miniF2F VS torchtyping

Compare miniF2F vs torchtyping and see what are their differences.


Formal to Formal Mathematics Benchmark (by openai)


Type annotations and dynamic checking for a tensor's shape, dtype, names, etc. (by patrick-kidger)
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miniF2F torchtyping
4 7
259 1,338
3.9% -
0.0 3.2
9 months ago 11 months ago
Objective-C++ Python
- Apache License 2.0
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Posts with mentions or reviews of miniF2F. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-11.


Posts with mentions or reviews of torchtyping. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-11.
  • [D] Have their been any attempts to create a programming language specifically for machine learning?
    12 projects | /r/MachineLearning | 11 Feb 2023
    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. or
  • What's New in Python 3.11?
    14 projects | | 26 Jun 2022
    I disagree. I've had a serious attempt at array typing using variadic generics and I'm not impressed. Python's type system has numerous issues... and now they just apply to any "ArrayWithNDimensions" type as well as any "ArrayWith2Dimenensions" type.

    Variadic protocols don't exist; many operations like stacking are inexpressible; the synatx is awful and verbose; etc. etc.

    I've written more about this here as part of my TorchTyping project: [0]


  • Can anyone point out the mistakes in my input layer or dimension?
    1 project | /r/learnmachinelearning | 6 Jun 2022
  • [D] Anyone using named tensors or a tensor annotation lib productively?
    2 projects | /r/MachineLearning | 18 Apr 2022
    FWIW I'm the author of torchtyping so happy to answer any questions about that. :) I think people are using it!
  • [D] Ideal deep learning library
    9 projects | /r/MachineLearning | 5 Jan 2022
    The one thing I really *really* wish got more attention was named tensors and the tensor type system. Tensor misalignment errors are a constant source of silently-failing bugs. While 3rd party libraries have attempted to fill this gap, it really needs better native support. In particular it seems like bad form to me for programmers to have to remember the specific alignment and broadcasting rules, and then have to apply them to an often poorly documented order of tensor indices. I'd really like to see something like tsalib's warp operator made part of the main library and generalized to arbitrary function application, like a named-tensor version of fold. But preferably using notation closer to that of torchtyping.
  • [P] torchtyping -- documentation + runtime type checking of tensor shapes (and dtypes, ...)
    2 projects | /r/MachineLearning | 7 Apr 2021
    Yes it does work with numerical literals! It support using integers to specify an absolute size, strings to specify names for dimensions that should all be consistently sized (and optionally also checks named tensors), "..." to indicate batch dimensions, and so on. See the full list here.

What are some alternatives?

When comparing miniF2F and torchtyping you can also consider the following projects:

tensor_annotations - Annotating tensor shapes using Python types

jaxtyping - Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays.

einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

equinox - Elegant easy-to-use neural networks + scientific computing in JAX.

FL - FL language specification and reference implementations

tsalib - Tensor Shape Annotation Library (numpy, tensorflow, pytorch, ...)

dex-lang - Research language for array processing in the Haskell/ML family

mypy - Optional static typing for Python

functorch - functorch is JAX-like composable function transforms for PyTorch.

hasktorch - Tensors and neural networks in Haskell