chore: add top_p sampling example (#34)

Libraries/LLM/Evaluate.swift

@@ -5,10 +5,33 @@ import Foundation
 import MLX
 import MLXRandom
 
-private func sample(logits: MLXArray, temp: Float) -> MLXArray {
+private func topPSampling(logits: MLXArray, topP: Float, temp: Float) -> MLXArray {
+    var logits = logits
+    if logits.dtype == .bfloat16 {
+        logits = logits.asType(.float32)
+    }
+
+    let probs = softMax(logits / temp, axis: -1)
+    let sortedIndices = argSort(probs, axis: -1)
+
+    // probs shape is [B,V] and after take it will be [1, B, V], so we squeeze it back to [B, V]
+    let sortedProbs = take(probs, sortedIndices, axis: -1).squeezed(axis: 0)
+
+    let cumulativeProbs = cumsum(sortedProbs, axis: -1)
+
+    let topProbs = MLX.where(cumulativeProbs .> (1 - topP), sortedProbs, zeros(like: sortedProbs))
+
+    let sortedToken = categorical(log(topProbs))
+    return sortedIndices.squeezed(axis: 0)[sortedToken]
+}
+
+private func sample(logits: MLXArray, temp: Float, topP: Float = 1.0) -> MLXArray {
     if temp == 0 {
         return argMax(logits, axis: -1)
     } else {
+        if topP > 0 && topP < 1 {
+            return topPSampling(logits: logits, topP: topP, temp: temp)
+        }
         return categorical(logits * (1 / temp))
     }
 }
@@ -19,15 +42,16 @@ private func sample(logits: MLXArray, temp: Float) -> MLXArray {
 public struct TokenIterator: Sequence, IteratorProtocol {
     let model: LLMModel
     let temp: Float
-
+    let topP: Float
     var y: MLXArray
     var cache: [(MLXArray, MLXArray)]
 
     var first = true
 
-    public init(prompt: MLXArray, model: LLMModel, temp: Float = 0.0) {
+    public init(prompt: MLXArray, model: LLMModel, temp: Float = 0.0, topP: Float = 1.0) {
         self.model = model
         self.temp = temp
+        self.topP = topP
         self.y = prompt
         self.cache = []
     }
@@ -35,7 +59,7 @@ public struct TokenIterator: Sequence, IteratorProtocol {
     mutating public func next() -> MLXArray? {
         var logits: MLXArray
         (logits, cache) = model(expandedDimensions(y, axis: 0), cache: cache.isEmpty ? nil : cache)
-        y = sample(logits: logits[-1, axis: 1], temp: temp)
+        y = sample(logits: logits[-1, axis: 1], temp: temp, topP: topP)
 
         return y
     }