Complete 2 pass sdpav

mlx/backend/cuda/scaled_dot_product_attention.cu

@@ -2,6 +2,7 @@
 
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/device/config.h"
+#include "mlx/backend/cuda/device/utils.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/backend/cuda/lru_cache.h"
 #include "mlx/backend/gpu/copy.h"
@@ -217,11 +218,11 @@ __global__ void kernel_sdpav_2pass_1(
   U k[v_per_thread];
   U o[v_per_thread];
 
-  __shared__ U outputs[BD][BN + 1];
+  __shared__ U outputs[BN][BD + 1];
   __shared__ U max_scores[BN];
   __shared__ U sum_exp_scores[BN];
 
-  const U scale_log2 = params.scale; // * 1.44269504089f;
+  const U scale_log2 = params.scale * 1.44269504089f;
 
   auto block = cg::this_thread_block();
   auto warp = cg::tiled_partition<32>(block);
@@ -230,7 +231,7 @@ __global__ void kernel_sdpav_2pass_1(
   const int warp_idx = warp.meta_group_rank();
 
   // Adjust to thread block and thread
-  const int batch_idx = 0; // blockIdx.z / blocks;
+  const int batch_idx = blockIdx.z / blocks;
   const int block_idx = blockIdx.z % blocks;
   const int head_idx = blockIdx.x;
   const int kv_head_idx = head_idx / params.gqa_factor;
@@ -302,8 +303,8 @@ __global__ void kernel_sdpav_2pass_1(
 
       // Update the accumulators
       U new_max = max(max_score, score);
-      U factor = expf(max_score - new_max);
+      U factor = exp2f(max_score - new_max);
-      U exp_score = expf(score - new_max);
+      U exp_score = exp2f(score - new_max);
 
       max_score = new_max;
       sum_exp_score = sum_exp_score * factor + exp_score;
@@ -330,7 +331,7 @@ __global__ void kernel_sdpav_2pass_1(
 
   max_score = (lane_idx < BN) ? max_scores[lane_idx] : -1e9;
   U new_max = cg::reduce(warp, max_score, cg::greater<U>());
-  U factor = expf(max_score - new_max);
+  U factor = exp2f(max_score - new_max);
   sum_exp_score = (lane_idx < BN) ? sum_exp_scores[lane_idx] : 0.f;
   sum_exp_score = cg::reduce(warp, sum_exp_score * factor, cg::plus<U>());
 
@@ -341,31 +342,30 @@ __global__ void kernel_sdpav_2pass_1(
   }
 
   // Now we need to aggregate all the outputs
+  auto ff = exp2f(max_scores[warp_idx] - new_max);
   PRAGMA_LOOP_UNROLL
   for (int i = 0; i < v_per_thread; i++) {
-    outputs[lane_idx][warp_idx] = o[i] * expf(max_scores[warp_idx] - new_max);
+    outputs[warp_idx][lane_idx] = o[i] * ff;
     block.sync();
 
     if (warp_idx == 0) {
-      U ot = outputs[lane_idx][0];
+      U ot = outputs[0][lane_idx];
-
       PRAGMA_LOOP_UNROLL
       for (int j = 1; j < BN; j++) {
-        ot += outputs[lane_idx][0];
+        ot += outputs[j][lane_idx];
+        warp.sync();
       }
-
+      o[i] = ot;
-      // o[i] = ot;
-      partials[v_per_thread * lane_idx + i] = ot;
     }
     block.sync();
   }
 
-  // if(warp_idx == 0) {
+  if (warp_idx == 0) {
-  //   PRAGMA_LOOP_UNROLL
+    PRAGMA_LOOP_UNROLL
-  //   for (int i = 0; i < v_per_thread; i++) {
+    for (int i = 0; i < v_per_thread; i++) {
-  //     partials[v_per_thread * lane_idx + i] = o[i];
+      partials[v_per_thread * lane_idx + i] = o[i];
-  //   }
+    }
-  // }
+  }
 }
 
 template <typename T, bool do_causal, int D>
@@ -414,9 +414,9 @@ __global__ void kernel_sdpav_2pass_2(
 
   U max_score = maxs[lane_idx];
   U new_max = cg::reduce(warp, max_score, cg::greater<U>());
-  U factor = expf(max_score - new_max);
+  U factor = exp2f(max_score - new_max);
   U sum_exp_score = cg::reduce(warp, sums[lane_idx] * factor, cg::plus<U>());
-  // sum_exp_score = __frcp_rn(sum_exp_score);
+  sum_exp_score = __frcp_rn(sum_exp_score);
 
   PRAGMA_LOOP_UNROLL
   for (int i = 0; i < v_per_thread; i++) {
@@ -429,7 +429,7 @@ __global__ void kernel_sdpav_2pass_2(
     outputs[lane_idx][warp_idx] = o[i];
     block.sync();
     U ot = outputs[warp_idx][lane_idx] * factor;
-    o[i] = cg::reduce(warp, ot, cg::plus<U>()) / sum_exp_score;
+    o[i] = cg::reduce(warp, ot, cg::plus<U>()) * sum_exp_score;
     block.sync();
   }
 
@@ -504,6 +504,7 @@ void sdpa_vector_1pass_fallback(
             kernel,
             grid_dim,
             block_dim,
+            0,
             q.data<DataType>(),
             k.data<DataType>(),
             v.data<DataType>(),
@@ -585,6 +586,7 @@ void sdpa_vector_2pass_fallback(
               kernel,
               grid_dim,
               block_dim,
+              0,
               q.data<DataType>(),
               k.data<DataType>(),
               v.data<DataType>(),
@@ -610,6 +612,7 @@ void sdpa_vector_2pass_fallback(
               kernel,
               grid_dim,
               block_dim,
+              0,
               intermediate.data<float>(),
               sums.data<float>(),
               maxs.data<float>(),
@@ -632,7 +635,7 @@ void sdpa_vector_fallback(
     bool do_causal_ = false) {
   int kL = k.shape(2);
 
-  if (false && kL > 1024) {
+  if (kL > 1024) {
     return sdpa_vector_2pass_fallback(
         s, encoder, q, k, v, scale, o, do_causal_);
   } else {
@@ -1034,7 +1037,23 @@ void ScaledDotProductAttention::eval_gpu(
     if (q.is_donatable() && q.flags().row_contiguous && q.size() == o.size()) {
       o.copy_shared_buffer(q);
     } else {
-      o.set_data(allocator::malloc(o.nbytes()));
+      int64_t str_oD = 1;
+      int64_t str_oH = o.shape(3);
+      int64_t str_oL = o.shape(1) * str_oH;
+      int64_t str_oB = o.shape(2) * str_oL;
+      size_t data_size = o.shape(0) * str_oB;
+
+      array::Flags flags{
+          /* bool contiguous = */ 1,
+          /* bool row_contiguous = */ 0,
+          /* bool col_contiguous = */ 0,
+      };
+
+      o.set_data(
+          allocator::malloc(o.nbytes()),
+          data_size,
+          {str_oB, str_oH, str_oL, str_oD},
+          flags);
     }
 
     return sdpa_vector_fallback(s, encoder, q, k, v, scale_, o, do_causal_);