LocalAI/api/prediction.go

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package api
import (
"fmt"
"os"
"path/filepath"
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"regexp"
"strings"
"sync"
"github.com/donomii/go-rwkv.cpp"
"github.com/go-skynet/LocalAI/pkg/langchain"
model "github.com/go-skynet/LocalAI/pkg/model"
"github.com/go-skynet/LocalAI/pkg/stablediffusion"
"github.com/go-skynet/bloomz.cpp"
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bert "github.com/go-skynet/go-bert.cpp"
transformers "github.com/go-skynet/go-ggml-transformers.cpp"
llama "github.com/go-skynet/go-llama.cpp"
gpt4all "github.com/nomic-ai/gpt4all/gpt4all-bindings/golang"
)
// mutex still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
var mutexMap sync.Mutex
var mutexes map[string]*sync.Mutex = make(map[string]*sync.Mutex)
func defaultLLamaOpts(c Config) []llama.ModelOption {
llamaOpts := []llama.ModelOption{}
if c.ContextSize != 0 {
llamaOpts = append(llamaOpts, llama.SetContext(c.ContextSize))
}
if c.F16 {
llamaOpts = append(llamaOpts, llama.EnableF16Memory)
}
if c.Embeddings {
llamaOpts = append(llamaOpts, llama.EnableEmbeddings)
}
if c.NGPULayers != 0 {
llamaOpts = append(llamaOpts, llama.SetGPULayers(c.NGPULayers))
}
llamaOpts = append(llamaOpts, llama.SetMMap(c.MMap))
llamaOpts = append(llamaOpts, llama.SetMainGPU(c.MainGPU))
llamaOpts = append(llamaOpts, llama.SetTensorSplit(c.TensorSplit))
if c.Batch != 0 {
llamaOpts = append(llamaOpts, llama.SetNBatch(c.Batch))
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} else {
llamaOpts = append(llamaOpts, llama.SetNBatch(512))
}
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if c.NUMA {
llamaOpts = append(llamaOpts, llama.EnableNUMA)
}
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if c.LowVRAM {
llamaOpts = append(llamaOpts, llama.EnabelLowVRAM)
}
return llamaOpts
}
func ImageGeneration(height, width, mode, step, seed int, positive_prompt, negative_prompt, dst string, loader *model.ModelLoader, c Config, o *Option) (func() error, error) {
if c.Backend != model.StableDiffusionBackend {
return nil, fmt.Errorf("endpoint only working with stablediffusion models")
}
inferenceModel, err := loader.BackendLoader(c.Backend, c.ImageGenerationAssets, []llama.ModelOption{}, uint32(c.Threads), o.assetsDestination)
if err != nil {
return nil, err
}
var fn func() error
switch model := inferenceModel.(type) {
case *stablediffusion.StableDiffusion:
fn = func() error {
return model.GenerateImage(height, width, mode, step, seed, positive_prompt, negative_prompt, dst)
}
default:
fn = func() error {
return fmt.Errorf("creation of images not supported by the backend")
}
}
return func() error {
// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
mutexMap.Lock()
l, ok := mutexes[c.Backend]
if !ok {
m := &sync.Mutex{}
mutexes[c.Backend] = m
l = m
}
mutexMap.Unlock()
l.Lock()
defer l.Unlock()
return fn()
}, nil
}
func ModelEmbedding(s string, tokens []int, loader *model.ModelLoader, c Config, o *Option) (func() ([]float32, error), error) {
if !c.Embeddings {
return nil, fmt.Errorf("endpoint disabled for this model by API configuration")
}
modelFile := c.Model
llamaOpts := defaultLLamaOpts(c)
var inferenceModel interface{}
var err error
if c.Backend == "" {
inferenceModel, err = loader.GreedyLoader(modelFile, llamaOpts, uint32(c.Threads), o.assetsDestination)
} else {
inferenceModel, err = loader.BackendLoader(c.Backend, modelFile, llamaOpts, uint32(c.Threads), o.assetsDestination)
}
if err != nil {
return nil, err
}
var fn func() ([]float32, error)
switch model := inferenceModel.(type) {
case *llama.LLama:
fn = func() ([]float32, error) {
predictOptions := buildLLamaPredictOptions(c, loader.ModelPath)
if len(tokens) > 0 {
return model.TokenEmbeddings(tokens, predictOptions...)
}
return model.Embeddings(s, predictOptions...)
}
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// bert embeddings
case *bert.Bert:
fn = func() ([]float32, error) {
if len(tokens) > 0 {
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return model.TokenEmbeddings(tokens, bert.SetThreads(c.Threads))
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}
return model.Embeddings(s, bert.SetThreads(c.Threads))
}
default:
fn = func() ([]float32, error) {
return nil, fmt.Errorf("embeddings not supported by the backend")
}
}
return func() ([]float32, error) {
// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
mutexMap.Lock()
l, ok := mutexes[modelFile]
if !ok {
m := &sync.Mutex{}
mutexes[modelFile] = m
l = m
}
mutexMap.Unlock()
l.Lock()
defer l.Unlock()
embeds, err := fn()
if err != nil {
return embeds, err
}
// Remove trailing 0s
for i := len(embeds) - 1; i >= 0; i-- {
if embeds[i] == 0.0 {
embeds = embeds[:i]
} else {
break
}
}
return embeds, nil
}, nil
}
func buildLLamaPredictOptions(c Config, modelPath string) []llama.PredictOption {
// Generate the prediction using the language model
predictOptions := []llama.PredictOption{
llama.SetTemperature(c.Temperature),
llama.SetTopP(c.TopP),
llama.SetTopK(c.TopK),
llama.SetTokens(c.Maxtokens),
llama.SetThreads(c.Threads),
}
if c.PromptCacheAll {
predictOptions = append(predictOptions, llama.EnablePromptCacheAll)
}
if c.PromptCacheRO {
predictOptions = append(predictOptions, llama.EnablePromptCacheRO)
}
if c.PromptCachePath != "" {
// Create parent directory
p := filepath.Join(modelPath, c.PromptCachePath)
os.MkdirAll(filepath.Dir(p), 0755)
predictOptions = append(predictOptions, llama.SetPathPromptCache(p))
}
if c.Mirostat != 0 {
predictOptions = append(predictOptions, llama.SetMirostat(c.Mirostat))
}
if c.MirostatETA != 0 {
predictOptions = append(predictOptions, llama.SetMirostatETA(c.MirostatETA))
}
if c.MirostatTAU != 0 {
predictOptions = append(predictOptions, llama.SetMirostatTAU(c.MirostatTAU))
}
if c.Debug {
predictOptions = append(predictOptions, llama.Debug)
}
predictOptions = append(predictOptions, llama.SetStopWords(c.StopWords...))
if c.RepeatPenalty != 0 {
predictOptions = append(predictOptions, llama.SetPenalty(c.RepeatPenalty))
}
if c.Keep != 0 {
predictOptions = append(predictOptions, llama.SetNKeep(c.Keep))
}
if c.Batch != 0 {
predictOptions = append(predictOptions, llama.SetBatch(c.Batch))
}
if c.F16 {
predictOptions = append(predictOptions, llama.EnableF16KV)
}
if c.IgnoreEOS {
predictOptions = append(predictOptions, llama.IgnoreEOS)
}
if c.Seed != 0 {
predictOptions = append(predictOptions, llama.SetSeed(c.Seed))
}
//predictOptions = append(predictOptions, llama.SetLogitBias(c.Seed))
predictOptions = append(predictOptions, llama.SetFrequencyPenalty(c.FrequencyPenalty))
predictOptions = append(predictOptions, llama.SetMlock(c.MMlock))
predictOptions = append(predictOptions, llama.SetMemoryMap(c.MMap))
predictOptions = append(predictOptions, llama.SetPredictionMainGPU(c.MainGPU))
predictOptions = append(predictOptions, llama.SetPredictionTensorSplit(c.TensorSplit))
predictOptions = append(predictOptions, llama.SetTailFreeSamplingZ(c.TFZ))
predictOptions = append(predictOptions, llama.SetTypicalP(c.TypicalP))
return predictOptions
}
func ModelInference(s string, loader *model.ModelLoader, c Config, o *Option, tokenCallback func(string) bool) (func() (string, error), error) {
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supportStreams := false
modelFile := c.Model
llamaOpts := defaultLLamaOpts(c)
var inferenceModel interface{}
var err error
if c.Backend == "" {
inferenceModel, err = loader.GreedyLoader(modelFile, llamaOpts, uint32(c.Threads), o.assetsDestination)
} else {
inferenceModel, err = loader.BackendLoader(c.Backend, modelFile, llamaOpts, uint32(c.Threads), o.assetsDestination)
}
if err != nil {
return nil, err
}
var fn func() (string, error)
switch model := inferenceModel.(type) {
case *rwkv.RwkvState:
supportStreams = true
fn = func() (string, error) {
stopWord := "\n"
if len(c.StopWords) > 0 {
stopWord = c.StopWords[0]
}
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if err := model.ProcessInput(s); err != nil {
return "", err
}
response := model.GenerateResponse(c.Maxtokens, stopWord, float32(c.Temperature), float32(c.TopP), tokenCallback)
return response, nil
}
case *transformers.GPTNeoX:
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fn = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []transformers.PredictOption{
transformers.SetTemperature(c.Temperature),
transformers.SetTopP(c.TopP),
transformers.SetTopK(c.TopK),
transformers.SetTokens(c.Maxtokens),
transformers.SetThreads(c.Threads),
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}
if c.Batch != 0 {
predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
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}
if c.Seed != 0 {
predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
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}
return model.Predict(
s,
predictOptions...,
)
}
case *transformers.Replit:
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fn = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []transformers.PredictOption{
transformers.SetTemperature(c.Temperature),
transformers.SetTopP(c.TopP),
transformers.SetTopK(c.TopK),
transformers.SetTokens(c.Maxtokens),
transformers.SetThreads(c.Threads),
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}
if c.Batch != 0 {
predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
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}
if c.Seed != 0 {
predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
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}
return model.Predict(
s,
predictOptions...,
)
}
case *transformers.Starcoder:
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fn = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []transformers.PredictOption{
transformers.SetTemperature(c.Temperature),
transformers.SetTopP(c.TopP),
transformers.SetTopK(c.TopK),
transformers.SetTokens(c.Maxtokens),
transformers.SetThreads(c.Threads),
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}
if c.Batch != 0 {
predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
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}
if c.Seed != 0 {
predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
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}
return model.Predict(
s,
predictOptions...,
)
}
case *transformers.MPT:
fn = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []transformers.PredictOption{
transformers.SetTemperature(c.Temperature),
transformers.SetTopP(c.TopP),
transformers.SetTopK(c.TopK),
transformers.SetTokens(c.Maxtokens),
transformers.SetThreads(c.Threads),
}
if c.Batch != 0 {
predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
}
if c.Seed != 0 {
predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
}
return model.Predict(
s,
predictOptions...,
)
}
case *bloomz.Bloomz:
fn = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []bloomz.PredictOption{
bloomz.SetTemperature(c.Temperature),
bloomz.SetTopP(c.TopP),
bloomz.SetTopK(c.TopK),
bloomz.SetTokens(c.Maxtokens),
bloomz.SetThreads(c.Threads),
}
if c.Seed != 0 {
predictOptions = append(predictOptions, bloomz.SetSeed(c.Seed))
}
return model.Predict(
s,
predictOptions...,
)
}
case *transformers.Falcon:
fn = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []transformers.PredictOption{
transformers.SetTemperature(c.Temperature),
transformers.SetTopP(c.TopP),
transformers.SetTopK(c.TopK),
transformers.SetTokens(c.Maxtokens),
transformers.SetThreads(c.Threads),
}
if c.Batch != 0 {
predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
}
if c.Seed != 0 {
predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
}
return model.Predict(
s,
predictOptions...,
)
}
case *transformers.GPTJ:
fn = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []transformers.PredictOption{
transformers.SetTemperature(c.Temperature),
transformers.SetTopP(c.TopP),
transformers.SetTopK(c.TopK),
transformers.SetTokens(c.Maxtokens),
transformers.SetThreads(c.Threads),
}
if c.Batch != 0 {
predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
}
if c.Seed != 0 {
predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
}
return model.Predict(
s,
predictOptions...,
)
}
case *transformers.Dolly:
fn = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []transformers.PredictOption{
transformers.SetTemperature(c.Temperature),
transformers.SetTopP(c.TopP),
transformers.SetTopK(c.TopK),
transformers.SetTokens(c.Maxtokens),
transformers.SetThreads(c.Threads),
}
if c.Batch != 0 {
predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
}
if c.Seed != 0 {
predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
}
return model.Predict(
s,
predictOptions...,
)
}
case *transformers.GPT2:
fn = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []transformers.PredictOption{
transformers.SetTemperature(c.Temperature),
transformers.SetTopP(c.TopP),
transformers.SetTopK(c.TopK),
transformers.SetTokens(c.Maxtokens),
transformers.SetThreads(c.Threads),
}
if c.Batch != 0 {
predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
}
if c.Seed != 0 {
predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
}
return model.Predict(
s,
predictOptions...,
)
}
case *gpt4all.Model:
supportStreams = true
fn = func() (string, error) {
if tokenCallback != nil {
model.SetTokenCallback(tokenCallback)
}
// Generate the prediction using the language model
predictOptions := []gpt4all.PredictOption{
gpt4all.SetTemperature(c.Temperature),
gpt4all.SetTopP(c.TopP),
gpt4all.SetTopK(c.TopK),
gpt4all.SetTokens(c.Maxtokens),
}
if c.Batch != 0 {
predictOptions = append(predictOptions, gpt4all.SetBatch(c.Batch))
}
str, er := model.Predict(
s,
predictOptions...,
)
// Seems that if we don't free the callback explicitly we leave functions registered (that might try to send on closed channels)
// For instance otherwise the API returns: {"error":{"code":500,"message":"send on closed channel","type":""}}
// after a stream event has occurred
model.SetTokenCallback(nil)
return str, er
}
case *llama.LLama:
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supportStreams = true
fn = func() (string, error) {
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if tokenCallback != nil {
model.SetTokenCallback(tokenCallback)
}
predictOptions := buildLLamaPredictOptions(c, loader.ModelPath)
str, er := model.Predict(
s,
predictOptions...,
)
// Seems that if we don't free the callback explicitly we leave functions registered (that might try to send on closed channels)
// For instance otherwise the API returns: {"error":{"code":500,"message":"send on closed channel","type":""}}
// after a stream event has occurred
model.SetTokenCallback(nil)
return str, er
}
case *langchain.HuggingFace:
fn = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []langchain.PredictOption{
langchain.SetModel(c.Model),
langchain.SetMaxTokens(c.Maxtokens),
langchain.SetTemperature(c.Temperature),
langchain.SetStopWords(c.StopWords),
}
pred, er := model.PredictHuggingFace(s, predictOptions...)
if er != nil {
return "", er
}
return pred.Completion, nil
}
}
return func() (string, error) {
// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
mutexMap.Lock()
l, ok := mutexes[modelFile]
if !ok {
m := &sync.Mutex{}
mutexes[modelFile] = m
l = m
}
mutexMap.Unlock()
l.Lock()
defer l.Unlock()
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res, err := fn()
if tokenCallback != nil && !supportStreams {
tokenCallback(res)
}
return res, err
}, nil
}
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func ComputeChoices(predInput string, input *OpenAIRequest, config *Config, o *Option, loader *model.ModelLoader, cb func(string, *[]Choice), tokenCallback func(string) bool) ([]Choice, error) {
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result := []Choice{}
n := input.N
if input.N == 0 {
n = 1
}
// get the model function to call for the result
predFunc, err := ModelInference(predInput, loader, *config, o, tokenCallback)
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if err != nil {
return result, err
}
for i := 0; i < n; i++ {
prediction, err := predFunc()
if err != nil {
return result, err
}
prediction = Finetune(*config, predInput, prediction)
cb(prediction, &result)
//result = append(result, Choice{Text: prediction})
}
return result, err
}
var cutstrings map[string]*regexp.Regexp = make(map[string]*regexp.Regexp)
var mu sync.Mutex = sync.Mutex{}
func Finetune(config Config, input, prediction string) string {
if config.Echo {
prediction = input + prediction
}
for _, c := range config.Cutstrings {
mu.Lock()
reg, ok := cutstrings[c]
if !ok {
cutstrings[c] = regexp.MustCompile(c)
reg = cutstrings[c]
}
mu.Unlock()
prediction = reg.ReplaceAllString(prediction, "")
}
for _, c := range config.TrimSpace {
prediction = strings.TrimSpace(strings.TrimPrefix(prediction, c))
}
return prediction
}