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fix token type

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This commit is contained in:
Michael Yang 2025-04-23 12:40:05 -07:00 • committed by Michael Yang
parent 8d376acc9b
commit d26c18e25c
13 changed files with 36 additions and 25 deletions
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2
fs/config.go
View file

@ -8,6 +8,6 @@ type Config interface {
Bool(string, ...bool) bool
Strings(string, ...[]string) []string
Uints(string, ...[]uint32) []uint32
Ints(string, ...[]int32) []int32
Floats(string, ...[]float32) []float32
}

21
fs/ggml/ggml.go
View file

@ -108,6 +108,10 @@ func (kv KV) Strings(key string, defaultValue ...[]string) []string {
return keyValue(kv, key, &array[string]{}).values
}
func (kv KV) Ints(key string, defaultValue ...[]int32) []int32 {
return keyValue(kv, key, &array[int32]{}).values
}
func (kv KV) Uints(key string, defaultValue ...[]uint32) []uint32 {
return keyValue(kv, key, &array[uint32]{}).values
}
@ -124,11 +128,18 @@ func (kv KV) OllamaEngineRequired() bool {
}
type valueTypes interface {
string | uint32 | uint64 | float32 | bool |
*array[string] | *array[uint32] | *array[uint64] | *array[float32] | *array[bool]
uint8 | int8 | uint16 | int16 |
uint32 | int32 | uint64 | int64 |
string | float32 | float64 | bool
}
func keyValue[T valueTypes](kv KV, key string, defaultValue ...T) T {
type arrayValueTypes interface {
*array[uint8] | *array[int8] | *array[uint16] | *array[int16] |
*array[uint32] | *array[int32] | *array[uint64] | *array[int64] |
*array[string] | *array[float32] | *array[float64] | *array[bool]
}
func keyValue[T valueTypes | arrayValueTypes](kv KV, key string, defaultValue ...T) T {
if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
key = kv.Architecture() + "." + key
}
@ -450,9 +461,9 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
case "mllama":
var visionTokens, tiles uint64 = 1601, 4
crossAttentionLayers := f.KV().Uints("attention.cross_attention_layers")
crossAttentionLayers := f.KV().Ints("attention.cross_attention_layers")
for i := range kv {
if slices.Contains(crossAttentionLayers, uint32(i)) {
if slices.Contains(crossAttentionLayers, int32(i)) {
kv[i] = headsKV * (embeddingHeadsK + embeddingHeadsV) *
4 * // sizeof(float32)
visionTokens *

2
model/models/gemma2/model.go
View file

@ -42,7 +42,7 @@ func New(c fs.Config) (model.Model, error) {
&model.Vocabulary{
Values: c.Strings("tokenizer.ggml.tokens"),
Scores: c.Floats("tokenizer.ggml.scores"),
Types: c.Uints("tokenizer.ggml.token_type"),
Types: c.Ints("tokenizer.ggml.token_type"),
BOS: int32(c.Uint("tokenizer.ggml.bos_token_id")),
EOS: int32(c.Uint("tokenizer.ggml.eos_token_id")),
},

2
model/models/gemma3/model.go
View file

@ -59,7 +59,7 @@ func New(c fs.Config) (model.Model, error) {
&model.Vocabulary{
Values: c.Strings("tokenizer.ggml.tokens"),
Scores: c.Floats("tokenizer.ggml.scores"),
Types: c.Uints("tokenizer.ggml.token_type"),
Types: c.Ints("tokenizer.ggml.token_type"),
BOS: int32(c.Uint("tokenizer.ggml.bos_token_id")),
AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
EOS: int32(1),

2
model/models/gemma3/model_text.go
View file

@ -49,7 +49,7 @@ func newTextModel(c fs.Config) *TextModel {
&model.Vocabulary{
Values: c.Strings("tokenizer.ggml.tokens"),
Scores: c.Floats("tokenizer.ggml.scores"),
Types: c.Uints("tokenizer.ggml.token_type"),
Types: c.Ints("tokenizer.ggml.token_type"),
BOS: int32(c.Uint("tokenizer.ggml.bos_token_id")),
EOS: int32(c.Uint("tokenizer.ggml.eos_token_id")),
},

2
model/models/llama/model.go
View file

@ -41,7 +41,7 @@ func New(c fs.Config) (model.Model, error) {
c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
&model.Vocabulary{
Values: c.Strings("tokenizer.ggml.tokens"),
Types: c.Uints("tokenizer.ggml.token_type"),
Types: c.Ints("tokenizer.ggml.token_type"),
Merges: c.Strings("tokenizer.ggml.merges"),
BOS: int32(c.Uint("tokenizer.ggml.bos_token_id")),
AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),

2
model/models/mistral3/model_text.go
View file

@ -152,7 +152,7 @@ func NewTextModel(c fs.Config) (*TextModel, error) {
c.String("tokenizer.ggml.pretokenizer", `[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*|\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.Uints("tokenizer.ggml.token_type"),
Types: c.Ints("tokenizer.ggml.token_type"),
Merges: c.Strings("tokenizer.ggml.merges"),
BOS: int32(c.Uint("tokenizer.ggml.bos_token_id", 1)),
AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),

2
model/models/mllama/model.go
View file

@ -43,7 +43,7 @@ func New(c fs.Config) (model.Model, error) {
c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
&model.Vocabulary{
Values: c.Strings("tokenizer.ggml.tokens"),
Types: c.Uints("tokenizer.ggml.token_type"),
Types: c.Ints("tokenizer.ggml.token_type"),
Merges: c.Strings("tokenizer.ggml.merges"),
BOS: int32(c.Uint("tokenizer.ggml.bos_token_id")),
AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),

8
model/models/mllama/model_text.go
View file

@ -177,7 +177,7 @@ type TextDecoder struct {
func (d *TextDecoder) Forward(ctx ml.Context, hiddenState, positionIDs, outputs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
for i, layer := range d.Layers {
layerType := selfAttentionLayer
if slices.Contains(opts.crossAttentionLayers, uint32(i)) {
if slices.Contains(opts.crossAttentionLayers, int32(i)) {
layerType = crossAttentionLayer
}
@ -202,7 +202,7 @@ type TextModelOptions struct {
eps, ropeBase, ropeScale float32
ropeDim uint32
crossAttentionLayers []uint32
crossAttentionLayers []int32
}
type TextModel struct {
@ -225,7 +225,7 @@ func newTextModel(c fs.Config) *TextModel {
var decoderLayers []TextDecoderLayer
for i := range c.Uint("block_count") {
var textDecoderLayer TextDecoderLayer
if slices.Contains(c.Uints("attention.cross_attention_layers"), i) {
if slices.Contains(c.Ints("attention.cross_attention_layers"), int32(i)) {
textDecoderLayer = &TextCrossAttentionDecoderLayer{}
} else {
textDecoderLayer = &TextSelfAttentionDecoderLayer{}
@ -244,7 +244,7 @@ func newTextModel(c fs.Config) *TextModel {
ropeBase: c.Float("rope.freq_base"),
ropeScale: c.Float("rope.freq_scale", 1),
ropeDim: c.Uint("rope.dimension_count"),
crossAttentionLayers: c.Uints("attention.cross_attention_layers"),
crossAttentionLayers: c.Ints("attention.cross_attention_layers"),
},
}
}

8
model/models/mllama/model_vision.go
View file

@ -96,10 +96,10 @@ type VisionEncoder struct {
Layers []VisionEncoderLayer
}
func (e *VisionEncoder) Forward(ctx ml.Context, hiddenState ml.Tensor, intermediateLayersIndices []uint32, opts *VisionModelOptions) (ml.Tensor, []ml.Tensor) {
func (e *VisionEncoder) Forward(ctx ml.Context, hiddenState ml.Tensor, intermediateLayersIndices []int32, opts *VisionModelOptions) (ml.Tensor, []ml.Tensor) {
var intermediateHiddenStates []ml.Tensor
for i, layer := range e.Layers {
if slices.Contains(intermediateLayersIndices, uint32(i)) {
if slices.Contains(intermediateLayersIndices, int32(i)) {
intermediateHiddenStates = append(intermediateHiddenStates, hiddenState.Reshape(ctx, append([]int{1}, hiddenState.Shape()...)...))
}
@ -154,7 +154,7 @@ type VisionModelOptions struct {
imageSize, patchSize int
eps float32
intermediateLayersIndices []uint32
intermediateLayersIndices []int32
}
type VisionModel struct {
@ -229,7 +229,7 @@ func newVisionModel(c fs.Config) *VisionModel {
eps: c.Float("vision.attention.layer_norm_epsilon"),
intermediateLayersIndices: c.Uints("vision.intermediate_layers_indices"),
intermediateLayersIndices: c.Ints("vision.intermediate_layers_indices"),
},
}
}

2
model/process_text.go
View file

@ -37,7 +37,7 @@ type TextProcessor interface {
type Vocabulary struct {
Values []string
Types []uint32
Types []int32
Scores []float32
Merges []string

6
model/process_text_spm_test.go
View file

@ -35,9 +35,9 @@ func loadSentencePieceVocab(t *testing.T) SentencePieceModel {
sentencepiece.ModelProto_SentencePiece_CONTROL,
sentencepiece.ModelProto_SentencePiece_UNUSED,
sentencepiece.ModelProto_SentencePiece_BYTE:
v.Types = append(v.Types, uint32(t))
v.Types = append(v.Types, int32(t))
default:
tt := uint32(sentencepiece.ModelProto_SentencePiece_NORMAL)
tt := int32(sentencepiece.ModelProto_SentencePiece_NORMAL)
// todo parse the special tokens file
// - this will roundtrip correctly but the <start_of_turn> and
// <end_of_turn> tokens aren't processed
@ -124,7 +124,7 @@ func TestSentencePieceModelDecodeByteTokens(t *testing.T) {
"<0xC3>",
"<0xA3>",
},
Types: []uint32{
Types: []int32{
TOKEN_TYPE_NORMAL,
TOKEN_TYPE_BYTE,
TOKEN_TYPE_BYTE,

2
model/process_text_test.go
View file

@ -28,7 +28,7 @@ func llama(t testing.TB) BytePairEncoding {
t.Fatal(err)
}
types := make([]uint32, len(vocab))
types := make([]int32, len(vocab))
tokens := make([]string, len(vocab))
for token, id := range vocab {
tokens[id] = token