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# Quark ONNX Quantization Tutorial For Cross Layer Equalization (CLE)

This folder contains an example of quantizing a resnet152 model using the ONNX quantizer of Quark. Cross Layer Equalization (CLE) can equalize the weights of consecutive convolution layers, making the model weights easier to perform per-tensor quantization. For more details, please refer the paper https://arxiv.org/abs/1906.04721. Experiments show that the CLE technique improves PTQ accuracy for many models, especially those with depthwise convolutional layers, such as MobileNet and ShuffleNet.

The example has the following parts:

- Install requirements

- Prepare model

- Prepare data

- Quantizatize without CLE

- Quantizatize with CLE

- Evaluate 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 amd-quark
    %pip install -r ./requirements.txt

## 2) Export ONNX Model From Resnet152 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/resnet152

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

    !mkdir -p models

    import os
    import shutil

    import timm
    import torch

    model_name = "resnet152"

    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")

## 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. This example uses MinMax calibration and INT8 quantization for both weights and activations; it also enables the CLE procedure to reduce accuracy degradation during quantization.

    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

    from quark.onnx.operators.custom_ops import get_library_path

    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 CalibMethod, CLEConfig, Int8Spec, ModelQuantizer, QConfig, QLayerConfig

    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.get("include_cle"):
            algo_config = [CLEConfig(cle_steps=1, cle_total_layer_diff_threshold=2e-7)]
        else:
            algo_config = []
        activation_spec = Int8Spec()
        weight_spec = Int8Spec()
        activation_spec.set_symmetric(False)
        activation_spec.set_calibration_method(CalibMethod.MinMax)
        weight_spec.set_calibration_method(CalibMethod.MinMax)
        config = QConfig(
            global_config=QLayerConfig(activation=activation_spec, weight=weight_spec),
            algo_config=algo_config,
            AlignSlice=False,
            FoldRelu=True,
            AlignConcat=True,
        )
        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 quantization config with CLE disabled, and then generates a quantized model to the models directory using the default S8S8_AAWS configuration — symmetric INT8 quantization for both weights and activations.

    quant_config = {
        "model_name": "resnet152",
        "input_model_path": "models/resnet152.onnx",
        "output_model_path": "models/resnet152_quantized.onnx",
        "calibration_dataset_path": calib_data_path,
        "batch_size": 1,
        "workers": 1,
    }

    quantize_model(quant_config)

The cell applies the same default quantization scheme and adds the CLE option, then generates the quantized model into the models directory.

    quant_config_with_cle = copy.deepcopy(quant_config)
    quant_config_with_cle["output_model_path"] = "models/resnet152_cle_quantized.onnx"
    quant_config_with_cle["include_cle"] = True

    quantize_model(quant_config_with_cle)

## 5) Evaluation and Expected Results

Evaluation is performed on the ImageNet validation set. We compare three models — (1) full-precision, (2) quantized without CLE, and (3) quantized with CLE — to assess CLE’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

    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.")

First, define an evaluation config, and record accuracy of the Full Precision model on ImageNet val dataset

    eval_config = {
        "data": source_folder,
        "model_name": "resnet152",
        "batch_size": 1,
        "workers": 1,
        "gpu": False,
        "print_freq": 1000,
    }

    full_precision_eval_config = copy.deepcopy(eval_config)
    full_precision_eval_config["onnx_input"] = "models/resnet152.onnx"

    evaluate(full_precision_eval_config)

Then, specify the path to the quantized model without CLE and record its accuracy on ImageNet val dataset

    quant_eval_config = copy.deepcopy(eval_config)
    quant_eval_config["onnx_input"] = "models/resnet152_quantized.onnx"

    evaluate(quant_eval_config)

Last, specify the path to the quantized model with CLE and record its accuracy on ImageNet val dataset

    cle_eval_config = copy.deepcopy(eval_config)
    cle_eval_config["onnx_input"] = "models/resnet152_cle_quantized.onnx"

    evaluate(cle_eval_config)

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 CLE.

| Float Model | Quantized Model without CLE | Quantized Model with CLE
--- | --- | --- | ---
Model Size | 232 MB | 59 MB | 59 MB
Prec@1 | 83.456 % | 70.194 % | 79.610 %
Prec@5 | 96.894 % | 88.456 % | 94.894 %