mirror of
				https://github.com/ggml-org/llama.cpp.git
				synced 2025-11-03 09:22:01 +00:00 
			
		
		
		
	* vulkan: implement GGML_OP_ROPE_BACK * vulkan: implement GGML_OP_RMS_NORM_BACK * vulkan: implement GGML_OP_SILU_BACK * vulkan: implement GGML_OP_SOFTMAX_BACK
		
			
				
	
	
		
			56 lines
		
	
	
		
			1.8 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
			
		
		
	
	
			56 lines
		
	
	
		
			1.8 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
#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 G {A_TYPE data_a[];};
 | 
						|
layout (binding = 1) readonly buffer X {B_TYPE data_b[];};
 | 
						|
layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
 | 
						|
 | 
						|
shared FLOAT_TYPE sum_xx[BLOCK_SIZE];
 | 
						|
shared FLOAT_TYPE sum_xg[BLOCK_SIZE];
 | 
						|
 | 
						|
void main() {
 | 
						|
    const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
 | 
						|
    const uint tid = gl_LocalInvocationID.x;
 | 
						|
 | 
						|
    // Compute derivative of x[i]/norm(x) = g[i]/norm(x) - x[i] dot(x,g)/KX / norm(x)^1.5
 | 
						|
 | 
						|
    // partial sums for thread in warp
 | 
						|
    sum_xx[tid] = FLOAT_TYPE(0.0f);
 | 
						|
    sum_xg[tid] = FLOAT_TYPE(0.0f);
 | 
						|
 | 
						|
    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
 | 
						|
        const FLOAT_TYPE gi = FLOAT_TYPE(data_a[row*p.KX + col]);
 | 
						|
        const FLOAT_TYPE xi = FLOAT_TYPE(data_b[row*p.KX + col]);
 | 
						|
        sum_xx[tid] += xi * xi;
 | 
						|
        sum_xg[tid] += xi * gi;
 | 
						|
    }
 | 
						|
 | 
						|
    // sum up partial sums and write back result
 | 
						|
    barrier();
 | 
						|
    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
 | 
						|
        if (tid < s) {
 | 
						|
            sum_xx[tid] += sum_xx[tid + s];
 | 
						|
            sum_xg[tid] += sum_xg[tid + s];
 | 
						|
        }
 | 
						|
        barrier();
 | 
						|
    }
 | 
						|
 | 
						|
    const FLOAT_TYPE eps = FLOAT_TYPE(p.param1);
 | 
						|
    const FLOAT_TYPE mean = sum_xx[0] / FLOAT_TYPE(p.KX);
 | 
						|
    const FLOAT_TYPE scale_g = inversesqrt(mean + eps);
 | 
						|
    const FLOAT_TYPE scale_x = -scale_g * sum_xg[0] / (sum_xx[0] + FLOAT_TYPE(p.KX) * eps);
 | 
						|
 | 
						|
    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
 | 
						|
        data_d[row*p.KX + col] = D_TYPE(
 | 
						|
            scale_g * FLOAT_TYPE(data_a[row*p.KX + col]) +
 | 
						|
            scale_x * FLOAT_TYPE(data_b[row*p.KX + col]));
 | 
						|
    }
 | 
						|
}
 |