nomic-embed-text:v2: model implementation (#13162)

convert/convert.go

@@ -202,6 +202,8 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 		conv = &qwen3VLModel{}
 	case "BertModel":
 		conv = &bertModel{}
+	case "NomicBertModel", "NomicBertMoEModel":
+		conv = &nomicbertModel{}
 	case "CohereForCausalLM":
 		conv = &commandrModel{}
 	case "GptOssForCausalLM":

convert/convert_nomicbert.go

+package convert
+
+import (
+	"cmp"
+	"encoding/json"
+	"io/fs"
+	"path/filepath"
+	"slices"
+	"strings"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type nomicbertModel struct {
+	ModelParameters
+	NLayers               uint32  `json:"n_layers"`
+	NumHiddenLayers       uint32  `json:"num_hidden_layers"`
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	LayerNormEPS          float32 `json:"layer_norm_eps"`
+	LayerNormEpsilon      float32 `json:"layer_norm_epsilon"`
+	RopeFreqBase          float32 `json:"rope_theta"`
+	normalizeEmbeddings   bool
+	PoolingType           uint32
+
+	// MoE parameters (only present in v2 models)
+	NumExperts      uint32 `json:"num_local_experts"`
+	NumExpertsUsed  uint32 `json:"num_experts_per_tok"`
+	MoEEveryNLayers uint32 `json:"moe_every_n_layers"`
+}
+
+var (
+	_ ModelConverter = (*nomicbertModel)(nil)
+	_ moreParser     = (*nomicbertModel)(nil)
+)
+
+func (p *nomicbertModel) parseMore(fsys fs.FS) error {
+	bts, err := fs.ReadFile(fsys, "modules.json")
+	if err != nil {
+		return err
+	}
+
+	var modules []struct {
+		Type string `json:"type"`
+		Path string `json:"path"`
+	}
+
+	if err := json.Unmarshal(bts, &modules); err != nil {
+		return err
+	}
+
+	var pooling string
+	for _, m := range modules {
+		switch m.Type {
+		case "sentence_transformers.models.Pooling":
+			pooling = m.Path
+		case "sentence_transformers.models.Normalize":
+			p.normalizeEmbeddings = true
+		}
+	}
+
+	if pooling != "" {
+		bts, err := fs.ReadFile(fsys, filepath.Join(pooling, "config.json"))
+		if err != nil {
+			return err
+		}
+
+		var pc struct {
+			PoolingModeCLSToken   bool `json:"pooling_mode_cls_token"`
+			PoolingModeMeanTokens bool `json:"pooling_mode_mean_tokens"`
+		}
+
+		if err := json.Unmarshal(bts, &pc); err != nil {
+			return err
+		}
+
+		if pc.PoolingModeMeanTokens {
+			p.PoolingType = 1
+		} else if pc.PoolingModeCLSToken {
+			p.PoolingType = 2
+		}
+	}
+
+	return nil
+}
+
+func (p *nomicbertModel) KV(t *Tokenizer) ggml.KV {
+	kv := p.ModelParameters.KV(t)
+
+	// Determine architecture based on MoE parameters (following qwen3 pattern)
+	arch := "nomic-bert"
+	if p.MoEEveryNLayers > 0 {
+		arch += "-moe"
+	}
+
+	kv["general.architecture"] = arch
+	kv["attention.causal"] = false
+	kv["pooling_type"] = p.PoolingType
+	kv["normalize_embeddings"] = p.normalizeEmbeddings
+
+	kv["block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers)
+
+	if contextLength := p.MaxPositionEmbeddings; contextLength > 0 {
+		kv["context_length"] = contextLength
+	}
+
+	if embeddingLength := p.HiddenSize; embeddingLength > 0 {
+		kv["embedding_length"] = p.HiddenSize
+	}
+
+	if feedForwardLength := p.IntermediateSize; feedForwardLength > 0 {
+		kv["feed_forward_length"] = p.IntermediateSize
+	}
+
+	if headCount := p.NumAttentionHeads; headCount > 0 {
+		kv["attention.head_count"] = p.NumAttentionHeads
+	}
+
+	if kvHeadCount := p.NumKeyValueHeads; kvHeadCount > 0 {
+		kv["attention.head_count_kv"] = p.NumKeyValueHeads
+	}
+
+	if layerNormEpsilon := cmp.Or(p.LayerNormEPS, p.LayerNormEpsilon); layerNormEpsilon > 0 {
+		kv["attention.layer_norm_epsilon"] = layerNormEpsilon
+	}
+
+	if p.RopeFreqBase > 0 {
+		kv["rope.freq_base"] = p.RopeFreqBase
+	}
+
+	// MoE specific parameters (only if MoE is enabled)
+	if p.NumExperts > 0 {
+		kv["expert_count"] = p.NumExperts
+	}
+
+	if p.NumExpertsUsed > 0 {
+		kv["expert_used_count"] = p.NumExpertsUsed
+	}
+
+	if p.MoEEveryNLayers > 0 {
+		kv["moe_every_n_layers"] = p.MoEEveryNLayers
+	}
+
+	kv["tokenizer.ggml.model"] = "bert"
+	kv["tokenizer.ggml.token_type_count"] = uint32(2)
+
+	// convert to phantom space tokens
+	for i, e := range t.Tokens {
+		switch {
+		case strings.HasPrefix(e, "[") && strings.HasSuffix(e, "]"):
+			// noop - keep special tokens as-is
+		case strings.HasPrefix(e, "##"):
+			t.Tokens[i] = e[2:]
+		default:
+			t.Tokens[i] = "\u2581" + e
+		}
+	}
+
+	kv["tokenizer.ggml.tokens"] = t.Tokens
+
+	return kv
+}
+
+func (p *nomicbertModel) Tensors(ts []Tensor) []*ggml.Tensor {
+	out := make([]*ggml.Tensor, 0, len(ts))
+	for _, t := range ts {
+		if slices.Contains([]string{
+			"embeddings.position_ids",
+			"pooler.dense.weight",
+			"pooler.dense.bias",
+		}, t.Name()) {
+			continue
+		}
+
+		out = append(out, &ggml.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (nomicbertModel) Replacements() []string {
+	return []string{
+		"encoder.layer", "blk",
+		"encoder.layers", "blk",
+		"embeddings.word_embeddings", "token_embd",
+		"embeddings.token_type_embeddings", "token_types",
+		"embeddings.LayerNorm", "token_embd_norm",
+
+		"attention.self.qkv", "attn_qkv",
+
+		"attention.output.dense", "attn_output",
+		"attention.output.LayerNorm", "attn_output_norm",
+
+		"mlp.up", "ffn_up",
+		"mlp.down", "ffn_down",
+
+		"mlp.router", "ffn_gate_inp",
+		"mlp.experts.up", "ffn_up_exps",
+		"mlp.experts.down", "ffn_down_exps",
+
+		"intermediate.dense", "ffn_up",
+		"output.dense", "ffn_down",
+		"output.LayerNorm", "layer_output_norm",
+	}
+}

model/models/nomicbert/model.go

@@ -34,19 +34,23 @@ type Options struct {
 	poolingType  pooling.Type
 	normalize    bool
 	ropeFreqBase float32
+
+	// MoE specific options (used by v2 / MoE models only)
+	numExperts      int
+	numExpertsUsed  int
+	moeEveryNLayers int
 }
 
 func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions ml.Tensor) ml.Tensor {
 	return nn.RoPE(ctx, states, positions, o.headDim, o.ropeFreqBase, 1.0, rope.WithTypeNeoX())
 }
 
-// Single Encoder Layer
 type EncoderLayer struct {
 	*Attention
 
 	AttentionNorm *nn.LayerNorm `gguf:"attn_output_norm"`
 
-	*MLP
+	FeedForward FeedForward
 
 	MLPNorm *nn.LayerNorm `gguf:"layer_output_norm"`
 }
@@ -56,12 +60,63 @@ type Attention struct {
 	Output *nn.Linear `gguf:"attn_output"`
 }
 
-type MLP struct {
+type FeedForward interface {
+	Forward(ml.Context, ml.Tensor, *Options) ml.Tensor
+}
+
+type dense struct {
 	Gate *nn.Linear `gguf:"ffn_gate"`
 	Up   *nn.Linear `gguf:"ffn_up"`
 	Down *nn.Linear `gguf:"ffn_down"`
 }
 
+func (mlp *dense) Forward(ctx ml.Context, hiddenStates ml.Tensor, _ *Options) ml.Tensor {
+	hidden := mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	return mlp.Down.Forward(ctx, hidden)
+}
+
+// denseGELU implements MLP with GELU activation for v2 MoE dense layers
+type denseGELU struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+}
+
+func (mlp *denseGELU) Forward(ctx ml.Context, hiddenStates ml.Tensor, _ *Options) ml.Tensor {
+	return mlp.Down.Forward(ctx, mlp.Up.Forward(ctx, hiddenStates).GELU(ctx))
+}
+
+// sparse implements MoE with expert routing
+type sparse struct {
+	Router *nn.Linear      `gguf:"ffn_gate_inp"`
+	Up     *nn.LinearBatch `gguf:"ffn_up_exps"`
+	Down   *nn.LinearBatch `gguf:"ffn_down_exps"`
+}
+
+func (moe *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
+	hiddenDim, sequenceLength, batchSize := hiddenStates.Dim(0), hiddenStates.Dim(1), hiddenStates.Dim(2)
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenDim, sequenceLength*batchSize)
+
+	routerLogits := moe.Router.Forward(ctx, hiddenStates)
+	routingWeights := routerLogits.Softmax(ctx)
+	selectedExperts := routingWeights.TopK(ctx, opts.numExpertsUsed)
+
+	routingWeights = routingWeights.Reshape(ctx, 1, opts.numExperts, hiddenStates.Dim(1)).Rows(ctx, selectedExperts)
+
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))
+
+	hiddenStates = moe.Up.Forward(ctx, hiddenStates, selectedExperts).GELU(ctx)
+	experts := moe.Down.Forward(ctx, hiddenStates, selectedExperts)
+
+	experts = experts.Mul(ctx, routingWeights)
+
+	nextStates := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))
+	for i := 1; i < opts.numExpertsUsed; i++ {
+		nextStates = nextStates.Add(ctx, experts.View(ctx, i*experts.Stride(1), experts.Dim(0), experts.Stride(2), experts.Dim(2)))
+	}
+
+	return nextStates
+}
+
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 
@@ -92,7 +147,7 @@ func (e *EncoderLayer) Forward(ctx ml.Context, hiddenStates ml.Tensor, positions
 	hiddenStates = e.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
 
 	residual = hiddenStates
-	hiddenStates = e.MLP.Forward(ctx, hiddenStates)
+	hiddenStates = e.FeedForward.Forward(ctx, hiddenStates, opts)
 	hiddenStates = hiddenStates.Add(ctx, residual)
 	hiddenStates = e.MLPNorm.Forward(ctx, hiddenStates, opts.eps)
 
@@ -118,12 +173,6 @@ func (a *Attention) Forward(ctx ml.Context, hiddenStates ml.Tensor, positions ml
 	return a.Output.Forward(ctx, attention)
 }
 
-func (m *MLP) Forward(ctx ml.Context, hiddenStates ml.Tensor) ml.Tensor {
-	hidden := m.Gate.Forward(ctx, hiddenStates).SILU(ctx, m.Up.Forward(ctx, hiddenStates))
-
-	return m.Down.Forward(ctx, hidden)
-}
-
 func New(c fs.Config) (model.Model, error) {
 	hiddenSize := int(c.Uint("embedding_length"))
 	numHeads := int(c.Uint("attention.head_count"))
@@ -152,9 +201,26 @@ func New(c fs.Config) (model.Model, error) {
 		false,
 	)
 
+	blockCount := int(c.Uint("block_count"))
+	moeEveryNLayers := int(c.Uint("moe_every_n_layers", 0))
+	layers := make([]EncoderLayer, blockCount)
+
+	for i := range layers {
+		if moeEveryNLayers > 0 {
+			// Layer uses MoE if (i+1) % moe_every_n_layers == 0
+			if (i+1)%moeEveryNLayers == 0 {
+				layers[i].FeedForward = &sparse{}
+			} else {
+				layers[i].FeedForward = &denseGELU{}
+			}
+		} else {
+			layers[i].FeedForward = &dense{}
+		}
+	}
+
 	return &Model{
 		TextProcessor: processor,
-		Layers:        make([]EncoderLayer, c.Uint("block_count")),
+		Layers:        layers,
 		Options: Options{
 			hiddenSize:      hiddenSize,
 			numHeads:        numHeads,
@@ -163,6 +229,9 @@ func New(c fs.Config) (model.Model, error) {
 			poolingType:     pooling.Type(c.Uint("pooling_type")),
 			normalize:       c.Bool("normalize_embeddings", false),
 			ropeFreqBase:    c.Float("rope.freq_base", 1000.0),
+			numExperts:      int(c.Uint("expert_count")),
+			numExpertsUsed:  int(c.Uint("expert_used_count")),
+			moeEveryNLayers: moeEveryNLayers,
 		},
 	}, nil
 }
@@ -170,4 +239,6 @@ func New(c fs.Config) (model.Model, error) {
 func init() {
 	model.Register("nomic-bert", New)
 	model.Register("nomic-bert_embed", New)
+	model.Register("nomic-bert-moe", New)
+	model.Register("nomic-bert-moe_embed", New)
 }