Kernels assume dim <= kTensorDimensionLimit

🐛 Describe the bug

While reviewing https://github.com/pytorch/executorch/pull/7943 it surfaced that a lot of kernels and kernel utilities assume that tensors will never have more than kTensorDimensionLimit, or in some cases defining their own values for kTensorDimensionLimit and not checking that the tensor dimensions are actually smaller than that value.

Nothing in the runtime today puts a limit on the number of dimensions of a tensor. It would be legal to construct a PTE file that contains a tensor with kTensorDimensionLimit + 1 dimensions, and it would be legal for a model to define an input with kTensorDimensionLimit + dimensions.

And even if the model's tensors fit within the assumed size, a PTE file could become corrupted in a way that increases a tensor's dimensions.

If a kernel or backend encountered a tensor with an unexpectedly-large number of dimensions, they may overrun stack-resident buffers, causing subtle or not-so-subtle problems, corruption, and crashes. It's also a potential security attack vector.

Auditing existing code is one step, followed by adding regression tests to ensure that fixes stay fixed.

Fuzzing is another important tool, along with tools like @manuelcandales's kernel testing tool. These would help us uncover unknown assumptions, and help us run regression tests against the code.

cc: @swolchok

Versions

ExecuTorch 0.5.0

cc @larryliu0820 @manuelcandales

Start with runtime/core/exec_aten/util/tensor_util.h at kTensorDimensionLimit, then audit the kernels and kernel utilities for fixed-dimension assumptions and locally defined limits. Use the kernel testing tool and fuzzing where available, and add regression coverage so oversized or corrupted tensor dimensions are rejected without buffer overruns.