[testing] assert no duplicate in method_tests for an OpInfo entry (#53492)

kshitij12345 · facebook-github-bot · commit df7c0a06d669 · 2021-03-14T21:58:39.000-07:00
Summary: Assert no duplicate in method_tests for an OpInfo entry Pull Request resolved: #53492 Reviewed By: izdeby Differential Revision: D26882441 Pulled By: mruberry fbshipit-source-id: f0631ea2b46b74285c76365c679bd45abc917d63
diff --git a/test/test_ops.py b/test/test_ops.py
@@ -8,9 +8,9 @@
 from torch.testing._internal.common_utils import \
     (TestCase, run_tests, IS_SANDCASTLE, clone_input_helper, make_tensor)
 from torch.testing._internal.common_methods_invocations import \
-    (op_db)
+    (op_db, method_tests)
 from torch.testing._internal.common_device_type import \
-    (instantiate_device_type_tests, ops, onlyOnCPUAndCUDA, skipCUDAIfRocm, OpDTypes)
+    (instantiate_device_type_tests, ops, onlyCPU, onlyOnCPUAndCUDA, skipCUDAIfRocm, OpDTypes)
 from torch.testing._internal.common_jit import JitCommonTestCase, check_against_reference
 from torch.autograd.gradcheck import gradcheck, gradgradcheck
 
@@ -19,6 +19,9 @@
 from torch.testing._internal.jit_utils import disable_autodiff_subgraph_inlining
 
 
+# Get names of all the operators which have entry in `method_tests` (legacy testing infra)
+method_tested_operators = set(map(lambda test_details: test_details[0], method_tests()))
+
 # Tests that apply to all operators
 
 class TestOpInfo(TestCase):
@@ -56,6 +59,12 @@ def test_supported_dtypes(self, device, dtype, op):
         sample = samples[0]
         op(*sample.input, *sample.args, **sample.kwargs)
 
+    # Verifies that ops do not have an entry in
+    # `method_tests` (legacy testing infra).
+    @onlyCPU
+    @ops(op_db, allowed_dtypes=[torch.float32])
+    def test_duplicate_method_tests(self, device, dtype, op):
+        self.assertFalse(op.name in method_tested_operators)
 
 # gradcheck requires double precision
 _gradcheck_ops = partial(ops, dtypes=OpDTypes.supported,
diff --git a/torch/testing/_internal/common_methods_invocations.py b/torch/testing/_internal/common_methods_invocations.py
@@ -1574,6 +1574,7 @@ def _make_tensor_helper(shape, low=None, high=None):
            skips=(
                SkipInfo('TestCommon', 'test_variant_consistency_jit',
                         dtypes=[torch.bfloat16, torch.float16, torch.cfloat, torch.cdouble]),
+               SkipInfo('TestOpInfo', 'test_duplicate_method_tests'),
                # addmm does not correctly warn when resizing out= inputs
                SkipInfo('TestCommon', 'test_out')),
            sample_inputs_func=sample_inputs_addmm),
@@ -1857,6 +1858,7 @@ def _make_tensor_helper(shape, low=None, high=None):
                         dtypes=[torch.bool]),
                # cumsum does not correctly warn when resizing out= inputs
                SkipInfo('TestCommon', 'test_out'),
+               SkipInfo('TestOpInfo', 'test_duplicate_method_tests'),
            ),
            sample_inputs_func=sample_inputs_cumsum),
     UnaryUfuncInfo('deg2rad',
@@ -1881,21 +1883,25 @@ def _make_tensor_helper(shape, low=None, high=None):
            variant_test_name='no_rounding_mode',
            dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16),
            sample_inputs_func=sample_inputs_div,
+           skips=(SkipInfo('TestOpInfo', 'test_duplicate_method_tests'),),
            assert_autodiffed=True),
     OpInfo('div',
            variant_test_name='true_rounding',
            dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16),
            sample_inputs_func=partial(sample_inputs_div, rounding_mode='true'),
+           skips=(SkipInfo('TestOpInfo', 'test_duplicate_method_tests'),),
            assert_autodiffed=True),
     OpInfo('div',
            variant_test_name='trunc_rounding',
            dtypes=all_types_and(torch.half, torch.bfloat16),
            sample_inputs_func=partial(sample_inputs_div, rounding_mode='trunc'),
+           skips=(SkipInfo('TestOpInfo', 'test_duplicate_method_tests'),),
            assert_autodiffed=True),
     OpInfo('div',
            variant_test_name='floor_rounding',
            dtypes=all_types_and(torch.half, torch.bfloat16),
            sample_inputs_func=partial(sample_inputs_div, rounding_mode='floor'),
+           skips=(SkipInfo('TestOpInfo', 'test_duplicate_method_tests'),),
            assert_autodiffed=True),
     UnaryUfuncInfo('exp',
                    ref=np_unary_ufunc_integer_promotion_wrapper(np.exp),
@@ -2050,6 +2056,10 @@ def _make_tensor_helper(shape, low=None, high=None):
            dtypes=all_types_and(torch.half, torch.bfloat16),
            sample_inputs_func=sample_inputs_floor_divide,
            decorators=[_wrap_warn_once("floor_divide is deprecated, and will be removed")],
+           skips=(
+               # `test_duplicate_method_tests` doesn't raise any warning, as it doesn't actually
+               # call the operator.
+               SkipInfo('TestOpInfo', 'test_duplicate_method_tests'),),
            supports_autograd=False,
            ),
     OpInfo('inverse',
@@ -2241,6 +2251,7 @@ def _make_tensor_helper(shape, low=None, high=None):
                         device_type='cuda', dtypes=[torch.complex128]),
                SkipInfo('TestCommon', 'test_variant_consistency_jit',
                         dtypes=[torch.cfloat, torch.cdouble]),
+               SkipInfo('TestOpInfo', 'test_duplicate_method_tests'),
            ),
            supports_out=False),
     OpInfo('masked_select',
@@ -2494,6 +2505,7 @@ def _make_tensor_helper(shape, low=None, high=None):
            dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16),
            supports_out=False,
            test_inplace_grad=False,
+           skips=(SkipInfo('TestOpInfo', 'test_duplicate_method_tests'),),
            sample_inputs_func=sample_inputs_tensor_split,),
     OpInfo('triangular_solve',
            op=torch.triangular_solve,
@@ -2723,6 +2735,7 @@ def _make_tensor_helper(shape, low=None, high=None):
     OpInfo('index_fill',
            dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16),
            test_inplace_grad=False,
+           skips=(SkipInfo('TestOpInfo', 'test_duplicate_method_tests'),),
            supports_out=False,
            sample_inputs_func=sample_inputs_index_fill),
     OpInfo('index_select',
