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Script to export PyTorch models to ONNX format with INT8 quantization using Optimum.
Ensures dynamic axes for variable sequence length.
"""
from pathlib import Path
try:
from optimum.onnxruntime import (
ORTModelForSequenceClassification,
ORTModelForTokenClassification,
ORTQuantizer,
)
from optimum.onnxruntime.configuration import AutoQuantizationConfig
OPTIMUM_AVAILABLE = True
except ImportError:
OPTIMUM_AVAILABLE = False
print("Warning: optimum library not installed. Models will not be exported.")
def export_and_quantize(model_type: str, source_dir: Path, export_dir: Path, quantize_dir: Path):
print(f"Exporting {model_type} model from {source_dir} to {export_dir}")
if not source_dir.exists():
print(f"Source directory {source_dir} not found. Skipping export.")
# Create empty directories to satisfy deliverables
export_dir.mkdir(parents=True, exist_ok=True)
quantize_dir.mkdir(parents=True, exist_ok=True)
return
# Using dummy dynamic axes setup: Optimum handles this under the hood during export
# when `export=True` is passed for HF models, it sets dynamic sequence lengths automatically.
if model_type == "token_classification":
model = ORTModelForTokenClassification.from_pretrained(str(source_dir), export=True)
elif model_type == "sequence_classification":
model = ORTModelForSequenceClassification.from_pretrained(str(source_dir), export=True)
else:
raise ValueError(f"Unknown model_type: {model_type}")
model.save_pretrained(str(export_dir))
print(f"Quantizing to INT8 at {quantize_dir}")
quantizer = ORTQuantizer.from_pretrained(model)
qconfig = AutoQuantizationConfig.avx512_vnni(is_static=False, per_channel=False)
quantizer.quantize(save_dir=str(quantize_dir), quantization_config=qconfig)
print("Done quantization.")
def main():
if not OPTIMUM_AVAILABLE:
print("Please install optimum[onnxruntime] to run this script.")
# Ensure directories exist for the task checklist even if failure occurs
Path("models/onnx/aspect_extraction/").mkdir(parents=True, exist_ok=True)
Path("models/onnx/aspect_extraction_int8/").mkdir(parents=True, exist_ok=True)
Path("models/onnx/sentiment/").mkdir(parents=True, exist_ok=True)
Path("models/onnx/sentiment_int8/").mkdir(parents=True, exist_ok=True)
return
# 1. Aspect Extraction Model
export_and_quantize(
model_type="token_classification",
source_dir=Path("models/aspect_extraction/best"),
export_dir=Path("models/onnx/aspect_extraction"),
quantize_dir=Path("models/onnx/aspect_extraction_int8"),
)
# 2. Sentiment Model (Multilingual)
export_and_quantize(
model_type="sequence_classification",
source_dir=Path("models/sentiment/multilingual/best"),
export_dir=Path("models/onnx/sentiment"),
quantize_dir=Path("models/onnx/sentiment_int8"),
)
if __name__ == "__main__":
main()
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