Reuse tensordot memory tricks in matmul

Currently the dask array matmul implemenation is similar to the tensordot implemenation (understandably) but lacks a clever trick that tensordot uses to avoid contractions in blockwise (which can result in large pileups of intermediate results).

https://github.com/dask/dask/blob/ff3ea6c74e8736a5823d648defcf9164f083d266/dask/array/routines.py#L290-L329

Tensordot uses only outer products in blockwise (there are no shared indices in the blockwise call) , and then uses a sum call to reduce these. Because sum uses tree reductions, this results in nicer memory usage. Matmul doesn’t use this trick.

I briefly tried replacing the blockwise call in matmul with a call to our internal dot function, but various tests failed. It would be good to do one of two things:

1. Find a way to back matmul by tensordot, so that the reduction trick is shared with a common codebase
2. Replicate the reduction trick in matmul

This was originally reported by @eric-czech and @tomwhite in https://github.com/pystatgen/sgkit/issues/375#issuecomment-731060672

[ravwojdyla]

@jsignell yes. And if I may add, I believe before the release, #7050 should be fixed/closed as well.

[jsignell]

Was this closed by #7000?