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import torch
import json
# ------------ Hyperparameters ------------
def hyperparameters(config_path: str):
with open(config_path) as f:
config = json.load(f)
batch_size = config['batch_size']
block_size = config['block_size']
max_iters = config['max_iters']
eval_interval = config['eval_interval']
learning_rate = config['learning_rate']
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
eval_iters = config['eval_iters']
n_embd = config['n_embd']
n_head = config['n_head']
n_layer = config['n_layer']
dropout = config['dropout']
return (batch_size, block_size, max_iters, eval_interval, learning_rate,
device, eval_iters, n_embd, n_head, n_layer, dropout)
# ----------------------------------------
def load_data(path) -> tuple[torch.Tensor, torch.Tensor, int, callable, callable]:
with open(path, 'r', encoding='utf-8') as f:
text = f.read()
# words = text.split()
# vocab_size = len(words)
# stoi = {word: i for i, word in enumerate(words)}
# itos = {i: word for i, word in enumerate(words)}
# def encode(s): return [stoi[w] for w in s.split()]
# def decode(ids): return ' '.join([itos[i] for i in ids])
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)}
def encode(s): return [stoi[c] for c in s]
def decode(l): return ''.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:]
return train_data, val_data, vocab_size, encode, decode
def get_batch(split, train_data, val_data, device, block_size, batch_size):
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(model, get_batch, eval_iters, train_data, val_data, device, block_size, batch_size):
out = {}
model.eval()
for split in ['train', 'val']:
losses = torch.zeros(eval_iters)
for k in range(eval_iters):
X, Y = get_batch(split, train_data, val_data, device, block_size, batch_size)
logits, loss = model(X, Y)
losses[k] = loss.item()
out[split] = losses.mean()
model.train()
return out
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