Introduction to MPSGraph

Metal Performance Shaders Graph (MPSGraph) is a powerful framework provided by Apple for accelerating machine learning and numerical computing tasks on Apple Silicon GPUs. In this article, we'll explore the key features of MPSGraph, its role in machine learning inference, and recent updates that enhance its capabilities.

What is MPSGraph?

MPSGraph is a framework for constructing and running general-purpose compute graphs using Metal. It provides a high-level API for defining complex computational graphs that can be efficiently executed on Apple's GPUs. MPSGraph is designed to work seamlessly with other Apple frameworks and technologies, making it an ideal choice for developers looking to leverage the full power of Apple Silicon in their machine learning applications.

Key Features of MPSGraph:

Efficient execution of compute graphs on Apple GPUs
Support for a wide range of mathematical and machine learning operations
Integration with Metal for low-level GPU control
Optimized performance for Apple Silicon
Compatibility with popular machine learning frameworks like Core ML, PyTorch, and TensorFlow

ML Inference with MPSGraph

MPSGraph plays a crucial role in accelerating machine learning inference on Apple devices. Let's explore some of the recent enhancements and features that make MPSGraph a powerful tool for ML inference.

MPSGraphPackage: Optimizing Load Times

One of the significant improvements introduced to MPSGraph is the new serialization format called MPSGraphPackage. This format addresses the issue of high application launch times for complex graphs with many layers.

Here's how you can create and use an MPSGraphPackage:


// Creating an MPSGraphPackage
let serializationDescriptor = MPSGraphSerializationDescriptor()
let executable = // Your existing MPSGraphExecutable
try executable.serialize(descriptor: serializationDescriptor, to: packagePath)

// Loading an MPSGraphPackage
let compilationDescriptor = MPSGraphCompilationDescriptor()
let executable = try MPSGraphExecutable(url: packagePath, options: compilationDescriptor)

This new serialization format allows you to create the MPSGraphExecutable ahead of time, significantly reducing load times in your application.

MPSGraphTool: Easy Migration from Other Frameworks

For developers coming from other frameworks like Core ML or ONNX, Apple has introduced the MPSGraphTool. This command-line tool simplifies the process of converting existing models to the MPSGraphPackage format.

Here's an example of how to use MPSGraphTool:


MPSGraphTool --input-format=coreml --output-path=/path/to/output --output-name=MyModel --target-platform=macos --minimum-deployment-target=14.0 /path/to/input.mlpackage

8-bit Integer Quantization

To improve the efficiency of computations, MPSGraph now supports 8-bit integer quantization. This feature helps in saving memory bandwidth and reducing the memory footprint of models.

MPSGraph supports both symmetric and asymmetric quantization. Here's an example of how to use quantization in your graph:


let graph = MPSGraph()
let input = graph.placeholder(shape: [1, 3, 224, 224], dataType: .float32)
let weights = graph.constant(...)  // Your quantized weights
let scale = graph.constant(...)    // Quantization scale
let zeroPoint = graph.constant(...) // Quantization zero point

let dequantizedWeights = graph.dequantizeTensor(weights, scale: scale, zeroPoint: zeroPoint)
let convOutput = graph.convolution2D(input, weights: dequantizedWeights, ...)
let quantizedOutput = graph.quantizeTensor(convOutput, scale: scale, zeroPoint: zeroPoint)

Recent Updates in MPSGraph (WWDC 2024)

Apple continues to enhance MPSGraph with new features and optimizations. Some of the recent updates include:

Improved Transformer Model Performance

New Scaled Dot-Product Attention (SDPA) operation for efficient attention computation
Support for KV-cache to optimize transformer inference
4-bit integer quantization for further memory savings

Fast Fourier Transform (FFT) Support

MPSGraph now includes operations for computing Fast Fourier Transforms, which are commonly used in audio processing and scientific computing applications.


let graph = MPSGraph()
let input = graph.placeholder(shape: [batchSize, windowSize], dataType: .float32)
let windowedInput = graph.arrayView(input, shape: [batchSize, numWindows, windowSize], strides: [1, stride, 1])
let fftOutput = graph.fft(windowedInput, inverse: false, dimension: -1)

MPSGraph Viewer

A new tool introduced in Xcode 16 that allows developers to visualize and analyze their MPSGraph structures. This viewer provides insights into how operations are connected and how the graph is optimized for specific devices.

Conclusion

MPSGraph is a powerful framework that enables developers to leverage the full potential of Apple Silicon GPUs for machine learning and numerical computing tasks. With recent enhancements like MPSGraphPackage, quantization support, and improved transformer model performance, MPSGraph continues to evolve as a crucial tool for high-performance ML inference on Apple devices.

As the field of machine learning on Apple platforms continues to advance, staying up-to-date with MPSGraph's capabilities will be essential for developers looking to create cutting-edge AI applications that take full advantage of Apple's hardware.