ONNX Runtime (ORT)

Introduction

ONNX Runtime (ORT) is a cross-platform machine learning inference accelerator designed to speed up the inference process of ONNX (Open Neural Network Exchange) models. Developed and open-sourced by Microsoft, it supports various hardware platforms and operating systems, providing high-performance inference capabilities.

Core Objectives:

• Accelerate ONNX Model Inference: Improve the inference speed of ONNX models through execution graph optimization, hardware acceleration, and other techniques.
• Cross-Platform Support: Support operating systems such as Windows, Linux, and macOS, as well as various hardware platforms including CPUs, GPUs, and FPGAs.
• Easy Integration: Provide APIs in multiple programming languages such as C/C++, Python, Java, and C#, making it easy to integrate into various applications.
• High Performance: Achieve high-performance inference through various optimization techniques, such as graph optimization, operator fusion, and memory optimization.
• Extensibility: Allow users to define custom operators and execution providers to support new hardware platforms and algorithms.

Key Features

• ONNX Model Support: Fully supports the ONNX standard, allowing loading and execution of models conforming to the ONNX specification.
• Multiple Execution Providers (EPs):
  • CPU EP: Uses the CPU for inference, supporting various CPU instruction set optimizations (e.g., AVX2, AVX512).
  • CUDA EP: Uses NVIDIA GPUs for inference, leveraging CUDA acceleration.
  • TensorRT EP: Uses NVIDIA TensorRT for inference, further optimizing GPU performance.
  • OpenVINO EP: Uses the Intel OpenVINO toolkit for inference, optimizing Intel CPU and GPU performance.
  • DirectML EP: Uses the Windows DirectML API for inference, utilizing GPU resources on Windows.
  • CoreML EP: Uses the Apple CoreML framework for inference, optimizing performance on Apple devices.
  • Other EPs: Also supports other hardware platforms, such as ARM NN, ACL, etc.
• Graph Optimization: Automatically optimizes the ONNX model graph, such as operator fusion, constant folding, node elimination, etc., reducing computation and memory footprint.
• Operator Fusion: Merges multiple operators into a single operator, reducing communication overhead between operators.
• Quantization Support: Supports model quantization, converting floating-point models to integer models, reducing model size and computation, and improving inference speed.
• Dynamic Shape Support: Supports ONNX models with dynamic shapes, allowing processing of input data of varying sizes.
• Session Options: Provides rich session options to control various aspects of the inference process, such as the number of threads, memory allocation, and graph optimization level.
• Debugging Tools: Provides debugging tools to help users analyze performance bottlenecks in ONNX models.
• Performance Analysis: Provides performance analysis tools to help users understand the performance metrics of ONNX models, such as inference time and memory usage.
• Distributed Inference: Supports distributed inference, allowing ONNX models to be deployed on multiple devices for inference, improving inference throughput.

Architecture

The architecture of ONNX Runtime mainly includes the following parts:

1. Frontend: Responsible for loading the ONNX model and converting it into ONNX Runtime's internal representation.
2. Graph Optimizer: Responsible for optimizing the ONNX model graph, such as operator fusion and constant folding.
3. Executor: Responsible for executing the ONNX model graph, allocating operators to different hardware platforms for execution based on different execution providers.
4. Execution Provider: Responsible for executing operators on specific hardware platforms, such as CPU, GPU, and FPGA.
5. Session: Responsible for managing the lifecycle of the ONNX model, including loading the model, optimizing the model, executing the model, and releasing resources.

Usage

1. Install ONNX Runtime: You can install ONNX Runtime via pip, for example: pip install onnxruntime (CPU version) or pip install onnxruntime-gpu (GPU version).
2. Load ONNX Model: Use onnxruntime.InferenceSession to load the ONNX model.
3. Prepare Input Data: Convert the input data into a format that ONNX Runtime can accept, such as NumPy arrays.
4. Run Inference: Use the InferenceSession.run() method to run inference and obtain the output results.

Example Code (Python):

import onnxruntime
import numpy as np

# Load ONNX model
session = onnxruntime.InferenceSession("model.onnx")

# Get input and output information
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name

# Prepare input data
input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)

# Run inference
output_data = session.run([output_name], {input_name: input_data})

# Print output results
print(output_data)

Applicable Scenarios

• Image Recognition: Accelerate inference for tasks such as image classification, object detection, and image segmentation.
• Natural Language Processing: Accelerate inference for tasks such as text classification, machine translation, and text generation.
• Speech Recognition: Accelerate inference for tasks such as speech recognition and speech synthesis.
• Recommendation Systems: Accelerate inference for recommendation models, improving recommendation efficiency.
• Other Machine Learning Tasks: Can accelerate inference for various machine learning tasks, such as regression and clustering.

Advantages

• High Performance: Achieve high-performance inference through various optimization techniques.
• Cross-Platform: Supports multiple operating systems and hardware platforms.
• Easy Integration: Provides APIs in multiple programming languages.
• Flexible and Extensible: Allows users to define custom operators and execution providers.
• Active Community: Has an active community where you can get technical support and exchange experiences.

Limitations

• Dependency on ONNX Model Format: Can only execute models in ONNX format, requiring conversion of models in other formats to ONNX format.
• Potentially Incomplete Support for Certain Operators: Support for some special operators may be incomplete in ONNX Runtime, requiring users to define custom operators.
• Requires Some Configuration and Tuning: To achieve optimal performance, some configuration and tuning of ONNX Runtime may be required.

Summary

ONNX Runtime is a powerful machine learning inference accelerator that can help users accelerate the inference process of ONNX models and improve application performance. It has advantages such as high performance, cross-platform compatibility, ease of integration, and flexibility and extensibility, making it suitable for various machine learning tasks.

For all detailed information, please refer to the official website (https://github.com/microsoft/onnxruntime)