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 mobilenetv2_050 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 mobilenetv2_050 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

• Customize evaluator

• 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) Export ONNX Model From mobilenetv2_050.lamb_in1k Torch 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_050.lamb_in1k

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

!mkdir -p models

import os
import shutil

import timm
import torch
from timm.data import resolve_data_config

model_name = "mobilenetv2_050.lamb_in1k"

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

import torch

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) 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 time

import numpy as np
import onnxruntime
import torchvision
from onnxruntime.quantization.calibrate import CalibrationMethod
from onnxruntime.quantization.quant_utils import QuantType
from torchvision import transforms

from quark.onnx import Config, ExtendedQuantFormat, ExtendedQuantType, 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],
        "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],
        },
    }

    # for s8s8 aaws/asws
    search_space_s8s8: dict[str, any] = {
        "calibrate_method": [CalibrationMethod.MinMax, CalibrationMethod.Percentile],
        "activation_type": [
            QuantType.QInt8,
        ],
        "weight_type": [
            QuantType.QInt8,
        ],
        "include_cle": [False],
        "include_fast_ft": [False],
        "extra_options": {
            "ActivationSymmetric": [True, False],
            "WeightSymmetric": [True],
            "CalibMovingAverage": [False, True],
            "CalibMovingAverageConstant": [0.01],
            "AlignSlice": [False],
            "FoldRelu": [True],
            "AlignConcat": [True],
        },
    }

    search_space_s8s8_advanced: dict[str, any] = {
        "calibrate_method": [CalibrationMethod.MinMax, CalibrationMethod.Percentile],
        "activation_type": [QuantType.QInt8],
        "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],
            "AlignSlice": [False],
            "FoldRelu": [True],
            "AlignConcat": [True],
            "FastFinetune": {
                "DataSize": [
                    200,
                ],
                "NumIterations": [1000],
                "OptimAlgorithm": ["adaround"],
                "LearningRate": [0.1],
                "OptimDevice": ["cuda:0"],
                "InferDevice": ["cuda:0"],
                "EarlyStop": [False],
            },
        },
    }

    search_space_s8s8_advanced2: dict[str, any] = {
        "calibrate_method": [CalibrationMethod.MinMax, CalibrationMethod.Percentile],
        "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],
            "AlignSlice": [False],
            "FoldRelu": [True],
            "AlignConcat": [True],
            "FastFinetune": {
                "DataSize": [
                    200,
                ],
                "NumIterations": [5000],
                "OptimAlgorithm": ["adaquant"],
                "LearningRate": [
                    1e-5,
                ],
                "OptimDevice": ["cuda:0"],
                "InferDevice": ["cuda:0"],
                "EarlyStop": [False],
            },
        },
    }

    # for s16s8 aaws/asws
    search_space_s16s8: dict[str, any] = {
        "calibrate_method": [CalibrationMethod.MinMax, CalibrationMethod.Percentile],
        "activation_type": [
            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],
            "AlignSlice": [False],
            "FoldRelu": [True],
            "AlignConcat": [True],
            "AlignEltwiseQuantType": [True],
        },
    }

    search_space_s16s8_advanced: dict[str, any] = {
        "calibrate_method": [CalibrationMethod.MinMax, CalibrationMethod.Percentile],
        "activation_type": [
            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],
            "AlignSlice": [False],
            "FoldRelu": [True],
            "AlignConcat": [True],
            "AlignEltwiseQuantType": [True],
            "FastFinetune": {
                "DataSize": [
                    200,
                ],
                "NumIterations": [1000],
                "OptimAlgorithm": ["adaround"],
                "LearningRate": [0.1],
                "OptimDevice": ["cuda:0"],
                "InferDevice": ["cuda:0"],
                "EarlyStop": [False],
            },
        },
    }

    search_space_s16s8_advanced2: dict[str, any] = {
        "calibrate_method": [CalibrationMethod.MinMax, CalibrationMethod.Percentile],
        "activation_type": [
            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],
            "AlignSlice": [False],
            "FoldRelu": [True],
            "AlignConcat": [True],
            "AlignEltwiseQuantType": [True],
            "FastFinetune": {
                "DataSize": [
                    200,
                ],
                "NumIterations": [5000],
                "OptimAlgorithm": ["adaquant"],
                "LearningRate": [
                    1e-5,
                ],
                "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 Imagenet dataset

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 load_loader(model_name, data_dir, batch_size, workers):
    timm_model = timm.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

The following cell demonstrates how to customize an evaluator. ImageNet has 1,000 classes, so normally 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. However, we need to limit to one evaluation metric to determine if pre-defined criteria is met. Therefore, this evaluator only uses Prec@1 metric.

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]
    top1 = 100 * np.equal(max_indices[:, 0], target).mean()
    return top1


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

    batch_time = AverageMeter()
    top1 = 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 = accuracy_np(output, target.numpy())
        top1.update(prec1.item(), input.size(0))

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

    return top1


def evaluator(model_path):
    # model_path = "models/mobilenetv2_050.lamb_in1k.onnx"
    args = {
        "data": source_folder,
        "model_name": "mobilenetv2_050.lamb_in1k",
        "batch_size": 1,
        "workers": 2,
        "gpu": False,
        "print_freq": 1000,
    }
    # 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))

    val_loader = load_loader(args["model_name"], args["data"], args["batch_size"], args["workers"])
    top1 = metrics(model_path, sess_options, providers, val_loader)
    return top1

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 13 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.

data_loader = load_loader("mobilenetv2_050.lamb_in1k", source_folder, 1, 1)

auto_search_config = AutoSearchConfig_Default()
auto_search_config.search_evaluator = evaluator

# 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="models/mobilenetv2_050.lamb_in1k.onnx",
    calibration_data_reader=CalibrationDataReader(data_loader),
)

# 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_s8s8)
space3 = auto_search_ins.build_all_configs(auto_search_config.search_space_s16s8)
space4 = auto_search_ins.build_all_configs(auto_search_config.search_space_XINT8_advanced)
space5 = auto_search_ins.build_all_configs(auto_search_config.search_space_XINT8_advanced2)
space6 = auto_search_ins.build_all_configs(auto_search_config.search_space_s8s8_advanced)
space7 = auto_search_ins.build_all_configs(auto_search_config.search_space_s8s8_advanced2)
space8 = auto_search_ins.build_all_configs(auto_search_config.search_space_s16s8_advanced)
space9 = auto_search_ins.build_all_configs(auto_search_config.search_space_s16s8_advanced2)
# bf16 and bfp16
space10 = auto_search_ins.build_all_configs(auto_search_config.search_space_bf16)
space11 = auto_search_ins.build_all_configs(auto_search_config.search_space_bfp16)
space12 = auto_search_ins.build_all_configs(auto_search_config.search_space_bf16_advanced)
space13 = 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 + space11 + space12 + space13
auto_search_ins.all_configs = space1 + space2

# 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.