gclm.py

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)