model: add rnj-1 inference support (#13354)

convert/convert_gemma3.go

@@ -2,6 +2,7 @@ package convert
 
 import (
 	"cmp"
+	"slices"
 
 	"github.com/ollama/ollama/fs/ggml"
 )
@@ -26,16 +27,26 @@ type gemma3Model struct {
 		NumChannels       uint32  `json:"num_channels"`        // num_channels 3
 		PatchSize         uint32  `json:"patch_size"`          // patch_size 14
 	} `json:"vision_config"`
-	MaxPositionEmbeddings    uint32  `json:"max_position_embeddings"`
-	NumAttentionHeads        uint32  `json:"num_attention_heads"`
-	NumKeyValueHeads         uint32  `json:"num_key_value_heads"`
-	RMSNormEPS               float32 `json:"rms_norm_eps"`
-	HeadDim                  uint32  `json:"head_dim"`
-	FinalLogitSoftcap        float32 `json:"final_logit_softcapping"`
-	RopeLocalTheta           float32 `json:"rope_local_base_freq"`
-	RopeGlobalTheta          float32 `json:"rope_global_base_freq"`
-	SlidingWindow            uint32  `json:"sliding_window"`
-	MultiModalTokensPerImage uint32  `json:"mm_tokens_per_image"`
+	MaxPositionEmbeddings    uint32   `json:"max_position_embeddings"`
+	NumAttentionHeads        uint32   `json:"num_attention_heads"`
+	NumKeyValueHeads         uint32   `json:"num_key_value_heads"`
+	RMSNormEPS               float32  `json:"rms_norm_eps"`
+	HeadDim                  uint32   `json:"head_dim"`
+	FinalLogitSoftcap        float32  `json:"final_logit_softcapping"`
+	RopeLocalTheta           float32  `json:"rope_local_base_freq"`
+	RopeTheta                float32  `json:"rope_theta"`
+	SlidingWindow            uint32   `json:"sliding_window"`
+	SlidingWindowPattern     *uint32  `json:"sliding_window_pattern"`
+	LayerTypes               []string `json:"layer_types"`
+	MultiModalTokensPerImage uint32   `json:"mm_tokens_per_image"`
+	RopeScaling              *struct {
+		Type                          string  `json:"rope_type"`
+		Factor                        float32 `json:"factor"`
+		OriginalMaxPositionEmbeddings uint32  `json:"original_max_position_embeddings"`
+		ExtrapolationFactor           float32 `json:"extrapolation_factor"`
+		BetaFast                      float32 `json:"beta_fast"`
+		BetaSlow                      float32 `json:"beta_slow"`
+	} `json:"rope_scaling"`
 }
 
 const (
@@ -81,9 +92,38 @@ func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
 		kv["gemma3.attention.key_length"] = p.HeadDim
 		kv["gemma3.attention.value_length"] = p.HeadDim
 		kv["gemma3.attention.sliding_window"] = p.SlidingWindow
-		kv["gemma3.final_logit_softcapping"] = cmp.Or(p.FinalLogitSoftcap, 30)
+
+		// The sliding window pattern is either provided as the sliding_window_pattern
+		// key (an int) or as the layer_types key (a list of strings).
+		if p.SlidingWindowPattern != nil || len(p.LayerTypes) > 0 {
+			kv["gemma3.attention.sliding_window_pattern"] = slices.Collect(func(yield func(bool) bool) {
+				for i := range numBlocks {
+					var isLocal bool
+					if len(p.LayerTypes) > 0 && int(i) < len(p.LayerTypes) {
+						isLocal = p.LayerTypes[i] == "sliding_attention"
+					} else if p.SlidingWindowPattern != nil && *p.SlidingWindowPattern > 0 {
+						isLocal = (i+1)%*p.SlidingWindowPattern != 0
+					}
+					if !yield(isLocal) {
+						break
+					}
+				}
+			})
+		}
+		if p.FinalLogitSoftcap > 0 {
+			kv["gemma3.final_logit_softcapping"] = p.FinalLogitSoftcap
+		}
 		kv["gemma3.rope.local.freq_base"] = cmp.Or(p.RopeLocalTheta, 10000.0)
-		kv["gemma3.rope.global.freq_base"] = cmp.Or(p.RopeGlobalTheta, 1000000.0)
+		kv["gemma3.rope.freq_base"] = cmp.Or(p.RopeTheta, 1000000.0)
+		if p.RopeScaling != nil && p.RopeScaling.Type == "yarn" && p.RopeScaling.Factor > 0 {
+			kv["gemma3.rope.scaling.type"] = "yarn"
+			kv["gemma3.rope.scaling.factor"] = p.RopeScaling.Factor
+			kv["gemma3.rope.scaling.original_context_length"] = p.RopeScaling.OriginalMaxPositionEmbeddings
+			kv["gemma3.rope.scaling.extrapolation_factor"] = cmp.Or(p.RopeScaling.ExtrapolationFactor, float32(1.0))
+			kv["gemma3.rope.scaling.beta_fast"] = cmp.Or(p.RopeScaling.BetaFast, float32(64.0))
+			kv["gemma3.rope.scaling.beta_slow"] = cmp.Or(p.RopeScaling.BetaSlow, float32(1.0))
+		}
+
 		kv["gemma3.embedding_length"] = p.HiddenSize
 		kv["gemma3.feed_forward_length"] = p.IntermediateSize
 	default:

ml/nn/rope/options.go

@@ -58,6 +58,18 @@ func WithAttentionFactor(attentionFactor float32) func(*Options) {
 	}
 }
 
+func WithBetaFast(betaFast float32) func(*Options) {
+	return func(opts *Options) {
+		opts.YaRN.BetaFast = betaFast
+	}
+}
+
+func WithBetaSlow(betaSlow float32) func(*Options) {
+	return func(opts *Options) {
+		opts.YaRN.BetaSlow = betaSlow
+	}
+}
+
 func WithMRoPE(sections []int) func(*Options) {
 	return func(opts *Options) {
 		opts.Type |= 1 << 3

model/models/gemma3/model.go

@@ -16,7 +16,7 @@ import (
 
 type Model struct {
 	model.Base
-	model.SentencePiece
+	model.TextProcessor
 
 	*VisionModel `gguf:"v"`
 	*TextModel
@@ -54,24 +54,35 @@ func (p *MultiModalProjector) Forward(ctx ml.Context, visionOutputs ml.Tensor, i
 }
 
 func New(c fs.Config) (model.Model, error) {
-	m := Model{
-		SentencePiece: model.NewSentencePiece(
-			&model.Vocabulary{
-				Values: c.Strings("tokenizer.ggml.tokens"),
-				Scores: c.Floats("tokenizer.ggml.scores"),
-				Types:  c.Ints("tokenizer.ggml.token_type"),
-				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")),
-						int32(c.Uint("tokenizer.ggml.eot_token_id", 106)),
-					},
-					c.Ints("tokenizer.ggml.eos_token_ids")...,
-				),
+	vocabulary := model.Vocabulary{
+		Values: c.Strings("tokenizer.ggml.tokens"),
+		Scores: c.Floats("tokenizer.ggml.scores"),
+		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")...,
 		),
+	}
+
+	var processor model.TextProcessor
+	switch c.String("tokenizer.ggml.model") {
+	case "gpt2":
+		processor = model.NewBytePairEncoding(&vocabulary)
+	default:
+		// Previous uploads of Gemma 3 on Ollama did not have token 106
+		// (i.e. "<end_of_turn>") so we need to add in case it's not already present
+		vocabulary.EOS = append(vocabulary.EOS, int32(c.Uint("tokenizer.ggml.eot_token_id", 106)))
+		processor = model.NewSentencePiece(&vocabulary)
+	}
+
+	m := Model{
+		TextProcessor:  processor,
 		ImageProcessor: newImageProcessor(c),
 		VisionModel:    newVisionModel(c),
 		TextModel:      newTextModel(c),
@@ -141,8 +152,16 @@ func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	hiddenStates := m.TextModel.Forward(ctx, batch, m.Cache)
-	return m.Output.Forward(ctx, hiddenStates), nil
+	hiddenState := m.TextModel.Forward(ctx, batch, m.Cache)
+	hiddenState = m.Output.Forward(ctx, hiddenState)
+
+	if m.TextConfig.finalLogitSoftcap > 0.0 {
+		hiddenState = hiddenState.Scale(ctx, 1.0/float64(m.TextConfig.finalLogitSoftcap))
+		hiddenState = hiddenState.Tanh(ctx)
+		hiddenState = hiddenState.Scale(ctx, float64(m.TextConfig.finalLogitSoftcap))
+	}
+
+	return hiddenState, nil
 }
 
 func init() {

model/models/gemma3/model_text.go

@@ -2,6 +2,7 @@ package gemma3
 
 import (
 	"math"
+	"slices"
 
 	"github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/kvcache"
@@ -15,12 +16,32 @@ type TextConfig struct {
 	hiddenSize, numHeads, numKVHeads int
 	attnKeyLen, attnValLen           int
 	eps, ropeScale                   float32
-	ropeLocalBase, ropeGlobalBase    float32
+	ropeLocalBase                    float32
 	largeModelScaling                bool
+	slidingWindowPattern             []bool
+	ropeBase                         float32
+	ropeType                         string
+	ropeOriginalContext              int
+	ropeExtrapolation                float32
+	ropeBetaFast                     float32
+	ropeBetaSlow                     float32
+	finalLogitSoftcap                float32
 }
 
 func (o TextConfig) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions ml.Tensor, base float32) ml.Tensor {
-	return nn.RoPE(ctx, states, positions, o.attnKeyLen, base, 1./o.ropeScale, rope.WithTypeNeoX())
+	ropeOpts := []func(*rope.Options){rope.WithTypeNeoX()}
+	if o.ropeType == "yarn" {
+		attnFactor := float32(1.0 / (1.0 + 0.1*math.Log(float64(o.ropeScale))))
+		ropeOpts = append(ropeOpts,
+			rope.WithOriginalContextLength(o.ropeOriginalContext),
+			rope.WithExtrapolationFactor(o.ropeExtrapolation),
+			rope.WithAttentionFactor(attnFactor),
+			rope.WithBetaFast(o.ropeBetaFast),
+			rope.WithBetaSlow(o.ropeBetaSlow),
+		)
+	}
+
+	return nn.RoPE(ctx, states, positions, o.attnKeyLen, base, 1./o.ropeScale, ropeOpts...)
 }
 
 type TextModel struct {
@@ -48,21 +69,35 @@ func newTextModel(c fs.Config) *TextModel {
 	m := TextModel{
 		Layers: make([]TextLayer, numBlocks),
 		TextConfig: &TextConfig{
-			hiddenSize:     int(c.Uint("embedding_length")),
-			numHeads:       int(c.Uint("attention.head_count")),
-			numKVHeads:     int(c.Uint("attention.head_count_kv")),
-			attnKeyLen:     int(c.Uint("attention.key_length", 256)),
-			attnValLen:     int(c.Uint("attention.value_length", 256)),
-			eps:            c.Float("attention.layer_norm_rms_epsilon", 1e-06),
-			ropeLocalBase:  c.Float("rope.local.freq_base", 10000.0),
-			ropeGlobalBase: c.Float("rope.global.freq_base", 1000000.0),
-			ropeScale:      1,
-			// NOTE: the rope.scaling.factor is set incorrectly in the official QAT weights
-			//       (8 instead of 1)
-			// ropeScale:      c.Float("rope.scaling.factor", 1.0),
+			hiddenSize:           int(c.Uint("embedding_length")),
+			numHeads:             int(c.Uint("attention.head_count")),
+			numKVHeads:           int(c.Uint("attention.head_count_kv")),
+			attnKeyLen:           int(c.Uint("attention.key_length", 256)),
+			attnValLen:           int(c.Uint("attention.value_length", 256)),
+			eps:                  c.Float("attention.layer_norm_rms_epsilon", 1e-06),
+			ropeLocalBase:        c.Float("rope.local.freq_base", 10000.0),
+			ropeBase:             c.Float("rope.freq_base", 1000000.0),
+			slidingWindowPattern: c.Bools("attention.sliding_window_pattern"),
+			ropeType:             c.String("rope.scaling.type"),
+			ropeOriginalContext:  int(c.Uint("rope.scaling.original_context_length")),
+			ropeExtrapolation:    c.Float("rope.scaling.extrapolation_factor", 1.0),
+			ropeBetaFast:         c.Float("rope.scaling.beta_fast", 64.0),
+			ropeBetaSlow:         c.Float("rope.scaling.beta_slow", 1.0),
+			ropeScale:            c.Float("rope.scaling.factor", 1.0),
+			finalLogitSoftcap:    c.Float("final_logit_softcapping", 0.0),
 		},
 	}
 
+	// Google's Gemma 3 release with sliding window attention does
+	// not use final logit softcapping, and so force it to 0.0
+	// TODO (jmorganca): this should ideally be set to 0.0 in the
+	// model configuration instead of here, as future versions of
+	// models may include both sliding window attention and final
+	// logit softcapping.
+	if slices.Contains(m.TextConfig.slidingWindowPattern, true) {
+		m.TextConfig.finalLogitSoftcap = 0.0
+	}
+
 	if numBlocks == gemma27BLayerCount {
 		m.largeModelScaling = true
 	}
@@ -79,13 +114,26 @@ type TextSelfAttention struct {
 	Output    *nn.Linear  `gguf:"attn_output"`
 }
 
+func (opts *TextConfig) ropeBaseForLayer(layer int) float32 {
+	if opts.slidingWindowPattern != nil && opts.slidingWindowPattern[layer] {
+		return opts.ropeLocalBase
+	}
+
+	// Standard Gemma3: only every n-th layer is global,
+	// where n = gemmaGlobalCacheCount, otherwise use
+	// the local rope base
+	if (layer+1)%gemmaGlobalCacheCount > 0 {
+		return opts.ropeLocalBase
+	}
+
+	// default to global rope base
+	return opts.ropeBase
+}
+
 func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextConfig) ml.Tensor {
 	batchSize := hiddenState.Dim(1)
 
-	ropeBase := opts.ropeLocalBase
-	if (layer+1)%gemmaGlobalCacheCount == 0 {
-		ropeBase = opts.ropeGlobalBase
-	}
+	ropeBase := opts.ropeBaseForLayer(layer)
 
 	q := sa.Query.Forward(ctx, hiddenState)
 	q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize)
@@ -114,12 +162,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, pos
 }
 
 func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	ropeBase := m.TextConfig.ropeLocalBase
-	if (layer+1)%gemmaGlobalCacheCount == 0 {
-		ropeBase = m.TextConfig.ropeGlobalBase
-	}
-
-	return m.applyRotaryPositionEmbeddings(ctx, key, shift, ropeBase), nil
+	return m.applyRotaryPositionEmbeddings(ctx, key, shift, m.TextConfig.ropeBaseForLayer(layer)), nil
 }
 
 type TextMLP struct {
@@ -207,6 +250,5 @@ func (m *TextModel) Forward(ctx ml.Context, batch input.Batch, cache kvcache.Cac
 		hiddenState = layer.Forward(ctx, i, hiddenState, positions, lastLayerOutputs, cache, m.TextConfig)
 	}
 
-	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
-	return hiddenState
+	return m.OutputNorm.Forward(ctx, hiddenState, m.eps)
 }

parser/parser.go

@@ -300,18 +300,13 @@ func filesForModel(path string) ([]string, error) {
 	}
 	files = append(files, js...)
 
-	// only include tokenizer.model is tokenizer.json is not present
-	if !slices.ContainsFunc(files, func(s string) bool {
-		return slices.Contains(strings.Split(s, string(os.PathSeparator)), "tokenizer.json")
-	}) {
-		if tks, _ := glob(filepath.Join(path, "tokenizer.model"), "application/octet-stream"); len(tks) > 0 {
-			// add tokenizer.model if it exists, tokenizer.json is automatically picked up by the previous glob
-			// tokenizer.model might be a unresolved git lfs reference; error if it is
-			files = append(files, tks...)
-		} else if tks, _ := glob(filepath.Join(path, "**/tokenizer.model"), "text/plain"); len(tks) > 0 {
-			// some times tokenizer.model is in a subdirectory (e.g. meta-llama/Meta-Llama-3-8B)
-			files = append(files, tks...)
-		}
+	// add tokenizer.model if it exists (tokenizer.json is automatically picked up by the previous glob)
+	// tokenizer.model might be a unresolved git lfs reference; error if it is
+	if tks, _ := glob(filepath.Join(path, "tokenizer.model"), "application/octet-stream"); len(tks) > 0 {
+		files = append(files, tks...)
+	} else if tks, _ := glob(filepath.Join(path, "**/tokenizer.model"), "text/plain"); len(tks) > 0 {
+		// some times tokenizer.model is in a subdirectory (e.g. meta-llama/Meta-Llama-3-8B)
+		files = append(files, tks...)
 	}
 
 	return files, nil

parser/parser_test.go

@@ -888,6 +888,37 @@ func TestFilesForModel(t *testing.T) {
 				"tokenizer.json",
 			},
 		},
+		{
+			name: "safetensors with both tokenizer.json and tokenizer.model",
+			setup: func(dir string) error {
+				// Create binary content for tokenizer.model (application/octet-stream)
+				binaryContent := make([]byte, 512)
+				for i := range binaryContent {
+					binaryContent[i] = byte(i % 256)
+				}
+				files := []string{
+					"model-00001-of-00001.safetensors",
+					"config.json",
+					"tokenizer.json",
+				}
+				for _, file := range files {
+					if err := os.WriteFile(filepath.Join(dir, file), []byte("test content"), 0o644); err != nil {
+						return err
+					}
+				}
+				// Write tokenizer.model as binary
+				if err := os.WriteFile(filepath.Join(dir, "tokenizer.model"), binaryContent, 0o644); err != nil {
+					return err
+				}
+				return nil
+			},
+			wantFiles: []string{
+				"model-00001-of-00001.safetensors",
+				"config.json",
+				"tokenizer.json",
+				"tokenizer.model",
+			},
+		},
 		{
 			name: "safetensors with consolidated files - prefers model files",
 			setup: func(dir string) error {