145 lines
6.9 KiB
Python
145 lines
6.9 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 models.bge_reranker import BGERerankerModel
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from data.dataset import BGEM3Dataset, BGERerankerDataset
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from transformers import AutoTokenizer
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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("benchmark")
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def parse_args():
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"""Parse command line arguments for benchmarking."""
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parser = argparse.ArgumentParser(description="Benchmark BGE fine-tuned models.")
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parser.add_argument('--model_type', type=str, choices=['retriever', 'reranker'], required=True, help='Model type to benchmark')
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parser.add_argument('--model_path', type=str, required=True, help='Path to fine-tuned 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='benchmark_results.txt', help='File to save benchmark results')
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return parser.parse_args()
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def measure_memory():
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"""Measure the current memory usage of the process."""
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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 benchmark_retriever(model_path, data_path, batch_size, max_samples, device):
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"""Benchmark the BGE retriever model."""
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logger.info(f"Loading retriever model from {model_path}")
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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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logger.info(f"Loaded {len(dataset)} samples for benchmarking")
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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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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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# Only dense embedding for speed
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_ = 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_samples += batch['query_input_ids'].shape[0]
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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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logger.info(f"Retriever throughput: {throughput:.2f} samples/sec, latency: {latency*1000:.2f} ms/sample, peak mem: {mem_mb:.2f} MB")
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return throughput, latency, mem_mb
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def benchmark_reranker(model_path, data_path, batch_size, max_samples, device):
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"""Benchmark the BGE reranker model."""
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logger.info(f"Loading reranker model from {model_path}")
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model = BGERerankerModel(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 = BGERerankerDataset(data_path, tokenizer, is_train=False)
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logger.info(f"Loaded {len(dataset)} samples for benchmarking")
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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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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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_ = model.model(
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input_ids=batch['input_ids'].to(device),
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attention_mask=batch['attention_mask'].to(device)
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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_samples += batch['input_ids'].shape[0]
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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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logger.info(f"Reranker throughput: {throughput:.2f} samples/sec, latency: {latency*1000:.2f} ms/sample, peak mem: {mem_mb:.2f} MB")
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return throughput, latency, mem_mb
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def main():
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"""Run benchmark with robust error handling and config-driven defaults."""
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import argparse
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parser = argparse.ArgumentParser(description="Benchmark BGE 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('--model_type', type=str, choices=['retriever', 'reranker'], required=True, help='Model type to benchmark')
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parser.add_argument('--model_path', type=str, required=True, help='Path to fine-tuned 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='benchmark_results.txt', help='File to save benchmark results')
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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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if args.model_type == 'retriever':
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throughput, latency, mem_mb = benchmark_retriever(
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args.model_path, args.data_path, args.batch_size, args.max_samples, device)
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else:
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throughput, latency, mem_mb = benchmark_reranker(
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args.model_path, args.data_path, args.batch_size, args.max_samples, device)
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# Save results
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with open(args.output, 'w') as f:
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f.write(f"Model: {args.model_path}\n")
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f.write(f"Data: {args.data_path}\n")
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f.write(f"Batch size: {args.batch_size}\n")
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f.write(f"Throughput: {throughput:.2f} samples/sec\n")
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f.write(f"Latency: {latency*1000:.2f} ms/sample\n")
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f.write(f"Peak memory: {mem_mb:.2f} MB\n")
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logger.info(f"Benchmark results saved to {args.output}")
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logging.info(f"Benchmark completed on device: {device}")
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except Exception as e:
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logging.error(f"Benchmark failed: {e}")
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raise
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if __name__ == '__main__':
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main()
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