rocm

I see comments suggesting adding this to understand how loops are being handled by numba, and in the their own FAQ (https://numba.pydata.org/numba-doc/latest/user/faq.html)

from llvmlite import binding as llvm
llvm.set_option('','--debug-only=loop-vectorize')

You would then create your njit function and run it, and I believe the idea is that it prints debug information about whether

Please go to https://github.com/deepmodeling/deepmd-kit/discussions to ask questions. Before asking questions, you can

• search the previous discussions
• check the document, especially training parameters

Please provide necessary information including the version of software and installation way,

Bug summary
There is evidence that sub_group::get_group_id() does not return the same value as threadIdx.x / warpSize (assuming 1D kernel), as expected on CUDA. We should check the implementation of this function. Our implementation of this function performs bit manipulation magic, presumably the optimization went to far...

To Reproduce
Compare sub_group{}.get_group_id() or `sub

Since arrays may not actually be modified by a given operation, or might only be partially modified (or the user has some other way to ensure correctness).