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ce3a60d 8c50d16 ce3a60d 8c50d16 ce3a60d 116c87c ce3a60d 8c50d16 ce3a60d 8c50d16 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | """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()
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