Unskip Navi inductor UTs

Author: jataylo

test/inductor/test_torchinductor.py

@@ -1654,7 +1654,6 @@ def fn(x):
             # make sure things also work if they aren't unrolled
             self.common(fn, (torch.randn(8, 3),))
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_multilayer_sum_low_prec(self):
         # fp16 nyi for cpu
         if self.device == "cpu":
@@ -1665,7 +1664,6 @@ def fn(a):
 
         self.common(fn, ((torch.rand((10, 3, 352, 352), dtype=torch.float16),)))
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_multilayer_prime_size(self):
         def fn(a):
             return torch.max(a), torch.sum(a)
@@ -1676,7 +1674,6 @@ def fn(a):
         self.common(fn, (sample,))
 
     @skipCPUIf(IS_MACOS, "fails on macos")
-    @skipIfRocmArch(NAVI_ARCH)
     def test_multilayer_var(self):
         def fn(a):
             return torch.var(a)
@@ -2676,7 +2673,6 @@ def fn(a, b):
 
         self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_large_tensor_reduction(self):
         if not _has_sufficient_memory(self.device, 4.5 * 1024**3):  # 4.5 GiB
             raise unittest.SkipTest("insufficient memory")
@@ -2697,7 +2693,6 @@ def fn(a):
         expect = torch.tensor(2, dtype=torch.int8, device=self.device)
         self.assertEqual(actual, expect)
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_large_broadcast_reduction(self):
         if self.device == "cpu":
             raise unittest.SkipTest("Fails on CPU")
@@ -3772,7 +3767,6 @@ def test_conv2d_channels_last(self):
             check_lowp=False,
         )
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_conv2d_backward_channels_last(self):
         def fn(grad_output, inp, weight):
             convolution_backward_8 = torch.ops.aten.convolution_backward.default(
@@ -4532,7 +4526,6 @@ def fn(x, y):
         self.assertEqual(a.stride(), c.stride())
         self.assertEqual(c.stride()[2], 1)
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_std(self):
         def fn(x):
             return (
@@ -4575,7 +4568,6 @@ def test_batch_norm_2d(self):
 
     # From yolov3
     @with_tf32_off
-    @skipIfRocmArch(NAVI_ARCH)
     def test_batch_norm_2d_2(self):
         if self.device == "cpu":
             raise unittest.SkipTest(f"requires {GPU_TYPE}")
@@ -4711,7 +4703,6 @@ def fn(x):
 
         self.common(fn, (x,))
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_cauchy(self):
         def fn(x, y):
             return torch.sum(1 / (torch.unsqueeze(x, -1) - y))
@@ -6049,7 +6040,6 @@ def fn(a):
         y = fn_compiled(x)
         self.assertTrue(y is not x)
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_l1_loss(self):
         def fn(a, b):
             return torch.nn.functional.l1_loss(a, b), torch.nn.functional.mse_loss(a, b)
@@ -6447,7 +6437,6 @@ def fn(x):
             fn, (torch.tensor([1, float("inf"), 2, float("-inf"), float("nan")]),)
         )
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_any(self):
         def fn(x):
             return (
@@ -7196,7 +7185,6 @@ def fn(a, dim, index, b, reduce):
             )
 
     # issue #1150
-    @skipIfRocmArch(NAVI_ARCH)
     def test_dense_mask_index(self):
         r"""
         There will be a little difference for reduce order between aten and inductor
@@ -8172,7 +8160,6 @@ def fn(a, b):
         b = torch.rand(2, 2, 1, 4, 1).int()
         self.common(fn, (a, b))
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_argmax_argmin1(self):
         def fn(x):
             return (aten.argmax(x), aten.argmin(x))
@@ -8184,7 +8171,6 @@ def fn(x):
             ],
         )
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_argmax_argmin2(self):
         def fn(x):
             return (
@@ -8196,7 +8182,6 @@ def fn(x):
 
         self.common(fn, (torch.randn([144, 144]),))
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_argmax_argmin_with_duplicates(self):
         def fn(x):
             return (
@@ -8218,7 +8203,6 @@ def fn(x):
         t1 = torch.randint(8, size=(1028, 1028))
         self.common(fn, (t1,))
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_argmax_argmin_with_nan(self):
         def fn(x):
             return (
@@ -8351,7 +8335,6 @@ def fn(x):
             ],
         )
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_tmp_not_defined_issue1(self):
         def forward(
             primals_3,
@@ -8746,7 +8729,6 @@ def __torch_dispatch__(self, func, types, args=(), kwargs=None):
                 else:
                     self.assertEqual(len(inps), 0)
 
-    @skipIfRocmArch(NAVI_ARCH)
     def test_dtype_mismatch_issue(self):
         def fn(x):
             attn = torch.nn.functional.pad(x, [0, 1])
@@ -11414,7 +11396,6 @@ def test_rnn_compile_safe(self):
 
     class NanCheckerTest(TestCase):
         @config.patch("nan_asserts", True)
-        @skipIfRocmArch(NAVI_ARCH)
         def test_nan_checker_pass(self):
             def f(x):
                 return torch.softmax(x, dim=-1)