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https://github.com/andresnowak/Micro-Mojograd


https://github.com/andresnowak/Micro-Mojograd

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# Micro-Mojograd

This is for now going to be a **Toy project** to learn about how do tensors work, how to make them efficient, how to do the computational graph efficiently (like joining element wise operationss or making a tensors contiguous when it used in more than one operation *use more memory but have better performance*, etc...) and the Autograd part.

When the language is a bit more mature and has complete lifetime and ownership support and Traits support, I will try to make a more complete version of this library, or maybe help another mojo project like [numojo](https://github.com/MadAlex1997/Mojo-Arrays) or [Infermo](https://github.com/TilliFe/Infermo)

And it is going to be inspired by:

- [Micrograd](https://github.com/karpathy/micrograd)
- [Pytorch](https://github.com/pytorch/pytorch)
- [Tinygrad](https://github.com/tinygrad/tinygrad)

## To-do

- [x] Tensor: using 1d memory and not using MLIR
- [x] Tensor Matmul
- [x] Tensor sum elementwise
- [x] Tensor Mul elementwise
- [x] Tensor equal operator
- [ ] Tensor broadcasting
- [ ] Fix problem where parallel version of element-wise operations (add, mul, eq, etc.) are slower than the sequential version.
- [ ] Autograd: _For now doing the autogradient part is difficult, because mojo still doesn't have complete ownership and lifetime support._