vulkan: Optimize mul_mat_vec p021 and nc shaders (#12505)

* tests: add mul_mat perf/functional tests for p021/nc vulkan shaders

* vulkan: Optimize mul_mat_vec p021 and nc shaders.

These shaders are used in attention calculations, and when the KV cache grows
large they start to dominate the run time. For the nc shader (which is called
with large 'k' dimension), use unrolling and vector loads. For the p021 shader
(which is called with large 'm' and small 'k' dimensions), take advantage of
grouped query attention to reuse loads from the A matrix for the whole group,
and reduce the number of workgroups (too much overhead from tiny dispatches).

Using subgroupAdd in the p021 shader also helps, use that conditionally.

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp

@@ -12,6 +12,9 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
 layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
 
+layout (binding = 0) readonly buffer AV4 {A_TYPE_VEC4 data_a_v4[];};
+layout (binding = 1) readonly buffer BV4 {B_TYPE_VEC4 data_b_v4[];};
+
 layout (push_constant) uniform parameter
 {
     uint ncols_x;
@@ -37,25 +40,66 @@ void main() {
 
     const uint idst = channel*nrows_dst + row_dst;
 
-    tmp[tid] = 0.0f;
+    FLOAT_TYPE temp = 0.0f;
 
-    for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) {
-        const uint col_x = col_x0 + tid;
+    // Detect alignment for vector loads
+    bool is_aligned = (p.ncols_x % 4) == 0 && (p.row_stride_x % 4) == 0 && (p.channel_stride_x % 4) == 0;
 
-        if (col_x >= p.ncols_x) {
-            break;
+    for (uint col_x0 = 0; col_x0 < p.ncols_x;) {
+
+        // Unroll 2x and do vec4 loads if aligned
+        const uint unroll_count = 2;
+        if (col_x0 + unroll_count * 4 * BLOCK_SIZE <= p.ncols_x && is_aligned) {
+            [[unroll]] for (uint i = 0; i < unroll_count; ++i) {
+                const uint col_x = col_x0 + 4*tid;
+
+                const uint row_y = col_x;
+
+                const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+                const uint iy = channel*nrows_y + row_y;
+
+                const vec4 av4 = vec4(data_a_v4[ix / 4]);
+                const vec4 bv4 = vec4(data_b_v4[iy / 4]);
+
+                temp += dot(av4, bv4);
+
+                col_x0 += 4*BLOCK_SIZE;
+            }
+        // do vec4 loads if aligned
+        } else if (col_x0 + 4*BLOCK_SIZE <= p.ncols_x && is_aligned) {
+            const uint col_x = col_x0 + 4*tid;
+
+            const uint row_y = col_x;
+
+            const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+            const uint iy = channel*nrows_y + row_y;
+
+            const vec4 av4 = vec4(data_a_v4[ix / 4]);
+            const vec4 bv4 = vec4(data_b_v4[iy / 4]);
+
+            temp += dot(av4, bv4);
+
+            col_x0 += 4*BLOCK_SIZE;
+        } else {
+            const uint col_x = col_x0 + tid;
+            if (col_x >= p.ncols_x) {
+                break;
+            }
+
+            const uint row_y = col_x;
+
+            const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+            const uint iy = channel*nrows_y + row_y;
+
+            const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);
+
+            temp = fma(xi, FLOAT_TYPE(data_b[iy]), temp);
+            col_x0 += BLOCK_SIZE;
         }
-
-        const uint row_y = col_x;
-
-        const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
-        const uint iy = channel*nrows_y + row_y;
-
-        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);
-
-        tmp[tid] = fma(xi, FLOAT_TYPE(data_b[iy]), tmp[tid]);
     }
 
+    tmp[tid] = temp;
+
     // sum up partial sums and write back result
     barrier();
     [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {