ik_llama_opt/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_bn.cu

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#include "../iqk_mmvq_templates.cuh"
__device__ __forceinline__ void vec_dot_iq1_bn_q8_1(
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) {
half d16; memcpy(&d16, vbq, sizeof(d16));
float scale = d16;
const block_iq1_bn * bq1 = (const block_iq1_bn *)((const char *)vbq + sizeof(d16)) + kbx;
// iqs is 0 or 1
int sumi = 0;
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
uint16_t mult[2];
mult[1] = iqs == 0 ? 27 : 3;
mult[0] = mult[1] + (mult[1] << 1);
const int * q8 = (const int *)bq8_1[iqs].qs;
int val[4];
for (int l = 0; l < 2; ++l) {
int8_t * a = (int8_t *)val;
const int i16 = 2*iqs + l;
for (int k = 0; k < 3; ++k) {
uint16_t q = bq1->ql[3*i16+k];
for (int j = 4; j >= 0; --j) {
uint16_t v = q & 0xff;
v += v << 1;
a[j] = v >> 8;
q += q << 1;
}
a += 5;
}
uint16_t v = (mult[l]*bq1->extra) & 0xff;
v += v << 1;
*a = v >> 8;
sumi = ggml_cuda_dp4a(val[0], q8[4*l+0], ggml_cuda_dp4a(val[1], q8[4*l+1], ggml_cuda_dp4a(val[2], q8[4*l+2], ggml_cuda_dp4a(val[3], q8[4*l+3], sumi))));
}
float2 d8 = __half22float2(bq8_1[iqs].ds);
*result += scale * (d8.x * sumi - d8.y);
#else
static const uint16_t k_mult[5] = {81, 27, 9, 3, 1};
const int8_t * q8 = bq8_1[iqs].qs;
for (int l = 0; l < 2; ++l) {
const int i16 = 2*iqs + l;
for (int k = 0; k < 3; ++k) {
uint8_t q = bq1->ql[3*i16+k];
for (int j = 0; j < 5; ++j) {
uint8_t v = k_mult[j]*q;
int8_t vs = (v + (v >> 1)) >> 7;
sumi += q8[j]*(vs - 1);
}
q8 += 5;
}
uint8_t v = k_mult[i16]*bq1->extra;
int8_t vs = (v + (v >> 1)) >> 7;
sumi += q8[0]*(vs - 1);
q8++;
}
*result += scale * __low2float(bq8_1[iqs].ds) * sumi;
#endif
}
void mul_mat_vec_iq1_bn_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) {
iqk_mul_mat_vec_q_cuda<GGML_TYPE_IQ1_BN, 1, vec_dot_iq1_bn_q8_1>(args, stream);
}