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* feat: Bump llama.cpp to df1b612 Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(mtmd): Correctly encode text chunks during mtmd tokenization There can be text chunks that appear interspersed with the image embeddings that contain template delimiter tokens for some models. These need to be correctly translated to text tokens. Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * tests: Use MtmdChunk in image_test Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix unnecessary conversion linting Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(ggml): Revert changes to ggml_hip.cpp These changes were done largely by our code assistant and are likely wrong Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Revert changes in mem_nvml.cpp Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Update sync point to 1deee0 This brings in several more optimization commits and model support for EmbeddingGemma Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Update patches for 1deee0 Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: sync for bump to 1deee0 Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Bad patch updates with errant `+` Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Bump llama.cpp/ggml to 7049736 Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: format-patches after latest bump Branch: LlamaCPPBump-GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
150 lines
3.0 KiB
Go
150 lines
3.0 KiB
Go
package llamarunner
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import (
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"errors"
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"fmt"
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"hash/maphash"
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"log/slog"
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"sync"
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"time"
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"github.com/ollama/ollama/llama"
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)
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const imageCacheSize = 4
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type ImageContext struct {
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// mu is required to be held when generating embeddings or accessing the cache
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mu sync.Mutex
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mtmd *llama.MtmdContext
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// cache of images to embeddings
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images []imageCache
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imageHash maphash.Hash
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}
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func NewImageContext(llamaContext *llama.Context, modelPath string) (*ImageContext, error) {
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arch, err := llama.GetModelArch(modelPath)
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if err != nil {
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return nil, fmt.Errorf("unable to determine vision architecture: %w (%s)", err, modelPath)
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}
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var c ImageContext
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if arch == "clip" {
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c.mtmd, err = llama.NewMtmdContext(llamaContext, modelPath)
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} else {
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return nil, fmt.Errorf("unknown vision model architecture: %s", arch)
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}
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if err != nil {
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return nil, err
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}
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c.images = make([]imageCache, imageCacheSize)
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return &c, nil
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}
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func (c *ImageContext) Free(modelPath string) {
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if c == nil {
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return
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}
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if c.mtmd != nil {
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c.mtmd.Free()
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}
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}
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func (c *ImageContext) MultimodalTokenize(llamaContext *llama.Context, data []byte) ([]llama.MtmdChunk, error) {
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if c == nil {
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return nil, nil
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}
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if len(data) <= 0 {
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return nil, errors.New("received zero length image")
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}
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hash := c.hashImage(data)
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c.mu.Lock()
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defer c.mu.Unlock()
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chunks, err := c.findImage(hash)
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if err != nil {
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if c.mtmd != nil {
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chunks, err = c.mtmd.MultimodalTokenize(llamaContext, data)
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if err != nil {
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return nil, err
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}
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} else {
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return nil, errors.New("received image but vision model not loaded")
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}
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c.addImage(hash, chunks)
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}
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return chunks, nil
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}
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func (c *ImageContext) BatchSize(configuredBatchSize int) int {
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// If images are not supported, we don't need to allocate embedding batches
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if c == nil {
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return 0
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}
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return configuredBatchSize
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}
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func (c *ImageContext) EmbedSize(llamaContext *llama.Context) int {
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return llamaContext.Model().NEmbd()
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}
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type imageCache struct {
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key uint64
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val []llama.MtmdChunk
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lastUsed time.Time
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}
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func (c *ImageContext) hashImage(image []byte) uint64 {
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c.imageHash.Reset()
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_, _ = c.imageHash.Write(image)
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return c.imageHash.Sum64()
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}
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var errImageNotFound = errors.New("image not found in cache")
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func (c *ImageContext) findImage(hash uint64) ([]llama.MtmdChunk, error) {
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for i := range c.images {
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if c.images[i].key == hash {
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slog.Debug("loading image embeddings from cache", "entry", i)
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c.images[i].lastUsed = time.Now()
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return c.images[i].val, nil
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}
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}
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return nil, errImageNotFound
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}
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func (c *ImageContext) addImage(hash uint64, embed []llama.MtmdChunk) {
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best := time.Now()
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var bestImage int
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for i := range c.images {
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if c.images[i].key == hash {
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bestImage = i
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break
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}
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if c.images[i].lastUsed.Compare(best) < 0 {
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best = c.images[i].lastUsed
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bestImage = i
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}
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}
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slog.Debug("storing image embeddings in cache", "entry", bestImage, "used", c.images[bestImage].lastUsed)
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c.images[bestImage].key = hash
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c.images[bestImage].val = embed
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c.images[bestImage].lastUsed = time.Now()
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}
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