package kvcache

// import (
// 	"errors"
// 	"fmt"
// 	"log/slog"
// 	"math"
// 	"slices"

// 	"github.com/ollama/ollama/ml"
// 	"github.com/ollama/ollama/model/input"
// )

// 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

// 	// swaWindowSize is the number of tokens that will be included in the mask
// 	// during attention operations. swaMemorySize is the number of tokens that
// 	// will be retained in memory for partial prefix caching. Set to math.MaxInt32
// 	// for unlimited or if sliding window attention is not being used.
// 	swaWindowSize int32
// 	swaMemorySize int32

// 	chunkSize int32

// 	opts CausalOptions

// 	// maxBatch is the largest batch that we might receive
// 	maxBatch int

// 	// config controls mostly backend-specific optimizations
// 	config *ml.CacheConfig

// 	// ** current forward pass **

// 	// size of the current batch
// 	curBatchSize int

// 	// locations for data storage for this batch
// 	curLoc ml.Tensor

// 	// mask of the cache as used by this batch
// 	curMask ml.Tensor

// 	// the active layer for Get and Put
// 	curLayer int

// 	// locations in the cache that are needed for this batch
// 	curCellRange cellRange

// 	// curSequences is the sequences corresponding to this pass's entries in the cache
// 	curSequences []int

// 	// curPositions is the positions corresponding to this pass's entries in the cache
// 	curPositions []int32

// 	// ** 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
// 	ctxs         map[int]ml.Context
// 	keys, values map[int]ml.Tensor

// 	kHeadDims, vHeadDims, numKVHeads map[int]int
// }

// type cacheCell struct {
// 	pos       int32
// 	sequences []int
// }

// type cellRange struct {
// 	min int
// 	max int
// }

// func NewCausalCache(shift shiftFn) *Causal {
// 	return &Causal{
// 		shiftFn:    shift,
// 		ctxs:       make(map[int]ml.Context),
// 		keys:       make(map[int]ml.Tensor),
// 		values:     make(map[int]ml.Tensor),
// 		kHeadDims:  make(map[int]int),
// 		vHeadDims:  make(map[int]int),
// 		numKVHeads: make(map[int]int),
// 	}
// }

// func NewSWACache(windowSize int32, shift shiftFn) *Causal {
// 	return &Causal{
// 		swaWindowSize: windowSize,
// 		shiftFn:       shift,
// 		ctxs:          make(map[int]ml.Context),
// 		keys:          make(map[int]ml.Tensor),
// 		values:        make(map[int]ml.Tensor),
// 		kHeadDims:     make(map[int]int),
// 		vHeadDims:     make(map[int]int),
// 		numKVHeads:    make(map[int]int),
// 	}
// }

// func NewSWAMemCache(windowSize int32, memorySize int32, shift shiftFn) *Causal {
// 	return &Causal{
// 		swaWindowSize: windowSize,
// 		swaMemorySize: memorySize,
// 		shiftFn:       shift,
// 		ctxs:          make(map[int]ml.Context),
// 		keys:          make(map[int]ml.Tensor),
// 		values:        make(map[int]ml.Tensor),
// 		kHeadDims:     make(map[int]int),
// 		vHeadDims:     make(map[int]int),
// 		numKVHeads:    make(map[int]int),
// 	}
// }

// func NewChunkedAttentionCache(chunkSize int32, shift shiftFn) *Causal {
// 	return &Causal{
// 		chunkSize:  chunkSize,
// 		shiftFn:    shift,
// 		ctxs:       make(map[int]ml.Context),
// 		keys:       make(map[int]ml.Tensor),
// 		values:     make(map[int]ml.Tensor),
// 		kHeadDims:  make(map[int]int),
// 		vHeadDims:  make(map[int]int),
// 		numKVHeads: make(map[int]int),
// 	}
// }

// func (c *Causal) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
// 	if c.config == nil {
// 		var config ml.CacheConfig
// 		if cc, ok := backend.(ml.BackendCacheConfig); ok {
// 			config = cc.CacheConfig()
// 		}
// 		c.config = &config
// 	}

// 	if c.config.CachePadding == 0 {
// 		c.config.CachePadding = 1
// 	}

// 	if c.config.MaskBatchPadding == 0 {
// 		c.config.MaskBatchPadding = 1
// 	}

// 	// TODO what types do we handle here?
// 	// if c.config.MaskDType == ml.DTypeOther {
// 	// 	c.config.MaskDType = ml.DTypeFloat32
// 	// }

// 	if c.swaWindowSize == 0 {
// 		c.swaWindowSize = math.MaxInt32
// 	}
// 	if c.swaMemorySize == 0 {
// 		c.swaMemorySize = c.swaWindowSize
// 	}
// 	// We will allocate space in the cache for the stop token, which won't be part of a follow on
// 	// sequence, so allocate an extra token of storage to ensure that we can jump back without
// 	// causing a cache break. As an optimization, only do this when we have parallel sequences
// 	// because the extra token will live in the batch buffer and won't get overwritten if we
// 	// only have a single sequence.
// 	if c.swaMemorySize != math.MaxInt32 && maxSequences > 1 {
// 		c.swaMemorySize = max(c.swaMemorySize, c.swaWindowSize+1)
// 	}
// 	if int(c.swaMemorySize) >= capacity {
// 		c.swaMemorySize = math.MaxInt32
// 	}

// 	if c.swaMemorySize < c.swaWindowSize {
// 		panic(fmt.Errorf("sliding window memory (%v) must be at least as large as the window (%v)", c.swaMemorySize, c.swaWindowSize))
// 	}

// 	var cacheSize int
// 	if c.swaMemorySize == math.MaxInt32 {
// 		cacheSize = maxSequences * capacity
// 	} else {
// 		cacheSize = (maxSequences * int(c.swaMemorySize)) + maxBatch
// 	}
// 	cacheSize = roundUp(cacheSize, c.config.CachePadding)
// 	c.cells = make([]cacheCell, cacheSize)

// 	c.DType = dtype
// 	c.cellRanges = make(map[int]cellRange)
// 	c.backend = backend
// 	c.maxBatch = maxBatch
// }

// func (c *Causal) SetConfig(config ml.CacheConfig) {
// 	if c.config != nil {
// 		panic("config cannot be changed after being previously set, either by the model or backend")
// 	}

// 	c.config = &config
// }

// func (c *Causal) Close() {
// 	slog.Info("XXX Causal.Close called", "number of contexts", len(c.ctxs))
// 	for _, ctx := range c.ctxs {
// 		ctx.Close()
// 	}
// }

// func (c *Causal) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
// 	slog.Info("XXX Causal.StartForward", "cell count", len(c.cells), "prior batch size", c.curBatchSize, "positions", len(batch.Positions), "reserve", reserve, "batch", batch)
// 	// panic("XXX Causal.StartForward")
// 	c.curBatchSize = len(batch.Positions)
// 	c.curSequences = batch.Sequences
// 	c.curPositions = batch.Positions
// 	c.opts.Except = nil

// 	var locs []int32
// 	if !reserve {
// 		c.updateSlidingWindow()

// 		var err error
// 		locs, err = c.findLocs()
// 		if err != nil {
// 			return err
// 		}
// 		slog.Info("XXX Causal.StartForward", "findLocs len", len(locs))

// 		for i, pos := range batch.Positions {
// 			seq := batch.Sequences[i]
// 			loc := int(locs[i])

// 			c.cells[loc] = cacheCell{pos: pos, sequences: []int{seq}}

// 			seqRange, ok := c.cellRanges[seq]
// 			if !ok {
// 				seqRange = newRange()
// 			}

// 			seqRange.min = min(seqRange.min, loc)
// 			c.curCellRange.min = min(c.curCellRange.min, loc)

// 			seqRange.max = max(seqRange.max, loc)
// 			c.curCellRange.max = max(c.curCellRange.max, loc)

// 			c.cellRanges[seq] = seqRange
// 		}
// 	} else {
// 		// If we are reserving memory, don't update any of the cache metadata but set the size
// 		// to the worst case.
// 		locs = make([]int32, c.curBatchSize)
// 		for i := range locs {
// 			locs[i] = int32(i)
// 		}
// 		c.curCellRange.min = 0
// 		c.curCellRange.max = len(c.cells) - 1
// 	}

// 	// XXX Building up the locs for what's already processed (if any)
// 	dummyLocs := []int{}
// 	c.curCellRange.min = roundDown(c.curCellRange.min, c.config.CachePadding)
// 	c.curCellRange.max = roundUp(c.curCellRange.max+1, c.config.CachePadding) - 1

// 	for i := range c.curBatchSize {
// 		enabled := !slices.Contains(c.opts.Except, i)
// 		for j := c.curCellRange.min; j <= c.curCellRange.max; j++ {
// 			if !slices.Contains(c.cells[j].sequences, c.curSequences[i]) ||
// 				(enabled && c.cells[j].pos > c.curPositions[i]) ||
// 				c.chunkSize > 0 && c.cells[j].pos < c.curPositions[i]-c.curPositions[i]%c.chunkSize ||
// 				c.cells[j].pos < c.curPositions[i]-c.swaWindowSize {
// 				// mask[i*length+(j-c.curCellRange.min)] = float32(math.Inf(-1))
// 			} else {
// 				if len(dummyLocs) == 0 || dummyLocs[len(dummyLocs)-1] != i {
// 					dummyLocs = append(dummyLocs, i)
// 				}
// 			}
// 		}
// 	}
// 	slog.Info("XXX Causa.StartForward calculated locations", "locs", dummyLocs)

// 	slog.Info("XXX Causal.StartForward", "locs", locs)
// 	c.curLoc = ctx.Input().FromInts(locs, len(locs))
// 	c.curMask = c.buildMask(ctx)

// 	return nil
// }

// func newRange() cellRange {
// 	return cellRange{
// 		min: math.MaxInt,
// 		max: 0,
// 	}
// }

// // Returns a slice of locations where each token in the batch should be stored
// func (c *Causal) findLocs() ([]int32, error) {
// 	loc := make([]int32, 0, c.curBatchSize)

// 	for i := range c.cells {
// 		if len(c.cells[i].sequences) == 0 {
// 			loc = append(loc, int32(i))
// 			if len(loc) >= c.curBatchSize {
// 				return loc, nil
// 			}
// 		}
// 	}

// 	return nil, fmt.Errorf("%w (cache: %v batch: %v)", ErrKvCacheFull, len(c.cells), c.curBatchSize)
// }

// func (c *Causal) updateSlidingWindow() {
// 	c.curCellRange = newRange()

// 	if c.swaMemorySize == math.MaxInt32 {
// 		for _, seq := range c.curSequences {
// 			if seqRange, ok := c.cellRanges[seq]; ok {
// 				c.curCellRange.min = min(c.curCellRange.min, seqRange.min)
// 				c.curCellRange.max = max(c.curCellRange.max, seqRange.max)
// 			}
// 		}

// 		return
// 	}

// 	type lowestPosition struct {
// 		pos      int32
// 		curBatch bool
// 	}

// 	// create a map of unique sequences to the lowest position in that sequence
// 	lowestPos := make(map[int]lowestPosition)
// 	for i := range c.curPositions {
// 		seq := c.curSequences[i]

// 		lowest, ok := lowestPos[seq]
// 		if !ok {
// 			lowest = lowestPosition{pos: c.curPositions[i], curBatch: true}
// 		} else if c.curPositions[i] < lowest.pos {
// 			lowest.pos = c.curPositions[i]
// 		}

// 		lowestPos[seq] = lowest
// 	}

// 	// for any sequences are not part of this batch, clean up any tokens
// 	// that are no longer needed after the processing of the previous
// 	// batch
// 	for seq, seqRange := range c.cellRanges {
// 		if _, ok := lowestPos[seq]; !ok {
// 			var last int32
// 			for i := seqRange.min; i <= seqRange.max; i++ {
// 				if slices.Contains(c.cells[i].sequences, seq) {
// 					last = max(last, c.cells[i].pos)
// 				}
// 			}

// 			lowestPos[seq] = lowestPosition{pos: last + 1, curBatch: false}
// 		}
// 	}

// 	// delete any entries that are beyond the window of the oldest position in the sequence
// 	for seq, lowest := range lowestPos {
// 		oldRange, ok := c.cellRanges[seq]
// 		if !ok {
// 			continue
// 		}

// 		newRange := newRange()

// 		for i := oldRange.min; i <= oldRange.max; i++ {
// 			if slices.Contains(c.cells[i].sequences, seq) {
// 				if c.cells[i].pos < lowest.pos-c.swaMemorySize {
// 					c.cells[i].sequences = slices.DeleteFunc(c.cells[i].sequences, func(s int) bool { return s == seq })
// 				} else {
// 					newRange.min = min(newRange.min, i)
// 					newRange.max = max(newRange.max, i)
// 				}
// 				if lowest.curBatch && c.cells[i].pos >= lowest.pos-c.swaWindowSize {
// 					c.curCellRange.min = min(c.curCellRange.min, i)
// 					c.curCellRange.max = max(c.curCellRange.max, i)
// 				}
// 			}
// 		}

// 		c.cellRanges[seq] = newRange
// 	}
// }

// func roundDown(length, pad int) int {
// 	return (length / pad) * pad
// }

// func roundUp(length, pad int) int {
// 	return ((length + pad - 1) / pad) * pad
// }

// // 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) ml.Tensor {
// 	// Align and pad the two dimensions as required by the backend
// 	batchSize := roundUp(c.curBatchSize, c.config.MaskBatchPadding)

// 	c.curCellRange.min = roundDown(c.curCellRange.min, c.config.CachePadding)
// 	c.curCellRange.max = roundUp(c.curCellRange.max+1, c.config.CachePadding) - 1

// 	length := c.curCellRange.max - c.curCellRange.min + 1

// 	mask := make([]float32, batchSize*length)

// 	for i := range c.curBatchSize {
// 		enabled := !slices.Contains(c.opts.Except, i)
// 		for j := c.curCellRange.min; j <= c.curCellRange.max; j++ {
// 			if !slices.Contains(c.cells[j].sequences, c.curSequences[i]) ||
// 				(enabled && c.cells[j].pos > c.curPositions[i]) ||
// 				c.chunkSize > 0 && c.cells[j].pos < c.curPositions[i]-c.curPositions[i]%c.chunkSize ||
// 				c.cells[j].pos < c.curPositions[i]-c.swaWindowSize {
// 				mask[i*length+(j-c.curCellRange.min)] = float32(math.Inf(-1))
// 			}
// 		}
// 	}

// 	// Mask out any padding tokens we added. For padding that we added to the cache history, this
// 	// has already been masked out because the sequence doesn't match.
// 	for i := c.curBatchSize * length; i < len(mask); i++ {
// 		mask[i] = float32(math.Inf(-1))
// 	}

// 	maskTensor := ctx.Input().FromFloats(mask, batchSize, length)

// 	// if c.config.MaskDType != ml.DTypeFloat32 {
// 	// 	maskTensor = maskTensor.Cast(ctx, c.config.MaskDType)
// 	// }

// 	slog.Info("XXX Causal.buildMask", "c.curBatchSize", c.curBatchSize, "c.config.MaskBatchPadding", c.config.MaskBatchPadding, "c.curCellRange.min", c.curCellRange.min, "c.curCellRange.max", c.curCellRange.max, "size", len(mask), "shape", []int{1, batchSize, length})

// 	return maskTensor
// }

// func (c *Causal) SetLayer(layer int) {
// 	c.curLayer = layer
// }

// type CausalOptions struct {
// 	// Enabled controls whether the causal mask is generated for a particular index in a batch
// 	Except []int
// }

// // SetCausal disables causal mask generation for a particular range of indicies in
// // the current batch for subsequent calls to Get. The state resets for the next forward pass.
// func (c *Causal) SetCausal(ctx ml.Context, opts CausalOptions) {
// 	if !slices.Equal(c.opts.Except, opts.Except) {
// 		c.opts = opts
// 		if ctx != nil {
// 			c.curMask = c.buildMask(ctx)
// 		}
// 	}
// }

// func (c *Causal) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
// 	key := c.keys[c.curLayer]
// 	value := c.values[c.curLayer]

// 	kHeadDim := c.kHeadDims[c.curLayer]
// 	vHeadDim := c.vHeadDims[c.curLayer]
// 	numKVHeads := c.numKVHeads[c.curLayer]
// 	// rowSize := numKVHeads * c.curBatchSize
// 	// cachedSize := c.curMask.Dim(1)
// 	cachedSize := c.curLoc.Dim(0)
// 	// kCellSize := kHeadDim * numKVHeads
// 	// vCellSize := vHeadDim * numKVHeads

// 	slog.Info("XXX Causal.Get full cache", "key", key)
// 	slog.Info("XXX Causal.Get full cache", "value", value)
// 	slog.Info("XXX Causal.Get full cache", "curloc", c.curLoc)
// 	slog.Info("XXX Causal.Get", "curMask", c.curMask)
// 	slog.Info("XXX Causal.Get", "kHeadDim", kHeadDim, "numKVHeads", numKVHeads, "cachedSize", cachedSize, "kHeadDim", kHeadDim)
// 	// panic("XXX")

// 	// fmt.Fprintln(os.Stderr, key.ToString())
// 	// panic("full cache value")

// 	// TODO we should use TakeAxes to gather the cells from curLoc, but for now to be consistent with GGML, just grab a larger chunk and mask
// 	key = key.TakeAxes(ctx, c.curLoc, 0).Reshape(ctx, 1, numKVHeads, cachedSize, kHeadDim)
// 	// key = key.AsStrided(ctx, []int{1, numKVHeads, cachedSize, kHeadDim}, []int{}, rowSize*c.curCellRange.min)

// 	// slog.Info("XXX Causal.Get after AsStrided", "key", key)
// 	// panic("XXX")

// 	// if c.config.PermutedV {
// 	// 	panic("permuted")
// 	// 	// TODO not converted
// 	// 	vHeadDim := value.Dim(1)
// 	// 	elemSize := value.Stride(2)

// 	// 	value = value.AsStrided(ctx,
// 	// 		[]int{numKVHeads, vHeadDim, cachedSize},
// 	// 		[]int{value.Stride(0), value.Stride(1)},
// 	// 		elemSize*c.curCellRange.min,
// 	// 	)
// 	// } else {
// 	// vHeadDim := c.vHeadDims[c.curLayer]
// 	// rowSize := value.Stride(2)
// 	// slog.Info("XXX Causal.Get before AsStrided", "vHeadDim", vHeadDim, "rowSize", rowSize)
// 	// panic("XXX")

// 	// TODO we should use TakeAxes to gather the cells from curLoc, but for now to be consistent with GGML, just grab a larger chunk and mask
// 	value = value.TakeAxes(ctx, c.curLoc, 0).Reshape(ctx, 1, numKVHeads, cachedSize, vHeadDim)
// 	// value = value.AsStrided(ctx, []int{1, numKVHeads, cachedSize, vHeadDim}, []int{}, rowSize*c.curCellRange.min)

// 	// slog.Info("XXX Causal.Get after AsStrided", "value", value)
// 	// panic("XXX")

// 	// }

// 	// // TODO The mask changes from X,X to 1,X, and with the Row-order change
// 	// // the 1 becomes trailing and messes up later operations
// 	// // This isn't the right solution, but works around it...
// 	// if c.curMask.Dim(1) == 1 {
// 	// 	return key, value, c.curMask.Transpose(ctx, 1, 0, 2, 3)
// 	// }
// 	// fmt.Fprintln(os.Stderr, key.ToString())
// 	// fmt.Fprintln(os.Stderr, value.ToString())
// 	// panic("XXX")
// 	slog.Info("XXX Mask", "curLayer", c.curLayer, "shape", c.curMask.Shape())

// 	return key, value, c.curMask
// }

// func (c *Causal) Put(ctx ml.Context, key, value ml.Tensor) {
// 	kHeadDim := key.Dim(3)
// 	vHeadDim := value.Dim(3)
// 	numKVHeads := key.Dim(1)
// 	batchSize := key.Dim(2)
// 	kCellSize := kHeadDim * numKVHeads
// 	vCellSize := vHeadDim * numKVHeads

// 	// slog.Info("XXX Causal.Put", "key", key, "value", value)
// 	slog.Info("XXX Causal.Put", "kHeadDim", kHeadDim, "vHeadDim", vHeadDim, "numKVHeads", numKVHeads, "batchSize", batchSize)
// 	// panic("XXX")

// 	if c.curBatchSize != batchSize {
// 		panic(fmt.Errorf("inconsistent batch sizes (layer: %v, batch size: %v layer batch size: %v)", c.curLayer, c.curBatchSize, batchSize))
// 	}

// 	// slog.Info("XXX", "c.ctxs", c.ctxs, "c.curLayer", c.curLayer, "backend", c.backend)
// 	if _, ok := c.ctxs[c.curLayer]; !ok {
// 		slog.Info("XXX Causal.Put creating new context", "c.curLayer", c.curLayer)
// 		c.ctxs[c.curLayer] = c.backend.NewContext().Layer(c.curLayer)
// 	}

// 	if _, ok := c.keys[c.curLayer]; !ok {
// 		slog.Info("XXX Causal.Put allocating keys", "c.curLayer", c.curLayer, "shape", []int{len(c.cells), kCellSize})

// 		c.keys[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, len(c.cells), kCellSize)
// 		c.kHeadDims[c.curLayer] = kHeadDim
// 		c.vHeadDims[c.curLayer] = vHeadDim
// 		c.numKVHeads[c.curLayer] = numKVHeads
// 	}

// 	if _, ok := c.values[c.curLayer]; !ok {
// 		// if c.config.PermutedV {
// 		// 	c.values[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, numKVHeads, vHeadDim, len(c.cells))
// 		// } else {
// 		c.values[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, len(c.cells), vCellSize)
// 		// }
// 	}

// 	key = key.Reshape(ctx, batchSize, 1, kCellSize) //.Contiguous(ctx, false) // TODO contiguous may not be needed

// 	// slog.Info("XXX Causal.Put after reshape", "keyCache", keyCache)
// 	// panic("XXX")
// 	// curLoc := 0 // TODO c.curLoc is now a tensor
// 	// kSize := numKVHeads * kHeadDim
// 	// vSize := numKVHeads * vHeadDim
// 	// start := []int{int(curLoc), 0}
// 	// kStop := []int{int(curLoc + batchSize), int(kSize)}
// 	// vStop := []int{int(curLoc + batchSize), int(vSize)}
// 	// strides := []int{1, 1}

// 	// slog.Info("XXX Causal.Put Key SliceUpdate", "keyCache", keyCache)
// 	// slog.Info("XXX Causal.Put Key SliceUpdate", "key", key)

// 	// slog.Info("XXX Causal.Put Key SliceUpdate", "start", start, "kStop", kStop, "strides", strides)

// 	// ctx.Forward(c.keys[c.curLayer].SliceUpdate(ctx, key, start, kStop, strides))
// 	ctx.Forward(c.keys[c.curLayer].Scatter(ctx, []ml.Tensor{c.curLoc}, key, []int{0}))
// 	// fmt.Fprintln(os.Stderr, keyCache.ToString())
// 	// panic("input value")

// 	// fmt.Fprintln(os.Stderr, t.ToString())
// 	// panic("XXX")

// 	// if c.config.PermutedV {
// 	// 	panic("permuted")
// 	// 	// TODO not adjusted
// 	// 	value = value.Reshape(ctx, vHeadDim*numKVHeads, 1, batchSize)
// 	// 	value = value.Transpose(ctx, 2, 0, 1, 3)

// 	// 	valueCache := c.values[c.curLayer]
// 	// 	valueCache = valueCache.Reshape(ctx, 1, len(c.cells), vHeadDim*numKVHeads)

// 	// 	ctx.Forward(valueCache.SliceUpdate(ctx, value, start, vStop, strides))
// 	// } else {
// 	value = value.Reshape(ctx, batchSize, 1, vCellSize) //.Contiguous(ctx, false) // TODO contiguous may not be needed
// 	// slog.Info("XXX Causal.Put Value SliceUpdate", "valueCache", valueCache)
// 	// slog.Info("XXX Causal.Put Value SliceUpdate", "value", value)
// 	// slog.Info("XXX Causal.Put Value SliceUpdate", "start", start, "vStop", vStop, "strides", strides)

// 	ctx.Forward(c.values[c.curLayer].Scatter(ctx, []ml.Tensor{c.curLoc}, value, []int{0}))
// 	// }
// 	// fmt.Fprintln(os.Stderr, c.keys[c.curLayer].ToString())
// 	// fmt.Fprintln(os.Stderr, c.values[c.curLayer].ToString())
// 	// panic("XXX")

// }

// 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) CanResume(seq int, pos int32) bool {
// 	if c.swaMemorySize == math.MaxInt32 {
// 		return true
// 	}

// 	seqRange, ok := c.cellRanges[seq]
// 	if !ok {
// 		return false
// 	}

// 	// for sliding window, check that the window of the new sequence is contained in
// 	// the window of what we are storing
// 	var first int32 = math.MaxInt32
// 	var last int32 = -1
// 	for i := seqRange.min; i <= seqRange.max; i++ {
// 		if slices.Contains(c.cells[i].sequences, seq) {
// 			first = min(first, c.cells[i].pos)
// 			last = max(last, c.cells[i].pos)
// 		}
// 	}

// 	if last == -1 {
// 		return false
// 	}

// 	posWindowStart := max(0, pos-c.swaWindowSize)
// 	return posWindowStart >= first && pos <= last+1
// }

// func (c *Causal) shift(seq int, beginIndex, offset int32) error {
// 	if c.shiftFn == nil {
// 		return ErrNotSupported
// 	}

// 	seqRange := c.cellRanges[seq]

// 	for start := seqRange.min; start <= seqRange.max; start += c.maxBatch {
// 		size := min(seqRange.max-start+1, c.maxBatch)
// 		offsets := make([]int32, size)

// 		var batchFirst, batchLast int

// 		batchFirst = -1
// 		for i := range offsets {
// 			cell := c.cells[start+i]

// 			if slices.Contains(cell.sequences, seq) && cell.pos >= beginIndex {
// 				offsets[i] = offset
// 				if batchFirst < 0 {
// 					batchFirst = i
// 				}
// 				batchLast = i
// 			}
// 		}

// 		if batchFirst < 0 {
// 			continue
// 		}

// 		offsets = offsets[batchFirst : batchLast+1]

// 		slog.Info("XXX Causal.shift creating new temporary context")
// 		ctx := c.backend.NewContext()
// 		kShift := ctx.Input().FromInts(offsets, len(offsets))

// 		for i, key := range c.keys {
// 			if key == nil {
// 				continue
// 			}

// 			kHeadDim := key.Dim(2)
// 			numKVHeads := key.Dim(1)
// 			rowSize := key.Stride(0)

// 			key = key.AsStrided(ctx,
// 				[]int{len(offsets), numKVHeads, kHeadDim},
// 				[]int{key.Stride(0), key.Stride(1)},
// 				rowSize*(start+batchFirst),
// 			)

// 			roped, err := c.shiftFn(ctx, i, key, kShift)
// 			if err != nil {
// 				ctx.Close()
// 				return err
// 			}

// 			ctx.Forward(roped.Copy(ctx, key))
// 		}

// 		ctx.Compute()
// 		ctx.Close()
// 	}

// 	return nil
// }

// func (c *Causal) Remove(seq int, beginIndex, endIndex int32) error {
// 	// TODO(jessegross): We should check to see if removing the middle of the sequence will
// 	// cause the sliding window to encompass tokens that we no longer have. If so, then we
// 	// should return an error, which will trigger the runner to evaluate the full history and
// 	// rebuild the window. However, if we have multimodal inputs in our history, this reuse
// 	// results in use after free, so we don't do it for now.

// 	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 }) {
// 						return errors.New("shifting cells shared by multiple sequences not supported")
// 					}

// 					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
// }