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Merged
merged 4 commits into from
Aug 19, 2024
Merged

[Navi] [Inductor] Unskip Navi inductor UTs #1514

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704ccde
Unskip Navi inductor UTs
jataylo Jul 31, 2024
9248079
Remove skips in opinfo
jataylo Jul 31, 2024
299ea9f
Unskip test_cuda_repro
jataylo Jul 31, 2024
bed48d9
Fix merge conflict issue
jataylo Aug 14, 2024
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3 changes: 0 additions & 3 deletions test/inductor/test_cuda_repro.py
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Original file line number Diff line number Diff line change
Expand Up @@ -333,7 +333,6 @@ def foo(x):
out_ref.add_(2)
# self.assertEqual(out_ref, out)

@skipIfRocmArch(NAVI_ARCH)
def test_accuracy_issue1(self):
class Repro(torch.nn.Module):
def __init__(self) -> None:
Expand Down Expand Up @@ -370,7 +369,6 @@ def forward(self, start_positions: torch.Tensor, x: torch.Tensor):
assert same_two_models(mod, opt_mod, args), "Dynamo failed"

@config.patch(allow_buffer_reuse=False)
@skipIfRocmArch(NAVI_ARCH)
def test_issue103461(self):
def forward(add_1):
var_mean = torch.ops.aten.var_mean.correction(
Expand Down Expand Up @@ -869,7 +867,6 @@ def forward(self, x):
res2 = jit_func(x)
self.assertEqual(res1, res2)

@skipIfRocmArch(NAVI_ARCH)
def test_issue103481(self):
def fn(x, y):
# NOTE: 6 dimensions is important! does not fail for 5 dimensions
Expand Down
19 changes: 0 additions & 19 deletions test/inductor/test_torchinductor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1804,7 +1804,6 @@ 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":
Expand All @@ -1815,7 +1814,6 @@ 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)
Expand All @@ -1827,7 +1825,6 @@ def fn(a):

@skip_if_gpu_halide
@skipCPUIf(IS_MACOS, "fails on macos")
@skipIfRocmArch(NAVI_ARCH)
def test_multilayer_var(self):
def fn(a):
return torch.var(a)
Expand Down Expand Up @@ -2940,7 +2937,6 @@ def fn(a, b):
self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))

@skip_if_halide # only 32-bit indexing
@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")
Expand All @@ -2962,7 +2958,6 @@ def fn(a):
self.assertEqual(actual, expect)

@skip_if_gpu_halide # only 32-bit indexing
@skipIfRocmArch(NAVI_ARCH)
def test_large_broadcast_reduction(self):
if self.device == "cpu":
raise unittest.SkipTest("Fails on CPU")
Expand Down Expand Up @@ -4113,7 +4108,6 @@ 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(
Expand Down Expand Up @@ -4899,7 +4893,6 @@ def fn(x, y):
self.assertEqual(c.stride()[2], 1)

@skip_if_gpu_halide
@skipIfRocmArch(NAVI_ARCH)
def test_std(self):
def fn(x):
return (
Expand Down Expand Up @@ -4942,7 +4935,6 @@ 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}")
Expand Down Expand Up @@ -5090,7 +5082,6 @@ def fn(dist, angle):
self.common(fn, (*inp,))

@skip_if_gpu_halide # incorrect result on CUDA
@skipIfRocmArch(NAVI_ARCH)
def test_cauchy(self):
def fn(x, y):
return torch.sum(1 / (torch.unsqueeze(x, -1) - y))
Expand Down Expand Up @@ -6491,7 +6482,6 @@ 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)
Expand Down Expand Up @@ -6899,7 +6889,6 @@ 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 (
Expand Down Expand Up @@ -7652,7 +7641,6 @@ def fn(a, dim, index, b, reduce):

@skip_if_gpu_halide
# 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
Expand Down Expand Up @@ -8662,7 +8650,6 @@ 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))
Expand All @@ -8674,7 +8661,6 @@ def fn(x):
],
)

@skipIfRocmArch(NAVI_ARCH)
def test_argmax_argmin2(self):
def fn(x):
return (
Expand All @@ -8686,7 +8672,6 @@ def fn(x):

self.common(fn, (torch.randn([144, 144]),))

@skipIfRocmArch(NAVI_ARCH)
def test_argmax_argmin_with_duplicates(self):
def fn(x):
return (
Expand All @@ -8709,7 +8694,6 @@ def fn(x):
self.common(fn, (t1,))

@skip_if_halide # nan behavior
@skipIfRocmArch(NAVI_ARCH)
def test_argmax_argmin_with_nan(self):
def fn(x):
return (
Expand Down Expand Up @@ -8833,7 +8817,6 @@ def fn(x):
],
)

@skipIfRocmArch(NAVI_ARCH)
def test_tmp_not_defined_issue1(self):
def forward(
primals_3,
Expand Down Expand Up @@ -9234,7 +9217,6 @@ 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])
Expand Down Expand Up @@ -12235,7 +12217,6 @@ 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)
Expand Down
14 changes: 0 additions & 14 deletions test/inductor/test_torchinductor_opinfo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,6 @@
dtype_abbrs,
IS_MACOS,
IS_X86,
is_navi_arch,
skipCUDAMemoryLeakCheckIf,
skipIfCrossRef,
skipIfTorchDynamo,
Expand Down Expand Up @@ -204,19 +203,6 @@ def format_op(op):
# Tensors are not alike
inductor_skips["cuda"]["logcumsumexp"] = {f32}
inductor_skips["cuda"]["special.modified_bessel_i1"] = {f64}
if is_navi_arch():
inductor_skips["cuda"]["aminmax"] = {b8, f16, f32, f64, i32, i64}
inductor_skips["cuda"]["dist"] = {b8, f16, f32, f64, i32, i64}
inductor_skips["cuda"]["kron"] = {b8, f16, f32, f64, i32, i64}
inductor_skips["cuda"]["masked.std"] = {b8, f16, f32, f64, i32, i64}
inductor_skips["cuda"]["masked.var"] = {b8, f16, f32, f64, i32, i64}
inductor_skips["cuda"][("max", "reduction_no_dim")] = {b8, f16, f32, f64, i32, i64}
inductor_skips["cuda"][("min", "reduction_no_dim")] = {b8, f16, f32, f64, i32, i64}
inductor_skips["cuda"]["nn.functional.conv_transpose3d"] = {b8, f16, f32, f64, i32, i64}
inductor_skips["cuda"]["std"] = {b8, f16, f32, f64, i32, i64}
inductor_skips["cuda"]["std_mean"] = {b8, f16, f32, f64, i32, i64}
inductor_skips["cuda"]["var"] = {b8, f16, f32, f64, i32, i64}
inductor_skips["cuda"]["var_mean"] = {b8, f16, f32, f64, i32, i64}

inductor_expected_failures_single_sample = defaultdict(dict)

Expand Down