V6.5-7ds cleanup: remove legacy scripts/train_fast.py

scripts/train_fast.py

@@ -1,165 +0,0 @@
-#!/usr/bin/env python3
-"""train_fast.py — Treino rápido de bug-detection (2 épocas, modelo pequeno).
-
-Versão otimizada para rodar em < 5 minutos no ambiente Xeon 2-core:
-- max_modules=4 (em vez de 8)
-- bigru_hidden=16 (em vez de 32)
-- d_transformer=32 (em vez de 64)
-- num_layers_train=1, num_layers_hyp=1
-- max_samples=8 por dataset
-- 1 dataset (CEIA-POSITIVO/ultrachat_br_clustred_balanced_v1)
-
-Valida todos os 4 lemas sem pretender produzir modelo SOTA.
-"""
-from __future__ import annotations
-
-import os
-import sys
-import time
-import json
-import logging
-from pathlib import Path
-
-# Adiciona src/ ao path
-sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
-
-# Otimização Xeon
-os.environ.setdefault("OMP_NUM_THREADS", "2")
-os.environ.setdefault("MKL_NUM_THREADS", "2")
-os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
-
-import torch
-torch.set_num_threads(2)
-
-try:
-    from bigru_t.utils.xeon_runtime import optimize_xeon_environment
-    optimize_xeon_environment()
-except Exception as e:
-    logging.warning(f"Could not apply Xeon optimization: {e}")
-
-from bigru_t import (
-    UnifiedModel, UnifiedModelConfig, create_unified_model,
-    BiGRU_T_Trainer, TrainerConfig,
-)
-from bigru_t.data.streaming_datasets import stream_dataset
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(message)s",
-    datefmt="%H:%M:%S",
-    handlers=[logging.StreamHandler()],
-)
-logger = logging.getLogger(__name__)
-
-
-def main():
-    # HF token
-    hf_token = os.environ.get("HF_TOKEN") or None
-
-    # Modelo pequeno para bug-detection rápido
-    model_config = UnifiedModelConfig(
-        vocab_size=16384,
-        d_model=64,
-        max_seq_len=32,
-        pad_token_id=1,
-        max_modules=4,           # 4 módulos em vez de 8
-        bigru_hidden=16,         # 16 em vez de 32
-        d_transformer=32,        # 32 em vez de 64
-        nhead_tu=2,              # 2 em vez de 4 (32/2=16 ok)
-        d_ff_tu=64,              # 64 em vez de 128
-        output_dim_u8cell=32,    # 32 em vez de 64
-        cache_len=8,             # 8 em vez de 16
-        d_cache=64,              # 64 em vez de 128
-        nhead_orq=2,             # 2 em vez de 8 (64/2=32 ok)
-        d_ff_orq=128,            # 128 em vez de 256
-        trainT_dim=64,
-        nhead_train=2,
-        d_ff_train=128,
-        num_layers_train=1,      # 1 em vez de 2
-        hypT_dim=64,
-        nhead_hyp=2,
-        d_ff_hyp=128,
-        num_layers_hyp=1,        # 1 em vez de 2
-        num_bits=8,
-        dropout=0.1,
-    )
-
-    logger.info("Criando UnifiedModel (small config)...")
-    model, _ = create_unified_model(model_config)
-    params = model.count_parameters()
-    logger.info(f"  params: {params['total']:,} ({params['total_M']:.2f}M)")
-
-    # Tokenizer
-    from tokenizers import Tokenizer
-    tok_path = "/home/z/my-project/source/model_final/tokenizer/tokenizer.json"
-    tokenizer = Tokenizer.from_file(tok_path)
-
-    # Carrega 1 dataset
-    ds_name = "CEIA-POSITIVO/ultrachat_br_clustred_balanced_v1"
-    max_samples = 8
-    logger.info(f"Loading {max_samples} samples from {ds_name}...")
-    samples = []
-    t0 = time.time()
-    for sample in stream_dataset(ds_name, max_samples=max_samples, hf_token=hf_token):
-        samples.append(sample)
-        if len(samples) >= max_samples:
-            break
-        if time.time() - t0 > 120:
-            logger.warning(f"Timeout após {len(samples)} amostras")
-            break
-    logger.info(f"Loaded {len(samples)} samples in {time.time()-t0:.1f}s")
-
-    if not samples:
-        logger.error("Nenhuma amostra carregada")
-        sys.exit(1)
-
-    # Split 90/10
-    import random
-    random.seed(42)
-    random.shuffle(samples)
-    split = max(1, int(0.9 * len(samples)))
-    train_samples = samples[:split]
-    val_samples = samples[split:] or samples[:1]
-    logger.info(f"  train: {len(train_samples)} | val: {len(val_samples)}")
-
-    # Trainer config
-    trainer_config = TrainerConfig(
-        epochs=2,
-        datasets=ds_name,
-        max_samples_per_dataset=max_samples,
-        max_seq_len=32,
-        per_device_batch_size=1,
-        grad_accum=2,
-        lr=1e-3,
-        use_hypothesis=True,
-        meta_interval=5,
-        log_every=1,
-        save_temp_every=100,  # não salvar temp (modelo pequeno)
-        output_dir="/home/z/my-project/BiGRU_T_version/model_final",
-        temp_dir="/home/z/my-project/BiGRU_T_version/_temp_checkpoints",
-        keep_temp=False,
-    )
-
-    trainer = BiGRU_T_Trainer(
-        model=model,
-        tokenizer=tokenizer,
-        config=trainer_config,
-        train_samples=train_samples,
-        val_samples=val_samples,
-    )
-
-    result = trainer.train()
-
-    print("\n=== Resultado Final ===")
-    print(json.dumps(result, indent=2, default=str))
-
-    # Apaga HF token
-    if "HF_TOKEN" in os.environ:
-        del os.environ["HF_TOKEN"]
-        logger.info("HF_TOKEN apagado do ambiente")
-
-    sys.exit(0 if not result.get("killed") else 1)
-
-
-if __name__ == "__main__":
-    main()