tensor_annotations
dex-lang
| tensor_annotations | dex-lang | |
|---|---|---|
| 2 | 25 | |
| 158 | 1,538 | |
| -0.6% | 0.5% | |
| 5.8 | 8.8 | |
| 10 months ago | 6 days ago | |
| Python | Haskell | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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tensor_annotations
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Not really an answer to your question, but there are Python packages that try to solve the problem of tensor shapes that you mentioned, e.g. https://github.com/patrick-kidger/torchtyping or https://github.com/deepmind/tensor_annotations
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Matrix Multiplication Inches Closer to Mythic Goal
I've explored this space quite a bit. In my view, static checking should be the goal.
https://github.com/deepmind/tensor_annotations and tsastanley seem to be the most far along. I've developed a mypy plugin that does similarly off of the "Named Tensor" dynamic feature (which isn't well supported yet), but haven't released it yet.
dex-lang
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Thinking in an Array Language
A really nice approach to this I've seen recently is Google's research on [Dex](https://github.com/google-research/dex-lang).
- Function Composition in Programming Languages – Conor Hoekstra – CppNorth 2023 [video]
- Dex Lang: Research language for array processing in the Haskell/ML family
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Dex
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[D] PyTorch 2.0 Announcement
Have you tried Dex? https://github.com/google-research/dex-lang It is in a relatively early stage, but it is exploring some interesting parts of the design space.
- Mangle, a programming language for deductive database programming
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Looking for languages that combine algebraic effects with parallel execution
I think [Dex](https://github.com/google-research/dex-lang) might be along the lines of what you're looking for, although its focus is on SIMD GPU-style parallelism rather than thread-level parallelism.
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“Why I still recommend Julia”
Dex proves indexing correctness without a full dependent type system, including loops.
See: https://github.com/google-research/dex-lang/pull/969
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Haskell for Artificial Intelligence?
In case you want to see one research direction that's combining practical machine learning and functional programming, one of the authors of JAX (and the main author of its predecessor, Autograd) is writing Dex (https://github.com/google-research/dex-lang), a functional language for array processing. The compiler itself is written in Haskell. JAX is one of the most popular libraries for doing a lot of machine learning these days, along with Tensorflow and PyTorch. You might also want to see the bug in the JAX repo about adding Haskell support, for some context: https://github.com/google/jax/issues/185
What are some alternatives?
torchtyping - Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
miniF2F - Formal to Formal Mathematics Benchmark
futhark - :boom::computer::boom: A data-parallel functional programming language
TablaM - The practical relational programing language for data-oriented applications
julia - The Julia Programming Language
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
MindsDB - The platform for customizing AI from enterprise data
hasktorch - Tensors and neural networks in Haskell
FL - FL language specification and reference implementations
CIPs