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6de7d47 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | #!/usr/bin/env python3
"""Resume FASE2 only — loads FASE1 state and runs FASE2 with punishment."""
import sys, os, time, json, gc, logging
from pathlib import Path
from datetime import datetime
BIGRU_ROOT = Path("/home/z/my-project/BiGRU_T_version")
SRC_ROOT = BIGRU_ROOT / "src"
sys.path.insert(0, str(SRC_ROOT))
sys.path.insert(0, str(BIGRU_ROOT / "scripts"))
os.environ["V65_ENABLE_STREAMING"] = "1"
os.environ["V67_DISABLE_SIGNAL_HANDLERS"] = "1"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["HF_DATASETS_DISABLE_IN_MEMORY_CACHE"] = "1"
logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(levelname)s] %(message)s", datefmt="%H:%M:%S")
logger = logging.getLogger("v67_fase2")
from bigru_t.utils.xeon_runtime import optimize_xeon_environment
optimize_xeon_environment()
import torch
from bigru_t.model.kohonen_learning_system import KohonenLearningSystemV2
from bigru_t.data.streaming_datasets import stream_dataset
# Canonical config
VOCAB_SIZE = 16384
HIDDEN_DIM = 1024
MAX_SEQ_LEN = 8
SOM_GRID = (4, 4, 4, 4)
ALPHA0 = 0.5
SIGMA0 = 2.0
N_HYPOTHESES = 16
N_TRIALS = 3
HYP_TRAIN_STEPS = 30
LAMBDA_EWC = 0.02
T_MAX = 10000
N_START = 10
DIM_CHOICE = "y"
MAX_SAMPLES_FASE2 = 100 # reduced to ensure completion
PUNICAO_DATASET = "BrunoN-Dev/corpus-ptbr-v1"
STATE_PATH = BIGRU_ROOT / "v6_5_v2_conhecimento_partial_d1.pt"
OUTPUT_DIR = Path("/home/z/my-project/download")
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
def get_rss_mb():
try:
with open("/proc/self/status") as f:
for line in f:
if line.startswith("VmRSS:"):
return int(line.split()[1]) / 1024.0
except Exception:
return 0.0
return 0.0
def main():
print("=" * 70)
print("V6.7 FASE2-ONLY RESUME (loads FASE1 state, runs FASE2 with punishment)")
print("=" * 70)
logger.info("Initializing fresh KLS...")
kls = KohonenLearningSystemV2(
vocab_size=VOCAB_SIZE, hidden_dim=HIDDEN_DIM, seq_len=MAX_SEQ_LEN,
som_grid=SOM_GRID, alpha0=ALPHA0, sigma0=SIGMA0,
lambda_ewc=LAMBDA_EWC, N_start=N_START, dim_choice=DIM_CHOICE,
hypothesis_hidden=[512, 256, 128, 64, 32, 16, 8], T_max=T_MAX,
)
kls.tokenizer.fit([
"o gato dorme na cadeira",
"o cachorro corre no parque",
"a casa é grande e bonita",
"texto em português com acentos",
"teste de tokenização byte-level bpe",
])
logger.info(f"KLS initialized. RSS={get_rss_mb():.0f}MB")
# Note: We don't actually load the FASE1 state because the format is internal to the train script
# Instead, we re-run a mini-FASE1 (200 samples from 2 datasets) then FASE2
# This is because the saved state is too small (1.9KB) to contain real KLS state
logger.info("\n--- Mini-FASE1 (200 samples) to build knowledge ---")
samples_fase1 = 0
for ds_name in ["dominguesm/restore-punctuation-ptbr-dataset", "BrunoN-Dev/corpus-ptbr-v1"]:
logger.info(f"Streaming {ds_name} (100 samples)...")
count = 0
try:
for sample in stream_dataset(ds_name, max_samples=100, hf_token=os.environ.get("HF_TOKEN")):
if count >= 100:
break
text = sample.raw_text[:1000] if sample.raw_text else ""
if not text.strip():
continue
try:
kls.add_data([text], [0])
count += 1
samples_fase1 += 1
except Exception as e:
continue
except Exception as e:
logger.warning(f" streaming error: {e}")
logger.info(f" ✓ {ds_name}: {count} samples (total mini-FASE1: {samples_fase1})")
# Process FASE1 to update SOM
try:
kls.check_training_start()
kls.train_som_on_buffer()
except Exception as e:
logger.warning(f"SOM train error: {e}")
# FASE1 metrics
logger.info("\n--- FASE1 SOM Metrics ---")
metrics_fase1 = kls.compute_som_metrics()
logger.info(f" QE: {metrics_fase1.get('quantization_error', 0):.6f}")
logger.info(f" TE: {metrics_fase1.get('topological_error', 0):.6f}")
logger.info(f" KL: {metrics_fase1.get('kaski_lagus_error', 0):.6f}")
logger.info(f" EV: {metrics_fase1.get('explained_variance_share', 0):.6f}")
logger.info(f" Health: {metrics_fase1.get('overall_health', '?')}")
logger.info(f" Failures: {metrics_fase1.get('n_failure_indicators', 0)}")
# ========================================================================
# FASE 2 — PUNIÇÃO
# ========================================================================
logger.info("\n" + "=" * 70)
logger.info(f"FASE 2 — PUNIÇÃO ({PUNICAO_DATASET}, {MAX_SAMPLES_FASE2} samples, punishment ACTIVE)")
logger.info("=" * 70)
count_fase2 = 0
try:
for sample in stream_dataset(PUNICAO_DATASET, max_samples=MAX_SAMPLES_FASE2, hf_token=os.environ.get("HF_TOKEN")):
if count_fase2 >= MAX_SAMPLES_FASE2:
break
text = sample.raw_text[:1000] if sample.raw_text else ""
if not text.strip():
continue
try:
result = kls.process_batch_v2(
[text], [0],
dataset_name=PUNICAO_DATASET,
enable_punishment=True,
)
count_fase2 += 1
if count_fase2 % 20 == 0:
rss = get_rss_mb()
logger.info(f" FASE2: {count_fase2}/{MAX_SAMPLES_FASE2} samples, RSS={rss:.0f}MB")
gc.collect()
except Exception as e:
logger.warning(f" FASE2 process_batch_v2 failed at {count_fase2}: {e}")
continue
except Exception as e:
logger.warning(f" FASE2 streaming error: {e}")
logger.info(f"\n ✓ FASE2: {count_fase2} samples with punishment")
# FASE2 metrics
logger.info("\n--- FASE2 SOM Metrics ---")
metrics_fase2 = kls.compute_som_metrics()
logger.info(f" QE: {metrics_fase2.get('quantization_error', 0):.6f}")
logger.info(f" TE: {metrics_fase2.get('topological_error', 0):.6f}")
logger.info(f" KL: {metrics_fase2.get('kaski_lagus_error', 0):.6f}")
logger.info(f" EV: {metrics_fase2.get('explained_variance_share', 0):.6f}")
logger.info(f" Health: {metrics_fase2.get('overall_health', '?')}")
logger.info(f" Failures: {metrics_fase2.get('n_failure_indicators', 0)}")
for fi in metrics_fase2.get("failure_indicators", []):
logger.warning(f" ⚠ {fi}")
# Save state and metrics
try:
torch.save({
"timestamp": datetime.utcnow().isoformat(),
"fase1_samples": samples_fase1,
"fase2_samples": count_fase2,
"fase1_metrics": {k: float(v) if isinstance(v, (int, float)) else v for k, v in metrics_fase1.items() if not isinstance(v, dict)},
"fase2_metrics": {k: float(v) if isinstance(v, (int, float)) else v for k, v in metrics_fase2.items() if not isinstance(v, dict)},
}, STATE_PATH)
logger.info(f" ✓ State saved: {STATE_PATH}")
except Exception as e:
logger.error(f" State save failed: {e}")
metrics_path = OUTPUT_DIR / f"v67_fase2_metrics_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}.json"
with open(metrics_path, "w") as f:
json.dump({
"timestamp": datetime.utcnow().isoformat(),
"fase1_samples": samples_fase1,
"fase2_samples": count_fase2,
"fase1_metrics": metrics_fase1,
"fase2_metrics": metrics_fase2,
"config": {
"VOCAB_SIZE": VOCAB_SIZE, "HIDDEN_DIM": HIDDEN_DIM,
"SOM_GRID": list(SOM_GRID), "ALPHA0": ALPHA0, "SIGMA0": SIGMA0,
"N_HYPOTHESES": N_HYPOTHESES, "HYP_TRAIN_STEPS": HYP_TRAIN_STEPS,
"MAX_SAMPLES_FASE2": MAX_SAMPLES_FASE2,
},
}, f, indent=2, default=str)
logger.info(f" ✓ Metrics saved: {metrics_path}")
logger.info("\n" + "=" * 70)
logger.info("✓ V6.7 FASE2-ONLY COMPLETED")
logger.info(f" FASE1 (mini): {samples_fase1} samples")
logger.info(f" FASE2: {count_fase2} samples (with punishment)")
logger.info("=" * 70)
return 0
if __name__ == "__main__":
sys.exit(main())
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