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"""
SHOREKEEPER-4B Training Pipeline
Runs on any CUDA device (RTX 3060, H100, etc.)
"""
import sys
import json
import torch
import torch.nn as nn
from pathlib import Path
from tqdm import tqdm
sys.path.insert(0, str(Path(__file__).parent.parent))
from src.shorekeeper import MemoryEfficientSHOREKEEPER
from transformers import AutoTokenizer
class SHOREKEEPERTrainer:
"""Simple training loop for SHOREKEEPER"""
def __init__(self, model, tokenizer, config):
self.model = model
self.tokenizer = tokenizer
self.device = next(model.parameters()).device
self.learning_rate = config.get('learning_rate', 1e-4)
self.epochs = config.get('epochs', 3)
self.batch_size = config.get('batch_size', 2)
self.gradient_accumulation = config.get('gradient_accumulation', 4)
self.optimizer = torch.optim.AdamW(
self.model.parameters(),
lr=self.learning_rate,
weight_decay=0.01
)
self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
self.optimizer,
T_max=1000,
eta_min=1e-6
)
self.step = 0
def train_step(self, batch):
"""Single training step"""
self.model.train()
# Prepare batch
texts = batch['text']
# Tokenize
inputs = self.tokenizer(
texts,
return_tensors="pt",
padding=True,
truncation=True,
max_length=512
)
input_ids = inputs['input_ids'].to(self.device)
# Forward pass
logits = self.model(input_ids)
# Calculate loss (next token prediction)
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = input_ids[..., 1:].contiguous()
# Cross entropy loss - ignore padding tokens
loss = nn.functional.cross_entropy(
shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1),
ignore_index=self.tokenizer.pad_token_id if self.tokenizer.pad_token_id else -100
)
# Backward
loss.backward()
# Gradient accumulation
if (self.step + 1) % self.gradient_accumulation == 0:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
self.optimizer.step()
self.scheduler.step()
self.optimizer.zero_grad()
self.step += 1
return loss.item()
def train(self, dataset, output_dir="./outputs"):
"""Full training loop"""
print(f"\n{'='*60}")
print("Starting Training")
print(f"{'='*60}")
print(f"Device: {self.device}")
print(f"Training samples: {len(dataset)}")
print(f"Batch size: {self.batch_size}")
print(f"Learning rate: {self.learning_rate}")
print(f"Epochs: {self.epochs}")
print(f"{'='*60}\n")
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for epoch in range(self.epochs):
print(f"\nEpoch {epoch + 1}/{self.epochs}")
print("-" * 40)
total_loss = 0
steps = 0
# Create progress bar
pbar = tqdm(dataset, desc=f"Training")
for i, item in enumerate(pbar):
# Format training text
prompt = item.get('prompt', '')
response = item.get('response', '')
if not prompt or not response:
continue
# Create training text (prompt + response)
text = f"{prompt}\n{response}"
batch = {'text': [text]}
try:
loss = self.train_step(batch)
total_loss += loss
steps += 1
# Update progress bar
pbar.set_postfix({'loss': f'{loss:.4f}'})
# Save checkpoint every 100 steps
if steps % 100 == 0:
checkpoint_path = output_dir / f"checkpoint_step_{steps}.pt"
torch.save({
'step': steps,
'model_state': self.model.state_dict(),
'optimizer_state': self.optimizer.state_dict(),
'loss': loss
}, checkpoint_path)
print(f"\n Saved checkpoint: {checkpoint_path}")
except Exception as e:
# Don't print every error to avoid spam
if steps < 5:
print(f"\n Error on step {steps}: {e}")
continue
avg_loss = total_loss / steps if steps > 0 else 0
print(f"\nEpoch {epoch + 1} complete: Avg Loss = {avg_loss:.4f}")
# Save epoch checkpoint
epoch_path = output_dir / f"epoch_{epoch + 1}.pt"
torch.save({
'epoch': epoch + 1,
'model_state': self.model.state_dict(),
'optimizer_state': self.optimizer.state_dict(),
'avg_loss': avg_loss
}, epoch_path)
print(f"Saved epoch checkpoint: {epoch_path}")
# Save final model
final_path = output_dir / "shorekeeper-4b-final.pt"
torch.save(self.model.state_dict(), final_path)
print(f"\n{'='*60}")
print(f"✅ Training complete! Final model saved to: {final_path}")
print(f"{'='*60}")
return self.model
def load_data(data_path, limit=None):
"""Load training data from JSONL file"""
data = []
data_path = Path(data_path)
if not data_path.exists():
print(f"Data file not found: {data_path}")
return data
with open(data_path, 'r') as f:
for i, line in enumerate(f):
if limit and i >= limit:
break
try:
item = json.loads(line)
data.append(item)
except:
continue
print(f"Loaded {len(data)} examples from {data_path}")
return data
def main():
print("=" * 60)
print("SHOREKEEPER-4B Training Pipeline")
print("=" * 60)
# Check device
if torch.cuda.is_available():
device = torch.device("cuda")
print(f"\n✓ CUDA available: {torch.cuda.get_device_name(0)}")
print(f" Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")
else:
device = torch.device("cpu")
print("\n⚠ No GPU detected, using CPU (will be slow)")
# Load model
print("\n1. Loading SHOREKEEPER model...")
model = MemoryEfficientSHOREKEEPER(use_4bit=False) # Use full precision for training
model = model.to(device)
print(f" Model loaded on {device}")
# Load tokenizer
print("\n2. Loading tokenizer...")
try:
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
print(" ✓ Using GPT-2 tokenizer")
except:
print(" ⚠ Could not load GPT-2 tokenizer")
return
# Load training data
print("\n3. Loading training data...")
data_path = Path("./data/processed/train.jsonl")
if not data_path.exists():
print(f"\n❌ No training data found at {data_path}")
print(" Run: python3 scripts/01_download_data.py")
print(" Then: python3 scripts/02_prepare_data.py")
return
print("\n Training options:")
print(" [1] Quick test (50 examples, 1 epoch) - ~2 minutes")
print(" [2] Small training (200 examples, 3 epochs) - ~10 minutes")
print(" [3] Medium training (500 examples, 5 epochs) - ~30 minutes")
print(" [4] Full training (all data, 10 epochs) - several hours")
choice = input("\nChoose option (1/2/3/4): ").strip()
if choice == "1":
limit = 50
epochs = 1
learning_rate = 1e-4
elif choice == "2":
limit = 200
epochs = 3
learning_rate = 5e-5
elif choice == "3":
limit = 500
epochs = 5
learning_rate = 3e-5
else:
limit = None
epochs = 10
learning_rate = 1e-5
# Load data
data = load_data(data_path, limit=limit)
if not data:
print("\n❌ No training data available!")
return
print(f"\n Training with {len(data)} examples, {epochs} epochs")
print(f" Learning rate: {learning_rate}")
# Training config
config = {
'learning_rate': learning_rate,
'epochs': epochs,
'batch_size': 2,
'gradient_accumulation': 4
}
# Create trainer
print("\n4. Initializing trainer...")
trainer = SHOREKEEPERTrainer(model, tokenizer, config)
# Start training
print("\n5. Starting training...")
print(" Press Ctrl+C to stop early\n")
try:
trained_model = trainer.train(data, output_dir="./outputs")
except KeyboardInterrupt:
print("\n\n⚠ Training interrupted by user")
print("Saving current model...")
torch.save(model.state_dict(), "./outputs/shorekeeper-interrupted.pt")
print("Model saved to: ./outputs/shorekeeper-interrupted.pt")
except Exception as e:
print(f"\n❌ Training failed: {e}")
import traceback
traceback.print_exc()
print("\n" + "=" * 60)
print("Next steps:")
print(" 1. Run GRPO training: python3 scripts/05_grpo_train.py")
print(" 2. Convert to 4-bit: python3 scripts/06_convert_to_4bit.py")
print(" 3. Run SHOREKEEPER: python3 scripts/07_run_shorekeeper.py")
print("=" * 60)
if __name__ == "__main__":
main()
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