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101 lines
3.0 KiB
Go
101 lines
3.0 KiB
Go
package convert
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import (
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"slices"
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"strings"
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"github.com/ollama/ollama/fs/ggml"
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)
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type lfm2Model struct {
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ModelParameters
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HiddenSize uint32 `json:"hidden_size"`
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NumHiddenLayers uint32 `json:"num_hidden_layers"`
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MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
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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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RopeTheta float32 `json:"rope_theta"`
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NormEps float32 `json:"norm_eps"`
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ConvLCache uint32 `json:"conv_L_cache"`
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LayerTypes []string `json:"layer_types"`
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TieEmbedding bool `json:"tie_embedding"`
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}
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var _ ModelConverter = (*lfm2Model)(nil)
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func (p *lfm2Model) KV(t *Tokenizer) KV {
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kv := p.ModelParameters.KV(t)
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kv["general.architecture"] = "lfm2"
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kv["lfm2.vocab_size"] = p.VocabSize
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kv["lfm2.block_count"] = p.NumHiddenLayers
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kv["lfm2.embedding_length"] = p.HiddenSize
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kv["lfm2.feed_forward_length"] = p.IntermediateSize
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kv["lfm2.context_length"] = p.MaxPositionEmbeddings
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// Build per-layer KV head count array based on layer_types
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// (0 = shortconv layer, non-zero = attention layer with that many KV heads)
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kvHeadCounts := make([]uint32, p.NumHiddenLayers)
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for i := range p.NumHiddenLayers {
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if int(i) < len(p.LayerTypes) && p.LayerTypes[i] == "full_attention" {
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kvHeadCounts[i] = p.NumKeyValueHeads
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}
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}
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kv["lfm2.attention.head_count"] = p.NumAttentionHeads
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kv["lfm2.attention.head_count_kv"] = kvHeadCounts
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kv["lfm2.attention.key_length"] = p.HiddenSize / p.NumAttentionHeads
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kv["lfm2.attention.value_length"] = p.HiddenSize / p.NumAttentionHeads
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kv["lfm2.attention.layer_norm_rms_epsilon"] = p.NormEps
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kv["lfm2.rope.freq_base"] = p.RopeTheta
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kv["lfm2.shortconv.l_cache"] = p.ConvLCache
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return kv
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}
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func (p *lfm2Model) 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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shape := t.Shape()
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// Squeeze conv weights: [D, 1, K] -> [D, K]
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if strings.HasSuffix(t.Name(), "shortconv.conv.weight") {
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if len(shape) == 3 && shape[1] == 1 {
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shape = []uint64{shape[0], shape[2]}
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}
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}
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out = append(out, &ggml.Tensor{
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Name: t.Name(),
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Kind: t.Kind(),
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Shape: slices.Clone(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 *lfm2Model) Replacements() []string {
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return []string{
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"model.embed_tokens", "token_embd",
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"model.embedding_norm", "output_norm",
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"model.layers", "blk",
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"operator_norm", "attn_norm",
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"self_attn.q_proj", "attn_q",
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"self_attn.k_proj", "attn_k",
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"self_attn.v_proj", "attn_v",
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"self_attn.out_proj", "attn_output",
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"self_attn.q_layernorm", "attn_q_norm",
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"self_attn.k_layernorm", "attn_k_norm",
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"conv.conv", "shortconv.conv",
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"conv.in_proj", "shortconv.in_proj",
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"conv.out_proj", "shortconv.out_proj",
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"feed_forward.w1", "ffn_gate",
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"feed_forward.w2", "ffn_down",
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"feed_forward.w3", "ffn_up",
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"ffn_norm", "ffn_norm",
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}
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}
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