Deploying Large Language Models on Edge Devices with ONNX Runtime and WebAssembly: A Beginner’s Guide

As artificial intelligence continues to evolve, large language models (LLMs) like GPT-3, BERT, and others are being utilized in various applications. However, deploying these models on edge devices—such as smartphones, IoT devices, and embedded systems—poses challenges due to hardware limitations and runtime constraints. The good news is that ONNX Runtime and WebAssembly (Wasm) make it possible to run LLMs efficiently on edge devices. This guide offers a step-by-step explanation for beginners on how to deploy large language models on edge devices using these technologies.

ONNX Runtime is an open-source, high-performance engine for executing machine learning models in the ONNX (Open Neural Network Exchange) format. It supports various platforms, including CPU, GPU, and accelerators, and provides optimized runtimes for edge devices.

WebAssembly (Wasm) is a binary instruction format for a stack-based virtual machine. It enables code to run directly in browsers and other environments like edge devices, offering near-native performance. When combined with ONNX Runtime, Wasm allows you to deploy and execute machine learning models efficiently on resource-limited devices.

Running large language models on edge devices offers several benefits: – **Reduced Latency:** Processing data locally avoids the latency of sending requests to remote servers. – **Privacy:** Sensitive data remains on the device, enhancing privacy. – **Offline Functionality:** Enables tasks even without an internet connection. – **Cost Savings:** Reduces reliance on cloud infrastructure.

Before you begin, ensure the following:

1. **ONNX Model:** The language model must be converted into ONNX format.
2. **Development Environment:** You need Python installed with necessary libraries.
3. **WebAssembly Support:** Your edge device or browser must support WebAssembly.

Most large language models, such as GPT-2 or BERT, can be converted to ONNX format using frameworks like PyTorch or TensorFlow.

import torch
from transformers import GPT2Model

# Load the pre-trained GPT-2 model
model = GPT2Model.from_pretrained("gpt2")

# Define dummy input for tracing
dummy_input = torch.randint(0, 100, (1, 10))

# Export to ONNX format
torch.onnx.export(
    model, 
    dummy_input, 
    "gpt2.onnx", 
    input_names=["input_ids"], 
    output_names=["outputs"]
)

ONNX Runtime provides a WebAssembly backend (`wasm`) for running models in browser environments or on edge devices. Install the ONNX Runtime Web package:

pip install onnxruntime

For WebAssembly support, you need the ONNX Runtime Web package, which can be used in JavaScript or TypeScript. Add the following script to your project:

<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js"></script>

Here’s how you can load the ONNX model and execute it using ONNX Runtime WebAssembly:

async function runModel() {
    const session = await ort.InferenceSession.create('gpt2.onnx');
    
    // Define input tensor
    const inputTensor = new ort.Tensor('int32', [1, 10]);

    // Run inference
    const results = await session.run({ input_ids: inputTensor });

    console.log('Model Output:', results.outputs);
}

runModel();

When deploying models on edge devices, optimization is critical. Techniques include:

1. **Quantization:** Reduce model size and precision (e.g., from FP32 to INT8).
2. **Model Pruning:** Remove redundant parameters to decrease complexity.
3. **Custom Runtime:** Use ONNX Runtime with specific optimizations for your hardware.

For quantization in ONNX, you can use the Python ONNX Runtime API:

from onnxruntime.quantization import quantize_dynamic, QuantType

# Quantize the model for reduced precision
quantized_model_path = quantize_dynamic(
    "gpt2.onnx", 
    "gpt2_quantized.onnx", 
    weight_type=QuantType.QInt8
)
print("Quantized model saved at:", quantized_model_path)

Once the model is deployed, test it on your edge device to ensure performance and accuracy meet your requirements. You can use browser-based tools like Chrome DevTools or device-specific profiling tools to evaluate runtime performance.

Deploying large language models on edge devices is now feasible thanks to ONNX Runtime and WebAssembly. These technologies enable efficient, low-latency, and privacy-preserving AI applications that can operate offline. Though edge deployment requires careful optimization and testing, it opens new possibilities for AI at the edge.