Model Quantization Technology and Practice in HarmonyOS Next

This article aims to deeply explore the model quantization technology in the Huawei HarmonyOS Next system (up to API 12 as of now), and summarize it based on actual development practices. It mainly serves as a vehicle for technical sharing and communication. There may be mistakes and omissions. Colleagues are welcome to put forward valuable opinions and questions so that we can make progress together. This article is original content, and any form of reprint must indicate the source and the original author.

I. Basic Concepts and Significance of Model Quantization

(1) Concept Explanation

In the model world of HarmonyOS Next, model quantization is like a “weight - loss journey” for the model. Simply put, it is the process of converting the parameters in the model originally represented by high - precision data types (such as 32 - bit floating - point numbers) into low - precision data types (such as 8 - bit integers). The purpose of this is to significantly reduce the storage size of the model with minimal loss of model performance (such as accuracy), and at the same time improve the computational efficiency, enabling the model to better adapt to the limited resource environment of HarmonyOS Next devices.

(2) Comparison of Differences before and after Quantization (in Tabular Form)

Comparison Items	Before Quantization	After Quantization
Storage Size	Take a model with 10 million parameters as an example. If each parameter is stored as a 32 - bit floating - point number, it will occupy 40MB of space (10 million * 4 bytes)	After 8 - bit integer quantization, each parameter occupies 1 byte, and the model storage size can be reduced to 10MB (10 million * 1 byte), with a storage requirement reduction of approximately 75%
Computational Efficiency	During the calculation process, the operations of 32 - bit floating - point numbers are relatively complex, consuming more computational resources and time	The calculations of 8 - bit integers are simpler and more efficient. On some hardware platforms, dedicated instruction sets can be used for acceleration, and the calculation speed can be increased several times, especially when performing large - scale matrix operations

(3) Analysis of the Impact of Different Quantization Strategies on Model Performance

1. Uniform Quantization Strategy Uniform quantization evenly divides the data range into several intervals, and then uses a representative value (usually the mid - point of the interval) to represent all the values within this interval. The advantage of this strategy is its simplicity and intuitiveness, with relatively low computational requirements, and it can achieve good results in many scenarios. For example, for models with relatively uniform data distribution, uniform quantization can effectively reduce the number of bits used to represent parameters, while having a relatively small impact on model accuracy. However, in the case of non - uniform data distribution, it may lead to significant accuracy loss. For instance, in an image recognition model, if the pixel values of the image are concentrated in a specific interval, uniform quantization may cause the loss of information in other intervals, thus affecting the model's ability to recognize image features.
2. Non - uniform Quantization Strategy Different from uniform quantization, non - uniform quantization divides the intervals according to the distribution characteristics of the data. Usually, the regions where the data distribution is more dense are divided more finely, while the regions with sparse data distribution are divided more coarsely. This strategy can better adapt to the data distribution, and to a certain extent, reduce accuracy loss. For example, when processing speech signal data, since the amplitude distribution of speech signals usually exhibits a logarithmic characteristic, non - uniform quantization can divide the intervals according to this characteristic, representing the features of speech signals more accurately and improving the model's accuracy in speech recognition. However, the computational complexity of non - uniform quantization is relatively high, requiring more computational resources to determine the quantization intervals and mapping relationships.

II. Implementation Methods and Tools for Model Quantization

(1) Usage of OMG Off - line Model Conversion Tool

1. Preparation Work First, ensure that the dependent environment required by the OMG off - line model conversion tool has been installed. Then, prepare the original model file to be quantized (such as a TensorFlow pb model or a PyTorch pt model) and the calibration dataset. The calibration dataset is used to analyze the distribution of model parameters during the quantization process, so as to determine appropriate quantization parameters.
2. Parameter Configuration When running the OMG off - line model conversion tool, a series of parameters need to be configured. For example, use the --mode parameter to specify the running mode as 0 (no - training mode, currently only this mode is supported); select the type of deep - learning framework through the --framework parameter, such as 3 for TensorFlow, 5 for PyTorch or ONNX; use the --model parameter to specify the path of the original model file; the --cal_conf parameter sets the path of the calibration - method quantization configuration file, which contains some key configuration information during the quantization process, such as the selection of quantization algorithms, the setting of quantization ranges, etc.; the --output parameter specifies the absolute path where the quantized model file will be stored; the --input_shape parameter sets the shape of the input data according to the input requirements of the model, ensuring that it is consistent with the actual input node shape of the model.
3. Execution of the Quantization Process After configuring the parameters, run the tool to start the model quantization. The tool will analyze the original model based on the calibration dataset, determine the quantization parameters, then convert the parameters in the model to low - precision data types, and generate the quantized model file. During the quantization process, pay attention to the log information output on the console, and promptly discover and solve potential problems, such as data format mismatches, path errors, etc.

(2) Quantization Process of a TensorFlow Model (Simplified Code Example)

Suppose we have a simple TensorFlow image classification model. The following is a rough example of the quantization process:

import tensorflow as tf
from tensorflow.python.tools import freeze_graph
from tensorflow.python.tools import optimize_for_inference_lib

# Load the original model
model_path = 'original_model.pb'
graph = tf.Graph()
with graph.as_default():
    od_graph_def = tf.compat.v1.GraphDef()
    with tf.io.gfile.GFile(model_path, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

# Define input and output nodes
input_tensor = graph.get_tensor_by_name('input:0')
output_tensor = graph.get_tensor_by_name('output:0')

# Prepare the calibration dataset
calibration_data = get_calibration_data()  # Assume the calibration dataset has been obtained

# Perform model quantization
with tf.compat.v1.Session(graph=graph) as sess:
    # Freeze the model
    frozen_graph = freeze_graph.freeze_graph_with_def_protos(
        input_graph_def=graph.as_graph_def(),
        input_saver_def=None,
        input_checkpoint=None,
        output_node_names='output',
        restore_op_name=None,
        filename_tensor_name=None,
        output_graph='frozen_model.pb',
        clear_devices=True,
        initializer_nodes=None
    )
    # Optimize the model
    optimized_graph = optimize_for_inference_lib.optimize_for_inference(
        input_graph_def=frozen_graph,
        input_node_names=['input'],
        output_node_names=['output'],
        placeholder_type_enum=tf.float32.as_datatype_enum
    )
    # Quantize the model
    converter = tf.lite.TFLiteConverter.from_session(sess, [input_tensor], [output_tensor])
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter.inference_input_type = tf.uint8
    converter.inference_output_type = tf.uint8
    tflite_model = converter.convert()
    # Save the quantized model
    with open('quantized_model.tflite', 'wb') as f:
        f.write(tflite_model)

In this example, first, the original TensorFlow model is loaded, then the input and output nodes are defined, and the calibration dataset is prepared. Then, through a series of steps, including freezing the model, optimizing the model, and finally using TFLiteConverter for quantization operations, the quantized model is saved in the .tflite format for deployment on HarmonyOS Next devices.

(3) Precautions during the Quantization Process

1. Selection of Calibration Dataset The selection of the calibration dataset is crucial. It should be able to represent the data distribution that the model may encounter in practical applications. If the calibration dataset is significantly different from the actual application data, it may lead to a decrease in the accuracy of the quantized model. For example, in a model for identifying animal pictures, if the calibration dataset only contains pictures of a few types of animals, while in actual applications, various types of animal pictures may be encountered, then the quantized model may have a high error rate when processing actual data.
2. Adjustment of Quantization Parameters During the quantization process, the adjustment of some parameters will directly affect the quantization effect. For example, the setting of the quantization range. If it is set improperly, it may lead to data overflow or excessive accuracy loss. For different data types and model structures, the quantization range needs to be adjusted reasonably according to the actual situation. At the same time, pay attention to the support of quantization parameters by different hardware platforms to ensure that the quantized model can run normally on the target device.

III. Deployment and Optimization of Quantized Models

(1) Deployment Process and Challenges

1. Overview of the Deployment Process To deploy the quantized model onto a HarmonyOS Next device, first, ensure that the device supports the running environment of the model. For example, relevant runtime libraries or interpreters (such as an inference engine for quantized models) need to be installed. Then, transfer the quantized model file to the device and integrate the model loading and inference code into the application. When loading the model, pay attention to whether the path and format of the model file are correct to ensure successful model loading.
2. Challenges Faced
   • Hardware Compatibility Issues: Different HarmonyOS Next devices may adopt different hardware architectures, such as CPU, GPU, or NPU, and their support for quantized models also varies. Some low - end devices may have performance bottlenecks when processing low - precision data after quantization, or may not support certain specific quantized data types or operations. For example, the CPU of some devices may not have optimized instruction sets for 8 - bit integer calculations, resulting in the quantized model running slower than expected on these devices.
   • Memory Management Challenges: Although the quantized model has a reduced storage size, it still requires a certain amount of memory to store model parameters and intermediate calculation results during operation. For devices with limited resources, especially some IoT devices, memory management becomes a key issue. If the memory is not allocated reasonably, it may lead to application crashes or slow running. For example, when multiple applications are running simultaneously or when processing large - scale data, the quantized model may not work properly due to insufficient memory.

(2) Optimization Strategies

1. Optimization of Computational Resource Allocation Allocate computational tasks reasonably according to the hardware resources of the device. For example, for devices with an NPU, the computationally intensive parts of the model (such as convolutional layer calculations) can be assigned to the NPU for execution, making full use of the acceleration capabilities of the NPU, while leaving some control logic and simple calculations to be processed on the CPU. At the same time, multi - threading or asynchronous computing methods can be adopted to improve the utilization rate of computational resources. For example, during the model inference process, different data blocks or computational tasks can be assigned to different threads for parallel processing to speed up the inference speed.
2. Optimization of Model Parameter Adjustment After the quantized model is deployed, if the performance does not meet the standard, consider adjusting and optimizing the model parameters. One method is to perform fine - tuning training on the model. Use a small amount of actual application data to further train the quantized model, enabling the model to better adapt to the actual data distribution and improve the accuracy of the model. Another method is to adjust the structure or parameter settings of the model according to the performance characteristics of the device. For example, reduce some unnecessary layers or parameters in the model, or adjust the quantization parameters to further improve the computational efficiency of the model while ensuring a certain level of accuracy.

(3) Demonstration of Optimization Effects in a Practical Case

Take an intelligent image recognition application based on HarmonyOS Next as an example. In the application, a quantized convolutional neural network model is used to recognize objects in images.

1. Situation before Optimization Before optimization, the quantized model was directly deployed on a mid - to - low - end HarmonyOS Next device. During the testing process, it was found that the inference speed of the model was slow, with an average inference time of 1.5 seconds per image, and the accuracy was only about 80% when processing images of complex scenes. At the same time, the memory occupation of the application was high during operation, and it was prone to freezing.
2. Implementation of Optimization Measures
   • Optimization of Computational Resource Allocation: By analyzing the device hardware, it was found that the device had certain GPU computing capabilities. Therefore, the model was optimized by assigning the convolutional layer calculation tasks to the GPU for execution, and at the same time, the task scheduling strategy between the CPU and GPU was adjusted to improve the collaborative efficiency of computational resources.
   • Optimization of Model Parameter Adjustment: Some images from the actual application scenario were used to perform fine - tuning training on the quantized model, and the quantization parameters were adjusted to further reduce the computational amount of the model without sacrificing too much accuracy.
3. Effects after Optimization After the above optimizations, the application was tested again. The inference speed of the model was significantly improved, with the average inference time per image shortened to less than 0.5 seconds, an increase of approximately 2 times. In terms of accuracy, it also increased to over 90%, enabling more accurate recognition of objects in images. At the same time, the memory occupation of the application was significantly reduced, and the operation became smoother, effectively alleviating the freezing phenomenon. Through this practical case, it can be seen that through reasonable deployment optimization strategies, the performance and resource utilization efficiency of the quantized model on HarmonyOS Next devices can be effectively improved, providing strong support for the development of intelligent applications. It is hoped that through the introduction of this article, everyone can better master the model quantization technology in HarmonyOS Next and create more efficient and lightweight intelligent models in actual development. If you encounter other problems during the practice process, you are welcome to communicate and discuss together! Haha!