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Oh hey, their slides mention Glow, which is the open source ML compiler I worked on at Facebook. Neat to see it getting used here :-)
by bertr4nd 5y ago
Oh hey, their slides mention Glow, which is the open source ML compiler I worked on at Facebook. Neat to see it getting used here :-)
- diskzero 5y agoCould you share more about Glow? I am also curious as to why Facebook would make this technology open source. I am happy to be able to benefit from the open source work large corporations do, but am not always clear why they do it.
- sitkack 5y agoThe main reasons are hiring, and depth and breadth of the product. Compilers are hard, device support is hard, the compiler community is small and closed source compilers quickly become weird tech islands. https://github.com/pytorch/glow https://github.com/pytorch/glow
- bertr4nd 5y agoThe link to our GitHub repo in a sibling comment probably does more justice than I could do in an HN comment, but it's essentially an ML-graph-to-machine-code compiler that focuses on accelerators. The rationale for open-sourcing here, in addition to the general recruiting/hiring benefit, is that we want vendors to target a common interface so that it's easy to make direct comparisons amongst different hardware. I'd say, though, that ML is moving somewhat away from the "graph compiler" approach. PyTorch (and users' experience with TPUs/XLA vs GPUs) has suggested that static graphs aren't desirable for usability or necessary for performance. These days, I'd say write a PyTorch device backend and a fast kernel library.
- zozbot234 5y agoFrom what I can gather about it, it's not actually a ML code compiler but more like a backend for traditional neural network dataflow graphs.