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import optuna
from optuna.samplers import TPESampler
from optuna.pruners import MedianPruner
import wandb
import pandas as pd
from model.train import train, init_model, create_dataloaders, ToxicDataset
from model.training_config import TrainingConfig
from transformers import XLMRobertaTokenizer
import json
import torch
def load_dataset(file_path: str):
"""Load and prepare dataset"""
df = pd.read_csv(file_path)
tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
config = TrainingConfig()
return ToxicDataset(df, tokenizer, config)
class HyperparameterTuner:
def __init__(self, train_dataset, val_dataset, n_trials=10):
self.train_dataset = train_dataset
self.val_dataset = val_dataset
self.n_trials = n_trials
# Make pruning more aggressive
self.study = optuna.create_study(
direction="maximize",
sampler=TPESampler(seed=42),
pruner=MedianPruner(
n_startup_trials=2,
n_warmup_steps=2,
interval_steps=1
)
)
def objective(self, trial):
"""Objective function for Optuna optimization with optimal ranges"""
# Define hyperparameter search space with optimal ranges
config_params = {
# Fixed architecture parameters
"model_name": "xlm-roberta-large",
"hidden_size": 1024, # Fixed to original
"num_attention_heads": 16, # Fixed to original
# Optimized ranges based on trials
"lr": trial.suggest_float("lr", 1e-5, 5e-5, log=True), # Best range from trial-8/4
"batch_size": trial.suggest_categorical("batch_size", [32, 64]), # Top performers
"model_dropout": trial.suggest_float("model_dropout", 0.3, 0.45), # Trial-8's 0.445 effective
"weight_decay": trial.suggest_float("weight_decay", 0.01, 0.03), # Best regularization
"grad_accum_steps": trial.suggest_int("grad_accum_steps", 1, 4), # Keep for throughput optimization
# Fixed training parameters
"epochs": 2,
"mixed_precision": "bf16",
"max_length": 128,
"fp16": False,
"distributed": False,
"world_size": 1,
"num_workers": 12,
"activation_checkpointing": True,
"tensor_float_32": True,
"gc_frequency": 500
}
# Create config
config = TrainingConfig(**config_params)
# Initialize wandb for this trial with better metadata
wandb.init(
project="toxic-classification-hparam-tuning",
name=f"trial-{trial.number}",
config={
**config_params,
'trial_number': trial.number,
'pruner': str(trial.study.pruner),
'sampler': str(trial.study.sampler)
},
reinit=True,
tags=['hyperparameter-optimization', f'trial-{trial.number}']
)
try:
# Create model and dataloaders
model = init_model(config)
train_loader, val_loader = create_dataloaders(
self.train_dataset,
self.val_dataset,
config
)
# Train and get metrics
metrics = train(model, train_loader, val_loader, config)
# Log detailed metrics
wandb.log({
'final_val_auc': metrics['val/auc'],
'final_val_loss': metrics['val/loss'],
'final_train_loss': metrics['train/loss'],
'peak_gpu_memory': torch.cuda.max_memory_allocated() / 1e9 if torch.cuda.is_available() else 0,
'trial_completed': True
})
# Report intermediate values for pruning
trial.report(metrics['val/auc'], step=config.epochs)
# Handle pruning
if trial.should_prune():
wandb.log({'pruned': True})
raise optuna.TrialPruned()
return metrics['val/auc']
except Exception as e:
wandb.log({
'error': str(e),
'trial_failed': True
})
print(f"Trial failed: {str(e)}")
raise optuna.TrialPruned()
finally:
# Cleanup
if 'model' in locals():
del model
torch.cuda.empty_cache()
wandb.finish()
def run_optimization(self):
"""Run the hyperparameter optimization"""
print("Starting hyperparameter optimization...")
print("Search space:")
print(" - Learning rate: 1e-5 to 5e-5")
print(" - Batch size: [32, 64]")
print(" - Dropout: 0.3 to 0.45")
print(" - Weight decay: 0.01 to 0.03")
print(" - Gradient accumulation steps: 1 to 4")
print("\nFixed parameters:")
print(" - Hidden size: 1024 (original)")
print(" - Attention heads: 16 (original)")
try:
self.study.optimize(
self.objective,
n_trials=self.n_trials,
timeout=None, # No timeout
callbacks=[self._log_trial]
)
# Print optimization results
print("\nBest trial:")
best_trial = self.study.best_trial
print(f" Value: {best_trial.value:.4f}")
print(" Params:")
for key, value in best_trial.params.items():
print(f" {key}: {value}")
# Save study results with more details
self._save_study_results()
except KeyboardInterrupt:
print("\nOptimization interrupted by user.")
self._save_study_results() # Save results even if interrupted
except Exception as e:
print(f"Optimization failed: {str(e)}")
raise
def _log_trial(self, study, trial):
"""Callback to log trial results with enhanced metrics"""
if trial.value is not None:
metrics = {
"best_auc": study.best_value,
"trial_auc": trial.value,
"trial_number": trial.number,
**trial.params
}
# Add optimization progress metrics
if len(study.trials) > 1:
metrics.update({
"optimization_progress": {
"trials_completed": len(study.trials),
"improvement_rate": (study.best_value - study.trials[0].value) / len(study.trials),
"best_trial_number": study.best_trial.number
}
})
wandb.log(metrics)
def _save_study_results(self):
"""Save optimization results with enhanced metadata"""
import joblib
from pathlib import Path
from datetime import datetime
# Create directory if it doesn't exist
results_dir = Path("optimization_results")
results_dir.mkdir(exist_ok=True)
# Save study object
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
study_path = results_dir / f"hparam_optimization_study_{timestamp}.pkl"
joblib.dump(self.study, study_path)
# Save comprehensive results
results = {
"best_trial": {
"number": self.study.best_trial.number,
"value": self.study.best_value,
"params": self.study.best_trial.params
},
"study_statistics": {
"n_trials": len(self.study.trials),
"n_completed": len([t for t in self.study.trials if t.state == optuna.trial.TrialState.COMPLETE]),
"n_pruned": len([t for t in self.study.trials if t.state == optuna.trial.TrialState.PRUNED]),
"datetime_start": self.study.trials[0].datetime_start.isoformat(),
"datetime_complete": datetime.now().isoformat()
},
"search_space": {
"lr": {"low": 1e-5, "high": 5e-5},
"batch_size": [32, 64],
"model_dropout": {"low": 0.3, "high": 0.45},
"weight_decay": {"low": 0.01, "high": 0.03},
"grad_accum_steps": {"low": 1, "high": 4}
},
"trial_history": [
{
"number": t.number,
"value": t.value,
"state": str(t.state),
"params": t.params if hasattr(t, 'params') else None
}
for t in self.study.trials
]
}
results_path = results_dir / f"optimization_results_{timestamp}.json"
with open(results_path, "w") as f:
json.dump(results, f, indent=4)
print(f"\nResults saved to:")
print(f" - Study: {study_path}")
print(f" - Results: {results_path}")
def main():
"""Main function to run hyperparameter optimization"""
# Load datasets
train_dataset = load_dataset("dataset/split/train.csv")
val_dataset = load_dataset("dataset/split/val.csv")
# Initialize tuner
tuner = HyperparameterTuner(
train_dataset=train_dataset,
val_dataset=val_dataset,
n_trials=10
)
# Run optimization
tuner.run_optimization()
if __name__ == "__main__":
main() |