ollama source for Momentry Core verification
This commit is contained in:
Accusys
563
convert/convert_qwen3next_test.go
Normal file
563
convert/convert_qwen3next_test.go
Normal file
@@ -0,0 +1,563 @@
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package convert
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import (
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"bytes"
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"encoding/binary"
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"os"
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"slices"
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"strings"
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"testing"
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"github.com/ollama/ollama/fs/ggml"
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)
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func boolPtr(v bool) *bool {
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return &v
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}
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func readTensorData(t *testing.T, tensor *ggml.Tensor) []float32 {
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t.Helper()
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var b bytes.Buffer
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if _, err := tensor.WriteTo(&b); err != nil {
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t.Fatal(err)
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}
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numel := 1
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for _, d := range tensor.Shape {
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numel *= int(d)
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}
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values := make([]float32, numel)
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if err := binary.Read(&b, binary.LittleEndian, &values); err != nil {
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t.Fatal(err)
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}
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return values
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}
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func TestQwen3NextLegacyModelTypeDisablesReorder(t *testing.T) {
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m := &qwen3NextModel{
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ModelParameters: ModelParameters{
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ModelType: "qwen3_next",
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},
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}
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if m.shouldReorderVHeads() {
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t.Fatalf("legacy qwen3_next model_type should not reorder v-head layout")
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}
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}
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func TestQwen3NextLegacyArchitectureDisablesReorder(t *testing.T) {
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m := &qwen3NextModel{
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ModelParameters: ModelParameters{
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Architectures: []string{"Qwen3NextForCausalLM"},
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},
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}
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if m.shouldReorderVHeads() {
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t.Fatalf("legacy Qwen3Next architecture should not reorder v-head layout")
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}
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}
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func TestQwen3NextKVLegacyConfig(t *testing.T) {
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m := &qwen3NextModel{
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ModelParameters: ModelParameters{
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ModelType: "qwen3_next",
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},
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qwen3NextTextConfig: qwen3NextTextConfig{
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MaxPositionEmbeddings: 8192,
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HiddenSize: 512,
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NumHiddenLayers: 4,
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IntermediateSize: 2048,
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NumAttentionHeads: 8,
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NumKeyValueHeads: 2,
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HeadDim: 64,
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RopeTheta: 1_000_000,
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RMSNormEPS: 1e-6,
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NumExperts: 8,
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NumExpertsPerToken: 2,
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NormTopkProb: boolPtr(true),
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MoEIntermediateSize: 256,
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SharedExpertIntermSize: 512,
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FullAttentionInterval: 2,
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LinearConvKernelDim: 4,
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LinearKeyHeadDim: 64,
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LinearNumKeyHeads: 2,
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LinearNumValueHeads: 4,
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LinearValueHeadDim: 64,
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PartialRotaryFactor: 0.25,
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},
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}
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if err := m.parseMore(os.DirFS(t.TempDir())); err != nil {
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t.Fatal(err)
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}
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kv := m.KV(&Tokenizer{Vocabulary: &Vocabulary{}})
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if got, want := kv["general.architecture"], "qwen35moe"; got != want {
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t.Fatalf("unexpected architecture: got %v want %v", got, want)
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}
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if got, want := kv["tokenizer.ggml.pre"], "qwen35"; got != want {
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t.Fatalf("unexpected tokenizer pre: got %v want %v", got, want)
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}
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headCountKV, ok := kv["attention.head_count_kv"].([]uint32)
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if !ok {
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t.Fatalf("attention.head_count_kv has unexpected type: %T", kv["attention.head_count_kv"])
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}
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if got, want := headCountKV, []uint32{0, 2, 0, 2}; !slices.Equal(got, want) {
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t.Fatalf("unexpected attention.head_count_kv: got %v want %v", got, want)
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}
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if _, ok := kv["ssm.v_head_reordered"]; ok {
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t.Fatalf("legacy qwen3next should not enable ssm.v_head_reordered")
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}
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if got, want := kv["norm_top_k_prob"], true; got != want {
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t.Fatalf("unexpected norm_top_k_prob: got %v want %v", got, want)
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}
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}
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func TestQwen35MoeOmitsNormTopKProbWhenUnset(t *testing.T) {
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m := &qwen3NextModel{
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ModelParameters: ModelParameters{
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ModelType: "qwen3_5",
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},
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qwen3NextTextConfig: qwen3NextTextConfig{
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MaxPositionEmbeddings: 4096,
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HiddenSize: 512,
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NumHiddenLayers: 4,
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IntermediateSize: 2048,
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NumAttentionHeads: 8,
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NumKeyValueHeads: 2,
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HeadDim: 64,
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RopeTheta: 1_000_000,
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RMSNormEPS: 1e-6,
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NumExperts: 8,
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NumExpertsPerToken: 2,
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FullAttentionInterval: 2,
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LinearConvKernelDim: 4,
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LinearKeyHeadDim: 64,
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LinearNumKeyHeads: 2,
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LinearNumValueHeads: 4,
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LinearValueHeadDim: 64,
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PartialRotaryFactor: 0.25,
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},
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}
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if err := m.parseMore(os.DirFS(t.TempDir())); err != nil {
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t.Fatal(err)
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}
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kv := m.KV(&Tokenizer{Vocabulary: &Vocabulary{}})
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if _, ok := kv["norm_top_k_prob"]; ok {
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t.Fatalf("expected norm_top_k_prob to be omitted when not set in config")
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}
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}
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func TestQwen35KVFromTextConfig(t *testing.T) {
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m := &qwen3NextModel{
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ModelParameters: ModelParameters{
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ModelType: "qwen3_5",
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},
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TextConfig: &qwen3NextTextConfig{
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MaxPositionEmbeddings: 16384,
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HiddenSize: 1024,
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NumHiddenLayers: 4,
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IntermediateSize: 4096,
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NumAttentionHeads: 8,
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NumKeyValueHeads: 4,
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HeadDim: 128,
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RMSNormEPS: 1e-6,
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LayerTypes: []string{
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"linear_attention",
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"full_attention",
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"linear_attention",
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"full_attention",
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},
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LinearConvKernelDim: 4,
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LinearKeyHeadDim: 128,
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LinearNumKeyHeads: 2,
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LinearNumValueHeads: 4,
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LinearValueHeadDim: 128,
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RopeParameters: qwen3NextRopeParams{
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MRopeInterleaved: true,
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MropeSection: []int32{11, 11, 10},
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RopeType: "default",
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RopeTheta: 10_000_000,
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PartialRotaryFactor: 0.25,
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},
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},
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VisionModel: qwen3NextVisionConfig{
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Depth: 2,
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HiddenSize: 128,
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NumHeads: 4,
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InChannels: 3,
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PatchSize: 16,
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SpatialMergeSize: 2,
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RMSNormEps: 1e-6,
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RopeTheta: 10_000,
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TemporalPatchSize: 2,
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DeepstackVisualIndexes: []int32{1},
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},
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ImageTokenID: 1001,
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VisionStartTokenID: 1002,
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VisionEndTokenID: 1003,
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}
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m.VisionModel.Size.ShortestEdge = 224
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m.VisionModel.Size.LongestEdge = 4096
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m.VisionModel.ImageMean = []float32{0.5, 0.5, 0.5}
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m.VisionModel.ImageStd = []float32{0.2, 0.2, 0.2}
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if err := m.parseMore(os.DirFS(t.TempDir())); err != nil {
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t.Fatal(err)
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}
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kv := m.KV(&Tokenizer{Vocabulary: &Vocabulary{}})
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if got, want := kv["general.architecture"], "qwen35"; got != want {
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t.Fatalf("unexpected architecture: got %v want %v", got, want)
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}
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headCountKV, ok := kv["attention.head_count_kv"].([]uint32)
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if !ok {
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t.Fatalf("attention.head_count_kv has unexpected type: %T", kv["attention.head_count_kv"])
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}
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if got, want := headCountKV, []uint32{0, 4, 0, 4}; !slices.Equal(got, want) {
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t.Fatalf("unexpected attention.head_count_kv: got %v want %v", got, want)
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}
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if got, ok := kv["ssm.v_head_reordered"].(bool); !ok || !got {
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t.Fatalf("expected ssm.v_head_reordered=true, got %v (%T)", kv["ssm.v_head_reordered"], kv["ssm.v_head_reordered"])
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}
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mrope, ok := kv["mrope_sections"].([]int32)
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if !ok {
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t.Fatalf("mrope_sections has unexpected type: %T", kv["mrope_sections"])
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}
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if got, want := mrope, []int32{11, 11, 10}; !slices.Equal(got, want) {
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t.Fatalf("unexpected mrope_sections: got %v want %v", got, want)
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}
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ropeSections, ok := kv["rope.dimension_sections"].([]int32)
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if !ok {
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t.Fatalf("rope.dimension_sections has unexpected type: %T", kv["rope.dimension_sections"])
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}
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if got, want := ropeSections, []int32{11, 11, 10}; !slices.Equal(got, want) {
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t.Fatalf("unexpected rope.dimension_sections: got %v want %v", got, want)
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}
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if got, ok := kv["rope.mrope_interleaved"].(bool); !ok || !got {
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t.Fatalf("expected rope.mrope_interleaved=true, got %v (%T)", kv["rope.mrope_interleaved"], kv["rope.mrope_interleaved"])
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}
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if got, want := kv["vision.block_count"], uint32(2); got != want {
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t.Fatalf("unexpected vision.block_count: got %v want %v", got, want)
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}
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}
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func TestQwen3NextReplacements(t *testing.T) {
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r := strings.NewReplacer((&qwen3NextModel{}).Replacements()...)
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if got, want := r.Replace("model.language_model.layers.1.linear_attn.in_proj_qkv.weight"), "blk.1.attn_qkv.weight"; got != want {
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t.Fatalf("unexpected language-model replacement: got %q want %q", got, want)
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}
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if got, want := r.Replace("model.visual.blocks.0.attn.qkv.weight"), "v.blk.0.attn_qkv.weight"; got != want {
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t.Fatalf("unexpected vision replacement: got %q want %q", got, want)
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}
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if got, want := r.Replace("model.layers.1.linear_attn.in_proj_qkvz.weight"), "blk.1.ssm_in.weight"; got != want {
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t.Fatalf("unexpected legacy replacement: got %q want %q", got, want)
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}
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}
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func TestQwen35ReordersVHeads(t *testing.T) {
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m := &qwen3NextModel{
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ModelParameters: ModelParameters{
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ModelType: "qwen3_5",
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},
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qwen3NextTextConfig: qwen3NextTextConfig{
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LinearNumKeyHeads: 2,
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LinearNumValueHeads: 4,
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LinearValueHeadDim: 1,
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},
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}
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out := m.Tensors([]Tensor{
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&fakeTensor{
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name: "blk.0.attn_gate.weight",
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shape: []uint64{4, 2},
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data: []float32{0, 1, 2, 3, 4, 5, 6, 7},
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},
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})
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if len(out) != 1 {
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t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
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}
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if got, want := readTensorData(t, out[0]), []float32{0, 1, 4, 5, 2, 3, 6, 7}; !slices.Equal(got, want) {
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t.Fatalf("unexpected data: got %v want %v", got, want)
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}
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}
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func TestQwen35ReordersAttnQKVOutputDim(t *testing.T) {
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m := &qwen3NextModel{
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ModelParameters: ModelParameters{
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ModelType: "qwen3_5",
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},
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qwen3NextTextConfig: qwen3NextTextConfig{
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LinearNumKeyHeads: 2,
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LinearNumValueHeads: 4,
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LinearKeyHeadDim: 1,
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LinearValueHeadDim: 1,
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},
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}
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out := m.Tensors([]Tensor{
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&fakeTensor{
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name: "blk.0.attn_qkv.weight",
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shape: []uint64{8, 2}, // [out_features, in_features] (HF layout)
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data: []float32{
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0, 1, // q0
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2, 3, // q1
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4, 5, // k0
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6, 7, // k1
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10, 11, // v(k0,v0)
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12, 13, // v(k0,v1)
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20, 21, // v(k1,v0)
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22, 23, // v(k1,v1)
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},
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},
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})
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if len(out) != 1 {
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t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
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}
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if got, want := readTensorData(t, out[0]), []float32{
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0, 1, 2, 3, 4, 5, 6, 7,
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10, 11, 20, 21, 12, 13, 22, 23,
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}; !slices.Equal(got, want) {
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t.Fatalf("unexpected qkv data: got %v want %v", got, want)
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}
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}
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func TestQwen35ReordersSsmOutInputDim(t *testing.T) {
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m := &qwen3NextModel{
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ModelParameters: ModelParameters{
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ModelType: "qwen3_5",
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},
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qwen3NextTextConfig: qwen3NextTextConfig{
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LinearNumKeyHeads: 2,
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LinearNumValueHeads: 4,
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LinearValueHeadDim: 1,
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},
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}
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out := m.Tensors([]Tensor{
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&fakeTensor{
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name: "blk.0.ssm_out.weight",
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shape: []uint64{2, 4},
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data: []float32{0, 1, 2, 3, 4, 5, 6, 7},
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},
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})
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if len(out) != 1 {
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t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
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}
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if got, want := readTensorData(t, out[0]), []float32{0, 2, 1, 3, 4, 6, 5, 7}; !slices.Equal(got, want) {
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t.Fatalf("unexpected ssm_out data: got %v want %v", got, want)
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}
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}
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func TestQwen35ReordersSsmBetaRows(t *testing.T) {
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m := &qwen3NextModel{
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ModelParameters: ModelParameters{
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ModelType: "qwen3_5",
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},
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qwen3NextTextConfig: qwen3NextTextConfig{
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LinearNumKeyHeads: 2,
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LinearNumValueHeads: 4,
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},
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}
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out := m.Tensors([]Tensor{
|
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&fakeTensor{
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name: "blk.0.ssm_beta.weight",
|
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shape: []uint64{4, 2},
|
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data: []float32{0, 1, 2, 3, 4, 5, 6, 7},
|
||||
},
|
||||
})
|
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if len(out) != 1 {
|
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t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
|
||||
}
|
||||
|
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if got, want := readTensorData(t, out[0]), []float32{0, 1, 4, 5, 2, 3, 6, 7}; !slices.Equal(got, want) {
|
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t.Fatalf("unexpected ssm_beta data: got %v want %v", got, want)
|
||||
}
|
||||
}
|
||||
|
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func TestQwen35ReordersConv1DChannelDim(t *testing.T) {
|
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m := &qwen3NextModel{
|
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ModelParameters: ModelParameters{
|
||||
ModelType: "qwen3_5",
|
||||
},
|
||||
qwen3NextTextConfig: qwen3NextTextConfig{
|
||||
LinearNumKeyHeads: 2,
|
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LinearNumValueHeads: 4,
|
||||
LinearKeyHeadDim: 1,
|
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LinearValueHeadDim: 1,
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},
|
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}
|
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|
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out := m.Tensors([]Tensor{
|
||||
&fakeTensor{
|
||||
name: "blk.0.ssm_conv1d.weight",
|
||||
shape: []uint64{8, 2}, // [channels, kernel] after squeeze
|
||||
data: []float32{
|
||||
0, 1, // q0
|
||||
2, 3, // q1
|
||||
4, 5, // k0
|
||||
6, 7, // k1
|
||||
10, 11, // v(k0,v0)
|
||||
12, 13, // v(k0,v1)
|
||||
20, 21, // v(k1,v0)
|
||||
22, 23, // v(k1,v1)
|
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},
|
||||
},
|
||||
})
|
||||
if len(out) != 1 {
|
||||
t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
|
||||
}
|
||||
|
||||
if got, want := readTensorData(t, out[0]), []float32{
|
||||
0, 1, 2, 3, 4, 5, 6, 7,
|
||||
10, 11, 20, 21, 12, 13, 22, 23,
|
||||
}; !slices.Equal(got, want) {
|
||||
t.Fatalf("unexpected conv1d data: got %v want %v", got, want)
|
||||
}
|
||||
}
|
||||
|
||||
func TestLegacyQwen3NextDoesNotReorderVHeads(t *testing.T) {
|
||||
m := &qwen3NextModel{
|
||||
ModelParameters: ModelParameters{
|
||||
ModelType: "qwen3_next",
|
||||
},
|
||||
qwen3NextTextConfig: qwen3NextTextConfig{
|
||||
LinearNumKeyHeads: 2,
|
||||
LinearNumValueHeads: 4,
|
||||
LinearValueHeadDim: 1,
|
||||
},
|
||||
}
|
||||
|
||||
out := m.Tensors([]Tensor{
|
||||
&fakeTensor{
|
||||
name: "blk.0.attn_gate.weight",
|
||||
shape: []uint64{4, 1},
|
||||
data: []float32{0, 1, 2, 3},
|
||||
},
|
||||
})
|
||||
if len(out) != 1 {
|
||||
t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
|
||||
}
|
||||
|
||||
if got, want := readTensorData(t, out[0]), []float32{0, 1, 2, 3}; !slices.Equal(got, want) {
|
||||
t.Fatalf("unexpected data for legacy qwen3next: got %v want %v", got, want)
|
||||
}
|
||||
}
|
||||
|
||||
func TestQwen35MoePackedExperts(t *testing.T) {
|
||||
m := &qwen3NextModel{
|
||||
qwen3NextTextConfig: qwen3NextTextConfig{
|
||||
NumHiddenLayers: 1,
|
||||
},
|
||||
}
|
||||
|
||||
out := m.Tensors([]Tensor{
|
||||
&fakeTensor{
|
||||
name: "blk.0.mlp.experts.gate_up_proj",
|
||||
shape: []uint64{2, 4, 3},
|
||||
data: []float32{
|
||||
0, 1, 2,
|
||||
3, 4, 5,
|
||||
6, 7, 8,
|
||||
9, 10, 11,
|
||||
12, 13, 14,
|
||||
15, 16, 17,
|
||||
18, 19, 20,
|
||||
21, 22, 23,
|
||||
},
|
||||
},
|
||||
&fakeTensor{
|
||||
name: "blk.0.mlp.experts.down_proj",
|
||||
shape: []uint64{2, 5, 3},
|
||||
data: make([]float32, 2*5*3),
|
||||
},
|
||||
})
|
||||
|
||||
get := func(name string) *ggml.Tensor {
|
||||
for _, tensor := range out {
|
||||
if tensor.Name == name {
|
||||
return tensor
|
||||
}
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
||||
gate := get("blk.0.ffn_gate_exps.weight")
|
||||
if gate == nil {
|
||||
t.Fatalf("missing tensor %q", "blk.0.ffn_gate_exps.weight")
|
||||
}
|
||||
if got, want := gate.Shape, []uint64{2, 2, 3}; !slices.Equal(got, want) {
|
||||
t.Fatalf("unexpected gate shape: got %v want %v", got, want)
|
||||
}
|
||||
if got, want := readTensorData(t, gate), []float32{
|
||||
0, 1, 2, 3, 4, 5,
|
||||
12, 13, 14, 15, 16, 17,
|
||||
}; !slices.Equal(got, want) {
|
||||
t.Fatalf("unexpected gate values: got %v want %v", got, want)
|
||||
}
|
||||
|
||||
up := get("blk.0.ffn_up_exps.weight")
|
||||
if up == nil {
|
||||
t.Fatalf("missing tensor %q", "blk.0.ffn_up_exps.weight")
|
||||
}
|
||||
if got, want := up.Shape, []uint64{2, 2, 3}; !slices.Equal(got, want) {
|
||||
t.Fatalf("unexpected up shape: got %v want %v", got, want)
|
||||
}
|
||||
if got, want := readTensorData(t, up), []float32{
|
||||
6, 7, 8, 9, 10, 11,
|
||||
18, 19, 20, 21, 22, 23,
|
||||
}; !slices.Equal(got, want) {
|
||||
t.Fatalf("unexpected up values: got %v want %v", got, want)
|
||||
}
|
||||
|
||||
down := get("blk.0.ffn_down_exps.weight")
|
||||
if down == nil {
|
||||
t.Fatalf("missing tensor %q", "blk.0.ffn_down_exps.weight")
|
||||
}
|
||||
if got, want := down.Shape, []uint64{2, 5, 3}; !slices.Equal(got, want) {
|
||||
t.Fatalf("unexpected down shape: got %v want %v", got, want)
|
||||
}
|
||||
}
|
||||
|
||||
func TestQwen35SharedExpertGateKeepsMatrixShape(t *testing.T) {
|
||||
m := &qwen3NextModel{}
|
||||
|
||||
out := m.Tensors([]Tensor{
|
||||
&fakeTensor{
|
||||
name: "blk.0.ffn_gate_inp_shexp.weight",
|
||||
shape: []uint64{1, 4},
|
||||
data: []float32{0, 1, 2, 3},
|
||||
},
|
||||
})
|
||||
if len(out) != 1 {
|
||||
t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
|
||||
}
|
||||
|
||||
if got, want := out[0].Shape, []uint64{1, 4}; !slices.Equal(got, want) {
|
||||
t.Fatalf("unexpected shared gate shape: got %v want %v", got, want)
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user