convert gemma2

convert/convert.go

@@ -7,6 +7,7 @@ import (
 	"io"
 	"io/fs"
 	"log/slog"
+	"strings"
 
 	"github.com/ollama/ollama/llm"
 )
@@ -58,11 +59,13 @@ type Converter interface {
 	KV(*Tokenizer) llm.KV
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
 	Tensors([]Tensor) []llm.Tensor
+	// Replacements returns a list of string pairs to replace in tensor names.
+	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
+	Replacements() []string
 
-	// tensorName returns the LLM tensor name for a specific input name
-	tensorName(string) string
 	// specialTokenTypes returns any special token types the model uses
 	specialTokenTypes() []string
+	// writeFile writes the model to the provided io.WriteSeeker
 	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
 }
 
@@ -97,6 +100,8 @@ func Convert(fsys fs.FS, ws io.WriteSeeker) error {
 		conv = &mixtral{}
 	case "GemmaForCausalLM":
 		conv = &gemma{}
+	case "Gemma2ForCausalLM":
+		conv = &gemma2{}
 	case "Phi3ForCausalLM":
 		conv = &phi3{}
 	case "BertModel":
@@ -131,7 +136,7 @@ func Convert(fsys fs.FS, ws io.WriteSeeker) error {
 		slog.Debug("vocabulary", "size", len(t.Vocabulary.Tokens))
 	}
 
-	ts, err := parseTensors(fsys)
+	ts, err := parseTensors(fsys, strings.NewReplacer(conv.Replacements()...))
 	if err != nil {
 		return err
 	}

convert/convert_bert.go

@@ -144,9 +144,8 @@ func (p *bert) Tensors(ts []Tensor) []llm.Tensor {
 			continue
 		}
 
-		name := p.tensorName(t.Name())
 		out = append(out, llm.Tensor{
-			Name:     name,
+			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
 			WriterTo: t,
@@ -156,8 +155,8 @@ func (p *bert) Tensors(ts []Tensor) []llm.Tensor {
 	return out
 }
 
-func (bert) tensorName(n string) string {
-	return strings.NewReplacer(
+func (bert) Replacements() []string {
+	return []string{
 		"encoder.layer", "blk",
 		"encoder.layers", "blk",
 		"embeddings.word_embeddings", "token_embd",
@@ -172,5 +171,5 @@ func (bert) tensorName(n string) string {
 		"intermediate.dense", "ffn_up",
 		"output.dense", "ffn_down",
 		"output.LayerNorm", "layer_output_norm",
-	).Replace(n)
+	}
 }

convert/convert_gemma.go

@@ -44,15 +44,14 @@ func (p *gemma) KV(t *Tokenizer) llm.KV {
 }
 
 func (p *gemma) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+	out := make([]llm.Tensor, 0, len(ts))
 	for _, t := range ts {
-		name := p.tensorName(t.Name())
-		if strings.HasSuffix(name, "_norm.weight") {
+		if strings.HasSuffix(t.Name(), "_norm.weight") {
 			t.SetRepacker(p.addOne)
 		}
 
 		out = append(out, llm.Tensor{
-			Name:     name,
+			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
 			WriterTo: t,
@@ -62,8 +61,8 @@ func (p *gemma) Tensors(ts []Tensor) []llm.Tensor {
 	return out
 }
 
-func (p *gemma) tensorName(n string) string {
-	return strings.NewReplacer(
+func (p *gemma) Replacements() []string {
+	return []string{
 		"model.embed_tokens", "token_embd",
 		"model.norm", "output_norm",
 		"model.layers", "blk",
@@ -76,8 +75,7 @@ func (p *gemma) tensorName(n string) string {
 		"mlp.down_proj", "ffn_down",
 		"mlp.up_proj", "ffn_up",
 		"post_attention_layernorm", "ffn_norm",
-		"block_sparse_moe.gate", "ffn_inp",
-	).Replace(n)
+	}
 }
 
 func (*gemma) addOne(_ string, data []float32, shape []uint64) ([]float32, error) {

convert/convert_gemma2.go

@@ -0,0 +1,44 @@
+package convert
+
+import (
+	"github.com/ollama/ollama/llm"
+)
+
+type gemma2 struct {
+	gemma
+	SlidingWindow         uint32  `json:"sliding_window"`
+	AttentionLogitSoftcap float32 `json:"attn_logit_softcapping"`
+	FinalLogitSoftcap     float32 `json:"final_logit_softcapping"`
+}
+
+func (p *gemma2) KV(t *Tokenizer) llm.KV {
+	kv := p.Parameters.KV(t)
+	kv["general.architecture"] = "gemma2"
+	kv["general.name"] = "gemma2"
+	kv["gemma2.context_length"] = p.MaxPositionEmbeddings
+	kv["gemma2.embedding_length"] = p.HiddenSize
+	kv["gemma2.block_count"] = p.HiddenLayers
+	kv["gemma2.feed_forward_length"] = p.IntermediateSize
+	kv["gemma2.attention.head_count"] = p.NumAttentionHeads
+	kv["gemma2.attention.head_count_kv"] = p.NumKeyValueHeads
+	kv["gemma2.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	kv["gemma2.attention.key_length"] = p.HeadDim
+	kv["gemma2.attention.value_length"] = p.HeadDim
+	kv["gemma2.attention.sliding_window"] = p.SlidingWindow
+	kv["gemma2.attn_logit_softcapping"] = p.AttentionLogitSoftcap
+	kv["gemma2.final_logit_softcapping"] = p.FinalLogitSoftcap
+	kv["tokenizer.ggml.eot_token_id"] = uint32(107)
+	kv["tokenizer.ggml.middle_token_id"] = uint32(68)
+	kv["tokenizer.ggml.prefix_token_id"] = uint32(67)
+	kv["tokenizer.ggml.suffix_token_id"] = uint32(69)
+	return kv
+}
+
+func (p *gemma2) Replacements() []string {
+	return append(
+		p.gemma.Replacements(),
+		"post_attention_layernorm", "post_attention_norm",
+		"pre_feedforward_layernorm", "ffn_norm",
+		"post_feedforward_layernorm", "post_ffw_norm",
+	)
+}

convert/convert_llama.go

@@ -96,14 +96,13 @@ func (p *llama) KV(t *Tokenizer) llm.KV {
 func (p *llama) Tensors(ts []Tensor) []llm.Tensor {
 	var out []llm.Tensor
 	for _, t := range ts {
-		name := p.tensorName(t.Name())
-		if strings.HasSuffix(name, "attn_q.weight") ||
-			strings.HasSuffix(name, "attn_k.weight") {
+		if strings.HasSuffix(t.Name(), "attn_q.weight") ||
+			strings.HasSuffix(t.Name(), "attn_k.weight") {
 			t.SetRepacker(p.repack)
 		}
 
 		out = append(out, llm.Tensor{
-			Name:     name,
+			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
 			WriterTo: t,
@@ -113,8 +112,8 @@ func (p *llama) Tensors(ts []Tensor) []llm.Tensor {
 	return out
 }
 
-func (p *llama) tensorName(n string) string {
-	return strings.NewReplacer(
+func (p *llama) Replacements() []string {
+	return []string{
 		"lm_head", "output",
 		"model.embed_tokens", "token_embd",
 		"model.norm", "output_norm",
@@ -128,9 +127,7 @@ func (p *llama) tensorName(n string) string {
 		"mlp.down_proj", "ffn_down",
 		"mlp.up_proj", "ffn_up",
 		"post_attention_layernorm", "ffn_norm",
-		// mixtral
-		"block_sparse_moe.gate", "ffn_gate_inp",
-	).Replace(n)
+	}
 }
 
 func (p *llama) repack(name string, data []float32, shape []uint64) ([]float32, error) {
@@ -140,9 +137,9 @@ func (p *llama) repack(name string, data []float32, shape []uint64) ([]float32,
 	}
 
 	var heads uint32
-	if strings.HasSuffix(name, "q_proj.weight") {
+	if strings.HasSuffix(name, "attn_q.weight") {
 		heads = p.NumAttentionHeads
-	} else if strings.HasSuffix(name, "k_proj.weight") {
+	} else if strings.HasSuffix(name, "attn_k.weight") {
 		heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
 	} else {
 		return nil, fmt.Errorf("unknown tensor for repack: %s", name)

convert/convert_mixtral.go

@@ -15,8 +15,6 @@ type mixtral struct {
 	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
 }
 
-var _ Converter = (*mixtral)(nil)
-
 func (p *mixtral) KV(t *Tokenizer) llm.KV {
 	kv := p.llama.KV(t)
 
@@ -72,6 +70,13 @@ func (p *mixtral) Tensors(ts []Tensor) []llm.Tensor {
 	return append(out, p.llama.Tensors(ts)...)
 }
 
+func (p *mixtral) Replacements() []string {
+	return append(
+		p.llama.Replacements(),
+		"block_sparse_moe.gate", "ffn_gate_inp",
+	)
+}
+
 type experts []Tensor
 
 func (e experts) WriteTo(w io.Writer) (int64, error) {

convert/convert_phi3.go

@@ -74,8 +74,7 @@ func (p *phi3) Tensors(ts []Tensor) []llm.Tensor {
 
 	out := make([]llm.Tensor, 0, len(ts)+2)
 	for _, t := range ts {
-		name := p.tensorName(t.Name())
-		if strings.HasPrefix(name, "blk.0.") {
+		if strings.HasPrefix(t.Name(), "blk.0.") {
 			addRopeFactors.Do(func() {
 				out = append(out, llm.Tensor{
 					Name:     "rope_factors_long.weight",
@@ -92,7 +91,7 @@ func (p *phi3) Tensors(ts []Tensor) []llm.Tensor {
 		}
 
 		out = append(out, llm.Tensor{
-			Name:     name,
+			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
 			WriterTo: t,
@@ -102,8 +101,8 @@ func (p *phi3) Tensors(ts []Tensor) []llm.Tensor {
 	return out
 }
 
-func (p *phi3) tensorName(n string) string {
-	return strings.NewReplacer(
+func (p *phi3) Replacements() []string {
+	return []string{
 		"lm_head", "output",
 		"model.embed_tokens", "token_embd",
 		"model.norm", "output_norm",
@@ -114,7 +113,7 @@ func (p *phi3) tensorName(n string) string {
 		"mlp.down_proj", "ffn_down",
 		"mlp.gate_up_proj", "ffn_up",
 		"post_attention_layernorm", "ffn_norm",
-	).Replace(n)
+	}
 }
 
 type ropeFactor []float32

convert/convert_test.go

@@ -68,6 +68,7 @@ func TestConvertFull(t *testing.T) {
 		// microsoft/Phi-3-mini-128-instruct@d548c233192db00165d842bf8edff054bb3212f8
 		"Phi-3-mini-128k-instruct",
 		"all-MiniLM-L6-v2",
+		"gemma-2-9b-it",
 	}
 
 	for i := range cases {

convert/reader.go

@@ -35,9 +35,9 @@ const (
 )
 
 func (t tensorBase) Kind() uint32 {
-	if strings.HasSuffix(t.name, ".block_sparse_moe.gate.weight") {
-		return 0
-	} else if t.name == "embeddings.token_type_embeddings.weight" {
+	if strings.HasSuffix(t.name, ".ffn_gate_inp.weight") ||
+		t.name == "token_types.weight" {
+		// these tensors are always F32
 		return 0
 	}
 
@@ -57,10 +57,10 @@ func (t *tensorBase) SetRepacker(fn repacker) {
 
 type repacker func(string, []float32, []uint64) ([]float32, error)
 
-func parseTensors(fsys fs.FS) ([]Tensor, error) {
+func parseTensors(fsys fs.FS, replacer *strings.Replacer) ([]Tensor, error) {
 	patterns := []struct {
 		Pattern string
-		Func    func(fs.FS, ...string) ([]Tensor, error)
+		Func    func(fs.FS, *strings.Replacer, ...string) ([]Tensor, error)
 	}{
 		{"model-*-of-*.safetensors", parseSafetensors},
 		{"model.safetensors", parseSafetensors},
@@ -76,7 +76,7 @@ func parseTensors(fsys fs.FS) ([]Tensor, error) {
 		}
 
 		if len(matches) > 0 {
-			return pattern.Func(fsys, matches...)
+			return pattern.Func(fsys, replacer, matches...)
 		}
 	}
 

convert/reader_safetensors.go

@@ -8,6 +8,7 @@ import (
 	"io"
 	"io/fs"
 	"slices"
+	"strings"
 
 	"github.com/d4l3k/go-bfloat16"
 	"github.com/x448/float16"
@@ -20,7 +21,7 @@ type safetensorMetadata struct {
 	Offsets []int64  `json:"data_offsets"`
 }
 
-func parseSafetensors(fsys fs.FS, ps ...string) ([]Tensor, error) {
+func parseSafetensors(fsys fs.FS, replacer *strings.Replacer, ps ...string) ([]Tensor, error) {
 	var ts []Tensor
 	for _, p := range ps {
 		f, err := fsys.Open(p)
@@ -56,7 +57,7 @@ func parseSafetensors(fsys fs.FS, ps ...string) ([]Tensor, error) {
 					offset: safetensorsPad(n, value.Offsets[0]),
 					size:   safetensorsPad(n, value.Offsets[1]) - safetensorsPad(n, value.Offsets[0]),
 					tensorBase: &tensorBase{
-						name:  key,
+						name:  replacer.Replace(key),
 						shape: value.Shape,
 					},
 				})

convert/reader_torch.go

@@ -3,12 +3,13 @@ package convert
 import (
 	"io"
 	"io/fs"
+	"strings"
 
 	"github.com/nlpodyssey/gopickle/pytorch"
 	"github.com/nlpodyssey/gopickle/types"
 )
 
-func parseTorch(fsys fs.FS, ps ...string) ([]Tensor, error) {
+func parseTorch(fsys fs.FS, replacer *strings.Replacer, ps ...string) ([]Tensor, error) {
 	var ts []Tensor
 	for _, p := range ps {
 		pt, err := pytorch.Load(p)
@@ -27,7 +28,7 @@ func parseTorch(fsys fs.FS, ps ...string) ([]Tensor, error) {
 			ts = append(ts, torch{
 				storage: t.(*pytorch.Tensor).Source,
 				tensorBase: &tensorBase{
-					name:  k.(string),
+					name:  replacer.Replace(k.(string)),
 					shape: shape,
 				},
 			})

convert/testdata/gemma-2-9b-it.json

@@ -0,0 +1,6 @@
+{
+  "general.architecture": "gemma2",
+  "gemma2.attention.sliding_window": "4096",
+  "gemma2.attn_logit_softcapping": "50",
+  "gemma2.final_logit_softcapping": "30"
+}

convert/tokenizer_spm.go

@@ -15,6 +15,11 @@ import (
 )
 
 func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
+	ast, err := parseAdditionalSpecialTokens(fsys)
+	if err != nil {
+		return nil, err
+	}
+
 	bts, err := fs.ReadFile(fsys, "tokenizer.model")
 	if err != nil {
 		return nil, err
@@ -37,7 +42,12 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 			sentencepiece.ModelProto_SentencePiece_BYTE:
 			v.Types = append(v.Types, int32(t))
 		default:
-			v.Types = append(v.Types, int32(sentencepiece.ModelProto_SentencePiece_NORMAL))
+			tt := int32(sentencepiece.ModelProto_SentencePiece_NORMAL)
+			if slices.Contains(ast, piece.GetPiece()) {
+				tt = int32(sentencepiece.ModelProto_SentencePiece_CONTROL)
+			}
+
+			v.Types = append(v.Types, tt)
 		}
 	}
 
@@ -81,3 +91,23 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 
 	return &v, nil
 }
+
+func parseAdditionalSpecialTokens(fsys fs.FS) ([]string, error) {
+	f, err := fsys.Open("special_tokens_map.json")
+	if errors.Is(err, os.ErrNotExist) {
+		return nil, nil
+	} else if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	var m struct {
+		AdditionalSpecialTokens []string `json:"additional_special_tokens"`
+	}
+
+	if err := json.NewDecoder(f).Decode(&m); err != nil {
+		return nil, err
+	}
+
+	return m.AdditionalSpecialTokens, nil
+}