tntorch
torchtyping
tntorch | torchtyping | |
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1 | 7 | |
271 | 1,337 | |
- | - | |
1.6 | 3.2 | |
about 1 year ago | 11 months ago | |
Python | Python | |
GNU Lesser General Public License v3.0 only | Apache License 2.0 |
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tntorch
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ETH Zurich AI Researchers Introduce ‘tntorch’: a PyTorch-Powered Tensor Learning Python Library That Supports Multiple Decompositions Under a Unified Interface
Continue reading | Checkout the paper and github
torchtyping
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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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What's New in Python 3.11?
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]
[0] https://github.com/patrick-kidger/torchtyping/issues/37#issu...
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Can anyone point out the mistakes in my input layer or dimension?
also https://github.com/patrick-kidger/torchtyping
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[D] Anyone using named tensors or a tensor annotation lib productively?
FWIW I'm the author of torchtyping so happy to answer any questions about that. :) I think people are using it!
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[D] Ideal deep learning library
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.
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[P] torchtyping -- documentation + runtime type checking of tensor shapes (and dtypes, ...)
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?
ITensors.jl - A Julia library for efficient tensor computations and tensor network calculations
jaxtyping - Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. https://docs.kidger.site/jaxtyping/
ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
norse - Deep learning for spiking neural networks
tsalib - Tensor Shape Annotation Library (numpy, tensorflow, pytorch, ...)
mypy - Optional static typing for Python
functorch - functorch is JAX-like composable function transforms for PyTorch.
tensor_annotations - Annotating tensor shapes using Python types
Roslyn - The Roslyn .NET compiler provides C# and Visual Basic languages with rich code analysis APIs.
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
dex-lang - Research language for array processing in the Haskell/ML family