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authorKawrakow <48489457+ikawrakow@users.noreply.github.com>2024-07-27 07:55:01 +0200
committerGitHub <noreply@github.com>2024-07-27 07:55:01 +0200
commit154e0d75fccf1784fe9ff6fd76a630b66563da3d (patch)
tree81ce6dbb5b1900c1aa78a879f0593c694cab9d27 /ggml/src/vulkan-shaders/norm.comp
parent0684c3e9c70d49323b4fc517128cbe222cab7f96 (diff)
Merge mainline llama.cpp (#3)
* Merging mainline - WIP * Merging mainline - WIP AVX2 and CUDA appear to work. CUDA performance seems slightly (~1-2%) lower as it is so often the case with llama.cpp/ggml after some "improvements" have been made. * Merging mainline - fix Metal * Remove check --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Diffstat (limited to 'ggml/src/vulkan-shaders/norm.comp')
-rw-r--r--ggml/src/vulkan-shaders/norm.comp44
1 files changed, 44 insertions, 0 deletions
diff --git a/ggml/src/vulkan-shaders/norm.comp b/ggml/src/vulkan-shaders/norm.comp
new file mode 100644
index 00000000..803dbdcb
--- /dev/null
+++ b/ggml/src/vulkan-shaders/norm.comp
@@ -0,0 +1,44 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+shared vec2 sum[BLOCK_SIZE];
+
+void main() {
+ const uint row = gl_WorkGroupID.x;
+ const uint tid = gl_LocalInvocationID.x;
+
+ sum[tid] = vec2(0.0f, 0.0f);
+
+ [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+ const float xi = float(data_a[row*p.KX + col]);
+ sum[tid].x += xi;
+ sum[tid].y += xi * xi;
+ }
+
+ // sum up partial sums and write back result
+ barrier();
+ [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+ if (tid < s) {
+ sum[tid] += sum[tid + s];
+ }
+ barrier();
+ }
+
+ const float mean = sum[0].x / p.KX;
+ const float var = sum[0].y / p.KX - mean * mean;
+ const float inv_std = inversesqrt(var + p.param1);
+
+ [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+ data_d[row*p.KX + col] = D_TYPE((float(data_a[row*p.KX + col]) - mean) * inv_std);
+ }
+}