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I wonder about this. Is ML just plugging together prebuilt blocks because that covers the entire solution space, or are we limiting the solution space to that b

I wonder about this. Is ML just plugging together prebuilt blocks because that covers the entire solution space, or are we limiting the solution space to that because that’s the only thing you can realistically do in Python?

robertlagrant 3y ago
My sense is it's more the latter, at least when exploring how to solve things, but it's hard to say. What is the only thing you can realistically do in Python?
brrrrrm 3y ago
It's worth noting that these pre-built blocks are both convenient and incredibly flexible. Before I started getting my hands dirty, it was hard to really see this. For example, here's a masked gather operation along the innermost dimension of an arbitrary rank tensor A: A[..., idx[mask]] I'd hate to have to express that in serial code and then pray a compiler could lift the correct vectorized version out of it.
- sanderjd 3y ago
  Sure, but any language can provide that kind of pre-built block in a library. (Indeed, I suspect the library we're discussing has this operation, though I didn't check.) But if you want to implement a new operation that performs well, not every language can do that. In python, people tend to drop down to C or C++ or increasingly Rust to do that. But it seems (to me) like it would be nice to be able to use a single language for both things.
  - brrrrrm 3y ago
    At least with the more common modern ML (LLMs etc) operations, no one drops down to Rust. It's really only good for IO/CPU work, but that's rarely a spot for new operations, just for data loading and high-latency network stuff. People typically use Triton or CUDA (usually the C++ flavor) to implement operations. It'd be cool if Rust had a CUDA dialect.
    - sanderjd 3y ago
      The point is that someone dropped down to implement those operations, at some point. It seems like you can target CUDA with Rust, because this project seems to be doing that?
      - brrrrrm 3y ago
        https://github.com/huggingface/candle/blob/main/candle-kernels/src/conv.cu https://github.com/huggingface/candle/blob/main/candle-kerne... That’s the C++ variant of cuda, not Rust. They’re just binding it as one might in Python. At which point, what’s the value add?
        sanderjd 3y ago
        Aha! Thanks for digging that up!
ElectricalUnion 3y ago
You're limiting your solution space because it's the only thing exposed to you in Python. You're not really doing "ML in Python", you're doing ML with those bunch of libraries that happens to expose limited bindings in Python.
PartiallyTyped 3y ago
With ANNs all you are doing is matmul, addition, log, exp, cosine, sine, and indexing. I am saying “all” because all operations we are doing are reduced to a tree of just these operations. It turns out that these are enough, and that’s good because floating point units are limited. You don’t want to waste transistors on operations that don’t provide a lot of value.

robertlagrant 3y ago

My sense is it's more the latter, at least when exploring how to solve things, but it's hard to say. What is the only thing you can realistically do in Python?

brrrrrm 3y ago

It's worth noting that these pre-built blocks are both convenient and incredibly flexible. Before I started getting my hands dirty, it was hard to really see this. For example, here's a masked gather operation along the innermost dimension of an arbitrary rank tensor A: A[..., idx[mask]] I'd hate to have to express that in serial code and then pray a compiler could lift the correct vectorized version out of it.

sanderjd 3y ago
Sure, but any language can provide that kind of pre-built block in a library. (Indeed, I suspect the library we're discussing has this operation, though I didn't check.) But if you want to implement a new operation that performs well, not every language can do that. In python, people tend to drop down to C or C++ or increasingly Rust to do that. But it seems (to me) like it would be nice to be able to use a single language for both things.
- brrrrrm 3y ago
  At least with the more common modern ML (LLMs etc) operations, no one drops down to Rust. It's really only good for IO/CPU work, but that's rarely a spot for new operations, just for data loading and high-latency network stuff. People typically use Triton or CUDA (usually the C++ flavor) to implement operations. It'd be cool if Rust had a CUDA dialect.
  - sanderjd 3y ago
    The point is that someone dropped down to implement those operations, at some point. It seems like you can target CUDA with Rust, because this project seems to be doing that?
    - brrrrrm 3y ago
      https://github.com/huggingface/candle/blob/main/candle-kernels/src/conv.cu https://github.com/huggingface/candle/blob/main/candle-kerne... That’s the C++ variant of cuda, not Rust. They’re just binding it as one might in Python. At which point, what’s the value add?
      - sanderjd 3y ago
        Aha! Thanks for digging that up!

ElectricalUnion 3y ago

You're limiting your solution space because it's the only thing exposed to you in Python. You're not really doing "ML in Python", you're doing ML with those bunch of libraries that happens to expose limited bindings in Python.

PartiallyTyped 3y ago

With ANNs all you are doing is matmul, addition, log, exp, cosine, sine, and indexing. I am saying “all” because all operations we are doing are reduced to a tree of just these operations. It turns out that these are enough, and that’s good because floating point units are limited. You don’t want to waste transistors on operations that don’t provide a lot of value.