run.py

import torch
import json
import os
current_directory = os.path.dirname(os.path.abspath(__file__))
os.chdir(current_directory)
device = 'cuda' if torch.cuda.is_available() else 'cpu'

with open('training files/file1.txt', 'r', encoding='utf-8') as f:
  test_data = f.read().lower()
  print("file opened!")
f.close()

print(f"{(len(test_data)/1e6):.2f} million letters")

from tokenizer import KMerTokenizer

tokenizer = KMerTokenizer(k_mers=4)
tokenizer.load_model('tokenizer/vocabs/base_4k.json')
vocab_size = tokenizer.vocab_size

# Train and test splits
data = torch.tensor(tokenizer.encode(test_data), dtype=torch.long)
n = int(0.9*len(data)) # first 90% will be train, rest val
train_data = data[:n]
val_data = data[n:]

print(train_data[:10])
print(val_data[:10])

with open('enigma2/config.json', 'r', encoding='utf-8') as file:
  params = json.load(file)

# required parameters
batch_size = params['batch_size']
block_size = params['block_size']
max_iters = params['max_iters']
eval_interval = params['eval_interval']
eval_iters = params['eval_iters']
learning_rate = params['learning_rate']

torch.manual_seed(19)
# data loading
def get_batch(split):
  # generate a small batch of data of inputs x and targets y
  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

from enigma2 import Transformer
model = Transformer(vocab_size=vocab_size)
m = model.to(device)

# no of parameters
n_param = sum(p.numel() for p in m.parameters())/1e6
print(f"{n_param:.2f} million")
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
steps = []
train_losses = []
val_losses = []

for iter in range(max_iters):

  if iter % eval_interval == 0 or iter == max_iters - 1:
    losses = estimate_loss()
    print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")

    steps.append(iter)
    train_losses.append(losses['train'])
    val_losses.append(losses['val'])

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

torch.save(model.state_dict(), f'enigma_{n_param:.0f}m.pth')