"""Benchmark AniFileBERT PyTorch and ONNX Runtime inference. The benchmark measures end-to-end parser latency after model/session loading. It includes tokenization, model forward pass, constrained BIO decoding, and field postprocessing. """ import argparse import json import statistics import time from pathlib import Path from typing import Callable, Dict, List import torch import onnxruntime as ort from transformers import BertForTokenClassification from anifilebert.config import Config from tools.evaluate_parser_cases import DEFAULT_CASE_FILE, load_cases from anifilebert.inference import parse_filename from tools.onnx_inference import OnnxFilenameParser from anifilebert.tokenizer import load_tokenizer DEFAULT_OUTPUT_FILE = Path("reports") / "benchmark_results.json" def percentile(values: List[float], pct: float) -> float: if not values: return 0.0 ordered = sorted(values) index = (len(ordered) - 1) * pct lower = int(index) upper = min(lower + 1, len(ordered) - 1) if lower == upper: return ordered[lower] weight = index - lower return ordered[lower] * (1 - weight) + ordered[upper] * weight def summarize(name: str, load_ms: float, latencies_ms: List[float]) -> Dict: total_ms = sum(latencies_ms) count = len(latencies_ms) return { "name": name, "load_ms": load_ms, "runs": count, "avg_ms": statistics.fmean(latencies_ms) if latencies_ms else 0.0, "p50_ms": percentile(latencies_ms, 0.50), "p95_ms": percentile(latencies_ms, 0.95), "p99_ms": percentile(latencies_ms, 0.99), "min_ms": min(latencies_ms) if latencies_ms else 0.0, "max_ms": max(latencies_ms) if latencies_ms else 0.0, "throughput_fps": (count / (total_ms / 1000.0)) if total_ms > 0 else 0.0, } def run_benchmark( name: str, parser_fn: Callable[[str], Dict], filenames: List[str], warmup: int, repeat: int, ) -> Dict: for idx in range(warmup): parser_fn(filenames[idx % len(filenames)]) latencies: List[float] = [] for _ in range(repeat): for filename in filenames: start = time.perf_counter() parser_fn(filename) latencies.append((time.perf_counter() - start) * 1000.0) return {"name": name, "latencies_ms": latencies} def load_case_filenames(case_file: str, limit: int | None) -> List[str]: cases = load_cases(case_file) filenames = [case["filename"] for case in cases if case.get("filename")] if limit is not None and limit > 0: filenames = filenames[:limit] if not filenames: raise ValueError(f"No filenames found in {case_file}") return filenames def main() -> None: parser = argparse.ArgumentParser(description="Benchmark AniFileBERT inference speed") parser.add_argument("--model-dir", default=".", help="Directory containing the PyTorch checkpoint") parser.add_argument("--onnx", default="exports/anime_filename_parser.onnx", help="ONNX model path") parser.add_argument("--case-file", default=DEFAULT_CASE_FILE, help="JSON regression case file") parser.add_argument("--max-length", type=int, default=None, help="Override sequence length") parser.add_argument("--limit-cases", type=int, default=None, help="Use only the first N cases") parser.add_argument("--repeat", type=int, default=5, help="Repeat the case set this many times") parser.add_argument("--warmup", type=int, default=10, help="Warmup parses per backend") parser.add_argument("--backend", choices=["both", "torch", "onnx"], default="both") parser.add_argument("--torch-threads", type=int, default=1, help="torch intra-op thread count") parser.add_argument("--ort-threads", type=int, default=1, help="ONNX Runtime intra/inter-op thread count") parser.add_argument("--no-constrained-bio", action="store_true", help="Use greedy labels for PyTorch backend") parser.add_argument("--output", default=str(DEFAULT_OUTPUT_FILE), help="JSON output path") args = parser.parse_args() filenames = load_case_filenames(args.case_file, args.limit_cases) model_dir = Path(args.model_dir) max_length = args.max_length if args.torch_threads and args.torch_threads > 0: torch.set_num_threads(args.torch_threads) torch.set_num_interop_threads(args.torch_threads) results: List[Dict] = [] if args.backend in {"both", "torch"}: cfg = Config() load_start = time.perf_counter() tokenizer = load_tokenizer(str(model_dir)) model = BertForTokenClassification.from_pretrained(model_dir) model.eval() resolved_max_length = max_length or int(getattr(model.config, "max_seq_length", 128)) id2label = {int(k): v for k, v in getattr(model.config, "id2label", cfg.id2label).items()} load_ms = (time.perf_counter() - load_start) * 1000.0 def parse_torch(filename: str) -> Dict: return parse_filename( filename, model, tokenizer, id2label, max_length=resolved_max_length, debug=False, constrain_bio=not args.no_constrained_bio, ) raw = run_benchmark("pytorch", parse_torch, filenames, args.warmup, args.repeat) results.append(summarize(raw["name"], load_ms, raw["latencies_ms"])) if args.backend in {"both", "onnx"}: session_options = ort.SessionOptions() if args.ort_threads and args.ort_threads > 0: session_options.intra_op_num_threads = args.ort_threads session_options.inter_op_num_threads = args.ort_threads load_start = time.perf_counter() onnx_parser = OnnxFilenameParser( model_dir=model_dir, onnx_path=Path(args.onnx), max_length=max_length or 128, session_options=session_options, ) load_ms = (time.perf_counter() - load_start) * 1000.0 def parse_onnx(filename: str) -> Dict: return onnx_parser.parse(filename) raw = run_benchmark("onnxruntime", parse_onnx, filenames, args.warmup, args.repeat) results.append(summarize(raw["name"], load_ms, raw["latencies_ms"])) report = { "model_dir": str(model_dir), "onnx": args.onnx, "case_file": args.case_file, "case_count": len(filenames), "repeat": args.repeat, "warmup": args.warmup, "torch_threads": args.torch_threads, "ort_threads": args.ort_threads, "constrain_bio": not args.no_constrained_bio, "results": results, } print(json.dumps(report, ensure_ascii=False, indent=2)) print("\nSummary:") print("| Backend | Load ms | Avg ms | P50 ms | P95 ms | P99 ms | Throughput files/s |") print("| --- | ---: | ---: | ---: | ---: | ---: | ---: |") for item in results: print( f"| {item['name']} | {item['load_ms']:.2f} | {item['avg_ms']:.3f} | " f"{item['p50_ms']:.3f} | {item['p95_ms']:.3f} | {item['p99_ms']:.3f} | " f"{item['throughput_fps']:.1f} |" ) output_path = Path(args.output) output_path.parent.mkdir(parents=True, exist_ok=True) output_path.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8") if __name__ == "__main__": main()