Remove det_singular OpInfo (#145533)

Fixes https://github.com/pytorch/pytorch/issues/93045 https://github.com/pytorch/pytorch/issues/93044

From previous discussion https://github.com/pytorch/pytorch/issues/93045#issuecomment-1477674083 the resolution is that we're okay with removing this.

Some older attempts:
- https://github.com/pytorch/pytorch/pull/102581
- https://github.com/pytorch/pytorch/pull/109249

Pull Request resolved: https://github.com/pytorch/pytorch/pull/145533
Approved by: https://github.com/lezcano, https://github.com/malfet
ghstack dependencies: #145520, #145531

test/distributed/tensor/test_dtensor_ops.py

@@ -190,7 +190,6 @@ dtensor_fails = {
     xfail("linalg.cholesky_ex"),
     xfail("linalg.cross"),
     xfail("linalg.det"),
-    xfail("linalg.det", "singular"),
     xfail("linalg.eig"),
     xfail("linalg.eigvals"),
     xfail("linalg.householder_product"),

test/functorch/test_ops.py

@@ -46,8 +46,6 @@ from torch.testing._internal.common_device_type import (
 from torch.testing._internal.common_methods_invocations import op_db
 from torch.testing._internal.common_utils import (
     is_iterable_of_tensors,
-    IS_MACOS,
-    IS_X86,
     noncontiguous_like,
     parametrize,
     run_tests,
@@ -587,11 +585,6 @@ class TestOperators(TestCase):
                 xfail("as_strided"),
                 xfail("as_strided", "partial_views"),
                 xfail("as_strided_scatter"),
-                decorate(
-                    "linalg.det",
-                    "singular",
-                    decorator=expectedFailureIf(IS_MACOS and IS_X86),
-                ),
             }
         ),
     )
@@ -877,9 +870,6 @@ class TestOperators(TestCase):
             tol1("masked.cumprod", {torch.float32: tol(atol=5e-04, rtol=5e-04)}),
             tol1("cumprod", {torch.float32: tol(atol=5e-04, rtol=5e-04)}),
             tol1("linalg.vander", {torch.float32: tol(atol=5e-04, rtol=5e-04)}),
-            tol2(
-                "linalg.det", "singular", {torch.float32: tol(atol=2e-05, rtol=2e-05)}
-            ),
         ),
     )
     def test_vjpvjp(self, device, dtype, op):
@@ -1357,11 +1347,6 @@ class TestOperators(TestCase):
         vmapjvpall_fail.union(
             {
                 xfail("as_strided_copy"),
-                decorate(
-                    "linalg.det",
-                    "singular",
-                    decorator=expectedFailureIf(IS_MACOS and IS_X86),
-                ),
             }
         ),
     )

test/inductor/test_torchinductor_opinfo.py

@@ -282,7 +282,6 @@ inductor_expected_failures_single_sample["xpu"] = {
     "inner": {f64},
     "linalg.cholesky_ex": {f64},
     "linalg.cholesky": {f64},
-    ("linalg.det", "singular"): {f64},
     "linalg.ldl_factor_ex": {f64},
     "linalg.ldl_factor": {f64},
     "linalg.ldl_solve": {f64},

test/test_mps.py

@@ -701,7 +701,6 @@ def mps_ops_modifier(ops):
         'linalg.cholesky': None,
         'linalg.cholesky_ex': None,
         'linalg.cond': None,
-        'linalg.detsingular': None,
         'linalg.det': None,
         'linalg.eigh': None,
         'linalg.eigvalsh': None,

torch/testing/_internal/opinfo/definitions/linalg.py

@@ -34,11 +34,9 @@ from torch.testing._internal.common_dtype import (
     all_types_and_complex_and,
     floating_and_complex_types,
     floating_and_complex_types_and,
-    get_all_complex_dtypes,
 )
 from torch.testing._internal.common_utils import (
     GRADCHECK_NONDET_TOL,
-    IS_MACOS,
     make_fullrank_matrices_with_distinct_singular_values,
     skipIfSlowGradcheckEnv,
     slowTest,
@@ -188,37 +186,6 @@ def sample_inputs_householder_product(op_info, device, dtype, requires_grad, **k
     yield SampleInput(make_arg((S, S - 1)), make_arg((S - 2,), low=None, high=None))
 
 
-def sample_inputs_linalg_det_singular(op_info, device, dtype, requires_grad, **kwargs):
-    make_arg = partial(make_tensor, device=device, dtype=dtype)
-
-    def make_singular_matrix_batch_base(size, rank):
-        assert size[-1] == size[-2]
-        assert rank > 0 and rank < size[-1]
-
-        n = size[-1]
-        a = make_arg(size[:-2] + (n, rank)) / 10
-        b = make_arg(size[:-2] + (rank, n)) / 10
-        x = a @ b
-        lu, pivs, _ = torch.linalg.lu_factor_ex(x)
-        p, l, u = torch.lu_unpack(lu, pivs)
-        u_diag_abs = u.diagonal(0, -2, -1).abs()
-        u_diag_abs_largest = u_diag_abs.max(dim=-1, keepdim=True).values
-        u_diag_abs_smallest_idxs = torch.topk(
-            u_diag_abs, k=(n - rank), largest=False
-        ).indices
-        u.diagonal(0, -2, -1).div_(u_diag_abs_largest)
-        u.diagonal(0, -2, -1)[..., u_diag_abs_smallest_idxs] = torch.finfo(dtype).eps
-        matrix = p @ l @ u
-
-        matrix.requires_grad_(requires_grad)
-        return matrix
-
-    for batch, size in product(((), (2,), (2, 2)), range(6)):
-        shape = batch + (size, size)
-        for rank in range(1, size):
-            yield SampleInput(make_singular_matrix_batch_base(shape, rank))
-
-
 def sample_inputs_linalg_matrix_power(op_info, device, dtype, requires_grad, **kwargs):
     make_fullrank = make_fullrank_matrices_with_distinct_singular_values
     make_arg = partial(
@@ -1201,87 +1168,6 @@ op_db: list[OpInfo] = [
         decorators=[skipCPUIfNoLapack, skipCUDAIfNoMagmaAndNoCusolver],
         check_batched_gradgrad=False,
     ),
-    OpInfo(
-        "linalg.det",
-        aten_name="linalg_det",
-        op=torch.linalg.det,
-        variant_test_name="singular",
-        aliases=("det",),
-        dtypes=floating_and_complex_types(),
-        supports_forward_ad=True,
-        supports_fwgrad_bwgrad=True,
-        check_batched_gradgrad=False,
-        sample_inputs_func=sample_inputs_linalg_det_singular,
-        decorators=[skipCPUIfNoLapack, skipCUDAIfNoMagmaAndNoCusolver],
-        skips=(
-            DecorateInfo(
-                unittest.skip("The backward may give different results"),
-                "TestCommon",
-                "test_noncontiguous_samples",
-            ),
-            DecorateInfo(
-                unittest.skip("Gradients are incorrect on macos"),
-                "TestBwdGradients",
-                "test_fn_grad",
-                device_type="cpu",
-                dtypes=(torch.float64,),
-                active_if=IS_MACOS,
-            ),
-            DecorateInfo(
-                unittest.skip("Gradients are incorrect on macos"),
-                "TestFwdGradients",
-                "test_forward_mode_AD",
-                device_type="cpu",
-                dtypes=(torch.float64,),
-                active_if=IS_MACOS,
-            ),
-            # Both Hessians are incorrect on complex inputs??
-            DecorateInfo(
-                unittest.expectedFailure,
-                "TestBwdGradients",
-                "test_fn_gradgrad",
-                dtypes=(torch.complex128,),
-            ),
-            DecorateInfo(
-                unittest.expectedFailure,
-                "TestFwdGradients",
-                "test_fn_fwgrad_bwgrad",
-                dtypes=(torch.complex128,),
-            ),
-            DecorateInfo(
-                unittest.skip("Skipped, see https://github.com//issues/84192"),
-                "TestBwdGradients",
-                "test_fn_gradgrad",
-                device_type="cuda",
-            ),
-            DecorateInfo(
-                unittest.skip("Skipped, see https://github.com//issues/84192"),
-                "TestFwdGradients",
-                "test_fn_fwgrad_bwgrad",
-                device_type="cuda",
-            ),
-            DecorateInfo(
-                unittest.skip(
-                    "Flaky on ROCm https://github.com/pytorch/pytorch/issues/93044"
-                ),
-                "TestBwdGradients",
-                "test_fn_grad",
-                device_type="cuda",
-                dtypes=get_all_complex_dtypes(),
-                active_if=TEST_WITH_ROCM,
-            ),
-            DecorateInfo(
-                unittest.skip(
-                    "Flaky on ROCm https://github.com/pytorch/pytorch/issues/93045"
-                ),
-                "TestFwdGradients",
-                "test_forward_mode_AD",
-                device_type="cuda",
-                dtypes=get_all_complex_dtypes(),
-                active_if=TEST_WITH_ROCM,
-            ),
-        ),
-    ),
     OpInfo(
         "linalg.diagonal",
         aten_name="linalg_diagonal",