mlx: make array management thread-safe

Use atomic.Int32 for Array.pinned and a sync.Mutex for the global
arrays slice so MLX arrays can be safely created and managed from
multiple goroutines. Convert Array value receivers to pointer
receivers and struct fields from Array to *Array to avoid copying
the atomic.

x/mlxrunner/mlx/array.go

@@ -10,6 +10,8 @@ import (
 	"reflect"
 	"sort"
 	"strings"
+	"sync"
+	"sync/atomic"
 	"unsafe"
 
 	"github.com/ollama/ollama/logutil"
@@ -18,15 +20,20 @@ import (
 type Array struct {
 	ctx    C.mlx_array
 	name   string
-	pinned int
+	pinned atomic.Int32
 }
 
-var arrays []*Array
+var (
+	arrays   []*Array
+	arraysMu sync.Mutex
+)
 
 // constructor utilities
 
 func New(name string) *Array {
 	t := &Array{name: name}
+	arraysMu.Lock()
+	defer arraysMu.Unlock()
 	arrays = append(arrays, t)
 	return t
 }
@@ -127,7 +134,7 @@ func (t *Array) Clone() *Array {
 func Pin(s ...*Array) {
 	for _, t := range s {
 		if t != nil {
-			t.pinned++
+			t.pinned.Add(1)
 		}
 	}
 }
@@ -136,8 +143,7 @@ func Pin(s ...*Array) {
 func Unpin(s ...*Array) {
 	for _, t := range s {
 		if t != nil {
-			t.pinned--
-			if t.pinned < 0 {
+			if t.pinned.Add(-1) < 0 {
 				panic(fmt.Sprintf("mlx.Unpin: negative pin count on array %q", t.name))
 			}
 		}
@@ -147,9 +153,11 @@ func Unpin(s ...*Array) {
 // Sweep releases all unpinned arrays, primarily intermediate tensors. MLX will truly
 // free them when there are no other references, including dependencies in the graph.
 func Sweep() {
+	arraysMu.Lock()
+	defer arraysMu.Unlock()
 	n := 0
 	for _, t := range arrays {
-		if t.pinned > 0 && t.Valid() {
+		if t.pinned.Load() > 0 && t.Valid() {
 			arrays[n] = t
 			n++
 		} else if t.Valid() {
@@ -176,7 +184,7 @@ func (t *Array) String() string {
 func (t *Array) LogValue() slog.Value {
 	attrs := []slog.Attr{
 		slog.String("name", t.name),
-		slog.Int("pinned", t.pinned),
+		slog.Int("pinned", int(t.pinned.Load())),
 	}
 	if t.Valid() {
 		attrs = append(attrs,
@@ -190,19 +198,19 @@ func (t *Array) LogValue() slog.Value {
 
 // shape utilities
 
-func (t Array) Size() int {
+func (t *Array) Size() int {
 	return int(C.mlx_array_size(t.ctx))
 }
 
-func (t Array) NumBytes() int {
+func (t *Array) NumBytes() int {
 	return int(C.mlx_array_nbytes(t.ctx))
 }
 
-func (t Array) NumDims() int {
+func (t *Array) NumDims() int {
 	return int(C.mlx_array_ndim(t.ctx))
 }
 
-func (t Array) Dims() []int {
+func (t *Array) Dims() []int {
 	dims := make([]int, t.NumDims())
 	for i := range dims {
 		dims[i] = t.Dim(i)
@@ -211,29 +219,29 @@ func (t Array) Dims() []int {
 	return dims
 }
 
-func (t Array) Dim(dim int) int {
+func (t *Array) Dim(dim int) int {
 	return int(C.mlx_array_dim(t.ctx, C.int(dim)))
 }
 
-func (t Array) DType() DType {
+func (t *Array) DType() DType {
 	return DType(C.mlx_array_dtype(t.ctx))
 }
 
 // data utilities
 
-func (t Array) Int() int {
+func (t *Array) Int() int {
 	var item C.int64_t
 	C.mlx_array_item_int64(&item, t.ctx)
 	return int(item)
 }
 
-func (t Array) Float() float64 {
+func (t *Array) Float() float64 {
 	var item C.double
 	C.mlx_array_item_float64(&item, t.ctx)
 	return float64(item)
 }
 
-func (t Array) Ints() []int {
+func (t *Array) Ints() []int {
 	ints := make([]int, t.Size())
 	for i, f := range unsafe.Slice(C.mlx_array_data_int32(t.ctx), len(ints)) {
 		ints[i] = int(f)
@@ -241,7 +249,7 @@ func (t Array) Ints() []int {
 	return ints
 }
 
-func (t Array) Floats() []float32 {
+func (t *Array) Floats() []float32 {
 	floats := make([]float32, t.Size())
 	for i, f := range unsafe.Slice(C.mlx_array_data_float32(t.ctx), len(floats)) {
 		floats[i] = float32(f)
@@ -249,7 +257,7 @@ func (t Array) Floats() []float32 {
 	return floats
 }
 
-func (t Array) Save(name string) error {
+func (t *Array) Save(name string) error {
 	cName := C.CString(name)
 	defer C.free(unsafe.Pointer(cName))
 	C.mlx_save(cName, t.ctx)
@@ -258,6 +266,8 @@ func (t Array) Save(name string) error {
 
 // LogArrays logs all live arrays, sorted by size
 func LogArrays() {
+	arraysMu.Lock()
+	defer arraysMu.Unlock()
 	sort.Slice(arrays, func(i, j int) bool {
 		return arrays[i].NumBytes() > arrays[j].NumBytes()
 	})
@@ -266,7 +276,7 @@ func LogArrays() {
 	for _, t := range arrays {
 		nb := t.NumBytes()
 		total += nb
-		logutil.Trace(fmt.Sprintf("tensor %-60s %5s %5s pinned=%d %v", t.name, t.DType(), PrettyBytes(nb), t.pinned, t.Dims()))
+		logutil.Trace(fmt.Sprintf("tensor %-60s %5s %5s pinned=%d %v", t.name, t.DType(), PrettyBytes(nb), t.pinned.Load(), t.Dims()))
 	}
 	logutil.Trace(fmt.Sprintf("tensors total: %d, size: %s, active: %s", len(arrays), PrettyBytes(total), PrettyBytes(ActiveMemory())))
 }

x/mlxrunner/mlx/fast.go

@@ -24,8 +24,8 @@ func ScaledDotProductAttention(query, key, value, mask *Array, scale float32) *A
 }
 
 type LayerNorm struct {
-	Weight Array `weight:"weight"`
-	Bias   Array `weight:"bias"`
+	Weight *Array `weight:"weight"`
+	Bias   *Array `weight:"bias"`
 }
 
 func (r *LayerNorm) Forward(x *Array, eps float32) *Array {
@@ -35,10 +35,10 @@ func (r *LayerNorm) Forward(x *Array, eps float32) *Array {
 }
 
 type RMSNorm struct {
-	Weight Array `weight:"weight"`
+	Weight *Array `weight:"weight"`
 }
 
-func (r RMSNorm) Forward(x *Array, eps float32) *Array {
+func (r *RMSNorm) Forward(x *Array, eps float32) *Array {
 	out := New("FAST_RMSNORM")
 	C.mlx_fast_rms_norm(&out.ctx, x.ctx, r.Weight.ctx, C.float(eps), DefaultStream().ctx)
 	return out

x/mlxrunner/mlx/nn.go

@@ -1,12 +1,12 @@
 package mlx
 
 type Linear struct {
-	Weight Array `weight:"weight"`
-	Bias   Array `weight:"bias"`
+	Weight *Array `weight:"weight"`
+	Bias   *Array `weight:"bias"`
 }
 
 // Forward computes the linear transformation: x @ Weight.T + Bias
-func (m Linear) Forward(x *Array) *Array {
+func (m *Linear) Forward(x *Array) *Array {
 	w := m.Weight.Transpose(1, 0)
 	if m.Bias.Valid() {
 		return m.Bias.Addmm(x, w, 1.0, 1.0)
@@ -15,14 +15,14 @@ func (m Linear) Forward(x *Array) *Array {
 	return x.Matmul(w)
 }
 
-func (m Linear) Gather(x, lhs, rhs *Array, sorted bool) *Array {
+func (m *Linear) Gather(x, lhs, rhs *Array, sorted bool) *Array {
 	w := m.Weight.Transpose(0, 2, 1)
 	// TODO: bias
 	return x.GatherMM(w, lhs, rhs, sorted)
 }
 
 type Embedding struct {
-	Weight Array `weight:"weight"`
+	Weight *Array `weight:"weight"`
 }
 
 func (e *Embedding) Forward(indices *Array) *Array {