mirror of https://github.com/ollama/ollama
183 lines
6.9 KiB
Go
183 lines
6.9 KiB
Go
package convert
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import (
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"cmp"
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"slices"
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"github.com/ollama/ollama/fs/ggml"
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)
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type gemma3Model struct {
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gemmaModel
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Architecture string
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TextModel struct {
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HeadDim uint32 `json:"head_dim"`
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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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NumHiddenLayers uint32 `json:"num_hidden_layers"` // block_count 32
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HiddenSize uint32 `json:"hidden_size"` // embedding_length 1280
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IntermediateSize uint32 `json:"intermediate_size"` // feed_forward_length 5120
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ImageSize uint32 `json:"image_size"` // image_size 560
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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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MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
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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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FinalLogitSoftcap float32 `json:"final_logit_softcapping"`
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RopeLocalTheta float32 `json:"rope_local_base_freq"`
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RopeTheta float32 `json:"rope_theta"`
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SlidingWindow uint32 `json:"sliding_window"`
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SlidingWindowPattern *uint32 `json:"sliding_window_pattern"`
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LayerTypes []string `json:"layer_types"`
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MultiModalTokensPerImage uint32 `json:"mm_tokens_per_image"`
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RopeScaling *struct {
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Type string `json:"rope_type"`
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Factor float32 `json:"factor"`
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OriginalMaxPositionEmbeddings uint32 `json:"original_max_position_embeddings"`
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ExtrapolationFactor float32 `json:"extrapolation_factor"`
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BetaFast float32 `json:"beta_fast"`
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BetaSlow float32 `json:"beta_slow"`
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} `json:"rope_scaling"`
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}
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const (
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gemma4BLayerCount = 34
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gemma12BLayerCount = 48
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gemma27BLayerCount = 62
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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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numBlocks := cmp.Or(p.HiddenLayers, p.TextModel.HiddenLayers)
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kv["gemma3.block_count"] = numBlocks
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var (
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numHeads uint32
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numKVHeads uint32
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)
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switch numBlocks {
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case gemma4BLayerCount:
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numHeads = 8
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numKVHeads = 4
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case gemma12BLayerCount:
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numHeads = 16
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numKVHeads = 8
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case gemma27BLayerCount:
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numHeads = 32
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numKVHeads = 16
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default:
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numHeads = p.NumAttentionHeads
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numKVHeads = p.NumKeyValueHeads
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}
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kv["gemma3.attention.head_count"] = numHeads
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kv["gemma3.attention.head_count_kv"] = numKVHeads
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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.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.attention.sliding_window"] = p.SlidingWindow
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// The sliding window pattern is either provided as the sliding_window_pattern
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// key (an int) or as the layer_types key (a list of strings).
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if p.SlidingWindowPattern != nil || len(p.LayerTypes) > 0 {
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kv["gemma3.attention.sliding_window_pattern"] = slices.Collect(func(yield func(bool) bool) {
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for i := range numBlocks {
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var isLocal bool
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if len(p.LayerTypes) > 0 && int(i) < len(p.LayerTypes) {
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isLocal = p.LayerTypes[i] == "sliding_attention"
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} else if p.SlidingWindowPattern != nil && *p.SlidingWindowPattern > 0 {
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isLocal = (i+1)%*p.SlidingWindowPattern != 0
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}
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if !yield(isLocal) {
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break
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}
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}
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})
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}
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if p.FinalLogitSoftcap > 0 {
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kv["gemma3.final_logit_softcapping"] = p.FinalLogitSoftcap
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}
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kv["gemma3.rope.local.freq_base"] = cmp.Or(p.RopeLocalTheta, 10000.0)
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kv["gemma3.rope.freq_base"] = cmp.Or(p.RopeTheta, 1000000.0)
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if p.RopeScaling != nil && p.RopeScaling.Type == "yarn" && p.RopeScaling.Factor > 0 {
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kv["gemma3.rope.scaling.type"] = "yarn"
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kv["gemma3.rope.scaling.factor"] = p.RopeScaling.Factor
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kv["gemma3.rope.scaling.original_context_length"] = p.RopeScaling.OriginalMaxPositionEmbeddings
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kv["gemma3.rope.scaling.extrapolation_factor"] = cmp.Or(p.RopeScaling.ExtrapolationFactor, float32(1.0))
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kv["gemma3.rope.scaling.beta_fast"] = cmp.Or(p.RopeScaling.BetaFast, float32(64.0))
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kv["gemma3.rope.scaling.beta_slow"] = cmp.Or(p.RopeScaling.BetaSlow, float32(1.0))
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}
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kv["gemma3.embedding_length"] = p.HiddenSize
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kv["gemma3.feed_forward_length"] = p.IntermediateSize
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default:
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kv["gemma3.context_length"] = cmp.Or(p.MaxPositionEmbeddings, 131072)
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kv["gemma3.embedding_length"] = p.TextModel.HiddenSize
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kv["gemma3.feed_forward_length"] = p.TextModel.IntermediateSize
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kv["gemma3.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"] = cmp.Or(p.VisionModel.NumChannels, 3)
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kv["gemma3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
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kv["gemma3.vision.attention.layer_norm_epsilon"] = cmp.Or(p.VisionModel.LayerNormEpsilon, 1e-6)
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kv["gemma3.attention.key_length"] = cmp.Or(p.TextModel.HeadDim, 256)
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kv["gemma3.attention.value_length"] = cmp.Or(p.TextModel.HeadDim, 256)
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}
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if p.MultiModalTokensPerImage > 0 {
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kv["gemma3.mm.tokens_per_image"] = p.MultiModalTokensPerImage
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}
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return kv
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}
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func (p *gemma3Model) Replacements() []string {
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return []string{
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"lm_head", "output",
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"model.embed_tokens", "token_embd",
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"model.norm", "output_norm",
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"vision_tower.vision_model.embeddings", "v",
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"vision_tower.vision_model", "v",
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"vision_model.vision_model.embeddings", "v",
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"vision_model.vision_model", "v",
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"language_model.", "",
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"model.layers", "blk",
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"encoder.layers", "blk",
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"input_layernorm", "attn_norm",
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"self_attn.q_proj", "attn_q",
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"self_attn.q_norm", "attn_q_norm",
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"self_attn.k_proj", "attn_k",
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"self_attn.k_norm", "attn_k_norm",
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"self_attn.v_proj", "attn_v",
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"self_attn.o_proj", "attn_output",
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"self_attn.out_proj", "attn_output",
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"mlp.gate_proj", "ffn_gate",
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"mlp.down_proj", "ffn_down",
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"mlp.up_proj", "ffn_up",
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"post_attention_layernorm", "post_attention_norm",
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"pre_feedforward_layernorm", "ffn_norm",
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"post_feedforward_layernorm", "post_ffw_norm",
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"input_projection_weight", "input_projection.weight",
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"multi_modal_projector", "mm",
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}
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}
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