LocalAI/api/backend/llm.go

package backend

import (
	"context"
	"os"
	"regexp"
	"strings"
	"sync"

	config "github.com/go-skynet/LocalAI/api/config"
	"github.com/go-skynet/LocalAI/api/options"
	"github.com/go-skynet/LocalAI/pkg/gallery"
	"github.com/go-skynet/LocalAI/pkg/grpc"
	model "github.com/go-skynet/LocalAI/pkg/model"
	"github.com/go-skynet/LocalAI/pkg/utils"
)

type LLMResponse struct {
	Response string // should this be []byte?
	Usage    TokenUsage
}

type TokenUsage struct {
	Prompt     int
	Completion int
}

func ModelInference(ctx context.Context, s string, loader *model.ModelLoader, c config.Config, o *options.Option, tokenCallback func(string, TokenUsage) bool) (func() (LLMResponse, error), error) {
	modelFile := c.Model

	grpcOpts := gRPCModelOpts(c)

	var inferenceModel *grpc.Client
	var err error

	opts := modelOpts(c, o, []model.Option{
		model.WithLoadGRPCLoadModelOpts(grpcOpts),
		model.WithThreads(uint32(c.Threads)), // some models uses this to allocate threads during startup
		model.WithAssetDir(o.AssetsDestination),
		model.WithModel(modelFile),
		model.WithContext(o.Context),
	})

	if c.Backend != "" {
		opts = append(opts, model.WithBackendString(c.Backend))
	}

	// Check if the modelFile exists, if it doesn't try to load it from the gallery
	if o.AutoloadGalleries { // experimental
		if _, err := os.Stat(modelFile); os.IsNotExist(err) {
			utils.ResetDownloadTimers()
			// if we failed to load the model, we try to download it
			err := gallery.InstallModelFromGalleryByName(o.Galleries, modelFile, loader.ModelPath, gallery.GalleryModel{}, utils.DisplayDownloadFunction)
			if err != nil {
				return nil, err
			}
		}
	}

	if c.Backend == "" {
		inferenceModel, err = loader.GreedyLoader(opts...)
	} else {
		inferenceModel, err = loader.BackendLoader(opts...)
	}

	if err != nil {
		return nil, err
	}

	// in GRPC, the backend is supposed to answer to 1 single token if stream is not supported
	fn := func() (LLMResponse, error) {
		opts := gRPCPredictOpts(c, loader.ModelPath)
		opts.Prompt = s

		tokenUsage := TokenUsage{}

		// check the per-model feature flag for usage, since tokenCallback may have a cost.
		// Defaults to off as for now it is still experimental
		if c.FeatureFlag.Enabled("usage") {
			userTokenCallback := tokenCallback
			if userTokenCallback == nil {
				userTokenCallback = func(token string, usage TokenUsage) bool {
					return true
				}
			}

			promptInfo, pErr := inferenceModel.TokenizeString(ctx, opts)
			if pErr == nil && promptInfo.Length > 0 {
				tokenUsage.Prompt = int(promptInfo.Length)
			}

			tokenCallback = func(token string, usage TokenUsage) bool {
				tokenUsage.Completion++
				return userTokenCallback(token, tokenUsage)
			}
		}

		if tokenCallback != nil {
			ss := ""
			err := inferenceModel.PredictStream(ctx, opts, func(s []byte) {
				tokenCallback(string(s), tokenUsage)
				ss += string(s)
			})
			return LLMResponse{
				Response: ss,
				Usage:    tokenUsage,
			}, err
		} else {
			// TODO: Is the chicken bit the only way to get here? is that acceptable?
			reply, err := inferenceModel.Predict(ctx, opts)
			if err != nil {
				return LLMResponse{}, err
			}
			return LLMResponse{
				Response: string(reply.Message),
				Usage:    tokenUsage,
			}, err
		}
	}

	return fn, nil
}

var cutstrings map[string]*regexp.Regexp = make(map[string]*regexp.Regexp)
var mu sync.Mutex = sync.Mutex{}

func Finetune(config 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

}