Revert D30993855: [pytorch][PR] OpInfo: nn.functional.conv2d

Test Plan: revert-hammer

Differential Revision:
D30993855 (https://github.com/pytorch/pytorch/commit/873255c6d95342d144e9d1b633c16410844b934e)

Original commit changeset: 7402f99addb4

fbshipit-source-id: b0539daa195dc6a3739bce5c264cb2177b7721ff

diff --git a/test/test_fx_experimental.py b/test/test_fx_experimental.py
index a46501b..9238a10 100644 (file)
--- a/test/test_fx_experimental.py
+++ b/test/test_fx_experimental.py
@@ -1471,7 +1471,6 @@ class TestNormalizeOperators(JitTestCase):
             "igamma",
             "igammac",
             "index_put",
-            "nn.functional.conv2d",
             "nn.functional.dropout",
             "polygamma",
             "special.polygamma",

diff --git a/torch/testing/_internal/common_methods_invocations.py b/torch/testing/_internal/common_methods_invocations.py
index 19b3712..6331c31 100644 (file)
--- a/torch/testing/_internal/common_methods_invocations.py
+++ b/torch/testing/_internal/common_methods_invocations.py
@@ -2667,49 +2667,6 @@ def sample_inputs_conv_transpose2d(op_info, device, dtype, requires_grad, **kwar
 
     return list(generator())
 
-
-def sample_inputs_conv2d(op_info, device, dtype, requires_grad, jit_fail_sample=False, **kwargs):
-    make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
-
-    # Ordered as shapes for input, weight, bias
-    # and a dict of values of (stride, padding, groups, dilation)
-    cases: Tuple = (
-        ((1, 3, 4, 4), (3, 3, 3, 3), (3,),
-            {'stride': (2, 2), 'padding': 2, 'groups': 1}),
-        ((2, 4, 8, 8), (2, 2, 3, 3), (2,),
-            {'stride': (3, 2), 'padding': (2, 1), 'groups': 2, 'dilation': (4, 4)}),
-        ((1, 4, 5, 5), (1, 4, 2, 3), (1,),
-            {'stride': 2, 'padding': 1, 'groups': 1, 'dilation': (2, 3)}),
-        ((1, 4, 5, 5), (1, 4, 2, 3), (1,),
-            {'stride': 2, 'padding': 1, 'groups': 1, 'dilation': (2, 3)}),
-        ((1, 2, 4, 3), (4, 2, 3, 4), None,
-            {'stride': 2, 'padding': 1, 'groups': 1}),
-        ((1, 4, 5, 5), (1, 4, 2, 3), (1,),
-            {'stride': 2, 'padding': "valid"}),
-        ((1, 4, 5, 5), (1, 4, 2, 3), (1,),
-            {'stride': 1, 'padding': "same", 'dilation': 3}),
-        # Below are the group related samples from common_nn.py
-        ((2, 4, 6, 6), (4, 1, 3, 3), (4,), {'groups': 4}),
-        ((2, 4, 6, 6), (8, 1, 3, 3), (8,), {'groups': 4}),
-        ((2, 4, 6, 6), (8, 1, 3, 3), None, {'groups': 4}),
-        ((2, 4, 6, 6), (4, 1, 3, 3), (4,), {'groups': 4, 'stride': (3, 2)}),
-        ((2, 4, 6, 6), (4, 1, 3, 3), (4,), {'groups': 4, 'padding': (1, 1)}),
-        ((2, 4, 5, 5), (4, 1, 2, 2), (4,), {'groups': 4, 'dilation': (2, 2)}),
-        ((2, 4, 6, 5), (6, 2, 3, 2), (6,), {'groups': 2}),
-        # With defaults
-        ((1, 4, 5, 5), (3, 4, 3, 3), None, {}),
-    )
-
-    def generator():
-        for input_shape, weight, bias, kwargs in cases:
-            yield SampleInput(make_arg(input_shape), args=(
-                make_arg(weight),
-                make_arg(bias) if bias is not None else bias
-            ), kwargs=kwargs)
-
-    return list(generator())
-
-
 def sample_inputs_layer_norm(opinfo, device, dtype, requires_grad, **kwargs):
     make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
 
@@ -7593,21 +7550,6 @@ op_db: List[OpInfo] = [
                DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'),
            ),
            supports_out=False,),
-    # Added 2 entries for conv2d
-    # One runs with cudnn and other without.
-    OpInfo('nn.functional.conv2d',
-           aliases=('conv2d',),
-           aten_name='conv2d',
-           dtypes=floating_types_and(torch.int64),
-           dtypesIfCUDA=floating_types_and(torch.float16, *[torch.bfloat16] if CUDA11OrLater else []),
-           sample_inputs_func=partial(sample_inputs_conv2d),
-           gradcheck_nondet_tol=GRADCHECK_NONDET_TOL if CUDA11OrLater else 0.,
-           skips=(
-               # RuntimeError: !lhs.isAliasOf(rhs)INTERNAL ASSERT FAILED at
-               # "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":103, please report a bug to PyTorch.
-               DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'),
-           ),
-           supports_out=False,),
     OpInfo('nn.functional.layer_norm',
            aten_name='layer_norm',
            aliases=('layer_norm',),