UForm Swift SDK

UForm offers first-party support for Swift. To get started, add UForm to your project using Swift Package Manager.

swift package init --type executable
swift package add uform

Then, import UForm in your Swift code:

import UForm

Embeddings

Text Embeddings

let textModel = try await TextEncoder(
    modelName: "unum-cloud/uform3-image-text-english-small",
    computeUnits: .cpuAndNeuralEngine
)
let text = "A group of friends enjoy a barbecue on a sandy beach, with one person grilling over a large black grill, while the other sits nearby, laughing and enjoying the camaraderie."
let textEmbedding: Embedding = try textModel.encode(text)
let textVector: [Float32] = textEmbedding.asFloats()

Image Embeddings

let imageModel = try await ImageEncoder(
    modelName: "unum-cloud/uform3-image-text-english-small",
    computeUnits: .cpuAndNeuralEngine
)
let imageURL = "https://github.com/ashvardanian/ashvardanian/blob/master/demos/bbq-on-beach.jpg?raw=true"
guard let url = URL(string: imageURL),
    let imageSource = CGImageSourceCreateWithURL(url as CFURL, nil),
    let cgImage = CGImageSourceCreateImageAtIndex(imageSource, 0, nil) {
    throw Exception("Could not load image from URL: \(imageURL)")
}

var imageEmbedding: Embedding = try imageModel.encode(cgImage)
var imageVector: [Float32] = embedding.asFloats()

Choosing Target Device

Apple chips provide several functional units capable of high-throughput matrix multiplication and AI inference. Those computeUnits include the CPU, GPU, and Neural Engine. For maximum compatibility, the .all option is used by default. Sadly, Apple's scheduler is not always optimal, and it might be beneficial to specify the target device explicitly, especially if the models are pre-compiled for the Apple Neural Engine, as it may yield significant performance gains.

Model	GPU Text E.	ANE Text E.	GPU Image E.	ANE Image E.
`english-small`	2.53 ms	0.53 ms	6.57 ms	1.23 ms
`english-base`	2.54 ms	0.61 ms	18.90 ms	3.79 ms
`english-large`	2.30 ms	0.61 ms	79.68 ms	20.94 ms
`multilingual-base`	2.34 ms	0.50 ms	18.98 ms	3.77 ms

On Apple M4 iPad, running iOS 18.2. Batch size is 1, and the model is pre-loaded into memory. The original encoders use f32 single-precision numbers for maximum compatibility, and mostly rely on GPU for computation. The quantized encoders use a mixture of i8, f16, and f32 numbers for maximum performance, and mostly rely on the Apple Neural Engine (ANE) for computation. The median latency is reported.

Computing Distances

There are several ways to compute distances between embeddings, once you have them. Naive Swift code might look like this:

func cosineSimilarity(_ a: [Float32], _ b: [Float32]) -> Float32 {
    let dotProduct = zip(a, b).map(*).reduce(0, +)
    let normA = sqrt(a.map { $0 * $0 }.reduce(0, +))
    let normB = sqrt(b.map { $0 * $0 }.reduce(0, +))
    return dotProduct / (normA * normB)
}

A faster way to compute distances is to use the Accelerate framework:

import Accelerate

func cosineSimilarity(_ a: [Float32], _ b: [Float32]) -> Float32 {
    var result: Float32 = 0
    var aNorm: Float32 = 0
    var bNorm: Float32 = 0
    vDSP_dotpr(a, 1, b, 1, &result, vDSP_Length(a.count))
    vDSP_svesq(a, 1, &aNorm, vDSP_Length(a.count))
    vDSP_svesq(b, 1, &bNorm, vDSP_Length(b.count))
    return result / sqrt(aNorm * bNorm)
}

An even faster approach would be to use USearch or SimSIMD, that work not only for Float32 and Float64, but also for Float16, Int8, and binary embeddings.

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