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ef18673 b4f432f ef18673 b4f432f ef18673 | 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 | """Main training loop for SAGE."""
from __future__ import annotations
import argparse
import json
import time
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Optional
import torch
from torch.utils.data import DataLoader
import yaml
from data.dataset import DatasetConfig, PackedDataset
from eval.perplexity import evaluate_perplexity
from model.config import ModelConfig
from model.model import SageTransformer
from train.checkpoint import load_latest_checkpoint, save_checkpoint
from train.hardware import HardwareConfig
from train.loss import masked_cross_entropy
from train.optimizer import ScheduleConfig, create_optimizer, create_scheduler
@dataclass
class TrainerConfig:
"""High-level trainer settings."""
output_dir: str = "runs/default"
checkpoint_interval: int = 1000
log_interval: int = 10
eval_interval: int = 1000
total_steps: int = 25_000
seed: int = 42
use_wandb: bool = True
def collate_batch(batch: list[dict[str, torch.Tensor]]) -> dict[str, torch.Tensor]:
"""Stack packed dataset examples into a batch."""
keys = batch[0].keys()
return {key: torch.stack([item[key] for item in batch], dim=0) for key in keys}
def create_dataloader(dataset: PackedDataset, batch_size: int) -> DataLoader:
"""Create the training DataLoader."""
return DataLoader(dataset, batch_size=batch_size, collate_fn=collate_batch)
def train(
model: SageTransformer,
train_dataset: PackedDataset,
validation_dataset: PackedDataset | None,
model_config: ModelConfig,
schedule_config: ScheduleConfig,
trainer_config: TrainerConfig,
) -> dict[str, object]:
"""Run the training loop and return the final summary."""
torch.manual_seed(trainer_config.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(trainer_config.seed)
hw = HardwareConfig(model_size_b=1.0, context_length=model_config.context_length)
device = torch.device(hw.device)
model = model.to(device)
optimizer = create_optimizer(model, schedule_config)
scheduler = create_scheduler(optimizer, schedule_config)
scaler = torch.GradScaler("cuda", enabled=(hw.device == "cuda" and hw.dtype == torch.float16))
start_step = load_latest_checkpoint(model, optimizer, scheduler, scaler, trainer_config.output_dir, device)
train_dataset.skip(start_step * hw.grad_accum)
train_loader = create_dataloader(train_dataset, batch_size=hw.micro_batch)
train_iter = iter(train_loader)
Path(trainer_config.output_dir).mkdir(parents=True, exist_ok=True)
metrics_path = Path(trainer_config.output_dir) / "metrics.jsonl"
tokens_seen = start_step * hw.micro_batch * model_config.context_length
last_log_time = time.perf_counter()
wandb_run = _init_wandb(trainer_config, model_config, schedule_config, hw.summary())
model.train()
for step in range(start_step, trainer_config.total_steps):
optimizer.zero_grad(set_to_none=True)
step_loss = 0.0
for _ in range(hw.grad_accum):
try:
batch = next(train_iter)
except StopIteration:
train_iter = iter(train_loader)
batch = next(train_iter)
input_ids = batch["input_ids"].to(device)
labels = batch["labels"].to(device)
loss_mask = batch["loss_mask"].to(device)
if hw.use_amp:
with torch.autocast(device_type=hw.device, dtype=hw.dtype):
logits, _ = model(input_ids)
loss = masked_cross_entropy(logits, labels, loss_mask) / hw.grad_accum
else:
logits, _ = model(input_ids)
loss = masked_cross_entropy(logits, labels, loss_mask) / hw.grad_accum
scaler.scale(loss).backward()
step_loss += loss.item()
tokens_seen += int(input_ids.numel())
scaler.unscale_(optimizer)
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
scaler.step(optimizer)
scaler.update()
scheduler.step()
if (step + 1) % trainer_config.log_interval == 0:
now = time.perf_counter()
elapsed = max(now - last_log_time, 1.0e-6)
tokens_per_second = (hw.micro_batch * hw.grad_accum * model_config.context_length) / elapsed
metrics = {
"step": step + 1,
"loss": step_loss,
"learning_rate": scheduler.get_last_lr()[0],
"tokens_seen": tokens_seen,
"tokens_per_second": tokens_per_second,
"grad_norm": float(grad_norm),
}
with metrics_path.open("a", encoding="utf-8") as handle:
handle.write(json.dumps(metrics) + "\n")
if wandb_run is not None:
wandb_run.log(metrics, step=step + 1)
last_log_time = now
if (step + 1) % trainer_config.eval_interval == 0 and validation_dataset is not None:
val_loader = create_dataloader(validation_dataset, batch_size=1)
evaluation = evaluate_perplexity(model, val_loader, device=device, dtype=hw.dtype if hw.use_amp else None)
with metrics_path.open("a", encoding="utf-8") as handle:
handle.write(json.dumps({"step": step + 1, **evaluation}) + "\n")
if wandb_run is not None:
wandb_run.log(evaluation, step=step + 1)
if (step + 1) % trainer_config.checkpoint_interval == 0:
save_checkpoint(
model=model,
optimizer=optimizer,
scheduler=scheduler,
scaler=scaler,
step=step + 1,
config={"model": model_config.to_dict(), "schedule": asdict(schedule_config), "trainer": asdict(trainer_config)},
output_dir=trainer_config.output_dir,
)
if wandb_run is not None:
wandb_run.finish()
return {"output_dir": trainer_config.output_dir, "tokens_seen": tokens_seen, "hardware": hw.summary()}
def _init_wandb(
trainer_config: TrainerConfig,
model_config: ModelConfig,
schedule_config: ScheduleConfig,
hardware_summary: dict[str, object],
):
"""Start a wandb run when available and enabled."""
if not trainer_config.use_wandb:
return None
try:
import wandb
except ImportError:
return None
return wandb.init(
project="sage-llm",
name=Path(trainer_config.output_dir).name,
config={
"model": model_config.to_dict(),
"schedule": asdict(schedule_config),
"trainer": asdict(trainer_config),
"hardware": hardware_summary,
},
mode="offline",
)
def build_argparser() -> argparse.ArgumentParser:
"""Build the trainer CLI."""
parser = argparse.ArgumentParser(description="Train the SAGE dense language model.")
parser.add_argument("--model-config", default="configs/model/1b.yaml")
parser.add_argument("--schedule-config", default="configs/train/schedule.yaml")
parser.add_argument("--train-shards", nargs="+", default=[])
parser.add_argument("--validation-shards", nargs="*", default=[])
parser.add_argument("--output-dir", default="runs/default")
parser.add_argument("--steps", type=int, default=None)
parser.add_argument("--disable-wandb", action="store_true")
return parser
def main(argv: Optional[list[str]] = None) -> None:
"""CLI entrypoint for local training runs."""
parser = build_argparser()
args = parser.parse_args(argv)
model_config = ModelConfig.from_yaml(args.model_config)
schedule_payload = yaml.safe_load(Path(args.schedule_config).read_text(encoding="utf-8"))
schedule = ScheduleConfig(
peak_learning_rate=schedule_payload["peak_learning_rate"],
min_learning_rate=schedule_payload["min_learning_rate"],
warmup_steps=schedule_payload["warmup_steps"],
weight_decay=schedule_payload["weight_decay"],
betas=tuple(schedule_payload["betas"]),
adam_eps=schedule_payload["adam_eps"],
total_steps=args.steps or schedule_payload["total_steps"] if "total_steps" in schedule_payload else (args.steps or 25_000),
)
trainer_config = TrainerConfig(
output_dir=args.output_dir,
checkpoint_interval=schedule_payload.get("checkpoint_interval", 1000),
log_interval=schedule_payload.get("log_interval", 10),
eval_interval=schedule_payload.get("eval_interval", 1000),
total_steps=args.steps or schedule_payload.get("total_steps", 25_000),
seed=schedule_payload.get("seed", 42),
use_wandb=not args.disable_wandb,
)
if not args.train_shards:
print("No training shards provided. The trainer entrypoint is configured correctly but requires shard paths to run.")
return
train_dataset = PackedDataset(DatasetConfig(tuple(args.train_shards), model_config.context_length, split="train"))
validation_dataset = None
if args.validation_shards:
validation_dataset = PackedDataset(DatasetConfig(tuple(args.validation_shards), model_config.context_length, split="validation"))
model = SageTransformer(model_config)
summary = train(model, train_dataset, validation_dataset, model_config, schedule, trainer_config)
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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