mirror of
https://github.com/ollama/ollama.git
synced 2025-04-16 07:31:35 +02:00
mistral3 arch
This commit is contained in:
Bruce MacDonald
jmorganca
parent
9a12fd1067
commit
3b4ad00a4b
@ -185,7 +185,7 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
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case "LlamaForCausalLM":
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conv = &llamaModel{}
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case "Mistral3ForConditionalGeneration":
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conv = &mistralModel{}
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conv = &mistral3Model{}
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case "MixtralForCausalLM":
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conv = &mixtralModel{}
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case "GemmaForCausalLM":
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@ -11,8 +11,11 @@ import (
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"github.com/ollama/ollama/fs/ggml"
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)
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type mistralModel struct {
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type mistral3Model struct {
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ModelParameters
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// ImageTokenIndex uint32 `json:"image_token_index"`
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// SpatialMergeSize uint32 `json:"spatial_merge_size"`
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// VisionFeatureLayer int32 `json:"vision_feature_layer"`
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TextModel struct {
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NumHiddenLayers uint32 `json:"num_hidden_layers"`
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MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
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@ -23,30 +26,62 @@ type mistralModel struct {
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RopeTheta float32 `json:"rope_theta"`
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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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HiddenAct string `json:"hidden_act"`
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VocabSize uint32 `json:"vocab_size"`
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} `json:"text_config"`
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// VisionModel struct {
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// NumAttentionHeads uint32 `json:"num_attention_heads"`
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// NumHiddenLayers uint32 `json:"num_hidden_layers"`
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// HiddenSize uint32 `json:"hidden_size"`
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// IntermediateSize uint32 `json:"intermediate_size"`
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// ImageSize uint32 `json:"image_size"`
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// NumChannels uint32 `json:"num_channels"`
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// PatchSize uint32 `json:"patch_size"`
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// HeadDim uint32 `json:"head_dim"`
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// HiddenAct string `json:"hidden_act"`
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// RopeTheta float32 `json:"rope_theta"`
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// } `json:"vision_config"`
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// MultiModalProjectorBias bool `json:"multimodal_projector_bias"`
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// ProjectorHiddenAct string `json:"projector_hidden_act"`
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}
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func (p *mistralModel) KV(t *Tokenizer) ggml.KV {
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func (p *mistral3Model) KV(t *Tokenizer) ggml.KV {
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kv := p.ModelParameters.KV(t)
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kv["general.architecture"] = "mistral"
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kv["mistral.vocab_size"] = p.VocabSize
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kv["general.architecture"] = "mistral3"
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kv["mistral3.vocab_size"] = p.TextModel.VocabSize
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kv["mistral.block_count"] = p.TextModel.NumHiddenLayers
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kv["mistral.context_length"] = p.TextModel.MaxPositionEmbeddings
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kv["mistral.embedding_length"] = p.TextModel.HiddenSize
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kv["mistral.feed_forward_length"] = p.TextModel.IntermediateSize
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kv["mistral.attention.head_count"] = p.TextModel.NumAttentionHeads
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kv["mistral.rope.dimension_count"] = p.TextModel.HiddenSize / p.TextModel.NumHiddenLayers
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kv["mistral.rope.freq_base"] = p.TextModel.RopeTheta
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kv["mistral.attention.head_count_kv"] = p.TextModel.NumKeyValueHeads
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kv["mistral.attention.layer_norm_rms_epsilon"] = p.TextModel.RMSNormEPS
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kv["mistral.attention.key_length"] = p.TextModel.HeadDim
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kv["mistral.attention.value_length"] = p.TextModel.HeadDim
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// Text configuration
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kv["mistral3.block_count"] = p.TextModel.NumHiddenLayers
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kv["mistral3.context_length"] = p.TextModel.MaxPositionEmbeddings
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kv["mistral3.embedding_length"] = p.TextModel.HiddenSize
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kv["mistral3.feed_forward_length"] = p.TextModel.IntermediateSize
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kv["mistral3.attention.head_count"] = p.TextModel.NumAttentionHeads
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kv["mistral3.attention.head_count_kv"] = p.TextModel.NumKeyValueHeads
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kv["mistral3.attention.layer_norm_rms_epsilon"] = p.TextModel.RMSNormEPS
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kv["mistral3.attention.key_length"] = p.TextModel.HeadDim
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kv["mistral3.attention.value_length"] = p.TextModel.HeadDim
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kv["mistral3.rope.dimension_count"] = p.TextModel.HiddenSize / p.TextModel.NumHiddenLayers
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kv["mistral3.rope.freq_base"] = p.TextModel.RopeTheta
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// Multimodal configuration
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// kv["mistral3.image_token_index"] = p.ImageTokenIndex
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// kv["mistral3.spatial_merge_size"] = p.SpatialMergeSize
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// if p.VisionFeatureLayer != 0 {
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// kv["mistral3.vision_feature_layer"] = p.VisionFeatureLayer
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// }
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// kv["mistral3.mm.projector_bias"] = p.MultiModalProjectorBias
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// if p.ProjectorHiddenAct != "" {
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// kv["mistral3.mm.projector_hidden_act"] = p.ProjectorHiddenAct
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// }
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return kv
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}
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func (p *mistralModel) Tensors(ts []Tensor) []ggml.Tensor {
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func (p *mistral3Model) Tensors(ts []Tensor) []ggml.Tensor {
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var out []ggml.Tensor
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for _, t := range ts {
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@ -55,10 +90,8 @@ func (p *mistralModel) Tensors(ts []Tensor) []ggml.Tensor {
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t.SetRepacker(p.repack)
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}
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if strings.HasPrefix(t.Name(), "patch_merger.") ||
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strings.HasPrefix(t.Name(), "pre_mm_projector_output_norm.") ||
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strings.HasPrefix(t.Name(), "vision_encoder.") ||
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strings.HasPrefix(t.Name(), "vision_language_adapter.") {
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// Skip certain vision model tensors that might need special handling
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if strings.HasPrefix(t.Name(), "patch_merger.") || strings.HasPrefix(t.Name(), "pre_mm_projector_output_norm.") {
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continue
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}
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@ -73,8 +106,9 @@ func (p *mistralModel) Tensors(ts []Tensor) []ggml.Tensor {
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return out
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}
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func (p *mistralModel) Replacements() []string {
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func (p *mistral3Model) Replacements() []string {
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return []string{
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// Text model replacements
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"model.layers", "blk",
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"input_layernorm", "attn_norm",
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"post_attention_layernorm", "ffn_norm",
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@ -121,14 +155,21 @@ func (p *mistralModel) Replacements() []string {
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"vision_tower.transformer.layers.*.ffn_norm", "v.ffn_norm",
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"vision_tower.ln_pre", "v.encoder_norm",
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"vision_tower.patch_conv", "v.patch_conv",
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"vision_tower.embeddings", "v.embeddings",
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// Alternative vision model paths
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"vision_model.vision_model.embeddings", "v.embeddings",
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"vision_model.vision_model", "v",
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"vision_model.layers", "v.blk",
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// Multimodal projector components
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"multi_modal_projector.patch_merger", "mm.patch_merger",
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"multi_modal_projector.norm", "mm.norm",
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"multi_modal_projector.linear", "mm.projection",
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}
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}
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func (p *mistralModel) repack(name string, data []float32, shape []uint64) ([]float32, error) {
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func (p *mistral3Model) repack(name string, data []float32, shape []uint64) ([]float32, error) {
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var dims []int
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for _, dim := range shape {
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dims = append(dims, int(dim))
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@ -10,7 +10,7 @@ import (
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"github.com/ollama/ollama/model/input"
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)
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type TextOptions struct {
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type TextConfig struct {
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hiddenSize, numHeads, numKVHeads int
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attnKeyLen, attnValLen int
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eps, ropeScale float32
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@ -27,7 +27,7 @@ type TextModel struct {
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OutputNorm *nn.RMSNorm `gguf:"output_norm"`
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Output *nn.Linear `gguf:"output,alt:token_embd"`
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*TextOptions
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*TextConfig
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}
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const (
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@ -55,7 +55,7 @@ func newTextModel(c ml.Config) *TextModel {
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},
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),
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Layers: make([]TextLayer, numBlocks),
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TextOptions: &TextOptions{
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TextConfig: &TextConfig{
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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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@ -84,7 +84,7 @@ type TextSelfAttention struct {
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Output *nn.Linear `gguf:"attn_output"`
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}
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func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
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func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextConfig) ml.Tensor {
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batchSize := hiddenState.Dim(1)
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ropeType := uint32(2)
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@ -120,12 +120,12 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, pos
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}
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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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ropeBase := m.TextConfig.ropeLocalBase
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if (layer+1)%gemmaGlobalCacheCount == 0 {
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ropeBase = m.TextOptions.ropeGlobalBase
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ropeBase = m.TextConfig.ropeGlobalBase
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}
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return key.RoPE(ctx, shift, nil, uint32(m.TextOptions.attnKeyLen), uint32(2), ropeBase, m.TextOptions.ropeScale), nil
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return key.RoPE(ctx, shift, nil, uint32(m.TextConfig.attnKeyLen), uint32(2), ropeBase, m.TextConfig.ropeScale), nil
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}
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type TextMLP struct {
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@ -134,7 +134,7 @@ type TextMLP struct {
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Gate *nn.Linear `gguf:"ffn_gate"`
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}
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func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
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func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextConfig) ml.Tensor {
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hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
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return mlp.Down.Forward(ctx, hiddenState)
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}
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@ -148,7 +148,7 @@ type TextLayer struct {
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PostMLPNorm *nn.RMSNorm `gguf:"post_ffw_norm"`
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}
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func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
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func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *TextConfig) ml.Tensor {
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residual := hiddenState
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hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
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@ -173,7 +173,7 @@ func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs,
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func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, batch input.Batch, cache kvcache.Cache) ml.Tensor {
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hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
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hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextOptions.hiddenSize)))
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hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextConfig.hiddenSize)))
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// set image embeddings
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var except []int
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@ -206,7 +206,7 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
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lastLayerOutputs = outputs
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}
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hiddenState = layer.Forward(ctx, i, hiddenState, positions, lastLayerOutputs, cache, m.TextOptions)
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hiddenState = layer.Forward(ctx, i, hiddenState, positions, lastLayerOutputs, cache, m.TextConfig)
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}
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hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
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@ -1,4 +1,4 @@
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package llama
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package mistral3
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import (
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"fmt"
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@ -12,7 +12,7 @@ import (
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"github.com/ollama/ollama/model/input"
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)
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type Options struct {
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type TextOptions struct {
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hiddenSize, numHeads, numKVHeads, headDim int
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eps, ropeBase, ropeScale float32
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ropeDim uint32
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@ -27,7 +27,7 @@ type Model struct {
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OutputNorm *nn.RMSNorm `gguf:"output_norm"`
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Output *nn.Linear `gguf:"output,alt:token_embd"`
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*Options
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*TextOptions
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}
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func New(c ml.Config) (model.Model, error) {
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@ -49,7 +49,7 @@ func New(c ml.Config) (model.Model, error) {
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},
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),
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Layers: make([]Layer, c.Uint("block_count")),
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Options: &Options{
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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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@ -74,7 +74,7 @@ type SelfAttention struct {
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RopeFactors ml.Tensor `gguf:"rope_freqs.weight"`
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}
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func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
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func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
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batchSize := hiddenState.Dim(1)
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ropeType := uint32(0)
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// Get head dimension - use explicit value if available, otherwise calculate
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@ -119,7 +119,7 @@ type MLP struct {
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Gate *nn.Linear `gguf:"ffn_gate"`
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}
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func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
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func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
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hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
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return mlp.Down.Forward(ctx, hiddenState)
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}
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@ -131,7 +131,7 @@ type Layer struct {
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MLP *MLP
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}
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func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
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func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
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residual := hiddenState
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hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
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@ -168,8 +168,10 @@ func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) {
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return nil, err
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}
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// Process text inputs
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hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
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// Process through text transformer layers
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for i, layer := range m.Layers {
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m.Cache.SetLayer(i)
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@ -178,7 +180,7 @@ func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) {
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lastLayerOutputs = outputs
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}
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hiddenState = layer.Forward(ctx, hiddenState, positions, lastLayerOutputs, m.Cache, m.Options)
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hiddenState = layer.Forward(ctx, hiddenState, positions, lastLayerOutputs, m.Cache, m.TextOptions)
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}
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hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
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@ -186,5 +188,5 @@ func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) {
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}
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func init() {
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model.Register("mistral", New)
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model.Register("mistral3", New)
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}
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