ollama source for Momentry Core verification

kvcache/cache.go

@@ -0,0 +1,84 @@
+package kvcache
+
+import (
+	"errors"
+
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/input"
+)
+
+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)
+
+	// SetConfig controls optimizations (mostly backend-specific) that may transform
+	// the output of the cache to work better with specific kernels. If not called,
+	// the backend settings will be used. This works well when calling Attention.
+	//
+	// The config can be overridden by models, especially if they require vanilla
+	// output when implementing their own version of attention. To do this, pass
+	// an empty ml.CacheConfig.
+	//
+	// Most models will not need to use this.
+	SetConfig(ml.CacheConfig)
+
+	// ** cache management **
+
+	// Init sets up runtime parameters.
+	// backend: Used to allocate cache data storage and execute management operations (such as defrag)
+	// dtype: The data type for storing cache entries
+	// maxSequences: The maximum number of sequences stored in the cache - across all batches
+	// capacity: The number of cache entries to store, per sequence
+	// maxBatch: The maximum number of tokens that can occur in a single batch
+	Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int)
+
+	// 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. reserve is to preallocate memory
+	// without actually storing data in the cache.
+	StartForward(ctx ml.Context, batch input.Batch, reserve bool) error
+
+	// CopyPrefix copies tokens in the range [0, len) from srcSeq to dstSeq
+	CopyPrefix(srcSeq, dstSeq int, len int32)
+
+	// CanResume returns true if the cache can continue with the next token at
+	// the given position and sequence. Assumes that the caller has already
+	// verified the contents of the cache.
+	CanResume(seq int, pos int32) bool
+
+	// 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
+}
+
+// CheckpointCache optionally supports restoring recurrent state to a prior
+// position to avoid full prompt reprocessing when a prefix mismatch occurs.
+// The returned position is the number of tokens that can be kept (prefix length).
+type CheckpointCache interface {
+	PrepareRestore(seq int, targetPos int32) (int32, bool)
+}