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[torchfuzz] add test suite of fuzzer repros that we xfail (#164430)
i'll add the rest of the repros once in a follow up PR once we agree on a good test harness Pull Request resolved: https://github.com/pytorch/pytorch/pull/164430 Approved by: https://github.com/ezyang
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bobrenjc93
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PyTorch MergeBot
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@ -18,6 +18,7 @@ exclude_patterns = [
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'torch/_inductor/autoheuristic/artifacts/**',
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'scripts/**',
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'test/generated_type_hints_smoketest.py',
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'test/test_torchfuzz_repros.py',
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# CPython tests
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'test/dynamo/cpython/**',
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# Tests from the NumPy test suite
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176
test/test_torchfuzz_repros.py
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176
test/test_torchfuzz_repros.py
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@ -0,0 +1,176 @@
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# Owner(s): ["module: tests"]
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"""
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Fuzzer-discovered eager/compile divergence test cases.
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All tests are marked as xfail since they represent known compilation bugs.
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"""
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import pytest
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import torch
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from torch.testing._internal.common_utils import run_tests, TestCase
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class TestFuzzerCompileIssues(TestCase):
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"""Test cases for fuzzer-discovered eager/compile divergence issues."""
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def setUp(self):
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"""Configure common test settings."""
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torch._dynamo.config.capture_scalar_outputs = True
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torch._dynamo.config.capture_dynamic_output_shape_ops = True
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torch._inductor.config.emulate_precision_casts = True
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@pytest.mark.xfail(reason="Issue #164428")
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def test_fuzzer_issue_164428(self):
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torch.manual_seed(6804)
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def foo(
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arg0,
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arg1,
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arg2,
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arg3,
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arg4,
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arg5,
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arg6,
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arg7,
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arg8,
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arg9,
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arg10,
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arg11,
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arg12,
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arg13,
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arg14,
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):
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t0 = arg0 # size=(241,), stride=(4,), dtype=float16, device=cuda
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t1 = t0.contiguous() # size=(241,), stride=(1,), dtype=float16, device=cuda
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t2 = arg1 # size=(3, 241), stride=(241, 1), dtype=float16, device=cuda
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t3 = t2.max(
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dim=0
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).values # size=(241,), stride=(1,), dtype=float16, device=cuda
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t4 = arg2 # size=(13,), stride=(1,), dtype=float16, device=cuda
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t5 = arg3 # size=(90,), stride=(1,), dtype=float16, device=cuda
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t6 = arg4 # size=(1,), stride=(1,), dtype=float16, device=cuda
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t7 = arg5 # size=(26,), stride=(1,), dtype=float16, device=cuda
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t8 = arg6 # size=(111,), stride=(1,), dtype=float16, device=cuda
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t9 = torch.cat(
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[t4, t5, t6, t7, t8], dim=0
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) # size=(241,), stride=(1,), dtype=float16, device=cuda
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t10 = arg7 # size=(241,), stride=(1,), dtype=float16, device=cuda
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t11 = arg8 # size=(241,), stride=(1,), dtype=float16, device=cuda
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t12 = t9 + t10 + t11 # size=(241,), stride=(1,), dtype=float16, device=cuda
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t13 = arg9 # size=(241,), stride=(1,), dtype=float16, device=cuda
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t14 = torch.exp(t13) # size=(241,), stride=(1,), dtype=float16, device=cuda
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t15 = torch.pow(
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torch.pow(torch.pow(torch.pow(t1, t3), t12), t9), t14
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) # size=(241,), stride=(1,), dtype=float16, device=cuda
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t16 = arg10 # size=(5, 103), stride=(103, 1), dtype=float16, device=cuda
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t17 = t16.var(dim=0) # size=(103,), stride=(1,), dtype=float16, device=cuda
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t18 = arg11 # size=(68, 2), stride=(2, 1), dtype=float16, device=cuda
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t19 = t18.sum(dim=1) # size=(68,), stride=(1,), dtype=float16, device=cuda
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t20 = arg12 # size=(5, 14), stride=(14, 1), dtype=float16, device=cuda
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t21 = t20.std(dim=0) # size=(14,), stride=(1,), dtype=float16, device=cuda
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t22 = arg13 # size=(47,), stride=(3,), dtype=float16, device=cuda
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t23 = (
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t22.contiguous()
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) # size=(47,), stride=(1,), dtype=float16, device=cuda
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t24 = arg14 # size=(9,), stride=(1,), dtype=float16, device=cuda
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t25 = t24.clone()
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t25.zero_() # size=(9,), stride=(1,), dtype=float16, device=cuda
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t26 = torch.cat(
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[t17, t19, t21, t23, t25], dim=0
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) # size=(241,), stride=(1,), dtype=float16, device=cuda
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t27 = (
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((t15) / t15) / t26
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) / t26 # size=(241,), stride=(1,), dtype=float16, device=cuda
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output = t27 # output tensor
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return output
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arg0 = torch.rand(
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[241], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(241,), stride=(4,), dtype=float16, device=cuda
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arg1 = torch.rand(
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[3, 241], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(3, 241), stride=(241, 1), dtype=float16, device=cuda
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arg2 = torch.rand(
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[13], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(13,), stride=(1,), dtype=float16, device=cuda
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arg3 = torch.rand(
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[90], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(90,), stride=(1,), dtype=float16, device=cuda
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arg4 = torch.rand(
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[1], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(1,), stride=(1,), dtype=float16, device=cuda
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arg5 = torch.rand(
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[26], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(26,), stride=(1,), dtype=float16, device=cuda
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arg6 = torch.rand(
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[111], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(111,), stride=(1,), dtype=float16, device=cuda
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arg7 = torch.rand(
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[241], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(241,), stride=(1,), dtype=float16, device=cuda
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arg8 = torch.rand(
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[241], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(241,), stride=(1,), dtype=float16, device=cuda
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arg9 = torch.rand(
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[241], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(241,), stride=(1,), dtype=float16, device=cuda
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arg10 = torch.rand(
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[5, 103], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(5, 103), stride=(103, 1), dtype=float16, device=cuda
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arg11 = torch.rand(
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[68, 2], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(68, 2), stride=(2, 1), dtype=float16, device=cuda
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arg12 = torch.rand(
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[5, 14], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(5, 14), stride=(14, 1), dtype=float16, device=cuda
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arg13 = torch.rand(
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[47], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(47,), stride=(3,), dtype=float16, device=cuda
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arg14 = torch.rand(
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[9], dtype=torch.float16, device="cuda", requires_grad=True
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) # size=(9,), stride=(1,), dtype=float16, device=cuda
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out_eager = foo(
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arg0,
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arg1,
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arg2,
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arg3,
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arg4,
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arg5,
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arg6,
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arg7,
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arg8,
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arg9,
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arg10,
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arg11,
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arg12,
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arg13,
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arg14,
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)
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out_eager.sum().backward()
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print("Eager Success! ✅")
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compiled_foo = torch.compile(foo, fullgraph=True, dynamic=True)
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out_compiled = compiled_foo(
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arg0,
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arg1,
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arg2,
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arg3,
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arg4,
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arg5,
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arg6,
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arg7,
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arg8,
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arg9,
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arg10,
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arg11,
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arg12,
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arg13,
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arg14,
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)
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out_compiled.sum().backward()
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print("Compile Success! ✅")
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if __name__ == "__main__":
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run_tests()
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