97 lines
2.9 KiB
Swift
97 lines
2.9 KiB
Swift
// Copyright © 2024 Apple Inc.
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import AsyncAlgorithms
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import Foundation
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import MLX
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import MLXRandom
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private func topPSampling(logits: MLXArray, topP: Float, temp: Float) -> MLXArray {
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var logits = logits
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if logits.dtype == .bfloat16 {
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logits = logits.asType(.float32)
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}
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let probs = softMax(logits / temp, axis: -1)
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let sortedIndices = argSort(probs, axis: -1)
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// probs shape is [B,V] and after take it will be [1, B, V], so we squeeze it back to [B, V]
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let sortedProbs = take(probs, sortedIndices, axis: -1).squeezed(axis: 0)
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let cumulativeProbs = cumsum(sortedProbs, axis: -1)
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let topProbs = MLX.where(cumulativeProbs .> (1 - topP), sortedProbs, zeros(like: sortedProbs))
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let sortedToken = categorical(log(topProbs))
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return sortedIndices.squeezed(axis: 0)[sortedToken]
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}
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private func sample(logits: MLXArray, temp: Float, topP: Float = 1.0) -> MLXArray {
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if temp == 0 {
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return argMax(logits, axis: -1)
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} else {
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if topP > 0 && topP < 1 {
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return topPSampling(logits: logits, topP: topP, temp: temp)
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}
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return categorical(logits * (1 / temp))
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}
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}
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/// Synchronous generator of tokens.
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///
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/// Port of `generate_step()` from https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/utils.py
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public struct TokenIterator: Sequence, IteratorProtocol {
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let model: LLMModel
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let temp: Float
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let topP: Float
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var y: MLXArray
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var cache: [(MLXArray, MLXArray)]
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var first = true
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public init(prompt: MLXArray, model: LLMModel, temp: Float = 0.0, topP: Float = 1.0) {
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self.model = model
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self.temp = temp
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self.topP = topP
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self.y = prompt
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self.cache = []
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}
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mutating public func next() -> MLXArray? {
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var logits: MLXArray
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(logits, cache) = model(expandedDimensions(y, axis: 0), cache: cache.isEmpty ? nil : cache)
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y = sample(logits: logits[-1, axis: 1], temp: temp, topP: topP)
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return y
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}
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}
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/// Async generator of tokens.
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///
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/// Port of `generate_step()` from https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/utils.py.
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///
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/// Note that because MLXArray is not thread safe this eval's the result and sends the TokenId back
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/// to the caller.
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public func generate(prompt: MLXArray, model: LLMModel, temp: Float = 0.0) -> (
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Task<Void, Never>, AsyncBufferSequence<AsyncChannel<Int>>
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) {
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let channel = AsyncChannel<Int>()
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let buffer = channel.buffer(policy: .bounded(10))
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let task = Task {
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var y = prompt
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var cache = [(MLXArray, MLXArray)]()
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while !Task.isCancelled {
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var logits: MLXArray
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(logits, cache) = model(
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expandedDimensions(y, axis: 0), cache: cache.isEmpty ? nil : cache)
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y = sample(logits: logits[-1, axis: 1], temp: temp)
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eval(y)
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await channel.send(y.item(Int.self))
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}
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}
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return (task, buffer)
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}
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