Runner for Ollama engine

This provides integration with the new Ollama engine
(58245413 next ollama runner (#7913)) and the rest of the Ollama
infrastructure such as the runner and Ollama server.

In addition, it also builds out the KV cache infrastructure to
support requirements of how Ollama runs models such as:
 - Parallel processing
 - Memory management for defragmentation and shifting
 - Multi-modal modals

Both old and new engines continue to be supported. By default, only
the old engine is used. To enable the new engine:

Start the server with the OLLAMA_NEW_ENGINE environment variable set:
OLLAMA_NEW_ENGINE=1 ./ollama serve

Start a model that is supported by the Ollama engine. This one is Llama 3.1 8b Q4_K_M:
./ollama run jessegross/llama3.1

cache/cache.go

-package cache
-
-import (
-	"github.com/ollama/ollama/ml"
-)
-
-type Options struct {
-	Position int
-}
-
-type Cache interface {
-	Sub(i int) Cache
-	Put(ctx ml.Context, key, value ml.Tensor, opts Options) (ml.Tensor, ml.Tensor)
-}
-
-type Simple struct {
-	DType    ml.DType
-	Capacity int
-
-	keys, values []ml.Tensor
-}
-
-func (c *Simple) Sub(i int) Cache {
-	if i >= len(c.keys) {
-		c.keys = append(c.keys, make([]ml.Tensor, i-len(c.keys)+1)...)
-		c.values = append(c.values, make([]ml.Tensor, i-len(c.values)+1)...)
-	}
-
-	return &Simple{
-		keys:     c.keys[i : i+1],
-		values:   c.values[i : i+1],
-		Capacity: c.Capacity,
-		DType:    c.DType,
-	}
-}
-
-func (c *Simple) Put(ctx ml.Context, key, value ml.Tensor, opts Options) (ml.Tensor, ml.Tensor) {
-	if c.keys[0] == nil || c.values[0] == nil {
-		c.keys[0] = ctx.Zeros(c.DType, key.Dim(0)*key.Dim(1)*c.Capacity)
-		c.values[0] = ctx.Zeros(c.DType, value.Dim(0)*value.Dim(1)*c.Capacity)
-	}
-
-	ctx.Forward(key.Copy(ctx, c.keys[0].View(ctx, key.Stride(2)*opts.Position, key.Dim(0)*key.Dim(1)*key.Dim(2))))
-	ctx.Forward(value.Copy(ctx, c.values[0].View(ctx, value.Stride(2)*opts.Position, value.Dim(0)*value.Dim(1)*value.Dim(2))))
-
-	n := min(c.Capacity, key.Dim(2)+opts.Position)
-
-	key = c.keys[0].View(ctx, 0,
-		key.Dim(0), key.Stride(1),
-		key.Dim(1), key.Stride(2),
-		n,
-	)
-
-	value = c.values[0].View(ctx, 0,
-		value.Dim(0), value.Stride(1),
-		value.Dim(1), value.Stride(2),
-		n,
-	)
-
-	// TODO shift context if necessary
-
-	return key, value
-}

cmd/cmd.go

@@ -35,9 +35,9 @@ import (
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/llama"
-	"github.com/ollama/ollama/llama/runner"
 	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/progress"
+	"github.com/ollama/ollama/runner"
 	"github.com/ollama/ollama/server"
 	"github.com/ollama/ollama/types/model"
 	"github.com/ollama/ollama/version"
@@ -338,7 +338,10 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	opts.MultiModal = len(info.ProjectorInfo) != 0
+	// TODO(jessegross): We should either find another way to know if this is
+	// a vision model or remove the logic. Also consider that other modalities will
+	// need different behavior anyways.
+	opts.MultiModal = len(info.ProjectorInfo) != 0 || envconfig.NewEngine()
 	opts.ParentModel = info.Details.ParentModel
 
 	if interactive {

cmd/runner/main.go

@@ -4,7 +4,7 @@ import (
 	"fmt"
 	"os"
 
-	"github.com/ollama/ollama/llama/runner"
+	"github.com/ollama/ollama/runner"
 )
 
 func main() {

envconfig/config.go

@@ -165,6 +165,8 @@ var (
 	IntelGPU = Bool("OLLAMA_INTEL_GPU")
 	// MultiUserCache optimizes prompt caching for multi-user scenarios
 	MultiUserCache = Bool("OLLAMA_MULTIUSER_CACHE")
+	// Enable the new Ollama engine
+	NewEngine = Bool("OLLAMA_NEW_ENGINE")
 )
 
 func String(s string) func() string {
@@ -250,6 +252,7 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", Origins(), "A comma separated list of allowed origins"},
 		"OLLAMA_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
 		"OLLAMA_MULTIUSER_CACHE":   {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
+		"OLLAMA_NEW_ENGINE":        {"OLLAMA_NEW_ENGINE", NewEngine(), "Enable the new Ollama engine"},
 
 		// Informational
 		"HTTP_PROXY":  {"HTTP_PROXY", String("HTTP_PROXY")(), "HTTP proxy"},

kvcache/cache.go

+package kvcache
+
+import (
+	"errors"
+
+	"github.com/ollama/ollama/ml"
+)
+
+var (
+	ErrKvCacheFull  = errors.New("could not find a kv cache slot")
+	ErrNotSupported = errors.New("model does not support operation")
+)
+
+type Cache interface {
+	// ** used by model implementations **
+
+	// SetLayer sets the active layer of the cache
+	SetLayer(layer int)
+
+	// Get returns the history of key and value tensors plus a mask
+	//
+	// The shape of the tensors is documented in the specific
+	// cache implementation used.
+	Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor)
+
+	// Put stores a batch of key and value in the cache
+	//
+	// The shape of the tensors is documented in the specific
+	// cache implementation used.
+	Put(ctx ml.Context, key, value ml.Tensor)
+
+	// ** cache management **
+
+	// Init sets up runtime parameters
+	Init(backend ml.Backend, dtype ml.DType, capacity int32)
+
+	// Close closes the cache and frees resources associated with it
+	Close()
+
+	// StartForward is called before the start of the model's forward pass.
+	// For each token in the coming batch, there must be a corresponding
+	// entry in positions and seqs.
+	StartForward(ctx ml.Context, positions []int32, seqs []int) error
+
+	// CopyPrefix copies tokens in the range [0, len) from srcSeq to dstSeq
+	CopyPrefix(srcSeq, dstSeq int, len int32)
+
+	// Remove deletes tokens in the range [beginIndex, endIndex) from seq. Set
+	// endIndex to math.MaxInt32 to remove everything starting at beginIndex.
+	//
+	// If an error occurs, the entire context for the sequence should be
+	// removed by calling Remove(seq, 0, math.MaxInt32)
+	Remove(seq int, beginIndex, endIndex int32) error
+}

kvcache/causal.go

+package kvcache
+
+import (
+	"errors"
+	"fmt"
+	"log/slog"
+	"math"
+	"slices"
+
+	"github.com/ollama/ollama/ml"
+)
+
+type shiftFn func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error)
+
+// Causal cache stores K and V tensors according to their position in the
+// sequence. Returns the history and a mask for attending to past tokens
+//
+// The tensors are of shape embed dim, kv heads, batch size
+// The mask is of shape history size, batch size
+type Causal struct {
+	DType      ml.DType
+	Capacity   int32
+	windowSize int32
+
+	// ** current forward pass **
+
+	// the active layer for Get and Put
+	curLayer int
+
+	// starting location for data storage for this batch
+	curLoc int
+
+	// size of the current batch
+	curBatchSize int
+
+	// mask of the cache as used by this batch
+	curMask ml.Tensor
+
+	// locations in the cache that are needed for this batch
+	curCellRange cellRange
+
+	// ** cache metadata **
+
+	// for each possible location in the cache, stores the position and set of sequences
+	// that reference the data there
+	cells []cacheCell
+
+	// maps from sequence to the range of locations where it is stored in the cache
+	cellRanges map[int]cellRange
+
+	// ** cache data storage **
+
+	shiftFn      shiftFn
+	backend      ml.Backend
+	cacheCtx     ml.Context
+	keys, values []ml.Tensor
+}
+
+type cacheCell struct {
+	pos       int32
+	sequences []int
+}
+
+type cellRange struct {
+	min int
+	max int
+}
+
+func NewCausalCache(shift shiftFn) *Causal {
+	return &Causal{windowSize: math.MaxInt32, shiftFn: shift}
+}
+
+func NewSWACache(windowSize int32, shift shiftFn) *Causal {
+	return &Causal{windowSize: windowSize, shiftFn: shift}
+}
+
+func (c *Causal) Init(backend ml.Backend, dtype ml.DType, capacity int32) {
+	c.DType = dtype
+	c.Capacity = capacity
+	c.cells = make([]cacheCell, capacity)
+	c.cellRanges = make(map[int]cellRange)
+	c.backend = backend
+	c.cacheCtx = backend.NewContext()
+}
+
+func (c *Causal) Close() {
+	c.cacheCtx.Close()
+}
+
+func (c *Causal) StartForward(ctx ml.Context, positions []int32, seqs []int) error {
+	c.curBatchSize = len(positions)
+
+	var err error
+	c.curLoc, err = c.findStartLoc()
+	if errors.Is(err, ErrKvCacheFull) {
+		c.defrag()
+		c.curLoc, err = c.findStartLoc()
+	}
+	if err != nil {
+		return err
+	}
+
+	c.curCellRange = newRange()
+	for i, pos := range positions {
+		seq := seqs[i]
+
+		c.cells[c.curLoc+i] = cacheCell{pos: pos, sequences: []int{seq}}
+
+		seqRange, ok := c.cellRanges[seq]
+		if !ok {
+			seqRange = newRange()
+		}
+
+		if c.curLoc+i > seqRange.max {
+			seqRange.max = c.curLoc + i
+		}
+		if seqRange.max > c.curCellRange.max {
+			c.curCellRange.max = seqRange.max
+		}
+
+		if c.curLoc+i < seqRange.min {
+			seqRange.min = c.curLoc + i
+		}
+		if seqRange.min < c.curCellRange.min {
+			c.curCellRange.min = seqRange.min
+		}
+		c.cellRanges[seq] = seqRange
+	}
+
+	c.curMask, err = c.buildMask(ctx, positions, seqs)
+
+	return err
+}
+
+func newRange() cellRange {
+	return cellRange{
+		min: math.MaxInt,
+		max: 0,
+	}
+}
+
+// Find the first contiguous block of at least curBatchSize
+func (c *Causal) findStartLoc() (int, error) {
+	var start, count int
+	for i := range c.cells {
+		if len(c.cells[i].sequences) == 0 {
+			count++
+			if count >= c.curBatchSize {
+				return start, nil
+			}
+		} else {
+			start = i + 1
+			count = 0
+		}
+	}
+
+	return 0, fmt.Errorf("%w (length: %v)", ErrKvCacheFull, c.Capacity)
+}
+
+// Builds a mask of history x batch indicating whether for each token in the batch the
+// token in the history should apply. This is based on both the sequence and causality (the
+// position of the history is not ahead of the token in the batch).
+func (c *Causal) buildMask(ctx ml.Context, positions []int32, seqs []int) (ml.Tensor, error) {
+	// TODO(jessegross): This does not do padding, which is required for flash attention
+	len := c.curCellRange.max - c.curCellRange.min + 1
+	mask := make([]float32, c.curBatchSize*len)
+
+	for i := range c.curBatchSize {
+		for j := c.curCellRange.min; j <= c.curCellRange.max; j++ {
+			if !slices.Contains(c.cells[j].sequences, seqs[i]) || c.cells[j].pos > positions[i] ||
+				c.cells[j].pos < positions[i]-c.windowSize {
+				mask[i*len+(j-c.curCellRange.min)] = float32(math.Inf(-1))
+			}
+		}
+	}
+
+	return ctx.FromFloatSlice(mask, len, c.curBatchSize)
+}
+
+func moveCell(ctx ml.Context, objs []ml.Tensor, src, dst, len int) {
+	for _, obj := range objs {
+		if obj == nil {
+			continue
+		}
+
+		srcView := obj.View(ctx, obj.Stride(2)*src, obj.Dim(0)*obj.Dim(1)*len)
+		dstView := obj.View(ctx, obj.Stride(2)*dst, obj.Dim(0)*obj.Dim(1)*len)
+
+		ctx.Forward(srcView.Copy(ctx, dstView))
+	}
+}
+
+func (c *Causal) defrag() {
+	slog.Debug("defragmenting kv cache")
+
+	// Defrag strategy:
+	// - Search for empty holes at the beginning of the cache,
+	//   filling them with active data starting at the end
+	// - If there are contiguous elements that need to be moved,
+	//   combine them into a single operation by holding new moves
+	//   until we see that the next one is non-contiguous
+	// - Fill up the context with the maximum number of operations it
+	//   can hold then compute that and continue with a new context
+	//
+	// We could try to optimize placement by grouping blocks from
+	// the same sequences together but most likely the next forward
+	// pass will disrupt this anyways, so the real world benefit
+	// seems limited as this time.
+
+	ctx := c.backend.NewContext()
+
+	// For every move, 6 tensors are required per layer (2 views and a
+	// copy for each of k and v).
+	layers := 0
+	for _, key := range c.keys {
+		if key == nil {
+			continue
+		}
+		layers++
+	}
+
+	maxMoves := ctx.MaxTensors() / (6 * layers)
+	moves := 0
+
+	var pendingSrc, pendingDst, pendingLen int
+	src := len(c.cells) - 1
+
+	for dst := 0; dst < src; dst++ {
+		if len(c.cells[dst].sequences) == 0 {
+			for ; src > dst; src-- {
+				if len(c.cells[src].sequences) != 0 {
+					c.cells[dst] = c.cells[src]
+					c.cells[src] = cacheCell{}
+
+					if pendingLen > 0 {
+						if src == pendingSrc-pendingLen && dst == pendingDst+pendingLen {
+							pendingSrc = src
+							pendingLen++
+							break
+						} else {
+							moveCell(ctx, c.keys, pendingSrc, pendingDst, pendingLen)
+							moveCell(ctx, c.values, pendingSrc, pendingDst, pendingLen)
+							moves++
+						}
+					}
+
+					pendingSrc = src
+					pendingDst = dst
+					pendingLen = 1
+
+					break
+				}
+			}
+		}
+
+		if moves >= maxMoves {
+			ctx.Compute()
+			ctx.Close()
+			ctx = c.backend.NewContext()
+
+			moves = 0
+		}
+	}
+
+	if pendingLen > 0 {
+		moveCell(ctx, c.keys, pendingSrc, pendingDst, pendingLen)
+		moveCell(ctx, c.values, pendingSrc, pendingDst, pendingLen)
+		moves++
+	}
+
+	if moves > 0 {
+		ctx.Compute()
+	}
+	ctx.Close()
+
+	// Reset range metadata
+	for seq := range c.cellRanges {
+		seqRange := newRange()
+
+		for i, cell := range c.cells {
+			if slices.Contains(cell.sequences, seq) {
+				if i < seqRange.min {
+					seqRange.min = i
+				}
+				if i > seqRange.max {
+					seqRange.max = i
+				}
+			}
+		}
+
+		c.cellRanges[seq] = seqRange
+	}
+}
+
+func (c *Causal) SetLayer(layer int) {
+	if layer >= len(c.keys) {
+		c.keys = append(c.keys, make([]ml.Tensor, layer-len(c.keys)+1)...)
+		c.values = append(c.values, make([]ml.Tensor, layer-len(c.values)+1)...)
+	}
+
+	c.curLayer = layer
+}
+
+func (c *Causal) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
+	key := c.keys[c.curLayer]
+	value := c.values[c.curLayer]
+
+	key = key.View(ctx, key.Stride(2)*c.curCellRange.min,
+		key.Dim(0), key.Stride(1),
+		key.Dim(1), key.Stride(2),
+		c.curMask.Dim(0),
+	)
+
+	value = value.View(ctx, key.Stride(2)*c.curCellRange.min,
+		value.Dim(0), value.Stride(1),
+		value.Dim(1), value.Stride(2),
+		c.curMask.Dim(0),
+	)
+
+	return key, value, c.curMask
+}
+
+func (c *Causal) Put(ctx ml.Context, key, value ml.Tensor) {
+	if c.curBatchSize != key.Dim(2) {
+		panic(fmt.Errorf("inconsistent batch sizes (layer: %v, batch size: %v layer batch size: %v)", c.curLayer, c.curBatchSize, key.Dim(2)))
+	}
+
+	if c.keys[c.curLayer] == nil || c.values[c.curLayer] == nil {
+		c.keys[c.curLayer] = c.cacheCtx.Zeros(c.DType, key.Dim(0), key.Dim(1), int(c.Capacity))
+		c.values[c.curLayer] = c.cacheCtx.Zeros(c.DType, value.Dim(0), value.Dim(1), int(c.Capacity))
+	}
+
+	ctx.Forward(key.Copy(ctx, c.keys[c.curLayer].View(ctx, c.keys[c.curLayer].Stride(2)*c.curLoc, key.Dim(0)*key.Dim(1)*key.Dim(2))))
+	ctx.Forward(value.Copy(ctx, c.values[c.curLayer].View(ctx, c.values[c.curLayer].Stride(2)*c.curLoc, value.Dim(0)*value.Dim(1)*value.Dim(2))))
+}
+
+func (c *Causal) CopyPrefix(srcSeq, dstSeq int, len int32) {
+	seqRange := newRange()
+
+	for i := range c.cells {
+		// Remove the contents of dstSeq so that we only have the copied prefix, metadata will be reset at the end
+		if slices.Contains(c.cells[i].sequences, dstSeq) {
+			c.cells[i].sequences = slices.DeleteFunc(c.cells[i].sequences, func(s int) bool { return s == dstSeq })
+		}
+
+		if slices.Contains(c.cells[i].sequences, srcSeq) && c.cells[i].pos < len {
+			c.cells[i].sequences = append(c.cells[i].sequences, dstSeq)
+			if i < seqRange.min {
+				seqRange.min = i
+			}
+			if i > seqRange.max {
+				seqRange.max = i
+			}
+		}
+	}
+
+	c.cellRanges[dstSeq] = seqRange
+}
+
+func (c *Causal) shift(seq int, beginIndex, offset int32) error {
+	if c.shiftFn == nil {
+		return ErrNotSupported
+	}
+
+	ctx := c.backend.NewContext()
+	defer ctx.Close()
+
+	seqRange := c.cellRanges[seq]
+	size := seqRange.max - seqRange.min + 1
+
+	offsets := make([]int32, size)
+	for i := range offsets {
+		cell := c.cells[seqRange.min+i]
+
+		if slices.Contains(cell.sequences, seq) && cell.pos >= beginIndex {
+			offsets[i] = offset
+		}
+	}
+
+	kShift, err := ctx.FromIntSlice(offsets, len(offsets))
+	if err != nil {
+		return err
+	}
+
+	for i, key := range c.keys {
+		if key == nil {
+			continue
+		}
+
+		key = key.View(ctx, key.Stride(2)*seqRange.min,
+			key.Dim(0), key.Stride(1),
+			key.Dim(1), key.Stride(2),
+			size,
+		)
+
+		roped, err := c.shiftFn(ctx, i, key, kShift)
+		if err != nil {
+			return err
+		}
+
+		ctx.Forward(roped.Copy(ctx, key))
+	}
+
+	ctx.Compute()
+
+	return nil
+}
+
+func (c *Causal) Remove(seq int, beginIndex, endIndex int32) error {
+	var offset int32
+	if endIndex != math.MaxInt32 {
+		offset = beginIndex - endIndex
+	}
+
+	seqRange := newRange()
+
+	for i := range c.cells {
+		if slices.Contains(c.cells[i].sequences, seq) {
+			if c.cells[i].pos >= beginIndex && c.cells[i].pos < endIndex {
+				c.cells[i].sequences = slices.DeleteFunc(c.cells[i].sequences, func(s int) bool { return s == seq })
+			} else {
+				if c.cells[i].pos >= endIndex {
+					if slices.ContainsFunc(c.cells[i].sequences, func(s int) bool { return s != seq }) {
+						// TODO(jessegross): Need to be careful about data shared between sequences
+						return errors.New("shifting on cells shared by multiple sequences not yet implemented")
+					}
+
+					c.cells[i].pos += offset
+				}
+				if i < seqRange.min {
+					seqRange.min = i
+				}
+				if i > seqRange.max {
+					seqRange.max = i
+				}
+			}
+		}
+	}
+
+	if seqRange == newRange() {
+		delete(c.cellRanges, seq)
+		return nil
+	}
+
+	c.cellRanges[seq] = seqRange
+
+	if endIndex != math.MaxInt32 {
+		err := c.shift(seq, endIndex+offset, offset)
+		if err != nil {
+			return err
+		}
+	}
+
+	return nil
+}

kvcache/causal_test.go

+package kvcache
+
+import (
+	"math"
+	"slices"
+	"testing"
+
+	"github.com/ollama/ollama/ml"
+)
+
+type testCase struct {
+	name          string
+	in            []float32
+	inShape       []int
+	seqs          []int
+	pos           []int32
+	expected      []float32
+	expectedShape []int
+	expectedMask  []float32
+}
+
+func TestStore(t *testing.T) {
+	backend := &testBackend{}
+	cache := NewCausalCache(nil)
+	defer cache.Close()
+
+	cache.Init(backend, ml.DTypeF16, 16)
+
+	tests := []testCase{
+		{
+			name:          "FirstBatch",
+			in:            []float32{111, 211, 121, 221, 131, 231, 112, 212, 122, 222, 132, 232, 113, 213, 123, 223, 133, 233, 114, 214, 124, 224, 134, 234},
+			inShape:       []int{2, 3, 4},
+			seqs:          []int{0, 0, 0, 0},
+			pos:           []int32{0, 1, 2, 3},
+			expected:      []float32{111, 211, 121, 221, 131, 231, 112, 212, 122, 222, 132, 232, 113, 213, 123, 223, 133, 233, 114, 214, 124, 224, 134, 234},
+			expectedShape: []int{2, 3, 4},
+			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0},
+		},
+		{
+			name:          "SecondBatch",
+			in:            []float32{115, 215, 125, 225, 135, 235},
+			inShape:       []int{2, 3, 1},
+			seqs:          []int{0},
+			pos:           []int32{4},
+			expected:      []float32{111, 211, 121, 221, 131, 231, 112, 212, 122, 222, 132, 232, 113, 213, 123, 223, 133, 233, 114, 214, 124, 224, 134, 234, 115, 215, 125, 225, 135, 235},
+			expectedShape: []int{2, 3, 5},
+			expectedMask:  []float32{0, 0, 0, 0, 0},
+		},
+	}
+
+	testCache(t, backend, cache, tests)
+}
+
+func TestSWA(t *testing.T) {
+	backend := &testBackend{}
+	cache := NewSWACache(1, nil)
+	defer cache.Close()
+
+	cache.Init(backend, ml.DTypeF32, 16)
+
+	tests := []testCase{
+		{
+			name:          "SlidingWindow",
+			in:            []float32{1, 2, 3, 4},
+			inShape:       []int{1, 1, 4},
+			seqs:          []int{0, 0, 0, 0},
+			pos:           []int32{0, 1, 2, 3},
+			expected:      []float32{1, 2, 3, 4},
+			expectedShape: []int{1, 1, 4},
+			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
+		},
+	}
+
+	testCache(t, backend, cache, tests)
+}
+
+func TestSequences(t *testing.T) {
+	backend := &testBackend{}
+	cache := NewCausalCache(nil)
+	defer cache.Close()
+
+	cache.Init(backend, ml.DTypeF16, 16)
+
+	tests := []testCase{
+		{
+			name:          "FirstBatch",
+			in:            []float32{1, 2, 3, 4},
+			inShape:       []int{1, 1, 4},
+			seqs:          []int{0, 0, 1, 1},
+			pos:           []int32{0, 1, 0, 1},
+			expected:      []float32{1, 2, 3, 4},
+			expectedShape: []int{1, 1, 4},
+			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
+		},
+		{
+			name:          "SecondBatch",
+			in:            []float32{5, 6},
+			inShape:       []int{1, 1, 2},
+			seqs:          []int{0, 1},
+			pos:           []int32{2, 2},
+			expected:      []float32{1, 2, 3, 4, 5, 6},
+			expectedShape: []int{1, 1, 6},
+			expectedMask:  []float32{0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), 0},
+		},
+	}
+
+	testCache(t, backend, cache, tests)
+}
+
+func TestRemove(t *testing.T) {
+	backend := &testBackend{}
+	cache := NewCausalCache(func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+		return key.Add(ctx, shift), nil
+	})
+	defer cache.Close()
+
+	cache.Init(backend, ml.DTypeF16, 16)
+
+	tests := []testCase{
+		{
+			name:          "FirstBatch",
+			in:            []float32{1, 2, 3, 4},
+			inShape:       []int{1, 1, 4},
+			seqs:          []int{0, 0, 1, 1},
+			pos:           []int32{0, 1, 0, 1},
+			expected:      []float32{1, 2, 3, 4},
+			expectedShape: []int{1, 1, 4},
+			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
+		},
+	}
+
+	testCache(t, backend, cache, tests)
+
+	err := cache.Remove(0, 1, math.MaxInt32)
+	if err != nil {
+		panic(err)
+	}
+
+	tests = []testCase{
+		{
+			name:          "RemoveEnd",
+			in:            []float32{5, 6},
+			inShape:       []int{1, 1, 2},
+			seqs:          []int{0, 1},
+			pos:           []int32{1, 2},
+			expected:      []float32{1, 2, 3, 4, 5, 6},
+			expectedShape: []int{1, 1, 6},
+			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), 0},
+		},
+	}
+
+	testCache(t, backend, cache, tests)
+
+	err = cache.Remove(0, 0, 1)
+	if err != nil {
+		panic(err)
+	}
+
+	tests = []testCase{
+		{
+			name:          "RemoveMiddle",
+			in:            []float32{7, 8},
+			inShape:       []int{1, 1, 2},
+			seqs:          []int{0, 0},
+			pos:           []int32{1, 2},
+			expected:      []float32{7, 8, 3, 4, 4},
+			expectedShape: []int{1, 1, 5},
+			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0},
+		},
+	}
+
+	testCache(t, backend, cache, tests)
+}
+
+func TestDefrag(t *testing.T) {
+	backend := &testBackend{}
+	cache := NewCausalCache(func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+		return key.Add(ctx, shift), nil
+	})
+	defer cache.Close()
+
+	cache.Init(backend, ml.DTypeF16, 16)
+
+	tests := []testCase{
+		{
+			name:          "FirstBatch",
+			in:            []float32{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
+			inShape:       []int{1, 1, 16},
+			seqs:          []int{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+			pos:           []int32{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15},
+			expected:      []float32{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
+			expectedShape: []int{1, 1, 16},
+			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+		},
+	}
+
+	testCache(t, backend, cache, tests)
+
+	err := cache.Remove(0, 2, 4)
+	if err != nil {
+		panic(err)
+	}
+
+	err = cache.Remove(0, 13, math.MaxInt32)
+	if err != nil {
+		panic(err)
+	}
+
+	tests = []testCase{
+		{
+			name:          "Defrag",
+			in:            []float32{17, 18, 19},
+			inShape:       []int{1, 1, 3},
+			seqs:          []int{0, 0, 0},
+			pos:           []int32{16, 17, 18},
+			expected:      []float32{1, 2, 12, 13, 3, 4, 5, 6, 7, 8, 9, 10, 11, 17, 18, 19},
+			expectedShape: []int{1, 1, 16},
+			expectedMask:  []float32{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+		},
+	}
+
+	testCache(t, backend, cache, tests)
+}
+
+func TestCopy(t *testing.T) {
+	backend := &testBackend{}
+	cache := NewCausalCache(func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) { return key, nil })
+	defer cache.Close()
+
+	cache.Init(backend, ml.DTypeF16, 16)
+
+	tests := []testCase{
+		{
+			name:          "FirstBatch",
+			in:            []float32{1, 2, 3, 4},
+			inShape:       []int{1, 1, 4},
+			seqs:          []int{0, 0, 0, 0},
+			pos:           []int32{0, 1, 2, 3},
+			expected:      []float32{1, 2, 3, 4},
+			expectedShape: []int{1, 1, 4},
+			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0},
+		},
+	}
+
+	testCache(t, backend, cache, tests)
+
+	cache.CopyPrefix(0, 1, 2)
+
+	tests = []testCase{
+		{
+			name:          "Copy",
+			in:            []float32{5, 6},
+			inShape:       []int{1, 1, 2},
+			seqs:          []int{1, 1},
+			pos:           []int32{3, 4},
+			expected:      []float32{1, 2, 3, 4, 5, 6},
+			expectedShape: []int{1, 1, 6},
+			expectedMask:  []float32{0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
+		},
+	}
+
+	testCache(t, backend, cache, tests)
+}
+
+func testCache(t *testing.T, backend ml.Backend, cache Cache, tests []testCase) {
+	for _, test := range tests {
+		t.Run(test.name, func(t *testing.T) {
+			context := backend.NewContext()
+			defer context.Close()
+
+			err := cache.StartForward(context, test.pos, test.seqs)
+			if err != nil {
+				panic(err)
+			}
+
+			cache.SetLayer(0)
+			tensor, _ := context.FromFloatSlice(test.in, test.inShape...)
+			cache.Put(context, tensor, tensor)
+
+			out, _, mask := cache.Get(context)
+
+			context.Forward(out)
+			context.Forward(mask)
+			context.Compute(out, mask)
+
+			if !slices.Equal(out.Floats(), test.expected) || !slices.Equal(out.Shape(), test.expectedShape) || !slices.Equal(mask.Floats(), test.expectedMask) {
+				t.Errorf("TestCache: have %v (shape %v); want %v (shape %v); mask: have %v (shape %v) want %v", out.Floats(), out.Shape(), test.expected, test.expectedShape, mask.Floats(), mask.Shape(), test.expectedMask)
+			}
+		})
+	}
+}
+
+type testBackend struct{}
+
+func (b *testBackend) Config() ml.Config {
+	panic("not implemented")
+}
+
+func (b *testBackend) Get(name string) ml.Tensor {
+	panic("not implemented")
+}
+
+func (b *testBackend) NewContext() ml.Context {
+	return &testContext{}
+}
+
+type testContext struct{}
+
+func (c *testContext) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
+	total := 0
+
+	if len(shape) > 0 {
+		total = 1
+		for _, s := range shape {
+			total *= s
+		}
+	}
+
+	return &testTensor{dtype: dtype, elementSize: 4, data: make([]float32, total), shape: shape}
+}
+
+func (c *testContext) FromFloatSlice(s []float32, shape ...int) (ml.Tensor, error) {
+	t := c.Zeros(ml.DTypeF32, shape...).(*testTensor)
+
+	copy(t.data, s)
+
+	return t, nil
+}
+
+func (c *testContext) FromIntSlice(s []int32, shape ...int) (ml.Tensor, error) {
+	f := make([]float32, len(s))
+	for i := range f {
+		f[i] = float32(s[i])
+	}
+
+	out, _ := c.FromFloatSlice(f, shape...)
+	out.(*testTensor).dtype = ml.DTypeI32
+
+	return out, nil
+}
+
+func (c *testContext) Forward(ml.Tensor) {}
+
+func (c *testContext) Compute(...ml.Tensor) {}
+
+func (c *testContext) MaxTensors() int {
+	return 10
+}
+
+func (c *testContext) Close() {}
+
+type testTensor struct {
+	dtype       ml.DType
+	elementSize int
+	data        []float32
+	shape       []int
+}
+
+func (t *testTensor) Dim(n int) int {
+	return t.shape[n]
+}
+
+func (t *testTensor) Stride(n int) int {
+	stride := t.elementSize
+	for i := range n {
+		stride *= t.shape[i]
+	}
+
+	return stride
+}
+
+func (t *testTensor) Shape() []int {
+	return t.shape
+}
+
+func (t *testTensor) DType() ml.DType {
+	return t.dtype
+}
+
+func (t *testTensor) Bytes() []byte {
+	panic("not implemented")
+}
+
+func (t *testTensor) Floats() []float32 {
+	out := make([]float32, len(t.data))
+	copy(out, t.data)
+	return out
+}
+
+func (t *testTensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
+	out := ctx.Zeros(t.DType(), t.Shape()...).(*testTensor)
+
+	for i := range out.data {
+		out.data[i] = t.data[i] + t2.(*testTensor).data[i]
+	}
+
+	return out
+}
+
+func (t *testTensor) Mul(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Mulmat(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) MulmatFullPrec(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Softmax(ctx ml.Context) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) LayerNorm(ctx ml.Context, weight, bias ml.Tensor, eps float32) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) RMSNorm(ctx ml.Context, weight ml.Tensor, eps float32) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Scale(ctx ml.Context, s float64) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Conv2D(ctx ml.Context, weight ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, dim uint32, base, scale float32) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Tanh(ctx ml.Context) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) GELU(ctx ml.Context) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) SILU(ctx ml.Context) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Reshape(ctx ml.Context, shape ...int) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
+	offset /= t.elementSize
+
+	var s []int
+
+	switch len(shape) {
+	case 1:
+		s = []int{shape[0]}
+	case 5:
+		s = []int{shape[0], shape[2], shape[4]}
+	default:
+		panic("unsupported number of dimensions")
+	}
+
+	context := &testContext{}
+
+	view := context.Zeros(t.dtype, s...).(*testTensor)
+	view.data = t.data[offset : offset+len(view.data)]
+
+	return view
+}
+
+func (t *testTensor) Permute(ctx ml.Context, shape ...int) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Contiguous(ctx ml.Context) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Pad(ctx ml.Context, shape ...int) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Unpad(ctx ml.Context, shape ...int) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Stack(ctx ml.Context, dim int, s ...ml.Tensor) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Concat(ctx ml.Context, t2 ml.Tensor, dim int) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Rows(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) Copy(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
+	copy(t2.(*testTensor).data, t.data)
+	return nil
+}

kvcache/encoder.go

+package kvcache
+
+import (
+	"github.com/ollama/ollama/ml"
+)
+
+// Encoder cache stores K and V tensors that are position independent
+//
+// The tensors can be of any shape and will be returned as they were stored
+// The mask is currently always nil
+//
+// Not currently safe for multiple sequences
+type EncoderCache struct {
+	// ** current forward pass **
+
+	// the active layer for Get and Put
+	curLayer int
+
+	// if something is stored during this pass, this
+	// will be the position (but there is no guarantee
+	// anything will be stored)
+	curPos int32
+
+	// ** cache metadata **
+
+	// was something stored in the cache?
+	encoderCached bool
+
+	// position of the cached data
+	encoderPos int32
+
+	// ** cache data storage **
+
+	cacheCtx     ml.Context
+	keys, values []ml.Tensor
+}
+
+func NewEncoderCache() *EncoderCache {
+	return &EncoderCache{}
+}
+
+func (c *EncoderCache) Init(backend ml.Backend, dtype ml.DType, capacity int32) {
+	c.cacheCtx = backend.NewContext()
+}
+
+func (c *EncoderCache) Close() {
+	c.cacheCtx.Close()
+}
+
+func (c *EncoderCache) StartForward(ctx ml.Context, positions []int32, seqs []int) error {
+	// The image is always in the first position
+	c.curPos = positions[0]
+
+	return nil
+}
+
+func (c *EncoderCache) SetLayer(layer int) {
+	if layer >= len(c.keys) {
+		c.keys = append(c.keys, make([]ml.Tensor, layer-len(c.keys)+1)...)
+		c.values = append(c.values, make([]ml.Tensor, layer-len(c.values)+1)...)
+	}
+
+	c.curLayer = layer
+}
+
+func (c *EncoderCache) EncoderCached() bool {
+	return c.encoderCached
+}
+
+func (c *EncoderCache) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
+	return c.keys[c.curLayer], c.values[c.curLayer], nil
+}
+
+func (c *EncoderCache) Put(ctx ml.Context, key, value ml.Tensor) {
+	c.encoderPos = c.curPos
+	c.encoderCached = true
+
+	if c.keys[c.curLayer] == nil || c.values[c.curLayer] == nil {
+		c.keys[c.curLayer] = c.cacheCtx.Zeros(key.DType(), key.Shape()...)
+		c.values[c.curLayer] = c.cacheCtx.Zeros(value.DType(), value.Shape()...)
+	}
+
+	ctx.Forward(key.Copy(ctx, c.keys[c.curLayer]))
+	ctx.Forward(value.Copy(ctx, c.values[c.curLayer]))
+}
+
+func (c *EncoderCache) CopyPrefix(srcSeq, dstSeq int, len int32) {
+	panic("encoder cache does not support multiple sequences")
+}
+
+func (c *EncoderCache) Remove(seq int, beginIndex, endIndex int32) error {
+	if c.encoderPos >= beginIndex && c.encoderPos < endIndex {
+		c.encoderCached = false
+	}
+
+	return nil
+}

kvcache/wrapper.go

+package kvcache
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/ml"
+)
+
+// Wrapper cache is a container for multiple types of caches,
+// such as for the encoding and decoding portions of a model.
+type WrapperCache struct {
+	// caches we are wrapping
+	caches []Cache
+
+	// cache to be used for this layer
+	curType int
+}
+
+func NewWrapperCache(caches ...Cache) *WrapperCache {
+	return &WrapperCache{
+		caches: caches,
+	}
+}
+
+func (c *WrapperCache) Init(backend ml.Backend, dtype ml.DType, capacity int32) {
+	for _, cache := range c.caches {
+		cache.Init(backend, dtype, capacity)
+	}
+}
+
+func (c *WrapperCache) Close() {
+	for _, cache := range c.caches {
+		cache.Close()
+	}
+}
+
+func (c *WrapperCache) StartForward(ctx ml.Context, positions []int32, seqs []int) error {
+	for i, cache := range c.caches {
+		err := cache.StartForward(ctx, positions, seqs)
+		if err != nil {
+			// unwind on error - Remove with endIndex set to math.MaxInt32 does not fail
+			for j := i - 1; j >= 0; j-- {
+				for k := range positions {
+					_ = c.caches[j].Remove(seqs[k], positions[k], math.MaxInt32)
+				}
+			}
+			return err
+		}
+	}
+
+	c.curType = 0
+	return nil
+}
+
+func (c *WrapperCache) SetLayer(layer int) {
+	for _, cache := range c.caches {
+		cache.SetLayer(layer)
+	}
+}
+
+func (c *WrapperCache) SetLayerType(layerType int) {
+	c.curType = layerType
+}
+
+func (c *WrapperCache) UnderlyingCache() Cache {
+	return c.caches[c.curType]
+}
+
+func (c *WrapperCache) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
+	return c.caches[c.curType].Get(ctx)
+}
+
+func (c *WrapperCache) Put(ctx ml.Context, key, value ml.Tensor) {
+	c.caches[c.curType].Put(ctx, key, value)
+}
+
+func (c *WrapperCache) CopyPrefix(srcSeq, dstSeq int, len int32) {
+	for _, cache := range c.caches {
+		cache.CopyPrefix(srcSeq, dstSeq, len)
+	}
+}
+
+func (c *WrapperCache) Remove(seq int, beginIndex, endIndex int32) error {
+	// If the one of these fails, the caller is supposed to retry with endIndex set to math.MaxInt32, which should not fail
+	for _, cache := range c.caches {
+		err := cache.Remove(seq, beginIndex, endIndex)
+		if err != nil {
+			return err
+		}
+	}
+
+	return nil
+}

llm/server.go

@@ -275,6 +275,9 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapt
 			port = rand.Intn(65535-49152) + 49152 // get a random port in the ephemeral range
 		}
 		finalParams := []string{"runner"}
+		if envconfig.NewEngine() {
+			finalParams = append(finalParams, "--ollama-engine")
+		}
 		finalParams = append(finalParams, params...)
 		finalParams = append(finalParams, "--port", strconv.Itoa(port))
 

ml/backend.go

@@ -50,6 +50,7 @@ type Context interface {
 
 	Forward(Tensor)
 	Compute(...Tensor)
+	MaxTensors() int
 	Close()
 }
 
@@ -118,7 +119,7 @@ type DumpOptions struct {
 	Precision int
 }
 
-func Dump(t Tensor, opts ...DumpOptions) string {
+func Dump(ctx Context, t Tensor, opts ...DumpOptions) string {
 	if len(opts) < 1 {
 		opts = append(opts, DumpOptions{
 			Items:     3,
@@ -128,11 +129,17 @@ func Dump(t Tensor, opts ...DumpOptions) string {
 
 	switch t.DType() {
 	case DTypeF32:
-		return dump[[]float32](t, opts[0].Items, func(f float32) string {
+		return dump[[]float32](ctx, t, opts[0].Items, func(f float32) string {
+			return strconv.FormatFloat(float64(f), 'f', opts[0].Precision, 32)
+		})
+	case DTypeF16:
+		f32 := ctx.Zeros(DTypeF32, t.Shape()...)
+		f32 = t.Copy(ctx, f32)
+		return dump[[]float32](ctx, f32, opts[0].Items, func(f float32) string {
 			return strconv.FormatFloat(float64(f), 'f', opts[0].Precision, 32)
 		})
 	case DTypeI32:
-		return dump[[]int32](t, opts[0].Items, func(i int32) string {
+		return dump[[]int32](ctx, t, opts[0].Items, func(i int32) string {
 			return strconv.FormatInt(int64(i), 10)
 		})
 	default:
@@ -140,10 +147,10 @@ func Dump(t Tensor, opts ...DumpOptions) string {
 	}
 }
 
-func dump[S ~[]E, E number](t Tensor, items int, fn func(E) string) string {
-	bts := t.Bytes()
-	if bts == nil {
-		return "<nil>"
+func dump[S ~[]E, E number](ctx Context, t Tensor, items int, fn func(E) string) string {
+	if t.Bytes() == nil {
+		ctx.Forward(t)
+		ctx.Compute(t)
 	}
 
 	s := make(S, mul(t.Shape()...))
@@ -191,7 +198,8 @@ func dump[S ~[]E, E number](t Tensor, items int, fn func(E) string) string {
 type DType int
 
 const (
-	DTypeF32 DType = iota
+	DTypeOther DType = iota
+	DTypeF32
+	DTypeF16
 	DTypeI32
-	DTypeOther
 )

ml/backend/ggml/ggml.go

@@ -258,6 +258,10 @@ func (c *Context) Compute(tensors ...ml.Tensor) {
 	}
 }
 
+func (c *Context) MaxTensors() int {
+	return c.nodes
+}
+
 func shapeToGGML(shape []int) *C.int64_t {
 	sh := make([]C.int64_t, len(shape))
 	for i, s := range shape {
@@ -282,6 +286,8 @@ func (c Context) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
 	switch dtype {
 	case ml.DTypeF32:
 		t = C.ggml_new_tensor(c.ctx, C.GGML_TYPE_F32, C.int(len(shape)), shapeToGGML(shape))
+	case ml.DTypeF16:
+		t = C.ggml_new_tensor(c.ctx, C.GGML_TYPE_F16, C.int(len(shape)), shapeToGGML(shape))
 	case ml.DTypeI32:
 		t = C.ggml_new_tensor(c.ctx, C.GGML_TYPE_I32, C.int(len(shape)), shapeToGGML(shape))
 	default:
@@ -389,6 +395,8 @@ func (t *Tensor) DType() ml.DType {
 	switch t.t._type {
 	case C.GGML_TYPE_F32:
 		return ml.DTypeF32
+	case C.GGML_TYPE_F16:
+		return ml.DTypeF16
 	case C.GGML_TYPE_I32:
 		return ml.DTypeI32
 	default:
@@ -580,9 +588,14 @@ func (t *Tensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, ropeDi
 		ropeFactors = &Tensor{}
 	}
 
+	dequant := t.t
+	if C.ggml_is_quantized(t.t._type) {
+		dequant = C.ggml_cast(ctx.(*Context).ctx, t.t, C.GGML_TYPE_F32)
+	}
+
 	return &Tensor{
 		t: C.ggml_rope_ext(
-			ctx.(*Context).ctx, t.t, positionIDs.(*Tensor).t, ropeFactors.(*Tensor).t,
+			ctx.(*Context).ctx, dequant, positionIDs.(*Tensor).t, ropeFactors.(*Tensor).t,
 			C.int(ropeDim),
 			131072,       // YaRN n_ctx_train
 			ropeTypeNorm, // ROPE_TYPE_NORM

model/model.go

 package model
 
 import (
+	"errors"
 	"fmt"
 	"image"
 	_ "image/jpeg"
@@ -15,102 +16,42 @@ import (
 	_ "golang.org/x/image/tiff"
 	_ "golang.org/x/image/webp"
 
-	"github.com/ollama/ollama/cache"
+	"github.com/ollama/ollama/kvcache"
 	"github.com/ollama/ollama/ml"
 	_ "github.com/ollama/ollama/ml/backend"
 )
 
-type Cache struct {
-	cache.Cache
-	cache.Options
-}
-
-func (c Cache) Sub(i int) Cache {
-	if c.Cache != nil {
-		return Cache{
-			Cache:   c.Cache.Sub(i),
-			Options: c.Options,
-		}
-	}
-
-	return c
-}
-
-func (c Cache) Put(ctx ml.Context, key, value ml.Tensor, opts cache.Options) (ml.Tensor, ml.Tensor) {
-	if c.Cache != nil {
-		return c.Cache.Put(ctx, key, value, opts)
-	}
-
-	return key, value
-}
-
 type Options struct {
-	inputs []int32
-
-	Offset int
+	Inputs    []int32
+	Positions []int32
+	Sequences []int
+	Outputs   []int32
 
 	Images []image.Image
-
-	Cache
-}
-
-func (opts Options) Inputs() []int32 {
-	return opts.inputs[opts.Offset:]
-}
-
-func (opts Options) Positions() []int32 {
-	positions := make([]int32, len(opts.inputs)-opts.Offset)
-	for i := range positions {
-		positions[i] = int32(opts.Offset + i)
-	}
-
-	return positions
 }
 
-type OptionsFunc func(Model, *Options)
-
-func WithInputIDs(ids []int32) OptionsFunc {
-	return func(m Model, opts *Options) {
-		opts.inputs = ids
-	}
-}
-
-func WithOffset(offset int) OptionsFunc {
-	return func(m Model, opts *Options) {
-		opts.Offset = offset
-		opts.Cache.Position = offset
-	}
-}
-
-func WithImage(img image.Image) OptionsFunc {
-	return func(m Model, opts *Options) {
-		opts.Images = append(opts.Images, img)
-	}
-}
-
-func WithCache(c cache.Cache) OptionsFunc {
-	return func(m Model, opts *Options) {
-		opts.Cache = Cache{
-			Cache: c,
-			Options: cache.Options{
-				Position: opts.Offset,
-			},
-		}
-	}
+type config struct {
+	Cache kvcache.Cache
 }
 
 type Base struct {
 	b ml.Backend
+	config
 }
 
 func (m *Base) Backend() ml.Backend {
 	return m.b
 }
 
+func (m *Base) Config() config {
+	return m.config
+}
+
 type Model interface {
 	Forward(ml.Context, Options) (ml.Tensor, error)
 
 	Backend() ml.Backend
+	Config() config
 }
 
 var models = make(map[string]func(ml.Config) (Model, error))
@@ -146,12 +87,14 @@ func New(s string) (Model, error) {
 		return nil, err
 	}
 
+	base := Base{b: b, config: m.Config()}
+
 	v := reflect.ValueOf(m)
-	v.Elem().Set(populateFields(b, v.Elem()))
+	v.Elem().Set(populateFields(base, v.Elem()))
 	return m, nil
 }
 
-func populateFields(b ml.Backend, v reflect.Value, tags ...Tag) reflect.Value {
+func populateFields(base Base, v reflect.Value, tags ...Tag) reflect.Value {
 	t := v.Type()
 
 	if t.Kind() == reflect.Struct {
@@ -170,7 +113,7 @@ func populateFields(b ml.Backend, v reflect.Value, tags ...Tag) reflect.Value {
 			}
 
 			if tt == reflect.TypeOf((*Base)(nil)).Elem() {
-				vv.Set(reflect.ValueOf(Base{b: b}))
+				vv.Set(reflect.ValueOf(base))
 			} else if tt == reflect.TypeOf((*ml.Tensor)(nil)).Elem() {
 				var fn func([]Tag) [][]string
 				fn = func(tags []Tag) (values [][]string) {
@@ -196,21 +139,21 @@ func populateFields(b ml.Backend, v reflect.Value, tags ...Tag) reflect.Value {
 
 				names := fn(tagsCopy)
 				for _, name := range names {
-					if tensor := b.Get(strings.Join(name, ".")); tensor != nil {
+					if tensor := base.Backend().Get(strings.Join(name, ".")); tensor != nil {
 						slog.Debug("found tensor", "", tensor)
 						vv.Set(reflect.ValueOf(tensor))
 						break
 					}
 				}
 			} else if tt.Kind() == reflect.Pointer || tt.Kind() == reflect.Interface {
-				setPointer(b, vv, tagsCopy)
+				setPointer(base, vv, tagsCopy)
 			} else if tt.Kind() == reflect.Slice || tt.Kind() == reflect.Array {
 				for i := range vv.Len() {
 					vvv := vv.Index(i)
 					if vvv.Kind() == reflect.Pointer || vvv.Kind() == reflect.Interface {
-						setPointer(b, vvv, append(tagsCopy, Tag{Name: strconv.Itoa(i)}))
+						setPointer(base, vvv, append(tagsCopy, Tag{Name: strconv.Itoa(i)}))
 					} else {
-						vvv.Set(populateFields(b, vvv, append(tagsCopy, Tag{Name: strconv.Itoa(i)})...))
+						vvv.Set(populateFields(base, vvv, append(tagsCopy, Tag{Name: strconv.Itoa(i)})...))
 					}
 				}
 			}
@@ -228,7 +171,7 @@ func populateFields(b ml.Backend, v reflect.Value, tags ...Tag) reflect.Value {
 	return v
 }
 
-func setPointer(b ml.Backend, v reflect.Value, tags []Tag) {
+func setPointer(base Base, v reflect.Value, tags []Tag) {
 	vv := v
 	if v.Kind() == reflect.Interface {
 		if v.IsNil() {
@@ -243,7 +186,7 @@ func setPointer(b ml.Backend, v reflect.Value, tags []Tag) {
 		vv = reflect.New(v.Type().Elem()).Elem()
 	}
 
-	if f := populateFields(b, vv, tags...); f.CanAddr() {
+	if f := populateFields(base, vv, tags...); f.CanAddr() {
 		v.Set(f.Addr())
 	}
 }
@@ -277,18 +220,27 @@ func canNil(t reflect.Type) bool {
 		t.Kind() == reflect.Slice
 }
 
-func Forward(m Model, optsFuncs ...OptionsFunc) (ml.Tensor, error) {
-	var opts Options
-	for _, optsFunc := range optsFuncs {
-		optsFunc(m, &opts)
+func Forward(ctx ml.Context, m Model, opts Options) (ml.Tensor, error) {
+	if len(opts.Positions) != len(opts.Sequences) {
+		return nil, fmt.Errorf("length of positions (%v) must match length of seqs (%v)", len(opts.Positions), len(opts.Sequences))
+	}
+
+	if len(opts.Positions) < 1 {
+		return nil, errors.New("batch size cannot be less than 1")
+	}
+
+	cache := m.Config().Cache
+	if cache != nil {
+		err := cache.StartForward(ctx, opts.Positions, opts.Sequences)
+		if err != nil {
+			return nil, err
+		}
 	}
 
-	ctx := m.Backend().NewContext()
 	t, err := m.Forward(ctx, opts)
 	if err != nil {
 		return nil, err
 	}
-	defer ctx.Close()
 
 	ctx.Forward(t)
 	ctx.Compute(t)

model/model_test.go

@@ -78,7 +78,7 @@ func TestPopulateFields(t *testing.T) {
 
 	var m fakeModel
 	v := reflect.ValueOf(&m)
-	v.Elem().Set(populateFields(&fakeBackend{
+	v.Elem().Set(populateFields(Base{b: &fakeBackend{
 		names: []string{
 			"input.weight",
 			"blk.0.attn_q.weight",
@@ -90,7 +90,7 @@ func TestPopulateFields(t *testing.T) {
 			"output_norm.weight",
 			"output.weight",
 		},
-	}, v.Elem()))
+	}}, v.Elem()))
 
 	if diff := cmp.Diff(fakeModel{
 		Input:      &nn.Embedding{Weight: &fakeTensor{Name: "input.weight"}},
@@ -121,11 +121,11 @@ func TestPopulateFieldsAlternateName(t *testing.T) {
 
 	m := fakeModel{}
 	v := reflect.ValueOf(&m)
-	v.Elem().Set(populateFields(&fakeBackend{
+	v.Elem().Set(populateFields(Base{b: &fakeBackend{
 		names: []string{
 			"input.weight",
 		},
-	}, v.Elem()))
+	}}, v.Elem()))
 
 	if diff := cmp.Diff(fakeModel{
 		Input:  &nn.Embedding{Weight: &fakeTensor{Name: "input.weight"}},

model/models/llama/model.go

@@ -3,6 +3,7 @@ package llama
 import (
 	"math"
 
+	"github.com/ollama/ollama/kvcache"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/ml/nn"
 	"github.com/ollama/ollama/model"
@@ -28,7 +29,7 @@ type Model struct {
 }
 
 func New(c ml.Config) (model.Model, error) {
-	return &Model{
+	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+`),
 			&model.Vocabulary{
@@ -49,7 +50,11 @@ func New(c ml.Config) (model.Model, error) {
 			ropeScale:  c.Float("rope.freq_scale", 1),
 			ropeDim:    c.Uint("rope.dimension_count"),
 		},
-	}, nil
+	}
+
+	m.Cache = kvcache.NewCausalCache(m.Shift)
+
+	return &m, nil
 }
 
 type SelfAttention struct {
@@ -59,7 +64,7 @@ type SelfAttention struct {
 	Output *nn.Linear `gguf:"attn_output"`
 }
 
-func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache model.Cache, opts *Options) ml.Tensor {
+func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
 	batchSize := hiddenState.Dim(1)
 	headDim := opts.hiddenSize / opts.numHeads
 
@@ -74,7 +79,8 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 	v := sa.Value.Forward(ctx, hiddenState)
 	v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
 
-	k, v = cache.Put(ctx, k, v, cache.Options)
+	cache.Put(ctx, k, v)
+	k, v, mask := cache.Get(ctx)
 
 	q = q.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	k = k.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
@@ -82,6 +88,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 
 	kq := k.MulmatFullPrec(ctx, q)
 	kq = kq.Scale(ctx, 1.0/math.Sqrt(float64(headDim)))
+	kq = kq.Add(ctx, mask)
 	kq = kq.Softmax(ctx)
 
 	kqv := v.Mulmat(ctx, kq)
@@ -91,6 +98,10 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 	return sa.Output.Forward(ctx, kqv)
 }
 
+func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return key.RoPE(ctx, shift, m.Options.RopeFactors, m.Options.ropeDim, m.Options.ropeBase, m.Options.ropeScale), nil
+}
+
 type MLP struct {
 	Up   *nn.Linear `gguf:"ffn_up"`
 	Down *nn.Linear `gguf:"ffn_down"`
@@ -109,7 +120,7 @@ type Layer struct {
 	MLP           *MLP
 }
 
-func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache model.Cache, opts *Options) ml.Tensor {
+func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
 	residual := hiddenState
 
 	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
@@ -123,12 +134,12 @@ func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cach
 }
 
 func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) {
-	inputs, err := ctx.FromIntSlice(opts.Inputs(), len(opts.Inputs()))
+	inputs, err := ctx.FromIntSlice(opts.Inputs, len(opts.Inputs))
 	if err != nil {
 		return nil, err
 	}
 
-	positions, err := ctx.FromIntSlice(opts.Positions(), len(opts.Positions()))
+	positions, err := ctx.FromIntSlice(opts.Positions, len(opts.Positions))
 	if err != nil {
 		return nil, err
 	}
@@ -136,13 +147,14 @@ func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) {
 	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
 
 	for i, layer := range m.Layers {
-		hiddenState = layer.Forward(ctx, hiddenState, positions, opts.Cache.Sub(i), m.Options)
+		m.Cache.SetLayer(i)
+		hiddenState = layer.Forward(ctx, hiddenState, positions, m.Cache, m.Options)
 	}
 
 	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
 	hiddenState = m.Output.Forward(ctx, hiddenState)
 
-	outputs, err := ctx.FromIntSlice([]int32{int32(len(opts.Positions())) - 1}, 1)
+	outputs, err := ctx.FromIntSlice(opts.Outputs, len(opts.Outputs))
 	if err != nil {
 		return nil, err
 	}

model/models/mllama/model.go

 package mllama
 
 import (
+	"github.com/ollama/ollama/kvcache"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/ml/nn"
 	"github.com/ollama/ollama/model"
@@ -18,8 +19,13 @@ type Model struct {
 	ImageProcessor
 }
 
+const (
+	crossAttentionLayer = iota
+	selfAttentionLayer
+)
+
 func New(c ml.Config) (model.Model, error) {
-	return &Model{
+	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+`),
 			&model.Vocabulary{
@@ -33,7 +39,11 @@ func New(c ml.Config) (model.Model, error) {
 		ImageProcessor: newImageProcessor(c),
 		VisionModel:    newVisionModel(c),
 		TextModel:      newTextModel(c),
-	}, nil
+	}
+
+	m.Cache = kvcache.NewWrapperCache(kvcache.NewEncoderCache(), kvcache.NewCausalCache(m.TextModel.Shift))
+
+	return &m, nil
 }
 
 func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) {
@@ -73,20 +83,20 @@ func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) {
 		crossAttentionStates = m.Projector.Forward(ctx, crossAttentionStates)
 	}
 
-	inputs, err := ctx.FromIntSlice(opts.Inputs(), len(opts.Inputs()))
+	inputs, err := ctx.FromIntSlice(opts.Inputs, len(opts.Inputs))
 	if err != nil {
 		return nil, err
 	}
 
-	positions, err := ctx.FromIntSlice(opts.Positions(), len(opts.Positions()))
+	positions, err := ctx.FromIntSlice(opts.Positions, len(opts.Positions))
 	if err != nil {
 		return nil, err
 	}
 
 	// TODO: attention mask, cross attention mask
-	hiddenState := m.TextModel.Forward(ctx, inputs, positions, nil, crossAttentionStates, nil, opts.Cache)
+	hiddenState := m.TextModel.Forward(ctx, inputs, positions, nil, crossAttentionStates, nil, m.Cache.(*kvcache.WrapperCache))
 
-	outputs, err := ctx.FromIntSlice([]int32{int32(len(opts.Positions())) - 1}, 1)
+	outputs, err := ctx.FromIntSlice(opts.Outputs, len(opts.Outputs))
 	if err != nil {
 		return nil, err
 	}

model/models/mllama/model_text.go

@@ -4,9 +4,9 @@ import (
 	"math"
 	"slices"
 
+	"github.com/ollama/ollama/kvcache"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/ml/nn"
-	"github.com/ollama/ollama/model"
 )
 
 type TextSelfAttention struct {
@@ -16,7 +16,7 @@ type TextSelfAttention struct {
 	Output *nn.Linear `gguf:"attn_output"`
 }
 
-func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, mask ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor {
+func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, _ ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
 	batchSize := hiddenState.Dim(1)
 	headDim := opts.hiddenSize / opts.numHeads
 
@@ -31,7 +31,8 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, mas
 	value := sa.Value.Forward(ctx, hiddenState)
 	value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
 
-	key, value = cache.Put(ctx, key, value, cache.Options)
+	cache.Put(ctx, key, value)
+	key, value, mask := cache.Get(ctx)
 
 	query = query.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	key = key.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
@@ -39,11 +40,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, mas
 
 	scores := key.MulmatFullPrec(ctx, query)
 	scores = scores.Scale(ctx, 1.0/math.Sqrt(float64(headDim)))
-
-	if mask != nil {
 	scores = scores.Add(ctx, mask)
-	}
-
 	scores = scores.Softmax(ctx)
 
 	attention := value.Mulmat(ctx, scores)
@@ -53,6 +50,11 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, mas
 	return sa.Output.Forward(ctx, attention)
 }
 
+func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	// This will only get called for layers in the cache, which are just the self attention layers
+	return key.RoPE(ctx, shift, m.RopeFactors, m.ropeDim, m.ropeBase, m.ropeScale), nil
+}
+
 type TextMLP struct {
 	Up   *nn.Linear `gguf:"ffn_up"`
 	Down *nn.Linear `gguf:"ffn_down"`
@@ -72,7 +74,7 @@ type TextSelfAttentionDecoderLayer struct {
 	MLP     *TextMLP
 }
 
-func (d *TextSelfAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, positions, mask, _, _ ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor {
+func (d *TextSelfAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, positions, mask, _, _ ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
 	residual := hiddenState
 
 	hiddenState = d.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
@@ -94,23 +96,29 @@ type TextCrossAttention struct {
 	Output    *nn.Linear  `gguf:"cross_attn_o_proj"`
 }
 
-func (ca *TextCrossAttention) Forward(ctx ml.Context, hiddenState, crossAttentionStates ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor {
+func (ca *TextCrossAttention) Forward(ctx ml.Context, hiddenState, crossAttentionStates ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
 	batchSize := hiddenState.Dim(1)
 	headDim := opts.hiddenSize / opts.numHeads
-	numVisionTokens, numTiles := crossAttentionStates.Dim(1), crossAttentionStates.Dim(2)
 
 	query := ca.Query.Forward(ctx, hiddenState)
 	query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
 	query = ca.QueryNorm.Forward(ctx, query, opts.eps)
 
-	key := ca.Key.Forward(ctx, crossAttentionStates)
+	var key, value ml.Tensor
+	if crossAttentionStates != nil {
+		numVisionTokens, numTiles := crossAttentionStates.Dim(1), crossAttentionStates.Dim(2)
+
+		key = ca.Key.Forward(ctx, crossAttentionStates)
 		key = key.Reshape(ctx, headDim, opts.numKVHeads, numVisionTokens*numTiles)
 		key = ca.KeyNorm.Forward(ctx, key, opts.eps)
 
-	value := ca.Value.Forward(ctx, crossAttentionStates)
+		value = ca.Value.Forward(ctx, crossAttentionStates)
 		value = value.Reshape(ctx, headDim, opts.numKVHeads, numVisionTokens*numTiles)
 
-	// TODO cache key, value
+		cache.Put(ctx, key, value)
+	} else {
+		key, value, _ = cache.Get(ctx)
+	}
 
 	query = query.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	key = key.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
@@ -137,7 +145,7 @@ type TextCrossAttentionDecoderLayer struct {
 	MLPGate ml.Tensor `gguf:"cross_attn_mlp_gate"`
 }
 
-func (d TextCrossAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, _, _, crossAttentionStates, crossAttentionMask ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor {
+func (d *TextCrossAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, _, _, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
 	residual := hiddenState
 
 	hiddenState = d.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
@@ -153,17 +161,25 @@ func (d TextCrossAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, _,
 }
 
 type TextDecoderLayer interface {
-	Forward(ctx ml.Context, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor
+	Forward(ctx ml.Context, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor
 }
 
 type TextDecoder struct {
 	Layers []TextDecoderLayer
 }
 
-func (d *TextDecoder) Forward(ctx ml.Context, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor {
+func (d *TextDecoder) Forward(ctx ml.Context, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
 	for i, layer := range d.Layers {
-		if !slices.Contains(opts.crossAttentionLayers, uint32(i)) || crossAttentionStates != nil {
-			hiddenState = layer.Forward(ctx, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask, cache.Sub(i), opts)
+		layerType := selfAttentionLayer
+		if slices.Contains(opts.crossAttentionLayers, uint32(i)) {
+			layerType = crossAttentionLayer
+		}
+
+		cache.SetLayer(i)
+		cache.SetLayerType(layerType)
+
+		if layerType == selfAttentionLayer || crossAttentionStates != nil || cache.UnderlyingCache().(*kvcache.EncoderCache).EncoderCached() {
+			hiddenState = layer.Forward(ctx, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask, cache, opts)
 		}
 	}
 
@@ -189,7 +205,7 @@ type TextModel struct {
 	*TextModelOptions
 }
 
-func (m *TextModel) Forward(ctx ml.Context, inputIDs, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache model.Cache) ml.Tensor {
+func (m *TextModel) Forward(ctx ml.Context, inputIDs, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache) ml.Tensor {
 	hiddenState := m.TokenEmbedding.Forward(ctx, inputIDs)
 	hiddenState = m.Transformer.Forward(ctx, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask, cache, m.TextModelOptions)
 	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)

llama/runner/stop.go → runner/common/stop.go

-package runner
+package common
 
 import (
 	"strings"
 )
 
-func findStop(sequence string, stops []string) (bool, string) {
+func FindStop(sequence string, stops []string) (bool, string) {
 	for _, stop := range stops {
 		if strings.Contains(sequence, stop) {
 			return true, stop
@@ -14,7 +14,7 @@ func findStop(sequence string, stops []string) (bool, string) {
 	return false, ""
 }
 
-func containsStopSuffix(sequence string, stops []string) bool {
+func ContainsStopSuffix(sequence string, stops []string) bool {
 	for _, stop := range stops {
 		for i := 1; i <= len(stop); i++ {
 			if strings.HasSuffix(sequence, stop[:i]) {
@@ -29,7 +29,7 @@ func containsStopSuffix(sequence string, stops []string) bool {
 // truncateStop removes the provided stop string from pieces,
 // returning the partial pieces with stop removed, including truncating
 // the last piece if required (and signalling if this was the case)
-func truncateStop(pieces []string, stop string) ([]string, bool) {
+func TruncateStop(pieces []string, stop string) ([]string, bool) {
 	joined := strings.Join(pieces, "")
 
 	index := strings.Index(joined, stop)
@@ -65,7 +65,7 @@ func truncateStop(pieces []string, stop string) ([]string, bool) {
 	return result, tokenTruncated
 }
 
-func incompleteUnicode(token string) bool {
+func IncompleteUnicode(token string) bool {
 	incomplete := false
 
 	// check if there is incomplete UTF-8 character at the end

llama/runner/stop_test.go → runner/common/stop_test.go

-package runner
+package common
 
 import (
 	"reflect"
@@ -52,7 +52,7 @@ func TestTruncateStop(t *testing.T) {
 
 	for _, tt := range tests {
 		t.Run(tt.name, func(t *testing.T) {
-			result, resultTrunc := truncateStop(tt.pieces, tt.stop)
+			result, resultTrunc := TruncateStop(tt.pieces, tt.stop)
 			if !reflect.DeepEqual(result, tt.expected) || resultTrunc != tt.expectedTrunc {
 				t.Errorf("truncateStop(%v, %s): have %v (%v); want %v (%v)", tt.pieces, tt.stop, result, resultTrunc, tt.expected, tt.expectedTrunc)
 			}
@@ -120,7 +120,7 @@ func TestIncompleteUnicode(t *testing.T) {
 
 	for _, tt := range tests {
 		t.Run(tt.name, func(t *testing.T) {
-			result := incompleteUnicode(tt.input)
+			result := IncompleteUnicode(tt.input)
 			if result != tt.expected {
 				t.Errorf("incompleteUnicode(%s): have %v; want %v", tt.input, result, tt.expected)
 			}