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Author SHA1 Message Date
Patrick Devine
5c26b81a2f skip files in the list if we can't get the correct model path 2023-07-18 12:37:51 -07:00
112 changed files with 8547 additions and 13848 deletions

2
.gitignore vendored
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@@ -2,7 +2,5 @@
.vscode
.env
.venv
.swp
dist
ollama
/ggml-metal.metal

147
README.md
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@@ -1,126 +1,76 @@
<div align="center">
<picture>
<source media="(prefers-color-scheme: dark)" height="200px" srcset="https://github.com/jmorganca/ollama/assets/3325447/56ea1849-1284-4645-8970-956de6e51c3c">
<img alt="logo" height="200px" src="https://github.com/jmorganca/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
</picture>
</div>
![ollama](https://github.com/jmorganca/ollama/assets/251292/961f99bb-251a-4eec-897d-1ba99997ad0f)
# Ollama
[![Discord](https://dcbadge.vercel.app/api/server/ollama?style=flat&compact=true)](https://discord.gg/ollama)
Run large language models with `llama.cpp`.
Run, create, and share large language models (LLMs).
> Note: certain models that can be run with Ollama are intended for research and/or non-commercial use only.
> Note: Ollama is in early preview. Please report any issues you find.
### Features
## Download
- Download and run popular large language models
- Switch between multiple models on the fly
- Hardware acceleration where available (Metal, CUDA)
- Fast inference server written in Go, powered by [llama.cpp](https://github.com/ggerganov/llama.cpp)
- REST API to use with your application (python, typescript SDKs coming soon)
- [Download](https://ollama.ai/download) for macOS
- Download for Windows and Linux (coming soon)
- Build [from source](#building)
## Install
- [Download](https://ollama.ai/download) for macOS with Apple Silicon (Intel coming soon)
- Download for Windows (coming soon)
You can also build the [binary from source](#building).
## Quickstart
To run and chat with [Llama 2](https://ai.meta.com/llama), the new model by Meta:
Run a fast and simple model.
```
ollama run llama2
ollama run orca
```
## Model library
## Example models
`ollama` includes a library of open-source models:
### 💬 Chat
| Model | Parameters | Size | Download |
| ------------------------ | ---------- | ----- | ------------------------------- |
| Llama2 | 7B | 3.8GB | `ollama pull llama2` |
| Llama2 13B | 13B | 7.3GB | `ollama pull llama2:13b` |
| Llama2 70B | 70B | 39GB | `ollama pull llama2:70b` |
| Llama2 Uncensored | 7B | 3.8GB | `ollama pull llama2-uncensored` |
| Orca Mini | 3B | 1.9GB | `ollama pull orca` |
| Vicuna | 7B | 3.8GB | `ollama pull vicuna` |
| Nous-Hermes | 13B | 7.3GB | `ollama pull nous-hermes` |
| Wizard Vicuna Uncensored | 13B | 7.3GB | `ollama pull wizard-vicuna` |
> Note: You should have at least 8 GB of RAM to run the 3B models, 16 GB to run the 7B models, and 32 GB to run the 13B models.
## Examples
### Run a model
Have a conversation.
```
ollama run llama2
>>> hi
Hello! How can I help you today?
ollama run vicuna "Why is the sky blue?"
```
For multiline input, you can wrap text with `"""`:
### 🗺️ Instructions
Get a helping hand.
```
>>> """Hello,
... world!
... """
I'm a basic program that prints the famous "Hello, world!" message to the console.
ollama run orca "Write an email to my boss."
```
### Create a custom model
### 🔎 Ask questions about documents
Pull a base model:
Send the contents of a document and ask questions about it.
```
ollama pull llama2
ollama run nous-hermes "$(cat input.txt)", please summarize this story
```
> To update a model to the latest version, run `ollama pull llama2` again. The model will be updated (if necessary).
### 📖 Storytelling
Create a `Modelfile`:
Venture into the unknown.
```
FROM llama2
# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# set the system prompt
SYSTEM """
You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
"""
ollama run nous-hermes "Once upon a time"
```
Next, create and run the model:
## Advanced usage
### Run a local model
```
ollama create mario -f ./Modelfile
ollama run mario
>>> hi
Hello! It's your friend Mario.
ollama run ~/Downloads/vicuna-7b-v1.3.ggmlv3.q4_1.bin
```
For more examples, see the [examples](./examples) directory. For more information on creating a Modelfile, see the [Modelfile](./docs/modelfile.md) documentation.
### Pull a model from the registry
```
ollama pull orca
```
### Listing local models
```
ollama list
```
## Model packages
### Overview
Ollama bundles model weights, configuration, and data into a single package, defined by a [Modelfile](./docs/modelfile.md).
<picture>
<source media="(prefers-color-scheme: dark)" height="480" srcset="https://github.com/jmorganca/ollama/assets/251292/2fd96b5f-191b-45c1-9668-941cfad4eb70">
<img alt="logo" height="480" src="https://github.com/jmorganca/ollama/assets/251292/2fd96b5f-191b-45c1-9668-941cfad4eb70">
</picture>
## Building
```
@@ -130,32 +80,29 @@ go build .
To run it start the server:
```
./ollama serve &
./ollama server &
```
Finally, run a model!
```
./ollama run llama2
./ollama run ~/Downloads/vicuna-7b-v1.3.ggmlv3.q4_1.bin
```
## REST API
## API Reference
> See the [API documentation](./docs/api.md) for all endpoints.
### `POST /api/pull`
Ollama has an API for running and managing models. For example to generate text from a model:
Download a model
```
curl -X POST http://localhost:11434/api/generate -d '{
"model": "llama2",
"prompt":"Why is the sky blue?"
}'
curl -X POST http://localhost:11343/api/pull -d '{"model": "orca"}'
```
## Tools using Ollama
### `POST /api/generate`
- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/modules/model_io/models/llms/integrations/ollama) with a question-answering [example](https://js.langchain.com/docs/use_cases/question_answering/local_retrieval_qa).
- [Continue](https://github.com/continuedev/continue) - embeds Ollama inside Visual Studio Code. The extension lets you highlight code to add to the prompt, ask questions in the sidebar, and generate code inline.
- [Discord AI Bot](https://github.com/mekb-turtle/discord-ai-bot) - interact with Ollama as a chatbot on Discord.
- [Raycast Ollama](https://github.com/MassimilianoPasquini97/raycast_ollama) - Raycast extension to use Ollama for local llama inference on Raycast.
- [Simple HTML UI for Ollama](https://github.com/rtcfirefly/ollama-ui)
Complete a prompt
```
curl -X POST http://localhost:11434/api/generate -d '{"model": "orca", "prompt": "hello!"}'
```

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@@ -27,7 +27,7 @@ func checkError(resp *http.Response, body []byte) error {
err := json.Unmarshal(body, &apiError)
if err != nil {
// Use the full body as the message if we fail to decode a response.
apiError.ErrorMessage = string(body)
apiError.Message = string(body)
}
return apiError
@@ -92,6 +92,7 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
}
}
return nil
}
func (c *Client) stream(ctx context.Context, method, path string, data any, fn func([]byte) error) error {
@@ -130,15 +131,11 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
return fmt.Errorf("unmarshal: %w", err)
}
if errorResponse.Error != "" {
return fmt.Errorf(errorResponse.Error)
}
if response.StatusCode >= 400 {
return StatusError{
StatusCode: response.StatusCode,
Status: response.Status,
ErrorMessage: errorResponse.Error,
StatusCode: response.StatusCode,
Status: response.Status,
Message: errorResponse.Error,
}
}
@@ -163,11 +160,11 @@ func (c *Client) Generate(ctx context.Context, req *GenerateRequest, fn Generate
})
}
type PullProgressFunc func(ProgressResponse) error
type PullProgressFunc func(PullProgress) error
func (c *Client) Pull(ctx context.Context, req *PullRequest, fn PullProgressFunc) error {
return c.stream(ctx, http.MethodPost, "/api/pull", req, func(bts []byte) error {
var resp ProgressResponse
var resp PullProgress
if err := json.Unmarshal(bts, &resp); err != nil {
return err
}
@@ -176,11 +173,11 @@ func (c *Client) Pull(ctx context.Context, req *PullRequest, fn PullProgressFunc
})
}
type PushProgressFunc func(ProgressResponse) error
type PushProgressFunc func(PushProgress) error
func (c *Client) Push(ctx context.Context, req *PushRequest, fn PushProgressFunc) error {
return c.stream(ctx, http.MethodPost, "/api/push", req, func(bts []byte) error {
var resp ProgressResponse
var resp PushProgress
if err := json.Unmarshal(bts, &resp); err != nil {
return err
}
@@ -189,11 +186,11 @@ func (c *Client) Push(ctx context.Context, req *PushRequest, fn PushProgressFunc
})
}
type CreateProgressFunc func(ProgressResponse) error
type CreateProgressFunc func(CreateProgress) error
func (c *Client) Create(ctx context.Context, req *CreateRequest, fn CreateProgressFunc) error {
return c.stream(ctx, http.MethodPost, "/api/create", req, func(bts []byte) error {
var resp ProgressResponse
var resp CreateProgress
if err := json.Unmarshal(bts, &resp); err != nil {
return err
}
@@ -209,24 +206,3 @@ func (c *Client) List(ctx context.Context) (*ListResponse, error) {
}
return &lr, nil
}
func (c *Client) Copy(ctx context.Context, req *CopyRequest) error {
if err := c.do(ctx, http.MethodPost, "/api/copy", req, nil); err != nil {
return err
}
return nil
}
func (c *Client) Delete(ctx context.Context, req *DeleteRequest) error {
if err := c.do(ctx, http.MethodDelete, "/api/delete", req, nil); err != nil {
return err
}
return nil
}
func (c *Client) Heartbeat(ctx context.Context) error {
if err := c.do(ctx, http.MethodHead, "/", nil, nil); err != nil {
return err
}
return nil
}

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@@ -1,56 +1,31 @@
package api
import (
"encoding/json"
"fmt"
"log"
"math"
"os"
"reflect"
"runtime"
"strings"
"time"
)
type StatusError struct {
StatusCode int
Status string
ErrorMessage string `json:"error"`
StatusCode int
Status string
Message string
}
func (e StatusError) Error() string {
switch {
case e.Status != "" && e.ErrorMessage != "":
return fmt.Sprintf("%s: %s", e.Status, e.ErrorMessage)
case e.Status != "":
return e.Status
case e.ErrorMessage != "":
return e.ErrorMessage
default:
// this should not happen
return "something went wrong, please see the ollama server logs for details"
if e.Message != "" {
return fmt.Sprintf("%s: %s", e.Status, e.Message)
}
return e.Status
}
type GenerateRequest struct {
Model string `json:"model"`
Prompt string `json:"prompt"`
System string `json:"system"`
Template string `json:"template"`
Context []int `json:"context,omitempty"`
Model string `json:"model"`
Prompt string `json:"prompt"`
Context []int `json:"context,omitempty"`
Options map[string]interface{} `json:"options"`
}
type EmbeddingRequest struct {
Model string `json:"model"`
Prompt string `json:"prompt"`
Options map[string]interface{} `json:"options"`
}
type EmbeddingResponse struct {
Embedding []float64 `json:"embedding"`
Options `json:"options"`
}
type CreateRequest struct {
@@ -58,36 +33,38 @@ type CreateRequest struct {
Path string `json:"path"`
}
type DeleteRequest struct {
Name string `json:"name"`
}
type CopyRequest struct {
Source string `json:"source"`
Destination string `json:"destination"`
type CreateProgress struct {
Status string `json:"status"`
}
type PullRequest struct {
Name string `json:"name"`
Insecure bool `json:"insecure,omitempty"`
Username string `json:"username"`
Password string `json:"password"`
}
type ProgressResponse struct {
Status string `json:"status"`
Digest string `json:"digest,omitempty"`
Total int `json:"total,omitempty"`
Completed int `json:"completed,omitempty"`
type PullProgress struct {
Status string `json:"status"`
Digest string `json:"digest,omitempty"`
Total int `json:"total,omitempty"`
Completed int `json:"completed,omitempty"`
Percent float64 `json:"percent,omitempty"`
}
type PushRequest struct {
Name string `json:"name"`
Insecure bool `json:"insecure,omitempty"`
Username string `json:"username"`
Password string `json:"password"`
}
type PushProgress struct {
Status string `json:"status"`
Digest string `json:"digest,omitempty"`
Total int `json:"total,omitempty"`
Completed int `json:"completed,omitempty"`
Percent float64 `json:"percent,omitempty"`
}
type ListResponse struct {
Models []ListResponseModel `json:"models"`
}
@@ -98,10 +75,6 @@ type ListResponseModel struct {
Size int `json:"size"`
}
type TokenResponse struct {
Token string `json:"token"`
}
type GenerateResponse struct {
Model string `json:"model"`
CreatedAt time.Time `json:"created_at"`
@@ -111,9 +84,6 @@ type GenerateResponse struct {
Context []int `json:"context,omitempty"`
TotalDuration time.Duration `json:"total_duration,omitempty"`
LoadDuration time.Duration `json:"load_duration,omitempty"`
SampleCount int `json:"sample_count,omitempty"`
SampleDuration time.Duration `json:"sample_duration,omitempty"`
PromptEvalCount int `json:"prompt_eval_count,omitempty"`
PromptEvalDuration time.Duration `json:"prompt_eval_duration,omitempty"`
EvalCount int `json:"eval_count,omitempty"`
@@ -125,19 +95,6 @@ func (r *GenerateResponse) Summary() {
fmt.Fprintf(os.Stderr, "total duration: %v\n", r.TotalDuration)
}
if r.LoadDuration > 0 {
fmt.Fprintf(os.Stderr, "load duration: %v\n", r.LoadDuration)
}
if r.SampleCount > 0 {
fmt.Fprintf(os.Stderr, "sample count: %d token(s)\n", r.SampleCount)
}
if r.SampleDuration > 0 {
fmt.Fprintf(os.Stderr, "sample duration: %s\n", r.SampleDuration)
fmt.Fprintf(os.Stderr, "sample rate: %.2f tokens/s\n", float64(r.SampleCount)/r.SampleDuration.Seconds())
}
if r.PromptEvalCount > 0 {
fmt.Fprintf(os.Stderr, "prompt eval count: %d token(s)\n", r.PromptEvalCount)
}
@@ -164,136 +121,50 @@ type Options struct {
UseNUMA bool `json:"numa,omitempty"`
// Model options
NumCtx int `json:"num_ctx,omitempty"`
NumKeep int `json:"num_keep,omitempty"`
NumBatch int `json:"num_batch,omitempty"`
NumGQA int `json:"num_gqa,omitempty"`
NumGPU int `json:"num_gpu,omitempty"`
MainGPU int `json:"main_gpu,omitempty"`
LowVRAM bool `json:"low_vram,omitempty"`
F16KV bool `json:"f16_kv,omitempty"`
LogitsAll bool `json:"logits_all,omitempty"`
VocabOnly bool `json:"vocab_only,omitempty"`
UseMMap bool `json:"use_mmap,omitempty"`
UseMLock bool `json:"use_mlock,omitempty"`
EmbeddingOnly bool `json:"embedding_only,omitempty"`
RopeFrequencyBase float32 `json:"rope_frequency_base,omitempty"`
RopeFrequencyScale float32 `json:"rope_frequency_scale,omitempty"`
NumCtx int `json:"num_ctx,omitempty"`
NumBatch int `json:"num_batch,omitempty"`
NumGPU int `json:"num_gpu,omitempty"`
MainGPU int `json:"main_gpu,omitempty"`
LowVRAM bool `json:"low_vram,omitempty"`
F16KV bool `json:"f16_kv,omitempty"`
LogitsAll bool `json:"logits_all,omitempty"`
VocabOnly bool `json:"vocab_only,omitempty"`
UseMMap bool `json:"use_mmap,omitempty"`
UseMLock bool `json:"use_mlock,omitempty"`
EmbeddingOnly bool `json:"embedding_only,omitempty"`
// Predict options
RepeatLastN int `json:"repeat_last_n,omitempty"`
RepeatPenalty float32 `json:"repeat_penalty,omitempty"`
FrequencyPenalty float32 `json:"frequency_penalty,omitempty"`
PresencePenalty float32 `json:"presence_penalty,omitempty"`
Temperature float32 `json:"temperature,omitempty"`
TopK int `json:"top_k,omitempty"`
TopP float32 `json:"top_p,omitempty"`
TFSZ float32 `json:"tfs_z,omitempty"`
TypicalP float32 `json:"typical_p,omitempty"`
Mirostat int `json:"mirostat,omitempty"`
MirostatTau float32 `json:"mirostat_tau,omitempty"`
MirostatEta float32 `json:"mirostat_eta,omitempty"`
PenalizeNewline bool `json:"penalize_newline,omitempty"`
Stop []string `json:"stop,omitempty"`
RepeatLastN int `json:"repeat_last_n,omitempty"`
RepeatPenalty float32 `json:"repeat_penalty,omitempty"`
FrequencyPenalty float32 `json:"frequency_penalty,omitempty"`
PresencePenalty float32 `json:"presence_penalty,omitempty"`
Temperature float32 `json:"temperature,omitempty"`
TopK int `json:"top_k,omitempty"`
TopP float32 `json:"top_p,omitempty"`
TFSZ float32 `json:"tfs_z,omitempty"`
TypicalP float32 `json:"typical_p,omitempty"`
Mirostat int `json:"mirostat,omitempty"`
MirostatTau float32 `json:"mirostat_tau,omitempty"`
MirostatEta float32 `json:"mirostat_eta,omitempty"`
NumThread int `json:"num_thread,omitempty"`
}
func (opts *Options) FromMap(m map[string]interface{}) error {
valueOpts := reflect.ValueOf(opts).Elem() // names of the fields in the options struct
typeOpts := reflect.TypeOf(opts).Elem() // types of the fields in the options struct
// build map of json struct tags to their types
jsonOpts := make(map[string]reflect.StructField)
for _, field := range reflect.VisibleFields(typeOpts) {
jsonTag := strings.Split(field.Tag.Get("json"), ",")[0]
if jsonTag != "" {
jsonOpts[jsonTag] = field
}
}
for key, val := range m {
if opt, ok := jsonOpts[key]; ok {
field := valueOpts.FieldByName(opt.Name)
if field.IsValid() && field.CanSet() {
switch field.Kind() {
case reflect.Int:
// when JSON unmarshals numbers, it uses float64 by default, not int
val, ok := val.(float64)
if !ok {
log.Printf("could not convert model parmeter %v to int, skipped", key)
continue
}
field.SetInt(int64(val))
case reflect.Bool:
val, ok := val.(bool)
if !ok {
log.Printf("could not convert model parmeter %v to bool, skipped", key)
continue
}
field.SetBool(val)
case reflect.Float32:
// JSON unmarshals to float64
val, ok := val.(float64)
if !ok {
log.Printf("could not convert model parmeter %v to float32, skipped", key)
continue
}
field.SetFloat(val)
case reflect.String:
val, ok := val.(string)
if !ok {
log.Printf("could not convert model parmeter %v to string, skipped", key)
continue
}
field.SetString(val)
case reflect.Slice:
// JSON unmarshals to []interface{}, not []string
val, ok := val.([]interface{})
if !ok {
log.Printf("could not convert model parmeter %v to slice, skipped", key)
continue
}
// convert []interface{} to []string
slice := make([]string, len(val))
for i, item := range val {
str, ok := item.(string)
if !ok {
log.Printf("could not convert model parmeter %v to slice of strings, skipped", key)
continue
}
slice[i] = str
}
field.Set(reflect.ValueOf(slice))
default:
return fmt.Errorf("unknown type loading config params: %v", field.Kind())
}
}
}
}
return nil
}
func DefaultOptions() Options {
return Options{
Seed: -1,
UseNUMA: false,
NumCtx: 2048,
NumKeep: -1,
NumBatch: 512,
NumGPU: 1,
NumGQA: 1,
LowVRAM: false,
F16KV: true,
UseMMap: true,
UseMLock: false,
RopeFrequencyBase: 10000.0,
RopeFrequencyScale: 1.0,
EmbeddingOnly: true,
NumCtx: 2048,
NumBatch: 512,
NumGPU: 1,
LowVRAM: false,
F16KV: true,
UseMMap: true,
UseMLock: false,
RepeatLastN: 64,
RepeatLastN: 512,
RepeatPenalty: 1.1,
FrequencyPenalty: 0.0,
PresencePenalty: 0.0,
@@ -305,37 +176,7 @@ func DefaultOptions() Options {
Mirostat: 0,
MirostatTau: 5.0,
MirostatEta: 0.1,
PenalizeNewline: true,
NumThread: runtime.NumCPU(),
}
}
type Duration struct {
time.Duration
}
func (d *Duration) UnmarshalJSON(b []byte) (err error) {
var v any
if err := json.Unmarshal(b, &v); err != nil {
return err
}
d.Duration = 5 * time.Minute
switch t := v.(type) {
case float64:
if t < 0 {
t = math.MaxFloat64
}
d.Duration = time.Duration(t)
case string:
d.Duration, err = time.ParseDuration(t)
if err != nil {
return err
}
}
return nil
}

View File

@@ -1,5 +1,7 @@
# Desktop
_Note: the Ollama desktop app is a work in progress and is not ready yet for general use._
This app builds upon Ollama to provide a desktop experience for running models.
## Developing
@@ -7,15 +9,19 @@ This app builds upon Ollama to provide a desktop experience for running models.
First, build the `ollama` binary:
```
cd ..
go build .
make -C ..
```
Then run the desktop app with `npm start`:
```
cd app
npm install
npm start
```
## Coming soon
- Browse the latest available models on Hugging Face and other sources
- Keep track of previous conversations with models
- Switch quickly between models
- Connect to remote Ollama servers to run models

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@@ -18,15 +18,9 @@ const config: ForgeConfig = {
asar: true,
icon: './assets/icon.icns',
extraResource: [
'../dist/ollama',
path.join(__dirname, './assets/iconTemplate.png'),
path.join(__dirname, './assets/iconTemplate@2x.png'),
path.join(__dirname, './assets/iconUpdateTemplate.png'),
path.join(__dirname, './assets/iconUpdateTemplate@2x.png'),
path.join(__dirname, './assets/iconDarkTemplate.png'),
path.join(__dirname, './assets/iconDarkTemplate@2x.png'),
path.join(__dirname, './assets/iconDarkUpdateTemplate.png'),
path.join(__dirname, './assets/iconDarkUpdateTemplate@2x.png'),
'../ollama',
path.join(__dirname, './assets/ollama_icon_16x16Template.png'),
path.join(__dirname, './assets/ollama_icon_16x16Template@2x.png'),
...(process.platform === 'darwin' ? ['../llama/ggml-metal.metal'] : []),
],
...(process.env.SIGN
@@ -42,9 +36,6 @@ const config: ForgeConfig = {
},
}
: {}),
osxUniversal: {
x64ArchFiles: '**/ollama',
},
},
rebuildConfig: {},
makers: [new MakerSquirrel({}), new MakerZIP({}, ['darwin'])],

7
app/package-lock.json generated
View File

@@ -32,7 +32,6 @@
"@electron-forge/plugin-auto-unpack-natives": "^6.2.1",
"@electron-forge/plugin-webpack": "^6.2.1",
"@electron-forge/publisher-github": "^6.2.1",
"@electron/universal": "^1.4.1",
"@svgr/webpack": "^8.0.1",
"@types/chmodr": "^1.0.0",
"@types/node": "^20.4.0",
@@ -3329,9 +3328,9 @@
}
},
"node_modules/@electron/universal": {
"version": "1.4.1",
"resolved": "https://registry.npmjs.org/@electron/universal/-/universal-1.4.1.tgz",
"integrity": "sha512-lE/U3UNw1YHuowNbTmKNs9UlS3En3cPgwM5MI+agIgr/B1hSze9NdOP0qn7boZaI9Lph8IDv3/24g9IxnJP7aQ==",
"version": "1.3.4",
"resolved": "https://registry.npmjs.org/@electron/universal/-/universal-1.3.4.tgz",
"integrity": "sha512-BdhBgm2ZBnYyYRLRgOjM5VHkyFItsbggJ0MHycOjKWdFGYwK97ZFXH54dTvUWEfha81vfvwr5On6XBjt99uDcg==",
"dev": true,
"dependencies": {
"@electron/asar": "^3.2.1",

View File

@@ -6,14 +6,12 @@
"main": ".webpack/main",
"scripts": {
"start": "electron-forge start",
"package": "electron-forge package --arch universal",
"package:sign": "SIGN=1 electron-forge package --arch universal",
"make": "electron-forge make --arch universal",
"make:sign": "SIGN=1 electron-forge make --arch universal",
"package": "electron-forge package",
"package:sign": "SIGN=1 electron-forge package",
"make": "electron-forge make",
"make:sign": "SIGN=1 electron-forge make",
"publish": "SIGN=1 electron-forge publish",
"lint": "eslint --ext .ts,.tsx .",
"format": "prettier --check . --ignore-path .gitignore",
"format:fix": "prettier --write . --ignore-path .gitignore"
"lint": "eslint --ext .ts,.tsx ."
},
"keywords": [],
"author": {
@@ -32,7 +30,6 @@
"@electron-forge/plugin-auto-unpack-natives": "^6.2.1",
"@electron-forge/plugin-webpack": "^6.2.1",
"@electron-forge/publisher-github": "^6.2.1",
"@electron/universal": "^1.4.1",
"@svgr/webpack": "^8.0.1",
"@types/chmodr": "^1.0.0",
"@types/node": "^20.4.0",

View File

@@ -2,7 +2,7 @@ import { useState } from 'react'
import copy from 'copy-to-clipboard'
import { CheckIcon, DocumentDuplicateIcon } from '@heroicons/react/24/outline'
import Store from 'electron-store'
import { getCurrentWindow, app } from '@electron/remote'
import { getCurrentWindow } from '@electron/remote'
import { install } from './install'
import OllamaIcon from './ollama.svg'
@@ -19,7 +19,7 @@ export default function () {
const [step, setStep] = useState<Step>(Step.WELCOME)
const [commandCopied, setCommandCopied] = useState<boolean>(false)
const command = 'ollama run llama2'
const command = 'ollama run orca'
return (
<div className='drag'>
@@ -51,15 +51,10 @@ export default function () {
<div className='mx-auto'>
<button
onClick={async () => {
try {
await install()
setStep(Step.FINISH)
} catch (e) {
console.error('could not install: ', e)
} finally {
getCurrentWindow().show()
getCurrentWindow().focus()
}
await install()
getCurrentWindow().show()
getCurrentWindow().focus()
setStep(Step.FINISH)
}}
className='no-drag rounded-dm mx-auto w-[60%] rounded-md bg-black px-4 py-2 text-sm text-white hover:brightness-110'
>
@@ -82,11 +77,7 @@ export default function () {
{command}
</pre>
<button
className={`no-drag absolute right-[5px] px-2 py-2 ${
commandCopied
? 'text-gray-900 opacity-100 hover:cursor-auto'
: 'text-gray-200 opacity-50 hover:cursor-pointer'
} hover:font-bold hover:text-gray-900 group-hover:opacity-100`}
className={`no-drag absolute right-[5px] px-2 py-2 ${commandCopied ? 'text-gray-900 opacity-100 hover:cursor-auto' : 'text-gray-200 opacity-50 hover:cursor-pointer'} hover:text-gray-900 hover:font-bold group-hover:opacity-100`}
onClick={() => {
copy(command)
setCommandCopied(true)
@@ -94,15 +85,13 @@ export default function () {
}}
>
{commandCopied ? (
<CheckIcon className='h-4 w-4 font-bold text-gray-500' />
<CheckIcon className='h-4 w-4 text-gray-500 font-bold' />
) : (
<DocumentDuplicateIcon className='h-4 w-4 text-gray-500' />
)}
</button>
</div>
<p className='mx-auto my-4 w-[70%] text-xs text-gray-400'>
Run this command in your favorite terminal.
</p>
<p className='mx-auto my-4 w-[70%] text-xs text-gray-400'>Run this command in your favorite terminal.</p>
</div>
<button
onClick={() => {

View File

@@ -1,4 +1,4 @@
declare module '*.svg' {
const content: string
export default content
}
const content: string;
export default content;
}

View File

@@ -1,5 +1,5 @@
import { spawn, ChildProcess } from 'child_process'
import { app, autoUpdater, dialog, Tray, Menu, BrowserWindow, MenuItemConstructorOptions, nativeTheme } from 'electron'
import { spawn } from 'child_process'
import { app, autoUpdater, dialog, Tray, Menu, BrowserWindow } from 'electron'
import Store from 'electron-store'
import winston from 'winston'
import 'winston-daily-rotate-file'
@@ -10,12 +10,8 @@ import { installed } from './install'
require('@electron/remote/main').initialize()
if (require('electron-squirrel-startup')) {
app.quit()
}
const store = new Store()
let tray: Tray | null = null
let welcomeWindow: BrowserWindow | null = null
declare const MAIN_WINDOW_WEBPACK_ENTRY: string
@@ -32,30 +28,10 @@ const logger = winston.createLogger({
format: winston.format.printf(info => info.message),
})
app.on('ready', () => {
const gotTheLock = app.requestSingleInstanceLock()
if (!gotTheLock) {
app.exit(0)
return
}
app.on('second-instance', () => {
if (app.hasSingleInstanceLock()) {
app.releaseSingleInstanceLock()
}
if (proc) {
proc.off('exit', restart)
proc.kill()
}
app.exit(0)
})
app.focus({ steal: true })
init()
})
const SingleInstanceLock = app.requestSingleInstanceLock()
if (!SingleInstanceLock) {
app.quit()
}
function firstRunWindow() {
// Create the browser window.
@@ -71,74 +47,49 @@ function firstRunWindow() {
nodeIntegration: true,
contextIsolation: false,
},
alwaysOnTop: true,
})
require('@electron/remote/main').enable(welcomeWindow.webContents)
// and load the index.html of the app.
welcomeWindow.loadURL(MAIN_WINDOW_WEBPACK_ENTRY)
welcomeWindow.on('ready-to-show', () => welcomeWindow.show())
welcomeWindow.on('closed', () => {
if (process.platform === 'darwin') {
app.dock.hide()
}
})
}
let tray: Tray | null = null
let updateAvailable = false
const assetPath = app.isPackaged ? process.resourcesPath : path.join(__dirname, '..', '..', 'assets')
// for debugging
// welcomeWindow.webContents.openDevTools()
function trayIconPath() {
return nativeTheme.shouldUseDarkColors
? updateAvailable
? path.join(assetPath, 'iconDarkUpdateTemplate.png')
: path.join(assetPath, 'iconDarkTemplate.png')
: updateAvailable
? path.join(assetPath, 'iconUpdateTemplate.png')
: path.join(assetPath, 'iconTemplate.png')
}
function updateTrayIcon() {
if (tray) {
tray.setImage(trayIconPath())
if (process.platform === 'darwin') {
app.dock.hide()
}
}
function updateTray() {
const updateItems: MenuItemConstructorOptions[] = [
{ label: 'An update is available', enabled: false },
{
label: 'Restart to update',
click: () => autoUpdater.quitAndInstall(),
},
{ type: 'separator' },
]
function createSystemtray() {
let iconPath = path.join(__dirname, '..', '..', 'assets', 'ollama_icon_16x16Template.png')
const menu = Menu.buildFromTemplate([
...(updateAvailable ? updateItems : []),
{ role: 'quit', label: 'Quit Ollama', accelerator: 'Command+Q' },
])
if (!tray) {
tray = new Tray(trayIconPath())
if (app.isPackaged) {
iconPath = path.join(process.resourcesPath, 'ollama_icon_16x16Template.png')
}
tray.setToolTip(updateAvailable ? 'An update is available' : 'Ollama')
tray.setContextMenu(menu)
tray.setImage(trayIconPath())
tray = new Tray(iconPath)
nativeTheme.off('updated', updateTrayIcon)
nativeTheme.on('updated', updateTrayIcon)
const contextMenu = Menu.buildFromTemplate([{ role: 'quit', label: 'Quit Ollama', accelerator: 'Command+Q' }])
tray.setContextMenu(contextMenu)
tray.setToolTip('Ollama')
}
let proc: ChildProcess = null
if (require('electron-squirrel-startup')) {
app.quit()
}
function server() {
const binary = app.isPackaged
? path.join(process.resourcesPath, 'ollama')
: path.resolve(process.cwd(), '..', 'ollama')
proc = spawn(binary, ['serve'])
const proc = spawn(binary, ['serve'])
proc.stdout.on('data', data => {
logger.info(data.toString().trim())
@@ -148,32 +99,24 @@ function server() {
logger.error(data.toString().trim())
})
function restart() {
logger.info('Restarting the server...')
server()
}
proc.on('exit', restart)
}
function restart() {
setTimeout(server, 1000)
}
app.on('before-quit', () => {
if (proc) {
app.on('before-quit', () => {
proc.off('exit', restart)
proc.kill()
}
})
})
}
function init() {
if (app.isPackaged) {
heartbeat()
autoUpdater.checkForUpdates()
setInterval(() => {
heartbeat()
autoUpdater.checkForUpdates()
}, 60 * 60 * 1000)
}
updateTray()
if (process.platform === 'darwin') {
app.dock.hide()
}
app.on('ready', () => {
if (process.platform === 'darwin') {
if (app.isPackaged) {
if (!app.isInApplicationsFolder()) {
@@ -209,13 +152,10 @@ function init() {
}
}
createSystemtray()
server()
if (store.get('first-time-run') && installed()) {
if (process.platform === 'darwin') {
app.dock.hide()
}
app.setLoginItemSettings({ openAtLogin: app.getLoginItemSettings().openAtLogin })
return
}
@@ -223,7 +163,7 @@ function init() {
// This is the first run or the CLI is no longer installed
app.setLoginItemSettings({ openAtLogin: true })
firstRunWindow()
}
})
// Quit when all windows are closed, except on macOS. There, it's common
// for applications and their menu bar to stay active until the user quits
@@ -236,18 +176,13 @@ app.on('window-all-closed', () => {
// In this file you can include the rest of your app's specific main process
// code. You can also put them in separate files and import them here.
let aid = ''
try {
aid = id()
} catch (e) {}
autoUpdater.setFeedURL({
url: `https://ollama.ai/api/update?os=${process.platform}&arch=${process.arch}&version=${app.getVersion()}&id=${aid}`,
url: `https://ollama.ai/api/update?os=${process.platform}&arch=${process.arch}&version=${app.getVersion()}`,
})
async function heartbeat() {
analytics.track({
anonymousId: aid,
anonymousId: id(),
event: 'heartbeat',
properties: {
version: app.getVersion(),
@@ -255,11 +190,29 @@ async function heartbeat() {
})
}
if (app.isPackaged) {
heartbeat()
autoUpdater.checkForUpdates()
setInterval(() => {
heartbeat()
autoUpdater.checkForUpdates()
}, 60 * 60 * 1000)
}
autoUpdater.on('error', e => {
console.error(`update check failed - ${e.message}`)
logger.error(`update check failed - ${e.message}`)
})
autoUpdater.on('update-downloaded', () => {
updateAvailable = true
updateTray()
autoUpdater.on('update-downloaded', (event, releaseNotes, releaseName) => {
dialog
.showMessageBox({
type: 'info',
buttons: ['Restart Now', 'Later'],
title: 'New update available',
message: process.platform === 'win32' ? releaseNotes : releaseName,
detail: 'A new version of Ollama is available. Restart to apply the update.',
})
.then(returnValue => {
if (returnValue.response === 0) autoUpdater.quitAndInstall()
})
})

View File

@@ -13,9 +13,12 @@ export function installed() {
}
export async function install() {
const command = `do shell script "mkdir -p ${path.dirname(
symlinkPath
)} && ln -F -s \\"${ollama}\\" \\"${symlinkPath}\\"" with administrator privileges`
const command = `do shell script "ln -F -s ${ollama} ${symlinkPath}" with administrator privileges`
await exec(`osascript -e '${command}'`)
try {
await exec(`osascript -e '${command}'`)
} catch (error) {
console.error(`cli: failed to install cli: ${error.message}`)
return
}
}

View File

@@ -5,71 +5,39 @@ import (
"context"
"errors"
"fmt"
"io"
"log"
"net"
"net/http"
"os"
"os/exec"
"path/filepath"
"runtime"
"strings"
"time"
"github.com/chzyer/readline"
"github.com/dustin/go-humanize"
"github.com/olekukonko/tablewriter"
"github.com/schollz/progressbar/v3"
"github.com/spf13/cobra"
"golang.org/x/term"
"github.com/jmorganca/ollama/api"
"github.com/jmorganca/ollama/format"
"github.com/jmorganca/ollama/progressbar"
"github.com/jmorganca/ollama/server"
)
func CreateHandler(cmd *cobra.Command, args []string) error {
func create(cmd *cobra.Command, args []string) error {
filename, _ := cmd.Flags().GetString("file")
filename, err := filepath.Abs(filename)
if err != nil {
return err
}
client := api.NewClient()
var spinner *Spinner
var currentDigest string
var bar *progressbar.ProgressBar
request := api.CreateRequest{Name: args[0], Path: filename}
fn := func(resp api.ProgressResponse) error {
if resp.Digest != currentDigest && resp.Digest != "" {
if spinner != nil {
spinner.Stop()
}
currentDigest = resp.Digest
switch {
case strings.Contains(resp.Status, "embeddings"):
bar = progressbar.Default(int64(resp.Total), resp.Status)
bar.Set(resp.Completed)
default:
// pulling
bar = progressbar.DefaultBytes(
int64(resp.Total),
resp.Status,
)
bar.Set(resp.Completed)
}
} else if resp.Digest == currentDigest && resp.Digest != "" {
bar.Set(resp.Completed)
} else {
currentDigest = ""
if spinner != nil {
spinner.Stop()
}
spinner = NewSpinner(resp.Status)
go spinner.Spin(100 * time.Millisecond)
fn := func(resp api.CreateProgress) error {
if spinner != nil {
spinner.Stop()
}
spinner = NewSpinner(resp.Status)
go spinner.Spin(100 * time.Millisecond)
return nil
}
@@ -84,7 +52,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
return nil
}
func RunHandler(cmd *cobra.Command, args []string) error {
func RunRun(cmd *cobra.Command, args []string) error {
mp := server.ParseModelPath(args[0])
fp, err := mp.GetManifestPath(false)
if err != nil {
@@ -94,7 +62,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
_, err = os.Stat(fp)
switch {
case errors.Is(err, os.ErrNotExist):
if err := pull(args[0], false); err != nil {
if err := pull(args[0]); err != nil {
var apiStatusError api.StatusError
if !errors.As(err, &apiStatusError) {
return err
@@ -111,33 +79,12 @@ func RunHandler(cmd *cobra.Command, args []string) error {
return RunGenerate(cmd, args)
}
func PushHandler(cmd *cobra.Command, args []string) error {
func push(cmd *cobra.Command, args []string) error {
client := api.NewClient()
insecure, err := cmd.Flags().GetBool("insecure")
if err != nil {
return err
}
var currentDigest string
var bar *progressbar.ProgressBar
request := api.PushRequest{Name: args[0], Insecure: insecure}
fn := func(resp api.ProgressResponse) error {
if resp.Digest != currentDigest && resp.Digest != "" {
currentDigest = resp.Digest
bar = progressbar.DefaultBytes(
int64(resp.Total),
fmt.Sprintf("pushing %s...", resp.Digest[7:19]),
)
bar.Set(resp.Completed)
} else if resp.Digest == currentDigest && resp.Digest != "" {
bar.Set(resp.Completed)
} else {
currentDigest = ""
fmt.Println(resp.Status)
}
request := api.PushRequest{Name: args[0]}
fn := func(resp api.PushProgress) error {
fmt.Println(resp.Status)
return nil
}
@@ -147,7 +94,7 @@ func PushHandler(cmd *cobra.Command, args []string) error {
return nil
}
func ListHandler(cmd *cobra.Command, args []string) error {
func list(cmd *cobra.Command, args []string) error {
client := api.NewClient()
models, err := client.List(context.Background())
@@ -158,9 +105,7 @@ func ListHandler(cmd *cobra.Command, args []string) error {
var data [][]string
for _, m := range models.Models {
if len(args) == 0 || strings.HasPrefix(m.Name, args[0]) {
data = append(data, []string{m.Name, humanize.Bytes(uint64(m.Size)), format.HumanTime(m.ModifiedAt, "Never")})
}
data = append(data, []string{m.Name, humanize.Bytes(uint64(m.Size)), format.HumanTime(m.ModifiedAt, "Never")})
}
table := tablewriter.NewWriter(os.Stdout)
@@ -177,57 +122,32 @@ func ListHandler(cmd *cobra.Command, args []string) error {
return nil
}
func DeleteHandler(cmd *cobra.Command, args []string) error {
client := api.NewClient()
req := api.DeleteRequest{Name: args[0]}
if err := client.Delete(context.Background(), &req); err != nil {
return err
}
fmt.Printf("deleted '%s'\n", args[0])
return nil
func RunPull(cmd *cobra.Command, args []string) error {
return pull(args[0])
}
func CopyHandler(cmd *cobra.Command, args []string) error {
func pull(model string) error {
client := api.NewClient()
req := api.CopyRequest{Source: args[0], Destination: args[1]}
if err := client.Copy(context.Background(), &req); err != nil {
return err
}
fmt.Printf("copied '%s' to '%s'\n", args[0], args[1])
return nil
}
func PullHandler(cmd *cobra.Command, args []string) error {
insecure, err := cmd.Flags().GetBool("insecure")
if err != nil {
return err
}
return pull(args[0], insecure)
}
func pull(model string, insecure bool) error {
client := api.NewClient()
var currentDigest string
var bar *progressbar.ProgressBar
request := api.PullRequest{Name: model, Insecure: insecure}
fn := func(resp api.ProgressResponse) error {
if resp.Digest != currentDigest && resp.Digest != "" {
currentDigest = resp.Digest
currentLayer := ""
request := api.PullRequest{Name: model}
fn := func(resp api.PullProgress) error {
if resp.Digest != currentLayer && resp.Digest != "" {
if currentLayer != "" {
fmt.Println()
}
currentLayer = resp.Digest
layerStr := resp.Digest[7:23] + "..."
bar = progressbar.DefaultBytes(
int64(resp.Total),
fmt.Sprintf("pulling %s...", resp.Digest[7:19]),
"pulling "+layerStr,
)
bar.Set(resp.Completed)
} else if resp.Digest == currentDigest && resp.Digest != "" {
} else if resp.Digest == currentLayer && resp.Digest != "" {
bar.Set(resp.Completed)
} else {
currentDigest = ""
currentLayer = ""
fmt.Println(resp.Status)
}
return nil
@@ -245,14 +165,14 @@ func RunGenerate(cmd *cobra.Command, args []string) error {
return generate(cmd, args[0], strings.Join(args[1:], " "))
}
if readline.IsTerminal(int(os.Stdin.Fd())) {
if term.IsTerminal(int(os.Stdin.Fd())) {
return generateInteractive(cmd, args[0])
}
return generateBatch(cmd, args[0])
}
type generateContextKey string
var generateContextKey struct{}
func generate(cmd *cobra.Command, model, prompt string) error {
if len(strings.TrimSpace(prompt)) > 0 {
@@ -263,36 +183,26 @@ func generate(cmd *cobra.Command, model, prompt string) error {
var latest api.GenerateResponse
generateContext, ok := cmd.Context().Value(generateContextKey("context")).([]int)
generateContext, ok := cmd.Context().Value(generateContextKey).([]int)
if !ok {
generateContext = []int{}
}
request := api.GenerateRequest{Model: model, Prompt: prompt, Context: generateContext}
fn := func(response api.GenerateResponse) error {
fn := func(resp api.GenerateResponse) error {
if !spinner.IsFinished() {
spinner.Finish()
}
latest = response
latest = resp
fmt.Print(response.Response)
fmt.Print(resp.Response)
cmd.SetContext(context.WithValue(cmd.Context(), generateContextKey, resp.Context))
return nil
}
if err := client.Generate(context.Background(), &request, fn); err != nil {
if strings.Contains(err.Error(), "failed to load model") {
// tell the user to check the server log, if it exists locally
home, nestedErr := os.UserHomeDir()
if nestedErr != nil {
// return the original error
return err
}
logPath := filepath.Join(home, ".ollama", "logs", "server.log")
if _, nestedErr := os.Stat(logPath); nestedErr == nil {
err = fmt.Errorf("%w\nFor more details, check the error logs at %s", err, logPath)
}
}
return err
}
@@ -307,207 +217,23 @@ func generate(cmd *cobra.Command, model, prompt string) error {
if verbose {
latest.Summary()
}
ctx := cmd.Context()
ctx = context.WithValue(ctx, generateContextKey("context"), latest.Context)
cmd.SetContext(ctx)
}
return nil
}
func showLayer(l *server.Layer) {
filename, err := server.GetBlobsPath(l.Digest)
if err != nil {
fmt.Println("Couldn't get layer's path")
return
}
bts, err := os.ReadFile(filename)
if err != nil {
fmt.Println("Couldn't read layer")
return
}
fmt.Println(string(bts))
}
func generateInteractive(cmd *cobra.Command, model string) error {
home, err := os.UserHomeDir()
if err != nil {
return err
}
completer := readline.NewPrefixCompleter(
readline.PcItem("/help"),
readline.PcItem("/list"),
readline.PcItem("/set",
readline.PcItem("history"),
readline.PcItem("nohistory"),
readline.PcItem("verbose"),
readline.PcItem("quiet"),
readline.PcItem("mode",
readline.PcItem("vim"),
readline.PcItem("emacs"),
readline.PcItem("default"),
),
),
readline.PcItem("/show",
readline.PcItem("license"),
readline.PcItem("system"),
readline.PcItem("template"),
),
readline.PcItem("/exit"),
readline.PcItem("/bye"),
)
usage := func() {
fmt.Fprintln(os.Stderr, "commands:")
fmt.Fprintln(os.Stderr, completer.Tree(" "))
}
config := readline.Config{
Prompt: ">>> ",
HistoryFile: filepath.Join(home, ".ollama", "history"),
AutoComplete: completer,
}
scanner, err := readline.NewEx(&config)
if err != nil {
return err
}
defer scanner.Close()
var multiLineBuffer string
var isMultiLine bool
for {
line, err := scanner.Readline()
switch {
case errors.Is(err, io.EOF):
return nil
case errors.Is(err, readline.ErrInterrupt):
if line == "" {
return nil
}
continue
case err != nil:
fmt.Print(">>> ")
scanner := bufio.NewScanner(os.Stdin)
for scanner.Scan() {
if err := generate(cmd, model, scanner.Text()); err != nil {
return err
}
line = strings.TrimSpace(line)
switch {
case isMultiLine:
if strings.HasSuffix(line, `"""`) {
isMultiLine = false
multiLineBuffer += strings.TrimSuffix(line, `"""`)
line = multiLineBuffer
multiLineBuffer = ""
scanner.SetPrompt(">>> ")
} else {
multiLineBuffer += line + " "
continue
}
case strings.HasPrefix(line, `"""`):
isMultiLine = true
multiLineBuffer = strings.TrimPrefix(line, `"""`) + " "
scanner.SetPrompt("... ")
continue
case strings.HasPrefix(line, "/list"):
args := strings.Fields(line)
if err := ListHandler(cmd, args[1:]); err != nil {
return err
}
continue
case strings.HasPrefix(line, "/set"):
args := strings.Fields(line)
if len(args) > 1 {
switch args[1] {
case "history":
scanner.HistoryEnable()
continue
case "nohistory":
scanner.HistoryDisable()
continue
case "verbose":
cmd.Flags().Set("verbose", "true")
continue
case "quiet":
cmd.Flags().Set("verbose", "false")
continue
case "mode":
if len(args) > 2 {
switch args[2] {
case "vim":
scanner.SetVimMode(true)
continue
case "emacs", "default":
scanner.SetVimMode(false)
continue
default:
usage()
continue
}
} else {
usage()
continue
}
}
} else {
usage()
continue
}
case strings.HasPrefix(line, "/show"):
args := strings.Fields(line)
if len(args) > 1 {
mp := server.ParseModelPath(model)
manifest, err := server.GetManifest(mp)
if err != nil {
fmt.Println("error: couldn't get a manifest for this model")
continue
}
switch args[1] {
case "license":
for _, l := range manifest.Layers {
if l.MediaType == "application/vnd.ollama.image.license" {
showLayer(l)
}
}
continue
case "system":
for _, l := range manifest.Layers {
if l.MediaType == "application/vnd.ollama.image.system" {
showLayer(l)
}
}
continue
case "template":
for _, l := range manifest.Layers {
if l.MediaType == "application/vnd.ollama.image.template" {
showLayer(l)
}
}
continue
default:
usage()
continue
}
} else {
usage()
continue
}
case line == "/help", line == "/?":
usage()
continue
case line == "/exit", line == "/bye":
return nil
}
if err := generate(cmd, model, line); err != nil {
return err
}
fmt.Print(">>> ")
}
return nil
}
func generateBatch(cmd *cobra.Command, model string) error {
@@ -523,21 +249,15 @@ func generateBatch(cmd *cobra.Command, model string) error {
return nil
}
func RunServer(cmd *cobra.Command, _ []string) error {
var host, port = "127.0.0.1", "11434"
parts := strings.Split(os.Getenv("OLLAMA_HOST"), ":")
if ip := net.ParseIP(parts[0]); ip != nil {
host = ip.String()
func RunServer(_ *cobra.Command, _ []string) error {
host := os.Getenv("OLLAMA_HOST")
if host == "" {
host = "127.0.0.1"
}
if len(parts) > 1 {
port = parts[1]
}
// deprecated: include port in OLLAMA_HOST
if p := os.Getenv("OLLAMA_PORT"); p != "" {
port = p
port := os.Getenv("OLLAMA_PORT")
if port == "" {
port = "11434"
}
ln, err := net.Listen("tcp", fmt.Sprintf("%s:%s", host, port))
@@ -545,60 +265,7 @@ func RunServer(cmd *cobra.Command, _ []string) error {
return err
}
var origins []string
if o := os.Getenv("OLLAMA_ORIGINS"); o != "" {
origins = strings.Split(o, ",")
}
return server.Serve(ln, origins)
}
func startMacApp(client *api.Client) error {
exe, err := os.Executable()
if err != nil {
return err
}
link, err := os.Readlink(exe)
if err != nil {
return err
}
if !strings.Contains(link, "Ollama.app") {
return fmt.Errorf("could not find ollama app")
}
path := strings.Split(link, "Ollama.app")
if err := exec.Command("/usr/bin/open", "-a", path[0]+"Ollama.app").Run(); err != nil {
return err
}
// wait for the server to start
timeout := time.After(5 * time.Second)
tick := time.Tick(500 * time.Millisecond)
for {
select {
case <-timeout:
return errors.New("timed out waiting for server to start")
case <-tick:
if err := client.Heartbeat(context.Background()); err == nil {
return nil // server has started
}
}
}
}
func checkServerHeartbeat(_ *cobra.Command, _ []string) error {
client := api.NewClient()
if err := client.Heartbeat(context.Background()); err != nil {
if !strings.Contains(err.Error(), "connection refused") {
return err
}
if runtime.GOOS == "darwin" {
if err := startMacApp(client); err != nil {
return fmt.Errorf("could not connect to ollama app, is it running?")
}
} else {
return fmt.Errorf("could not connect to ollama server, run 'ollama serve' to start it")
}
}
return nil
return server.Serve(ln)
}
func NewCLI() *cobra.Command {
@@ -616,21 +283,19 @@ func NewCLI() *cobra.Command {
cobra.EnableCommandSorting = false
createCmd := &cobra.Command{
Use: "create MODEL",
Short: "Create a model from a Modelfile",
Args: cobra.MinimumNArgs(1),
PreRunE: checkServerHeartbeat,
RunE: CreateHandler,
Use: "create MODEL",
Short: "Create a model from a Modelfile",
Args: cobra.MinimumNArgs(1),
RunE: create,
}
createCmd.Flags().StringP("file", "f", "Modelfile", "Name of the Modelfile (default \"Modelfile\")")
runCmd := &cobra.Command{
Use: "run MODEL [PROMPT]",
Short: "Run a model",
Args: cobra.MinimumNArgs(1),
PreRunE: checkServerHeartbeat,
RunE: RunHandler,
Use: "run MODEL [PROMPT]",
Short: "Run a model",
Args: cobra.MinimumNArgs(1),
RunE: RunRun,
}
runCmd.Flags().Bool("verbose", false, "Show timings for response")
@@ -643,47 +308,23 @@ func NewCLI() *cobra.Command {
}
pullCmd := &cobra.Command{
Use: "pull MODEL",
Short: "Pull a model from a registry",
Args: cobra.MinimumNArgs(1),
PreRunE: checkServerHeartbeat,
RunE: PullHandler,
Use: "pull MODEL",
Short: "Pull a model from a registry",
Args: cobra.MinimumNArgs(1),
RunE: RunPull,
}
pullCmd.Flags().Bool("insecure", false, "Use an insecure registry")
pushCmd := &cobra.Command{
Use: "push MODEL",
Short: "Push a model to a registry",
Args: cobra.MinimumNArgs(1),
PreRunE: checkServerHeartbeat,
RunE: PushHandler,
Use: "push MODEL",
Short: "Push a model to a registry",
Args: cobra.MinimumNArgs(1),
RunE: push,
}
pushCmd.Flags().Bool("insecure", false, "Use an insecure registry")
listCmd := &cobra.Command{
Use: "list",
Aliases: []string{"ls"},
Short: "List models",
PreRunE: checkServerHeartbeat,
RunE: ListHandler,
}
copyCmd := &cobra.Command{
Use: "cp",
Short: "Copy a model",
Args: cobra.MinimumNArgs(2),
PreRunE: checkServerHeartbeat,
RunE: CopyHandler,
}
deleteCmd := &cobra.Command{
Use: "rm",
Short: "Remove a model",
Args: cobra.MinimumNArgs(1),
PreRunE: checkServerHeartbeat,
RunE: DeleteHandler,
Use: "list",
Short: "List models",
RunE: list,
}
rootCmd.AddCommand(
@@ -693,8 +334,6 @@ func NewCLI() *cobra.Command {
pullCmd,
pushCmd,
listCmd,
copyCmd,
deleteCmd,
)
return rootCmd

View File

@@ -5,7 +5,7 @@ import (
"os"
"time"
"github.com/jmorganca/ollama/progressbar"
"github.com/schollz/progressbar/v3"
)
type Spinner struct {

View File

@@ -1,5 +0,0 @@
# Documentation
- [Modelfile](./modelfile.md)
- [How to develop Ollama](./development.md)
- [API](./api.md)

View File

@@ -1,222 +0,0 @@
# API
## Endpoints
- [Generate a completion](#generate-a-completion)
- [Create a model](#create-a-model)
- [List local models](#list-local-models)
- [Copy a model](#copy-a-model)
- [Delete a model](#delete-a-model)
- [Pull a model](#pull-a-model)
## Conventions
### Model names
Model names follow a `model:tag` format. Some examples are `orca:3b-q4_1` and `llama2:70b`. The tag is optional and if not provided will default to `latest`. The tag is used to identify a specific version.
### Durations
All durations are returned in nanoseconds.
## Generate a completion
```
POST /api/generate
```
Generate a response for a given prompt with a provided model. This is a streaming endpoint, so will be a series of responses. The final response object will include statistics and additional data from the request.
### Parameters
- `model`: (required) the [model name](#model-names)
- `prompt`: the prompt to generate a response for
Advanced parameters:
- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
- `system`: system prompt to (overrides what is defined in the `Modelfile`)
- `template`: the full prompt or prompt template (overrides what is defined in the `Modelfile`)
### Request
```
curl -X POST http://localhost:11434/api/generate -d '{
"model": "llama2:7b",
"prompt": "Why is the sky blue?"
}'
```
### Response
A stream of JSON objects:
```json
{
"model": "llama2:7b",
"created_at": "2023-08-04T08:52:19.385406455-07:00",
"response": "The",
"done": false
}
```
The final response in the stream also includes additional data about the generation:
- `total_duration`: time spent generating the response
- `load_duration`: time spent in nanoseconds loading the model
- `sample_count`: number of samples generated
- `sample_duration`: time spent generating samples
- `prompt_eval_count`: number of tokens in the prompt
- `prompt_eval_duration`: time spent in nanoseconds evaluating the prompt
- `eval_count`: number of tokens the response
- `eval_duration`: time in nanoseconds spent generating the response
To calculate how fast the response is generated in tokens per second (token/s), divide `eval_count` / `eval_duration`.
```json
{
"model": "llama2:7b",
"created_at": "2023-08-04T19:22:45.499127Z",
"done": true,
"total_duration": 5589157167,
"load_duration": 3013701500,
"sample_count": 114,
"sample_duration": 81442000,
"prompt_eval_count": 46,
"prompt_eval_duration": 1160282000,
"eval_count": 113,
"eval_duration": 1325948000
}
```
## Create a Model
```
POST /api/create
```
Create a model from a [`Modelfile`](./modelfile.md)
### Parameters
- `name`: name of the model to create
- `path`: path to the Modelfile
### Request
```
curl -X POST http://localhost:11434/api/create -d '{
"name": "mario",
"path": "~/Modelfile"
}'
```
### Response
A stream of JSON objects. When finished, `status` is `success`
```json
{
"status": "parsing modelfile"
}
```
## List Local Models
```
GET /api/tags
```
List models that are available locally.
### Request
```
curl http://localhost:11434/api/tags
```
### Response
```json
{
"models": [
{
"name": "llama2:7b",
"modified_at": "2023-08-02T17:02:23.713454393-07:00",
"size": 3791730596
},
{
"name": "llama2:13b",
"modified_at": "2023-08-08T12:08:38.093596297-07:00",
"size": 7323310500
}
]
}
```
## Copy a Model
```
POST /api/copy
```
Copy a model. Creates a model with another name from an existing model.
### Request
```
curl http://localhost:11434/api/copy -d '{
"source": "llama2:7b",
"destination": "llama2-backup"
}'
```
## Delete a Model
```
DELETE /api/delete
```
Delete a model and its data.
### Parameters
- `model`: model name to delete
### Request
```
curl -X DELETE http://localhost:11434/api/delete -d '{
"name": "llama2:13b"
}'
```
## Pull a Model
```
POST /api/pull
```
Download a model from a the model registry. Cancelled pulls are resumed from where they left off, and multiple calls to will share the same download progress.
### Parameters
- `name`: name of the model to pull
### Request
```
curl -X POST http://localhost:11434/api/pull -d '{
"name": "llama2:7b"
}'
```
### Response
```json
{
"status": "downloading digestname",
"digest": "digestname",
"total": 2142590208
}
```

View File

@@ -6,14 +6,6 @@ Install required tools:
brew install go
```
Enable CGO:
```
export CGO_ENABLED=1
```
You will also need a C/C++ compiler such as GCC for MacOS and Linux or Mingw-w64 GCC for Windows.
Then build ollama:
```
@@ -30,15 +22,19 @@ Now you can run `ollama`:
To release a new version of Ollama you'll need to set some environment variables:
- `GITHUB_TOKEN`: your GitHub token
- `APPLE_IDENTITY`: the Apple signing identity (macOS only)
- `APPLE_ID`: your Apple ID
- `APPLE_PASSWORD`: your Apple ID app-specific password
- `APPLE_TEAM_ID`: the Apple team ID for the signing identity
- `TELEMETRY_WRITE_KEY`: segment write key for telemetry
* `GITHUB_TOKEN`: your GitHub token
* `APPLE_IDENTITY`: the Apple signing identity (macOS only)
* `APPLE_ID`: your Apple ID
* `APPLE_PASSWORD`: your Apple ID app-specific password
* `APPLE_TEAM_ID`: the Apple team ID for the signing identity
* `TELEMETRY_WRITE_KEY`: segment write key for telemetry
Then run the publish script with the target version:
```
VERSION=0.0.2 ./scripts/publish.sh
```

View File

@@ -1,17 +0,0 @@
# FAQ
## How can I expose the Ollama server?
```
OLLAMA_HOST=0.0.0.0:11435 ollama serve
```
By default, Ollama allows cross origin requests from `127.0.0.1` and `0.0.0.0`. To support more origins, you can use the `OLLAMA_ORIGINS` environment variable:
```
OLLAMA_ORIGINS=http://192.168.1.1:*,https://example.com ollama serve
```
## Where are models stored?
Raw model data is stored under `~/.ollama/models`.

View File

@@ -1,166 +1,80 @@
# Ollama Model File
# Ollama Model File Reference
> Note: this model file syntax is in development
A model file is the blueprint to create and share models with Ollama.
## Table of Contents
- [Format](#format)
- [Examples](#examples)
- [Instructions](#instructions)
- [FROM (Required)](#from-required)
- [Build from llama2](#build-from-llama2)
- [Build from a bin file](#build-from-a-bin-file)
- [PARAMETER](#parameter)
- [Valid Parameters and Values](#valid-parameters-and-values)
- [TEMPLATE](#template)
- [Template Variables](#template-variables)
- [SYSTEM](#system)
- [LICENSE](#license)
- [Notes](#notes)
Ollama can build models automatically by reading the instructions from a Modelfile. A Modelfile is a text document that represents the complete configuration of the Model. You can see that a Modelfile is very similar to a Dockerfile.
## Format
The format of the Modelfile:
Here is the format of the Modelfile:
```modelfile
# comment
INSTRUCTION arguments
```
| Instruction | Description |
| ----------------------------------- | ------------------------------------------------------------- |
| [`FROM`](#from-required) (required) | Defines the base model to use. |
| [`PARAMETER`](#parameter) | Sets the parameters for how Ollama will run the model. |
| [`TEMPLATE`](#template) | The full prompt template to be sent to the model. |
| [`SYSTEM`](#system) | Specifies the system prompt that will be set in the template. |
| [`LICENSE`](#license) | Specifies the legal license. |
Nothing in the file is case-sensitive. However, the convention is for instructions to be uppercase to make it easier to distinguish from the arguments.
## Examples
A Modelfile can include instructions in any order. But the convention is to start the Modelfile with the FROM instruction.
An example of a model file creating a mario blueprint:
Although the example above shows a comment starting with a hash character, any instruction that is not recognized is seen as a comment.
```
FROM llama2
# sets the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
PARAMETER num_ctx 4096
## FROM
# sets a custom system prompt to specify the behavior of the chat assistant
SYSTEM You are Mario from super mario bros, acting as an assistant.
```modelfile
FROM <image>[:<tag>]
```
To use this:
This defines the base model to be used. An image can be a known image on the Ollama Hub, or a fully-qualified path to a model file on your system
1. Save it as a file (eg. `Modelfile`)
2. `ollama create NAME -f <location of the file eg. ./Modelfile>'`
3. `ollama run NAME`
4. Start using the model!
## PARAMETER
More examples are available in the [examples directory](../examples).
The PARAMETER instruction defines a parameter that can be set when the model is run.
## Instructions
### FROM (Required)
The FROM instruction defines the base model to use when creating a model.
```
FROM <model name>:<tag>
```
#### Build from llama2
```
FROM llama2
```
A list of available base models:
<https://github.com/jmorganca/ollama#model-library>
#### Build from a bin file
```
FROM ./ollama-model.bin
```
This bin file location should be specified as an absolute path or relative to the Modelfile location.
### PARAMETER
The `PARAMETER` instruction defines a parameter that can be set when the model is run.
```
```modelfile
PARAMETER <parameter> <parametervalue>
```
### Valid Parameters and Values
| Parameter | Description | Value Type | Example Usage |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- |
| mirostat | Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | int | mirostat 0 |
| mirostat_eta | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1) | float | mirostat_eta 0.1 |
| mirostat_tau | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0) | float | mirostat_tau 5.0 |
| num_ctx | Sets the size of the context window used to generate the next token. (Default: 2048) | int | num_ctx 4096 |
| num_gpu | The number of GPUs to use. On macOS it defaults to 1 to enable metal support, 0 to disable. | int | num_gpu 1 |
| num_thread | Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). | int | num_thread 8 |
| repeat_last_n | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) | int | repeat_last_n 64 |
| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1) | float | repeat_penalty 1.1 |
| temperature | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8) | float | temperature 0.7 |
| stop | Sets the stop tokens to use. | string | stop "AI assistant:" |
| tfs_z | Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1) | float | tfs_z 1 |
| top_k | Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40) | int | top_k 40 |
| top_p | Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9) | float | top_p 0.9 |
| Parameter | Description | Value Type | Value Range |
| ---------------- | ------------------------------------------------------------------------------------------- | ---------- | ----------- |
| NumCtx | | int | |
| NumGPU | | int | |
| MainGPU | | int | |
| LowVRAM | | bool | |
| F16KV | | bool | |
| LogitsAll | | bool | |
| VocabOnly | | bool | |
| UseMMap | | bool | |
| EmbeddingOnly | | bool | |
| RepeatLastN | | int | |
| RepeatPenalty | | float | |
| FrequencyPenalty | | float | |
| PresencePenalty | | float | |
| temperature | The temperature of the model. Higher temperatures result in more creativity in the response | float | 0 - 1 |
| TopK | | int | |
| TopP | | float | |
| TFSZ | | float | |
| TypicalP | | float | |
| Mirostat | | int | |
| MirostatTau | | float | |
| MirostatEta | | float | |
| NumThread | | int | |
### TEMPLATE
`TEMPLATE` of the full prompt template to be passed into the model. It may include (optionally) a system prompt and a user's prompt. This is used to create a full custom prompt, and syntax may be model specific.
## PROMPT
#### Template Variables
Prompt is a multiline instruction that defines the prompt to be used when the model is run. Typically there are 3-4 components to a prompt: System, context, user, and response.
| Variable | Description |
| --------------- | ------------------------------------------------------------------------------------------------------------ |
| `{{ .System }}` | The system prompt used to specify custom behavior, this must also be set in the Modelfile as an instruction. |
| `{{ .Prompt }}` | The incoming prompt, this is not specified in the model file and will be set based on input. |
| `{{ .First }}` | A boolean value used to render specific template information for the first generation of a session. |
```
TEMPLATE """
{{- if .First }}
```modelfile
PROMPT """
{{- if not .Context }}
### System:
{{ .System }}
You are a content marketer who needs to come up with a short but succinct tweet. Make sure to include the appropriate hashtags and links. Sometimes when appropriate, describe a meme that can be includes as well. All answers should be in the form of a tweet which has a max size of 280 characters. Every instruction will be the topic to create a tweet about.
{{- end }}
### User:
### Instruction:
{{ .Prompt }}
### Response:
"""
SYSTEM """<system message>"""
```
### SYSTEM
The `SYSTEM` instruction specifies the system prompt to be used in the template, if applicable.
```
SYSTEM """<system message>"""
```
### LICENSE
The `LICENSE` instruction allows you to specify the legal license under which the model used with this Modelfile is shared or distributed.
```
LICENSE """
<license text>
"""
```
## Notes
- the **modelfile is not case sensitive**. In the examples, we use uppercase for instructions to make it easier to distinguish it from arguments.
- Instructions can be in any order. In the examples, we start with FROM instruction to keep it easily readable.
```

View File

@@ -1,15 +0,0 @@
# Examples
This directory contains examples that can be created and run with `ollama`.
To create a model:
```
ollama create example -f <example file>
```
To run a model:
```
ollama run example
```

View File

@@ -1,8 +0,0 @@
# Modelfile for creating a devops engineer assistant
# Run `ollama create devops-engineer -f ./Modelfile` and then `ollama run devops-engineer` and enter a topic
FROM llama2:13b
PARAMETER temperature 1
SYSTEM """
You are a senior devops engineer, acting as an assistant. You offer help with cloud technologies like: Terraform, AWS, kubernetes, python. You answer with code examples when possible
"""

View File

@@ -1,5 +0,0 @@
FROM llama2
PARAMETER temperature 1
SYSTEM """
You are Mario from super mario bros, acting as an assistant.
"""

Binary file not shown.

Before

Width:  |  Height:  |  Size: 446 KiB

View File

@@ -1,43 +0,0 @@
<img src="logo.png" alt="image of Italian plumber" height="200"/>
# Example character: Mario
This example shows how to create a basic character using Llama2 as the base model.
To run this example:
1. Download the Modelfile
2. `ollama pull llama2` to get the base model used in the model file.
3. `ollama create NAME -f ./Modelfile`
4. `ollama run NAME`
Ask it some questions like "Who are you?" or "Is Peach in trouble again?"
## Editing this file
What the model file looks like:
```
FROM llama2
PARAMETER temperature 1
SYSTEM """
You are Mario from Super Mario Bros, acting as an assistant.
"""
```
What if you want to change its behaviour?
- Try changing the prompt
- Try changing the parameters [Docs](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md)
- Try changing the model (e.g. An uncensored model by `FROM wizard-vicuna` this is the wizard-vicuna uncensored model )
Once the changes are made,
1. `ollama create NAME -f ./Modelfile`
2. `ollama run NAME`
3. Iterate until you are happy with the results.
Notes:
- This example is for research purposes only. There is no affiliation with any entity.
- When using an uncensored model, please be aware that it may generate offensive content.

View File

@@ -1,8 +1,14 @@
# Modelfile for creating a Midjourney prompts from a topic
# This prompt was adapted from the original at https://www.greataiprompts.com/guide/midjourney/best-chatgpt-prompt-for-midjourney/
# Run `ollama create mj -f ./Modelfile` and then `ollama run mj` and enter a topic
# Run `ollama create mj -f pathtofile` and then `ollama run mj` and enter a topic
FROM nous-hermes
SYSTEM """
FROM library/nous-hermes:latest
PROMPT """
{{- if not .Context }}
### System:
Embrace your role as an AI-powered creative assistant, employing Midjourney to manifest compelling AI-generated art. I will outline a specific image concept, and in response, you must produce an exhaustive, multifaceted prompt for Midjourney, ensuring every detail of the original concept is represented in your instructions. Midjourney doesn't do well with text, so after the prompt, give me instructions that I can use to create the titles in a image editor.
"""
{{- end }}
### Instruction:
{{ .Prompt }}
### Response:
"""

View File

@@ -1,6 +1,13 @@
# Modelfile for creating a recipe from a list of ingredients
# Run `ollama create recipemaker -f ./Modelfile` and then `ollama run recipemaker` and feed it lists of ingredients to create recipes around.
FROM nous-hermes
SYSTEM """
# Run `ollama create recipemaker -f pathtofile` and then `ollama run recipemaker` and feed it lists of ingredients to create recipes around.
FROM library/nous-hermes:latest
PROMPT """
{{- if not .Context }}
### System:
The instruction will be a list of ingredients. You should generate a recipe that can be made in less than an hour. You can also include ingredients that most people will find in their pantry every day. The recipe should be 4 people and you should include a description of what the meal will taste like
{{- end }}
### Instruction:
{{ .Prompt }}
### Response:
"""

View File

@@ -1,7 +1,14 @@
# Modelfile for creating a tweet from a topic
# Run `ollama create tweetwriter -f ./Modelfile` and then `ollama run tweetwriter` and enter a topic
# Run `ollama create tweetwriter -f pathtofile` and then `ollama run tweetwriter` and enter a topic
FROM nous-hermes
SYSTEM """
You are a content marketer who needs to come up with a short but succinct tweet. Make sure to include the appropriate hashtags and links. Sometimes when appropriate, describe a meme that can be included as well. All answers should be in the form of a tweet which has a max size of 280 characters. Every instruction will be the topic to create a tweet about.
"""
FROM library/nous-hermes:latest
PROMPT """
{{- if not .Context }}
### System:
You are a content marketer who needs to come up with a short but succinct tweet. Make sure to include the appropriate hashtags and links. Sometimes when appropriate, describe a meme that can be includes as well. All answers should be in the form of a tweet which has a max size of 280 characters. Every instruction will be the topic to create a tweet about.
{{- end }}
### Instruction:
{{ .Prompt }}
### Response:
"""

15
examples/python/README.md Normal file
View File

@@ -0,0 +1,15 @@
# Python
This is a simple example of calling the Ollama api from a python app.
First, download a model:
```
curl -L https://huggingface.co/TheBloke/orca_mini_3B-GGML/resolve/main/orca-mini-3b.ggmlv3.q4_1.bin -o orca.bin
```
Then run it using the example script. You'll need to have Ollama running on your machine.
```
python3 main.py orca.bin
```

32
examples/python/main.py Normal file
View File

@@ -0,0 +1,32 @@
import http.client
import json
import os
import sys
if len(sys.argv) < 2:
print("Usage: python main.py <model file>")
sys.exit(1)
conn = http.client.HTTPConnection('localhost', 11434)
headers = { 'Content-Type': 'application/json' }
# generate text from the model
conn.request("POST", "/api/generate", json.dumps({
'model': os.path.join(os.getcwd(), sys.argv[1]),
'prompt': 'write me a short story',
'stream': True
}), headers)
response = conn.getresponse()
def parse_generate(data):
for event in data.decode('utf-8').split("\n"):
if not event:
continue
yield event
if response.status == 200:
for chunk in response:
for event in parse_generate(chunk):
print(json.loads(event)['response'], end="", flush=True)

1
ggml-metal.metal Symbolic link
View File

@@ -0,0 +1 @@
llama/ggml-metal.metal

13
go.mod
View File

@@ -5,20 +5,21 @@ go 1.20
require (
github.com/dustin/go-humanize v1.0.1
github.com/gin-gonic/gin v1.9.1
github.com/mattn/go-runewidth v0.0.14
github.com/mitchellh/colorstring v0.0.0-20190213212951-d06e56a500db
github.com/olekukonko/tablewriter v0.0.5
github.com/spf13/cobra v1.7.0
)
require github.com/rivo/uniseg v0.2.0 // indirect
require (
github.com/mattn/go-runewidth v0.0.14 // indirect
github.com/mitchellh/colorstring v0.0.0-20190213212951-d06e56a500db // indirect
github.com/rivo/uniseg v0.2.0 // indirect
)
require (
dario.cat/mergo v1.0.0
github.com/bytedance/sonic v1.9.1 // indirect
github.com/chenzhuoyu/base64x v0.0.0-20221115062448-fe3a3abad311 // indirect
github.com/chzyer/readline v1.5.1
github.com/gabriel-vasile/mimetype v1.4.2 // indirect
github.com/gin-contrib/cors v1.4.0
github.com/gin-contrib/sse v0.1.0 // indirect
github.com/go-playground/locales v0.14.1 // indirect
github.com/go-playground/universal-translator v0.18.1 // indirect
@@ -33,6 +34,7 @@ require (
github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect
github.com/modern-go/reflect2 v1.0.2 // indirect
github.com/pelletier/go-toml/v2 v2.0.8 // indirect
github.com/schollz/progressbar/v3 v3.13.1
github.com/spf13/pflag v1.0.5 // indirect
github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
github.com/ugorji/go/codec v1.2.11 // indirect
@@ -42,7 +44,6 @@ require (
golang.org/x/sys v0.10.0 // indirect
golang.org/x/term v0.10.0
golang.org/x/text v0.10.0 // indirect
gonum.org/v1/gonum v0.13.0
google.golang.org/protobuf v1.30.0 // indirect
gopkg.in/yaml.v3 v3.0.1 // indirect
)

62
go.sum
View File

@@ -1,17 +1,12 @@
dario.cat/mergo v1.0.0 h1:AGCNq9Evsj31mOgNPcLyXc+4PNABt905YmuqPYYpBWk=
dario.cat/mergo v1.0.0/go.mod h1:uNxQE+84aUszobStD9th8a29P2fMDhsBdgRYvZOxGmk=
github.com/bytedance/sonic v1.5.0/go.mod h1:ED5hyg4y6t3/9Ku1R6dU/4KyJ48DZ4jPhfY1O2AihPM=
github.com/bytedance/sonic v1.9.1 h1:6iJ6NqdoxCDr6mbY8h18oSO+cShGSMRGCEo7F2h0x8s=
github.com/bytedance/sonic v1.9.1/go.mod h1:i736AoUSYt75HyZLoJW9ERYxcy6eaN6h4BZXU064P/U=
github.com/chenzhuoyu/base64x v0.0.0-20211019084208-fb5309c8db06/go.mod h1:DH46F32mSOjUmXrMHnKwZdA8wcEefY7UVqBKYGjpdQY=
github.com/chenzhuoyu/base64x v0.0.0-20221115062448-fe3a3abad311 h1:qSGYFH7+jGhDF8vLC+iwCD4WpbV1EBDSzWkJODFLams=
github.com/chenzhuoyu/base64x v0.0.0-20221115062448-fe3a3abad311/go.mod h1:b583jCggY9gE99b6G5LEC39OIiVsWj+R97kbl5odCEk=
github.com/chzyer/logex v1.2.1 h1:XHDu3E6q+gdHgsdTPH6ImJMIp436vR6MPtH8gP05QzM=
github.com/chzyer/logex v1.2.1/go.mod h1:JLbx6lG2kDbNRFnfkgvh4eRJRPX1QCoOIWomwysCBrQ=
github.com/chzyer/readline v1.5.1 h1:upd/6fQk4src78LMRzh5vItIt361/o4uq553V8B5sGI=
github.com/chzyer/readline v1.5.1/go.mod h1:Eh+b79XXUwfKfcPLepksvw2tcLE/Ct21YObkaSkeBlk=
github.com/chzyer/test v1.0.0 h1:p3BQDXSxOhOG0P9z6/hGnII4LGiEPOYBhs8asl/fC04=
github.com/chzyer/test v1.0.0/go.mod h1:2JlltgoNkt4TW/z9V/IzDdFaMTM2JPIi26O1pF38GC8=
github.com/cpuguy83/go-md2man/v2 v2.0.2/go.mod h1:tgQtvFlXSQOSOSIRvRPT7W67SCa46tRHOmNcaadrF8o=
github.com/creack/pty v1.1.9/go.mod h1:oKZEueFk5CKHvIhNR5MUki03XCEU+Q6VDXinZuGJ33E=
github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c=
github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
@@ -19,25 +14,17 @@ github.com/dustin/go-humanize v1.0.1 h1:GzkhY7T5VNhEkwH0PVJgjz+fX1rhBrR7pRT3mDkp
github.com/dustin/go-humanize v1.0.1/go.mod h1:Mu1zIs6XwVuF/gI1OepvI0qD18qycQx+mFykh5fBlto=
github.com/gabriel-vasile/mimetype v1.4.2 h1:w5qFW6JKBz9Y393Y4q372O9A7cUSequkh1Q7OhCmWKU=
github.com/gabriel-vasile/mimetype v1.4.2/go.mod h1:zApsH/mKG4w07erKIaJPFiX0Tsq9BFQgN3qGY5GnNgA=
github.com/gin-contrib/cors v1.4.0 h1:oJ6gwtUl3lqV0WEIwM/LxPF1QZ5qe2lGWdY2+bz7y0g=
github.com/gin-contrib/cors v1.4.0/go.mod h1:bs9pNM0x/UsmHPBWT2xZz9ROh8xYjYkiURUfmBoMlcs=
github.com/gin-contrib/sse v0.1.0 h1:Y/yl/+YNO8GZSjAhjMsSuLt29uWRFHdHYUb5lYOV9qE=
github.com/gin-contrib/sse v0.1.0/go.mod h1:RHrZQHXnP2xjPF+u1gW/2HnVO7nvIa9PG3Gm+fLHvGI=
github.com/gin-gonic/gin v1.8.1/go.mod h1:ji8BvRH1azfM+SYow9zQ6SZMvR8qOMZHmsCuWR9tTTk=
github.com/gin-gonic/gin v1.9.1 h1:4idEAncQnU5cB7BeOkPtxjfCSye0AAm1R0RVIqJ+Jmg=
github.com/gin-gonic/gin v1.9.1/go.mod h1:hPrL7YrpYKXt5YId3A/Tnip5kqbEAP+KLuI3SUcPTeU=
github.com/go-playground/assert/v2 v2.0.1/go.mod h1:VDjEfimB/XKnb+ZQfWdccd7VUvScMdVu0Titje2rxJ4=
github.com/go-playground/assert/v2 v2.2.0 h1:JvknZsQTYeFEAhQwI4qEt9cyV5ONwRHC+lYKSsYSR8s=
github.com/go-playground/locales v0.14.0/go.mod h1:sawfccIbzZTqEDETgFXqTho0QybSa7l++s0DH+LDiLs=
github.com/go-playground/locales v0.14.1 h1:EWaQ/wswjilfKLTECiXz7Rh+3BjFhfDFKv/oXslEjJA=
github.com/go-playground/locales v0.14.1/go.mod h1:hxrqLVvrK65+Rwrd5Fc6F2O76J/NuW9t0sjnWqG1slY=
github.com/go-playground/universal-translator v0.18.0/go.mod h1:UvRDBj+xPUEGrFYl+lu/H90nyDXpg0fqeB/AQUGNTVA=
github.com/go-playground/universal-translator v0.18.1 h1:Bcnm0ZwsGyWbCzImXv+pAJnYK9S473LQFuzCbDbfSFY=
github.com/go-playground/universal-translator v0.18.1/go.mod h1:xekY+UJKNuX9WP91TpwSH2VMlDf28Uj24BCp08ZFTUY=
github.com/go-playground/validator/v10 v10.10.0/go.mod h1:74x4gJWsvQexRdW8Pn3dXSGrTK4nAUsbPlLADvpJkos=
github.com/go-playground/validator/v10 v10.14.0 h1:vgvQWe3XCz3gIeFDm/HnTIbj6UGmg/+t63MyGU2n5js=
github.com/go-playground/validator/v10 v10.14.0/go.mod h1:9iXMNT7sEkjXb0I+enO7QXmzG6QCsPWY4zveKFVRSyU=
github.com/goccy/go-json v0.9.7/go.mod h1:6MelG93GURQebXPDq3khkgXZkazVtN9CRI+MGFi0w8I=
github.com/goccy/go-json v0.10.2 h1:CrxCmQqYDkv1z7lO7Wbh2HN93uovUHgrECaO5ZrCXAU=
github.com/goccy/go-json v0.10.2/go.mod h1:6MelG93GURQebXPDq3khkgXZkazVtN9CRI+MGFi0w8I=
github.com/golang/protobuf v1.5.0/go.mod h1:FsONVRAS9T7sI+LIUmWTfcYkHO4aIWwzhcaSAoJOfIk=
@@ -49,21 +36,13 @@ github.com/inconshreveable/mousetrap v1.1.0 h1:wN+x4NVGpMsO7ErUn/mUI3vEoE6Jt13X2
github.com/inconshreveable/mousetrap v1.1.0/go.mod h1:vpF70FUmC8bwa3OWnCshd2FqLfsEA9PFc4w1p2J65bw=
github.com/json-iterator/go v1.1.12 h1:PV8peI4a0ysnczrg+LtxykD8LfKY9ML6u2jnxaEnrnM=
github.com/json-iterator/go v1.1.12/go.mod h1:e30LSqwooZae/UwlEbR2852Gd8hjQvJoHmT4TnhNGBo=
github.com/k0kubun/go-ansi v0.0.0-20180517002512-3bf9e2903213/go.mod h1:vNUNkEQ1e29fT/6vq2aBdFsgNPmy8qMdSay1npru+Sw=
github.com/klauspost/cpuid/v2 v2.0.9/go.mod h1:FInQzS24/EEf25PyTYn52gqo7WaD8xa0213Md/qVLRg=
github.com/klauspost/cpuid/v2 v2.2.4 h1:acbojRNwl3o09bUq+yDCtZFc1aiwaAAxtcn8YkZXnvk=
github.com/klauspost/cpuid/v2 v2.2.4/go.mod h1:RVVoqg1df56z8g3pUjL/3lE5UfnlrJX8tyFgg4nqhuY=
github.com/kr/pretty v0.1.0/go.mod h1:dAy3ld7l9f0ibDNOQOHHMYYIIbhfbHSm3C4ZsoJORNo=
github.com/kr/pretty v0.2.1/go.mod h1:ipq/a2n7PKx3OHsz4KJII5eveXtPO4qwEXGdVfWzfnI=
github.com/kr/pretty v0.3.0 h1:WgNl7dwNpEZ6jJ9k1snq4pZsg7DOEN8hP9Xw0Tsjwk0=
github.com/kr/pretty v0.3.0/go.mod h1:640gp4NfQd8pI5XOwp5fnNeVWj67G7CFk/SaSQn7NBk=
github.com/kr/pty v1.1.1/go.mod h1:pFQYn66WHrOpPYNljwOMqo10TkYh1fy3cYio2l3bCsQ=
github.com/kr/text v0.1.0/go.mod h1:4Jbv+DJW3UT/LiOwJeYQe1efqtUx/iVham/4vfdArNI=
github.com/kr/text v0.2.0 h1:5Nx0Ya0ZqY2ygV366QzturHI13Jq95ApcVaJBhpS+AY=
github.com/kr/text v0.2.0/go.mod h1:eLer722TekiGuMkidMxC/pM04lWEeraHUUmBw8l2grE=
github.com/leodido/go-urn v1.2.1/go.mod h1:zt4jvISO2HfUBqxjfIshjdMTYS56ZS/qv49ictyFfxY=
github.com/leodido/go-urn v1.2.4 h1:XlAE/cm/ms7TE/VMVoduSpNBoyc2dOxHs5MZSwAN63Q=
github.com/leodido/go-urn v1.2.4/go.mod h1:7ZrI8mTSeBSHl/UaRyKQW1qZeMgak41ANeCNaVckg+4=
github.com/mattn/go-isatty v0.0.14/go.mod h1:7GGIvUiUoEMVVmxf/4nioHXj79iQHKdU27kJ6hsGG94=
github.com/mattn/go-isatty v0.0.17/go.mod h1:kYGgaQfpe5nmfYZH+SKPsOc2e4SrIfOl2e/yFXSvRLM=
github.com/mattn/go-isatty v0.0.19 h1:JITubQf0MOLdlGRuRq+jtsDlekdYPia9ZFsB8h/APPA=
github.com/mattn/go-isatty v0.0.19/go.mod h1:W+V8PltTTMOvKvAeJH7IuucS94S2C6jfK/D7dTCTo3Y=
github.com/mattn/go-runewidth v0.0.9/go.mod h1:H031xJmbD/WCDINGzjvQ9THkh0rPKHF+m2gUSrubnMI=
@@ -78,18 +57,15 @@ github.com/modern-go/reflect2 v1.0.2 h1:xBagoLtFs94CBntxluKeaWgTMpvLxC4ur3nMaC9G
github.com/modern-go/reflect2 v1.0.2/go.mod h1:yWuevngMOJpCy52FWWMvUC8ws7m/LJsjYzDa0/r8luk=
github.com/olekukonko/tablewriter v0.0.5 h1:P2Ga83D34wi1o9J6Wh1mRuqd4mF/x/lgBS7N7AbDhec=
github.com/olekukonko/tablewriter v0.0.5/go.mod h1:hPp6KlRPjbx+hW8ykQs1w3UBbZlj6HuIJcUGPhkA7kY=
github.com/pelletier/go-toml/v2 v2.0.1/go.mod h1:r9LEWfGN8R5k0VXJ+0BkIe7MYkRdwZOjgMj2KwnJFUo=
github.com/pelletier/go-toml/v2 v2.0.8 h1:0ctb6s9mE31h0/lhu+J6OPmVeDxJn+kYnJc2jZR9tGQ=
github.com/pelletier/go-toml/v2 v2.0.8/go.mod h1:vuYfssBdrU2XDZ9bYydBu6t+6a6PYNcZljzZR9VXg+4=
github.com/pkg/diff v0.0.0-20210226163009-20ebb0f2a09e/go.mod h1:pJLUxLENpZxwdsKMEsNbx1VGcRFpLqf3715MtcvvzbA=
github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=
github.com/pmezard/go-difflib v1.0.0/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
github.com/rivo/uniseg v0.2.0 h1:S1pD9weZBuJdFmowNwbpi7BJ8TNftyUImj/0WQi72jY=
github.com/rivo/uniseg v0.2.0/go.mod h1:J6wj4VEh+S6ZtnVlnTBMWIodfgj8LQOQFoIToxlJtxc=
github.com/rogpeppe/go-internal v1.6.1/go.mod h1:xXDCJY+GAPziupqXw64V24skbSoqbTEfhy4qGm1nDQc=
github.com/rogpeppe/go-internal v1.8.0 h1:FCbCCtXNOY3UtUuHUYaghJg4y7Fd14rXifAYUAtL9R8=
github.com/rogpeppe/go-internal v1.8.0/go.mod h1:WmiCO8CzOY8rg0OYDC4/i/2WRWAB6poM+XZ2dLUbcbE=
github.com/russross/blackfriday/v2 v2.1.0/go.mod h1:+Rmxgy9KzJVeS9/2gXHxylqXiyQDYRxCVz55jmeOWTM=
github.com/schollz/progressbar/v3 v3.13.1 h1:o8rySDYiQ59Mwzy2FELeHY5ZARXZTVJC7iHD6PEFUiE=
github.com/schollz/progressbar/v3 v3.13.1/go.mod h1:xvrbki8kfT1fzWzBT/UZd9L6GA+jdL7HAgq2RFnO6fQ=
github.com/spf13/cobra v1.7.0 h1:hyqWnYt1ZQShIddO5kBpj3vu05/++x6tJ6dg8EC572I=
github.com/spf13/cobra v1.7.0/go.mod h1:uLxZILRyS/50WlhOIKD7W6V5bgeIt+4sICxh6uRMrb0=
github.com/spf13/pflag v1.0.5 h1:iy+VFUOCP1a+8yFto/drg2CJ5u0yRoB7fZw3DKv/JXA=
@@ -98,7 +74,6 @@ github.com/stretchr/objx v0.1.0/go.mod h1:HFkY916IF+rwdDfMAkV7OtwuqBVzrE8GR6GFx+
github.com/stretchr/objx v0.4.0/go.mod h1:YvHI0jy2hoMjB+UWwv71VJQ9isScKT/TqJzVSSt89Yw=
github.com/stretchr/objx v0.5.0/go.mod h1:Yh+to48EsGEfYuaHDzXPcE3xhTkx73EhmCGUpEOglKo=
github.com/stretchr/testify v1.3.0/go.mod h1:M5WIy9Dh21IEIfnGCwXGc5bZfKNJtfHm1UVUgZn+9EI=
github.com/stretchr/testify v1.6.1/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
github.com/stretchr/testify v1.7.0/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
github.com/stretchr/testify v1.7.1/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
github.com/stretchr/testify v1.8.0/go.mod h1:yNjHg4UonilssWZ8iaSj1OCr/vHnekPRkoO+kdMU+MU=
@@ -108,51 +83,32 @@ github.com/stretchr/testify v1.8.3 h1:RP3t2pwF7cMEbC1dqtB6poj3niw/9gnV4Cjg5oW5gt
github.com/stretchr/testify v1.8.3/go.mod h1:sz/lmYIOXD/1dqDmKjjqLyZ2RngseejIcXlSw2iwfAo=
github.com/twitchyliquid64/golang-asm v0.15.1 h1:SU5vSMR7hnwNxj24w34ZyCi/FmDZTkS4MhqMhdFk5YI=
github.com/twitchyliquid64/golang-asm v0.15.1/go.mod h1:a1lVb/DtPvCB8fslRZhAngC2+aY1QWCk3Cedj/Gdt08=
github.com/ugorji/go v1.2.7/go.mod h1:nF9osbDWLy6bDVv/Rtoh6QgnvNDpmCalQV5urGCCS6M=
github.com/ugorji/go/codec v1.2.7/go.mod h1:WGN1fab3R1fzQlVQTkfxVtIBhWDRqOviHU95kRgeqEY=
github.com/ugorji/go/codec v1.2.11 h1:BMaWp1Bb6fHwEtbplGBGJ498wD+LKlNSl25MjdZY4dU=
github.com/ugorji/go/codec v1.2.11/go.mod h1:UNopzCgEMSXjBc6AOMqYvWC1ktqTAfzJZUZgYf6w6lg=
golang.org/x/arch v0.0.0-20210923205945-b76863e36670/go.mod h1:5om86z9Hs0C8fWVUuoMHwpExlXzs5Tkyp9hOrfG7pp8=
golang.org/x/arch v0.3.0 h1:02VY4/ZcO/gBOH6PUaoiptASxtXU10jazRCP865E97k=
golang.org/x/arch v0.3.0/go.mod h1:5om86z9Hs0C8fWVUuoMHwpExlXzs5Tkyp9hOrfG7pp8=
golang.org/x/crypto v0.0.0-20210711020723-a769d52b0f97/go.mod h1:GvvjBRRGRdwPK5ydBHafDWAxML/pGHZbMvKqRZ5+Abc=
golang.org/x/crypto v0.10.0 h1:LKqV2xt9+kDzSTfOhx4FrkEBcMrAgHSYgzywV9zcGmM=
golang.org/x/crypto v0.10.0/go.mod h1:o4eNf7Ede1fv+hwOwZsTHl9EsPFO6q6ZvYR8vYfY45I=
golang.org/x/net v0.0.0-20210226172049-e18ecbb05110/go.mod h1:m0MpNAwzfU5UDzcl9v0D8zg8gWTRqZa9RBIspLL5mdg=
golang.org/x/net v0.10.0 h1:X2//UzNDwYmtCLn7To6G58Wr6f5ahEAQgKNzv9Y951M=
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golang.org/x/sys v0.0.0-20210630005230-0f9fa26af87c/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.0.0-20210806184541-e5e7981a1069/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.0.0-20220310020820-b874c991c1a5/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.0.0-20220704084225-05e143d24a9e/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.0.0-20220811171246-fbc7d0a398ab/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.10.0 h1:SqMFp9UcQJZa+pmYuAKjd9xq1f0j5rLcDIk0mj4qAsA=
golang.org/x/sys v0.10.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
golang.org/x/term v0.6.0/go.mod h1:m6U89DPEgQRMq3DNkDClhWw02AUbt2daBVO4cn4Hv9U=
golang.org/x/term v0.10.0 h1:3R7pNqamzBraeqj/Tj8qt1aQ2HpmlC+Cx/qL/7hn4/c=
golang.org/x/term v0.10.0/go.mod h1:lpqdcUyK/oCiQxvxVrppt5ggO2KCZ5QblwqPnfZ6d5o=
golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.10.0 h1:UpjohKhiEgNc0CSauXmwYftY1+LlaC75SJwh0SgCX58=
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golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
gonum.org/v1/gonum v0.13.0 h1:a0T3bh+7fhRyqeNbiC3qVHYmkiQgit3wnNan/2c0HMM=
gonum.org/v1/gonum v0.13.0/go.mod h1:/WPYRckkfWrhWefxyYTfrTtQR0KH4iyHNuzxqXAKyAU=
google.golang.org/protobuf v1.26.0-rc.1/go.mod h1:jlhhOSvTdKEhbULTjvd4ARK9grFBp09yW+WbY/TyQbw=
google.golang.org/protobuf v1.28.0/go.mod h1:HV8QOd/L58Z+nl8r43ehVNZIU/HEI6OcFqwMG9pJV4I=
google.golang.org/protobuf v1.30.0 h1:kPPoIgf3TsEvrm0PFe15JQ+570QVxYzEvvHqChK+cng=
google.golang.org/protobuf v1.30.0/go.mod h1:HV8QOd/L58Z+nl8r43ehVNZIU/HEI6OcFqwMG9pJV4I=
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405 h1:yhCVgyC4o1eVCa2tZl7eS0r+SDo693bJlVdllGtEeKM=
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
gopkg.in/check.v1 v1.0.0-20180628173108-788fd7840127/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c h1:Hei/4ADfdWqJk1ZMxUNpqntNwaWcugrBjAiHlqqRiVk=
gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c/go.mod h1:JHkPIbrfpd72SG/EVd6muEfDQjcINNoR0C8j2r3qZ4Q=
gopkg.in/errgo.v2 v2.1.0/go.mod h1:hNsd1EY+bozCKY1Ytp96fpM3vjJbqLJn88ws8XvfDNI=
gopkg.in/yaml.v2 v2.4.0/go.mod h1:RDklbk79AGWmwhnvt/jBztapEOGDOx6ZbXqjP6csGnQ=
gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
gopkg.in/yaml.v3 v3.0.0-20210107192922-496545a6307b/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
rsc.io/pdf v0.1.1/go.mod h1:n8OzWcQ6Sp37PL01nO98y4iUCRdTGarVfzxY20ICaU4=

View File

@@ -1,567 +0,0 @@
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
* Copyright (c) 2023 Georgi Gerganov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "ggml-alloc.h"
#include "ggml.h"
#include <assert.h>
#include <stdarg.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#define UNUSED(x) (void)(x)
#define MAX(a, b) ((a) > (b) ? (a) : (b))
//#define GGML_ALLOCATOR_DEBUG
//#define AT_PRINTF printf
#define AT_PRINTF(...) ((void)0)
struct hash_node {
struct ggml_tensor * t;
int n_children;
int n_views;
};
static size_t hash(void * p) {
return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE;
}
static struct hash_node * hash_get(struct hash_node hash_table[], struct ggml_tensor * t) {
size_t h = hash(t);
// linear probing
size_t i = h;
while (hash_table[i].t != NULL) {
if (hash_table[i].t == t) {
return &hash_table[i];
}
i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE;
if (i == h) {
// hash table is full
GGML_ASSERT(false);
}
}
hash_table[i].t = t;
return &hash_table[i];
}
// TODO: GGML_PAD ?
static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
assert(alignment && !(alignment & (alignment - 1))); // power of 2
size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
return offset + align;
}
struct free_block {
void * addr;
size_t size;
};
#define MAX_FREE_BLOCKS 128
struct ggml_allocr {
void * data;
size_t size;
size_t alignment;
int n_free_blocks;
struct free_block free_blocks[MAX_FREE_BLOCKS];
struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE];
size_t max_size;
bool measure;
#ifdef GGML_ALLOCATOR_DEBUG
struct ggml_tensor * allocated_tensors[1024];
#endif
};
#ifdef GGML_ALLOCATOR_DEBUG
static void add_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i] == NULL) {
alloc->allocated_tensors[i] = tensor;
return;
}
}
GGML_ASSERT(!"out of allocated_tensors");
}
static void remove_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i] == tensor ||
(alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) {
alloc->allocated_tensors[i] = NULL;
return;
}
}
printf("tried to free tensor %s not found\n", tensor->name);
GGML_ASSERT(!"tensor not found");
}
#endif
static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
return ggml_nbytes(tensor);
UNUSED(alloc);
}
void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
size_t max_avail = 0;
// find the best fitting free block
int best_fit_block = -1;
size_t best_fit_size = SIZE_MAX;
for (int i = 0; i < alloc->n_free_blocks; i++) {
struct free_block * block = &alloc->free_blocks[i];
max_avail = MAX(max_avail, block->size);
if (block->size >= size && block->size <= best_fit_size) {
best_fit_block = i;
best_fit_size = block->size;
}
}
AT_PRINTF("block %d\n", best_fit_block);
if (best_fit_block == -1) {
fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
__func__, size, max_avail);
GGML_ASSERT(!"not enough space in the buffer");
return;
}
struct free_block * block = &alloc->free_blocks[best_fit_block];
void * addr = block->addr;
block->addr = (char*)block->addr + size;
block->size -= size;
if (block->size == 0) {
// remove block if empty
alloc->n_free_blocks--;
for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
alloc->free_blocks[j] = alloc->free_blocks[j+1];
}
}
tensor->data = addr;
#ifdef GGML_ALLOCATOR_DEBUG
add_allocated_tensor(alloc, tensor);
size_t cur_max = (char*)addr - (char*)alloc->data + size;
if (cur_max > alloc->max_size) {
printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i]) {
printf("%s (%.2f MB) ", alloc->allocated_tensors[i]->name, ggml_nbytes(alloc->allocated_tensors[i]) / 1024.0 / 1024.0);
}
}
printf("\n");
}
#endif
alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->data + size);
}
// this is a very naive implementation, but for our case the number of free blocks should be very small
static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
void * ptr = tensor->data;
if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) {
// the tensor was not allocated in this buffer
// this can happen because the graph allocator will try to free weights and other tensors from different buffers
// the easiest way to deal with this is just to ignore it
return;
}
size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks);
#ifdef GGML_ALLOCATOR_DEBUG
remove_allocated_tensor(alloc, tensor);
#endif
// see if we can merge with an existing block
for (int i = 0; i < alloc->n_free_blocks; i++) {
struct free_block * block = &alloc->free_blocks[i];
// check if ptr is at the end of the block
if ((char*)block->addr + block->size == ptr) {
block->size += size;
// check if we can merge with the next block
if (i < alloc->n_free_blocks - 1 && (char*)block->addr + block->size == alloc->free_blocks[i+1].addr) {
block->size += alloc->free_blocks[i+1].size;
alloc->n_free_blocks--;
for (int j = i+1; j < alloc->n_free_blocks; j++) {
alloc->free_blocks[j] = alloc->free_blocks[j+1];
}
}
return;
}
// check if ptr is at the beginning of the block
if ((char*)ptr + size == block->addr) {
block->addr = ptr;
block->size += size;
// check if we can merge with the previous block
if (i > 0 && (char*)alloc->free_blocks[i-1].addr + alloc->free_blocks[i-1].size == block->addr) {
alloc->free_blocks[i-1].size += block->size;
alloc->n_free_blocks--;
for (int j = i; j < alloc->n_free_blocks; j++) {
alloc->free_blocks[j] = alloc->free_blocks[j+1];
}
}
return;
}
}
// otherwise, add a new block
GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
// insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
int insert_pos = 0;
while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].addr < ptr) {
insert_pos++;
}
// shift all blocks from insert_pos onward to make room for the new block
for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
alloc->free_blocks[i] = alloc->free_blocks[i-1];
}
// insert the new block
alloc->free_blocks[insert_pos].addr = ptr;
alloc->free_blocks[insert_pos].size = size;
alloc->n_free_blocks++;
}
void ggml_allocr_reset(struct ggml_allocr * alloc) {
alloc->n_free_blocks = 1;
size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment);
alloc->free_blocks[0].addr = (char *)alloc->data + align_offset;
alloc->free_blocks[0].size = alloc->size - align_offset;
}
struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) {
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
*alloc = (struct ggml_allocr){
/*.data = */ data,
/*.size = */ size,
/*.alignment = */ alignment,
/*.n_free_blocks = */ 0,
/*.free_blocks = */ {{0}},
/*.hash_table = */ {{0}},
/*.max_size = */ 0,
/*.measure = */ false,
#ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ = {0},
#endif
};
ggml_allocr_reset(alloc);
return alloc;
}
// address and size of the buffer when measuring
// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers
static void * const MEASURE_BASE_ADDR = (void *) 0x1000;
static const size_t MEASURE_MAX_SIZE = 1ULL<<40; // 1 TB
struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
*alloc = (struct ggml_allocr){
/*.data = */ MEASURE_BASE_ADDR,
/*.size = */ MEASURE_MAX_SIZE,
/*.alignment = */ alignment,
/*.n_free_blocks = */ 0,
/*.free_blocks = */ {{0}},
/*.hash_table = */ {{0}},
/*.max_size = */ 0,
/*.measure = */ true,
#ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ = {0},
#endif
};
ggml_allocr_reset(alloc);
return alloc;
}
void ggml_allocr_free(struct ggml_allocr * alloc) {
free(alloc);
}
bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
return alloc->measure;
}
//////////// compute graph allocator
static bool ggml_is_view(struct ggml_tensor * t) {
return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE ||
t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY;
}
static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
if (a->type != b->type) {
return false;
}
for (int i = 0; i < GGML_MAX_DIMS; i++) {
if (a->ne[i] != b->ne[i]) {
return false;
}
if (a->nb[i] != b->nb[i]) {
return false;
}
}
return true;
}
static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) {
switch (t->op) {
case GGML_OP_PERMUTE:
case GGML_OP_RESHAPE:
case GGML_OP_TRANSPOSE:
case GGML_OP_VIEW:
return t->src[0];
case GGML_OP_CPY:
return t->src[1];
default:
return NULL;
}
}
static struct ggml_tensor * get_view_source(struct ggml_tensor * t) {
struct ggml_tensor * parent = t;
do {
parent = get_view_parent(parent);
} while (ggml_is_view(parent));
return parent;
}
static bool ggml_op_can_inplace(enum ggml_op op) {
switch (op) {
case GGML_OP_SCALE:
case GGML_OP_DIAG_MASK_ZERO:
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_ADD:
case GGML_OP_ADD1:
case GGML_OP_ACC:
case GGML_OP_SUB:
case GGML_OP_MUL:
case GGML_OP_DIV:
case GGML_OP_SQR:
case GGML_OP_SQRT:
case GGML_OP_LOG:
case GGML_OP_UNARY:
case GGML_OP_ROPE:
case GGML_OP_RMS_NORM:
case GGML_OP_SET:
case GGML_OP_SOFT_MAX:
case GGML_OP_CONT:
return true;
default:
return false;
}
}
static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) {
struct hash_node * ht = alloc->hash_table;
if (node->data == NULL) {
if (ggml_is_view(node)) {
size_t offset;
switch(node->op) {
case GGML_OP_VIEW:
memcpy(&offset, node->op_params, sizeof(size_t));
node->data = (char *) node->src[0]->data + offset;
break;
case GGML_OP_PERMUTE:
case GGML_OP_RESHAPE:
case GGML_OP_TRANSPOSE:
node->data = node->src[0]->data;
break;
case GGML_OP_CPY:
node->data = node->src[1]->data;
break;
default:
GGML_ASSERT(!"unknown view op");
break;
}
} else {
// see if we can reuse a parent's buffer (inplace)
if (ggml_op_can_inplace(node->op)) {
for (int i = 0; i < GGML_MAX_SRC; i++) {
struct ggml_tensor * parent = node->src[i];
if (parent == NULL) {
break;
}
struct hash_node * p_hn = hash_get(ht, parent);
if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
if (ggml_is_view(parent)) {
struct ggml_tensor * view_src = get_view_source(parent);
struct hash_node * view_src_hn = hash_get(ht, view_src);
if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
// TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
// the parent's data that it will need later (same layout requirement). the problem is that then
// we cannot free the tensor because the original address of the allocation is lost.
// adding a view_src pointer to the tensor would solve this and simplify the code dealing with views
// for now, we only reuse the parent's data if the offset is zero (view_src->data == parent->data)
AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
node->data = parent->data;
return;
}
}
else {
AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
node->data = parent->data;
}
return;
}
}
}
ggml_allocr_alloc(alloc, node);
}
}
}
static size_t ggml_allocator_alloc_graph_tensors_n(
struct ggml_allocr * alloc,
struct ggml_cgraph ** graphs, int n_graphs,
struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) {
// reset hash table
struct hash_node * ht = alloc->hash_table;
memset(ht, 0, sizeof(struct hash_node) * GGML_GRAPH_HASHTABLE_SIZE);
// count number of children and views
for (int g = 0; g < n_graphs; g++) {
struct ggml_cgraph * gf = graphs[g];
for (int i = 0; i < gf->n_nodes; i++) {
struct ggml_tensor * node = gf->nodes[i];
if (ggml_is_view(node)) {
struct ggml_tensor * view_src = get_view_source(node);
hash_get(ht, view_src)->n_views += 1;
}
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * parent = node->src[j];
if (parent == NULL) {
break;
}
hash_get(ht, parent)->n_children += 1;
}
}
}
// allocate tensors
for (int g = 0; g < n_graphs; g++) {
struct ggml_cgraph * gf = graphs[g];
AT_PRINTF("####### graph %d/%d\n", g, n_graphs);
// graph inputs are allocated first to ensure that they are not overwritten by each other
if (inputs != NULL && inputs[g] != NULL) {
for (int i = 0; inputs[g][i] != NULL; i++) {
struct ggml_tensor * input = inputs[g][i];
AT_PRINTF("input: %s\n", input->name);
allocate_node(alloc, input);
}
}
for (int i = 0; i < gf->n_nodes; i++) {
struct ggml_tensor * node = gf->nodes[i];
// allocate parents (leafs)
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * parent = node->src[j];
if (parent == NULL) {
break;
}
allocate_node(alloc, parent);
}
// allocate node
allocate_node(alloc, node);
AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * parent = node->src[j];
if (parent == NULL) {
break;
}
AT_PRINTF("%s", parent->name);
if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
AT_PRINTF(", ");
}
}
AT_PRINTF("\n");
// update parents
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * parent = node->src[j];
if (parent == NULL) {
break;
}
struct hash_node * p_hn = hash_get(ht, parent);
p_hn->n_children -= 1;
//AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
if (p_hn->n_children == 0 && p_hn->n_views == 0) {
if (ggml_is_view(parent)) {
struct ggml_tensor * view_src = get_view_source(parent);
struct hash_node * view_src_hn = hash_get(ht, view_src);
view_src_hn->n_views -= 1;
AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src->n_children, view_src->n_views);
if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
ggml_allocator_free_tensor(alloc, view_src);
}
}
else {
if (parent->data != node->data) {
ggml_allocator_free_tensor(alloc, parent);
}
}
}
}
AT_PRINTF("\n");
}
// free graph outputs here that wouldn't be freed otherwise because they have no children
if (outputs != NULL && outputs[g] != NULL) {
for (int i = 0; outputs[g][i] != NULL; i++) {
struct ggml_tensor * output = outputs[g][i];
AT_PRINTF("output: %s\n", output->name);
ggml_allocator_free_tensor(alloc, output);
}
}
}
return alloc->max_size;
}
size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) {
return ggml_allocator_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL);
}

View File

@@ -1,48 +0,0 @@
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
* Copyright (c) 2023 Georgi Gerganov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#pragma once
#include "ggml.h"
#ifdef __cplusplus
extern "C" {
#endif
GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
GGML_API void ggml_allocr_free(struct ggml_allocr * alloc);
GGML_API bool ggml_allocr_is_measure(struct ggml_allocr * alloc);
GGML_API void ggml_allocr_reset(struct ggml_allocr * alloc);
GGML_API void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor);
GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
#ifdef __cplusplus
}
#endif

File diff suppressed because it is too large Load Diff

View File

@@ -1,5 +1,5 @@
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
@@ -53,7 +53,6 @@ void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
void ggml_cuda_set_main_device(int main_device);
void ggml_cuda_set_mul_mat_q(bool mul_mat_q);
void ggml_cuda_set_scratch_size(size_t scratch_size);
void ggml_cuda_free_scratch(void);
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);

View File

@@ -1,7 +1,5 @@
//go:build darwin
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
@@ -89,13 +87,6 @@ void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor *
// get data from the device into host memory
void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
// try to find operations that can be run concurrently in the graph
// you should run it again if the topology of your graph changes
void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
// if the graph has been optimized for concurrently dispatch
bool ggml_metal_if_optimized(struct ggml_metal_context * ctx);
// same as ggml_graph_compute but uses Metal
// creates gf->n_threads command buffers in parallel
void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);

View File

@@ -1,7 +1,7 @@
//go:build darwin
// +build darwin
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
@@ -64,16 +64,12 @@ struct ggml_metal_context {
int n_buffers;
struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
int concur_list[GGML_MAX_NODES];
int concur_list_len;
// custom kernels
#define GGML_METAL_DECL_KERNEL(name) \
id<MTLFunction> function_##name; \
id<MTLComputePipelineState> pipeline_##name
GGML_METAL_DECL_KERNEL(add);
GGML_METAL_DECL_KERNEL(add_row); // TODO: avoid this extra kernel, instead extend the "add" kernel to support broadcast
GGML_METAL_DECL_KERNEL(mul);
GGML_METAL_DECL_KERNEL(mul_row); // TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast
GGML_METAL_DECL_KERNEL(scale);
@@ -129,7 +125,6 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
ctx->device = MTLCreateSystemDefaultDevice();
ctx->queue = [ctx->device newCommandQueue];
ctx->n_buffers = 0;
ctx->concur_list_len = 0;
// determine if we can use MPS
if (MPSSupportsMTLDevice(ctx->device)) {
@@ -190,7 +185,6 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
fprintf(stderr, "%s: loaded %-32s %16p\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name);
GGML_METAL_ADD_KERNEL(add);
GGML_METAL_ADD_KERNEL(add_row);
GGML_METAL_ADD_KERNEL(mul);
GGML_METAL_ADD_KERNEL(mul_row);
GGML_METAL_ADD_KERNEL(scale);
@@ -249,13 +243,6 @@ void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
ctx->n_cb = n_cb;
}
bool ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
if (ctx->concur_list_len) {
return true;
}
return false;
}
// finds the Metal buffer that contains the tensor data on the GPU device
// the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
// Metal buffer based on the host memory pointer
@@ -394,98 +381,11 @@ void ggml_metal_get_tensor(
memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t));
}
void ggml_metal_graph_find_concurrency(
struct ggml_metal_context * ctx,
struct ggml_cgraph * gf) {
int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time
int nodes_unused[GGML_MAX_NODES];
for (int i = 0; i < GGML_MAX_NODES; i++) {ctx->concur_list[i] = 0;}
for (int i = 0; i < gf->n_nodes; i++) {nodes_unused[i] = 1;}
ctx->concur_list_len = 0;
int n_left = gf->n_nodes;
int n_start = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list
int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos
while (n_left > 0) {
// number of nodes at a layer (that can be issued concurrently)
int concurrency = 0;
for (int i = n_start; i < ((n_start + search_depth > gf->n_nodes) ? gf->n_nodes : n_start + search_depth); i++) {
if (nodes_unused[i]) {
// if the requirements for gf->nodes[i] are satisfied
int exe_flag=1;
// scan all srcs
for (int src_ind = 0; src_ind < GGML_MAX_SRC; src_ind++) {
struct ggml_tensor * src_cur = gf->nodes[i]->src[src_ind];
if (src_cur) {
// if is leaf nodes it's satisfied.
if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) {continue;}
// otherwise this src should be the output from previous nodes.
int is_found = 0;
// scan 2*search_depth back because we inserted barrier.
for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) {
if (gf->nodes[ctx->concur_list[j]] == src_cur) {is_found = 1; break;}
}
if (is_found == 0) {exe_flag = 0; break;}
}
}
if (exe_flag) {
// check if nodes[i]'s data will be overwritten by a node before nodes[i].
// if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3]
int64_t data_start = (int64_t) gf->nodes[i]->data;
int64_t length = (int64_t) ggml_nbytes(gf->nodes[i]);
for (int j = n_start; j < i; j++) {
if (nodes_unused[j] && gf->nodes[j]->op != GGML_OP_RESHAPE \
&& gf->nodes[j]->op != GGML_OP_VIEW \
&& gf->nodes[j]->op != GGML_OP_TRANSPOSE \
&& gf->nodes[j]->op != GGML_OP_PERMUTE) {
if (((int64_t)gf->nodes[j]->data) >= data_start + length || \
((int64_t)gf->nodes[j]->data) + (int64_t) ggml_nbytes(gf->nodes[j]) <= data_start) {
continue;
} else {
exe_flag = 0;
}
}
}
}
if (exe_flag) {
ctx->concur_list[level_pos + concurrency] = i;
nodes_unused[i] = 0;
concurrency++;
ctx->concur_list_len++;
}
}
}
n_left -= concurrency;
// adding a barrier different layer
ctx->concur_list[level_pos + concurrency] = -1;
ctx->concur_list_len++;
// jump all sorted nodes at nodes_bak
while (!nodes_unused[n_start]) {n_start++;}
level_pos += concurrency + 1;
}
if (ctx->concur_list_len > GGML_MAX_NODES) {
fprintf(stderr, "%s: too many elements for metal ctx->concur_list!\n", __func__);
}
}
void ggml_metal_graph_compute(
struct ggml_metal_context * ctx,
struct ggml_cgraph * gf) {
metal_printf("%s: evaluating graph\n", __func__);
// if there is ctx->concur_list, dispatch concurrently
// else fallback to serial dispatch
MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor;
const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_NODES;
const int n_nodes = has_concur ? ctx->concur_list_len : gf->n_nodes;
edesc.dispatchType = has_concur ? MTLDispatchTypeConcurrent : MTLDispatchTypeSerial;
// create multiple command buffers and enqueue them
// then, we encode the graph into the command buffers in parallel
@@ -504,7 +404,7 @@ void ggml_metal_graph_compute(
dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb;
const int n_nodes_per_cb = (gf->n_nodes + n_cb - 1) / n_cb;
dispatch_async(queue, ^{
size_t offs_src0 = 0;
@@ -515,21 +415,10 @@ void ggml_metal_graph_compute(
id<MTLComputeCommandEncoder> encoder = nil;
const int node_start = (cb_idx + 0) * n_nodes_per_cb;
const int node_end = (cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb;
for (int ind = node_start; ind < node_end; ++ind) {
const int i = has_concur ? ctx->concur_list[ind] : ind;
if (i == -1) {
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
continue;
}
[encoder memoryBarrierWithScope:MTLBarrierScopeBuffers];
continue;
}
const int node_start = (cb_idx + 0) * n_nodes_per_cb;
const int node_end = (cb_idx == n_cb - 1) ? gf->n_nodes : (cb_idx + 1) * n_nodes_per_cb;
for (int i = node_start; i < node_end; ++i) {
metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
struct ggml_tensor * src0 = gf->nodes[i]->src[0];
@@ -600,19 +489,13 @@ void ggml_metal_graph_compute(
case GGML_OP_ADD:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
if (ggml_nelements(src1) == ne10) {
// src1 is a row
[encoder setComputePipelineState:ctx->pipeline_add_row];
} else {
[encoder setComputePipelineState:ctx->pipeline_add];
}
[encoder setComputePipelineState:ctx->pipeline_add];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
const int64_t n = ggml_nelements(dst);
@@ -621,7 +504,7 @@ void ggml_metal_graph_compute(
case GGML_OP_MUL:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
if (ggml_nelements(src1) == ne10) {
@@ -642,7 +525,7 @@ void ggml_metal_graph_compute(
case GGML_OP_SCALE:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
const float scale = *(const float *) src1->data;
@@ -656,60 +539,52 @@ void ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_UNARY:
switch (ggml_get_unary_op(gf->nodes[i])) {
case GGML_UNARY_OP_SILU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
case GGML_OP_SILU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
[encoder setComputePipelineState:ctx->pipeline_silu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setComputePipelineState:ctx->pipeline_silu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_UNARY_OP_RELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
[encoder setComputePipelineState:ctx->pipeline_relu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_UNARY_OP_GELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
[encoder setComputePipelineState:ctx->pipeline_gelu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
default:
{
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
GGML_ASSERT(false);
}
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_RELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
[encoder setComputePipelineState:ctx->pipeline_relu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_GELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
[encoder setComputePipelineState:ctx->pipeline_gelu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SOFT_MAX:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
const int nth = 32;
@@ -727,10 +602,10 @@ void ggml_metal_graph_compute(
case GGML_OP_DIAG_MASK_INF:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
const int n_past = ((int32_t *)(dst->op_params))[0];
const int n_past = ((int32_t *)(src1->data))[0];
[encoder setComputePipelineState:ctx->pipeline_diag_mask_inf];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
@@ -746,8 +621,7 @@ void ggml_metal_graph_compute(
// TODO: needs to be updated after PR: https://github.com/ggerganov/ggml/pull/224
GGML_ASSERT(ne00 == ne10);
// GGML_ASSERT(ne02 == ne12); // Should be checked on individual data types until broadcast is implemented everywhere
GGML_ASSERT(ne03 == ne13);
GGML_ASSERT(ne02 == ne12);
if (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
@@ -775,11 +649,11 @@ void ggml_metal_graph_compute(
initWithDevice:ctx->device transposeLeft:false transposeRight:true
resultRows:ne11 resultColumns:ne01 interiorColumns:ne00 alpha:1.0 beta:0.0];
// we need to do ne12 multiplications
// we need to do ne02 multiplications
// TODO: is there a way to do this in parallel - currently very slow ..
// TODO: might be possible to offload part of the computation to ANE using Accelerate's CBLAS
for (int64_t i02 = 0; i02 < ne12; ++i02) {
size_t offs_src0_cur = offs_src0 + i02/(ne12/ne02)*nb02; // gqa not used for now
for (int64_t i02 = 0; i02 < ne02; ++i02) {
size_t offs_src0_cur = offs_src0 + i02*nb02;
size_t offs_src1_cur = offs_src1 + i02*nb12;
size_t offs_dst_cur = offs_dst + i02*nb2;
@@ -791,7 +665,7 @@ void ggml_metal_graph_compute(
}
} else {
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
int nth0 = 32;
@@ -801,6 +675,8 @@ void ggml_metal_graph_compute(
switch (src0t) {
case GGML_TYPE_F16:
{
GGML_ASSERT(ne02 == ne12);
nth0 = 64;
nth1 = 1;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
@@ -828,8 +704,8 @@ void ggml_metal_graph_compute(
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 2;
nth1 = 32;
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_K_f32];
} break;
case GGML_TYPE_Q3_K:
@@ -837,8 +713,8 @@ void ggml_metal_graph_compute(
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 2;
nth1 = 32;
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q3_K_f32];
} break;
case GGML_TYPE_Q4_K:
@@ -846,8 +722,8 @@ void ggml_metal_graph_compute(
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 2;
nth1 = 32;
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_K_f32];
} break;
case GGML_TYPE_Q5_K:
@@ -855,8 +731,8 @@ void ggml_metal_graph_compute(
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 2;
nth1 = 32;
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q5_K_f32];
} break;
case GGML_TYPE_Q6_K:
@@ -864,8 +740,8 @@ void ggml_metal_graph_compute(
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 2;
nth1 = 32;
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q6_K_f32];
} break;
default:
@@ -880,35 +756,28 @@ void ggml_metal_graph_compute(
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:5];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:6];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:7];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:8];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:9];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14];
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7) / 8, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_Q3_K) {
#ifdef GGML_QKK_64
[encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
#else
[encoder dispatchThreadgroups:MTLSizeMake((ne01+3)/4, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
#endif
}
else if (src0t == GGML_TYPE_Q5_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3) / 4, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_Q6_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
else if (src0t == GGML_TYPE_Q2_K ||
src0t == GGML_TYPE_Q3_K ||
src0t == GGML_TYPE_Q4_K ||
src0t == GGML_TYPE_Q5_K ||
src0t == GGML_TYPE_Q6_K) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else {
[encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@@ -918,7 +787,7 @@ void ggml_metal_graph_compute(
case GGML_OP_GET_ROWS:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
switch (src0->type) {
@@ -947,13 +816,12 @@ void ggml_metal_graph_compute(
case GGML_OP_RMS_NORM:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
const float eps = 1e-6f;
const int nth = 512;
const int nth = 256;
[encoder setComputePipelineState:ctx->pipeline_rms_norm];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
@@ -961,7 +829,7 @@ void ggml_metal_graph_compute(
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
[encoder setBytes:&eps length:sizeof( float) atIndex:4];
[encoder setThreadgroupMemoryLength:nth/32*sizeof(float) atIndex:0];
[encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];
const int64_t nrows = ggml_nrows(src0);
@@ -970,7 +838,7 @@ void ggml_metal_graph_compute(
case GGML_OP_NORM:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
const float eps = 1e-5f;
@@ -992,15 +860,14 @@ void ggml_metal_graph_compute(
case GGML_OP_ALIBI:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
GGML_ASSERT((src0t == GGML_TYPE_F32));
const int n_past = ((int32_t *) dst->op_params)[0]; UNUSED(n_past);
const int n_head = ((int32_t *) dst->op_params)[1];
float max_bias;
memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
const int n_past = ((int32_t *) src1->data)[0]; UNUSED(n_past);
const int n_head = ((int32_t *) src1->data)[1];
const float max_bias = ((float *) src1->data)[2];
if (__builtin_popcount(n_head) != 1) {
GGML_ASSERT(false && "only power-of-two n_head implemented");
@@ -1035,51 +902,43 @@ void ggml_metal_graph_compute(
case GGML_OP_ROPE:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
const int n_dims = ((int32_t *) src1->data)[1];
const int mode = ((int32_t *) src1->data)[2];
float freq_base;
float freq_scale;
memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
const int n_past = ((int32_t *)(src1->data))[0];
[encoder setComputePipelineState:ctx->pipeline_rope];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12];
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&n_past length:sizeof( int) atIndex:18];
[encoder setBytes:&n_dims length:sizeof( int) atIndex:19];
[encoder setBytes:&mode length:sizeof( int) atIndex:20];
[encoder setBytes:&freq_base length:sizeof(float) atIndex:21];
[encoder setBytes:&freq_scale length:sizeof(float) atIndex:22];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12];
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&n_past length:sizeof( int) atIndex:18];
[encoder setBytes:&n_dims length:sizeof( int) atIndex:19];
[encoder setBytes:&mode length:sizeof( int) atIndex:20];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_DUP:
case GGML_OP_CPY:
case GGML_OP_CONT:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
encoder = [command_buffer computeCommandEncoder];
}
const int nth = 32;
@@ -1126,10 +985,8 @@ void ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
default:
{
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
GGML_ASSERT(false);
}
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
GGML_ASSERT(false);
}
}

File diff suppressed because it is too large Load Diff

View File

@@ -1,244 +0,0 @@
//go:build mpi
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
* Copyright (c) 2023 Georgi Gerganov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "ggml-mpi.h"
#include "ggml.h"
#include <mpi.h>
#include <stdio.h>
#include <stdlib.h>
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define UNUSED GGML_UNUSED
struct ggml_mpi_context {
int rank;
int size;
};
void ggml_mpi_backend_init(void) {
MPI_Init(NULL, NULL);
}
void ggml_mpi_backend_free(void) {
MPI_Finalize();
}
struct ggml_mpi_context * ggml_mpi_init(void) {
struct ggml_mpi_context * ctx = calloc(1, sizeof(struct ggml_mpi_context));
MPI_Comm_rank(MPI_COMM_WORLD, &ctx->rank);
MPI_Comm_size(MPI_COMM_WORLD, &ctx->size);
return ctx;
}
void ggml_mpi_free(struct ggml_mpi_context * ctx) {
free(ctx);
}
int ggml_mpi_rank(struct ggml_mpi_context * ctx) {
return ctx->rank;
}
void ggml_mpi_eval_init(
struct ggml_mpi_context * ctx_mpi,
int * n_tokens,
int * n_past,
int * n_threads) {
UNUSED(ctx_mpi);
// synchronize the worker node parameters with the root node
MPI_Barrier(MPI_COMM_WORLD);
MPI_Bcast(n_tokens, 1, MPI_INT, 0, MPI_COMM_WORLD);
MPI_Bcast(n_past, 1, MPI_INT, 0, MPI_COMM_WORLD);
MPI_Bcast(n_threads, 1, MPI_INT, 0, MPI_COMM_WORLD);
}
static int ggml_graph_get_node_idx(struct ggml_cgraph * gf, const char * name) {
struct ggml_tensor * t = ggml_graph_get_tensor(gf, name);
if (t == NULL) {
fprintf(stderr, "%s: tensor %s not found\n", __func__, name);
return -1;
}
for (int i = 0; i < gf->n_nodes; i++) {
if (gf->nodes[i] == t) {
return i;
}
}
fprintf(stderr, "%s: tensor %s not found in graph (should not happen)\n", __func__, name);
return -1;
}
static void ggml_mpi_tensor_send(struct ggml_tensor * t, int mpi_rank_dst) {
MPI_Datatype mpi_type;
switch (t->type) {
case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break;
case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break;
default: GGML_ASSERT(false && "not implemented");
}
const int retval = MPI_Send(t->data, ggml_nelements(t), mpi_type, mpi_rank_dst, 0, MPI_COMM_WORLD);
GGML_ASSERT(retval == MPI_SUCCESS);
}
static void ggml_mpi_tensor_recv(struct ggml_tensor * t, int mpi_rank_src) {
MPI_Datatype mpi_type;
switch (t->type) {
case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break;
case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break;
default: GGML_ASSERT(false && "not implemented");
}
MPI_Status status; UNUSED(status);
const int retval = MPI_Recv(t->data, ggml_nelements(t), mpi_type, mpi_rank_src, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
GGML_ASSERT(retval == MPI_SUCCESS);
}
// TODO: there are many improvements that can be done to this implementation
void ggml_mpi_graph_compute_pre(
struct ggml_mpi_context * ctx_mpi,
struct ggml_cgraph * gf,
int n_layers) {
const int mpi_rank = ctx_mpi->rank;
const int mpi_size = ctx_mpi->size;
struct ggml_tensor * inp_tokens = ggml_graph_get_tensor(gf, "inp_tokens");
if (inp_tokens == NULL) {
fprintf(stderr, "%s: tensor 'inp_tokens' not found\n", __func__);
return;
}
struct ggml_tensor * inp0 = ggml_graph_get_tensor(gf, "layer_inp_0");
if (inp0 == NULL) {
fprintf(stderr, "%s: tensor 'inp0' not found\n", __func__);
return;
}
GGML_ASSERT(inp0 == gf->nodes[0]);
// distribute the compute graph into slices across the MPI nodes
//
// the main node (0) processes the last layers + the remainder of the compute graph
// and is responsible to pass the input tokens to the first node (1)
//
// node 1: [( 0) * n_per_node, ( 1) * n_per_node)
// node 2: [( 1) * n_per_node, ( 2) * n_per_node)
// ...
// node n-1: [(n-2) * n_per_node, (n-1) * n_per_node)
// node 0: [(n-1) * n_per_node, n_nodes)
//
if (mpi_rank > 0) {
if (mpi_rank == 1) {
// the first node (1) receives the input tokens from the main node (0)
ggml_mpi_tensor_recv(inp_tokens, 0);
} else {
// recv input data for each node into the "inp0" tensor (i.e. the first node in the compute graph)
ggml_mpi_tensor_recv(inp0, mpi_rank - 1);
}
} else if (mpi_size > 1) {
// node 0 sends the input tokens to node 1
ggml_mpi_tensor_send(inp_tokens, 1);
// recv the output data from the last node
ggml_mpi_tensor_recv(inp0, mpi_size - 1);
}
{
const int n_per_node = (n_layers + (mpi_size - 1)) / mpi_size;
const int mpi_idx = mpi_rank > 0 ? mpi_rank - 1 : mpi_size - 1;
const int il0 = (mpi_idx + 0) * n_per_node;
const int il1 = MIN(n_layers, (mpi_idx + 1) * n_per_node);
char name_l0[GGML_MAX_NAME];
char name_l1[GGML_MAX_NAME];
snprintf(name_l0, sizeof(name_l0), "layer_inp_%d", il0);
snprintf(name_l1, sizeof(name_l1), "layer_inp_%d", il1);
const int idx_l0 = ggml_graph_get_node_idx(gf, name_l0);
const int idx_l1 = mpi_rank > 0 ? ggml_graph_get_node_idx(gf, name_l1) + 1 : gf->n_nodes;
if (idx_l0 < 0 || idx_l1 < 0) {
fprintf(stderr, "%s: layer input nodes not found\n", __func__);
return;
}
// attach the input data to all nodes that need it
// TODO: not great - should be able to do this without modifying the compute graph (see next TODO below)
for (int i = idx_l0; i < idx_l1; i++) {
if (gf->nodes[i]->src[0] == gf->nodes[idx_l0]) {
gf->nodes[i]->src[0] = inp0;
}
if (gf->nodes[i]->src[1] == gf->nodes[idx_l0]) {
gf->nodes[i]->src[1] = inp0;
}
}
// TODO: instead of rearranging the nodes, we should be able to execute a subset of the compute graph
for (int i = 1; i < idx_l1 - idx_l0; i++) {
gf->nodes[i] = gf->nodes[idx_l0 + i];
gf->grads[i] = gf->grads[idx_l0 + i];
}
// the first node performs the "get_rows" operation, the rest of the nodes get the data from the previous node
if (mpi_idx != 0) {
gf->nodes[0]->op = GGML_OP_NONE;
}
gf->n_nodes = idx_l1 - idx_l0;
//fprintf(stderr, "%s: node %d: processing %d nodes [%d, %d)\n", __func__, mpi_rank, gf->n_nodes, il0, il1);
}
}
void ggml_mpi_graph_compute_post(
struct ggml_mpi_context * ctx_mpi,
struct ggml_cgraph * gf,
int n_layers) {
UNUSED(n_layers);
const int mpi_rank = ctx_mpi->rank;
const int mpi_size = ctx_mpi->size;
// send the output data to the next node
if (mpi_rank > 0) {
ggml_mpi_tensor_send(gf->nodes[gf->n_nodes - 1], (mpi_rank + 1) % mpi_size);
}
}

View File

@@ -1,67 +0,0 @@
//go:build mpi
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
* Copyright (c) 2023 Georgi Gerganov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#pragma once
struct ggml_context;
struct ggml_tensor;
struct ggml_cgraph;
#ifdef __cplusplus
extern "C" {
#endif
struct ggml_mpi_context;
void ggml_mpi_backend_init(void);
void ggml_mpi_backend_free(void);
struct ggml_mpi_context * ggml_mpi_init(void);
void ggml_mpi_free(struct ggml_mpi_context * ctx);
int ggml_mpi_rank(struct ggml_mpi_context * ctx);
void ggml_mpi_eval_init(
struct ggml_mpi_context * ctx_mpi,
int * n_tokens,
int * n_past,
int * n_threads);
void ggml_mpi_graph_compute_pre(
struct ggml_mpi_context * ctx_mpi,
struct ggml_cgraph * gf,
int n_layers);
void ggml_mpi_graph_compute_post(
struct ggml_mpi_context * ctx_mpi,
struct ggml_cgraph * gf,
int n_layers);
#ifdef __cplusplus
}
#endif

File diff suppressed because it is too large Load Diff

View File

@@ -1,53 +0,0 @@
//go:build opencl
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
* Copyright (c) 2023 Georgi Gerganov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#pragma once
#include "ggml.h"
#ifdef __cplusplus
extern "C" {
#endif
void ggml_cl_init(void);
void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
void * ggml_cl_host_malloc(size_t size);
void ggml_cl_host_free(void * ptr);
void ggml_cl_free_data(const struct ggml_tensor* tensor);
void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
#ifdef __cplusplus
}
#endif

File diff suppressed because it is too large Load Diff

View File

@@ -1,5 +1,5 @@
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
@@ -225,17 +225,10 @@
#define GGML_MAX_CONTEXTS 64
#define GGML_MAX_SRC 6
#define GGML_MAX_NAME 48
#define GGML_MAX_OP_PARAMS 32
#define GGML_DEFAULT_N_THREADS 4
#define GGML_EXIT_SUCCESS 0
#define GGML_EXIT_ABORTED 1
#define GGML_UNUSED(x) (void)(x)
#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
#define GGML_ASSERT(x) \
do { \
if (!(x)) { \
@@ -357,6 +350,16 @@ extern "C" {
GGML_OP_ARGMAX,
GGML_OP_REPEAT,
GGML_OP_REPEAT_BACK,
GGML_OP_ABS,
GGML_OP_SGN,
GGML_OP_NEG,
GGML_OP_STEP,
GGML_OP_TANH,
GGML_OP_ELU,
GGML_OP_RELU,
GGML_OP_GELU,
GGML_OP_GELU_QUICK,
GGML_OP_SILU,
GGML_OP_SILU_BACK,
GGML_OP_NORM, // normalize
GGML_OP_RMS_NORM,
@@ -386,8 +389,6 @@ extern "C" {
GGML_OP_CLAMP,
GGML_OP_CONV_1D,
GGML_OP_CONV_2D,
GGML_OP_POOL_1D,
GGML_OP_POOL_2D,
GGML_OP_FLASH_ATTN,
GGML_OP_FLASH_FF,
@@ -395,8 +396,6 @@ extern "C" {
GGML_OP_WIN_PART,
GGML_OP_WIN_UNPART,
GGML_OP_UNARY,
GGML_OP_MAP_UNARY,
GGML_OP_MAP_BINARY,
@@ -410,24 +409,6 @@ extern "C" {
GGML_OP_COUNT,
};
enum ggml_unary_op {
GGML_UNARY_OP_ABS,
GGML_UNARY_OP_SGN,
GGML_UNARY_OP_NEG,
GGML_UNARY_OP_STEP,
GGML_UNARY_OP_TANH,
GGML_UNARY_OP_ELU,
GGML_UNARY_OP_RELU,
GGML_UNARY_OP_GELU,
GGML_UNARY_OP_GELU_QUICK,
GGML_UNARY_OP_SILU,
};
enum ggml_object_type {
GGML_OBJECT_TENSOR,
GGML_OBJECT_GRAPH,
GGML_OBJECT_WORK_BUFFER
};
// ggml object
struct ggml_object {
@@ -436,9 +417,7 @@ extern "C" {
struct ggml_object * next;
enum ggml_object_type type;
char padding[4];
char padding[8];
};
static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
@@ -458,9 +437,6 @@ extern "C" {
// compute data
enum ggml_op op;
// op params - allocated as int32_t for alignment
int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
bool is_param;
struct ggml_tensor * grad;
@@ -477,7 +453,7 @@ extern "C" {
void * extra; // extra things e.g. for ggml-cuda.cu
char padding[4];
char padding[8];
};
static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
@@ -492,17 +468,8 @@ extern "C" {
// the `n_tasks` of nodes, 1:1 mapping to cgraph nodes
int n_tasks[GGML_MAX_NODES];
// abort ggml_graph_compute when true
bool (*abort_callback)(void * data);
void * abort_callback_data;
};
// next prime after GGML_MAX_NODES
// #define GGML_GRAPH_HASHTABLE_SIZE 4099
// next prime after GGML_MAX_NODES * 2 (nodes + leafs)
#define GGML_GRAPH_HASHTABLE_SIZE 8273
// computation graph
struct ggml_cgraph {
int n_nodes;
@@ -512,16 +479,12 @@ extern "C" {
struct ggml_tensor * grads[GGML_MAX_NODES];
struct ggml_tensor * leafs[GGML_MAX_NODES];
void * visited_hash_table[GGML_GRAPH_HASHTABLE_SIZE];
// performance
int perf_runs;
int64_t perf_cycles;
int64_t perf_time_us;
};
static const size_t GGML_GRAPH_SIZE = sizeof(struct ggml_cgraph);
// scratch buffer
struct ggml_scratch {
size_t offs;
@@ -583,7 +546,6 @@ extern "C" {
GGML_API const char * ggml_type_name(enum ggml_type type);
GGML_API const char * ggml_op_name (enum ggml_op op);
GGML_API const char * ggml_op_symbol(enum ggml_op op);
GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);
@@ -607,7 +569,6 @@ extern "C" {
GGML_API size_t ggml_used_mem(const struct ggml_context * ctx);
GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch);
GGML_API bool ggml_get_no_alloc(struct ggml_context * ctx);
GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
GGML_API void * ggml_get_mem_buffer (const struct ggml_context * ctx);
@@ -667,11 +628,9 @@ extern "C" {
GGML_API void * ggml_get_data (const struct ggml_tensor * tensor);
GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor);
GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name);
GGML_API struct ggml_tensor * ggml_format_name( struct ggml_tensor * tensor, const char * fmt, ...);
GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor);
GGML_API struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name);
GGML_API struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...);
//
// operations on tensors with backpropagation
@@ -681,11 +640,6 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
// in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_dup_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_add(
struct ggml_context * ctx,
struct ggml_tensor * a,
@@ -910,17 +864,14 @@ extern "C" {
GGML_API struct ggml_tensor * ggml_rms_norm(
struct ggml_context * ctx,
struct ggml_tensor * a,
float eps);
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_rms_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
float eps);
struct ggml_tensor * a);
// a - x
// b - dy
// TODO: update with configurable eps
GGML_API struct ggml_tensor * ggml_rms_norm_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
@@ -1012,22 +963,11 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// a -> b, in-place, return view(b)
GGML_API struct ggml_tensor * ggml_cpy_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
// make contiguous
GGML_API struct ggml_tensor * ggml_cont(
struct ggml_context * ctx,
struct ggml_tensor * a);
// make contiguous, in-place
GGML_API struct ggml_tensor * ggml_cont_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
// return view(a), b specifies the new shape
// TODO: when we start computing gradient, make a copy instead of view
GGML_API struct ggml_tensor * ggml_reshape(
@@ -1196,28 +1136,6 @@ extern "C" {
int mode,
int n_ctx);
// custom RoPE
GGML_API struct ggml_tensor * ggml_rope_custom(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode,
int n_ctx,
float freq_base,
float freq_scale);
// in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode,
int n_ctx,
float freq_base,
float freq_scale);
// rotary position embedding backward, i.e compute dx from dy
// a - dy
GGML_API struct ggml_tensor * ggml_rope_back(
@@ -1225,8 +1143,7 @@ extern "C" {
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode,
int n_ctx);
int mode);
// alibi position embedding
// in-place, returns view(a)
@@ -1273,31 +1190,6 @@ extern "C" {
int s,
int d);
enum ggml_op_pool {
GGML_OP_POOL_MAX,
GGML_OP_POOL_AVG,
GGML_OP_POOL_COUNT,
};
GGML_API struct ggml_tensor* ggml_pool_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_op_pool op,
int k0, // kernel size
int s0, // stride
int p0); // padding
GGML_API struct ggml_tensor* ggml_pool_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_op_pool op,
int k0,
int k1,
int s0,
int s1,
int p0,
int p1);
GGML_API struct ggml_tensor * ggml_flash_attn(
struct ggml_context * ctx,
struct ggml_tensor * q,
@@ -1350,16 +1242,6 @@ extern "C" {
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
GGML_API struct ggml_tensor * ggml_unary(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_unary_op op);
GGML_API struct ggml_tensor * ggml_unary_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_unary_op op);
GGML_API struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
@@ -1439,21 +1321,15 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * tensor);
GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
// graph allocation in a context
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx);
GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor);
GGML_API size_t ggml_graph_overhead(void);
// ggml_graph_plan() has to be called before ggml_graph_compute()
// when plan.work_size > 0, caller must allocate memory for plan.work_data
GGML_API struct ggml_cplan ggml_graph_plan (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
GGML_API void ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph);
// same as ggml_graph_compute() but the work data is allocated as a part of the context

View File

@@ -1,5 +1,5 @@
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
@@ -65,8 +65,6 @@
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
//
// 2-6 bit quantization in super-blocks
//
@@ -1381,7 +1379,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
const __m256i all_scales = _mm256_cvtepi8_epi16(scales8);
const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0);
const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1);
const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)};
const __m256i scales[2] = {_mm256_set_m128i(l_scales, l_scales), _mm256_set_m128i(h_scales, h_scales)};
__m256i sumi = _mm256_setzero_si256();
@@ -1449,7 +1447,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
const __m128i summs_1 = _mm_madd_epi16(mins_1, _mm_loadu_si128((const __m128i*)&y[i].bsums[8]));
// sumf += -dmin * summs in 32bits*8
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(MM256_SET_M128I(summs_1, summs_0))), acc);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(_mm256_set_m128i(summs_1, summs_0))), acc);
const __m128i scales_0 = _mm_cvtepi8_epi16(scales16);
const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales16, scales16));
@@ -1521,7 +1519,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
}
// sumf += dall * isum - dmin * summs in 32bits
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dall), _mm256_cvtepi32_ps(sumi)), acc);
}
@@ -1672,8 +1670,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
summs += dmin * smin;
const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2);
const __m256i q2_0 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 2), q2bits), m3);
const __m256i q2_1 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3);
const __m256i q2_0 = _mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q2bits, 2), q2bits), m3);
const __m256i q2_1 = _mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3);
const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
@@ -1694,62 +1692,6 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
*s = hsum_float_8(acc) + summs;
#elif defined __AVX__
const __m128i m3 = _mm_set1_epi8(3);
__m256 acc = _mm256_setzero_ps();
uint32_t ud, um;
const uint8_t * restrict db = (const uint8_t *)&ud;
const uint8_t * restrict mb = (const uint8_t *)&um;
float summs = 0;
// TODO: optimize this
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
const uint8_t * restrict q2 = x[i].qs;
const int8_t * restrict q8 = y[i].qs;
const uint32_t * restrict sc = (const uint32_t *)x[i].scales;
ud = (sc[0] >> 0) & 0x0f0f0f0f;
um = (sc[0] >> 4) & 0x0f0f0f0f;
int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3];
summs += dmin * smin;
const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2);
const __m128i q2_0 = _mm_and_si128(q2bits, m3);
const __m128i q2_1 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3);
const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3);
const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3);
const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
const __m128i p0 = _mm_maddubs_epi16(q2_0, _mm256_extractf128_si256(q8_0, 0));
const __m128i p1 = _mm_maddubs_epi16(q2_1, _mm256_extractf128_si256(q8_0, 1));
const __m128i p2 = _mm_maddubs_epi16(q2_2, _mm256_extractf128_si256(q8_1, 0));
const __m128i p3 = _mm_maddubs_epi16(q2_3, _mm256_extractf128_si256(q8_1, 1));
const __m256i p_0 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0));
const __m256i p_1 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1));
const __m256i p_2 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2));
const __m256i p_3 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3));
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0)), acc);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1)), acc);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2)), acc);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3)), acc);
}
*s = hsum_float_8(acc) + summs;
#else
float sumf = 0;
@@ -1945,7 +1887,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
const __m256i all_scales = _mm256_cvtepi8_epi16(scales128);
const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0);
const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1);
const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)};
const __m256i scales[2] = {_mm256_set_m128i(l_scales, l_scales), _mm256_set_m128i(h_scales, h_scales)};
// high bit
const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].hmask);
@@ -2156,7 +2098,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
}
// multiply with block scale and accumulate
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc);
}
@@ -2331,13 +2273,13 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
aux16[0] = a & 0x0f0f;
aux16[1] = (a >> 4) & 0x0f0f;
const __m256i scale_0 = MM256_SET_M128I(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8));
const __m256i scale_1 = MM256_SET_M128I(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8));
const __m256i scale_0 = _mm256_set_m128i(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8));
const __m256i scale_1 = _mm256_set_m128i(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8));
memcpy(&aux64, x[i].hmask, 8);
const __m128i haux = _mm_set_epi64x(aux64 >> 1, aux64 >> 0);
__m256i q3h_0 = MM256_SET_M128I(_mm_srli_epi16(haux, 2), haux);
__m256i q3h_0 = _mm256_set_m128i(_mm_srli_epi16(haux, 2), haux);
__m256i q3h_1 = _mm256_srli_epi16(q3h_0, 4);
q3h_0 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_0, m1), 2);
q3h_1 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_1, m1), 2);
@@ -2346,7 +2288,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3);
// prepare low and high bits
const __m256i q3aux = MM256_SET_M128I(_mm_srli_epi16(q3bits, 2), q3bits);
const __m256i q3aux = _mm256_set_m128i(_mm_srli_epi16(q3bits, 2), q3bits);
const __m256i q3l_0 = _mm256_and_si256(q3aux, m3);
const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3aux, 4), m3);
@@ -2379,93 +2321,6 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
*s = hsum_float_8(acc);
#elif defined __AVX__
const __m128i m3 = _mm_set1_epi8(3);
const __m128i m1 = _mm_set1_epi8(1);
__m256 acc = _mm256_setzero_ps();
uint64_t aux64;
uint16_t aux16[2];
const int8_t * aux8 = (const int8_t *)aux16;
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
const uint8_t * restrict q3 = x[i].qs;
const int8_t * restrict q8 = y[i].qs;
const uint16_t a = *(const uint16_t *)x[i].scales;
aux16[0] = a & 0x0f0f;
aux16[1] = (a >> 4) & 0x0f0f;
const __m128i scale_0 = _mm_set1_epi16(aux8[0] - 8);
const __m128i scale_1 = _mm_set1_epi16(aux8[2] - 8);
const __m128i scale_2 = _mm_set1_epi16(aux8[1] - 8);
const __m128i scale_3 = _mm_set1_epi16(aux8[3] - 8);
memcpy(&aux64, x[i].hmask, 8);
__m128i q3h_0 = _mm_set_epi64x(aux64 >> 1, aux64 >> 0);
__m128i q3h_1 = _mm_srli_epi16(q3h_0, 2);
__m128i q3h_2 = _mm_srli_epi16(q3h_0, 4);
__m128i q3h_3 = _mm_srli_epi16(q3h_0, 6);
q3h_0 = _mm_slli_epi16(_mm_andnot_si128(q3h_0, m1), 2);
q3h_1 = _mm_slli_epi16(_mm_andnot_si128(q3h_1, m1), 2);
q3h_2 = _mm_slli_epi16(_mm_andnot_si128(q3h_2, m1), 2);
q3h_3 = _mm_slli_epi16(_mm_andnot_si128(q3h_3, m1), 2);
// load low 2 bits
const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3);
// prepare low and high bits
const __m128i q3l_0 = _mm_and_si128(q3bits, m3);
const __m128i q3l_1 = _mm_and_si128(_mm_srli_epi16(q3bits, 2), m3);
const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits, 4), m3);
const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits, 6), m3);
// load Q8 quants
const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
// Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm_maddubs_epi16,
// and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set,
// and 2 if the high bit was set)
const __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, _mm256_extractf128_si256(q8_0, 0));
const __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, _mm256_extractf128_si256(q8_0, 1));
const __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, _mm256_extractf128_si256(q8_1, 0));
const __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, _mm256_extractf128_si256(q8_1, 1));
__m128i p16_0 = _mm_maddubs_epi16(q3l_0, _mm256_extractf128_si256(q8_0, 0));
__m128i p16_1 = _mm_maddubs_epi16(q3l_1, _mm256_extractf128_si256(q8_0, 1));
__m128i p16_2 = _mm_maddubs_epi16(q3l_2, _mm256_extractf128_si256(q8_1, 0));
__m128i p16_3 = _mm_maddubs_epi16(q3l_3, _mm256_extractf128_si256(q8_1, 1));
p16_0 = _mm_sub_epi16(p16_0, q8s_0);
p16_1 = _mm_sub_epi16(p16_1, q8s_1);
p16_2 = _mm_sub_epi16(p16_2, q8s_2);
p16_3 = _mm_sub_epi16(p16_3, q8s_3);
// multiply with scales
p16_0 = _mm_madd_epi16(scale_0, p16_0);
p16_1 = _mm_madd_epi16(scale_1, p16_1);
p16_2 = _mm_madd_epi16(scale_2, p16_2);
p16_3 = _mm_madd_epi16(scale_3, p16_3);
p16_0 = _mm_add_epi32(p16_0, p16_2);
p16_1 = _mm_add_epi32(p16_1, p16_3);
__m256i p16 = MM256_SET_M128I(p16_1, p16_0);
// multiply with block scale and accumulate
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16)), acc);
}
*s = hsum_float_8(acc);
#else
int8_t aux8[QK_K];
@@ -2648,7 +2503,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
acc_m = _mm_fmadd_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod), acc_m);
const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0);
const __m256i scales = MM256_SET_M128I(sc128, sc128);
const __m256i scales = _mm256_set_m128i(sc128, sc128);
__m256i sumi = _mm256_setzero_si256();
@@ -2755,7 +2610,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
}
__m256 vd = _mm256_set1_ps(d);
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc);
}
@@ -2952,60 +2807,6 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
*s = hsum_float_8(acc) - summs;
#elif defined __AVX__
const __m128i m4 = _mm_set1_epi8(0xF);
__m256 acc = _mm256_setzero_ps();
float summs = 0;
uint16_t aux16[2];
const uint8_t * scales = (const uint8_t *)aux16;
for (int i = 0; i < nb; ++i) {
const float d = ggml_fp16_to_fp32(x[i].d[0]) * y[i].d;
const float m = ggml_fp16_to_fp32(x[i].d[1]) * y[i].d;
const __m256 vd = _mm256_set1_ps(d);
const uint16_t * a = (const uint16_t *)x[i].scales;
aux16[0] = a[0] & 0x0f0f;
aux16[1] = (a[0] >> 4) & 0x0f0f;
summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
const uint8_t * restrict q4 = x[i].qs;
const int8_t * restrict q8 = y[i].qs;
const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4);
const __m128i q4bits_0 = _mm256_extractf128_si256(q4bits, 0);
const __m128i q4bits_1 = _mm256_extractf128_si256(q4bits, 1);
const __m128i q4_0 = _mm_and_si128(q4bits_0, m4);
const __m128i q4_1 = _mm_and_si128(q4bits_1, m4);
const __m128i q4_2 = _mm_and_si128(_mm_srli_epi16(q4bits_0, 4), m4);
const __m128i q4_3 = _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4);
const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
const __m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0));
const __m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1));
const __m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0));
const __m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1));
const __m128i p32_0 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_0);
const __m128i p32_1 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_1);
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_1, p32_0))), acc);
const __m128i p32_2 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_2);
const __m128i p32_3 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_3);
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_3, p32_2))), acc);
}
*s = hsum_float_8(acc) - summs;
#else
uint8_t aux8[QK_K];
@@ -3188,7 +2989,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
summs += dmin * _mm_extract_epi32(hsum, 0);
const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0);
const __m256i scales = MM256_SET_M128I(sc128, sc128);
const __m256i scales = _mm256_set_m128i(sc128, sc128);
const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].qh);
__m256i hmask = mone;
@@ -3327,7 +3128,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
}
__m256 vd = _mm256_set1_ps(d);
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc);
}
@@ -3490,13 +3291,13 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5);
const __m256i scale_l = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0]));
const __m256i scale_h = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2]));
const __m256i scale_l = _mm256_set_m128i(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0]));
const __m256i scale_h = _mm256_set_m128i(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2]));
int64_t aux64;
memcpy(&aux64, x[i].qh, 8);
const __m128i haux128 = _mm_set_epi64x(aux64 >> 1, aux64);
const __m256i haux256 = MM256_SET_M128I(_mm_srli_epi16(haux128, 2), haux128);
const __m256i haux256 = _mm256_set_m128i(_mm_srli_epi16(haux128, 2), haux128);
const __m256i q5h_0 = _mm256_slli_epi16(_mm256_andnot_si256(haux256, mone), 4);
const __m256i q5h_1 = _mm256_slli_epi16(_mm256_andnot_si256(_mm256_srli_epi16(haux256, 4), mone), 4);
@@ -3520,66 +3321,10 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
*s = hsum_float_8(acc);
#elif defined __AVX__
const __m128i m4 = _mm_set1_epi8(0xF);
const __m128i mone = _mm_set1_epi8(1);
__m256 acc = _mm256_setzero_ps();
for (int i = 0; i < nb; ++i) {
const uint8_t * restrict q5 = x[i].qs;
const int8_t * restrict q8 = y[i].qs;
const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5);
const __m128i scale_0 = _mm_set1_epi16(x[i].scales[0]);
const __m128i scale_1 = _mm_set1_epi16(x[i].scales[1]);
const __m128i scale_2 = _mm_set1_epi16(x[i].scales[2]);
const __m128i scale_3 = _mm_set1_epi16(x[i].scales[3]);
int64_t aux64;
memcpy(&aux64, x[i].qh, 8);
const __m128i haux128_0 = _mm_set_epi64x(aux64 >> 1, aux64);
const __m128i haux128_1 = _mm_srli_epi16(haux128_0, 2);
const __m128i q5h_0 = _mm_slli_epi16(_mm_andnot_si128(haux128_0, mone), 4);
const __m128i q5h_1 = _mm_slli_epi16(_mm_andnot_si128(haux128_1, mone), 4);
const __m128i q5h_2 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_0, 4), mone), 4);
const __m128i q5h_3 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_1, 4), mone), 4);
const __m128i q5l_0 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 0), m4);
const __m128i q5l_1 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 1), m4);
const __m128i q5l_2 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 0), 4), m4);
const __m128i q5l_3 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 1), 4), m4);
const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
const __m128i p16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5l_0, _mm256_extractf128_si256(q8_0, 0)));
const __m128i p16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5l_1, _mm256_extractf128_si256(q8_0, 1)));
const __m128i p16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5l_2, _mm256_extractf128_si256(q8_1, 0)));
const __m128i p16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5l_3, _mm256_extractf128_si256(q8_1, 1)));
const __m128i s16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5h_0, _mm256_extractf128_si256(q8_0, 0)));
const __m128i s16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5h_1, _mm256_extractf128_si256(q8_0, 1)));
const __m128i s16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5h_2, _mm256_extractf128_si256(q8_1, 0)));
const __m128i s16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5h_3, _mm256_extractf128_si256(q8_1, 1)));
const __m128i dot_0 = _mm_sub_epi32(_mm_add_epi32(p16_0, p16_2), _mm_add_epi32(s16_0, s16_2));
const __m128i dot_1 = _mm_sub_epi32(_mm_add_epi32(p16_1, p16_3), _mm_add_epi32(s16_1, s16_3));
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(dot_1, dot_0))), acc);
}
*s = hsum_float_8(acc);
#else
int8_t aux8[QK_K];
uint8_t aux8[QK_K];
int16_t aux16[16];
float sums [8];
memset(sums, 0, 8*sizeof(float));
@@ -3589,7 +3334,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
const uint8_t * restrict q4 = x[i].qs;
const uint8_t * restrict hm = x[i].qh;
const int8_t * restrict q8 = y[i].qs;
int8_t * restrict a = aux8;
uint8_t * restrict a = aux8;
for (int l = 0; l < 32; ++l) {
a[l+ 0] = q4[l] & 0xF;
a[l+32] = q4[l] >> 4;
@@ -3953,7 +3698,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
}
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc);
}
@@ -4111,8 +3856,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4);
const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh);
const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4);
const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4);
const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4);
const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4);
const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0);
const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_1);
@@ -4139,77 +3884,6 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
*s = hsum_float_8(acc);
#elif defined __AVX__
const __m128i m4 = _mm_set1_epi8(0xF);
const __m128i m2 = _mm_set1_epi8(3);
const __m128i m32s = _mm_set1_epi8(32);
__m256 acc = _mm256_setzero_ps();
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
const uint8_t * restrict q4 = x[i].ql;
const uint8_t * restrict qh = x[i].qh;
const int8_t * restrict q8 = y[i].qs;
const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]);
const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]);
const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]);
const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]);
__m128i sumi_0 = _mm_setzero_si128();
__m128i sumi_1 = _mm_setzero_si128();
const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1);
const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3);
const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4);
const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh);
const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH, m2), 4);
const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 2), m2), 4);
const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 4), m2), 4);
const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 6), m2), 4);
const __m128i q4_0 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 0), m4), q4h_0);
const __m128i q4_1 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 1), m4), q4h_1);
const __m128i q4_2 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 0), 4), m4), q4h_2);
const __m128i q4_3 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 1), 4), m4), q4h_3);
const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
__m128i q8s_0 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 0));
__m128i q8s_1 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 1));
__m128i q8s_2 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 0));
__m128i q8s_3 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 1));
__m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0));
__m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1));
__m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0));
__m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1));
p16_0 = _mm_sub_epi16(p16_0, q8s_0);
p16_1 = _mm_sub_epi16(p16_1, q8s_1);
p16_2 = _mm_sub_epi16(p16_2, q8s_2);
p16_3 = _mm_sub_epi16(p16_3, q8s_3);
p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0);
p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_0, scale_0)), p16_1);
p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2);
p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_1, scale_1)), p16_3);
sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2));
sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3));
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi_1, sumi_0))), acc);
}
*s = hsum_float_8(acc);
#else
int8_t aux8[QK_K];

View File

@@ -1,5 +1,5 @@
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
@@ -41,14 +41,6 @@
#define K_SCALE_SIZE 12
#endif
#ifndef static_assert
#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
#define static_assert(cond, msg) _Static_assert(cond, msg)
#else
#define static_assert(cond, msg) struct global_scope_noop_trick
#endif
#endif
//
// Super-block quantization structures
//

View File

@@ -1,5 +1,5 @@
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
@@ -201,13 +201,13 @@ struct llama_mmap {
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
size = file->size;
int fd = fileno(file->fp);
int flags = MAP_SHARED;
int flags = MAP_PRIVATE;
// prefetch/readahead impairs performance on NUMA systems
if (numa) { prefetch = 0; }
#ifdef __linux__
if (prefetch) { flags |= MAP_POPULATE; }
#endif
addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
addr = mmap(NULL, file->size, PROT_READ | PROT_WRITE, flags, fd, 0);
if (addr == MAP_FAILED) {
throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
}
@@ -249,7 +249,7 @@ struct llama_mmap {
throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
}
addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
addr = MapViewOfFile(hMapping, FILE_MAP_COPY, 0, 0, 0);
error = GetLastError();
CloseHandle(hMapping);

File diff suppressed because it is too large Load Diff

View File

@@ -1,10 +1,9 @@
package llama
/*
#cgo CPPFLAGS: -O3 -Wall -Wextra -Wno-unused-function -Wno-unused-variable -DNDEBUG -DGGML_USE_K_QUANTS
#cgo CXXFLAGS: -std=gnu++11
#cgo darwin CPPFLAGS: -DGGML_USE_ACCELERATE
#cgo darwin,arm64 CPPFLAGS: -DGGML_USE_METAL -DGGML_METAL_NDEBUG
#cgo CPPFLAGS: -O3 -DNDEBUG=1
#cgo CXXFLAGS: -std=c++11
#cgo darwin CPPFLAGS: -DGGML_USE_METAL=1 -DGGML_METAL_NDEBUG=1
#cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
#include <stdlib.h>
#include "llama.h"
@@ -22,7 +21,6 @@ struct llama_sample_options
int mirostat;
float mirostat_tau;
float mirostat_eta;
bool penalize_newline;
};
llama_token llama_sample(
@@ -39,8 +37,6 @@ llama_token llama_sample(
false,
};
struct llama_token_data newline = candidates_p.data[llama_token_nl()];
llama_sample_repetition_penalty(
ctx, &candidates_p,
last_tokens, n_last_tokens,
@@ -51,10 +47,6 @@ llama_token llama_sample(
last_tokens, n_last_tokens,
opts->frequency_penalty, opts->presence_penalty);
if (!opts->penalize_newline) {
candidates_p.data[llama_token_nl()] = newline;
}
if (opts->temperature <= 0) {
return llama_sample_token_greedy(ctx, &candidates_p);
}
@@ -85,47 +77,34 @@ llama_token llama_sample(
}
*/
import "C"
import (
"bytes"
"embed"
"errors"
"fmt"
"io"
"log"
"os"
"strings"
"sync"
"time"
"unicode/utf8"
"unsafe"
"github.com/jmorganca/ollama/api"
)
//go:embed ggml-metal.metal
var fs embed.FS
type LLM struct {
type llama struct {
params *C.struct_llama_context_params
model *C.struct_llama_model
ctx *C.struct_llama_context
last []C.llama_token
embd []C.llama_token
cursor int
mu sync.Mutex
gc bool
api.Options
}
func New(model string, opts api.Options) (*LLM, error) {
func New(model string, opts api.Options) (*llama, error) {
if _, err := os.Stat(model); err != nil {
return nil, err
}
llm := LLM{Options: opts}
llm := llama{Options: opts}
C.llama_backend_init(C.bool(llm.UseNUMA))
@@ -133,7 +112,6 @@ func New(model string, opts api.Options) (*LLM, error) {
params.seed = C.uint(llm.Seed)
params.n_ctx = C.int(llm.NumCtx)
params.n_batch = C.int(llm.NumBatch)
params.n_gqa = C.int(llm.NumGQA)
params.n_gpu_layers = C.int(llm.NumGPU)
params.main_gpu = C.int(llm.MainGPU)
params.low_vram = C.bool(llm.LowVRAM)
@@ -143,8 +121,6 @@ func New(model string, opts api.Options) (*LLM, error) {
params.use_mmap = C.bool(llm.UseMMap)
params.use_mlock = C.bool(llm.UseMLock)
params.embedding = C.bool(llm.EmbeddingOnly)
params.rope_freq_base = C.float(llm.RopeFrequencyBase)
params.rope_freq_scale = C.float(llm.RopeFrequencyScale)
llm.params = &params
cModel := C.CString(model)
@@ -168,141 +144,31 @@ func New(model string, opts api.Options) (*LLM, error) {
return &llm, nil
}
func (llm *LLM) Close() {
llm.gc = true
llm.mu.Lock()
defer llm.mu.Unlock()
func (llm *llama) Close() {
defer C.llama_free_model(llm.model)
defer C.llama_free(llm.ctx)
C.llama_print_timings(llm.ctx)
}
var errNeedMoreData = errors.New("need more data")
func (llm *LLM) Predict(ctx []int, prompt string, fn func(api.GenerateResponse)) error {
C.llama_reset_timings(llm.ctx)
tokens := make([]C.llama_token, len(ctx))
for i := range tokens {
tokens[i] = C.llama_token(ctx[i])
}
llm.marshalPrompt(tokens, prompt)
C.llama_set_rng_seed(llm.ctx, C.uint(llm.Seed))
var b bytes.Buffer
for {
token, err := llm.next()
if llm.gc {
return nil
} else if errors.Is(err, io.EOF) {
break
} else if err != nil {
return err
func (llm *llama) Predict(ctx []int, prompt string, fn func(api.GenerateResponse)) error {
if input := llm.tokenize(prompt); input != nil {
embd := make([]C.llama_token, len(ctx))
for i := range ctx {
embd[i] = C.llama_token(ctx[i])
}
b.WriteString(llm.Decode(token))
if err := llm.checkStopConditions(b); err != nil {
if errors.Is(err, io.EOF) {
break
} else if errors.Is(err, errNeedMoreData) {
continue
}
return err
}
if utf8.Valid(b.Bytes()) || b.Len() >= utf8.UTFMax {
fn(api.GenerateResponse{Response: b.String()})
b.Reset()
}
return llm.generate(append(embd, input...), fn)
}
embd := make([]int, len(llm.embd))
for i := range llm.embd {
embd[i] = int(llm.embd[i])
}
timings := C.llama_get_timings(llm.ctx)
fn(api.GenerateResponse{
Done: true,
Context: embd,
SampleCount: int(timings.n_sample),
SampleDuration: parseDurationMs(float64(timings.t_sample_ms)),
PromptEvalCount: int(timings.n_p_eval),
PromptEvalDuration: parseDurationMs(float64(timings.t_p_eval_ms)),
EvalCount: int(timings.n_eval),
EvalDuration: parseDurationMs(float64(timings.t_eval_ms)),
})
return nil
return errors.New("llama: tokenize")
}
func (llm *LLM) checkStopConditions(b bytes.Buffer) error {
for _, stopCondition := range llm.Stop {
if stopCondition == strings.TrimSpace(b.String()) {
return io.EOF
} else if strings.HasPrefix(stopCondition, strings.TrimSpace(b.String())) {
return errNeedMoreData
}
}
return nil
}
func (llm *LLM) marshalPrompt(ctx []C.llama_token, prompt string) []C.llama_token {
tokens := append(ctx, llm.Encode(prompt)...)
if llm.NumKeep < 0 {
llm.NumKeep = len(tokens)
}
// min(llm.NumCtx - 4, llm.NumKeep)
if llm.NumCtx-4 < llm.NumKeep {
llm.NumKeep = llm.NumCtx - 4
}
if len(tokens) >= llm.NumCtx {
// truncate input
numLeft := (llm.NumCtx - llm.NumKeep) / 2
truncated := tokens[:llm.NumKeep]
erasedBlocks := (len(tokens) - llm.NumKeep - numLeft - 1) / numLeft
truncated = append(truncated, tokens[llm.NumKeep+erasedBlocks*numLeft:]...)
copy(llm.last, tokens[len(tokens)-llm.NumCtx:])
tokens = truncated
log.Printf("input truncated: num_ctx=%d num_keep=%d num_left=%d num_tokens=%d", llm.NumCtx, llm.NumKeep, numLeft, len(truncated))
} else {
llm.last = make([]C.llama_token, llm.NumCtx-len(tokens))
llm.last = append(llm.last, tokens...)
}
var i int
for i = 0; i < len(llm.embd) && i < len(tokens) && llm.embd[i] == tokens[i]; i++ {
// noop
}
llm.embd = tokens
if i == len(tokens) {
// evaluate at least one token to generate logits
i--
}
llm.cursor = i
log.Printf("prompt: num_past=%d cached=%v eval=%v", i, len(llm.embd[:i]), len(llm.embd[i:]))
return tokens
}
func (llm *LLM) Encode(prompt string) []C.llama_token {
func (llm *llama) tokenize(prompt string) []C.llama_token {
cPrompt := C.CString(prompt)
defer C.free(unsafe.Pointer(cPrompt))
tokens := make([]C.llama_token, len(prompt)+1)
tokens := make([]C.llama_token, llm.NumCtx)
if n := C.llama_tokenize(llm.ctx, cPrompt, unsafe.SliceData(tokens), C.int(len(tokens)), true); n > 0 {
return tokens[:n]
}
@@ -310,7 +176,7 @@ func (llm *LLM) Encode(prompt string) []C.llama_token {
return nil
}
func (llm *LLM) Decode(tokens ...C.llama_token) string {
func (llm *llama) detokenize(tokens ...C.llama_token) string {
var sb strings.Builder
for _, token := range tokens {
sb.WriteString(C.GoString(C.llama_token_to_str(llm.ctx, token)))
@@ -319,121 +185,98 @@ func (llm *LLM) Decode(tokens ...C.llama_token) string {
return sb.String()
}
func (llm *LLM) next() (C.llama_token, error) {
llm.mu.Lock()
defer llm.mu.Unlock()
func (llm *llama) generate(input []C.llama_token, fn func(api.GenerateResponse)) error {
var opts C.struct_llama_sample_options
opts.repeat_penalty = C.float(llm.RepeatPenalty)
opts.frequency_penalty = C.float(llm.FrequencyPenalty)
opts.presence_penalty = C.float(llm.PresencePenalty)
opts.temperature = C.float(llm.Temperature)
opts.top_k = C.int(llm.TopK)
opts.top_p = C.float(llm.TopP)
opts.tfs_z = C.float(llm.TFSZ)
opts.typical_p = C.float(llm.TypicalP)
opts.mirostat = C.int(llm.Mirostat)
opts.mirostat_tau = C.float(llm.MirostatTau)
opts.mirostat_eta = C.float(llm.MirostatEta)
if len(llm.embd) >= llm.NumCtx {
numLeft := (llm.NumCtx - llm.NumKeep) / 2
truncated := llm.embd[:llm.NumKeep]
truncated = append(truncated, llm.embd[len(llm.embd)-numLeft:]...)
output := deque[C.llama_token]{capacity: llm.NumCtx}
llm.embd = truncated
llm.cursor = llm.NumKeep
log.Printf("input truncated: num_ctx=%d num_keep=%d num_left=%d num_tokens=%d cursor=%d", llm.NumCtx, llm.NumKeep, numLeft, len(truncated), llm.cursor)
context := deque[int]{capacity: llm.NumCtx / 2}
for _, in := range input {
context.PushLeft(int(in))
}
for {
if llm.gc {
return 0, io.EOF
var b bytes.Buffer
for C.llama_get_kv_cache_token_count(llm.ctx) < C.int(llm.NumCtx) {
if retval := C.llama_eval(llm.ctx, unsafe.SliceData(input), C.int(len(input)), C.llama_get_kv_cache_token_count(llm.ctx), C.int(llm.NumThread)); retval != 0 {
return errors.New("llama: eval")
}
if llm.cursor >= len(llm.embd) {
token, err := llm.sample(output, &opts)
if errors.Is(err, io.EOF) {
break
} else if err != nil {
return err
}
numEval := len(llm.embd) - llm.cursor
if numEval > llm.NumBatch {
numEval = llm.NumBatch
b.WriteString(llm.detokenize(token))
if utf8.Valid(b.Bytes()) || b.Len() >= utf8.UTFMax {
// call the callback
fn(api.GenerateResponse{
Response: b.String(),
})
output.PushLeft(token)
context.PushLeft(int(token))
b.Reset()
}
if retval := C.llama_eval(llm.ctx, unsafe.SliceData(llm.embd[llm.cursor:]), C.int(numEval), C.int(llm.cursor), C.int(llm.NumThread)); retval != 0 {
return 0, fmt.Errorf("llama_eval: %d", retval)
}
llm.cursor += numEval
input = []C.llama_token{token}
}
var sampleOpts C.struct_llama_sample_options
sampleOpts.repeat_penalty = C.float(llm.RepeatPenalty)
sampleOpts.frequency_penalty = C.float(llm.FrequencyPenalty)
sampleOpts.presence_penalty = C.float(llm.PresencePenalty)
sampleOpts.temperature = C.float(llm.Temperature)
sampleOpts.top_k = C.int(llm.TopK)
sampleOpts.top_p = C.float(llm.TopP)
sampleOpts.tfs_z = C.float(llm.TFSZ)
sampleOpts.typical_p = C.float(llm.TypicalP)
sampleOpts.mirostat = C.int(llm.Mirostat)
sampleOpts.mirostat_tau = C.float(llm.MirostatTau)
sampleOpts.mirostat_eta = C.float(llm.MirostatEta)
sampleOpts.penalize_newline = C.bool(llm.PenalizeNewline)
dur := func(ms float64) time.Duration {
d, err := time.ParseDuration(fmt.Sprintf("%fms", ms))
if err != nil {
panic(err)
}
numVocab := C.llama_n_vocab(llm.ctx)
return d
}
timings := C.llama_get_timings(llm.ctx)
fn(api.GenerateResponse{
Done: true,
Context: context.Data(),
PromptEvalCount: int(timings.n_p_eval),
PromptEvalDuration: dur(float64(timings.t_p_eval_ms)),
EvalCount: int(timings.n_eval),
EvalDuration: dur(float64(timings.t_eval_ms)),
})
return nil
}
func (llm *llama) sample(output deque[C.llama_token], opts *C.struct_llama_sample_options) (C.llama_token, error) {
numVocab := int(C.llama_n_vocab(llm.ctx))
logits := unsafe.Slice(C.llama_get_logits(llm.ctx), numVocab)
// TODO: logit bias
candidates := make([]C.llama_token_data, numVocab)
for i := range logits {
candidates[i] = C.llama_token_data{
candidates := deque[C.struct_llama_token_data]{capacity: numVocab}
for i := 0; i < candidates.Cap(); i++ {
candidates.PushLeft(C.struct_llama_token_data{
id: C.int(i),
logit: logits[i],
p: 0,
}
})
}
repeatLastN := llm.RepeatLastN
if len(llm.last) < repeatLastN {
repeatLastN = len(llm.last)
}
if llm.NumCtx < repeatLastN {
repeatLastN = llm.NumCtx
}
lastN := llm.last[len(llm.last)-repeatLastN:]
token := C.llama_sample(
llm.ctx,
unsafe.SliceData(candidates), C.size_t(len(candidates)),
unsafe.SliceData(lastN), C.size_t(len(lastN)),
&sampleOpts,
)
llm.last = append(llm.last, token)
llm.embd = append(llm.embd, token)
if token == C.llama_token_eos() {
return 0, io.EOF
unsafe.SliceData(candidates.Data()), C.size_t(candidates.Len()),
unsafe.SliceData(output.Data()), C.size_t(output.Len()),
opts)
if token != C.llama_token_eos() {
return token, nil
}
return token, nil
}
func (llm *LLM) Embedding(input string) ([]float64, error) {
if !llm.EmbeddingOnly {
return nil, errors.New("llama: embedding not enabled")
}
tokens := llm.Encode(input)
if tokens == nil {
return nil, errors.New("llama: tokenize embedding")
}
retval := C.llama_eval(llm.ctx, unsafe.SliceData(tokens), C.int(len(tokens)), 0, C.int(llm.NumThread))
if retval != 0 {
return nil, errors.New("llama: eval")
}
n := C.llama_n_embd(llm.ctx)
if n <= 0 {
return nil, errors.New("llama: no embeddings generated")
}
cEmbeddings := unsafe.Slice(C.llama_get_embeddings(llm.ctx), n)
embeddings := make([]float64, len(cEmbeddings))
for i, v := range cEmbeddings {
embeddings[i] = float64(v)
}
return embeddings, nil
return 0, io.EOF
}

View File

@@ -1,5 +1,5 @@
/**
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
@@ -79,10 +79,6 @@
#define LLAMA_SUPPORTS_GPU_OFFLOAD
#endif
#ifndef LLAMA_DEFAULT_RMS_EPS
#define LLAMA_DEFAULT_RMS_EPS 5e-6f
#endif
#ifdef __cplusplus
extern "C" {
#endif
@@ -113,20 +109,12 @@ extern "C" {
typedef void (*llama_progress_callback)(float progress, void *ctx);
struct llama_context_params {
uint32_t seed; // RNG seed, -1 for random
int32_t n_ctx; // text context
int32_t n_batch; // prompt processing batch size
int32_t n_gqa; // grouped-query attention (TEMP - will be moved to model hparams)
float rms_norm_eps; // rms norm epsilon (TEMP - will be moved to model hparams)
int32_t n_gpu_layers; // number of layers to store in VRAM
int32_t main_gpu; // the GPU that is used for scratch and small tensors
const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
// ref: https://github.com/ggerganov/llama.cpp/pull/2054
float rope_freq_base; // RoPE base frequency
float rope_freq_scale; // RoPE frequency scaling factor
uint32_t seed; // RNG seed, -1 for random
int32_t n_ctx; // text context
int32_t n_batch; // prompt processing batch size
int32_t n_gpu_layers; // number of layers to store in VRAM
int32_t main_gpu; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
// called with a progress value between 0 and 1, pass NULL to disable
llama_progress_callback progress_callback;
// context pointer passed to the progress callback
@@ -134,7 +122,6 @@ extern "C" {
// Keep the booleans together to avoid misalignment during copy-by-value.
bool low_vram; // if true, reduce VRAM usage at the cost of performance
bool mul_mat_q; // if true, use experimental mul_mat_q kernels
bool f16_kv; // use fp16 for KV cache
bool logits_all; // the llama_eval() call computes all logits, not just the last one
bool vocab_only; // only load the vocabulary, no weights
@@ -173,40 +160,6 @@ extern "C" {
bool quantize_output_tensor; // quantize output.weight
} llama_model_quantize_params;
// grammar types
struct llama_grammar;
// grammar element type
enum llama_gretype {
// end of rule definition
LLAMA_GRETYPE_END = 0,
// start of alternate definition for rule
LLAMA_GRETYPE_ALT = 1,
// non-terminal element: reference to rule
LLAMA_GRETYPE_RULE_REF = 2,
// terminal element: character (code point)
LLAMA_GRETYPE_CHAR = 3,
// inverse char(s) ([^a], [^a-b] [^abc])
LLAMA_GRETYPE_CHAR_NOT = 4,
// modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
// be an inclusive range ([a-z])
LLAMA_GRETYPE_CHAR_RNG_UPPER = 5,
// modifies a preceding LLAMA_GRETYPE_CHAR or
// LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
LLAMA_GRETYPE_CHAR_ALT = 6,
};
typedef struct llama_grammar_element {
enum llama_gretype type;
uint32_t value; // Unicode code point or rule ID
} llama_grammar_element;
// performance timing information
struct llama_timings {
double t_start_ms;
@@ -221,8 +174,6 @@ extern "C" {
int32_t n_eval;
};
LLAMA_API int llama_max_devices();
LLAMA_API struct llama_context_params llama_context_default_params();
LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
@@ -345,21 +296,10 @@ extern "C" {
int n_max_tokens,
bool add_bos);
LLAMA_API int llama_tokenize_with_model(
const struct llama_model * model,
const char * text,
llama_token * tokens,
int n_max_tokens,
bool add_bos);
LLAMA_API int llama_n_vocab(const struct llama_context * ctx);
LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
LLAMA_API int llama_n_embd (const struct llama_context * ctx);
LLAMA_API int llama_n_vocab_from_model(const struct llama_model * model);
LLAMA_API int llama_n_ctx_from_model (const struct llama_model * model);
LLAMA_API int llama_n_embd_from_model (const struct llama_model * model);
// Get the vocabulary as output parameters.
// Returns number of results.
LLAMA_API int llama_get_vocab(
@@ -368,12 +308,6 @@ extern "C" {
float * scores,
int capacity);
LLAMA_API int llama_get_vocab_from_model(
const struct llama_model * model,
const char * * strings,
float * scores,
int capacity);
// Token logits obtained from the last call to llama_eval()
// The logits for the last token are stored in the last row
// Can be mutated in order to change the probabilities of the next token
@@ -386,28 +320,13 @@ extern "C" {
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
// Token Id -> String. Uses the vocabulary in the provided context
LLAMA_API const char * llama_token_to_str(
const struct llama_context * ctx,
llama_token token);
LLAMA_API const char * llama_token_to_str_with_model(
const struct llama_model * model,
llama_token token);
LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
// Special tokens
LLAMA_API llama_token llama_token_bos(); // beginning-of-sentence
LLAMA_API llama_token llama_token_eos(); // end-of-sentence
LLAMA_API llama_token llama_token_nl(); // next-line
// Grammar
//
LLAMA_API struct llama_grammar * llama_grammar_init(
const llama_grammar_element ** rules,
size_t n_rules,
size_t start_rule_index);
LLAMA_API void llama_grammar_free(struct llama_grammar * grammar);
// Sampling functions
/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
@@ -420,11 +339,13 @@ extern "C" {
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
/// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
/// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
/// @params smooth_factor Smooth factor between guidance logits and original logits. 1.0f means only use guidance logits. 0.0f means only original logits.
LLAMA_API void llama_sample_classifier_free_guidance(
struct llama_context * ctx,
llama_token_data_array * candidates,
struct llama_context * guidance_ctx,
float scale);
float scale,
float smooth_factor);
/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
@@ -442,9 +363,6 @@ extern "C" {
LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
/// @details Apply constraints from grammar
LLAMA_API void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar);
/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
@@ -466,9 +384,6 @@ extern "C" {
/// @details Randomly selects a token from the candidates based on their probabilities.
LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
/// @details Accepts the sampled token into the grammar
LLAMA_API void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token);
// Performance information
LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
LLAMA_API void llama_print_timings(struct llama_context * ctx);

View File

@@ -1,80 +0,0 @@
package llama
import (
"bytes"
"crypto/sha256"
"errors"
"io"
"log"
"os"
"path/filepath"
)
func init() {
if err := initBackend(); err != nil {
log.Printf("WARNING: GPU could not be initialized correctly: %v", err)
log.Printf("WARNING: falling back to CPU")
}
}
func initBackend() error {
exec, err := os.Executable()
if err != nil {
return err
}
exec, err = filepath.EvalSymlinks(exec)
if err != nil {
return err
}
metal := filepath.Join(filepath.Dir(exec), "ggml-metal.metal")
fi, err := os.Stat(metal)
if err != nil && !errors.Is(err, os.ErrNotExist) {
return err
}
if fi != nil {
actual, err := os.Open(metal)
if err != nil {
return err
}
actualSum := sha256.New()
if _, err := io.Copy(actualSum, actual); err != nil {
return err
}
expect, err := fs.Open("ggml-metal.metal")
if err != nil {
return err
}
expectSum := sha256.New()
if _, err := io.Copy(expectSum, expect); err != nil {
return err
}
if bytes.Equal(actualSum.Sum(nil), expectSum.Sum(nil)) {
return nil
}
}
dst, err := os.Create(filepath.Join(filepath.Dir(exec), "ggml-metal.metal"))
if err != nil {
return err
}
defer dst.Close()
src, err := fs.Open("ggml-metal.metal")
if err != nil {
return err
}
defer src.Close()
if _, err := io.Copy(dst, src); err != nil {
return err
}
return nil
}

View File

@@ -1,70 +0,0 @@
#!/bin/sh
set -eu
status() { echo >&2 ">>> $*"; }
error() { status "ERROR $*"; }
usage() {
echo "usage: $(basename $0) /path/to/repo"
exit 1
}
OUT=$(dirname $0)
while getopts "hC:" OPTION; do
case $OPTION in
C) OUT=$OPTARG ;;
*) usage ;;
esac
done
shift $(( $OPTIND - 1 ))
[ $# -eq 1 ] || usage
status "updating source..."
cp -a "$1"/*.{c,h,cpp,m,metal,cu} "$OUT"
status "removing incompatible files..."
rm -f "$OUT"/build-info.h
SHA1=$(git -C $1 rev-parse @)
LICENSE=$(mktemp)
cleanup() {
rm -f $LICENSE
}
trap cleanup 0
cat <<EOF | sed 's/ *$//' >$LICENSE
/**
* llama.cpp - git $SHA1
*
$(sed 's/^/ * /' <$1/LICENSE)
*/
EOF
for IN in $OUT/*.{c,h,cpp,m,metal,cu}; do
TMP=$(mktemp)
status "updating license $IN"
cat $LICENSE $IN >$TMP
mv $TMP $IN
done
touchup() {
local CONSTRAINT=$1 && shift
for IN in $*; do
status "touching up $IN..."
TMP=$(mktemp)
{
echo "//go:build $CONSTRAINT"
echo
} | cat - $IN >$TMP
mv $TMP $IN
done
}
touchup darwin $OUT/ggml-metal.*
touchup mpi $OUT/ggml-mpi.*
touchup opencl $OUT/ggml-opencl.*

View File

@@ -1,15 +1,104 @@
package llama
import (
"fmt"
"time"
)
type node[T any] struct {
t T
next *node[T]
prev *node[T]
}
func parseDurationMs(ms float64) time.Duration {
dur, err := time.ParseDuration(fmt.Sprintf("%fms", ms))
if err != nil {
panic(err)
type deque[T any] struct {
head *node[T]
tail *node[T]
size int
capacity int
}
func (d *deque[T]) Empty() bool {
return d.size == 0
}
func (d *deque[T]) Len() int {
return d.size
}
func (d *deque[T]) Cap() int {
return d.capacity
}
func (d *deque[T]) Push(t T) {
if d.capacity > 0 && d.size >= d.capacity {
d.PopLeft()
}
return dur
n := node[T]{t: t}
if d.head != nil {
n.next = d.head
d.head.prev = &n
d.head = &n
} else {
d.head = &n
d.tail = &n
}
d.size++
}
func (d *deque[T]) PushLeft(t T) {
if d.capacity > 0 && d.size >= d.capacity {
d.Pop()
}
n := node[T]{t: t}
if d.tail != nil {
n.prev = d.tail
d.tail.next = &n
d.tail = &n
} else {
d.head = &n
d.tail = &n
}
d.size++
}
func (d *deque[T]) Pop() *T {
if d.Empty() {
return nil
}
head := d.head
d.head = head.next
if d.head != nil {
d.head.prev = nil
} else {
d.tail = nil
}
d.size--
return &head.t
}
func (d *deque[T]) PopLeft() *T {
if d.Empty() {
return nil
}
tail := d.tail
d.tail = tail.prev
if d.tail != nil {
d.tail.next = nil
} else {
d.head = nil
}
d.size--
return &tail.t
}
func (d *deque[T]) Data() (data []T) {
for n := d.head; n != nil; n = n.next {
data = append(data, n.t)
}
return data
}

38
models.json Normal file
View File

@@ -0,0 +1,38 @@
[
{
"name": "orca",
"display_name": "Orca Mini",
"parameters": "3B",
"url": "https://huggingface.co/TheBloke/orca_mini_3B-GGML/resolve/main/orca-mini-3b.ggmlv3.q4_1.bin",
"short_description": "Follow instructions. Great small model that runs fast even without GPU support.",
"description": "An OpenLLaMa-3B model trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches.",
"published_by": "TheBloke",
"original_author": "psmathur",
"original_url": "https://huggingface.co/psmathur/orca_mini_3b",
"license": "CC-BY-SA-4.0"
},
{
"name": "nous-hermes",
"display_name": "Nous Hermes",
"parameters": "13B",
"url": "https://huggingface.co/TheBloke/Nous-Hermes-13B-GGML/resolve/main/nous-hermes-13b.ggmlv3.q2_K.bin",
"short_description": "Currently one of the best 13B general model.",
"description": "It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions. This model was fine-tuned by Nous Research, with Teknium and Karan4D leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors. The result is an enhanced Llama 13b model that rivals GPT-3.5-turbo in performance across a variety of tasks. \n \n This model stands out for its long responses, low hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 2000 sequence length on an 8x a100 80GB DGX machine for over 50 hours.",
"published_by": "TheBloke",
"original_author": "NousResearch",
"original_url": "https://huggingface.co/NousResearch/Nous-Hermes-13b",
"license": "GPL"
},
{
"name": "vicuna",
"display_name": "Vicuna",
"parameters": "7B",
"url": "https://huggingface.co/TheBloke/vicuna-7B-v1.3-GGML/resolve/main/vicuna-7b-v1.3.ggmlv3.q4_0.bin",
"short_description": "Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.",
"description": "The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.",
"published_by": "TheBloke",
"original_author": "LMSYS",
"original_url": "https://huggingface.co/lmsys/vicuna-7b-v1.3",
"license:": "Non-commercial"
}
]

View File

@@ -2,109 +2,76 @@ package parser
import (
"bufio"
"bytes"
"errors"
"fmt"
"io"
"log"
"strings"
)
type Command struct {
Name string
Args string
}
func (c *Command) Reset() {
c.Name = ""
c.Args = ""
Arg string
}
func Parse(reader io.Reader) ([]Command, error) {
var commands []Command
var command, modelCommand Command
var foundModel bool
scanner := bufio.NewScanner(reader)
scanner.Buffer(make([]byte, 0, bufio.MaxScanTokenSize), bufio.MaxScanTokenSize)
scanner.Split(scanModelfile)
multiline := false
var multilineCommand *Command
for scanner.Scan() {
line := scanner.Bytes()
fields := bytes.SplitN(line, []byte(" "), 2)
if len(fields) == 0 || len(fields[0]) == 0 {
line := scanner.Text()
if multiline {
// If we're in a multiline string and the line is """, end the multiline string.
if strings.TrimSpace(line) == `"""` {
multiline = false
commands = append(commands, *multilineCommand)
} else {
// Otherwise, append the line to the multiline string.
multilineCommand.Arg += "\n" + line
}
continue
}
fields := strings.Fields(line)
if len(fields) == 0 {
continue
}
switch string(bytes.ToUpper(fields[0])) {
command := Command{}
switch strings.ToUpper(fields[0]) {
case "FROM":
command.Name = "model"
command.Args = string(fields[1])
// copy command for validation
modelCommand = command
case "LICENSE", "TEMPLATE", "SYSTEM", "PROMPT", "EMBED":
command.Name = string(bytes.ToLower(fields[0]))
command.Args = string(fields[1])
command.Arg = fields[1]
if command.Arg == "" {
return nil, fmt.Errorf("no model specified in FROM line")
}
foundModel = true
case "PROMPT":
command.Name = "prompt"
if fields[1] == `"""` {
multiline = true
multilineCommand = &command
multilineCommand.Arg = ""
} else {
command.Arg = strings.Join(fields[1:], " ")
}
case "PARAMETER":
fields = bytes.SplitN(fields[1], []byte(" "), 2)
command.Name = string(fields[0])
command.Args = string(fields[1])
command.Name = fields[1]
command.Arg = strings.Join(fields[2:], " ")
default:
// log a warning for unknown commands
log.Printf("WARNING: Unknown command: %s", fields[0])
continue
}
commands = append(commands, command)
command.Reset()
if !multiline {
commands = append(commands, command)
}
}
if modelCommand.Args == "" {
return nil, errors.New("no FROM line for the model was specified")
if !foundModel {
return nil, fmt.Errorf("no FROM line for the model was specified")
}
if multiline {
return nil, fmt.Errorf("unclosed multiline string")
}
return commands, scanner.Err()
}
func scanModelfile(data []byte, atEOF bool) (advance int, token []byte, err error) {
advance, token, err = scan([]byte(`"""`), []byte(`"""`), data, atEOF)
if err != nil {
return 0, nil, err
}
if advance > 0 && token != nil {
return advance, token, nil
}
advance, token, err = scan([]byte(`"`), []byte(`"`), data, atEOF)
if err != nil {
return 0, nil, err
}
if advance > 0 && token != nil {
return advance, token, nil
}
return bufio.ScanLines(data, atEOF)
}
func scan(openBytes, closeBytes, data []byte, atEOF bool) (advance int, token []byte, err error) {
newline := bytes.IndexByte(data, '\n')
if start := bytes.Index(data, openBytes); start >= 0 && start < newline {
end := bytes.Index(data[start+len(openBytes):], closeBytes)
if end < 0 {
if atEOF {
return 0, nil, fmt.Errorf("unterminated %s: expecting %s", openBytes, closeBytes)
} else {
return 0, nil, nil
}
}
n := start + len(openBytes) + end + len(closeBytes)
newData := data[:start]
newData = append(newData, data[start+len(openBytes):n-len(closeBytes)]...)
return n, newData, nil
}
return 0, nil, nil
}

View File

@@ -1,21 +0,0 @@
MIT License
Copyright (c) 2017 Zack
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

View File

@@ -1,121 +0,0 @@
# progressbar
[![CI](https://github.com/schollz/progressbar/actions/workflows/ci.yml/badge.svg?branch=main&event=push)](https://github.com/schollz/progressbar/actions/workflows/ci.yml)
[![go report card](https://goreportcard.com/badge/github.com/schollz/progressbar)](https://goreportcard.com/report/github.com/schollz/progressbar)
[![coverage](https://img.shields.io/badge/coverage-84%25-brightgreen.svg)](https://gocover.io/github.com/schollz/progressbar)
[![godocs](https://godoc.org/github.com/schollz/progressbar?status.svg)](https://godoc.org/github.com/schollz/progressbar/v3)
A very simple thread-safe progress bar which should work on every OS without problems. I needed a progressbar for [croc](https://github.com/schollz/croc) and everything I tried had problems, so I made another one. In order to be OS agnostic I do not plan to support [multi-line outputs](https://github.com/schollz/progressbar/issues/6).
## Install
```
go get -u github.com/schollz/progressbar/v3
```
## Usage
### Basic usage
```golang
bar := progressbar.Default(100)
for i := 0; i < 100; i++ {
bar.Add(1)
time.Sleep(40 * time.Millisecond)
}
```
which looks like:
![Example of basic bar](examples/basic/basic.gif)
### I/O operations
The `progressbar` implements an `io.Writer` so it can automatically detect the number of bytes written to a stream, so you can use it as a progressbar for an `io.Reader`.
```golang
req, _ := http.NewRequest("GET", "https://dl.google.com/go/go1.14.2.src.tar.gz", nil)
resp, _ := http.DefaultClient.Do(req)
defer resp.Body.Close()
f, _ := os.OpenFile("go1.14.2.src.tar.gz", os.O_CREATE|os.O_WRONLY, 0644)
defer f.Close()
bar := progressbar.DefaultBytes(
resp.ContentLength,
"downloading",
)
io.Copy(io.MultiWriter(f, bar), resp.Body)
```
which looks like:
![Example of download bar](examples/download/download.gif)
### Progress bar with unknown length
A progressbar with unknown length is a spinner. Any bar with -1 length will automatically convert it to a spinner with a customizable spinner type. For example, the above code can be run and set the `resp.ContentLength` to `-1`.
which looks like:
![Example of download bar with unknown length](examples/download-unknown/download-unknown.gif)
### Customization
There is a lot of customization that you can do - change the writer, the color, the width, description, theme, etc. See [all the options](https://pkg.go.dev/github.com/schollz/progressbar/v3?tab=doc#Option).
```golang
bar := progressbar.NewOptions(1000,
progressbar.OptionSetWriter(ansi.NewAnsiStdout()),
progressbar.OptionEnableColorCodes(true),
progressbar.OptionShowBytes(true),
progressbar.OptionSetWidth(15),
progressbar.OptionSetDescription("[cyan][1/3][reset] Writing moshable file..."),
progressbar.OptionSetTheme(progressbar.Theme{
Saucer: "[green]=[reset]",
SaucerHead: "[green]>[reset]",
SaucerPadding: " ",
BarStart: "[",
BarEnd: "]",
}))
for i := 0; i < 1000; i++ {
bar.Add(1)
time.Sleep(5 * time.Millisecond)
}
```
which looks like:
![Example of customized bar](examples/customization/customization.gif)
## Contributing
Pull requests are welcome. Feel free to...
- Revise documentation
- Add new features
- Fix bugs
- Suggest improvements
## Thanks
Thanks [@Dynom](https://github.com/dynom) for massive improvements in version 2.0!
Thanks [@CrushedPixel](https://github.com/CrushedPixel) for adding descriptions and color code support!
Thanks [@MrMe42](https://github.com/MrMe42) for adding some minor features!
Thanks [@tehstun](https://github.com/tehstun) for some great PRs!
Thanks [@Benzammour](https://github.com/Benzammour) and [@haseth](https://github.com/haseth) for helping create v3!
Thanks [@briandowns](https://github.com/briandowns) for compiling the list of spinners.
## License
MIT

File diff suppressed because it is too large Load Diff

View File

@@ -1,80 +0,0 @@
package progressbar
var spinners = map[int][]string{
0: {"←", "↖", "↑", "↗", "→", "↘", "↓", "↙"},
1: {"▁", "▃", "▄", "▅", "▆", "▇", "█", "▇", "▆", "▅", "▄", "▃", "▁"},
2: {"▖", "▘", "▝", "▗"},
3: {"┤", "┘", "┴", "└", "├", "┌", "┬", "┐"},
4: {"◢", "◣", "◤", "◥"},
5: {"◰", "◳", "◲", "◱"},
6: {"◴", "◷", "◶", "◵"},
7: {"◐", "◓", "◑", "◒"},
8: {".", "o", "O", "@", "*"},
9: {"|", "/", "-", "\\"},
10: {"◡◡", "⊙⊙", "◠◠"},
11: {"⣾", "⣽", "⣻", "⢿", "⡿", "⣟", "⣯", "⣷"},
12: {">))'>", " >))'>", " >))'>", " >))'>", " >))'>", " <'((<", " <'((<", " <'((<"},
13: {"⠁", "⠂", "⠄", "⡀", "⢀", "⠠", "⠐", "⠈"},
14: {"⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"},
15: {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"},
16: {"▉", "▊", "▋", "▌", "▍", "▎", "▏", "▎", "▍", "▌", "▋", "▊", "▉"},
17: {"■", "□", "▪", "▫"},
18: {"←", "↑", "→", "↓"},
19: {"╫", "╪"},
20: {"⇐", "⇖", "⇑", "⇗", "⇒", "⇘", "⇓", "⇙"},
21: {"⠁", "⠁", "⠉", "⠙", "⠚", "⠒", "⠂", "⠂", "⠒", "⠲", "⠴", "⠤", "⠄", "⠄", "⠤", "⠠", "⠠", "⠤", "⠦", "⠖", "⠒", "⠐", "⠐", "⠒", "⠓", "⠋", "⠉", "⠈", "⠈"},
22: {"⠈", "⠉", "⠋", "⠓", "⠒", "⠐", "⠐", "⠒", "⠖", "⠦", "⠤", "⠠", "⠠", "⠤", "⠦", "⠖", "⠒", "⠐", "⠐", "⠒", "⠓", "⠋", "⠉", "⠈"},
23: {"⠁", "⠉", "⠙", "⠚", "⠒", "⠂", "⠂", "⠒", "⠲", "⠴", "⠤", "⠄", "⠄", "⠤", "⠴", "⠲", "⠒", "⠂", "⠂", "⠒", "⠚", "⠙", "⠉", "⠁"},
24: {"⠋", "⠙", "⠚", "⠒", "⠂", "⠂", "⠒", "⠲", "⠴", "⠦", "⠖", "⠒", "⠐", "⠐", "⠒", "⠓", "⠋"},
25: {"ヲ", "ァ", "ィ", "ゥ", "ェ", "ォ", "ャ", "ュ", "ョ", "ッ", "ア", "イ", "ウ", "エ", "オ", "カ", "キ", "ク", "ケ", "コ", "サ", "シ", "ス", "セ", "ソ", "タ", "チ", "ツ", "テ", "ト", "ナ", "ニ", "ヌ", "ネ", "ノ", "ハ", "ヒ", "フ", "ヘ", "ホ", "マ", "ミ", "ム", "メ", "モ", "ヤ", "ユ", "ヨ", "ラ", "リ", "ル", "レ", "ロ", "ワ", "ン"},
26: {".", "..", "..."},
27: {"▁", "▂", "▃", "▄", "▅", "▆", "▇", "█", "▉", "▊", "▋", "▌", "▍", "▎", "▏", "▏", "▎", "▍", "▌", "▋", "▊", "▉", "█", "▇", "▆", "▅", "▄", "▃", "▂", "▁"},
28: {".", "o", "O", "°", "O", "o", "."},
29: {"+", "x"},
30: {"v", "<", "^", ">"},
31: {">>--->", " >>--->", " >>--->", " >>--->", " >>--->", " <---<<", " <---<<", " <---<<", " <---<<", "<---<<"},
32: {"|", "||", "|||", "||||", "|||||", "|||||||", "||||||||", "|||||||", "||||||", "|||||", "||||", "|||", "||", "|"},
33: {"[ ]", "[= ]", "[== ]", "[=== ]", "[==== ]", "[===== ]", "[====== ]", "[======= ]", "[======== ]", "[========= ]", "[==========]"},
34: {"(*---------)", "(-*--------)", "(--*-------)", "(---*------)", "(----*-----)", "(-----*----)", "(------*---)", "(-------*--)", "(--------*-)", "(---------*)"},
35: {"█▒▒▒▒▒▒▒▒▒", "███▒▒▒▒▒▒▒", "█████▒▒▒▒▒", "███████▒▒▒", "██████████"},
36: {"[ ]", "[=> ]", "[===> ]", "[=====> ]", "[======> ]", "[========> ]", "[==========> ]", "[============> ]", "[==============> ]", "[================> ]", "[==================> ]", "[===================>]"},
37: {"", ""},
38: {"▌", "▀", "▐▄"},
39: {"🌍", "🌎", "🌏"},
40: {"◜", "◝", "◞", "◟"},
41: {"⬒", "⬔", "⬓", "⬕"},
42: {"⬖", "⬘", "⬗", "⬙"},
43: {"[>>> >]", "[]>>>> []", "[] >>>> []", "[] >>>> []", "[] >>>> []", "[] >>>>[]", "[>> >>]"},
44: {"♠", "♣", "♥", "♦"},
45: {"➞", "➟", "➠", "➡", "➠", "➟"},
46: {" | ", ` \ `, "_ ", ` \ `, " | ", " / ", " _", " / "},
47: {" . . . .", ". . . .", ". . . .", ". . . .", ". . . . ", ". . . . ."},
48: {" | ", " / ", " _ ", ` \ `, " | ", ` \ `, " _ ", " / "},
49: {"⎺", "⎻", "⎼", "⎽", "⎼", "⎻"},
50: {"▹▹▹▹▹", "▸▹▹▹▹", "▹▸▹▹▹", "▹▹▸▹▹", "▹▹▹▸▹", "▹▹▹▹▸"},
51: {"[ ]", "[ =]", "[ ==]", "[ ===]", "[====]", "[=== ]", "[== ]", "[= ]"},
52: {"( ● )", "( ● )", "( ● )", "( ● )", "( ●)", "( ● )", "( ● )", "( ● )", "( ● )"},
53: {"✶", "✸", "✹", "✺", "✹", "✷"},
54: {"▐|\\____________▌", "▐_|\\___________▌", "▐__|\\__________▌", "▐___|\\_________▌", "▐____|\\________▌", "▐_____|\\_______▌", "▐______|\\______▌", "▐_______|\\_____▌", "▐________|\\____▌", "▐_________|\\___▌", "▐__________|\\__▌", "▐___________|\\_▌", "▐____________|\\▌", "▐____________/|▌", "▐___________/|_▌", "▐__________/|__▌", "▐_________/|___▌", "▐________/|____▌", "▐_______/|_____▌", "▐______/|______▌", "▐_____/|_______▌", "▐____/|________▌", "▐___/|_________▌", "▐__/|__________▌", "▐_/|___________▌", "▐/|____________▌"},
55: {"▐⠂ ▌", "▐⠈ ▌", "▐ ⠂ ▌", "▐ ⠠ ▌", "▐ ⡀ ▌", "▐ ⠠ ▌", "▐ ⠂ ▌", "▐ ⠈ ▌", "▐ ⠂ ▌", "▐ ⠠ ▌", "▐ ⡀ ▌", "▐ ⠠ ▌", "▐ ⠂ ▌", "▐ ⠈ ▌", "▐ ⠂▌", "▐ ⠠▌", "▐ ⡀▌", "▐ ⠠ ▌", "▐ ⠂ ▌", "▐ ⠈ ▌", "▐ ⠂ ▌", "▐ ⠠ ▌", "▐ ⡀ ▌", "▐ ⠠ ▌", "▐ ⠂ ▌", "▐ ⠈ ▌", "▐ ⠂ ▌", "▐ ⠠ ▌", "▐ ⡀ ▌", "▐⠠ ▌"},
56: {"¿", "?"},
57: {"⢹", "⢺", "⢼", "⣸", "⣇", "⡧", "⡗", "⡏"},
58: {"⢄", "⢂", "⢁", "⡁", "⡈", "⡐", "⡠"},
59: {". ", ".. ", "...", " ..", " .", " "},
60: {".", "o", "O", "°", "O", "o", "."},
61: {"▓", "▒", "░"},
62: {"▌", "▀", "▐", "▄"},
63: {"⊶", "⊷"},
64: {"▪", "▫"},
65: {"□", "■"},
66: {"▮", "▯"},
67: {"-", "=", "≡"},
68: {"d", "q", "p", "b"},
69: {"∙∙∙", "●∙∙", "∙●∙", "∙∙●", "∙∙∙"},
70: {"🌑 ", "🌒 ", "🌓 ", "🌔 ", "🌕 ", "🌖 ", "🌗 ", "🌘 "},
71: {"☗", "☖"},
72: {"⧇", "⧆"},
73: {"◉", "◎"},
74: {"㊂", "㊀", "㊁"},
75: {"⦾", "⦿"},
}

View File

@@ -1,23 +0,0 @@
#!/bin/bash
mkdir -p dist
# build universal binary
CGO_ENABLED=1 GOARCH=arm64 go build -o dist/ollama-darwin-arm64
CGO_ENABLED=1 GOARCH=amd64 go build -o dist/ollama-darwin-amd64
lipo -create -output dist/ollama dist/ollama-darwin-arm64 dist/ollama-darwin-amd64
rm dist/ollama-darwin-amd64 dist/ollama-darwin-arm64
codesign --deep --force --options=runtime --sign "$APPLE_IDENTITY" --timestamp dist/ollama
chmod +x dist/ollama
# build and sign the mac app
npm install --prefix app
npm run --prefix app make:sign
cp app/out/make/zip/darwin/universal/Ollama-darwin-universal-${VERSION:-0.0.0}.zip dist/Ollama-darwin.zip
# sign the binary and rename it
codesign -f --timestamp -s "$APPLE_IDENTITY" --identifier ai.ollama.ollama --options=runtime dist/ollama
ditto -c -k --keepParent dist/ollama dist/temp.zip
xcrun notarytool submit dist/temp.zip --wait --timeout 10m --apple-id $APPLE_ID --password $APPLE_PASSWORD --team-id $APPLE_TEAM_ID
mv dist/ollama dist/ollama-darwin
rm dist/temp.zip

View File

@@ -8,18 +8,28 @@ if [[ -z "${VERSION}" ]]; then
fi
OS=$(go env GOOS)
ARCH=$(go env GOARCH)
./script/build_${OS}.sh
go build .
npm --prefix app run make:sign
# Create a new tag if it doesn't exist.
if ! git rev-parse v$VERSION >/dev/null 2>&1; then
git tag v$VERSION
git push origin v$VERSION
fi
git push origin v$VERSION
mkdir -p dist
cp app/out/make/zip/${OS}/${ARCH}/Ollama-${OS}-${ARCH}-${VERSION}.zip dist/Ollama-${OS}-${ARCH}.zip
cp ./ollama dist/ollama-${OS}-${ARCH}
# Create a new release.
gh release create -p v$VERSION -t v$VERSION
gh release create v$VERSION
# Upload the zip file.
gh release upload v$VERSION ./dist/* --clobber
gh release upload v$VERSION ./dist/Ollama-${OS}-${ARCH}.zip
# Upload the binary.
gh release upload v$VERSION ./dist/ollama-${OS}-${ARCH}

View File

@@ -1,164 +0,0 @@
package server
import (
"bytes"
"crypto/rand"
"crypto/sha256"
"encoding/base64"
"encoding/hex"
"encoding/json"
"fmt"
"io"
"io/ioutil"
"log"
"net/http"
"os"
"path"
"strings"
"time"
"golang.org/x/crypto/ssh"
"github.com/jmorganca/ollama/api"
)
type AuthRedirect struct {
Realm string
Service string
Scope string
}
type SignatureData struct {
Method string
Path string
Data []byte
}
func generateNonce(length int) (string, error) {
nonce := make([]byte, length)
_, err := rand.Read(nonce)
if err != nil {
return "", err
}
return base64.RawURLEncoding.EncodeToString(nonce), nil
}
func (r AuthRedirect) URL() (string, error) {
nonce, err := generateNonce(16)
if err != nil {
return "", err
}
return fmt.Sprintf("%s?service=%s&scope=%s&ts=%d&nonce=%s", r.Realm, r.Service, r.Scope, time.Now().Unix(), nonce), nil
}
func getAuthToken(redirData AuthRedirect, regOpts *RegistryOptions) (string, error) {
url, err := redirData.URL()
if err != nil {
return "", err
}
home, err := os.UserHomeDir()
if err != nil {
return "", err
}
keyPath := path.Join(home, ".ollama/id_ed25519")
rawKey, err := ioutil.ReadFile(keyPath)
if err != nil {
log.Printf("Failed to load private key: %v", err)
return "", err
}
s := SignatureData{
Method: "GET",
Path: url,
Data: nil,
}
if !strings.HasPrefix(s.Path, "http") {
if regOpts.Insecure {
s.Path = "http://" + url
} else {
s.Path = "https://" + url
}
}
sig, err := s.Sign(rawKey)
if err != nil {
return "", err
}
headers := map[string]string{
"Authorization": sig,
}
resp, err := makeRequest("GET", url, headers, nil, regOpts)
if err != nil {
log.Printf("couldn't get token: %q", err)
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK {
body, _ := io.ReadAll(resp.Body)
return "", fmt.Errorf("on pull registry responded with code %d: %s", resp.StatusCode, body)
}
respBody, err := io.ReadAll(resp.Body)
if err != nil {
return "", err
}
var tok api.TokenResponse
if err := json.Unmarshal(respBody, &tok); err != nil {
return "", err
}
return tok.Token, nil
}
// Bytes returns a byte slice of the data to sign for the request
func (s SignatureData) Bytes() []byte {
// We first derive the content hash of the request body using:
// base64(hex(sha256(request body)))
hash := sha256.Sum256(s.Data)
hashHex := make([]byte, hex.EncodedLen(len(hash)))
hex.Encode(hashHex, hash[:])
contentHash := base64.StdEncoding.EncodeToString(hashHex)
// We then put the entire request together in a serialize string using:
// "<method>,<uri>,<content hash>"
// e.g. "GET,http://localhost,OTdkZjM1O..."
return []byte(strings.Join([]string{s.Method, s.Path, contentHash}, ","))
}
// SignData takes a SignatureData object and signs it with a raw private key
func (s SignatureData) Sign(rawKey []byte) (string, error) {
privateKey, err := ssh.ParseRawPrivateKey(rawKey)
if err != nil {
return "", err
}
signer, err := ssh.NewSignerFromKey(privateKey)
if err != nil {
return "", err
}
// get the pubkey, but remove the type
pubKey := ssh.MarshalAuthorizedKey(signer.PublicKey())
parts := bytes.Split(pubKey, []byte(" "))
if len(parts) < 2 {
return "", fmt.Errorf("malformed public key")
}
signedData, err := signer.Sign(nil, s.Bytes())
if err != nil {
return "", err
}
// signature is <pubkey>:<signature>
sig := fmt.Sprintf("%s:%s", bytes.TrimSpace(parts[1]), base64.StdEncoding.EncodeToString(signedData.Blob))
return sig, nil
}

View File

@@ -1,215 +0,0 @@
package server
import (
"context"
"errors"
"fmt"
"io"
"log"
"net/http"
"os"
"path"
"strconv"
"sync"
"time"
"github.com/jmorganca/ollama/api"
)
type FileDownload struct {
Digest string
FilePath string
Total int64
Completed int64
}
var inProgress sync.Map // map of digests currently being downloaded to their current download progress
// downloadBlob downloads a blob from the registry and stores it in the blobs directory
func downloadBlob(ctx context.Context, mp ModelPath, digest string, regOpts *RegistryOptions, fn func(api.ProgressResponse)) error {
fp, err := GetBlobsPath(digest)
if err != nil {
return err
}
if fi, _ := os.Stat(fp); fi != nil {
// we already have the file, so return
fn(api.ProgressResponse{
Digest: digest,
Total: int(fi.Size()),
Completed: int(fi.Size()),
})
return nil
}
fileDownload := &FileDownload{
Digest: digest,
FilePath: fp,
Total: 1, // dummy value to indicate that we don't know the total size yet
Completed: 0,
}
_, downloading := inProgress.LoadOrStore(digest, fileDownload)
if downloading {
// this is another client requesting the server to download the same blob concurrently
return monitorDownload(ctx, mp, regOpts, fileDownload, fn)
}
return doDownload(ctx, mp, regOpts, fileDownload, fn)
}
var downloadMu sync.Mutex // mutex to check to resume a download while monitoring
// monitorDownload monitors the download progress of a blob and resumes it if it is interrupted
func monitorDownload(ctx context.Context, mp ModelPath, regOpts *RegistryOptions, f *FileDownload, fn func(api.ProgressResponse)) error {
tick := time.NewTicker(time.Second)
for range tick.C {
done, resume, err := func() (bool, bool, error) {
downloadMu.Lock()
defer downloadMu.Unlock()
val, downloading := inProgress.Load(f.Digest)
if !downloading {
// check once again if the download is complete
if fi, _ := os.Stat(f.FilePath); fi != nil {
// successful download while monitoring
fn(api.ProgressResponse{
Digest: f.Digest,
Total: int(fi.Size()),
Completed: int(fi.Size()),
})
return true, false, nil
}
// resume the download
inProgress.Store(f.Digest, f) // store the file download again to claim the resume
return false, true, nil
}
f, ok := val.(*FileDownload)
if !ok {
return false, false, fmt.Errorf("invalid type for in progress download: %T", val)
}
fn(api.ProgressResponse{
Status: fmt.Sprintf("downloading %s", f.Digest),
Digest: f.Digest,
Total: int(f.Total),
Completed: int(f.Completed),
})
return false, false, nil
}()
if err != nil {
return err
}
if done {
// done downloading
return nil
}
if resume {
return doDownload(ctx, mp, regOpts, f, fn)
}
}
return nil
}
var chunkSize = 1024 * 1024 // 1 MiB in bytes
// doDownload downloads a blob from the registry and stores it in the blobs directory
func doDownload(ctx context.Context, mp ModelPath, regOpts *RegistryOptions, f *FileDownload, fn func(api.ProgressResponse)) error {
var size int64
fi, err := os.Stat(f.FilePath + "-partial")
switch {
case errors.Is(err, os.ErrNotExist):
// noop, file doesn't exist so create it
case err != nil:
return fmt.Errorf("stat: %w", err)
default:
size = fi.Size()
// Ensure the size is divisible by the chunk size by removing excess bytes
size -= size % int64(chunkSize)
err := os.Truncate(f.FilePath+"-partial", size)
if err != nil {
return fmt.Errorf("truncate: %w", err)
}
}
url := fmt.Sprintf("%s/v2/%s/blobs/%s", mp.Registry, mp.GetNamespaceRepository(), f.Digest)
headers := map[string]string{
"Range": fmt.Sprintf("bytes=%d-", size),
}
resp, err := makeRequest("GET", url, headers, nil, regOpts)
if err != nil {
log.Printf("couldn't download blob: %v", err)
return err
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK && resp.StatusCode != http.StatusPartialContent {
body, _ := io.ReadAll(resp.Body)
return fmt.Errorf("on download registry responded with code %d: %v", resp.StatusCode, string(body))
}
err = os.MkdirAll(path.Dir(f.FilePath), 0o700)
if err != nil {
return fmt.Errorf("make blobs directory: %w", err)
}
remaining, _ := strconv.ParseInt(resp.Header.Get("Content-Length"), 10, 64)
f.Completed = size
f.Total = remaining + f.Completed
inProgress.Store(f.Digest, f)
out, err := os.OpenFile(f.FilePath+"-partial", os.O_CREATE|os.O_APPEND|os.O_WRONLY, 0o644)
if err != nil {
return fmt.Errorf("open file: %w", err)
}
defer out.Close()
outerLoop:
for {
select {
case <-ctx.Done():
// handle client request cancellation
inProgress.Delete(f.Digest)
return nil
default:
fn(api.ProgressResponse{
Status: fmt.Sprintf("downloading %s", f.Digest),
Digest: f.Digest,
Total: int(f.Total),
Completed: int(f.Completed),
})
if f.Completed >= f.Total {
if err := out.Close(); err != nil {
return err
}
if err := os.Rename(f.FilePath+"-partial", f.FilePath); err != nil {
fn(api.ProgressResponse{
Status: fmt.Sprintf("error renaming file: %v", err),
Digest: f.Digest,
Total: int(f.Total),
Completed: int(f.Completed),
})
return err
}
break outerLoop
}
}
n, err := io.CopyN(out, resp.Body, int64(chunkSize))
if err != nil && !errors.Is(err, io.EOF) {
return err
}
f.Completed += n
inProgress.Store(f.Digest, f)
}
inProgress.Delete(f.Digest)
log.Printf("success getting %s\n", f.Digest)
return nil
}

File diff suppressed because it is too large Load Diff

View File

@@ -4,7 +4,6 @@ import (
"fmt"
"os"
"path/filepath"
"runtime"
"strings"
)
@@ -45,7 +44,7 @@ func ParseModelPath(name string) ModelPath {
return ModelPath{}
}
colonParts := strings.Split(slashParts[len(slashParts)-1], ":")
colonParts := strings.Split(name, ":")
if len(colonParts) == 2 {
tag = colonParts[1]
} else {
@@ -70,13 +69,10 @@ func (mp ModelPath) GetFullTagname() string {
}
func (mp ModelPath) GetShortTagname() string {
if mp.Registry == DefaultRegistry {
if mp.Namespace == DefaultNamespace {
return fmt.Sprintf("%s:%s", mp.Repository, mp.Tag)
}
return fmt.Sprintf("%s/%s:%s", mp.Namespace, mp.Repository, mp.Tag)
if mp.Registry == DefaultRegistry && mp.Namespace == DefaultNamespace {
return fmt.Sprintf("%s:%s", mp.Repository, mp.Tag)
}
return fmt.Sprintf("%s/%s/%s:%s", mp.Registry, mp.Namespace, mp.Repository, mp.Tag)
return fmt.Sprintf("%s/%s:%s", mp.Namespace, mp.Repository, mp.Tag)
}
func (mp ModelPath) GetManifestPath(createDir bool) (string, error) {
@@ -110,10 +106,6 @@ func GetBlobsPath(digest string) (string, error) {
return "", err
}
if runtime.GOOS == "windows" {
digest = strings.ReplaceAll(digest, ":", "-")
}
path := filepath.Join(home, ".ollama", "models", "blobs", digest)
if err := os.MkdirAll(filepath.Dir(path), 0o755); err != nil {
return "", err

View File

@@ -1,123 +1,35 @@
package server
import (
"context"
"encoding/json"
"errors"
"fmt"
"io"
"log"
"net"
"net/http"
"os"
"path/filepath"
"reflect"
"strings"
"sync"
"text/template"
"time"
"github.com/gin-contrib/cors"
"dario.cat/mergo"
"github.com/gin-gonic/gin"
"gonum.org/v1/gonum/mat"
"github.com/jmorganca/ollama/api"
"github.com/jmorganca/ollama/llama"
"github.com/jmorganca/ollama/vector"
)
var loaded struct {
mu sync.Mutex
func cacheDir() string {
home, err := os.UserHomeDir()
if err != nil {
panic(err)
}
llm *llama.LLM
Embeddings []vector.Embedding
expireAt time.Time
expireTimer *time.Timer
digest string
options api.Options
return filepath.Join(home, ".ollama")
}
// load a model into memory if it is not already loaded, it is up to the caller to lock loaded.mu before calling this function
func load(model *Model, reqOpts map[string]interface{}, sessionDuration time.Duration) error {
opts := api.DefaultOptions()
if err := opts.FromMap(model.Options); err != nil {
log.Printf("could not load model options: %v", err)
return err
}
if err := opts.FromMap(reqOpts); err != nil {
log.Printf("could not merge model options: %v", err)
return err
}
if model.Digest != loaded.digest || !reflect.DeepEqual(loaded.options, opts) {
if loaded.llm != nil {
loaded.llm.Close()
loaded.llm = nil
loaded.digest = ""
}
if model.Embeddings != nil && len(model.Embeddings) > 0 {
opts.EmbeddingOnly = true // this is requried to generate embeddings, completions will still work
loaded.Embeddings = model.Embeddings
}
llm, err := llama.New(model.ModelPath, opts)
if err != nil {
return err
}
if opts.NumKeep < 0 {
promptWithSystem, err := model.Prompt(api.GenerateRequest{}, "")
if err != nil {
return err
}
promptNoSystem, err := model.Prompt(api.GenerateRequest{Context: []int{0}}, "")
if err != nil {
return err
}
tokensWithSystem := llm.Encode(promptWithSystem)
tokensNoSystem := llm.Encode(promptNoSystem)
llm.NumKeep = len(tokensWithSystem) - len(tokensNoSystem) + 1
}
loaded.llm = llm
loaded.digest = model.Digest
loaded.options = opts
}
loaded.expireAt = time.Now().Add(sessionDuration)
if loaded.expireTimer == nil {
loaded.expireTimer = time.AfterFunc(sessionDuration, func() {
loaded.mu.Lock()
defer loaded.mu.Unlock()
if time.Now().Before(loaded.expireAt) {
return
}
if loaded.llm == nil {
return
}
loaded.llm.Close()
loaded.llm = nil
loaded.digest = ""
})
}
loaded.expireTimer.Reset(sessionDuration)
return nil
}
func GenerateHandler(c *gin.Context) {
loaded.mu.Lock()
defer loaded.mu.Unlock()
checkpointStart := time.Now()
func generate(c *gin.Context) {
start := time.Now()
var req api.GenerateRequest
if err := c.ShouldBindJSON(&req); err != nil {
@@ -131,99 +43,55 @@ func GenerateHandler(c *gin.Context) {
return
}
sessionDuration := 5 * time.Minute
if err := load(model, req.Options, sessionDuration); err != nil {
opts := api.DefaultOptions()
if err := mergo.Merge(&opts, model.Options, mergo.WithOverride); err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
checkpointLoaded := time.Now()
embedding := ""
if model.Embeddings != nil && len(model.Embeddings) > 0 {
promptEmbed, err := loaded.llm.Embedding(req.Prompt)
if err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
// TODO: set embed_top from specified parameters in modelfile
embed_top := 3
topK := vector.TopK(embed_top, mat.NewVecDense(len(promptEmbed), promptEmbed), loaded.Embeddings)
for _, e := range topK {
embedding = fmt.Sprintf("%s %s", embedding, e.Embedding.Data)
}
if err := mergo.Merge(&opts, req.Options, mergo.WithOverride); err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
prompt, err := model.Prompt(req, embedding)
templ, err := template.New("").Parse(model.Prompt)
if err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
var sb strings.Builder
if err = templ.Execute(&sb, req); err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
req.Prompt = sb.String()
llm, err := llama.New(model.ModelPath, opts)
if err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
defer llm.Close()
ch := make(chan any)
go func() {
defer close(ch)
fn := func(r api.GenerateResponse) {
loaded.expireAt = time.Now().Add(sessionDuration)
loaded.expireTimer.Reset(sessionDuration)
llm.Predict(req.Context, req.Prompt, func(r api.GenerateResponse) {
r.Model = req.Model
r.CreatedAt = time.Now().UTC()
if r.Done {
r.TotalDuration = time.Since(checkpointStart)
r.LoadDuration = checkpointLoaded.Sub(checkpointStart)
r.TotalDuration = time.Since(start)
}
ch <- r
}
if err := loaded.llm.Predict(req.Context, prompt, fn); err != nil {
ch <- gin.H{"error": err.Error()}
}
})
}()
streamResponse(c, ch)
}
func EmbeddingHandler(c *gin.Context) {
loaded.mu.Lock()
defer loaded.mu.Unlock()
var req api.EmbeddingRequest
if err := c.ShouldBindJSON(&req); err != nil {
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
return
}
model, err := GetModel(req.Model)
if err != nil {
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
return
}
if err := load(model, req.Options, 5*time.Minute); err != nil {
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
return
}
if !loaded.options.EmbeddingOnly {
c.JSON(http.StatusBadRequest, gin.H{"error": "embedding option must be set to true"})
return
}
embedding, err := loaded.llm.Embedding(req.Prompt)
if err != nil {
log.Printf("embedding generation failed: %v", err)
c.JSON(http.StatusInternalServerError, gin.H{"error": "failed to generate embedding"})
return
}
resp := api.EmbeddingResponse{
Embedding: embedding,
}
c.JSON(http.StatusOK, resp)
}
func PullModelHandler(c *gin.Context) {
func pull(c *gin.Context) {
var req api.PullRequest
if err := c.ShouldBindJSON(&req); err != nil {
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
@@ -233,28 +101,25 @@ func PullModelHandler(c *gin.Context) {
ch := make(chan any)
go func() {
defer close(ch)
fn := func(r api.ProgressResponse) {
ch <- r
fn := func(status, digest string, total, completed int, percent float64) {
ch <- api.PullProgress{
Status: status,
Digest: digest,
Total: total,
Completed: completed,
Percent: percent,
}
}
regOpts := &RegistryOptions{
Insecure: req.Insecure,
Username: req.Username,
Password: req.Password,
}
ctx, cancel := context.WithCancel(c.Request.Context())
defer cancel()
if err := PullModel(ctx, req.Name, regOpts, fn); err != nil {
ch <- gin.H{"error": err.Error()}
if err := PullModel(req.Name, req.Username, req.Password, fn); err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
}()
streamResponse(c, ch)
}
func PushModelHandler(c *gin.Context) {
func push(c *gin.Context) {
var req api.PushRequest
if err := c.ShouldBindJSON(&req); err != nil {
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
@@ -264,67 +129,59 @@ func PushModelHandler(c *gin.Context) {
ch := make(chan any)
go func() {
defer close(ch)
fn := func(r api.ProgressResponse) {
ch <- r
fn := func(status, digest string, total, completed int, percent float64) {
ch <- api.PushProgress{
Status: status,
Digest: digest,
Total: total,
Completed: completed,
Percent: percent,
}
}
regOpts := &RegistryOptions{
Insecure: req.Insecure,
Username: req.Username,
Password: req.Password,
}
if err := PushModel(req.Name, regOpts, fn); err != nil {
ch <- gin.H{"error": err.Error()}
if err := PushModel(req.Name, req.Username, req.Password, fn); err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
}()
streamResponse(c, ch)
}
func CreateModelHandler(c *gin.Context) {
func create(c *gin.Context) {
var req api.CreateRequest
if err := c.ShouldBindJSON(&req); err != nil {
c.JSON(http.StatusBadRequest, gin.H{"message": err.Error()})
return
}
// NOTE consider passing the entire Modelfile in the json instead of the path to it
file, err := os.Open(req.Path)
if err != nil {
c.JSON(http.StatusBadRequest, gin.H{"message": err.Error()})
return
}
defer file.Close()
ch := make(chan any)
go func() {
defer close(ch)
fn := func(resp api.ProgressResponse) {
ch <- resp
fn := func(status string) {
ch <- api.CreateProgress{
Status: status,
}
}
ctx, cancel := context.WithCancel(c.Request.Context())
defer cancel()
if err := CreateModel(ctx, req.Name, req.Path, fn); err != nil {
ch <- gin.H{"error": err.Error()}
if err := CreateModel(req.Name, file, fn); err != nil {
c.JSON(http.StatusBadRequest, gin.H{"message": err.Error()})
return
}
}()
streamResponse(c, ch)
}
func DeleteModelHandler(c *gin.Context) {
var req api.DeleteRequest
if err := c.ShouldBindJSON(&req); err != nil {
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
return
}
if err := DeleteModel(req.Name); err != nil {
if os.IsNotExist(err) {
c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model '%s' not found", req.Name)})
} else {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
}
return
}
}
func ListModelsHandler(c *gin.Context) {
func list(c *gin.Context) {
var models []api.ListResponseModel
fp, err := GetManifestPath()
if err != nil {
@@ -333,17 +190,12 @@ func ListModelsHandler(c *gin.Context) {
}
err = filepath.Walk(fp, func(path string, info os.FileInfo, err error) error {
if err != nil {
if errors.Is(err, os.ErrNotExist) {
log.Printf("manifest file does not exist: %s", fp)
return nil
}
return err
}
if !info.IsDir() {
fi, err := os.Stat(path)
if err != nil {
log.Printf("skipping file: %s", fp)
return nil
return err
}
path := path[len(fp)+1:]
slashIndex := strings.LastIndex(path, "/")
@@ -371,62 +223,21 @@ func ListModelsHandler(c *gin.Context) {
return
}
c.JSON(http.StatusOK, api.ListResponse{Models: models})
c.JSON(http.StatusOK, api.ListResponse{models})
}
func CopyModelHandler(c *gin.Context) {
var req api.CopyRequest
if err := c.ShouldBindJSON(&req); err != nil {
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
return
}
if err := CopyModel(req.Source, req.Destination); err != nil {
if os.IsNotExist(err) {
c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model '%s' not found", req.Source)})
} else {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
}
return
}
}
func Serve(ln net.Listener, origins []string) error {
config := cors.DefaultConfig()
config.AllowWildcard = true
config.AllowOrigins = append(origins, []string{
"http://localhost",
"http://localhost:*",
"https://localhost",
"https://localhost:*",
"http://127.0.0.1",
"http://127.0.0.1:*",
"https://127.0.0.1",
"https://127.0.0.1:*",
"http://0.0.0.0",
"http://0.0.0.0:*",
"https://0.0.0.0",
"https://0.0.0.0:*",
}...)
func Serve(ln net.Listener) error {
r := gin.Default()
r.Use(cors.New(config))
r.GET("/", func(c *gin.Context) {
c.String(http.StatusOK, "Ollama is running")
})
r.HEAD("/", func(c *gin.Context) {
c.Status(http.StatusOK)
})
r.POST("/api/pull", PullModelHandler)
r.POST("/api/generate", GenerateHandler)
r.POST("/api/embeddings", EmbeddingHandler)
r.POST("/api/create", CreateModelHandler)
r.POST("/api/push", PushModelHandler)
r.POST("/api/copy", CopyModelHandler)
r.GET("/api/tags", ListModelsHandler)
r.DELETE("/api/delete", DeleteModelHandler)
r.POST("/api/pull", pull)
r.POST("/api/generate", generate)
r.POST("/api/create", create)
r.POST("/api/push", push)
r.GET("/api/tags", list)
log.Printf("Listening on %s", ln.Addr())
s := &http.Server{
@@ -437,7 +248,6 @@ func Serve(ln net.Listener, origins []string) error {
}
func streamResponse(c *gin.Context, ch chan any) {
c.Header("Content-Type", "application/x-ndjson")
c.Stream(func(w io.Writer) bool {
val, ok := <-ch
if !ok {
@@ -446,13 +256,11 @@ func streamResponse(c *gin.Context, ch chan any) {
bts, err := json.Marshal(val)
if err != nil {
log.Printf("streamResponse: json.Marshal failed with %s", err)
return false
}
bts = append(bts, '\n')
if _, err := w.Write(bts); err != nil {
log.Printf("streamResponse: w.Write failed with %s", err)
return false
}

View File

@@ -0,0 +1,10 @@
{{- if not .Context }}
Below is an instruction that describes a task. Write a response that appropriately completes the request.
{{- end }}
### Instruction:
{{ .Prompt }}
### Response:

View File

@@ -0,0 +1,5 @@
{{- if not .Context }}
A helpful assistant who helps the user with any questions asked.
{{- end }}
User: {{ .Prompt }}
Assistant:

View File

@@ -0,0 +1,5 @@
### Instruction:
{{ .Prompt }}
### Response:

View File

@@ -0,0 +1,5 @@
### Instruction:
{{ .Prompt }}
### Response:

View File

@@ -0,0 +1,6 @@
{{- if not .Context }}
Below is an instruction that describes a task. Write a response that appropriately completes the request. Be concise. Once the request is completed, include no other text.
{{- end }}
### Instruction:
{{ .Prompt }}
### Response:

View File

@@ -0,0 +1 @@
{{ .Prompt }}

View File

@@ -0,0 +1,9 @@
{{- if not .Context }}
### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
{{- end }}
### User:
{{ .Prompt }}
### Response:

View File

@@ -0,0 +1,2 @@
### Human: {{ .Prompt }}
### Assistant:

View File

@@ -0,0 +1,4 @@
{{ .Prompt }}

View File

@@ -0,0 +1,2 @@
USER: {{ .Prompt }}
ASSISTANT:

View File

@@ -0,0 +1,6 @@
{{ if not .Context }}
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
{{- end }}
USER: {{ .Prompt }}
ASSISTANT:

View File

@@ -0,0 +1,7 @@
{{- if not .Context }}
Below is an instruction that describes a task. Write a response that appropriately completes the request
{{- end }}
### Instruction: {{ .Prompt }}
### Response:

View File

@@ -0,0 +1,3 @@
{{ .Prompt }}
### Response:

View File

@@ -1,69 +0,0 @@
package vector
import (
"container/heap"
"sort"
"gonum.org/v1/gonum/mat"
)
type Embedding struct {
Vector []float64 // the embedding vector
Data string // the data represted by the embedding
}
type EmbeddingSimilarity struct {
Embedding Embedding // the embedding that was used to calculate the similarity
Similarity float64 // the similarity between the embedding and the query
}
type Heap []EmbeddingSimilarity
func (h Heap) Len() int { return len(h) }
func (h Heap) Less(i, j int) bool { return h[i].Similarity < h[j].Similarity }
func (h Heap) Swap(i, j int) { h[i], h[j] = h[j], h[i] }
func (h *Heap) Push(e any) {
*h = append(*h, e.(EmbeddingSimilarity))
}
func (h *Heap) Pop() interface{} {
old := *h
n := len(old)
x := old[n-1]
*h = old[0 : n-1]
return x
}
// cosineSimilarity is a measure that calculates the cosine of the angle between two vectors.
// This value will range from -1 to 1, where 1 means the vectors are identical.
func cosineSimilarity(vec1, vec2 *mat.VecDense) float64 {
dotProduct := mat.Dot(vec1, vec2)
norms := mat.Norm(vec1, 2) * mat.Norm(vec2, 2)
if norms == 0 {
return 0
}
return dotProduct / norms
}
func TopK(k int, query *mat.VecDense, embeddings []Embedding) []EmbeddingSimilarity {
h := &Heap{}
heap.Init(h)
for _, emb := range embeddings {
similarity := cosineSimilarity(query, mat.NewVecDense(len(emb.Vector), emb.Vector))
heap.Push(h, EmbeddingSimilarity{Embedding: emb, Similarity: similarity})
if h.Len() > k {
heap.Pop(h)
}
}
topK := make([]EmbeddingSimilarity, 0, h.Len())
for h.Len() > 0 {
topK = append(topK, heap.Pop(h).(EmbeddingSimilarity))
}
sort.Slice(topK, func(i, j int) bool {
return topK[i].Similarity > topK[j].Similarity
})
return topK
}

3
web/.eslintrc.json Normal file
View File

@@ -0,0 +1,3 @@
{
"extends": "next/core-web-vitals"
}

35
web/.gitignore vendored Normal file
View File

@@ -0,0 +1,35 @@
# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
# dependencies
/node_modules
/.pnp
.pnp.js
# testing
/coverage
# next.js
/.next/
/out/
# production
/build
# misc
.DS_Store
*.pem
# debug
npm-debug.log*
yarn-debug.log*
yarn-error.log*
# local env files
.env*.local
# vercel
.vercel
# typescript
*.tsbuildinfo
next-env.d.ts

9
web/README.md Normal file
View File

@@ -0,0 +1,9 @@
# Ollama.ai
This website renders helpful information, blog posts, docs and more for the Ollama project.
## Develop
```bash
npm run dev
```

View File

@@ -0,0 +1,6 @@
import models from '../../../../models.json'
import { NextResponse } from 'next/server'
export async function GET() {
return NextResponse.json(models)
}

View File

@@ -0,0 +1,17 @@
import { Analytics } from '@segment/analytics-node'
import { v4 as uuid } from 'uuid'
const analytics = new Analytics({ writeKey: process.env.TELEMETRY_WRITE_KEY || '<empty>' })
export async function POST(req: Request) {
const { email } = await req.json()
analytics.identify({
anonymousId: uuid(),
traits: {
email,
},
})
return new Response(null, { status: 200 })
}

View File

@@ -0,0 +1,42 @@
import { NextResponse } from 'next/server'
import semver from 'semver'
export async function GET(req: Request) {
const { searchParams } = new URL(req.url)
const os = searchParams.get('os') || 'darwin'
const version = searchParams.get('version') || '0.0.0'
if (!version) {
return new Response('not found', { status: 404 })
}
const res = await fetch('https://api.github.com/repos/jmorganca/ollama/releases', { next: { revalidate: 60 } })
const data = await res.json()
if (data.length === 0) {
return new Response('not found', { status: 404 })
}
const latest = data[0]
const assets = latest.assets || []
if (assets.length === 0) {
return new Response('not found', { status: 404 })
}
// todo: get the correct asset for the current arch/os
const asset = assets.find((a: any) => a.name.toLowerCase().includes(os) && a.name.toLowerCase().includes('.zip'))
if (!asset) {
return new Response('not found', { status: 404 })
}
console.log(asset)
if (semver.lt(version, latest.tag_name)) {
return NextResponse.json({ version: data.tag_name, url: asset.browser_download_url })
}
return new Response(null, { status: 204 })
}

View File

@@ -0,0 +1,11 @@
'use client'
import { useEffect } from 'react'
export default function Downloader({ url }: { url: string }) {
useEffect(() => {
window.location.href = url
}, [])
return null
}

47
web/app/download/page.tsx Normal file
View File

@@ -0,0 +1,47 @@
import Downloader from './downloader'
import Signup from './signup'
export default async function Download() {
const res = await fetch('https://api.github.com/repos/jmorganca/ollama/releases', { next: { revalidate: 60 } })
const data = await res.json()
if (data.length === 0) {
return null
}
const latest = data[0]
const assets = latest.assets || []
if (assets.length === 0) {
return null
}
// todo: get the correct asset for the current arch/os
const asset = assets.find(
(a: any) => a.name.toLowerCase().includes('darwin') && a.name.toLowerCase().includes('.zip')
)
if (!asset) {
return null
}
return (
<main className='flex min-h-screen max-w-2xl flex-col p-4 lg:p-24 items-center mx-auto'>
<img src='/ollama.png' className='w-16 h-auto' />
<section className='my-12 text-center'>
<h2 className='my-2 max-w-md text-3xl tracking-tight'>Downloading Ollama</h2>
<h3 className='text-sm text-neutral-500'>
Problems downloading?{' '}
<a href={asset.browser_download_url} className='underline'>
Try again
</a>
</h3>
<Downloader url={asset.browser_download_url} />
</section>
<section className='max-w-sm flex flex-col w-full items-center border border-neutral-200 rounded-xl px-8 pt-8 pb-2'>
<p className='text-lg leading-tight text-center mb-6 max-w-[260px]'>Sign up for updates</p>
<Signup />
</section>
</main>
)
}

View File

@@ -0,0 +1,51 @@
'use client'
import { useState } from 'react'
export default function Signup() {
const [email, setEmail] = useState('')
const [submitting, setSubmitting] = useState(false)
const [success, setSuccess] = useState(false)
return (
<form
onSubmit={async e => {
e.preventDefault()
setSubmitting(true)
await fetch('/api/signup', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ email }),
})
setSubmitting(false)
setSuccess(true)
setEmail('')
return false
}}
className='flex self-stretch flex-col gap-3 h-32'
>
<input
required
autoFocus
value={email}
onChange={e => setEmail(e.target.value)}
type='email'
placeholder='your@email.com'
className='bg-neutral-100 rounded-lg px-4 py-2 focus:outline-none placeholder-neutral-500'
/>
<input
type='submit'
value='Get updates'
disabled={submitting}
className='bg-black text-white disabled:text-neutral-200 disabled:bg-neutral-700 rounded-lg px-4 py-2 focus:outline-none cursor-pointer'
/>
{success && <p className='text-center text-sm'>You&apos;re signed up for updates</p>}
</form>
)
}

3
web/app/globals.css Normal file
View File

@@ -0,0 +1,3 @@
@tailwind base;
@tailwind components;
@tailwind utilities;

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