PyTorch in JavaScript

• JS-Torch is a Deep Learning JavaScript library built from scratch, to closely follow PyTorch's syntax.
• It contains a fully functional Tensor object, which can track gradients, Deep Learning Layers and functions, and an Automatic Differentiation engine.
• Feel free to try out the Web Demo!

Note: You can install the package locally with: npm install js-pytorch

Implemented Tensor Operations:

• Add
• Subtract
• Multiply
• Divide
• Matrix Multiply
• Power
• Square Root
• Exponentiate
• Log
• Sum
• Mean
• Variance
• Transpose
• At
• MaskedFill
• Reshape

Implemented Deep Learning Layers:

• nn.Linear
• nn.MultiHeadSelfAttention
• nn.FullyConnected
• nn.Block
• nn.Embedding
• nn.PositionalEmbedding
• nn.ReLU
• nn.Softmax
• nn.Dropout
• nn.LayerNorm
• nn.CrossEntropyLoss

1. Project Structure

• assets/ : Folder to store images and the Demo.
  • assets/demo/ : JS-Torch's Web Demo.
• src/ : Framework with JavaScript files.
  • src/tensor.ts: File with the Tensor class and all of the tensor Operations.
  • src/utils.ts: File with operations and helper functions.
  • src/layers.ts: Submodule of the framework. Contains full layers.
  • src/optim.ts: Submodule of the framework. Contains Adam Optimizer.
• tests/: Folder with unit tests. Contains test.ts.

2. Running it Yourself

Simple Autograd Example:

const { torch } = require("js-pytorch");

// Instantiate Tensors:
let x = torch.randn([8, 4, 5]);
let w = torch.randn([8, 5, 4], (requires_grad = true));
let b = torch.tensor([0.2, 0.5, 0.1, 0.0], (requires_grad = 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):

const { torch } = require("js-pytorch");
const nn = torch.nn;
const optim = torch.optim;

// 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) {
    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);
    this.b2 = new nn.Block(hidden_size, hidden_size, n_heads, n_timesteps,dropout_p);
    this.ln = new nn.LayerNorm(hidden_size);
    this.linear = new nn.Linear(hidden_size, vocab_size);
  }

  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);

// 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)}`)
}

3. 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.
• 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.

4. Results

• The models implemented in the unit tests all converged to near-zero losses.
• Run them with npm test!
• This package is not as optimized as PyTorch yet, but I tried making it more interpretable. Efficiency improvements are incoming!
• Hope you enjoy!