Files
llama.cpp/ggml/src/vulkan-shaders/group_norm.comp
0cc4m a3738b2fa7 vulkan : implement Stable Diffusion operators (ggml/904)
* Fix Vulkan repeat op

* Implement Vulkan concat op

* Delete old Vulkan shader generator

* Implement Vulkan im2col op

* Implement Vulkan unary gelu_quick op

* Implement Vulkan group_norm op

* Implement Vulkan timestep_embedding op

* Implement Vulkan upscale op

* Fix Vulkan vk_context tensor extra index issue

* Fix Vulkan matmul shader parameter bug

* Properly fix Vulkan matmul shader parameter bug

* Add Vulkan ADD f16 + f32 -> f16 operator support

* Implement Vulkan tanh op

* Fix Vulkan group count too large Validation error on non-Nvidia GPUs

* Throw error when too much memory is requested

* Fix another Vulkan group count too large Validation error on non-Nvidia GPUs

* Fix matmul MMQ condition

* Implement Vulkan pad op

* Fix Vulkan crash when tensor is used multiple times in a compute graph

* Add Vulkan CONCAT f16 + f16 -> f16 op

* Add Vulkan LEAKY_RELU op
2024-08-05 08:50:57 +03:00

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#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 float tmp[BLOCK_SIZE];
void main() {
const uint group_size = p.KX;
const float eps = p.param1;
const uint tid = gl_LocalInvocationID.x;
const uint start = gl_WorkGroupID.x * group_size + tid;
const uint end = start + group_size;
tmp[tid] = 0.0f;
// Calculate mean
[[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) {
tmp[tid] += float(data_a[col]);
}
// tmp up partial tmps and write back result
barrier();
[[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) {
tmp[tid] += tmp[tid + s];
}
barrier();
}
const float mean = tmp[0] / group_size;
barrier();
tmp[tid] = 0.0f;
// Calculate variance
[[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) {
const float xi = float(data_a[col]) - mean;
data_d[col] = D_TYPE(xi);
tmp[tid] += 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) {
tmp[tid] += tmp[tid + s];
}
barrier();
}
const float variance = tmp[0] / group_size;
const float scale = inversesqrt(variance + eps);
[[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) {
data_d[col] *= D_TYPE(scale);
}
}