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

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"))
@@ -152,17 +201,37 @@ 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,
-			headDim:      headDim,
-			eps:          c.Float("attention.layer_norm_epsilon"),
-			poolingType:  pooling.Type(c.Uint("pooling_type")),
-			normalize:    c.Bool("normalize_embeddings", false),
-			ropeFreqBase: c.Float("rope.freq_base", 1000.0),
+			hiddenSize:      hiddenSize,
+			numHeads:        numHeads,
+			headDim:         headDim,
+			eps:             c.Float("attention.layer_norm_epsilon"),
+			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)
 }