add more runner params

llama/llama.go

@@ -31,6 +31,7 @@ package llama
 // #include "sampling_ext.h"
 import "C"
 import (
+	"errors"
 	"fmt"
 	"runtime"
 	"strings"
@@ -49,13 +50,14 @@ type ContextParams struct {
 	c C.struct_llama_context_params
 }
 
-func NewContextParams() ContextParams {
+func NewContextParams(numCtx int, threads int, flashAttention bool) ContextParams {
 	params := C.llama_context_default_params()
-	params.seed = C.uint(1234)
+	params.n_ctx = C.uint(numCtx)
-	params.n_ctx = C.uint(2048)
 	params.n_threads = C.uint(runtime.NumCPU())
 	params.n_threads_batch = params.n_threads
 	params.embeddings = C.bool(true)
+	params.flash_attn = C.bool(flashAttention)
+	params.n_threads = C.uint(threads)
 	return ContextParams{c: params}
 }
 
@@ -63,9 +65,10 @@ type ModelParams struct {
 	c C.struct_llama_model_params
 }
 
-func NewModelParams() ModelParams {
+func NewModelParams(numGpuLayers int, mainGpu int) ModelParams {
 	params := C.llama_model_default_params()
-	params.n_gpu_layers = 999
+	params.n_gpu_layers = C.int(numGpuLayers)
+	params.main_gpu = C.int32_t(mainGpu)
 	return ModelParams{c: params}
 }
 
@@ -155,6 +158,23 @@ func (m *Model) TokenIsEog(token int) bool {
 	return bool(C.llama_token_is_eog(m.c, C.llama_token(token)))
 }
 
+func (m *Model) ApplyLoraFromFile(loraPath string, scale float32, baseModelPath string, threads int) error {
+	cLoraPath := C.CString(loraPath)
+	defer C.free(unsafe.Pointer(cLoraPath))
+
+	var cBaseModelPath *C.char
+	if baseModelPath != "" {
+		cBaseModelPath = C.CString(baseModelPath)
+	}
+
+	code := int(C.llama_model_apply_lora_from_file(m.c, cLoraPath, C.float(scale), cBaseModelPath, C.int32_t(threads)))
+	if code != 0 {
+		return errors.New("error applying lora from file")
+	}
+
+	return nil
+}
+
 type Batch struct {
 	c C.struct_llama_batch
 }

llama/runner/runner.go

@@ -9,6 +9,7 @@ import (
 	"log/slog"
 	"net"
 	"net/http"
+	"runtime"
 	"strconv"
 	"strings"
 	"sync"
@@ -73,6 +74,8 @@ type Server struct {
 	lc    *llama.Context
 	cc    *llama.ClipContext
 
+	batchSize int
+
 	// parallel is the number of parallel requests to handle
 	parallel int
 
@@ -154,7 +157,7 @@ func truncateStop(pieces []string, stop string) []string {
 }
 
 func (s *Server) run(ctx context.Context) {
-	batch := llama.NewBatch(512, 0, s.parallel)
+	batch := llama.NewBatch(s.batchSize, 0, s.parallel)
 	defer batch.Free()
 
 	// build up stop sequences as we recognize them
@@ -182,7 +185,7 @@ func (s *Server) run(ctx context.Context) {
 
 				for j, t := range seq.tokens {
 					// todo: make this n_batch
-					if j > 512 {
+					if j > s.batchSize {
 						break
 					}
 
@@ -207,10 +210,10 @@ func (s *Server) run(ctx context.Context) {
 
 				// don't sample prompt processing
 				if seq.prompt() {
-					if len(seq.tokens) < 512 {
+					if len(seq.tokens) < s.batchSize {
 						seq.tokens = []int{}
 					} else {
-						seq.tokens = seq.tokens[512:]
+						seq.tokens = seq.tokens[s.batchSize:]
 					}
 
 					continue
@@ -412,14 +415,26 @@ func main() {
 	mpath := flag.String("model", "", "Path to model binary file")
 	ppath := flag.String("projector", "", "Path to projector binary file")
 	parallel := flag.Int("parallel", 1, "Number of sequences to handle simultaneously")
+	batchSize := flag.Int("batch-size", 512, "Batch size")
+	nGpuLayers := flag.Int("n-gpu-layers", 0, "Number of layers to offload to GPU")
+	mainGpu := flag.Int("main-gpu", 0, "Main GPU")
+	flashAttention := flag.Bool("flash-attention", false, "Enable flash attention")
+	numCtx := flag.Int("num-ctx", 2048, "Context (or KV cache) size")
+	lpath := flag.String("lora", "", "Path to lora layer file")
 	port := flag.Int("port", 8080, "Port to expose the server on")
+	threads := flag.Int("threads", runtime.NumCPU(), "Number of threads to use during generation")
 	flag.Parse()
 
 	// load the model
 	llama.BackendInit()
-	params := llama.NewModelParams()
+	params := llama.NewModelParams(*nGpuLayers, *mainGpu)
 	model := llama.LoadModelFromFile(*mpath, params)
-	ctxParams := llama.NewContextParams()
+
+	if *lpath != "" {
+		model.ApplyLoraFromFile(*lpath, 1.0, "", *threads)
+	}
+
+	ctxParams := llama.NewContextParams(*numCtx, *threads, *flashAttention)
 	lc := llama.NewContextWithModel(model, ctxParams)
 	if lc == nil {
 		panic("Failed to create context")
@@ -437,6 +452,7 @@ func main() {
 		model:     model,
 		lc:        lc,
 		cc:        cc,
+		batchSize: *batchSize,
 		parallel:  *parallel,
 		seqs:      make([]*Sequence, *parallel),
 	}