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