ollama/llama/runner/image.go
Jesse Gross 26acdcf44e runner.go: Don't set cross attention before sending embeddings
Currently if an input has embeddings at any point then we will set
cross attention to true from the beginning. This means that any
tokens before the embeddings are sent will incorrectly have cross
attention layers applied.

This only sets cross attention when we have an embedding, either
previously in this sequence or in the cache. It also makes cross
attention capable of supporting parallelism at the runner level,
though the mllama implementation doesn't support that yet.
2024-10-31 13:56:08 -07:00

156 lines
3.2 KiB
Go

package main
import (
"errors"
"fmt"
"hash/maphash"
"log/slog"
"slices"
"sync"
"time"
"github.com/ollama/ollama/llama"
)
const imageCacheSize = 4
type ImageContext struct {
// mu is required to be held when generating embeddings or accessing the cache
mu sync.Mutex
clip *llama.ClipContext
mllama *llama.MllamaContext
// cache of images to embeddings
images []imageCache
imageHash maphash.Hash
}
func NewImageContext(llamaContext *llama.Context, modelPath string) (*ImageContext, error) {
arch, err := llama.GetModelArch(modelPath)
if err != nil {
return nil, fmt.Errorf("unable to determine vision architecture: %w (%s)", err, modelPath)
}
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)
}
if err != nil {
return nil, err
}
c.images = make([]imageCache, imageCacheSize)
return &c, nil
}
func (c *ImageContext) Free(modelPath string) {
if c == nil {
return
}
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 {
if c == nil {
return nil
}
hash := c.hashImage(data)
c.mu.Lock()
defer c.mu.Unlock()
embed, err := c.findImage(hash)
if err != nil {
if c.mllama != nil {
embed = c.mllama.NewEmbed(llamaContext, data, aspectRatioId)
} else if c.clip != nil {
embed = c.clip.NewEmbed(llamaContext, data)
} else {
return nil
}
c.addImage(hash, embed)
}
return embed
}
func (c *ImageContext) EmbedSize(llamaContext *llama.Context) int {
if c != nil && c.mllama != nil {
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 {
key uint64
val [][]float32
lastUsed time.Time
}
func (c *ImageContext) hashImage(image []byte) uint64 {
c.imageHash.Reset()
_, _ = c.imageHash.Write(image)
return c.imageHash.Sum64()
}
var errImageNotFound = errors.New("image not found in cache")
func (c *ImageContext) findImage(hash uint64) ([][]float32, error) {
for i := range c.images {
if c.images[i].key == hash {
slog.Debug("loading image embeddings from cache", "entry", i)
c.images[i].lastUsed = time.Now()
return c.images[i].val, nil
}
}
return nil, errImageNotFound
}
func (c *ImageContext) addImage(hash uint64, embed [][]float32) {
best := time.Now()
var bestImage int
for i := range c.images {
if c.images[i].key == hash {
bestImage = i
break
}
if c.images[i].lastUsed.Compare(best) < 0 {
best = c.images[i].lastUsed
bestImage = i
}
}
slog.Debug("storing image embeddings in cache", "entry", bestImage, "used", c.images[bestImage].lastUsed)
c.images[bestImage].key = hash
c.images[bestImage].val = embed
c.images[bestImage].lastUsed = time.Now()
}