ONNX Quantization#

ONNX is an open graph format to represent machine learning models. ONNX Runtime is a cross-platform machine-learning model accelerator, with a flexible interface to integrate hardware-specific libraries.

Quantize with onnxruntime#

Quantization is a technique to compress deep learning models by reducing the precision of the model weights from 32 bits to 8 bits. This technique is used to reduce the memory footprint and improve the inference performance of the model. Quantization can be applied to the weights of the model, the activations of the model, or both.

There are two ways to quantize a model in onnxruntime:

  1. Dynamic Quantization: Dynamic quantization calculates the quantization parameters (scale and zero point) for activations dynamically, which means there is no any requirement for the calibration dataset. These calculations increase the cost of inference, while usually achieve higher accuracy comparing to static ones.

  2. Static Quantization: Static quantization method runs the model using a set of inputs called calibration data. In this way, user must provide a calibration dataset to calculate the quantization parameters (scale and zero point) for activations before quantizing the model.

Olive consolidates the dynamic and static quantization into a single pass called OnnxQuantization, and provide the user with the ability to tune both quantization methods and hyperparameter at the same time. If the user desires to only tune either of dynamic or static quantization, Olive also supports them through OnnxDynamicQuantization and OnnxStaticQuantization respectively.

Please refer to OnnxQuantization, OnnxDynamicQuantization and OnnxStaticQuantization for more details about the passes and their config parameters.

Note: If target execution provider is QNN EP, the model might need to be preprocessed before quantization. Please refer to QnnPreprocess for more details about the pass and its config parameters. This preprocessing step fuses operators unsupported by QNN EP and inserts necessary operators to make the model compatible with QNN EP.

Example Configuration#

a. Tune the parameters of the OlivePass with pre-defined searchable values

{
    "type": "OnnxQuantization",
    "data_config": "calib_data_config"
}

b. Select parameters to tune

{
    "type": "OnnxQuantization",
    // select per_channel to tune with "SEARCHABLE_VALUES".
    // other parameters will use the default value, not to be tuned.
    "per_channel": "SEARCHABLE_VALUES",
    "data_config": "calib_data_config"
}

c. Use default values of the OlivePass (no tuning in this way)

{
    "type": "OnnxQuantization",
    // set per_channel to "DEFAULT_VALUE"
    "per_channel": "DEFAULT_VALUE",
    "data_config": "calib_data_config"
}

d. Specify parameters with user defined values

"onnx_quantization": {
    "type": "OnnxQuantization",
    // set per_channel to True.
    "per_channel": true,
    "data_config": "calib_data_config"
}

Check out this file for an example implementation of "user_script.py" and "calib_data_config/dataloader_config/type".

check out this file for an example for Intel® Neural Compressor quantization.

Quantize with Intel® Neural Compressor#

In addition to the default onnxruntime quantization tool, Olive also integrates Intel® Neural Compressor.

Intel® Neural Compressor is a model compression tool across popular deep learning frameworks including TensorFlow, PyTorch, ONNX Runtime (ORT) and MXNet, which supports a variety of powerful model compression techniques, e.g., quantization, pruning, distillation, etc. As a user-experience-driven and hardware friendly tool, Intel® Neural Compressor focuses on providing users with an easy-to-use interface and strives to reach “quantize once, run everywhere” goal.

Olive consolidates the Intel® Neural Compressor dynamic and static quantization into a single pass called IncQuantization, and provide the user with the ability to tune both quantization methods and hyperparameter at the same time. If the user desires to only tune either of dynamic or static quantization, Olive also supports them through IncDynamicQuantization and IncStaticQuantization respectively.

Example Configuration#

"inc_quantization": {
    "type": "IncStaticQuantization",
    "approach": "weight_only",
    "weight_only_config": {
        "bits": 4,
        "algorithm": "GPTQ"
    },
    "data_config": "calib_data_config",
    "calibration_sampling_size": [8],
    "save_as_external_data": true,
    "all_tensors_to_one_file": true
}

Please refer to IncQuantization, IncDynamicQuantization and IncStaticQuantization for more details about the passes and their config parameters.

Quantize with AMD Vitis AI Quantizer#

Olive also integrates AMD Vitis AI Quantizer for quantization.

The Vitis™ AI development environment accelerates AI inference on AMD® hardware platforms. The Vitis AI quantizer can reduce the computing complexity by converting the 32-bit floating-point weights and activations to fixed-point like INT8. The fixed-point network model requires less memory bandwidth, thus providing faster speed and higher power efficiency than the floating-point model. Olive consolidates the Vitis™ AI quantization into a single pass called VitisAIQuantization which supports power-of-2 scale quantization methods and supports Vitis AI Execution Provider.

Example Configuration#

"vitis_ai_quantization": {
    "type": "VitisAIQuantization",
    "calibrate_method":"NonOverflow",
    "quant_format":"QDQ",
    "activation_type":"QUInt8",
    "weight_type":"QInt8",
    "data_config": "calib_data_config"
}

Please refer to VitisAIQuantization for more details about the pass and its config parameters.

NVIDIA TensorRT Model Optimizer-Windows#

Olive also integrates TensorRT Model Optimizer-Windows

The TensorRT Model Optimizer-Windows is engineered to deliver advanced model compression techniques, including quantization, to Windows RTX PC systems. Specifically tailored to meet the needs of Windows users,it is optimized for rapid and efficient quantization, featuring local GPU calibration, reduced system and video memory consumption, and swift processing times.

The primary objective of the TensorRT Model Optimizer-Windows is to generate optimized, standards-compliant ONNX-format models for DirectML backends. This makes it an ideal solution for seamless integration with ONNX Runtime (ORT) and DirectML (DML) frameworks, ensuring broad compatibility with any inference framework supporting the ONNX standard.

Olive consolidates the NVIDIA TensorRT Model Optimizer-Windows quantization into a single pass called NVModelOptQuantization which supports AWQ algorithm.

Example Configuration#

"quantization": {
    "type": "NVModelOptQuantization",
    "algorithm": "AWQ",
    "tokenizer_dir": "microsoft/Phi-3-mini-4k-instruct",
    "calibration": "awq_lite"
}

Please refer to Phi3 example for usability and setup details.