Implement tensor parallel SGMV #79
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This PR combines tensor parallelism with the SGMV vectorized CUDA kernel code path. In effect, this also addresses most of the pain points identified #6, as we only require one collective op per rank instead of one per adapter. The one caveat here is that the SGMV kernel doesn't work with ranks < 8, and since tensor parallelism reduces the effective rank of the tensor, this means we need to fallback on the loop implementation any time
rank / world_size < 8. We'll try to address this in future iterations by extending the SGMV kernel to support ranks < 8 (#78).