# PyTorch in JavaScript
- JS-PyTorch is a Deep Learning **JavaScript library** built from scratch, to closely follow PyTorch's syntax.
- This library has **GPU support**, using GPU.js.
- If you want to run it yourself, check out the Documentation.
- Try out the Web Demo!
> **Note:** You can install the package locally with: `npm install js-pytorch`
Implemented Tensor Operations:
- [Add](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L346-L401)
- [Subtract](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L404-L438)
- [Multiply](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L441-L496)
- [Divide](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L498-L557)
- [Matrix Multiply](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L560-L621)
- [Power](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L625-L663)
- [Square Root](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L666-L704)
- [Exponentiate](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#706-L744)
- [Log](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L746-L785)
- [Sum](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L790-L842)
- [Mean](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L844-L894)
- [Variance](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L896-L949)
- [Transpose](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L953-L1008)
- [At](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L1010-L1060)
- [MaskedFill](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L1062-L1095)
- [Reshape](https://github.com/eduardoleao052/js-torch/blob/07c1286867b952f32c0e904033214253e8812090/src/tensor.js#L1097-L1129)
Implemented Deep Learning Layers:
- [nn.Linear](https://github.com/eduardoleao052/js-torch/blob/a158c91db9775a88fae6ed2d0f76d6d8ee6f9d23/src/layers.js#L60-L88)
- [nn.MultiHeadSelfAttention](https://github.com/eduardoleao052/js-torch/blob/a158c91db9775a88fae6ed2d0f76d6d8ee6f9d23/src/layers.js#L90-L163)
- [nn.FullyConnected](https://github.com/eduardoleao052/js-torch/blob/a158c91db9775a88fae6ed2d0f76d6d8ee6f9d23/src/layers.js#L165-L194)
- [nn.Block](https://github.com/eduardoleao052/js-torch/blob/a158c91db9775a88fae6ed2d0f76d6d8ee6f9d23/src/layers.js#L196-L226)
- [nn.Embedding](https://github.com/eduardoleao052/js-torch/blob/a158c91db9775a88fae6ed2d0f76d6d8ee6f9d23/src/layers.js#L231-L260)
- [nn.PositionalEmbedding](https://github.com/eduardoleao052/js-torch/blob/a158c91db9775a88fae6ed2d0f76d6d8ee6f9d23/src/layers.js#L262-L291)
- [nn.ReLU](https://github.com/eduardoleao052/js-torch/blob/a158c91db9775a88fae6ed2d0f76d6d8ee6f9d23/src/layers.js#L296-L325)
- [nn.Softmax](https://github.com/eduardoleao052/js-torch/blob/a158c91db9775a88fae6ed2d0f76d6d8ee6f9d23/src/layers.js#L327-L346)
- [nn.Dropout](https://github.com/eduardoleao052/js-torch/blob/a158c91db9775a88fae6ed2d0f76d6d8ee6f9d23/src/layers.js#L351-L376)
- [nn.LayerNorm](https://github.com/eduardoleao052/js-torch/blob/a158c91db9775a88fae6ed2d0f76d6d8ee6f9d23/src/layers.js#L378-L397)
- [nn.CrossEntropyLoss](https://github.com/eduardoleao052/js-torch/blob/a158c91db9775a88fae6ed2d0f76d6d8ee6f9d23/src/layers.js#L400-L441)
## 1.Table of Contents
* [Installation](#2-installation)
* [Running it Yourself](#3-Running-it-Yourself)
* [Simple Autograd Example](#simple-autograd-example)
* [Complex Autograd Example (Transformer)](#complex-autograd-example-transformer)
* [Saving and Loading models](#saving-and-loading-models)
* [Distribution & Devtools](#4-distribution--devtools)
* [Future Work](#5-future-work)
## 2. Installation
- On **MacOS**, **Windows**, and **Ubuntu**, you can install the library with `npm install js-pytorch`.
- On **Windows**, if you run into an error, you might need to install the latest version of [Visual Studio](https://visualstudio.microsoft.com/downloads/?cid=learn-navbar-download-cta), including the "Desktop development with C++" workload.
- To run in the **Browser**, paste the following tag in the `` of your HTML file:
```html
```
- After that, you can use JS-PyTorch freely in any `
```
## 3. Running it Yourself
### Simple Autograd Example:
```typescript
// Require the Library if running in node (not necessary in the browser):
const { torch } = require("js-pytorch");
// Pass device as an argument to a Tensor or nn.Module (same as PyTorch):
const device = 'gpu';
// Instantiate Tensors:
let x = torch.randn([8, 4, 5]);
let w = torch.randn([8, 5, 4], true, device);
let b = torch.tensor([0.2, 0.5, 0.1, 0.0], true);
// Make calculations:
let out = torch.matmul(x, w);
out = torch.add(out, b);
// Compute gradients on whole graph:
out.backward();
// Get gradients from specific Tensors:
console.log(w.grad);
console.log(b.grad);
```
### Complex Autograd Example (Transformer):
```typescript
// Require the Library if running in node (not necessary in the browser):
const { torch } = require("js-pytorch");
const nn = torch.nn;
const optim = torch.optim;
const device = 'gpu';
// Define training hyperparameters:
const vocab_size = 52;
const hidden_size = 32;
const n_timesteps = 16;
const n_heads = 4;
const dropout_p = 0;
const batch_size = 8;
// Create Transformer decoder Module:
class Transformer extends nn.Module {
constructor(vocab_size, hidden_size, n_timesteps, n_heads, dropout_p, device) {
super();
// Instantiate Transformer's Layers:
this.embed = new nn.Embedding(vocab_size, hidden_size);
this.pos_embed = new nn.PositionalEmbedding(n_timesteps, hidden_size);
this.b1 = new nn.Block(hidden_size, hidden_size, n_heads, n_timesteps, dropout_p, device);
this.b2 = new nn.Block(hidden_size, hidden_size, n_heads, n_timesteps, dropout_p, device);
this.ln = new nn.LayerNorm(hidden_size);
this.linear = new nn.Linear(hidden_size, vocab_size, device);
}
forward(x) {
let z;
z = torch.add(this.embed.forward(x), this.pos_embed.forward(x));
z = this.b1.forward(z);
z = this.b2.forward(z);
z = this.ln.forward(z);
z = this.linear.forward(z);
return z;
}
}
// Instantiate your custom nn.Module:
const model = new Transformer(vocab_size, hidden_size, n_timesteps, n_heads, dropout_p, device);
// Define loss function and optimizer:
const loss_func = new nn.CrossEntropyLoss();
const optimizer = new optim.Adam(model.parameters(), (lr = 5e-3), (reg = 0));
// Instantiate sample input and output:
let x = torch.randint(0, vocab_size, [batch_size, n_timesteps, 1]);
let y = torch.randint(0, vocab_size, [batch_size, n_timesteps]);
let loss;
// Training Loop:
for (let i = 0; i < 40; i++) {
// Forward pass through the Transformer:
let z = model.forward(x);
// Get loss:
loss = loss_func.forward(z, y);
// Backpropagate the loss using torch.tensor's backward() method:
loss.backward();
// Update the weights:
optimizer.step();
// Reset the gradients to zero after each training step:
optimizer.zero_grad();
// Print loss at every iteration:
console.log(`Iter ${i} - Loss ${loss.data[0].toFixed(4)}`)
}
```
### Saving and Loading models:
```typescript
// Instantiate your model:
const model = new Transformer(vocab_size, hidden_size, n_timesteps, n_heads, dropout_p);
// Train the model:
trainModel(model);
// Save model to JSON file:
torch.save(model, 'model.json')
// To load, instantiate placeHolder using the original model's architecture:
const placeHolder = new Transformer(vocab_size, hidden_size, n_timesteps, n_heads, dropout_p);
// Load weights into placeHolder:
const newModel = torch.load(placeHolder, 'model.json')
```
## 4. Distribution & Devtools
- **Build for Distribution** by running `npm run build`. CJS and ESM modules and `index.d.ts` will be output in the `dist/` folder.
- **Check the Code** with ESLint at any time, running `npm run lint`.
- **Run tests** run `npm test`.
- **Improve Code Formatting** with prettier, running `npm run prettier`.
- **Performance Benchmarks** are also included in the `tests/benchmarks/` directory. Run all benchmarks with `npm run bench` and save new benchmarks with `npm run bench:update`.
## 5. Future Work
- This package is not as optimized as PyTorch yet, but I tried making it more interpretable. Efficiency improvements are incoming!
- Feel free to **contribute**! Create a merge request to the `develop` branch, and also feel free to reach out. I'll try to answer as soon as possible.
- Hope you enjoy!