103 lines
2.7 KiB
Go
103 lines
2.7 KiB
Go
package convert
|
|
|
|
import (
|
|
"strings"
|
|
|
|
"github.com/pdevine/tensor"
|
|
"github.com/pdevine/tensor/native"
|
|
|
|
"github.com/ollama/ollama/llm"
|
|
)
|
|
|
|
type gemma struct {
|
|
Parameters
|
|
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
|
|
HiddenSize uint32 `json:"hidden_size"`
|
|
HiddenLayers uint32 `json:"num_hidden_layers"`
|
|
IntermediateSize uint32 `json:"intermediate_size"`
|
|
NumAttentionHeads uint32 `json:"num_attention_heads"`
|
|
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
|
|
RMSNormEPS float32 `json:"rms_norm_eps"`
|
|
HeadDim uint32 `json:"head_dim"`
|
|
}
|
|
|
|
var _ Converter = (*gemma)(nil)
|
|
|
|
func (p *gemma) KV(t *Tokenizer) llm.KV {
|
|
kv := p.Parameters.KV(t)
|
|
kv["general.architecture"] = "gemma"
|
|
kv["general.name"] = "gemma"
|
|
kv["gemma.context_length"] = p.MaxPositionEmbeddings
|
|
kv["gemma.embedding_length"] = p.HiddenSize
|
|
kv["gemma.block_count"] = p.HiddenLayers
|
|
kv["gemma.feed_forward_length"] = p.IntermediateSize
|
|
kv["gemma.attention.head_count"] = p.NumAttentionHeads
|
|
kv["gemma.attention.head_count_kv"] = p.NumKeyValueHeads
|
|
kv["gemma.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
|
|
kv["gemma.attention.key_length"] = p.HeadDim
|
|
kv["gemma.attention.value_length"] = p.HeadDim
|
|
kv["tokenizer.ggml.eot_token_id"] = uint32(107)
|
|
kv["tokenizer.ggml.middle_token_id"] = uint32(68)
|
|
kv["tokenizer.ggml.prefix_token_id"] = uint32(67)
|
|
kv["tokenizer.ggml.suffix_token_id"] = uint32(69)
|
|
return kv
|
|
}
|
|
|
|
func (p *gemma) Tensors(ts []Tensor) []llm.Tensor {
|
|
var out []llm.Tensor
|
|
for _, t := range ts {
|
|
name := p.tensorName(t.Name())
|
|
if strings.HasSuffix(name, "_norm.weight") {
|
|
t.SetRepacker(p.addOne)
|
|
}
|
|
|
|
out = append(out, llm.Tensor{
|
|
Name: name,
|
|
Kind: t.Kind(),
|
|
Shape: t.Shape(),
|
|
WriterTo: t,
|
|
})
|
|
}
|
|
|
|
return out
|
|
}
|
|
|
|
func (p *gemma) tensorName(n string) string {
|
|
return strings.NewReplacer(
|
|
"model.embed_tokens", "token_embd",
|
|
"model.norm", "output_norm",
|
|
"model.layers", "blk",
|
|
"input_layernorm", "attn_norm",
|
|
"self_attn.q_proj", "attn_q",
|
|
"self_attn.k_proj", "attn_k",
|
|
"self_attn.v_proj", "attn_v",
|
|
"self_attn.o_proj", "attn_output",
|
|
"mlp.gate_proj", "ffn_gate",
|
|
"mlp.down_proj", "ffn_down",
|
|
"mlp.up_proj", "ffn_up",
|
|
"post_attention_layernorm", "ffn_norm",
|
|
"block_sparse_moe.gate", "ffn_inp",
|
|
).Replace(n)
|
|
}
|
|
|
|
func (*gemma) addOne(_ string, data []float32, shape []uint64) ([]float32, error) {
|
|
n := tensor.New(tensor.WithShape(int(shape[0])), tensor.WithBacking(data))
|
|
ones := tensor.Ones(tensor.Float32, int(shape[0]))
|
|
|
|
n, err := n.Add(ones)
|
|
if err != nil {
|
|
return nil, err
|
|
}
|
|
|
|
ts, err := native.SelectF32(n, 0)
|
|
if err != nil {
|
|
return nil, err
|
|
}
|
|
|
|
var f32s []float32
|
|
for _, t := range ts {
|
|
f32s = append(f32s, t...)
|
|
}
|
|
|
|
return f32s, nil
|
|
}
|