Gotch

Overview

gotch creates a thin wrapper to Pytorch C++ APIs (Libtorch) to make use of its already optimized C++ tensor APIs (3039) and dynamic graph computation with CUDA support and provides idiomatic Go APIs for developing and implementing Deep Learning in Go.

Some features are

• Comprehensive Pytorch tensor APIs (2525)
• Fully featured Pytorch dynamic graph computation
• JIT interface to run model trained/saved using PyTorch Python API
• Load pretrained Pytorch models and run inference
• Pure Go APIs to build and train neural network models with both CPU and GPU support
• Most recent image models
• NLP Language models - Transformer in separate package built with gotch and pure Go Tokenizer.

gotch is in active development mode and may have API breaking changes. Feel free to pull request, report issues or discuss any concerns. All contributions are welcome.

gotch current version is v0.9.1

Dependencies

• Libtorch C++ v2.1.0 library of Pytorch
• Clang-17/Clang++-17 compilers

Installation

• Default CUDA version is 11.8 if CUDA is available otherwise using CPU version.
• Default Pytorch C++ API version is 2.1.0

NOTE: libtorch will be installed at /usr/local/lib

CPU

Step 1: Setup libtorch (skip this step if a valid libtorch already installed in your machine!)

    wget https://github.com/sugarme/gotch/releases/download/v0.9.0/setup-libtorch.sh
    chmod +x setup-libtorch.sh
    export CUDA_VER=cpu && bash setup-libtorch.sh

Update Environment: in Debian/Ubuntu, add/update the following lines to .bashrc file

    export GOTCH_LIBTORCH="/usr/local/lib/libtorch"
    export LIBRARY_PATH="$LIBRARY_PATH:$GOTCH_LIBTORCH/lib"
    export CPATH="$CPATH:$GOTCH_LIBTORCH/lib:$GOTCH_LIBTORCH/include:$GOTCH_LIBTORCH/include/torch/csrc/api/include"
    export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$GOTCH_LIBTORCH/lib"

Step 2: Setup gotch

    wget https://github.com/sugarme/gotch/releases/download/v0.9.0/setup-gotch.sh
    chmod +x setup-gotch.sh
    export CUDA_VER=cpu && export GOTCH_VER=v0.9.1 && bash setup-gotch.sh

GPU

NOTE: make sure your machine has working CUDA.

• Check version: nvidia-smi
• Install nvidia driver here
• Install CUDA here
• Install CuDNN here

Step 1: Setup libtorch (skip this step if a valid libtorch already installed in your machine!)

    wget https://github.com/sugarme/gotch/releases/download/v0.9.0/setup-libtorch.sh
    chmod +x setup-libtorch.sh

    export CUDA_VER=11.8 && bash setup-libtorch.sh

Update Environment: in Debian/Ubuntu, add/update the following lines to .bashrc file

    export GOTCH_LIBTORCH="/usr/local/lib/libtorch"
    export LIBRARY_PATH="$LIBRARY_PATH:$GOTCH_LIBTORCH/lib"
    export CPATH="$CPATH:$GOTCH_LIBTORCH/lib:$GOTCH_LIBTORCH/include:$GOTCH_LIBTORCH/include/torch/csrc/api/include"
    LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$GOTCH_LIBTORCH/lib:/usr/lib64-nvidia:/usr/local/cuda-${CUDA_VERSION}/lib64"

Step 2: Setup gotch

    wget https://github.com/sugarme/gotch/releases/download/v0.9.0/setup-gotch.sh
    chmod +x setup-gotch.sh
    export CUDA_VER=11.8 && export GOTCH_VER=v0.9.1 && bash setup-gotch.sh

Examples

Basic tensor operations

import (
	"fmt"

	"github.com/sugarme/gotch"
	"github.com/sugarme/gotch/ts"
)

func basicOps() {

xs := ts.MustRand([]int64{3, 5, 6}, gotch.Float, gotch.CPU)
fmt.Printf("%8.3f\n", xs)
fmt.Printf("%i", xs)

/*
(1,.,.) =
   0.391     0.055     0.638     0.514     0.757     0.446  
   0.817     0.075     0.437     0.452     0.077     0.492  
   0.504     0.945     0.863     0.243     0.254     0.640  
   0.850     0.132     0.763     0.572     0.216     0.116  
   0.410     0.660     0.156     0.336     0.885     0.391  

(2,.,.) =
   0.952     0.731     0.380     0.390     0.374     0.001  
   0.455     0.142     0.088     0.039     0.862     0.939  
   0.621     0.198     0.728     0.914     0.168     0.057  
   0.655     0.231     0.680     0.069     0.803     0.243  
   0.853     0.729     0.983     0.534     0.749     0.624  

(3,.,.) =
   0.734     0.447     0.914     0.956     0.269     0.000  
   0.427     0.034     0.477     0.535     0.440     0.972  
   0.407     0.945     0.099     0.184     0.778     0.058  
   0.482     0.996     0.085     0.605     0.282     0.671  
   0.887     0.029     0.005     0.216     0.354     0.262  



TENSOR INFO:
        Shape:          [3 5 6]
        DType:          float32
        Device:         {CPU 1}
        Defined:        true
*/

// Basic tensor operations
ts1 := ts.MustArange(ts.IntScalar(6), gotch.Int64, gotch.CPU).MustView([]int64{2, 3}, true)
defer ts1.MustDrop()
ts2 := ts.MustOnes([]int64{3, 4}, gotch.Int64, gotch.CPU)
defer ts2.MustDrop()

mul := ts1.MustMatmul(ts2, false)
defer mul.MustDrop()

fmt.Printf("ts1:\n%2d", ts1)
fmt.Printf("ts2:\n%2d", ts2)
fmt.Printf("mul tensor (ts1 x ts2):\n%2d", mul)

/*
ts1:
 0   1   2  
 3   4   5  

ts2:
 1   1   1   1  
 1   1   1   1  
 1   1   1   1  

mul tensor (ts1 x ts2):
 3   3   3   3  
12  12  12  12  
*/


// In-place operation
ts3 := ts.MustOnes([]int64{2, 3}, gotch.Float, gotch.CPU)
fmt.Printf("Before:\n%v", ts3)
ts3.MustAddScalar_(ts.FloatScalar(2.0))
fmt.Printf("After (ts3 + 2.0):\n%v", ts3)

/*
Before:
1  1  1  
1  1  1  

After (ts3 + 2.0):
3  3  3  
3  3  3  
*/
}

Simplified Convolutional neural network

import (
    "fmt"

    "github.com/sugarme/gotch"
    "github.com/sugarme/gotch/nn"
    "github.com/sugarme/gotch/ts"
)

type Net struct {
    conv1 *nn.Conv2D
    conv2 *nn.Conv2D
    fc    *nn.Linear
}

func newNet(vs *nn.Path) *Net {
    conv1 := nn.NewConv2D(vs, 1, 16, 2, nn.DefaultConv2DConfig())
    conv2 := nn.NewConv2D(vs, 16, 10, 2, nn.DefaultConv2DConfig())
    fc := nn.NewLinear(vs, 10, 10, nn.DefaultLinearConfig())

    return &Net{
        conv1,
        conv2,
        fc,
    }
}

func (n Net) ForwardT(xs *ts.Tensor, train bool) *ts.Tensor {
    xs = xs.MustView([]int64{-1, 1, 8, 8}, false)

    outC1 := xs.Apply(n.conv1)
    outMP1 := outC1.MaxPool2DDefault(2, true)
    defer outMP1.MustDrop()

    outC2 := outMP1.Apply(n.conv2)
    outMP2 := outC2.MaxPool2DDefault(2, true)
    outView2 := outMP2.MustView([]int64{-1, 10}, true)
    defer outView2.MustDrop()

    outFC := outView2.Apply(n.fc)
    return outFC.MustRelu(true)
}

func main() {

    vs := nn.NewVarStore(gotch.CPU)
    net := newNet(vs.Root())

    xs := ts.MustOnes([]int64{8, 8}, gotch.Float, gotch.CPU)

    logits := net.ForwardT(xs, false)
    fmt.Printf("Logits: %0.3f", logits)
}

//Logits: 0.000  0.000  0.000  0.225  0.321  0.147  0.000  0.207  0.000  0.000

Play with gotch on Google Colab or locally

• Tensor Initiation
• Tensor Indexing
• MNIST
• YOLO v3 model infering
• RNN model training
• CIFAR model training
• JIT ResNet18 Torch Script model load and inference
• Neural style transfer
• Image pretrained models - inference
• Translation
• Convert Pytorch Python model to Go
• Load Python Pytorch JIT model then train/finetune in Go
• Image augmentation

Getting Started

• See pkg.go.dev for APIs detail.

License

gotch is Apache 2.0 licensed.

Acknowledgement

• This project has been inspired and used many concepts from tch-rs Libtorch Rust binding.