[Distributed] fix matmul error for Distribute-stable ci (#74989)

* trigger ci

* fix matmul with #74945

* disable matmul sinking into c++

* use Decorator to alias

* add unit test

* comments

Author: DanielSun11

paddle/fluid/pir/dialect/op_generator/python_c_gen.py

@@ -894,3 +894,5 @@ def ParseArguments():
         python_c_def_h_file,
         python_c_def_cc_file,
     )
+
+#

paddle/phi/ops/yaml/python_api_info.yaml

@@ -8,10 +8,10 @@
   args_alias :
     use_default_mapping : True
 
-- op : matmul
-  name : [paddle.matmul,paddle.Tensor.matmul]
-  args_alias :
-    use_default_mapping : True
+# - op : matmul
+#   name : [paddle.matmul,paddle.Tensor.matmul]
+#   args_alias :
+#     use_default_mapping : True
 - op : multiply
   name : [paddle.multiply,paddle.Tensor.multiply]
   args_alias :

python/paddle/_paddle_docs.py

@@ -522,111 +522,111 @@ def argmin(
     """,
 )
 
-add_doc_and_signature(
-    "matmul",
-    """
-    Applies matrix multiplication to two tensors. `matmul` follows
-    the complete broadcast rules,
-    and its behavior is consistent with `np.matmul`.
-
-    Currently, the input tensors' number of dimensions can be any, `matmul` can be used to
-    achieve the `dot`, `matmul` and `batchmatmul`.
-
-    The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
-    flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
-
-    - If a transpose flag is specified, the last two dimensions of the tensor
-      are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor
-      is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas
-      for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.
-
-    The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:
-
-    - If both tensors are 1-dimensional, the dot product result is obtained.
-
-    - If both tensors are 2-dimensional, the matrix-matrix product is obtained.
-
-    - If the `x` is 1-dimensional and the `y` is 2-dimensional,
-      a `1` is prepended to its dimension in order to conduct the matrix multiply.
-      After the matrix multiply, the prepended dimension is removed.
-
-    - If the `x` is 2-dimensional and `y` is 1-dimensional,
-      the matrix-vector product is obtained.
-
-    - If both arguments are at least 1-dimensional and at least one argument
-      is N-dimensional (where N > 2), then a batched matrix multiply is obtained.
-      If the first argument is 1-dimensional, a 1 is prepended to its dimension
-      in order to conduct the batched matrix multiply and removed after.
-      If the second argument is 1-dimensional, a 1 is appended to its
-      dimension for the purpose of the batched matrix multiple and removed after.
-      The non-matrix (exclude the last two dimensions) dimensions are
-      broadcasted according the broadcast rule.
-      For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,
-      out will be a (j, k, n, p) tensor.
-
-    Args:
-        x (Tensor): The input tensor which is a Tensor.
-        y (Tensor): The input tensor which is a Tensor.
-        transpose_x (bool, optional): Whether to transpose :math:`x` before multiplication. Default is False.
-        transpose_y (bool, optional): Whether to transpose :math:`y` before multiplication. Default is False.
-        name (str|None, optional): If set None, the layer will be named automatically. For more information, please refer to :ref:`api_guide_Name`. Default is None.
-        out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None.
-
-    Returns:
-        Tensor: The output Tensor.
-
-    Examples:
-
-        .. code-block:: python
-
-            >>> import paddle
-
-            >>> # vector * vector
-            >>> x = paddle.rand([10])
-            >>> y = paddle.rand([10])
-            >>> z = paddle.matmul(x, y)
-            >>> print(z.shape)
-            []
-
-            >>> # matrix * vector
-            >>> x = paddle.rand([10, 5])
-            >>> y = paddle.rand([5])
-            >>> z = paddle.matmul(x, y)
-            >>> print(z.shape)
-            [10]
-
-            >>> # batched matrix * broadcasted vector
-            >>> x = paddle.rand([10, 5, 2])
-            >>> y = paddle.rand([2])
-            >>> z = paddle.matmul(x, y)
-            >>> print(z.shape)
-            [10, 5]
-
-            >>> # batched matrix * batched matrix
-            >>> x = paddle.rand([10, 5, 2])
-            >>> y = paddle.rand([10, 2, 5])
-            >>> z = paddle.matmul(x, y)
-            >>> print(z.shape)
-            [10, 5, 5]
-
-            >>> # batched matrix * broadcasted matrix
-            >>> x = paddle.rand([10, 1, 5, 2])
-            >>> y = paddle.rand([1, 3, 2, 5])
-            >>> z = paddle.matmul(x, y)
-            >>> print(z.shape)
-            [10, 3, 5, 5]
-
-    """,
-    """    def matmul(
-    x: Tensor,
-    y: Tensor,
-    transpose_x: bool = False,
-    transpose_y: bool = False,
-    name: str | None = None,
-    *,
-    out: Tensor | None = None,
-) -> Tensor""",
-)
+# add_doc_and_signature(
+#     "matmul",
+#     """
+#     Applies matrix multiplication to two tensors. `matmul` follows
+#     the complete broadcast rules,
+#     and its behavior is consistent with `np.matmul`.
+
+#     Currently, the input tensors' number of dimensions can be any, `matmul` can be used to
+#     achieve the `dot`, `matmul` and `batchmatmul`.
+
+#     The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
+#     flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
+
+#     - If a transpose flag is specified, the last two dimensions of the tensor
+#       are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor
+#       is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas
+#       for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.
+
+#     The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:
+
+#     - If both tensors are 1-dimensional, the dot product result is obtained.
+
+#     - If both tensors are 2-dimensional, the matrix-matrix product is obtained.
+
+#     - If the `x` is 1-dimensional and the `y` is 2-dimensional,
+#       a `1` is prepended to its dimension in order to conduct the matrix multiply.
+#       After the matrix multiply, the prepended dimension is removed.
+
+#     - If the `x` is 2-dimensional and `y` is 1-dimensional,
+#       the matrix-vector product is obtained.
+
+#     - If both arguments are at least 1-dimensional and at least one argument
+#       is N-dimensional (where N > 2), then a batched matrix multiply is obtained.
+#       If the first argument is 1-dimensional, a 1 is prepended to its dimension
+#       in order to conduct the batched matrix multiply and removed after.
+#       If the second argument is 1-dimensional, a 1 is appended to its
+#       dimension for the purpose of the batched matrix multiple and removed after.
+#       The non-matrix (exclude the last two dimensions) dimensions are
+#       broadcasted according the broadcast rule.
+#       For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,
+#       out will be a (j, k, n, p) tensor.
+
+#     Args:
+#         x (Tensor): The input tensor which is a Tensor.
+#         y (Tensor): The input tensor which is a Tensor.
+#         transpose_x (bool, optional): Whether to transpose :math:`x` before multiplication. Default is False.
+#         transpose_y (bool, optional): Whether to transpose :math:`y` before multiplication. Default is False.
+#         name (str|None, optional): If set None, the layer will be named automatically. For more information, please refer to :ref:`api_guide_Name`. Default is None.
+#         out (Tensor, optional): The output tensor. If set, the result will be stored in this tensor. Default is None.
+
+#     Returns:
+#         Tensor: The output Tensor.
+
+#     Examples:
+
+#         .. code-block:: python
+
+#             >>> import paddle
+
+#             >>> # vector * vector
+#             >>> x = paddle.rand([10])
+#             >>> y = paddle.rand([10])
+#             >>> z = paddle.matmul(x, y)
+#             >>> print(z.shape)
+#             []
+
+#             >>> # matrix * vector
+#             >>> x = paddle.rand([10, 5])
+#             >>> y = paddle.rand([5])
+#             >>> z = paddle.matmul(x, y)
+#             >>> print(z.shape)
+#             [10]
+
+#             >>> # batched matrix * broadcasted vector
+#             >>> x = paddle.rand([10, 5, 2])
+#             >>> y = paddle.rand([2])
+#             >>> z = paddle.matmul(x, y)
+#             >>> print(z.shape)
+#             [10, 5]
+
+#             >>> # batched matrix * batched matrix
+#             >>> x = paddle.rand([10, 5, 2])
+#             >>> y = paddle.rand([10, 2, 5])
+#             >>> z = paddle.matmul(x, y)
+#             >>> print(z.shape)
+#             [10, 5, 5]
+
+#             >>> # batched matrix * broadcasted matrix
+#             >>> x = paddle.rand([10, 1, 5, 2])
+#             >>> y = paddle.rand([1, 3, 2, 5])
+#             >>> z = paddle.matmul(x, y)
+#             >>> print(z.shape)
+#             [10, 3, 5, 5]
+
+#     """,
+#     """    def matmul(
+#     x: Tensor,
+#     y: Tensor,
+#     transpose_x: bool = False,
+#     transpose_y: bool = False,
+#     name: str | None = None,
+#     *,
+#     out: Tensor | None = None,
+# ) -> Tensor""",
+# )
 add_doc_and_signature(
     "multiply",
     """