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Fix flaky test_inductor_multiple_specializations (#159264)

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Summary: This test was using do_bench, so it was flaky performance is non-deterministic.

Test Plan:
buck test 'fbcode//mode/opt' fbcode//caffe2/test/inductor:compile_subprocess -- --exact 'caffe2/test/inductor:compile_subprocess - test_inductor_multiple_specializations_cuda (caffe2.test.inductor.test_compile_subprocess.GPUTests)' --run-disabled

Rollback Plan:

Differential Revision: D79098692

Pull Request resolved: https://github.com/pytorch/pytorch/pull/159264
Approved by: https://github.com/jingsh
This commit is contained in:
Jason Ansel 2025-07-29 05:16:55 +00:00 committed by PyTorch MergeBot
parent 27ae72036d
commit 6de24135e5
1 changed files with 4 additions and 8 deletions
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12
test/inductor/test_torchinductor.py
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@ -10553,8 +10553,6 @@ def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "Sym(s53)", ar
not IS_BIG_GPU, "Skipping triton backend only since not big GPU (not enough SM)" not IS_BIG_GPU, "Skipping triton backend only since not big GPU (not enough SM)"
) )
def test_inductor_multiple_specializations(self): def test_inductor_multiple_specializations(self):
from triton.testing import do_bench
@torch.compile( @torch.compile(
options={ options={
"max_autotune": True, "max_autotune": True,
@ -10569,7 +10567,7 @@ def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "Sym(s53)", ar
m = 16 m = 16
k = 1280 k = 1280
dynamic_a = torch.randn(m, k, device=GPU_TYPE, dtype=torch.bfloat16) dynamic_a = torch.randn(m, k, device=GPU_TYPE, dtype=torch.bfloat16)
dynamic_specialized_a = torch.randn(m, k, device=GPU_TYPE, dtype=torch.bfloat16) dynamic_specialized_a = dynamic_a.clone()
b = torch.randn(k, m, device=GPU_TYPE, dtype=torch.bfloat16) b = torch.randn(k, m, device=GPU_TYPE, dtype=torch.bfloat16)
torch._dynamo.decorators.mark_dynamic( torch._dynamo.decorators.mark_dynamic(
dynamic_a, dynamic_a,
@ -10584,12 +10582,10 @@ def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "Sym(s53)", ar
b, b,
1, 1,
) )
dynamic = do_bench(lambda: inductor_matmul(dynamic_a, b)) dynamic = inductor_matmul(dynamic_a, b)
torch._dynamo.reset() torch._dynamo.reset()
dynamic_specialized = do_bench( dynamic_specialized = inductor_matmul(dynamic_specialized_a, b)
lambda: inductor_matmul(dynamic_specialized_a, b) self.assertEqual(dynamic, dynamic_specialized)
)
self.assertGreaterEqual(dynamic, dynamic_specialized)
@requires_gpu() @requires_gpu()
def test_stride_preservation_with_stride_modifying_fx_pass(self): def test_stride_preservation_with_stride_modifying_fx_pass(self):