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https://github.com/sinkingsugar/nimtorch

PyTorch - Python + Nim
https://github.com/sinkingsugar/nimtorch

artificial-intelligence artificial-neural-networks cuda machine-learning nim pytorch wasm

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PyTorch - Python + Nim

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README 

        
![NimTorch](media/NimTorchBanner.png)



| Master | Release |

|-------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------

| [![Build Status](https://travis-ci.com/fragcolor-xyz/nimtorch.svg?branch=master)  ](https://travis-ci.com/fragcolor-xyz/nimtorch  ) | [![Build Status](https://travis-ci.com/fragcolor-xyz/nimtorch.svg?branch=release)  ](https://travis-ci.com/fragcolor-xyz/nimtorch  )



Pytorch - Py + Nim



A Nim frontend for pytorch, aiming to be mostly auto-generated and internally using ATen.



**Because Nim compiles to C++, this is not a wrapper or binding library. It generates 1-to-1 native ATen code.**



The only requirement from pytorch is ATen's core tensor library. Because of this, nimtorch is extremely versatile and can compile on any kind of device.



## Current status



**Early stage**



- [x] Automatically generated, from `Declarations.yaml`, the full ATen API

- [x] Cuda support ( add -d:cuda when compiling with nim )

- [x] WASM support ( add -d:wasm when compiling with nim )

- [x] Automatically generated, from `derivatives.yaml`, gradient procs

- [x] Autograd

- [ ] Add missing derivatives

- [ ] More high level pytorch API (Module, Models etc)

- [ ] ...



The final aim is to be as compatible as possible with the pytorch API.



## Why



Ease of use of the python language while keeping fully bare metal native C++ performance



### Python code



```python

# GRUCell

gi = x.matmul(w_input.t()) + b_input

gh = hidden.matmul(w_recur.t()) + b_recur

i_r, i_i, i_n = gi.chunk(3, 1)

h_r, h_i, h_n = gh.chunk(3, 1)

resetgate = (i_r + h_r).sigmoid()

inputgate = torch.sigmoid(i_i + h_i)

newgate = (i_n + resetgate * h_n).tanh()

hy = newgate + inputgate * (hidden - newgate)

```



### Nim code



```nimrod

# GRUCell

let

  gi = x.matmul(w_input.t()) + b_input

  gh = hidden.matmul(w_recur.t()) + b_recur

  (i_r, i_i, i_nn) = gi.chunk(3, 1)

  (h_r, h_i, h_n)  = gh.chunk(3, 1)

  resetgate = (i_r + h_r).sigmoid()

  inputgate = torch.sigmoid(i_i + h_i)

  newgate = (i_nn + resetgate * h_n).tanh()

  hy = newgate + inputgate * (hidden - newgate)

```



## Getting started



### Requirements



**Linux**: A recent distribution on par with ubuntu 18.04 in terms of libc and basic libraries, gcc compiler



**macOS**: We compile with 10.13 min version flags but might work even on lower versions, XCode for the compilers



**Windows**: Windows 10, Visual Studio Runtime 2017 and Visual Studio 2017 (any edition)



**WASM**: Latest Emscripten compiler and tools



### Super easy, using conda



*Linux, macOS and Windows*



`conda create -n nimtorch -c fragcolor nimtorch` (add `cuda10.0` for cuda 10 linux only or add `wasm` for wasm version)



`source activate nimtorch` or on windows: `conda activate nimtorch`



This will install: nim and ATen binaries, fragments and nimtorch all in one command, nothing else needed.



Make sure you use a recent version of conda and have a compiler installed in your system, on windows you have to add `--cc:vcc` and be on a developer prompt.



Make sure your system is recent (ubuntu 18.04 reference / macOS High Sierra / Windows 10) and you have cuda 9.2 installed (if you need cuda, linux only, more cuda versions coming, please open a issue if you need a specific version).



Test with with something like:



`nim cpp -o:test -r $ATEN/dist/pkgs/nimtorch-\#head/tests/test_xor.nim`



or on windows... (because dlls need to be side by side)



`nim cpp -o:%ATEN%/lib/test.exe -r %ATEN%/dist/pkgs/nimtorch-#head/tests/test_xor.nim`



### Semi manual way



*Linux, macOS and Windows*



Check what version of ATen/PyTorch we need in `conda/nimtorch/meta.yaml` - should be something like `aten ==2018.10.10.1089`



Note the version as you will need it in the next step



`conda create -n aten -c fragcolor aten={version}`



or



**WASM**



`conda create -n aten -c fragcolor aten={version} wasm`



or Cuda 10.0 (linux only)



`conda create -n aten -c fragcolor aten={version} cuda10.0`



activate aten environment



`source activate aten` or on windows: `conda activate aten`



1. Make sure you have a recent Nim and Nimble version in your path

  * [Easy option: install nim with choosenim](https://github.com/dom96/choosenim)

    * `choosenim devel`

2. clone the **release** branch `git clone -b release https://github.com/fragcolor-xyz/nimtorch.git`

3. `cd nimtorch`

4. `nimble develop`



*finally*



run self test `nim cpp -o:test -r torch.nim` (use `-o:%ATEN%/lib/test.exe` instead on windows because of dll location)



in the case of WASM:



run self test `nim cpp -d:wasm -o:test.js torch.nim && node test.js` (needs node.js)



### Manual way without requiring conda

**Build ATEN**

```sh

pip2 install pyyaml typing

git clone -b fragcolor-devel https://github.com/fragcolor-xyz/pytorch.git

cd pytorch

git reset --hard  # from torch/commit.txt

git submodule update --init

mkdir build && cd build

cmake -DCMAKE_BUILD_TYPE=Release -DUSE_CUDA=OFF -DBUILD_ATEN_ONLY=ON -DCMAKE_INSTALL_PREFIX=`pwd`/output ../

make -j4

make install



# also copy derivatives if we want to run generator.nim or nimble test

# notice generator.nim might need python3 and pyyaml

cp ../tools/autograd/derivatives.yaml `pwd`/output/share/

```



**Test the build**

```

cd 

ATEN= nim cpp -r -f -o:/tmp/z01 torch.nim # for eg: ATEN=pathto/pytorch/build/output/

```



## Notes



* We suggest setting `OMP_WAIT_POLICY` environment variable to `PASSIVE` when running on CPU.