Support 32x32 scaling for weights in MXFP8 weight quantization kernel

[danielvegamyhre]

32x32 scaling is more performant by avoiding an extra scale calculation in the backward pass, and it's also better for accuracy by ensuring that there's no params getting underflowed in forward (and not contributing to output) but not getting underflowed in backward (and thus getting a gradient).