Quark ONNX Quantization Tutorial For AdaQuant

In this tutorial, you will learn how to use AMD Quark to quantize image classification models from the Hugging Face timm library using three different quantization configurations: XINT8, A8W8, and A16W8.

Quantization is a powerful optimization technique that reduces model precision to improve inference speed and reduce memory footprint while maintaining model accuracy. The tutorial provides a step-by-step guide on setting up the environment, loading pretrained timm models, and applying AMD Quark’s quantization workflow.

By the end of this tutorial, you will understand the trade-offs between XINT8, A8W8, and A16W8 quantization schemes, and learn how to select the best configuration for achieving optimal performance and accuracy to suit your needs.

The example has the following parts:

• Install requirements

• Prepare model

• Prepare data

• Quantizatize model

• Evaluate Models

• Apply More Models

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 "cmake<4.0" amd-quark
%pip install -r ./requirements.txt

2) Export ONNX Model From mobilenetv2_100.ra_in1k Model.

You don’t need to download this model manually. If you’re curious about its source, the corresponding model link is: https://huggingface.co/timm/mobilenetv2_100.ra_in1k

Before exporting, let’s create a directory for models:

!mkdir -p models

import os
import shutil

import timm
import torch


def timm_to_onnx(model_name):
    model = timm.create_model(model_name, pretrained=True)
    model = model.eval()
    device = torch.device("cpu")

    data_config = timm.data.resolve_model_data_config(
        model=model,
        use_test_size=True,
    )

    batch_size = 1
    torch.manual_seed(42)
    dummy_input = torch.randn((batch_size,) + tuple(data_config["input_size"])).to(device)

    torch.onnx.export(
        model,
        dummy_input,
        "models/" + model_name + ".onnx",
        export_params=True,
        do_constant_folding=True,
        opset_version=17,
        input_names=["input"],
        output_names=["output"],
        dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}},
        verbose=False,
        dynamo=False,
    )
    print("Onnx model is saved at models/" + model_name + ".onnx")


timm_to_onnx("mobilenetv2_100.ra_in1k")

3) Prepare data

ILSVRC 2012, commonly known as ‘ImageNet’. This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 50,000 validation images.

If you already have an ImageNet datasets, you can directly use your dataset path.

To prepare the test data, please check the download section of the main website: https://huggingface.co/datasets/imagenet-1k/tree/main/data. You need to register and download val_images.tar.gz to the current directory.

Then, create a val_data folder and decompress the .gz file to the folder.

!mkdir -p val_data && tar -xzf val_images.tar.gz -C val_data

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, and validation dataset and calibration dataset will be created under current directory.

import sys

source_folder = "val_data"
calib_data_path = "calib_data"

if os.environ.get("LOCAL_DATA_CACHE") is not None:
    data_path = os.environ["LOCAL_DATA_CACHE"]
    source_folder = os.path.join(data_path, "Imagenet/val")
    calib_data_path = os.path.join(data_path, "Imagenet/calib_100")
else:
    files = os.listdir(source_folder)

    for filename in files:
        if not filename.startswith("ILSVRC2012_val_") or not filename.endswith(".JPEG"):
            continue

        n_identifier = filename.split("_")[-1].split(".")[0]
        folder_name = n_identifier
        folder_path = os.path.join(source_folder, folder_name)
        if not os.path.exists(folder_path):
            os.makedirs(folder_path)
        file_path = os.path.join(source_folder, filename)
        destination = os.path.join(folder_path, filename)
        shutil.move(file_path, destination)

    print("File organization complete.")

    if not os.path.exists(calib_data_path):
        os.makedirs(calib_data_path)

    destination_folder = calib_data_path

    subfolders = os.listdir(source_folder)

    for subfolder in subfolders:
        source_subfolder = os.path.join(source_folder, subfolder)
        destination_subfolder = os.path.join(destination_folder, subfolder)
        os.makedirs(destination_subfolder, exist_ok=True)
        files = os.listdir(source_subfolder)

        if files:
            file_to_copy = files[0]
            source_file = os.path.join(source_subfolder, file_to_copy)
            destination_file = os.path.join(destination_subfolder, file_to_copy)

            shutil.copy(source_file, destination_file)

    print("Creating calibration dataset complete.")

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

The storage format of the val_data of the ImageNet dataset organized as follows:

• val_data

  • n01440764

    • ILSVRC2012_val_00000293.JPEG

    • ILSVRC2012_val_00002138.JPEG

    • …

  • n01443537

    • ILSVRC2012_val_00000236.JPEG

    • ILSVRC2012_val_00000262.JPEG

    • …

  • …

The storage format of the calib_data of the ImageNet dataset organized as follows:

• calib_data

  • n01440764

    • ILSVRC2012_val_00000293.JPEG

  • n01443537

    • ILSVRC2012_val_00000236.JPEG

  • …

4) Quantization Procedure

First, create a data reader that gathers calibration statistics from the target dataset. Next, inside quantize_model, construct the quantized model and pass in your configuration.

import numpy as np
import onnxruntime
import torchvision
from timm.data import resolve_data_config
from timm.models import create_model
from torchvision import transforms


def load_loader(model_name, data_dir, batch_size, workers):
    timm_model = create_model(
        model_name,
        pretrained=False,
    )
    data_config = resolve_data_config(model=timm_model, use_test_size=True)
    crop_pct = data_config["crop_pct"]
    input_size = data_config["input_size"]
    width = input_size[-1]
    data_transform = transforms.Compose(
        [
            transforms.Resize(int(width / crop_pct)),
            transforms.CenterCrop(width),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ]
    )
    dataset = torchvision.datasets.ImageFolder(data_dir, data_transform)
    data_loader = torch.utils.data.DataLoader(
        dataset, batch_size=batch_size, shuffle=False, num_workers=workers, pin_memory=True
    )
    return data_loader

import copy

from quark.onnx import A8W8_QCONFIG, A16W8_QCONFIG, XINT8_QCONFIG, ModelQuantizer


class CalibrationDataReader:
    def __init__(self, dataloader):
        super().__init__()
        self.iterator = iter(dataloader)

    def get_next(self) -> dict:
        try:
            return {"input": next(self.iterator)[0].numpy()}
        except Exception:
            return None


def quantize_model(args: dict) -> None:
    # `dr` (Data Reader) is an instance of ResNet50DataReader, which is a utility class that
    # reads the calibration dataset and prepares it for the quantization process.
    if args["calibration_dataset_path"] == "":
        dr = None
    else:
        data_loader = load_loader(
            args["model_name"], args["calibration_dataset_path"], args["batch_size"], args["workers"]
        )
        dr = CalibrationDataReader(data_loader)

    # Get quantization configuration
    if args["config"] == "XINT8":
        config = XINT8_QCONFIG
    elif args["config"] == "A8W8":
        config = A8W8_QCONFIG
    elif args["config"] == "A16W8":
        config = A16W8_QCONFIG
    else:
        raise ValueError(
            "No config is specified in the default quantize_model function. Please adjust it to fit your need."
        )

    print(f"The configuration for quantization is {config}")

    # Create an ONNX quantizer
    quantizer = ModelQuantizer(config)

    # Quantize the ONNX model
    quantizer.quantize_model(args["input_model_path"], args["output_model_path"], dr)

The cell defines a basic config inputs without specifying the quantization yet

quant_config = {
    "model_name": "mobilenetv2_100.ra_in1k",
    "input_model_path": "models/mobilenetv2_100.ra_in1k.onnx",
    "output_model_path": "models/mobilenetv2_100.ra_in1k_quantized.onnx",
    "calibration_dataset_path": calib_data_path,
    "batch_size": 1,
    "workers": 1,
}

Quantize with XINT8 config

quant_config_with_xint8 = copy.deepcopy(quant_config)
quant_config_with_xint8["config"] = "XINT8"
quant_config_with_xint8["output_model_path"] = "models/mobilenetv2_100.ra_in1k_xint8.onnx"

quantize_model(quant_config_with_xint8)

Quantize with A8W8 config

quant_config_with_a8w8 = copy.deepcopy(quant_config)
quant_config_with_a8w8["config"] = "A8W8"
quant_config_with_a8w8["output_model_path"] = "models/mobilenetv2_100.ra_in1k_a8w8.onnx"

quantize_model(quant_config_with_a8w8)

Quantize with A16W8 config

quant_config_with_a16w8 = copy.deepcopy(quant_config)
quant_config_with_a16w8["config"] = "A16W8"
quant_config_with_a16w8["output_model_path"] = "models/mobilenetv2_100.ra_in1k_a16w8.onnx"

quantize_model(quant_config_with_a16w8)

5) Evaluation and Expected Results

Evaluation is performed on the ImageNet validation set. We compare three models — (1) full-precision, (2) quantized without AdaQuant, and (3) quantized with AdaQuant — to assess AdaQuant’s effectiveness. The full-precision model serves as the baseline for measuring any accuracy change caused by quantization.

ImageNet has 1,000 classes, so we report both Prec@1 and Prec@5 to capture strict and relaxed accuracy. Both metrics are reported as percentages (higher is better). Prec@1 shows exact single-label correctness; Prec@5 is useful on large, fine-grained label spaces because it captures near-misses where the correct class is among the model’s top candidates.

import time

from quark.onnx.operators.custom_ops import get_library_path


class AverageMeter:
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def accuracy_np(output, target):
    max_indices = np.argsort(output, axis=1)[:, ::-1]
    top5 = 100 * np.equal(max_indices[:, :5], target[:, np.newaxis]).sum(axis=1).mean()
    top1 = 100 * np.equal(max_indices[:, 0], target).mean()
    return top1, top5


def metrics(onnx_model_path, sess_options, providers, data_loader, print_freq):
    session = onnxruntime.InferenceSession(onnx_model_path, sess_options, providers=providers)
    input_name = session.get_inputs()[0].name

    batch_time = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()
    end = time.time()
    for i, (input, target) in enumerate(data_loader):
        # run the net and return prediction
        output = session.run([], {input_name: input.data.numpy()})
        output = output[0]

        # measure accuracy and record loss
        prec1, prec5 = accuracy_np(output, target.numpy())
        top1.update(prec1.item(), input.size(0))
        top5.update(prec5.item(), input.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % print_freq == 0:
            print(
                f"Test: [{i}/{len(data_loader)}]\t"
                f"Time {batch_time.val:.3f} ({batch_time.avg:.3f}, {input.size(0) / batch_time.avg:.3f}/s, "
                f"{100 * batch_time.avg / input.size(0):.3f} ms/sample) \t"
                f"Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t"
                f"Prec@5 {top5.val:.3f} ({top5.avg:.3f})"
            )

    return top1, top5


def evaluate(args: dict):
    args["gpu_id"] = 0

    # Set graph optimization level
    sess_options = onnxruntime.SessionOptions()
    sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
    if args.get("profile"):
        sess_options.enable_profiling = True
    if args.get("onnx_output_opt"):
        sess_options.optimized_model_filepath = args["onnx_output_opt"]
    if args.get("gpu"):
        if "ROCMExecutionProvider" in onnxruntime.get_available_providers():
            device = "ROCM"
            providers = ["ROCMExecutionProvider"]
        elif "CUDAExecutionProvider" in onnxruntime.get_available_providers():
            device = "CUDA"
            providers = ["CUDAExecutionProvider"]
        else:
            device = "CPU"
            providers = ["CPUExecutionProvider"]
            print("Warning: GPU is not available, use CPU instead.")
    else:
        device = "CPU"
        providers = ["CPUExecutionProvider"]
    sess_options.register_custom_ops_library(get_library_path(device))

    if args.get("onnx_input"):
        val_loader = load_loader(args["model_name"], args["data"], args["batch_size"], args["workers"])
        f_top1, f_top5 = metrics(args["onnx_input"], sess_options, providers, val_loader, args["print_freq"])
        print(f" * Prec@1 {f_top1.avg:.3f} ({100 - f_top1.avg:.3f}) Prec@5 {f_top5.avg:.3f} ({100.0 - f_top5.avg:.3f})")
    elif args.get("onnx_float") and args.get("onnx_quant"):
        val_loader = load_loader(args[""], args["data"], args["batch_size"], args["workers"])
        f_top1, f_top5 = metrics(args["onnx_float"], sess_options, providers, val_loader, args["print_freq"])
        f_top1 = format(f_top1.avg, ".2f")
        f_top5 = format(f_top5.avg, ".2f")

        q_top1, q_top5 = metrics(args["onnx_quant"], sess_options, providers, val_loader, args["print_freq"])
        q_top1 = format(q_top1.avg, ".2f")
        q_top5 = format(q_top5.avg, ".2f")

        f_size = format(os.path.getsize(args["onnx_float"]) / (1024 * 1024), ".2f")
        q_size = format(os.path.getsize(args["onnx_quant"]) / (1024 * 1024), ".2f")
        """
        --------------------------------------------------------
        |             | float model    | quantized model |
        --------------------------------------------------------
        | ****        | ****           | ****             |
        --------------------------------------------------------
        | Model Size  | ****           | ****             |
        --------------------------------------------------------
        """
        from rich.console import Console
        from rich.table import Table

        console = Console()

        table = Table()
        table.add_column("")
        table.add_column("Float Model")
        table.add_column("Quantized Model", style="bold green1")

        table.add_row("Model", args["onnx_float"], args["onnx_quant"])
        table.add_row("Model Size", str(f_size) + " MB", str(q_size) + " MB")
        table.add_row("Prec@1", str(f_top1) + " %", str(q_top1) + " %")
        table.add_row("Prec@5", str(f_top5) + " %", str(q_top5) + " %")

        console.print(table)

    else:
        print("Please specify both model-float and model-quant or model-input for evaluation.")

Let’s define a basic evaluation config inputs, then record accuracy of each method on ImageNet val dataset

eval_config = {
    "data": source_folder,
    "model_name": "mobilenetv2_100.ra_in1k",
    "batch_size": 1,
    "workers": 1,
    "gpu": False,
    "print_freq": 1000,
}

Evaluate on the full-precision modeland set it as a measurement base

full_precision_eval_config = copy.deepcopy(eval_config)
full_precision_eval_config["onnx_input"] = "models/mobilenetv2_100.ra_in1k.onnx"

evaluate(full_precision_eval_config)

Evaluate on XINT8 quantized model

eval_config_xint8 = copy.deepcopy(eval_config)
eval_config_xint8["onnx_input"] = "models/mobilenetv2_100.ra_in1k_xint8.onnx"

evaluate(eval_config_xint8)

Evaluate on A8W8 quantized model

eval_config_a8w8 = copy.deepcopy(eval_config)
eval_config_a8w8["onnx_input"] = "models/mobilenetv2_100.ra_in1k_a8w8.onnx"

evaluate(eval_config_a8w8)

Evaluate on A16W8 quantized model

eval_config_a16w8 = copy.deepcopy(eval_config)
eval_config_a16w8["onnx_input"] = "models/mobilenetv2_100.ra_in1k_a16w8.onnx"

evaluate(eval_config_a16w8)

The following table contains the expected results, but please note that different machines can lead to minor variations in the accuracy of quantized model with AdaQuant.

	Float Model	Quantized Model with XINT8	Quantized Model with A8W8	Quantized Model with A16W8
Model Size	15 MB	4.0 MB	4.0 MB	4.0 MB
Prec@1	72.890 %	66.640 %	70.486 %	70.562 %
Prec@5	90.996 %	87.122 %	89.614 %	89.586 %