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kvcache: Pass granular cache size into implementations

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Currently the runner computes the kv size needed and creates a
cache of that size. This is the context size times number of
parallel sequences.

Cache implementations can make better decisions about their memory
usage, so instead pass in the required capacity, number of sequences
and maximum batch size. For now, the causal cache just uses this to
compute the size in the same way as before.
This commit is contained in:
Jesse Gross
2025-03-18 14:31:52 -07:00
committed by Jesse Gross
parent 6d1103048e
commit 3ed7ad3ab3
7 changed files with 44 additions and 32 deletions
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33
kvcache/causal.go
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@@ -20,7 +20,6 @@ type shiftFn func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, e
// The mask is of shape history size, batch size
type Causal struct {
DType ml.DType
Capacity int32
windowSize int32
opts CausalOptions
@@ -98,7 +97,7 @@ func NewSWACache(windowSize int32, shift shiftFn) *Causal {
}
}
func (c *Causal) Init(backend ml.Backend, dtype ml.DType, capacity int32) {
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 {
@@ -119,9 +118,11 @@ func (c *Causal) Init(backend ml.Backend, dtype ml.DType, capacity int32) {
c.config.MaskDType = ml.DTypeF32
}
cacheSize := maxSequences * capacity
cacheSize = roundUp(cacheSize, c.config.CachePadding)
c.cells = make([]cacheCell, cacheSize)
c.DType = dtype
c.Capacity = int32(roundUp(int(capacity), c.config.CachePadding))
c.cells = make([]cacheCell, c.Capacity)
c.cellRanges = make(map[int]cellRange)
c.backend = backend
}
@@ -210,7 +211,7 @@ func (c *Causal) findStartLoc() (int, error) {
}
}
return 0, fmt.Errorf("%w (length: %v)", ErrKvCacheFull, c.Capacity)
return 0, fmt.Errorf("%w (length: %v)", ErrKvCacheFull, len(c.cells))
}
func roundDown(length, pad int) int {
@@ -265,7 +266,7 @@ func (c *Causal) buildMask(ctx ml.Context) (ml.Tensor, error) {
return maskTensor, nil
}
func (c *Causal) moveCells(ctx ml.Context, src, dst, len int) {
func (c *Causal) moveCells(ctx ml.Context, src, dst, length int) {
for i, key := range c.keys {
if key == nil {
continue
@@ -275,8 +276,8 @@ func (c *Causal) moveCells(ctx ml.Context, src, dst, len int) {
numKVHeads := key.Dim(1)
rowSize := key.Stride(2)
kSrcView := key.View(ctx, rowSize*src, kHeadDim*numKVHeads*len)
kDstView := key.View(ctx, rowSize*dst, kHeadDim*numKVHeads*len)
kSrcView := key.View(ctx, rowSize*src, kHeadDim*numKVHeads*length)
kDstView := key.View(ctx, rowSize*dst, kHeadDim*numKVHeads*length)
value := c.values[i]
var vSrcView, vDstView ml.Tensor
@@ -284,14 +285,14 @@ func (c *Causal) moveCells(ctx ml.Context, src, dst, len int) {
vHeadDim := value.Dim(1)
elemSize := value.Stride(0)
vSrcView = value.View(ctx, elemSize*src, len, int(c.Capacity)*elemSize, vHeadDim*numKVHeads)
vDstView = value.View(ctx, elemSize*dst, len, int(c.Capacity)*elemSize, vHeadDim*numKVHeads)
vSrcView = value.View(ctx, elemSize*src, length, len(c.cells)*elemSize, vHeadDim*numKVHeads)
vDstView = value.View(ctx, elemSize*dst, length, len(c.cells)*elemSize, vHeadDim*numKVHeads)
} else {
vHeadDim := value.Dim(0)
rowSize := value.Stride(2)
vSrcView = value.View(ctx, rowSize*src, vHeadDim*numKVHeads*len)
vDstView = value.View(ctx, rowSize*dst, vHeadDim*numKVHeads*len)
vSrcView = value.View(ctx, rowSize*src, vHeadDim*numKVHeads*length)
vDstView = value.View(ctx, rowSize*dst, vHeadDim*numKVHeads*length)
}
ctx.Forward(
@@ -480,14 +481,14 @@ func (c *Causal) Put(ctx ml.Context, key, value ml.Tensor) {
}
if _, ok := c.keys[c.curLayer]; !ok {
c.keys[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, kHeadDim, numKVHeads, int(c.Capacity))
c.keys[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, kHeadDim, numKVHeads, len(c.cells))
}
if _, ok := c.values[c.curLayer]; !ok {
if c.config.PermutedV {
c.values[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, int(c.Capacity), vHeadDim, numKVHeads)
c.values[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, len(c.cells), vHeadDim, numKVHeads)
} else {
c.values[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, vHeadDim, numKVHeads, int(c.Capacity))
c.values[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, vHeadDim, numKVHeads, len(c.cells))
}
}
@@ -498,7 +499,7 @@ func (c *Causal) Put(ctx ml.Context, key, value ml.Tensor) {
elemSize := c.values[c.curLayer].Stride(0)
value = value.Permute(ctx, 1, 2, 0, 3)
ctx.Forward(value.Copy(ctx, c.values[c.curLayer].View(ctx, elemSize*c.curLoc, batchSize, int(c.Capacity)*elemSize, vHeadDim*numKVHeads)))
ctx.Forward(value.Copy(ctx, c.values[c.curLayer].View(ctx, elemSize*c.curLoc, batchSize, len(c.cells)*elemSize, vHeadDim*numKVHeads)))
} else {
rowSize := c.values[c.curLayer].Stride(2)