property
+    def monitor_name(self):
+        """
+        返回 monitor 的名字，如果 monitor 是个 callable 的函数，则返回该函数的名称。
+
+        :return:
+        """
+        if callable(self.monitor):
+            try:
+                monitor_name = self.monitor.__qualname__
+            except:
+                monitor_name = self.monitor.__name__
+        elif self.monitor is None:
+            return None
+        else:
+            # 这里是能是monitor，而不能是real_monitor，因为用户再次运行的时候real_monitor被初始化为monitor了
+            monitor_name = str(self.monitor)
+        return monitor_name

fastNLP/core/callbacks/checkpoint_callback.py

@@ -19,11 +19,11 @@ from fastNLP.core.utils import synchronize_safe_rm, synchronize_mkdir
 class CheckpointCallback(HasMonitorCallback):
     def __init__(
             self,
-            monitor,
+            monitor:Optional[Union[str, Callable]]=None,
             save_folder: Optional[Union[str, Path]] = None,
             save_every_n_epochs: Optional[int] = None,
             save_every_n_batches: Optional[int] = None,
-            save_last: bool = True,
+            save_last: bool = False,
             save_topk: Optional[int] = None,
             save_on_exception: Optional[Union[BaseException, Sequence[BaseException]]] = None,
             larger_better: bool = True,
@@ -31,12 +31,32 @@ class CheckpointCallback(HasMonitorCallback):
             model_save_fn: Optional[Callable] = None,
             **kwargs,
     ):
+        """
+        请使用 ModelCheckpointCallback 与 TrainerCheckpointCallback 。
+
+        :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 最短公共字符串算法 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 save_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 save_every_n_epochs: 多少个 epoch 保存一次。
+        :param save_every_n_batches: 多少个 batch 保存一次。
+        :param save_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 save_topk: 保存 monitor 结果 topK 个。
+        :param save_on_exception: 在出异常信息时，是否保存。传入需要捕获的异常的类。
+        :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。
+            如果传入了 model_save_fn 函数，fastNLP 将不再进行模型相关的保存。在多卡场景下，我们只在 rank 0 上会运行该函数。
+        :param kwargs:
+        """
         super().__init__(monitor=monitor, larger_better=larger_better,
                          must_have_monitor=save_topk is not None)
         if save_folder is None:
             logger.warning(
                 "Parameter `path` is None, and we will use the current work directory to find and load your model.")
             save_folder = Path.cwd()
+        save_folder = Path(save_folder)
         if not save_folder.exists():
             raise NotADirectoryError(f"Path '{save_folder.absolute()}' is not existed!")
         elif save_folder.is_file():
@@ -71,7 +91,7 @@ class CheckpointCallback(HasMonitorCallback):
         else:
             save_on_exception = []
 
-        self.save_folder = Path(save_folder)
+        self.save_folder = save_folder
         self.save_every_n_epochs = save_every_n_epochs
         self.save_every_n_batches = save_every_n_batches
         self.save_last = save_last
@@ -88,18 +108,15 @@ class CheckpointCallback(HasMonitorCallback):
         # 注意这里应当保证只有进程 0 在执行这个操作，因为当用户使用 python -m torch.distributed.launch 来拉起进程的时候，
         #  FASTNLP_LAUNCH_TIME 在每一个进程上的值是不一样的；
         self.timestamp_path = self.save_folder.joinpath(os.environ[FASTNLP_LAUNCH_TIME])
-        # 我们只需要保证这个创建文件夹的操作只在进程 0 上进行即可；因为后续的实际的保存操作，其它进程实际并不会去执行；
-        synchronize_mkdir(self.timestamp_path)
+        # 该 folder 只在保存真的要发生的时候再创建。
 
     def on_after_trainer_initialized(self, trainer, driver):
         if self.save_topk is not None:
             super().on_after_trainer_initialized(trainer, driver)
         if self.save_topk is not None and trainer.evaluator is None:
-            logger.warning("You set `save_topk`, but `validate_dataloaders` is not set in Trainer.")
+            logger.warning("You set `save_topk`, but `evaluate_dataloaders` is not set in Trainer.")
 
     def on_validate_end(self, trainer, results):
-        if len(results) == 0:
-            return
         self._save_topk(trainer, results)
 
     def on_train_epoch_end(self, trainer: "fastNLP.Trainer"):
@@ -136,16 +153,17 @@ class CheckpointCallback(HasMonitorCallback):
         states['timestamp_path'] = str(self.timestamp_path.absolute())
         states['_topk_model'] = deepcopy(self._topk_model)
         states['save_topk'] = 0 if self.save_topk is None else self.save_topk
-        states['_real_monitor'] = self._real_monitor
+        if isinstance(self._real_monitor, str):
+            states['_real_monitor'] = self._real_monitor
         return states
 
     def on_load_checkpoint(self, trainer, states: Optional[Dict]):
         timestamp_path = states['timestamp_path']
         if not os.path.exists(timestamp_path):
-            logger.info(f"The resuming save folder {timestamp_path} is not exists, will checkpoint save to "
+            logger.info(f"The resuming checkpoint folder {timestamp_path} is not exists, will checkpoint save to "
                         f" {self.timestamp_path.absolute()}.")
         else:
-            logger.info(f"Resume to save in path: {timestamp_path}.")
+            logger.info(f"Resume to checkpoint in path: {timestamp_path}.")
             self.timestamp_path = Path(timestamp_path)
             _topk_model = states['_topk_model']
             save_topk = None if int(states['save_topk']) == 0 else int(states['save_topk'])
@@ -153,7 +171,8 @@ class CheckpointCallback(HasMonitorCallback):
                 assert self.save_topk == save_topk, f"The checkpoint set save_topk={save_topk}, while this callback set it " \
                                                     f"as {save_topk}."
             self._topk_model.update(self._topk_model)
-        self._real_monitor = states["real_monitor"]
+
+        self._real_monitor = states["_real_monitor"]
 
     def _save_topk(self, trainer: "fastNLP.Trainer", results: Dict):
         """
@@ -231,9 +250,9 @@ class ModelCheckpointCallback(CheckpointCallback):
     model_save_fn 为 None ，则以上每个 folder 中，将生成 fastnlp_model.pkl.tar 文件。
     若 model_save_fn 不为 None，则 fastNLP 将 folder 绝对路径传递给该函数，fastNLP 不在该 folder 下创建任何文件。
 
-    :param monitor: 监控的 metric 的名称。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最短公共字符串算法 找到最匹配
-        的那个作为 monitor 。如果为 None 将尝试从 Trainer 中获取该值。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。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最短公共字符串算法 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 save_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 save_every_n_epochs: 多少个 epoch 保存一次。
@@ -249,6 +268,11 @@ class ModelCheckpointCallback(CheckpointCallback):
     """
     @property
     def save_fn_name(self):
+        """
+        调用 Trainer 中的哪个函数。
+
+        :return:
+        """
         return 'save_model'
 
     @property
@@ -257,7 +281,7 @@ class ModelCheckpointCallback(CheckpointCallback):
         通过该值决定两个 CheckpointCallback 实例是否可以共用断点重训的状态；
         :return:
         """
-        return f"model_checkpoint#monitor-{self.monitor}#topK-{self.save_topk}#only_state_dict-{self.only_state_dict}"
+        return f"model_checkpoint#monitor-{self.monitor_name}#topK-{self.save_topk}#only_state_dict-{self.only_state_dict}"
 
     @property
     def folder_prefix(self):
@@ -279,9 +303,9 @@ class TrainerCheckpointCallback(CheckpointCallback):
     model_save_fn 为 None ，则以上每个 folder 中，将生成两个文件：fastnlp_trainer.pkl.tar 以及 fastnlp_model.pkl.tar 。
     若 model_save_fn 不为 None，则 fastNLP 只会在每个 folder 下生成 fastnlp_trainer.pkl.tar 文件。
 
-    :param monitor: 监控的 metric 的名称。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最短公共字符串算法 找到最匹配
-        的那个作为 monitor 。如果为 None 将尝试从 Trainer 中获取该值。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。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最短公共字符串算法 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 save_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 save_every_n_epochs: 多少个 epoch 保存一次。
@@ -297,6 +321,11 @@ class TrainerCheckpointCallback(CheckpointCallback):
     """
     @property
     def save_fn_name(self):
+        """
+        调用 Trainer 中的哪个函数。
+
+        :return:
+        """
         return 'save'
 
     @property
@@ -305,7 +334,8 @@ class TrainerCheckpointCallback(CheckpointCallback):
         通过该值决定两个 CheckpointCallback 实例是否可以共用断点重训的状态；
         :return:
         """
-        return f"trainer_checkpoint#monitor-{self.monitor}#topK-{self.save_topk}#only_state_dict-{self.only_state_dict}"
+
+        return f"trainer_checkpoint#monitor-{self.monitor_name}#topK-{self.save_topk}#only_state_dict-{self.only_state_dict}"
 
     @property
     def folder_prefix(self):

fastNLP/core/callbacks/early_stop_callback.py

@@ -12,8 +12,9 @@ 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 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 最短公共字符串算法 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 larger_better: monitor 的值是否是越大越好。
         :param patience: 多少次 validate 不没有提升就停止。
         """
@@ -46,17 +47,20 @@ class EarlyStopCallback(HasMonitorCallback):
         states = {
             'patience': self.patience,
             'wait': self.wait,
-            'monitor': self.monitor,
             'monitor_value': self.monitor_value
         }
+        if not callable(self._real_monitor):
+            states['_real_monitor'] = self._real_monitor
         return states
 
     def on_load_checkpoint(self, trainer, states):
         self.patience = states['patience']
         self.wait = states['wait']
-        self.monitor = states['monitor']
         self.monitor_value = float(states['monitor_value'])
+        if '_real_monitor' in states:
+            self._real_monitor = states['_real_monitor']
 
+    @property
     def callback_name(self):
-        return f'EarlyStopCallback#monitor-{self.monitor}#patience-{self.patience}'
+        return f'EarlyStopCallback#monitor-{self.monitor_name}#patience-{self.patience}'
 

fastNLP/core/callbacks/load_best_model_callback.py

@@ -21,8 +21,9 @@ 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。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 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 最短公共字符串算法 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 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。

fastNLP/core/callbacks/progress_callback.py

@@ -44,10 +44,11 @@ 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。如果为 None ，会尝试使用 trainer 中设置的 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 larger_better: 是否是monitor的结果越大越好。
-        :param format_json: 是否format json再打印
+        :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 最短公共字符串算法 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 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
         """
         super().__init__(monitor=monitor, larger_better=larger_better, must_have_monitor=False)
         self.print_every = print_every
@@ -136,8 +137,9 @@ 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。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: 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 最短公共字符串算法 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 larger_better: 是否是monitor的结果越大越好。
         :param format_json: 是否format json再打印
         """

fastNLP/core/controllers/evaluator.py

@@ -36,10 +36,10 @@ class Evaluator:
             model,
             dataloaders,
             metrics: Optional[Union[Dict, Metric]] = None,
-            driver: Union[str, Driver] = 'single',
+            driver: Union[str, Driver] = 'torch',
             device: Optional[Union[int, List[int], str]] = None,
             batch_step_fn: Optional[callable] = None,
-            mode: Optional[Union[str, callable]] = 'validate',  # 首先尝试找 evaluate_step, 找不到 forward, callable
+            evaluate_fn: Optional[str] = None,  # 首先尝试找 evaluate_step, 找不到 forward, callable
             input_mapping: Optional[Union[Callable, Dict]] = None,
             output_mapping: Optional[Union[Callable, Dict]] = None,
             model_wo_auto_param_call: bool = False,
@@ -49,8 +49,8 @@ class Evaluator:
     ):
         """
 
-        :param dataloaders:
         :param model:
+        :param dataloaders:
         :param metrics: 使用的 metric 。必须为 dict 类型，其中 key 为 metric 的名称，value 为一个 Metric 对象。支持 fastNLP 的
             metric ，torchmetrics，allennlpmetrics等。
         :param driver: 使用 driver 。
@@ -58,14 +58,13 @@ class Evaluator:
         :param batch_step_fn: callable的对象，接受 (evaluator, batch) 作为参数，其中 evaluator 为 Evaluator 对象，batch 为
             DataLoader 中返回的对象。一个 batch_step_fn 的例子可参考 fastNLP.core.controller.loops.evaluate_batch_loop 的
             batch_step_fn 函数。
-        :param mode: 可选 ["validate", "test"], 当为 "validate" 时将首先尝试寻找 model 是否有 validate_step 函数，没有的话则尝试
-            寻找 test_step 函数，都没找到则使用 model 的前向运算函数。当为 "test" 是将首先尝试寻找 model 是否有 test_step 函数，
-            没有的话尝试 "validate_step"  函数，都没找到则使用 model 的前向运算函数。
+        :param evaluate_fn: 用来控制 `Evaluator` 在评测的前向传播过程中是调用哪一个函数，例如是 `model.evaluate_step` 还是 `model.forward`；
+         默认为 None，如果该值是 None，那么我们会默认使用 `evaluate_step` 当做前向传播的函数，如果在模型中没有找到该方法，则使用 `model.forward` 函数；
         :param input_mapping: 对 dataloader 中输出的内容将通过 input_mapping 处理之后再输入到 model 以及 metric 中
         :param output_mapping: 对 model 输出的内容，将通过 output_mapping 处理之后再输入到 metric 中。
         :param model_wo_auto_param_call: 是否关闭在训练时调用我们的 auto_param_call 来自动匹配 batch 和 forward 函数的参数的行为；
          如果该值为 True，并且当 batch 为字典时，我们会根据 forward 所需要的参数从 batch 中提取对应的对象，传入到 forward 函数中；如果该值
-         为 False，那么我们会将 batch 直接透传给 forward 函数。注意上述逻辑同样应用于 `train_step`, `validate_step` 和 `test_step`；
+         为 False，那么我们会将 batch 直接透传给 forward 函数。注意上述逻辑同样应用于 `train_step`, `evaluate_step` 和 `test_step`；
         :param fp16: 是否使用 fp16 。
         :param verbose: 是否打印 evaluate 的结果。
         :param kwargs:
@@ -87,9 +86,11 @@ class Evaluator:
 
         self.model = model
         self.metrics = metrics
-
         self.driver = choose_driver(model, driver, device, fp16=fp16, model_wo_auto_param_call=model_wo_auto_param_call, **kwargs)
 
+        if dataloaders is None:
+            raise ValueError("Parameter `dataloaders` can not be None.")
+        self.dataloaders = dataloaders
         self.device = device
         self.verbose = verbose
 
@@ -97,21 +98,12 @@ class Evaluator:
             _check_valid_parameters_number(batch_step_fn, ['trainer', 'batch'], fn_name='batch_step_fn')
         self.batch_step_fn = batch_step_fn
 
-        self.mode = mode
-        assert mode in {'validate', 'test'}, "Parameter `mode` should only be 'validate' or 'test'."
-
         self.input_mapping = input_mapping
         self.output_mapping = output_mapping
 
         if not isinstance(dataloaders, dict):
             dataloaders = {None: dataloaders}
-        if mode == "validate":
-            self._evaluate_step = self.driver.validate_step
-            self.driver.set_dataloader(validate_dataloaders=dataloaders)
-        else:
-            self._evaluate_step = self.driver.test_step
-            self.driver.set_dataloader(test_dataloaders=dataloaders)
-        self.mode = mode
+
         self.evaluate_batch_loop = EvaluateBatchLoop(batch_step_fn=batch_step_fn)
         self.separator = kwargs.get('separator', '#')
         self.model_use_eval_mode = kwargs.get('model_use_eval_mode', True)
@@ -123,10 +115,15 @@ class Evaluator:
         self._metric_wrapper = None
         _ = self.metrics_wrapper  # 触发检查
 
-        assert self.driver.has_validate_dataloaders() or self.driver.has_test_dataloaders()
         self.driver.setup()
         self.driver.barrier()
 
+        if evaluate_fn is not None and not isinstance(evaluate_fn, str):
+            raise TypeError("Parameter `train_fn` can only be `str` type when it is not None.")
+        self._evaluate_step, self._evaluate_step_signature_fn = \
+            self.driver.get_model_call_fn("evaluate_step" if evaluate_fn is None else evaluate_fn)
+        self.evaluate_fn = evaluate_fn
+
         self.dataloaders = {}
         for name, dl in dataloaders.items():  # 替换为正确的 sampler
             dl = self.driver.set_dist_repro_dataloader(dataloader=dl, dist=self._dist_sampler, reproducible=False)
@@ -136,7 +133,6 @@ class Evaluator:
         if self.progress_bar == 'auto':
             self.progress_bar = 'rich' if (sys.stdin and sys.stdin.isatty()) else 'raw'
 
-        self.driver.check_evaluator_mode(self.mode)
         self.driver.barrier()
 
     def run(self, num_eval_batch_per_dl: int = -1, **kwargs) -> Dict:
@@ -156,11 +152,6 @@ class Evaluator:
         assert isinstance(num_eval_batch_per_dl, int), "num_eval_batch_per_dl must be of int type."
         assert num_eval_batch_per_dl > 0 or num_eval_batch_per_dl == -1, "num_eval_batch_per_dl must be -1 or larger than 0."
 
-        if self.mode == 'validate':
-            assert self.driver.has_validate_dataloaders()
-        else:
-            assert self.driver.has_test_dataloaders()
-
         metric_results = {}
         self.reset()
         evaluate_context = self.driver.get_evaluate_context()
@@ -235,13 +226,6 @@ class Evaluator:
             f_rich_progress.destroy_task(self._rich_task_id)
             delattr(self, '_rich_task_id')
 
-    @property
-    def eval_dataloaders(self):
-        if self.mode == "validate":
-            return self.driver.validate_dataloaders
-        else:
-            return self.driver.test_dataloaders
-
     @property
     def evaluate_batch_loop(self):
         return self._evaluate_batch_loop
@@ -296,13 +280,13 @@ class Evaluator:
 
     def evaluate_step(self, batch):
         """
-        将 batch 传递到model中进行处理，根据当前 mode 选择进行 evaluate 还是 test 。会将返回结果经过 output_mapping 处理后再
+        将 batch 传递到model中进行处理，根据当前 evaluate_fn 选择进行 evaluate 还是 test 。会将返回结果经过 output_mapping 处理后再
             返回。
 
         :param batch:
         :return:
         """
-        outputs = self._evaluate_step(batch)
+        outputs = self.driver.model_call(batch, self._evaluate_step, self._evaluate_step_signature_fn)
         outputs = match_and_substitute_params(self.output_mapping, outputs)
         return outputs
 

fastNLP/core/controllers/loops/train_batch_loop.py

@@ -20,7 +20,7 @@ class TrainBatchLoop(Loop):
             else lambda *args, **kwargs: None
         dataloader = iter(dataloader)
         indices = None
-        while True:
+        while trainer.batch_idx_in_epoch<=trainer.num_batches_per_epoch:
             try:
                 trainer.on_fetch_data_begin()
                 batch = next(dataloader)
@@ -30,10 +30,8 @@ class TrainBatchLoop(Loop):
                 batch = trainer.move_data_to_device(batch)
             except StopIteration:
                 break
-            except EarlyStopException:  # 在 Trainer 处理 earlystop 的 exception
-                break
             except BaseException as e:
-                if indices:
+                if indices and not isinstance(e, EarlyStopException):
                     logger.debug(f"The following exception happens when running on samples: {indices}")
                 raise e
 

fastNLP/core/controllers/trainer.py

@@ -41,19 +41,20 @@ class Trainer(TrainerEventTrigger):
             optimizers,
             device: Optional[Union[int, List[int], str]] = "cpu",
             n_epochs: int = 20,
-            validate_dataloaders=None,
+            evaluate_dataloaders=None,
             batch_step_fn: Optional[Callable] = None,
-            validate_batch_step_fn: Optional[Callable] = None,
-            validate_mode: Union[str, callable] = 'validate',
+            evaluate_batch_step_fn: Optional[Callable] = None,
+            train_fn: Optional[str] = None,
+            evaluate_fn: Optional[str] = None,
             callbacks: Union[List[Callback], Callback, None] = None,
             metrics: Optional[dict] = None,
-            validate_every: Optional[Union[int, callable]] = -1,
+            evaluate_every: Optional[Union[int, Callable]] = -1,
             input_mapping: Optional[Union[Callable, Dict]] = None,
             output_mapping: Optional[Union[Callable, Dict]] = None,
             model_wo_auto_param_call: bool = False,
             accumulation_steps: int = 1,
             fp16: bool = False,
-            monitor: Union[str, callable] = None,
+            monitor: Union[str, Callable] = None,
             larger_better: bool = True,
             marker: Optional[str] = None,
             **kwargs
@@ -79,19 +80,19 @@ class Trainer(TrainerEventTrigger):
             4. list(int)：如果多于1个device，应当通过该种方式进行设定；当 `device` 为一个 list 时，我们默认使用 `TorchDDPDriver`；
             5. None： 为None则不对模型进行任何处理；
         :param n_epochs: 训练总共的 epoch 的数量，默认为 20；
-        :param validate_dataloaders: 验证数据集，其可以是单独的一个数据集，也可以是多个数据集；当为多个数据集时，注意其必须是 Dict；默认
+        :param evaluate_dataloaders: 验证数据集，其可以是单独的一个数据集，也可以是多个数据集；当为多个数据集时，注意其必须是 Dict；默认
          为 None；
         :param batch_step_fn: 用来替换 `TrainBatchLoop` 中的 `batch_step_fn` 函数，注意该函数的两个参数必须为 `trainer` 和
          `batch`；默认为 None；
-        :param validate_batch_step_fn: 用来替换 'Evaluator' 中的 `EvaluateBatchLoop` 中的 `batch_step_fn` 函数，注意该函数的
+        :param evaluate_batch_step_fn: 用来替换 'Evaluator' 中的 `EvaluateBatchLoop` 中的 `batch_step_fn` 函数，注意该函数的
          两个参数必须为 `evaluator` 和 `batch`；默认为 None；
-        :param validate_mode: 用来控制 `Trainer` 中内置的 `Evaluator` 的模式，其值应当为以下之一：["validate", "test"]；
-            默认为 "validate"；当为 "validate" 时将首先尝试寻找 model 是否有 validate_step 函数，没有的话则尝试
-            寻找 test_step 函数，都没找到则使用 model 的前向运算函数。当为 "test" 是将首先尝试寻找 model 是否有 test_step 函数，
-            没有的话尝试 "validate_step"  函数，都没找到则使用 model 的前向运算函数。
+        :param train_fn: 用来控制 `Trainer` 在训练的前向传播过程中是调用哪一个函数，例如是 `model.train_step` 还是 `model.forward`；
+         默认为 None，如果该值是 None，那么我们会默认使用 `train_step` 当做前向传播的函数，如果在模型中没有找到该方法，则使用 `model.forward` 函数；
+        :param evaluate_fn: 用来控制 `Trainer` 中内置的 `Evaluator` 的模式，应当为 None 或者一个字符串；其使用方式和 train_fn 类似；
+         注意该参数我们会直接传给 Trainer 中内置的 Evaluator（如果不为 None）；
         :param callbacks: 训练当中触发的 callback 类，该参数应当为一个列表，其中的每一个元素都应当继承 `Callback` 类；
         :param metrics: 应当为一个字典，其中 key 表示 monitor，例如 {"acc1": AccMetric(), "acc2": AccMetric()}；
-        :param validate_every: 可以为负数、正数或者函数；为负数时表示每隔几个 epoch validate 一次；为正数则表示每隔几个 batch validate 一次；
+        :param evaluate_every: 可以为负数、正数或者函数；为负数时表示每隔几个 epoch validate 一次；为正数则表示每隔几个 batch validate 一次；
          为函数时表示用户自己传入的用于控制 Trainer 中的 validate 的频率的函数，该函数的应该接受当前 trainer 对象作为参数，并
          返回一个 bool 值，返回为 True 说明需要进行 validate ；将在每个 batch 结束后调用该函数判断是否需要 validate 。
         :param input_mapping: 应当为一个字典或者一个函数，表示在当前 step 拿到一个 batch 的训练数据后，应当做怎样的映射处理；如果其是
@@ -105,10 +106,10 @@ class Trainer(TrainerEventTrigger):
          如果 batch 是一个 `dataclass`，那么我们会先将该 dataclass 转换为一个 Dict，然后再进行上述转换；
         :param model_wo_auto_param_call: 是否关闭在训练时调用我们的 auto_param_call 来自动匹配 batch 和 forward 函数的参数的行为；
          如果该值为 False，并且当 batch 为字典时，我们会根据 forward 所需要的参数从 batch 中提取对应的对象，传入到 forward 函数中；如果该值
-         为 True，那么我们会将 batch 直接透传给模型。注意该参数应用于 `train_step`, `validate_step` 和 `test_step`；
+         为 True，那么我们会将 batch 直接透传给模型。注意该参数应用于 `train_step`, `evaluate_step` 和 `test_step`；
         :param accumulation_steps: 梯度累积的步数，表示每隔几个 batch 优化器迭代一次；默认为 1；
         :param fp16: 是否开启混合精度训练；默认为 False；
-        :param monitor: 当存在 validate_dataloaders 时，默认的 monitor metric 的名字。传入的 callback 如果有 monitor 参数且没有
+        :param monitor: 当存在 evaluate_dataloaders 时，默认的 monitor metric 的名字。传入的 callback 如果有 monitor 参数且没有
             在 callback 初始化设定的，将采取这个值。如果在 evaluation 结果中没有找到完全一致的名称，将使用 最短公共字符串算法 找到最匹配
             的那个作为 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 larger_better: monitor 的值是否是越大越好。
@@ -136,10 +137,15 @@ class Trainer(TrainerEventTrigger):
         else:
             self.driver_name = driver.__class__.__name__
         self.device = device
+        if train_dataloader is None:
+            raise ValueError("Parameter `train_dataloader` can not be None.")
+        self.train_dataloader = train_dataloader
+        self.evaluate_dataloaders = evaluate_dataloaders
         self.optimizers = optimizers
         self.fp16 = fp16
         self.input_mapping = input_mapping
         self.output_mapping = output_mapping
+        self.evaluate_fn = evaluate_fn
 
         self.batch_step_fn = batch_step_fn
         if batch_step_fn is not None:
@@ -168,13 +174,12 @@ class Trainer(TrainerEventTrigger):
             optimizers=optimizers,
             device=device,
             n_epochs=n_epochs,
-            validate_dataloaders=validate_dataloaders,
             batch_step_fn=batch_step_fn,
-            validate_batch_step_fn=validate_batch_step_fn,
-            validate_mode=validate_mode,
+            z=evaluate_batch_step_fn,
+            evaluate_fn=evaluate_fn,
             callbacks=callbacks,
             metrics=metrics,
-            validate_every=validate_every,
+            validate_every=evaluate_every,
             input_mapping=input_mapping,
             output_mapping=output_mapping,
             model_wo_auto_param_call=model_wo_auto_param_call,
@@ -185,9 +190,6 @@ class Trainer(TrainerEventTrigger):
         )
         self.driver.set_optimizers(optimizers=optimizers)
 
-        if train_dataloader is not None:
-            self.driver.set_dataloader(train_dataloader=train_dataloader)
-
         # 初始化 callback manager；
         self.callback_manager = CallbackManager(callbacks, kwargs.get('progress_bar', 'auto'))
         # 添加所有的函数式 callbacks；
@@ -213,25 +215,25 @@ class Trainer(TrainerEventTrigger):
             _dist_sampler = None
 
         """ 设置内部的 Evaluator """
-        if metrics is None and validate_dataloaders is not None:
+        if metrics is None and evaluate_dataloaders is not None:
             raise ValueError("You have set 'validate_dataloader' but forget to set 'metrics'.")
 
-        if metrics is not None and validate_dataloaders is None:
+        if metrics is not None and evaluate_dataloaders is None:
             raise ValueError("You have set 'metrics' but forget to set 'validate_dataloader'.")
 
         self.evaluator = None
         self.monitor = monitor
         self.larger_better = larger_better
-        if metrics is not None and validate_dataloaders is not None:
-            check_validate_every(validate_every)
+        if metrics is not None and evaluate_dataloaders is not None:
+            check_validate_every(evaluate_every)
             self.evaluator = Evaluator(
                 model=model,
-                dataloaders=validate_dataloaders,
+                dataloaders=evaluate_dataloaders,
                 metrics=metrics,
                 driver=self.driver,
                 device=device,
-                batch_step_fn=validate_batch_step_fn,
-                mode=validate_mode,
+                batch_step_fn=evaluate_batch_step_fn,
+                evaluate_fn=evaluate_fn,
                 input_mapping=input_mapping,
                 output_mapping=output_mapping,
                 fp16=fp16,
@@ -241,12 +243,16 @@ class Trainer(TrainerEventTrigger):
             )
 
         self.metrics = metrics
-        self.validate_every = validate_every
+        self.validate_every = evaluate_every
 
-        assert self.driver.has_train_dataloader()
         self.driver.setup()
         self.driver.barrier()
 
+        if train_fn is not None and not isinstance(train_fn, str):
+            raise TypeError("Parameter `train_fn` can only be `str` type when it is not None.")
+        self._train_step, self._train_step_signature_fn = self.driver.get_model_call_fn("train_step" if train_fn is None else train_fn)
+        self.train_fn = train_fn
+
         self.dataloader = self.train_dataloader
         self.driver.set_deterministic_dataloader(self.dataloader)
 
@@ -273,6 +279,7 @@ class Trainer(TrainerEventTrigger):
             行。默认如果非 distributed 的 driver 会 catch ，distributed 不会 catch （无法 catch ）
         :return:
         """
+
         if catch_KeyboardInterrupt is None:
             catch_KeyboardInterrupt = not self.driver.is_distributed()
         else:
@@ -301,7 +308,7 @@ class Trainer(TrainerEventTrigger):
 
         self.num_batches_per_epoch = len(self.dataloader)
         self.total_batches = self.num_batches_per_epoch * self.n_epochs
-
+        self.global_forward_batches = self.num_batches_per_epoch * self.cur_epoch_idx + self.batch_idx_in_epoch
         self.on_train_begin()
         self.driver.barrier()
         self.driver.zero_grad(self.set_grad_to_none)
@@ -343,7 +350,8 @@ class Trainer(TrainerEventTrigger):
             _validate_res: dict = validate_fn()
             trainer.on_validate_end(_validate_res)
 
-        self.run_evaluate = partial(_validate_fn, self, partial(self.evaluator.run, num_eval_batch_per_dl))
+        if self.evaluator is not None:
+            self.run_evaluate = partial(_validate_fn, self, partial(self.evaluator.run, num_eval_batch_per_dl))
 
     def step_validate(self):
         """
@@ -489,11 +497,6 @@ class Trainer(TrainerEventTrigger):
         self.has_checked_train_batch_loop = True
 
     """ Trainer 需要的一些 property """
-
-    @property
-    def train_dataloader(self):
-        return self.driver.train_dataloader
-
     @property
     def driver(self):
         return self._driver
@@ -632,6 +635,8 @@ class Trainer(TrainerEventTrigger):
         :param folder: 保存断点重训 states 的文件地址；
         :param resume_training: 是否从上次的 batch 开始训练，或者只从最近的 epoch 开始训练；注意如果 resume_training=True，那么我们
          只会加载 model 和 optimizers 的状态；而其余的对象的值则根据用户的 Trainer 的初始化直接重置；
+        :param only_state_dict: 保存的 model 是否只包含了权重。
+        :param model_load_fn: 使用的模型加载函数，参数应为一个 文件夹，不返回任何内容。
         """
         self.driver.barrier()
         if isinstance(folder, str):
@@ -670,8 +675,6 @@ class Trainer(TrainerEventTrigger):
         # 这里的原则就是应当使得    '还会产生的batch数量' + 'batch_idx_in_epoch' = '原来不断点训练的batch的总数'。其中由于
         #    '还会产生的batch数量' 是由还剩多少 sample 决定的，因此只能通过调整 'batch_idx_in_epoch' 使得等式成立
         self.trainer_state.batch_idx_in_epoch = states.pop('batch_idx_in_epoch')
-        self.trainer_state.global_forward_batches = self.num_batches_per_epoch * self.cur_epoch_idx + \
-                                                    self.batch_idx_in_epoch
         # 这个是防止用户在 Trainer.load 之后还没结束当前 epoch 又继续 save
         self.start_batch_idx_in_epoch = self.trainer_state.batch_idx_in_epoch
 
@@ -684,7 +687,7 @@ class Trainer(TrainerEventTrigger):
 
     def train_step(self, batch):
         with self.driver.auto_cast():
-            outputs = self.driver.train_step(batch)
+            outputs = self.driver.model_call(batch, self._train_step, self._train_step_signature_fn)
             outputs = match_and_substitute_params(self.output_mapping, outputs)
             return outputs
 
@@ -814,6 +817,24 @@ class Trainer(TrainerEventTrigger):
     def data_device(self):
         return self.driver.data_device
 
+    """ dataloader property """
+
+    @property
+    def train_dataloader(self):
+        return self._train_dataloader
+
+    @train_dataloader.setter
+    def train_dataloader(self, train_dataloader):
+        self._train_dataloader = train_dataloader
+
+    @property
+    def evaluate_dataloaders(self):
+        return self._evaluate_dataloaders
+
+    @evaluate_dataloaders.setter
+    def evaluate_dataloaders(self, evaluate_dataloaders):
+        self._evaluate_dataloaders = evaluate_dataloaders
+
 
 
 

fastNLP/core/controllers/utils/state.py

@@ -65,10 +65,10 @@ class TrainerState:
     """
     n_epochs: Optional[int] = None  # 无论如何重新算
 
-    cur_epoch_idx: Optional[int] = None  # 断点重训; 仅当 resume=False 时为0；
-    global_forward_batches: Optional[int] = None  # 断点重训
+    cur_epoch_idx: Optional[int] = 0  # 断点重训; 仅当 resume=False 时为0；
+    global_forward_batches: Optional[int] = 0  # 断点重训
 
-    batch_idx_in_epoch: Optional[int] = None  # 断点重训
+    batch_idx_in_epoch: Optional[int] = 0  # 断点重训
 
     num_batches_per_epoch: Optional[int] = None  # 无论如何重新算
 

fastNLP/core/controllers/utils/utils.py

@@ -128,6 +128,6 @@ class _TruncatedDataLoader:
 
 def check_validate_every(validate_every):
     if not callable(validate_every) and (not isinstance(validate_every, int) or validate_every == 0):
-        raise ValueError("Parameter 'validate_every' should be set to 'int' type and either < 0 or > 0.")
+        raise ValueError("Parameter 'evaluate_every' should be set to 'int' type and either < 0 or > 0.")
     if callable(validate_every):
         _check_valid_parameters_number(validate_every, expected_params=['trainer'])

fastNLP/core/drivers/driver.py

@@ -1,7 +1,7 @@
 import os
 import signal
 import sys
-from typing import Any, Sequence, List, Optional, Callable, Dict, Union
+from typing import Any, Sequence, List, Optional, Callable, Dict, Union, Tuple
 from abc import ABC, abstractmethod
 from datetime import datetime
 from pathlib import Path
@@ -79,41 +79,44 @@ class Driver(ABC):
         """
 
     @abstractmethod
-    def train_step(self, batch):
+    def model_call(self, batch, fn: Callable, signature_fn: Optional[Callable]) -> Dict:
         """
-        通过调用模型自带的 `train_step` 或者 `forward` 方法来实现训练的前向过程；
-        如果检测到用户模型实现了 train_step
+        通过调用 `fn` 来实现训练时的前向传播过程；
+        注意 Trainer 和 Evaluator 会调用该函数来实现网络的前向传播过程，其中传入该函数的参数 `fn` 是函数 `get_model_call_fn` 所返回的
+        函数；
 
         :param batch: 当前的一个 batch 的数据；可以为字典或者其它类型；
-        :return: 返回由模型的 `train_step` 或者 `forward` 方法返回的结果（应当为一个 dict 或者 dataclass，但是不需要我们去检查）；
+        :param fn: 调用该函数进行一次计算。
+        :param signature_fn: 由 Trainer 传入的用于网络前向传播一次的签名函数，因为当 batch 是一个 Dict 的时候，我们会自动调用 auto_param_call
+        函数，而一些被包裹的模型需要暴露其真正的函数签名，例如 DistributedDataParallel 的调用函数是 forward，但是需要其函数签名为 model.module.forward；
+        :return: 返回由 `fn` 返回的结果（应当为一个 dict 或者 dataclass，但是不需要我们去检查）；
         """
-        raise NotImplementedError("Each specific driver should implemented its own `train_step` function.")
+        raise NotImplementedError("Each specific driver should implemented its own `model_call` function.")
 
-    def validate_step(self, batch):
-        """
-        通过调用模型自带的 `validate_step` 或者 `forward` 方法来实现模型评测的前向过程；
-
-        :param batch: 当前的一个 batch 的数据；可以为字典或者其它类型；
-        :return: 返回由模型的 `validate_step` 或者 `forward` 方法返回的结果（应当为一个 dict 或者 dataclass，但是不需要我们去检查）；
+    @abstractmethod
+    def get_model_call_fn(self, fn: str) -> Tuple:
         """
-        raise NotImplementedError("Each specific driver should implemented its own `validate_step` function.")
+        该函数会接受 Trainer 的 train_fn 或者 Evaluator 的 evaluate_fn，返回一个实际用于调用 driver.model_call 时传入的函数参数；
+        该函数会在 Trainer 和 Evaluator 在 driver.setup 函数之后调用；
 
-    def test_step(self, batch):
-        """
-        通过调用模型自带的 `test_step` 或者 `forward` 方法来实现模型评测的前向过程；
+        之所以设置该函数的目的在于希望将具体的 model_call function 从 driver 中抽离出来，然后将其附着在 Trainer 或者 Evaluator 身上；
+        这样是因为在新版的设计中，使用 model 的哪种方法来进行 `train step` 或者 `evaluate step` 是通过额外的参数 `train_fn` 和
+         `evaluate_fn` 来确定的，而二者又分别是通过 Trainer 和 Evaluator 来控制的；因此不能将确定具体的 `train step fn` 和
+         `evaluate step fn` 的逻辑放在每一个 driver 的初始化的时候（因此在 Trainer 初始化第一个 driver 时，Evaluator 还没有初始化，但是
+         `evaluate step fn` 的确定却需要 Evaluator 的初始化），因此我们将这一逻辑抽象到这一函数当中；
 
-        :param batch: 当前的一个 batch 的数据；可以为字典或者其它类型；
-        :return: 返回由模型的 `test_step` 或者 `forward` 方法返回的结果（应当为一个 dict 或者 dataclass，但是不需要我们去检查）；
-        """
-        raise NotImplementedError("Each specific driver should implemented its own `test_step` function.")
+        这一函数应当通过参数 `fn` 来判断应当返回的实际的调用的函数，具体逻辑如下所示：
+            1. 如果 fn == "train_step" or "evaluate_step"，那么对传入的模型进行检测，如果模型没有定义方法 `fn`，则默认调用模型的 `forward`
+             函数，然后给出 warning；
+            2. 如果 fn 是其他字符串，那么如果模型没有定义方法 `fn` 则直接报错；
+        注意不同的 driver 需要做额外的检测处理，例如在 DDPDriver 中，当传入的模型本身就是 DistributedDataParallel 中，我们只能调用模型的
+         forward 函数，因此需要额外的 warning；这一点特别需要注意的问题在于 driver 自己在 setup 时也会对模型进行改变（DDPDriver），因此
+         可能需要额外标记最初传入 driver 的模型是哪种形式的；
 
-    def check_evaluator_mode(self, mode: str):
-        r"""
-        因为我们在具体的 driver 的 validate_step 和 test_step 的逻辑是如果模型没有实现本函数，那么就去检测模型是否实现了另一个函数；
-        因此如果用户的 evaluator mode 是 validate，但是传入的 model 却没有实现 validate_step 函数，而是实现了 test_step 函数，那么
-         我们应当提醒用户这一行为；
+        :param fn: 应当为一个字符串，该函数通过该字符串判断要返回模型的哪种方法；
+        :return: 返回一个元组，包含两个函数，用于在调用 driver.model_call 时传入；
         """
-        raise NotImplementedError("Each specific driver should implemented its own `check_evaluator_mode` function.")
+        raise NotImplementedError("Each specific driver should implemented its own `get_model_call_fn` function.")
 
     @property
     def model(self):
@@ -123,80 +126,6 @@ class Driver(ABC):
     def model(self, model):
         self._model = model
 
-    @property
-    def train_dataloader(self):
-        return self._train_dataloader
-
-    @train_dataloader.setter
-    def train_dataloader(self, train_dataloader: Any):
-        self._train_dataloader = train_dataloader
-
-    @property
-    def validate_dataloaders(self):
-        return self._validate_dataloaders
-
-    @validate_dataloaders.setter
-    def validate_dataloaders(self, validate_dataloaders: Any):
-        self._validate_dataloaders = validate_dataloaders
-
-    @property
-    def test_dataloaders(self):
-        return self._test_dataloaders
-
-    @test_dataloaders.setter
-    def test_dataloaders(self, test_dataloaders: Any):
-        self._test_dataloaders = test_dataloaders
-
-    @property
-    def predict_dataloaders(self):
-        return self._predict_dataloaders
-
-    @predict_dataloaders.setter
-    def predict_dataloaders(self, predict_dataloaders: Any):
-        self._predict_dataloaders = predict_dataloaders
-
-    def set_dataloader(self, **kwargs):
-        r"""
-        设置训练或者检验过程中的数据；用于在 trainer 和 evaluator 中将数据 dataloader 挂载到每一个具体的 driver 上；
-
-        :param kwargs: 输入的数据，应当使用 'keyword-only' 的参数进行设置；
-        """
-        if "train_dataloader" in kwargs:
-            self.train_dataloader = kwargs["train_dataloader"]
-            self._check_dataloader_legality(self.train_dataloader, "train_dataloader", is_train=True)
-        if "validate_dataloaders" in kwargs:
-            self.validate_dataloaders = kwargs["validate_dataloaders"]
-            self._check_dataloader_legality(self.validate_dataloaders, "validate_dataloaders", is_train=False)
-        if "test_dataloaders" in kwargs:
-            self.test_dataloaders = kwargs["test_dataloaders"]
-            self._check_dataloader_legality(self.test_dataloaders, "test_dataloaders", is_train=False)
-        if "predict_dataloaders" in kwargs:
-            self.predict_dataloaders = kwargs["predict_dataloaders"]
-            self._check_dataloader_legality(self.predict_dataloaders, "predict_dataloaders", is_train=False)
-
-    @staticmethod
-    def _check_dataloader_legality(dataloader, dataloader_name, is_train: bool = False):
-        r"""
-        该函数会在 trainer 或者 evaluator 设置 dataloader 后检测 dataloader 的合法性，因为不同的深度学习的框架需要的 dataloader 的
-         行为是不相同的；
-
-        :param dataloader: 需要检测的输入的 `dataloader`；
-        :param dataloader_name:
-        """
-        raise NotImplementedError("Each specific driver should implemented its own `_check_dataloader_legality` function.")
-
-    def has_train_dataloader(self):
-        return "_train_dataloader" in self.__dict__
-
-    def has_validate_dataloaders(self):
-        return "_validate_dataloaders" in self.__dict__
-
-    def has_test_dataloaders(self):
-        return "_test_dataloaders" in self.__dict__
-
-    def has_predict_dataloaders(self):
-        return "_predict_dataloaders" in self.__dict__
-
     @property
     def optimizers(self) -> List:
         r"""

fastNLP/core/drivers/jittor_driver/jittor_driver.py

@@ -39,7 +39,7 @@ class JittorDriver(Driver):
         self.grad_scaler = _grad_scaler()
 
     @staticmethod
-    def _check_dataloader_legality(dataloader, dataloader_name, is_train: bool = False):
+    def check_dataloader_legality(dataloader, dataloader_name, is_train: bool = False):
         # 在fastnlp中实现了JittorDataLoader
         # TODO: 是否允许传入Dataset？
         if is_train:
@@ -64,18 +64,18 @@ class JittorDriver(Driver):
     def check_evaluator_mode(self, mode: str):
         model = self.unwrap_model()
         if mode == "validate":
-            if not hasattr(model, "validate_step"):
+            if not hasattr(model, "evaluate_step"):
                 if hasattr(model, "test_step"):
                     logger.warning_once(
-                        "Your model does not have 'validate_step' method but has 'test_step' method, but you"
-                        "are using 'mode=validate', we are going to use 'test_step' to substitute for"
-                        "'validate_step'.")
+                        "Your model does not have 'evaluate_step' method but has 'test_step' method, but you"
+                        "are using 'evaluate_fn=validate', we are going to use 'test_step' to substitute for"
+                        "'evaluate_step'.")
 
         else:
             if not hasattr(model, "test_step"):
-                if hasattr(model, "validate_step"):
+                if hasattr(model, "evaluate_step"):
                     logger.warning_once("Your model does not have 'test_step' method but has 'validate' method, but you"
-                                   "are using 'mode=test', we are going to use 'validate_step' to substitute for"
+                                   "are using 'evaluate_fn=test', we are going to use 'evaluate_step' to substitute for"
                                    "'test_step'.")
 
     def save_model(self, filepath: str, only_state_dict: bool = False, model_save_fn: Optional[Callable]=None):

fastNLP/core/drivers/jittor_driver/single_device.py

@@ -35,8 +35,8 @@ class JittorSingleDriver(JittorDriver):
             model = self.unwrap_model()
             self._train_signature_fn = model.execute
 
-        if hasattr(self.model, "validate_step"):
-            self._validate_step = self.model.validate_step
+        if hasattr(self.model, "evaluate_step"):
+            self._validate_step = self.model.evaluate_step
             self._validate_signature_fn = None
         elif hasattr(self.model, "test_step"):
             self._validate_step = self.model.test_step
@@ -49,9 +49,9 @@ class JittorSingleDriver(JittorDriver):
         if hasattr(self.model, "test_step"):
             self._test_step = self.model.test_step
             self._test_signature_fn = None
-        elif hasattr(self.model, "validate_step"):
-            self._test_step = self.model.validate_step
-            self._test_signature_fn = self.model.validate_step
+        elif hasattr(self.model, "evaluate_step"):
+            self._test_step = self.model.evaluate_step
+            self._test_signature_fn = self.model.evaluate_step
         else:
             self._test_step = self.model
             model = self.unwrap_model()

fastNLP/core/drivers/paddle_driver/fleet.py

@@ -118,11 +118,11 @@ class PaddleFleetDriver(PaddleDriver):
                     " call `forward` function instead of `train_step` and you should note that.")
             self._train_step = partial(_running_fn_, step_fn=self.model, signature_fn=model.forward, wo_auto_param_call=self.wo_auto_param_call)
 
-            if hasattr(model, "validate_step"):
+            if hasattr(model, "evaluate_step"):
                 logger.warning(
                     "Notice your model is a `paddle.DataParallel` model. And your "
-                    "model also implements the `validate_step` method, which we can not call actually, "
-                    "we will call `forward` function instead of `validate_step` and you should note that.")
+                    "model also implements the `evaluate_step` method, which we can not call actually, "
+                    "we will call `forward` function instead of `evaluate_step` and you should note that.")
             self._validate_step = partial(_running_fn_, step_fn=self.model, signature_fn=model.forward, wo_auto_param_call=self.wo_auto_param_call)
 
             if hasattr(model, "test_step"):

fastNLP/core/drivers/paddle_driver/paddle_driver.py

@@ -72,7 +72,7 @@ class PaddleDriver(Driver):
             optimizer.clear_grad()
 
     @staticmethod
-    def _check_dataloader_legality(dataloader, dataloader_name, is_train: bool = False):
+    def check_dataloader_legality(dataloader, dataloader_name, is_train: bool = False):
         r"""
         该函数会在 trainer 或者 evaluator 设置 dataloader 后检测 dataloader 的合法性。
         要求传入的 dataloader 必须为 `paddle.io.DataLoader` 或包含该类型的字典。
@@ -117,24 +117,24 @@ class PaddleDriver(Driver):
 
     def check_evaluator_mode(self, mode: str):
         r"""
-        因为我们在具体的 driver 的 validate_step 和 test_step 的逻辑是如果模型没有实现本函数，那么就去检测模型是否实现了另一个函数；
-        因此如果用户的 evaluator mode 是 validate，但是传入的 model 却没有实现 validate_step 函数，而是实现了 test_step 函数，那么
+        因为我们在具体的 driver 的 evaluate_step 和 test_step 的逻辑是如果模型没有实现本函数，那么就去检测模型是否实现了另一个函数；
+        因此如果用户的 evaluator evaluate_fn 是 validate，但是传入的 model 却没有实现 evaluate_step 函数，而是实现了 test_step 函数，那么
          我们应当提醒用户这一行为；
         """
         model = self.unwrap_model()
         if mode == "validate":
-            if not hasattr(model, "validate_step"):
+            if not hasattr(model, "evaluate_step"):
                 if hasattr(model, "test_step"):
                     logger.warning(
-                        "Your model does not have 'validate_step' method but has 'test_step' method, but you"
+                        "Your model does not have 'evaluate_step' method but has 'test_step' method, but you"
                         "are using 'Evaluator.validate', we are going to use 'test_step' to substitute for"
-                        "'validate_step'.")
+                        "'evaluate_step'.")
 
         else:
             if not hasattr(model, "test_step"):
-                if hasattr(model, "validate_step"):
+                if hasattr(model, "evaluate_step"):
                     logger.warning_once("Your model does not have 'test_step' method but has 'validate' method, but you"
-                                    "are using 'Evaluator.test', we are going to use 'validate_step' to substitute for"
+                                    "are using 'Evaluator.test', we are going to use 'evaluate_step' to substitute for"
                                     "'test_step'.")
 
     @staticmethod

fastNLP/core/drivers/paddle_driver/single_device.py

@@ -50,10 +50,10 @@ class PaddleSingleDriver(PaddleDriver):
             self._train_step = self.model
             self._train_signature_fn = model.forward
 
-            if hasattr(model, "validate_step"):
+            if hasattr(model, "evaluate_step"):
                 logger.warning("Notice your model is a `paddle.DataParallel` model. And your model also "
-                                "implements the `validate_step` method, which we can not call actually, we "
-                                "will call `forward` function instead of `validate_step` and you should note that.")
+                                "implements the `evaluate_step` method, which we can not call actually, we "
+                                "will call `forward` function instead of `evaluate_step` and you should note that.")
             self._validate_step = self.model
             self._validate_signature_fn = model.forward
 
@@ -73,8 +73,8 @@ class PaddleSingleDriver(PaddleDriver):
                 model = self.unwrap_model()
                 self._train_signature_fn = model.forward
 
-            if hasattr(self.model, "validate_step"):
-                self._validate_step = self.model.validate_step
+            if hasattr(self.model, "evaluate_step"):
+                self._validate_step = self.model.evaluate_step
                 self._validate_signature_fn = None
             elif hasattr(self.model, "test_step"):
                 self._validate_step = self.model.test_step
@@ -87,9 +87,9 @@ class PaddleSingleDriver(PaddleDriver):
             if hasattr(self.model, "test_step"):
                 self._test_step = self.model.test_step
                 self._test_signature_fn = None
-            elif hasattr(self.model, "validate_step"):
-                self._test_step = self.model.validate_step
-                self._test_signature_fn = self.model.validate_step
+            elif hasattr(self.model, "evaluate_step"):
+                self._test_step = self.model.evaluate_step
+                self._test_signature_fn = self.model.evaluate_step
             else:
                 self._test_step = self.model
                 model = self.unwrap_model()

fastNLP/core/drivers/paddle_driver/utils.py

@@ -108,11 +108,11 @@ class _FleetWrappingModel(Layer):
             self._train_step = self.model
             self._train_signature_fn = model.forward
 
-            if hasattr(model, "validate_step"):
+            if hasattr(model, "evaluate_step"):
                 logger.warning(
                     "Notice your model is a `paddle.DataParallel` model. And your "
-                    "model also implements the `validate_step` method, which we can not call actually, "
-                    "we will call `forward` function instead of `validate_step` and you should note that.")
+                    "model also implements the `evaluate_step` method, which we can not call actually, "
+                    "we will call `forward` function instead of `evaluate_step` and you should note that.")
             self._validate_step = self.model
             self._validate_signature_fn = model.forward
 
@@ -131,7 +131,7 @@ class _FleetWrappingModel(Layer):
                 self._train_step = model
                 self._train_signature_fn = model.forward
 
-            if hasattr(model, "validate_step"):
+            if hasattr(model, "evaluate_step"):
                 self._validate_step = model.validate_step
                 self._validate_signature_fn = None
             elif hasattr(model, "test_step"):
@@ -144,7 +144,7 @@ class _FleetWrappingModel(Layer):
             if hasattr(model, "test_step"):
                 self._test_step = model.test_step
                 self._test_signature_fn = None
-            elif hasattr(model, "validate_step"):
+            elif hasattr(model, "evaluate_step"):
                 self._test_step = model.validate_step
                 self._test_signature_fn = None
             else:
@@ -172,9 +172,9 @@ class _FleetWrappingModel(Layer):
             else:
                 return self._test_step(batch)
         elif forward_state == ForwardState.PREDICT:
-            raise NotImplementedError("'PREDICT' mode has not been implemented.")
+            raise NotImplementedError("'PREDICT' evaluate_fn has not been implemented.")
         else:
-            raise NotImplementedError("You should direct a concrete mode.")
+            raise NotImplementedError("You should direct a concrete evaluate_fn.")
 
 class DummyGradScaler:
     """

fastNLP/core/drivers/torch_driver/ddp.py

@@ -4,7 +4,7 @@ import __main__
 import socket
 import numpy as np
 from time import sleep
-from typing import List, Optional, Union, Dict
+from typing import List, Optional, Union, Dict, Tuple, Callable
 from functools import partial
 
 from fastNLP.envs.imports import _NEED_IMPORT_TORCH
@@ -21,8 +21,6 @@ __all__ = [
 from .torch_driver import TorchDriver
 from fastNLP.core.drivers.torch_driver.utils import (
     _DDPWrappingModel,
-    ForwardState,
-    _MODE_PARAMETER,
     reset_seed,
     replace_sampler,
     replace_batch_sampler
@@ -158,10 +156,10 @@ class TorchDDPDriver(TorchDriver):
           ————————————————————————————————————————————————————————————————————————————————————————————————————————
 
             3. _DDPWrappingModel 的作用；
-            因为我们即需要调用模型的 `train_step`、`validate_step`、`test_step` 方法，又需要通过 `DistributedDataParallel` 的
+            因为我们即需要调用模型的 `train_step`、`evaluate_step`、`test_step` 方法，又需要通过 `DistributedDataParallel` 的
              forward 函数来帮助我们同步各个设备上的梯度，因此我们需要先将模型单独包裹一层，然后在 forward 的时候，其先经过 `DistributedDataParallel`
              的 forward 方法，然后再经过 `_DDPWrappingModel` 的 forward 方法，我们会在该 forward 函数中进行判断，确定调用的是模型自己的
-             forward 函数，还是 `train_step`、`validate_step`、`test_step` 方法。
+             forward 函数，还是 `train_step`、`evaluate_step`、`test_step` 方法。
 
             4. 当某一个进程出现 exception 后，`TorchDDPDriver` 的处理；
 
@@ -204,37 +202,6 @@ class TorchDDPDriver(TorchDriver):
             #  我们就直接将 model_device 置为 None；
             self.model_device = None
 
-            def _running_fn_(batch, step_fn, signature_fn, wo_auto_param_call):
-                if isinstance(batch, Dict) and not wo_auto_param_call:
-                    return auto_param_call(step_fn, batch, signature_fn=signature_fn)
-                else:
-                    return step_fn(batch)
-
-            model = model.module
-            if hasattr(model, "train_step"):
-                logger.warning(
-                    "Notice your model is a `DistributedDataParallel` model. And your "
-                    "model also implements the `train_step` method, which we can not call actually, we will"
-                    " call `forward` function instead of `train_step` and you should note that.")
-            self._train_step = partial(_running_fn_, step_fn=self.model, signature_fn=model.forward, wo_auto_param_call=self.wo_auto_param_call)
-            # self._train_signature_fn = model.forward
-
-            if hasattr(model, "validate_step"):
-                logger.warning(
-                    "Notice your model is a `DistributedDataParallel` model. And your "
-                    "model also implements the `validate_step` method, which we can not call actually, "
-                    "we will call `forward` function instead of `validate_step` and you should note that.")
-            self._validate_step = partial(_running_fn_, step_fn=self.model, signature_fn=model.forward, wo_auto_param_call=self.wo_auto_param_call)
-            # self._validate_signature_fn = model.forward
-
-            if hasattr(model, "test_step"):
-                logger.warning(
-                    "Notice your model is a `DistributedDataParallel` model. And your "
-                    "model also implements the `test_step` method, which we can not call actually, we will"
-                    " call `forward` function instead of `test_step` and you should note that.")
-            self._test_step = partial(_running_fn_, step_fn=self.model, signature_fn=model.forward, wo_auto_param_call=self.wo_auto_param_call)
-            # self._test_signature_fn = model.forward
-
         # 当用户自己在外面初始化 DDP 时我们会将 model_device 置为 None，这是用户可以通过 `data_device` 将对应的数据移到指定的机器上;
         self._data_device = kwargs.get("data_device", None)
         if isinstance(self._data_device, int):
@@ -253,7 +220,6 @@ class TorchDDPDriver(TorchDriver):
         # world_size 表示的就是全局的显卡的数量；
         self.world_size = None  # int(os.environ.get("WORLD_SIZE"))  len(self.parallel_device)
         self.global_rank = 0
-        self._configured = False  # 防止重复调用 configure_ddp() 函数使用的
 
         self._ddp_kwargs = kwargs.get("torch_ddp_kwargs", {})
         check_user_specific_params(self._ddp_kwargs, DistributedDataParallel.__init__)
@@ -268,8 +234,8 @@ class TorchDDPDriver(TorchDriver):
             os.makedirs(name=self.output_from_new_proc, exist_ok=True)
             self.output_from_new_proc = os.path.abspath(self.output_from_new_proc)
 
-        # 设置这一参数是因为 evaluator 中也会进行 setup 操作，但是显然是不需要的也不应该的；
-        self._has_setup = False
+        self._has_setup = False # 设置这一参数是因为 evaluator 中也会进行 setup 操作，但是显然是不需要的也不应该的；
+        self._has_ddpwrapped = False  # 判断传入的模型是否经过 _has_ddpwrapped 包裹；
 
     def setup(self):
         if self._has_setup:
@@ -341,24 +307,16 @@ class TorchDDPDriver(TorchDriver):
         self._pids = self.tensor_to_numeric(self._pids)
 
     def configure_ddp(self):
-        if not self._configured and not isinstance(self.model, DistributedDataParallel):
+        if not isinstance(self.model, DistributedDataParallel):
             self.model = DistributedDataParallel(
                 # 注意这里的 self.model_device 是 `torch.device` type，因此 self.model_device.index；
                 _DDPWrappingModel(self.model), device_ids=[self.model_device.index],
                 **self._ddp_kwargs
             )
-
-            self._train_step = partial(self.model, **{_MODE_PARAMETER: ForwardState.TRAIN}, wo_auto_param_call=self.wo_auto_param_call)
-            self._validate_step = partial(self.model, **{_MODE_PARAMETER: ForwardState.VALIDATE}, wo_auto_param_call=self.wo_auto_param_call)
-            self._test_step = partial(self.model, **{_MODE_PARAMETER: ForwardState.TEST}, wo_auto_param_call=self.wo_auto_param_call)
-
-        self._configured = True
+            self._has_ddpwrapped = True
 
     def open_subprocess(self):
         if self.local_rank == 0:
-            # self._consensus_file = Path(tempfile.mkstemp()[1])
-            # self._consensus_file.unlink()
-
             # Script called as `python a/b/c.py`
             if __main__.__spec__ is None:  # pragma: no-cover
                 # pull out the commands used to run the script and resolve the abs file path
@@ -432,18 +390,39 @@ class TorchDDPDriver(TorchDriver):
             return self._data_device
         return self.model_device
 
-    def train_step(self, batch):
-        # 注意这里的 self.model 已经是 'fastNLP.drivers.utils._DDPWrappingModel'；
-        # return self.model(batch, **{_MODE_PARAMETER: ForwardState.TRAIN})
-        return self._train_step(batch)
-
-    def validate_step(self, batch):
-        # return self.model(batch, **{_MODE_PARAMETER: ForwardState.VALIDATE})
-        return self._validate_step(batch)
-
-    def test_step(self, batch):
-        # return self.model(batch, **{_MODE_PARAMETER: ForwardState.TEST})
-        return self._test_step(batch)
+    def model_call(self, batch, fn: Callable, signature_fn: Optional[Callable]) -> Dict:
+        if self._has_ddpwrapped:
+            return self.model(batch, fastnlp_fn=fn, fastnlp_signature_fn=signature_fn,
+                              wo_auto_param_call=self.wo_auto_param_call)
+        else:
+            if isinstance(batch, Dict) and not self.wo_auto_param_call:
+                return auto_param_call(fn, batch, signature_fn=signature_fn)
+            else:
+                return fn(batch)
+
+    def get_model_call_fn(self, fn: str) -> Tuple:
+        model = self.unwrap_model()
+        if self._has_ddpwrapped:
+            if hasattr(model, fn):
+                fn = getattr(model, fn)
+                if not callable(fn):
+                    raise RuntimeError(f"The `{fn}` attribute of model is not `Callable`.")
+                return fn, None
+            elif fn in {"train_step", "evaluate_step"}:
+                return model, model.forward
+            else:
+                raise RuntimeError(f"There is no `{fn}` method in your model.")
+        else:
+            if hasattr(model, fn):
+                logger.warning("Notice your model is a `DistributedDataParallel` model. And your model also implements "
+                               f"the `{fn}` method, which we can not call actually, we will"
+                               " call `forward` function instead of `train_step` and you should note that.")
+            elif fn not in {"train_step", "evaluate_step"}:
+                raise RuntimeError(f"There is no `{fn}` method in your model. And also notice that your model is a "
+                                   "`DistributedDataParallel` model, which means that we will only call model.forward "
+                                   "function when we are in forward propagation.")
+
+            return self.model, model.forward
 
     def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleSampler, ReproducibleBatchSampler]]=None,
                                   reproducible: bool = False):

fastNLP/core/drivers/torch_driver/single_device.py

@@ -1,5 +1,5 @@
 import os
-from typing import Dict, Union
+from typing import Dict, Union, Callable, Tuple, Optional
 from fastNLP.envs.imports import _NEED_IMPORT_TORCH
 if _NEED_IMPORT_TORCH:
     import torch
@@ -42,84 +42,40 @@ class TorchSingleDriver(TorchDriver):
         self.global_rank = 0
         self.world_size = 1
 
-        if isinstance(model, DataParallel):
-            model = self.unwrap_model()
-            if hasattr(model, "train_step"):
-                logger.warning("Notice your model is a `DataParallel` or `DistributedDataParallel` model. And your "
-                               "model also implements the `train_step` method, which we can not call actually, we will"
-                               " call `forward` function instead of `train_step` and you should note that.")
-            self._train_step = self.model
-            self._train_signature_fn = model.forward
-
-            if hasattr(model, "validate_step"):
-                logger.warning("Notice your model is a `DataParallel` or `DistributedDataParallel` model. And your "
-                               "model also implements the `validate_step` method, which we can not call actually, "
-                               "we will call `forward` function instead of `validate_step` and you should note that.")
-            self._validate_step = self.model
-            self._validate_signature_fn = model.forward
-
-            if hasattr(model, "test_step"):
-                logger.warning("Notice your model is a `DataParallel` or `DistributedDataParallel` model. And your "
-                               "model also implements the `test_step` method, which we can not call actually, we will"
-                               " call `forward` function instead of `test_step` and you should note that.")
-            self._test_step = self.model
-            self._test_signature_fn = model.forward
-        else:
-            if hasattr(self.model, "train_step"):
-                self._train_step = self.model.train_step
-                self._train_signature_fn = None
-            else:
-                self._train_step = self.model
-                # 输入的模型是 `DataParallel` 或者 `DistributedDataParallel`，我们需要保证其 signature_fn 是正确的；
-                model = self.unwrap_model()
-                self._train_signature_fn = model.forward
-
-            if hasattr(self.model, "validate_step"):
-                self._validate_step = self.model.validate_step
-                self._validate_signature_fn = None
-            elif hasattr(self.model, "test_step"):
-                self._validate_step = self.model.test_step
-                self._validate_signature_fn = self.model.test_step
-            else:
-                self._validate_step = self.model
-                model = self.unwrap_model()
-                self._validate_signature_fn = model.forward
-
-            if hasattr(self.model, "test_step"):
-                self._test_step = self.model.test_step
-                self._test_signature_fn = None
-            elif hasattr(self.model, "validate_step"):
-                self._test_step = self.model.validate_step
-                self._test_signature_fn = self.model.validate_step
-            else:
-                self._test_step = self.model
-                model = self.unwrap_model()
-                self._test_signature_fn = model.forward
-
     def setup(self):
         if self.model_device is not None:
             self.model.to(self.model_device)
 
-    def train_step(self, batch) -> Dict:
-        # 如果 batch 是一个 Dict，我们就默认帮其做参数匹配，否则就直接传入到 `train_step` 函数中，让用户自己处理；
+    def model_call(self, batch, fn: Callable, signature_fn: Optional[Callable]) -> Dict:
         if isinstance(batch, Dict) and not self.wo_auto_param_call:
-            return auto_param_call(self._train_step, batch, signature_fn=self._train_signature_fn)
+            return auto_param_call(fn, batch, signature_fn=signature_fn)
         else:
-            return self._train_step(batch)
+            return fn(batch)
 
-    def validate_step(self, batch) -> Dict:
-        # 因为我们 Tester 的逻辑就是将所有的 metric 传给 tester，然后 tester 控制具体 metric 的 update 和 compute；因此不管用户是否
-        # 实现 validate_step 函数，其都应该返回一个字典，具体使用哪些东西则是在 validate_batch_loop 中每一个具体的 metric 自己去拿的；
-        if isinstance(batch, Dict) and not self.wo_auto_param_call:
-            return auto_param_call(self._validate_step, batch, signature_fn=self._validate_signature_fn)
-        else:
-            return self._validate_step(batch)
+    def get_model_call_fn(self, fn: str) -> Tuple:
+        if isinstance(self.model, DataParallel):
+            model = self.unwrap_model()
+            if hasattr(model, fn):
+                logger.warning("Notice your model is a `DataParallel` model. And your model also implements the "
+                               f"`{fn}` method, which we can not call actually, we will"
+                               " call `forward` function instead of `train_step` and you should note that.")
 
-    def test_step(self, batch) -> Dict:
-        if isinstance(batch, Dict) and not self.wo_auto_param_call:
-            return auto_param_call(self._test_step, batch, signature_fn=self._test_signature_fn)
+            elif fn not in {"train_step", "evaluate_step"}:
+                raise RuntimeError(f"There is no `{fn}` method in your model. And also notice that your model is a "
+                                   f"`DataParallel` model, which means that we will only call model.forward function "
+                                   f"when we are in forward propagation.")
+
+            return self.model, model.forward
         else:
-            return self._test_step(batch)
+            if hasattr(self.model, fn):
+                fn = getattr(self.model, fn)
+                if not callable(fn):
+                    raise RuntimeError(f"The `{fn}` attribute is not `Callable`.")
+                return fn, None
+            elif fn in {"train_step", "evaluate_step"}:
+                return self.model, self.model.forward
+            else:
+                raise RuntimeError(f"There is no `{fn}` method in your model.")
 
     def set_dist_repro_dataloader(self, dataloader, dist: Union[str, ReproducibleBatchSampler, ReproducibleSampler]=None,
                                   reproducible: bool = False):

fastNLP/core/drivers/torch_driver/torch_driver.py

@@ -81,7 +81,7 @@ class TorchDriver(Driver):
             self.grad_scaler.update()
 
     @staticmethod
-    def _check_dataloader_legality(dataloader, dataloader_name, is_train: bool = False):
+    def check_dataloader_legality(dataloader, dataloader_name, is_train: bool = False):
         if is_train:
             if not isinstance(dataloader, DataLoader):
                 raise ValueError(f"Parameter `{dataloader_name}` should be 'DataLoader' type, not {type(dataloader)}.")
@@ -108,23 +108,6 @@ class TorchDriver(Driver):
                 raise ValueError(f"Each optimizer of parameter `optimizers` should be 'Optimizer' type, "
                                  f"not {type(each_optimizer)}.")
 
-    def check_evaluator_mode(self, mode: str):
-        model = self.unwrap_model()
-        if mode == "validate":
-            if not hasattr(model, "validate_step"):
-                if hasattr(model, "test_step"):
-                    logger.warning_once(
-                        "Your model does not have 'validate_step' method but has 'test_step' method, but you"
-                        "are using 'mode=validate', we are going to use 'test_step' to substitute for"
-                        "'validate_step'.")
-
-        else:
-            if not hasattr(model, "test_step"):
-                if hasattr(model, "validate_step"):
-                    logger.warning("Your model does not have 'test_step' method but has 'validate' method, but you"
-                                   "are using 'mode=test', we are going to use 'validate_step' to substitute for"
-                                   "'test_step'.")
-
     @staticmethod
     def tensor_to_numeric(tensor, reduce=None):
         if tensor is None:
@@ -216,6 +199,7 @@ class TorchDriver(Driver):
                         num_consumed_batches = sampler_states['num_consumed_samples']
                 sampler_states['num_consumed_samples'] = num_consumed_samples_array[num_consumed_batches]
                 assert sampler_states['num_consumed_samples'] != -1, "This is a bug, please report."
+            states['sampler_states'] = sampler_states
         else:
             raise RuntimeError(
                 'The sampler has no `state_dict()` method, it will fail to recover to the specific batch.')

fastNLP/core/drivers/torch_driver/utils.py

@@ -90,14 +90,11 @@ class ForwardState(IntEnum):
     PREDICT = 3
 
 
-_MODE_PARAMETER = "_forward_state"
-
-
 class _DDPWrappingModel(Module):
     """
     该函数用于 DDP 训练时处理用户自己定制的 train_step 等函数；
     之所以要使用这一额外的包裹模型，是因为在使用 DDP 时，必须使用 DistributedDataParallel 的 forward 函数才能实现正常的运行；
-    另一方面，我们要求用户在使用我们的框架时，需要针对不用的模式实现不同的处理函数，例如 'train_step', 'validate_step' 等；
+    另一方面，我们要求用户在使用我们的框架时，需要针对不用的模式实现不同的处理函数，例如 'train_step', 'evaluate_step' 等；
     然而，当使用 DistributedDataParallel 包裹 model 后，模型看不见其除了 forward 之外的方法；并且当我们尝试在训练过程中主动提取
     `model = model.module`，这同样会导致错误，会使得每一个gpu上的模型参数不同；
 
@@ -109,60 +106,18 @@ class _DDPWrappingModel(Module):
         super(_DDPWrappingModel, self).__init__()
         self.model = model
 
-        if hasattr(model, "train_step"):
-            self._train_step = model.train_step
-            self._train_signature_fn = None
-        else:
-            self._train_step = model
-            self._train_signature_fn = model.forward
-
-        if hasattr(model, "validate_step"):
-            self._validate_step = model.validate_step
-            self._validate_signature_fn = None
-        elif hasattr(model, "test_step"):
-            self._validate_step = model.test_step
-            self._validate_signature_fn = None
-        else:
-            self._validate_step = model
-            self._validate_signature_fn = model.forward
-
-        if hasattr(model, "test_step"):
-            self._test_step = model.test_step
-            self._test_signature_fn = None
-        elif hasattr(model, "validate_step"):
-            self._test_step = model.validate_step
-            self._test_signature_fn = None
-        else:
-            self._test_step = model
-            self._test_signature_fn = model.forward
-
     def forward(self, batch, **kwargs) -> Dict:
         """
         pytorch lightning 实现了先 unwrapping_model 的操作，但是感觉对于我们来说没有什么必须要，先写个注释放这里，之后有需求了再看；
         """
-
-        forward_state = kwargs.pop(_MODE_PARAMETER)
+        fn = kwargs.pop("fastnlp_fn")
+        signature_fn = kwargs.pop("fastnlp_signature_fn")
         wo_auto_param_call = kwargs.pop("wo_auto_param_call")
 
-        if forward_state == ForwardState.TRAIN:
-            if isinstance(batch, Dict) and not wo_auto_param_call:
-                return auto_param_call(self._train_step, batch, signature_fn=self._train_signature_fn)
-            else:
-                return self._train_step(batch)
-        elif forward_state == ForwardState.VALIDATE:
-            if isinstance(batch, Dict) and not wo_auto_param_call:
-                return auto_param_call(self._validate_step, batch, signature_fn=self._validate_signature_fn)
-            else:
-                return self._validate_step(batch)
-        elif forward_state == ForwardState.TEST:
-            if isinstance(batch, Dict) and not wo_auto_param_call:
-                return auto_param_call(self._test_step, batch, signature_fn=self._test_signature_fn)
-            else:
-                return self._test_step(batch)
-        elif forward_state == ForwardState.PREDICT:
-            raise NotImplementedError("'PREDICT' mode has not been implemented.")
+        if isinstance(batch, Dict) and not wo_auto_param_call:
+            return auto_param_call(fn, batch, signature_fn=signature_fn)
         else:
-            raise NotImplementedError("You should direct a concrete mode.")
+            return fn(batch)
 
 
 class DummyGradScaler:

fastNLP/core/drivers/torch_paddle_driver/torch_paddle_driver.py

@@ -55,8 +55,8 @@ class TorchPaddleDriver(Driver):
             self._train_step = self.model
             self._train_signature_fn = self.model.forward
 
-        if hasattr(self.model, "validate_step"):
-            self._validate_step = self.model.validate_step
+        if hasattr(self.model, "evaluate_step"):
+            self._validate_step = self.model.evaluate_step
             self._validate_signature_fn = None
         elif hasattr(self.model, "test_step"):
             self._validate_step = self.model.test_step
@@ -68,8 +68,8 @@ class TorchPaddleDriver(Driver):
         if hasattr(self.model, "test_step"):
             self._test_step = self.model.test_step
             self._test_signature_fn = None
-        elif hasattr(self.model, "validate_step"):
-            self._test_step = self.model.validate_step
+        elif hasattr(self.model, "evaluate_step"):
+            self._test_step = self.model.evaluate_step
             self._test_signature_fn = self.model.forward
         else:
             self._test_step = self.model
@@ -81,7 +81,7 @@ class TorchPaddleDriver(Driver):
             self.model.to(self.model_device)
 
     @staticmethod
-    def _check_dataloader_legality(dataloader, dataloader_name, is_train: bool = False):
+    def check_dataloader_legality(dataloader, dataloader_name, is_train: bool = False):
         if is_train:
             if not isinstance(dataloader, (TorchDataLoader, PaddleDataLoader)):
                 raise ValueError(f"Parameter `{dataloader_name}` should be 'torch.util.data.DataLoader' or `paddle.io.dataloader` type, not {type(dataloader)}.")

fastNLP/core/log/logger.py

@@ -211,9 +211,9 @@ def _add_file_handler(_logger: logging.Logger, path: Optional[Union[str, Path]]
         raise TypeError("Parameter `remove_other_handlers` can only be `bool` type.")
 
     if not isinstance(mode, str):
-        raise TypeError("Parameter 'mode' can only be `str` type.")
+        raise TypeError("Parameter 'evaluate_fn' can only be `str` type.")
     if mode not in {"w", "a"}:
-        raise ValueError("Parameter `mode` can only be one of these values: ('w', 'a').")
+        raise ValueError("Parameter `evaluate_fn` can only be one of these values: ('w', 'a').")
 
     for h in _logger.handlers:
         if isinstance(h, logging.FileHandler):
@@ -230,7 +230,7 @@ def _add_file_handler(_logger: logging.Logger, path: Optional[Union[str, Path]]
     dirname = os.path.abspath(os.path.dirname(path))
     os.makedirs(dirname, exist_ok=True)
 
-    # 这里只要检测到是分布式训练，我们就将 mode 改为 "a"；这样会导致的一个问题在于，如果第二次训练也是分布式训练，logger记录的log不会重新
+    # 这里只要检测到是分布式训练，我们就将 evaluate_fn 改为 "a"；这样会导致的一个问题在于，如果第二次训练也是分布式训练，logger记录的log不会重新
     #  覆盖掉原文件，而是会接着上一次的 log 继续添加；
     # 这样做主要是为了解决这样的情形所导致的问题：在分布式训练中，进程 1 比 进程 0 先运行到这里，然后使得进程 0 将进程 1 的 log 覆盖掉；
     if is_cur_env_distributed():# and int(os.environ.get(FASTNLP_GLOBAL_RANK, 0)) != 0:

tests/core/callbacks/test_checkpoint_callback_torch.py

@@ -124,7 +124,7 @@ def test_model_checkpoint_callback_1(
             device=device,
             optimizers=model_and_optimizers.optimizers,
             train_dataloader=model_and_optimizers.train_dataloader,
-            validate_dataloaders=model_and_optimizers.validate_dataloaders,
+            evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
             input_mapping=model_and_optimizers.input_mapping,
             output_mapping=model_and_optimizers.output_mapping,
             metrics=model_and_optimizers.metrics,
@@ -204,7 +204,7 @@ def test_model_checkpoint_callback_1(
                 device=device,
                 optimizers=model_and_optimizers.optimizers,
                 train_dataloader=model_and_optimizers.train_dataloader,
-                validate_dataloaders=model_and_optimizers.validate_dataloaders,
+                evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
                 input_mapping=model_and_optimizers.input_mapping,
                 output_mapping=model_and_optimizers.output_mapping,
                 metrics=model_and_optimizers.metrics,
@@ -264,7 +264,7 @@ def test_model_checkpoint_callback_2(
                 device=device,
                 optimizers=model_and_optimizers.optimizers,
                 train_dataloader=model_and_optimizers.train_dataloader,
-                validate_dataloaders=model_and_optimizers.validate_dataloaders,
+                evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
                 input_mapping=model_and_optimizers.input_mapping,
                 output_mapping=model_and_optimizers.output_mapping,
                 metrics=model_and_optimizers.metrics,
@@ -302,7 +302,7 @@ def test_model_checkpoint_callback_2(
                 device=4,
                 optimizers=model_and_optimizers.optimizers,
                 train_dataloader=model_and_optimizers.train_dataloader,
-                validate_dataloaders=model_and_optimizers.validate_dataloaders,
+                evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
                 input_mapping=model_and_optimizers.input_mapping,
                 output_mapping=model_and_optimizers.output_mapping,
                 metrics=model_and_optimizers.metrics,
@@ -370,7 +370,7 @@ def test_trainer_checkpoint_callback_1(
             device=device,
             optimizers=model_and_optimizers.optimizers,
             train_dataloader=model_and_optimizers.train_dataloader,
-            validate_dataloaders=model_and_optimizers.validate_dataloaders,
+            evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
             input_mapping=model_and_optimizers.input_mapping,
             output_mapping=model_and_optimizers.output_mapping,
             metrics=model_and_optimizers.metrics,
@@ -448,7 +448,7 @@ def test_trainer_checkpoint_callback_1(
                 device=device,
                 optimizers=model_and_optimizers.optimizers,
                 train_dataloader=model_and_optimizers.train_dataloader,
-                validate_dataloaders=model_and_optimizers.validate_dataloaders,
+                evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
                 input_mapping=model_and_optimizers.input_mapping,
                 output_mapping=model_and_optimizers.output_mapping,
                 metrics=model_and_optimizers.metrics,
@@ -626,7 +626,7 @@ def test_trainer_checkpoint_callback_2(
             train_dataloader=test_bert_dataloader_train,
             optimizers=test_bert_optimizers,
 
-            validate_dataloaders=test_bert_dataloader_validate,
+            evaluate_dataloaders=test_bert_dataloader_validate,
             input_mapping=bert_input_mapping,
             output_mapping=bert_output_mapping,
             metrics={"acc": acc},
@@ -700,7 +700,7 @@ def test_trainer_checkpoint_callback_2(
                 train_dataloader=test_bert_dataloader_train,
                 optimizers=test_bert_optimizers,
 
-                validate_dataloaders=test_bert_dataloader_validate,
+                evaluate_dataloaders=test_bert_dataloader_validate,
                 input_mapping=bert_input_mapping,
                 output_mapping=bert_output_mapping,
                 metrics={"acc": acc},

tests/core/callbacks/test_load_best_model_callback_torch.py

@@ -92,7 +92,7 @@ def test_load_best_model_callback(
         device=device,
         optimizers=model_and_optimizers.optimizers,
         train_dataloader=model_and_optimizers.train_dataloader,
-        validate_dataloaders=model_and_optimizers.validate_dataloaders,
+        evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
         input_mapping=model_and_optimizers.input_mapping,
         output_mapping=lambda output: output if ('loss' in output) else {'pred':output['preds'], 'target': output['target']},
         metrics=model_and_optimizers.metrics,

tests/core/controllers/_test_distributed_launch_torch_1.py

@@ -89,7 +89,7 @@ def _test_trainer_torch_with_evaluator_fp16_accumulation_steps(
         device=None,
         optimizers=optimizers,
         train_dataloader=train_dataloader,
-        validate_dataloaders=validate_dataloaders,
+        evaluate_dataloaders=validate_dataloaders,
         metrics=metrics,
 
         n_epochs=2,

tests/core/controllers/_test_distributed_launch_torch_2.py

@@ -77,7 +77,7 @@ def _test_trainer_torch_with_evaluator_fp16_accumulation_steps(
         device=None,
         optimizers=optimizers,
         train_dataloader=train_dataloader,
-        validate_dataloaders=validate_dataloaders,
+        evaluate_dataloaders=validate_dataloaders,
         metrics=metrics,
 
         n_epochs=2,

tests/core/controllers/test_trainer_event_trigger.py

@@ -82,7 +82,7 @@ def test_trainer_event_trigger(
                 device=device,
                 optimizers=model_and_optimizers.optimizers,
                 train_dataloader=model_and_optimizers.train_dataloader,
-                validate_dataloaders=model_and_optimizers.validate_dataloaders,
+                evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
                 input_mapping=model_and_optimizers.input_mapping,
                 output_mapping=model_and_optimizers.output_mapping,
                 metrics=model_and_optimizers.metrics,

tests/core/controllers/test_trainer_fleet.py

@@ -64,8 +64,8 @@ def test_trainer_fleet(
         device=device,
         optimizers=optimizers,
         train_dataloader=train_dataloader,
-        validate_dataloaders=validate_dataloaders,
-        validate_every=validate_every,
+        evaluate_dataloaders=validate_dataloaders,
+        evaluate_every=validate_every,
         input_mapping=None,
         output_mapping=None,
         metrics=metrics,

tests/core/controllers/test_trainer_fleet_outside.py

@@ -70,8 +70,8 @@ def test_trainer_fleet(
         device=device,
         optimizers=optimizers,
         train_dataloader=train_dataloader,
-        validate_dataloaders=validate_dataloaders,
-        validate_every=validate_every,
+        evaluate_dataloaders=validate_dataloaders,
+        evaluate_every=validate_every,
         input_mapping=None,
         output_mapping=None,
         metrics=metrics,

tests/core/controllers/test_trainer_paddle.py

@@ -96,4 +96,4 @@ def test_trainer_paddle(
         n_epochs=n_epochs,
         callbacks=callbacks,
     )
-    trainer.run()
+    trainer.run()

tests/core/controllers/test_trainer_w_evaluator_torch.py

@@ -98,16 +98,16 @@ def model_and_optimizers(request):
 
 
 # 测试一下普通的情况；
-@pytest.mark.parametrize("driver,device", [("torch", [0, 1])])  #  ("torch", "cpu"), ("torch", 1), ("torch", [0, 1])
+@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", 1), ("torch", [0, 1])])  #  ("torch", "cpu"), ("torch", 1), ("torch", [0, 1])
 @pytest.mark.parametrize("callbacks", [[RecordMetricCallback(monitor="acc", metric_threshold=0.2, larger_better=True)]])
-@pytest.mark.parametrize("validate_every", [-3])
+@pytest.mark.parametrize("evaluate_every", [-3, -1, 100])
 @magic_argv_env_context
 def test_trainer_torch_with_evaluator(
         model_and_optimizers: TrainerParameters,
         driver,
         device,
         callbacks,
-        validate_every,
+        evaluate_every,
         n_epochs=10,
 ):
     trainer = Trainer(
@@ -116,11 +116,11 @@ def test_trainer_torch_with_evaluator(
         device=device,
         optimizers=model_and_optimizers.optimizers,
         train_dataloader=model_and_optimizers.train_dataloader,
-        validate_dataloaders=model_and_optimizers.validate_dataloaders,
+        evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
         input_mapping=model_and_optimizers.input_mapping,
         output_mapping=model_and_optimizers.output_mapping,
         metrics=model_and_optimizers.metrics,
-        validate_every=validate_every,
+        evaluate_every=evaluate_every,
 
         n_epochs=n_epochs,
         callbacks=callbacks,
@@ -152,7 +152,7 @@ def test_trainer_torch_with_evaluator_fp16_accumulation_steps(
         device=device,
         optimizers=model_and_optimizers.optimizers,
         train_dataloader=model_and_optimizers.train_dataloader,
-        validate_dataloaders=model_and_optimizers.validate_dataloaders,
+        evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
         input_mapping=model_and_optimizers.input_mapping,
         output_mapping=model_and_optimizers.output_mapping,
         metrics=model_and_optimizers.metrics,
@@ -193,14 +193,14 @@ def test_trainer_validate_every(
         device=device,
         optimizers=model_and_optimizers.optimizers,
         train_dataloader=model_and_optimizers.train_dataloader,
-        validate_dataloaders=model_and_optimizers.validate_dataloaders,
+        evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
         input_mapping=model_and_optimizers.input_mapping,
         output_mapping=model_and_optimizers.output_mapping,
         metrics=model_and_optimizers.metrics,
 
         n_epochs=n_epochs,
         output_from_new_proc="all",
-        validate_every=validate_every
+        evaluate_every=validate_every
     )
 
     trainer.run()

tests/core/controllers/test_trainer_wo_evaluator_torch.py

@@ -91,7 +91,7 @@ def test_trainer_torch_without_evaluator(
         device=device,
         optimizers=model_and_optimizers.optimizers,
         train_dataloader=model_and_optimizers.train_dataloader,
-        validate_dataloaders=model_and_optimizers.validate_dataloaders,
+        evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
         input_mapping=model_and_optimizers.input_mapping,
         output_mapping=model_and_optimizers.output_mapping,
         metrics=model_and_optimizers.metrics,
@@ -126,7 +126,7 @@ def test_trainer_torch_without_evaluator_fp16_accumulation_steps(
         device=device,
         optimizers=model_and_optimizers.optimizers,
         train_dataloader=model_and_optimizers.train_dataloader,
-        validate_dataloaders=model_and_optimizers.validate_dataloaders,
+        evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
         input_mapping=model_and_optimizers.input_mapping,
         output_mapping=model_and_optimizers.output_mapping,
         metrics=model_and_optimizers.metrics,
@@ -163,7 +163,7 @@ def test_trainer_torch_without_evaluator_accumulation_steps(
 
         optimizers=model_and_optimizers.optimizers,
         train_dataloader=model_and_optimizers.train_dataloader,
-        validate_dataloaders=model_and_optimizers.validate_dataloaders,
+        evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
         input_mapping=model_and_optimizers.input_mapping,
         output_mapping=model_and_optimizers.output_mapping,
         metrics=model_and_optimizers.metrics,
@@ -202,7 +202,7 @@ def test_trainer_output_from_new_proc(
 
             optimizers=model_and_optimizers.optimizers,
             train_dataloader=model_and_optimizers.train_dataloader,
-            validate_dataloaders=model_and_optimizers.validate_dataloaders,
+            evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
             input_mapping=model_and_optimizers.input_mapping,
             output_mapping=model_and_optimizers.output_mapping,
             metrics=model_and_optimizers.metrics,
@@ -267,7 +267,7 @@ def test_trainer_on_exception(
 
             optimizers=model_and_optimizers.optimizers,
             train_dataloader=model_and_optimizers.train_dataloader,
-            validate_dataloaders=model_and_optimizers.validate_dataloaders,
+            evaluate_dataloaders=model_and_optimizers.validate_dataloaders,
             input_mapping=model_and_optimizers.input_mapping,
             output_mapping=model_and_optimizers.output_mapping,
             metrics=model_and_optimizers.metrics,

tests/core/drivers/paddle_driver/test_single_device.py

@@ -423,12 +423,12 @@ class TestPaddleDriverFunctions:
         测试is_train参数为True时，_check_dataloader_legality函数的表现
         """
         dataloader = paddle.io.DataLoader(PaddleNormalDataset())
-        PaddleSingleDriver._check_dataloader_legality(dataloader, "dataloader", True)
+        PaddleSingleDriver.check_dataloader_legality(dataloader, "dataloader", True)
 
         # batch_size 和 batch_sampler 均为 None 的情形
         dataloader = paddle.io.DataLoader(PaddleNormalDataset(), batch_size=None)
         with pytest.raises(ValueError):
-            PaddleSingleDriver._check_dataloader_legality(dataloader, "dataloader", True)
+            PaddleSingleDriver.check_dataloader_legality(dataloader, "dataloader", True)
 
         # 创建torch的dataloader
         dataloader = torch.utils.data.DataLoader(
@@ -436,7 +436,7 @@ class TestPaddleDriverFunctions:
             batch_size=32, shuffle=True
         )
         with pytest.raises(ValueError):
-            PaddleSingleDriver._check_dataloader_legality(dataloader, "dataloader", True)
+            PaddleSingleDriver.check_dataloader_legality(dataloader, "dataloader", True)
 
     def test_check_dataloader_legality_in_test(self):
         """
@@ -447,7 +447,7 @@ class TestPaddleDriverFunctions:
             "train": paddle.io.DataLoader(PaddleNormalDataset()),
             "test":paddle.io.DataLoader(PaddleNormalDataset())
         }
-        PaddleSingleDriver._check_dataloader_legality(dataloader, "dataloader", False)
+        PaddleSingleDriver.check_dataloader_legality(dataloader, "dataloader", False)
 
         # batch_size 和 batch_sampler 均为 None 的情形
         dataloader = {
@@ -455,12 +455,12 @@ class TestPaddleDriverFunctions:
             "test":paddle.io.DataLoader(PaddleNormalDataset(), batch_size=None)
         }
         with pytest.raises(ValueError):
-            PaddleSingleDriver._check_dataloader_legality(dataloader, "dataloader", False)
+            PaddleSingleDriver.check_dataloader_legality(dataloader, "dataloader", False)
 
         # 传入的不是dict，应该报错
         dataloader = paddle.io.DataLoader(PaddleNormalDataset())
         with pytest.raises(ValueError):
-            PaddleSingleDriver._check_dataloader_legality(dataloader, "dataloader", False)
+            PaddleSingleDriver.check_dataloader_legality(dataloader, "dataloader", False)
 
         # 创建torch的dataloader
         train_loader = torch.utils.data.DataLoader(
@@ -473,7 +473,7 @@ class TestPaddleDriverFunctions:
         )
         dataloader = {"train": train_loader, "test": test_loader}
         with pytest.raises(ValueError):
-            PaddleSingleDriver._check_dataloader_legality(dataloader, "dataloader", False)
+            PaddleSingleDriver.check_dataloader_legality(dataloader, "dataloader", False)
 
     def test_tensor_to_numeric(self):
         """

tests/core/utils/test_utils.py

@@ -181,6 +181,7 @@ class TestCheckNumberOfParameters:
 
 
 def test_get_fun_msg():
+    # 测试运行
     def demo(x):
         pass
 

tests/helpers/datasets/normal_data.py

@@ -1,3 +1,6 @@
+import numpy as np
+
+
 class NormalIterator:
     def __init__(self, num_of_data=1000):
         self._num_of_data = num_of_data
@@ -15,4 +18,15 @@ class NormalIterator:
         return self._data
 
     def __len__(self):
-        return self._num_of_data
+        return self._num_of_data
+
+
+class RandomDataset:
+    def __init__(self, num_data=10):
+        self.data = np.random.rand(num_data)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, item):
+        return self.data[item]

tests/helpers/models/torch_model.py

@@ -28,7 +28,7 @@ class TorchNormalModel_Classification_1(nn.Module):
         x = self(x)
         return {"loss": self.loss_fn(x, y)}
 
-    def validate_step(self, x, y):
+    def evaluate_step(self, x, y):
         """
         如果不加参数 y，那么应该在 trainer 中设置 output_mapping = {"y": "target"}；
         """