optimum_benchmark/backends/onnxruntime/utils.py

from typing import Any, Dict

from onnxruntime.quantization import CalibrationMethod, QuantFormat, QuantizationMode, QuantType
from optimum.pipelines import ORT_SUPPORTED_TASKS

TASKS_TO_ORTMODELS = {
    task: f"optimum.onnxruntime.{task_dict['class'][0].__name__}" for task, task_dict in ORT_SUPPORTED_TASKS.items()
}

TASKS_TO_ORTPIPELINES = {
    "inpainting": "optimum.onnxruntime.ORTPipelineForInpainting",
    "text-to-image": "optimum.onnxruntime.ORTPipelineForText2Image",
    "image-to-image": "optimum.onnxruntime.ORTPipelineForImage2Image",
}


def format_calibration_config(calibration_config: Dict[str, Any]) -> None:
    if calibration_config.get("method", None) is not None:
        calibration_config["method"] = CalibrationMethod[calibration_config["method"]]

    return calibration_config


def format_quantization_config(quantization_config: Dict[str, Any]) -> None:
    """Format the quantization dictionary for onnxruntime."""
    # the conditionals are here because some quantization strategies don't have all the options
    if quantization_config.get("format", None) is not None:
        quantization_config["format"] = QuantFormat.from_string(quantization_config["format"])
    if quantization_config.get("mode", None) is not None:
        quantization_config["mode"] = QuantizationMode.from_string(quantization_config["mode"])
    if quantization_config.get("activations_dtype", None) is not None:
        quantization_config["activations_dtype"] = QuantType.from_string(quantization_config["activations_dtype"])
    if quantization_config.get("weights_dtype", None) is not None:
        quantization_config["weights_dtype"] = QuantType.from_string(quantization_config["weights_dtype"])

    return quantization_config