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#!/usr/bin/env python3
"""
ULTRA-FAST training script for morphological reinflection using aggressive optimizations
"""
import argparse
import json
import logging
import os
import time
import gc
from pathlib import Path
from typing import Dict, Tuple, Optional
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.cuda.amp import GradScaler, autocast
import torch.backends.cudnn as cudnn
from transformer import TagTransformer, PAD_IDX, DEVICE
from morphological_dataset import MorphologicalDataset, build_vocabulary, collate_fn
# Aggressive optimizations
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.allow_tf32 = True
torch.backends.cuda.matmul.allow_tf32 = True
# Minimal logging for speed
logging.basicConfig(level=logging.WARNING)
logger = logging.getLogger(__name__)
def create_optimized_model(config: Dict, src_vocab: Dict[str, int], tgt_vocab: Dict[str, int]) -> TagTransformer:
"""Create and initialize the TagTransformer model with aggressive optimizations"""
# Count feature tokens
feature_tokens = [token for token in src_vocab.keys()
if token.startswith('<') and token.endswith('>')]
nb_attr = len(feature_tokens)
model = TagTransformer(
src_vocab_size=len(src_vocab),
trg_vocab_size=len(tgt_vocab),
embed_dim=config['embed_dim'],
nb_heads=config['nb_heads'],
src_hid_size=config['src_hid_size'],
src_nb_layers=config['src_nb_layers'],
trg_hid_size=config['trg_hid_size'],
trg_nb_layers=config['trg_nb_layers'],
dropout_p=config['dropout_p'],
tie_trg_embed=config['tie_trg_embed'],
label_smooth=config['label_smooth'],
nb_attr=nb_attr,
src_c2i=src_vocab,
trg_c2i=tgt_vocab,
attr_c2i={},
)
# Aggressive weight initialization
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
elif p.dim() == 1:
nn.init.uniform_(p, -0.1, 0.1)
# Compile model for speed (PyTorch 2.0+)
if hasattr(torch, 'compile'):
try:
model = torch.compile(model, mode="max-autotune")
print("✓ Model compiled with torch.compile")
except Exception as e:
print(f"⚠ torch.compile failed: {e}")
return model
def create_ultra_fast_dataloader(dataset, config: Dict, src_vocab: Dict, tgt_vocab: Dict):
"""Create ultra-fast DataLoader with minimal overhead"""
# Calculate optimal workers based on system
num_workers = min(16, os.cpu_count() or 1)
# Use shared memory for faster inter-process communication
dataloader = DataLoader(
dataset,
batch_size=config['batch_size'],
shuffle=True,
collate_fn=lambda batch: collate_fn(batch, src_vocab, tgt_vocab, config['max_length']),
num_workers=num_workers,
pin_memory=True,
persistent_workers=True,
prefetch_factor=4, # Increased prefetching
drop_last=True,
generator=torch.Generator(device='cpu'), # Deterministic shuffling
)
return dataloader
def train_epoch_ultra_fast(model: TagTransformer,
dataloader: DataLoader,
optimizer: optim.Optimizer,
device: torch.device,
epoch: int,
config: Dict,
scaler: GradScaler) -> float:
"""Ultra-fast training with minimal overhead"""
model.train()
total_loss = 0.0
num_batches = 0
# Pre-allocate tensors to avoid repeated allocation
accumulation_steps = config.get('gradient_accumulation_steps', 1)
optimizer.zero_grad(set_to_none=True) # Faster than zero_grad()
# Disable gradient computation for validation tensors
torch.set_grad_enabled(True)
start_time = time.time()
for batch_idx, (src, src_mask, tgt, tgt_mask) in enumerate(dataloader):
# Move to device with minimal overhead
src = src.to(device, non_blocking=True, memory_format=torch.channels_last)
src_mask = src_mask.to(device, non_blocking=True)
tgt = tgt.to(device, non_blocking=True, memory_format=torch.channels_last)
tgt_mask = tgt_mask.to(device, non_blocking=True)
# Mixed precision forward pass
with autocast(enabled=config.get('use_amp', True)):
# Forward pass
output = model(src, src_mask, tgt, tgt_mask)
# Compute loss (shift sequences for next-token prediction)
loss = model.loss(output[:-1], tgt[1:])
loss = loss / accumulation_steps
# Mixed precision backward pass
scaler.scale(loss).backward()
# Gradient accumulation
if (batch_idx + 1) % accumulation_steps == 0:
# Gradient clipping
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['gradient_clip'])
# Optimizer step
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad(set_to_none=True)
total_loss += loss.item() * accumulation_steps
num_batches += 1
# Minimal logging - only every 100 batches
if batch_idx % 100 == 0:
elapsed = time.time() - start_time
samples_per_sec = (batch_idx + 1) * config['batch_size'] / elapsed
print(f'Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item() * accumulation_steps:.4f}, Speed: {samples_per_sec:.0f} samples/sec')
# Handle remaining gradients
if num_batches % accumulation_steps != 0:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['gradient_clip'])
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad(set_to_none=True)
avg_loss = total_loss / num_batches
return avg_loss
def validate_fast(model: TagTransformer,
dataloader: DataLoader,
device: torch.device,
config: Dict) -> float:
"""Fast validation with minimal overhead"""
model.eval()
total_loss = 0.0
num_batches = 0
with torch.no_grad():
for src, src_mask, tgt, tgt_mask in dataloader:
src = src.to(device, non_blocking=True, memory_format=torch.channels_last)
src_mask = src_mask.to(device, non_blocking=True)
tgt = tgt.to(device, non_blocking=True, memory_format=torch.channels_last)
tgt_mask = tgt_mask.to(device, non_blocking=True)
with autocast(enabled=config.get('use_amp', True)):
output = model(src, src_mask, tgt, tgt_mask)
loss = model.loss(output[:-1], tgt[1:])
total_loss += loss.item()
num_batches += 1
avg_loss = total_loss / num_batches
return avg_loss
def save_checkpoint_fast(model: TagTransformer,
optimizer: optim.Optimizer,
epoch: int,
loss: float,
save_path: str,
scaler: GradScaler = None):
"""Fast checkpoint saving"""
# Save only essential data
checkpoint = {
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': loss,
}
if scaler is not None:
checkpoint['scaler_state_dict'] = scaler.state_dict()
# Use torch.save with _use_new_zipfile_serialization=False for speed
torch.save(checkpoint, save_path, _use_new_zipfile_serialization=False)
print(f'Checkpoint saved: {save_path}')
def load_checkpoint_fast(model: TagTransformer,
optimizer: optim.Optimizer,
checkpoint_path: str,
scaler: GradScaler = None) -> int:
"""Fast checkpoint loading"""
checkpoint = torch.load(checkpoint_path, map_location=DEVICE, weights_only=False)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
if scaler is not None and 'scaler_state_dict' in checkpoint:
scaler.load_state_dict(checkpoint['scaler_state_dict'])
epoch = checkpoint['epoch']
loss = checkpoint['loss']
print(f'Checkpoint loaded: {checkpoint_path}, Epoch: {epoch}, Loss: {loss:.4f}')
return epoch
def main():
parser = argparse.ArgumentParser(description='ULTRA-FAST training for morphological reinflection')
parser.add_argument('--resume', type=str, help='Path to checkpoint to resume from')
parser.add_argument('--output_dir', type=str, default='./models', help='Output directory')
parser.add_argument('--no_amp', action='store_true', help='Disable mixed precision training')
parser.add_argument('--profile', action='store_true', help='Enable profiling for debugging')
args = parser.parse_args()
# Ultra-aggressive configuration for maximum speed
config = {
'embed_dim': 256,
'nb_heads': 4,
'src_hid_size': 1024,
'src_nb_layers': 4,
'trg_hid_size': 1024,
'trg_nb_layers': 4,
'dropout_p': 0.1,
'tie_trg_embed': True,
'label_smooth': 0.1,
'batch_size': 800, # Larger batch size for better GPU utilization
'learning_rate': 0.001,
'max_epochs': 1000,
'max_updates': 10000,
'warmup_steps': 4000,
'weight_decay': 0.01,
'gradient_clip': 1.0,
'save_every': 20, # Save less frequently for speed
'eval_every': 10, # Evaluate less frequently for speed
'max_length': 100,
'use_amp': not args.no_amp,
'gradient_accumulation_steps': 1, # No accumulation for maximum speed
'pin_memory': True,
'persistent_workers': True,
'prefetch_factor': 4,
}
# Create output directory
os.makedirs(args.output_dir, exist_ok=True)
os.makedirs(os.path.join(args.output_dir, 'checkpoints'), exist_ok=True)
# Save config
with open(os.path.join(args.output_dir, 'config.json'), 'w') as f:
json.dump(config, f, indent=2)
# Set device and enable optimizations
device = DEVICE
print(f'Using device: {device}')
# Aggressive CUDA optimizations
if torch.cuda.is_available():
# Set memory fraction to avoid fragmentation
torch.cuda.set_per_process_memory_fraction(0.95)
# Enable memory pool for faster allocation
torch.cuda.empty_cache()
gc.collect()
print("✓ CUDA optimizations enabled")
print(f"GPU: {torch.cuda.get_device_name()}")
print(f"Memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB")
# Data file paths
train_src = '../10L_90NL/train/run1/train.10L_90NL_1_1.src'
train_tgt = '../10L_90NL/train/run1/train.10L_90NL_1_1.tgt'
dev_src = '../10L_90NL/dev/run1/dev.10L_90NL_1_1.src'
dev_tgt = '../10L_90NL/dev/run1/dev.10L_90NL_1_1.tgt'
# Build vocabulary efficiently
print("Building vocabulary...")
src_vocab = build_vocabulary([train_src, dev_src])
tgt_vocab = build_vocabulary([train_tgt, dev_tgt])
print(f"Source vocabulary size: {len(src_vocab)}")
print(f"Target vocabulary size: {len(tgt_vocab)}")
# Create datasets
train_dataset = MorphologicalDataset(train_src, train_tgt, src_vocab, tgt_vocab, config['max_length'])
dev_dataset = MorphologicalDataset(dev_src, dev_tgt, src_vocab, tgt_vocab, config['max_length'])
# Create ultra-fast dataloaders
train_loader = create_ultra_fast_dataloader(train_dataset, config, src_vocab, tgt_vocab)
dev_loader = create_ultra_fast_dataloader(dev_dataset, config, src_vocab, tgt_vocab)
# Create optimized model
model = create_optimized_model(config, src_vocab, tgt_vocab)
model = model.to(device)
# Use channels_last memory format for better performance
if hasattr(model, 'to'):
model = model.to(memory_format=torch.channels_last)
# Count parameters
total_params = model.count_nb_params()
print(f'Total parameters: {total_params:,}')
# Create optimizer with aggressive settings
optimizer = optim.AdamW(
model.parameters(),
lr=config['learning_rate'],
weight_decay=config['weight_decay'],
betas=(0.9, 0.999),
eps=1e-8,
foreach=True, # Use foreach implementation for speed
)
# Learning rate scheduler
def lr_lambda(step):
if step < config['warmup_steps']:
return float(step) / float(max(1, config['warmup_steps']))
progress = (step - config['warmup_steps']) / (config['max_updates'] - config['warmup_steps'])
return max(0.0, 0.5 * (1.0 + torch.cos(torch.pi * progress)))
scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
# Mixed precision training
scaler = GradScaler(enabled=config['use_amp'])
if config['use_amp']:
print("✓ Mixed precision training enabled")
# Resume from checkpoint if specified
start_epoch = 0
if args.resume:
start_epoch = load_checkpoint_fast(model, optimizer, args.resume, scaler)
# Training loop
best_val_loss = float('inf')
global_step = 0
updates = 0
print(f"\nStarting training with {len(train_loader)} batches per epoch")
print(f"Effective batch size: {config['batch_size'] * config['gradient_accumulation_steps']}")
for epoch in range(start_epoch, config['max_epochs']):
epoch_start_time = time.time()
# Train
train_loss = train_epoch_ultra_fast(
model, train_loader, optimizer, device, epoch, config, scaler
)
# Update learning rate
scheduler.step()
current_lr = scheduler.get_last_lr()[0]
# Validate less frequently for speed
if epoch % config['eval_every'] == 0:
val_loss = validate_fast(model, dev_loader, device, config)
print(f'Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, LR: {current_lr:.6f}')
# Save best model
if val_loss < best_val_loss:
best_val_loss = val_loss
save_checkpoint_fast(
model, optimizer, epoch, val_loss,
os.path.join(args.output_dir, 'checkpoints', 'best_model.pth'),
scaler
)
else:
print(f'Epoch {epoch}: Train Loss: {train_loss:.4f}, LR: {current_lr:.6f}')
# Save checkpoint less frequently for speed
if epoch % config['save_every'] == 0:
save_checkpoint_fast(
model, optimizer, epoch, train_loss,
os.path.join(args.output_dir, 'checkpoints', f'checkpoint_epoch_{epoch}.pth'),
scaler
)
epoch_time = time.time() - epoch_start_time
samples_per_sec = len(train_loader) * config['batch_size'] / epoch_time
print(f'Epoch {epoch} completed in {epoch_time:.2f}s ({samples_per_sec:.0f} samples/sec)')
# Count updates
updates += len(train_loader)
global_step += len(train_loader)
# Check if we've reached max updates
if updates >= config['max_updates']:
print(f'Reached maximum updates ({config["max_updates"]}), stopping training')
break
# Clear cache periodically
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Save final model
save_checkpoint_fast(
model, optimizer, epoch, train_loss,
os.path.join(args.output_dir, 'checkpoints', 'final_model.pth'),
scaler
)
print('Training completed!')
if __name__ == '__main__':
main()
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