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Comment by eduardoleao052 | Hacker News Reader

Could not agree more hahaha! I tried to work around it building methods like “torch.add(a, b)” for operator overloading and “torch.at(index)” for slicing. But it’s definitely not as seamless as these features you proposed.

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You should do it! If you actually had a solution for operator overloading you'd really stand out from the other various JS deep learning libraries. Save me from pip and conda please :)

I can try implementing it in the future lol It would surely be a quality of life improvement. But with the current tools, I tried my best to make the syntax as similar as possible to PyTorch’s!

If you do want to add evaluation of mathematical expressions you should check out Math.js since they provide a parser among other utilities. Please make it optional though, it would be a nightmare to debug if everything was written in strings.

https://mathjs.org/docs/expressions/parsing.html

Thanks for the tip, will look into it! Yes, I think it would always be better to leave a "vanilla" option available.

Curious, why do you need to construct these as class instances, like operation = new Exp() ? Seems like a lot of extra overhead constructing those objects. Why not just have Exp contain static methods for forwards and backwards?

[edit] nevermind, I missed the cache step. Still not sure it wouldn't be more performant to centralize caches as plain objects somewhere rather than to call new() on every op...?

I centralized the entire backpropagation around the Operation objects. They store the data about the forward prop in the cache, and serve as the connections in the graphs between tensors. Each tensor has a “parent”, “child” and “operation”. These store who generated the tensor, what tensors it generated, and how it was generated (what operation). I could store the backward function inside of each tensor instead of an Operation object, but I chose the slightly more verbose option because I think it is a little more interpretable and simpler to add new operations.

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