[Typing][A-27,A-28,A-32,A-33,A-35,A-36] Add type annotations for paddle/nn/initializer/*

python/paddle/nn/initializer/assign.py

@@ -11,6 +11,11 @@
 # 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
+
 import paddle
 from paddle import _C_ops
 
@@ -23,6 +28,9 @@
 )
 from .initializer import Initializer
 
+if TYPE_CHECKING:
+    import numpy
+
 __all__ = []
 
 
@@ -38,19 +46,21 @@ class NumpyArrayInitializer(Initializer):
 
     """
 
-    def __init__(self, value):
+    def __init__(self, value: numpy.ndarray):
         import numpy
 
         assert isinstance(value, numpy.ndarray)
         super().__init__()
         self._value = value
 
-    def forward(self, var, block=None):
+    def forward(
+        self, var: paddle.Tensor, block: paddle.pir.Block | None = None
+    ) -> paddle.Tensor | None:
         """Initialize the input tensor with Numpy array.
 
         Args:
             var(Tensor): Tensor that needs to be initialized.
-            block(Block, optional): The block in which initialization ops
+            block(Block|None, optional): The block in which initialization ops
                    should be added. Used in static graph only, default None.
 
         Returns:
@@ -172,7 +182,7 @@ class Assign(NumpyArrayInitializer):
 
     Args:
         value (Tensor|numpy.ndarray|list|tuple): numpy array, list, tuple, or tensor to initialize the parameter.
-        name(str, optional): Normally there is no need for user to set this
+        name(str|None, optional): Normally there is no need for user to set this
             property. For more information, please refer to :ref:`api_guide_Name`. Default is None.
 
     Returns:
@@ -239,7 +249,11 @@ class Assign(NumpyArrayInitializer):
             [6.]
     """
 
-    def __init__(self, value, name=None):
+    def __init__(
+        self,
+        value: numpy.ndarray | list[int] | tuple[int] | paddle.Tensor,
+        name: str | None = None,
+    ):
         import numpy
 
         check_type(

python/paddle/nn/initializer/bilinear.py

@@ -12,8 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import numpy as np
 
+import paddle
 from paddle import _C_ops, pir
 
 from ...base import core, framework, unique_name
@@ -78,12 +81,14 @@ def __init__(self):
         """Constructor for BilinearInitializer."""
         super().__init__()
 
-    def forward(self, var, block=None):
+    def forward(
+        self, var: paddle.Tensor, block: pir.Block | None = None
+    ) -> paddle.Tensor | None:
         """Initialize the input tensor with Bilinear initialization.
 
         Args:
             var(Tensor): Tensor that needs to be initialized.
-            block(Block, optional): The block in which initialization ops
+            block(Block|None, optional): The block in which initialization ops
                    should be added. Used in static graph only, default None.
 
         Returns:

python/paddle/nn/initializer/kaiming.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 # TODO: define the initializers of Kaiming functions in neural network
 import math
 
@@ -64,11 +66,11 @@ class MSRAInitializer(Initializer):
 
     def __init__(
         self,
-        uniform=True,
-        fan_in=None,
-        seed=0,
-        negative_slope=0,
-        nonlinearity='relu',
+        uniform: bool = True,
+        fan_in: float | None = None,
+        seed: int = 0,
+        negative_slope: float = 0,
+        nonlinearity: str = 'relu',
     ):
         """Constructor for MSRAInitializer"""
         assert uniform is not None
@@ -80,12 +82,14 @@ def __init__(
         self._negative_slope = negative_slope
         self._nonlinearity = nonlinearity
 
-    def forward(self, var, block=None):
+    def forward(
+        self, var: paddle.Tensor, block: paddle.pir.Block | None = None
+    ) -> paddle.Tensor | None:
         """Initialize the input tensor with MSRA initialization.
 
         Args:
             var(Tensor): Tensor that needs to be initialized.
-            block(Block, optional): The block in which initialization ops
+            block(Block|None, optional): The block in which initialization ops
                    should be added. Used in static graph only, default None.
 
         Returns:
@@ -271,7 +275,12 @@ class KaimingNormal(MSRAInitializer):
 
     """
 
-    def __init__(self, fan_in=None, negative_slope=0.0, nonlinearity='relu'):
+    def __init__(
+        self,
+        fan_in: float | None = None,
+        negative_slope: float = 0.0,
+        nonlinearity: str = 'relu',
+    ):
         super().__init__(
             uniform=False,
             fan_in=fan_in,
@@ -317,7 +326,12 @@ class KaimingUniform(MSRAInitializer):
 
     """
 
-    def __init__(self, fan_in=None, negative_slope=0.0, nonlinearity='relu'):
+    def __init__(
+        self,
+        fan_in: float | None = None,
+        negative_slope: float = 0.0,
+        nonlinearity: str = 'relu',
+    ):
         super().__init__(
             uniform=True,
             fan_in=fan_in,

python/paddle/nn/initializer/normal.py

@@ -12,6 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
+import paddle
 from paddle import _C_ops, pir
 
 from ...base import core, framework, unique_name
@@ -36,7 +39,7 @@ class NormalInitializer(Initializer):
 
     """
 
-    def __init__(self, loc=0.0, scale=1.0, seed=0):
+    def __init__(self, loc: float = 0.0, scale: float = 1.0, seed: int = 0):
         assert loc is not None
         assert scale is not None
         assert seed is not None
@@ -45,12 +48,12 @@ def __init__(self, loc=0.0, scale=1.0, seed=0):
         self._std_dev = scale
         self._seed = seed
 
-    def forward(self, var, block=None):
+    def forward(self, var: paddle.Tensor, block: pir.Block | None = None):
         """Initialize the input tensor with Normal distribution.
 
         Args:
             var(Tensor): Tensor that needs to be initialized.
-            block(Block, optional): The block in which initialization ops
+            block(Block|None, optional): The block in which initialization ops
                    should be added. Used in static graph only, default None.
 
         Returns:
@@ -119,7 +122,7 @@ class Normal(NormalInitializer):
     Args:
         mean (float, optional): mean of the normal distribution. Default is 0.0.
         std (float, optional): standard deviation of the normal distribution. Default is 1.0.
-        name(str, optional): The default value is None. Normally there is no need for user to set this
+        name(str|None, optional): The default value is None. Normally there is no need for user to set this
             property. For more information, please refer to :ref:`api_guide_Name`. Default: None.
 
     Returns:
@@ -156,7 +159,9 @@ class Normal(NormalInitializer):
              [[ 1.0754838 -4.071067 ]]])
     """
 
-    def __init__(self, mean=0.0, std=1.0, name=None):
+    def __init__(
+        self, mean: float = 0.0, std: float = 1.0, name: str | None = None
+    ):
         assert mean is not None, 'mean should not be None'
         assert std is not None, 'std should not be None'
         super().__init__(loc=mean, scale=std, seed=0)
@@ -178,7 +183,14 @@ class TruncatedNormalInitializer(Initializer):
 
     """
 
-    def __init__(self, loc=0.0, scale=1.0, seed=0, a=-2.0, b=2.0):
+    def __init__(
+        self,
+        loc: float = 0.0,
+        scale: float = 1.0,
+        seed: int = 0,
+        a: float = -2.0,
+        b: float = 2.0,
+    ):
         assert loc is not None
         assert scale is not None
         assert seed is not None
@@ -191,12 +203,14 @@ def __init__(self, loc=0.0, scale=1.0, seed=0, a=-2.0, b=2.0):
         self._a = a
         self._b = b
 
-    def forward(self, var, block=None):
+    def forward(
+        self, var: paddle.Tensor, block: pir.Block | None = None
+    ) -> None | paddle.Tensor:
         """Initialize the input tensor with TruncatedNormal distribution.
 
         Args:
             var(Tensor): Tensor that needs to be initialized.
-            block(Block, optional): The block in which initialization ops
+            block(Block|None, optional): The block in which initialization ops
                    should be added. Used in static graph only, default None.
 
         Returns:
@@ -289,7 +303,7 @@ class TruncatedNormal(TruncatedNormalInitializer):
         std (float, optional): Standard deviation of the normal distribution. Default is :math:`1.0`.
         a (float, optional): The minimum cutoff value. Default is -2.0.
         b (float, optional): The maximum cutoff value. Default is 2.0.
-        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
+        name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
 
     Returns:
         A parameter initialized by truncated normal distribution (Gaussian distribution).
@@ -325,7 +339,14 @@ class TruncatedNormal(TruncatedNormalInitializer):
              [[-0.11380529 -3.0696259 ]]])
     """
 
-    def __init__(self, mean=0.0, std=1.0, a=-2.0, b=2.0, name=None):
+    def __init__(
+        self,
+        mean: float = 0.0,
+        std: float = 1.0,
+        a: float = -2.0,
+        b: float = 2.0,
+        name: str | None = None,
+    ):
         assert mean is not None, 'mean should not be None'
         assert std is not None, 'std should not be None'
         assert a is not None, 'a should not be None'

python/paddle/nn/initializer/uniform.py

@@ -12,6 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
+import paddle
 from paddle import _C_ops, pir
 
 from ...base import core, framework, unique_name
@@ -43,7 +46,13 @@ class UniformInitializer(Initializer):
     """
 
     def __init__(
-        self, low=-1.0, high=1.0, seed=0, diag_num=0, diag_step=0, diag_val=1.0
+        self,
+        low: float = -1.0,
+        high: float = 1.0,
+        seed: int = 0,
+        diag_num: int = 0,
+        diag_step: int = 0,
+        diag_val: float = 1.0,
     ):
         assert low is not None
         assert high is not None
@@ -62,12 +71,14 @@ def __init__(
         self._diag_step = diag_step
         self._diag_val = diag_val
 
-    def forward(self, var, block=None):
+    def forward(
+        self, var: paddle.Tensor, block: pir.Block | None = None
+    ) -> paddle.Tensor | None:
         """Initialize the input tensor with Uniform distribution.
 
         Args:
             var(Tensor): Tensor that needs to be initialized.
-            block(Block, optional): The block in which initialization ops
+            block(Block|None, optional): The block in which initialization ops
                    should be added. Used in static graph only, default None.
 
         Returns:
@@ -176,7 +187,7 @@ class Uniform(UniformInitializer):
     Args:
         low (float, optional): Lower boundary of the uniform distribution. Default is :math:`-1.0`.
         high (float, optional): Upper boundary of the uniform distribution. Default is :math:`1.0`.
-        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
+        name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
 
     Returns:
         A parameter initialized by uniform distribution.
@@ -213,7 +224,9 @@ class Uniform(UniformInitializer):
              [[-0.41843393,  0.27575102]]])
     """
 
-    def __init__(self, low=-1.0, high=1.0, name=None):
+    def __init__(
+        self, low: float = -1.0, high: float = 1.0, name: str | None = None
+    ):
         assert low is not None, 'low should not be None'
         assert high is not None, 'high should not be None'
         assert high >= low, 'high should greater or equal than low'