Transform Huggingface Models into Native Apple Models

Table of Contents

1. Introduction
2. Running Transformer Models on M1 Mac
3. Reformatting Transformer Models for Apple Neural Engine
4. Deploying Transformers on Apple Neural Engine
5. Optimizing Transformers for the Neural Engine
6. Picking the Right Data Format
7. Converting Models to Apple's Format
8. Applying Optimizations to DistilBERT Model
9. Profiling the Model in Xcode
10. Conclusion

Running Transformer Models on M1 Mac in Native Format

In this article, we will discuss how to run Transformer models on the M1 Mac using their native format. By default, if You're running Transformer models in Python, you're likely using the hugging faces native format, which is not optimized for the Apple Neural Engine. We will learn how to reformat these models into a data structure that can be efficiently used on the Ane, where it can run on 16 cores instead of 8, freeing up the CPU and GPU for other tasks. We will also explore the article "Deploying Transformers on the Apple Neural Engine" by Apple, which provides guidance on the deployment of Transformer models.

To begin, let's take a look at the steps involved in running Transformer models on the M1 Mac.

1. Introduction

Introduce the topic and provide Context on running Transformer models on the M1 Mac.

2. Running Transformer Models on M1 Mac

Discuss the challenges of running Transformer models on the M1 Mac and the need for optimization.

3. Reformatting Transformer Models for Apple Neural Engine

Explain the process of reformatting Transformer models into a data structure suitable for the Apple Neural Engine.

4. Deploying Transformers on Apple Neural Engine

Discuss the article by Apple on deploying Transformers on the Apple Neural Engine.

5. Optimizing Transformers for the Neural Engine

Explore the principles behind optimizing Transformers for efficient execution on the Apple Neural Engine.

6. Picking the Right Data Format

Explain the importance of choosing the right data format for Transformer models and the compatibility with the Apple Neural Engine.

7. Converting Models to Apple's Format

Guide on how to convert Transformer models into Apple's format using the AE Transformers library.

8. Applying Optimizations to DistilBERT Model

Take a case study of applying optimizations to the popular DistilBERT model from the Hugging Face Model Hub to achieve faster and memory-efficient inference.

9. Profiling the Model in Xcode

Demonstrate how to profile the optimized model in Xcode using the Core ML Performance Report feature.

10. Conclusion

Sum up the key points discussed in the article and highlight the benefits of running Transformer models on the M1 Mac in their native format.

Now, let's dive into each section and explore the topic of running Transformer models on the M1 Mac in Detail.