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https://github.com/ollama/ollama.git
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fix conversion
This commit is contained in:
Patrick Devine
Michael Yang
parent
0df1800436
commit
c62861f4fa
@ -190,8 +190,8 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
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conv = &gemmaModel{}
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case "Gemma2ForCausalLM":
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conv = &gemma2Model{}
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case "Gemma3ForConditionalGeneration":
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conv = &gemma3Model{}
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case "Gemma3ForCausalLM", "Gemma3ForConditionalGeneration":
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conv = &gemma3Model{Architecture: p.Architectures[0]}
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case "Phi3ForCausalLM":
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conv = &phi3Model{}
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case "Qwen2ForCausalLM":
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@ -226,6 +226,9 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
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}
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switch {
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case vocabSize == 0:
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slog.Warn("vocabulary size was not explicitly set by the model", "default size", len(t.Vocabulary.Tokens))
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vocabSize = len(t.Vocabulary.Tokens)
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case vocabSize > len(t.Vocabulary.Tokens):
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slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", vocabSize, "actual", len(t.Vocabulary.Tokens))
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for i := range vocabSize - len(t.Vocabulary.Tokens) {
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@ -4,7 +4,13 @@ import "github.com/ollama/ollama/fs/ggml"
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type gemma3Model struct {
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gemmaModel
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TextModel gemma3TextModel `json:"text_config"`
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Architecture string
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TextModel struct {
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HiddenSize uint32 `json:"hidden_size"`
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HiddenLayers uint32 `json:"num_hidden_layers"`
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IntermediateSize uint32 `json:"intermediate_size"`
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SlidingWindow uint32 `json:"sliding_window"`
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} `json:"text_config"`
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VisionModel struct {
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NumAttentionHeads uint32 `json:"num_attention_heads"` // attention.head_count 16
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LayerNormEpsilon float32 `json:"layer_norm_eps"` // attention.layer_norm_epsilon 1e-05
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@ -15,49 +21,54 @@ type gemma3Model struct {
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NumChannels uint32 `json:"num_channels"` // num_channels 3
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PatchSize uint32 `json:"patch_size"` // patch_size 14
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} `json:"vision_config"`
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}
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type gemma3TextModel struct {
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MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
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HiddenSize uint32 `json:"hidden_size"`
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HiddenLayers uint32 `json:"num_hidden_layers"`
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IntermediateSize uint32 `json:"intermediate_size"`
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NumAttentionHeads uint32 `json:"num_attention_heads"`
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NumKeyValueHeads uint32 `json:"num_key_value_heads"`
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RMSNormEPS float32 `json:"rms_norm_eps"`
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HeadDim uint32 `json:"head_dim"`
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SlidingWindow uint32 `json:"sliding_window"`
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AttentionLogitSoftcap float32 `json:"attn_logit_softcapping"`
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FinalLogitSoftcap float32 `json:"final_logit_softcapping"`
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RopeLocalTheta float32 `json:"rope_local_base_freq"`
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RopeGlobalTheta float32 `json:"rope_global_base_freq"`
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SlidingWindow uint32 `json:"sliding_window"`
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}
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func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
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kv := p.ModelParameters.KV(t)
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kv["general.architecture"] = "gemma3"
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kv["gemma3.context_length"] = p.TextModel.MaxPositionEmbeddings
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kv["gemma3.embedding_length"] = p.TextModel.HiddenSize
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kv["gemma3.block_count"] = p.TextModel.HiddenLayers
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kv["gemma3.text.feed_forward_length"] = p.TextModel.IntermediateSize
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kv["gemma3.attention.head_count"] = p.TextModel.NumAttentionHeads
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kv["gemma3.attention.head_count_kv"] = p.TextModel.NumKeyValueHeads
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kv["gemma3.text.attention.layer_norm_rms_epsilon"] = p.TextModel.RMSNormEPS
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kv["gemma3.attention.key_length"] = p.TextModel.HeadDim
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kv["gemma3.attention.value_length"] = p.TextModel.HeadDim
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kv["gemma3.text.attention.sliding_window"] = p.TextModel.SlidingWindow
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kv["gemma3.text.final_logit_softcapping"] = p.TextModel.FinalLogitSoftcap
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kv["gemma3.text.rope.local.freq_base"] = p.TextModel.RopeLocalTheta
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kv["gemma3.text.rope.global.freq_base"] = p.TextModel.RopeGlobalTheta
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kv["gemma3.vision.block_count"] = p.VisionModel.NumHiddenLayers
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kv["gemma3.vision.embedding_length"] = p.VisionModel.HiddenSize
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kv["gemma3.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
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kv["gemma3.vision.image_size"] = p.VisionModel.ImageSize
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kv["gemma3.vision.patch_size"] = p.VisionModel.PatchSize
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kv["gemma3.vision.num_channels"] = p.VisionModel.NumChannels
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kv["gemma3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
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kv["gemma3.vision.attention.layer_norm_epsilon"] = p.VisionModel.LayerNormEpsilon
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switch p.Architecture {
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case "Gemma3ForCausalLM":
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kv["gemma3.context_length"] = p.MaxPositionEmbeddings
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kv["gemma3.attention.head_count"] = p.NumAttentionHeads
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kv["gemma3.attention.head_count_kv"] = p.NumKeyValueHeads
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kv["gemma3.text.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
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kv["gemma3.attention.key_length"] = p.HeadDim
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kv["gemma3.attention.value_length"] = p.HeadDim
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kv["gemma3.text.attention.sliding_window"] = p.SlidingWindow
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kv["gemma3.text.final_logit_softcapping"] = p.FinalLogitSoftcap
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kv["gemma3.text.rope.local.freq_base"] = p.RopeLocalTheta
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kv["gemma3.text.rope.global.freq_base"] = p.RopeGlobalTheta
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kv["gemma3.embedding_length"] = p.HiddenSize
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kv["gemma3.block_count"] = p.HiddenLayers
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kv["gemma3.text.feed_forward_length"] = p.IntermediateSize
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default:
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kv["gemma3.embedding_length"] = p.TextModel.HiddenSize
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kv["gemma3.block_count"] = p.TextModel.HiddenLayers
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kv["gemma3.text.feed_forward_length"] = p.TextModel.IntermediateSize
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kv["gemma3.text.attention.sliding_window"] = p.TextModel.SlidingWindow
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kv["gemma3.vision.block_count"] = p.VisionModel.NumHiddenLayers
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kv["gemma3.vision.embedding_length"] = p.VisionModel.HiddenSize
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kv["gemma3.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
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kv["gemma3.vision.image_size"] = p.VisionModel.ImageSize
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kv["gemma3.vision.patch_size"] = p.VisionModel.PatchSize
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kv["gemma3.vision.num_channels"] = p.VisionModel.NumChannels
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kv["gemma3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
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kv["gemma3.vision.attention.layer_norm_epsilon"] = p.VisionModel.LayerNormEpsilon
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}
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kv["tokenizer.ggml.bos_token_id"] = uint32(2)
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kv["tokenizer.ggml.eot_token_id"] = uint32(1)
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return kv
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}
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@ -32,7 +32,8 @@ type TextModel struct {
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}
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const (
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gemma27BLayerCount = 46
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gemmaGlobalCacheCount = 6
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gemma27BLayerCount = 46
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)
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const (
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@ -55,15 +56,15 @@ func newTextModel(c ml.Config) *TextModel {
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Layers: make([]TextLayer, c.Uint("block_count")),
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TextOptions: &TextOptions{
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hiddenSize: int(c.Uint("embedding_length")),
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numHeads: int(c.Uint("attention.head_count")),
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numKVHeads: int(c.Uint("attention.head_count_kv")),
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attnKeyLen: int(c.Uint("attention.key_length")),
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attnValLen: int(c.Uint("attention.value_length")),
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eps: c.Float("text.attention.layer_norm_rms_epsilon"),
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numHeads: int(c.Uint("attention.head_count", 8)),
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numKVHeads: int(c.Uint("attention.head_count_kv", 4)),
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attnKeyLen: int(c.Uint("attention.key_length", 256)),
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attnValLen: int(c.Uint("attention.value_length", 256)),
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eps: c.Float("text.attention.layer_norm_rms_epsilon", 1e-06),
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ropeLocalBase: c.Float("text.rope.local.freq_base", 10000.0),
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ropeGlobalBase: c.Float("text.rope.global.freq_base", 1000000.0),
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ropeScale: c.Float("text.rope.freq_scale", 1.0),
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finalLogitSoftcap: c.Float("text.final_logit_softcapping"),
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finalLogitSoftcap: c.Float("text.final_logit_softcapping", 30.0),
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},
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}
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@ -84,7 +85,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, pos
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ropeType := uint32(2)
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ropeBase := opts.ropeLocalBase
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if (layer+1)%6 == 0 {
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if (layer+1)%gemmaGlobalCacheCount == 0 {
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ropeBase = opts.ropeGlobalBase
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}
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@ -116,7 +117,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, pos
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func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
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ropeBase := m.TextOptions.ropeLocalBase
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if (layer+1)%6 == 0 {
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if (layer+1)%gemmaGlobalCacheCount == 0 {
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ropeBase = m.TextOptions.ropeGlobalBase
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}
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@ -184,7 +185,7 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
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// gemma alternates between the sliding window (local) and causal (global)
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// kv cache every 6 layers
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cacheType := cacheTypeSWA
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if (i+1)%6 == 0 {
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if (i+1)%gemmaGlobalCacheCount == 0 {
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cacheType = cacheTypeCausal
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}
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cache.SetLayer(i)
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