123 lines
3.7 KiB
Go
123 lines
3.7 KiB
Go
package convert
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import (
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"cmp"
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"encoding/binary"
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"io"
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"math"
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"strings"
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"sync"
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"github.com/ollama/ollama/llm"
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)
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type phi3Model struct {
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ModelParameters
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NumHiddenLayers uint32 `json:"num_hidden_layers"`
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NLayers uint32 `json:"n_layers"`
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HiddenSize uint32 `json:"hidden_size"`
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NEmbd uint32 `json:"n_embd"`
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IntermediateSize uint32 `json:"intermediate_size"`
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NumAttentionHeads uint32 `json:"num_attention_heads"`
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NHead uint32 `json:"n_head"`
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NumKeyValueHeads uint32 `json:"num_key_value_heads"`
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NHeadKV uint32 `json:"n_head_kv"`
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RopeTheta float32 `json:"rope_theta"`
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RopeScaling struct {
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Type string `json:"type"`
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LongFactor ropeFactor `json:"long_factor"`
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ShortFactor ropeFactor `json:"short_factor"`
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} `json:"rope_scaling"`
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RMSNormEPS float32 `json:"rms_norm_eps"`
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NPositions uint32 `json:"n_positions"`
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MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
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OriginalMaxPositionEmbeddings uint32 `json:"original_max_position_embeddings"`
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SlidingWindow uint32 `json:"sliding_window"`
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}
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var _ ModelConverter = (*phi3Model)(nil)
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func (p *phi3Model) KV(t *Tokenizer) llm.KV {
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kv := p.ModelParameters.KV(t)
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kv["general.architecture"] = "phi3"
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kv["phi3.context_length"] = p.MaxPositionEmbeddings
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kv["phi3.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
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kv["phi3.feed_forward_length"] = p.IntermediateSize
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kv["phi3.block_count"] = cmp.Or(p.NumHiddenLayers, p.NLayers)
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kv["phi3.attention.head_count"] = cmp.Or(p.NumAttentionHeads, p.NHead)
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kv["phi3.attention.head_count_kv"] = cmp.Or(p.NumKeyValueHeads, p.NHeadKV)
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kv["phi3.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
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kv["phi3.rope.dimension_count"] = p.HiddenSize / cmp.Or(p.NumAttentionHeads, p.NHead)
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kv["phi3.rope.freq_base"] = p.RopeTheta
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kv["phi3.rope.scaling.original_context_length"] = p.OriginalMaxPositionEmbeddings
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kv["phi3.attention.sliding_window"] = p.SlidingWindow
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scale := float64(p.MaxPositionEmbeddings) / float64(p.OriginalMaxPositionEmbeddings)
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switch p.RopeScaling.Type {
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case "":
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// no scaling
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case "su", "longrope":
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kv["phi3.rope.scaling.attn_factor"] = float32(max(math.Sqrt(1+math.Log(scale)/math.Log(float64(p.OriginalMaxPositionEmbeddings))), 1.0))
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case "yarn":
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kv["phi3.rope.scaling.attn_factor"] = float32(max(0.1*math.Log(scale)+1.0, 1.0))
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default:
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panic("unknown rope scaling type")
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}
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return kv
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}
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func (p *phi3Model) Tensors(ts []Tensor) []llm.Tensor {
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var addRopeFactors sync.Once
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out := make([]llm.Tensor, 0, len(ts)+2)
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for _, t := range ts {
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if strings.HasPrefix(t.Name(), "blk.0.") {
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addRopeFactors.Do(func() {
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out = append(out, llm.Tensor{
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Name: "rope_factors_long.weight",
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Kind: 0,
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Shape: []uint64{uint64(len(p.RopeScaling.LongFactor))},
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WriterTo: p.RopeScaling.LongFactor,
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}, llm.Tensor{
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Name: "rope_factors_short.weight",
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Kind: 0,
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Shape: []uint64{uint64(len(p.RopeScaling.ShortFactor))},
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WriterTo: p.RopeScaling.ShortFactor,
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})
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})
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}
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out = append(out, llm.Tensor{
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Name: t.Name(),
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Kind: t.Kind(),
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Shape: t.Shape(),
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WriterTo: t,
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})
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}
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return out
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}
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func (p *phi3Model) 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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"model.layers", "blk",
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"input_layernorm", "attn_norm",
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"self_attn.qkv_proj", "attn_qkv",
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"self_attn.o_proj", "attn_output",
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"mlp.down_proj", "ffn_down",
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"mlp.gate_up_proj", "ffn_up",
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"post_attention_layernorm", "ffn_norm",
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}
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}
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type ropeFactor []float32
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func (r ropeFactor) WriteTo(w io.Writer) (int64, error) {
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err := binary.Write(w, binary.LittleEndian, r)
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return 0, err
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}
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