- 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:
Implemented Deep Learning Layers:
- 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, including the "Desktop development with C++" workload.
- To run in the Browser, paste the following tag in the
<head>of your HTML file:
<script src="https://cdnjs.cloudflare.com/ajax/libs/js-pytorch/0.7.2/js-pytorch-browser.js"
integrity="sha512-l22t7GnqXvHBMCBvPUBdFO2TEYxnb1ziCGcDQcpTB2un16IPA4FE5SIZ8bUR+RwoDZGikQkWisO+fhnakXt9rg=="
crossorigin="anonymous"
referrerpolicy="no-referrer"></script>- After that, you can use JS-PyTorch freely in any
<script>in your HTML file:
<head>
<title>My Project</title>
<!-- New script goes here -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/js-pytorch/0.7.2/js-pytorch-browser.js"
integrity="sha512-l22t7GnqXvHBMCBvPUBdFO2TEYxnb1ziCGcDQcpTB2un16IPA4FE5SIZ8bUR+RwoDZGikQkWisO+fhnakXt9rg=="
crossorigin="anonymous"
referrerpolicy="no-referrer">
</script>
<!
8000
---->
</head>
<body>
<script>
let x = torch.randn([10,5])
let linear = new torch.nn.Linear(5,1,'gpu',true)
let z = linear.forward(x)
console.log(z.data)
</script>
</body>// 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);// 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)}`)
}// 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')- Build for Distribution by running
npm run build. CJS and ESM modules andindex.d.tswill be output in thedist/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 withnpm run benchand save new benchmarks withnpm run bench:update.
- 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
developbranch, and also feel free to reach out. I'll try to answer as soon as possible. - Hope you enjoy!