epfml / powersgd

Practical low-rank gradient compression for distributed optimization: https://arxiv.org/abs/1905.13727

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Random Block Algorithm seems like not definitely true

ducviet00 opened this issue · comments

size = self.rank * (m.shape[0] + m.shape[1])

what does it mean ? I think with rank > 1, it always be the number of tensor elements.

Hey! This should be different from the number of tensor elements (note the +)

Say 'm' is a matrix of shape (100, 50), then size = (100 * rank) + (rank * 50), for the size of the two components that together make up a low-rank approximation of m.

Hope this clarifies it. Let me know if you have more questions!