Fix Numberic Numeric(#69148)

Author: co63oc

python/paddle/_typing/__init__.py

@@ -18,11 +18,11 @@
 # Basic
 from .basic import (
     NestedList as NestedList,
-    NestedNumbericSequence as NestedNumbericSequence,
+    NestedNumericSequence as NestedNumericSequence,
     NestedSequence as NestedSequence,
     NestedStructure as NestedStructure,
-    Numberic as Numberic,
-    NumbericSequence as NumbericSequence,
+    Numeric as Numeric,
+    NumericSequence as NumericSequence,
     ParamAttrLike as ParamAttrLike,
     TensorIndex as TensorIndex,
     TensorLike as TensorLike,

python/paddle/_typing/basic.py

@@ -36,8 +36,8 @@
     from paddle.regularizer import WeightDecayRegularizer
 
 
-Numberic: TypeAlias = Union[int, float, complex, np.number, "Tensor"]
-TensorLike: TypeAlias = Union[npt.NDArray[Any], "Tensor", Numberic]
+Numeric: TypeAlias = Union[int, float, complex, np.number, "Tensor"]
+TensorLike: TypeAlias = Union[npt.NDArray[Any], "Tensor", Numeric]
 _TensorIndexItem: TypeAlias = Union[
     None, bool, int, slice, "Tensor", EllipsisType
 ]
@@ -55,8 +55,8 @@
 NestedStructure = Union[
     _T, Dict[str, "NestedStructure[_T]"], Sequence["NestedStructure[_T]"]
 ]
-NumbericSequence = Sequence[Numberic]
-NestedNumbericSequence: TypeAlias = NestedSequence[Numberic]
+NumericSequence = Sequence[Numeric]
+NestedNumericSequence: TypeAlias = NestedSequence[Numeric]
 TensorOrTensors: TypeAlias = Union["Tensor", Sequence["Tensor"]]
 
 ParamAttrLike: TypeAlias = Union[

python/paddle/distributed/auto_parallel/api.py

@@ -84,7 +84,7 @@
     from paddle import Tensor
     from paddle._typing import (
         DTypeLike,
-        NestedNumbericSequence,
+        NestedNumericSequence,
         PlaceLike,
         TensorLike,
     )
@@ -210,7 +210,7 @@ def sharding_specs(self):
 
 
 def shard_tensor(
-    data: Tensor | TensorLike | NestedNumbericSequence,
+    data: Tensor | TensorLike | NestedNumericSequence,
     mesh: ProcessMesh,
     placements: list[Placement],
     dtype: DTypeLike | None = None,

python/paddle/distributed/auto_parallel/process_mesh.py

@@ -28,7 +28,7 @@
 
     import numpy.typing as npt
 
-    from paddle._typing import NestedNumbericSequence
+    from paddle._typing import NestedNumericSequence
 
     _NumpyShapeLike = Union[SupportsIndex, Sequence[SupportsIndex]]
 
@@ -109,7 +109,7 @@ class ProcessMesh(core.ProcessMesh):
 
     def __init__(
         self,
-        mesh: npt.NDArray[Any] | NestedNumbericSequence | None = None,
+        mesh: npt.NDArray[Any] | NestedNumericSequence | None = None,
         dim_names: list[str] | None = None,
         shape: _NumpyShapeLike | None = None,
         process_ids: Iterable[Any] | None = None,

python/paddle/distributed/fleet/base/util_factory.py

@@ -40,7 +40,7 @@
     import numpy.typing as npt
 
     from paddle import Tensor
-    from paddle._typing import NestedNumbericSequence
+    from paddle._typing import NestedNumericSequence
     from paddle.base.framework import Block
     from paddle.distributed.fleet.base.distributed_strategy import (
         DistributedStrategy,
@@ -80,7 +80,7 @@ def _set_file_system(self, fs_client: FS) -> None:
 
     def all_reduce(
         self,
-        input: NestedNumbericSequence | npt.NDArray[Any],
+        input: NestedNumericSequence | npt.NDArray[Any],
         mode: Literal["sum", "min", "max"] = "sum",
         comm_world: Literal["worker", "server", "all"] = "worker",
     ) -> npt.NDArray[Any] | None:

python/paddle/distribution/distribution.py

@@ -34,7 +34,7 @@
     from typing_extensions import TypeGuard
 
     from paddle import Tensor
-    from paddle._typing import NestedNumbericSequence, TensorLike
+    from paddle._typing import NestedNumericSequence, TensorLike
 
 
 class Distribution:
@@ -149,7 +149,7 @@ def _extend_shape(self, sample_shape: Sequence[int] | Tensor) -> Tensor:
         )
 
     def _validate_args(
-        self, *args: TensorLike | NestedNumbericSequence
+        self, *args: TensorLike | NestedNumericSequence
     ) -> TypeGuard[Tensor]:
         """
         Argument validation for distribution args
@@ -174,7 +174,7 @@ def _validate_args(
         return is_variable
 
     def _to_tensor(
-        self, *args: TensorLike | NestedNumbericSequence
+        self, *args: TensorLike | NestedNumericSequence
     ) -> tuple[Tensor, ...]:
         """
         Argument convert args to Tensor
@@ -308,7 +308,7 @@ def _logits_to_probs(
         )
 
     def _broadcast_all(
-        self, *args: TensorLike | NestedNumbericSequence
+        self, *args: TensorLike | NestedNumericSequence
     ) -> tuple[Tensor, ...]:
         r"""
         Returns a list where each arg is broadcasted. Scalar args are upcast to tensors

python/paddle/nn/functional/pooling.py

@@ -640,7 +640,7 @@ def max_pool1d(
         padding, 1, ceil_mode=ceil_mode
     )
 
-    # use 2d to implenment 1d should expand padding in advance.
+    # use 2d to implement 1d should expand padding in advance.
     padding = _expand_low_nd_padding(padding)
 
     if in_dynamic_or_pir_mode():
@@ -831,7 +831,7 @@ def max_unpool1d(
     else:
         stride = [1, *convert_to_list(stride, 1, 'pool_stride')]
     padding, padding_algorithm = _update_padding_nd(padding, 1)
-    # use 2d to implenment 1d should expand padding in advance.
+    # use 2d to implement 1d should expand padding in advance.
     padding = _expand_low_nd_padding(padding)
 
     if output_size is not None:

python/paddle/nn/layer/activation.py

@@ -189,7 +189,7 @@ class GELU(Layer):
         GELU(x) = 0.5 * x * (1 + erf(\frac{x}{\sqrt{2}}))
 
     Parameters:
-        approximate (bool, optional): Wether to enable approximation. Default is False.
+        approximate (bool, optional): Whether to enable approximation. Default is False.
         name (str|None, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
 
@@ -1190,7 +1190,7 @@ class ThresholdedReLU(Layer):
 
     Parameters:
         threshold (float, optional): The value of threshold for ThresholdedReLU. Default is 1.0
-        value (float, optinal): The value to replace with when x is less than threshold. Default is 0.0
+        value (float, optional): The value to replace with when x is less than threshold. Default is 0.0
         name (str|None, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
 

python/paddle/sparse/binary.py

@@ -506,7 +506,7 @@ def mask_as(x: Tensor, mask: Tensor, name: str | None = None) -> Tensor:
     Filter the input dense tensor `x` using the `indices` of the sparse matrix `mask`,
     which in turn generates a sparse matrix of the corresponding format.
     The input `x` and `mask` must have the same shape, and the sparse tensor returned has the same indices as `mask`
-    even `zero` values exist in the coresponding indices.
+    even `zero` values exist in the corresponding indices.
 
     Args:
         x (Tensor): The input tensor. It should be a DenseTensor.

python/paddle/sparse/creation.py

@@ -35,7 +35,7 @@
     import numpy.typing as npt
 
     from paddle import CPUPlace, CUDAPinnedPlace, CUDAPlace, Tensor
-    from paddle._typing import DTypeLike, NumbericSequence, ShapeLike
+    from paddle._typing import DTypeLike, NumericSequence, ShapeLike
 
 __all__ = [
     'sparse_coo_tensor',
@@ -87,7 +87,7 @@ def sparse_coo_tensor(
         | npt.NDArray[np.int_]
         | Tensor
     ),
-    values: NumbericSequence | npt.NDArray[Any] | Tensor,
+    values: NumericSequence | npt.NDArray[Any] | Tensor,
     shape: ShapeLike | None = None,
     dtype: DTypeLike | None = None,
     place: CPUPlace | CUDAPinnedPlace | CUDAPlace | str | None = None,
@@ -220,7 +220,7 @@ def _infer_dense_csr_shape(crows, cols):
 def sparse_csr_tensor(
     crows: list[int] | tuple[int, ...] | npt.NDArray[np.int_] | Tensor,
     cols: list[int] | tuple[int, ...] | npt.NDArray[np.int_] | Tensor,
-    values: NumbericSequence | npt.NDArray[Any] | Tensor,
+    values: NumericSequence | npt.NDArray[Any] | Tensor,
     shape: ShapeLike | None = None,
     dtype: DTypeLike | None = None,
     place: CPUPlace | CUDAPinnedPlace | CUDAPlace | str | None = None,