def _gen_sparse(self, d, nnz, with_size):
# TODO: Consider implementing this in the CUDA case by directly
# performing the operations on the GPU. You won't be able to
# use torch.rand/torch.randn in this case because they are
# CPU-only. If you do this, you can remove the is_cuda branch
# at the end.
#
# If you do this, be sure to update assert_uncoalesced too
if isinstance(with_size, Number):
with_size = [with_size] * d
if self.is_uncoalesced:
# We want to generate a tensor with a lot of uncoalesced
# entries to stress test whether or not we handle this
# (subtle) case correctly
v_size = [nnz * 2] + list(with_size[d:])
v = torch.randn(*v_size)
r = torch.rand(d, nnz)
# Repeat the indexes, so every position shows up twice
i = torch.cat([r, r], dim=1) * \
torch.Tensor(with_size[:d]).repeat(nnz * 2, 1).transpose(0, 1)
i = i.type(torch.LongTensor)
x = torch.sparse.DoubleTensor(i, v, torch.Size(with_size))
self.assert_uncoalesced(x)
else:
# Generate a sparse tensor with d sparse dimensions; the
# rest the dimensions with_size[d:] are dense.
v_size = [nnz] + list(with_size[d:])
v = torch.randn(*v_size)
i = torch.rand(d, nnz) * \
torch.Tensor(with_size[:d]).repeat(nnz, 1).transpose(0, 1)
i = i.type(torch.LongTensor)
x = torch.sparse.DoubleTensor(i, v, torch.Size(with_size))
if self.is_cuda:
return x.cuda(), i.cuda(), v.cuda()
else:
return x, i.clone(), v.clone()
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