Gotch

Overview

Gotch creates a thin wrapper to Pytorch C++ APIs (Libtorch) to make use of its already optimized C++ tensor APIs (~ over 1400) 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 (~ 1404)
• 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.

Dependencies

• Libtorch C++ v1.7.0 library of Pytorch

Installation

• Default CUDA version is 10.1 if CUDA is available otherwise using CPU version.
• Default Pytorch C++ API version is 1.7.0

    wget https://raw.githubusercontent.com/sugarme/gotch/v0.3.6/setup.sh
    chmod +x setup.sh

    # Default
    bash setup.sh

    # Specify CUDA version
    export CUDA_VER=YOUR_PC_CUDA_VERSION && bash setup.sh

    # CPU
    export CUDA_VER=cpu && bash setup.sh

Examples

Basic tensor operations

import (
	"fmt"

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

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.MustAdd1_(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"
    ts "github.com/sugarme/gotch/tensor"
)

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

More coming soon...

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.