ollama source for Momentry Core verification
This commit is contained in:
Accusys
752
kvcache/recurrent.go
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752
kvcache/recurrent.go
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@@ -0,0 +1,752 @@
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package kvcache
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import (
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"errors"
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"fmt"
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"math"
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"slices"
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"github.com/ollama/ollama/ml"
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"github.com/ollama/ollama/model/input"
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)
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const (
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DefaultCheckpointCount = 24
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DefaultCheckpointMinPos = int32(16)
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DefaultCheckpointInterval = int32(1664)
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)
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var ErrInvalidRecurrentShape = errors.New("kvcache: invalid recurrent state shape")
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// Config configures a shared hybrid recurrent cache.
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type RecurrentConfig struct {
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Shift func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error)
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ConvDim int
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ConvChannels int
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RecurrentStateSize int
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CheckpointLogPrefix string
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}
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var (
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_ Cache = (*Recurrent)(nil)
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_ CheckpointCache = (*Recurrent)(nil)
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)
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// Cache stores:
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// - a standard causal KV cache
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// - per-sequence conv state for recurrent operators
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// - per-sequence recurrent state for recurrent operators
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//
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// Conv state shape (per layer, per sequence): [convDim, convChannels]
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// Recurrent state shape (per layer, per sequence): [recurrentStateSize]
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type Recurrent struct {
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kv *Causal
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backend ml.Backend
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dtype ml.DType
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maxSequences int
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// Conv state dimensions
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convDim int
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convChannels int
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// Recurrent state dimensions
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recurrentStateSize int
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logPrefix string
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// slot mapping for recurrent state (copy-on-write)
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slotForSeq map[int]int
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refCount []int
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freeSlots []int
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seqCounts map[int]int
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slotScratch [1]int32
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// per-layer conv state buffers (allocated lazily)
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convCtxs map[int]ml.Context
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convStates map[int]ml.Tensor // [convDim*convChannels, maxSlots]
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// per-layer recurrent state buffers (allocated lazily)
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recurrentCtxs map[int]ml.Context
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recurrentStates map[int]ml.Tensor // [recurrentStateSize, maxSlots]
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// recurrent checkpoints (per slot)
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checkpointCount int
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checkpointMinPos int32
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checkpointInterval int32
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checkpointCtxSize int
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checkpoints map[int]*slotCheckpointStore
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pendingRestore map[int]checkpointRestore
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curCheckpointPos []int32
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curCheckpointSlots map[int]int
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reserveCheckpoints bool
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checkpointConvCtxs map[int]ml.Context
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checkpointRecurCtxs map[int]ml.Context
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checkpointReserved map[int]struct{}
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// current forward batch (derived in StartForward)
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curSeqs []int
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curSlots []int
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curSlotsInput ml.Tensor
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curSeqTokens int
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// track if EnsureWritable has been called for this forward pass
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writableEnsured bool
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writableError error
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}
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func NewRecurrentCache(config RecurrentConfig) *Recurrent {
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return &Recurrent{
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kv: NewCausalCache(config.Shift),
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convDim: config.ConvDim,
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convChannels: config.ConvChannels,
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recurrentStateSize: config.RecurrentStateSize,
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logPrefix: config.CheckpointLogPrefix,
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slotForSeq: make(map[int]int),
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seqCounts: make(map[int]int),
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convCtxs: make(map[int]ml.Context),
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convStates: make(map[int]ml.Tensor),
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recurrentCtxs: make(map[int]ml.Context),
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recurrentStates: make(map[int]ml.Tensor),
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checkpointCount: DefaultCheckpointCount,
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checkpointMinPos: DefaultCheckpointMinPos,
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checkpointInterval: DefaultCheckpointInterval,
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checkpoints: make(map[int]*slotCheckpointStore),
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pendingRestore: make(map[int]checkpointRestore),
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curCheckpointSlots: make(map[int]int),
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checkpointConvCtxs: make(map[int]ml.Context),
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checkpointRecurCtxs: make(map[int]ml.Context),
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checkpointReserved: make(map[int]struct{}),
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}
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}
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func (c *Recurrent) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
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c.backend = backend
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c.dtype = dtype
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c.maxSequences = maxSequences
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c.checkpoints = make(map[int]*slotCheckpointStore)
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c.pendingRestore = make(map[int]checkpointRestore)
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c.curCheckpointPos = c.curCheckpointPos[:0]
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c.curCheckpointSlots = make(map[int]int)
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c.checkpointReserved = make(map[int]struct{})
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c.checkpointCtxSize = c.checkpointCount * c.maxSequences
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if c.checkpointCtxSize < 8 {
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c.checkpointCtxSize = 8
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}
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// initialize slot allocator
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c.refCount = make([]int, maxSequences)
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c.freeSlots = c.freeSlots[:0]
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for i := maxSequences - 1; i >= 0; i-- {
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c.freeSlots = append(c.freeSlots, i)
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}
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c.kv.Init(backend, dtype, maxSequences, capacity, maxBatch)
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}
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func (c *Recurrent) Close() {
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for _, ctx := range c.convCtxs {
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ctx.Close()
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}
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for _, ctx := range c.recurrentCtxs {
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ctx.Close()
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}
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for _, ctx := range c.checkpointConvCtxs {
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ctx.Close()
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}
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for _, ctx := range c.checkpointRecurCtxs {
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ctx.Close()
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}
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c.kv.Close()
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}
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func (c *Recurrent) SetConfig(config ml.CacheConfig) {
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c.kv.SetConfig(config)
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}
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func (c *Recurrent) SetLayer(layer int) {
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c.kv.SetLayer(layer)
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}
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func (c *Recurrent) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
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return c.kv.Get(ctx)
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}
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func (c *Recurrent) Put(ctx ml.Context, key, value ml.Tensor) {
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c.kv.Put(ctx, key, value)
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}
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func (c *Recurrent) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
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if err := c.kv.StartForward(ctx, batch, reserve); err != nil {
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return err
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}
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nTokens := len(batch.Sequences)
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if nTokens == 0 {
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c.curSeqs = c.curSeqs[:0]
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c.curSlots = c.curSlots[:0]
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c.curSlotsInput = nil
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c.curSeqTokens = 0
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c.reserveCheckpoints = false
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c.writableEnsured = false
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c.writableError = nil
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return nil
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}
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// Fast path for single-sequence batches (common during decode and prefill).
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firstSeq := batch.Sequences[0]
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singleSeq := true
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for _, s := range batch.Sequences[1:] {
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if s != firstSeq {
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singleSeq = false
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break
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}
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}
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if singleSeq {
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return c.startForwardSingleSeq(ctx, firstSeq, nTokens, batch, reserve)
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}
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// Derive equal-length sequence layout for recurrent layers.
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seqCounts := c.seqCounts
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for s := range seqCounts {
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delete(seqCounts, s)
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}
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c.curSeqs = c.curSeqs[:0]
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for _, s := range batch.Sequences {
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if seqCounts[s] == 0 {
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c.curSeqs = append(c.curSeqs, s)
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}
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seqCounts[s]++
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}
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nSeqs := len(c.curSeqs)
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want := nTokens / nSeqs
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for _, s := range c.curSeqs {
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if seqCounts[s] != want {
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return ErrNotSupported
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}
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}
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c.curSeqTokens = want
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if reserve {
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c.curSlots = c.curSlots[:0]
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for i := range nSeqs {
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c.curSlots = append(c.curSlots, i)
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}
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c.finalizeStartForward(ctx, batch, true)
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return nil
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}
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// Ensure slots exist for sequences in this batch.
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c.curSlots = c.curSlots[:0]
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var newSlots []int
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for _, s := range c.curSeqs {
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slot, ok := c.slotForSeq[s]
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if !ok {
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var err error
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slot, err = c.allocSlot()
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if err != nil {
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return err
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}
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c.slotForSeq[s] = slot
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c.refCount[slot] = 1
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newSlots = append(newSlots, slot)
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}
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c.curSlots = append(c.curSlots, slot)
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}
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if len(newSlots) > 0 {
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c.zeroSlots(ctx, newSlots)
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}
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c.finalizeStartForward(ctx, batch, false)
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return nil
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}
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func (c *Recurrent) startForwardSingleSeq(ctx ml.Context, seq, seqTokens int, batch input.Batch, reserve bool) error {
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c.curSeqs = append(c.curSeqs[:0], seq)
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c.curSeqTokens = seqTokens
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if reserve {
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c.curSlots = append(c.curSlots[:0], 0)
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c.finalizeStartForward(ctx, batch, true)
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return nil
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}
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slot, ok := c.slotForSeq[seq]
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if !ok {
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var err error
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slot, err = c.allocSlot()
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if err != nil {
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return err
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}
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c.slotForSeq[seq] = slot
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c.refCount[slot] = 1
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slotList := [1]int{slot}
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c.zeroSlots(ctx, slotList[:])
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}
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c.curSlots = append(c.curSlots[:0], slot)
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c.finalizeStartForward(ctx, batch, false)
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return nil
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}
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func (c *Recurrent) finalizeStartForward(ctx ml.Context, batch input.Batch, reserve bool) {
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c.setCurSlotsInput(ctx)
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c.writableEnsured = false
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c.writableError = nil
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c.reserveCheckpoints = reserve
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c.planCheckpoints(batch)
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}
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func (c *Recurrent) setCurSlotsInput(ctx ml.Context) {
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c.curSlotsInput = c.slotsInput(ctx, c.curSlots)
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}
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func (c *Recurrent) slotsInput(ctx ml.Context, slots []int) ml.Tensor {
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switch len(slots) {
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case 0:
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return nil
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case 1:
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c.slotScratch[0] = int32(slots[0])
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return ctx.Input().FromInts(c.slotScratch[:], 1)
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default:
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slotIndices := make([]int32, len(slots))
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for i, v := range slots {
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slotIndices[i] = int32(v)
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}
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return ctx.Input().FromInts(slotIndices, len(slotIndices))
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}
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}
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func (c *Recurrent) allocSlot() (int, error) {
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if len(c.freeSlots) == 0 {
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return 0, ErrKvCacheFull
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}
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slot := c.freeSlots[len(c.freeSlots)-1]
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c.freeSlots = c.freeSlots[:len(c.freeSlots)-1]
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return slot, nil
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}
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func (c *Recurrent) freeSlot(slot int) {
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if slot >= 0 && slot < c.maxSequences {
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c.freeSlots = append(c.freeSlots, slot)
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}
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}
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// zeroSlots zeros recurrent state for the given slots across all cached layers.
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func (c *Recurrent) zeroSlots(ctx ml.Context, slots []int) {
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if len(slots) == 0 {
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return
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}
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inputCtx := ctx.Input()
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slotsTensor := c.slotsInput(ctx, slots)
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if len(c.convStates) > 0 {
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zeros := inputCtx.Zeros(ml.DTypeF32, c.convDim*c.convChannels, len(slots))
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for _, buf := range c.convStates {
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ctx.Forward(buf.SetRows(ctx, zeros, slotsTensor))
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}
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}
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if len(c.recurrentStates) > 0 {
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zeros := inputCtx.Zeros(ml.DTypeF32, c.recurrentStateSize, len(slots))
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for _, buf := range c.recurrentStates {
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ctx.Forward(buf.SetRows(ctx, zeros, slotsTensor))
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}
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}
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}
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// EnsureWritable ensures sequences have private slots (copy-on-write).
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func (c *Recurrent) EnsureWritable(ctx ml.Context) error {
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for i, seq := range c.curSeqs {
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slot, ok := c.slotForSeq[seq]
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if !ok {
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continue
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}
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if slot < 0 || slot >= len(c.refCount) {
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continue
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}
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if c.refCount[slot] <= 1 {
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continue
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}
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newSlot, err := c.allocSlot()
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if err != nil {
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return err
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}
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c.refCount[slot]--
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c.refCount[newSlot] = 1
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c.slotForSeq[seq] = newSlot
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c.curSlots[i] = newSlot
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c.copyRecurrentState(ctx, slot, newSlot)
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c.copyCheckpoints(ctx, slot, newSlot)
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}
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c.setCurSlotsInput(ctx)
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return nil
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}
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func (c *Recurrent) copyRecurrentState(ctx ml.Context, srcSlot, dstSlot int) {
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src := ctx.Input().FromInts([]int32{int32(srcSlot)}, 1)
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dst := ctx.Input().FromInts([]int32{int32(dstSlot)}, 1)
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for _, buf := range c.convStates {
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rows := buf.Rows(ctx, src)
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if rows.DType() != ml.DTypeF32 {
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rows = rows.Cast(ctx, ml.DTypeF32)
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}
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ctx.Forward(buf.SetRows(ctx, rows, dst))
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}
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for _, buf := range c.recurrentStates {
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rows := buf.Rows(ctx, src)
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if rows.DType() != ml.DTypeF32 {
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rows = rows.Cast(ctx, ml.DTypeF32)
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}
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ctx.Forward(buf.SetRows(ctx, rows, dst))
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}
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}
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func (c *Recurrent) CopyPrefix(srcSeq, dstSeq int, prefixLen int32) {
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c.kv.CopyPrefix(srcSeq, dstSeq, prefixLen)
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if dstSlot, ok := c.slotForSeq[dstSeq]; ok {
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if c.validSlot(dstSlot) {
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c.refCount[dstSlot]--
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if c.refCount[dstSlot] <= 0 {
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c.refCount[dstSlot] = 0
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c.freeSlot(dstSlot)
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}
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}
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delete(c.slotForSeq, dstSeq)
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}
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srcSlot, ok := c.slotForSeq[srcSeq]
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if !ok {
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return
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}
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if c.validSlot(srcSlot) {
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c.slotForSeq[dstSeq] = srcSlot
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c.refCount[srcSlot]++
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}
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}
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func (c *Recurrent) CanResume(seq int, pos int32) bool {
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if !c.kv.CanResume(seq, pos) {
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return false
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}
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if pos == 0 {
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return true
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}
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return c.hasCheckpoint(seq, pos)
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}
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func (c *Recurrent) Remove(seq int, beginIndex, endIndex int32) error {
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if beginIndex > 0 && endIndex != math.MaxInt32 {
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if err := c.kv.Remove(seq, beginIndex, endIndex); err != nil {
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return err
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}
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delete(c.pendingRestore, seq)
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slot, ok := c.slotForSeq[seq]
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if !ok || !c.validSlot(slot) {
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return nil
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}
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// Detach shared recurrent state/checkpoints before mutating checkpoint positions.
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if c.refCount[slot] > 1 {
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newSlot, err := c.allocSlot()
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if err != nil {
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return err
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}
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ctx := c.backend.NewContext()
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c.copyRecurrentState(ctx, slot, newSlot)
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c.copyCheckpoints(ctx, slot, newSlot)
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if len(c.convStates) > 0 || len(c.recurrentStates) > 0 {
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ctx.Compute()
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}
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ctx.Close()
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c.refCount[slot]--
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c.refCount[newSlot] = 1
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c.slotForSeq[seq] = newSlot
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slot = newSlot
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}
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c.shiftCheckpoints(slot, beginIndex, endIndex)
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return nil
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}
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||||
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if beginIndex > 0 {
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restore, ok := c.pendingRestore[seq]
|
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if !ok || restore.pos+1 != beginIndex {
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return ErrNotSupported
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||||
}
|
||||
if !c.restoreComplete(restore) {
|
||||
return ErrNotSupported
|
||||
}
|
||||
if slot, ok := c.slotForSeq[seq]; ok && c.validSlot(slot) && c.refCount[slot] > 1 {
|
||||
newSlot, err := c.allocSlot()
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
ctx := c.backend.NewContext()
|
||||
c.copyRecurrentState(ctx, slot, newSlot)
|
||||
c.copyCheckpoints(ctx, slot, newSlot)
|
||||
if len(c.convStates) > 0 || len(c.recurrentStates) > 0 {
|
||||
ctx.Compute()
|
||||
}
|
||||
ctx.Close()
|
||||
|
||||
c.refCount[slot]--
|
||||
c.refCount[newSlot] = 1
|
||||
c.slotForSeq[seq] = newSlot
|
||||
|
||||
restore.slot = newSlot
|
||||
c.pendingRestore[seq] = restore
|
||||
}
|
||||
}
|
||||
|
||||
if err := c.kv.Remove(seq, beginIndex, endIndex); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if beginIndex > 0 {
|
||||
restore := c.pendingRestore[seq]
|
||||
delete(c.pendingRestore, seq)
|
||||
return c.applyCheckpointRestore(restore)
|
||||
}
|
||||
|
||||
slot, ok := c.slotForSeq[seq]
|
||||
delete(c.pendingRestore, seq)
|
||||
if !ok {
|
||||
return nil
|
||||
}
|
||||
|
||||
if !c.validSlot(slot) {
|
||||
delete(c.slotForSeq, seq)
|
||||
return nil
|
||||
}
|
||||
|
||||
c.refCount[slot]--
|
||||
if c.refCount[slot] <= 0 {
|
||||
c.refCount[slot] = 0
|
||||
c.clearCheckpoints(slot)
|
||||
c.freeSlot(slot)
|
||||
}
|
||||
delete(c.slotForSeq, seq)
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
func (c *Recurrent) validSlot(slot int) bool {
|
||||
return slot >= 0 && slot < len(c.refCount)
|
||||
}
|
||||
|
||||
func (c *Recurrent) SlotsTensor() ml.Tensor {
|
||||
return c.curSlotsInput
|
||||
}
|
||||
|
||||
// contiguousSlots returns the starting slot if current slots are contiguous and ordered.
|
||||
func (c *Recurrent) contiguousSlots() (int, bool) {
|
||||
if len(c.curSlots) == 0 {
|
||||
return 0, false
|
||||
}
|
||||
start := c.curSlots[0]
|
||||
for i, s := range c.curSlots {
|
||||
if s != start+i {
|
||||
return 0, false
|
||||
}
|
||||
}
|
||||
return start, true
|
||||
}
|
||||
|
||||
func (c *Recurrent) SeqTokens() int {
|
||||
return c.curSeqTokens
|
||||
}
|
||||
|
||||
func (c *Recurrent) NumSeqs() int {
|
||||
return len(c.curSeqs)
|
||||
}
|
||||
|
||||
func (c *Recurrent) convBuffer(layer int) ml.Tensor {
|
||||
if buf, ok := c.convStates[layer]; ok {
|
||||
return buf
|
||||
}
|
||||
|
||||
if _, ok := c.convCtxs[layer]; !ok {
|
||||
c.convCtxs[layer] = c.backend.NewContextSize(1).Layer(layer)
|
||||
}
|
||||
|
||||
buf := c.convCtxs[layer].Zeros(ml.DTypeF32, c.convDim*c.convChannels, c.maxSequences)
|
||||
c.convStates[layer] = buf
|
||||
return buf
|
||||
}
|
||||
|
||||
func (c *Recurrent) recurrentBuffer(layer int) ml.Tensor {
|
||||
if buf, ok := c.recurrentStates[layer]; ok {
|
||||
return buf
|
||||
}
|
||||
|
||||
if _, ok := c.recurrentCtxs[layer]; !ok {
|
||||
c.recurrentCtxs[layer] = c.backend.NewContextSize(1).Layer(layer)
|
||||
}
|
||||
|
||||
buf := c.recurrentCtxs[layer].Zeros(ml.DTypeF32, c.recurrentStateSize, c.maxSequences)
|
||||
c.recurrentStates[layer] = buf
|
||||
return buf
|
||||
}
|
||||
|
||||
func (c *Recurrent) ensureWritable(ctx ml.Context) error {
|
||||
c.ensureWritableOnce(ctx)
|
||||
return c.writableError
|
||||
}
|
||||
|
||||
func (c *Recurrent) currentSlotRows(ctx ml.Context, buf ml.Tensor, rowSize int) ml.Tensor {
|
||||
if start, ok := c.contiguousSlots(); ok {
|
||||
offset := start * buf.Stride(1)
|
||||
return buf.View(ctx, offset, rowSize, buf.Stride(1), c.NumSeqs())
|
||||
}
|
||||
|
||||
return buf.Rows(ctx, c.SlotsTensor())
|
||||
}
|
||||
|
||||
func (c *Recurrent) writeCurrentSlotRows(ctx ml.Context, buf ml.Tensor, rowSize int, src ml.Tensor) {
|
||||
if start, ok := c.contiguousSlots(); ok {
|
||||
offset := start * buf.Stride(1)
|
||||
view := buf.View(ctx, offset, rowSize, buf.Stride(1), c.NumSeqs())
|
||||
ctx.Forward(src.Copy(ctx, view))
|
||||
return
|
||||
}
|
||||
|
||||
ctx.Forward(buf.SetRows(ctx, src, c.SlotsTensor()))
|
||||
}
|
||||
|
||||
func (c *Recurrent) ensureWritableOnce(ctx ml.Context) {
|
||||
if !c.writableEnsured {
|
||||
needsWritable := false
|
||||
for _, seq := range c.curSeqs {
|
||||
slot, ok := c.slotForSeq[seq]
|
||||
if !ok {
|
||||
continue
|
||||
}
|
||||
if slot >= 0 && slot < len(c.refCount) && c.refCount[slot] > 1 {
|
||||
needsWritable = true
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
if needsWritable {
|
||||
if err := c.EnsureWritable(ctx); err != nil {
|
||||
c.writableError = err
|
||||
}
|
||||
}
|
||||
c.writableEnsured = true
|
||||
}
|
||||
}
|
||||
|
||||
// ConvState returns conv state for current batch sequences as [convDim, convChannels, nSeqs].
|
||||
func (c *Recurrent) ConvState(ctx ml.Context, layer int) (ml.Tensor, error) {
|
||||
if err := c.ensureWritable(ctx); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
buf := c.convBuffer(layer)
|
||||
cur := c.currentSlotRows(ctx, buf, c.convDim*c.convChannels)
|
||||
return cur.Reshape(ctx, c.convDim, c.convChannels, c.NumSeqs()), nil
|
||||
}
|
||||
|
||||
// UpdateConvState writes new conv state for current batch sequences.
|
||||
func (c *Recurrent) UpdateConvState(ctx ml.Context, layer int, newState ml.Tensor) {
|
||||
buf := c.convBuffer(layer)
|
||||
src := newState.Reshape(ctx, c.convDim*c.convChannels, c.NumSeqs())
|
||||
srcF32 := src
|
||||
if src.DType() != ml.DTypeF32 {
|
||||
srcF32 = src.Cast(ctx, ml.DTypeF32)
|
||||
}
|
||||
c.writeCurrentSlotRows(ctx, buf, c.convDim*c.convChannels, srcF32)
|
||||
|
||||
c.captureConvCheckpoint(ctx, layer, srcF32)
|
||||
}
|
||||
|
||||
// RecurrentState returns recurrent state for current batch sequences with shape [dims..., nSeqs].
|
||||
func (c *Recurrent) RecurrentState(ctx ml.Context, layer int, dims ...int) (ml.Tensor, error) {
|
||||
if err := c.ensureWritable(ctx); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
if len(dims) == 0 {
|
||||
return nil, ErrInvalidRecurrentShape
|
||||
}
|
||||
|
||||
size := 1
|
||||
for _, d := range dims {
|
||||
if d <= 0 {
|
||||
return nil, ErrInvalidRecurrentShape
|
||||
}
|
||||
size *= d
|
||||
}
|
||||
if size != c.recurrentStateSize {
|
||||
return nil, fmt.Errorf("%w: got %v (size %d), want size %d", ErrInvalidRecurrentShape, dims, size, c.recurrentStateSize)
|
||||
}
|
||||
|
||||
buf := c.recurrentBuffer(layer)
|
||||
cur := c.currentSlotRows(ctx, buf, c.recurrentStateSize)
|
||||
shape := make([]int, 0, len(dims)+1)
|
||||
shape = append(shape, dims...)
|
||||
shape = append(shape, c.NumSeqs())
|
||||
return cur.Reshape(ctx, shape...), nil
|
||||
}
|
||||
|
||||
// RecurrentState4D returns recurrent state as [dim0, dim1, dim2, nSeqs].
|
||||
func (c *Recurrent) RecurrentState4D(ctx ml.Context, layer int, dim0, dim1, dim2 int) (ml.Tensor, error) {
|
||||
if err := c.ensureWritable(ctx); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
if dim0 <= 0 || dim1 <= 0 || dim2 <= 0 {
|
||||
return nil, ErrInvalidRecurrentShape
|
||||
}
|
||||
|
||||
size := dim0 * dim1 * dim2
|
||||
if size != c.recurrentStateSize {
|
||||
return nil, fmt.Errorf("%w: got [%d %d %d] (size %d), want size %d", ErrInvalidRecurrentShape, dim0, dim1, dim2, size, c.recurrentStateSize)
|
||||
}
|
||||
|
||||
buf := c.recurrentBuffer(layer)
|
||||
cur := c.currentSlotRows(ctx, buf, c.recurrentStateSize)
|
||||
return cur.Reshape(ctx, dim0, dim1, dim2, c.NumSeqs()), nil
|
||||
}
|
||||
|
||||
// UpdateRecurrentState writes new recurrent state for current batch sequences.
|
||||
func (c *Recurrent) UpdateRecurrentState(ctx ml.Context, layer int, newState ml.Tensor) {
|
||||
buf := c.recurrentBuffer(layer)
|
||||
src := newState.Reshape(ctx, c.recurrentStateSize, c.NumSeqs())
|
||||
srcF32 := src
|
||||
if src.DType() != ml.DTypeF32 {
|
||||
srcF32 = src.Cast(ctx, ml.DTypeF32)
|
||||
}
|
||||
c.writeCurrentSlotRows(ctx, buf, c.recurrentStateSize, srcF32)
|
||||
|
||||
c.captureRecurrentCheckpoint(ctx, layer, srcF32)
|
||||
}
|
||||
|
||||
// IsSupportedForBatch returns true if the current batch layout supports recurrent layers.
|
||||
func (c *Recurrent) IsSupportedForBatch() bool {
|
||||
return c.curSeqTokens > 0 && len(c.curSeqs) > 0
|
||||
}
|
||||
|
||||
// Seqs returns the ordered unique sequences for the current forward pass.
|
||||
func (c *Recurrent) Seqs() []int {
|
||||
return slices.Clone(c.curSeqs)
|
||||
}
|
||||
Reference in New Issue
Block a user