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8ee90f4 79c851f 8ee90f4 79c851f 8ee90f4 79c851f 8ee90f4 79c851f 8ee90f4 79c851f 8ee90f4 79c851f 8ee90f4 79c851f 8ee90f4 79c851f 8ee90f4 79c851f 8ee90f4 79c851f 8ee90f4 79c851f | 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 | """Run multi-label inference with a published PyTorch or CPU INT8 ONNX model."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
DEFAULT_MODEL_ID = "NajahUniv/AraUni-MARBERTv2-Intent-Classifier"
def choose_device(requested: str) -> str:
if requested != "auto":
return requested
if torch.cuda.is_available():
return "cuda"
if torch.backends.mps.is_available():
return "mps"
return "cpu"
def resolve_runtime(backend: str, precision: str, device: str) -> tuple[str, str]:
resolved_backend = "pytorch" if backend == "auto" else backend
resolved_precision = precision
if precision == "auto":
if resolved_backend == "onnx":
resolved_precision = "int8"
elif device == "cuda":
resolved_precision = "bf16" if torch.cuda.is_bf16_supported() else "fp16"
else:
resolved_precision = "fp32"
if resolved_backend == "onnx" and (device != "cpu" or resolved_precision != "int8"):
raise ValueError("the published ONNX artifact supports CPU INT8 only")
if resolved_backend == "pytorch" and resolved_precision == "int8":
raise ValueError("precision=int8 requires backend=onnx")
if resolved_backend == "pytorch" and resolved_precision in {"bf16", "fp16"} and device != "cuda":
raise ValueError("the example enables bf16/fp16 only on CUDA")
return resolved_backend, resolved_precision
def hub_or_local_file(model_id: str, filename: str, revision: str | None) -> str:
local = Path(model_id) / filename
if local.is_file():
return str(local)
return hf_hub_download(model_id, filename, revision=revision)
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--model-id", default=DEFAULT_MODEL_ID)
parser.add_argument("--revision", help="Pin a release tag or reviewed commit SHA")
parser.add_argument("--text", required=True)
parser.add_argument("--device", default="auto", choices=("auto", "cpu", "mps", "cuda"))
parser.add_argument("--backend", default="auto", choices=("auto", "pytorch", "onnx"))
parser.add_argument(
"--precision",
default="auto",
choices=("auto", "fp32", "bf16", "fp16", "int8"),
)
parser.add_argument("--top-k", type=int, default=5)
args = parser.parse_args()
device = "cpu" if args.backend == "onnx" and args.device == "auto" else choose_device(args.device)
backend, precision = resolve_runtime(args.backend, args.precision, device)
load_kwargs = {"revision": args.revision} if args.revision else {}
tokenizer = AutoTokenizer.from_pretrained(
args.model_id,
trust_remote_code=True,
**load_kwargs,
)
config = AutoConfig.from_pretrained(
args.model_id,
trust_remote_code=True,
**load_kwargs,
)
thresholds = config.thresholds
if backend == "onnx":
import onnxruntime as ort
onnx_config_path = hub_or_local_file(
args.model_id,
"onnx/onnx_config.json",
args.revision,
)
onnx_config = json.loads(Path(onnx_config_path).read_text(encoding="utf-8"))
thresholds = onnx_config["thresholds"]
model_path = hub_or_local_file(
args.model_id,
"onnx/model_int8.onnx",
args.revision,
)
session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
encoded = tokenizer(
args.text,
return_tensors="np",
truncation=True,
max_length=config.max_length,
)
logits = session.run(
["logits"],
{
"input_ids": encoded["input_ids"].astype(np.int64),
"attention_mask": encoded["attention_mask"].astype(np.int64),
},
)[0][0]
probabilities = 1.0 / (1.0 + np.exp(-logits))
else:
dtype = {
"fp32": torch.float32,
"bf16": torch.bfloat16,
"fp16": torch.float16,
}[precision]
model = AutoModelForSequenceClassification.from_pretrained(
args.model_id,
trust_remote_code=True,
torch_dtype=dtype,
**load_kwargs,
).to(device).eval()
encoded = tokenizer(
args.text,
return_tensors="pt",
truncation=True,
max_length=model.config.max_length,
).to(device)
with torch.inference_mode():
probabilities = torch.sigmoid(model(**encoded).logits[0]).cpu().float().numpy()
labels = [config.id2label[index] for index in range(config.num_labels)]
ranked = sorted(
(
{
"label": label,
"probability": float(probabilities[index]),
"threshold": float(thresholds[label]),
"selected": float(probabilities[index]) >= float(thresholds[label]),
}
for index, label in enumerate(labels)
),
key=lambda item: item["probability"],
reverse=True,
)
print(
json.dumps(
{
"text": args.text,
"backend": backend,
"precision": precision,
"device": device,
"selected_labels": [item["label"] for item in ranked if item["selected"]],
"scores": ranked[: max(1, args.top_k)],
},
ensure_ascii=False,
indent=2,
)
)
if __name__ == "__main__":
main()
|