[inductor] [silent incorrectness] Multiple internal torch.rand can lead to inconsistent results with eager #151524
Description
🐛 Describe the bug
symptom: If we just use one-time torch.rand in forward function, the output is right. However, output is inconsistent when we use at least two times torch.rand. The multiple uses of internal torch.rand don't respect the fallback_random (?)
device backend: both CPP and triton
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch._inductor import config
config.fallback_random = True
torch.set_grad_enabled(False)
torch.manual_seed(0)
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self):
x = torch.rand(1)
x = torch.rand(1)
return x
model = Model()
inputs = []
def run_test(model, inputs, backend):
if backend != "eager":
model = torch.compile(model, backend=backend)
torch.manual_seed(0)
output = model()
return output
output = run_test(model, inputs, 'eager')
c_output = run_test(model, inputs, 'inductor')
print(output)
print(c_output)Error logs
tensor([0.7682])
tensor([0.4963])
Versions
nightly 20250414
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