init commit

scripts/benchmark.py

@@ -0,0 +1,144 @@
+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 models.bge_reranker import BGERerankerModel
+from data.dataset import BGEM3Dataset, BGERerankerDataset
+from transformers import AutoTokenizer
+from torch.utils.data import DataLoader
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("benchmark")
+
+def parse_args():
+    """Parse command line arguments for benchmarking."""
+    parser = argparse.ArgumentParser(description="Benchmark BGE fine-tuned models.")
+    parser.add_argument('--model_type', type=str, choices=['retriever', 'reranker'], required=True, help='Model type to benchmark')
+    parser.add_argument('--model_path', type=str, required=True, help='Path to fine-tuned 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='benchmark_results.txt', help='File to save benchmark results')
+    return parser.parse_args()
+
+def measure_memory():
+    """Measure the current memory usage of the process."""
+    process = psutil.Process(os.getpid())
+    return process.memory_info().rss / (1024 * 1024)  # MB
+
+def benchmark_retriever(model_path, data_path, batch_size, max_samples, device):
+    """Benchmark the BGE retriever model."""
+    logger.info(f"Loading retriever model from {model_path}")
+    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)
+    logger.info(f"Loaded {len(dataset)} samples for benchmarking")
+    dataloader = DataLoader(dataset, batch_size=batch_size)
+    model.eval()
+    model.to(device)
+    n_samples = 0
+    times = []
+    tracemalloc.start()
+    with torch.no_grad():
+        for batch in dataloader:
+            if n_samples >= max_samples:
+                break
+            start = time.time()
+            # Only dense embedding for speed
+            _ = 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_samples += batch['query_input_ids'].shape[0]
+    current, peak = tracemalloc.get_traced_memory()
+    tracemalloc.stop()
+    throughput = n_samples / sum(times)
+    latency = np.mean(times) / batch_size
+    mem_mb = peak / (1024 * 1024)
+    logger.info(f"Retriever throughput: {throughput:.2f} samples/sec, latency: {latency*1000:.2f} ms/sample, peak mem: {mem_mb:.2f} MB")
+    return throughput, latency, mem_mb
+
+def benchmark_reranker(model_path, data_path, batch_size, max_samples, device):
+    """Benchmark the BGE reranker model."""
+    logger.info(f"Loading reranker model from {model_path}")
+    model = BGERerankerModel(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 = BGERerankerDataset(data_path, tokenizer, is_train=False)
+    logger.info(f"Loaded {len(dataset)} samples for benchmarking")
+    dataloader = DataLoader(dataset, batch_size=batch_size)
+    model.eval()
+    model.to(device)
+    n_samples = 0
+    times = []
+    tracemalloc.start()
+    with torch.no_grad():
+        for batch in dataloader:
+            if n_samples >= max_samples:
+                break
+            start = time.time()
+            _ = model.model(
+                input_ids=batch['input_ids'].to(device),
+                attention_mask=batch['attention_mask'].to(device)
+            )
+            torch.cuda.synchronize() if device.startswith('cuda') else None
+            end = time.time()
+            times.append(end - start)
+            n_samples += batch['input_ids'].shape[0]
+    current, peak = tracemalloc.get_traced_memory()
+    tracemalloc.stop()
+    throughput = n_samples / sum(times)
+    latency = np.mean(times) / batch_size
+    mem_mb = peak / (1024 * 1024)
+    logger.info(f"Reranker throughput: {throughput:.2f} samples/sec, latency: {latency*1000:.2f} ms/sample, peak mem: {mem_mb:.2f} MB")
+    return throughput, latency, mem_mb
+
+def main():
+    """Run benchmark with robust error handling and config-driven defaults."""
+    import argparse
+    parser = argparse.ArgumentParser(description="Benchmark BGE models.")
+    parser.add_argument('--device', type=str, default=None, help='Device to use (cuda, npu, cpu)')
+    parser.add_argument('--model_type', type=str, choices=['retriever', 'reranker'], required=True, help='Model type to benchmark')
+    parser.add_argument('--model_path', type=str, required=True, help='Path to fine-tuned 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='benchmark_results.txt', help='File to save benchmark results')
+    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:
+        if args.model_type == 'retriever':
+            throughput, latency, mem_mb = benchmark_retriever(
+                args.model_path, args.data_path, args.batch_size, args.max_samples, device)
+        else:
+            throughput, latency, mem_mb = benchmark_reranker(
+                args.model_path, args.data_path, args.batch_size, args.max_samples, device)
+        # Save results
+        with open(args.output, 'w') as f:
+            f.write(f"Model: {args.model_path}\n")
+            f.write(f"Data: {args.data_path}\n")
+            f.write(f"Batch size: {args.batch_size}\n")
+            f.write(f"Throughput: {throughput:.2f} samples/sec\n")
+            f.write(f"Latency: {latency*1000:.2f} ms/sample\n")
+            f.write(f"Peak memory: {mem_mb:.2f} MB\n")
+        logger.info(f"Benchmark results saved to {args.output}")
+        logging.info(f"Benchmark completed on device: {device}")
+    except Exception as e:
+        logging.error(f"Benchmark failed: {e}")
+        raise
+
+if __name__ == '__main__':
+    main()