Hasktorch

Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the core C++ libraries shared by PyTorch.

This project is in active development, so expect changes to the library API as it evolves. We would like to invite new users to join our Hasktorch slack space for questions and discussions. Contributions/PR are encouraged.

Currently we are developing the second major release of Hasktorch (0.2). Note the 1st release, Hasktorch 0.1, on hackage is outdated and should not be used.

Documentation

The documentation is divided into several sections:

Introductory Videos
Getting Started
Known Issues
Contributing
Notes for Library Developers

Introductory Videos

Getting Started

The following steps will get you started. They assume the hasktorch repository has just been cloned. After setup is done, read the online tutorials and API documents.

linux+cabal+cpu
linux+cabal+cuda11
macos+cabal+cpu
linux+stack+cpu
macos+stack+cpu
nixos+cabal+cpu
nixos+cabal+cuda11
docker+jupyterlab+cuda11

linux+cabal+cpu

Starting from the top-level directory of the project, run:

$ pushd deps       # Change to the deps directory and save the current directory.
$ ./get-deps.sh    # Run the shell script to retrieve the libtorch dependencies.
$ popd             # Go back to the root directory of the project.
$ source setenv    # Set the shell environment to reference the shared library locations.
$ ./setup-cabal.sh # Create a cabal project file

To build and test the Hasktorch library, run:

$ cabal build hasktorch  # Build the Hasktorch library.
$ cabal test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ cabal build examples  # Build the Hasktorch examples.
$ cabal test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE=cpu             # Set device to CPU for the MNIST CNN example.
$ cabal run static-mnist-cnn    # Run the MNIST CNN example.

linux+cabal+cuda11

Starting from the top-level directory of the project, run:

$ pushd deps              # Change to the deps directory and save the current directory.
$ ./get-deps.sh -a cu111  # Run the shell script to retrieve the libtorch dependencies.
$ popd                    # Go back to the root directory of the project.
$ source setenv           # Set the shell environment to reference the shared library locations.
$ ./setup-cabal.sh        # Create a cabal project file

To build and test the Hasktorch library, run:

$ cabal build hasktorch  # Build the Hasktorch library.
$ cabal test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ cabal build examples  # Build the Hasktorch examples.
$ cabal test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE="cuda:0"        # Set device to CUDA for the MNIST CNN example.
$ cabal run static-mnist-cnn    # Run the MNIST CNN example.

macos+cabal+cpu

Starting from the top-level directory of the project, run:

$ pushd deps       # Change to the deps directory and save the current directory.
$ ./get-deps.sh    # Run the shell script to retrieve the libtorch dependencies.
$ popd             # Go back to the root directory of the project.
$ source setenv    # Set the shell environment to reference the shared library locations.
$ ./setup-cabal.sh # Create a cabal project file

To build and test the Hasktorch library, run:

$ cabal build hasktorch  # Build the Hasktorch library.
$ cabal test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ cabal build examples  # Build the Hasktorch examples.
$ cabal test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE=cpu             # Set device to CPU for the MNIST CNN example.
$ cabal run static-mnist-cnn    # Run the MNIST CNN example.

linux+stack+cpu

Install the Haskell Tool Stack if you haven't already, following instructions here

Starting from the top-level directory of the project, run:

$ pushd deps     # Change to the deps directory and save the current directory.
$ ./get-deps.sh  # Run the shell script to retrieve the libtorch dependencies.
$ popd           # Go back to the root directory of the project.
$ source setenv  # Set the shell environment to reference the shared library locations.

To build and test the Hasktorch library, run:

$ stack build hasktorch  # Build the Hasktorch library.
$ stack test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ stack build examples  # Build the Hasktorch examples.
$ stack test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE=cpu             # Set device to CPU for the MNIST CNN example.
$ stack run static-mnist-cnn     # Run the MNIST CNN example.

macos+stack+cpu

Install the Haskell Tool Stack if you haven't already, following instructions here

Starting from the top-level directory of the project, run:

$ pushd deps     # Change to the deps directory and save the current directory.
$ ./get-deps.sh  # Run the shell script to retrieve the libtorch dependencies.
$ popd           # Go back to the root directory of the project.
$ source setenv  # Set the shell environment to reference the shared library locations.

To build and test the Hasktorch library, run:

$ stack build hasktorch  # Build the Hasktorch library.
$ stack test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ stack build examples  # Build the Hasktorch examples.
$ stack test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE=cpu             # Set device to CPU for the MNIST CNN example.
$ stack run static-mnist-cnn     # Run the MNIST CNN example.

nixos+cabal+cpu

(Optional) Install and set up Cachix:

$ nix-env -iA cachix -f https://cachix.org/api/v1/install  # (Optional) Install Cachix.
$ cachix use iohk                                          # (Optional) Use IOHK's cache.
$ cachix use hasktorch                                     # (Optional) Use hasktorch's cache.

Starting from the top-level directory of the project, run:

$ nix-shell  # Enter the nix shell environment for Hasktorch.

To build and test the Hasktorch library, run:

$ cabal build hasktorch  # Build the Hasktorch library.
$ cabal test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ cabal build examples  # Build the Hasktorch examples.
$ cabal test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE=cpu             # Set device to CPU for the MNIST CNN example.
$ cabal run static-mnist-cnn    # Run the MNIST CNN example.

nixos+cabal+cuda11

(Optional) Install and set up Cachix:

$ nix-env -iA cachix -f https://cachix.org/api/v1/install  # (Optional) Install Cachix.
$ cachix use iohk                                          # (Optional) Use IOHK's cache.
$ cachix use hasktorch                                     # (Optional) Use hasktorch's cache.

Starting from the top-level directory of the project, run:

$ nix-shell --arg cudaSupport true --argstr cudaMajorVersion 11  # Enter the nix shell environment for Hasktorch.

To build and test the Hasktorch library, run:

$ cabal build hasktorch  # Build the Hasktorch library.
$ cabal test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ cabal build examples  # Build the Hasktorch examples.
$ cabal test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE="cuda:0"        # Set device to CUDA for the MNIST CNN example.
$ cabal run static-mnist-cnn    # Run the MNIST CNN example.

docker+jupyterlab+cuda11

This dockerhub repository provides the docker-image of jupyterlab. It supports cuda11, cuda10 and cpu only. When you use jupyterlab with hasktorch, type following command, then click a url in a console.

$ docker run --gpus all -it --rm -p 8888:8888 htorch/hasktorch-jupyter
or
$ docker run --gpus all -it --rm -p 8888:8888 htorch/hasktorch-jupyter:latest-cu11

Known Issues

Tensors Cannot Be Moved to CUDA

In rare cases, you may see errors like

cannot move tensor to "CUDA:0"

although you have CUDA capable hardware in your machine and have followed the getting-started instructions for CUDA support.

If that happens, check if /run/opengl-driver/lib exists. If not, make sure your CUDA drivers are installed correctly.

Weird Behaviour When Switching from CPU-Only to CUDA-Enabled Nix Shell

If you have run cabal in a CPU-only Hasktorch Nix shell before, you may need to:

Clean the dist-newstyle folder using cabal clean.
Delete the .ghc.environment* file in the Hasktorch root folder.

Otherwise, at best, you will not be able to move tensors to CUDA, and, at worst, you will see weird linker errors like

gcc: error: hasktorch/dist-newstyle/build/x86_64-linux/ghc-8.8.3/libtorch-ffi-1.5.0.0/build/Torch/Internal/Unmanaged/Autograd.dyn_o: No such file or directory
`cc' failed in phase `Linker'. (Exit code: 1)

Contributing

We welcome new contributors.

Contact us for access to the hasktorch slack channel. You can send an email to [email protected] or on twitter as @austinvhuang, @SamStites, @tscholak, or @junjihashimoto3.

Notes for library developers

See the wiki for developer notes.

Project Folder Structure

Basic functionality:

deps/ - submodules and downloads for build dependencies (libtorch, mklml, pytorch) -- you can ignore this if you are on Nix
examples/ - high level example models (xor mlp, typed cnn, etc.)
experimental/ - experimental projects or tips
hasktorch/ - higher level user-facing library, calls into ffi/, used by examples/

Internals (for contributing developers):

codegen/ - code generation, parses Declarations.yaml spec from pytorch and produces ffi/ contents
inline-c/ - submodule to inline-cpp fork used for C++ FFI
libtorch-ffi/- low level FFI bindings to libtorch
spec/ - specification files used for codegen/

Tensors and neural networks in Haskell

Related tags

Overview

Hasktorch

Documentation

Introductory Videos

Getting Started

linux+cabal+cpu

linux+cabal+cuda11

macos+cabal+cpu

linux+stack+cpu

macos+stack+cpu

nixos+cabal+cpu

nixos+cabal+cuda11

docker+jupyterlab+cuda11

Known Issues

Tensors Cannot Be Moved to CUDA

Weird Behaviour When Switching from CPU-Only to CUDA-Enabled Nix Shell

Contributing

Notes for library developers

Project Folder Structure

Owner

hasktorch

This is the official pytorch implementation for the paper: Instance Similarity Learning for Unsupervised Feature Representation.

This is the source code of the 1st place solution for segmentation task (with Dice 90.32%) in 2021 CCF BDCI challenge.

A Partition Filter Network for Joint Entity and Relation Extraction EMNLP 2021

This is the implementation of GGHL (A General Gaussian Heatmap Labeling for Arbitrary-Oriented Object Detection)

"3D Human Texture Estimation from a Single Image with Transformers", ICCV 2021

Minecraft agent to farm resources using reinforcement learning

CRLT: A Unified Contrastive Learning Toolkit for Unsupervised Text Representation Learning

A CV toolkit for my papers.

Hydra: an Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems

Code for CVPR2019 Towards Natural and Accurate Future Motion Prediction of Humans and Animals

[ICLR 2021, Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

Bayesian inference for Permuton-induced Chinese Restaurant Process (NeurIPS2021).

Testability-Aware Low Power Controller Design with Evolutionary Learning, ITC2021

DiSECt: Differentiable Simulator for Robotic Cutting

Code for project: "Learning to Minimize Remainder in Supervised Learning".

A repository built on the Flow software package to explore cyber-security attacks on intelligent transportation systems.

Text completion with Hugging Face and TensorFlow.js running on Node.js

Swin-Transformer is basically a hierarchical Transformer whose representation is computed with shifted windows.

Official implementation of Sparse Transformer-based Action Recognition

Crosslingual Segmental Language Model