const torch = require("js-pytorch"); // Instantiate Tensors: x = torch.randn([8,4,5]); w = torch.randn([8,5,4], requires_grad = true); b = torch.tensor([0.2, 0.5, 0.1, 0.0], requires_grad = true); // Make calculations: 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);

const torch = require("js-pytorch"); const nn = torch.nn; class Transformer extends nn.Module { constructor(vocab_size, hidden_size, n_timesteps, n_heads, 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=p); this.b2 = new nn.Block(hidden_size, hidden_size, n_heads, n_timesteps, dropout_p=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(); };