sample: use container/heap for top_k

This commit is contained in:
ParthSareen
2025-03-12 00:45:41 -04:00
committed by Parth Sareen
parent a70820daa0
commit 1b7433b71e
3 changed files with 223 additions and 117 deletions

View File

@@ -1,10 +1,30 @@
package sample
import (
"container/heap"
"math"
"slices"
)
// tokenHeap implements heap.Interface and holds tokens as a min-heap to track k largest elements
type tokenHeap []token
func (h tokenHeap) Len() int { return len(h) }
func (h tokenHeap) Less(i, j int) bool { return h[i].value < h[j].value } // Use < for min-heap to track largest elements
func (h tokenHeap) Swap(i, j int) { h[i], h[j] = h[j], h[i] }
func (h *tokenHeap) Push(x any) {
*h = append(*h, x.(token))
}
func (h *tokenHeap) Pop() any {
old := *h
n := len(old)
x := old[n-1]
*h = old[0 : n-1]
return x
}
// temperature applies scaling and softmax to the logits
func temperature(ts []token, temp float32) []token {
// Find max logit for numerical stability
@@ -31,62 +51,33 @@ func temperature(ts []token, temp float32) []token {
return ts
}
// siftDown maintains a min-heap property by recursively moving larger elements down the heap.
//
// The heap is represented as an array where for any node at index i:
// - Left child is at index 2i + 1
// - Right child is at index 2i + 2
// - Parent is at index (i-1)/2
//
// The function compares a node with its children and:
// 1. Finds the smallest value between the node and its children
// 2. If the node is not the smallest, swaps it with its smallest child
// 3. Continues this process down the affected path until the min-heap property is restored
func siftDown(data []token, start, end int) {
root := start
for {
child := 2*root + 1
if child >= end {
break
}
// Find smaller child (we want min heap)
if child+1 < end && data[child+1].value < data[child].value {
child++
}
// Exit if root is already smaller than children
if data[root].value <= data[child].value {
break
}
// Swap with smaller child and continue
data[root], data[child] = data[child], data[root]
root = child
}
}
// topK limits the number of tokens considered to the k highest logits
func topK(ts []token, k int) []token {
if k >= len(ts) {
sortLogits(ts)
return ts
}
// Heapify + siftDown - O(nlog(k))
// Build min-heap of first k elements
heap := ts[:k]
for i := k/2 - 1; i >= 0; i-- {
siftDown(heap, i, k)
}
// Process remaining elements - if larger than heap root, replace root
// Initialize min-heap with first k elements
h := make(tokenHeap, k)
copy(h, ts[:k])
heap.Init(&h)
// Process remaining elements
for i := k; i < len(ts); i++ {
if ts[i].value > heap[0].value {
heap[0] = ts[i]
siftDown(heap, 0, k)
if ts[i].value > h[0].value {
heap.Pop(&h)
heap.Push(&h, ts[i])
}
}
slices.Reverse(heap)
// Convert heap to sorted slice in descending order
result := make([]token, k)
for i := k - 1; i >= 0; i-- {
result[i] = heap.Pop(&h).(token)
}
ts = heap
return ts
return result
}
// topP limits tokens to those with cumulative probability p
@@ -135,61 +126,77 @@ func minP(ts []token, p float32) []token {
return ts
}
// TODO(parthsareen): possibly replace with simpler implementation https://github.com/ollama/ollama/issues/9584
// sortLogits sorts implementation to sort tokens by logits using counting sort
// counting sort is faster than built-in sort for this use case
func sortLogits(tokens []token) {
if len(tokens) <= 1 {
return
// partialSortLogits uses quickselect to efficiently find and sort the top n tokens
func partialSortLogits(ts []token, n int) []token {
if n >= len(ts) {
n = len(ts)
}
// Find max/min in a single pass
minLogit, maxLogit := tokens[0].value, tokens[0].value
for _, t := range tokens[1:] {
if t.value < minLogit {
minLogit = t.value
} else if t.value > maxLogit {
maxLogit = t.value
left, right := 0, len(ts)-1
target := n - 1
// Quickselect algorithm to partition array around pivot
for left < right {
// Choose middle element as pivot and move it to the end
pivot := left + (right-left)/2
ts[pivot], ts[right] = ts[right], ts[pivot]
// storeIndex tracks where to put next element greater than pivot
storeIndex := left
pivotValue := ts[right].value
// Partition array into elements >= pivot and < pivot
// Elements >= pivot go to the left side
for i := left; i < right; i++ {
if ts[i].value >= pivotValue {
ts[storeIndex], ts[i] = ts[i], ts[storeIndex]
storeIndex++
}
}
// Move pivot to its final position
ts[right], ts[storeIndex] = ts[storeIndex], ts[right]
// If pivot is at target position, we're done
// Otherwise recursively partition the half containing target
if storeIndex == target {
break
} else if storeIndex < target {
left = storeIndex + 1 // Target is in right half
} else {
right = storeIndex - 1 // Target is in left half
}
}
// Calculate scaling to map to uint32 range
logitRange := maxLogit - minLogit
if logitRange < 1e-6 {
return // All values effectively equal
}
// Sort just the top n elements in descending order
slices.SortFunc(ts[:n], func(a, b token) int {
if a.value > b.value {
return -1
}
if a.value < b.value {
return 1
}
return 0
})
// Count frequencies directly from tokens
const maxInt = (1 << 24) - 1 // Use 24 bits for good granularity
var counts [256]int // For first byte
// First pass: count frequencies
for _, t := range tokens {
// Map to [0, maxInt] range
score := min(uint32((t.value-minLogit)*float32(maxInt)/logitRange), maxInt)
counts[score>>16]++
}
// Calculate offsets
var offset int
for i := range counts {
count := counts[i]
counts[i] = offset
offset += count
}
// Second pass: place elements in correct position
output := make([]token, len(tokens))
// Track current positions
countsCopy := counts
for i, t := range tokens {
score := min(uint32((t.value-minLogit)*float32(maxInt)/logitRange), maxInt)
pos := countsCopy[score>>16]
countsCopy[score>>16]++
output[len(tokens)-1-pos] = tokens[i]
}
copy(tokens, output)
return ts[:n]
}
// sortLogits uses partialSortLogits to efficiently sort tokens
// It sorts approximately sqrt(len(tokens)) elements which balances
// between having enough tokens for sampling while avoiding full sort
func sortLogits(ts []token) {
// Use sqrt of token length as a heuristic for partial sort size
// This provides a good balance between performance and having enough tokens
n := int(math.Sqrt(float64(len(ts)))) + 1
// Ensure we have at least 100 tokens and at most 1000
switch {
case n < 100:
n = 100
case n > 1000:
n = 1000
}
partialSortLogits(ts, n)
}