Add cgo implementation for llama.cpp

Run the server.cpp directly inside the Go runtime via cgo
while retaining the LLM Go abstractions.

.dockerignore

@@ -2,7 +2,7 @@
 ollama
 app
 dist
-scripts
 llm/llama.cpp/gguf
 .env
 .cache
+test_data

.gitignore

@@ -8,4 +8,5 @@ ollama
 ggml-metal.metal
 .cache
 *.exe
-.idea
+.idea
+test_data

go.mod

@@ -7,7 +7,7 @@ require (
 	github.com/gin-gonic/gin v1.9.1
 	github.com/olekukonko/tablewriter v0.0.5
 	github.com/spf13/cobra v1.7.0
-	github.com/stretchr/testify v1.8.3
+	github.com/stretchr/testify v1.8.4
 	golang.org/x/sync v0.3.0
 )
 

go.sum

@@ -98,8 +98,9 @@ github.com/stretchr/testify v1.7.1/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/
 github.com/stretchr/testify v1.8.0/go.mod h1:yNjHg4UonilssWZ8iaSj1OCr/vHnekPRkoO+kdMU+MU=
 github.com/stretchr/testify v1.8.1/go.mod h1:w2LPCIKwWwSfY2zedu0+kehJoqGctiVI29o6fzry7u4=
 github.com/stretchr/testify v1.8.2/go.mod h1:w2LPCIKwWwSfY2zedu0+kehJoqGctiVI29o6fzry7u4=
-github.com/stretchr/testify v1.8.3 h1:RP3t2pwF7cMEbC1dqtB6poj3niw/9gnV4Cjg5oW5gtY=
 github.com/stretchr/testify v1.8.3/go.mod h1:sz/lmYIOXD/1dqDmKjjqLyZ2RngseejIcXlSw2iwfAo=
+github.com/stretchr/testify v1.8.4 h1:CcVxjf3Q8PM0mHUKJCdn+eZZtm5yQwehR5yeSVQQcUk=
+github.com/stretchr/testify v1.8.4/go.mod h1:sz/lmYIOXD/1dqDmKjjqLyZ2RngseejIcXlSw2iwfAo=
 github.com/twitchyliquid64/golang-asm v0.15.1 h1:SU5vSMR7hnwNxj24w34ZyCi/FmDZTkS4MhqMhdFk5YI=
 github.com/twitchyliquid64/golang-asm v0.15.1/go.mod h1:a1lVb/DtPvCB8fslRZhAngC2+aY1QWCk3Cedj/Gdt08=
 github.com/ugorji/go v1.2.7/go.mod h1:nF9osbDWLy6bDVv/Rtoh6QgnvNDpmCalQV5urGCCS6M=

llm/ext_server.go

@@ -0,0 +1,325 @@
+package llm
+
+/*
+#cgo CFLAGS: -I${SRCDIR}/llama.cpp/gguf -I${SRCDIR}/llama.cpp/gguf/common
+#cgo CFLAGS: -DNDEBUG -DLLAMA_SERVER_LIBRARY=1 -D_XOPEN_SOURCE=600 -DACCELERATE_NEW_LAPACK -DACCELERATE_LAPACK_ILP64
+#cgo CFLAGS: -Wmissing-noreturn -Wall -Wextra -Wcast-qual -Wno-unused-function -Wno-array-bounds
+#cgo CPPFLAGS: -Ofast -Wall -Wextra -Wno-unused-function -Wno-unused-variable -Wno-deprecated-declarations -Wno-unused-but-set-variable
+#cgo darwin CFLAGS: -D_DARWIN_C_SOURCE
+#cgo darwin CPPFLAGS:  -DGGML_USE_ACCELERATE
+#cgo darwin,arm64 CPPFLAGS: -DGGML_USE_METAL -DGGML_METAL_NDEBUG
+#cgo darwin LDFLAGS: -lc++ -framework Accelerate
+#cgo darwin,arm64 LDFLAGS: -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
+#cgo darwin,arm64 LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/metal/common/libcommon.a
+#cgo darwin,arm64 LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/metal/examples/server/libext_server.a
+#cgo darwin,arm64 LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/metal/libllama.a
+#cgo darwin,arm64 LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/metal/libggml_static.a
+#cgo darwin,amd64 LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/cpu/common/libcommon.a
+#cgo darwin,amd64 LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/cpu/examples/server/libext_server.a
+#cgo darwin,amd64 LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/cpu/libllama.a
+#cgo darwin,amd64 LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/cpu/libggml_static.a
+#cgo linux CFLAGS: -D_GNU_SOURCE
+#cgo linux windows CFLAGS: -DGGML_CUDA_DMMV_X=32 -DGGML_CUDA_MMV_Y=1 -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 -DGGML_USE_CUBLAS
+#cgo linux LDFLAGS: -L/usr/local/cuda/targets/x86_64-linux/lib -L/usr/local/cuda/lib64 -L/usr/local/cuda/targets/x86_64-linux/lib/stubs
+#cgo linux LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/cuda/examples/server/libext_server.a
+#cgo linux LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/cuda/common/libcommon.a
+#cgo linux LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/cuda/libllama.a
+#cgo linux LDFLAGS: ${SRCDIR}/llama.cpp/gguf/build/cuda/libggml_static.a
+#cgo linux LDFLAGS: /usr/local/cuda/lib64/libcudart_static.a
+#cgo linux LDFLAGS: /usr/local/cuda/lib64/libcublas_static.a
+#cgo linux LDFLAGS: /usr/local/cuda/lib64/libcublasLt_static.a
+#cgo linux LDFLAGS: /usr/local/cuda/lib64/libcudadevrt.a
+#cgo linux LDFLAGS: /usr/local/cuda/lib64/libculibos.a
+#cgo linux LDFLAGS: -lrt -lpthread -ldl -lstdc++ -lm
+#cgo windows LDFLAGS: -L${SRCDIR}/llama.cpp/gguf/build/wincuda/dist/bin
+#cgo windows LDFLAGS: -lext_server_shared -lpthread
+
+#include <stdlib.h>
+#include "examples/server/server.h"
+
+*/
+import "C"
+import (
+	"bytes"
+	"context"
+	"encoding/json"
+	"errors"
+	"fmt"
+	"log"
+	"os"
+	"runtime"
+	"sync"
+	"time"
+	"unsafe"
+
+	"github.com/jmorganca/ollama/api"
+)
+
+func errWrap(resp C.ext_server_err) error {
+	if resp.code == 0 {
+		return nil
+	}
+	err := fmt.Errorf(C.GoString(resp.err))
+	C.free(unsafe.Pointer(resp.err))
+	return err
+}
+
+type llamaExtServer struct {
+	api.Options
+}
+
+// Note: current implementation does not support concurrent instantiations
+var mutex sync.Mutex
+
+func newLlamaExtServer(model string, adapters, projectors []string, numLayers int64, opts api.Options) (*llamaExtServer, error) {
+	if !mutex.TryLock() {
+		log.Printf("concurrent llm servers not yet supported, waiting for prior server to complete")
+		mutex.Lock()
+	}
+	server := &llamaExtServer{opts}
+	fileInfo, err := os.Stat(model)
+	if err != nil {
+		return nil, err
+	}
+	var sparams C.ext_server_params
+	sparams.model = C.CString(model)
+	defer C.free(unsafe.Pointer(sparams.model))
+
+	numGPU := NumGPU(numLayers, fileInfo.Size(), opts)
+
+	sparams.embedding = true
+	sparams.n_ctx = C.uint(opts.NumCtx)
+	sparams.n_batch = C.uint(opts.NumBatch)
+	sparams.n_gpu_layers = C.int(numGPU)
+	sparams.main_gpu = C.int(opts.MainGPU)
+	sparams.n_parallel = 2 // TODO - wire up concurrency
+
+	// Always use the value encoded in the model
+	sparams.rope_freq_base = 0.0
+	sparams.rope_freq_scale = 0.0
+
+	sparams.lora_adapters = nil
+	for i := 0; i < len(adapters); i++ {
+		la := (*C.ext_server_lora_adapter)(C.malloc(C.sizeof_struct_ext_server_lora_adapter))
+		defer C.free(unsafe.Pointer(la))
+		la.adapter = C.CString(adapters[i])
+		defer C.free(unsafe.Pointer(la.adapter))
+		la.scale = C.float(1.0) // TODO expose scale/weights up through ollama UX
+		la.next = nil
+		if i == 0 {
+			sparams.lora_adapters = la
+		} else {
+			tmp := sparams.lora_adapters
+			for ; tmp.next != nil; tmp = tmp.next {
+			}
+			tmp.next = la
+		}
+	}
+
+	// TODO - implement ME
+	// if len(projectors) > 0 {
+	// 	// TODO: applying multiple projectors is not supported by the llama.cpp server yet
+	// 	params = append(params, "--mmproj", projectors[0])
+	// }
+
+	if opts.NumThread > 0 {
+		sparams.n_threads = C.uint(opts.NumThread)
+	} else {
+		sparams.n_threads = C.uint(runtime.NumCPU())
+	}
+
+	sparams.memory_f16 = false
+	if opts.F16KV {
+		sparams.memory_f16 = true
+	}
+	sparams.use_mlock = false
+	if opts.UseMLock {
+		sparams.use_mlock = true
+	}
+	sparams.use_mmap = true
+	if !opts.UseMMap {
+		sparams.use_mmap = false
+	}
+	sparams.numa = false
+	if opts.UseNUMA {
+		sparams.numa = true
+	}
+
+	log.Printf("Initializing internal llama server")
+	err = errWrap(C.llama_server_init(&sparams))
+	if err != nil {
+		return nil, err
+	}
+
+	log.Printf("Starting internal llama main loop")
+	C.llama_server_start()
+	return server, nil
+}
+
+func (llm *llamaExtServer) Predict(ctx context.Context, predict PredictOpts, fn func(PredictResult)) error {
+
+	request := map[string]any{
+		"prompt":            predict.Prompt,
+		"stream":            true,
+		"n_predict":         llm.NumPredict,
+		"n_keep":            llm.NumKeep,
+		"temperature":       llm.Temperature,
+		"top_k":             llm.TopK,
+		"top_p":             llm.TopP,
+		"tfs_z":             llm.TFSZ,
+		"typical_p":         llm.TypicalP,
+		"repeat_last_n":     llm.RepeatLastN,
+		"repeat_penalty":    llm.RepeatPenalty,
+		"presence_penalty":  llm.PresencePenalty,
+		"frequency_penalty": llm.FrequencyPenalty,
+		"mirostat":          llm.Mirostat,
+		"mirostat_tau":      llm.MirostatTau,
+		"mirostat_eta":      llm.MirostatEta,
+		"penalize_nl":       llm.PenalizeNewline,
+		"seed":              llm.Seed,
+		"stop":              llm.Stop,
+	}
+
+	if predict.Format == "json" {
+		request["grammar"] = jsonGrammar
+	}
+
+	// Handling JSON marshaling with special characters unescaped.
+	buffer := &bytes.Buffer{}
+	enc := json.NewEncoder(buffer)
+	enc.SetEscapeHTML(false)
+
+	if err := enc.Encode(request); err != nil {
+		return fmt.Errorf("failed to marshal data: %w", err)
+	}
+
+	req := C.CString(buffer.String())
+	defer C.free(unsafe.Pointer(req))
+
+	cmpCtx := C.llama_server_completion(req)
+	if cmpCtx.task_id < 0 {
+		defer C.free(unsafe.Pointer(cmpCtx.err))
+		return fmt.Errorf(C.GoString(cmpCtx.err))
+	}
+
+	for {
+		select {
+		case <-ctx.Done():
+			// This handles the request cancellation
+			return errWrap(C.llama_server_completion_cancel(cmpCtx.task_id))
+		default:
+			result := C.llama_server_completion_next_result(cmpCtx.task_id)
+			if result.result_json != nil {
+				defer C.free(unsafe.Pointer(result.result_json))
+			}
+			var p prediction
+			if err := json.Unmarshal([]byte(C.GoString(result.result_json)), &p); err != nil {
+				err2 := errWrap(C.llama_server_completion_cancel(cmpCtx.task_id))
+				return errors.Join(fmt.Errorf("error unmarshaling llm prediction response: %w", err), err2)
+			}
+
+			if p.Content != "" {
+				fn(PredictResult{
+					// Model:     predict.Model, // XXX remove or replace?
+					CreatedAt: time.Now().UTC(),
+					Content:   p.Content,
+				})
+			}
+
+			if p.Stop {
+				fn(PredictResult{
+					// Model:              predict.Model, // XXX remove or replace?
+					CreatedAt:          time.Now().UTC(),
+					TotalDuration:      time.Since(predict.CheckpointStart),
+					Done:               true,
+					PromptEvalCount:    p.Timings.PromptN,
+					PromptEvalDuration: parseDurationMs(p.Timings.PromptMS),
+					EvalCount:          p.Timings.PredictedN,
+					EvalDuration:       parseDurationMs(p.Timings.PredictedMS),
+				})
+				return nil
+			}
+		}
+	}
+}
+
+func (llm *llamaExtServer) Encode(ctx context.Context, prompt string) ([]int, error) {
+	data, err := json.Marshal(TokenizeRequest{Content: prompt})
+	if err != nil {
+		return nil, fmt.Errorf("marshaling encode data: %w", err)
+	}
+	req := C.CString(string(data))
+	defer C.free(unsafe.Pointer(req))
+	var resp C.ext_server_resp
+	err = errWrap(C.llama_server_tokenize(req, &resp))
+	if resp.json_resp != nil {
+		defer C.free(unsafe.Pointer(resp.json_resp))
+	}
+
+	var encoded TokenizeResponse
+	if err2 := json.Unmarshal([]byte(C.GoString(resp.json_resp)), &encoded); err2 != nil {
+		return nil, fmt.Errorf("unmarshal encode response: %w", err2)
+	}
+
+	return encoded.Tokens, err
+}
+
+func (llm *llamaExtServer) Decode(ctx context.Context, tokens []int) (string, error) {
+	if len(tokens) == 0 {
+		return "", nil
+	}
+	data, err := json.Marshal(DetokenizeRequest{Tokens: tokens})
+	if err != nil {
+		return "", fmt.Errorf("marshaling decode data: %w", err)
+	}
+
+	req := C.CString(string(data))
+	defer C.free(unsafe.Pointer(req))
+	var resp C.ext_server_resp
+	err = errWrap(C.llama_server_detokenize(req, &resp))
+	if resp.json_resp != nil {
+		defer C.free(unsafe.Pointer(resp.json_resp))
+	}
+
+	var decoded DetokenizeResponse
+	if err2 := json.Unmarshal([]byte(C.GoString(resp.json_resp)), &decoded); err2 != nil {
+		return "", fmt.Errorf("unmarshal encode response: %w", err2)
+	}
+
+	return decoded.Content, err
+}
+
+func (llm *llamaExtServer) Embedding(ctx context.Context, input string) ([]float64, error) {
+	data, err := json.Marshal(TokenizeRequest{Content: input})
+	if err != nil {
+		return nil, fmt.Errorf("error marshaling embed data: %w", err)
+	}
+
+	req := C.CString(string(data))
+	defer C.free(unsafe.Pointer(req))
+	var resp C.ext_server_resp
+	err = errWrap(C.llama_server_embedding(req, &resp))
+	if resp.json_resp != nil {
+		defer C.free(unsafe.Pointer(resp.json_resp))
+	}
+	if err != nil {
+		return nil, err
+	}
+
+	var embedding EmbeddingResponse
+	if err := json.Unmarshal([]byte(C.GoString(resp.json_resp)), &embedding); err != nil {
+		return nil, fmt.Errorf("unmarshal tokenize response: %w", err)
+	}
+
+	return embedding.Embedding, nil
+}
+
+func (llm *llamaExtServer) Ping(ctx context.Context) error {
+	// TODO - consider some mechanism to check if the main loop and llama.cpp are in a good state
+	return nil
+}
+
+func (llm *llamaExtServer) Close() {
+	C.llama_server_stop()
+	mutex.Unlock()
+}

llm/gpu_cuda.go

@@ -0,0 +1,57 @@
+//go:build linux || windows
+
+package llm
+
+import (
+	"errors"
+	"log"
+
+	"github.com/jmorganca/ollama/api"
+)
+
+/*
+#cgo windows LDFLAGS: -L"/Program Files/NVIDIA Corporation/NVSMI/"
+#cgo linux LDFLAGS: -lnvidia-ml
+
+#include <stdlib.h>
+#include "examples/server/server.h"
+*/
+import "C"
+
+// CheckVRAM returns the free VRAM in bytes on Linux machines with NVIDIA GPUs
+func CheckVRAM() (int64, error) {
+	return int64(C.check_vram()), nil
+}
+
+func NumGPU(numLayer, fileSizeBytes int64, opts api.Options) int {
+	if opts.NumGPU != -1 {
+		return opts.NumGPU
+	}
+	freeBytes, err := CheckVRAM()
+	if err != nil {
+		if !errors.Is(err, errNvidiaSMI) {
+			log.Print(err.Error())
+		}
+		// nvidia driver not installed or no nvidia GPU found
+		return 0
+	}
+
+	/*
+		Calculate bytes per layer, this will roughly be the size of the model file divided by the number of layers.
+		We can store the model weights and the kv cache in vram,
+		to enable kv chache vram storage add two additional layers to the number of layers retrieved from the model file.
+	*/
+	bytesPerLayer := fileSizeBytes / numLayer
+
+	// 75% of the absolute max number of layers we can fit in available VRAM, off-loading too many layers to the GPU can cause OOM errors
+	layers := int(freeBytes/bytesPerLayer) * 3 / 4
+
+	// TODO - not sure on this part... if we can't fit all the layers, just fallback to CPU
+	// if int64(layers) < numLayer {
+	// 	log.Printf("%d MB VRAM available, insufficient to load current model (reuires %d MB) - falling back to CPU %d", freeBytes/(1024*1024), fileSizeBytes/(1024*1024))
+	// 	return 0
+	// }
+	log.Printf("%d MB VRAM available, loading up to %d GPU layers out of %d", freeBytes/(1024*1024), layers, numLayer)
+
+	return layers
+}

llm/gpu_darwin.go

@@ -0,0 +1,19 @@
+//go:build darwin
+
+package llm
+
+import (
+	"github.com/jmorganca/ollama/api"
+)
+
+// CheckVRAM returns the free VRAM in bytes on Linux machines with NVIDIA GPUs
+func CheckVRAM() (int64, error) {
+	// TODO - assume metal, and return free memory?
+	return 0, errNvidiaSMI
+
+}
+
+func NumGPU(numLayer, fileSizeBytes int64, opts api.Options) int {
+	// default to enable metal on macOS
+	return 1
+}

llm/llama.cpp/gen_common.sh

@@ -0,0 +1,34 @@
+# common logic accross linux and darwin
+
+init_vars() {
+    PATCHES="0001-Expose-callable-API-for-server.patch"
+    CMAKE_DEFS="-DLLAMA_ACCELERATE=on"
+    # TODO - LLAMA_K_QUANTS is stale and needs to be mapped to newer cmake settings
+    CMAKE_TARGETS="--target ggml --target ggml_static --target llama --target build_info --target common --target ext_server"
+    if echo "${CGO_CFLAGS}" | grep -- '-g' > /dev/null ; then
+        CMAKE_DEFS="-DCMAKE_BUILD_TYPE=RelWithDebInfo -DCMAKE_VERBOSE_MAKEFILE=on -DLLAMA_GPROF=on ${CMAKE_DEFS}"
+    else
+        # TODO - add additional optimization flags...
+        CMAKE_DEFS="-DCMAKE_BUILD_TYPE=Release ${CMAKE_DEFS}"
+    fi
+}
+
+git_module_setup() {
+    # TODO add flags to skip the init/patch logic to make it easier to mod llama.cpp code in-repo
+    git submodule init
+    git submodule update --force gguf
+
+}
+
+apply_patches() {
+    # Workaround git apply not handling creation well for iteration
+    rm -f gguf/examples/server/server.h
+    for patch in ${PATCHES} ; do
+        git -C gguf apply ../patches/${patch}
+    done
+}
+
+build() {
+    cmake -S gguf -B ${BUILD_DIR} ${CMAKE_DEFS}
+    cmake --build ${BUILD_DIR} ${CMAKE_TARGETS} -j8 
+}

llm/llama.cpp/gen_darwin.sh

@@ -0,0 +1,36 @@
+#!/bin/sh
+# This script is intended to run inside the go generate
+# working directory must be ../llm/llama.cpp
+
+# TODO - add hardening to detect missing tools (cmake, etc.)
+
+set -ex
+set -o pipefail
+echo "Starting darwin generate script"
+source $(dirname $0)/gen_common.sh
+init_vars
+CMAKE_DEFS="-DCMAKE_OSX_DEPLOYMENT_TARGET=11.0 ${CMAKE_DEFS}"
+case "${GOARCH}" in
+    "amd64")
+        CMAKE_DEFS="-DLLAMA_METAL=off -DCMAKE_SYSTEM_PROCESSOR=x86_64 -DCMAKE_OSX_ARCHITECTURES=x86_64 ${CMAKE_DEFS}"
+        BUILD_DIR="gguf/build/cpu"
+        ;;
+     "arm64")
+        CMAKE_DEFS="-DLLAMA_METAL=on -DCMAKE_SYSTEM_PROCESSOR=arm64 -DCMAKE_OSX_ARCHITECTURES=arm64 ${CMAKE_DEFS}"
+        BUILD_DIR="gguf/build/metal"
+        ;;
+    *)
+        echo "GOARCH must be set"
+        echo "this script is meant to be run from within go generate"
+        exit 1
+        ;;
+esac
+
+git_module_setup
+apply_patches
+build
+
+# Enable local debug/run usecase
+if [ -e "gguf/ggml-metal.metal" ]; then
+    cp gguf/ggml-metal.metal ../../
+fi

llm/llama.cpp/gen_linux.sh

@@ -0,0 +1,17 @@
+#!/bin/sh
+# This script is intended to run inside the go generate
+# working directory must be ../llm/llama.cpp
+
+set -ex
+set -o pipefail
+
+# TODO - stopped here - map the variables from above over and refine the case statement below
+
+echo "Starting linux generate script"
+source $(dirname $0)/gen_common.sh
+init_vars
+CMAKE_DEFS="-DLLAMA_CUBLAS=on -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
+BUILD_DIR="gguf/build/cuda"
+git_module_setup
+apply_patches
+build

llm/llama.cpp/gen_windows.ps1

@@ -0,0 +1,51 @@
+#!powershell
+
+$ErrorActionPreference = "Stop"
+
+function init_vars {
+    $script:buildDir="gguf/build/wincuda"
+    $script:installDir="gguf/build/wincuda/dist"
+    $script:patches = @("0001-Expose-callable-API-for-server.patch")
+    $script:cmakeDefs = @("-DLLAMA_NATIVE=off", "-DLLAMA_F16C=off", "-DLLAMA_FMA=off", "-DLLAMA_AVX512=off", "-DLLAMA_AVX2=off", "-DLLAMA_AVX=on", "-DLLAMA_K_QUANTS=on", "-DLLAMA_ACCELERATE=on", "-DLLAMA_CUBLAS=ON","-DCMAKE_VERBOSE_MAKEFILE=ON","-DBUILD_SHARED_LIBS=on","-A","x64")
+
+    if ($env:CGO_CFLAGS -contains "-g") {
+        $script:cmakeDefs += @("-DCMAKE_VERBOSE_MAKEFILE=on")
+        $script:config += "RelWithDebInfo"
+    } else {
+        $script:config += "Release"
+    }
+}
+
+function git_module_setup {
+    # TODO add flags to skip the init/patch logic to make it easier to mod llama.cpp code in-repo
+    & git submodule init
+    & git submodule update --force gguf
+}
+
+function apply_patches {
+    rm -erroraction ignore -path "gguf/examples/server/server.h"
+    foreach ($patch in $patches) {
+        write-host "Applying patch $patch"
+        & git -C gguf apply ../patches/$patch
+    }
+}
+
+function build {
+    write-host "generating config with: cmake -S gguf -B $buildDir $cmakeDefs"
+    & cmake --version
+    & cmake -S gguf -B $buildDir $cmakeDefs
+    write-host "building with: cmake --build $buildDir --config $config"
+    & cmake --build $buildDir --config $config
+}
+
+function install {
+    rm -erroraction ignore -recurse -force -path $installDir
+    & cmake --install $buildDir --prefix $installDir --config $config
+
+}
+
+init_vars
+git_module_setup
+apply_patches
+build
+install

llm/llama.cpp/generate_darwin.go

@@ -0,0 +1,3 @@
+package llm
+
+//go:generate sh ./gen_darwin.sh

llm/llama.cpp/generate_darwin_amd64.go

@@ -1,9 +0,0 @@
-package llm
-
-//go:generate git submodule init
-
-//go:generate git submodule update --force gguf
-//go:generate git -C gguf apply ../patches/0001-update-default-log-target.patch
-//go:generate cmake -S gguf -B gguf/build/cpu -DLLAMA_METAL=off -DLLAMA_ACCELERATE=on -DLLAMA_K_QUANTS=on -DCMAKE_SYSTEM_NAME=Darwin -DCMAKE_SYSTEM_PROCESSOR=x86_64 -DCMAKE_OSX_ARCHITECTURES=x86_64 -DCMAKE_OSX_DEPLOYMENT_TARGET=11.0 -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=on
-//go:generate cmake --build gguf/build/cpu --target server --config Release
-//go:generate mv gguf/build/cpu/bin/server gguf/build/cpu/bin/ollama-runner

llm/llama.cpp/generate_darwin_arm64.go

@@ -1,9 +0,0 @@
-package llm
-
-//go:generate git submodule init
-
-//go:generate git submodule update --force gguf
-//go:generate git -C gguf apply ../patches/0001-update-default-log-target.patch
-//go:generate cmake -S gguf -B gguf/build/metal -DLLAMA_METAL=on -DLLAMA_ACCELERATE=on -DLLAMA_K_QUANTS=on -DCMAKE_SYSTEM_PROCESSOR=arm64 -DCMAKE_OSX_ARCHITECTURES=arm64 -DCMAKE_OSX_DEPLOYMENT_TARGET=11.0
-//go:generate cmake --build gguf/build/metal --target server --config Release
-//go:generate mv gguf/build/metal/bin/server gguf/build/metal/bin/ollama-runner

llm/llama.cpp/generate_linux.go

@@ -1,14 +1,3 @@
 package llm
 
-//go:generate git submodule init
-
-//go:generate git submodule update --force gguf
-//go:generate git -C gguf apply ../patches/0001-copy-cuda-runtime-libraries.patch
-//go:generate git -C gguf apply ../patches/0001-update-default-log-target.patch
-//go:generate cmake -S gguf -B gguf/build/cpu -DLLAMA_K_QUANTS=on -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off
-//go:generate cmake --build gguf/build/cpu --target server --config Release
-//go:generate mv gguf/build/cpu/bin/server gguf/build/cpu/bin/ollama-runner
-
-//go:generate cmake -S gguf -B gguf/build/cuda -DLLAMA_CUBLAS=on -DLLAMA_ACCELERATE=on -DLLAMA_K_QUANTS=on -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off -DLLAMA_CUDA_PEER_MAX_BATCH_SIZE=0
-//go:generate cmake --build gguf/build/cuda --target server --config Release
-//go:generate mv gguf/build/cuda/bin/server gguf/build/cuda/bin/ollama-runner
+//go:generate sh ./gen_linux.sh

llm/llama.cpp/generate_windows.go

@@ -1,17 +1,3 @@
 package llm
 
-//go:generate git submodule init
-
-//go:generate git submodule update --force gguf
-//go:generate git -C gguf apply ../patches/0001-update-default-log-target.patch
-//go:generate cmake -S gguf -B gguf/build/cpu -DLLAMA_K_QUANTS=on -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off
-//go:generate cmake --build gguf/build/cpu --target server --config Release
-//go:generate cmd /c move gguf\build\cpu\bin\Release\server.exe gguf\build\cpu\bin\Release\ollama-runner.exe
-
-//go:generate cmake -S ggml -B ggml/build/cuda -DLLAMA_CUBLAS=on -DLLAMA_ACCELERATE=on -DLLAMA_K_QUANTS=on
-//go:generate cmake --build ggml/build/cuda --target server --config Release
-//go:generate cmd /c move ggml\build\cuda\bin\Release\server.exe ggml\build\cuda\bin\Release\ollama-runner.exe
-
-//go:generate cmake -S gguf -B gguf/build/cuda -DLLAMA_CUBLAS=on -DLLAMA_ACCELERATE=on -DLLAMA_K_QUANTS=on -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off
-//go:generate cmake --build gguf/build/cuda --target server --config Release
-//go:generate cmd /c move gguf\build\cuda\bin\Release\server.exe gguf\build\cuda\bin\Release\ollama-runner.exe
+//go:generate powershell -ExecutionPolicy Bypass -File ./gen_windows.ps1

llm/llama.cpp/patches/0001-Expose-callable-API-for-server.patch

@@ -0,0 +1,422 @@
+From 64b3fbb150d12b3ca63ac2fb4e57bc46f41d2ccd Mon Sep 17 00:00:00 2001
+From: Daniel Hiltgen <daniel@ollama.com>
+Date: Mon, 13 Nov 2023 12:25:58 -0800
+Subject: [PATCH] Expose callable API for server
+
+This adds an extern "C" interface within the example server
+---
+ examples/server/CMakeLists.txt |  24 ++++
+ examples/server/server.cpp     | 247 +++++++++++++++++++++++++++++++++
+ examples/server/server.h       |  83 +++++++++++
+ ggml-cuda.cu                   |   1 +
+ 4 files changed, 355 insertions(+)
+ create mode 100644 examples/server/server.h
+
+diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt
+index 859cd12..4ea47a7 100644
+--- a/examples/server/CMakeLists.txt
++++ b/examples/server/CMakeLists.txt
+@@ -11,3 +11,27 @@ if (WIN32)
+     TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
+ endif()
+ target_compile_features(${TARGET} PRIVATE cxx_std_11)
++
++set(TARGET ext_server)
++option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
++add_library(${TARGET} STATIC server.cpp)
++target_include_directories(${TARGET} PRIVATE ../../common)
++target_include_directories(${TARGET} PRIVATE ../..)
++target_compile_features(${TARGET} PRIVATE cxx_std_11)
++target_compile_definitions(${TARGET} PUBLIC LLAMA_SERVER_LIBRARY=1)
++target_link_libraries(${TARGET} PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT})
++
++if (BUILD_SHARED_LIBS)
++    set_target_properties(ext_server PROPERTIES POSITION_INDEPENDENT_CODE ON)
++    target_compile_definitions(ext_server PRIVATE LLAMA_SHARED LLAMA_BUILD)
++    add_library(ext_server_shared SHARED $<TARGET_OBJECTS:ext_server>)
++    target_link_libraries(ext_server_shared PRIVATE ggml llama llava common ${CMAKE_THREAD_LIBS_INIT})
++    install(TARGETS ext_server_shared LIBRARY)
++endif()
++
++if (CUDAToolkit_FOUND)
++    target_include_directories(${TARGET} PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
++    if (WIN32)
++        target_link_libraries(ext_server_shared PRIVATE nvml)
++    endif()
++endif()
+\ No newline at end of file
+diff --git a/examples/server/server.cpp b/examples/server/server.cpp
+index 895f751..f939590 100644
+--- a/examples/server/server.cpp
++++ b/examples/server/server.cpp
+@@ -5,6 +5,9 @@
+ #include "../llava/clip.h"
+ 
+ #include "stb_image.h"
++#if defined(LLAMA_SERVER_LIBRARY)
++#include "server.h"
++#endif
+ 
+ #ifndef NDEBUG
+ // crash the server in debug mode, otherwise send an http 500 error
+@@ -2631,6 +2634,7 @@ static void append_to_generated_text_from_generated_token_probs(llama_server_con
+     }
+ }
+ 
++#ifndef LLAMA_SERVER_LIBRARY
+ int main(int argc, char **argv)
+ {
+     // own arguments required by this example
+@@ -3065,3 +3069,246 @@ int main(int argc, char **argv)
+     llama_backend_free();
+     return 0;
+ }
++
++#else // LLAMA_SERVER_LIBRARY
++// Expose the llama server as a callable extern "C" API
++llama_server_context llama;
++std::atomic<bool> ext_server_running(false);
++std::thread ext_server_thread;
++inline ext_server_err makeErr(uint32_t code, std::string msg) {
++    if (code == 0) {
++        return ext_server_err{0, NULL};
++    }
++    const std::string::size_type size = msg.size();
++    ext_server_err ret = {
++        code,
++        new char[size + 1],
++    };
++    memcpy(ret.err, msg.c_str(), size + 1);
++    return ret;
++}
++
++ext_server_err llama_server_init(ext_server_params *sparams)
++{
++    log_set_target(stdout);
++    gpt_params params;
++    params.n_ctx = sparams->n_ctx;
++    params.n_batch = sparams->n_batch;
++    params.n_threads = sparams->n_threads;
++    params.n_parallel = sparams->n_parallel;
++    params.rope_freq_base = sparams->rope_freq_base;
++    params.rope_freq_scale = sparams->rope_freq_scale;
++
++    if (sparams->memory_f16)  {
++        params.cache_type_k = "f16";
++        params.cache_type_v = "f16";
++    } else {
++        params.cache_type_k = "f32";
++        params.cache_type_v = "f32";
++    }
++    
++    params.n_gpu_layers = sparams->n_gpu_layers;
++    params.main_gpu = sparams->main_gpu;
++    params.use_mlock = sparams->use_mlock;
++    params.use_mmap = sparams->use_mmap;
++    params.numa = sparams->numa;
++    params.embedding = sparams->embedding;
++    if (sparams->model != NULL) {
++        params.model = sparams->model;
++    }
++
++    for (ext_server_lora_adapter *la = sparams->lora_adapters; la != NULL; la = la->next) {
++        params.lora_adapter.push_back(std::make_tuple(la->adapter, la->scale));
++    }
++    
++    try {
++        llama_backend_init(params.numa);
++
++        // load the model
++        if (!llama.load_model(params))
++        {
++            // TODO - consider modifying the logging logic or patching load_model so we can capture more detailed error messages
++            // and pass them back to the caller for better UX
++            return makeErr(1, "error loading model " + params.model);
++        }
++
++        llama.initialize();
++    } catch (std::exception &e) {
++        return makeErr(1, e.what());
++    } catch (...) {
++        return makeErr(1, "Unknown Exception initializing llama server");
++    }
++    return makeErr(0, "");
++}
++
++void llama_server_start()
++{
++     // TODO mutex to protect thread creation
++    ext_server_thread = std::thread([&]()
++    {
++        ext_server_running = true;
++        try {
++            LOG_TEE("llama server main loop starting\n");
++            ggml_time_init();
++            while (ext_server_running.load())
++            {
++                if (!llama.update_slots()) {
++                    LOG_TEE("unexpected error in llama server update_slots - exiting main loop\n");
++                    break;
++                }
++            }
++        } catch (std::exception &e) {
++            LOG_TEE("caught exception in llama server main loop: %s\n", e.what());
++        } catch (...) {
++            LOG_TEE("caught unknown exception in llama server main loop\n");
++        }
++        LOG_TEE("\nllama server shutting down\n");
++        llama_backend_free();
++    });
++}
++
++void llama_server_stop() {
++    // TODO - too verbose, remove once things are solid
++    LOG_TEE("requesting llama server shutdown\n");
++    ext_server_running = false;
++    ext_server_thread.join();
++    LOG_TEE("llama server shutdown complete\n");
++}
++
++ext_server_completion_resp llama_server_completion(const char *json_req) {
++    std::string msg;
++    ext_server_completion_resp resp = {
++        0,
++        NULL,
++    };
++    try {
++        json data = json::parse(json_req);
++        resp.task_id = llama.request_completion(data, false, false, -1);
++        return resp;
++    } catch (std::exception &e) {
++        msg = e.what();
++    } catch (...) {
++        msg = "Unknown Exception during completion";
++    }
++    const std::string::size_type size = msg.size();
++    resp.task_id = 0;
++    resp.err = new char[size + 1];
++    memcpy(resp.err, msg.c_str(), size + 1);
++    return resp;
++}
++
++ext_task_result llama_server_completion_next_result(const int task_id) {
++    std::string msg;
++    ext_task_result resp = {-1,false,false,NULL};
++    try {
++        task_result result = llama.next_result(task_id);
++        std::string result_json = result.result_json.dump(-1, ' ', false, json::error_handler_t::replace);
++        const std::string::size_type size = result_json.size();
++        resp.id = result.id;
++        resp.stop = result.stop;
++        resp.error = result.error;
++        resp.result_json = new char[size + 1];
++        memcpy(resp.result_json, result_json.c_str(), size + 1);
++        if (result.error) {
++            llama.request_cancel(task_id);
++        } else if (result.stop) {
++            llama.request_cancel(task_id);
++        }
++        return resp;
++    } catch (std::exception &e) {
++        msg = e.what(); // TODO - json?
++    } catch (...) {
++        msg = "Unknown Exception during completion";
++    }
++    resp.error = true;
++    const std::string::size_type size = msg.size();
++    resp.result_json = new char[size + 1];
++    memcpy(resp.result_json, msg.c_str(), size + 1);
++    return resp;
++}
++
++ext_server_err llama_server_completion_cancel(const int task_id) {
++    try {
++        llama.request_cancel(task_id);
++    } catch (std::exception &e) {
++        return makeErr(1, e.what());
++    } catch (...) {
++        return makeErr(1, "Unknown Exception running llama server");
++    }
++    return makeErr(0, "");
++}
++
++
++ext_server_err llama_server_tokenize(const char *json_req, ext_server_resp *resp) {
++    resp->json_resp = NULL;
++    try {
++        const json body = json::parse(json_req);
++        std::vector<llama_token> tokens;
++        if (body.count("content") != 0)
++        {
++            tokens = llama.tokenize(body["content"], false);
++        }
++        const json data = format_tokenizer_response(tokens);
++        std::string result_json = data.dump();
++        const std::string::size_type size = result_json.size();
++        resp->json_resp = new char[size + 1];
++        memcpy(resp->json_resp, result_json.c_str(), size + 1);
++    } catch (std::exception &e) {
++        return makeErr(1, e.what());
++    } catch (...) {
++        return makeErr(1, "Unknown Exception during tokenize");
++    }
++    return makeErr(0, "");
++}
++
++ext_server_err llama_server_detokenize(const char *json_req, ext_server_resp *resp) {
++    resp->json_resp = NULL;
++    try {
++        const json body = json::parse(json_req);
++        std::string content;
++        if (body.count("tokens") != 0)
++        {
++            const std::vector<llama_token> tokens = body["tokens"];
++            content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend());
++        }
++        const json data = format_detokenized_response(content);
++        std::string result_json = data.dump();
++        const std::string::size_type size = result_json.size();
++        resp->json_resp = new char[size + 1];
++        memcpy(resp->json_resp, result_json.c_str(), size + 1);
++    } catch (std::exception &e) {
++        return makeErr(1, e.what());
++    } catch (...) {
++        return makeErr(1, "Unknown Exception during detokenize");
++    }
++    return makeErr(0, "");
++}
++
++ext_server_err llama_server_embedding(const char *json_req, ext_server_resp *resp) {
++    resp->json_resp = NULL;
++    try {
++        const json body = json::parse(json_req);
++        json prompt;
++        if (body.count("content") != 0)
++        {
++            prompt = body["content"];
++        }
++        else
++        {
++            prompt = "";
++        }
++        const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true, -1);
++        task_result result = llama.next_result(task_id);
++        std::string result_json = result.result_json.dump();
++        const std::string::size_type size = result_json.size();
++        resp->json_resp = new char[size + 1];
++        memcpy(resp->json_resp, result_json.c_str(), size + 1);
++    } catch (std::exception &e) {
++        return makeErr(1, e.what());
++    } catch (...) {
++        return makeErr(1, "Unknown Exception during detokenize");
++    }
++    return makeErr(0, "");
++}
++
++#endif // LLAMA_SERVER_LIBRARY
+\ No newline at end of file
+diff --git a/examples/server/server.h b/examples/server/server.h
+new file mode 100644
+index 0000000..4d03b1e
+--- /dev/null
++++ b/examples/server/server.h
+@@ -0,0 +1,83 @@
++#if defined(LLAMA_SERVER_LIBRARY)
++#ifndef LLAMA_SERVER_H
++#define LLAMA_SERVER_H
++#include <stddef.h>
++#include <stdint.h>
++#include <stdio.h>
++#include <stdbool.h>
++
++// This exposes extern C entrypoints into the llama_server 
++// To enable the server compile with LLAMA_SERVER_LIBRARY
++
++#ifdef __cplusplus
++extern "C"
++{
++#endif
++    // TODO - clean the type def's up a bit for better consistency
++    typedef struct ext_server_err {
++        uint32_t code; // 0 on success, > 0 on error
++        char *err; // null if code == 0; else contains error message.  Caller responsible for freeing memory
++    } ext_server_err;
++
++    typedef struct ext_server_lora_adapter {
++        char *adapter;
++        float scale;
++        struct ext_server_lora_adapter *next;
++    } ext_server_lora_adapter;
++    typedef struct ext_server_params
++    {
++        char *model;
++        uint32_t n_ctx;         // text context, 0 = from model
++        uint32_t n_batch;       // prompt processing maximum batch size
++        uint32_t n_threads;     // number of threads to use for generation
++        int32_t n_parallel;     // number of parallel sequences to decodewra
++        float rope_freq_base;   // RoPE base frequency, 0 = from model
++        float rope_freq_scale;  // RoPE frequency scaling factor, 0 = from model
++        bool memory_f16;        // use f16 instead of f32 for memory kv
++        int32_t n_gpu_layers;   // number of layers to store in VRAM (-1 - use default)
++        int32_t main_gpu;       // the GPU that is used for scratch and small tensors
++        bool use_mlock;         // force system to keep model in RAM
++        bool use_mmap;          // use mmap if possible
++        bool numa;              // attempt optimizations that help on some NUMA systems
++        bool embedding;         // get only sentence embedding
++        ext_server_lora_adapter* lora_adapters;
++    } ext_server_params;
++
++    // Initialize the server once per process
++    ext_server_err llama_server_init(ext_server_params *sparams);
++
++    // Run the main loop
++    void llama_server_start();
++    // Stop the main loop
++    void llama_server_stop();
++
++    typedef struct ext_task_result
++    {
++        int id;
++        bool stop;
++        bool error;
++        char* result_json; // caller responsible to free this memory
++    } ext_task_result;
++    
++    typedef struct ext_server_completion_resp {
++        int task_id; // < 0 on error, >= 0 on success
++        char *err; // null if task_id >= 0; else contains error message.  Caller responsible for freeing memory
++    } ext_server_completion_resp;
++    ext_server_completion_resp llama_server_completion(const char *json_req);
++    ext_task_result llama_server_completion_next_result(const int task_id);
++    ext_server_err llama_server_completion_cancel(const int task_id);
++
++    // Caller responsible for freeing json_resp
++    typedef struct ext_server_resp {
++        char *json_resp; // Caller responsible for freeing string
++    } ext_server_resp;
++    ext_server_err llama_server_tokenize(const char *json_req, ext_server_resp *resp);
++    ext_server_err llama_server_detokenize(const char *json_req, ext_server_resp *resp);
++    ext_server_err llama_server_embedding(const char *json_req, ext_server_resp *resp);
++
++#ifdef __cplusplus
++}
++#endif
++
++#endif
++#endif // LLAMA_SERVER_LIBRARY
+\ No newline at end of file
+diff --git a/ggml-cuda.cu b/ggml-cuda.cu
+index 85f7a29..ce51364 100644
+--- a/ggml-cuda.cu
++++ b/ggml-cuda.cu
+@@ -6410,6 +6410,7 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
+         CUDA_CHECK(cudaGetDevice(&id));
+         src_ptr = (char *) extra->data_device[id];
+     } else {
++        fprintf(stderr, "ggml_cuda_cpy_tensor_2d assert: backend: %d\n", src->backend);
+         GGML_ASSERT(false);
+     }
+     char * dst_ptr = (char *) dst;
+-- 
+2.39.3 (Apple Git-145)
+

llm/llama.cpp/patches/0001-copy-cuda-runtime-libraries.patch

@@ -1,27 +0,0 @@
-From 5dd02993e8cc2ce309157736b95bb572f274a3fd Mon Sep 17 00:00:00 2001
-From: Michael Yang <mxyng@pm.me>
-Date: Wed, 20 Sep 2023 14:19:52 -0700
-Subject: [PATCH] copy cuda runtime libraries
-
----
- CMakeLists.txt | 4 ++++
- 1 file changed, 4 insertions(+)
-
-diff --git a/CMakeLists.txt b/CMakeLists.txt
-index 824d9f2..dd24137 100644
---- a/CMakeLists.txt
-+++ b/CMakeLists.txt
-@@ -274,6 +274,10 @@ if (LLAMA_CUBLAS)
-             set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
-         endif()
- 
-+        configure_file(${CUDAToolkit_LIBRARY_DIR}/libcudart.so ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/libcudart.so.${CUDAToolkit_VERSION_MAJOR}.0 COPYONLY)
-+        configure_file(${CUDAToolkit_LIBRARY_DIR}/libcublas.so ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/libcublas.so.${CUDAToolkit_VERSION_MAJOR} COPYONLY)
-+        configure_file(${CUDAToolkit_LIBRARY_DIR}/libcublasLt.so ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/libcublasLt.so.${CUDAToolkit_VERSION_MAJOR} COPYONLY)
-+
-     if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
-         # 52 == lowest CUDA 12 standard
-         # 60 == f16 CUDA intrinsics
--- 
-2.42.0
-

llm/llama.cpp/patches/0001-update-default-log-target.patch

@@ -1,25 +0,0 @@
-From 6465fec6290f0a7f5d4d0fbe6bcf634e4810dde6 Mon Sep 17 00:00:00 2001
-From: Michael Yang <mxyng@pm.me>
-Date: Mon, 23 Oct 2023 10:39:34 -0700
-Subject: [PATCH] default log stderr
-
----
- common/log.h | 2 +-
- 1 file changed, 1 insertion(+), 1 deletion(-)
-
-diff --git a/common/log.h b/common/log.h
-index b8953fd..25522cd 100644
---- a/common/log.h
-+++ b/common/log.h
-@@ -90,7 +90,7 @@
- //  }
- //
- #ifndef LOG_TARGET
--    #define LOG_TARGET log_handler()
-+    #define LOG_TARGET nullptr
- #endif
- 
- #ifndef LOG_TEE_TARGET
--- 
-2.42.0
-

llm/llama.go

@@ -1,25 +1,12 @@
 package llm
 
 import (
-	"bufio"
 	"bytes"
 	"context"
-	"embed"
-	"encoding/json"
 	"errors"
 	"fmt"
-	"io"
-	"io/fs"
-	"log"
-	"math/rand"
-	"net/http"
 	"os"
 	"os/exec"
-	"path"
-	"path/filepath"
-	"runtime"
-	"strconv"
-	"strings"
 	"sync"
 	"time"
 
@@ -55,107 +42,6 @@ number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
 ws ::= ([ \t\n] ws)?
 `
 
-//go:embed llama.cpp/*/build/*/bin/*
-var llamaCppEmbed embed.FS
-
-type ModelRunner struct {
-	Path        string // path to the model runner executable
-	Accelerated bool
-}
-
-func chooseRunners(workDir string) []ModelRunner {
-	buildPath := path.Join("llama.cpp", "gguf", "build")
-	var runners []ModelRunner
-
-	// set the runners based on the OS
-	// IMPORTANT: the order of the runners in the array is the priority order
-	switch runtime.GOOS {
-	case "darwin":
-		if runtime.GOARCH == "arm64" {
-			runners = []ModelRunner{{Path: path.Join(buildPath, "metal", "bin", "ollama-runner")}}
-		} else {
-			runners = []ModelRunner{{Path: path.Join(buildPath, "cpu", "bin", "ollama-runner")}}
-		}
-	case "linux":
-		runners = []ModelRunner{
-			{Path: path.Join(buildPath, "cuda", "bin", "ollama-runner"), Accelerated: true},
-			{Path: path.Join(buildPath, "cpu", "bin", "ollama-runner")},
-		}
-	case "windows":
-		// TODO: select windows GPU runner here when available
-		runners = []ModelRunner{
-			{Path: path.Join(buildPath, "cuda", "bin", "Release", "ollama-runner.exe"), Accelerated: true},
-			{Path: path.Join(buildPath, "cpu", "bin", "Release", "ollama-runner.exe")},
-		}
-	default:
-		log.Printf("unknown OS, running on CPU: %s", runtime.GOOS)
-		runners = []ModelRunner{
-			{Path: path.Join(buildPath, "cpu", "bin", "ollama-runner")},
-		}
-	}
-
-	runnerAvailable := false // if no runner files are found in the embed, this flag will cause a fast fail
-	for _, r := range runners {
-		// find all the files in the runner's bin directory
-		files, err := fs.Glob(llamaCppEmbed, path.Join(path.Dir(r.Path), "*"))
-		if err != nil {
-			// this is expected, ollama may be compiled without all runners packed in
-			log.Printf("%s runner not found: %v", r.Path, err)
-			continue
-		}
-
-		for _, f := range files {
-			runnerAvailable = true
-
-			srcFile, err := llamaCppEmbed.Open(f)
-			if err != nil {
-				log.Fatalf("read llama runner %s: %v", f, err)
-			}
-			defer srcFile.Close()
-
-			// create the directory in case it does not exist, filepath.Dir() converts the file path to the OS's format
-			destPath := filepath.Join(workDir, filepath.Dir(f))
-			if err := os.MkdirAll(destPath, 0o755); err != nil {
-				log.Fatalf("create runner temp dir %s: %v", filepath.Dir(f), err)
-			}
-
-			// create the path to the destination file, filepath.Base() converts the file path to the OS's format
-			destFile := filepath.Join(destPath, filepath.Base(f))
-
-			_, err = os.Stat(destFile)
-			switch {
-			case errors.Is(err, os.ErrNotExist):
-				destFile, err := os.OpenFile(destFile, os.O_WRONLY|os.O_CREATE|os.O_TRUNC, 0o755)
-				if err != nil {
-					log.Fatalf("write llama runner %s: %v", f, err)
-				}
-				defer destFile.Close()
-
-				if _, err := io.Copy(destFile, srcFile); err != nil {
-					log.Fatalf("copy llama runner %s: %v", f, err)
-				}
-			case err != nil:
-				log.Fatalf("stat llama runner %s: %v", f, err)
-			}
-		}
-	}
-	if !runnerAvailable {
-		log.Fatalf("gguf runner not found")
-	}
-
-	// return the runners to try in priority order
-	localRunnersByPriority := []ModelRunner{}
-	for _, r := range runners {
-		// clean the ModelRunner paths so that they match the OS we are running on
-		localRunnersByPriority = append(localRunnersByPriority, ModelRunner{
-			Path:        filepath.Clean(path.Join(workDir, r.Path)),
-			Accelerated: r.Accelerated,
-		})
-	}
-
-	return localRunnersByPriority
-}
-
 type llamaModel struct {
 	hyperparameters llamaHyperparameters
 }
@@ -237,72 +123,6 @@ var (
 	errAvailableVRAM = errors.New("not enough VRAM available, falling back to CPU only")
 )
 
-// CheckVRAM returns the free VRAM in bytes on Linux machines with NVIDIA GPUs
-func CheckVRAM() (int64, error) {
-	cmd := exec.Command("nvidia-smi", "--query-gpu=memory.free", "--format=csv,noheader,nounits")
-	var stdout bytes.Buffer
-	cmd.Stdout = &stdout
-	err := cmd.Run()
-	if err != nil {
-		return 0, errNvidiaSMI
-	}
-
-	var freeMiB int64
-	scanner := bufio.NewScanner(&stdout)
-	for scanner.Scan() {
-		line := scanner.Text()
-		if strings.Contains(line, "[Insufficient Permissions]") {
-			return 0, fmt.Errorf("GPU support may not enabled, check you have installed GPU drivers and have the necessary permissions to run nvidia-smi")
-		}
-
-		vram, err := strconv.ParseInt(strings.TrimSpace(line), 10, 64)
-		if err != nil {
-			return 0, fmt.Errorf("failed to parse available VRAM: %v", err)
-		}
-
-		freeMiB += vram
-	}
-
-	freeBytes := freeMiB * 1024 * 1024
-	if freeBytes < 2*format.GigaByte {
-		log.Printf("less than 2 GB VRAM available")
-		return 0, errAvailableVRAM
-	}
-
-	return freeBytes, nil
-}
-
-func NumGPU(numLayer, fileSizeBytes int64, opts api.Options) int {
-	if opts.NumGPU != -1 {
-		return opts.NumGPU
-	}
-	if runtime.GOOS == "linux" || runtime.GOOS == "windows" {
-		freeBytes, err := CheckVRAM()
-		if err != nil {
-			if !errors.Is(err, errNvidiaSMI) {
-				log.Print(err.Error())
-			}
-			// nvidia driver not installed or no nvidia GPU found
-			return 0
-		}
-
-		/*
-		 Calculate bytes per layer, this will roughly be the size of the model file divided by the number of layers.
-		 We can store the model weights and the kv cache in vram,
-		 to enable kv chache vram storage add two additional layers to the number of layers retrieved from the model file.
-		*/
-		bytesPerLayer := fileSizeBytes / numLayer
-
-		// 75% of the absolute max number of layers we can fit in available VRAM, off-loading too many layers to the GPU can cause OOM errors
-		layers := int(freeBytes/bytesPerLayer) * 3 / 4
-		log.Printf("%d MB VRAM available, loading up to %d GPU layers", freeBytes/(1024*1024), layers)
-
-		return layers
-	}
-	// default to enable metal on macOS
-	return 1
-}
-
 // StatusWriter is a writer that captures error messages from the llama runner process
 type StatusWriter struct {
 	ErrCh      chan error
@@ -331,204 +151,6 @@ func (w *StatusWriter) Write(b []byte) (int, error) {
 	return os.Stderr.Write(b)
 }
 
-func newLlama(model string, adapters, projectors []string, runners []ModelRunner, numLayers int64, opts api.Options) (*llama, error) {
-	fileInfo, err := os.Stat(model)
-	if err != nil {
-		return nil, err
-	}
-
-	if len(adapters) > 1 {
-		return nil, errors.New("ollama supports only one lora adapter, but multiple were provided")
-	}
-
-	numGPU := NumGPU(numLayers, fileInfo.Size(), opts)
-	params := []string{
-		"--model", model,
-		"--ctx-size", fmt.Sprintf("%d", opts.NumCtx),
-		"--batch-size", fmt.Sprintf("%d", opts.NumBatch),
-		"--n-gpu-layers", fmt.Sprintf("%d", numGPU),
-		"--embedding",
-		"--parallel", "2",
-	}
-
-	if opts.MainGPU > 0 {
-		params = append(params, "--main-gpu", fmt.Sprintf("%d", opts.MainGPU))
-	}
-
-	if opts.RopeFrequencyBase > 0 {
-		params = append(params, "--rope-freq-base", fmt.Sprintf("%f", opts.RopeFrequencyBase))
-	}
-
-	if opts.RopeFrequencyScale > 0 {
-		params = append(params, "--rope-freq-scale", fmt.Sprintf("%f", opts.RopeFrequencyScale))
-	}
-
-	if opts.NumGQA > 0 {
-		params = append(params, "--gqa", fmt.Sprintf("%d", opts.NumGQA))
-	}
-
-	if len(adapters) > 0 {
-		// TODO: applying multiple adapters is not supported by the llama.cpp server yet
-		params = append(params, "--lora", adapters[0])
-	}
-
-	if len(projectors) > 0 {
-		// TODO: applying multiple projectors is not supported by the llama.cpp server yet
-		params = append(params, "--mmproj", projectors[0])
-	}
-
-	if opts.NumThread > 0 {
-		params = append(params, "--threads", fmt.Sprintf("%d", opts.NumThread))
-	}
-
-	if !opts.F16KV {
-		params = append(params, "--memory-f32")
-	}
-	if opts.UseMLock {
-		params = append(params, "--mlock")
-	}
-	if !opts.UseMMap {
-		params = append(params, "--no-mmap")
-	}
-	if opts.UseNUMA {
-		params = append(params, "--numa")
-	}
-
-	var runnerErr error
-
-	// start the llama.cpp server with a retry in case the port is already in use
-	for _, runner := range runners {
-		if runner.Accelerated && numGPU == 0 {
-			log.Printf("skipping accelerated runner because num_gpu=0")
-			continue
-		}
-
-		if _, err := os.Stat(runner.Path); err != nil {
-			log.Printf("llama runner not found: %v", err)
-			continue
-		}
-
-		port := rand.Intn(65535-49152) + 49152 // get a random port in the ephemeral range
-		params := append(params, "--port", strconv.Itoa(port))
-
-		ctx, cancel := context.WithCancel(context.Background())
-		cmd := exec.CommandContext(
-			ctx,
-			runner.Path,
-			params...,
-		)
-
-		var libraryPaths []string
-		if libraryPath, ok := os.LookupEnv("LD_LIBRARY_PATH"); ok {
-			libraryPaths = append(libraryPaths, libraryPath)
-		}
-
-		libraryPaths = append(libraryPaths, filepath.Dir(runner.Path))
-
-		cmd.Env = append(os.Environ(), fmt.Sprintf("LD_LIBRARY_PATH=%s", strings.Join(libraryPaths, ":")))
-		cmd.Stdout = os.Stderr
-		statusWriter := NewStatusWriter()
-		cmd.Stderr = statusWriter
-
-		llm := &llama{Options: opts, Running: Running{Port: port, Cmd: cmd, Cancel: cancel, exitCh: make(chan error)}}
-
-		log.Print("starting llama runner")
-		if err := llm.Cmd.Start(); err != nil {
-			log.Printf("error starting the external llama runner: %v", err)
-			continue
-		}
-
-		// monitor the llama runner process and signal when it exits
-		go func() {
-			err := llm.Cmd.Wait()
-			// default to printing the exit message of the command process, it will probably just say 'exit staus 1'
-			errMsg := err.Error()
-			// try to set a better error message if llama runner logs captured an error
-			if statusWriter.LastErrMsg != "" {
-				errMsg = statusWriter.LastErrMsg
-			}
-			log.Println(errMsg)
-			// llm.Cmd.Wait() can only be called once, use this exit channel to signal that the process has exited
-			llm.exitOnce.Do(func() {
-				close(llm.exitCh)
-			})
-		}()
-
-		if err := waitForServer(llm); err != nil {
-			log.Printf("error starting llama runner: %v", err)
-			llm.Close()
-
-			// default the runnerErr to the error returned by the most recent llama runner process
-			runnerErr = err
-
-			// capture the error directly from the runner process, if any
-			select {
-			case runnerErr = <-statusWriter.ErrCh:
-			default:
-				// the runner process probably timed out
-			}
-
-			// try again
-			continue
-		}
-
-		// server started successfully
-		return llm, nil
-	}
-
-	if runnerErr != nil {
-		// this is the error returned from the llama runner process that failed most recently
-		return nil, runnerErr
-	}
-
-	return nil, fmt.Errorf("failed to start a llama runner")
-}
-
-func waitForServer(llm *llama) error {
-	start := time.Now()
-	expiresAt := time.Now().Add(3 * time.Minute) // be generous with timeout, large models can take a while to load
-	ticker := time.NewTicker(200 * time.Millisecond)
-	defer ticker.Stop()
-
-	log.Print("waiting for llama runner to start responding")
-	for {
-		select {
-		case <-llm.exitCh:
-			// failed to start subprocess
-			return fmt.Errorf("llama runner process has terminated")
-		case <-ticker.C:
-			if time.Now().After(expiresAt) {
-				// timeout
-				return fmt.Errorf("timed out waiting for llama runner to start")
-			}
-
-			if err := llm.Ping(context.Background()); err == nil {
-				// success
-				log.Printf("llama runner started in %f seconds", time.Since(start).Seconds())
-				return nil
-			}
-		}
-	}
-}
-
-func (llm *llama) Close() {
-	// signal the sub-process to terminate
-	llm.Cancel()
-
-	// wait for the command to exit to prevent race conditions with the next run
-	<-llm.exitCh
-
-	if llm.StatusWriter != nil && llm.StatusWriter.LastErrMsg != "" {
-		log.Printf("llama runner stopped with error: %v", llm.StatusWriter.LastErrMsg)
-	} else {
-		log.Print("llama runner stopped successfully")
-	}
-}
-
-func (llm *llama) SetOptions(opts api.Options) {
-	llm.Options = opts
-}
-
 type prediction struct {
 	Content string `json:"content"`
 	Model   string `json:"model"`
@@ -561,158 +183,6 @@ type PredictResult struct {
 	EvalDuration       time.Duration
 }
 
-// IsRetryable checks if the line matches a condition that can be retried
-func isRetryable(line []byte) bool {
-	return bytes.Contains(line, []byte("slot unavailable"))
-}
-
-func (llm *llama) Predict(ctx context.Context, predict PredictOpts, fn func(PredictResult)) error {
-	imageData := llm.ImageData
-	if len(predict.Images) > 0 {
-		for cnt, i := range predict.Images {
-			imageData = append(imageData, ImageData{Data: i, ID: cnt})
-		}
-	}
-	log.Printf("loaded %d images", len(imageData))
-
-	request := map[string]any{
-		"prompt":            predict.Prompt,
-		"stream":            true,
-		"n_predict":         llm.NumPredict,
-		"n_keep":            llm.NumKeep,
-		"main_gpu":          llm.MainGPU,
-		"temperature":       llm.Temperature,
-		"top_k":             llm.TopK,
-		"top_p":             llm.TopP,
-		"tfs_z":             llm.TFSZ,
-		"typical_p":         llm.TypicalP,
-		"repeat_last_n":     llm.RepeatLastN,
-		"repeat_penalty":    llm.RepeatPenalty,
-		"presence_penalty":  llm.PresencePenalty,
-		"frequency_penalty": llm.FrequencyPenalty,
-		"mirostat":          llm.Mirostat,
-		"mirostat_tau":      llm.MirostatTau,
-		"mirostat_eta":      llm.MirostatEta,
-		"penalize_nl":       llm.PenalizeNewline,
-		"seed":              llm.Seed,
-		"stop":              llm.Stop,
-		"image_data":        imageData,
-	}
-
-	if predict.Format == "json" {
-		request["grammar"] = jsonGrammar
-	}
-
-	retryDelay := 100 * time.Microsecond
-	for retries := 0; retries < maxRetries; retries++ {
-		if retries > 0 {
-			time.Sleep(retryDelay) // wait before retrying
-			retryDelay *= 2        // exponential backoff
-		}
-
-		// Handling JSON marshaling with special characters unescaped.
-		buffer := &bytes.Buffer{}
-		enc := json.NewEncoder(buffer)
-		enc.SetEscapeHTML(false)
-
-		if err := enc.Encode(request); err != nil {
-			return fmt.Errorf("failed to marshal data: %v", err)
-		}
-
-		endpoint := fmt.Sprintf("http://127.0.0.1:%d/completion", llm.Port)
-		req, err := http.NewRequestWithContext(ctx, http.MethodPost, endpoint, buffer)
-		if err != nil {
-			return fmt.Errorf("error creating POST request: %v", err)
-		}
-		req.Header.Set("Content-Type", "application/json")
-
-		resp, err := http.DefaultClient.Do(req)
-		if err != nil {
-			return fmt.Errorf("POST predict: %v", err)
-		}
-		defer resp.Body.Close()
-
-		if resp.StatusCode >= 400 {
-			bodyBytes, err := io.ReadAll(resp.Body)
-			if err != nil {
-				return fmt.Errorf("failed reading llm error response: %w", err)
-			}
-			log.Printf("llm predict error: %s", bodyBytes)
-			return fmt.Errorf("%s", bodyBytes)
-		}
-
-		scanner := bufio.NewScanner(resp.Body)
-		// increase the buffer size to avoid running out of space
-		buf := make([]byte, 0, maxBufferSize)
-		scanner.Buffer(buf, maxBufferSize)
-
-		retryNeeded := false
-		for scanner.Scan() {
-			select {
-			case <-ctx.Done():
-				// This handles the request cancellation
-				return ctx.Err()
-			default:
-				line := scanner.Bytes()
-				if len(line) == 0 {
-					continue
-				}
-
-				if isRetryable(line) {
-					retryNeeded = true
-					break
-				}
-
-				evt, ok := bytes.CutPrefix(line, []byte("data: "))
-				if !ok {
-					return fmt.Errorf("error parsing llm response stream: %s", line)
-				}
-
-				var p prediction
-				if err := json.Unmarshal(evt, &p); err != nil {
-					return fmt.Errorf("error unmarshaling llm prediction response: %v", err)
-				}
-
-				if p.Content != "" {
-					fn(PredictResult{
-						Content: p.Content,
-					})
-				}
-
-				if p.Stop {
-					fn(PredictResult{
-						Done:               true,
-						PromptEvalCount:    p.Timings.PromptN,
-						PromptEvalDuration: parseDurationMs(p.Timings.PromptMS),
-						EvalCount:          p.Timings.PredictedN,
-						EvalDuration:       parseDurationMs(p.Timings.PredictedMS),
-					})
-					return nil
-				}
-			}
-		}
-
-		if err := scanner.Err(); err != nil {
-			if strings.Contains(err.Error(), "unexpected EOF") {
-				// this means the llama runner subprocess crashed
-				llm.Close()
-				if llm.StatusWriter != nil && llm.StatusWriter.LastErrMsg != "" {
-					return fmt.Errorf("llama runner exited: %v", llm.StatusWriter.LastErrMsg)
-				}
-				return fmt.Errorf("llama runner exited, you may not have enough available memory to run this model")
-			}
-			return fmt.Errorf("error reading llm response: %v", err)
-		}
-
-		if !retryNeeded {
-			return nil // success
-		}
-	}
-
-	// should never reach here ideally
-	return fmt.Errorf("max retries exceeded")
-}
-
 type TokenizeRequest struct {
 	Content string `json:"content"`
 }
@@ -721,43 +191,6 @@ type TokenizeResponse struct {
 	Tokens []int `json:"tokens"`
 }
 
-func (llm *llama) Encode(ctx context.Context, prompt string) ([]int, error) {
-	endpoint := fmt.Sprintf("http://127.0.0.1:%d/tokenize", llm.Port)
-	data, err := json.Marshal(TokenizeRequest{Content: prompt})
-	if err != nil {
-		return nil, fmt.Errorf("marshaling encode data: %w", err)
-	}
-
-	req, err := http.NewRequestWithContext(ctx, http.MethodPost, endpoint, bytes.NewBuffer(data))
-	if err != nil {
-		return nil, fmt.Errorf("encode request: %w", err)
-	}
-	req.Header.Set("Content-Type", "application/json")
-
-	resp, err := http.DefaultClient.Do(req)
-	if err != nil {
-		return nil, fmt.Errorf("do encode request: %w", err)
-	}
-	defer resp.Body.Close()
-
-	body, err := io.ReadAll(resp.Body)
-	if err != nil {
-		return nil, fmt.Errorf("read encode request: %w", err)
-	}
-
-	if resp.StatusCode >= 400 {
-		log.Printf("llm encode error: %s", body)
-		return nil, fmt.Errorf("%s", body)
-	}
-
-	var encoded TokenizeResponse
-	if err := json.Unmarshal(body, &encoded); err != nil {
-		return nil, fmt.Errorf("unmarshal encode response: %w", err)
-	}
-
-	return encoded.Tokens, nil
-}
-
 type DetokenizeRequest struct {
 	Tokens []int `json:"tokens"`
 }
@@ -766,46 +199,6 @@ type DetokenizeResponse struct {
 	Content string `json:"content"`
 }
 
-func (llm *llama) Decode(ctx context.Context, tokens []int) (string, error) {
-	if len(tokens) == 0 {
-		return "", nil
-	}
-	endpoint := fmt.Sprintf("http://127.0.0.1:%d/detokenize", llm.Port)
-	data, err := json.Marshal(DetokenizeRequest{Tokens: tokens})
-	if err != nil {
-		return "", fmt.Errorf("marshaling decode data: %w", err)
-	}
-
-	req, err := http.NewRequestWithContext(ctx, http.MethodPost, endpoint, bytes.NewBuffer(data))
-	if err != nil {
-		return "", fmt.Errorf("decode request: %w", err)
-	}
-	req.Header.Set("Content-Type", "application/json")
-
-	resp, err := http.DefaultClient.Do(req)
-	if err != nil {
-		return "", fmt.Errorf("do decode request: %w", err)
-	}
-	defer resp.Body.Close()
-
-	body, err := io.ReadAll(resp.Body)
-	if err != nil {
-		return "", fmt.Errorf("read decode request: %w", err)
-	}
-
-	if resp.StatusCode >= 400 {
-		log.Printf("llm decode error: %s", body)
-		return "", fmt.Errorf("%s", body)
-	}
-
-	var decoded DetokenizeResponse
-	if err := json.Unmarshal(body, &decoded); err != nil {
-		return "", fmt.Errorf("unmarshal encode response: %w", err)
-	}
-
-	return decoded.Content, nil
-}
-
 type EmbeddingRequest struct {
 	Content string `json:"content"`
 }
@@ -813,52 +206,3 @@ type EmbeddingRequest struct {
 type EmbeddingResponse struct {
 	Embedding []float64 `json:"embedding"`
 }
-
-func (llm *llama) Embedding(ctx context.Context, input string) ([]float64, error) {
-	endpoint := fmt.Sprintf("http://127.0.0.1:%d/embedding", llm.Port)
-	data, err := json.Marshal(TokenizeRequest{Content: input})
-	if err != nil {
-		return nil, fmt.Errorf("error marshaling embed data: %w", err)
-	}
-
-	req, err := http.NewRequestWithContext(ctx, http.MethodPost, endpoint, bytes.NewBuffer(data))
-	if err != nil {
-		return nil, fmt.Errorf("error creating embed request: %w", err)
-	}
-	req.Header.Set("Content-Type", "application/json")
-
-	resp, err := http.DefaultClient.Do(req)
-	if err != nil {
-		return nil, fmt.Errorf("POST embedding: %w", err)
-	}
-	defer resp.Body.Close()
-
-	body, err := io.ReadAll(resp.Body)
-	if err != nil {
-		return nil, fmt.Errorf("error reading embed response: %w", err)
-	}
-
-	if resp.StatusCode >= 400 {
-		log.Printf("llm encode error: %s", body)
-		return nil, fmt.Errorf("%s", body)
-	}
-
-	var embedding EmbeddingResponse
-	if err := json.Unmarshal(body, &embedding); err != nil {
-		return nil, fmt.Errorf("unmarshal tokenize response: %w", err)
-	}
-
-	return embedding.Embedding, nil
-}
-
-// Ping checks that the server subprocess is still running and responding to requests
-func (llm *llama) Ping(ctx context.Context) error {
-	resp, err := http.Head(fmt.Sprintf("http://127.0.0.1:%d", llm.Port))
-	if err != nil {
-		return fmt.Errorf("ping resp: %w", err)
-	}
-	if resp.StatusCode != http.StatusOK {
-		return fmt.Errorf("unexpected ping status: %s", resp.Status)
-	}
-	return nil
-}

llm/llm.go

@@ -18,7 +18,6 @@ type LLM interface {
 	Embedding(context.Context, string) ([]float64, error)
 	Encode(context.Context, string) ([]int, error)
 	Decode(context.Context, []int) (string, error)
-	SetOptions(api.Options)
 	Close()
 	Ping(context.Context) error
 }
@@ -79,5 +78,5 @@ func New(workDir, model string, adapters, projectors []string, opts api.Options)
 	opts.NumGQA = 0
 	opts.RopeFrequencyBase = 0.0
 	opts.RopeFrequencyScale = 0.0
-	return newLlama(model, adapters, projectors, chooseRunners(workDir), ggml.NumLayers(), opts)
+	return newLlamaExtServer(model, adapters, projectors, ggml.NumLayers(), opts)
 }

scripts/build_darwin.sh

@@ -9,7 +9,7 @@ mkdir -p dist
 
 for TARGETARCH in arm64 amd64; do
     GOOS=darwin GOARCH=$TARGETARCH go generate ./...
-    GOOS=darwin GOARCH=$TARGETARCH go build -o dist/ollama-darwin-$TARGETARCH
+    CGO_ENABLED=1 GOOS=darwin GOARCH=$TARGETARCH go build -o dist/ollama-darwin-$TARGETARCH
     rm -rf llm/llama.cpp/*/build
 done
 

scripts/build_linux.sh

@@ -7,7 +7,7 @@ export GOFLAGS="'-ldflags=-w -s \"-X=github.com/jmorganca/ollama/version.Version
 
 mkdir -p dist
 
-for TARGETARCH in arm64 amd64; do
+for TARGETARCH in amd64 arm64; do
     docker buildx build --load --platform=linux/$TARGETARCH --build-arg=VERSION --build-arg=GOFLAGS -f Dockerfile.build -t builder:$TARGETARCH .
     docker create --platform linux/$TARGETARCH --name builder-$TARGETARCH builder:$TARGETARCH
     docker cp builder-$TARGETARCH:/go/src/github.com/jmorganca/ollama/ollama ./dist/ollama-linux-$TARGETARCH

scripts/setup_integration_tests.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+
+# This script sets up integration tests which run the full stack to verify
+# inference locally
+set -e
+set -o pipefail
+
+REPO=$(dirname $0)/../
+export OLLAMA_MODELS=${REPO}/test_data/models
+REGISTRY_SCHEME=https
+REGISTRY=registry.ollama.ai
+TEST_MODEL=library/orca-mini
+TEST_MODEL_TAG=latest
+ACCEPT_HEADER="Accept: application/vnd.docker.distribution.manifest.v2+json"
+
+mkdir -p ${OLLAMA_MODELS}/manifests/${REGISTRY}/${TEST_MODEL}/
+mkdir -p ${OLLAMA_MODELS}/blobs/
+
+echo "Pulling manifest for ${TEST_MODEL}:${TEST_MODEL_TAG}"
+curl -s --header "${ACCEPT_HEADER}" \
+    -o ${OLLAMA_MODELS}/manifests/${REGISTRY}/${TEST_MODEL}/${TEST_MODEL_TAG} \
+    ${REGISTRY_SCHEME}://${REGISTRY}/v2/${TEST_MODEL}/manifests/${TEST_MODEL_TAG} 
+
+CFG_HASH=$(cat ${OLLAMA_MODELS}/manifests/${REGISTRY}/${TEST_MODEL}/${TEST_MODEL_TAG} | jq -r ".config.digest")
+echo "Pulling config blob ${CFG_HASH}"
+curl -L -C - --header "${ACCEPT_HEADER}" \
+        -o ${OLLAMA_MODELS}/blobs/${CFG_HASH} \
+        ${REGISTRY_SCHEME}://${REGISTRY}/v2/${TEST_MODEL}/blobs/${CFG_HASH}
+
+for LAYER in $(cat ${OLLAMA_MODELS}/manifests/${REGISTRY}/${TEST_MODEL}/${TEST_MODEL_TAG} | jq -r ".layers[].digest" ) ; do
+    echo "Pulling blob ${LAYER}"
+    curl -L -C - --header "${ACCEPT_HEADER}" \
+        -o ${OLLAMA_MODELS}/blobs/${LAYER} \
+        ${REGISTRY_SCHEME}://${REGISTRY}/v2/${TEST_MODEL}/blobs/${LAYER}
+done

server/llm_test.go

@@ -0,0 +1,103 @@
+package server
+
+import (
+	"context"
+	"strings"
+	"sync"
+	"testing"
+	"time"
+
+	"github.com/stretchr/testify/assert"
+
+	"github.com/jmorganca/ollama/api"
+)
+
+// TODO - this would ideally be in the llm package, but that would require some refactoring of interfaces in the server
+//        package to avoid circular dependencies
+
+// WARNING - these tests will fail on mac if you don't manually copy ggml-metal.metal to this dir (./server)
+//
+// TODO - Fix this ^^
+
+var (
+	req = [2]api.GenerateRequest{
+		{
+			Model:   "orca-mini",
+			Prompt:  "tell me a short story about agi?",
+			Options: map[string]interface{}{},
+		}, {
+			Model:   "orca-mini",
+			Prompt:  "what is the origin of the us thanksgiving holiday?",
+			Options: map[string]interface{}{},
+		},
+	}
+	resp = [2]string{
+		"once upon a time",
+		"fourth thursday",
+	}
+)
+
+func TestIntegrationSimpleOrcaMini(t *testing.T) {
+	SkipIFNoTestData(t)
+	ctx, cancel := context.WithTimeout(context.Background(), time.Second*60)
+	defer cancel()
+	opts := api.DefaultOptions()
+	opts.Seed = 42
+	opts.Temperature = 0.0
+	model, llmRunner := PrepareModelForPrompts(t, req[0].Model, opts)
+	defer llmRunner.Close()
+	response := OneShotPromptResponse(t, ctx, req[0], model, llmRunner)
+	assert.Contains(t, strings.ToLower(response), resp[0])
+}
+
+// TODO
+// The server always loads a new runner and closes the old one, which forces serial execution
+// At present this test case fails with concurrency problems.  Eventually we should try to
+// get true concurrency working with n_parallel support in the backend
+func TestIntegrationConcurrentPredictOrcaMini(t *testing.T) {
+	SkipIFNoTestData(t)
+	t.Skip("concurrent prediction on single runner not currently supported")
+	ctx, cancel := context.WithTimeout(context.Background(), time.Second*60)
+	defer cancel()
+	opts := api.DefaultOptions()
+	opts.Seed = 42
+	opts.Temperature = 0.0
+	var wg sync.WaitGroup
+	wg.Add(len(req))
+	model, llmRunner := PrepareModelForPrompts(t, req[0].Model, opts)
+	defer llmRunner.Close()
+	for i := 0; i < len(req); i++ {
+		go func(i int) {
+			defer wg.Done()
+			response := OneShotPromptResponse(t, ctx, req[i], model, llmRunner)
+			t.Logf("Prompt: %s\nResponse: %s", req[0].Prompt, response)
+			assert.Contains(t, strings.ToLower(response), resp[i], "error in thread %d (%s)", i, req[i].Prompt)
+		}(i)
+	}
+	wg.Wait()
+}
+
+func TestIntegrationConcurrentRunnersOrcaMini(t *testing.T) {
+	SkipIFNoTestData(t)
+	ctx, cancel := context.WithTimeout(context.Background(), time.Second*60)
+	defer cancel()
+	opts := api.DefaultOptions()
+	opts.Seed = 42
+	opts.Temperature = 0.0
+	var wg sync.WaitGroup
+	wg.Add(len(req))
+
+	for i := 0; i < len(req); i++ {
+		go func(i int) {
+			defer wg.Done()
+			model, llmRunner := PrepareModelForPrompts(t, req[0].Model, opts)
+			defer llmRunner.Close()
+			response := OneShotPromptResponse(t, ctx, req[i], model, llmRunner)
+			t.Logf("Prompt: %s\nResponse: %s", req[0].Prompt, response)
+			assert.Contains(t, strings.ToLower(response), resp[i], "error in thread %d (%s)", i, req[i].Prompt)
+		}(i)
+	}
+	wg.Wait()
+}
+
+// TODO - create a parallel test with 2 different models once we support concurrency

server/llm_utils_test.go

@@ -0,0 +1,76 @@
+package server
+
+import (
+	"context"
+	"errors"
+	"os"
+	"path"
+	"runtime"
+	"testing"
+	"time"
+
+	"github.com/jmorganca/ollama/api"
+	"github.com/jmorganca/ollama/llm"
+	"github.com/stretchr/testify/require"
+)
+
+func SkipIFNoTestData(t *testing.T) {
+	modelDir := getModelDir()
+	if _, err := os.Stat(modelDir); errors.Is(err, os.ErrNotExist) {
+		t.Skipf("%s does not exist - skipping integration tests", modelDir)
+	}
+}
+
+func getModelDir() string {
+	_, filename, _, _ := runtime.Caller(0)
+	return path.Dir(path.Dir(filename) + "/../test_data/models/.")
+}
+
+func PrepareModelForPrompts(t *testing.T, modelName string, opts api.Options) (*Model, llm.LLM) {
+	modelDir := getModelDir()
+	os.Setenv("OLLAMA_MODELS", modelDir)
+	model, err := GetModel(modelName)
+	require.NoError(t, err, "GetModel ")
+	err = opts.FromMap(model.Options)
+	require.NoError(t, err, "opts from model ")
+	runner, err := llm.New("unused", model.ModelPath, model.AdapterPaths, model.ProjectorPaths, opts)
+	require.NoError(t, err, "llm.New failed")
+	return model, runner
+}
+
+func OneShotPromptResponse(t *testing.T, ctx context.Context, req api.GenerateRequest, model *Model, runner llm.LLM) string {
+	checkpointStart := time.Now()
+	prompt, err := model.Prompt(PromptVars{
+		System: req.System,
+		Prompt: req.Prompt,
+		First:  len(req.Context) == 0,
+	})
+	require.NoError(t, err, "prompt generation failed")
+	success := make(chan bool, 1)
+	response := ""
+	cb := func(r llm.PredictResult) {
+
+		if !r.Done {
+			response += r.Content
+		} else {
+			success <- true
+		}
+	}
+	checkpointLoaded := time.Now()
+	predictReq := llm.PredictOpts{
+		Prompt:           prompt,
+		Format:           req.Format,
+		CheckpointStart:  checkpointStart,
+		CheckpointLoaded: checkpointLoaded,
+	}
+	err = runner.Predict(ctx, predictReq, cb)
+	require.NoError(t, err, "predict call failed")
+
+	select {
+	case <-ctx.Done():
+		t.Errorf("failed to complete before timeout: \n%s", response)
+		return ""
+	case <-success:
+		return response
+	}
+}

server/routes.go

@@ -126,10 +126,6 @@ func load(c *gin.Context, modelName string, reqOpts map[string]interface{}, sess
 		loaded.Options = &opts
 	}
 
-	// update options for the loaded llm
-	// TODO(mxyng): this isn't thread safe, but it should be fine for now
-	loaded.runner.SetOptions(opts)
-
 	loaded.expireAt = time.Now().Add(sessionDuration)
 
 	if loaded.expireTimer == nil {