bigram.py

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

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

torch.manual_seed(15)

with open('mahabharata.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])
  x, y = x.to(device), y.to(device)
  return x, y


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


class BigramModel(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)
    
    if targets is None: 
      loss = None
    else:
      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):
    # generate max_new_tokens new indices and concatenate to idx
    # idx is (B, T) array of indices. row is number of batches, and column is context length
    for _ in range(max_new_tokens):
      logits, loss = self(idx)
      # we only need the last value in the sequence to generate the next sequence, in this particular model
      logits = logits[:, -1, :] 
      # getting the probabilites from the logits
      probs = F.softmax(logits, dim = -1)
      # sampling from the distrbution
      idx_next = torch.multinomial(probs, num_samples = 1)
      idx = torch.cat((idx, idx_next), dim = 1)
    return idx
  
model = BigramModel(vocab_size)
m = model.to(device)

optimizer = torch.optim.AdamW(m.parameters(), lr=1e-3)

batch_size = 32
for iter in range(max_iters):
  
  if iter%eval_interval == 0:
    losses = estimate_loss()
    print(f"step: {iter} train loss:{losses['train']:.4f} val loss:{losses['val']:.4f}")

  xb, yb = get_batch('train')
  logits, loss = m(xb, yb)
  optimizer.zero_grad(set_to_none=True)
  loss.backward()
  optimizer.step()

context = torch.zeros((1, 1), dtype=torch.long, device=device)
print(decode(m.generate(context, max_new_tokens = 500)[0].tolist()))