🐛 Describe the bug

From #85583 cc @ezyang @albanD @zou3519 @gqchen @pearu @nikitaved @soulitzer @lezcano @Varal7 @mikaylagawarecki @bdhirsh @soumith @amjames

When I updated the sample_inputs function to produce deterministic inputs, we expected the fn_grad and fn_gradgrad tests to pass for as as_strided_scatter operator. however they still produce an xfail with this message:

  File "/home/srossross/pytorch/torch/autograd/gradcheck.py", line 549, in _check_analytical_jacobian_attributes
    raise GradcheckError('Backward is not reentrant, i.e., running backward with '
torch.autograd.gradcheck.GradcheckError: Backward is not reentrant, i.e., running backward with same input and grad_output multiple times gives different values, although analytical gradient matches numerical gradient.The tolerance for nondeterminism was 0.0.

NOTE: If your op relies on non-deterministic operations i.e., it is listed here:
https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html
this failure might be expected.

If you are adding a new operator, please file an issue and then use one of the
workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck.
If the test
- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
  with `nondet_tol=<tol>` as a keyword argument.
- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
  to have `gradcheck_nondet_tol=<tol>`.
- is a Module test (e.g., in common_nn.py), then modify the corresponding
  module_test entry to have `gradcheck_nondet_tol=<tol>`

Versions

Collecting environment information...
PyTorch version: 1.13.0a0+gitd62a6ca
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.24.1
Libc version: glibc-2.27

Python version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:10) [GCC 10.3.0] (64-bit runtime)
Python platform: Linux-4.15.0-147-generic-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: Quadro RTX 8000
GPU 1: Quadro RTX 8000

Nvidia driver version: 515.65.01
cuDNN version: Probably one of the following:
/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcudnn.so.7
/usr/local/cuda-10.2.89/targets/x86_64-linux/lib/libcudnn.so.7
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] functorch==0.3.0a0+9166625
[pip3] mypy==0.812
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.23.2
[pip3] torch==1.14.0a0+git08da210
[conda] functorch 0.3.0a0+9166625 dev_0
[conda] magma-cuda117 2.6.1 1 pytorch
[conda] mkl 2022.1.0 h84fe81f_915 conda-forge
[conda] mkl-include 2022.1.0 h84fe81f_915 conda-forge
[conda] numpy 1.23.2 py38h3a7f9d9_0 conda-forge
[conda] torch 1.14.0a0+git08da210 dev_0