examples/llada_diffuse.swift

import Foundation
import CoreML

struct InputPayload: Codable {
    let prompt: String
    let prompt_ids: [Int32]
    let seq_len: Int
    let max_new_tokens: Int
    let steps: Int
    let mask_token_id: Int32
    let eos_token_id: Int32
    let pad_token_id: Int32
    let compute_units: String?
}

struct StepStat: Codable {
    let step: Int
    let masked_before: Int
    let fixed_this_step: Int
    let masked_after: Int
    let avg_fixed_score: Float
}

struct OutputPayload: Codable {
    let prompt: String
    let prompt_ids: [Int32]
    let final_ids: [Int32]
    let generated_ids: [Int32]
    let generated_ids_untrimmed: [Int32]
    let prompt_len: Int
    let total_len: Int
    let step_stats: [StepStat]
    let load_seconds: Double
    let total_predict_seconds: Double
    let loop_seconds: Double
}

func argValue(_ name: String) -> String? {
    let args = CommandLine.arguments
    guard let idx = args.firstIndex(of: name), idx + 1 < args.count else {
        return nil
    }
    return args[idx + 1]
}

func usageAndExit() -> Never {
    fputs("Usage: swift scripts/llada_diffuse.swift --model <path.mlmodelc|mlpackage> --input <input.json> --output <output.json>\n", stderr)
    exit(2)
}

func computeUnits(from value: String?) -> MLComputeUnits {
    guard let raw = value?.lowercased() else { return .all }
    switch raw {
    case "cpu", "cpuonly":
        return .cpuOnly
    case "cpugpu", "cpuandgpu":
        return .cpuAndGPU
    case "cpune", "cpuandneuralengine":
        return .cpuAndNeuralEngine
    default:
        return .all
    }
}

func int32Array(_ values: [Int32], shape: [NSNumber]) throws -> MLMultiArray {
    let arr = try MLMultiArray(shape: shape, dataType: .int32)
    for i in 0..<values.count {
        arr[i] = NSNumber(value: values[i])
    }
    return arr
}

do {
    guard let modelPath = argValue("--model"),
          let inputPath = argValue("--input"),
          let outputPath = argValue("--output") else {
        usageAndExit()
    }

    let inputData = try Data(contentsOf: URL(fileURLWithPath: inputPath))
    let input = try JSONDecoder().decode(InputPayload.self, from: inputData)

    let seqLen = max(1, input.seq_len)
    let promptLen = min(input.prompt_ids.count, seqLen)
    let maxNew = max(0, min(input.max_new_tokens, seqLen - promptLen))
    let totalLen = promptLen + maxNew
    let stepCount = max(1, input.steps)

    var tokenBuffer = Array(repeating: input.pad_token_id, count: seqLen)
    for i in 0..<promptLen {
        tokenBuffer[i] = input.prompt_ids[i]
    }

    var attentionMask = Array(repeating: Int32(0), count: seqLen)
    if totalLen > 0 {
        for i in 0..<totalLen {
            attentionMask[i] = 1
        }
    }

    var fixed = Array(repeating: false, count: seqLen)
    if promptLen > 0 {
        for i in 0..<promptLen {
            fixed[i] = true
        }
    }
    if totalLen > promptLen {
        for i in promptLen..<totalLen {
            tokenBuffer[i] = input.mask_token_id
        }
    }

    let cfg = MLModelConfiguration()
    cfg.computeUnits = computeUnits(from: input.compute_units)

    let modelURL = URL(fileURLWithPath: modelPath)
    let modelLoadStart = Date()
    let resolvedModelURL: URL
    if modelURL.pathExtension.lowercased() == "mlpackage" {
        resolvedModelURL = try MLModel.compileModel(at: modelURL)
    } else {
        resolvedModelURL = modelURL
    }
    let model = try MLModel(contentsOf: resolvedModelURL, configuration: cfg)
    let modelLoadSeconds = Date().timeIntervalSince(modelLoadStart)

    func predict(ids: [Int32], mask: [Int32]) throws -> ([Int32], [Float], Double) {
        let idsMA = try int32Array(ids, shape: [1, NSNumber(value: seqLen)])
        let maskMA = try int32Array(mask, shape: [1, NSNumber(value: seqLen)])
        let provider = try MLDictionaryFeatureProvider(dictionary: [
            "input_ids": MLFeatureValue(multiArray: idsMA),
            "attention_mask": MLFeatureValue(multiArray: maskMA)
        ])
        let t0 = Date()
        let out = try model.prediction(from: provider)
        let dt = Date().timeIntervalSince(t0)

        guard let predMA = out.featureValue(for: "var_4801")?.multiArrayValue,
              let scoreMA = out.featureValue(for: "var_4806")?.multiArrayValue else {
            throw NSError(domain: "llada_diffuse", code: 1, userInfo: [NSLocalizedDescriptionKey: "Model outputs var_4801/var_4806 not found"])
        }

        var pred = Array(repeating: Int32(0), count: seqLen)
        var score = Array(repeating: Float(0), count: seqLen)
        for i in 0..<seqLen {
            pred[i] = predMA[i].int32Value
            score[i] = scoreMA[i].floatValue
        }
        return (pred, score, dt)
    }

    var stepStats: [StepStat] = []
    var totalPredictSeconds = 0.0
    let loopStart = Date()

    for step in 1...stepCount {
        var maskedPositions: [Int] = []
        if totalLen > promptLen {
            for i in promptLen..<totalLen where !fixed[i] {
                maskedPositions.append(i)
            }
        }
        if maskedPositions.isEmpty {
            break
        }

        let (pred, score, predictSeconds) = try predict(ids: tokenBuffer, mask: attentionMask)
        totalPredictSeconds += predictSeconds

        let remainingAfter = Int(Double(maxNew) * Double(max(0, stepCount - step)) / Double(stepCount))
        let leaveMasked = min(remainingAfter, maskedPositions.count)
        let fixCount = max(0, maskedPositions.count - leaveMasked)

        let ranked = maskedPositions.sorted { score[$0] > score[$1] }

        var scoreSum: Float = 0
        if fixCount > 0 {
            for j in 0..<fixCount {
                let pos = ranked[j]
                tokenBuffer[pos] = pred[pos]
                fixed[pos] = true
                scoreSum += score[pos]
            }
        }
        if fixCount < ranked.count {
            for j in fixCount..<ranked.count {
                tokenBuffer[ranked[j]] = input.mask_token_id
            }
        }

        var maskedAfter = 0
        if totalLen > promptLen {
            for i in promptLen..<totalLen where !fixed[i] {
                maskedAfter += 1
            }
        }
        let avgScore = fixCount > 0 ? scoreSum / Float(fixCount) : 0
        stepStats.append(StepStat(
            step: step,
            masked_before: maskedPositions.count,
            fixed_this_step: fixCount,
            masked_after: maskedAfter,
            avg_fixed_score: avgScore
        ))
    }

    // Safety: if any generation positions are still masked, fill them from one final pass.
    var remainingPositions: [Int] = []
    if totalLen > promptLen {
        for i in promptLen..<totalLen where !fixed[i] {
            remainingPositions.append(i)
        }
    }
    if !remainingPositions.isEmpty {
        let (pred, _, predictSeconds) = try predict(ids: tokenBuffer, mask: attentionMask)
        totalPredictSeconds += predictSeconds
        for pos in remainingPositions {
            tokenBuffer[pos] = pred[pos]
            fixed[pos] = true
        }
        stepStats.append(StepStat(
            step: stepCount + 1,
            masked_before: remainingPositions.count,
            fixed_this_step: remainingPositions.count,
            masked_after: 0,
            avg_fixed_score: 0
        ))
    }

    let loopSeconds = Date().timeIntervalSince(loopStart)
    let untrimmedGenerated = totalLen > promptLen ? Array(tokenBuffer[promptLen..<totalLen]) : []
    var generated: [Int32] = []
    generated.reserveCapacity(untrimmedGenerated.count)
    for token in untrimmedGenerated {
        if token == input.eos_token_id {
            break
        }
        generated.append(token)
    }

    let output = OutputPayload(
        prompt: input.prompt,
        prompt_ids: Array(tokenBuffer.prefix(promptLen)),
        final_ids: tokenBuffer,
        generated_ids: generated,
        generated_ids_untrimmed: untrimmedGenerated,
        prompt_len: promptLen,
        total_len: totalLen,
        step_stats: stepStats,
        load_seconds: modelLoadSeconds,
        total_predict_seconds: totalPredictSeconds,
        loop_seconds: loopSeconds
    )

    let encoded = try JSONEncoder().encode(output)
    try encoded.write(to: URL(fileURLWithPath: outputPath))
    print("Wrote \(outputPath)")
    print(String(format: "load=%.2fs predict_total=%.2fs loop=%.2fs", output.load_seconds, output.total_predict_seconds, output.loop_seconds))
} catch {
    fputs("ERROR: \(error)\n", stderr)
    exit(1)
}