experiments/bigram.py

import torch
from torch import nn
import torch.nn.functional as F

batch_size = 32
block_size = 8
max_iters = 3000
eval_interval = 300
learning_rate = 1e-2
device = "cuda:1" if torch.cuda.is_available() else "cpu"
eval_iters = 200

torch.manual_seed(1123)

with open("input.txt") as f:
    text = f.read()

chars = sorted(list(set(text)))
vocab_size = len(chars)

stoi = {ch: i for i, ch in enumerate(chars)}
itos = {i: ch for i, ch in enumerate(chars)}

encode = lambda s: [stoi[c] for c in s]
decode = lambda l: "".join([itos[i] for i in l])

data = torch.tensor(encode(text), dtype=torch.long)
n = int(0.9 * len(data))
train_data = data[:n]
val_data = data[n:]


def get_batch(split):
    data = train_data if split == "train" else val_data
    ix = torch.randint(len(data) - block_size, (batch_size,))
    x = torch.stack([data[i : i + block_size] for i in ix])
    y = torch.stack([data[i + 1 : i + block_size + 1] for i in ix])
    return x, y


@torch.no_grad()
def estimate_loss(model: nn.Module):
    out = {}
    model.eval()
    for split in ["train", "val"]:
        losses = torch.zeros(eval_iters)
        for k in range(eval_iters):
            X, Y = get_batch(split)
            X, Y = X.to(device), Y.to(device)
            logits, loss = model(X, Y)
            losses[k] = loss.item()
        out[split] = losses.mean()
    model.train()
    return out


class BigramLanguageModel(nn.Module):
    def __init__(self, vocab_size):
        super().__init__()
        self.token_embedding_table = nn.Embedding(vocab_size, vocab_size)

    def forward(self, idx, targets=None):
        logits = self.token_embedding_table(idx)  # BTC
        loss = None
        if targets is not None:
            B, T, C = logits.shape
            logits = logits.view(B * T, C)
            targets = targets.view(B * T)
            loss = F.cross_entropy(logits, targets)
        return logits, loss

    def generate(self, idx, max_new_tokens):
        for _ in range(max_new_tokens):
            logits, loss = self(idx)  # BxTxC
            logits = logits[:, -1, :]  # BxC
            probs = F.softmax(logits, dim=-1)  # BxC
            idx_next = torch.multinomial(probs, num_samples=1)  # Bx1
            idx = torch.cat((idx, idx_next), dim=1)  # BxT+1

        return idx


model = BigramLanguageModel(vocab_size)

model = model.to(device)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)

for iter in range(max_iters):
    if iter % eval_interval == 0:
        losses = estimate_loss(model)
        print(
            f"Step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}"
        )

    xb, yb = get_batch("train")
    xb, yb = xb.to(device), yb.to(device)

    logits, loss = model(xb, yb)

    optimizer.zero_grad(set_to_none=True)
    loss.backward()
    optimizer.step()


context = torch.zeros((1, 1), dtype=torch.long, device=device)
results = decode(model.generate(context, max_new_tokens=100)[0].tolist())
print(results)