IMLLM.py

import torch 
import torch.nn as nn
import torch.nn.functional as F
import tiktoken as tk

encoder = tk.get_encoding('gpt2')

# ----- Hyperparameters
batch_size = 64
block_size = 384
max_iters = 10000
eval_interval = 100
learning_rate = 1e-3
eval_iters = 200
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
n_embd = 64
n_head = 4
n_layer = 4
dropout = 0.2
# ------

torch.manual_seed(1337)
print('Reading data...')
#with open('data/2024-02-febrero.csv', 'r', encoding='utf-8') as f:
with open('data/inmo_2024-02-01_23-41-27.csv', 'r', encoding='utf-8') as f:
    text = f.read()

chars = sorted(list(set(text)))
#vocab_size =  len(chars)
vocab_size =  encoder.n_vocab

#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])

stoi = {encoder.decode([k]):k for k in range(encoder.n_vocab)}
itos = {k:encoder.decode([k]) for k in range(encoder.n_vocab)}
encode = encoder.encode
decode = encoder.decode

print('Encoding data examples...')
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 Head(nn.Module):
    def __init__(self, head_size):
        super().__init__()
        self.key = nn.Linear(n_embd, head_size, bias=False)
        self.query = nn.Linear(n_embd, head_size, bias=False)
        self.value = nn.Linear(n_embd, head_size, bias=False)
        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        B, T, C = x.shape
        k = self.key(x)
        q = self.query(x)
        wei = q @ k.transpose(-2, -1) * C **-.5
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
        wei = F.softmax(wei, dim=-1)
        wei = self.dropout(wei)
        v = self.value(x)
        out = wei @ v
        return out

class MultiHead(nn.Module):
    def __init__(self, num_heads, head_size):
        super().__init__()
        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
        self.proj = nn.Linear(n_embd, n_embd)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        out = torch.cat([h(x) for h in self.heads], dim=-1)
        out = self.dropout(self.proj(out))
        return out

class FeedForward(nn.Module):
    def __init__(self, n_embd):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(n_embd, 4 * n_embd),
            nn.ReLU(),
            nn.Linear(4 * n_embd, n_embd),
            nn.Dropout(dropout),
        )
    def forward(self, x):
        return self.net(x)

class Block(nn.Module):
    def __init__(self, n_embd, n_head):
        super().__init__()
        head_size = n_embd // n_head
        self.sa = MultiHead(n_head, head_size)
        self.ffwd = FeedForward(n_embd)
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)

    def forward(self, x):
        x = x + self.sa(self.ln1(x))
        x = x + self.ffwd(self.ln2(x))
        return x


class LayerNorm:

    def __init__(self, dim, eps=1e-5, momentum=0.1):
        self.eps = eps
        # parameters (trained with backprop)
        self.gamma = torch.ones(dim)
        self.beta = torch.zeros(dim)

    def __call__(self, x):
        xmean = x.mean(1, keepdim=True) # batch mean
        xvar = x.var(1, keepdim=True) # batch variance
        xhat = (x - xmean) / torch.sqrt(xvar + self.eps) # normalize to unit variance
        self.out = self.gamma * xhat + self.beta
        return self.out

    def parameters(self):
        return [self.gamma, self.beta]


class IMLanguageModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(block_size, n_embd)
        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd)
        self.lm_head = nn.Linear(n_embd, vocab_size)

    def forward(self, idx, targets=None):
        B, T = idx.shape
        # idx and targets are bot (B,T) tensor of int
        tok_emb = self.token_embedding_table(idx)  # (B, T, C)
        pos_emb = self.position_embedding_table(torch.arange(T, device=device))
        x = tok_emb + pos_emb
        x = self.blocks(x)
        logits = self.lm_head(x)  # (B, T, vocab_size)
        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):
        #idx is (B, T) array of indexes in the current context
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -block_size:]
            logits, loss = self(idx_cond)
            logits = logits[:, -1, :]  # becomes (B, C)
            probs = F.softmax(logits, dim=--1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, idx_next), dim=1)
        return idx

print('Loading the model...')
model = IMLanguageModel()
print(f'Model has {sum(p.numel() for p in model.parameters())/1e6} M parameters')
model = model.to(device)

optimizer = torch.optim.AdamW(model.parameters(),lr=learning_rate)
print('Training!')
for step in range(max_iters):
    if step % eval_interval == 0:
        losses = estimate_loss()
        print(f'step {step:5d}: train loss: {losses["train"]:.4f}, val loss: {losses["val"]:.4f}')

    xb, yb = get_batch('train')

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

print('Test generation >>>>>>>>>')
context = torch.zeros((1,1), dtype=torch.long, device=device)
print(decode(model.generate(context, max_new_tokens=50000)[0].tolist()))
print('<<<<<<<<<<<<<<<<< END')

torch.save(model, 'models/IMLLM.3.torch')