import time import torch import torch.nn as nn import torch.nn.functional as F # --- Configuration & Data --- data = """To be, or not to be, that is the question: Whether 'tis nobler in the mind to suffer The slings and arrows of outrageous fortune, Or to take arms against a sea of troubles And by opposing end them. To die—to sleep, No more; and by a sleep to say we end The heart-ache and the thousand natural shocks That flesh is heir to: 'tis a consummation Devoutly to be wish'd. To die, to sleep; To sleep, perchance to dream—ay, there's the rub: For in that sleep of death what dreams may come, When we have shuffled off this mortal coil, Must give us pause—there's the respect That makes calamity of so long life. For who would bear the whips and scorns of time, Th'oppressor's wrong, the proud man's contumely, The pangs of dispriz'd love, the law's delay, The insolence of office, and the spurns That patient merit of th'unworthy takes, When he himself might his quietus make With a bare bodkin? Who would fardels bear, To grunt and sweat under a weary life, But that the dread of something after death, The undiscovere'd country, from whose bourn No traveller returns, puzzles the will, And makes us rather bear those ills we have Than fly to others that we know not of? Thus conscience doth make cowards of us all, And thus the native hue of resolution Is sicklied o'er with the pale cast of thought, And enterprises of great pith and moment With this regard their currents turn awry And lose the name of action.""" chars = sorted(list(set(data))) vocab_size = len(chars) stoi = {ch: i for i, ch in enumerate(chars)} itos = {i: ch for i, ch in enumerate(chars)} encoded = torch.tensor([stoi[c] for c in data], dtype=torch.long) # Hyperparameters based on your architecture D_MODEL = 256 N_LAYERS = 4 MAX_SEQ_LEN = 64 LOCAL_K = 5 GLOBAL_K = 128 FFT_SIZE = 256 TRAIN_TIME = 60 BATCH_SIZE = 8 # --- Architecture Components --- class GlobalConv1D(nn.Module): def __init__(self, d_model, kernel_size, fft_size): super().__init__() self.kernel = nn.Parameter(torch.randn(d_model, kernel_size) * 0.01) self.kernel_size = kernel_size self.fft_size = fft_size def forward(self, x): B, C, T = x.shape K = min(self.kernel_size, T) overlap = K - 1 block = self.fft_size - overlap x = F.pad(x, (overlap, 0)) k = self.kernel[:, :K] k = F.pad(k, (0, self.fft_size - K)) k_f = torch.fft.rfft(k, n=self.fft_size) outs = [] pos = 0 while pos < T: seg = x[..., pos:pos + self.fft_size] if seg.shape[-1] < self.fft_size: seg = F.pad(seg, (0, self.fft_size - seg.shape[-1])) y = torch.fft.irfft(torch.fft.rfft(seg, n=self.fft_size) * k_f.unsqueeze(0), n=self.fft_size) outs.append(y[..., overlap:overlap + block]) pos += block return torch.cat(outs, dim=-1)[..., :T] class LocalConv1D(nn.Module): def __init__(self, d_model, k): super().__init__() self.k = k self.dw = nn.Conv1d(d_model, d_model, k, groups=d_model) self.pw = nn.Conv1d(d_model, d_model, 1) def forward(self, x): x = F.pad(x, (self.k - 1, 0)) return self.pw(F.relu(self.dw(x))) class Block(nn.Module): def __init__(self, d_model, use_global): super().__init__() self.use_global = use_global self.ln1 = nn.LayerNorm(d_model) self.local = LocalConv1D(d_model, LOCAL_K) if use_global: self.ln2 = nn.LayerNorm(d_model) self.global_conv = GlobalConv1D(d_model, GLOBAL_K, FFT_SIZE) self.ln3 = nn.LayerNorm(d_model) self.ff = nn.Sequential( nn.Linear(d_model, d_model * 4), nn.GELU(), nn.Linear(d_model * 4, d_model) ) def forward(self, x): x = x + self.local(self.ln1(x).transpose(1, 2)).transpose(1, 2) if self.use_global: x = x + self.global_conv(self.ln2(x).transpose(1, 2)).transpose(1, 2) return x + self.ff(self.ln3(x)) class GCLM(nn.Module): def __init__(self, vocab): super().__init__() self.emb = nn.Embedding(vocab, D_MODEL) self.pos = nn.Embedding(MAX_SEQ_LEN, D_MODEL) self.layers = nn.ModuleList([Block(D_MODEL, i % 2 == 0) for i in range(N_LAYERS)]) self.ln = nn.LayerNorm(D_MODEL) self.head = nn.Linear(D_MODEL, vocab) self.head.weight = self.emb.weight # Weight Tying def forward(self, x): T = x.size(1) h = self.emb(x) + self.pos(torch.arange(T, device=x.device)) for layer in self.layers: h = layer(h) return self.head(self.ln(h)) # --- Training Setup --- device = "cuda" if torch.cuda.is_available() else "cpu" model = GCLM(vocab_size).to(device) optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4) print(f"Training on {device} for {TRAIN_TIME} seconds...") start_time = time.time() step = 0 model.train() while (time.time() - start_time) < TRAIN_TIME: # Random batching ix = torch.randint(0, len(encoded) - MAX_SEQ_LEN, (BATCH_SIZE,)) x = torch.stack([encoded[i : i + MAX_SEQ_LEN] for i in ix]).to(device) y = torch.stack([encoded[i + 1 : i + MAX_SEQ_LEN + 1] for i in ix]).to(device) logits = model(x) loss = F.cross_entropy(logits.view(-1, vocab_size), y.view(-1)) optimizer.zero_grad(set_to_none=True) loss.backward() optimizer.step() if step % 10 == 0: elapsed = time.time() - start_time print(f"\rStep {step} | Loss: {loss.item():.4f} | Progress: {min(100, (elapsed/TRAIN_TIME)*100):.1f}%", end="") step += 1 # --- Generation --- print("\n\nTraining Complete. Generating:\n" + "-"*30) model.eval() prompt = "To be, " ctx = torch.tensor([[stoi[c] for c in prompt]], dtype=torch.long, device=device) print(prompt, end="", flush=True) with torch.no_grad(): for _ in range(MAX_SEQ_LEN * 2): # Crop context to model's MAX_SEQ_LEN inp = ctx[:, -MAX_SEQ_LEN:] logits = model(inp) logits = logits[:, -1, :] / 0.8 # Temperature # Simple top-k to keep it clean v, _ = torch.topk(logits, min(10, vocab_size)) logits[logits < v[:, [-1]]] = -float('Inf') probs = F.softmax(logits, dim=-1) next_char_idx = torch.multinomial(probs, num_samples=1) ctx = torch.cat((ctx, next_char_idx), dim=1) print(itos[next_char_idx.item()], end="", flush=True) print("\n" + "-"*30)