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47dfda4 382bae9 47dfda4 382bae9 47dfda4 382bae9 47dfda4 382bae9 47dfda4 382bae9 47dfda4 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 | import os
import time
import math
import random
import argparse
import numpy as np
import tensorflow as tf
from safetensors.numpy import load_file, save_file
from indigotf.bpe import BPETokenizer
from indigotf.common import (
build_tokenizer,
collect_text_files,
load_meta,
read_clean,
save_meta,
)
from indigotf.model import build_gpt
from indigotf.tokenizer import CharTokenizer
CONFIG_KEYS = ("vocab_size", "block_size", "n_layer", "n_head", "n_embd", "dropout")
def get_batch(np_data, block_size, batch_size):
ix = np.random.randint(0, len(np_data) - block_size - 1, size=batch_size)
idx = ix[:, None] + np.arange(block_size)
x = np_data[idx]
y = np_data[idx + 1]
return tf.constant(x), tf.constant(y)
@tf.function(reduce_retracing=True)
def train_step(model, optimizer, loss_fn, x, y):
with tf.GradientTape() as tape:
logits = model(x, training=True)
loss = loss_fn(y, logits)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
return loss
@tf.function(reduce_retracing=True)
def eval_step(model, loss_fn, x, y):
logits = model(x, training=False)
return loss_fn(y, logits)
def save_weights_tf(model, base_path, config, vocab, step, val_loss, tinfo):
tensors = {v.path: np.asarray(v) for v in model.weights}
save_file(tensors, base_path)
save_meta(base_path, config, vocab, step, val_loss, backend="tensorflow", tokenizer=tinfo)
def main(argv=None):
parser = argparse.ArgumentParser(description="Latih model Indigo-TF (backend TensorFlow/Keras)")
parser.add_argument("--data", nargs="+", default=["data/sample.txt"])
parser.add_argument("--out", default="out")
parser.add_argument("--steps", type=int, default=2000)
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--block-size", type=int, default=128)
parser.add_argument("--n-layer", type=int, default=4)
parser.add_argument("--n-head", type=int, default=4)
parser.add_argument("--n-embd", type=int, default=128)
parser.add_argument("--dropout", type=float, default=0.1)
parser.add_argument("--lr", type=float, default=3e-4)
parser.add_argument("--warmup", type=int, default=100)
parser.add_argument("--weight-decay", type=float, default=0.1)
parser.add_argument("--eval-interval", type=int, default=200)
parser.add_argument("--eval-iters", type=int, default=20)
parser.add_argument("--seed", type=int, default=1337)
parser.add_argument("--init-from", default=None, help="checkpoint safetensors sebelumnya")
parser.add_argument("--tokenizer", default="char", choices=["char", "bpe"])
parser.add_argument("--vocab-size", type=int, default=512)
parser.add_argument("--device", default="auto", choices=["auto", "cpu", "gpu"])
args = parser.parse_args(argv)
if args.device == "cpu":
tf.config.set_visible_devices([], "GPU")
gpus = tf.config.list_physical_devices("GPU")
device_label = f"gpu({len(gpus)})" if gpus and args.device != "cpu" else "cpu"
random.seed(args.seed)
np.random.seed(args.seed)
tf.random.set_seed(args.seed)
os.makedirs(args.out, exist_ok=True)
files = sorted(collect_text_files(args.data))
if not files:
raise SystemExit("tidak ada file teks ditemukan")
rng = random.Random(args.seed)
rng.shuffle(files)
n_val = max(1, round(len(files) * 0.1)) if len(files) > 1 else 0
print(f"file latih={len(files) - n_val} | file validasi={n_val}")
train_text = "".join(read_clean(p) for p in files[n_val:])
val_text = "".join(read_clean(p) for p in files[:n_val])
all_text = train_text + val_text
start_step = 0
init_state = None
if args.init_from:
meta = load_meta(args.init_from)
if meta.get("backend") != "tensorflow":
raise SystemExit(f"{args.init_from} bukan checkpoint backend TensorFlow")
config_d = meta["config"]
start_step = meta.get("step", 0)
init_state = {k.replace("/", "_"): v for k, v in load_file(args.init_from).items()}
print(f"melanjutkan dari {args.init_from} (step {start_step})")
tokenizer = build_tokenizer(meta.get("tokenizer") or {"type": "char"}, meta.get("vocab"))
tinfo = meta.get("tokenizer") or {"type": "char"}
else:
if args.tokenizer == "bpe":
tokenizer = BPETokenizer.train(all_text, args.vocab_size)
tinfo = tokenizer.state()
else:
tokenizer = CharTokenizer.from_text(all_text)
tinfo = {"type": "char"}
config_d = {
"vocab_size": tokenizer.vocab_size,
"block_size": args.block_size,
"n_layer": args.n_layer,
"n_head": args.n_head,
"n_embd": args.n_embd,
"dropout": args.dropout,
"bias": False,
}
if config_d["vocab_size"] != tokenizer.vocab_size:
raise SystemExit(
f"vocab tidak cocok: checkpoint={config_d['vocab_size']}, tokenizer={tokenizer.vocab_size}"
)
train_np = np.array(tokenizer.encode(train_text), dtype=np.int64)
val_np = np.array(tokenizer.encode(val_text), dtype=np.int64)
if len(train_np) < config_d["block_size"] * 2:
raise SystemExit(f"data latih terlalu pendek ({len(train_np)} token)")
print(
f"tokenizer={tinfo['type']} | tokens latih={len(train_np):,} | "
f"tokens validasi={len(val_np):,} | vocab={tokenizer.vocab_size}"
)
total_steps = start_step + args.steps
model = build_gpt(
vocab_size=config_d["vocab_size"],
block_size=config_d["block_size"],
n_layer=config_d["n_layer"],
n_head=config_d["n_head"],
n_embd=config_d["n_embd"],
dropout=config_d["dropout"],
)
if init_state is not None:
by_path = {v.path.replace("/", "_"): v for v in model.weights}
missing = [p for p in by_path if p not in init_state]
if missing:
raise SystemExit(f"bobot tidak cocok dengan checkpoint: {missing[:5]}")
model.set_weights([init_state[v.path.replace("/", "_")] for v in model.weights])
n_params = int(sum(int(np.prod(v.shape)) for v in model.weights))
print(
f"device={device_label} | params={n_params / 1e6:.2f}M | "
f"vocab={config_d['vocab_size']} | total_steps={total_steps}"
)
optimizer = tf.keras.optimizers.AdamW(
learning_rate=args.lr, beta_1=0.9, beta_2=0.95, weight_decay=args.weight_decay, clipnorm=1.0
)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
def lr_at(step):
if step < args.warmup:
return args.lr * (step + 1) / args.warmup
progress = (step - args.warmup) / max(1, total_steps - args.warmup)
return 0.1 * args.lr + 0.45 * args.lr * (1 + math.cos(math.pi * progress))
best_val = float("inf")
last_val = None
t0 = time.time()
for step in range(start_step, total_steps):
optimizer.learning_rate.assign(lr_at(step))
x, y = get_batch(train_np, config_d["block_size"], args.batch_size)
loss = train_step(model, optimizer, loss_fn, x, y)
if step % args.eval_interval == 0 or step == total_steps - 1:
if len(val_np) > config_d["block_size"] + 1:
losses = []
for _ in range(args.eval_iters):
vx, vy = get_batch(val_np, config_d["block_size"], args.batch_size)
losses.append(float(eval_step(model, loss_fn, vx, vy)))
val_loss = sum(losses) / len(losses)
marker = ""
if val_loss < best_val:
best_val = val_loss
save_weights_tf(
model,
os.path.join(args.out, "indigo_best.safetensors"),
config_d,
tokenizer.itos if hasattr(tokenizer, "itos") else None,
total_steps,
val_loss,
tinfo,
)
marker = " <- best"
last_val = val_loss
val_str = f"{val_loss:.4f}{marker}"
else:
val_str = "n/a"
print(
f"step {step + 1:5d}/{total_steps} | "
f"loss {float(loss):.4f} | val {val_str} | {time.time() - t0:.1f}s"
)
final_path = os.path.join(args.out, "indigo.safetensors")
save_weights_tf(model, final_path, config_d, tokenizer.itos if hasattr(tokenizer, "itos") else None,
total_steps, last_val, tinfo)
print(f"model tersimpan di {final_path} (+_meta.json)")
comp_ratio = 1.0
if tinfo.get("type") == "bpe":
n_chars = len((train_text + val_text).encode("utf-8"))
comp_ratio = n_chars / max(1, len(train_np))
stats = {
"out": args.out,
"device": device_label,
"backend": "tensorflow",
"tokenizer": tinfo.get("type", "char"),
"vocab_size": tokenizer.vocab_size,
"compression_ratio": round(comp_ratio, 4),
"tokens_train": len(train_np),
"tokens_val": len(val_np),
"files_train": max(0, len(files) - n_val),
"files_val": n_val,
"steps_trained": args.steps,
"total_steps": total_steps,
"best_val": best_val if best_val != float("inf") else None,
"last_val": last_val,
"nats_per_char_best": (
round(best_val / comp_ratio, 4) if best_val != float("inf") and comp_ratio else None
),
"params_million": round(n_params / 1e6, 4),
"config": config_d,
"args": {k: v for k, v in vars(args).items() if k != "data"},
"elapsed_sec": round(time.time() - t0, 1),
}
print(
f"ringkasan: best_val={stats['best_val']} | "
f"nats/karakter={stats['nats_per_char_best']} | params={stats['params_million']}M"
)
return stats
if __name__ == "__main__":
main()
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