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[torchfuzz] remove fixed xfail (#165116)

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Pull Request resolved: https://github.com/pytorch/pytorch/pull/165116
Approved by: https://github.com/PaulZhang12
This commit is contained in:
bobrenjc93
2025-10-09 20:00:34 -07:00
committed by PyTorch MergeBot
parent 253fd765bd
commit 3f27100d3e
1 changed files with 0 additions and 75 deletions
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test/test_torchfuzz_repros.py
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@ -428,81 +428,6 @@ class TestFuzzerCompileIssues(TestCase):
out_compiled.sum().backward()
print("Compile Success! ✅")
@pytest.mark.xfail(reason="Issue #163876")
def test_fuzzer_issue_163876(self):
torch.manual_seed(0)
def foo(arg0, arg1, arg2, arg3, arg4, arg5, arg6, arg7):
t0 = arg0 # size=(29, 50, 32, 5), stride=(46400, 1600, 32, 1), dtype=float16, device=cuda
t1 = arg1 # size=(29, 50, 32, 5), stride=(46400, 1600, 32, 1), dtype=float16, device=cuda
t2 = arg2 # size=(29, 50, 32, 5), stride=(46400, 1600, 32, 1), dtype=float16, device=cuda
t3 = torch.nn.functional.scaled_dot_product_attention(
t0, t1, t2
) # size=(29, 50, 32, 5), stride=(8000, 160, 5, 1), dtype=float16, device=cuda
t4 = (
t3.min(dim=3).values
) # size=(29, 50, 32), stride=(1600, 32, 1), dtype=float16, device=cuda
t5 = arg3 # size=(3, 10, 4640), stride=(46400, 4640, 1), dtype=float16, device=cuda
t6 = t5.var(
dim=0
) # size=(10, 4640), stride=(4640, 1), dtype=float16, device=cuda
t7 = t6.reshape(
(29, 50, 32)
) # size=(29, 50, 32), stride=(1600, 32, 1), dtype=float16, device=cuda
t8 = arg4 # size=(29, 50, 32), stride=(1600, 32, 1), dtype=float16, device=cuda
t9 = arg5 # size=(32, 50, 29), stride=(1, 32, 1600), dtype=float16, device=cuda
t10 = t9.clone()
t10.zero_() # size=(32, 50, 29), stride=(1, 32, 1600), dtype=float16, device=cuda
t11 = t10.transpose(
0, 2
) # size=(29, 50, 32), stride=(1600, 32, 1), dtype=float16, device=cuda
t12 = torch.pow(
torch.pow(t4, t8), t11
) # size=(29, 50, 32), stride=(1600, 32, 1), dtype=float16, device=cuda
t13 = arg6 # size=(29, 50, 32), stride=(1450, 50, 1), dtype=float16, device=cuda
t15 = torch.nn.functional.layer_norm(
t13, (32,)
) # size=(29, 50, 32), stride=(1600, 32, 1), dtype=float16, device=cuda
t16 = (
(t12) / t15
) # size=(29, 50, 32), stride=(1600, 32, 1), dtype=float16, device=cuda
t17 = (
((((t4) - t7) - t16) - t11) - t16
) # size=(29, 50, 32), stride=(1600, 32, 1), dtype=float16, device=cuda
output = t17
return output
arg0 = torch.rand(
[29, 50, 32, 5], dtype=torch.float16, device="cuda", requires_grad=True
)
arg1 = torch.rand(
[29, 50, 32, 5], dtype=torch.float16, device="cuda", requires_grad=True
)
arg2 = torch.rand(
[29, 50, 32, 5], dtype=torch.float16, device="cuda", requires_grad=True
)
arg3 = torch.rand(
[3, 10, 4640], dtype=torch.float16, device="cuda", requires_grad=True
)
arg4 = torch.rand(
[29, 50, 32], dtype=torch.float16, device="cuda", requires_grad=True
)
arg5 = torch.rand(
[32, 50, 29], dtype=torch.float16, device="cuda", requires_grad=True
)
arg6 = torch.rand(
[29, 50, 32], dtype=torch.float16, device="cuda", requires_grad=True
)
arg7 = torch.randint(0, 1000, [1], dtype=torch.int64, device="cuda")
out_eager = foo(arg0, arg1, arg2, arg3, arg4, arg5, arg6, arg7)
out_eager.sum().backward()
print("Eager Success! ✅")
compiled_foo = torch.compile(foo, fullgraph=True, dynamic=True)
out_compiled = compiled_foo(arg0, arg1, arg2, arg3, arg4, arg5, arg6, arg7)
out_compiled.sum().backward()
print("Compile Success! ✅")
@pytest.mark.xfail(reason="Issue #163877")
def test_fuzzer_issue_163877(self):
torch.manual_seed(0)