Revert "Add Opinfos for the Tensor overload of linspace/logspace (#107958)"

pytorchmergebot · pytorchmergebot · commit e276d70451ef · 2023-09-11T17:38:04.000Z
This reverts commit 106e0a0. Reverted #107958 on behalf of https://github.com/clee2000 due to I think the newly added test test_mps.py::TestConsistencyCPU::test_output_match_logspace_tensor_overload_cpu_complex64 is broken, probably a landrace since the mergebase seems to be 21 days old https://hud.pytorch.org/pytorch/pytorch/commit/106e0a0ef19c8dad088fc1ec10d7d93d76409352 https://github.com/pytorch/pytorch/actions/runs/6149523234/job/16685849126 ([comment](#107958 (comment)))
diff --git a/torch/testing/_internal/common_methods_invocations.py b/torch/testing/_internal/common_methods_invocations.py
@@ -1004,30 +1004,6 @@ def sample_inputs_linspace(op, device, dtype, requires_grad, **kwargs):
     yield SampleInput(1, args=(3, 1))
 
 
-def sample_inputs_linspace_tensor_overload(op, device, dtype, requires_grad, **kwargs):
-    ends = (-3, 0, 1, 4, 50)
-    starts = (-2., 0, 4.3, 50)
-    nsteps = (0, 1, 50)
-    is_start_end_tensors = ((True, True), (True, False), (False, True))
-    make_arg = partial(torch.tensor, device=device, requires_grad=False)
-
-    # Extra case to replicate off-by-one issue on CUDA
-    cases = list(product(starts, ends, nsteps, is_start_end_tensors)) + [(0, 7, 50, (True, True))]
-    for start, end, nstep, (is_start_tensor, is_end_tensor) in cases:
-        if dtype == torch.uint8 and (end < 0 or start < 0):
-            continue
-
-        tensor_options = {"dtype": dtype, "device": device}
-        if is_start_tensor:
-            start = make_arg(start, dtype=torch.float32 if isinstance(start, float) else torch.int64)
-        if is_end_tensor:
-            end = make_arg(end, dtype=torch.float32 if isinstance(end, float) else torch.int64)
-
-        yield SampleInput(start, args=(end, nstep), kwargs=tensor_options)
-
-    yield SampleInput(1, args=(3, 1))
-
-
 def sample_inputs_logspace(op, device, dtype, requires_grad, **kwargs):
     ends = (-3, 0, 1.2, 2, 4)
     starts = (-2., 0, 1, 2, 4.3)
@@ -1047,35 +1023,6 @@ def sample_inputs_logspace(op, device, dtype, requires_grad, **kwargs):
     yield SampleInput(1, args=(3, 1, 2.))
 
 
-def sample_inputs_logspace_tensor_overload(op, device, dtype, requires_grad, **kwargs):
-    ends = (-3, 0, 1.2, 2, 4)
-    starts = (-2., 0, 1, 2, 4.3)
-    nsteps = (0, 1, 2, 4)
-    bases = (2., 1.1) if dtype in (torch.int8, torch.uint8) else (None, 2., 3., 1.1, 5.)
-    is_start_end_tensors = ((True, True), (True, False), (False, True))
-    make_arg = partial(torch.tensor, device=device)
-    for start, end, nstep, base, (is_start_tensor, is_end_tensor) in product(starts, ends, nsteps, bases, is_start_end_tensors):
-        if dtype == torch.uint8 and end < 0 or start < 0:
-            continue
-        if nstep == 1 and isinstance(start, float) and not (dtype.is_complex or dtype.is_floating_point):
-            # https://github.com/pytorch/pytorch/issues/82242
-            continue
-
-        tensor_options = {"dtype": dtype, "device": device}
-
-        if (is_start_tensor):
-            start = make_arg(start, dtype=torch.float32 if isinstance(start, float) else torch.int64)
-        if (is_end_tensor):
-            end = make_arg(end, dtype=torch.float32 if isinstance(end, float) else torch.int64)
-
-        if base is None:
-            yield SampleInput(start, args=(end, nstep), kwargs=tensor_options)
-        else:
-            yield SampleInput(start, args=(end, nstep, base), kwargs=tensor_options)
-
-    yield SampleInput(1, args=(3, 1, 2.))
-
-
 def sample_inputs_isclose(op, device, dtype, requires_grad, **kwargs):
     yield from sample_inputs_elementwise_binary(op, device, dtype, requires_grad, **kwargs)
 
@@ -11370,45 +11317,6 @@ def reference_flatten(input, start_dim=0, end_dim=-1):
                DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit',
                             dtypes=(torch.cfloat,), device_type="cuda"),
            )),
-    OpInfo('linspace',
-           dtypes=all_types_and_complex_and(torch.bfloat16, torch.float16),
-           is_factory_function=True,
-           supports_out=True,
-           supports_autograd=False,
-           error_inputs_func=error_inputs_linspace,
-           sample_inputs_func=sample_inputs_linspace_tensor_overload,
-           variant_test_name="tensor_overload",
-           skips=(
-               # FX failed to normalize op - add the op to the op_skip list.
-               DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'),
-               # TypeError: 'int' object is not subscriptable
-               DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_variant_consistency_eager'),
-               DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'),
-               DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view'),
-
-               # Same failure as arange: cannot find linspace in captured graph
-               DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32,)),
-
-               # UserWarning not triggered : Resized a non-empty tensor but did not warn about it.
-               DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning'),
-               # UserWarning: CUDA caching allocator reports a memory leak not verified by the driver API
-               # in __main__.TestJitCUDA.test_variant_consistency_jit_logspace_cuda_complex64!
-               # Caching allocator allocated memory was 0 and is now reported as 307200 on device 0.
-               # CUDA driver allocated memory was 1254555648 and is now 1242955776.
-               DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit',
-                            dtypes=(torch.cfloat,), device_type="cuda"),
-
-               # https://github.com/pytorch/pytorch/pull/107958#pullrequestreview-1611367760
-               DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'),
-               DecorateInfo(unittest.expectedFailure, 'TestFakeTensor', 'test_fake'),
-               DecorateInfo(unittest.expectedFailure, 'TestFakeTensor', 'test_fake_autocast'),
-               DecorateInfo(unittest.expectedFailure, 'TestMeta', 'test_meta_outplace'),
-               DecorateInfo(unittest.expectedFailure, 'TestProxyTensorOpInfo', 'test_make_fx_exhaustive'),
-               DecorateInfo(unittest.expectedFailure, 'TestProxyTensorOpInfo', 'test_make_fx_fake_exhaustive'),
-               DecorateInfo(unittest.expectedFailure, 'TestProxyTensorOpInfo', 'test_make_fx_symbolic_exhaustive'),
-               DecorateInfo(unittest.expectedFailure, "TestVmapOperatorsOpInfo", "test_vmap_exhaustive"),
-               DecorateInfo(unittest.expectedFailure, "TestVmapOperatorsOpInfo", "test_op_has_batch_rule"),
-           )),
     OpInfo('logspace',
            dtypes=all_types_and_complex_and(torch.bfloat16),
            dtypesIfCUDA=all_types_and_complex_and(torch.half, torch.bfloat16),
@@ -11441,51 +11349,6 @@ def reference_flatten(input, start_dim=0, end_dim=-1):
                DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit',
                             dtypes=(torch.cfloat,), device_type="cuda"),
            )),
-    OpInfo('logspace',
-           dtypes=all_types_and_complex_and(torch.bfloat16),
-           dtypesIfCUDA=all_types_and_complex_and(torch.half, torch.bfloat16),
-           is_factory_function=True,
-           supports_out=True,
-           supports_autograd=False,
-           error_inputs_func=error_inputs_linspace,
-           sample_inputs_func=sample_inputs_logspace_tensor_overload,
-           variant_test_name="tensor_overload",
-           skips=(
-               # FX failed to normalize op - add the op to the op_skip list.
-               DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'),
-               # TypeError: 'int' object is not subscriptable
-               DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_variant_consistency_eager'),
-               DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'),
-               DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view'),
-               # Same failure as arange: cannot find linspace in captured graph
-               DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32,)),
-
-               # UserWarning not triggered : Resized a non-empty tensor but did not warn about it.
-               DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning'),
-
-               # Off-by-one issue when casting floats to ints
-               DecorateInfo(unittest.expectedFailure, 'TestDecomp', 'test_quick',
-                            dtypes=(torch.int16, torch.int32, torch.int64), device_type="cuda"),
-               DecorateInfo(unittest.expectedFailure, 'TestDecomp', 'test_comprehensive',
-                            dtypes=(torch.int16, torch.int32, torch.int64), device_type="cuda"),
-               # UserWarning: CUDA caching allocator reports a memory leak not verified by the driver API
-               # in __main__.TestJitCUDA.test_variant_consistency_jit_logspace_cuda_complex64!
-               # Caching allocator allocated memory was 0 and is now reported as 307200 on device 0.
-               # CUDA driver allocated memory was 1254555648 and is now 1242955776.
-               DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit',
-                            dtypes=(torch.cfloat,), device_type="cuda"),
-
-               # https://github.com/pytorch/pytorch/pull/107958#pullrequestreview-1611367760
-               DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'),
-               DecorateInfo(unittest.expectedFailure, 'TestFakeTensor', 'test_fake'),
-               DecorateInfo(unittest.expectedFailure, 'TestFakeTensor', 'test_fake_autocast'),
-               DecorateInfo(unittest.expectedFailure, 'TestMeta', 'test_meta_outplace'),
-               DecorateInfo(unittest.expectedFailure, 'TestProxyTensorOpInfo', 'test_make_fx_exhaustive'),
-               DecorateInfo(unittest.expectedFailure, 'TestProxyTensorOpInfo', 'test_make_fx_fake_exhaustive'),
-               DecorateInfo(unittest.expectedFailure, 'TestProxyTensorOpInfo', 'test_make_fx_symbolic_exhaustive'),
-               DecorateInfo(unittest.expectedFailure, "TestVmapOperatorsOpInfo", "test_vmap_exhaustive"),
-               DecorateInfo(unittest.expectedFailure, "TestVmapOperatorsOpInfo", "test_op_has_batch_rule"),
-           )),
     UnaryUfuncInfo('log',
                    ref=np.log,
                    domain=(0, None),
@@ -19136,41 +18999,6 @@ def reference_flatten(input, start_dim=0, end_dim=-1):
                          device_type="cuda"),
         ),
     ),
-    PythonRefInfo(
-        "_refs.linspace",
-        torch_opinfo_name="linspace",
-        torch_opinfo_variant_name="tensor_overload",
-        skips=(
-            # TypeError: 'int' object is not subscriptable
-            DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'),
-            DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view'),
-
-            # cpu implementation is wrong on some integral types
-            # https://github.com/pytorch/pytorch/issues/81996
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref_torch_fallback',
-                         dtypes=(torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64), device_type="cpu"),
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref',
-                         dtypes=(torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64), device_type="cpu"),
-
-            # cuda implementation is off-by-one on some inputs due to precision issues
-            # https://github.com/pytorch/pytorch/issues/82230
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref_torch_fallback',
-                         dtypes=(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64),
-                         device_type="cuda"),
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref',
-                         dtypes=(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64),
-                         device_type="cuda"),
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref_executor',
-                         dtypes=(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64),
-                         device_type="cuda"),
-
-            # https://github.com/pytorch/pytorch/pull/107958#pullrequestreview-1611367760
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref_torch_fallback',
-                         dtypes=(torch.float64, torch.complex128)),
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref'),
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref_executor'),
-        ),
-    ),
     PythonRefInfo(
         "_refs.logspace",
         torch_opinfo_name="logspace",
@@ -19192,33 +19020,6 @@ def reference_flatten(input, start_dim=0, end_dim=-1):
                          device_type="cuda"),
         ),
     ),
-    PythonRefInfo(
-        "_refs.logspace",
-        torch_opinfo_name="logspace",
-        torch_opinfo_variant_name="tensor_overload",
-        skips=(
-            # TypeError: 'int' object is not subscriptable
-            DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'),
-            DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view'),
-
-            # Off-by-one issue when casting floats to ints
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref_torch_fallback',
-                         dtypes=(torch.int16, torch.int32, torch.int64),
-                         device_type="cuda"),
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref',
-                         dtypes=(torch.int16, torch.int32, torch.int64),
-                         device_type="cuda"),
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref_executor',
-                         dtypes=(torch.int16, torch.int32, torch.int64),
-                         device_type="cuda"),
-
-            # https://github.com/pytorch/pytorch/pull/107958#pullrequestreview-1611367760
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref_torch_fallback',
-                         dtypes=(torch.int16, torch.int32, torch.int64, torch.int8, torch.uint8)),
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref'),
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref_executor'),
-        ),
-    ),
     PythonRefInfo(
         "_refs.meshgrid",
         torch_opinfo_name="meshgrid",
