remove mllama integration, use ollama engine

llama/llama.go

@@ -509,63 +509,6 @@ func (c *ClipContext) NewEmbed(llamaContext *Context, data []byte) ([][]float32,
 	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)
 }

model/models/mllama/model.go

@@ -3,7 +3,6 @@ package mllama
 import (
 	"bytes"
 	"encoding/binary"
-	"fmt"
 	"hash/fnv"
 	"image"
 	"slices"
@@ -34,10 +33,6 @@ const (
 )
 
 func New(c fs.Config) (model.Model, error) {
-	// Verify unified config
-	if c.Uint("vision.block_count") == 0 {
-		return nil, fmt.Errorf("non-unified vision model not supported")
-	}
 	m := Model{
 		BytePairEncoding: model.NewBytePairEncoding(
 			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),

runner/llamarunner/image.go

@@ -5,7 +5,6 @@ import (
 	"fmt"
 	"hash/maphash"
 	"log/slog"
-	"slices"
 	"sync"
 	"time"
 
@@ -18,8 +17,7 @@ type ImageContext struct {
 	// mu is required to be held when generating embeddings or accessing the cache
 	mu sync.Mutex
 
-	clip   *llama.ClipContext
+	clip *llama.ClipContext
-	mllama *llama.MllamaContext
 
 	// cache of images to embeddings
 	images    []imageCache
@@ -35,8 +33,6 @@ func NewImageContext(llamaContext *llama.Context, modelPath string) (*ImageConte
 	var c ImageContext
 	if arch == "clip" {
 		c.clip, err = llama.NewClipContext(llamaContext, modelPath)
-	} else if arch == "mllama" {
-		c.mllama, err = llama.NewMllamaContext(llamaContext, modelPath)
 	} else {
 		return nil, fmt.Errorf("unknown vision model architecture: %s", arch)
 	}
@@ -58,9 +54,6 @@ func (c *ImageContext) Free(modelPath string) {
 	if c.clip != nil {
 		c.clip.Free()
 	}
-	if c.mllama != nil {
-		c.mllama.Free()
-	}
 }
 
 func (c *ImageContext) NewEmbed(llamaContext *llama.Context, data []byte, aspectRatioId int) ([][]float32, error) {
@@ -79,12 +72,7 @@ func (c *ImageContext) NewEmbed(llamaContext *llama.Context, data []byte, aspect
 
 	embed, err := c.findImage(hash)
 	if err != nil {
-		if c.mllama != nil {
+		if c.clip != nil {
-			embed, err = c.mllama.NewEmbed(llamaContext, data, aspectRatioId)
-			if err != nil {
-				return nil, err
-			}
-		} else if c.clip != nil {
 			embed, err = c.clip.NewEmbed(llamaContext, data)
 			if err != nil {
 				return nil, err
@@ -109,29 +97,11 @@ func (c *ImageContext) BatchSize(configuredBatchSize int) int {
 	// and doesn't support more than a single image per request.
 	// The embeddings are large (100 MB), so allocating a big batch can fail
 	// on some systems
-	if c.mllama != nil {
-		return 1
-	}
-
 	return configuredBatchSize
 }
 
 func (c *ImageContext) EmbedSize(llamaContext *llama.Context) int {
-	if c != nil && c.mllama != nil {
+	return llamaContext.Model().NEmbd()
-		return c.mllama.EmbedSize(llamaContext)
-	} else {
-		return llamaContext.Model().NEmbd()
-	}
-}
-
-func (c *ImageContext) NeedCrossAttention(inputs ...input) bool {
-	if c == nil || c.mllama == nil {
-		return false
-	}
-
-	return slices.ContainsFunc(inputs, func(input input) bool {
-		return input.embed != nil
-	})
 }
 
 type imageCache struct {

runner/llamarunner/runner.go

@@ -413,9 +413,6 @@ func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch)
 			if batch == nil {
 				if !embedding {
 					batch = tokenBatch
-				} else {
-					batch = embedBatch
-					seq.crossAttention = s.image.NeedCrossAttention(input)
 				}
 			} else if embedding != batch.IsEmbedding() || crossAttention != seq.crossAttention {
 				s.nextSeq = seqIdx
@@ -439,8 +436,6 @@ func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch)
 		return nil
 	}
 
-	s.lc.SetCrossAttention(crossAttention)
-
 	err := s.lc.Decode(batch)
 	if err != nil {
 		return fmt.Errorf("failed to decode batch: %w", err)
@@ -621,8 +616,6 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 				return
 			}
 
-			seq.crossAttention = s.image.NeedCrossAttention(seq.cache.Inputs...)
-
 			s.seqs[i] = seq
 			s.cond.Signal()
 			found = true

server/prompt.go

@@ -3,7 +3,6 @@ package server
 import (
 	"bytes"
 	"context"
-	"encoding/binary"
 	"errors"
 	"fmt"
 	"log/slog"
@@ -11,7 +10,6 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/llm"
-	"github.com/ollama/ollama/model/models/mllama"
 	"github.com/ollama/ollama/template"
 )
 
@@ -25,25 +23,14 @@ var errTooManyImages = errors.New("vision model only supports a single image per
 func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.Options, msgs []api.Message, tools []api.Tool) (prompt string, images []llm.ImageData, _ error) {
 	var system []api.Message
 
-	isMllama := checkMllamaModelFamily(m)
-
 	var imageNumTokens int
 	// TODO: Ideally we would compute this from the projector metadata but some pieces are implementation dependent
-	if isMllama {
+	// Clip images are represented as 768 tokens, each an embedding
-		// Our mllama implementation packs all of the embeddings into a single token
+	imageNumTokens = 768
-		imageNumTokens = 1
-	} else {
-		// Clip images are represented as 768 tokens, each an embedding
-		imageNumTokens = 768
-	}
 
 	n := len(msgs) - 1
 	// in reverse, find all messages that fit into context window
 	for i := n; i >= 0; i-- {
-		if isMllama && len(msgs[i].Images) > 1 {
-			return "", nil, errTooManyImages
-		}
-
 		// always include the last message
 		if i == n {
 			continue
@@ -91,41 +78,9 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
 		for _, i := range msg.Images {
 			var imgData llm.ImageData
 
-			if isMllama {
+			imgData = llm.ImageData{
-				if len(m.ProjectorPaths) == 0 {
+				ID:   len(images),
-					imgData = llm.ImageData{
+				Data: i,
-						ID:   len(images),
-						Data: i,
-					}
-				} else {
-					data, opts, err := mllama.Preprocess(bytes.NewReader(i))
-					if err != nil {
-						return "", nil, err
-					}
-
-					buf := new(bytes.Buffer)
-					err = binary.Write(buf, binary.LittleEndian, data)
-					if err != nil {
-						return "", nil, err
-					}
-
-					ar, ok := opts["aspectRatioIndex"].(int)
-					if !ok {
-						return "", nil, fmt.Errorf("missing aspect ratio for image")
-					}
-
-					imgData = llm.ImageData{
-						ID:            len(images),
-						Data:          buf.Bytes(),
-						AspectRatioID: ar,
-					}
-				}
-				imgPrompt = "<|image|>"
-			} else {
-				imgData = llm.ImageData{
-					ID:   len(images),
-					Data: i,
-				}
 			}
 
 			imgTag := fmt.Sprintf("[img-%d]", imgData.ID)
@@ -148,12 +103,3 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
 
 	return b.String(), images, nil
 }
-
-func checkMllamaModelFamily(m *Model) bool {
-	for _, arch := range m.Config.ModelFamilies {
-		if arch == "mllama" {
-			return true
-		}
-	}
-	return false
-}

server/routes.go

@@ -4,7 +4,6 @@ import (
 	"bytes"
 	"cmp"
 	"context"
-	"encoding/binary"
 	"encoding/json"
 	"errors"
 	"fmt"
@@ -33,7 +32,6 @@ import (
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/llm"
-	"github.com/ollama/ollama/model/models/mllama"
 	"github.com/ollama/ollama/openai"
 	"github.com/ollama/ollama/server/internal/client/ollama"
 	"github.com/ollama/ollama/server/internal/registry"
@@ -204,38 +202,9 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		return
 	}
 
-	isMllama := checkMllamaModelFamily(m)
-	if isMllama && len(req.Images) > 1 {
-		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "this model only supports one image: more than one image sent"})
-		return
-	}
-
 	images := make([]llm.ImageData, len(req.Images))
 	for i := range req.Images {
-		if isMllama && len(m.ProjectorPaths) > 0 {
+		images[i] = llm.ImageData{ID: i, Data: req.Images[i]}
-			data, opts, err := mllama.Preprocess(bytes.NewReader(req.Images[i]))
-			if err != nil {
-				c.AbortWithStatusJSON(http.StatusInternalServerError, gin.H{"error": "error processing image"})
-				return
-			}
-
-			ar, ok := opts["aspectRatioIndex"].(int)
-			if !ok {
-				c.AbortWithStatusJSON(http.StatusInternalServerError, gin.H{"error": "error processing image"})
-				return
-			}
-
-			buf := new(bytes.Buffer)
-			err = binary.Write(buf, binary.LittleEndian, data)
-			if err != nil {
-				c.AbortWithStatusJSON(http.StatusInternalServerError, gin.H{"error": "error processing image"})
-				return
-			}
-
-			images[i] = llm.ImageData{ID: i, Data: buf.Bytes(), AspectRatioID: ar}
-		} else {
-			images[i] = llm.ImageData{ID: i, Data: req.Images[i]}
-		}
 	}
 
 	prompt := req.Prompt
@@ -267,9 +236,6 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 
 			for _, i := range images {
 				imgPrompt := ""
-				if isMllama {
-					imgPrompt = "<|image|>"
-				}
 				msgs = append(msgs, api.Message{Role: "user", Content: fmt.Sprintf("[img-%d]"+imgPrompt, i.ID)})
 			}
 

server/sched.go

@@ -8,6 +8,7 @@ import (
 	"os"
 	"reflect"
 	"runtime"
+	"slices"
 	"sort"
 	"strconv"
 	"strings"
@@ -132,11 +133,11 @@ func (s *Scheduler) processPending(ctx context.Context) {
 				continue
 			}
 			numParallel := int(envconfig.NumParallel())
-			// TODO (jmorganca): mllama doesn't support parallel yet
+			// `mllama` is a snowflake and uses an encoder cache which cannot be used with num_parallel > 1
-			// see https://github.com/ollama/ollama/issues/4165
+			// ref: https://github.com/ollama/ollama/issues/4165
-			if checkMllamaModelFamily(pending.model) && numParallel != 1 {
+			if slices.Contains(pending.model.Config.ModelFamilies, "mllama") && numParallel != 1 {
 				numParallel = 1
-				slog.Warn("mllama doesn't support parallel requests yet")
+				slog.Warn("mllama does not currently support parallel requests")
 			}
 
 			for {