llama: Improve error handling

Check for NULL return values from llama.cpp in more places and
convert them into Go errors, which should make debugging easier
in the future rather than having hidden surprises in our data
structures.

llama/llama.go

@@ -88,6 +88,7 @@ import (
 	"fmt"
 	"runtime"
 	"runtime/cgo"
+	"slices"
 	"strings"
 	"unsafe"
 )
@@ -260,7 +261,7 @@ func LoadModelFromFile(modelPath string, params ModelParams) (*Model, error) {
 	}
 
 	m := Model{c: C.llama_load_model_from_file(C.CString(modelPath), cparams)}
-	if m.c == (*C.struct_llama_model)(C.NULL) {
+	if m.c == nil {
 		return nil, fmt.Errorf("unable to load model: %s", modelPath)
 	}
 
@@ -276,7 +277,7 @@ func NewContextWithModel(model *Model, params ContextParams) (*Context, error) {
 		c:          C.llama_new_context_with_model(model.c, params.c),
 		numThreads: int(params.c.n_threads),
 	}
-	if c.c == (*C.struct_llama_context)(C.NULL) {
+	if c.c == nil {
 		return nil, errors.New("unable to create llama context")
 	}
 
@@ -300,6 +301,9 @@ func (m *Model) ApplyLoraFromFile(context *Context, loraPath string, scale float
 	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 {
@@ -322,13 +326,25 @@ type Batch struct {
 // 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 {
-	return &Batch{
+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 {
@@ -484,6 +500,9 @@ func NewClipContext(llamaContext *Context, modelPath string) (*ClipContext, erro
 	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()
@@ -498,8 +517,11 @@ func (c *ClipContext) Free() {
 	C.clip_free(c.c)
 }
 
-func (c *ClipContext) NewEmbed(llamaContext *Context, data []byte) [][]float32 {
+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()
@@ -516,7 +538,7 @@ func (c *ClipContext) NewEmbed(llamaContext *Context, data []byte) [][]float32 {
 
 	C.llava_image_embed_free(l)
 
-	return embed
+	return embed, nil
 }
 
 type MllamaContext struct {
@@ -527,6 +549,9 @@ func NewMllamaContext(llamaContext *Context, modelPath string) (*MllamaContext,
 	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()
@@ -541,19 +566,25 @@ func (m *MllamaContext) Free() {
 	C.mllama_free(m.c)
 }
 
-func (m *MllamaContext) NewEmbed(llamaContext *Context, data []byte, aspectRatioId int) [][]float32 {
+func (m *MllamaContext) NewEmbed(llamaContext *Context, data []byte, aspectRatioId int) ([][]float32, error) {
 	img := C.mllama_image_init()
 	defer C.mllama_image_free(img)
 
-	C.mllama_image_load_from_data(unsafe.Pointer(&data[0]), C.int(len(data)), 560, 560, 3, 4, C.int(aspectRatioId), 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))
-	C.mllama_image_encode(m.c, C.int(llamaContext.numThreads), img, (*C.float)(unsafe.Pointer(&rows[0])))
+	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
+	return embed, nil
 }
 
 func (m *MllamaContext) EmbedSize(llamaContext *Context) int {
@@ -592,7 +623,7 @@ type SamplingParams struct {
 	Grammar        string
 }
 
-func NewSamplingContext(model *Model, params SamplingParams) *SamplingContext {
+func NewSamplingContext(model *Model, params SamplingParams) (*SamplingContext, error) {
 	var cparams C.struct_gpt_sampler_cparams
 	cparams.top_k = C.int32_t(params.TopK)
 	cparams.top_p = C.float(params.TopP)
@@ -615,9 +646,13 @@ func NewSamplingContext(model *Model, params SamplingParams) *SamplingContext {
 
 	cparams.grammar = grammar
 	context := &SamplingContext{c: C.gpt_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.gpt_sampler_cfree(s.c) })
 
-	return context
+	return context, nil
 }
 
 func (s *SamplingContext) Reset() {

llama/runner/image.go

@@ -63,9 +63,9 @@ func (c *ImageContext) Free(modelPath string) {
 	}
 }
 
-func (c *ImageContext) NewEmbed(llamaContext *llama.Context, data []byte, aspectRatioId int) [][]float32 {
+func (c *ImageContext) NewEmbed(llamaContext *llama.Context, data []byte, aspectRatioId int) ([][]float32, error) {
 	if c == nil {
-		return nil
+		return nil, nil
 	}
 
 	hash := c.hashImage(data)
@@ -76,17 +76,23 @@ func (c *ImageContext) NewEmbed(llamaContext *llama.Context, data []byte, aspect
 	embed, err := c.findImage(hash)
 	if err != nil {
 		if c.mllama != nil {
-			embed = c.mllama.NewEmbed(llamaContext, data, aspectRatioId)
+			embed, err = c.mllama.NewEmbed(llamaContext, data, aspectRatioId)
+			if err != nil {
+				return nil, err
+			}
 		} else if c.clip != nil {
-			embed = c.clip.NewEmbed(llamaContext, data)
+			embed, err = c.clip.NewEmbed(llamaContext, data)
+			if err != nil {
+				return nil, err
+			}
 		} else {
-			return nil
+			return nil, errors.New("received image but vision model not loaded")
 		}
 
 		c.addImage(hash, embed)
 	}
 
-	return embed
+	return embed, nil
 }
 
 func (c *ImageContext) BatchSize(configuredBatchSize int) int {

llama/runner/runner.go

@@ -131,7 +131,10 @@ func (s *Server) NewSequence(prompt string, images []ImageData, params NewSequen
 
 	var sc *llama.SamplingContext
 	if params.samplingParams != nil {
-		sc = llama.NewSamplingContext(s.model, *params.samplingParams)
+		sc, err = llama.NewSamplingContext(s.model, *params.samplingParams)
+		if err != nil {
+			return nil, err
+		}
 		for _, input := range inputs {
 			if input.embed == nil {
 				sc.Accept(input.token, false)
@@ -194,7 +197,11 @@ func (s *Server) inputs(prompt string, images []ImageData) ([]input, error) {
 				return nil, fmt.Errorf("invalid image index: %d", n)
 			}
 
-			embed := s.image.NewEmbed(s.lc, images[imageIndex].Data, images[imageIndex].AspectRatioID)
+			embed, err := s.image.NewEmbed(s.lc, images[imageIndex].Data, images[imageIndex].AspectRatioID)
+			if err != nil {
+				return nil, err
+			}
+
 			for _, e := range embed {
 				inputs = append(inputs, input{embed: e})
 			}
@@ -305,13 +312,19 @@ func (s *Server) run(ctx context.Context) {
 
 	// Logically these batches are used only within the context of processBatch
 	// but it is better for performance to allocate them once here
-	tokenBatch := llama.NewBatch(s.batchSize, len(s.seqs), 0)
+	tokenBatch, err := llama.NewBatch(s.batchSize, len(s.seqs), 0)
+	if err != nil {
+		panic(err)
+	}
 	defer tokenBatch.Free()
 
 	var embedBatch *llama.Batch
 	embedBatchSize := s.image.BatchSize(s.batchSize)
 	if embedBatchSize != 0 {
-		embedBatch = llama.NewBatch(embedBatchSize, len(s.seqs), s.image.EmbedSize(s.lc))
+		embedBatch, err = llama.NewBatch(embedBatchSize, len(s.seqs), s.image.EmbedSize(s.lc))
+		if err != nil {
+			panic(err)
+		}
 		defer embedBatch.Free()
 	} else {
 		embedBatch = &llama.Batch{}

llama/sampling_ext.cpp

@@ -5,24 +5,28 @@
 struct gpt_sampler *gpt_sampler_cinit(
     const struct llama_model *model, struct gpt_sampler_cparams *params)
 {
-    gpt_sampler_params sparams;
-    sparams.top_k = params->top_k;
-    sparams.top_p = params->top_p;
-    sparams.min_p = params->min_p;
-    sparams.tfs_z = params->tfs_z;
-    sparams.typ_p = params->typical_p;
-    sparams.temp = params->temp;
-    sparams.penalty_last_n = params->penalty_last_n;
-    sparams.penalty_repeat = params->penalty_repeat;
-    sparams.penalty_freq = params->penalty_freq;
-    sparams.penalty_present = params->penalty_present;
-    sparams.mirostat = params->mirostat;
-    sparams.mirostat_tau = params->mirostat_tau;
-    sparams.mirostat_eta = params->mirostat_eta;
-    sparams.penalize_nl = params->penalize_nl;
-    sparams.seed = params->seed;
-    sparams.grammar = params->grammar;
-    return gpt_sampler_init(model, sparams);
+    try {
+        gpt_sampler_params sparams;
+        sparams.top_k = params->top_k;
+        sparams.top_p = params->top_p;
+        sparams.min_p = params->min_p;
+        sparams.tfs_z = params->tfs_z;
+        sparams.typ_p = params->typical_p;
+        sparams.temp = params->temp;
+        sparams.penalty_last_n = params->penalty_last_n;
+        sparams.penalty_repeat = params->penalty_repeat;
+        sparams.penalty_freq = params->penalty_freq;
+        sparams.penalty_present = params->penalty_present;
+        sparams.mirostat = params->mirostat;
+        sparams.mirostat_tau = params->mirostat_tau;
+        sparams.mirostat_eta = params->mirostat_eta;
+        sparams.penalize_nl = params->penalize_nl;
+        sparams.seed = params->seed;
+        sparams.grammar = params->grammar;
+        return gpt_sampler_init(model, sparams);
+    } catch (const std::exception & err) {
+        return nullptr;
+    }
 }
 
 void gpt_sampler_cfree(struct gpt_sampler *sampler)