WIP: Add tutorials about ragged tensors.

[csukuangfj]

No description provided.

[csukuangfj]

A preview can be found at

https://csukuangfj.github.io/k2/python_tutorials/ragged/basics.html#

[danpovey]

Looks cool!

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On Sat, Sep 11, 2021 at 5:56 PM Fangjun Kuang ***@***.***> wrote: A preview can be found at https://csukuangfj.github.io/k2/python_tutorials/ragged/basics.html# — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#823 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAZFLO3YI3NPQ22GMIDTWETUBMRTVANCNFSM5D2ZJRUA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

[GNroy]

@csukuangfj Thanks for this tutorial!
Could you please clarify how ragged tensors relate to, say, PyTorch sparse matrices? They look quite similar.

[csukuangfj]

@csukuangfj Thanks for this tutorial!
Could you please clarify how ragged tensors relate to, say, PyTorch sparse matrices? They look quite similar.

TensorFlow has sparse matrices and ragged tensors, see

• https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor
• https://www.tensorflow.org/api_docs/python/tf/RaggedTensor

PyTorch also has sparse matrices and nested tensors, see

• https://github.com/pytorch/nestedtensor
• https://pytorch.org/docs/stable/sparse.html and https://github.com/Quansight-Labs/rfcs/tree/pearu/rfc0005/RFC0003-sparse-roadmap

We use the same terminology, i.e., row splits, row ids, etc, as the one used in tf.RaggedTensor, though ragged tensors in k2 were designed by @danpovey independently. We were later told that TensorFlow was using the same ideas.

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A ragged tensor with 2 axes looks similar to a sparse matrix in CSR format, but they are different.

From https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format) , a sparse matrix in CSR format has the following components:

• ROW_INDEX
• COL_INDEX
• V

The ROW_INDEX is called row_splits in k2 and V is called values in k2. That's why I said a ragged tensor in k2
shares some similarities with sparse matrices.

However, there is no COL_INDEX in ragged tensors. We are not viewing a ragged tensor as a ragged matrix.
For a ragged tensor of 2 axes, what we care about is the number of elements in each row, we don't assign a column index to entries in a row.

PyTorch's sparse matrices use COO format. But anyway, they are still matrices with row indexes and column indexes.

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Also, ragged tensors in k2 are not designed for linear algebra operations, i.e., there are no matrix-vector or matrix-matrix multiplications. Instead, they are designed for efficiently manipulating irregular data structures on GPU.

[GNroy]

Many thanks for the clarification!

A humble suggestion: you might consider including this information in the tutorial because I am hardly the last person to ask questions like this.