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This is one of the feature of LASER but currently the code doesn't do that. This is because we do SVD on a mxn W matrix which gives us U, S, V matrices of size m x m, mxn and nxn respectively, but then we take the top-k dimensions and multiply them back giving back a mxn low-rank approximation of W. To save memory, we should not multiple these matrices back but instead store them as 3 separate matrices of size mxk, kxk, kxn where k is the required low-rank.
The code needs to be modified so that instead of using the modified W parameter we trigger matrix multiplication with 3 separate matrices. This will cut down memory time albeit it will increase the number of sequential steps.
I am adding this as a feature request and we should be able to support this.
Hi,
Thanks for releasing this code. Does this codebase decrease the size of the model (ie file size, required VRAM)?
Thank you!
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