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https://github.com/laurentmazare/ocaml-torch

OCaml bindings for PyTorch
https://github.com/laurentmazare/ocaml-torch

artificial-intelligence deep-learning gpu machine-learning neural-network ocaml pytorch tensor

Last synced: 1 day ago
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OCaml bindings for PyTorch

Host: GitHub
URL: https://github.com/laurentmazare/ocaml-torch
Owner: LaurentMazare
License: apache-2.0
Created: 2018-10-02T23:14:32.000Z (about 6 years ago)
Default Branch: main
Last Pushed: 2024-10-17T13:03:35.000Z (29 days ago)
Last Synced: 2024-11-13T19:02:01.224Z (1 day ago)
Topics: artificial-intelligence, deep-learning, gpu, machine-learning, neural-network, ocaml, pytorch, tensor
Language: OCaml
Homepage:
Size: 8.87 MB
Stars: 412
Watchers: 14
Forks: 38
Open Issues: 10
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        > **Warning**

> 

> Development for this repo has moved to https://github.com/janestreet/torch .

# ocaml-torch

__ocaml-torch__ provides some ocaml bindings for the [PyTorch](https://pytorch.org) tensor library.

This brings to OCaml NumPy-like tensor computations with GPU acceleration and tape-based automatic

differentiation.

![Main workflow](https://github.com/LaurentMazare/ocaml-torch/workflows/Main%20workflow/badge.svg)

These bindings use the [PyTorch C++ API](https://pytorch.org/cppdocs/) and are

mostly automatically generated. The current GitHub tip and the opam package v0.7

corresponds to PyTorch **v2.0.0**.

On Linux note that you will need the PyTorch version using the cxx11 abi

[cpu version](https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.0.0%2Bcpu.zip),

[cuda 11.7 version](https://download.pytorch.org/libtorch/cu117/libtorch-cxx11-abi-shared-with-deps-2.0.0%2Bcu117.zip).

## Opam Installation

The [opam](https://opam.ocaml.org/) package can be installed using the following command.

This automatically installs the CPU version of libtorch.

```bash

opam install torch

```

You can then compile some sample code, see some instructions below.

__ocaml-torch__ can also be used in interactive mode via

[utop](https://github.com/ocaml-community/utop) or

[ocaml-jupyter](https://github.com/akabe/ocaml-jupyter).

Here is a sample utop session.

![utop](./images/utop.png)

### Build a Simple Example

To build a first torch program, create a file `example.ml` with the

following content.

```ocaml

open Torch

let () =

  let tensor = Tensor.randn [ 4; 2 ] in

  Tensor.print tensor

```

Then create a `dune` file with the following content:

```ocaml

(executables

  (names example)

  (libraries torch))

```

Run `dune exec example.exe` to compile the program and run it!

Alternatively you can first compile the code via `dune build example.exe` then run the executable

`_build/default/example.exe` (note that building the bytecode target `example.bc` may

not work on macos).

## Tutorials and Examples

* [MNIST tutorial](https://github.com/LaurentMazare/ocaml-torch/tree/master/examples/mnist).

* [Finetuning a ResNet-18 model](https://github.com/LaurentMazare/ocaml-torch/blob/master/examples/pretrained/).

* [Generative Adversarial Networks](https://github.com/LaurentMazare/ocaml-torch/blob/master/examples/gan).

* [Running some Python model](https://github.com/LaurentMazare/ocaml-torch/tree/master/examples/jit).

Some more advanced applications from external repos:

* An [OCaml port of mini-dalle](https://github.com/ArulselvanMadhavan/mini_dalle) by Arulselvan Madhavan.

* A clone of the [diffusers](https://github.com/huggingface/diffusers) library implementing

  stable-diffusion 1.5: [diffusers-ocaml](https://github.com/ArulselvanMadhavan/diffusers-ocaml) by Arulselvan Madhavan.

* Natural Language Processing models based on BERT can be found in the

[ocaml-bert repo](https://github.com/LaurentMazare/ocaml-bert).

## Sample Code

Below is an example of a linear model trained on the MNIST dataset ([full

code](https://github.com/LaurentMazare/ocaml-torch/blob/master/examples/mnist/linear.ml)).

```ocaml

  (* Create two tensors to store model weights. *)

  let ws = Tensor.zeros [image_dim; label_count] ~requires_grad:true in

  let bs = Tensor.zeros [label_count] ~requires_grad:true in

  let model xs = Tensor.(mm xs ws + bs) in

  for index = 1 to 100 do

    (* Compute the cross-entropy loss. *)

    let loss =

      Tensor.cross_entropy_for_logits (model train_images) ~targets:train_labels

    in

    Tensor.backward loss;

    (* Apply gradient descent, disable gradient tracking for these. *)

    Tensor.(no_grad (fun () ->

        ws -= grad ws * f learning_rate;

        bs -= grad bs * f learning_rate));

    (* Compute the validation error. *)

    let test_accuracy =

      Tensor.(argmax ~dim:(-1) (model test_images) = test_labels)

      |> Tensor.to_kind ~kind:(T Float)

      |> Tensor.sum

      |> Tensor.float_value

      |> fun sum -> sum /. test_samples

    in

    printf "%d %f %.2f%%\n%!" index (Tensor.float_value loss) (100. *. test_accuracy);

  done

```

* Some [ResNet examples on CIFAR-10](https://github.com/LaurentMazare/ocaml-torch/tree/master/examples/cifar).

* A simplified version of

  [char-rnn](https://github.com/LaurentMazare/ocaml-torch/blob/master/examples/char_rnn)

  illustrating character level language modeling using Recurrent Neural Networks.

* [Neural Style Transfer](https://github.com/LaurentMazare/ocaml-torch/blob/master/examples/neural_transfer)

  applies the style of an image to the content of another image. This uses some deep Convolutional Neural Network.

## Models and Weights

Various pre-trained computer vision models are implemented in the

[vision library](https://github.com/LaurentMazare/ocaml-torch/tree/master/src/vision).

The weight files can be downloaded at the following links:

* ResNet-18 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/resnet18.ot).

* ResNet-34 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/resnet34.ot).

* ResNet-50 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/resnet50.ot).

* ResNet-101 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/resnet101.ot).

* ResNet-152 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/resnet152.ot).

* DenseNet-121 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/densenet121.ot).

* DenseNet-161 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/densenet161.ot).

* DenseNet-169 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/densenet169.ot).

* SqueezeNet 1.0 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/squeezenet1_0.ot).

* SqueezeNet 1.1 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/squeezenet1_1.ot).

* VGG-13 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/vgg13.ot).

* VGG-16 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/vgg16.ot).

* AlexNet [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/alexnet.ot).

* Inception-v3 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/inception-v3.ot).

* MobileNet-v2 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/mobilenet-v2.ot).

* EfficientNet

  [b0 weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/efficientnet-b0.ot),

  [b1 weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/efficientnet-b1.ot),

  [b2 weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/efficientnet-b2.ot),

  [b3 weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/efficientnet-b3.ot),

  [b4 weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/efficientnet-b4.ot).

Running the pre-trained models on some sample images can the easily be done via the following commands.

```bash

dune exec examples/pretrained/predict.exe path/to/resnet18.ot tiger.jpg

```

## Alternative Installation Option

This alternative way to install __ocaml-torch__ could be useful to run with GPU

acceleration enabled.

The libtorch library can be downloaded from the [PyTorch

website](https://pytorch.org/resources) ([2.0.0 cpu

version](https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.0.0+cpu.zip)).

Download and extract the libtorch library then to build all the examples run:

```bash

export LIBTORCH=/path/to/libtorch

git clone https://github.com/LaurentMazare/ocaml-torch.git

cd ocaml-torch

make all

```