From b333b06772c89d96aacb5490d6a219fba7c09cc6 Mon Sep 17 00:00:00 2001
From: Mitja Felicijan <mitja.felicijan@gmail.com>
Date: Thu, 12 Feb 2026 20:57:17 +0100
Subject: Engage!

---
 llama.cpp/ggml/src/ggml-cuda/reduce_rows.cuh | 39 ++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)
 create mode 100644 llama.cpp/ggml/src/ggml-cuda/reduce_rows.cuh

(limited to 'llama.cpp/ggml/src/ggml-cuda/reduce_rows.cuh')

diff --git a/llama.cpp/ggml/src/ggml-cuda/reduce_rows.cuh b/llama.cpp/ggml/src/ggml-cuda/reduce_rows.cuh
new file mode 100644
index 0000000..de240fd
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-cuda/reduce_rows.cuh
@@ -0,0 +1,39 @@
+#include "common.cuh"
+
+// Row reduction kernel template - compute sum (norm=false) or mean (norm=true)
+template <bool norm>
+static __global__ void reduce_rows_f32(const float * __restrict__ x, float * __restrict__ dst, const int ncols) {
+    const int row = blockIdx.x;
+    const int col = threadIdx.x;
+
+    float     sum        = 0.0f;
+    const int num_unroll = 8;
+    float     temp[num_unroll];
+    float     sum_temp[num_unroll] = { 0.0f };
+    for (int i = col; i < ncols;) {
+        for (int j = 0; j < num_unroll; ++j) {
+            if (i < ncols) {
+                temp[j] = x[row * ncols + i];
+            } else {
+                temp[j] = 0;
+            }
+            i += blockDim.x;
+        }
+        for (int j = 0; j < num_unroll; ++j) {
+            sum_temp[j] += temp[j];
+        }
+    }
+    for (int j = 0; j < num_unroll; ++j) {
+        sum += sum_temp[j];
+    }
+
+    // sum up partial sums
+    __shared__ float shared_vals[32];
+    sum = block_reduce<block_reduce_method::SUM>(sum, shared_vals);
+
+    if (col != 0) {
+        return;
+    }
+
+    dst[row] = norm ? sum / ncols : sum;
+}
-- 
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