Day 49: Serving LLMs with ONNX Runtime

Introduction

Serving Large Language Models (LLMs) efficiently is crucial for real-world applications. ONNX Runtime is a powerful tool designed to optimize and serve models across different hardware platforms with high performance. By converting LLMs to ONNX format and leveraging its runtime, you can achieve faster inference and cross-platform compatibility.

Why Use ONNX Runtime for Serving LLMs?

1. High Performance: Accelerated inference with optimizations like graph pruning and kernel fusion.
2. Cross-Platform Support: Runs on diverse hardware like CPUs, GPUs, and specialized accelerators.
3. Interoperability: Supports models trained in frameworks like PyTorch and TensorFlow.
4. Scalability: Suitable for both edge and cloud deployments.

Steps to Serve LLMs with ONNX Runtime

1. Export the Model to ONNX Format

Use tools like Hugging Face Transformers or PyTorch’s torch.onnx.export to convert your LLM to ONNX format.

from transformers import AutoModelForSequenceClassification
import torch

# Load a pre-trained model
model_name = "bert-base-uncased"
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Dummy input for tracing
dummy_input = torch.ones(1, 16, dtype=torch.int64)

# Export to ONNX
torch.onnx.export(
    model, 
    dummy_input, 
    "bert_model.onnx", 
    input_names=["input_ids"], 
    output_names=["output"],
    dynamic_axes={"input_ids": {0: "batch_size", 1: "sequence_length"}}
)

2. Optimize the ONNX Model

Optimize the model for faster inference using ONNX Runtime’s optimization tools.

python -m onnxruntime.transformers.optimizer --input bert_model.onnx --output optimized_bert.onnx

3. Serve with ONNX Runtime

Load and run the optimized ONNX model in your application.

import onnxruntime as ort
import numpy as np

# Load the optimized model
session = ort.InferenceSession("optimized_bert.onnx")

# Prepare input
input_ids = np.ones((1, 16), dtype=np.int64)

# Run inference
outputs = session.run(None, {"input_ids": input_ids})
print("Model Output:", outputs)

Performance Comparison

Challenges in Using ONNX Runtime

1. Compatibility Issues: Not all operations are supported during conversion.
2. Optimization Complexity: Requires tuning for specific hardware.
3. Model Size: Some models may need quantization or pruning for deployment.

Tools and Resources

1. ONNX Runtime Documentation: ONNX Runtime
2. Hugging Face Transformers: Pre-trained models ready for ONNX export.
3. Azure Machine Learning: Scalable deployment with ONNX Runtime integration.

Applications of ONNX Runtime

• Real-Time Chatbots: Faster response times in conversational systems.
• Edge AI: Deploying lightweight models on mobile and IoT devices.
• Enterprise AI: Scalable cloud-based solutions for NLP tasks.

Conclusion

Serving LLMs with ONNX Runtime combines speed, scalability, and versatility. By converting models to ONNX format and leveraging its runtime, you can unlock high-performance inference across a variety of platforms. This approach is particularly valuable for production environments where efficiency is paramount.