Day 49: Serving LLMs with ONNX Runtime

introduce

Efficiently serving large language models (LLMs) is critical for real-world applications. ONNX Runtime is a powerful tool designed for high-performance optimization and service models across different hardware platforms. By converting LLM to the ONNX format and leveraging its runtime, you can achieve faster inference and cross-platform compatibility.

Why use the ONNX runtime to serve the LL.M.?

1. High efficiency: Accelerate inference through optimizations such as graph pruning and kernel fusion.
2. Cross-platform support: Run on different hardware, such as CPU, GPU and dedicated accelerators.
3. interoperability: Supports models trained in frameworks such as PyTorch and TensorFlow.
4. Scalability: Suitable for edge and cloud deployments.

Steps to serve the LL.M. using the ONNX runtime

1. Export model to ONNX format

Use tools like Hugging Face Transformers or PyTorch torch.onnx.export 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 ONNX model

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

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

3. Provide services when running using ONNX

Load and execute 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

Execution time challenges using ONNX

1. Compatibility issues: Not all operations are supported during conversion.
2. Optimize complexity: Requires tuning for specific hardware.
3. Model size: Some models may require quantization or pruning before deployment.

Tools and Resources

1. ONNX execution time documentation: When ONNX is running
2. Embrace the Transformer Face: Pre-trained models can be exported by ONNX.
3. Azure Machine Learning: Scalable deployment through ONNX runtime integration.

ONNX runtime applications

• Live chatbot: The dialogue system has faster reaction times.
• Edge Artificial Intelligence: Deploy lightweight models on mobile and IoT devices.
• Enterprise Artificial Intelligence: A scalable cloud-based solution for NLP tasks.

in conclusion

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