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[D] Looking for TF implementation of “PAY LESS ATTENTION WITH LIGHTWEIGHT AND DYNAMIC CONVOLUTIONS”

Ref. https://openreview.net/pdf?id=SkVhlh09tX and https://github.com/pytorch/fairseq/blob/master/examples/pay_less_attention_paper/README.md.

Looking for a TF implementation of LightConv and/or DynamicConv (at least the layers, don’t need full model/repro)–is anyone aware of one?

Exists well-documented state in Fairseq’s repo (https://github.com/pytorch/fairseq/blob/master/fairseq/models/lightconv.py being the core). I could re-implement in TF, but there are clearly some tricks-of-the-trade which make it a little hairy, so would rather not if could avoid (see both pytorch and paper commentary: “Implementation. Existing CUDA primitives for convolutions did not perform very well to implement LightConv and we found the following solution faster on short sequences: We copy and expand the normalized weights… We then reshape and transpose the inputs … and perform a batch matrix multiplication to get the outputs. We expect a dedicated CUDA kernel to be much more efficient.”).

Plus, last I checked, depthwise conv support has been a little wacky in tf (cf. https://github.com/tensorflow/tensorflow/issues?utf8=%E2%9C%93&q=is%3Aissue+depthwise), although perhaps those have been cleaned up?

Thanks…

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.