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llamacpp
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#version 450
#include "rope_common.comp"
layout(binding = 0) buffer restrict readonly tensorInA { float16_t inA[]; };
layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; };
layout(binding = 2) buffer restrict readonly tensorInC { float inC[]; };
layout(binding = 3) buffer restrict writeonly tensorOut { float16_t out_[]; };
void main() {
const uint i3 = gl_WorkGroupID.z;
const uint i2 = gl_WorkGroupID.y;
const uint i1 = gl_WorkGroupID.x;
float corr_dims[2];
rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims);
const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims);
float theta_base = float(inB[pcs.inBOff + i2]);
float inv_ndims = -1.f/pcs.n_dims;
float cos_theta;
float sin_theta;
for (uint i0 = 2*gl_LocalInvocationIndex; i0 < pcs.ne0; i0 += 2*gl_WorkGroupSize.x) {
if (i0 < pcs.n_dims) {
uint ic = i0/2;
float theta = theta_base * pow(pcs.freq_base, inv_ndims*i0);
const float freq_factor = pcs.has_freq_factors ? inC[pcs.inCOff + ic] : 1.0f;
rope_yarn(theta/freq_factor, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta);
const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + ic*pcs.nb00) / 2) + pcs.inAOff; // Based from in
const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + ic*pcs.nb0) / 2) + pcs.outOff; // Based from out_
const float x0 = float(inA[src]);
const float x1 = float(inA[src+pcs.n_dims/2]);
out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta);
out_[dst_data+pcs.n_dims/2] = float16_t(x0*sin_theta + x1*cos_theta);
} else {
const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in
const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_
out_[dst_data] = inA[src];
out_[dst_data+1] = inA[src+1];
}
}
}