Set the lr var's dtype to fp32 when create a fp16 lr var in optimizer if user...

Set the lr var's dtype to fp32 when create a fp16 lr var in optimizer if user not mean to use global fp16. (#44840)

python/paddle/optimizer/optimizer.py

@@ -385,19 +385,23 @@ class Optimizer(object):
         return self._opti_name_list
 
     def _create_global_learning_rate(self):
+        # lr var can't be float16, for pure fp16 training, should extra handle the dtype for lr
+        _lr_dtype = paddle.get_default_dtype(
+        ) if self._dtype is None else self._dtype
+        _lr_dtype = paddle.float32 if (
+            paddle.get_default_dtype() != "float16"
+            and _lr_dtype == paddle.float16) else _lr_dtype
         if isinstance(self._learning_rate, LRScheduler):
             lr_var = self._global_learning_rate()
             # only create global lr_var once
             if not isinstance(lr_var, framework.Variable):
                 lr_name = unique_name.generate('learning_rate')
                 self._learning_rate._var_name = lr_name
-                lr_var = self.helper.create_global_variable(
-                    name=lr_name,
-                    shape=[1],
-                    persistable=True,
-                    stop_gradient=True,
-                    dtype=paddle.get_default_dtype()
-                    if self._dtype is None else self._dtype)
+                lr_var = self.helper.create_global_variable(name=lr_name,
+                                                            shape=[1],
+                                                            persistable=True,
+                                                            stop_gradient=True,
+                                                            dtype=_lr_dtype)
                 main_prog = framework.default_main_program()
                 main_prog.lr_sheduler = self._learning_rate
                 main_prog.lr_var = lr_var
@@ -419,8 +423,7 @@ class Optimizer(object):
                     name=unique_name.generate("learning_rate"),
                     shape=[1],
                     value=float(self._learning_rate),
-                    dtype=paddle.get_default_dtype()
-                    if self._dtype is None else self._dtype,
+                    dtype=_lr_dtype,
                     persistable=True)
 
     @framework.dygraph_only