This enables performing several reductions in parallel, each smaller
than the size of the subgroup.
One potential application is flash attention with subgroup-wide matrix
multiplication and reduction combined in one kernel. The multiplication
operation requires a 2D matrix to be distributed over the lanes of the
subgroup, which then constrains the shape the following reduction can
have if we want to keep data in registers.
The new patterns break down subgroup reduce ops with vector values into
a sequence of subgroup reductions that fit the native shuffle size. The
maximum/native shuffle size is parametrized.
The overall goal is to be able to perform multi-element reductions with
a sequence of `gpu.shuffle` ops.
