feat: qwen3 embed (#12301)

* cleanup

* use pooling.TypeNone

* pooling test

* qwen3 embed

model/models/qwen3/embed.go

+package qwen3
+
+import (
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn/pooling"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type embedModel struct {
+	model.Base
+	model.BytePairEncoding
+
+	*Model
+	poolingType pooling.Type
+}
+
+func (m *embedModel) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	hiddenStates, err := m.forward(ctx, batch)
+	if err != nil {
+		return nil, err
+	}
+
+	hiddenStates = m.poolingType.Forward(ctx, hiddenStates)
+	hiddenStates = hiddenStates.L2Norm(ctx, 1e-12)
+	return hiddenStates, nil
+}
+
+func newEmbed(c fs.Config) (model.Model, error) {
+	layers := make([]Layer, c.Uint("block_count"))
+	for i := range layers {
+		layers[i].MLP = &dense{}
+	}
+	m := embedModel{
+		BytePairEncoding: model.NewBytePairEncoding(
+			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				Merges: c.Strings("tokenizer.ggml.merges"),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+				EOS: append(
+					[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+					c.Ints("tokenizer.ggml.eos_token_ids")...,
+				),
+			},
+		),
+		Model: &Model{
+			Layers: layers,
+			Options: &Options{
+				hiddenSize:     int(c.Uint("embedding_length")),
+				numHeads:       int(c.Uint("attention.head_count")),
+				numKVHeads:     int(c.Uint("attention.head_count_kv")),
+				keyLength:      int(c.Uint("attention.key_length")),
+				valueLength:    int(c.Uint("attention.value_length")),
+				eps:            c.Float("attention.layer_norm_rms_epsilon"),
+				ropeBase:       c.Float("rope.freq_base"),
+				ropeScale:      c.Float("rope.freq_scale", 1),
+				numExperts:     int(c.Uint("expert_count")),
+				numExpertsUsed: int(c.Uint("expert_used_count")),
+				normTopKProb:   c.Bool("norm_top_k_prob", true),
+			},
+		},
+		poolingType: pooling.Type(c.Uint("pooling_type")),
+	}
+
+	m.Cache = kvcache.NewCausalCache(m.Shift)
+	return &m, nil
+}

model/models/qwen3/model.go

@@ -151,14 +151,25 @@ type Model struct {
 	*Options
 }
 
-// Forward implements model.Model.
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	hiddenStates, err := m.forward(ctx, batch)
+	if err != nil {
+		return nil, err
+	}
+
+	return m.Output.Forward(ctx, hiddenStates), nil
+}
+
+// Forward implements model.Model.
+func (m *Model) forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
 
 	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 
 	for i, layer := range m.Layers {
-		m.Cache.SetLayer(i)
+		if m.Cache != nil {
+			m.Cache.SetLayer(i)
+		}
 
 		var outputs ml.Tensor
 		if i == len(m.Layers)-1 {
@@ -168,8 +179,7 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 		hiddenStates = layer.Forward(ctx, hiddenStates, positions, outputs, m.Cache, m.Options)
 	}
 
-	hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
-	return m.Output.Forward(ctx, hiddenStates), nil
+	return m.OutputNorm.Forward(ctx, hiddenStates, m.eps), nil
 }
 
 func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
@@ -227,4 +237,5 @@ func New(c fs.Config) (model.Model, error) {
 func init() {
 	model.Register("qwen3", New)
 	model.Register("qwen3moe", New)
+	model.Register("qwen3_embed", newEmbed)
 }