ikawrakow/ik_llama.cpp
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iq3_kt: CUDA dot product

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Iwan Kawrakow
2024-11-10 12:07:42 +02:00
parent e9e5879b94
commit 0ffc9b435c
1 changed files with 83 additions and 1 deletions
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84
ggml/src/ggml-cuda/dmmv.cu
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@@ -85,6 +85,88 @@ static __global__ void dequantize_mul_mat_vec_iq2_kt(const void * __restrict__ v
static __global__ void dequantize_mul_mat_vec_iq3_kt(const void * __restrict__ vx, const dfloat * __restrict__ yy, float * __restrict__ dst,
const int ncols, int nrows, int64_t row_size) {
constexpr uint32_t ka = 89226354;
constexpr uint32_t kb = 64248484;
constexpr uint32_t kmask = 0x8fff8fff;
constexpr uint32_t km32 = 0x3b603b60;
const int row = blockIdx.x*blockDim.y + threadIdx.y;
if (row > nrows) return;
const float * dptr = (const float *)((const char *)vx + row*row_size);
const float d = *dptr * 31.75f * 1.015f;
const block_iq3_kt * x = (const block_iq3_kt *)(dptr + 1);
const int num_blocks_per_row = ncols / QK_K;
dfloat2 tmp = {};
const int it = threadIdx.x/2;
const int ix = threadIdx.x%2;
uint32_t s[4];
const half * h = (const half *)s;
for (int i = ix; i < num_blocks_per_row; i += 2) {
const dfloat2 * y = (const dfloat2 *)(yy + i * QK_K + 8*it);
const uint8_t * ql = x[i].ql;
const uint8_t * qh = x[i].qh;
const dfloat scale1 = iq4k_values[(x[i].scales[it/4] & 0xf)+16];
const dfloat scale2 = iq4k_values[(x[i].scales[it/4] >> 4)+16];
const dfloat2 dl1 = {scale1, scale1};
const dfloat2 dl2 = {scale2, scale2};
dfloat2 bdot1 = {0, 0};
dfloat2 bdot2 = {0, 0};
uint32_t val1 = ql[2*it+ 0] + ((qh[2*it+0] << 8) & 0xf00) + 4096;
uint32_t val2 = ql[2*it+32] + ((qh[2*it+0] << 4) & 0xf00) + 4096;
for (int k = 0; k < 2; ++k) {
val1 = ka*val1 + kb; s[0] = (val1 & kmask) ^ km32;
val1 = ka*val1 + kb; s[1] = (val1 & kmask) ^ km32;
val2 = ka*val2 + kb; s[2] = (val2 & kmask) ^ km32;
val2 = ka*val2 + kb; s[3] = (val2 & kmask) ^ km32;
#ifdef GGML_CUDA_F16
bdot1 = __hfma2(y[k+ 0], {h[0]+h[1], h[2]+h[3]}, bdot1);
bdot2 = __hfma2(y[k+64], {h[4]+h[5], h[6]+h[7]}, bdot2);
#else
bdot1.x += y[k+ 0].x * (float)(h[0] + h[1]);
bdot1.y += y[k+ 0].y * (float)(h[2] + h[3]);
bdot2.x += y[k+64].x * (float)(h[4] + h[5]);
bdot2.y += y[k+64].y * (float)(h[6] + h[7]);
#endif
}
val1 = ql[2*it+ 1] + ((qh[2*it+1] << 8) & 0xf00) + 4096;
val2 = ql[2*it+33] + ((qh[2*it+1] << 4) & 0xf00) + 4096;
for (int k = 2; k < 4; ++k) {
val1 = ka*val1 + kb; s[0] = (val1 & kmask) ^ km32;
val1 = ka*val1 + kb; s[1] = (val1 & kmask) ^ km32;
val2 = ka*val2 + kb; s[2] = (val2 & kmask) ^ km32;
val2 = ka*val2 + kb; s[3] = (val2 & kmask) ^ km32;
#ifdef GGML_CUDA_F16
bdot1 = __hfma2(y[k+ 0], {h[0]+h[1], h[2]+h[3]}, bdot1);
bdot2 = __hfma2(y[k+64], {h[4]+h[5], h[6]+h[7]}, bdot2);
#else
bdot1.x += y[k+ 0].x * (float)(h[0] + h[1]);
bdot1.y += y[k+ 0].y * (float)(h[2] + h[3]);
bdot2.x += y[k+64].x * (float)(h[4] + h[5]);
bdot2.y += y[k+64].y * (float)(h[6] + h[7]);
#endif
}
#ifdef GGML_CUDA_F16
tmp = __hfma2(dl1, bdot1, tmp);
tmp = __hfma2(dl2, bdot2, tmp);
#else
tmp.x += dl1.x * bdot1.x + dl2.x * bdot2.x;
tmp.y += dl1.y * bdot1.y + dl2.y * bdot2.y;
#endif
}
// sum up partial sums and write back result
tmp = warp_reduce_sum(tmp);
if (threadIdx.x == 0) {
dst[row] = d * (float)(tmp.x + tmp.y);
}
}
static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
@@ -715,7 +797,7 @@ void ggml_cuda_op_dequantize_mul_mat_vec(
src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16 ||
src0->type == GGML_TYPE_IQ2_KT;
src0->type == GGML_TYPE_IQ2_KT || src0->type == GGML_TYPE_IQ3_KT;
if (src1_convert_f16) {
src1_dfloat = src1_dfloat_a.alloc(ne00);