Add memory_efficient_threshold kwarg to sdpa kernel (ml-explore#1319)

Allows opt-in to memory efficient GPU shader at proscribed sequence
length.  Otherwise, utilizes aggregate MLX primitives for best latency.

mlx/fast.cpp

@@ -465,6 +465,7 @@ array scaled_dot_product_attention(
     const array& values,
     const float scale,
     const std::optional<array>& mask,
+    const std::optional<int>& memory_efficient_threshold,
     StreamOrDevice s) {
   for (const auto& tensor : {queries, keys, values}) {
     if (tensor.ndim() != 4) {
@@ -535,6 +536,11 @@ array scaled_dot_product_attention(
    * * dtype is not fp32 or fp16
    */
 
+  int threshold = 1e6;
+  if (memory_efficient_threshold.has_value()) {
+    threshold = std::max(1, memory_efficient_threshold.value());
+  }
+
   bool needs_mask = mask.has_value();
   auto fallback = [scale, needs_mask, final_type, n_q_heads, n_kv_heads, &s](
                       const std::vector<array>& inputs) {
@@ -581,9 +587,10 @@ array scaled_dot_product_attention(
   bool implementation_supports_use_case =
       supports_sdpa || supports_full_self_attention;
 
-  // disabling full self attention until perf is tuned;
-  // likewise for sdpa
-  implementation_supports_use_case &= false;
+  // sdpa gpu shader is disabled except for memory efficient opt-in
+  const int seq_for_threshold = queries.shape(2);
+  bool use_memory_efficient_impl = seq_for_threshold >= threshold;
+  implementation_supports_use_case &= use_memory_efficient_impl;
 
   if (implementation_supports_use_case) {
     auto out_shape =

mlx/fast.h

@@ -37,6 +37,7 @@ array scaled_dot_product_attention(
     const array& values,
     const float scale,
     const std::optional<array>& mask = std::nullopt,
+    const std::optional<int>& memory_efficient_threshold = std::nullopt,
     StreamOrDevice s = {});
 
 std::tuple<array, array, array> affine_quantize(

python/src/fast.cpp

@@ -112,6 +112,7 @@ void init_fast(nb::module_& parent_module) {
       nb::kw_only(),
       "scale"_a,
       "mask"_a = nb::none(),
+      "memory_efficient_threshold"_a = nb::none(),
       "stream"_a = nb::none(),
       nb::sig(
           "def scaled_dot_product_attention(q: array, k: array, v: array, *, scale: float,  mask: Union[None, array] = None, stream: Union[None, Stream, Device] = None) -> array"),

python/tests/test_fast_sdpa.py

@@ -86,7 +86,7 @@ def test_fast_sdpa(self):
 
                 reference = mlx_primitives_sdpa_with_gqa(q_mlx, k_mlx, v_mlx, scale)
                 o_mlx = mx.fast.scaled_dot_product_attention(
-                    q_mlx, k_mlx, v_mlx, scale=scale
+                    q_mlx, k_mlx, v_mlx, scale=scale, memory_efficient_threshold=2
                 )
 
                 self.assertListEqual(list(reference.shape), list(o_mlx.shape))