Port linalg_qr to structured
This PR simplifies the logic of `linalg.qr` using structured kernels. I
also took this chance and merged a few `copy_` operations with other
ops.
This PR removes a the previous magma implementation as is never faster
than that of cusolver and it's rather buggy. This has the side-effect
that now `qr` is not supported in Rocm. Ivan confirmed that this is
fine, given how incredibly slow was QR on Rocm anyway (we were marking
some tests as slow because of this...).
This PR also corrects the dispatch in geqrf. Before, if we called it
with a matrix for which `input.size(-2) <= 256 && batchCount(input) >= std::max<int64_t>(2, input.size(-2) / 16)` is false, and we have cublas but not cusolver, we would end up calling magma rather than cublas. This is not what the heuristic suggested.
Probaly we should benchmark these heuristics again, but that's beyond the scope of this PR.
Note. It looks like `torch.geqrf` maybe broken in MAGMA as per the
previous comment in `linalg_qr_helper_magma`. IvanYashchuk wdyt?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79054
Approved by: https://github.com/IvanYashchuk, https://github.com/ezyang
