Add GPU implementation of QR factorization [wip]

[nicolov]

Proposed changes

Add a GPU implementation of QR factorization using the blocked Householder reflection algorithm, see:

Andrew Kerr, Dan Campbell, Mark Richards, QR Decomposition on GPUs Jan Priessnitz, GPU acceleration of matrix factorization

Here is the reference code in numpy for the algorithm.

Left todo

• [x] clean up handling of batched inputs: slice the inputs/outputs and only pass the slice to the algorithm. Temporaries need only be sized for a single input matrix.
• [ ] share some constants between the kernel and the driver function.
• [x] consider merging the two kernels to compute W.
• [ ] benchmark and optimize grid/block sizes.

Checklist

Put an x in the boxes that apply.

• [ ] I have read the CONTRIBUTING document
• [ ] I have run pre-commit run --all-files to format my code / installed pre-commit prior to committing changes
• [ ] I have added tests that prove my fix is effective or that my feature works
• [ ] I have updated the necessary documentation (if needed)