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Merge branch 'ollama:main' into main

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This commit is contained in:
likelovewant
2024-05-24 15:19:08 +08:00
committed by GitHub
parent fc2f25c1d5 afd2b058b4
commit 4e37e24b04
41 changed files with 1030 additions and 614 deletions
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1
.github/workflows/release.yaml vendored
View File

@@ -28,6 +28,7 @@ jobs:
security unlock-keychain -p password build.keychain
security import certificate.p12 -k build.keychain -P $MACOS_SIGNING_KEY_PASSWORD -T /usr/bin/codesign
security set-key-partition-list -S apple-tool:,apple:,codesign: -s -k password build.keychain
security set-keychain-settings -lut 3600 build.keychain
- uses: actions/setup-go@v5
with:
go-version-file: go.mod

28
README.md
View File

@@ -69,15 +69,17 @@ Here are some example models that can be downloaded:
| ------------------ | ---------- | ----- | ------------------------------ |
| Llama 3 | 8B | 4.7GB | `ollama run llama3` |
| Llama 3 | 70B | 40GB | `ollama run llama3:70b` |
| Phi-3 | 3.8B | 2.3GB | `ollama run phi3` |
| Phi 3 Mini | 3.8B | 2.3GB | `ollama run phi3` |
| Phi 3 Medium | 14B | 7.9GB | `ollama run phi3:medium` |
| Gemma | 2B | 1.4GB | `ollama run gemma:2b` |
| Gemma | 7B | 4.8GB | `ollama run gemma:7b` |
| Mistral | 7B | 4.1GB | `ollama run mistral` |
| Moondream 2 | 1.4B | 829MB | `ollama run moondream` |
| Neural Chat | 7B | 4.1GB | `ollama run neural-chat` |
| Starling | 7B | 4.1GB | `ollama run starling-lm` |
| Code Llama | 7B | 3.8GB | `ollama run codellama` |
| Llama 2 Uncensored | 7B | 3.8GB | `ollama run llama2-uncensored` |
| LLaVA | 7B | 4.5GB | `ollama run llava` |
| Gemma | 2B | 1.4GB | `ollama run gemma:2b` |
| Gemma | 7B | 4.8GB | `ollama run gemma:7b` |
| Solar | 10.7B | 6.1GB | `ollama run solar` |
> Note: You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
@@ -210,25 +212,7 @@ ollama list
## Building
Install `cmake` and `go`:
```
brew install cmake go
```
Then generate dependencies:
```
go generate ./...
```
Then build the binary:
```
go build .
```
More detailed instructions can be found in the [developer guide](https://github.com/ollama/ollama/blob/main/docs/development.md)
See the [developer guide](https://github.com/ollama/ollama/blob/main/docs/development.md)
### Running local builds

36
cmd/cmd.go
View File

@@ -35,6 +35,7 @@ import (
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/auth"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/parser"
"github.com/ollama/ollama/progress"
"github.com/ollama/ollama/server"
"github.com/ollama/ollama/types/errtypes"
@@ -63,7 +64,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
}
defer f.Close()
modelfile, err := model.ParseFile(f)
modelfile, err := parser.ParseFile(f)
if err != nil {
return err
}
@@ -207,7 +208,7 @@ func tempZipFiles(path string) (string, error) {
// pytorch files might also be unresolved git lfs references; skip if they are
// covers pytorch_model-x-of-y.bin, pytorch_model.fp32-x-of-y.bin, pytorch_model.bin
files = append(files, pt...)
} else if pt, _ := glob(filepath.Join(path, "consolidated*.pth"), "application/octet-stream"); len(pt) > 0 {
} else if pt, _ := glob(filepath.Join(path, "consolidated*.pth"), "application/zip"); len(pt) > 0 {
// pytorch files might also be unresolved git lfs references; skip if they are
// covers consolidated.x.pth, consolidated.pth
files = append(files, pt...)
@@ -1078,12 +1079,24 @@ func versionHandler(cmd *cobra.Command, _ []string) {
}
}
func appendHostEnvDocs(cmd *cobra.Command) {
const hostEnvDocs = `
type EnvironmentVar struct {
Name string
Description string
}
func appendEnvDocs(cmd *cobra.Command, envs []EnvironmentVar) {
if len(envs) == 0 {
return
}
envUsage := `
Environment Variables:
OLLAMA_HOST The host:port or base URL of the Ollama server (e.g. http://localhost:11434)
`
cmd.SetUsageTemplate(cmd.UsageTemplate() + hostEnvDocs)
for _, e := range envs {
envUsage += fmt.Sprintf(" %-16s %s\n", e.Name, e.Description)
}
cmd.SetUsageTemplate(cmd.UsageTemplate() + envUsage)
}
func NewCLI() *cobra.Command {
@@ -1220,6 +1233,10 @@ Environment Variables:
RunE: DeleteHandler,
}
ollamaHostEnv := EnvironmentVar{"OLLAMA_HOST", "The host:port or base URL of the Ollama server (e.g. http://localhost:11434)"}
ollamaNoHistoryEnv := EnvironmentVar{"OLLAMA_NOHISTORY", "Disable readline history"}
envs := []EnvironmentVar{ollamaHostEnv}
for _, cmd := range []*cobra.Command{
createCmd,
showCmd,
@@ -1231,7 +1248,12 @@ Environment Variables:
copyCmd,
deleteCmd,
} {
appendHostEnvDocs(cmd)
switch cmd {
case runCmd:
appendEnvDocs(cmd, []EnvironmentVar{ollamaHostEnv, ollamaNoHistoryEnv})
default:
appendEnvDocs(cmd, envs)
}
}
rootCmd.AddCommand(

5
cmd/interactive.go
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@@ -138,6 +138,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
fmt.Fprintln(os.Stderr, " Alt + f Move forward (right) one word")
fmt.Fprintln(os.Stderr, " Ctrl + k Delete the sentence after the cursor")
fmt.Fprintln(os.Stderr, " Ctrl + u Delete the sentence before the cursor")
fmt.Fprintln(os.Stderr, " Ctrl + w Delete the word before the cursor")
fmt.Fprintln(os.Stderr, "")
fmt.Fprintln(os.Stderr, " Ctrl + l Clear the screen")
fmt.Fprintln(os.Stderr, " Ctrl + c Stop the model from responding")
@@ -182,6 +183,10 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
return err
}
if os.Getenv("OLLAMA_NOHISTORY") != "" {
scanner.HistoryDisable()
}
fmt.Print(readline.StartBracketedPaste)
defer fmt.Printf(readline.EndBracketedPaste)

20
convert/convert.go
View File

@@ -18,6 +18,16 @@ import (
"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"`
@@ -37,6 +47,8 @@ type Params struct {
Experts int `json:"num_local_experts"`
ExpertsUsed int `json:"num_experts_per_tok"`
PreTokenizer string
ByteOrder
}
@@ -74,10 +86,9 @@ func GetModelFormat(dirname string) (ModelFormat, error) {
}
for _, fn := range files {
slog.Debug(fmt.Sprintf("file = %s", fn))
if strings.HasSuffix(fn, ".safetensors") {
return &SafetensorFormat{}, nil
} else if strings.HasSuffix(fn, ".bin") {
} else if strings.HasSuffix(fn, ".bin") || strings.HasSuffix(fn, ".pth") {
slog.Debug("model is torch")
return &TorchFormat{}, nil
}
@@ -92,6 +103,7 @@ type Vocab struct {
Tokens []string
Scores []float32
Types []int32
Merges []string
}
func LoadSentencePieceTokens(dirpath string, params *Params) (*Vocab, error) {
@@ -170,7 +182,7 @@ func LoadSentencePieceTokens(dirpath string, params *Params) (*Vocab, error) {
}
v.Tokens = append(v.Tokens, t.key)
v.Scores = append(v.Scores, -1000.0)
v.Types = append(v.Types, int32(llm.GGUFTokenUserDefined))
v.Types = append(v.Types, tokenTypeUserDefined)
}
slog.Info(fmt.Sprintf("vocab size w/ extra tokens: %d", len(v.Tokens)))
@@ -180,7 +192,7 @@ func LoadSentencePieceTokens(dirpath string, params *Params) (*Vocab, error) {
for cnt := 0; cnt < missingTokens; cnt++ {
v.Tokens = append(v.Tokens, fmt.Sprintf("<dummy%05d>", cnt+1))
v.Scores = append(v.Scores, -1)
v.Types = append(v.Types, int32(llm.GGUFTokenUserDefined))
v.Types = append(v.Types, tokenTypeUserDefined)
}
}

103
convert/convert_test.go Normal file
View File

@@ -0,0 +1,103 @@
//go:build slow
package convert
import (
"os"
"path/filepath"
"testing"
"github.com/ollama/ollama/llm"
)
func convertFull(t *testing.T, p string) (llm.KV, llm.Tensors) {
t.Helper()
mf, err := GetModelFormat(p)
if err != nil {
t.Fatal(err)
}
params, err := mf.GetParams(p)
if err != nil {
t.Fatal(err)
}
arch, err := mf.GetModelArch("", p, 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 {
t.Fatal(err)
}
r, err := os.Open(f.Name())
if err != nil {
t.Fatal(err)
}
defer r.Close()
m, _, err := llm.DecodeGGML(r)
if err != nil {
t.Fatal(err)
}
return m.KV(), m.Tensors()
}
func TestConvertFull(t *testing.T) {
cases := []struct {
path string
arch string
tensors int
layers int
}{
{"Meta-Llama-3-8B-Instruct", "llama", 291, 35},
{"Mistral-7B-Instruct-v0.2", "llama", 291, 35},
{"Mixtral-8x7B-Instruct-v0.1", "llama", 291, 35},
{"gemma-2b-it", "gemma", 164, 20},
}
for _, tt := range cases {
t.Run(tt.path, func(t *testing.T) {
p := filepath.Join("testdata", tt.path)
if _, err := os.Stat(p); err != nil {
t.Skipf("%s not found", p)
}
kv, tensors := convertFull(t, p)
if kv.Architecture() != tt.arch {
t.Fatalf("expected llama, got %s", kv.Architecture())
}
if kv.FileType().String() != "F16" {
t.Fatalf("expected F16, got %s", kv.FileType())
}
if len(tensors) != tt.tensors {
t.Fatalf("expected %d tensors, got %d", tt.tensors, len(tensors))
}
layers := tensors.Layers()
if len(layers) != tt.layers {
t.Fatalf("expected %d layers, got %d", tt.layers, len(layers))
}
})
}
}

51
convert/gemma.go
View File

@@ -1,14 +1,11 @@
package convert
import (
"encoding/binary"
"fmt"
"io"
"log/slog"
"os"
"strings"
"github.com/d4l3k/go-bfloat16"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
@@ -19,49 +16,27 @@ type GemmaModel struct {
ModelData
}
func gemmaLayerHandler(w io.Writer, r safetensorWriterTo, f *os.File) error {
slog.Debug(fmt.Sprintf("converting '%s'", r.t.Name))
data := make([]byte, r.end-r.start)
if err := binary.Read(f, r.bo, data); err != nil {
return err
}
tDataF32 := bfloat16.DecodeFloat32(data)
var err error
tDataF32, err = addOnes(tDataF32, int(r.t.Shape[0]))
if err != nil {
return err
}
if err := binary.Write(w, r.bo, tDataF32); err != nil {
return err
}
return nil
}
func addOnes(data []float32, vectorSize int) ([]float32, error) {
n := tensor.New(tensor.WithShape(vectorSize), tensor.WithBacking(data))
ones := tensor.Ones(tensor.Float32, vectorSize)
var err error
n, err = n.Add(ones)
n, err := n.Add(ones)
if err != nil {
return []float32{}, err
return nil, err
}
newN, err := native.SelectF32(n, 0)
ts, err := native.SelectF32(n, 0)
if err != nil {
return []float32{}, err
return nil, err
}
var fullTensor []float32
for _, v := range newN {
fullTensor = append(fullTensor, v...)
var f32s []float32
for _, t := range ts {
f32s = append(f32s, t...)
}
return fullTensor, nil
return f32s, nil
}
func (m *GemmaModel) GetTensors() error {
@@ -71,12 +46,10 @@ func (m *GemmaModel) GetTensors() error {
}
slog.Debug(fmt.Sprintf("Total tensors: %d", len(t)))
m.Tensors = []llm.Tensor{}
for _, l := range t {
if strings.HasSuffix(l.Name, "norm.weight") {
wt := l.WriterTo.(safetensorWriterTo)
wt.handler = gemmaLayerHandler
wt.repacker = m.Repack
l.WriterTo = wt
}
m.Tensors = append(m.Tensors, l)
@@ -94,6 +67,10 @@ func (m *GemmaModel) LoadVocab() error {
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",

176
convert/llama.go
View File

@@ -1,17 +1,17 @@
package convert
import (
"encoding/binary"
"cmp"
"errors"
"fmt"
"io"
"log/slog"
"os"
"path/filepath"
"regexp"
"strings"
"github.com/nlpodyssey/gopickle/pytorch"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
"github.com/x448/float16"
"github.com/ollama/ollama/llm"
)
@@ -20,81 +20,12 @@ type LlamaModel struct {
ModelData
}
func llamaLayerHandler(w io.Writer, r torchWriterTo) error {
slog.Debug(fmt.Sprintf("repacking layer '%s'", r.t.Name))
data := r.storage.(*pytorch.HalfStorage).Data
tData := make([]uint16, len(data))
for cnt, v := range data {
tData[cnt] = uint16(float16.Fromfloat32(v))
}
var err error
var heads uint32
if strings.Contains(r.t.Name, "attn_q") {
heads = uint32(r.params.AttentionHeads)
} else if strings.Contains(r.t.Name, "attn_k") {
heads = uint32(r.params.KeyValHeads)
if heads == 0 {
heads = uint32(r.params.AttentionHeads)
}
} else {
return fmt.Errorf("unknown layer type")
}
slog.Debug(fmt.Sprintf("heads = %d", heads))
tData, err = llamaRepack(tData, int(heads), r.t.Shape)
if err != nil {
return err
}
if err = binary.Write(w, r.bo, tData); err != nil {
return err
}
return nil
}
func llamaRepack(data []uint16, heads int, shape []uint64) ([]uint16, error) {
n := tensor.New(tensor.WithShape(int(shape[0]), int(shape[1])), tensor.WithBacking(data))
origShape := n.Shape().Clone()
// reshape the tensor and swap axes 1 and 2 to unpack the layer for gguf
if err := n.Reshape(heads, 2, origShape[0]/heads/2, origShape[1]); err != nil {
return nil, err
}
if err := n.T(0, 2, 1, 3); err != nil {
return nil, err
}
if err := n.Reshape(origShape...); err != nil {
return nil, err
}
if err := n.Transpose(); err != nil {
return nil, err
}
newN, err := native.SelectU16(n, 1)
if err != nil {
return nil, err
}
var fullTensor []uint16
for _, v := range newN {
fullTensor = append(fullTensor, v...)
}
return fullTensor, nil
}
func (m *LlamaModel) GetTensors() error {
t, err := m.Format.GetTensors(m.Path, m.Params)
if err != nil {
return err
}
m.Tensors = []llm.Tensor{}
pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
re, err := regexp.Compile(pattern)
if err != nil {
@@ -104,10 +35,16 @@ func (m *LlamaModel) GetTensors() error {
for _, l := range t {
matches := re.FindAllStringSubmatch(l.Name, -1)
if len(matches) > 0 {
slog.Debug(fmt.Sprintf("setting handler for: %s", l.Name))
wt := l.WriterTo.(torchWriterTo)
wt.handler = llamaLayerHandler
l.WriterTo = wt
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)
}
@@ -115,19 +52,22 @@ func (m *LlamaModel) GetTensors() error {
return nil
}
func (m *LlamaModel) LoadVocab() error {
var v *Vocab
var err error
slog.Debug("loading vocab")
v, err = LoadSentencePieceTokens(m.Path, m.Params)
if err != 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
}
slog.Debug("vocab loaded")
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 = v
m.Vocab.Merges = merges
m.Params.PreTokenizer = pre
return nil
}
@@ -140,23 +80,79 @@ func (m *LlamaModel) WriteGGUF(ws io.WriteSeeker) error {
"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": "llama",
"tokenizer.ggml.model": "gpt2",
"tokenizer.ggml.pre": m.Params.PreTokenizer,
"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,
}
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
if strings.HasSuffix(name, "attn_q.weight") {
heads = params.AttentionHeads
} else if strings.HasSuffix(name, "attn_k.weight") {
heads = cmp.Or(params.KeyValHeads, params.AttentionHeads)
} else {
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
}

93
convert/mistral.go
View File

@@ -1,17 +1,8 @@
package convert
import (
"encoding/binary"
"fmt"
"io"
"os"
"regexp"
"strings"
"github.com/d4l3k/go-bfloat16"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
"github.com/x448/float16"
"github.com/ollama/ollama/llm"
)
@@ -20,90 +11,12 @@ type MistralModel struct {
ModelData
}
func mistralLayerHandler(w io.Writer, r safetensorWriterTo, f *os.File) error {
layerSize := r.end - r.start
var err error
tData := make([]uint16, layerSize/2)
if err = binary.Read(f, r.bo, tData); err != nil {
return err
}
var heads uint32
if strings.Contains(r.t.Name, "attn_q") {
heads = uint32(r.params.AttentionHeads)
} else if strings.Contains(r.t.Name, "attn_k") {
heads = uint32(r.params.KeyValHeads)
if heads == 0 {
heads = uint32(r.params.AttentionHeads)
}
} else {
return fmt.Errorf("unknown layer type")
}
tData, err = repack(tData, int(heads), r.t.Shape)
if err != nil {
return err
}
var buf []byte
for _, n := range tData {
buf = r.bo.AppendUint16(buf, n)
}
tempBuf := make([]uint16, len(tData))
tDataF32 := bfloat16.DecodeFloat32(buf)
for cnt, v := range tDataF32 {
tDataF16 := float16.Fromfloat32(v)
tempBuf[cnt] = uint16(tDataF16)
}
if err = binary.Write(w, r.bo, tempBuf); err != nil {
return err
}
return nil
}
func repack(data []uint16, heads int, shape []uint64) ([]uint16, error) {
n := tensor.New(tensor.WithShape(int(shape[0]), int(shape[1])), tensor.WithBacking(data))
origShape := n.Shape().Clone()
// reshape the tensor and swap axes 1 and 2 to unpack the layer for gguf
if err := n.Reshape(heads, 2, origShape[0]/heads/2, origShape[1]); err != nil {
return nil, err
}
if err := n.T(0, 2, 1, 3); err != nil {
return nil, err
}
if err := n.Reshape(origShape...); err != nil {
return nil, err
}
if err := n.Transpose(); err != nil {
return nil, err
}
newN, err := native.SelectU16(n, 1)
if err != nil {
return nil, err
}
var fullTensor []uint16
for _, v := range newN {
fullTensor = append(fullTensor, v...)
}
return fullTensor, nil
}
func (m *MistralModel) GetTensors() error {
t, err := m.Format.GetTensors(m.Path, m.Params)
if err != nil {
return err
}
m.Tensors = []llm.Tensor{}
pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
re, err := regexp.Compile(pattern)
if err != nil {
@@ -114,7 +27,7 @@ func (m *MistralModel) GetTensors() error {
matches := re.FindAllStringSubmatch(l.Name, -1)
if len(matches) > 0 {
wt := l.WriterTo.(safetensorWriterTo)
wt.handler = mistralLayerHandler
wt.repacker = m.Repack
l.WriterTo = wt
}
m.Tensors = append(m.Tensors, l)
@@ -160,3 +73,7 @@ func (m *MistralModel) WriteGGUF(ws io.WriteSeeker) error {
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)
}

8
convert/mixtral.go
View File

@@ -17,8 +17,6 @@ func (m *MixtralModel) GetTensors() error {
return err
}
m.Tensors = []llm.Tensor{}
pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
re, err := regexp.Compile(pattern)
if err != nil {
@@ -29,7 +27,7 @@ func (m *MixtralModel) GetTensors() error {
matches := re.FindAllStringSubmatch(l.Name, -1)
if len(matches) > 0 {
wt := l.WriterTo.(safetensorWriterTo)
wt.handler = mistralLayerHandler
wt.repacker = m.Repack
l.WriterTo = wt
}
m.Tensors = append(m.Tensors, l)
@@ -83,3 +81,7 @@ func (m *MixtralModel) WriteGGUF(ws io.WriteSeeker) error {
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)
}

186
convert/safetensors.go
View File

@@ -6,14 +6,13 @@ import (
"encoding/json"
"fmt"
"io"
"log/slog"
"os"
"path/filepath"
"regexp"
"slices"
"strings"
"github.com/d4l3k/go-bfloat16"
"github.com/mitchellh/mapstructure"
"github.com/x448/float16"
"github.com/ollama/ollama/llm"
@@ -26,39 +25,38 @@ type safetensorWriterTo struct {
bo ByteOrder
filename string
dtype string
start, end, padding uint64
handler func(w io.Writer, r safetensorWriterTo, f *os.File) error
offset, size int64
repacker func(string, []float32, []uint64) ([]float32, error)
}
type tensorMetaData struct {
Type string `mapstructure:"dtype"`
Shape []int `mapstructure:"shape"`
Offsets []int `mapstructure:"data_offsets"`
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) {
slog.Debug("getting tensor data")
var tensors []llm.Tensor
files, err := filepath.Glob(filepath.Join(dirpath, "/model-*.safetensors"))
matches, err := filepath.Glob(filepath.Join(dirpath, "*.safetensors"))
if err != nil {
return nil, err
}
var offset uint64
for _, f := range files {
for _, f := range matches {
var t []llm.Tensor
var err error
t, offset, err = m.readTensors(f, offset, params)
if err != nil {
slog.Error(err.Error())
return nil, err
}
tensors = append(tensors, t...)
}
slog.Debug(fmt.Sprintf("all tensors = %d", len(tensors)))
return tensors, nil
}
@@ -69,70 +67,57 @@ func (m *SafetensorFormat) readTensors(fn string, offset uint64, params *Params)
}
defer f.Close()
var jsonSize uint64
if err := binary.Read(f, binary.LittleEndian, &jsonSize); err != nil {
var n int64
if err := binary.Read(f, binary.LittleEndian, &n); err != nil {
return nil, 0, err
}
buf := make([]byte, jsonSize)
_, err = io.ReadFull(f, buf)
if err != nil {
b := bytes.NewBuffer(make([]byte, 0, n))
if _, err = io.CopyN(b, f, n); err != nil {
return nil, 0, err
}
d := json.NewDecoder(bytes.NewBuffer(buf))
d.UseNumber()
var parsed map[string]interface{}
if err = d.Decode(&parsed); err != nil {
var headers map[string]safetensorMetadata
if err := json.NewDecoder(b).Decode(&headers); err != nil {
return nil, 0, err
}
var keys []string
for k := range parsed {
keys = append(keys, k)
for key := range headers {
if !strings.HasSuffix(key, "self_attn.rotary_embd.inv_freq") {
keys = append(keys, key)
}
}
slices.Sort(keys)
slog.Info("converting layers")
var tensors []llm.Tensor
for _, k := range keys {
vals := parsed[k].(map[string]interface{})
var data tensorMetaData
if err = mapstructure.Decode(vals, &data); err != nil {
slog.Error("couldn't decode properly")
return nil, 0, err
}
for _, key := range keys {
value := headers[key]
var size uint64
var kind uint32
switch len(data.Shape) {
switch len(value.Shape) {
case 0:
// metadata
// valuedata
continue
case 1:
// convert to float32
kind = 0
size = uint64(data.Shape[0] * 4)
case 2:
// convert to float16
kind = 1
size = uint64(data.Shape[0] * data.Shape[1] * 2)
}
ggufName, err := m.GetLayerName(k)
name, err := m.GetLayerName(key)
if err != nil {
slog.Error(err.Error())
return nil, 0, err
}
shape := []uint64{0, 0, 0, 0}
for i := range data.Shape {
shape[i] = uint64(data.Shape[i])
shape := make([]uint64, len(value.Shape))
copy(shape, value.Shape)
pad := func(s int64) int64 {
return 8 + n + s
}
t := llm.Tensor{
Name: ggufName,
Name: name,
Kind: kind,
Offset: offset,
Shape: shape[:],
@@ -143,18 +128,15 @@ func (m *SafetensorFormat) readTensors(fn string, offset uint64, params *Params)
params: params,
bo: params.ByteOrder,
filename: fn,
start: uint64(data.Offsets[0]),
end: uint64(data.Offsets[1]),
padding: 8 + jsonSize,
dtype: value.Type,
offset: pad(value.Offsets[0]),
size: pad(value.Offsets[1]) - pad(value.Offsets[0]),
}
offset += size
offset += t.Size()
tensors = append(tensors, t)
}
slog.Debug(fmt.Sprintf("total tensors for file = %d", len(tensors)))
slog.Debug(fmt.Sprintf("offset = %d", offset))
return tensors, offset, nil
}
@@ -167,9 +149,7 @@ func (m *SafetensorFormat) GetParams(dirpath string) (*Params, error) {
var params Params
d := json.NewDecoder(f)
err = d.Decode(&params)
if err != nil {
if err := json.NewDecoder(f).Decode(&params); err != nil {
return nil, err
}
@@ -224,55 +204,58 @@ func (r safetensorWriterTo) WriteTo(w io.Writer) (n int64, err error) {
}
defer f.Close()
if _, err = f.Seek(int64(r.padding+r.start), 0); err != nil {
if _, err = f.Seek(r.offset, io.SeekStart); err != nil {
return 0, err
}
// use the handler if one is present
if r.handler != nil {
return 0, r.handler(w, r, f)
}
remaining := r.end - r.start
bufSize := uint64(10240)
var finished bool
for {
data := make([]byte, min(bufSize, remaining))
b, err := io.ReadFull(f, data)
remaining -= uint64(b)
if err == io.EOF || remaining <= 0 {
finished = true
} else if err != nil {
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
}
// convert bfloat16 -> ieee float32
tDataF32 := bfloat16.DecodeFloat32(data)
switch r.t.Kind {
case 0:
if err := binary.Write(w, r.bo, tDataF32); err != nil {
return 0, err
}
case 1:
// convert float32 -> float16
tempBuf := make([]uint16, len(data)/2)
for cnt, v := range tDataF32 {
tDataF16 := float16.Fromfloat32(v)
tempBuf[cnt] = uint16(tDataF16)
}
if err := binary.Write(w, r.bo, tempBuf); err != nil {
return 0, err
}
for _, b := range u16s {
f32s = append(f32s, float16.Frombits(b).Float32())
}
if finished {
break
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
}
}
return 0, nil
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) {
@@ -281,6 +264,15 @@ func (m *SafetensorFormat) GetModelArch(name, dirPath string, params *Params) (M
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{

109
convert/tokenizer.go Normal file
View File

@@ -0,0 +1,109 @@
package convert
import (
"cmp"
"crypto/sha256"
"encoding/json"
"fmt"
"log/slog"
"os"
"slices"
"golang.org/x/exp/maps"
)
type Tokenizer struct {
Version string `json:"version"`
AddedTokens []Token `json:"added_tokens"`
Model TokenizerModel `json:"model"`
PreTokenizer struct {
PreTokenizers []struct {
Type string `json:"type"`
Pattern struct {
Regex string `json:"Regex"`
} `json:"pattern"`
} `json:"pretokenizers"`
} `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 {
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
}
}
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)
if err != nil {
panic(err)
}
defer f.Close()
var t Tokenizer
if err := json.NewDecoder(f).Decode(&t); err != nil {
return "", nil, nil, err
}
tokens = make([]Token, t.maxID()+1)
for k, v := range t.Model.Vocab {
tokens[v] = Token{ID: v, Content: k, Special: false, UserDefined: false}
}
for _, v := range t.AddedTokens {
v.UserDefined = true
tokens[v.ID] = v
}
sha256sum := sha256.New()
for _, pt := range t.PreTokenizer.PreTokenizers {
switch pt.Type {
case "Split":
if pt.Pattern.Regex != "" {
sha256sum.Write([]byte(pt.Pattern.Regex))
}
}
}
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 pre, tokens, t.Model.Merges, nil
}

72
convert/torch.go
View File

@@ -24,8 +24,8 @@ type torchWriterTo struct {
params *Params
bo ByteOrder
storage pytorch.StorageInterface
handler func(w io.Writer, r torchWriterTo) error
storage pytorch.StorageInterface
repacker func(string, []float32, []uint64) ([]float32, error)
}
type TorchFormat struct{}
@@ -33,14 +33,14 @@ type TorchFormat struct{}
func (tf *TorchFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
slog.Debug("getting torch tensors")
files, err := filepath.Glob(filepath.Join(dirpath, "pytorch_model-*.bin"))
if err != nil {
slog.Error("didn't find any torch files")
return nil, err
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)
@@ -77,7 +77,7 @@ func (tf *TorchFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor,
slog.Error(err.Error())
return nil, err
}
slog.Debug(fmt.Sprintf("finding name for '%s' -> '%s'", k.(string), ggufName))
slog.Debug(fmt.Sprintf("'%35s': '%30s' %10d [%#v]", k.(string), ggufName, size, tshape))
shape := []uint64{0, 0, 0, 0}
for i := range tshape {
@@ -120,7 +120,7 @@ func getAltParams(dirpath string) (*Params, error) {
AttentionHeads int `json:"n_heads"`
KeyValHeads int `json:"n_kv_heads"`
HiddenLayers int `json:"n_layers"`
RopeTheta int `json:"rope_theta"`
RopeTheta float64 `json:"rope_theta"`
NormEPS float64 `json:"norm_eps"`
}
@@ -133,6 +133,7 @@ func getAltParams(dirpath string) (*Params, error) {
}
params := &Params{
Architectures: []string{"LlamaForCausalLM"},
HiddenSize: tparams.HiddenSize,
AttentionHeads: tparams.AttentionHeads,
KeyValHeads: tparams.KeyValHeads,
@@ -229,37 +230,38 @@ func (m *TorchFormat) GetLayerName(n string) (string, error) {
}
func (r torchWriterTo) WriteTo(w io.Writer) (n int64, err error) {
// use the handler if one is present
if r.handler != nil {
return 0, r.handler(w, r)
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)
}
switch r.storage.(type) {
case *pytorch.FloatStorage:
slog.Warn(fmt.Sprintf("unexpected storage found for layer '%s'; skipping", r.t.Name))
return 0, nil
case *pytorch.HalfStorage:
switch r.t.Kind {
case 0:
data := r.storage.(*pytorch.HalfStorage).Data
slog.Debug(fmt.Sprintf("%35s F32 (%d)", r.t.Name, len(data)))
if err := binary.Write(w, r.bo, data); err != nil {
return 0, err
}
case 1:
data := r.storage.(*pytorch.HalfStorage).Data
tData := make([]uint16, len(data))
for cnt, v := range data {
tData[cnt] = uint16(float16.Fromfloat32(v))
}
slog.Debug(fmt.Sprintf("%35s F16 (%d)", r.t.Name, len(tData)))
if err := binary.Write(w, r.bo, tData); err != nil {
return 0, err
}
if r.repacker != nil {
f32s, err = r.repacker(r.t.Name, f32s, r.t.Shape)
if err != nil {
return 0, err
}
}
return 0, nil
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) {

2
docs/development.md
View File

@@ -6,6 +6,8 @@ Install required tools:
- go version 1.22 or higher
- gcc version 11.4.0 or higher
### MacOS
```bash
brew install go cmake gcc
```

93
docs/faq.md
View File

@@ -6,7 +6,7 @@ Ollama on macOS and Windows will automatically download updates. Click on the ta
On Linux, re-run the install script:
```
```shell
curl -fsSL https://ollama.com/install.sh | sh
```
@@ -30,7 +30,7 @@ To change this when using `ollama run`, use `/set parameter`:
When using the API, specify the `num_ctx` parameter:
```
```shell
curl http://localhost:11434/api/generate -d '{
"model": "llama3",
"prompt": "Why is the sky blue?",
@@ -40,6 +40,21 @@ curl http://localhost:11434/api/generate -d '{
}'
```
## How can I tell if my model was loaded onto the GPU?
Use the `ollama ps` command to see what models are currently loaded into memory.
```shell
ollama ps
NAME ID SIZE PROCESSOR UNTIL
llama3:70b bcfb190ca3a7 42 GB 100% GPU 4 minutes from now
```
The `Processor` column will show which memory the model was loaded in to:
* `100% GPU` means the model was loaded entirely into the GPU
* `100% CPU` means the model was loaded entirely in system memory
* `48%/52% CPU/GPU` means the model was loaded partially onto both the GPU and into system memory
## How do I configure Ollama server?
Ollama server can be configured with environment variables.
@@ -94,6 +109,34 @@ On Windows, Ollama inherits your user and system environment variables.
6. Start the Ollama application from the Windows Start menu.
## How do I use Ollama behind a proxy?
Ollama is compatible with proxy servers if `HTTP_PROXY` or `HTTPS_PROXY` are configured. When using either variables, ensure it is set where `ollama serve` can access the values. When using `HTTPS_PROXY`, ensure the proxy certificate is installed as a system certificate. Refer to the section above for how to use environment variables on your platform.
### How do I use Ollama behind a proxy in Docker?
The Ollama Docker container image can be configured to use a proxy by passing `-e HTTPS_PROXY=https://proxy.example.com` when starting the container.
Alternatively, the Docker daemon can be configured to use a proxy. Instructions are available for Docker Desktop on [macOS](https://docs.docker.com/desktop/settings/mac/#proxies), [Windows](https://docs.docker.com/desktop/settings/windows/#proxies), and [Linux](https://docs.docker.com/desktop/settings/linux/#proxies), and Docker [daemon with systemd](https://docs.docker.com/config/daemon/systemd/#httphttps-proxy).
Ensure the certificate is installed as a system certificate when using HTTPS. This may require a new Docker image when using a self-signed certificate.
```dockerfile
FROM ollama/ollama
COPY my-ca.pem /usr/local/share/ca-certificates/my-ca.crt
RUN update-ca-certificates
```
Build and run this image:
```shell
docker build -t ollama-with-ca .
docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-with-ca
```
## Does Ollama send my prompts and answers back to ollama.com?
No. Ollama runs locally, and conversation data does not leave your machine.
## How can I expose Ollama on my network?
@@ -120,7 +163,7 @@ server {
Ollama can be accessed using a range of tools for tunneling tools. For example with Ngrok:
```
```shell
ngrok http 11434 --host-header="localhost:11434"
```
@@ -128,7 +171,7 @@ ngrok http 11434 --host-header="localhost:11434"
To use Ollama with Cloudflare Tunnel, use the `--url` and `--http-host-header` flags:
```
```shell
cloudflared tunnel --url http://localhost:11434 --http-host-header="localhost:11434"
```
@@ -150,39 +193,10 @@ If a different directory needs to be used, set the environment variable `OLLAMA_
Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
## Does Ollama send my prompts and answers back to ollama.com?
No. Ollama runs locally, and conversation data does not leave your machine.
## How can I use Ollama in Visual Studio Code?
There is already a large collection of plugins available for VSCode as well as other editors that leverage Ollama. See the list of [extensions & plugins](https://github.com/ollama/ollama#extensions--plugins) at the bottom of the main repository readme.
## How do I use Ollama behind a proxy?
Ollama is compatible with proxy servers if `HTTP_PROXY` or `HTTPS_PROXY` are configured. When using either variables, ensure it is set where `ollama serve` can access the values. When using `HTTPS_PROXY`, ensure the proxy certificate is installed as a system certificate. Refer to the section above for how to use environment variables on your platform.
### How do I use Ollama behind a proxy in Docker?
The Ollama Docker container image can be configured to use a proxy by passing `-e HTTPS_PROXY=https://proxy.example.com` when starting the container.
Alternatively, the Docker daemon can be configured to use a proxy. Instructions are available for Docker Desktop on [macOS](https://docs.docker.com/desktop/settings/mac/#proxies), [Windows](https://docs.docker.com/desktop/settings/windows/#proxies), and [Linux](https://docs.docker.com/desktop/settings/linux/#proxies), and Docker [daemon with systemd](https://docs.docker.com/config/daemon/systemd/#httphttps-proxy).
Ensure the certificate is installed as a system certificate when using HTTPS. This may require a new Docker image when using a self-signed certificate.
```dockerfile
FROM ollama/ollama
COPY my-ca.pem /usr/local/share/ca-certificates/my-ca.crt
RUN update-ca-certificates
```
Build and run this image:
```shell
docker build -t ollama-with-ca .
docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-with-ca
```
## How do I use Ollama with GPU acceleration in Docker?
The Ollama Docker container can be configured with GPU acceleration in Linux or Windows (with WSL2). This requires the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit). See [ollama/ollama](https://hub.docker.com/r/ollama/ollama) for more details.
@@ -197,7 +211,7 @@ Open `Control Panel > Networking and Internet > View network status and tasks` a
Click on `Configure` and open the `Advanced` tab. Search through each of the properties until you find `Large Send Offload Version 2 (IPv4)` and `Large Send Offload Version 2 (IPv6)`. *Disable* both of these
properties.
## How can I pre-load a model to get faster response times?
## How can I preload a model into Ollama to get faster response times?
If you are using the API you can preload a model by sending the Ollama server an empty request. This works with both the `/api/generate` and `/api/chat` API endpoints.
@@ -211,6 +225,11 @@ To use the chat completions endpoint, use:
curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
```
To preload a model using the CLI, use the command:
```shell
ollama run llama3 ""
```
## How do I keep a model loaded in memory or make it unload immediately?
By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you are making numerous requests to the LLM. You may, however, want to free up the memory before the 5 minutes have elapsed or keep the model loaded indefinitely. Use the `keep_alive` parameter with either the `/api/generate` and `/api/chat` API endpoints to control how long the model is left in memory.
@@ -235,8 +254,6 @@ Alternatively, you can change the amount of time all models are loaded into memo
If you wish to override the `OLLAMA_KEEP_ALIVE` setting, use the `keep_alive` API parameter with the `/api/generate` or `/api/chat` API endpoints.
## How do I manage the maximum number of requests the server can queue
## How do I manage the maximum number of requests the Ollama server can queue?
If too many requests are sent to the server, it will respond with a 503 error
indicating the server is overloaded. You can adjust how many requests may be
queue by setting `OLLAMA_MAX_QUEUE`
If too many requests are sent to the server, it will respond with a 503 error indicating the server is overloaded. You can adjust how many requests may be queue by setting `OLLAMA_MAX_QUEUE`.

190
docs/troubleshooting.md
View File

@@ -1,104 +1,86 @@
# How to troubleshoot issues
Sometimes Ollama may not perform as expected. One of the best ways to figure out what happened is to take a look at the logs. Find the logs on **Mac** by running the command:
```shell
cat ~/.ollama/logs/server.log
```
On **Linux** systems with systemd, the logs can be found with this command:
```shell
journalctl -u ollama
```
When you run Ollama in a **container**, the logs go to stdout/stderr in the container:
```shell
docker logs <container-name>
```
(Use `docker ps` to find the container name)
If manually running `ollama serve` in a terminal, the logs will be on that terminal.
When you run Ollama on **Windows**, there are a few different locations. You can view them in the explorer window by hitting `<cmd>+R` and type in:
- `explorer %LOCALAPPDATA%\Ollama` to view logs
- `explorer %LOCALAPPDATA%\Programs\Ollama` to browse the binaries (The installer adds this to your user PATH)
- `explorer %HOMEPATH%\.ollama` to browse where models and configuration is stored
- `explorer %TEMP%` where temporary executable files are stored in one or more `ollama*` directories
To enable additional debug logging to help troubleshoot problems, first **Quit the running app from the tray menu** then in a powershell terminal
```powershell
$env:OLLAMA_DEBUG="1"
& "ollama app.exe"
```
Join the [Discord](https://discord.gg/ollama) for help interpreting the logs.
## LLM libraries
Ollama includes multiple LLM libraries compiled for different GPUs and CPU
vector features. Ollama tries to pick the best one based on the capabilities of
your system. If this autodetection has problems, or you run into other problems
(e.g. crashes in your GPU) you can workaround this by forcing a specific LLM
library. `cpu_avx2` will perform the best, followed by `cpu_avx` an the slowest
but most compatible is `cpu`. Rosetta emulation under MacOS will work with the
`cpu` library.
In the server log, you will see a message that looks something like this (varies
from release to release):
```
Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
```
**Experimental LLM Library Override**
You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass
autodetection, so for example, if you have a CUDA card, but want to force the
CPU LLM library with AVX2 vector support, use:
```
OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
```
You can see what features your CPU has with the following.
```
cat /proc/cpuinfo| grep flags | head -1
```
## Installing older or pre-release versions on Linux
If you run into problems on Linux and want to install an older version, or you'd
like to try out a pre-release before it's officially released, you can tell the
install script which version to install.
```sh
curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION="0.1.29" sh
```
## Linux tmp noexec
If your system is configured with the "noexec" flag where Ollama stores its
temporary executable files, you can specify an alternate location by setting
OLLAMA_TMPDIR to a location writable by the user ollama runs as. For example
OLLAMA_TMPDIR=/usr/share/ollama/
## Container fails to run on NVIDIA GPU
Make sure you've set up the conatiner runtime first as described in [docker.md](./docker.md)
Sometimes the container runtime can have difficulties initializing the GPU.
When you check the server logs, this can show up as various error codes, such
as "3" (not initialized), "46" (device unavailable), "100" (no device), "999"
(unknown), or others. The following troubleshooting techniques may help resolve
the problem
- Is the uvm driver not loaded? `sudo nvidia-modprobe -u`
- Try reloading the nvidia_uvm driver - `sudo rmmod nvidia_uvm` then `sudo modprobe nvidia_uvm`
- Try rebooting
- Make sure you're running the latest nvidia drivers
If none of those resolve the problem, gather additional information and file an issue:
- Set `CUDA_ERROR_LEVEL=50` and try again to get more diagnostic logs
- Check dmesg for any errors `sudo dmesg | grep -i nvrm` and `sudo dmesg | grep -i nvidia`
# How to troubleshoot issues
Sometimes Ollama may not perform as expected. One of the best ways to figure out what happened is to take a look at the logs. Find the logs on **Mac** by running the command:
```shell
cat ~/.ollama/logs/server.log
```
On **Linux** systems with systemd, the logs can be found with this command:
```shell
journalctl -u ollama
```
When you run Ollama in a **container**, the logs go to stdout/stderr in the container:
```shell
docker logs <container-name>
```
(Use `docker ps` to find the container name)
If manually running `ollama serve` in a terminal, the logs will be on that terminal.
When you run Ollama on **Windows**, there are a few different locations. You can view them in the explorer window by hitting `<cmd>+R` and type in:
- `explorer %LOCALAPPDATA%\Ollama` to view logs
- `explorer %LOCALAPPDATA%\Programs\Ollama` to browse the binaries (The installer adds this to your user PATH)
- `explorer %HOMEPATH%\.ollama` to browse where models and configuration is stored
- `explorer %TEMP%` where temporary executable files are stored in one or more `ollama*` directories
To enable additional debug logging to help troubleshoot problems, first **Quit the running app from the tray menu** then in a powershell terminal
```powershell
$env:OLLAMA_DEBUG="1"
& "ollama app.exe"
```
Join the [Discord](https://discord.gg/ollama) for help interpreting the logs.
## LLM libraries
Ollama includes multiple LLM libraries compiled for different GPUs and CPU vector features. Ollama tries to pick the best one based on the capabilities of your system. If this autodetection has problems, or you run into other problems (e.g. crashes in your GPU) you can workaround this by forcing a specific LLM library. `cpu_avx2` will perform the best, followed by `cpu_avx` an the slowest but most compatible is `cpu`. Rosetta emulation under MacOS will work with the `cpu` library.
In the server log, you will see a message that looks something like this (varies from release to release):
```
Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
```
**Experimental LLM Library Override**
You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass autodetection, so for example, if you have a CUDA card, but want to force the CPU LLM library with AVX2 vector support, use:
```
OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
```
You can see what features your CPU has with the following.
```
cat /proc/cpuinfo| grep flags | head -1
```
## Installing older or pre-release versions on Linux
If you run into problems on Linux and want to install an older version, or you'd like to try out a pre-release before it's officially released, you can tell the install script which version to install.
```sh
curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION="0.1.29" sh
```
## Linux tmp noexec
If your system is configured with the "noexec" flag where Ollama stores its temporary executable files, you can specify an alternate location by setting OLLAMA_TMPDIR to a location writable by the user ollama runs as. For example OLLAMA_TMPDIR=/usr/share/ollama/
## Container fails to run on NVIDIA GPU
Make sure you've set up the container runtime first as described in [docker.md](./docker.md)
Sometimes the container runtime can have difficulties initializing the GPU. When you check the server logs, this can show up as various error codes, such as "3" (not initialized), "46" (device unavailable), "100" (no device), "999" (unknown), or others. The following troubleshooting techniques may help resolve the problem
- Is the uvm driver not loaded? `sudo nvidia-modprobe -u`
- Try reloading the nvidia_uvm driver - `sudo rmmod nvidia_uvm` then `sudo modprobe nvidia_uvm`
- Try rebooting
- Make sure you're running the latest nvidia drivers
If none of those resolve the problem, gather additional information and file an issue:
- Set `CUDA_ERROR_LEVEL=50` and try again to get more diagnostic logs
- Check dmesg for any errors `sudo dmesg | grep -i nvrm` and `sudo dmesg | grep -i nvidia`

2
docs/windows.md
View File

@@ -33,7 +33,7 @@ Here's a quick example showing API access from `powershell`
## Troubleshooting
While we're in preview, `OLLAMA_DEBUG` is always enabled, which adds
a "view logs" menu item to the app, and increses logging for the GUI app and
a "view logs" menu item to the app, and increases logging for the GUI app and
server.
Ollama on Windows stores files in a few different locations. You can view them in

5
go.mod
View File

@@ -4,12 +4,10 @@ go 1.22.0
require (
github.com/containerd/console v1.0.3
github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1
github.com/emirpasic/gods v1.18.1
github.com/gin-gonic/gin v1.10.0
github.com/golang/protobuf v1.5.4 // indirect
github.com/google/uuid v1.1.2
github.com/mitchellh/mapstructure v1.5.0
github.com/olekukonko/tablewriter v0.0.5
github.com/spf13/cobra v1.7.0
github.com/stretchr/testify v1.9.0
@@ -18,6 +16,8 @@ require (
)
require (
github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1
github.com/mattn/go-runewidth v0.0.14
github.com/nlpodyssey/gopickle v0.3.0
github.com/pdevine/tensor v0.0.0-20240510204454-f88f4562727c
)
@@ -33,7 +33,6 @@ require (
github.com/gogo/protobuf v1.3.2 // indirect
github.com/google/flatbuffers v24.3.25+incompatible // indirect
github.com/kr/text v0.2.0 // indirect
github.com/mattn/go-runewidth v0.0.14 // indirect
github.com/pkg/errors v0.9.1 // indirect
github.com/pmezard/go-difflib v1.0.0 // indirect
github.com/rivo/uniseg v0.2.0 // indirect

2
go.sum
View File

@@ -135,8 +135,6 @@ github.com/mattn/go-isatty v0.0.20/go.mod h1:W+V8PltTTMOvKvAeJH7IuucS94S2C6jfK/D
github.com/mattn/go-runewidth v0.0.9/go.mod h1:H031xJmbD/WCDINGzjvQ9THkh0rPKHF+m2gUSrubnMI=
github.com/mattn/go-runewidth v0.0.14 h1:+xnbZSEeDbOIg5/mE6JF0w6n9duR1l3/WmbinWVwUuU=
github.com/mattn/go-runewidth v0.0.14/go.mod h1:Jdepj2loyihRzMpdS35Xk/zdY8IAYHsh153qUoGf23w=
github.com/mitchellh/mapstructure v1.5.0 h1:jeMsZIYE/09sWLaz43PL7Gy6RuMjD2eJVyuac5Z2hdY=
github.com/mitchellh/mapstructure v1.5.0/go.mod h1:bFUtVrKA4DC2yAKiSyO/QUcy7e+RRV2QTWOzhPopBRo=
github.com/modern-go/concurrent v0.0.0-20180228061459-e0a39a4cb421/go.mod h1:6dJC0mAP4ikYIbvyc7fijjWJddQyLn8Ig3JB5CqoB9Q=
github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd h1:TRLaZ9cD/w8PVh93nsPXa1VrQ6jlwL5oN8l14QlcNfg=
github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd/go.mod h1:6dJC0mAP4ikYIbvyc7fijjWJddQyLn8Ig3JB5CqoB9Q=

32
llm/ext_server/server.cpp vendored
View File

@@ -334,6 +334,7 @@ struct server_metrics {
struct llama_server_context
{
llama_model *model = nullptr;
float modelProgress = 0.0;
llama_context *ctx = nullptr;
clip_ctx *clp_ctx = nullptr;
@@ -737,7 +738,7 @@ struct llama_server_context
sampler_names.emplace_back(sampler_name);
}
}
slot->sparams.samplers_sequence = sampler_types_from_names(sampler_names, false);
slot->sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, false);
}
else
{
@@ -1095,7 +1096,7 @@ struct llama_server_context
std::vector<std::string> samplers_sequence;
for (const auto &sampler_type : slot.sparams.samplers_sequence)
{
samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type));
samplers_sequence.emplace_back(llama_sampling_type_to_str(sampler_type));
}
return json {
@@ -2104,6 +2105,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel);
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
printf(" -fa, --flash-attn enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
printf(" -spf FNAME, --system-prompt-file FNAME\n");
printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
printf(" -ctk TYPE, --cache-type-k TYPE\n");
@@ -2501,7 +2503,8 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
{
params.use_mmap = false;
}
else if (arg == "--numa") {
else if (arg == "--numa")
{
if (++i >= argc) {
invalid_param = true;
break;
@@ -2521,6 +2524,10 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
{
params.cont_batching = true;
}
else if (arg == "-fa" || arg == "--flash-attn")
{
params.flash_attn = true;
}
else if (arg == "-np" || arg == "--parallel")
{
if (++i >= argc)
@@ -2529,7 +2536,8 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
break;
}
params.n_parallel = std::stoi(argv[i]);
} else if (arg == "-n" || arg == "--n-predict")
}
else if (arg == "-n" || arg == "--n-predict")
{
if (++i >= argc)
{
@@ -2537,7 +2545,8 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
break;
}
params.n_predict = std::stoi(argv[i]);
} else if (arg == "-spf" || arg == "--system-prompt-file")
}
else if (arg == "-spf" || arg == "--system-prompt-file")
{
if (++i >= argc)
{
@@ -2771,6 +2780,12 @@ inline void signal_handler(int signal) {
shutdown_handler(signal);
}
static bool update_load_progress(float progress, void *data)
{
((llama_server_context*)data)->modelProgress = progress;
return true;
}
#if defined(_WIN32)
char* wchar_to_char(const wchar_t* wstr) {
if (wstr == nullptr) return nullptr;
@@ -2876,7 +2891,9 @@ int main(int argc, char **argv) {
break;
}
case SERVER_STATE_LOADING_MODEL:
res.set_content(R"({"status": "loading model"})", "application/json");
char buf[128];
snprintf(&buf[0], 128, R"({"status": "loading model", "progress": %0.2f})", llama.modelProgress);
res.set_content(buf, "application/json");
res.status = 503; // HTTP Service Unavailable
break;
case SERVER_STATE_ERROR:
@@ -3071,6 +3088,9 @@ int main(int argc, char **argv) {
});
// load the model
params.progress_callback = update_load_progress;
params.progress_callback_user_data = (void*)&llama;
if (!llama.load_model(params))
{
state.store(SERVER_STATE_ERROR);

50
llm/filetype.go
View File

@@ -27,8 +27,16 @@ const (
fileTypeIQ2_XXS
fileTypeIQ2_XS
fileTypeQ2_K_S
fileTypeQ3_K_XS
fileTypeIQ3_XS
fileTypeIQ3_XXS
fileTypeIQ1_S
fileTypeIQ4_NL
fileTypeIQ3_S
fileTypeIQ2_S
fileTypeIQ4_XS
fileTypeIQ2_M
fileTypeIQ1_M
fileTypeBF16
fileTypeUnknown
)
@@ -75,10 +83,26 @@ func ParseFileType(s string) (fileType, error) {
return fileTypeIQ2_XS, nil
case "Q2_K_S":
return fileTypeQ2_K_S, nil
case "Q3_K_XS":
return fileTypeQ3_K_XS, nil
case "IQ3_XS":
return fileTypeIQ3_XS, nil
case "IQ3_XXS":
return fileTypeIQ3_XXS, nil
case "IQ1_S":
return fileTypeIQ1_S, nil
case "IQ4_NL":
return fileTypeIQ4_NL, nil
case "IQ3_S":
return fileTypeIQ3_S, nil
case "IQ2_S":
return fileTypeIQ2_S, nil
case "IQ4_XS":
return fileTypeIQ4_XS, nil
case "IQ2_M":
return fileTypeIQ2_M, nil
case "IQ1_M":
return fileTypeIQ1_M, nil
case "BF16":
return fileTypeBF16, nil
default:
return fileTypeUnknown, fmt.Errorf("unknown fileType: %s", s)
}
@@ -126,10 +150,26 @@ func (t fileType) String() string {
return "IQ2_XS"
case fileTypeQ2_K_S:
return "Q2_K_S"
case fileTypeQ3_K_XS:
return "Q3_K_XS"
case fileTypeIQ3_XS:
return "IQ3_XS"
case fileTypeIQ3_XXS:
return "IQ3_XXS"
case fileTypeIQ1_S:
return "IQ1_S"
case fileTypeIQ4_NL:
return "IQ4_NL"
case fileTypeIQ3_S:
return "IQ3_S"
case fileTypeIQ2_S:
return "IQ2_S"
case fileTypeIQ4_XS:
return "IQ4_XS"
case fileTypeIQ2_M:
return "IQ2_M"
case fileTypeIQ1_M:
return "IQ1_M"
case fileTypeBF16:
return "BF16"
default:
return "unknown"
}

2
llm/ggla.go
View File

@@ -119,7 +119,7 @@ func (llm *ggla) decode(rs io.ReadSeeker) error {
t.Offset = uint64(offset)
if _, err := rs.Seek(int64(t.size()), io.SeekCurrent); err != nil {
if _, err := rs.Seek(int64(t.Size()), io.SeekCurrent); err != nil {
return err
}

38
llm/ggml.go
View File

@@ -106,7 +106,7 @@ type Layer map[string]*Tensor
func (l Layer) size() (size uint64) {
for _, t := range l {
size += t.size()
size += t.Size()
}
return size
@@ -124,12 +124,12 @@ type Tensor struct {
}
func (t Tensor) blockSize() uint64 {
switch {
case t.Kind < 2:
switch t.Kind {
case 0, 1, 24, 25, 26, 27, 28, 31: // F32, F16, I8, I16, I32, I64, F64, BF16
return 1
case t.Kind < 10:
case 2, 3, 8, 9, 20: // Q4_0, Q4_1, Q8_0, Q8_1, IQ4_NL
return 32
default:
default: // All others
return 256
}
}
@@ -171,7 +171,29 @@ func (t Tensor) typeSize() uint64 {
case 17: // IQ2_XS
return 2 + 2*blockSize/8 + blockSize/32
case 18: // IQ3_XXS
return 2 + 3*blockSize/8
return 2 + blockSize/4 + blockSize/8
case 19: // IQ1_S
return 2 + blockSize/8 + blockSize/16
case 20: // IQ4_NL
return 2 + blockSize/2
case 21: // IQ3_S
return 2 + blockSize/4 + blockSize/8 + blockSize/32 + 4
case 22: // IQ2_S
return 2 + blockSize/4 + blockSize/16
case 23: // IQ4_XS
return 2 + 2 + blockSize/2 + blockSize/64
case 24: // I8
return 1
case 25: // I16
return 2
case 26: // I32
return 4
case 27: // I64
return 8
case 28: // F64
return 8
case 29: // IQ1_M
return blockSize/8 + blockSize/16 + blockSize/32
default:
return 0
}
@@ -185,7 +207,7 @@ func (t Tensor) parameters() uint64 {
return count
}
func (t Tensor) size() uint64 {
func (t Tensor) Size() uint64 {
return t.parameters() * t.typeSize() / t.blockSize()
}
@@ -288,7 +310,7 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
// mixtral 8x22b
ff := uint64(llm.KV()["llama.feed_forward_length"].(uint32))
partialOffload = max(
3*ffnGateExpsWeight.size()+4*batch*(2*ff+headsKV+embedding+context+embedding/heads*headsKV),
3*ffnGateExpsWeight.Size()+4*batch*(2*ff+headsKV+embedding+context+embedding/heads*headsKV),
4*(context*batch*heads+context*embedding/heads*headsKV+batch*1024+embedding/heads*headsKV*batch),
)
} else if ffnGateWeight, ok := layers["blk.0"]["ffn_gate.0.weight"]; ok {

16
llm/gguf.go
View File

@@ -62,16 +62,6 @@ func (c *containerGGUF) Decode(rs io.ReadSeeker) (model, error) {
return model, nil
}
const (
_ uint32 = iota
GGUFTokenNormal
GGUFTokenUnknown
GGUFTokenControl
GGUFTokenUserDefined
GGUFTokenUnused
GGUFTokenByte
)
const (
ggufTypeUint8 uint32 = iota
ggufTypeInt8
@@ -251,11 +241,11 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
}
for _, tensor := range llm.tensors {
if _, err := rs.Seek(int64(tensor.size()), io.SeekCurrent); err != nil {
if _, err := rs.Seek(int64(tensor.Size()), io.SeekCurrent); err != nil {
return err
}
padding := llm.padding(int64(tensor.size()), int64(alignment))
padding := llm.padding(int64(tensor.Size()), int64(alignment))
if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
return err
}
@@ -480,9 +470,11 @@ var ggufKVOrder = map[string][]string{
"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",

2
llm/llama.cpp

Submodule llm/llama.cpp updated: 614d3b914e...74f33adf5f

31
llm/patches/01-load-progress.diff Normal file
View File

@@ -0,0 +1,31 @@
diff --git a/common/common.cpp b/common/common.cpp
index ba1ecf0e..cead57cc 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1836,6 +1836,8 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
mparams.use_mmap = params.use_mmap;
mparams.use_mlock = params.use_mlock;
mparams.check_tensors = params.check_tensors;
+ mparams.progress_callback = params.progress_callback;
+ mparams.progress_callback_user_data = params.progress_callback_user_data;
if (params.kv_overrides.empty()) {
mparams.kv_overrides = NULL;
} else {
diff --git a/common/common.h b/common/common.h
index d80344f2..71e84834 100644
--- a/common/common.h
+++ b/common/common.h
@@ -174,6 +174,13 @@ struct gpt_params {
// multimodal models (see examples/llava)
std::string mmproj = ""; // path to multimodal projector
std::vector<std::string> image; // path to image file(s)
+
+ // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
+ // If the provided progress_callback returns true, model loading continues.
+ // If it returns false, model loading is immediately aborted.
+ llama_progress_callback progress_callback = NULL;
+ // context pointer passed to the progress callback
+ void * progress_callback_user_data;
};
void gpt_params_handle_model_default(gpt_params & params);

23
llm/patches/03-load_exception.diff
View File

@@ -1,8 +1,17 @@
From 544a2d2e646d39e878d87dfbb3398a356bc560ab Mon Sep 17 00:00:00 2001
From: Michael Yang <mxyng@pm.me>
Date: Thu, 23 May 2024 11:18:45 -0700
Subject: [PATCH] throw exception on load errors
---
llama.cpp | 25 ++++++++++++++++---------
1 file changed, 16 insertions(+), 9 deletions(-)
diff --git a/llama.cpp b/llama.cpp
index 4225f955..7b762f86 100644
index 15c66077..8ba90b6a 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -4756,7 +4756,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
@@ -6346,7 +6346,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
}
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
@@ -11,10 +20,10 @@ index 4225f955..7b762f86 100644
}
return 0;
@@ -12102,16 +12102,22 @@ struct llama_model * llama_load_model_from_file(
};
@@ -15600,16 +15600,23 @@ struct llama_model * llama_load_model_from_file(
}
model->rpc_servers.push_back(servers);
}
- int status = llama_model_load(path_model, *model, params);
- GGML_ASSERT(status <= 0);
- if (status < 0) {
@@ -22,6 +31,7 @@ index 4225f955..7b762f86 100644
- LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
- } else if (status == -2) {
- LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
+
+ try {
+ int status = llama_model_load(path_model, *model, params);
+ GGML_ASSERT(status <= 0);
@@ -42,3 +52,6 @@ index 4225f955..7b762f86 100644
}
return model;
--
2.45.1

35
llm/patches/05-default-pretokenizer.diff Normal file
View File

@@ -0,0 +1,35 @@
From d02a06f3f45a09255ace8684a66590e06ce44605 Mon Sep 17 00:00:00 2001
From: Michael Yang <mxyng@pm.me>
Date: Thu, 23 May 2024 11:33:20 -0700
Subject: [PATCH] default pretokenizer on unrecognized type
---
llama.cpp | 5 +----
1 file changed, 1 insertion(+), 4 deletions(-)
diff --git a/llama.cpp b/llama.cpp
index 15c66077..af1aede3 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -4504,9 +4504,6 @@ static void llm_load_vocab(
LLAMA_LOG_WARN("%s: ************************************ \n", __func__);
LLAMA_LOG_WARN("%s: \n", __func__);
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
- } else if (
- tokenizer_pre == "default") {
- vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
} else if (
tokenizer_pre == "llama3" ||
tokenizer_pre == "llama-v3" ||
@@ -4553,7 +4550,7 @@ static void llm_load_vocab(
tokenizer_pre == "dbrx") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX;
} else {
- throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
}
} else {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
--
2.45.1

41
llm/server.go
View File

@@ -55,6 +55,7 @@ type llmServer struct {
totalLayers uint64
gpuCount int
loadDuration time.Duration // Record how long it took the model to load
loadProgress float32
sem *semaphore.Weighted
}
@@ -200,6 +201,23 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
params = append(params, "--numa")
}
flashAttnEnabled := envconfig.FlashAttention
// partial offloading does not support flash attention
if uint64(opts.NumGPU) < ggml.KV().BlockCount()+1 {
flashAttnEnabled = false
}
// only cuda (compute capability 7+) and metal support flash attention
for _, g := range gpus {
if g.Library != "metal" && (g.Library != "cuda" || g.DriverMajor < 7) {
flashAttnEnabled = false
}
}
if flashAttnEnabled {
params = append(params, "--flash-attn")
}
numParallel := envconfig.NumParallel
// TODO (jmorganca): multimodal models don't support parallel yet
@@ -408,10 +426,11 @@ func (s ServerStatus) ToString() string {
}
type ServerStatusResp struct {
Status string `json:"status"`
SlotsIdle int `json:"slots_idle"`
SlotsProcessing int `json:"slots_processing"`
Error string `json:"error"`
Status string `json:"status"`
SlotsIdle int `json:"slots_idle"`
SlotsProcessing int `json:"slots_processing"`
Error string `json:"error"`
Progress float32 `json:"progress"`
}
func (s *llmServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
@@ -459,6 +478,7 @@ func (s *llmServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
case "no slot available":
return ServerStatusNoSlotsAvailable, nil
case "loading model":
s.loadProgress = status.Progress
return ServerStatusLoadingModel, nil
default:
return ServerStatusError, fmt.Errorf("server error: %+v", status)
@@ -499,7 +519,8 @@ func (s *llmServer) Ping(ctx context.Context) error {
func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
start := time.Now()
expiresAt := time.Now().Add(10 * time.Minute) // be generous with timeout, large models can take a while to load
stallDuration := 60 * time.Second
stallTimer := time.Now().Add(stallDuration) // give up if we stall for
slog.Info("waiting for llama runner to start responding")
var lastStatus ServerStatus = -1
@@ -517,13 +538,13 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
return fmt.Errorf("llama runner process has terminated: %v %s", err, msg)
default:
}
if time.Now().After(expiresAt) {
if time.Now().After(stallTimer) {
// timeout
msg := ""
if s.status != nil && s.status.LastErrMsg != "" {
msg = s.status.LastErrMsg
}
return fmt.Errorf("timed out waiting for llama runner to start: %s", msg)
return fmt.Errorf("timed out waiting for llama runner to start - progress %0.2f - %s", s.loadProgress, msg)
}
if s.cmd.ProcessState != nil {
msg := ""
@@ -534,6 +555,7 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
}
ctx, cancel := context.WithTimeout(ctx, 200*time.Millisecond)
defer cancel()
priorProgress := s.loadProgress
status, _ := s.getServerStatus(ctx)
if lastStatus != status && status != ServerStatusReady {
// Only log on status changes
@@ -546,6 +568,11 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
return nil
default:
lastStatus = status
// Reset the timer as long as we're making forward progress on the load
if priorProgress != s.loadProgress {
slog.Debug(fmt.Sprintf("model load progress %0.2f", s.loadProgress))
stallTimer = time.Now().Add(stallDuration)
}
time.Sleep(time.Millisecond * 250)
continue
}

2
macapp/src/index.ts
View File

@@ -162,7 +162,7 @@ app.on('before-quit', () => {
}
})
const updateURL = `https://ollama.ai/api/update?os=${process.platform}&arch=${
const updateURL = `https://ollama.com/api/update?os=${process.platform}&arch=${
process.arch
}&version=${app.getVersion()}&id=${id()}`

30
types/model/file.go → parser/parser.go
View File

@@ -1,4 +1,4 @@
package model
package parser
import (
"bufio"
@@ -8,6 +8,7 @@ import (
"io"
"strconv"
"strings"
"unicode"
)
type File struct {
@@ -68,6 +69,11 @@ func ParseFile(r io.Reader) (*File, error) {
var b bytes.Buffer
var role string
var lineCount int
var linePos int
var utf16 bool
var f File
br := bufio.NewReader(r)
@@ -79,6 +85,17 @@ func ParseFile(r io.Reader) (*File, error) {
return nil, err
}
// the utf16 byte order mark will be read as "unreadable" by ReadRune()
if isUnreadable(r) && lineCount == 0 && linePos == 0 {
utf16 = true
continue
}
// skip the second byte if we're reading utf16
if utf16 && r == 0 {
continue
}
next, r, err := parseRuneForState(r, curr)
if errors.Is(err, io.ErrUnexpectedEOF) {
return nil, fmt.Errorf("%w: %s", err, b.String())
@@ -86,6 +103,13 @@ func ParseFile(r io.Reader) (*File, error) {
return nil, err
}
if isNewline(r) {
lineCount++
linePos = 0
} else {
linePos++
}
// process the state transition, some transitions need to be intercepted and redirected
if next != curr {
switch curr {
@@ -285,6 +309,10 @@ func isNewline(r rune) bool {
return r == '\r' || r == '\n'
}
func isUnreadable(r rune) bool {
return r == unicode.ReplacementChar
}
func isValidMessageRole(role string) bool {
return role == "system" || role == "user" || role == "assistant"
}

38
types/model/file_test.go → parser/parser_test.go
View File

@@ -1,11 +1,13 @@
package model
package parser
import (
"bytes"
"encoding/binary"
"fmt"
"io"
"strings"
"testing"
"unicode/utf16"
"github.com/stretchr/testify/assert"
)
@@ -509,3 +511,37 @@ SYSTEM ""
}
}
func TestParseFileUTF16ParseFile(t *testing.T) {
data := `FROM bob
PARAMETER param1 1
PARAMETER param2 4096
SYSTEM You are a utf16 file.
`
// simulate a utf16 le file
utf16File := utf16.Encode(append([]rune{'\ufffe'}, []rune(data)...))
buf := new(bytes.Buffer)
err := binary.Write(buf, binary.LittleEndian, utf16File)
assert.NoError(t, err)
actual, err := ParseFile(buf)
assert.NoError(t, err)
expected := []Command{
{Name: "model", Args: "bob"},
{Name: "param1", Args: "1"},
{Name: "param2", Args: "4096"},
{Name: "system", Args: "You are a utf16 file."},
}
assert.Equal(t, expected, actual.Commands)
// simulate a utf16 be file
buf = new(bytes.Buffer)
err = binary.Write(buf, binary.BigEndian, utf16File)
assert.NoError(t, err)
actual, err = ParseFile(buf)
assert.NoError(t, err)
assert.Equal(t, expected, actual.Commands)
}

10
server/envconfig/config.go
View File

@@ -31,6 +31,8 @@ var (
RunnersDir string
// Set via OLLAMA_TMPDIR in the environment
TmpDir string
// Experimental flash attention
FlashAttention bool
)
func AsMap() map[string]string {
@@ -45,6 +47,7 @@ func AsMap() map[string]string {
"OLLAMA_NUM_PARALLEL": fmt.Sprintf("%v", NumParallel),
"OLLAMA_RUNNERS_DIR": fmt.Sprintf("%v", RunnersDir),
"OLLAMA_TMPDIR": fmt.Sprintf("%v", TmpDir),
"OLLAMA_FLASH_ATTENTION": fmt.Sprintf("%v", FlashAttention),
}
}
@@ -78,6 +81,13 @@ func LoadConfig() {
}
}
if fa := clean("OLLAMA_FLASH_ATTENTION"); fa != "" {
d, err := strconv.ParseBool(fa)
if err == nil {
FlashAttention = d
}
}
RunnersDir = clean("OLLAMA_RUNNERS_DIR")
if runtime.GOOS == "windows" && RunnersDir == "" {
// On Windows we do not carry the payloads inside the main executable

3
server/envconfig/config_test.go
View File

@@ -17,4 +17,7 @@ func TestConfig(t *testing.T) {
t.Setenv("OLLAMA_DEBUG", "1")
LoadConfig()
require.True(t, Debug)
t.Setenv("OLLAMA_FLASH_ATTENTION", "1")
LoadConfig()
require.True(t, FlashAttention)
}

55
server/images.go
View File

@@ -27,6 +27,7 @@ import (
"github.com/ollama/ollama/auth"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/llm"
"github.com/ollama/ollama/parser"
"github.com/ollama/ollama/server/envconfig"
"github.com/ollama/ollama/types/errtypes"
"github.com/ollama/ollama/types/model"
@@ -61,36 +62,36 @@ func (m *Model) IsEmbedding() bool {
}
func (m *Model) String() string {
var modelfile model.File
var modelfile parser.File
modelfile.Commands = append(modelfile.Commands, model.Command{
modelfile.Commands = append(modelfile.Commands, parser.Command{
Name: "model",
Args: m.ModelPath,
})
for _, adapter := range m.AdapterPaths {
modelfile.Commands = append(modelfile.Commands, model.Command{
modelfile.Commands = append(modelfile.Commands, parser.Command{
Name: "adapter",
Args: adapter,
})
}
for _, projector := range m.ProjectorPaths {
modelfile.Commands = append(modelfile.Commands, model.Command{
modelfile.Commands = append(modelfile.Commands, parser.Command{
Name: "model",
Args: projector,
})
}
if m.Template != "" {
modelfile.Commands = append(modelfile.Commands, model.Command{
modelfile.Commands = append(modelfile.Commands, parser.Command{
Name: "template",
Args: m.Template,
})
}
if m.System != "" {
modelfile.Commands = append(modelfile.Commands, model.Command{
modelfile.Commands = append(modelfile.Commands, parser.Command{
Name: "system",
Args: m.System,
})
@@ -100,13 +101,13 @@ func (m *Model) String() string {
switch v := v.(type) {
case []any:
for _, s := range v {
modelfile.Commands = append(modelfile.Commands, model.Command{
modelfile.Commands = append(modelfile.Commands, parser.Command{
Name: k,
Args: fmt.Sprintf("%v", s),
})
}
default:
modelfile.Commands = append(modelfile.Commands, model.Command{
modelfile.Commands = append(modelfile.Commands, parser.Command{
Name: k,
Args: fmt.Sprintf("%v", v),
})
@@ -114,14 +115,14 @@ func (m *Model) String() string {
}
for _, license := range m.License {
modelfile.Commands = append(modelfile.Commands, model.Command{
modelfile.Commands = append(modelfile.Commands, parser.Command{
Name: "license",
Args: license,
})
}
for _, msg := range m.Messages {
modelfile.Commands = append(modelfile.Commands, model.Command{
modelfile.Commands = append(modelfile.Commands, parser.Command{
Name: "message",
Args: fmt.Sprintf("%s %s", msg.Role, msg.Content),
})
@@ -314,7 +315,7 @@ func realpath(rel, from string) string {
return abspath
}
func CreateModel(ctx context.Context, name, modelFileDir, quantization string, modelfile *model.File, fn func(resp api.ProgressResponse)) (err error) {
func CreateModel(ctx context.Context, name, modelFileDir, quantization string, modelfile *parser.File, fn func(resp api.ProgressResponse)) (err error) {
config := ConfigV2{
OS: "linux",
Architecture: "amd64",
@@ -339,7 +340,24 @@ func CreateModel(ctx context.Context, name, modelFileDir, quantization string, m
return err
}
} else if strings.HasPrefix(c.Args, "@") {
blobpath, err := GetBlobsPath(strings.TrimPrefix(c.Args, "@"))
digest := strings.TrimPrefix(c.Args, "@")
if ib, ok := intermediateBlobs[digest]; ok {
p, err := GetBlobsPath(ib)
if err != nil {
return err
}
if _, err := os.Stat(p); errors.Is(err, os.ErrNotExist) {
// pass
} else if err != nil {
return err
} else {
fn(api.ProgressResponse{Status: fmt.Sprintf("using cached layer %s", ib)})
digest = ib
}
}
blobpath, err := GetBlobsPath(digest)
if err != nil {
return err
}
@@ -350,14 +368,14 @@ func CreateModel(ctx context.Context, name, modelFileDir, quantization string, m
}
defer blob.Close()
baseLayers, err = parseFromFile(ctx, blob, fn)
baseLayers, err = parseFromFile(ctx, blob, digest, fn)
if err != nil {
return err
}
} else if file, err := os.Open(realpath(modelFileDir, c.Args)); err == nil {
defer file.Close()
baseLayers, err = parseFromFile(ctx, file, fn)
baseLayers, err = parseFromFile(ctx, file, "", fn)
if err != nil {
return err
}
@@ -397,10 +415,17 @@ func CreateModel(ctx context.Context, name, modelFileDir, quantization string, m
return err
}
baseLayer.Layer, err = NewLayer(temp, baseLayer.Layer.MediaType)
layers, err := parseFromFile(ctx, temp, "", fn)
if err != nil {
return err
}
if len(layers) != 1 {
return errors.New("quantization failed")
}
baseLayer.Layer = layers[0].Layer
baseLayer.GGML = layers[0].GGML
}
}

2
server/layer.go
View File

@@ -80,7 +80,7 @@ func NewLayerFromLayer(digest, mediatype, from string) (*Layer, error) {
}, nil
}
func (l *Layer) Open() (io.ReadCloser, error) {
func (l *Layer) Open() (io.ReadSeekCloser, error) {
blob, err := GetBlobsPath(l.Digest)
if err != nil {
return nil, err

22
server/model.go
View File

@@ -17,6 +17,8 @@ import (
"github.com/ollama/ollama/types/model"
)
var intermediateBlobs map[string]string = make(map[string]string)
type layerWithGGML struct {
*Layer
*llm.GGML
@@ -76,7 +78,7 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
return layers, nil
}
func parseFromZipFile(_ context.Context, file *os.File, fn func(api.ProgressResponse)) (layers []*layerWithGGML, err error) {
func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerWithGGML, err error) {
stat, err := file.Stat()
if err != nil {
return nil, err
@@ -165,16 +167,11 @@ func parseFromZipFile(_ context.Context, file *os.File, fn func(api.ProgressResp
}
layer, err := NewLayer(temp, "application/vnd.ollama.image.model")
if err != nil {
return nil, fmt.Errorf("aaa: %w", err)
}
blobpath, err := GetBlobsPath(layer.Digest)
if err != nil {
return nil, err
}
bin, err := os.Open(blobpath)
bin, err := layer.Open()
if err != nil {
return nil, err
}
@@ -185,16 +182,13 @@ func parseFromZipFile(_ context.Context, file *os.File, fn func(api.ProgressResp
return nil, err
}
layer, err = NewLayerFromLayer(layer.Digest, layer.MediaType, "")
if err != nil {
return nil, err
}
layers = append(layers, &layerWithGGML{layer, ggml})
intermediateBlobs[digest] = layer.Digest
return layers, nil
}
func parseFromFile(ctx context.Context, file *os.File, fn func(api.ProgressResponse)) (layers []*layerWithGGML, err error) {
func parseFromFile(ctx context.Context, file *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerWithGGML, err error) {
sr := io.NewSectionReader(file, 0, 512)
contentType, err := detectContentType(sr)
if err != nil {
@@ -205,7 +199,7 @@ func parseFromFile(ctx context.Context, file *os.File, fn func(api.ProgressRespo
case "gguf", "ggla":
// noop
case "application/zip":
return parseFromZipFile(ctx, file, fn)
return parseFromZipFile(ctx, file, digest, fn)
default:
return nil, fmt.Errorf("unsupported content type: %s", contentType)
}

22
server/routes.go
View File

@@ -29,6 +29,7 @@ import (
"github.com/ollama/ollama/gpu"
"github.com/ollama/ollama/llm"
"github.com/ollama/ollama/openai"
"github.com/ollama/ollama/parser"
"github.com/ollama/ollama/server/envconfig"
"github.com/ollama/ollama/types/errtypes"
"github.com/ollama/ollama/types/model"
@@ -539,7 +540,7 @@ func (s *Server) CreateModelHandler(c *gin.Context) {
r = f
}
modelfile, err := model.ParseFile(r)
modelfile, err := parser.ParseFile(r)
if err != nil {
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
return
@@ -840,6 +841,25 @@ func (s *Server) HeadBlobHandler(c *gin.Context) {
}
func (s *Server) CreateBlobHandler(c *gin.Context) {
if ib, ok := intermediateBlobs[c.Param("digest")]; ok {
p, err := GetBlobsPath(ib)
if err != nil {
c.AbortWithStatusJSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
if _, err := os.Stat(p); errors.Is(err, os.ErrNotExist) {
slog.Info("evicting intermediate blob which no longer exists", "digest", ib)
delete(intermediateBlobs, c.Param("digest"))
} else if err != nil {
c.AbortWithStatusJSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
} else {
c.Status(http.StatusOK)
return
}
}
path, err := GetBlobsPath(c.Param("digest"))
if err != nil {
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})

4
server/routes_test.go
View File

@@ -17,7 +17,7 @@ import (
"github.com/stretchr/testify/assert"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/types/model"
"github.com/ollama/ollama/parser"
"github.com/ollama/ollama/version"
)
@@ -56,7 +56,7 @@ func Test_Routes(t *testing.T) {
fname := createTestFile(t, "ollama-model")
r := strings.NewReader(fmt.Sprintf("FROM %s\nPARAMETER seed 42\nPARAMETER top_p 0.9\nPARAMETER stop foo\nPARAMETER stop bar", fname))
modelfile, err := model.ParseFile(r)
modelfile, err := parser.ParseFile(r)
assert.Nil(t, err)
fn := func(resp api.ProgressResponse) {
t.Logf("Status: %s", resp.Status)

2
server/sched.go
View File

@@ -220,7 +220,7 @@ func (s *Scheduler) processCompleted(ctx context.Context) {
runner := s.loaded[finished.model.ModelPath]
s.loadedMu.Unlock()
if runner == nil {
slog.Error("finished requeset signal received after model unloaded", "modelPath", finished.model.ModelPath)
slog.Error("finished request signal received after model unloaded", "modelPath", finished.model.ModelPath)
continue
}
runner.refMu.Lock()

2
server/sched_test.go
View File

@@ -151,7 +151,7 @@ func newScenario(t *testing.T, ctx context.Context, modelName string, estimatedV
}
func TestRequests(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 500*time.Millisecond)
ctx, done := context.WithTimeout(context.Background(), time.Second)
defer done()
// Same model, same request