ollama/llama/llama.go

package llama

//go:generate make -j 8

/*
#cgo CFLAGS: -O3 -std=c17 -DGGML_BUILD=1 -DNDEBUG -DLOG_DISABLE_LOGS -DGGML_USE_LLAMAFILE -DGGML_USE_CPU -DGGML_USE_CPU_AARCH64
#cgo CXXFLAGS: -O3 -std=c++17 -DGGML_BUILD=1 -DNDEBUG -DLOG_DISABLE_LOGS -DGGML_USE_LLAMAFILE -DGGML_USE_CPU -DGGML_USE_CPU_AARCH64
#cgo amd64,avx CFLAGS: -mavx
#cgo amd64,avx CXXFLAGS: -mavx
#cgo amd64,avx2 CFLAGS: -mavx2 -mfma -mf16c
#cgo amd64,avx2 CXXFLAGS: -mavx2 -mfma -mf16c
#cgo amd64,avx512 CFLAGS: -mavx512f -mavx512dq -mavx512bw
#cgo amd64,avx512 CXXFLAGS: -mavx512f -mavx512dq -mavx512bw
#cgo amd64,avx512bf16 CFLAGS: -mavx512bf16 -D__AVX512BF16__
#cgo amd64,avx512bf16 CXXFLAGS: -mavx512bf16 -D__AVX512BF16__
#cgo amd64,avx512vbmi CFLAGS: -mavx512vbmi -D__AVX512VBMI__
#cgo amd64,avx512vbmi CXXFLAGS: -mavx512vbmi -D__AVX512VBMI__
#cgo amd64,avx512vnni CFLAGS: -mavx512vnni -D__AVX512VNNI__
#cgo amd64,avx512vnni CXXFLAGS: -mavx512vnni -D__AVX512VNNI__
#cgo amd64,f16c CFLAGS: -mf16c
#cgo amd64,f16c CXXFLAGS: -mf16c
#cgo amd64,fma CFLAGS: -mfma
#cgo amd64,fma CXXFLAGS: -mfma
#cgo cuda CFLAGS: -fPIE -DGGML_USE_CUDA -DGGML_CUDA_DMMV_X=32 -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 -DGGML_CUDA_MMV_Y=1 -DGGML_BUILD=1
#cgo cuda CXXFLAGS: -DGGML_USE_CUDA -DGGML_CUDA_DMMV_X=32 -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 -DGGML_CUDA_MMV_Y=1 -DGGML_BUILD=1
#cgo cuda_jetpack5 LDFLAGS: -lggml_cuda_jetpack5
#cgo cuda_jetpack6 LDFLAGS: -lggml_cuda_jetpack6
#cgo cuda_v11 LDFLAGS: -lggml_cuda_v11
#cgo cuda_v12 LDFLAGS: -lggml_cuda_v12
#cgo darwin,amd64 CFLAGS: -Wno-incompatible-pointer-types-discards-qualifiers
#cgo darwin,amd64 CXXFLAGS: -Wno-incompatible-pointer-types-discards-qualifiers
#cgo darwin,amd64 LDFLAGS: -framework Foundation
#cgo darwin,amd64,avx2 CFLAGS: -DGGML_USE_ACCELERATE -DACCELERATE_NEW_LAPACK -DACCELERATE_LAPACK_ILP64
#cgo darwin,amd64,avx2 CXXFLAGS: -DGGML_USE_ACCELERATE -DACCELERATE_NEW_LAPACK -DACCELERATE_LAPACK_ILP64
#cgo darwin,amd64,avx2 LDFLAGS: -framework Accelerate
#cgo darwin,arm64 CFLAGS: -DGGML_USE_METAL -DGGML_USE_ACCELERATE -DGGML_METAL_EMBED_LIBRARY -DACCELERATE_NEW_LAPACK -DACCELERATE_LAPACK_ILP64 -DGGML_USE_BLAS -DGGML_BLAS_USE_ACCELERATE
#cgo darwin,arm64 CXXFLAGS: -DGGML_USE_METAL -DGGML_USE_ACCELERATE -DGGML_METAL_EMBED_LIBRARY -DACCELERATE_NEW_LAPACK -DACCELERATE_LAPACK_ILP64 -DGGML_USE_BLAS -DGGML_BLAS_USE_ACCELERATE
#cgo darwin,arm64 LDFLAGS: -framework Foundation -framework Metal -framework MetalKit -framework Accelerate
#cgo linux CFLAGS: -D_GNU_SOURCE
#cgo linux CXXFLAGS: -D_GNU_SOURCE
#cgo linux LDFLAGS: -ldl
#cgo linux,amd64 LDFLAGS: -L${SRCDIR}/build/linux-amd64
#cgo linux,arm64 CFLAGS: -D__aarch64__ -D__ARM_NEON -D__ARM_FEATURE_FMA
#cgo linux,arm64 CXXFLAGS: -D__aarch64__ -D__ARM_NEON -D__ARM_FEATURE_FMA
#cgo linux,arm64 LDFLAGS: -L${SRCDIR}/build/linux-arm64
#cgo linux,arm64,sve CFLAGS: -march=armv8.6-a+sve
#cgo linux,arm64,sve CXXFLAGS: -march=armv8.6-a+sve
#cgo linux,cuda LDFLAGS: -lcuda -lcudart -lcublas -lcublasLt -lpthread -lrt -lresolv
#cgo linux,rocm LDFLAGS: -lpthread -lrt -lresolv
#cgo rocm CFLAGS: -DGGML_USE_CUDA -DGGML_USE_HIP -DGGML_CUDA_DMMV_X=32 -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 -DGGML_CUDA_MMV_Y=1 -DGGML_BUILD=1
#cgo rocm CXXFLAGS: -DGGML_USE_CUDA -DGGML_USE_HIP -DGGML_CUDA_DMMV_X=32 -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 -DGGML_CUDA_MMV_Y=1 -DGGML_BUILD=1
#cgo rocm LDFLAGS: -L${SRCDIR} -lggml_rocm -lhipblas -lamdhip64 -lrocblas
#cgo windows CFLAGS: -Wno-discarded-qualifiers -D_WIN32_WINNT=0x602
#cgo windows CXXFLAGS: -D_WIN32_WINNT=0x602
#cgo windows LDFLAGS: -lmsvcrt -static-libstdc++ -static-libgcc -static
#cgo windows,amd64 LDFLAGS: -L${SRCDIR}/build/windows-amd64
#cgo windows,arm64 CFLAGS: -D__aarch64__ -D__ARM_NEON -D__ARM_FEATURE_FMA
#cgo windows,arm64 CXXFLAGS: -D__aarch64__ -D__ARM_NEON -D__ARM_FEATURE_FMA
#cgo windows,arm64 LDFLAGS: -L${SRCDIR}/build/windows-arm64
#cgo windows,cuda LDFLAGS: -lcuda -lcudart -lcublas -lcublasLt
#cgo windows,rocm LDFLAGS: -lggml_rocm -lhipblas -lamdhip64 -lrocblas

#include <stdlib.h>
#include "llama.h"
#include "clip.h"
#include "ggml.h"
#include "llava.h"
#include "mllama.h"
#include "sampling_ext.h"

extern bool llamaProgressCallback(float progress, void *user_data);
extern void llamaLog(int level, char* text, void* user_data);

typedef enum {COMP_UNKNOWN,COMP_GCC,COMP_CLANG} COMPILER;
COMPILER inline get_compiler() {
#if defined(__clang__)
	return COMP_CLANG;
#elif defined(__GNUC__)
	return COMP_GCC;
#else
	return UNKNOWN_COMPILER;
#endif
}

*/
import "C"

import (
	_ "embed"
	"errors"
	"fmt"
	"os"
	"runtime"
	"runtime/cgo"
	"slices"
	"strings"
	"sync/atomic"
	"unsafe"
)

func BackendInit() {
	C.llama_backend_init()
}

func PrintSystemInfo() string {
	var compiler string
	switch C.get_compiler() {
	case C.COMP_UNKNOWN:
		compiler = "cgo(unknown_compiler)"
	case C.COMP_GCC:
		compiler = "cgo(gcc)"
	case C.COMP_CLANG:
		compiler = "cgo(clang)"
	}
	return C.GoString(C.llama_print_system_info()) + compiler
}

var logLevel atomic.Int32

func init() {
	logLevel.Store(int32(C.GGML_LOG_LEVEL_INFO))
	C.llama_log_set((C.ggml_log_callback)(C.llamaLog), nil)
}

func EnableDebug() {
	logLevel.Store(int32(C.GGML_LOG_LEVEL_DEBUG))
}

//export llamaLog
func llamaLog(level int32, text *C.char, _ unsafe.Pointer) {
	if level < logLevel.Load() {
		return
	}

	fmt.Fprint(os.Stderr, C.GoString(text))
}

func GetModelArch(modelPath string) (string, error) {
	mp := C.CString(modelPath)
	defer C.free(unsafe.Pointer(mp))

	gguf_ctx := C.gguf_init_from_file(mp, C.struct_gguf_init_params{no_alloc: true, ctx: (**C.struct_ggml_context)(C.NULL)})
	if gguf_ctx == nil {
		return "", errors.New("unable to load model file")
	}
	defer C.gguf_free(gguf_ctx)

	key := C.CString("general.architecture")
	defer C.free(unsafe.Pointer(key))
	arch_index := C.gguf_find_key(gguf_ctx, key)
	if int(arch_index) < 0 {
		return "", errors.New("unknown model architecture")
	}

	arch := C.gguf_get_val_str(gguf_ctx, arch_index)

	return C.GoString(arch), nil
}

type ContextParams struct {
	c C.struct_llama_context_params
}

func NewContextParams(numCtx int, batchSize int, numSeqMax int, threads int, flashAttention bool, kvCacheType string) ContextParams {
	params := C.llama_context_default_params()
	params.n_ctx = C.uint(numCtx)
	params.n_batch = C.uint(batchSize)
	params.n_seq_max = C.uint(numSeqMax)
	params.n_threads = C.int(threads)
	params.n_threads_batch = params.n_threads
	params.embeddings = C.bool(true)
	params.flash_attn = C.bool(flashAttention)
	params.type_k = kvCacheTypeFromStr(strings.ToLower(kvCacheType))
	params.type_v = kvCacheTypeFromStr(strings.ToLower(kvCacheType))

	return ContextParams{c: params}
}

// kvCacheTypeFromStr converts a string cache type to the corresponding GGML type value
func kvCacheTypeFromStr(s string) C.enum_ggml_type {
	if s == "" {
		return C.GGML_TYPE_F16
	}

	switch s {
	case "q8_0":
		return C.GGML_TYPE_Q8_0
	case "q4_0":
		return C.GGML_TYPE_Q4_0
	default:
		return C.GGML_TYPE_F16
	}
}

type Context struct {
	c          *C.struct_llama_context
	numThreads int
}

var ErrKvCacheFull = errors.New("could not find a kv cache slot")

func (c *Context) Decode(batch *Batch) error {
	// Positive return values does not mean a fatal error, but rather a warning.
	//   0 - success
	//   1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
	// < 0 - error
	code := int(C.llama_decode(c.c, batch.c))

	if code < 0 {
		return fmt.Errorf("llama_decode failed with code %d", code)
	}

	if code > 0 {
		return ErrKvCacheFull
	}

	return nil
}

func (c *Context) Model() *Model {
	return &Model{c: C.llama_get_model(c.c)}
}

func (c *Context) KvCacheSeqAdd(seqId int, p0 int, p1 int, delta int) {
	C.llama_kv_cache_seq_add(c.c, C.int(seqId), C.int(p0), C.int(p1), C.int(delta))
}

func (c *Context) KvCacheSeqRm(seqId int, p0 int, p1 int) bool {
	return bool(C.llama_kv_cache_seq_rm(c.c, C.int(seqId), C.int(p0), C.int(p1)))
}

func (c *Context) KvCacheSeqCp(srcSeqId int, dstSeqId int, p0 int, p1 int) {
	C.llama_kv_cache_seq_cp(c.c, C.int(srcSeqId), C.int(dstSeqId), C.int(p0), C.int(p1))
}

func (c *Context) KvCacheClear() {
	C.llama_kv_cache_clear(c.c)
}

func (c *Context) KvCacheDefrag() {
	C.llama_kv_cache_defrag(c.c)
}

// Get the embeddings for a sequence id
func (c *Context) GetEmbeddingsSeq(seqId int) []float32 {
	embeddings := unsafe.Pointer(C.llama_get_embeddings_seq(c.c, C.int(seqId)))
	if embeddings == nil {
		return nil
	}

	return unsafe.Slice((*float32)(embeddings), c.Model().NEmbd())
}

func (c *Context) GetEmbeddingsIth(i int) []float32 {
	embeddings := unsafe.Pointer(C.llama_get_embeddings_ith(c.c, C.int32_t(i)))
	if embeddings == nil {
		return nil
	}

	return unsafe.Slice((*float32)(embeddings), c.Model().NEmbd())
}

// GetLogits returns the logits from the last decode operation.
// The returned slice has length equal to the vocabulary size.
func (c *Context) GetLogits() []float32 {
	logits := unsafe.Pointer(C.llama_get_logits(c.c))
	if logits == nil {
		return nil
	}

	// Get the number of vocabulary tokens to determine array size
	vocabSize := c.Model().NumVocab()
	return unsafe.Slice((*float32)(logits), vocabSize)
}

func (m *Model) Detokenize(tokens []int) (string, error) {
	var text string
	for _, token := range tokens {
		piece := m.TokenToPiece(token)
		if piece == "" {
			return "", fmt.Errorf("failed to convert token %d to piece", token)
		}
		text += piece
	}
	return text, nil
}

type ModelParams struct {
	NumGpuLayers int
	MainGpu      int
	UseMmap      bool
	UseMlock     bool
	TensorSplit  []float32
	Progress     func(float32)
	VocabOnly    bool
}

//export llamaProgressCallback
func llamaProgressCallback(progress C.float, userData unsafe.Pointer) C.bool {
	handle := *(*cgo.Handle)(userData)
	callback := handle.Value().(func(float32))
	callback(float32(progress))
	return true
}

func LoadModelFromFile(modelPath string, params ModelParams) (*Model, error) {
	cparams := C.llama_model_default_params()
	cparams.n_gpu_layers = C.int(params.NumGpuLayers)
	cparams.main_gpu = C.int32_t(params.MainGpu)
	cparams.use_mmap = C.bool(params.UseMmap)
	cparams.use_mlock = C.bool(params.UseMlock)
	cparams.vocab_only = C.bool(params.VocabOnly)

	if len(params.TensorSplit) > 0 {
		tensorSplitData := &params.TensorSplit[0]

		var tensorSplitPin runtime.Pinner
		tensorSplitPin.Pin(tensorSplitData)
		defer tensorSplitPin.Unpin()

		cparams.tensor_split = (*C.float)(unsafe.Pointer(tensorSplitData))
	}

	if params.Progress != nil {
		handle := cgo.NewHandle(params.Progress)
		defer handle.Delete()

		var handlePin runtime.Pinner
		handlePin.Pin(&handle)
		defer handlePin.Unpin()

		cparams.progress_callback = C.llama_progress_callback(C.llamaProgressCallback)
		cparams.progress_callback_user_data = unsafe.Pointer(&handle)
	}

	m := Model{c: C.llama_load_model_from_file(C.CString(modelPath), cparams)}
	if m.c == nil {
		return nil, fmt.Errorf("unable to load model: %s", modelPath)
	}

	return &m, nil
}

func FreeModel(model *Model) {
	C.llama_free_model(model.c)
}

func NewContextWithModel(model *Model, params ContextParams) (*Context, error) {
	c := Context{
		c:          C.llama_new_context_with_model(model.c, params.c),
		numThreads: int(params.c.n_threads),
	}
	if c.c == nil {
		return nil, errors.New("unable to create llama context")
	}

	return &c, nil
}

func (m *Model) NumVocab() int {
	return int(C.llama_n_vocab(m.c))
}

func (m *Model) TokenIsEog(token int) bool {
	return bool(C.llama_token_is_eog(m.c, C.llama_token(token)))
}

func (m *Model) AddBOSToken() bool {
	return bool(C.llama_add_bos_token(m.c))
}

func (m *Model) ApplyLoraFromFile(context *Context, loraPath string, scale float32, threads int) error {
	cLoraPath := C.CString(loraPath)
	defer C.free(unsafe.Pointer(cLoraPath))

	loraAdapter := C.llama_lora_adapter_init(m.c, cLoraPath)
	if loraAdapter == nil {
		return errors.New("unable to load lora")
	}

	err := -1
	if loraAdapter != nil {
		err = int(C.llama_lora_adapter_set(context.c, loraAdapter, C.float(scale)))
	}
	if err != 0 {
		return errors.New("error applying lora from file")
	}

	return nil
}

type Batch struct {
	c         C.struct_llama_batch
	batchSize int
	maxSeq    int
	embedSize int
}

// Creates a new batch for either word tokens or image embeddings (if embedSize is non-zero).
// Batches cannot contain both types at the same time. batchSize is the maximum number of entries
// that can be added per sequence
func NewBatch(batchSize int, maxSeq int, embedSize int) (*Batch, error) {
	b := Batch{
		c:         C.llama_batch_init(C.int(batchSize*maxSeq), C.int(embedSize), C.int(maxSeq)),
		batchSize: batchSize,
		maxSeq:    maxSeq,
		embedSize: embedSize,
	}

	// Check to see if any of the allocations in llama_batch_init() failed
	nilPointer := (embedSize == 0 && b.c.token == nil) || (embedSize != 0 && b.c.embd == nil) ||
		b.c.pos == nil || b.c.n_seq_id == nil || b.c.seq_id == nil || b.c.logits == nil ||
		slices.Contains(unsafe.Slice(b.c.seq_id, b.allocSize()), nil)

	if nilPointer {
		C.llama_batch_free(b.c)
		return nil, fmt.Errorf("unable to allocate batch (batchSize=%v maxSeq=%v embedSize=%v)", batchSize, maxSeq, embedSize)
	}

	return &b, nil
}

func (b *Batch) Size() int {
	return b.batchSize
}

func (b *Batch) allocSize() int {
	return b.batchSize * b.maxSeq
}

func (b *Batch) NumTokens() int {
	return int(b.c.n_tokens)
}

func (b *Batch) IsEmbedding() bool {
	return b.embedSize != 0
}

// Add adds either a token or an image embedding to the batch depending on the type
// when the batch was initialized. The other argument will be ignored. Adds to the
// batch with the given position for the given sequence ids, and optionally instructs
// to include logits.
func (b *Batch) Add(token int, embed []float32, pos int, logits bool, seqIds ...int) {
	if !b.IsEmbedding() {
		unsafe.Slice(b.c.token, b.allocSize())[b.c.n_tokens] = C.llama_token(token)
	} else {
		copy(unsafe.Slice((*float32)(b.c.embd), b.allocSize()*b.embedSize)[int(b.c.n_tokens)*b.embedSize:], embed)
	}
	unsafe.Slice(b.c.pos, b.allocSize())[b.c.n_tokens] = C.llama_pos(pos)
	unsafe.Slice(b.c.n_seq_id, b.allocSize())[b.c.n_tokens] = C.int(len(seqIds))

	for i, s := range seqIds {
		unsafe.Slice((unsafe.Slice(b.c.seq_id, b.allocSize())[b.c.n_tokens]), C.int(len(seqIds)))[i] = C.int32_t(s)
	}

	if logits {
		unsafe.Slice(b.c.logits, b.allocSize())[b.c.n_tokens] = 1
	} else {
		unsafe.Slice(b.c.logits, b.allocSize())[b.c.n_tokens] = 0
	}

	b.c.n_tokens += 1
}

func (b *Batch) Clear() {
	b.c.n_tokens = 0
}

func (b *Batch) Free() {
	b.batchSize = 0
	C.llama_batch_free(b.c)
}

type Model struct {
	c *C.struct_llama_model
}

func (m *Model) TokenToPiece(token int) string {
	tokenLen := 12
	buf := make([]byte, tokenLen)
	tokenLen = int(C.llama_token_to_piece(
		m.c,
		C.int32_t(token),
		(*C.char)(unsafe.Pointer(&buf[0])),
		C.int32_t(tokenLen),
		C.int32_t(0),
		C.bool(true),
	))
	if tokenLen < 0 {
		tokenLen = -tokenLen

		buf = make([]byte, tokenLen)
		C.llama_token_to_piece(
			m.c,
			C.int32_t(token),
			(*C.char)(unsafe.Pointer(&buf[0])),
			C.int32_t(tokenLen),
			C.int32_t(0),
			C.bool(true),
		)
	}
	return strings.TrimRight(string(buf), "\x00")
}

func (m *Model) Tokenize(text string, addSpecial bool, parseSpecial bool) ([]int, error) {
	maxTokens := len(text) + 2
	cTokens := make([]C.llama_token, maxTokens)
	cText := C.CString(text)
	defer C.free(unsafe.Pointer(cText))

	result := C.llama_tokenize(
		m.c,
		cText,
		C.int32_t(len(text)),
		&cTokens[0],
		C.int32_t(maxTokens),
		C.bool(addSpecial),
		C.bool(parseSpecial),
	)

	// if the result is negative, reallocate and retry with the correct buffer size
	if result < 0 {
		maxTokens = int(-result)
		cTokens = make([]C.llama_token, maxTokens)
		result = C.llama_tokenize(
			m.c,
			cText,
			C.int32_t(len(text)),
			&cTokens[0],
			C.int32_t(maxTokens),
			C.bool(addSpecial),
			C.bool(parseSpecial),
		)
		if result < 0 {
			return nil, fmt.Errorf("tokenization failed, required %d tokens", -result)
		}
	}

	tokens := make([]int, result)
	for i := range result {
		tokens[i] = int(cTokens[i])
	}

	return tokens, nil
}

func (m *Model) NEmbd() int {
	return int(C.llama_n_embd(m.c))
}

func Quantize(infile, outfile string, ftype uint32) error {
	cinfile := C.CString(infile)
	defer C.free(unsafe.Pointer(cinfile))

	coutfile := C.CString(outfile)
	defer C.free(unsafe.Pointer(coutfile))

	params := C.llama_model_quantize_default_params()
	params.nthread = -1
	params.ftype = ftype

	if rc := C.llama_model_quantize(cinfile, coutfile, &params); rc != 0 {
		return fmt.Errorf("llama_model_quantize: %d", rc)
	}

	return nil
}

// vision processing
type ClipContext struct {
	c *C.struct_clip_ctx
}

func NewClipContext(llamaContext *Context, modelPath string) (*ClipContext, error) {
	mp := C.CString(modelPath)
	defer C.free(unsafe.Pointer(mp))
	c := C.clip_model_load(mp, 1)
	if c == nil {
		return nil, fmt.Errorf("unable to load clip model: %v", modelPath)
	}

	projEmbedSize := int(C.clip_n_mmproj_embd(c))
	modelEmbedSize := llamaContext.Model().NEmbd()
	if projEmbedSize != modelEmbedSize {
		return nil, fmt.Errorf("projector embedding size (%d) does not match model (%d)", projEmbedSize, modelEmbedSize)
	}

	return &ClipContext{c: c}, nil
}

func (c *ClipContext) Free() {
	C.clip_free(c.c)
}

func (c *ClipContext) NewEmbed(llamaContext *Context, data []byte) ([][]float32, error) {
	l := C.llava_image_embed_make_with_bytes(c.c, C.int(llamaContext.numThreads), (*C.uchar)(unsafe.Pointer(&data[0])), C.int(len(data)))
	if l == nil {
		return nil, errors.New("unable to make llava embedding from image")
	}

	numTokens := int(l.n_image_pos)
	numEmbed := llamaContext.Model().NEmbd()

	s := unsafe.Slice((*float32)(l.embed), numEmbed*numTokens)

	embed := make([][]float32, numTokens)
	rows := make([]float32, len(s))
	copy(rows, s)

	for i := range embed {
		embed[i] = rows[i*numEmbed : (i+1)*numEmbed]
	}

	C.llava_image_embed_free(l)

	return embed, nil
}

type MllamaContext struct {
	c *C.struct_mllama_ctx
}

func NewMllamaContext(llamaContext *Context, modelPath string) (*MllamaContext, error) {
	mp := C.CString(modelPath)
	defer C.free(unsafe.Pointer(mp))
	c := C.mllama_model_load(mp, 1)
	if c == nil {
		return nil, fmt.Errorf("unable to load mllama model: %v", modelPath)
	}

	projEmbedSize := int(C.mllama_n_embd(c))
	modelEmbedSize := llamaContext.Model().NEmbd()
	if projEmbedSize != modelEmbedSize {
		return nil, fmt.Errorf("projector embedding size (%d) does not match model (%d)", projEmbedSize, modelEmbedSize)
	}

	return &MllamaContext{c: c}, nil
}

func (m *MllamaContext) Free() {
	C.mllama_free(m.c)
}

func (m *MllamaContext) NewEmbed(llamaContext *Context, data []byte, aspectRatioId int) ([][]float32, error) {
	img := C.mllama_image_init()
	defer C.mllama_image_free(img)

	ok := bool(C.mllama_image_load_from_data(unsafe.Pointer(&data[0]), C.int(len(data)), 560, 560, 3, 4, C.int(aspectRatioId), img))
	if !ok {
		return nil, errors.New("unable to load mllama image data")
	}

	rows := make([]float32, m.EmbedSize(llamaContext))
	ok = bool(C.mllama_image_encode(m.c, C.int(llamaContext.numThreads), img, (*C.float)(unsafe.Pointer(&rows[0]))))
	if !ok {
		return nil, errors.New("unable to make mllama embedding from image")
	}

	embed := make([][]float32, 1)
	embed[0] = rows

	return embed, nil
}

func (m *MllamaContext) EmbedSize(llamaContext *Context) int {
	numTokens := int(C.mllama_n_positions(m.c) * C.mllama_n_tiles(m.c))
	numEmbed := llamaContext.Model().NEmbd()

	return numTokens * numEmbed
}

func (c *Context) SetCrossAttention(state bool) {
	C.llama_set_cross_attention(c.c, C.bool(state))
}

func (c *Context) Synchronize() {
	C.llama_synchronize(c.c)
}

// sampling
// TODO: this is a temporary wrapper to allow calling C++ code from CGo
type SamplingContext struct {
	c *C.struct_common_sampler
}

type SamplingParams struct {
	TopK           int
	TopP           float32
	MinP           float32
	TypicalP       float32
	Temp           float32
	RepeatLastN    int
	PenaltyRepeat  float32
	PenaltyFreq    float32
	PenaltyPresent float32
	Mirostat       int
	MirostatTau    float32
	MirostatEta    float32
	PenalizeNl     bool
	Seed           uint32
	Grammar        string
}

func NewSamplingContext(model *Model, params SamplingParams) (*SamplingContext, error) {
	var cparams C.struct_common_sampler_cparams
	cparams.top_k = C.int32_t(params.TopK)
	cparams.top_p = C.float(params.TopP)
	cparams.min_p = C.float(params.MinP)
	cparams.typical_p = C.float(params.TypicalP)
	cparams.temp = C.float(params.Temp)
	cparams.penalty_last_n = C.int32_t(params.RepeatLastN)
	cparams.penalty_repeat = C.float(params.PenaltyRepeat)
	cparams.penalty_freq = C.float(params.PenaltyFreq)
	cparams.penalty_present = C.float(params.PenaltyFreq)
	cparams.mirostat = C.int32_t(params.Mirostat)
	cparams.mirostat_tau = C.float(params.MirostatTau)
	cparams.mirostat_eta = C.float(params.MirostatEta)
	cparams.penalize_nl = C.bool(params.PenalizeNl)
	cparams.seed = C.uint32_t(params.Seed)

	grammar := C.CString(params.Grammar)
	defer C.free(unsafe.Pointer(grammar))

	cparams.grammar = grammar
	context := &SamplingContext{c: C.common_sampler_cinit(model.c, &cparams)}
	if context.c == nil {
		return nil, errors.New("unable to create sampling context")
	}

	runtime.SetFinalizer(context, func(s *SamplingContext) { C.common_sampler_cfree(s.c) })

	return context, nil
}

func (s *SamplingContext) Reset() {
	C.common_sampler_creset(s.c)
}

func (s *SamplingContext) Sample(llamaContext *Context, idx int) int {
	return int(C.common_sampler_csample(s.c, llamaContext.c, C.int(idx)))
}

func (s *SamplingContext) Accept(id int, applyGrammar bool) {
	C.common_sampler_caccept(s.c, C.llama_token(id), C.bool(applyGrammar))
}

// SchemaToGrammar converts the provided JSON schema to a grammar. It returns
// nil if the provided schema is invalid JSON or an invalid JSON schema.
func SchemaToGrammar(schema []byte) []byte {
	cStr := C.CString(string(schema))
	defer C.free(unsafe.Pointer(cStr))

	// Allocate buffer for grammar output with reasonable size
	const maxLen = 32768 // 32KB
	buf := make([]byte, maxLen)

	// Call C function to convert schema to grammar
	n := C.schema_to_grammar(cStr, (*C.char)(unsafe.Pointer(&buf[0])), C.size_t(maxLen))
	if n == 0 {
		// preserve nil
		return nil
	}
	return buf[:n]
}