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feat: add llama-master backend (#752)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,25 @@ | ||
| package main | ||
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| // GRPC Falcon server | ||
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| // Note: this is started internally by LocalAI and a server is allocated for each model | ||
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| import ( | ||
| "flag" | ||
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| llama "github.com/go-skynet/LocalAI/pkg/grpc/llm/llama-master" | ||
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| grpc "github.com/go-skynet/LocalAI/pkg/grpc" | ||
| ) | ||
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| var ( | ||
| addr = flag.String("addr", "localhost:50051", "the address to connect to") | ||
| ) | ||
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| func main() { | ||
| flag.Parse() | ||
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| if err := grpc.StartServer(*addr, &llama.LLM{}); err != nil { | ||
| panic(err) | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,168 @@ | ||
| package llama | ||
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| // This is a wrapper to statisfy the GRPC service interface | ||
| // It is meant to be used by the main executable that is the server for the specific backend type (falcon, gpt3, etc) | ||
| import ( | ||
| "fmt" | ||
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| "github.com/go-skynet/LocalAI/pkg/grpc/base" | ||
| pb "github.com/go-skynet/LocalAI/pkg/grpc/proto" | ||
| "github.com/go-skynet/go-llama.cpp-master" | ||
| ) | ||
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| type LLM struct { | ||
| base.Base | ||
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| llama *llama.LLama | ||
| } | ||
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| func (llm *LLM) Load(opts *pb.ModelOptions) error { | ||
| llamaOpts := []llama.ModelOption{} | ||
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| if opts.ContextSize != 0 { | ||
| llamaOpts = append(llamaOpts, llama.SetContext(int(opts.ContextSize))) | ||
| } | ||
| if opts.F16Memory { | ||
| llamaOpts = append(llamaOpts, llama.EnableF16Memory) | ||
| } | ||
| if opts.Embeddings { | ||
| llamaOpts = append(llamaOpts, llama.EnableEmbeddings) | ||
| } | ||
| if opts.NGPULayers != 0 { | ||
| llamaOpts = append(llamaOpts, llama.SetGPULayers(int(opts.NGPULayers))) | ||
| } | ||
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| llamaOpts = append(llamaOpts, llama.SetMMap(opts.MMap)) | ||
| llamaOpts = append(llamaOpts, llama.SetMainGPU(opts.MainGPU)) | ||
| llamaOpts = append(llamaOpts, llama.SetTensorSplit(opts.TensorSplit)) | ||
| if opts.NBatch != 0 { | ||
| llamaOpts = append(llamaOpts, llama.SetNBatch(int(opts.NBatch))) | ||
| } else { | ||
| llamaOpts = append(llamaOpts, llama.SetNBatch(512)) | ||
| } | ||
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| if opts.NUMA { | ||
| llamaOpts = append(llamaOpts, llama.EnableNUMA) | ||
| } | ||
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| if opts.LowVRAM { | ||
| llamaOpts = append(llamaOpts, llama.EnabelLowVRAM) | ||
| } | ||
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| model, err := llama.New(opts.Model, llamaOpts...) | ||
| llm.llama = model | ||
| return err | ||
| } | ||
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| func buildPredictOptions(opts *pb.PredictOptions) []llama.PredictOption { | ||
| predictOptions := []llama.PredictOption{ | ||
| llama.SetTemperature(float64(opts.Temperature)), | ||
| llama.SetTopP(float64(opts.TopP)), | ||
| llama.SetTopK(int(opts.TopK)), | ||
| llama.SetTokens(int(opts.Tokens)), | ||
| llama.SetThreads(int(opts.Threads)), | ||
| } | ||
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| if opts.PromptCacheAll { | ||
| predictOptions = append(predictOptions, llama.EnablePromptCacheAll) | ||
| } | ||
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| if opts.PromptCacheRO { | ||
| predictOptions = append(predictOptions, llama.EnablePromptCacheRO) | ||
| } | ||
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| // Expected absolute path | ||
| if opts.PromptCachePath != "" { | ||
| predictOptions = append(predictOptions, llama.SetPathPromptCache(opts.PromptCachePath)) | ||
| } | ||
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| if opts.Mirostat != 0 { | ||
| predictOptions = append(predictOptions, llama.SetMirostat(int(opts.Mirostat))) | ||
| } | ||
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| if opts.MirostatETA != 0 { | ||
| predictOptions = append(predictOptions, llama.SetMirostatETA(float64(opts.MirostatETA))) | ||
| } | ||
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| if opts.MirostatTAU != 0 { | ||
| predictOptions = append(predictOptions, llama.SetMirostatTAU(float64(opts.MirostatTAU))) | ||
| } | ||
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| if opts.Debug { | ||
| predictOptions = append(predictOptions, llama.Debug) | ||
| } | ||
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| predictOptions = append(predictOptions, llama.SetStopWords(opts.StopPrompts...)) | ||
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| if opts.PresencePenalty != 0 { | ||
| predictOptions = append(predictOptions, llama.SetPenalty(float64(opts.PresencePenalty))) | ||
| } | ||
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| if opts.NKeep != 0 { | ||
| predictOptions = append(predictOptions, llama.SetNKeep(int(opts.NKeep))) | ||
| } | ||
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| if opts.Batch != 0 { | ||
| predictOptions = append(predictOptions, llama.SetBatch(int(opts.Batch))) | ||
| } | ||
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| if opts.F16KV { | ||
| predictOptions = append(predictOptions, llama.EnableF16KV) | ||
| } | ||
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| if opts.IgnoreEOS { | ||
| predictOptions = append(predictOptions, llama.IgnoreEOS) | ||
| } | ||
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| if opts.Seed != 0 { | ||
| predictOptions = append(predictOptions, llama.SetSeed(int(opts.Seed))) | ||
| } | ||
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| //predictOptions = append(predictOptions, llama.SetLogitBias(c.Seed)) | ||
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| predictOptions = append(predictOptions, llama.SetFrequencyPenalty(float64(opts.FrequencyPenalty))) | ||
| predictOptions = append(predictOptions, llama.SetMlock(opts.MLock)) | ||
| predictOptions = append(predictOptions, llama.SetMemoryMap(opts.MMap)) | ||
| predictOptions = append(predictOptions, llama.SetPredictionMainGPU(opts.MainGPU)) | ||
| predictOptions = append(predictOptions, llama.SetPredictionTensorSplit(opts.TensorSplit)) | ||
| predictOptions = append(predictOptions, llama.SetTailFreeSamplingZ(float64(opts.TailFreeSamplingZ))) | ||
| predictOptions = append(predictOptions, llama.SetTypicalP(float64(opts.TypicalP))) | ||
| return predictOptions | ||
| } | ||
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| func (llm *LLM) Predict(opts *pb.PredictOptions) (string, error) { | ||
| return llm.llama.Predict(opts.Prompt, buildPredictOptions(opts)...) | ||
| } | ||
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| func (llm *LLM) PredictStream(opts *pb.PredictOptions, results chan string) error { | ||
| predictOptions := buildPredictOptions(opts) | ||
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| predictOptions = append(predictOptions, llama.SetTokenCallback(func(token string) bool { | ||
| results <- token | ||
| return true | ||
| })) | ||
|
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| go func() { | ||
| _, err := llm.llama.Predict(opts.Prompt, predictOptions...) | ||
| if err != nil { | ||
| fmt.Println("err: ", err) | ||
| } | ||
| close(results) | ||
| }() | ||
|
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| return nil | ||
| } | ||
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| func (llm *LLM) Embeddings(opts *pb.PredictOptions) ([]float32, error) { | ||
| predictOptions := buildPredictOptions(opts) | ||
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| if len(opts.EmbeddingTokens) > 0 { | ||
| tokens := []int{} | ||
| for _, t := range opts.EmbeddingTokens { | ||
| tokens = append(tokens, int(t)) | ||
| } | ||
| return llm.llama.TokenEmbeddings(tokens, predictOptions...) | ||
| } | ||
|
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| return llm.llama.Embeddings(opts.Embeddings, predictOptions...) | ||
| } |
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