import os import time import argparse import logging import torch import psutil import tracemalloc import numpy as np from utils.config_loader import get_config from models.bge_m3 import BGEM3Model from data.dataset import BGEM3Dataset from transformers import AutoTokenizer from evaluation.metrics import compute_retrieval_metrics from torch.utils.data import DataLoader logging.basicConfig(level=logging.INFO) logger = logging.getLogger("compare_retriever") def parse_args(): """Parse command line arguments for retriever comparison.""" parser = argparse.ArgumentParser(description="Compare fine-tuned and baseline retriever models.") parser.add_argument('--finetuned_model_path', type=str, required=True, help='Path to fine-tuned retriever model') parser.add_argument('--baseline_model_path', type=str, required=True, help='Path to baseline retriever model') parser.add_argument('--data_path', type=str, required=True, help='Path to evaluation data (JSONL)') parser.add_argument('--batch_size', type=int, default=16, help='Batch size for inference') parser.add_argument('--max_samples', type=int, default=1000, help='Max samples to benchmark (for speed)') parser.add_argument('--device', type=str, default='cuda', help='Device to use (cuda, cpu, npu)') parser.add_argument('--output', type=str, default='compare_retriever_results.txt', help='File to save comparison results') parser.add_argument('--k_values', type=int, nargs='+', default=[1, 5, 10, 20, 100], help='K values for metrics@k') return parser.parse_args() def measure_memory(): """Measure the current process memory usage in MB.""" process = psutil.Process(os.getpid()) return process.memory_info().rss / (1024 * 1024) # MB def run_retriever(model_path, data_path, batch_size, max_samples, device): """ Run inference on a retriever model and measure throughput, latency, and memory. Args: model_path (str): Path to the retriever model. data_path (str): Path to the evaluation data. batch_size (int): Batch size for inference. max_samples (int): Maximum number of samples to process. device (str): Device to use (cuda, cpu, npu). Returns: tuple: throughput (samples/sec), latency (ms/sample), peak memory (MB), query_embeds (np.ndarray), passage_embeds (np.ndarray), labels (np.ndarray). """ model = BGEM3Model(model_name_or_path=model_path, device=device) tokenizer = model.tokenizer if hasattr(model, 'tokenizer') and model.tokenizer else AutoTokenizer.from_pretrained(model_path) dataset = BGEM3Dataset(data_path, tokenizer, is_train=False) dataloader = DataLoader(dataset, batch_size=batch_size) model.eval() model.to(device) n_samples = 0 times = [] query_embeds = [] passage_embeds = [] labels = [] tracemalloc.start() with torch.no_grad(): for batch in dataloader: if n_samples >= max_samples: break start = time.time() out = model( batch['query_input_ids'].to(device), batch['query_attention_mask'].to(device), return_dense=True, return_sparse=False, return_colbert=False ) torch.cuda.synchronize() if device.startswith('cuda') else None end = time.time() times.append(end - start) n = batch['query_input_ids'].shape[0] n_samples += n # For metrics: collect embeddings and labels query_embeds.append(out['query_embeds'].cpu().numpy()) passage_embeds.append(out['passage_embeds'].cpu().numpy()) labels.append(batch['labels'].cpu().numpy()) current, peak = tracemalloc.get_traced_memory() tracemalloc.stop() throughput = n_samples / sum(times) latency = np.mean(times) / batch_size mem_mb = peak / (1024 * 1024) # Prepare for metrics query_embeds = np.concatenate(query_embeds, axis=0) passage_embeds = np.concatenate(passage_embeds, axis=0) labels = np.concatenate(labels, axis=0) return throughput, latency, mem_mb, query_embeds, passage_embeds, labels def main(): """Run retriever comparison with robust error handling and config-driven defaults.""" import argparse parser = argparse.ArgumentParser(description="Compare retriever models.") parser.add_argument('--device', type=str, default=None, help='Device to use (cuda, npu, cpu)') parser.add_argument('--finetuned_model_path', type=str, required=True, help='Path to fine-tuned retriever model') parser.add_argument('--baseline_model_path', type=str, required=True, help='Path to baseline retriever model') parser.add_argument('--data_path', type=str, required=True, help='Path to evaluation data (JSONL)') parser.add_argument('--batch_size', type=int, default=16, help='Batch size for inference') parser.add_argument('--max_samples', type=int, default=1000, help='Max samples to benchmark (for speed)') parser.add_argument('--output', type=str, default='compare_retriever_results.txt', help='File to save comparison results') parser.add_argument('--k_values', type=int, nargs='+', default=[1, 5, 10, 20, 100], help='K values for metrics@k') args = parser.parse_args() # Load config and set defaults from utils.config_loader import get_config config = get_config() device = args.device or config.get('hardware', {}).get('device', 'cuda') # type: ignore[attr-defined] try: # Fine-tuned model logger.info("Running fine-tuned retriever...") ft_throughput, ft_latency, ft_mem, ft_qe, ft_pe, ft_labels = run_retriever( args.finetuned_model_path, args.data_path, args.batch_size, args.max_samples, device) ft_metrics = compute_retrieval_metrics(ft_qe, ft_pe, ft_labels, k_values=args.k_values) # Baseline model logger.info("Running baseline retriever...") bl_throughput, bl_latency, bl_mem, bl_qe, bl_pe, bl_labels = run_retriever( args.baseline_model_path, args.data_path, args.batch_size, args.max_samples, device) bl_metrics = compute_retrieval_metrics(bl_qe, bl_pe, bl_labels, k_values=args.k_values) # Output comparison with open(args.output, 'w') as f: f.write(f"Metric\tBaseline\tFine-tuned\tDelta\n") for k in args.k_values: for metric in [f'recall@{k}', f'precision@{k}', f'map@{k}', f'mrr@{k}', f'ndcg@{k}']: bl = bl_metrics.get(metric, 0) ft = ft_metrics.get(metric, 0) delta = ft - bl f.write(f"{metric}\t{bl:.4f}\t{ft:.4f}\t{delta:+.4f}\n") for metric in ['map', 'mrr']: bl = bl_metrics.get(metric, 0) ft = ft_metrics.get(metric, 0) delta = ft - bl f.write(f"{metric}\t{bl:.4f}\t{ft:.4f}\t{delta:+.4f}\n") f.write(f"Throughput (samples/sec)\t{bl_throughput:.2f}\t{ft_throughput:.2f}\t{ft_throughput-bl_throughput:+.2f}\n") f.write(f"Latency (ms/sample)\t{bl_latency*1000:.2f}\t{ft_latency*1000:.2f}\t{(ft_latency-bl_latency)*1000:+.2f}\n") f.write(f"Peak memory (MB)\t{bl_mem:.2f}\t{ft_mem:.2f}\t{ft_mem-bl_mem:+.2f}\n") logger.info(f"Comparison results saved to {args.output}") print(f"\nComparison complete. See {args.output} for details.") except Exception as e: logging.error(f"Retriever comparison failed: {e}") raise if __name__ == '__main__': main()