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# Quark ONNX Quantization Tutorial For Auto Search

NOTE This tutorial can be downloaded for local execution on a Jupyter Notebook environment. Click here to download the source file.

In this tutorial, you will learn how to use the AMD Quark Auto Search module to automatically find optimal quantization configurations for a YOLOv8 model. Quantization is a key step in optimizing deep learning models for efficient inference, reducing model size and improving performance without significantly impacting accuracy.

Using Quark’s Auto Search feature, you can streamline the quantization process by letting the tool explore different parameter combinations and select the best configuration automatically. This tutorial provides a step-by-step guide on setting up the environment, preparing the YOLOv8 model, running the Auto Search process, and evaluating the quantized model’s performance.

The example has the following parts:

- Install requirements

- Prepare model

- Prepare data

- Run auto search

## 1) Install The Necessary Python Packages:

In addition to Quark that must be installed as documented at here, extra packages are require for this tutorial.

    %pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
    %pip install amd-quark
    %pip install -r ./requirements.txt

## 2) Prepare YOLO Model

We are using YOLOv8-n model from Ultralytics to demonstrate Quark’s auto search process. First, we need to download the model, then convert it to onnx format.

    from ultralytics import YOLO

    model = YOLO("yolov8n.pt")
    model.export(format="onnx")

## 3) Prepare data

The COCO (Common Objects in Context) dataset is a large-scale benchmark designed for computer vision tasks such as object detection, segmentation, keypoint detection, and image captioning. It contains over 330,000 images, with more than 200,000 labeled images and around 1.5 million object instances spanning 80 object categories.

What makes COCO unique is its focus on objects in complex, real-world scenes, where multiple objects often appear together in varied contexts. Each image is richly annotated with detailed instance segmentation masks, bounding boxes, and object category labels, making COCO an essential dataset for training and evaluating deep learning models in visual recognition tasks.

Due to its diversity and annotation quality, COCO has become a standard benchmark for evaluating the performance of modern models.

If you do not have a COCO dataset in your local machine, you can download from COCO’s official website: https://cocodataset.org/#download, then decompress it.

    !wget http://images.cocodataset.org/zips/val2017.zip
    !unzip val2017.zip

If you have a local cache to store the dataset, you can use and environment variable like `LOCAL_DATA_CACHE` to specify its path. This is useful to organize and store all your datasets for different experiments in a central place. Otherwise, the current folder is used.

    import os
    import sys

    calibration_dataset_path = "val2017"

    if os.environ.get("LOCAL_DATA_CACHE") is not None:
        data_path = os.environ["LOCAL_DATA_CACHE"]
        calibration_dataset_path = os.path.join(data_path, "coco/2017/val2017")

    if not os.path.exists(calibration_dataset_path):
        print("The provided data path does not exist.")
        sys.exit(1)

The COCO dataset should be structured as follows:

- val2017

  - sample_1.jpg

  - sample_2.jpg

  - …

## 4) Auto Search Pipeline

The following cell defines an auto search config settings. You can customize the serch space to suit your requirements.

Search Tolerance Setting

The search tolerance is the acceptable margin between the accuracy of the original floating-point model and the quantized model. When the quantized model’s accuracy loss exceeds the set tolerance, the Auto Search framework will stop further searches.

- Tolerance Threshold: This is a value representing the maximum acceptable accuracy drop from the floating-point model.

- Auto-Stop Condition: When the search reaches a configuration with accuracy loss below the tolerance threshold, the framework will halt, saving the best configuration and corresponding quantized model.

Example: If the floating-point model has 95% accuracy and the tolerance is set to 1%, the Auto Search will stop if a configuration causes an accuracy drop greater than 1% (i.e., below 94%).

    import copy
    import os

    import cv2
    import numpy as np
    import onnxruntime as ort
    from onnxruntime.quantization.calibrate import CalibrationMethod
    from onnxruntime.quantization.quant_utils import QuantType

    from quark.onnx import Config, ExtendedQuantFormat, ExtendedQuantType, LayerWiseMethod, PowerOfTwoMethod
    from quark.onnx.quantization import auto_search
    from quark.onnx.quantization.config import get_default_config

    class AutoSearchConfig_Default:
        # for s8s8 & s16s8 aaws/asws
        search_space: dict[str, any] = {
            "calibrate_method": [
                CalibrationMethod.MinMax,
                CalibrationMethod.Percentile,
                LayerWiseMethod.LayerWisePercentile,
            ],
            "activation_type": [
                QuantType.QInt8,
                QuantType.QInt16,
            ],
            "weight_type": [
                QuantType.QInt8,
            ],
            "include_cle": [False],
            "include_fast_ft": [False],
            "extra_options": {
                "ActivationSymmetric": [True, False],
                "WeightSymmetric": [True],
                "CalibMovingAverage": [False, True],
                "CalibMovingAverageConstant": [0.01],
            },
        }

        search_space_advanced: dict[str, any] = {
            "calibrate_method": [
                CalibrationMethod.MinMax,
                CalibrationMethod.Percentile,
                LayerWiseMethod.LayerWisePercentile,
            ],
            "activation_type": [
                QuantType.QInt8,
                QuantType.QInt16,
            ],
            "weight_type": [
                QuantType.QInt8,
            ],
            "include_cle": [False, True],
            "include_fast_ft": [False, True],
            "extra_options": {
                "ActivationSymmetric": [True, False],
                "WeightSymmetric": [True],
                "CalibMovingAverage": [
                    False,
                    True,
                ],
                "CalibMovingAverageConstant": [0.01],
                "FastFinetune": {
                    "DataSize": [
                        200,
                    ],
                    "NumIterations": [1000, 5000, 10000],
                    "OptimAlgorithm": ["adaround"],
                    "LearningRate": [0.1, 0.01],
                    # 'OptimDevice': ['cuda:0'],
                    # 'InferDevice': ['cuda:0'],
                    "EarlyStop": [False],
                },
            },
        }

        search_space_advanced2: dict[str, any] = {
            "calibrate_method": [
                CalibrationMethod.MinMax,
                CalibrationMethod.Percentile,
                LayerWiseMethod.LayerWisePercentile,
            ],
            "activation_type": [
                QuantType.QInt8,
                QuantType.QInt16,
            ],
            "weight_type": [
                QuantType.QInt8,
            ],
            "include_cle": [False, True],
            "include_fast_ft": [False, True],
            "extra_options": {
                "ActivationSymmetric": [True, False],
                "WeightSymmetric": [True],
                "CalibMovingAverage": [
                    False,
                    True,
                ],
                "CalibMovingAverageConstant": [0.01],
                "FastFinetune": {
                    "DataSize": [
                        200,
                    ],
                    "NumIterations": [1000, 5000, 10000],
                    "OptimAlgorithm": ["adaquant"],
                    "LearningRate": [1e-5, 1e-6],
                    # 'OptimDevice': ['cuda:0'],
                    # 'InferDevice': ['cuda:0'],
                    "EarlyStop": [False],
                },
            },
        }

        # for XINT8
        search_space_XINT8: dict[str, any] = {
            "calibrate_method": [PowerOfTwoMethod.MinMSE],
            "activation_type": [QuantType.QUInt8],
            "weight_type": [
                QuantType.QInt8,
            ],
            "enable_npu_cnn": [True],
            "include_cle": [False],
            "include_fast_ft": [False],
            "extra_options": {
                "ActivationSymmetric": [True],
            },
        }

        search_space_XINT8_advanced: dict[str, any] = {
            "calibrate_method": [PowerOfTwoMethod.MinMSE],
            "activation_type": [
                QuantType.QUInt8,
            ],
            "weight_type": [
                QuantType.QInt8,
            ],
            "enable_npu_cnn": [True],
            "include_cle": [False, True],
            "include_fast_ft": [True],
            "extra_options": {
                "ActivationSymmetric": [
                    True,
                ],
                "WeightSymmetric": [True],
                "CalibMovingAverage": [
                    False,
                    True,
                ],
                "CalibMovingAverageConstant": [0.01],
                "FastFinetune": {
                    "DataSize": [
                        200,
                    ],
                    "NumIterations": [1000],
                    "OptimAlgorithm": ["adaround"],
                    "LearningRate": [
                        0.1,
                    ],
                    # 'OptimDevice': ['cuda:0'],
                    # 'InferDevice': ['cuda:0'],
                    "EarlyStop": [False],
                },
            },
        }

        search_space_XINT8_advanced2: dict[str, any] = {
            "calibrate_method": [PowerOfTwoMethod.MinMSE],
            "activation_type": [
                QuantType.QUInt8,
            ],
            "weight_type": [
                QuantType.QInt8,
            ],
            "enable_npu_cnn": [True],
            "include_cle": [False, True],
            "include_fast_ft": [True],
            "extra_options": {
                "ActivationSymmetric": [
                    True,
                ],
                "WeightSymmetric": [True],
                "CalibMovingAverage": [
                    False,
                    True,
                ],
                "CalibMovingAverageConstant": [0.01],
                "FastFinetune": {
                    "DataSize": [
                        200,
                    ],
                    "NumIterations": [5000],
                    "OptimAlgorithm": ["adaquant"],
                    "LearningRate": [
                        1e-5,
                    ],
                    # 'OptimDevice': ['cuda:0'],
                    # 'InferDevice': ['cuda:0'],
                    "EarlyStop": [False],
                },
            },
        }

        # for BF16
        search_space_bf16: dict[str, any] = {
            "calibrate_method": [CalibrationMethod.MinMax],
            "activation_type": [ExtendedQuantType.QBFloat16],
            "weight_type": [ExtendedQuantType.QBFloat16],
            "quant_format": [ExtendedQuantFormat.QDQ],
            "include_cle": [False],
            "include_fast_ft": [False],
        }

        search_space_bf16_advanced: dict[str, any] = {
            "calibrate_method": [CalibrationMethod.MinMax],
            "activation_type": [ExtendedQuantType.QBFloat16],
            "weight_type": [ExtendedQuantType.QBFloat16],
            "quant_format": [ExtendedQuantFormat.QDQ],
            "include_cle": [False],
            "include_fast_ft": [True],
            "extra_options": {
                "FastFinetune": {
                    "DataSize": [1000],
                    "FixedSeed": [1705472343],
                    "BatchSize": [2],
                    "NumIterations": [1000],
                    "LearningRate": [0.00001],
                    "OptimAlgorithm": ["adaquant"],
                    # 'OptimDevice': ['cuda:0'],
                    # 'InferDevice': ['cuda:0'],
                    "EarlyStop": [False],
                }
            },
        }

        #  for BFP16
        search_space_bfp16: dict[str, any] = {
            "calibrate_method": [CalibrationMethod.MinMax],
            "activation_type": [ExtendedQuantType.QBFP],
            "weight_type": [ExtendedQuantType.QBFP],
            "quant_format": [ExtendedQuantFormat.QDQ],
            "include_cle": [False],
            "include_fast_ft": [False],
            "extra_options": {
                "BFPAttributes": [
                    {
                        "bfp_method": "to_bfp",
                        "axis": 1,
                        "bit_width": 16,
                        "block_size": 8,
                        "rounding_mode": 2,
                    }
                ]
            },
        }

        search_space_bfp16_advanced: dict[str, any] = {
            "calibrate_method": [CalibrationMethod.MinMax],
            "activation_type": [ExtendedQuantType.QBFP],
            "weight_type": [ExtendedQuantType.QBFP],
            "quant_format": [ExtendedQuantFormat.QDQ],
            "include_cle": [False],
            "include_fast_ft": [True],
            "extra_options": {
                "BFPAttributes": [
                    {
                        "bfp_method": "to_bfp",
                        "axis": 1,
                        "bit_width": 16,
                        "block_size": 8,
                        "rounding_mode": 2,
                    }
                ],
                "FastFinetune": {
                    "DataSize": [1000],
                    "FixedSeed": [1705472343],
                    "BatchSize": [2],
                    "NumIterations": [1000],
                    "LearningRate": [0.00001],
                    "OptimAlgorithm": ["adaquant"],
                    # 'OptimDevice': ['cuda:0'],
                    # 'InferDevice': ['cuda:0'],
                    "EarlyStop": [False],
                },
            },
        }

        search_metric: str = "L2"
        search_algo: str = "grid_search"  # candidates: "grid_search", "random"
        search_evaluator = None
        search_metric_tolerance: float = 0.60001
        search_cache_dir: str = "./"
        search_output_dir: str = "./"
        search_log_path: str = "./auto_search.log"

        search_stop_condition: dict[str, any] = {
            "find_n_candidates": 1,
            "iteration_limit": 10000,
            "time_limit": 1000000.0,  # unit: second
        }

Now, let’s define an image reader for COCO dataset

    class ImageDataReader:
        def __init__(self, model_path: str, calibration_image_folder: str):
            self.enum_data = None
            self.data_list = self._preprocess_images(calibration_image_folder)
            session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
            self.input_name = session.get_inputs()[0].name

            self.datasize = len(self.data_list)

        def _preprocess_images(self, image_folder: str):
            data_list = []
            img_names = [
                f for f in os.listdir(image_folder) if f.endswith(".png") or f.endswith(".jpg") or f.endswith(".JPEG")
            ]
            for name in img_names:
                input_image = cv2.imread(os.path.join(image_folder, name))
                input_image = cv2.resize(input_image, (640, 640))
                input_data = np.array(input_image).astype(np.float32)
                # Customer Pre-Process
                input_data = input_data.transpose(2, 0, 1)
                input_size = input_data.shape
                if input_size[1] > input_size[2]:
                    input_data = input_data.transpose(0, 2, 1)
                input_data = np.expand_dims(input_data, axis=0)
                input_data = input_data / 255.0
                data_list.append(input_data)

            return data_list

        def get_next(self):
            if self.enum_data is None:
                self.enum_data = iter([{self.input_name: data} for data in self.data_list])
            return next(self.enum_data, None)

        def __getitem__(self, idx):
            return {self.input_name: self.data_list[idx]}

        def __len__(
            self,
        ):
            return self.datasize

        def rewind(self):
            self.enum_data = None

        def reset(self):
            self.enum_data = None

The following command generates a series of configurations from the auto_search settings. As long as the stop condition remains false, the instance samples configurations from the entire search space according to the selected search algorithm. Each sampled configuration is then used to quantize the input model with Quark ONNX. The evaluator computes the chosen metric on the quantized model and checks whether it falls within the defined tolerance. Models that meet the tolerance are added to the output dictionary; those that don’t are discarded.

To reduce computational load for this demo, we only set to run two search spaces, but we have defined 10 more spaces in the auto_search_model.py. You are welcome to test all of them or define your own search spaces based on their needs.

    input_model_path = "./yolov8n.onnx"

    auto_search_config = AutoSearchConfig_Default()

    # Get quantization configuration
    quant_config = get_default_config("S8S8_AAWS")
    config_copy = copy.deepcopy(quant_config)
    config_copy.calibrate_method = CalibrationMethod.MinMax
    config = Config(global_quant_config=config_copy)
    print(f"The configuration for quantization is {config}")

    # Create auto search instance
    auto_search_ins = auto_search.AutoSearch(
        config=config,
        auto_search_config=auto_search_config,
        model_input=input_model_path,
        calibration_data_reader=ImageDataReader(
            input_model_path,
            calibration_dataset_path,
        ),
    )

    # build search space
    # To reduce computational load for this demo, we have commented out the other predefined search spaces. Users are welcome to modify them based on their needs

    # fixed point
    # space1 = auto_search_ins.build_all_configs(auto_search_config.search_space_XINT8)
    # space2 = auto_search_ins.build_all_configs(auto_search_config.search_space)
    # space3 = auto_search_ins.build_all_configs(auto_search_config.search_space_XINT8_advanced)
    # space4 = auto_search_ins.build_all_configs(auto_search_config.search_space_XINT8_advanced2)
    space5 = auto_search_ins.build_all_configs(auto_search_config.search_space_advanced)
    space6 = auto_search_ins.build_all_configs(auto_search_config.search_space_advanced2)

    # bf16 and bfp16
    # space7 = auto_search_ins.build_all_configs(auto_search_config.search_space_bf16)
    # space8 = auto_search_ins.build_all_configs(auto_search_config.search_space_bfp16)
    # space9 = auto_search_ins.build_all_configs(auto_search_config.search_space_bf16_advanced)
    # space10 = auto_search_ins.build_all_configs(auto_search_config.search_space_bfp16_advanced)
    # auto_search_ins.all_configs = space1 + space2 + space3 + space4 + space5 + space6 + space7 + space8 + space9 + space10
    auto_search_ins.all_configs = space5 + space6

    # Excute the auto search process
    auto_search_ins.search_model()

The overall runtime of the AMD Quark Auto Search module varies depending on model complexity, dataset size, and available compute resources. Upon completion, the module automatically generates a comprehensive log file summarizing all evaluated configurations. These results are ranked according to the optimization criteria you specified.