[Typing][B-49] Add type annotations for python/paddle/base/layer_helper.py

python/paddle/base/layer_helper.py

@@ -11,8 +11,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from __future__ import annotations
 
 import copy
+from typing import TYPE_CHECKING, Any, Generator
 
 import paddle
 from paddle import _C_ops
@@ -28,9 +30,14 @@
 from .layer_helper_base import LayerHelperBase
 from .param_attr import ParamAttr
 
+if TYPE_CHECKING:
+    from paddle import Tensor
+    from paddle._typing import DTypeLike, ParamAttrLike
+    from paddle.base.framework import Operator
+
 
 class LayerHelper(LayerHelperBase):
-    def __init__(self, layer_type, **kwargs):
+    def __init__(self, layer_type: str, **kwargs: Any) -> None:
         self.kwargs = kwargs
         name = self.kwargs.get('name', None)
         # TODO(panyx0718, minqiyang): dygraph mode
@@ -46,10 +53,10 @@ def __init__(self, layer_type, **kwargs):
 
         super().__init__(self.kwargs['name'], layer_type=layer_type)
 
-    def append_op(self, *args, **kwargs):
+    def append_op(self, *args: Any, **kwargs: Any) -> Operator:
         return self.main_program.current_block().append_op(*args, **kwargs)
 
-    def multiple_input(self, input_param_name='input'):
+    def multiple_input(self, input_param_name: str = 'input') -> list[Tensor]:
         inputs = self.kwargs.get(input_param_name, [])
         ret = []
         if isinstance(inputs, (list, tuple)):
@@ -59,22 +66,22 @@ def multiple_input(self, input_param_name='input'):
             ret.append(self.to_variable(inputs))
         return ret
 
-    def input(self, input_param_name='input'):
+    def input(self, input_param_name: str = 'input') -> paddle.Tensor:
         inputs = self.multiple_input(input_param_name)
         if len(inputs) != 1:
             raise f"{self.layer_type} layer only takes one input"
         return inputs[0]
 
     @property
-    def param_attr(self):
+    def param_attr(self) -> ParamAttrLike | list[ParamAttrLike]:
         return ParamAttr._to_attr(self.kwargs.get('param_attr', None))
 
     @property
-    def bias_attr(self):
+    def bias_attr(self) -> ParamAttrLike | list[ParamAttrLike]:
         return ParamAttr._to_attr(self.kwargs.get('bias_attr', None))
 
     # TODO (jiabin): reconstruct this in LayerObjHelper and avoid dependency of param_attr
-    def multiple_param_attr(self, length):
+    def multiple_param_attr(self, length: int) -> list[ParamAttrLike]:
         param_attr = self.param_attr
         if isinstance(param_attr, ParamAttr):
             param_attr = [param_attr]
@@ -88,12 +95,14 @@ def multiple_param_attr(self, length):
             param_attr = tmp
         return param_attr
 
-    def iter_inputs_and_params(self, input_param_name='input'):
+    def iter_inputs_and_params(
+        self, input_param_name: str = 'input'
+    ) -> Generator[tuple[Tensor, ParamAttrLike]]:
         inputs = self.multiple_input(input_param_name)
         param_attrs = self.multiple_param_attr(len(inputs))
         yield from zip(inputs, param_attrs)
 
-    def input_dtype(self, input_param_name='input'):
+    def input_dtype(self, input_param_name: str = 'input') -> None | DTypeLike:
         inputs = self.multiple_input(input_param_name)
         dtype = None
         for each in inputs:
@@ -105,14 +114,16 @@ def input_dtype(self, input_param_name='input'):
                 )
         return dtype
 
-    def get_parameter(self, name):
+    def get_parameter(self, name: str) -> Parameter:
         param = self.main_program.global_block().var(name)
         if not isinstance(param, Parameter):
             raise ValueError(f"no Parameter name {name} found")
         return param
 
     # TODO (jiabin): reconstruct this in LayerObjHelper and avoid dependency of bias_attr
-    def append_bias_op(self, input_var, dim_start=1, dim_end=None):
+    def append_bias_op(
+        self, input_var: Tensor, dim_start: int = 1, dim_end: int | None = None
+    ) -> Tensor:
         """
         Append bias operator and return its output. If the user does not set
         bias_attr, append_bias_op will return input_var
@@ -146,7 +157,7 @@ def append_bias_op(self, input_var, dim_start=1, dim_end=None):
         return tmp
 
     # TODO (jiabin): reconstruct this in LayerObjHelper and avoid dependency of act
-    def append_activation(self, input_var):
+    def append_activation(self, input_var: Tensor) -> Tensor:
         act = self.kwargs.get('act', None)
         if act is None:
             return input_var
@@ -197,7 +208,7 @@ def _get_default_initializer(self, dtype):
             return paddle.nn.initializer.Constant()
 
     # TODO (jiabin): reconstruct this in LayerObjHelper and avoid dependency of kwargs
-    def is_instance(self, param_name, cls):
+    def is_instance(self, param_name: str, cls: Any) -> None:
         param = self.kwargs.get(param_name, None)
         if not isinstance(param, cls):
             raise TypeError(

python/paddle/base/param_attr.py

@@ -11,11 +11,17 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Sequence
 
 import paddle
 from paddle.base.data_feeder import check_type
 from paddle.regularizer import WeightDecayRegularizer
 
+if TYPE_CHECKING:
+    from paddle._typing import ParamAttrLike
+
 __all__ = []
 
 
@@ -154,7 +160,9 @@ def _set_default_bias_initializer(self):
         self._set_default_initializer(paddle.nn.initializer.Constant(0.0))
 
     @staticmethod
-    def _to_attr(arg):
+    def _to_attr(
+        arg: ParamAttrLike | Sequence[ParamAttrLike],
+    ) -> ParamAttrLike | list[ParamAttrLike]:
         """
         Create ParamAttr[s].