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ContentView.swift
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ContentView.swift
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// Copyright © 2024 Apple Inc.
import LLM
import MLX
import MLXRandom
import MarkdownUI
import Metal
import SwiftUI
import Tokenizers
struct ContentView: View {
@State var prompt = ""
@State var llm = LLMEvaluator()
@Environment(DeviceStat.self) private var deviceStat
enum displayStyle: String, CaseIterable, Identifiable {
case plain, markdown
var id: Self { self }
}
@State private var selectedDisplayStyle = displayStyle.markdown
var body: some View {
VStack(alignment: .leading) {
VStack {
HStack {
Text(llm.modelInfo)
.textFieldStyle(.roundedBorder)
Spacer()
Text(llm.stat)
}
HStack {
Spacer()
if llm.running {
ProgressView()
.frame(maxHeight: 20)
Spacer()
}
Picker("", selection: $selectedDisplayStyle) {
ForEach(displayStyle.allCases, id: \.self) { option in
Text(option.rawValue.capitalized)
.tag(option)
}
}
.pickerStyle(.segmented)
#if os(visionOS)
.frame(maxWidth: 250)
#else
.frame(maxWidth: 150)
#endif
}
}
// show the model output
ScrollView(.vertical) {
ScrollViewReader { sp in
Group {
if selectedDisplayStyle == .plain {
Text(llm.output)
.textSelection(.enabled)
} else {
Markdown(llm.output)
.textSelection(.enabled)
}
}
.onChange(of: llm.output) { _, _ in
sp.scrollTo("bottom")
}
Spacer()
.frame(width: 1, height: 1)
.id("bottom")
}
}
HStack {
TextField("prompt", text: $prompt)
.onSubmit(generate)
.disabled(llm.running)
#if os(visionOS)
.textFieldStyle(.roundedBorder)
#endif
Button("generate", action: generate)
.disabled(llm.running)
}
}
#if os(visionOS)
.padding(40)
#else
.padding()
#endif
.toolbar {
ToolbarItem {
Label(
"Memory Usage: \(deviceStat.gpuUsage.activeMemory.formatted(.byteCount(style: .memory)))",
systemImage: "info.circle.fill"
)
.labelStyle(.titleAndIcon)
.padding(.horizontal)
.help(
Text(
"""
Active Memory: \(deviceStat.gpuUsage.activeMemory.formatted(.byteCount(style: .memory)))/\(GPU.memoryLimit.formatted(.byteCount(style: .memory)))
Cache Memory: \(deviceStat.gpuUsage.cacheMemory.formatted(.byteCount(style: .memory)))/\(GPU.cacheLimit.formatted(.byteCount(style: .memory)))
Peak Memory: \(deviceStat.gpuUsage.peakMemory.formatted(.byteCount(style: .memory)))
"""
)
)
}
ToolbarItem(placement: .primaryAction) {
Button {
Task {
copyToClipboard(llm.output)
}
} label: {
Label("Copy Output", systemImage: "doc.on.doc.fill")
}
.disabled(llm.output == "")
.labelStyle(.titleAndIcon)
}
}
.task {
self.prompt = llm.modelConfiguration.defaultPrompt
// pre-load the weights on launch to speed up the first generation
_ = try? await llm.load()
}
}
private func generate() {
Task {
await llm.generate(prompt: prompt)
}
}
private func copyToClipboard(_ string: String) {
#if os(macOS)
NSPasteboard.general.clearContents()
NSPasteboard.general.setString(string, forType: .string)
#else
UIPasteboard.general.string = string
#endif
}
}
@Observable
class LLMEvaluator {
@MainActor
var running = false
var output = ""
var modelInfo = ""
var stat = ""
/// this controls which model loads -- phi4bit is one of the smaller ones so this will fit on
/// more devices
let modelConfiguration = ModelConfiguration.phi34bit
/// parameters controlling the output
let generateParameters = GenerateParameters(temperature: 0.6)
let maxTokens = 240
/// update the display every N tokens -- 4 looks like it updates continuously
/// and is low overhead. observed ~15% reduction in tokens/s when updating
/// on every token
let displayEveryNTokens = 4
enum LoadState {
case idle
case loaded(LLMModel, Tokenizers.Tokenizer)
}
var loadState = LoadState.idle
/// load and return the model -- can be called multiple times, subsequent calls will
/// just return the loaded model
func load() async throws -> (LLMModel, Tokenizers.Tokenizer) {
switch loadState {
case .idle:
// limit the buffer cache
MLX.GPU.set(cacheLimit: 20 * 1024 * 1024)
let (model, tokenizer) = try await LLM.load(configuration: modelConfiguration) {
[modelConfiguration] progress in
DispatchQueue.main.sync {
self.modelInfo =
"Downloading \(modelConfiguration.name): \(Int(progress.fractionCompleted * 100))%"
}
}
self.modelInfo =
"Loaded \(modelConfiguration.id). Weights: \(MLX.GPU.activeMemory / 1024 / 1024)M"
loadState = .loaded(model, tokenizer)
return (model, tokenizer)
case .loaded(let model, let tokenizer):
return (model, tokenizer)
}
}
func generate(prompt: String) async {
let canGenerate = await MainActor.run {
if running {
return false
} else {
running = true
self.output = ""
return true
}
}
guard canGenerate else { return }
do {
let (model, tokenizer) = try await load()
// augment the prompt as needed
let prompt = modelConfiguration.prepare(prompt: prompt)
let promptTokens = tokenizer.encode(text: prompt)
// each time you generate you will get something new
MLXRandom.seed(UInt64(Date.timeIntervalSinceReferenceDate * 1000))
let result = await LLM.generate(
promptTokens: promptTokens, parameters: generateParameters, model: model,
tokenizer: tokenizer, extraEOSTokens: modelConfiguration.extraEOSTokens
) { tokens in
// update the output -- this will make the view show the text as it generates
if tokens.count % displayEveryNTokens == 0 {
let text = tokenizer.decode(tokens: tokens)
await MainActor.run {
self.output = text
}
}
if tokens.count >= maxTokens {
return .stop
} else {
return .more
}
}
// update the text if needed, e.g. we haven't displayed because of displayEveryNTokens
await MainActor.run {
if result.output != self.output {
self.output = result.output
}
running = false
self.stat = " Tokens/second: \(String(format: "%.3f", result.tokensPerSecond))"
}
} catch {
await MainActor.run {
running = false
output = "Failed: \(error)"
}
}
}
}