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feat: move llama to a grpc

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Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
This commit is contained in:
Ettore Di Giacinto 2023-07-15 01:19:43 +02:00
parent b816009db0
commit 58f6aab637
13 changed files with 454 additions and 340 deletions
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9
Makefile
View File

@ -67,8 +67,8 @@ WHITE := $(shell tput -Txterm setaf 7)
CYAN := $(shell tput -Txterm setaf 6)
RESET := $(shell tput -Txterm sgr0)
C_INCLUDE_PATH=$(shell pwd)/go-llama:$(shell pwd)/go-stable-diffusion/:$(shell pwd)/gpt4all/gpt4all-bindings/golang/:$(shell pwd)/go-ggml-transformers:$(shell pwd)/go-rwkv:$(shell pwd)/whisper.cpp:$(shell pwd)/go-bert:$(shell pwd)/bloomz
LIBRARY_PATH=$(shell pwd)/go-piper:$(shell pwd)/go-llama:$(shell pwd)/go-stable-diffusion/:$(shell pwd)/gpt4all/gpt4all-bindings/golang/:$(shell pwd)/go-ggml-transformers:$(shell pwd)/go-rwkv:$(shell pwd)/whisper.cpp:$(shell pwd)/go-bert:$(shell pwd)/bloomz
C_INCLUDE_PATH=$(shell pwd)/go-stable-diffusion/:$(shell pwd)/gpt4all/gpt4all-bindings/golang/:$(shell pwd)/go-ggml-transformers:$(shell pwd)/go-rwkv:$(shell pwd)/whisper.cpp:$(shell pwd)/go-bert:$(shell pwd)/bloomz
LIBRARY_PATH=$(shell pwd)/go-piper:$(shell pwd)/go-stable-diffusion/:$(shell pwd)/gpt4all/gpt4all-bindings/golang/:$(shell pwd)/go-ggml-transformers:$(shell pwd)/go-rwkv:$(shell pwd)/whisper.cpp:$(shell pwd)/go-bert:$(shell pwd)/bloomz
ifeq ($(BUILD_TYPE),openblas)
CGO_LDFLAGS+=-lopenblas
@ -369,5 +369,8 @@ falcon-grpc: backend-assets/grpc
CGO_LDFLAGS="$(CGO_LDFLAGS)" C_INCLUDE_PATH=$(shell pwd)/go-ggllm LIBRARY_PATH=$(shell pwd)/go-ggllm \
$(GOCMD) build -x -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/falcon ./cmd/grpc/falcon/
llama-grpc: backend-assets/grpc
CGO_LDFLAGS="$(CGO_LDFLAGS)" C_INCLUDE_PATH=$(shell pwd)/go-llama LIBRARY_PATH=$(shell pwd)/go-llama \
$(GOCMD) build -x -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/llama ./cmd/grpc/llama/
grpcs: falcon-grpc
grpcs: falcon-grpc llama-grpc

282
api/prediction.go
View File

@ -18,7 +18,6 @@ import (
"github.com/go-skynet/bloomz.cpp"
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"
)
@ -36,6 +35,11 @@ func gRPCModelOpts(c Config) *pb.ModelOptions {
ContextSize: int32(c.ContextSize),
Seed: int32(c.Seed),
NBatch: int32(b),
F16Memory: c.F16,
MLock: c.MMlock,
NUMA: c.NUMA,
Embeddings: c.Embeddings,
LowVRAM: c.LowVRAM,
NGPULayers: int32(c.NGPULayers),
MMap: c.MMap,
MainGPU: c.MainGPU,
@ -43,32 +47,6 @@ func gRPCModelOpts(c Config) *pb.ModelOptions {
}
}
// func defaultGGLLMOpts(c Config) []ggllm.ModelOption {
// ggllmOpts := []ggllm.ModelOption{}
// if c.ContextSize != 0 {
// ggllmOpts = append(ggllmOpts, ggllm.SetContext(c.ContextSize))
// }
// // F16 doesn't seem to produce good output at all!
// //if c.F16 {
// // llamaOpts = append(llamaOpts, llama.EnableF16Memory)
// //}
// if c.NGPULayers != 0 {
// ggllmOpts = append(ggllmOpts, ggllm.SetGPULayers(c.NGPULayers))
// }
// ggllmOpts = append(ggllmOpts, ggllm.SetMMap(c.MMap))
// ggllmOpts = append(ggllmOpts, ggllm.SetMainGPU(c.MainGPU))
// ggllmOpts = append(ggllmOpts, ggllm.SetTensorSplit(c.TensorSplit))
// if c.Batch != 0 {
// ggllmOpts = append(ggllmOpts, ggllm.SetNBatch(c.Batch))
// } else {
// ggllmOpts = append(ggllmOpts, ggllm.SetNBatch(512))
// }
// return ggllmOpts
// }
func gRPCPredictOpts(c Config, modelPath string) *pb.PredictOptions {
promptCachePath := ""
if c.PromptCachePath != "" {
@ -85,6 +63,10 @@ func gRPCPredictOpts(c Config, modelPath string) *pb.PredictOptions {
PromptCacheAll: c.PromptCacheAll,
PromptCacheRO: c.PromptCacheRO,
PromptCachePath: promptCachePath,
F16KV: c.F16,
DebugMode: c.Debug,
Grammar: c.Grammar,
Mirostat: int32(c.Mirostat),
MirostatETA: float32(c.MirostatETA),
MirostatTAU: float32(c.MirostatTAU),
@ -105,200 +87,6 @@ func gRPCPredictOpts(c Config, modelPath string) *pb.PredictOptions {
}
}
// func buildGGLLMPredictOptions(c Config, modelPath string) []ggllm.PredictOption {
// // Generate the prediction using the language model
// predictOptions := []ggllm.PredictOption{
// ggllm.SetTemperature(c.Temperature),
// ggllm.SetTopP(c.TopP),
// ggllm.SetTopK(c.TopK),
// ggllm.SetTokens(c.Maxtokens),
// ggllm.SetThreads(c.Threads),
// }
// if c.PromptCacheAll {
// predictOptions = append(predictOptions, ggllm.EnablePromptCacheAll)
// }
// if c.PromptCacheRO {
// predictOptions = append(predictOptions, ggllm.EnablePromptCacheRO)
// }
// if c.PromptCachePath != "" {
// // Create parent directory
// p := filepath.Join(modelPath, c.PromptCachePath)
// os.MkdirAll(filepath.Dir(p), 0755)
// predictOptions = append(predictOptions, ggllm.SetPathPromptCache(p))
// }
// if c.Mirostat != 0 {
// predictOptions = append(predictOptions, ggllm.SetMirostat(c.Mirostat))
// }
// if c.MirostatETA != 0 {
// predictOptions = append(predictOptions, ggllm.SetMirostatETA(c.MirostatETA))
// }
// if c.MirostatTAU != 0 {
// predictOptions = append(predictOptions, ggllm.SetMirostatTAU(c.MirostatTAU))
// }
// if c.Debug {
// predictOptions = append(predictOptions, ggllm.Debug)
// }
// predictOptions = append(predictOptions, ggllm.SetStopWords(c.StopWords...))
// if c.RepeatPenalty != 0 {
// predictOptions = append(predictOptions, ggllm.SetPenalty(c.RepeatPenalty))
// }
// if c.Keep != 0 {
// predictOptions = append(predictOptions, ggllm.SetNKeep(c.Keep))
// }
// if c.Batch != 0 {
// predictOptions = append(predictOptions, ggllm.SetBatch(c.Batch))
// }
// if c.IgnoreEOS {
// predictOptions = append(predictOptions, ggllm.IgnoreEOS)
// }
// if c.Seed != 0 {
// predictOptions = append(predictOptions, ggllm.SetSeed(c.Seed))
// }
// //predictOptions = append(predictOptions, llama.SetLogitBias(c.Seed))
// predictOptions = append(predictOptions, ggllm.SetFrequencyPenalty(c.FrequencyPenalty))
// predictOptions = append(predictOptions, ggllm.SetMlock(c.MMlock))
// predictOptions = append(predictOptions, ggllm.SetMemoryMap(c.MMap))
// predictOptions = append(predictOptions, ggllm.SetPredictionMainGPU(c.MainGPU))
// predictOptions = append(predictOptions, ggllm.SetPredictionTensorSplit(c.TensorSplit))
// predictOptions = append(predictOptions, ggllm.SetTailFreeSamplingZ(c.TFZ))
// predictOptions = append(predictOptions, ggllm.SetTypicalP(c.TypicalP))
// return predictOptions
// }
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))
} else {
llamaOpts = append(llamaOpts, llama.SetNBatch(512))
}
if c.NUMA {
llamaOpts = append(llamaOpts, llama.EnableNUMA)
}
if c.LowVRAM {
llamaOpts = append(llamaOpts, llama.EnabelLowVRAM)
}
return llamaOpts
}
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)
}
predictOptions = append(predictOptions, llama.WithGrammar(c.Grammar))
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 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")
@ -351,14 +139,12 @@ func ModelEmbedding(s string, tokens []int, loader *model.ModelLoader, c Config,
modelFile := c.Model
llamaOpts := defaultLLamaOpts(c)
grpcOpts := gRPCModelOpts(c)
var inferenceModel interface{}
var err error
opts := []model.Option{
model.WithLlamaOpts(llamaOpts...),
model.WithLoadGRPCOpts(grpcOpts),
model.WithThreads(uint32(c.Threads)),
model.WithAssetDir(o.assetsDestination),
@ -377,14 +163,34 @@ func ModelEmbedding(s string, tokens []int, loader *model.ModelLoader, c Config,
var fn func() ([]float32, error)
switch model := inferenceModel.(type) {
case *llama.LLama:
case *grpc.Client:
fn = func() ([]float32, error) {
predictOptions := buildLLamaPredictOptions(c, loader.ModelPath)
predictOptions := gRPCPredictOpts(c, loader.ModelPath)
if len(tokens) > 0 {
return model.TokenEmbeddings(tokens, predictOptions...)
embeds := []int32{}
for _, t := range tokens {
embeds = append(embeds, int32(t))
}
return model.Embeddings(s, predictOptions...)
predictOptions.EmbeddingTokens = embeds
res, err := model.Embeddings(context.TODO(), predictOptions)
if err != nil {
return nil, err
}
return res.Embeddings, nil
}
predictOptions.Embeddings = s
res, err := model.Embeddings(context.TODO(), predictOptions)
if err != nil {
return nil, err
}
return res.Embeddings, nil
}
// bert embeddings
case *bert.Bert:
fn = func() ([]float32, error) {
@ -432,14 +238,12 @@ func ModelInference(s string, loader *model.ModelLoader, c Config, o *Option, to
supportStreams := false
modelFile := c.Model
llamaOpts := defaultLLamaOpts(c)
grpcOpts := gRPCModelOpts(c)
var inferenceModel interface{}
var err error
opts := []model.Option{
model.WithLlamaOpts(llamaOpts...),
model.WithLoadGRPCOpts(grpcOpts),
model.WithThreads(uint32(c.Threads)),
model.WithAssetDir(o.assetsDestination),
@ -708,26 +512,6 @@ func ModelInference(s string, loader *model.ModelLoader, c Config, o *Option, to
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:
supportStreams = true
fn = func() (string, error) {
if tokenCallback != nil {
model.SetTokenCallback(tokenCallback)
}
predictOptions := buildLLamaPredictOptions(c, loader.ModelPath)
str, er := model.Predict(
s,
predictOptions...,

25
cmd/grpc/llama/main.go Normal file
View File

@ -0,0 +1,25 @@
package main
// GRPC Falcon server
// Note: this is started internally by LocalAI and a server is allocated for each model
import (
"flag"
llama "github.com/go-skynet/LocalAI/pkg/grpc/llm/llama"
grpc "github.com/go-skynet/LocalAI/pkg/grpc"
)
var (
addr = flag.String("addr", "localhost:50051", "the address to connect to")
)
func main() {
flag.Parse()
if err := grpc.StartServer(*addr, &llama.LLM{}); err != nil {
panic(err)
}
}

11
pkg/grpc/client.go
View File

@ -47,6 +47,17 @@ func (c *Client) HealthCheck(ctx context.Context) bool {
return false
}
func (c *Client) Embeddings(ctx context.Context, in *pb.PredictOptions, opts ...grpc.CallOption) (*pb.EmbeddingResult, error) {
conn, err := grpc.Dial(c.address, grpc.WithTransportCredentials(insecure.NewCredentials()))
if err != nil {
return nil, err
}
defer conn.Close()
client := pb.NewLLMClient(conn)
return client.Embedding(ctx, in, opts...)
}
func (c *Client) Predict(ctx context.Context, in *pb.PredictOptions, opts ...grpc.CallOption) (*pb.Reply, error) {
conn, err := grpc.Dial(c.address, grpc.WithTransportCredentials(insecure.NewCredentials()))
if err != nil {

1
pkg/grpc/interface.go
View File

@ -8,4 +8,5 @@ type LLM interface {
Predict(*pb.PredictOptions) (string, error)
PredictStream(*pb.PredictOptions, chan string)
Load(*pb.ModelOptions) error
Embeddings(*pb.PredictOptions) ([]float32, error)
}

4
pkg/grpc/llm/falcon/falcon.go
View File

@ -42,6 +42,10 @@ func (llm *LLM) Load(opts *pb.ModelOptions) error {
return err
}
func (llm *LLM) Embeddings(opts *pb.PredictOptions) ([]float32, error) {
return nil, fmt.Errorf("not implemented")
}
func buildPredictOptions(opts *pb.PredictOptions) []ggllm.PredictOption {
predictOptions := []ggllm.PredictOption{
ggllm.SetTemperature(float64(opts.Temperature)),

165
pkg/grpc/llm/llama/llama.go Normal file
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@ -0,0 +1,165 @@
package llama
// 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"
pb "github.com/go-skynet/LocalAI/pkg/grpc/proto"
"github.com/go-skynet/go-llama.cpp"
)
type LLM struct {
llama *llama.LLama
}
func (llm *LLM) Load(opts *pb.ModelOptions) error {
llamaOpts := []llama.ModelOption{}
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)))
}
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))
}
if opts.NUMA {
llamaOpts = append(llamaOpts, llama.EnableNUMA)
}
if opts.LowVRAM {
llamaOpts = append(llamaOpts, llama.EnabelLowVRAM)
}
model, err := llama.New(opts.Model, llamaOpts...)
llm.llama = model
return err
}
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)),
}
if opts.PromptCacheAll {
predictOptions = append(predictOptions, llama.EnablePromptCacheAll)
}
if opts.PromptCacheRO {
predictOptions = append(predictOptions, llama.EnablePromptCacheRO)
}
predictOptions = append(predictOptions, llama.WithGrammar(opts.Grammar))
// Expected absolute path
if opts.PromptCachePath != "" {
predictOptions = append(predictOptions, llama.SetPathPromptCache(opts.PromptCachePath))
}
if opts.Mirostat != 0 {
predictOptions = append(predictOptions, llama.SetMirostat(int(opts.Mirostat)))
}
if opts.MirostatETA != 0 {
predictOptions = append(predictOptions, llama.SetMirostatETA(float64(opts.MirostatETA)))
}
if opts.MirostatTAU != 0 {
predictOptions = append(predictOptions, llama.SetMirostatTAU(float64(opts.MirostatTAU)))
}
if opts.Debug {
predictOptions = append(predictOptions, llama.Debug)
}
predictOptions = append(predictOptions, llama.SetStopWords(opts.StopPrompts...))
if opts.PresencePenalty != 0 {
predictOptions = append(predictOptions, llama.SetPenalty(float64(opts.PresencePenalty)))
}
if opts.NKeep != 0 {
predictOptions = append(predictOptions, llama.SetNKeep(int(opts.NKeep)))
}
if opts.Batch != 0 {
predictOptions = append(predictOptions, llama.SetBatch(int(opts.Batch)))
}
if opts.F16KV {
predictOptions = append(predictOptions, llama.EnableF16KV)
}
if opts.IgnoreEOS {
predictOptions = append(predictOptions, llama.IgnoreEOS)
}
if opts.Seed != 0 {
predictOptions = append(predictOptions, llama.SetSeed(int(opts.Seed)))
}
//predictOptions = append(predictOptions, llama.SetLogitBias(c.Seed))
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
}
func (llm *LLM) Predict(opts *pb.PredictOptions) (string, error) {
return llm.llama.Predict(opts.Prompt, buildPredictOptions(opts)...)
}
func (llm *LLM) PredictStream(opts *pb.PredictOptions, results chan string) {
predictOptions := buildPredictOptions(opts)
predictOptions = append(predictOptions, llama.SetTokenCallback(func(token string) bool {
results <- token
return true
}))
go func() {
_, err := llm.llama.Predict(opts.Prompt, predictOptions...)
if err != nil {
fmt.Println("err: ", err)
}
close(results)
}()
}
func (llm *LLM) Embeddings(opts *pb.PredictOptions) ([]float32, error) {
predictOptions := buildPredictOptions(opts)
if len(opts.EmbeddingTokens) > 0 {
tokens := []int{}
for _, t := range opts.EmbeddingTokens {
tokens = append(tokens, int(t))
}
return llm.llama.TokenEmbeddings(tokens, predictOptions...)
}
return llm.llama.Embeddings(opts.Embeddings, predictOptions...)
}

195
pkg/grpc/proto/llmserver.pb.go
View File

@ -87,7 +87,6 @@ type PredictOptions struct {
MirostatTAU float32 `protobuf:"fixed32,21,opt,name=MirostatTAU,proto3" json:"MirostatTAU,omitempty"`
PenalizeNL bool `protobuf:"varint,22,opt,name=PenalizeNL,proto3" json:"PenalizeNL,omitempty"`
LogitBias string `protobuf:"bytes,23,opt,name=LogitBias,proto3" json:"LogitBias,omitempty"`
PathPromptCache string `protobuf:"bytes,24,opt,name=PathPromptCache,proto3" json:"PathPromptCache,omitempty"`
MLock bool `protobuf:"varint,25,opt,name=MLock,proto3" json:"MLock,omitempty"`
MMap bool `protobuf:"varint,26,opt,name=MMap,proto3" json:"MMap,omitempty"`
PromptCacheAll bool `protobuf:"varint,27,opt,name=PromptCacheAll,proto3" json:"PromptCacheAll,omitempty"`
@ -98,6 +97,8 @@ type PredictOptions struct {
TopP float32 `protobuf:"fixed32,32,opt,name=TopP,proto3" json:"TopP,omitempty"`
PromptCachePath string `protobuf:"bytes,33,opt,name=PromptCachePath,proto3" json:"PromptCachePath,omitempty"`
Debug bool `protobuf:"varint,34,opt,name=Debug,proto3" json:"Debug,omitempty"`
EmbeddingTokens []int32 `protobuf:"varint,35,rep,packed,name=EmbeddingTokens,proto3" json:"EmbeddingTokens,omitempty"`
Embeddings string `protobuf:"bytes,36,opt,name=Embeddings,proto3" json:"Embeddings,omitempty"`
}
func (x *PredictOptions) Reset() {
@ -293,13 +294,6 @@ func (x *PredictOptions) GetLogitBias() string {
return ""
}
func (x *PredictOptions) GetPathPromptCache() string {
if x != nil {
return x.PathPromptCache
}
return ""
}
func (x *PredictOptions) GetMLock() bool {
if x != nil {
return x.MLock
@ -370,6 +364,20 @@ func (x *PredictOptions) GetDebug() bool {
return false
}
func (x *PredictOptions) GetEmbeddingTokens() []int32 {
if x != nil {
return x.EmbeddingTokens
}
return nil
}
func (x *PredictOptions) GetEmbeddings() string {
if x != nil {
return x.Embeddings
}
return ""
}
// The response message containing the result
type Reply struct {
state protoimpl.MessageState
@ -624,13 +632,60 @@ func (x *Result) GetSuccess() bool {
return false
}
type EmbeddingResult struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
Embeddings []float32 `protobuf:"fixed32,1,rep,packed,name=embeddings,proto3" json:"embeddings,omitempty"`
}
func (x *EmbeddingResult) Reset() {
*x = EmbeddingResult{}
if protoimpl.UnsafeEnabled {
mi := &file_pkg_grpc_proto_llmserver_proto_msgTypes[5]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *EmbeddingResult) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*EmbeddingResult) ProtoMessage() {}
func (x *EmbeddingResult) ProtoReflect() protoreflect.Message {
mi := &file_pkg_grpc_proto_llmserver_proto_msgTypes[5]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use EmbeddingResult.ProtoReflect.Descriptor instead.
func (*EmbeddingResult) Descriptor() ([]byte, []int) {
return file_pkg_grpc_proto_llmserver_proto_rawDescGZIP(), []int{5}
}
func (x *EmbeddingResult) GetEmbeddings() []float32 {
if x != nil {
return x.Embeddings
}
return nil
}
var File_pkg_grpc_proto_llmserver_proto protoreflect.FileDescriptor
var file_pkg_grpc_proto_llmserver_proto_rawDesc = []byte{
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0x6f, 0x74, 0x6f, 0x33,
}
var (
@ -758,25 +822,28 @@ func file_pkg_grpc_proto_llmserver_proto_rawDescGZIP() []byte {
return file_pkg_grpc_proto_llmserver_proto_rawDescData
}
var file_pkg_grpc_proto_llmserver_proto_msgTypes = make([]protoimpl.MessageInfo, 5)
var file_pkg_grpc_proto_llmserver_proto_msgTypes = make([]protoimpl.MessageInfo, 6)
var file_pkg_grpc_proto_llmserver_proto_goTypes = []interface{}{
(*HealthMessage)(nil), // 0: llm.HealthMessage
(*PredictOptions)(nil), // 1: llm.PredictOptions
(*Reply)(nil), // 2: llm.Reply
(*ModelOptions)(nil), // 3: llm.ModelOptions
(*Result)(nil), // 4: llm.Result
(*EmbeddingResult)(nil), // 5: llm.EmbeddingResult
}
var file_pkg_grpc_proto_llmserver_proto_depIdxs = []int32{
0, // 0: llm.LLM.Health:input_type -> llm.HealthMessage
1, // 1: llm.LLM.Predict:input_type -> llm.PredictOptions
3, // 2: llm.LLM.LoadModel:input_type -> llm.ModelOptions
1, // 3: llm.LLM.PredictStream:input_type -> llm.PredictOptions
2, // 4: llm.LLM.Health:output_type -> llm.Reply
2, // 5: llm.LLM.Predict:output_type -> llm.Reply
4, // 6: llm.LLM.LoadModel:output_type -> llm.Result
2, // 7: llm.LLM.PredictStream:output_type -> llm.Reply
4, // [4:8] is the sub-list for method output_type
0, // [0:4] is the sub-list for method input_type
1, // 4: llm.LLM.Embedding:input_type -> llm.PredictOptions
2, // 5: llm.LLM.Health:output_type -> llm.Reply
2, // 6: llm.LLM.Predict:output_type -> llm.Reply
4, // 7: llm.LLM.LoadModel:output_type -> llm.Result
2, // 8: llm.LLM.PredictStream:output_type -> llm.Reply
5, // 9: llm.LLM.Embedding:output_type -> llm.EmbeddingResult
5, // [5:10] is the sub-list for method output_type
0, // [0:5] is the sub-list for method input_type
0, // [0:0] is the sub-list for extension type_name
0, // [0:0] is the sub-list for extension extendee
0, // [0:0] is the sub-list for field type_name
@ -848,6 +915,18 @@ func file_pkg_grpc_proto_llmserver_proto_init() {
return nil
}
}
file_pkg_grpc_proto_llmserver_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*EmbeddingResult); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
}
type x struct{}
out := protoimpl.TypeBuilder{
@ -855,7 +934,7 @@ func file_pkg_grpc_proto_llmserver_proto_init() {
GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
RawDescriptor: file_pkg_grpc_proto_llmserver_proto_rawDesc,
NumEnums: 0,
NumMessages: 5,
NumMessages: 6,
NumExtensions: 0,
NumServices: 1,
},

8
pkg/grpc/proto/llmserver.proto
View File

@ -12,6 +12,7 @@ service LLM {
rpc Predict(PredictOptions) returns (Reply) {}
rpc LoadModel(ModelOptions) returns (Result) {}
rpc PredictStream(PredictOptions) returns (stream Reply) {}
rpc Embedding(PredictOptions) returns (EmbeddingResult) {}
}
message HealthMessage {}
@ -41,7 +42,6 @@ message PredictOptions {
float MirostatTAU = 21;
bool PenalizeNL = 22;
string LogitBias = 23;
string PathPromptCache = 24;
bool MLock = 25;
bool MMap = 26;
bool PromptCacheAll = 27;
@ -52,6 +52,8 @@ message PredictOptions {
float TopP = 32;
string PromptCachePath = 33;
bool Debug = 34;
repeated int32 EmbeddingTokens = 35;
string Embeddings = 36;
}
// The response message containing the result
@ -80,3 +82,7 @@ message Result {
string message = 1;
bool success = 2;
}
message EmbeddingResult {
repeated float embeddings = 1;
}

36
pkg/grpc/proto/llmserver_grpc.pb.go
View File

@ -26,6 +26,7 @@ type LLMClient interface {
Predict(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (*Reply, error)
LoadModel(ctx context.Context, in *ModelOptions, opts ...grpc.CallOption) (*Result, error)
PredictStream(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (LLM_PredictStreamClient, error)
Embedding(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (*EmbeddingResult, error)
}
type lLMClient struct {
@ -95,6 +96,15 @@ func (x *lLMPredictStreamClient) Recv() (*Reply, error) {
return m, nil
}
func (c *lLMClient) Embedding(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (*EmbeddingResult, error) {
out := new(EmbeddingResult)
err := c.cc.Invoke(ctx, "/llm.LLM/Embedding", in, out, opts...)
if err != nil {
return nil, err
}
return out, nil
}
// LLMServer is the server API for LLM service.
// All implementations must embed UnimplementedLLMServer
// for forward compatibility
@ -103,6 +113,7 @@ type LLMServer interface {
Predict(context.Context, *PredictOptions) (*Reply, error)
LoadModel(context.Context, *ModelOptions) (*Result, error)
PredictStream(*PredictOptions, LLM_PredictStreamServer) error
Embedding(context.Context, *PredictOptions) (*EmbeddingResult, error)
mustEmbedUnimplementedLLMServer()
}
@ -122,6 +133,9 @@ func (UnimplementedLLMServer) LoadModel(context.Context, *ModelOptions) (*Result
func (UnimplementedLLMServer) PredictStream(*PredictOptions, LLM_PredictStreamServer) error {
return status.Errorf(codes.Unimplemented, "method PredictStream not implemented")
}
func (UnimplementedLLMServer) Embedding(context.Context, *PredictOptions) (*EmbeddingResult, error) {
return nil, status.Errorf(codes.Unimplemented, "method Embedding not implemented")
}
func (UnimplementedLLMServer) mustEmbedUnimplementedLLMServer() {}
// UnsafeLLMServer may be embedded to opt out of forward compatibility for this service.
@ -210,6 +224,24 @@ func (x *lLMPredictStreamServer) Send(m *Reply) error {
return x.ServerStream.SendMsg(m)
}
func _LLM_Embedding_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(PredictOptions)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(LLMServer).Embedding(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/llm.LLM/Embedding",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(LLMServer).Embedding(ctx, req.(*PredictOptions))
}
return interceptor(ctx, in, info, handler)
}
// LLM_ServiceDesc is the grpc.ServiceDesc for LLM service.
// It's only intended for direct use with grpc.RegisterService,
// and not to be introspected or modified (even as a copy)
@ -229,6 +261,10 @@ var LLM_ServiceDesc = grpc.ServiceDesc{
MethodName: "LoadModel",
Handler: _LLM_LoadModel_Handler,
},
{
MethodName: "Embedding",
Handler: _LLM_Embedding_Handler,
},
},
Streams: []grpc.StreamDesc{
{

9
pkg/grpc/server.go
View File

@ -29,6 +29,15 @@ func (s *server) Health(ctx context.Context, in *pb.HealthMessage) (*pb.Reply, e
return &pb.Reply{Message: "OK"}, nil
}
func (s *server) Embedding(ctx context.Context, in *pb.PredictOptions) (*pb.EmbeddingResult, error) {
embeds, err := s.llm.Embeddings(in)
if err != nil {
return nil, err
}
return &pb.EmbeddingResult{Embeddings: embeds}, nil
}
func (s *server) LoadModel(ctx context.Context, in *pb.ModelOptions) (*pb.Result, error) {
err := s.llm.Load(in)
if err != nil {

15
pkg/model/initializers.go
View File

@ -17,7 +17,6 @@ import (
bloomz "github.com/go-skynet/bloomz.cpp"
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"
"github.com/hashicorp/go-multierror"
"github.com/hpcloud/tail"
gpt4all "github.com/nomic-ai/gpt4all/gpt4all-bindings/golang"
@ -135,11 +134,11 @@ var lcHuggingFace = func(repoId string) (interface{}, error) {
return langchain.NewHuggingFace(repoId)
}
func llamaLM(opts ...llama.ModelOption) func(string) (interface{}, error) {
return func(s string) (interface{}, error) {
return llama.New(s, opts...)
}
}
// func llamaLM(opts ...llama.ModelOption) func(string) (interface{}, error) {
// return func(s string) (interface{}, error) {
// return llama.New(s, opts...)
// }
// }
func gpt4allLM(opts ...gpt4all.ModelOption) func(string) (interface{}, error) {
return func(s string) (interface{}, error) {
@ -263,7 +262,8 @@ func (ml *ModelLoader) BackendLoader(opts ...Option) (model interface{}, err err
log.Debug().Msgf("Loading model %s from %s", o.backendString, o.modelFile)
switch strings.ToLower(o.backendString) {
case LlamaBackend:
return ml.LoadModel(o.modelFile, llamaLM(o.llamaOpts...))
// return ml.LoadModel(o.modelFile, llamaLM(o.llamaOpts...))
return ml.LoadModel(o.modelFile, ml.grpcModel(LlamaBackend, o))
case BloomzBackend:
return ml.LoadModel(o.modelFile, bloomzLM)
case GPTJBackend:
@ -325,7 +325,6 @@ func (ml *ModelLoader) GreedyLoader(opts ...Option) (interface{}, error) {
model, modelerr := ml.BackendLoader(
WithBackendString(b),
WithModelFile(o.modelFile),
WithLlamaOpts(o.llamaOpts...),
WithLoadGRPCOpts(o.gRPCOptions),
WithThreads(o.threads),
WithAssetDir(o.assetDir),

8
pkg/model/options.go
View File

@ -2,13 +2,11 @@ package model
import (
pb "github.com/go-skynet/LocalAI/pkg/grpc/proto"
llama "github.com/go-skynet/go-llama.cpp"
)
type Options struct {
backendString string
modelFile string
llamaOpts []llama.ModelOption
threads uint32
assetDir string
@ -35,12 +33,6 @@ func WithLoadGRPCOpts(opts *pb.ModelOptions) Option {
}
}
func WithLlamaOpts(opts ...llama.ModelOption) Option {
return func(o *Options) {
o.llamaOpts = append(o.llamaOpts, opts...)
}
}
func WithThreads(threads uint32) Option {
return func(o *Options) {
o.threads = threads