sample: simplify top_k=0 sorting

sample/transforms.go

@@ -10,7 +10,7 @@ import (
 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) Less(i, j int) bool { return h[i].value < h[j].value }
 func (h tokenHeap) Swap(i, j int)      { h[i], h[j] = h[j], h[i] }
 
 func (h *tokenHeap) Push(x any) {
@@ -72,7 +72,7 @@ func topK(ts []token, k int) []token {
 	}
 
 	// Convert heap to sorted slice in descending order
-	result := make([]token, k)
+	result := make([]token, len(h))
 	for i := k - 1; i >= 0; i-- {
 		result[i] = heap.Pop(&h).(token)
 	}
@@ -126,77 +126,16 @@ func minP(ts []token, p float32) []token {
 	return ts
 }
 
-// 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)
-	}
-
-	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
-		}
-	}
-
-	// 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 {
+// sortLogits sorts the tokens in descending order of logits
+func sortLogits(ts []token) {
+	slices.SortFunc(ts, func(a, b token) int {
+		switch {
+		case a.value < b.value:
 			return 1
+		case a.value > b.value:
+			return -1
+		default:
+			return 0
 		}
-		return 0
 	})
-
-	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)
 }

sample/transforms_test.go

 package sample
 
 import (
-	"encoding/binary"
-	"errors"
 	"math"
 	"math/rand/v2"
-	"os"
-	"path/filepath"
-	"runtime"
 	"testing"
 )
 
@@ -130,98 +125,6 @@ func TestSortLogits(t *testing.T) {
 	compareLogits(t, "sortLogits", want, tokens)
 }
 
-// TestSortLogitsWithRealData tests sorting behavior using real model logit distributions
-func TestSortLogitsWithRealData(t *testing.T) {
-	// This will be populated from testdata/logits.bin
-	// Format: 32-bit float array in binary format
-	logits, err := loadTestLogits(t)
-	if err != nil {
-		t.Skipf("Skipping real logit test: %v", err)
-		return
-	}
-
-	tokens := toTokens(logits)
-	sortLogits(tokens)
-
-	// Calculate n for verification
-	n := int(math.Sqrt(float64(len(tokens)))) + 1
-	if n > 1000 {
-		n = 1000
-	} else if n < 100 {
-		n = 100
-	}
-
-	t.Logf("Testing with %d tokens, partial sorting top %d", len(tokens), n)
-
-	// Only verify the top n elements are sorted (which is what we guarantee)
-	// This is much faster than checking the entire array
-	topN := tokens[:n]
-	for i := 1; i < len(topN); i++ {
-		if topN[i].value > topN[i-1].value {
-			t.Fatalf("top %d tokens not properly sorted at index %d: %.15f > %.15f",
-				n, i, topN[i].value, topN[i-1].value)
-		}
-	}
-
-	// Verify we didn't lose any high value tokens by checking that
-	// all tokens after position n are <= the nth token
-	// Do this in chunks to avoid timeouts on large arrays
-	nthValue := tokens[n-1].value
-	const chunkSize = 1000
-
-	for start := n; start < len(tokens); start += chunkSize {
-		end := min(start+chunkSize, len(tokens))
-		for i := start; i < end; i++ {
-			if tokens[i].value > nthValue {
-				t.Fatalf("found higher value token after position %d: tokens[%d].value = %.15f > %.15f",
-					n, i, tokens[i].value, nthValue)
-			}
-		}
-	}
-}
-
-// loadTestLogits loads logit test data from testdata/logits.bin
-func loadTestLogits(t *testing.T) ([]float32, error) {
-	t.Helper()
-
-	_, currFile, _, ok := runtime.Caller(0)
-	if !ok {
-		return nil, errors.New("could not determine test file path")
-	}
-	testDataPath := filepath.Join(filepath.Dir(currFile), "testdata", "logits.bin")
-
-	file, err := os.Open(testDataPath)
-	if err != nil {
-		return nil, err
-	}
-	defer file.Close()
-
-	stat, err := file.Stat()
-	if err != nil {
-		return nil, err
-	}
-
-	numFloats := stat.Size() / 4 // each float32 is 4 bytes
-	if numFloats*4 != stat.Size() {
-		return nil, errors.New("logits.bin has invalid size: not a multiple of 4 bytes")
-	}
-
-	logits := make([]float32, numFloats)
-	for i := range logits {
-		var val uint32
-		if err := binary.Read(file, binary.LittleEndian, &val); err != nil {
-			return nil, err
-		}
-		logits[i] = math.Float32frombits(val)
-	}
-
-	if len(logits) == 0 {
-		return nil, errors.New("logits.bin is empty")
-	}
-
-	return logits, nil
-}
-
 func BenchmarkTransforms(b *testing.B) {
 	// Generate random logits
 	tokens := make([]token, 1<<16)