allow concatenating "hybrid" (sparse/dense) tensors along their dense dimensions #13761
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hmm... I thought my tests were passing, looks like I probably screwed something up here. Closing until I figure out how to fix it. |
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should be fixed now |
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weiyangfb
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LGTM! Please address some minor nits
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… dimensions (#13761) Summary: Follow-up to #13577 The idea is to take each values tensor, concatenate it with zeros before and after itself (along the dimension corresponding to the one we're catting the tensors along), to get a tensor corresponding to the values for that tensor in the result. Then we concatenate all of those together to get the final values tensor. (Hopefully, this will be more clear from the example in the comments). The indices are more straightforward: since we aren't concatenating along a sparse dimension, they don't change at all, so all we need to do are concatenate the indices from the different tensors together. Pull Request resolved: pytorch/pytorch#13761 Differential Revision: D13160343 Pulled By: umanwizard fbshipit-source-id: 13d7adecd369e0eebdf5bce3d90a51029b66bd1d
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Follow-up to #13577
The idea is to take each values tensor, concatenate it with zeros before and after itself (along the dimension corresponding to the one we're catting the tensors along), to get a tensor corresponding to the values for that tensor in the result. Then we concatenate all of those together to get the final values tensor. (Hopefully, this will be more clear from the example in the comments).
The indices are more straightforward: since we aren't concatenating along a sparse dimension, they don't change at all, so all we need to do are concatenate the indices from the different tensors together.