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Roadmap to DiffSharp 1.0

See original GitHub issue

This is a catalog issue to track what needs doing for DiffSharp 1.0, based on 1:1 discussions with @gbaydin. There will be a long list of other things, we’ll extend this as necessary

  • Typed Backend.None CPU tensors (draft)

  • Add keep_dims on Mean (done)

  • Fix CompareTo in RawTensorFloat32CPU.fs (done)

  • Broadcasting. Full pytorch-style broadcasting for Add (see TODO here). The design principle expected here is “we should do the same thing as PyTorch”. Similarly full pytorch-style broadcasting for Mul and other operations. (done)

  • Convolutions (@gbaydin)

  • Transposed convolutions

  • Batchnorm

  • Dropout

  • Switch to Python-style casing on all operations to align with SciSharp

  • Remove excess overloads and use optional arguments instead

  • Finalize API

  • libtorch and cuda backends

  • Add Reshape (similar code to View)

  • Add OneHot

  • Differentiation API

  • Optimizers

  • Fix possible memory leak on Linux

  • Tensor save/load

  • General transpose

  • probability distributions

  • Generalization and batching of Transpose.

  • Zero-size tensors #150

  • Docs tooling #134

  • Docs #167

  • PyTorch Half support

  • Batching conv1d/2d/3d/…

  • Batching for MatMul. Currently no batching is supported, only 2D x 2D done

  • Einstein summation #92

  • norm #93

  • matrix inverse

Things out of scope for 1.0

  • Strided views

  • Quantized

  • Complex

  • Sparse

Issue Analytics

  • State:closed
  • Created 4 years ago
  • Reactions:3
  • Comments:10 (4 by maintainers)

github_iconTop GitHub Comments

pkesecommented, May 2, 2020

I’m not in favor of the new naming. While I agree with following PyTorch naming convetions, we should also respect our own F# casing convetions.

So couldn’t we just explain Pythonistas this one sentence:

Everything is the same as in PyTorch, except in F# we write object methods in PascalCase like t.ZerosLike and static functions in camelCase like Tensor.zerosLike t

… and avoid the damage to the F# side?

I think, sooner or later even the potential Pythonista converts will have to interact with the rest of the F# ecosystem and find such casing inconsistencies weird. Let’s not damage the F# API just for the ‘profit’ meme.

dsymecommented, Feb 14, 2020

Note that the direction of travel here is that the DiffSharp 1.0 API looks and acts a lot like an F# version of the PyTorch API.

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