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refactor convert

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This commit is contained in:
Michael Yang 2024-05-31 20:00:49 -07:00
parent 6b252918fb
commit 5e9db9fb0b
24 changed files with 1514 additions and 1494 deletions
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243
convert/convert.go
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@ -1,200 +1,123 @@
package convert
import (
"cmp"
"encoding/binary"
"encoding/json"
"errors"
"fmt"
"io"
"log/slog"
"os"
"path/filepath"
"slices"
"strings"
"google.golang.org/protobuf/proto"
"github.com/ollama/ollama/convert/sentencepiece"
"github.com/ollama/ollama/llm"
)
const (
_ int32 = iota
tokenTypeNormal
tokenTypeUnknown
tokenTypeControl
tokenTypeUserDefined
tokenTypeUnused
tokenTypeByte
)
type Params struct {
Architectures []string `json:"architectures"`
VocabSize int `json:"vocab_size"`
HiddenSize int `json:"hidden_size"` // n_embd
HiddenLayers int `json:"num_hidden_layers"` // n_layer
ContextSize int `json:"max_position_embeddings"`
IntermediateSize int `json:"intermediate_size"`
AttentionHeads int `json:"num_attention_heads"` // n_head
KeyValHeads int `json:"num_key_value_heads"`
NormEPS float64 `json:"rms_norm_eps"`
BoSTokenID int `json:"bos_token_id"`
EoSTokenID int `json:"eos_token_id"`
HeadDimension int `json:"head_dim"`
PaddingTokenID int `json:"pad_token_id"`
RopeFrequencyBase float64 `json:"rope_theta"`
Experts int `json:"num_local_experts"`
ExpertsUsed int `json:"num_experts_per_tok"`
PreTokenizer string
ByteOrder
type Parameters struct {
Architectures []string `json:"architectures"`
VocabSize uint32 `json:"vocab_size"`
}
type ByteOrder interface {
binary.ByteOrder
binary.AppendByteOrder
func (Parameters) KV(t *Tokenizer) llm.KV {
kv := llm.KV{
"general.file_type": uint32(1),
"general.quantization_version": uint32(2),
"tokenizer.ggml.pre": t.Pre,
"tokenizer.ggml.model": t.Vocabulary.Model,
"tokenizer.ggml.tokens": t.Vocabulary.Tokens,
"tokenizer.ggml.scores": t.Vocabulary.Scores,
"tokenizer.ggml.token_type": t.Vocabulary.Types,
}
if t.Template != "" {
kv["tokenizer.chat_template"] = t.Template
}
for _, sv := range t.SpecialVocabulary {
kv[fmt.Sprintf("tokenizer.ggml.%s_token_id", sv.Key())] = uint32(sv.ID)
kv[fmt.Sprintf("tokenizer.ggml.add_%s_token", sv.Key())] = sv.AddToken
}
return kv
}
type ModelArch interface {
GetTensors() error
LoadVocab() error
WriteGGUF(io.WriteSeeker) error
func (Parameters) specialTypes() []string {
return []string{
"bos", "eos", "unk", "sep", "pad", "cls", "mask",
}
}
type ModelFormat interface {
GetLayerName(string) (string, error)
GetTensors(string, *Params) ([]llm.Tensor, error)
GetParams(string) (*Params, error)
GetModelArch(string, string, *Params) (ModelArch, error)
func (Parameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []*llm.Tensor) error {
return llm.WriteGGUF(ws, kv, ts)
}
type ModelData struct {
Path string
Name string
Params *Params
Vocab *Vocab
Tensors []llm.Tensor
Format ModelFormat
type Converter interface {
// KV maps parameters to LLM key-values
KV(*Tokenizer) llm.KV
// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
Tensors([]Tensor) []*llm.Tensor
// tensorName returns the LLM tensor name for a specific input name
tensorName(string) string
// specialTypes returns any special token types the model uses
specialTypes() []string
writeFile(io.WriteSeeker, llm.KV, []*llm.Tensor) error
}
func GetModelFormat(dirname string) (ModelFormat, error) {
files, err := filepath.Glob(filepath.Join(dirname, "*"))
func Convert(d string, ws io.WriteSeeker) error {
f, err := os.Open(filepath.Join(d, "config.json"))
if err != nil {
return nil, err
return err
}
defer f.Close()
var p Parameters
if err := json.NewDecoder(f).Decode(&p); err != nil {
return err
}
for _, fn := range files {
if strings.HasSuffix(fn, ".safetensors") {
return &SafetensorFormat{}, nil
} else if strings.HasSuffix(fn, ".bin") || strings.HasSuffix(fn, ".pth") {
slog.Debug("model is torch")
return &TorchFormat{}, nil
}
if len(p.Architectures) < 1 {
return errors.New("unknown architecture")
}
return nil, fmt.Errorf("couldn't determine model format")
}
var c Converter
switch p.Architectures[0] {
case "LlamaForCausalLM", "MistralForCausalLM":
c = &llama{}
case "MixtralForCausalLM":
c = &mixtral{}
case "GemmaForCausalLM":
c = &gemma{}
default:
return errors.New("unsupported architecture")
}
// Details on gguf's tokenizer can be found at:
// https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#tokenizer
type Vocab struct {
Tokens []string
Scores []float32
Types []int32
Merges []string
}
func LoadSentencePieceTokens(dirpath string, params *Params) (*Vocab, error) {
slog.Info(fmt.Sprintf("reading vocab from %s", filepath.Join(dirpath, "tokenizer.model")))
in, err := os.ReadFile(filepath.Join(dirpath, "tokenizer.model"))
bts, err := os.ReadFile(filepath.Join(d, "config.json"))
if err != nil {
return nil, err
return err
}
// To regenerate sentencepiece from the protobufs use:
// protoc -I=./ --go_out=./ sentencepiece_model.proto
modelProto := &sentencepiece.ModelProto{}
if err := proto.Unmarshal(in, modelProto); err != nil {
return nil, err
if err := json.Unmarshal(bts, c); err != nil {
return err
}
v := &Vocab{
Tokens: make([]string, 0),
Scores: make([]float32, 0),
Types: make([]int32, 0),
t, err := parseTokenizer(d, c.specialTypes())
if err != nil {
return err
}
pieces := modelProto.GetPieces()
for _, p := range pieces {
v.Tokens = append(v.Tokens, p.GetPiece())
v.Scores = append(v.Scores, p.GetScore())
t := p.GetType()
switch t {
case sentencepiece.ModelProto_SentencePiece_UNKNOWN:
case sentencepiece.ModelProto_SentencePiece_CONTROL:
case sentencepiece.ModelProto_SentencePiece_UNUSED:
case sentencepiece.ModelProto_SentencePiece_BYTE:
default:
t = sentencepiece.ModelProto_SentencePiece_NORMAL
}
v.Types = append(v.Types, int32(t))
}
slog.Info(fmt.Sprintf("vocab size: %d", len(v.Tokens)))
// add any additional tokens
addIn, err := os.ReadFile(filepath.Join(dirpath, "added_tokens.json"))
if os.IsNotExist(err) {
return v, nil
} else if err != nil {
return nil, err
}
slog.Info("reading user defined tokens")
var extraTokenData map[string]int
if err := json.Unmarshal(addIn, &extraTokenData); err != nil {
return nil, err
}
type token struct {
key string
pos int
}
extraTokens := make([]token, 0)
for k, id := range extraTokenData {
extraTokens = append(extraTokens, token{k, id})
}
slices.SortFunc(extraTokens, func(a, b token) int {
return cmp.Compare(a.pos, b.pos)
})
numToks := len(v.Tokens)
for cnt, t := range extraTokens {
// the token id should match the specific index for the total number of tokens
if t.pos != cnt+numToks {
return nil, fmt.Errorf("token ID '%d' for '%s' doesn't match total token size", t.pos, t.key)
}
v.Tokens = append(v.Tokens, t.key)
v.Scores = append(v.Scores, -1000.0)
v.Types = append(v.Types, tokenTypeUserDefined)
}
slog.Info(fmt.Sprintf("vocab size w/ extra tokens: %d", len(v.Tokens)))
if params.VocabSize > len(v.Tokens) {
missingTokens := params.VocabSize - len(v.Tokens)
slog.Warn(fmt.Sprintf("vocab is missing %d tokens", missingTokens))
for cnt := range missingTokens {
v.Tokens = append(v.Tokens, fmt.Sprintf("<dummy%05d>", cnt+1))
v.Scores = append(v.Scores, -1)
v.Types = append(v.Types, tokenTypeUserDefined)
if vocabSize := int(p.VocabSize); vocabSize > len(t.Vocabulary.Tokens) {
slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", p.VocabSize, "actual", len(t.Vocabulary.Tokens))
for i := range vocabSize - len(t.Vocabulary.Tokens) {
t.Vocabulary.Tokens = append(t.Vocabulary.Tokens, fmt.Sprintf("[PAD%d]", i))
t.Vocabulary.Scores = append(t.Vocabulary.Scores, -1)
t.Vocabulary.Types = append(t.Vocabulary.Types, tokenTypeUserDefined)
}
}
return v, nil
ts, err := parseTensors(d)
if err != nil {
return err
}
return c.writeFile(ws, c.KV(t), c.Tensors(ts))
}

103
convert/convert_gemma.go Normal file
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@ -0,0 +1,103 @@
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
}

182
convert/convert_llama.go Normal file
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@ -0,0 +1,182 @@
package convert
import (
"cmp"
"fmt"
"strings"
"github.com/ollama/ollama/llm"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
)
type llama 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"`
RopeTheta float32 `json:"rope_theta"`
RopeScaling struct {
Type string `json:"type"`
Factor float32 `json:"factor"`
} `json:"rope_scaling"`
RMSNormEPS float32 `json:"rms_norm_eps"`
LayerNormEPS float32 `json:"layer_norm_eps"`
LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
NormEpsilon float32 `json:"norm_epsilon"`
HeadDim uint32 `json:"head_dim"`
}
var _ Converter = (*llama)(nil)
func (p *llama) KV(t *Tokenizer) llm.KV {
kv := p.Parameters.KV(t)
kv["general.architecture"] = "llama"
kv["general.name"] = "llama"
kv["llama.vocab_size"] = p.VocabSize
kv["llama.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
if contextLength := cmp.Or(p.MaxPositionEmbeddings, p.NCtx); contextLength > 0 {
kv["llama.context_length"] = contextLength
}
if embeddingLength := cmp.Or(p.HiddenSize, p.NEmbd); embeddingLength > 0 {
kv["llama.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
}
if feedForwardLength := cmp.Or(p.IntermediateSize, p.NInner); feedForwardLength > 0 {
kv["llama.feed_forward_length"] = cmp.Or(p.IntermediateSize, p.NInner)
}
if headCount := cmp.Or(p.NumAttentionHeads, p.NHead); headCount > 0 {
kv["llama.attention.head_count"] = cmp.Or(p.NumAttentionHeads, p.NHead)
kv["llama.rope.dimension_count"] = p.HiddenSize / headCount
}
if p.RopeTheta > 0 {
kv["llama.rope.freq_base"] = p.RopeTheta
}
if p.RopeScaling.Type == "linear" {
kv["llama.rope.scaling.type"] = p.RopeScaling.Type
kv["llama.rope.scaling.factor"] = p.RopeScaling.Factor
}
if p.NumKeyValueHeads > 0 {
kv["llama.attention.head_count_kv"] = p.NumKeyValueHeads
}
if p.RMSNormEPS > 0 {
kv["llama.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
}
if layerNormEpsilon := cmp.Or(p.LayerNormEPS, p.LayerNormEpsilon, p.NormEpsilon); layerNormEpsilon > 0 {
kv["llama.attention.layer_norm_epsilon"] = layerNormEpsilon
}
if p.HeadDim > 0 {
kv["llama.attention.key_length"] = p.HeadDim
kv["llama.attention.value_length"] = p.HeadDim
}
if len(t.Merges) > 0 {
kv["tokenizer.ggml.merges"] = t.Merges
}
return kv
}
func (p *llama) Tensors(ts []Tensor) []*llm.Tensor {
var out []*llm.Tensor
for _, t := range ts {
name := p.tensorName(t.Name())
if strings.HasSuffix(name, "attn_q.weight") ||
strings.HasSuffix(name, "attn_k.weight") {
t.SetRepacker(p.repack)
}
out = append(out, &llm.Tensor{
Name: name,
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
}
return out
}
func (p *llama) tensorName(n string) string {
return strings.NewReplacer(
"lm_head", "output",
"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",
// mixtral
"block_sparse_moe.gate", "ffn_gate_inp",
).Replace(n)
}
func (p *llama) repack(name string, data []float32, shape []uint64) ([]float32, error) {
var dims []int
for _, dim := range shape {
dims = append(dims, int(dim))
}
var heads uint32
if strings.HasSuffix(name, "q_proj.weight") {
heads = p.NumAttentionHeads
} else if strings.HasSuffix(name, "k_proj.weight") {
heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
} else {
return nil, fmt.Errorf("unknown tensor for repack: %s", name)
}
n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
if err := n.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
return nil, err
}
if err := n.T(0, 2, 1, 3); err != nil {
return nil, err
}
if err := n.Reshape(dims...); err != nil {
return nil, err
}
if err := n.Transpose(); err != nil {
return nil, err
}
ts, err := native.SelectF32(n, 1)
if err != nil {
return nil, err
}
var f32s []float32
for _, t := range ts {
f32s = append(f32s, t...)
}
return f32s, nil
}

89
convert/convert_mixtral.go Normal file
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@ -0,0 +1,89 @@
package convert
import (
"fmt"
"io"
"slices"
"strings"
"github.com/ollama/ollama/llm"
)
type mixtral struct {
llama
NumLocalExperts uint32 `json:"num_local_experts"`
NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
}
var _ Converter = (*mixtral)(nil)
func (p *mixtral) KV(t *Tokenizer) llm.KV {
kv := p.llama.KV(t)
if p.NumLocalExperts > 0 {
kv["llama.expert_count"] = p.NumLocalExperts
}
if p.NumExpertsPerToken > 0 {
kv["llama.expert_used_count"] = p.NumExpertsPerToken
}
return kv
}
func (p *mixtral) Tensors(ts []Tensor) []*llm.Tensor {
oldnew := []string{
"model.layers", "blk",
"w1", "ffn_gate_exps",
"w2", "ffn_down_exps",
"w3", "ffn_up_exps",
}
for i := range p.NumLocalExperts {
oldnew = append(oldnew, fmt.Sprintf(".block_sparse_moe.experts.%d.", i), ".")
}
// group experts of the same layer (model.layers.%d) and type (w[123]) into a single tensor
namer := strings.NewReplacer(oldnew...)
experts := make(map[string]experts)
// merge experts into a single tensor while removing them from ts
ts = slices.DeleteFunc(ts, func(t Tensor) bool {
if !strings.Contains(t.Name(), ".block_sparse_moe.experts.") {
return false
}
name := namer.Replace(t.Name())
experts[name] = append(experts[name], t)
return true
})
var out []*llm.Tensor
for n, e := range experts {
// TODO(mxyng): sanity check experts
out = append(out, &llm.Tensor{
Name: n,
Kind: e[0].Kind(),
Shape: append([]uint64{uint64(len(e))}, e[0].Shape()...),
WriterTo: e,
})
}
return append(out, p.llama.Tensors(ts)...)
}
type experts []Tensor
func (e experts) WriteTo(w io.Writer) (int64, error) {
// TODO(mxyng): experts _should_ be numerically sorted by expert but this should check
for _, t := range e {
// the canonical merged experts tensor stacks all experts along a new, 0 axis,
// e.g. `tensor.Stack(0, e[0], e[1:]...)`, which requires allocating temporary buffers
// this accomplishes the same thing by writing each expert tensor in sequence
if _, err := t.WriteTo(w); err != nil {
return 0, err
}
}
return 0, nil
}

25
convert/convert_test.go
View file

@ -20,36 +20,13 @@ import (
func convertFull(t *testing.T, d string) (*os.File, llm.KV, llm.Tensors) {
t.Helper()
mf, err := GetModelFormat(d)
if err != nil {
t.Fatal(err)
}
params, err := mf.GetParams(d)
if err != nil {
t.Fatal(err)
}
arch, err := mf.GetModelArch("", d, params)
if err != nil {
t.Fatal(err)
}
if err := arch.LoadVocab(); err != nil {
t.Fatal(err)
}
if err := arch.GetTensors(); err != nil {
t.Fatal(err)
}
f, err := os.CreateTemp(t.TempDir(), "f16")
if err != nil {
t.Fatal(err)
}
defer f.Close()
if err := arch.WriteGGUF(f); err != nil {
if err := Convert(d, f); err != nil {
t.Fatal(err)
}

102
convert/gemma.go
View file

@ -1,102 +0,0 @@
package convert
import (
"fmt"
"io"
"log/slog"
"strings"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
"github.com/ollama/ollama/llm"
)
type GemmaModel struct {
ModelData
}
func addOnes(data []float32, vectorSize int) ([]float32, error) {
n := tensor.New(tensor.WithShape(vectorSize), tensor.WithBacking(data))
ones := tensor.Ones(tensor.Float32, vectorSize)
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
}
func (m *GemmaModel) GetTensors() error {
t, err := m.Format.GetTensors(m.Path, m.Params)
if err != nil {
return err
}
slog.Debug(fmt.Sprintf("Total tensors: %d", len(t)))
for _, l := range t {
if strings.HasSuffix(l.Name, "norm.weight") {
wt := l.WriterTo.(safetensorWriterTo)
wt.repacker = m.Repack
l.WriterTo = wt
}
m.Tensors = append(m.Tensors, l)
}
return nil
}
func (m *GemmaModel) LoadVocab() error {
v, err := LoadSentencePieceTokens(m.Path, m.Params)
if err != nil {
return err
}
m.Vocab = v
return nil
}
func (m *GemmaModel) Repack(_ string, data []float32, shape []uint64) ([]float32, error) {
return addOnes(data, int(shape[0]))
}
func (m *GemmaModel) WriteGGUF(ws io.WriteSeeker) error {
kv := llm.KV{
"general.architecture": "gemma",
"general.name": m.Name,
"gemma.context_length": uint32(m.Params.ContextSize),
"gemma.embedding_length": uint32(m.Params.HiddenSize),
"gemma.block_count": uint32(m.Params.HiddenLayers),
"gemma.feed_forward_length": uint32(m.Params.IntermediateSize),
"gemma.attention.head_count": uint32(m.Params.AttentionHeads),
"gemma.attention.head_count_kv": uint32(m.Params.KeyValHeads),
"gemma.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
"gemma.attention.key_length": uint32(m.Params.HeadDimension),
"gemma.attention.value_length": uint32(m.Params.HeadDimension),
"general.file_type": uint32(1),
"tokenizer.ggml.model": "llama",
"tokenizer.ggml.tokens": m.Vocab.Tokens,
"tokenizer.ggml.scores": m.Vocab.Scores,
"tokenizer.ggml.token_type": m.Vocab.Types,
"tokenizer.ggml.bos_token_id": uint32(m.Params.BoSTokenID),
"tokenizer.ggml.eos_token_id": uint32(m.Params.EoSTokenID),
"tokenizer.ggml.padding_token_id": uint32(m.Params.PaddingTokenID),
"tokenizer.ggml.unknown_token_id": uint32(3),
"tokenizer.ggml.add_bos_token": true,
"tokenizer.ggml.add_eos_token": false,
}
return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
}

159
convert/llama.go
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@ -1,159 +0,0 @@
package convert
import (
"cmp"
"errors"
"fmt"
"io"
"os"
"path/filepath"
"regexp"
"strings"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
"github.com/ollama/ollama/llm"
)
type LlamaModel struct {
ModelData
}
func (m *LlamaModel) GetTensors() error {
t, err := m.Format.GetTensors(m.Path, m.Params)
if err != nil {
return err
}
pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
re, err := regexp.Compile(pattern)
if err != nil {
return err
}
for _, l := range t {
matches := re.FindAllStringSubmatch(l.Name, -1)
if len(matches) > 0 {
switch m.Format.(type) {
case *TorchFormat:
wt := l.WriterTo.(torchWriterTo)
wt.repacker = m.Repack
l.WriterTo = wt
case *SafetensorFormat:
wt := l.WriterTo.(safetensorWriterTo)
wt.repacker = m.Repack
l.WriterTo = wt
}
}
m.Tensors = append(m.Tensors, l)
}
return nil
}
func (m *LlamaModel) LoadVocab() (err error) {
pre, ts, merges, err := parseTokens(filepath.Join(m.Path, "tokenizer.json"))
if errors.Is(err, os.ErrNotExist) {
return nil
} else if err != nil {
return err
}
m.Vocab = &Vocab{}
for _, t := range ts {
m.Vocab.Tokens = append(m.Vocab.Tokens, t.Content)
m.Vocab.Types = append(m.Vocab.Types, t.Type())
}
m.Vocab.Merges = merges
m.Params.PreTokenizer = pre
return nil
}
func (m *LlamaModel) WriteGGUF(ws io.WriteSeeker) error {
kv := llm.KV{
"general.architecture": "llama",
"general.name": m.Name,
"llama.vocab_size": uint32(len(m.Vocab.Tokens)),
"llama.context_length": uint32(m.Params.ContextSize),
"llama.embedding_length": uint32(m.Params.HiddenSize),
"llama.block_count": uint32(m.Params.HiddenLayers),
"llama.feed_forward_length": uint32(m.Params.IntermediateSize),
"llama.rope.freq_base": float32(m.Params.RopeFrequencyBase),
"llama.rope.dimension_count": uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
"llama.attention.head_count": uint32(m.Params.AttentionHeads),
"llama.attention.head_count_kv": uint32(m.Params.KeyValHeads),
"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
"general.file_type": uint32(1),
"tokenizer.ggml.model": "gpt2",
"tokenizer.ggml.pre": m.Params.PreTokenizer,
"tokenizer.ggml.tokens": m.Vocab.Tokens,
"tokenizer.ggml.token_type": m.Vocab.Types,
"tokenizer.ggml.bos_token_id": uint32(m.Params.BoSTokenID),
"tokenizer.ggml.eos_token_id": uint32(m.Params.EoSTokenID),
"tokenizer.ggml.unknown_token_id": uint32(0),
}
if len(m.Vocab.Merges) > 0 {
kv["tokenizer.ggml.merges"] = m.Vocab.Merges
} else {
kv["tokenizer.ggml.scores"] = m.Vocab.Scores
}
return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
}
func (m *LlamaModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
return llamaRepack(name, m.Params, data, shape)
}
func llamaRepack(name string, params *Params, data []float32, shape []uint64) ([]float32, error) {
var dims []int
for _, dim := range shape {
if dim != 0 {
dims = append(dims, int(dim))
}
}
var heads int
switch {
case strings.HasSuffix(name, "attn_q.weight"):
heads = params.AttentionHeads
case strings.HasSuffix(name, "attn_k.weight"):
heads = cmp.Or(params.KeyValHeads, params.AttentionHeads)
default:
return nil, fmt.Errorf("unknown tensor name: %s", name)
}
n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
if err := n.Reshape(append([]int{heads, 2, dims[0] / heads / 2}, dims[1:]...)...); err != nil {
return nil, err
}
if err := n.T(0, 2, 1, 3); err != nil {
return nil, err
}
if err := n.Reshape(dims...); err != nil {
return nil, err
}
if err := n.Transpose(); err != nil {
return nil, err
}
ts, err := native.SelectF32(n, 1)
if err != nil {
return nil, err
}
var f32s []float32
for _, t := range ts {
f32s = append(f32s, t...)
}
return f32s, nil
}

84
convert/mistral.go
View file

@ -1,84 +0,0 @@
package convert
import (
"io"
"regexp"
"github.com/ollama/ollama/llm"
)
type MistralModel struct {
ModelData
}
func (m *MistralModel) GetTensors() error {
t, err := m.Format.GetTensors(m.Path, m.Params)
if err != nil {
return err
}
pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
re, err := regexp.Compile(pattern)
if err != nil {
return err
}
for _, l := range t {
matches := re.FindAllStringSubmatch(l.Name, -1)
if len(matches) > 0 {
wt := l.WriterTo.(safetensorWriterTo)
wt.repacker = m.Repack
l.WriterTo = wt
}
m.Tensors = append(m.Tensors, l)
}
return nil
}
func (m *MistralModel) LoadVocab() error {
v, err := LoadSentencePieceTokens(m.Path, m.Params)
if err != nil {
return err
}
m.Vocab = v
return nil
}
func (m *MistralModel) WriteGGUF(ws io.WriteSeeker) error {
kv := llm.KV{
"general.architecture": "llama",
"general.name": m.Name,
"llama.context_length": uint32(m.Params.ContextSize),
"llama.embedding_length": uint32(m.Params.HiddenSize),
"llama.block_count": uint32(m.Params.HiddenLayers),
"llama.feed_forward_length": uint32(m.Params.IntermediateSize),
"llama.rope.dimension_count": uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
"llama.attention.head_count": uint32(m.Params.AttentionHeads),
"llama.attention.head_count_kv": uint32(m.Params.KeyValHeads),
"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
"general.file_type": uint32(1),
"tokenizer.ggml.model": "llama",
"tokenizer.ggml.tokens": m.Vocab.Tokens,
"tokenizer.ggml.scores": m.Vocab.Scores,
"tokenizer.ggml.token_type": m.Vocab.Types,
"tokenizer.ggml.bos_token_id": uint32(m.Params.BoSTokenID),
"tokenizer.ggml.eos_token_id": uint32(m.Params.EoSTokenID),
"tokenizer.ggml.add_bos_token": true,
"tokenizer.ggml.add_eos_token": false,
"tokenizer.ggml.unknown_token_id": uint32(0),
}
if m.Params.HeadDimension > 0 {
kv["llama.attention.key_length"] = uint32(m.Params.HeadDimension)
kv["llama.attention.value_length"] = uint32(m.Params.HeadDimension)
}
return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
}
func (m *MistralModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
return llamaRepack(name, m.Params, data, shape)
}

87
convert/mixtral.go
View file

@ -1,87 +0,0 @@
package convert
import (
"io"
"regexp"
"github.com/ollama/ollama/llm"
)
type MixtralModel struct {
ModelData
}
func (m *MixtralModel) GetTensors() error {
t, err := m.Format.GetTensors(m.Path, m.Params)
if err != nil {
return err
}
pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
re, err := regexp.Compile(pattern)
if err != nil {
return err
}
for _, l := range t {
matches := re.FindAllStringSubmatch(l.Name, -1)
if len(matches) > 0 {
wt := l.WriterTo.(safetensorWriterTo)
wt.repacker = m.Repack
l.WriterTo = wt
}
m.Tensors = append(m.Tensors, l)
}
return nil
}
func (m *MixtralModel) LoadVocab() error {
v, err := LoadSentencePieceTokens(m.Path, m.Params)
if err != nil {
return err
}
m.Vocab = v
return nil
}
func (m *MixtralModel) WriteGGUF(ws io.WriteSeeker) error {
kv := llm.KV{
"general.architecture": "llama",
"general.name": m.Name,
"llama.block_count": uint32(m.Params.HiddenLayers),
"llama.context_length": uint32(m.Params.ContextSize),
"llama.embedding_length": uint32(m.Params.HiddenSize),
"llama.feed_forward_length": uint32(m.Params.IntermediateSize),
"llama.attention.head_count": uint32(m.Params.AttentionHeads),
"llama.attention.head_count_kv": uint32(m.Params.KeyValHeads),
"llama.rope.freq_base": float32(m.Params.RopeFrequencyBase),
"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
"llama.expert_count": uint32(m.Params.Experts),
"llama.expert_used_count": uint32(m.Params.ExpertsUsed),
"llama.vocab_size": uint32(len(m.Vocab.Tokens)),
"llama.rope.dimension_count": uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
"general.file_type": uint32(1),
"tokenizer.ggml.model": "llama",
"tokenizer.ggml.tokens": m.Vocab.Tokens,
"tokenizer.ggml.scores": m.Vocab.Scores,
"tokenizer.ggml.token_type": m.Vocab.Types,
"tokenizer.ggml.bos_token_id": uint32(m.Params.BoSTokenID),
"tokenizer.ggml.eos_token_id": uint32(m.Params.EoSTokenID),
"tokenizer.ggml.unknown_token_id": uint32(0),
"tokenizer.ggml.add_bos_token": true,
"tokenizer.ggml.add_eos_token": false,
}
return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
}
func (m *MixtralModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
return llamaRepack(name, m.Params, data, shape)
}

74
convert/reader.go Normal file
View file

@ -0,0 +1,74 @@
package convert
import (
"errors"
"io"
"path/filepath"
"strings"
)
type Tensor interface {
Name() string
Shape() []uint64
Kind() uint32
SetRepacker(repacker)
WriteTo(io.Writer) (int64, error)
}
type tensorBase struct {
name string
shape []uint64
repacker
}
func (t tensorBase) Name() string {
return t.name
}
func (t tensorBase) Shape() []uint64 {
return t.shape
}
func (t tensorBase) Kind() uint32 {
if strings.HasSuffix(t.name, ".block_sparse_moe.gate.weight") {
return 0
}
switch len(t.shape) {
case 0:
panic("invalid tensor shape")
case 1:
return 0
default:
return 1
}
}
func (t *tensorBase) SetRepacker(fn repacker) {
t.repacker = fn
}
type repacker func(string, []float32, []uint64) ([]float32, error)
func parseTensors(d string) ([]Tensor, error) {
patterns := map[string]func(...string) ([]Tensor, error){
"model-*-of-*.safetensors": parseSafetensors,
"model.safetensors": parseSafetensors,
"pytorch_model-*-of-*.bin": parseTorch,
"pytorch_model.bin": parseTorch,
"consolidated.*.pth": parseTorch,
}
for pattern, parseFn := range patterns {
matches, err := filepath.Glob(filepath.Join(d, pattern))
if err != nil {
return nil, err
}
if len(matches) > 0 {
return parseFn(matches...)
}
}
return nil, errors.New("unknown tensor format")
}

140
convert/reader_safetensors.go Normal file
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@ -0,0 +1,140 @@
package convert
import (
"bytes"
"encoding/binary"
"encoding/json"
"fmt"
"io"
"os"
"slices"
"github.com/d4l3k/go-bfloat16"
"github.com/x448/float16"
"golang.org/x/exp/maps"
)
type safetensorMetadata struct {
Type string `json:"dtype"`
Shape []uint64 `json:"shape"`
Offsets []int64 `json:"data_offsets"`
}
func parseSafetensors(ps ...string) ([]Tensor, error) {
var ts []Tensor
for _, p := range ps {
f, err := os.Open(p)
if err != nil {
return nil, err
}
defer f.Close()
var n int64
if err := binary.Read(f, binary.LittleEndian, &n); err != nil {
return nil, err
}
b := bytes.NewBuffer(make([]byte, 0, n))
if _, err = io.CopyN(b, f, n); err != nil {
return nil, err
}
var headers map[string]safetensorMetadata
if err := json.NewDecoder(b).Decode(&headers); err != nil {
return nil, err
}
keys := maps.Keys(headers)
slices.Sort(keys)
for _, key := range keys {
if value := headers[key]; value.Type != "" {
ts = append(ts, safetensor{
path: p,
dtype: value.Type,
offset: safetensorsPad(n, value.Offsets[0]),
size: safetensorsPad(n, value.Offsets[1]) - safetensorsPad(n, value.Offsets[0]),
tensorBase: &tensorBase{
name: key,
shape: value.Shape,
},
})
}
}
}
return ts, nil
}
func safetensorsPad(n, s int64) int64 {
return 8 + n + s
}
type safetensor struct {
path string
dtype string
offset int64
size int64
*tensorBase
}
func (st safetensor) WriteTo(w io.Writer) (int64, error) {
f, err := os.Open(st.path)
if err != nil {
return 0, err
}
defer f.Close()
if _, err = f.Seek(st.offset, io.SeekStart); err != nil {
return 0, err
}
var f32s []float32
switch st.dtype {
case "F32":
f32s = make([]float32, st.size/4)
if err = binary.Read(f, binary.LittleEndian, f32s); err != nil {
return 0, err
}
case "F16":
u16s := make([]uint16, st.size/2)
if err = binary.Read(f, binary.LittleEndian, u16s); err != nil {
return 0, err
}
for _, b := range u16s {
f32s = append(f32s, float16.Frombits(b).Float32())
}
case "BF16":
u8s := make([]uint8, st.size)
if err = binary.Read(f, binary.LittleEndian, u8s); err != nil {
return 0, err
}
f32s = bfloat16.DecodeFloat32(u8s)
default:
return 0, fmt.Errorf("unknown data type: %s", st.dtype)
}
if st.repacker != nil {
f32s, err = st.repacker(st.Name(), f32s, st.Shape())
if err != nil {
return 0, err
}
}
switch st.Kind() {
case 0:
return 0, binary.Write(w, binary.LittleEndian, f32s)
case 1:
f16s := make([]uint16, len(f32s))
for i := range f32s {
f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
}
return 0, binary.Write(w, binary.LittleEndian, f16s)
default:
return 0, fmt.Errorf("unknown storage type: %d", st.Kind())
}
}

46
convert/reader_torch.go Normal file
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@ -0,0 +1,46 @@
package convert
import (
"io"
"github.com/nlpodyssey/gopickle/pytorch"
"github.com/nlpodyssey/gopickle/types"
)
func parseTorch(ps ...string) ([]Tensor, error) {
var ts []Tensor
for _, p := range ps {
pt, err := pytorch.Load(p)
if err != nil {
return nil, err
}
for _, k := range pt.(*types.Dict).Keys() {
t := pt.(*types.Dict).MustGet(k)
var shape []uint64
for dim := range t.(*pytorch.Tensor).Size {
shape = append(shape, uint64(dim))
}
ts = append(ts, torch{
storage: t.(*pytorch.Tensor).Source,
tensorBase: &tensorBase{
name: k.(string),
shape: shape,
},
})
}
}
return ts, nil
}
type torch struct {
storage pytorch.StorageInterface
*tensorBase
}
func (pt torch) WriteTo(w io.Writer) (int64, error) {
return 0, nil
}

309
convert/safetensors.go
View file

@ -1,309 +0,0 @@
package convert
import (
"bytes"
"encoding/binary"
"encoding/json"
"fmt"
"io"
"os"
"path/filepath"
"regexp"
"slices"
"strings"
"github.com/d4l3k/go-bfloat16"
"github.com/x448/float16"
"github.com/ollama/ollama/llm"
)
type safetensorWriterTo struct {
t *llm.Tensor
params *Params
bo ByteOrder
filename string
dtype string
offset, size int64
repacker func(string, []float32, []uint64) ([]float32, error)
}
type safetensorMetadata struct {
Type string `json:"dtype"`
Shape []uint64 `json:"shape"`
Offsets []int64 `json:"data_offsets"`
}
type SafetensorFormat struct{}
func (m *SafetensorFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
var tensors []llm.Tensor
matches, err := filepath.Glob(filepath.Join(dirpath, "*.safetensors"))
if err != nil {
return nil, err
}
var offset uint64
for _, f := range matches {
var t []llm.Tensor
var err error
t, offset, err = m.readTensors(f, offset, params)
if err != nil {
return nil, err
}
tensors = append(tensors, t...)
}
return tensors, nil
}
func (m *SafetensorFormat) readTensors(fn string, offset uint64, params *Params) ([]llm.Tensor, uint64, error) {
f, err := os.Open(fn)
if err != nil {
return nil, 0, err
}
defer f.Close()
var n int64
if err := binary.Read(f, binary.LittleEndian, &n); err != nil {
return nil, 0, err
}
b := bytes.NewBuffer(make([]byte, 0, n))
if _, err = io.CopyN(b, f, n); err != nil {
return nil, 0, err
}
var headers map[string]safetensorMetadata
if err := json.NewDecoder(b).Decode(&headers); err != nil {
return nil, 0, err
}
var keys []string
for key := range headers {
if !strings.HasSuffix(key, "self_attn.rotary_embd.inv_freq") {
keys = append(keys, key)
}
}
slices.Sort(keys)
var tensors []llm.Tensor
for _, key := range keys {
value := headers[key]
var kind uint32
switch len(value.Shape) {
case 0:
// valuedata
continue
case 2:
kind = 1
}
name, err := m.GetLayerName(key)
if err != nil {
return nil, 0, err
}
shape := make([]uint64, len(value.Shape))
copy(shape, value.Shape)
pad := func(s int64) int64 {
return 8 + n + s
}
t := llm.Tensor{
Name: name,
Kind: kind,
Offset: offset,
Shape: shape,
}
t.WriterTo = safetensorWriterTo{
t: &t,
params: params,
bo: params.ByteOrder,
filename: fn,
dtype: value.Type,
offset: pad(value.Offsets[0]),
size: pad(value.Offsets[1]) - pad(value.Offsets[0]),
}
offset += t.Size()
tensors = append(tensors, t)
}
return tensors, offset, nil
}
func (m *SafetensorFormat) GetParams(dirpath string) (*Params, error) {
f, err := os.Open(filepath.Join(dirpath, "config.json"))
if err != nil {
return nil, err
}
defer f.Close()
var params Params
if err := json.NewDecoder(f).Decode(&params); err != nil {
return nil, err
}
params.ByteOrder = binary.LittleEndian
return &params, nil
}
func (m *SafetensorFormat) GetLayerName(n string) (string, error) {
directMap := map[string]string{
"model.embed_tokens.weight": "token_embd.weight",
"lm_head.weight": "output.weight",
"model.norm.weight": "output_norm.weight",
}
tMap := map[string]string{
"model.layers.(\\d+).input_layernorm.weight": "blk.$1.attn_norm.weight",
"model.layers.(\\d+).mlp.down_proj.weight": "blk.$1.ffn_down.weight",
"model.layers.(\\d+).mlp.gate_proj.weight": "blk.$1.ffn_gate.weight",
"model.layers.(\\d+).mlp.up_proj.weight": "blk.$1.ffn_up.weight",
"model.layers.(\\d+).post_attention_layernorm.weight": "blk.$1.ffn_norm.weight",
"model.layers.(\\d+).self_attn.k_proj.weight": "blk.$1.attn_k.weight",
"model.layers.(\\d+).self_attn.o_proj.weight": "blk.$1.attn_output.weight",
"model.layers.(\\d+).self_attn.q_proj.weight": "blk.$1.attn_q.weight",
"model.layers.(\\d+).self_attn.v_proj.weight": "blk.$1.attn_v.weight",
"model.layers.(\\d+).block_sparse_moe.gate.weight": "blk.$1.ffn_gate_inp.weight",
"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w1.weight": "blk.$1.ffn_gate.$2.weight",
"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w2.weight": "blk.$1.ffn_down.$2.weight",
"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w3.weight": "blk.$1.ffn_up.$2.weight",
}
v, ok := directMap[n]
if ok {
return v, nil
}
// quick hack to rename the layers to gguf format
for k, v := range tMap {
re := regexp.MustCompile(k)
newName := re.ReplaceAllString(n, v)
if newName != n {
return newName, nil
}
}
return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
}
func (r safetensorWriterTo) WriteTo(w io.Writer) (n int64, err error) {
f, err := os.Open(r.filename)
if err != nil {
return 0, err
}
defer f.Close()
if _, err = f.Seek(r.offset, io.SeekStart); err != nil {
return 0, err
}
var f32s []float32
switch r.dtype {
case "F32":
f32s = make([]float32, r.size/4)
if err = binary.Read(f, r.bo, f32s); err != nil {
return 0, err
}
case "F16":
u16s := make([]uint16, r.size/2)
if err = binary.Read(f, r.bo, u16s); err != nil {
return 0, err
}
for _, b := range u16s {
f32s = append(f32s, float16.Frombits(b).Float32())
}
case "BF16":
u8s := make([]uint8, r.size)
if err = binary.Read(f, r.bo, u8s); err != nil {
return 0, err
}
f32s = bfloat16.DecodeFloat32(u8s)
default:
return 0, fmt.Errorf("unknown data type: %s", r.dtype)
}
if r.repacker != nil {
f32s, err = r.repacker(r.t.Name, f32s, r.t.Shape)
if err != nil {
return 0, err
}
}
switch r.t.Kind {
case 0:
return 0, binary.Write(w, r.bo, f32s)
case 1:
f16s := make([]uint16, len(f32s))
for i := range f32s {
f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
}
return 0, binary.Write(w, r.bo, f16s)
default:
return 0, fmt.Errorf("unknown storage type: %d", r.t.Kind)
}
}
func (m *SafetensorFormat) GetModelArch(name, dirPath string, params *Params) (ModelArch, error) {
switch len(params.Architectures) {
case 0:
return nil, fmt.Errorf("No architecture specified to convert")
case 1:
switch params.Architectures[0] {
case "LlamaForCausalLM":
return &LlamaModel{
ModelData{
Name: name,
Path: dirPath,
Params: params,
Format: m,
},
}, nil
case "MistralForCausalLM":
return &MistralModel{
ModelData{
Name: name,
Path: dirPath,
Params: params,
Format: m,
},
}, nil
case "MixtralForCausalLM":
return &MixtralModel{
ModelData{
Name: name,
Path: dirPath,
Params: params,
Format: m,
},
}, nil
case "GemmaForCausalLM":
return &GemmaModel{
ModelData{
Name: name,
Path: dirPath,
Params: params,
Format: m,
},
}, nil
default:
return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
}
}
return nil, fmt.Errorf("Unknown error")
}

2
convert/testdata/Mistral-7B-Instruct-v0.2.json vendored
View file

@ -4,7 +4,7 @@
"general.quantization_version": "2",
"llama.block_count": "32",
"llama.context_length": "32768",
"llama.embedding_length": "",
"llama.embedding_length": "4096",
"llama.feed_forward_length": "14336",
"llama.attention.head_count": "32",
"llama.attention.head_count_kv": "8",

349
convert/testdata/Mixtral-8x7B-Instruct-v0.1.json vendored
View file

@ -1 +1,348 @@
{}
{
"general.architecture": "llama",
"general.file_type": "1",
"general.quantization_version": "2",
"llama.block_count": "32",
"llama.context_length": "32768",
"llama.embedding_length": "4096",
"llama.feed_forward_length": "14336",
"llama.rope.dimension_count": "128",
"llama.rope.freq_base": "1e+06",
"llama.attention.head_count": "32",
"llama.attention.head_count_kv": "8",
"llama.attention.layer_norm_rms_epsilon": "1e-05",
"llama.expert_count": "8",
"llama.expert_used_count": "2",
"tokenizer.ggml.model": "llama",
"tokenizer.ggml.add_bos_token": "true",
"tokenizer.ggml.add_eos_token": "false",
"tokenizer.ggml.bos_token_id": "1",
"tokenizer.ggml.eos_token_id": "2",
"tokenizer.ggml.unknown_token_id": "0",
"tokenizer.ggml.scores": "e3d3eea80bb41a1213f2d0aa3e8a38581d1f19323be77dbd779c9c7e3b72e676",
"tokenizer.ggml.token_type": "6040635e6bd38d98af06698feb75c1802bad35180ee6ae0a503e38c0f60fd71e",
"tokenizer.ggml.tokens": "604ac4bfbd019e430d7b6cdf18c6c0cd5b967900601f0307f714ec7773aa5ca6",
"token_embd.weight": "1d1d1d39a867d5a4bfb32792a47247d2638c10c95a6259391d02843583505cc4",
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"blk.26.ffn_norm.weight": "7f704e936fda28eb5c2cc339f0f6a5f78170b5aa43c01265b21668870d819c82",
"blk.26.attn_k.weight": "1cc62a0ce0ae251275d898c52c4a9fba5995fca10955d2011d10dd1a59e1afb8",
"blk.26.attn_output.weight": "636e881b1505f9cef656a4be98bec6a4765321d51f9bf1dac8933397cf44b765",
"blk.26.attn_q.weight": "89a3c4d202d7d6adebb9e0c1bcfd8b775f6456386f1be25e86e43acc949c1e16",
"blk.26.attn_v.weight": "ff2cc963b597cdf1a21703f3e7022af3bb4c65a34a19e19d9309a7c5e198b5bd",
"blk.27.ffn_gate_inp.weight": "6150139498fefe380bb99d11e72028da47a15ecb73dfc5b2774f726f4bed8f9e",
"blk.27.attn_k.weight": "f286eb9e5c56c7b801a497aedc40158c2a27877d7f9fb59b3fc67834798902d2",
"blk.27.attn_output.weight": "5dc3d3a05f9f7729509147fd09c16fb53f85f520cdab5cb69abf4bae3fd460c7",
"blk.27.attn_q.weight": "8462e40f86b24251960d6f35a9ea99b8793a01937faf1aec2859f2e5395dbb61",
"blk.27.attn_v.weight": "bac1a99e38e25953f8315f7212eb9777dc216cadb09b959977885ae62724ceca",
"blk.27.ffn_gate_exps.weight": "6a15eca7f0f6ecfd93db2e55c63875348ec4a78c4ff643ec46df9e958c0101e4",
"blk.27.ffn_down_exps.weight": "2e1c91247c4359e2073a8e5f26fd7f6426da7be3ed5bc65dcfff701f0a5022b2",
"blk.27.ffn_up_exps.weight": "65d6f5c553c9332085eae4aeadf25090b5d7768212ea7b08ed698102c21b29a1",
"blk.27.attn_norm.weight": "7fab8ae63ec8e91ce625cd130ab96d8427dad3a7413bb21b25ec5f408c5b9f5a",
"blk.27.ffn_norm.weight": "532720546b0fdcd423a02ca6e3e9d8aacb84b1b3e8269968f88a47fe2a69bab4",
"blk.28.ffn_gate_inp.weight": "a305ea58d98962d9dcf0c53ad2389b7acc8936fb35a0e3fc9410e7767cd49dea",
"blk.28.attn_k.weight": "8315e8a2e4f78dfdf36d4fc18fffc74bc95fe42c3ae4f9af2b6c874612c0f71b",
"blk.28.attn_output.weight": "9b5fdedd32d39ef46a22cca7cd5355d7b93bd07ea305f466a8aad6ca5a4f3778",
"blk.28.attn_q.weight": "4e8fb96997c30e231c437130f410d7c91d541a816f6c568b5f3bfdb4b8dece74",
"blk.28.attn_v.weight": "1fec739cf3bd7b4913f72ca358d4cf31391c304de44ac0ae31ecb825beaa7cfd",
"blk.28.ffn_gate_exps.weight": "9f259789d535e09268266b9a8020f32d6a6779966c909d91d3a10574f06238a2",
"blk.28.ffn_down_exps.weight": "516d3f8abaedb01b9916a4b67d4672159769138ef2850158bc1b32c41e31f0e8",
"blk.28.ffn_up_exps.weight": "f2f1d88d2c31ed588806fb5ad981d68f5134d7284c4fc022fd018de2eef437fc",
"blk.28.attn_norm.weight": "960fd005598deadaebd969996f4367a9dbfad90539a863674fe95730935acc64",
"blk.28.ffn_norm.weight": "e1993b37ced93d4049e9af2c47b0d9207d8f7e6f2cc3a52f57bef30bc806d805",
"blk.29.ffn_gate_exps.weight": "58927146338f443513337476b3cd30e6341742f096c2beb5890d400f10121298",
"blk.29.ffn_down_exps.weight": "03a3386e4f0b75a28c5608e23b2de8f0de25f21954e4aa7fc343431bde9db07e",
"blk.29.ffn_up_exps.weight": "6916b7490a7ae7b04a5d81cc1e7ac9b20c483434f3b186b12d87fe176bf1567b",
"blk.29.ffn_gate_inp.weight": "98e710e467a3d567abe4ce29d78b8e8dc033148762290c0c5e1ae4d78efd8c78",
"blk.29.attn_norm.weight": "4e64cb307d37be20d55f38c94faf7e451d11df5e60df347906cbaf9c5441be71",
"blk.29.ffn_norm.weight": "696c23a52f742679bd44440d687a4c44b4302d57f1e9dc5610d23374336187e7",
"blk.29.attn_k.weight": "e85253652fd6120c623634ba66b725bf7cd491318b54ccdad2c7df8851d64c0a",
"blk.29.attn_output.weight": "4f650a71efb150d1f24cd4d114d4187bf570ac424da3b92ea6455abdf1aea705",
"blk.29.attn_q.weight": "69fa7da901026ebcbbbc848455b425458b7e3295007d7fc093acf4b38e2166ea",
"blk.29.attn_v.weight": "17e2e7590b317b21f106de546aafd955579703d1e95d6aea044ee72ec3a514c9",
"blk.30.ffn_gate_inp.weight": "3a03284b4aa60d59d4a2ec86253469b61fc656372afca427cb77a5332fbcc62c",
"blk.30.attn_k.weight": "d518cfd0db9708e769eb1399e87ee49357dc54d5afdbac3d4c0ca46c64e789eb",
"blk.30.attn_output.weight": "9b44378714d784c5ef9ab604359091baca4e0ec222afa139b7f840eaefb371fd",
"blk.30.attn_q.weight": "cbb95365bbfbcad0c9cd99b4eebb5a5d32de68ce08e4063b5ec3e792b7548044",
"blk.30.attn_v.weight": "e7985c04fe1740e35a9598f43b67b0922b4fc2d00b68a92a9f917b82c3248de1",
"blk.30.ffn_gate_exps.weight": "8ac4bbd07935d98f895ba94dc174e5ad5046c3c222b53729d60f987c05e7eb70",
"blk.30.ffn_down_exps.weight": "dd672cc71e82abf05064a18121b8e55fe1a4f19bc1d7cb9a142f4add54bc336e",
"blk.30.ffn_up_exps.weight": "12282f664a2a12aa25e2deac58946108715ebb978bafed5274cef24569107646",
"blk.30.attn_norm.weight": "1a33458fee054c6c9c896a4bb0a4e1fbfa0293b2408c7dd2b81d692e966e7273",
"blk.30.ffn_norm.weight": "311e33b68051f507f1478ed8f2693fddb846170ddb7285a91be43f795c2ce31e",
"blk.31.ffn_gate_exps.weight": "8af43d9867a51cd8392fb48b981b0ceee0ae979c491c07d711b3b56b5162c786",
"blk.31.ffn_down_exps.weight": "5579cb7758c1600b19d1f540deffe081b575962e37437b3b2efb2fb0a2924e40",
"blk.31.ffn_up_exps.weight": "f2e7c005276b3a001fb40753f027fa10b4d5a346f43cf4b4bbdeec6e74e1cf6a",
"blk.31.ffn_gate_inp.weight": "89885dc0e30b6b16a90c0331d7fa3174671e941364e8102d934f02132237e61b",
"blk.31.attn_norm.weight": "99e4e9bf86a9edf8c404153a7e8a82324ba79da462622196e2faba161bd95172",
"blk.31.ffn_norm.weight": "55335997cf6de781bf332b943de96ff4646966b05d9fee86b76ea897e27b6ca7",
"blk.31.attn_k.weight": "cee570762b78da6316b637892cc4b080e40f57af5551ffb1866b9a8e80e96628",
"blk.31.attn_output.weight": "fa321ff55ec7819ead7b819fd45215262f39744569765ba2113c989c03588802",
"blk.31.attn_q.weight": "9e2c409b878f8a2a1436874abf428fceb1c534b21f9ad4dd6f532b8a469007f0",
"blk.31.attn_v.weight": "a845d0be68ba537b4a775bfba4d897faf7c82a811a2612b0b7420cc4f3574cb8",
"output.weight": "16101cbb74b54cda9ebc07ca3c762e3263a56efb3cc011156184b95807d7cf13",
"output_norm.weight": "d7aa61585baedd60157aafe157930785742c55989c288573566a971b02423564"
}

265
convert/tokenizer.go
View file

@ -3,19 +3,148 @@ package convert
import (
"cmp"
"crypto/sha256"
"encoding/hex"
"encoding/json"
"errors"
"fmt"
"log/slog"
"os"
"path/filepath"
"slices"
)
"golang.org/x/exp/maps"
const (
_ int32 = iota
tokenTypeNormal
tokenTypeUnknown
tokenTypeControl
tokenTypeUserDefined
tokenTypeUnused
tokenTypeByte
)
type Tokenizer struct {
Version string `json:"version"`
AddedTokens []Token `json:"added_tokens"`
Model TokenizerModel `json:"model"`
*Vocabulary
SpecialVocabulary []*SpecialVocabulary
Merges []string
Pre string
Template string
}
func parseTokenizer(d string, specialTypes []string) (*Tokenizer, error) {
v, err := parseVocabulary(d)
if err != nil {
return nil, err
}
t := &Tokenizer{
Vocabulary: v,
Pre: "default",
}
addedTokens := make(map[string]token)
if f, err := os.Open(filepath.Join(d, "tokenizer.json")); errors.Is(err, os.ErrNotExist) {
} else if err != nil {
return nil, err
} else {
defer f.Close()
var tt tokenizer
if err := json.NewDecoder(f).Decode(&tt); err != nil {
return nil, err
}
for _, t := range tt.AddedTokens {
addedTokens[t.Content] = t
}
t.Merges = tt.Model.Merges
sha256sum := sha256.New()
for _, pt := range tt.PreTokenizer.PreTokenizers {
switch pt.Type {
case "Split":
if pt.Pattern.Regex != "" {
sha256sum.Write([]byte(pt.Pattern.Regex))
}
}
}
switch digest := hex.EncodeToString(sha256sum.Sum(nil)); digest {
case "d98f9631be1e9607a9848c26c1f9eac1aa9fc21ac6ba82a2fc0741af9780a48f":
t.Pre = "llama-bpe"
case "03df5c5863ad70781dcfdef491ead25140f895fe8010964be0daefe27be32b02":
t.Pre = "deepseek-llm"
case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
t.Pre = "deepseek-coder"
case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
// noop, empty pretokenizer
default:
slog.Warn("unknown pretokenizer, using default", "digest", digest)
}
}
if f, err := os.Open(filepath.Join(d, "tokenizer_config.json")); errors.Is(err, os.ErrNotExist) {
} else if err != nil {
return nil, err
} else {
defer f.Close()
var p map[string]json.RawMessage
if err := json.NewDecoder(f).Decode(&p); err != nil {
return nil, err
}
if template, ok := p["chat_template"]; ok {
if err := json.Unmarshal(template, &t.Template); err != nil {
return nil, err
}
}
for _, st := range specialTypes {
sv := SpecialVocabulary{Type: st}
if bts, ok := p[fmt.Sprintf("add_%s_token", st)]; ok {
if err := json.Unmarshal(bts, &sv.AddToken); err != nil {
return nil, err
}
}
if bts, ok := p[fmt.Sprintf("%s_token", st)]; ok {
var content string
if err := json.Unmarshal(bts, &content); err != nil {
var mm map[string]any
if err := json.Unmarshal(bts, &mm); err != nil {
continue
}
content, ok = mm["content"].(string)
if !ok {
continue
}
}
sv.Content = content
}
if id, ok := addedTokens[sv.Content]; ok {
sv.ID = id.ID
t.SpecialVocabulary = append(t.SpecialVocabulary, &sv)
}
}
}
return t, nil
}
type tokenizer struct {
Version string `json:"version"`
AddedTokens []token `json:"added_tokens"`
Model struct {
Type string `json:"type"`
Vocab map[string]int `json:"vocab"`
Merges []string `json:"merges"`
} `json:"model"`
PreTokenizer struct {
PreTokenizers []struct {
@ -27,80 +156,106 @@ type Tokenizer struct {
} `json:"pre_tokenizer"`
}
type TokenizerModel struct {
Type string `json:"type"`
Vocab map[string]int `json:"vocab"`
Merges []string `json:"merges"`
Tokens []Token
}
type Token struct {
type token struct {
ID int `json:"id"`
Content string `json:"content"`
Special bool `json:"special"`
UserDefined bool
}
func (t *Token) Type() int32 {
switch {
case t.Special:
return tokenTypeControl
case t.UserDefined:
return tokenTypeUserDefined
default:
return tokenTypeNormal
}
type Vocabulary struct {
Model string
Tokens []string
Scores []float32
Types []int32
}
func (t *Tokenizer) maxID() int {
return max(
slices.Max(maps.Values(t.Model.Vocab)),
slices.MaxFunc(t.AddedTokens, func(a, b Token) int {
return cmp.Compare(a.ID, b.ID)
}).ID,
)
}
func parseTokens(dirpath string) (pre string, tokens []Token, merges []string, err error) {
f, err := os.Open(dirpath)
func parseVocabularyFromTokenizer(p string) (*Vocabulary, error) {
f, err := os.Open(filepath.Join(p, "tokenizer.json"))
if err != nil {
panic(err)
return nil, err
}
defer f.Close()
var t Tokenizer
var t tokenizer
if err := json.NewDecoder(f).Decode(&t); err != nil {
return "", nil, nil, err
return nil, err
}
tokens = make([]Token, t.maxID()+1)
var tokens []token
for k, v := range t.Model.Vocab {
tokens[v] = Token{ID: v, Content: k, Special: false, UserDefined: false}
tokens = append(tokens, token{
ID: v,
Content: k,
})
}
for _, v := range t.AddedTokens {
v.UserDefined = true
tokens[v.ID] = v
for _, t := range t.AddedTokens {
t.UserDefined = true
tokens = append(tokens, t)
}
sha256sum := sha256.New()
for _, pt := range t.PreTokenizer.PreTokenizers {
if pt.Type == "Split" && pt.Pattern.Regex != "" {
sha256sum.Write([]byte(pt.Pattern.Regex))
slices.SortFunc(tokens, func(i, j token) int {
return cmp.Compare(i.ID, j.ID)
})
v := Vocabulary{Model: "gpt2"}
for _, t := range tokens {
v.Tokens = append(v.Tokens, t.Content)
v.Scores = append(v.Scores, float32(t.ID))
switch {
case t.Special:
v.Types = append(v.Types, tokenTypeControl)
case t.UserDefined:
v.Types = append(v.Types, tokenTypeUserDefined)
default:
v.Types = append(v.Types, tokenTypeNormal)
}
}
switch digest := fmt.Sprintf("%x", sha256sum.Sum(nil)); digest {
case "d98f9631be1e9607a9848c26c1f9eac1aa9fc21ac6ba82a2fc0741af9780a48f":
pre = "llama-bpe"
case "03df5c5863ad70781dcfdef491ead25140f895fe8010964be0daefe27be32b02":
pre = "deepseek-llm"
case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
pre = "deepseek-coder"
default:
slog.Warn("unknown pretokenizer, using default", "digest", digest)
pre = "default"
return &v, nil
}
func parseVocabulary(d string) (*Vocabulary, error) {
patterns := map[string]func(string) (*Vocabulary, error){
"tokenizer.model": parseSentencePiece,
"tokenizer.json": parseVocabularyFromTokenizer,
}
return pre, tokens, t.Model.Merges, nil
for pattern, parseFn := range patterns {
matches, err := filepath.Glob(filepath.Join(d, pattern))
if err != nil {
return nil, err
}
if len(matches) > 0 {
return parseFn(d)
}
}
return nil, errors.New("unknown tensor format")
}
type SpecialVocabulary struct {
Type string
ID int
Content string
AddToken bool
}
func (sv SpecialVocabulary) Key() string {
switch t := sv.Type; t {
case "bos", "eos", "cls", "mask":
return t
case "unk":
return "unknown"
case "sep":
//nolint:misspell // this is an upstream typo
return "seperator"
case "pad":
return "padding"
}
panic("unknown special vocabulary type")
}

83
convert/tokenizer_spm.go Normal file
View file

@ -0,0 +1,83 @@
package convert
import (
"cmp"
"encoding/json"
"errors"
"fmt"
"os"
"path/filepath"
"slices"
"google.golang.org/protobuf/proto"
"github.com/ollama/ollama/convert/sentencepiece"
)
func parseSentencePiece(d string) (*Vocabulary, error) {
bts, err := os.ReadFile(filepath.Join(d, "tokenizer.model"))
if err != nil {
return nil, err
}
var spm sentencepiece.ModelProto
if err := proto.Unmarshal(bts, &spm); err != nil {
return nil, err
}
v := Vocabulary{Model: "llama"}
for _, piece := range spm.GetPieces() {
v.Tokens = append(v.Tokens, piece.GetPiece())
v.Scores = append(v.Scores, piece.GetScore())
switch t := piece.GetType(); t {
case sentencepiece.ModelProto_SentencePiece_UNKNOWN,
sentencepiece.ModelProto_SentencePiece_CONTROL,
sentencepiece.ModelProto_SentencePiece_UNUSED,
sentencepiece.ModelProto_SentencePiece_BYTE:
v.Types = append(v.Types, int32(t))
default:
v.Types = append(v.Types, int32(sentencepiece.ModelProto_SentencePiece_NORMAL))
}
}
f, err := os.Open(filepath.Join(d, "added_tokens.json"))
if errors.Is(err, os.ErrNotExist) {
return &v, nil
} else if err != nil {
return nil, err
}
defer f.Close()
var atm map[string]int
if err := json.NewDecoder(f).Decode(&atm); err != nil {
return nil, err
}
type t struct {
id int
content string
}
var ts []t
for content, id := range atm {
ts = append(ts, t{id, content})
}
slices.SortFunc(ts, func(i, j t) int {
return cmp.Compare(i.id, j.id)
})
n := len(v.Tokens)
for i, t := range ts {
if t.id != i+n {
return nil, fmt.Errorf("invalid token id: %d", t.id)
}
v.Tokens = append(v.Tokens, t.content)
v.Scores = append(v.Scores, -1000.0)
v.Types = append(v.Types, tokenTypeUserDefined)
}
return &v, nil
}

287
convert/torch.go
View file

@ -1,287 +0,0 @@
package convert
import (
"encoding/binary"
"encoding/json"
"fmt"
"io"
"log/slog"
"os"
"path/filepath"
"regexp"
"strings"
"github.com/nlpodyssey/gopickle/pytorch"
"github.com/nlpodyssey/gopickle/types"
"github.com/x448/float16"
"github.com/ollama/ollama/llm"
)
type torchWriterTo struct {
t *llm.Tensor
params *Params
bo ByteOrder
storage pytorch.StorageInterface
repacker func(string, []float32, []uint64) ([]float32, error)
}
type TorchFormat struct{}
func (tf *TorchFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
slog.Debug("getting torch tensors")
var files []string
if pt, _ := filepath.Glob(filepath.Join(dirpath, "consolidated*.pth")); len(pt) > 0 {
files = append(files, pt...)
} else if pt, _ := filepath.Glob(filepath.Join(dirpath, "pytorch_model*.pth")); len(pt) > 0 {
files = append(files, pt...)
}
var offset uint64
var tensors []llm.Tensor
for _, fn := range files {
m, err := pytorch.Load(fn)
if err != nil {
slog.Error(fmt.Sprintf("error unpickling: %q", err))
return []llm.Tensor{}, err
}
for _, k := range m.(*types.Dict).Keys() {
if strings.HasSuffix(k.(string), "self_attn.rotary_emb.inv_freq") {
continue
}
t, _ := m.(*types.Dict).Get(k)
tshape := t.(*pytorch.Tensor).Size
var size uint64
var kind uint32
switch len(tshape) {
case 0:
continue
case 1:
// convert to float32
kind = 0
size = uint64(tshape[0] * 4)
case 2:
// convert to float16
kind = 1
size = uint64(tshape[0] * tshape[1] * 2)
}
ggufName, err := tf.GetLayerName(k.(string))
if err != nil {
slog.Error(err.Error())
return nil, err
}
slog.Debug(fmt.Sprintf("'%35s': '%30s' %10d [%#v]", k.(string), ggufName, size, tshape))
shape := []uint64{0, 0, 0, 0}
for i := range tshape {
shape[i] = uint64(tshape[i])
}
tensor := llm.Tensor{
Name: ggufName,
Kind: kind,
Offset: offset, // calculate the offset
Shape: shape,
}
tensor.WriterTo = torchWriterTo{
t: &tensor,
params: params,
bo: params.ByteOrder,
storage: t.(*pytorch.Tensor).Source,
}
tensors = append(tensors, tensor)
offset += size
}
}
return tensors, nil
}
func getAltParams(dirpath string) (*Params, error) {
f, err := os.Open(filepath.Join(dirpath, "params.json"))
if err != nil {
slog.Error("no params.json")
return nil, err
}
defer f.Close()
type TorchParams struct {
HiddenSize int `json:"dim"`
AttentionHeads int `json:"n_heads"`
KeyValHeads int `json:"n_kv_heads"`
HiddenLayers int `json:"n_layers"`
RopeTheta float64 `json:"rope_theta"`
NormEPS float64 `json:"norm_eps"`
}
var tparams TorchParams
d := json.NewDecoder(f)
err = d.Decode(&tparams)
if err != nil {
return nil, err
}
params := &Params{
Architectures: []string{"LlamaForCausalLM"},
HiddenSize: tparams.HiddenSize,
AttentionHeads: tparams.AttentionHeads,
KeyValHeads: tparams.KeyValHeads,
HiddenLayers: tparams.HiddenLayers,
NormEPS: tparams.NormEPS,
}
switch {
case tparams.RopeTheta == 1000000:
// Codellama
params.ContextSize = 16384
case tparams.NormEPS == 1e-06:
// llama2
slog.Debug("Found llama2 - setting context size to 4096")
params.ContextSize = 4096
default:
params.ContextSize = 2048
}
params.ByteOrder = binary.LittleEndian
return params, nil
}
func (m *TorchFormat) GetParams(dirpath string) (*Params, error) {
f, err := os.Open(filepath.Join(dirpath, "config.json"))
if err != nil {
if os.IsNotExist(err) {
// try params.json instead
return getAltParams(dirpath)
} else {
return nil, err
}
}
var params Params
d := json.NewDecoder(f)
err = d.Decode(&params)
if err != nil {
return nil, err
}
params.ByteOrder = binary.LittleEndian
return &params, nil
}
func (m *TorchFormat) GetLayerName(n string) (string, error) {
directMap := map[string]string{
"tok_embeddings.weight": "token_embd.weight",
"output.weight": "output.weight",
"norm.weight": "output_norm.weight",
"rope.freqs": "rope_freqs.weight",
"model.embed_tokens.weight": "token_embd.weight",
"lm_head.weight": "output.weight",
"model.norm.weight": "output_norm.weight",
}
lMap := map[string]string{
"layers.(\\d+).attention_norm.weight": "blk.$1.attn_norm.weight",
"layers.(\\d+).attention_output_norm.weight": "blk.$1.attn_norm.weight",
"layers.(\\d+).feed_forward.w2.weight": "blk.$1.ffn_down.weight",
"layers.(\\d+).feed_forward.w1.weight": "blk.$1.ffn_gate.weight",
"layers.(\\d+).feed_forward.w3.weight": "blk.$1.ffn_up.weight",
"layers.(\\d+).ffn_norm.weight": "blk.$1.ffn_norm.weight",
"layers.(\\d+).attention.wk.weight": "blk.$1.attn_k.weight",
"layers.(\\d+).attention.wo.weight": "blk.$1.attn_output.weight",
"layers.(\\d+).attention.wq.weight": "blk.$1.attn_q.weight",
"layers.(\\d+).attention.wv.weight": "blk.$1.attn_v.weight",
"model.layers.(\\d+).input_layernorm.weight": "blk.$1.attn_norm.weight",
"model.layers.(\\d+).mlp.down_proj.weight": "blk.$1.ffn_down.weight",
"model.layers.(\\d+).mlp.gate_proj.weight": "blk.$1.ffn_gate.weight",
"model.layers.(\\d+).mlp.up_proj.weight": "blk.$1.ffn_up.weight",
"model.layers.(\\d+).post_attention_layernorm.weight": "blk.$1.ffn_norm.weight",
"model.layers.(\\d+).self_attn.k_proj.weight": "blk.$1.attn_k.weight",
"model.layers.(\\d+).self_attn.o_proj.weight": "blk.$1.attn_output.weight",
"model.layers.(\\d+).self_attn.q_proj.weight": "blk.$1.attn_q.weight",
"model.layers.(\\d+).self_attn.v_proj.weight": "blk.$1.attn_v.weight",
}
v, ok := directMap[n]
if ok {
return v, nil
}
// quick hack to rename the layers to gguf format
for k, v := range lMap {
re := regexp.MustCompile(k)
newName := re.ReplaceAllString(n, v)
if newName != n {
return newName, nil
}
}
return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
}
func (r torchWriterTo) WriteTo(w io.Writer) (n int64, err error) {
var f32s []float32
switch s := r.storage.(type) {
case *pytorch.FloatStorage:
f32s = s.Data
case *pytorch.HalfStorage:
f32s = s.Data
case *pytorch.BFloat16Storage:
f32s = s.Data
default:
return 0, fmt.Errorf("unknown data type: %T", s)
}
if r.repacker != nil {
f32s, err = r.repacker(r.t.Name, f32s, r.t.Shape)
if err != nil {
return 0, err
}
}
switch r.t.Kind {
case 0:
return 0, binary.Write(w, r.bo, f32s)
case 1:
f16s := make([]uint16, len(f32s))
for i := range f32s {
f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
}
return 0, binary.Write(w, r.bo, f16s)
default:
return 0, fmt.Errorf("unknown storage type: %d", r.t.Kind)
}
}
func (m *TorchFormat) GetModelArch(name, dirPath string, params *Params) (ModelArch, error) {
switch len(params.Architectures) {
case 0:
return nil, fmt.Errorf("No architecture specified to convert")
case 1:
switch params.Architectures[0] {
case "LlamaForCausalLM":
return &LlamaModel{
ModelData{
Name: name,
Path: dirPath,
Params: params,
Format: m,
},
}, nil
default:
return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
}
}
return nil, fmt.Errorf("Unknown error")
}

326
llm/gguf.go
View file

@ -2,11 +2,16 @@ package llm
import (
"bytes"
"cmp"
"encoding/binary"
"encoding/json"
"fmt"
"io"
"log/slog"
"slices"
"strings"
"golang.org/x/exp/maps"
)
type containerGGUF struct {
@ -88,7 +93,7 @@ type gguf struct {
kv KV
tensors []*Tensor
parameters uint64
parameters uint64
tensorOffset uint64
scratch [16 << 10]byte
@ -101,10 +106,6 @@ func newGGUF(container *containerGGUF) *gguf {
}
}
func NewGGUFV3(bo binary.ByteOrder) *gguf {
return newGGUF(&containerGGUF{ByteOrder: bo, Version: 3})
}
func (llm *gguf) KV() KV {
return llm.kv
}
@ -203,7 +204,7 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
return fmt.Errorf("failed to read tensor dimensions: %w", err)
}
shape := [4]uint64{1, 1, 1, 1}
shape := make([]uint64, dims)
for i := 0; uint32(i) < dims; i++ {
shape[i], err = readGGUF[uint64](llm, rs)
if err != nil {
@ -245,7 +246,7 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
return err
}
padding := llm.padding(offset, int64(alignment))
padding := ggufPadding(offset, int64(alignment))
llm.tensorOffset = uint64(offset + padding)
for _, tensor := range llm.tensors {
@ -254,7 +255,7 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
return fmt.Errorf("failed to get current offset: %w", err)
}
padding := llm.padding(offset, int64(alignment))
padding := ggufPadding(offset, int64(alignment))
if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
return fmt.Errorf("failed to seek to init padding: %w", err)
}
@ -273,12 +274,12 @@ func readGGUF[T any](llm *gguf, r io.Reader) (T, error) {
return t, err
}
func writeGGUF[V any](llm *gguf, w io.Writer, t uint32, v V) error {
if err := binary.Write(w, llm.ByteOrder, t); err != nil {
func writeGGUF[V any](w io.Writer, t uint32, v V) error {
if err := binary.Write(w, binary.LittleEndian, t); err != nil {
return err
}
return binary.Write(w, llm.ByteOrder, v)
return binary.Write(w, binary.LittleEndian, v)
}
func readGGUFV1String(llm *gguf, r io.Reader) (string, error) {
@ -342,12 +343,12 @@ func readGGUFString(llm *gguf, r io.Reader) (string, error) {
return string(buf), nil
}
func writeGGUFString(llm *gguf, w io.Writer, s string) error {
if err := binary.Write(w, llm.ByteOrder, ggufTypeString); err != nil {
func writeGGUFString(w io.Writer, s string) error {
if err := binary.Write(w, binary.LittleEndian, ggufTypeString); err != nil {
return err
}
if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
return err
}
@ -488,21 +489,21 @@ func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
return a, nil
}
func writeGGUFArray[S ~[]E, E any](llm *gguf, w io.Writer, t uint32, s S) error {
if err := binary.Write(w, llm.ByteOrder, ggufTypeArray); err != nil {
func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
if err := binary.Write(w, binary.LittleEndian, ggufTypeArray); err != nil {
return err
}
if err := binary.Write(w, llm.ByteOrder, t); err != nil {
if err := binary.Write(w, binary.LittleEndian, t); err != nil {
return err
}
if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
return err
}
for _, e := range s {
if err := binary.Write(w, llm.ByteOrder, e); err != nil {
if err := binary.Write(w, binary.LittleEndian, e); err != nil {
return err
}
}
@ -510,194 +511,55 @@ func writeGGUFArray[S ~[]E, E any](llm *gguf, w io.Writer, t uint32, s S) error
return nil
}
var ggufKVOrder = map[string][]string{
"llama": {
"general.architecture",
"general.name",
"llama.vocab_size",
"llama.context_length",
"llama.embedding_length",
"llama.block_count",
"llama.feed_forward_length",
"llama.attention.head_count",
"llama.attention.head_count_kv",
"llama.attention.layer_norm_rms_epsilon",
"llama.rope.freq_base",
"llama.rope.dimension_count",
"llama.expert_count",
"llama.expert_used_count",
"gemma.context_length",
"gemma.embedding_length",
"gemma.block_count",
"gemma.feed_forward_length",
"gemma.attention.head_count",
"gemma.attention.head_count_kv",
"gemma.attention.layer_norm_rms_epsilon",
"gemma.attention.key_length",
"gemma.attention.value_length",
"general.file_type",
"tokenizer.ggml.pre",
"tokenizer.ggml.model",
"tokenizer.ggml.tokens",
"tokenizer.ggml.scores",
"tokenizer.ggml.merges",
"tokenizer.ggml.token_type",
"tokenizer.ggml.bos_token_id",
"tokenizer.ggml.eos_token_id",
"tokenizer.ggml.unknown_token_id",
"tokenizer.ggml.padding_token_id",
"tokenizer.ggml.add_bos_token",
"tokenizer.ggml.add_eos_token",
"tokenizer.chat_template",
"bert.pooling_type",
},
}
func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
switch llm.Version {
case 3:
llm.V3.NumTensor = uint64(len(tensors))
llm.V3.NumKV = uint64(len(kv))
default:
return fmt.Errorf("not implemented: ggufv%d", llm.Version)
}
if err := binary.Write(ws, llm.ByteOrder, []byte("GGUF")); err != nil {
func WriteGGUF(ws io.WriteSeeker, kv KV, ts []*Tensor) error {
if err := binary.Write(ws, binary.LittleEndian, []byte("GGUF")); err != nil {
return err
}
if err := binary.Write(ws, llm.ByteOrder, llm.Version); err != nil {
if err := binary.Write(ws, binary.LittleEndian, uint32(3)); err != nil {
return err
}
if err := binary.Write(ws, llm.ByteOrder, llm.numTensor()); err != nil {
if err := binary.Write(ws, binary.LittleEndian, uint64(len(ts))); err != nil {
return err
}
if err := binary.Write(ws, llm.ByteOrder, llm.numKV()); err != nil {
if err := binary.Write(ws, binary.LittleEndian, uint64(len(kv))); err != nil {
return err
}
kvCheck := make(map[string]bool)
for k := range kv {
kvCheck[k] = false
}
keys := maps.Keys(kv)
slices.Sort(keys)
for _, k := range ggufKVOrder["llama"] {
v, ok := kv[k]
if !ok {
continue
}
kvCheck[k] = true
if err := binary.Write(ws, llm.ByteOrder, uint64(len(k))); err != nil {
return err
}
if err := binary.Write(ws, llm.ByteOrder, []byte(k)); err != nil {
return err
}
var err error
switch v := v.(type) {
case uint32:
err = writeGGUF(llm, ws, ggufTypeUint32, v)
case float32:
err = writeGGUF(llm, ws, ggufTypeFloat32, v)
case bool:
err = writeGGUF(llm, ws, ggufTypeBool, v)
case string:
err = writeGGUFString(llm, ws, v)
case []int32:
err = writeGGUFArray(llm, ws, ggufTypeInt32, v)
case []uint32:
err = writeGGUFArray(llm, ws, ggufTypeUint32, v)
case []float32:
err = writeGGUFArray(llm, ws, ggufTypeFloat32, v)
case []string:
if err := binary.Write(ws, llm.ByteOrder, ggufTypeArray); err != nil {
return err
}
if err := binary.Write(ws, llm.ByteOrder, ggufTypeString); err != nil {
return err
}
if err := binary.Write(ws, llm.ByteOrder, uint64(len(v))); err != nil {
return err
}
for _, e := range v {
if err := binary.Write(ws, llm.ByteOrder, uint64(len(e))); err != nil {
return err
}
if err := binary.Write(ws, llm.ByteOrder, []byte(e)); err != nil {
return err
}
}
default:
return fmt.Errorf("improper type for '%s'", k)
}
if err != nil {
for _, key := range keys {
if err := ggufWriteKV(ws, key, kv[key]); err != nil {
return err
}
}
for k, v := range kvCheck {
if !v {
return fmt.Errorf("Didn't know how to write kv %s", k)
slices.SortFunc(ts, func(a, b *Tensor) int {
var i, j int
if n, err := fmt.Sscanf(a.Name, "blk.%d", &i); err != nil || n != 1 {
return cmp.Compare(a.Name, b.Name)
} else if n, err := fmt.Sscanf(b.Name, "blk.%d", &j); err != nil || n != 1 {
return cmp.Compare(a.Name, b.Name)
}
}
for _, tensor := range tensors {
if err := binary.Write(ws, llm.ByteOrder, uint64(len(tensor.Name))); err != nil {
return err
}
if err := binary.Write(ws, llm.ByteOrder, []byte(tensor.Name)); err != nil {
return err
}
var dims int
for cnt := range len(tensor.Shape) {
if tensor.Shape[cnt] > 0 {
dims++
}
}
if err := binary.Write(ws, llm.ByteOrder, uint32(dims)); err != nil {
return err
}
for i := range dims {
if err := binary.Write(ws, llm.ByteOrder, tensor.Shape[dims-1-i]); err != nil {
return err
}
}
if err := binary.Write(ws, llm.ByteOrder, tensor.Kind); err != nil {
return err
}
if err := binary.Write(ws, llm.ByteOrder, tensor.Offset); err != nil {
return cmp.Compare(i, j)
})
var s uint64
for _, t := range ts {
t.Offset = s
if err := ggufWriteTensorInfo(ws, t); err != nil {
return err
}
s += t.Size()
}
var alignment int64 = 32
for _, tensor := range tensors {
offset, err := ws.Seek(0, io.SeekCurrent)
if err != nil {
return err
}
padding := llm.padding(offset, alignment)
if err := binary.Write(ws, llm.ByteOrder, bytes.Repeat([]byte{0}, int(padding))); err != nil {
return err
}
if _, err := tensor.WriteTo(ws); err != nil {
for _, t := range ts {
if err := ggufWriteTensor(ws, t, alignment); err != nil {
return err
}
}
@ -705,6 +567,102 @@ func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
return nil
}
func (gguf) padding(offset, align int64) int64 {
func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
slog.Debug(k, "type", fmt.Sprintf("%T", v))
if err := binary.Write(ws, binary.LittleEndian, uint64(len(k))); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, []byte(k)); err != nil {
return err
}
var err error
switch v := v.(type) {
case uint32:
err = writeGGUF(ws, ggufTypeUint32, v)
case float32:
err = writeGGUF(ws, ggufTypeFloat32, v)
case bool:
err = writeGGUF(ws, ggufTypeBool, v)
case string:
err = writeGGUFString(ws, v)
case []int32:
err = writeGGUFArray(ws, ggufTypeInt32, v)
case []uint32:
err = writeGGUFArray(ws, ggufTypeUint32, v)
case []float32:
err = writeGGUFArray(ws, ggufTypeFloat32, v)
case []string:
if err := binary.Write(ws, binary.LittleEndian, ggufTypeArray); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, ggufTypeString); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, uint64(len(v))); err != nil {
return err
}
for _, e := range v {
if err := binary.Write(ws, binary.LittleEndian, uint64(len(e))); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, []byte(e)); err != nil {
return err
}
}
default:
return fmt.Errorf("improper type for '%s'", k)
}
return err
}
func ggufWriteTensorInfo(ws io.WriteSeeker, t *Tensor) error {
slog.Debug(t.Name, "kind", t.Kind, "shape", t.Shape, "offset", t.Offset)
if err := binary.Write(ws, binary.LittleEndian, uint64(len(t.Name))); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, []byte(t.Name)); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, uint32(len(t.Shape))); err != nil {
return err
}
for i := range len(t.Shape) {
if err := binary.Write(ws, binary.LittleEndian, t.Shape[len(t.Shape)-i-1]); err != nil {
return err
}
}
if err := binary.Write(ws, binary.LittleEndian, t.Kind); err != nil {
return err
}
return binary.Write(ws, binary.LittleEndian, t.Offset)
}
func ggufWriteTensor(ws io.WriteSeeker, t *Tensor, alignment int64) error {
offset, err := ws.Seek(0, io.SeekCurrent)
if err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, bytes.Repeat([]byte{0}, int(ggufPadding(offset, alignment)))); err != nil {
return err
}
_, err = t.WriteTo(ws)
return err
}
func ggufPadding(offset, align int64) int64 {
return (align - offset%align) % align
}

6
llm/memory_test.go
View file

@ -2,7 +2,6 @@ package llm
import (
"bytes"
"encoding/binary"
"fmt"
"os"
"testing"
@ -20,10 +19,9 @@ func TestEstimateGPULayers(t *testing.T) {
f, err := os.CreateTemp(t.TempDir(), modelName)
require.NoError(t, err)
defer f.Close()
gguf := NewGGUFV3(binary.LittleEndian)
inputLayerCount := 5
tensors := []Tensor{
tensors := []*Tensor{
{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
{Name: "blk.1.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
{Name: "blk.2.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
@ -32,7 +30,7 @@ func TestEstimateGPULayers(t *testing.T) {
{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
}
assert.Len(t, tensors, inputLayerCount+1)
err = gguf.Encode(f, KV{
err = WriteGGUF(f, KV{
"general.architecture": "llama",
"general.name": "name",
"llama.context_length": uint32(32),

26
server/model.go
View file

@ -143,30 +143,6 @@ func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(a
return nil, err
}
mf, err := convert.GetModelFormat(tempDir)
if err != nil {
return nil, err
}
params, err := mf.GetParams(tempDir)
if err != nil {
return nil, err
}
mArch, err := mf.GetModelArch("", tempDir, params)
if err != nil {
return nil, err
}
fn(api.ProgressResponse{Status: "processing tensors"})
if err := mArch.GetTensors(); err != nil {
return nil, err
}
if err := mArch.LoadVocab(); err != nil {
return nil, err
}
fn(api.ProgressResponse{Status: "converting model"})
// TODO(mxyng): this should write directly into a layer
@ -178,7 +154,7 @@ func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(a
defer temp.Close()
defer os.Remove(temp.Name())
if err = mArch.WriteGGUF(temp); err != nil {
if err := convert.Convert(tempDir, temp); err != nil {
return nil, err
}

5
server/routes_create_test.go
View file

@ -2,7 +2,6 @@ package server
import (
"bytes"
"encoding/binary"
"encoding/json"
"fmt"
"io"
@ -20,7 +19,7 @@ import (
var stream bool = false
func createBinFile(t *testing.T, kv map[string]any, ti []llm.Tensor) string {
func createBinFile(t *testing.T, kv map[string]any, ti []*llm.Tensor) string {
t.Helper()
f, err := os.CreateTemp(t.TempDir(), "")
@ -29,7 +28,7 @@ func createBinFile(t *testing.T, kv map[string]any, ti []llm.Tensor) string {
}
defer f.Close()
if err := llm.NewGGUFV3(binary.LittleEndian).Encode(f, kv, ti); err != nil {
if err := llm.WriteGGUF(f, kv, ti); err != nil {
t.Fatal(err)
}

8
server/routes_generate_test.go
View file

@ -101,7 +101,7 @@ func TestGenerateChat(t *testing.T) {
"tokenizer.ggml.tokens": []string{""},
"tokenizer.ggml.scores": []float32{0},
"tokenizer.ggml.token_type": []int32{0},
}, []llm.Tensor{
}, []*llm.Tensor{
{Name: "token_embd.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
{Name: "blk.0.attn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
{Name: "blk.0.ffn_down.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
@ -149,7 +149,7 @@ func TestGenerateChat(t *testing.T) {
Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, llm.KV{
"general.architecture": "bert",
"bert.pooling_type": uint32(0),
}, []llm.Tensor{})),
}, []*llm.Tensor{})),
Stream: &stream,
})
@ -399,7 +399,7 @@ func TestGenerate(t *testing.T) {
"tokenizer.ggml.tokens": []string{""},
"tokenizer.ggml.scores": []float32{0},
"tokenizer.ggml.token_type": []int32{0},
}, []llm.Tensor{
}, []*llm.Tensor{
{Name: "token_embd.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
{Name: "blk.0.attn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
{Name: "blk.0.ffn_down.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
@ -447,7 +447,7 @@ func TestGenerate(t *testing.T) {
Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, llm.KV{
"general.architecture": "bert",
"bert.pooling_type": uint32(0),
}, []llm.Tensor{})),
}, []*llm.Tensor{})),
Stream: &stream,
})

8
server/sched_test.go
View file

@ -3,7 +3,6 @@ package server
import (
"bytes"
"context"
"encoding/binary"
"fmt"
"log/slog"
"os"
@ -114,8 +113,7 @@ func newScenarioRequest(t *testing.T, ctx context.Context, modelName string, est
require.NoError(t, err)
defer f.Close()
gguf := llm.NewGGUFV3(binary.LittleEndian)
err = gguf.Encode(f, llm.KV{
require.NoError(t, llm.WriteGGUF(f, llm.KV{
"general.architecture": "llama",
"general.name": "name",
"llama.context_length": uint32(32),
@ -126,10 +124,10 @@ func newScenarioRequest(t *testing.T, ctx context.Context, modelName string, est
"tokenizer.ggml.tokens": []string{" "},
"tokenizer.ggml.scores": []float32{0},
"tokenizer.ggml.token_type": []int32{0},
}, []llm.Tensor{
}, []*llm.Tensor{
{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
})
}))
require.NoError(t, err)
fname := f.Name()