添加在metric统计all_gather_object是否调用的逻辑；添加了一些可能引起bug处的注释

fastNLP/core/callbacks/has_monitor_callback.py

@@ -218,9 +218,10 @@ def on_after_trainer_initialized(self, trainer, driver):
         if self.must_have_monitor and self.monitor is None:
             raise RuntimeError(f"No `monitor` is set for {self.log_name}. "
                                f"You can set it in the initialization or through Trainer.")
-        if self.must_have_monitor and self.monitor is not None and trainer.evaluator is None:
-            raise RuntimeError(f"No `evaluate_dataloaders` is set for Trainer. But Callback: {self.log_name}"
-                               f" need to watch the monitor:`{self.monitor_name}`.")
+        # 用户可能会在自定义 Callback 中自行 evaluate 结果并且不使用 Evaluator，此时该限制会变得不合理，暂时注释掉
+        # if self.must_have_monitor and self.monitor is not None and trainer.evaluator is None:
+        #     raise RuntimeError(f"No `evaluate_dataloaders` is set for Trainer. But Callback: {self.log_name}"
+        #                        f" need to watch the monitor:`{self.monitor_name}`.")
 
     def on_sanity_check_end(self, trainer, sanity_check_res):
         # 主要核对一下 monitor 是否存在。

fastNLP/core/callbacks/topk_saver.py

@@ -75,6 +75,9 @@ def save(self, trainer, folder_name):
             model_save_fn=self.model_save_fn,
             **self.kwargs
         )
+        # TODO 如果 Metric 没有进行聚集操作，此时会创建出多个文件夹且只在 rank 0 的文件夹中进行保存
+        # 可能的解决方法：检测出空文件夹并且删除
+
         return str(os.path.abspath(folder))
 
     @rank_zero_call

fastNLP/core/metrics/metric.py

@@ -11,6 +11,7 @@
 
 from fastNLP.core.metrics.backend import Backend, AutoBackend
 from fastNLP.core.metrics.element import Element
+from fastNLP.envs import is_cur_env_distributed
 from fastNLP.core.log import logger
 
 
@@ -42,11 +43,9 @@ def __init__(self, backend: Union[str, Backend, None] = 'auto', aggregate_when_g
         self.get_metric = self._sync_get_metric(self.get_metric)
         self.update = self._wrap_update(self.update)
         self.reset = self._wrap_auto_reset_elements(self.reset)
-        self.get_metric = self._wrap_check_get_metric(self.get_metric)
         self.aggregate_when_get_metric = aggregate_when_get_metric
         self._cannot_change_element = False
-        self._call_gather_object = False
-        self._check_get_metric = False
+        self._call_gather_object = False # 用于检查用户是否在 get_metric 中调用了 all_gather_object
         self._elements = {}
 
     @property
@@ -108,7 +107,18 @@ def _wrap_get_metric(*args, **kwargs):
             assert self._updated, f"You have to call `{self.__class__.__name__}'s update() function before calling " \
                                   f"get_metric()."
             with self.sync(recover=True, aggregate=self.aggregate_when_get_metric):
+                self._call_gather_object = False
                 results = get_metric(*args, **kwargs)
+
+                # elements 为空、没有 call 则准备报错
+                if len(self._elements) == 0 and not self._call_gather_object:
+                    # 需要 aggregate 并且在多卡环境下
+                    if self.aggregate_when_get_metric and is_cur_env_distributed():
+                        logger.rank_zero_warning("There is no `<class 'Element'>` registered in metric `{}` and you didn't call "
+                                                "`Metric.all_gather_object()` in method `get_metric()` either. Therefore your "
+                                                "results may not be aggregated in distributed training."
+                                                .format(self.__class__), once=True)
+
             return results
 
         return _wrap_get_metric