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您的环境信息

-- CANN 版本 (e.g., 5.x.x, 8.x.x):  
-- Pytorch/Torch_npu 版本 (e.g., v2.6.0, v2.9.0):
-- Python 版本 (e.g., Python3.9.x, Python3.11.x):
-- 操作系统版本 (e.g., Ubuntu 18.04, EulerOs 2.0):

静态编译会单算子执行一遍图，以获取算子JSON，但在模型包含原地修改算子时会出现副作用（模型多执行一遍，输入会改变）

🐛 请描述bug

import torch
import torch_npu
import torch.nn as nn


class InplaceModel(nn.Module):
    def forward(self, x):
        x.add_(1)
        return x * 2


def main():
    if not hasattr(torch, "npu") or not torch.npu.is_available():
        raise RuntimeError("This reproducer requires an Ascend NPU and torch_npu.")

    model = InplaceModel().npu()
    compiled = torch.compile(
        model,
        backend="npugraph_ex",
        fullgraph=True,
        dynamic=False,
        options={
            "static_kernel_compile": True,
            # Force the dump path on every run so the repro is deterministic.
            "disable_static_kernel_compile_cache": True,
        },
    )

    original = torch.arange(4, dtype=torch.float32, device="npu")
    actual_input = original.clone()
    print("input before call:", actual_input.cpu())
    expected = (original + 1) * 2
    actual = compiled(actual_input)
    torch.npu.synchronize()

    print("expected:", expected.cpu())
    print("actual:  ", actual.cpu())
    print("input after call:", actual_input.cpu())
    torch.testing.assert_close(actual_input, original + 1)
    if not torch.allclose(actual, expected):
        raise AssertionError(
            "Regression: the fx graph was executed more than once in the static-kernel dump step."
        )
    print("PASS")


if __name__ == "__main__":
    main()