built a tiny pytorch clone in c after going through prof. vijay janapa reddi's mlsys book: mlsysbook.ai/tinytorch/
perfect for learning how ml frameworks work under the hood :)
woah, this got way more attention than i expected. thanks a lot.
if you are interested in the technical details, the design specs are here: https://github.com/sueszli/autograd.c/blob/main/docs/design....
if you are working on similar mlsys or compiler-style projects and think there could be overlap, please reach out: https://sueszli.github.io/
Any reason for creating a new tensor when accumulating grads over updating the existing one?
Edit: I asked this before I read the design decisions. Reasoning is, as far as I understand, that for simplificity no in-place operations hence accumulating it done on a new tensor.
yeah, exactly. it's for explicit ownership transfer. you always own what you receive, sum it, release both inputs, done. no mutation tracking, no aliasing concerns.
https://github.com/sueszli/autograd.c/blob/main/src/autograd...
i wonder whether there is a more clever way to do this without sacrificing simplicity.
Cool. But this makes me wonder. This negates most of the advantages of C. Is there a compiler-autograd "library"? Something that would compile into C specifically to execute as fast as possible on CPUs with no indirection at all.
At best you'd be restricted to the forward mode, which would still double stack pressure. If you needed reverse mode you'd need 2x stack, and the back sweep over the stack based tape would have the nearly perfectly unoptimal "grain". If you allows the higher order operators (both push out and pull back), you're going to end up with Jacobians & Hessians over nontrivial blocks. That's going to need the heap. It's still better than an unbounded loop tape, though.
We had all these issues back in 2006 when my group was implementing autograd for C++ and, later, a computer algebra system called Axiom. We knew it'd be ideal for NN; I was trying to build this out for my brother who was porting AI models to GPUs. (This did not work in 2006 for both HW & math reasons.)
We would need to mirror jax architecture more. Since the jax is sort of jit arch wise. Basically you somehow need a good way to convert computational graph to machine code while at compile time also perform a set of operations on the graph.
> Is there a compiler-autograd "library"?
Do you mean the method theano is using? Anyway, the performance bottleneck often lies in matrix multiplication or 2D-CNN (which can be reduced to matmul). Compiler autograd wouldn't save much time.
a heap-free implementation could be a really cool direction to explore. thanks!
i think you might be interested in MLIR/IREE: https://github.com/openxla/iree