Use std::optional for mask_arr arg (#2763)

mlx/fast.cpp

@@ -578,7 +578,7 @@ array scaled_dot_product_attention(
     const array& values,
     const float scale,
     const std::string& mask_mode /* = "" */,
-    const std::vector<array>& mask_arrs /* = {} */,
+    std::optional<array> mask_arr /* = {} */,
     const std::optional<array>& sinks /* = {} */,
     StreamOrDevice s /* = {}*/) {
   for (const auto& tensor : {queries, keys, values}) {
@@ -606,32 +606,22 @@ array scaled_dot_product_attention(
     has_mask = true;
     do_causal = true;
 
-    if (!mask_arrs.empty()) {
+    if (mask_arr) {
       std::ostringstream msg;
-      msg << "[scaled_dot_product_attention] Invalid mask_arrs for mask_mode "
+      msg << "[scaled_dot_product_attention] Invalid mask_arr for mask_mode "
-          << "'casusal'. No array masks supported.";
+          << "'casusal'. No array mask should be passed.";
       throw std::invalid_argument(msg.str());
     }
-  }
+  } else if (mask_arr) {
-
-  if (mask_mode == "array" || (mask_mode == "" && !mask_arrs.empty())) {
-    if (mask_arrs.size() != 1) {
-      std::ostringstream msg;
-      msg << "[scaled_dot_product_attention] Invalid mask_arrs for mask_mode "
-          << "'" << mask_mode << "'. Only 1 mask array is supported, got "
-          << mask_arrs.size() << "arrays.";
-      throw std::invalid_argument(msg.str());
-    }
-
     has_mask = true;
     has_arr_mask = true;
-    has_bool_mask = mask_arrs[0].dtype() == bool_;
+    has_bool_mask = mask_arr->dtype() == bool_;
   }
 
-  if (has_arr_mask && (mask_arrs[0]).ndim() > 4) {
+  if (has_arr_mask && mask_arr->ndim() > 4) {
     std::ostringstream msg;
     msg << "[scaled_dot_product_attention] the mask with shape "
-        << mask_arrs[0].shape() << " expected to have at most rank 4.";
+        << mask_arr->shape() << " expected to have at most rank 4.";
     throw std::invalid_argument(msg.str());
   }
 
@@ -764,20 +754,19 @@ array scaled_dot_product_attention(
   std::vector<array> inputs = {q, k, v};
   if (has_arr_mask) {
     // Check type
-    auto mask_arr = mask_arrs[0];
+    has_bool_mask = mask_arr->dtype() == bool_;
-    has_bool_mask = mask_arr.dtype() == bool_;
+    if (promote_types(mask_arr->dtype(), final_type) != final_type) {
-    if (promote_types(mask_arr.dtype(), final_type) != final_type) {
       std::ostringstream msg;
       msg << "[scaled_dot_product_attention] Mask type must promote to output type "
           << final_type << ".";
       throw std::invalid_argument(msg.str());
     } else if (!has_bool_mask) {
-      mask_arr = astype(mask_arr, final_type, stream);
+      mask_arr = astype(*mask_arr, final_type, stream);
     }
     // Broadcast mask
     auto mask_shape = queries.shape();
     mask_shape.back() = keys.shape(-2);
-    inputs.push_back(broadcast_to(mask_arr, mask_shape, stream));
+    inputs.push_back(broadcast_to(*mask_arr, mask_shape, stream));
   }
   if (has_sinks) {
     if (promote_types(sinks->dtype(), final_type) != final_type) {

mlx/fast.h

@@ -49,7 +49,7 @@ array scaled_dot_product_attention(
     const array& values,
     const float scale,
     const std::string& mask_mode = "",
-    const std::vector<array>& mask_arrs = {},
+    std::optional<array> mask_arr = {},
     const std::optional<array>& sinks = {},
     StreamOrDevice s = {});
 

python/src/fast.cpp

@@ -213,11 +213,11 @@ void init_fast(nb::module_& parent_module) {
               throw std::invalid_argument(msg.str());
             }
             return mx::fast::scaled_dot_product_attention(
-                queries, keys, values, scale, mask_str, {}, sinks, s);
+                queries, keys, values, scale, mask_str, std::nullopt, sinks, s);
           } else {
             auto mask_arr = std::get<mx::array>(mask);
             return mx::fast::scaled_dot_product_attention(
-                queries, keys, values, scale, "", {mask_arr}, sinks, s);
+                queries, keys, values, scale, "", mask_arr, sinks, s);
           }
 
         } else {