Fused attention for single query (#1497)

benchmarks/python/sdpa_vector_bench.py

@@ -0,0 +1,49 @@
+import argparse
+import math
+
+import mlx.core as mx
+from time_utils import time_fn
+
+L = 1024
+H = 32
+H_k = 32 // 4
+D = 128
+
+
+def attention(q, k, v):
+    B, Hq, L, D = q.shape
+    _, Hk, S, _ = k.shape
+    q = q.reshape(B, Hk, Hq // Hk, L, D)
+    k = k[:, :, None, :, :]
+    v = v[:, :, None, :, :]
+    s = q @ k.transpose(0, 1, 2, 4, 3)
+    p = mx.softmax(s.astype(mx.float32), axis=-1).astype(s.dtype)
+    o = p @ v
+    return o.reshape(B, Hq, L, D)
+
+
+def sdpa(q, k, v):
+    return mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0)
+
+
+def time_self_attention_primitives():
+    mx.random.seed(3)
+    q = mx.random.uniform(shape=(1, H, 1, D))
+    k = mx.random.uniform(shape=(1, H_k, L, D))
+    v = mx.random.uniform(shape=(1, H_k, L, D))
+    mx.eval(q, k, v)
+    time_fn(attention, q, k, v)
+
+
+def time_self_attention_sdpa():
+    mx.random.seed(3)
+    q = mx.random.uniform(shape=(1, H, 1, D))
+    k = mx.random.uniform(shape=(1, H_k, L, D))
+    v = mx.random.uniform(shape=(1, H_k, L, D))
+    mx.eval(q, k, v)
+    time_fn(sdpa, q, k, v)
+
+
+if __name__ == "__main__":
+    time_self_attention_sdpa()
+    time_self_attention_primitives()