Revert "Add __init__.pyi to torch/linalg (#160750)"

This reverts commit 9a665ca3c472384e9d722bddba79e5a7680f1abd.

Reverted https://github.com/pytorch/pytorch/pull/160750 on behalf of https://github.com/jeanschmidt due to Seems that those errors are legitimate, and there is no test plan. I'll be proceeding with a revert ([comment](https://github.com/pytorch/pytorch/pull/160750#issuecomment-3246095383))

test/typing/reveal/namedtuple.py

@@ -12,5 +12,5 @@ reveal_type(t_sort)  # E: torch.return_types.sort
 t_qr = torch.linalg.qr(t)
 t_qr[0].shape == [2, 2]  # noqa: B015
 t_qr.Q.shape == [2, 2]  # noqa: B015
-# Fixed: Now properly typed as torch.return_types.qr thanks to stub file
-reveal_type(t_qr)  # E: torch.return_types.qr
+# TODO: Fixme, should be Tuple[{Tensor}, {Tensor}, fallback=torch.return_types.qr]
+reveal_type(t_qr)  # E: Any

torch/linalg/__init__.pyi

@@ -1,208 +0,0 @@
-# Stub file for torch.linalg module
-# Type annotations for PyTorch Linear Algebra functions
-
-from collections.abc import Sequence
-from typing import Literal, Optional, Union
-
-import torch.return_types
-from torch import SymInt, Tensor
-from torch._C import dtype
-from torch.types import _float, _int
-
-# Exception class
-class LinAlgError(RuntimeError): ...
-
-# Common notes dictionary
-common_notes: dict[str, str]
-
-# Core linear algebra functions
-def cross(
-    input: Tensor, other: Tensor, *, dim: int = -1, out: Optional[Tensor] = None
-) -> Tensor: ...
-def cholesky(
-    A: Tensor, *, upper: bool = False, out: Optional[Tensor] = None
-) -> Tensor: ...
-def cholesky_ex(
-    A: Tensor,
-    *,
-    upper: bool = False,
-    check_errors: bool = False,
-    out: Optional[tuple[Tensor, Tensor]] = None,
-) -> torch.return_types._lu_with_info: ...
-def cond(
-    A: Tensor,
-    p: Optional[Union[_int, _float, str]] = None,
-    *,
-    out: Optional[Tensor] = None,
-) -> Tensor: ...
-def det(A: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
-def diagonal(
-    A: Tensor, *, offset: int = 0, dim1: int = -2, dim2: int = -1
-) -> Tensor: ...
-def eig(
-    A: Tensor, *, out: Optional[tuple[Tensor, Tensor]] = None
-) -> tuple[Tensor, Tensor]: ...
-def eigh(
-    A: Tensor, UPLO: str = "L", *, out: Optional[tuple[Tensor, Tensor]] = None
-) -> tuple[Tensor, Tensor]: ...
-def eigvals(A: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
-def eigvalsh(A: Tensor, UPLO: str = "L", *, out: Optional[Tensor] = None) -> Tensor: ...
-def householder_product(
-    A: Tensor, tau: Tensor, *, out: Optional[Tensor] = None
-) -> Tensor: ...
-def inv(A: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
-def inv_ex(
-    A: Tensor,
-    *,
-    check_errors: bool = False,
-    out: Optional[tuple[Tensor, Tensor]] = None,
-) -> tuple[Tensor, Tensor]: ...
-def ldl_factor(
-    A: Tensor, *, hermitian: bool = False, out: Optional[tuple[Tensor, Tensor]] = None
-) -> tuple[Tensor, Tensor]: ...
-def ldl_factor_ex(
-    A: Tensor,
-    *,
-    hermitian: bool = False,
-    check_errors: bool = False,
-    out: Optional[tuple[Tensor, Tensor, Tensor]] = None,
-) -> tuple[Tensor, Tensor, Tensor]: ...
-def ldl_solve(
-    LD: Tensor,
-    pivots: Tensor,
-    B: Tensor,
-    *,
-    hermitian: bool = False,
-    out: Optional[Tensor] = None,
-) -> Tensor: ...
-def lstsq(
-    A: Tensor,
-    B: Tensor,
-    rcond: Optional[_float] = None,
-    *,
-    driver: Optional[str] = None,
-    out: Optional[tuple[Tensor, Tensor, Tensor, Tensor]] = None,
-) -> tuple[Tensor, Tensor, Tensor, Tensor]: ...
-def lu(
-    A: Tensor,
-    *,
-    pivot: bool = True,
-    out: Optional[tuple[Tensor, Tensor, Tensor]] = None,
-) -> tuple[Tensor, Tensor, Tensor]: ...
-def lu_factor(
-    A: Tensor, *, pivot: bool = True, out: Optional[tuple[Tensor, Tensor]] = None
-) -> tuple[Tensor, Tensor]: ...
-def lu_factor_ex(
-    A: Tensor,
-    *,
-    pivot: bool = True,
-    check_errors: bool = False,
-    out: Optional[tuple[Tensor, Tensor, Tensor]] = None,
-) -> tuple[Tensor, Tensor, Tensor]: ...
-def lu_solve(
-    LU: Tensor,
-    pivots: Tensor,
-    B: Tensor,
-    *,
-    left: bool = True,
-    adjoint: bool = False,
-    out: Optional[Tensor] = None,
-) -> Tensor: ...
-def matmul(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
-def matrix_exp(A: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ...
-def matrix_norm(
-    A: Tensor,
-    ord: Union[_int, _float, str] = "fro",
-    dim: Union[int, tuple[int, int], Sequence[Union[int, SymInt]]] = (-2, -1),
-    keepdim: bool = False,
-    *,
-    dtype: Optional[dtype] = None,
-    out: Optional[Tensor] = None,
-) -> Tensor: ...
-def matrix_power(A: Tensor, n: int, *, out: Optional[Tensor] = None) -> Tensor: ...
-def matrix_rank(
-    A: Tensor,
-    tol: Optional[_float] = None,
-    hermitian: bool = False,
-    *,
-    out: Optional[Tensor] = None,
-) -> Tensor: ...
-def multi_dot(tensors: list[Tensor], *, out: Optional[Tensor] = None) -> Tensor: ...
-def norm(
-    A: Tensor,
-    ord: Optional[Union[_int, _float, str]] = None,
-    dim: Optional[Union[int, Sequence[Union[int, SymInt]]]] = None,
-    keepdim: bool = False,
-    *,
-    dtype: Optional[dtype] = None,
-    out: Optional[Tensor] = None,
-) -> Tensor: ...
-def pinv(
-    A: Tensor,
-    rcond: Optional[_float] = None,
-    hermitian: bool = False,
-    *,
-    out: Optional[Tensor] = None,
-) -> Tensor: ...
-def qr(
-    A: Tensor,
-    mode: Literal["reduced", "complete", "r"] = "reduced",
-    *,
-    out: Optional[tuple[Tensor, Tensor]] = None,
-) -> torch.return_types.qr: ...
-def slogdet(
-    A: Tensor, *, out: Optional[tuple[Tensor, Tensor]] = None
-) -> torch.return_types.slogdet: ...
-def solve(
-    A: Tensor, B: Tensor, *, left: bool = True, out: Optional[Tensor] = None
-) -> Tensor: ...
-def solve_ex(
-    A: Tensor,
-    B: Tensor,
-    *,
-    left: bool = True,
-    check_errors: bool = False,
-    out: Optional[tuple[Tensor, Tensor]] = None,
-) -> tuple[Tensor, Tensor]: ...
-def solve_triangular(
-    A: Tensor,
-    B: Tensor,
-    *,
-    upper: bool,
-    left: bool = True,
-    unitriangular: bool = False,
-    out: Optional[Tensor] = None,
-) -> Tensor: ...
-def svd(
-    A: Tensor,
-    full_matrices: bool = True,
-    *,
-    driver: Optional[str] = None,
-    out: Optional[tuple[Tensor, Tensor, Tensor]] = None,
-) -> tuple[Tensor, Tensor, Tensor]: ...
-def svdvals(
-    A: Tensor, *, driver: Optional[str] = None, out: Optional[Tensor] = None
-) -> Tensor: ...
-def tensorinv(A: Tensor, ind: int = 2, *, out: Optional[Tensor] = None) -> Tensor: ...
-def tensorsolve(
-    A: Tensor,
-    B: Tensor,
-    dims: Optional[Sequence[int]] = None,
-    *,
-    out: Optional[Tensor] = None,
-) -> Tensor: ...
-def vander(
-    x: Tensor, N: Optional[int] = None, *, out: Optional[Tensor] = None
-) -> Tensor: ...
-def vecdot(
-    x: Tensor, y: Tensor, *, dim: int = -1, out: Optional[Tensor] = None
-) -> Tensor: ...
-def vector_norm(
-    x: Tensor,
-    ord: Optional[Union[_int, _float, complex]] = 2,
-    dim: Optional[Union[int, Sequence[Union[int, SymInt]]]] = None,
-    keepdim: bool = False,
-    *,
-    dtype: Optional[dtype] = None,
-    out: Optional[Tensor] = None,
-) -> Tensor: ...