backend: Support graph computation that does not return an output

There are two cases where we may not have an output after computing:
 - Prompt processing where the length of the input exceeds the batch
   size
 - Internal memory management operations such as cache defrag and shift

ml/backend.go

@@ -49,7 +49,7 @@ type Context interface {
 	FromIntSlice(s []int32, shape ...int) (Tensor, error)
 
 	Forward(Tensor)
-	Compute(Tensor) Tensor
+	Compute(...Tensor)
 	Close()
 }
 

ml/backend/ggml/ggml.go

@@ -23,7 +23,7 @@ import (
 	"github.com/ollama/ollama/ml"
 	"golang.org/x/sync/errgroup"
 
-	"github.com/ollama/ollama/ml/backend/ggml/ggml/src"
+	ggml "github.com/ollama/ollama/ml/backend/ggml/ggml/src"
 )
 
 type device struct {
@@ -243,15 +243,17 @@ func (c *Context) Forward(t ml.Tensor) {
 	C.ggml_build_forward_expand(c.graph, t.(*Tensor).t)
 }
 
-func (c *Context) Compute(t ml.Tensor) ml.Tensor {
-	c.Forward(t)
+func (c *Context) Compute(tensors ...ml.Tensor) {
 	C.ggml_backend_sched_graph_compute_async(c.sched, c.graph)
 
-	backend := C.ggml_backend_sched_get_tensor_backend(c.sched, t.(*Tensor).t)
+	for _, t := range tensors {
+		if C.ggml_nbytes(t.(*Tensor).t) != 0 {
+			backend := C.ggml_backend_sched_get_tensor_backend(c.sched, t.(*Tensor).t)
 
-	t.(*Tensor).data = make([]byte, C.ggml_nbytes(t.(*Tensor).t))
-	C.ggml_backend_tensor_get_async(backend, t.(*Tensor).t, unsafe.Pointer(&t.(*Tensor).data[0]), 0, C.ggml_nbytes(t.(*Tensor).t))
-	return t
+			t.(*Tensor).data = make([]byte, C.ggml_nbytes(t.(*Tensor).t))
+			C.ggml_backend_tensor_get_async(backend, t.(*Tensor).t, unsafe.Pointer(&t.(*Tensor).data[0]), 0, C.ggml_nbytes(t.(*Tensor).t))
+		}
+	}
 }
 
 func shapeToGGML(shape []int) *C.int64_t {
@@ -292,6 +294,13 @@ func (c Context) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
 
 func fromSlice[S ~[]E, E float32 | int32](ctx Context, s S, shape []int, dtype uint32) (ml.Tensor, error) {
 	n := len(s)
+
+	if n == 0 {
+		var shape C.int64_t = 0
+		t := C.ggml_new_tensor(ctx.ctx, dtype, 1, &shape)
+		return &Tensor{t: t}, nil
+	}
+
 	for _, v := range shape {
 		n /= v
 	}
@@ -351,11 +360,7 @@ func (t *Tensor) Shape() []int {
 }
 
 func (t *Tensor) Bytes() []byte {
-	if bts := C.ggml_get_data(t.t); bts != nil {
-		return C.GoBytes(bts, C.int(C.ggml_nbytes(t.t)))
-	}
-
-	return nil
+	return t.data
 }
 
 func (t *Tensor) Floats() (f32s []float32) {

model/model.go

@@ -275,5 +275,8 @@ func Forward(m Model, optsFuncs ...OptionsFunc) (ml.Tensor, error) {
 	}
 	defer ctx.Close()
 
-	return ctx.Compute(t), nil
+	ctx.Forward(t)
+	ctx.Compute(t)
+
+	return t, nil
 }