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| 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.z * 262144 + gl_WorkGroupID.y * 512 + 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); | |
| } | |
| } | |