feat(model): add qwen3vl (#12665)

convert/convert.go

@@ -198,6 +198,8 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 		conv = &qwen2Model{}
 	case "Qwen2_5_VLForConditionalGeneration":
 		conv = &qwen25VLModel{}
+	case "Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration":
+		conv = &qwen3VLModel{}
 	case "BertModel":
 		conv = &bertModel{}
 	case "CohereForCausalLM":

convert/convert_qwen3.go

@@ -0,0 +1,157 @@
+package convert
+
+import (
+	"slices"
+	"strings"
+
+	"github.com/ollama/ollama/fs/ggml"
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+)
+
+type qwen3Model struct {
+	ModelParameters
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	HiddenLayers          uint32  `json:"num_hidden_layers"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	HeadDim               uint32  `json:"head_dim"`
+	NumExperts            uint32  `json:"num_experts"`
+	NumExpertsPerToken    uint32  `json:"num_experts_per_tok"`
+	NormTopkProb          bool    `json:"norm_topk_prob"`
+	RopeTheta             float32 `json:"rope_theta"`
+	RopeScaling           struct {
+		Type                          string     `json:"type"`
+		Factor                        ropeFactor `json:"factor"`
+		OriginalMaxPositionEmbeddings uint32     `json:"original_max_position_embeddings"`
+		MropeSection                  []int32    `json:"mrope_section"`
+	} `json:"rope_scaling"`
+	RMSNormEPS float32 `json:"rms_norm_eps"`
+}
+
+// KV implements ModelConverter.
+func (q *qwen3Model) KV(t *Tokenizer) ggml.KV {
+	arch := "qwen3"
+	if q.NumExperts > 0 {
+		arch += "moe"
+	}
+
+	kv := q.ModelParameters.KV(t)
+	kv["general.architecture"] = arch
+	kv["block_count"] = q.HiddenLayers
+	kv["context_length"] = q.MaxPositionEmbeddings
+	kv["embedding_length"] = q.HiddenSize
+	kv["feed_forward_length"] = q.IntermediateSize
+	kv["attention.head_count"] = q.NumAttentionHeads
+	kv["attention.head_count_kv"] = q.NumKeyValueHeads
+	kv["attention.key_length"] = q.HeadDim
+	kv["attention.value_length"] = q.HeadDim
+
+	if q.NumExperts > 0 {
+		kv["expert_count"] = q.NumExperts
+		kv["expert_used_count"] = q.NumExpertsPerToken
+		kv["norm_top_k_prob"] = q.NormTopkProb
+	}
+
+	kv["rope.freq_base"] = q.RopeTheta
+	kv["attention.layer_norm_rms_epsilon"] = q.RMSNormEPS
+
+	switch q.RopeScaling.Type {
+	case "":
+		// no scaling
+	case "yarn":
+		kv["rope.scaling.type"] = q.RopeScaling.Type
+		kv["rope.scaling.factor"] = q.RopeScaling.Factor
+	case "mrope", "default":
+		kv["rope.mrope_section"] = q.RopeScaling.MropeSection
+	default:
+		panic("unknown rope scaling type")
+	}
+	return kv
+}
+
+// Tensors implements ModelConverter.
+func (q *qwen3Model) Tensors(ts []Tensor) []*ggml.Tensor {
+	var out []*ggml.Tensor
+
+	// TODO: handle split experts
+
+	for _, t := range ts {
+		switch {
+		case strings.Contains(t.Name(), "ffn_gate_up_exps"):
+			afterFunc := func(t tensor.Tensor) (tensor.Tensor, error) { return tensor.Transpose(t, 0, 2, 1) }
+			for t := range splitDim(t, 2,
+				split{Replacer: strings.NewReplacer("gate_up", "gate"), afterFunc: afterFunc},
+				split{Replacer: strings.NewReplacer("gate_up", "up"), afterFunc: afterFunc},
+			) {
+				t.Shape[1], t.Shape[2] = t.Shape[2], t.Shape[1]
+				out = append(out, t)
+			}
+		case strings.Contains(t.Name(), "ffn_down_exps"):
+			shape := slices.Clone(t.Shape())
+			shape[1], shape[2] = shape[2], shape[1]
+			t.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
+				dims := make([]int, len(shape))
+				for i := range shape {
+					dims[i] = int(shape[i])
+				}
+
+				var tt tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+				tt, err := tensor.Transpose(tt, 0, 2, 1)
+				if err != nil {
+					return nil, err
+				}
+
+				// flatten tensor so it can be written as a vector
+				if err := tt.Reshape(tt.Shape().TotalSize()); err != nil {
+					return nil, err
+				}
+
+				return native.VectorF32(tt.(*tensor.Dense))
+			})
+			out = append(out, &ggml.Tensor{
+				Name:     t.Name(),
+				Kind:     t.Kind(),
+				Shape:    shape,
+				WriterTo: t,
+			})
+		default:
+			out = append(out, &ggml.Tensor{
+				Name:     t.Name(),
+				Kind:     t.Kind(),
+				Shape:    t.Shape(),
+				WriterTo: t,
+			})
+		}
+	}
+
+	return out
+}
+
+// Replacements implements ModelConverter.
+func (q *qwen3Model) Replacements() []string {
+	return []string{
+		"lm_head", "output",
+		"model.embed_tokens", "token_embd",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.k_norm", "attn_k_norm",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.q_norm", "attn_q_norm",
+		"self_attn.o_proj", "attn_output",
+		"mlp.down_proj", "ffn_down",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.up_proj", "ffn_up",
+		"mlp.gate.weight", "ffn_gate_inp.weight",
+		"mlp.experts.down_proj", "ffn_down_exps.weight",
+		"mlp.experts.gate_up_proj", "ffn_gate_up_exps.weight",
+		"post_attention_layernorm", "ffn_norm",
+		"model.norm", "output_norm",
+	}
+}
+
+var _ ModelConverter = (*qwen3Model)(nil)

convert/convert_qwen3vl.go

@@ -0,0 +1,116 @@
+package convert
+
+import (
+	"cmp"
+	"encoding/json"
+	"io/fs"
+	"slices"
+	"strings"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type qwen3VLModel struct {
+	qwen3Model `json:"text_config"`
+
+	VisionModel struct {
+		Depth                  uint32  `json:"depth"`
+		HiddenSize             uint32  `json:"hidden_size"`
+		NumHeads               uint32  `json:"num_heads"`
+		InChannels             uint32  `json:"in_channels"`
+		PatchSize              uint32  `json:"patch_size"`
+		SpatialMergeSize       uint32  `json:"spatial_merge_size"`
+		WindowSize             uint32  `json:"window_size"`
+		RMSNormEps             float32 `json:"layer_norm_epsilon"`
+		RopeTheta              float32 `json:"rope_theta"`
+		TemporalPatchSize      uint32  `json:"temporal_patch_size"`
+		DeepstackVisualIndexes []int32 `json:"deepstack_visual_indexes"`
+
+		Size struct {
+			ShortestEdge uint32 `json:"shortest_edge"`
+			LongestEdge  uint32 `json:"longest_edge"`
+		} `json:"size"`
+
+		ImageMean []float32 `json:"image_mean"`
+		ImageStd  []float32 `json:"image_std"`
+	} `json:"vision_config"`
+}
+
+func (m *qwen3VLModel) parseMore(fsys fs.FS) error {
+	bts, err := fs.ReadFile(fsys, "preprocessor_config.json")
+	if err != nil {
+		return err
+	}
+
+	return json.Unmarshal(bts, &m.VisionModel)
+}
+
+func (m *qwen3VLModel) KV(t *Tokenizer) ggml.KV {
+	kv := m.qwen3Model.KV(t)
+
+	arch := "qwen3vl"
+	if m.NumExperts > 0 {
+		arch += "moe"
+	}
+	// override architecture
+	kv["general.architecture"] = arch
+
+	kv["vision.block_count"] = cmp.Or(m.VisionModel.Depth, 32)
+	kv["vision.embedding_length"] = m.VisionModel.HiddenSize
+	kv["vision.attention.head_count"] = cmp.Or(m.VisionModel.NumHeads, 16)
+	kv["vision.num_channels"] = m.VisionModel.InChannels
+	kv["vision.patch_size"] = cmp.Or(m.VisionModel.PatchSize, 14)
+	kv["vision.spatial_merge_size"] = cmp.Or(m.VisionModel.SpatialMergeSize, 2)
+	kv["vision.attention.layer_norm_epsilon"] = cmp.Or(m.VisionModel.RMSNormEps, 1e-6)
+	kv["vision.rope.freq_base"] = cmp.Or(m.VisionModel.RopeTheta, 1e4)
+	kv["vision.temporal_patch_size"] = cmp.Or(m.VisionModel.TemporalPatchSize, 2)
+	kv["vision.deepstack_visual_indexes"] = m.VisionModel.DeepstackVisualIndexes
+
+	kv["vision.shortest_edge"] = m.VisionModel.Size.ShortestEdge
+	kv["vision.longest_edge"] = m.VisionModel.Size.LongestEdge
+
+	kv["vision.image_mean"] = m.VisionModel.ImageMean
+	kv["vision.image_std"] = m.VisionModel.ImageStd
+
+	return kv
+}
+
+func (m *qwen3VLModel) Tensors(ts []Tensor) []*ggml.Tensor {
+	var rest []Tensor
+	var out []*ggml.Tensor
+	for _, t := range ts {
+		switch {
+		case strings.Contains(t.Name(), "attn_qkv"):
+			out = append(out, slices.Collect(splitDim(t, 0,
+				split{Replacer: strings.NewReplacer("attn_qkv", "attn_q")},
+				split{Replacer: strings.NewReplacer("attn_qkv", "attn_k")},
+				split{Replacer: strings.NewReplacer("attn_qkv", "attn_v")},
+			))...)
+		case strings.Contains(t.Name(), "patch_embed") && strings.HasSuffix(t.Name(), "weight"):
+			shape := t.Shape()
+			out = append(out, &ggml.Tensor{
+				Name:     t.Name(),
+				Kind:     t.Kind(),
+				Shape:    append([]uint64{shape[0] * shape[1]}, shape[2:]...),
+				WriterTo: t,
+			})
+		default:
+			rest = append(rest, t)
+		}
+	}
+
+	return append(m.qwen3Model.Tensors(rest), out...)
+}
+
+func (m *qwen3VLModel) Replacements() []string {
+	return append(
+		m.qwen3Model.Replacements(),
+		"model.language_", "",
+		"model.visual", "v",
+		"patch_embed.proj", "patch_embed",
+		"blocks", "blk",
+		"attn.qkv", "attn_qkv",
+		"attn.proj", "attn_out",
+		"deepstack_merger_list", "deepstack_merger",
+	)
+}

convert/tensor.go

@@ -19,8 +19,8 @@ type split struct {
 	dim    int
 	slices []tensor.Slice
 
-	// fn is an optional function to apply to the tensor after slicing
-	fn func(tensor.Tensor) (tensor.Tensor, error)
+	// afterFunc is an optional function to apply to the tensor after slicing
+	afterFunc func(tensor.Tensor) (tensor.Tensor, error)
 }
 
 // splitDim splits a tensor along a specified dimension into multiple tensors. The dimension
@@ -54,8 +54,8 @@ func splitDim(t Tensor, dim int, splits ...split) iter.Seq[*ggml.Tensor] {
 
 				tt = tensor.Materialize(tt)
 
-				if split.fn != nil {
-					tt, err = split.fn(tt)
+				if split.afterFunc != nil {
+					tt, err = split.afterFunc(tt)
 					if err != nil {
 						return nil, err
 					}

convert/tensor_test.go

@@ -432,7 +432,7 @@ func TestSplitDim(t *testing.T) {
 		t.Run("split with transpose", func(t *testing.T) {
 			next, stop := iter.Pull(splitDim(&r, 1,
 				split{Replacer: strings.NewReplacer("a", "x")},
-				split{Replacer: strings.NewReplacer("b", "y"), fn: func(tt tensor.Tensor) (tensor.Tensor, error) {
+				split{Replacer: strings.NewReplacer("b", "y"), afterFunc: func(tt tensor.Tensor) (tensor.Tensor, error) {
 					return tensor.Transpose(tt, 1, 0)
 				}},
 			))