Add sparse tensor allreduce (#22036)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22036
Implemented only on ProcessGroupGloo, as an allgather of metadata
(sparse_dim, dense_dim, and nnz), followed by an allgather of indices,
followed by an allgather of values. Once these operations have
finished, all ranks locally compute a reduction over these sparse
tensors. Works for both CPU and CUDA tensors.
This surfaced a problem with the existing assumption of only modifying
tensors that are passed at the call site, because for sparse tensors
we don't know the dimensions of the output tensors before we run the
collective. To deal with this unknown, this commit adds a `result`
function to the `c10d::ProcessGroup::Work` class that returns a vector
of tensors.
It's a bit odd to have to retrieve the result through this function
only for operations on sparse tensors. To make this work irrespective
of tensor layout, we can create a follow-up commit to make all in
place operations make their results accessible through this function
as well. This doesn't break any existing contracts but does have the
potential to add interface ambiguity.
This is a resubmission of #19146.
Reviewed By: mrshenli
Differential Revision: D15926384
fbshipit-source-id: b6ee5d81606bfa8ed63c3d63a9e307613491e0ae
