Merge branch 'dev0.8.0' of github.com:fastnlp/fastNLP into dev0.8.0

fastNLP/core/callbacks/callback.py

@@ -71,7 +71,7 @@ class Callback:
         """
         pass
 
-    def on_train_batch_begin(self, trainer, batch, indices=None):
+    def on_train_batch_begin(self, trainer, batch, indices):
         r"""
         在训练过程中开始具体的一个 batch 前会被触发；
 

fastNLP/core/controllers/trainer.py

@@ -130,9 +130,12 @@ class Trainer(TrainerEventTrigger):
                 auto 表示如果检测到当前 terminal 为交互型 则使用 rich，否则使用 raw。
 
         """
-
+        self.model = model
         self.marker = marker
-        self.driver_name = driver
+        if isinstance(driver, str):
+            self.driver_name = driver
+        else:
+            self.driver_name = driver.__class__.__name__
         self.device = device
         self.fp16 = fp16
         self.input_mapping = input_mapping
@@ -157,6 +160,8 @@ class Trainer(TrainerEventTrigger):
         elif accumulation_steps < 0:
             raise ValueError("Parameter `accumulation_steps` can only be bigger than 0.")
         self.accumulation_steps = accumulation_steps
+
+        # todo 思路大概是，每个driver提供一下自己的参数是啥（需要对应回初始化的那个），然后trainer/evalutor在初始化的时候，就检测一下自己手上的参数和driver的是不是一致的，不一致的地方需要warn用户说这些值driver不太一样。感觉可以留到后面做吧
         self.driver = choose_driver(
             model=model,
             driver=driver,
@@ -403,9 +408,10 @@ class Trainer(TrainerEventTrigger):
 
         def wrapper(fn: Callable) -> Callable:
             cls._custom_callbacks[marker].append((event, fn))
-            assert check_fn_not_empty_params(fn, len(get_fn_arg_names(getattr(Callback, event.value))) - 1), "Your " \
-                                                                                                             "callback fn's allowed parameters seem not to be equal with the origin callback fn in class " \
-                                                                                                             "`Callback` with the same callback time."
+            callback_fn_args = get_fn_arg_names(getattr(Callback, event.value))[1:]
+            assert check_fn_not_empty_params(fn, len(callback_fn_args)), \
+                f"The callback function at `{event.value.lower()}`'s parameters should be {callback_fn_args}, but your "\
+                f"function {fn.__name__} only has these parameters: {get_fn_arg_names(fn)}."
             return fn
 
         return wrapper
@@ -807,10 +813,6 @@ class Trainer(TrainerEventTrigger):
     def data_device(self):
         return self.driver.data_device
 
-    @property
-    def model(self):
-        # 返回 driver 中的 model，注意该 model 可能被分布式的模型包裹，例如 `DistributedDataParallel`；
-        return self.driver.model
 
 
 

fastNLP/core/utils/utils.py

@@ -44,15 +44,11 @@ __all__ = [
 ]
 
 
-
-
-
 def get_fn_arg_names(fn: Callable) -> List[str]:
     r"""
     返回一个函数的所有参数的名字；
 
     :param fn: 需要查询的函数；
-
     :return: 一个列表，其中的元素则是查询函数的参数的字符串名字；
     """
     return list(inspect.signature(fn).parameters)

tests/core/callbacks/test_callback_events.py

@@ -1,7 +1,7 @@
 import pytest
 from functools import reduce
 
-from fastNLP.core.callbacks.callback_events import Filter
+from fastNLP.core.callbacks.callback_events import Events, Filter
 
 
 class TestFilter:

tests/core/controllers/test_trainer_other_things.py

@@ -0,0 +1,25 @@
+import pytest
+
+from fastNLP.core.controllers.trainer import Trainer
+from fastNLP.core.callbacks import Events
+from tests.helpers.utils import magic_argv_env_context
+
+
+@magic_argv_env_context
+def test_trainer_torch_without_evaluator():
+    @Trainer.on(Events.ON_TRAIN_EPOCH_BEGIN(every=10))
+    def fn1(trainer):
+        pass
+
+    @Trainer.on(Events.ON_TRAIN_BATCH_BEGIN(every=10))
+    def fn2(trainer, batch, indices):
+        pass
+
+    with pytest.raises(AssertionError):
+        @Trainer.on(Events.ON_TRAIN_BATCH_BEGIN(every=10))
+        def fn3(trainer, batch):
+            pass
+
+
+
+