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Commit 5a28b9cf authored by Michael Yang's avatar Michael Yang
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bert

parent a017cf2f
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Showing with 331 additions and 15 deletions
convert/convert.go
View file @ 5a28b9cf
......@@ -66,6 +66,10 @@ type Converter interface {
writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
}
type moreParser interface {
parseMore(fs.FS) error
}
// Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
// and files it finds in the input path.
// Supported input model formats include safetensors.
......@@ -95,6 +99,8 @@ func Convert(fsys fs.FS, ws io.WriteSeeker) error {
conv = &gemma{}
case "Phi3ForCausalLM":
conv = &phi3{}
case "BertModel":
conv = &bert{}
default:
return errors.New("unsupported architecture")
}
......@@ -103,6 +109,12 @@ func Convert(fsys fs.FS, ws io.WriteSeeker) error {
return err
}
if t, ok := conv.(moreParser); ok {
if err := t.parseMore(fsys); err != nil {
return err
}
}
t, err := parseTokenizer(fsys, conv.specialTokenTypes())
if err != nil {
return err
......
convert/convert_bert.go 0 → 100644
View file @ 5a28b9cf
package convert
import (
"cmp"
"encoding/json"
"io/fs"
"path/filepath"
"slices"
"strings"
"github.com/ollama/ollama/llm"
)
type bert struct {
Parameters
NLayers uint32 `json:"n_layers"`
NumHiddenLayers uint32 `json:"num_hidden_layers"`
NLayer uint32 `json:"n_layer"`
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
NCtx uint32 `json:"n_ctx"`
HiddenSize uint32 `json:"hidden_size"`
NEmbd uint32 `json:"n_embd"`
IntermediateSize uint32 `json:"intermediate_size"`
NInner uint32 `json:"n_inner"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
NHead uint32 `json:"n_head"`
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
LayerNormEPS float32 `json:"layer_norm_eps"`
LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
NormEpsilon float32 `json:"norm_epsilon"`
PoolingType uint32
}
var (
_ Converter = (*bert)(nil)
_ moreParser = (*bert)(nil)
)
func (p *bert) parseMore(fsys fs.FS) error {
bts, err := fs.ReadFile(fsys, "modules.json")
if err != nil {
return err
}
var modules []struct {
Type string `json:"type"`
Path string `json:"path"`
}
if err := json.Unmarshal(bts, &modules); err != nil {
return err
}
var pooling string
for _, m := range modules {
if m.Type == "sentence_transformers.models.Pooling" {
pooling = m.Path
break
}
}
if pooling != "" {
bts, err := fs.ReadFile(fsys, filepath.Join(pooling, "config.json"))
if err != nil {
return err
}
var pc struct {
PoolingModeCLSToken bool `json:"pooling_mode_cls_token"`
PoolingModeMeanTokens bool `json:"pooling_mode_mean_tokens"`
}
if err := json.Unmarshal(bts, &pc); err != nil {
return err
}
if pc.PoolingModeMeanTokens {
p.PoolingType = 1
} else if pc.PoolingModeCLSToken {
p.PoolingType = 2
}
}
return nil
}
func (p *bert) KV(t *Tokenizer) llm.KV {
kv := p.Parameters.KV(t)
kv["general.architecture"] = "bert"
kv["general.name"] = "bert"
kv["bert.attention.causal"] = false
kv["bert.pooling_type"] = p.PoolingType
kv["bert.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
if contextLength := cmp.Or(p.MaxPositionEmbeddings, p.NCtx); contextLength > 0 {
kv["bert.context_length"] = contextLength
}
if embeddingLength := cmp.Or(p.HiddenSize, p.NEmbd); embeddingLength > 0 {
kv["bert.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
}
if feedForwardLength := cmp.Or(p.IntermediateSize, p.NInner); feedForwardLength > 0 {
kv["bert.feed_forward_length"] = cmp.Or(p.IntermediateSize, p.NInner)
}
if headCount := cmp.Or(p.NumAttentionHeads, p.NHead); headCount > 0 {
kv["bert.attention.head_count"] = cmp.Or(p.NumAttentionHeads, p.NHead)
}
if layerNormEpsilon := cmp.Or(p.LayerNormEPS, p.LayerNormEpsilon, p.NormEpsilon); layerNormEpsilon > 0 {
kv["bert.attention.layer_norm_epsilon"] = layerNormEpsilon
}
kv["tokenizer.ggml.model"] = "bert"
kv["tokenizer.ggml.token_type_count"] = uint32(2)
// convert to phantom space tokens
for i, e := range t.Tokens {
if strings.HasPrefix(e, "[") && strings.HasSuffix(e, "]") {
// noop
} else if strings.HasPrefix(e, "##") {
t.Tokens[i] = e[2:]
} else {
t.Tokens[i] = "\u2581" + e
}
}
kv["tokenizer.ggml.tokens"] = t.Tokens
return kv
}
func (p *bert) Tensors(ts []Tensor) []llm.Tensor {
var out []llm.Tensor
for _, t := range ts {
if slices.Contains([]string{
"embeddings.position_ids",
"pooler.dense.weight",
"pooler.dense.bias",
}, t.Name()) {
continue
}
name := p.tensorName(t.Name())
out = append(out, llm.Tensor{
Name: name,
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
}
return out
}
func (bert) tensorName(n string) string {
return strings.NewReplacer(
"encoder.layer", "blk",
"encoder.layers", "blk",
"embeddings.word_embeddings", "token_embd",
"embeddings.token_type_embeddings", "token_types",
"embeddings.LayerNorm", "token_embd_norm",
"embeddings.position_embeddings", "position_embd",
"attention.self.query", "attn_q",
"attention.self.key", "attn_k",
"attention.self.value", "attn_v",
"attention.output.dense", "attn_output",
"attention.output.LayerNorm", "attn_output_norm",
"intermediate.dense", "ffn_up",
"output.dense", "ffn_down",
"output.LayerNorm", "layer_output_norm",
).Replace(n)
}
convert/convert_test.go
View file @ 5a28b9cf
......@@ -67,6 +67,7 @@ func TestConvertFull(t *testing.T) {
"gemma-2b-it",
// microsoft/Phi-3-mini-128-instruct@d548c233192db00165d842bf8edff054bb3212f8
"Phi-3-mini-128k-instruct",
"all-MiniLM-L6-v2",
}
for i := range cases {
......
convert/reader.go
View file @ 5a28b9cf
......@@ -37,6 +37,8 @@ const (
func (t tensorBase) Kind() uint32 {
if strings.HasSuffix(t.name, ".block_sparse_moe.gate.weight") {
return 0
} else if t.name == "embeddings.token_type_embeddings.weight" {
return 0
}
switch len(t.shape) {
......
convert/testdata/all-MiniLM-L6-v2.json 0 → 100644
View file @ 5a28b9cf
{
"general.architecture": "bert",
"general.file_type": "1",
"general.quantization_version": "2",
"bert.attention.causal": "false",
"bert.attention.head_count": "12",
"bert.attention.layer_norm_epsilon": "1e-12",
"bert.block_count": "6",
"bert.context_length": "512",
"bert.embedding_length": "384",
"bert.feed_forward_length": "1536",
"bert.pooling_type": "1",
"tokenizer.ggml.model": "bert",
"tokenizer.ggml.padding_token_id": "0",
"tokenizer.ggml.unknown_token_id": "100",
"tokenizer.ggml.cls_token_id": "101",
"tokenizer.ggml.seperator_token_id": "102",
"tokenizer.ggml.mask_token_id": "103",
"tokenizer.ggml.token_type_count": "2",
"tokenizer.ggml.scores": "6db964fe67338aca57790481a390121ff3dd643eebe49f7dd308029ad99abb6f",
"tokenizer.ggml.token_type": "98d247c5404b6b18f05f133b92dd56edf6efefefac326794b00d7b351f6c5aa1",
"tokenizer.ggml.tokens": "9efe405e229a45ff9916f54c475d151d2200cd2ab0006f347abfb069cf096c86",
"token_embd.weight": "8c1ee80a9ea4f65aa385ba30112010068af3d209bebc6e149d3d4589c2cd0a5a",
"position_embd.weight": "6c516f0b1c4e2388ab90394dd80ad69e4e4509b890982fc3408108ae66210eb6",
"token_types.weight": "f879f8e422ed211948f28b560d3c5e17aae7993f063b51196a28cf5c0fb3da21",
"token_embd_norm.weight": "75076e095d717aab96f8b6beeee503c27940d9a76f2b891a0e3de72f8a6043e4",
"token_embd_norm.bias": "298735285ffe944e1bf03e5d35c7280326b85cf121bde9874f1af5dc51ab939d",
"blk.0.attn_q.weight": "ab0923ce4c1549175112dcdfcc860fe30137f991e03ea6857fb5993670adaf6c",
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"blk.5.ffn_up.bias": "9d795388bb36fb33ad3a37fea3ccb4937838e02800a608fb47d363cd06b47370",
"blk.5.ffn_down.weight": "2fd628974e7f075479dd227b46fbd48ae8d3ca34d735b36f391ac06410730368",
"blk.5.ffn_down.bias": "cd213ba9eaa75fa541648097fbe9c96e58077e6c3ad6ad2fb1f21f8350f44291",
"blk.5.layer_output_norm.weight": "159a9df41d15b7022d136f86a2a2631c4635f9816e957472217077b522bcf52a",
"blk.5.layer_output_norm.bias": "24c1f27ffd1eb4e5be7e3a2909943e6f0980635d761fa1efdd0c19645da23766"
}
convert/tokenizer.go
View file @ 5a28b9cf
package convert
import (
"cmp"
"crypto/sha256"
"encoding/hex"
"encoding/json"
......@@ -11,6 +10,8 @@ import (
"log/slog"
"os"
"slices"
"golang.org/x/exp/maps"
)
const (
......@@ -184,32 +185,32 @@ func parseVocabularyFromTokenizer(fsys fs.FS) (*Vocabulary, error) {
return nil, err
}
var tokens []token
tokens := make(map[int]token, len(t.Model.Vocab))
for k, v := range t.Model.Vocab {
tokens = append(tokens, token{
tokens[v] = token{
ID: v,
Content: k,
})
}
}
for _, t := range t.AddedTokens {
t.UserDefined = true
tokens = append(tokens, t)
for _, token := range t.AddedTokens {
token.UserDefined = true
tokens[token.ID] = token
}
slices.SortFunc(tokens, func(i, j token) int {
return cmp.Compare(i.ID, j.ID)
})
keys := maps.Keys(tokens)
slices.Sort(keys)
v := Vocabulary{Model: "gpt2"}
for _, t := range tokens {
v.Tokens = append(v.Tokens, t.Content)
v.Scores = append(v.Scores, float32(t.ID))
for _, k := range keys {
token := tokens[k]
v.Tokens = append(v.Tokens, token.Content)
v.Scores = append(v.Scores, float32(token.ID))
switch {
case t.Special:
case token.Special:
v.Types = append(v.Types, tokenTypeControl)
case t.UserDefined:
case token.UserDefined:
v.Types = append(v.Types, tokenTypeUserDefined)
default:
v.Types = append(v.Types, tokenTypeNormal)
......
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