Redo add_transform! for GPU backed Tensors to be much, much faster

[kshyatt]

Should close #508

The short version of what I've done here is rework add_transform! into two kernels with associated helper structures, one for the Abelian case (which is quite simple) and one for the more generic case. Instead of parallelizing over the blocks, which are of pretty different sizes, I parallelize over the output data in data_dst. This means a lot more annoying bookkeeping but also a much more uniform workload across the GPU and better usage of the large number of available threads. There are still probably some performance optimizations to be done but I thought this was a) already hard enough to understand and b) pretty compelling!

Here are the results for the sample script I linked in the issue above, on an AMD MI210:

GPU permute time (ms)

case	trees	elems	bytes	main	ksh/add_transform	speedup
SU2 iPEPO tensor	42	329	2.6 KiB	1.915	0.033	58×
trivial symmetry	1	9604	75.0 KiB	0.022	0.020	1.1×
SU2 jmax=1	42	672	5.3 KiB	1.964	0.033	60×
SU2 jmax=2	323	5168	40.4 KiB	13.818	0.034	406×
SU2 jmax=3	1364	21824	170.5 KiB	53.537	0.046	1164×
U1	70	2226	17.4 KiB	1.064	0.020	53×

Cost per fusion tree (GPU, µs/tree)

case	trees	main	ksh/add_transform
SU2 iPEPO tensor	42	45.6	0.79
SU2 jmax=1	42	46.8	0.79
SU2 jmax=2	323	42.8	0.105
SU2 jmax=3	1364	39.2	0.034
U1	70	15.2	0.29

So this should hopefully finally let people doing complicated stuff (e.g. anything involving SU(2)) really benefit from the GPU. All the tests passed for me locally.

I still have some lingering questions about where stuff should live. I added some new caches of GPU objects as well to avoid sending things back to the GPU that don't need to go, but maybe those should live in the main package?

[kshyatt]

I'm also running the GPU branch of PEPSKit.jl against this to see how much/if it helps

[kshyatt]

Well, the short story for now is it doesn't help much because nearly all the time in our PEPSKit runs is spent on the CPU (GPU utilization is quite low). I'll look into that separately but I still think this is a nice bit of progress :)