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"""
Pré-treinamento de um pequeno modelo de linguagem
=================================================
Fluxo:
1. Usa o texto do README (artigo/resumo) como corpus inicial.
2. Treina um tokenizador SentencePiece (BPE) se ainda não existir.
3. Constrói um Dataset de LM (inputs + labels deslocados).
4. Treina `EpistemicLanguageModel` com cross-entropy e AdamW.
5. Salva pesos do modelo e reutiliza o tokenizador treinado.
"""
from dataclasses import dataclass, field
from typing import List, Tuple, Optional
import os
import torch
from torch.utils.data import Dataset, DataLoader
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR
from tqdm import tqdm
from custom_tokenizer import SPConfig, train_sentencepiece, CustomSPTokenizer
from custom_lm_model import LMConfig, EpistemicLanguageModel, save_lm, generate_text
from corpus_utils import load_main_corpus
@dataclass
class TrainLMConfig:
sp_config: SPConfig = field(default_factory=SPConfig)
max_seq_len: int = 128
batch_size: int = 16
num_epochs: int = 3
learning_rate: float = 3e-4
grad_clip: float = 1.0
grad_accum_steps: int = 1
save_dir: str = "checkpoints_lm"
class LMDataset(Dataset):
"""
Dataset de linguagem causal: divide o fluxo de tokens em blocos
de tamanho fixo e usa input_ids e labels deslocados em 1.
"""
def __init__(self, token_ids: List[int], block_size: int) -> None:
self.block_size = block_size
# Trunca para múltiplo de block_size
n = (len(token_ids) // block_size) * block_size
self.data = token_ids[:n]
def __len__(self) -> int:
return max(len(self.data) // self.block_size - 1, 0)
def __getitem__(self, idx: int):
start = idx * self.block_size
end = start + self.block_size
x = torch.tensor(self.data[start:end], dtype=torch.long)
y = torch.tensor(self.data[start + 1 : end + 1], dtype=torch.long)
return x, y
def ensure_tokenizer(config: TrainLMConfig) -> CustomSPTokenizer:
model_file = f"{config.sp_config.model_prefix}.model"
if not os.path.exists(model_file):
# Treina o SentencePiece a partir do corpus principal (README + artigo DOCX)
texts = load_main_corpus()
tmp_corpus = "sp_corpus_tmp.txt"
with open(tmp_corpus, "w", encoding="utf-8") as f:
for t in texts:
f.write(t.replace("\r\n", "\n") + "\n")
train_sentencepiece([tmp_corpus], config.sp_config)
os.remove(tmp_corpus)
return CustomSPTokenizer(model_prefix=config.sp_config.model_prefix)
def build_token_stream(tokenizer: CustomSPTokenizer) -> Tuple[List[int], List[int]]:
"""
Constrói streams de tokens para treino e validação a partir do corpus principal.
Usa divisão simples train/val em nível de documento.
"""
texts = load_main_corpus()
if len(texts) == 1:
train_texts = texts
val_texts = texts
else:
split = max(1, int(0.8 * len(texts)))
train_texts = texts[:split]
val_texts = texts[split:]
def encode_all(lst: List[str]) -> List[int]:
ids: List[int] = []
for t in lst:
ids.extend(tokenizer.encode(t, add_bos=True, add_eos=True))
return ids
return encode_all(train_texts), encode_all(val_texts)
def evaluate_lm(
model: EpistemicLanguageModel,
dataloader: DataLoader,
device: torch.device,
loss_fn,
) -> float:
model.eval()
total_loss, steps = 0.0, 0
with torch.no_grad():
for x, y in dataloader:
x = x.to(device)
y = y.to(device)
logits = model(x)
loss = loss_fn(logits.view(-1, logits.size(-1)), y.view(-1))
total_loss += float(loss.item())
steps += 1
avg_loss = total_loss / max(steps, 1)
return avg_loss
def train_lm(config: TrainLMConfig) -> EpistemicLanguageModel:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = ensure_tokenizer(config)
train_ids, val_ids = build_token_stream(tokenizer)
train_dataset = LMDataset(train_ids, block_size=config.max_seq_len)
val_dataset = LMDataset(val_ids, block_size=config.max_seq_len)
train_loader = DataLoader(train_dataset, batch_size=config.batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=config.batch_size)
lm_config = LMConfig(
vocab_size=tokenizer.vocab_size,
max_seq_len=config.max_seq_len,
)
model = EpistemicLanguageModel(lm_config).to(device)
# Suporte simples a múltiplas GPUs via DataParallel
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
optimizer = AdamW(model.parameters(), lr=config.learning_rate)
scheduler = CosineAnnealingLR(optimizer, T_max=config.num_epochs)
loss_fn = torch.nn.CrossEntropyLoss()
os.makedirs(config.save_dir, exist_ok=True)
for epoch in range(config.num_epochs):
model.train()
total_loss = 0.0
steps = 0
optimizer.zero_grad()
for step, (x, y) in enumerate(
tqdm(train_loader, desc=f"Epoch {epoch+1}/{config.num_epochs}")
):
x = x.to(device)
y = y.to(device)
logits = model(x) # (batch, seq, vocab)
loss = loss_fn(logits.view(-1, logits.size(-1)), y.view(-1))
loss = loss / max(config.grad_accum_steps, 1)
loss.backward()
if (step + 1) % config.grad_accum_steps == 0:
if config.grad_clip is not None and config.grad_clip > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip)
optimizer.step()
optimizer.zero_grad()
total_loss += float(loss.item())
steps += 1
scheduler.step()
avg_train_loss = total_loss / max(steps, 1)
# Validação
val_loss = evaluate_lm(
model.module if isinstance(model, torch.nn.DataParallel) else model,
val_loader,
device,
loss_fn,
)
ppl = torch.exp(torch.tensor(val_loss)).item()
print(
f"Epoch {epoch+1} - train loss: {avg_train_loss:.4f} | "
f"val loss: {val_loss:.4f} | ppl: {ppl:.2f}"
)
# Pequena geração de teste
base_model = model.module if isinstance(model, torch.nn.DataParallel) else model
prompt = "A inteligência artificial"
sample = generate_text(base_model, tokenizer, prompt, max_new_tokens=40)
print(f"Exemplo de geração: {sample}\n")
# Checkpoint por época
ckpt_path = os.path.join(config.save_dir, f"epistemic_lm_epoch{epoch+1}.pt")
save_lm(base_model, ckpt_path)
# retorna o último modelo (sem DataParallel)
return model.module if isinstance(model, torch.nn.DataParallel) else model
def main() -> None:
config = TrainLMConfig()
model = train_lm(config)
save_path = "epistemic_lm.pt"
save_lm(model, save_path)
print(f"Modelo de linguagem salvo em '{save_path}'")
if __name__ == "__main__":
main()
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