LocalAI/core/backend/options.go
Ettore Di Giacinto f895d06605
fix(config): set better defaults for inferencing (#1822)
* fix(defaults): set better defaults for inferencing

This changeset aim to have better defaults and to properly detect when
no inference settings are provided with the model.

If not specified, we defaults to mirostat sampling, and offload all the
GPU layers (if a GPU is detected).

Related to https://github.com/mudler/LocalAI/issues/1373 and https://github.com/mudler/LocalAI/issues/1723

* Adapt tests

* Also pre-initialize default seed
2024-03-13 10:05:30 +01:00

142 lines
4.5 KiB
Go

package backend
import (
"os"
"path/filepath"
"github.com/go-skynet/LocalAI/core/config"
pb "github.com/go-skynet/LocalAI/pkg/grpc/proto"
model "github.com/go-skynet/LocalAI/pkg/model"
)
func modelOpts(c config.BackendConfig, so *config.ApplicationConfig, opts []model.Option) []model.Option {
if so.SingleBackend {
opts = append(opts, model.WithSingleActiveBackend())
}
if so.ParallelBackendRequests {
opts = append(opts, model.EnableParallelRequests)
}
if c.GRPC.Attempts != 0 {
opts = append(opts, model.WithGRPCAttempts(c.GRPC.Attempts))
}
if c.GRPC.AttemptsSleepTime != 0 {
opts = append(opts, model.WithGRPCAttemptsDelay(c.GRPC.AttemptsSleepTime))
}
for k, v := range so.ExternalGRPCBackends {
opts = append(opts, model.WithExternalBackend(k, v))
}
return opts
}
func gRPCModelOpts(c config.BackendConfig) *pb.ModelOptions {
b := 512
if c.Batch != 0 {
b = c.Batch
}
return &pb.ModelOptions{
CUDA: c.CUDA || c.Diffusers.CUDA,
SchedulerType: c.Diffusers.SchedulerType,
PipelineType: c.Diffusers.PipelineType,
CFGScale: c.Diffusers.CFGScale,
LoraAdapter: c.LoraAdapter,
LoraScale: c.LoraScale,
F16Memory: *c.F16,
LoraBase: c.LoraBase,
IMG2IMG: c.Diffusers.IMG2IMG,
CLIPModel: c.Diffusers.ClipModel,
CLIPSubfolder: c.Diffusers.ClipSubFolder,
CLIPSkip: int32(c.Diffusers.ClipSkip),
ControlNet: c.Diffusers.ControlNet,
ContextSize: int32(*c.ContextSize),
Seed: int32(*c.Seed),
NBatch: int32(b),
NoMulMatQ: c.NoMulMatQ,
DraftModel: c.DraftModel,
AudioPath: c.VallE.AudioPath,
Quantization: c.Quantization,
GPUMemoryUtilization: c.GPUMemoryUtilization,
TrustRemoteCode: c.TrustRemoteCode,
EnforceEager: c.EnforceEager,
SwapSpace: int32(c.SwapSpace),
MaxModelLen: int32(c.MaxModelLen),
MMProj: c.MMProj,
YarnExtFactor: c.YarnExtFactor,
YarnAttnFactor: c.YarnAttnFactor,
YarnBetaFast: c.YarnBetaFast,
YarnBetaSlow: c.YarnBetaSlow,
NGQA: c.NGQA,
RMSNormEps: c.RMSNormEps,
MLock: *c.MMlock,
RopeFreqBase: c.RopeFreqBase,
RopeScaling: c.RopeScaling,
Type: c.ModelType,
RopeFreqScale: c.RopeFreqScale,
NUMA: c.NUMA,
Embeddings: c.Embeddings,
LowVRAM: *c.LowVRAM,
NGPULayers: int32(*c.NGPULayers),
MMap: *c.MMap,
MainGPU: c.MainGPU,
Threads: int32(*c.Threads),
TensorSplit: c.TensorSplit,
// AutoGPTQ
ModelBaseName: c.AutoGPTQ.ModelBaseName,
Device: c.AutoGPTQ.Device,
UseTriton: c.AutoGPTQ.Triton,
UseFastTokenizer: c.AutoGPTQ.UseFastTokenizer,
// RWKV
Tokenizer: c.Tokenizer,
}
}
func gRPCPredictOpts(c config.BackendConfig, modelPath string) *pb.PredictOptions {
promptCachePath := ""
if c.PromptCachePath != "" {
p := filepath.Join(modelPath, c.PromptCachePath)
os.MkdirAll(filepath.Dir(p), 0755)
promptCachePath = p
}
return &pb.PredictOptions{
Temperature: float32(*c.Temperature),
TopP: float32(*c.TopP),
NDraft: c.NDraft,
TopK: int32(*c.TopK),
Tokens: int32(*c.Maxtokens),
Threads: int32(*c.Threads),
PromptCacheAll: c.PromptCacheAll,
PromptCacheRO: c.PromptCacheRO,
PromptCachePath: promptCachePath,
F16KV: *c.F16,
DebugMode: *c.Debug,
Grammar: c.Grammar,
NegativePromptScale: c.NegativePromptScale,
RopeFreqBase: c.RopeFreqBase,
RopeFreqScale: c.RopeFreqScale,
NegativePrompt: c.NegativePrompt,
Mirostat: int32(*c.LLMConfig.Mirostat),
MirostatETA: float32(*c.LLMConfig.MirostatETA),
MirostatTAU: float32(*c.LLMConfig.MirostatTAU),
Debug: *c.Debug,
StopPrompts: c.StopWords,
Repeat: int32(c.RepeatPenalty),
NKeep: int32(c.Keep),
Batch: int32(c.Batch),
IgnoreEOS: c.IgnoreEOS,
Seed: int32(*c.Seed),
FrequencyPenalty: float32(c.FrequencyPenalty),
MLock: *c.MMlock,
MMap: *c.MMap,
MainGPU: c.MainGPU,
TensorSplit: c.TensorSplit,
TailFreeSamplingZ: float32(c.TFZ),
TypicalP: float32(c.TypicalP),
}
}