chunked attention

convert/convert_llama4.go

@@ -19,6 +19,7 @@ type llama4Model struct {
 		InterleaveMOELayerStep uint32 `json:"interleave_moe_layer_step"`
 		UseQKNorm              bool   `json:"use_qk_norm"`
 		IntermediateSizeMLP    uint32 `json:"intermediate_size_mlp"`
+		AttentionChunkSize     uint32 `json:"attention_chunk_size"`
 	} `json:"text_config"`
 	VisionModel struct {
 		NumHiddenLayers   uint32  `json:"num_hidden_layers"`
@@ -51,6 +52,7 @@ func (p *llama4Model) KV(t *Tokenizer) ggml.KV {
 	kv["llama4.expert_used_count"] = p.TextModel.NumExpertsPerToken
 	kv["llama4.interleave_moe_layer_step"] = p.TextModel.InterleaveMOELayerStep
 	kv["llama4.use_qk_norm"] = p.TextModel.UseQKNorm
+	kv["llama4.attention.chunk_size"] = p.TextModel.AttentionChunkSize
 
 	kv["llama4.vision.block_count"] = p.VisionModel.NumHiddenLayers
 	kv["llama4.vision.embedding_length"] = p.VisionModel.HiddenSize

kvcache/causal.go

@@ -21,6 +21,7 @@ type shiftFn func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, e
 type Causal struct {
 	DType      ml.DType
 	windowSize int32
+	chunkSize  int32
 
 	opts CausalOptions
 
@@ -97,6 +98,17 @@ func NewSWACache(windowSize int32, shift shiftFn) *Causal {
 	}
 }
 
+func NewChunkedAttentionCache(chunkSize int32, shift shiftFn) *Causal {
+	return &Causal{
+		windowSize: math.MaxInt32,
+		chunkSize:  chunkSize,
+		shiftFn:    shift,
+		ctxs:       make(map[int]ml.Context),
+		keys:       make(map[int]ml.Tensor),
+		values:     make(map[int]ml.Tensor),
+	}
+}
+
 func (c *Causal) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
 	if c.config == nil {
 		var config ml.CacheConfig
@@ -300,6 +312,7 @@ func (c *Causal) buildMask(ctx ml.Context) (ml.Tensor, error) {
 		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.windowSize {
 				mask[i*length+(j-c.curCellRange.min)] = float32(math.Inf(-1))
 			}

kvcache/causal_test.go

@@ -86,6 +86,64 @@ func TestSWA(t *testing.T) {
 	testCache(t, backend, cache, tests)
 }
 
+func TestChunkedAttention(t *testing.T) {
+	cache := NewChunkedAttentionCache(2, nil)
+	defer cache.Close()
+
+	var b testBackend
+	cache.Init(&b, ml.DTypeF16, 1, 16, 16)
+
+	x := float32(math.Inf(-1))
+
+	testCache(
+		t, &b, cache,
+		[]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, x, x, x,
+					0, 0, x, x,
+					x, x, 0, x,
+					x, x, 0, 0,
+				},
+			},
+			{
+				name:          "SecondBatch",
+				in:            []float32{5, 6, 7},
+				inShape:       []int{1, 1, 3},
+				seqs:          []int{0, 0, 0},
+				pos:           []int32{4, 5, 6},
+				expected:      []float32{1, 2, 3, 4, 5, 6, 7},
+				expectedShape: []int{1, 1, 7},
+				expectedMask: []float32{
+					x, x, x, x, 0, x, x,
+					x, x, x, x, 0, 0, x,
+					x, x, x, x, x, x, 0,
+				},
+			},
+			{
+				name:          "ThirdBatch",
+				in:            []float32{8, 9},
+				inShape:       []int{1, 1, 2},
+				seqs:          []int{0, 0},
+				pos:           []int32{7, 8},
+				expected:      []float32{1, 2, 3, 4, 5, 6, 7, 8, 9},
+				expectedShape: []int{1, 1, 9},
+				expectedMask: []float32{
+					x, x, x, x, x, x, 0, 0, x,
+					x, x, x, x, x, x, x, x, 0,
+				},
+			},
+		},
+	)
+}
+
 func TestSequences(t *testing.T) {
 	backend := &testBackend{}
 	cache := NewCausalCache(nil)
@@ -293,8 +351,16 @@ func testCache(t *testing.T, backend ml.Backend, cache Cache, tests []testCase)
 
 			context.Forward(out, mask).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)
+			if !slices.Equal(out.Floats(), test.expected) {
+				t.Errorf("TestCache: have %v; want %v", out.Floats(), test.expected)
+			}
+
+			if !slices.Equal(out.Shape(), test.expectedShape) {
+				t.Errorf("TestCache: has shape %v; want %v", out.Shape(), test.expectedShape)
+			}
+
+			if !slices.Equal(mask.Floats(), test.expectedMask) {
+				t.Errorf("TestCache: have mask: have %v want %v", mask.Floats(), test.expectedMask)
 			}
 		})
 	}

model/models/llama4/model.go

@@ -52,8 +52,7 @@ func New(c fs.Config) (model.Model, error) {
 	}
 
 	m.Cache = kvcache.NewWrapperCache(
-		// TODO: pretend this is chunked attention for now
-		kvcache.NewSWACache(8192, m.Shift),
+		kvcache.NewChunkedAttentionCache(int32(c.Uint("attention.chunk_size")), m.Shift),
 		kvcache.NewCausalCache(m.Shift),
 	)