diff --git a/sample/samplers.go b/sample/samplers.go index 8b0de3f54..8dd6ed855 100644 --- a/sample/samplers.go +++ b/sample/samplers.go @@ -1,11 +1,10 @@ package sample import ( - "errors" "math" - "math/rand/v2" - "slices" + "math/rand" "sync" + "time" "github.com/ollama/ollama/llama" ) @@ -87,53 +86,53 @@ func (s *Sampler) sample(tokens []token) (token, error) { // topK also sorts the tokens in descending order of logits tokens = topK(tokens, s.topK) - // token logit values are updated to probabilities - tokens = temperature(tokens, s.temperature) - tokens = topP(tokens, s.topP) tokens = minP(tokens, s.minP) - // TODO: this should fall back to greedy sampling - // or topP, topK values etc should be such that - // there are always tokens to sample from - if len(tokens) == 0 { - return token{}, errors.New("no tokens to sample from") - } + // token logit values are updated to probabilities + temperature(tokens, s.temperature) + softmax(tokens) + return tokens[dist(tokens, s.rng.Int63())], nil - var r float32 - if s.rng != nil { - r = s.rng.Float32() - } else { - r = rand.Float32() - } + // // TODO: this should fall back to greedy sampling + // // or topP, topK values etc should be such that + // // there are always tokens to sample from + // if len(tokens) == 0 { + // return token{}, errors.New("no tokens to sample from") + // } - // Calculate cumulative sum of probabilities - var sum float32 - for i := range tokens { - sum += tokens[i].value - tokens[i].value = sum - } - r *= tokens[len(tokens)-1].value + // var r float32 + // if s.rng != nil { + // r = s.rng.Float32() + // } else { + // r = rand.Float32() + // } - idx, _ := slices.BinarySearchFunc(tokens, r, func(token token, target float32) int { - if token.value < target { - return -1 - } - return 1 - }) + // // Calculate cumulative sum of probabilities + // var sum float32 + // for i := range tokens { + // sum += tokens[i].value + // tokens[i].value = sum + // } + // r *= tokens[len(tokens)-1].value - return tokens[idx], nil + // idx, _ := slices.BinarySearchFunc(tokens, r, func(token token, target float32) int { + // if token.value < target { + // return -1 + // } + // return 1 + // }) + + // return tokens[idx], nil } // TODO(parthsareen): update sampler interface to use json unmarshal https://github.com/ollama/ollama/issues/9278 func NewSampler(temperature float32, topK int, topP float32, minP float32, seed int, grammar *Grammar) Sampler { var rng *rand.Rand if seed != -1 { - // PCG requires two parameters: sequence and stream - // Use original seed for sequence - sequence := uint64(seed) - // Use golden ratio hash to generate statistically independent seeds - rng = rand.New(rand.NewPCG(sequence, sequence^0x9E3779B9)) + rng = rand.New(rand.NewSource(int64(seed))) + } else { + rng = rand.New(rand.NewSource(time.Now().UnixNano())) } if temperature < 0.0 { temperature = 0.0 diff --git a/sample/testdata/logits.bin b/sample/testdata/logits.bin new file 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Normalize - for i := range ts { - ts[i].value /= sum - } - - return ts -} - // topK limits the number of tokens considered to the k highest logits func topK(ts []token, k int) []token { if k >= len(ts) || k <= 0 { @@ -134,3 +109,59 @@ func minP(ts []token, p float32) []token { ts = validTokens return ts } + +func temperature(ts []token, temp float32) { + for i := range ts { + ts[i].value /= temp + } +} + +func softmax(ts []token) { + if len(ts) == 0 { + return + } + + // Find max logit for numerical stability + maxLogit := ts[0].value + for _, t := range ts { + if t.value > maxLogit { + maxLogit = t.value + } + } + + // Compute exp(logit - maxLogit) and sum them + var sumExp float32 + for i, t := range ts { + expVal := float32(math.Exp(float64(t.value - maxLogit))) + ts[i].value = expVal + sumExp += expVal + } + + // Normalize probabilities + for i := range ts { + ts[i].value /= sumExp + } +} + +// applyDist selects a token based on probabilities and seed +func dist(ts []token, seed int64) int { + rng := rand.New(rand.NewSource(seed)) + + cdf := make([]float32, len(ts)) + var cumSum float32 + for i, t := range ts { + cumSum += t.value + cdf[i] = cumSum + } + + r := rng.Float32() * cumSum + + // Select token based on CDF + for i, probSum := range cdf { + if r < probSum { + return i + } + } + + return len(ts) - 1 +} diff --git a/sample/transforms_test.go b/sample/transforms_test.go index 8f0a58b60..39b82c60c 100644 --- a/sample/transforms_test.go +++ b/sample/transforms_test.go @@ -1,8 +1,13 @@ package sample import ( + "encoding/binary" + "errors" "math" "math/rand/v2" + "os" + "path/filepath" + "runtime" "testing" ) @@ -143,6 +148,98 @@ 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)