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"""
Step 3: STREAMLINED Training - Minimal, Fast
"""
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import GPT2LMHeadModel, GPT2Config
import sentencepiece as spm
from tqdm import tqdm
import time
# ===== CONFIG =====
CONFIG = {
'train_file': './final_corpus/multilingual_corpus_train.txt',
'val_file': './final_corpus/multilingual_corpus_val.txt',
'tokenizer_path': './final_corpus/multilingual_spm.model',
# Tiny model for fast training
'n_positions': 128,
'n_embd': 256,
'n_layer': 4,
'n_head': 4,
'n_inner': 512,
# Training
'batch_size': 2, # Small batch for 4GB
'grad_accum': 8, # Effective batch = 16
'learning_rate': 2e-4,
'total_steps': 5000, # Train for 5000 steps only
'save_every': 1000,
}
class SimpleDataset(Dataset):
def __init__(self, filepath, tokenizer, block_size):
self.tokenizer = tokenizer
self.block_size = block_size
print("Loading data...")
with open(filepath, 'r', encoding='utf-8') as f:
lines = [line.strip() for line in f if line.strip()]
# Tokenize all at once
self.examples = []
for line in tqdm(lines[:600000], desc="Tokenizing"): # Use only 50K lines
tokens = tokenizer.encode(line)
if len(tokens) > 10:
if len(tokens) > block_size:
tokens = tokens[:block_size]
else:
tokens = tokens + [0] * (block_size - len(tokens))
self.examples.append(tokens)
print(f"Created {len(self.examples)} examples")
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
return torch.tensor(self.examples[idx], dtype=torch.long)
def train_streamlined():
print("\n" + "="*60)
print("STREAMLINED TRAINING - FASTEST POSSIBLE")
print("="*60)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Device: {device}")
# Load tokenizer
tokenizer = spm.SentencePieceProcessor()
tokenizer.load(CONFIG['tokenizer_path'])
vocab_size = tokenizer.get_piece_size()
# Create tiny model
config = GPT2Config(
vocab_size=vocab_size,
n_positions=CONFIG['n_positions'],
n_embd=CONFIG['n_embd'],
n_layer=CONFIG['n_layer'],
n_head=CONFIG['n_head'],
n_inner=CONFIG['n_inner'],
pad_token_id=0,
)
model = GPT2LMHeadModel(config)
model.to(device)
model.train()
# Enable gradient checkpointing
model.gradient_checkpointing_enable()
# Create dataset (small)
dataset = SimpleDataset(CONFIG['train_file'], tokenizer, CONFIG['n_positions'])
dataloader = DataLoader(dataset, batch_size=CONFIG['batch_size'], shuffle=True)
# Optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=CONFIG['learning_rate'])
print(f"\nModel: {sum(p.numel() for p in model.parameters())/1e6:.1f}M params")
print(f"Training steps: {CONFIG['total_steps']}")
print(f"Estimated time: {CONFIG['total_steps']*0.3/3600:.1f} hours\n")
# Training loop
global_step = 0
accumulation_steps = 0
start_time = time.time()
while global_step < CONFIG['total_steps']:
for batch in dataloader:
batch = batch.to(device)
# Forward
outputs = model(input_ids=batch, labels=batch)
loss = outputs.loss / CONFIG['grad_accum']
# Backward
loss.backward()
accumulation_steps += 1
# Gradient accumulation
if accumulation_steps == CONFIG['grad_accum']:
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
optimizer.zero_grad()
global_step += 1
accumulation_steps = 0
# Print progress
if global_step % 100 == 0:
elapsed = time.time() - start_time
steps_per_second = global_step / elapsed
remaining = (CONFIG['total_steps'] - global_step) / steps_per_second
print(f"Step {global_step}/{CONFIG['total_steps']} | "
f"Loss: {loss.item()*CONFIG['grad_accum']:.3f} | "
f"Remaining: {remaining/3600:.1f}h")
# Save checkpoint
if global_step % CONFIG['save_every'] == 0:
save_path = f"./checkpoints_tiny/step{global_step}"
model.save_pretrained(save_path)
print(f"Saved checkpoint: {save_path}")
# Stop if reached total steps
if global_step >= CONFIG['total_steps']:
break
print(f"\nTraining completed in {(time.time()-start_time)/3600:.2f} hours")
# Save final model
model.save_pretrained("./checkpoints_tiny/final")
print("Final model saved to ./checkpoints_tiny/final")
if __name__ == "__main__":
train_streamlined() |