Perf: Replace full SVD with torch.svd_lowrank for acceleration #115
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The code used torch.linalg.svd to compute the full singular value decomposition of the weight matrix. This proved to be computationally expensive and memory-intensive, especially since we only utilize the top rank components.
This PR replaces it with torch.svd_lowrank, utilizing a randomized algorithm to approximate the dominant singular values efficiently.
Changes:
Switched to torch.svd_lowrank for faster decomposition.
Set niter=4 and q=10 (oversampling) to achieve an optimal balance between speed and accuracy.
Adjusted tensor transposition logic to align with svd_lowrank's output format (which returns v instead of Vh).
Performance Impact In my local environment, this optimization significantly eliminates a major bottleneck during quantization:
Low-rank creation latency: Dropped from ~5s to ~100ms.
Total Runtime: The overall calibration and quantization process is approximately 6x faster.
This PR supersedes #111. I've resubmitted a clean version to resolve the conflicts caused by recent breaking changes in the main branch. Apologies for the inconvenience.