diff --git a/ggml/src/ggml-cuda/iqk_mmvq_templates.cuh b/ggml/src/ggml-cuda/iqk_mmvq_templates.cuh index 87f3c79a..466b1e9c 100644 --- a/ggml/src/ggml-cuda/iqk_mmvq_templates.cuh +++ b/ggml/src/ggml-cuda/iqk_mmvq_templates.cuh @@ -444,36 +444,6 @@ __device__ __forceinline__ void get_int_from_table_16_shift(const uint32_t & q4, val2 = v1 | (v2 << 16); } -#define VDR_IQ4_K_Q8_1_MMVQ 4 -#define VDR_IQ4_K_Q8_1_MMQ 4 - -__device__ __forceinline__ void vec_dot_iq4_k_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - const block_iq4_k * bq4 = (const block_iq4_k *) vbq + kbx; - const uint8_t * all_values = (const uint8_t *)iq4k_values; - - // iqs is 0...28 - const int ib32 = iqs/4; - // Why iqs/4 ? - const int32_t * q8 = (const int *)bq8_1[ib32].qs; - const uint16_t * q4 = (const uint16_t *)bq4->qs + 8*ib32; - const uint16_t extra = bq4->extra >> 2*ib32; - int v1, v2; - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < 4; ++j) { - const uint32_t aux32 = q4[2*j+0] | (q4[2*j+1] << 16); - get_int_from_table_16_shift(aux32, extra, all_values, v1, v2); - sumi1 = ggml_cuda_dp4a(v1, q8[j+0], sumi1); - sumi2 = ggml_cuda_dp4a(v2, q8[j+4], sumi2); - } - const float d = __half2float(bq4->d) * __low2float(bq8_1[ib32].ds); - const uint8_t sh = bq4->scales_h[ib32/2] >> 4*(ib32%2); - const int ls1 = ((bq4->scales_l[ib32] & 0xf) | ((sh << 4) & 0x30)) - 32; - const int ls2 = ((bq4->scales_l[ib32] >> 4) | ((sh << 2) & 0x30)) - 32; - *result += d * (sumi1 * ls1 + sumi2 * ls2); -} - static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4, const int8_t * values) { #if defined(__CUDA_ARCH__) uint32_t v1, v2, v3, v4, mask; @@ -506,1045 +476,36 @@ static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4, con #endif } -__device__ __forceinline__ void vec_dot_iq4_k_r4_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - const block_iq4_k_r4 * bq4 = (const block_iq4_k_r4 *)vbq + kbx; - - // iqs is 0...28 in steps of 2 - const int ib16 = iqs/2; - const float d8 = __low2float(bq8_1[ib16/2].ds); - const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); - - int ib32 = ib16/2; - int is = ib16%2; - int scales; - const uint32_t * scales_l = (const uint32_t *)bq4->scales_l; - const uint32_t * scales_h = (const uint32_t *)bq4->scales_h; - scales = __vsub4(((scales_l[2*(ib32%4)+is] >> 4*(ib32/4)) & 0x0f0f0f0f) | (((scales_h[2*(ib32%2)+is] >> 2*(ib32/2)) & 0x03030303) << 4), 0x20202020); - const int8_t * s8 = (const int8_t *)&scales; - int2 val1; - const int * q4 = (const int *)bq4->qs + 16*ib32; - for (int i = 0; i < 4; ++i) { - auto values1 = iq4k_values + (((bq4->extra[i+4*is] >> ib32) & 1) << 4); - int sumi1 = 0; - val1 = get_int_from_table_16(q4[i+4*is+0], values1); - sumi1 = ggml_cuda_dp4a(val1.x, q8[0], ggml_cuda_dp4a(val1.y, q8[2], sumi1)); - val1 = get_int_from_table_16(q4[i+4*is+8], values1); - sumi1 = ggml_cuda_dp4a(val1.x, q8[1], ggml_cuda_dp4a(val1.y, q8[3], sumi1)); - const float d = __half2float(bq4->d[i]) * d8; - result[i] += d * sumi1 * s8[i]; - } -} - -__device__ __forceinline__ void vec_dot_iq4_ks_r4_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - const float * dptr = (const float *)vbq; - const block_iq4_ks_r4 * bq4 = (const block_iq4_ks_r4 *)(dptr + 4) + kbx; - - // iqs is 0...28 in steps of 2 - const int ib16 = iqs/2; - const float d8 = __low2float(bq8_1[ib16/2].ds); - const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); - - int ib32 = ib16/2; - int is = ib16%2; - const uint32_t * scales32 = (const uint32_t *)bq4->scales; - int scales = __vsub4(scales32[ib32] & 0xfefefefe, 0x7f7f7f7f); - const int8_t * s8 = (const int8_t *)&scales; - int2 val; - const int * q4 = (const int *)bq4->qs + 16*ib32; - for (int i = 0; i < 4; ++i) { - auto values = iq4k_values + ((bq4->scales[4*ib32+i] & 1) << 4); - int sumi = 0; - val = get_int_from_table_16(q4[i+4*is+0], values); - sumi = ggml_cuda_dp4a(val.x, q8[0], ggml_cuda_dp4a(val.y, q8[2], sumi)); - val = get_int_from_table_16(q4[i+4*is+8], values); - sumi = ggml_cuda_dp4a(val.x, q8[1], ggml_cuda_dp4a(val.y, q8[3], sumi)); - const float d = dptr[i] * d8; - result[i] += d * sumi * s8[i]; - } -} - -__device__ __forceinline__ void vec_dot_iq1_s_r4_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - const half * dptr = (const half *)vbq; - const block_iq1_s_r4 * bq1 = (const block_iq1_s_r4 *)(dptr + 4) + kbx; - - // iqs is 0 or 2 - const float d8 = __low2float(bq8_1->ds); - const int32_t * q8 = (const int *)bq8_1->qs; - - int32_t grid32[2]; - const int * igrid = (const int *)grid32; - - int minus = 0; - for (int k = 0; k < 4; ++k) minus = ggml_cuda_dp4a(0x01010101, q8[4*(iqs/2)+k], minus); - - for (int i = 0; i < 4; ++i) { - float dl = __half2float(dptr[i])*(2*((bq1->qh[i] >> 12) & 7) + 1) * d8; - float ml = dl * (bq1->qh[i] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA); - grid32[0] = iq1s_grid_gpu[bq1->qs[4*iqs+i] | (((bq1->qh[i] >> 3*iqs) & 7) << 8)]; - grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; - grid32[0] &= 0x0f0f0f0f; - int sumi = ggml_cuda_dp4a(igrid[0], q8[4*(iqs/2)+0], ggml_cuda_dp4a(igrid[1], q8[4*(iqs/2)+1], 0)); - grid32[0] = iq1s_grid_gpu[bq1->qs[4*iqs+i+4] | (((bq1->qh[i] >> (3*iqs+3)) & 7) << 8)]; - grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; - grid32[0] &= 0x0f0f0f0f; - sumi = ggml_cuda_dp4a(igrid[0], q8[4*(iqs/2)+2], ggml_cuda_dp4a(igrid[1], q8[4*(iqs/2)+3], sumi)); - result[i] += dl * sumi + ml * minus; - } -} - -__device__ __forceinline__ void vec_dot_iq1_m_r4_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - const half * dptr = (const half *)vbq; - const block_iq1_m_r4 * bq1 = (const block_iq1_m_r4 *)(dptr + 4) + kbx; - - // iqs is 0 or 2 - const float d8 = __low2float(bq8_1->ds); - const int32_t * q8 = (const int *)bq8_1->qs; - - int32_t grid32[2]; - const int * igrid = (const int *)grid32; - - int minus1 = ggml_cuda_dp4a(0x01010101, q8[4*(iqs/2)+0], ggml_cuda_dp4a(0x01010101, q8[4*(iqs/2)+1], 0)); - int minus2 = ggml_cuda_dp4a(0x01010101, q8[4*(iqs/2)+2], ggml_cuda_dp4a(0x01010101, q8[4*(iqs/2)+3], 0)); - - for (int i = 0; i < 4; ++i) { - float dl = __half2float(dptr[i])*((bq1->scales[i] >> 4*(iqs/2)) & 0xf) * d8; - float ml1 = dl * (bq1->qh[4*(iqs/2)+i] & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA); - float ml2 = dl * (bq1->qh[4*(iqs/2)+i] & 0x80 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA); - grid32[0] = iq1s_grid_gpu[bq1->qs[4*iqs+i] | ((bq1->qh[4*(iqs/2)+i] & 0x07) << 8)]; - grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; - grid32[0] &= 0x0f0f0f0f; - int sumi = ggml_cuda_dp4a(igrid[0], q8[4*(iqs/2)+0], ggml_cuda_dp4a(igrid[1], q8[4*(iqs/2)+1], 0)); - grid32[0] = iq1s_grid_gpu[bq1->qs[4*iqs+i+4] | ((bq1->qh[4*(iqs/2)+i] & 0x70) << 4)]; - grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; - grid32[0] &= 0x0f0f0f0f; - sumi = ggml_cuda_dp4a(igrid[0], q8[4*(iqs/2)+2], ggml_cuda_dp4a(igrid[1], q8[4*(iqs/2)+3], sumi)); - result[i] += dl * sumi + ml1 * minus1 + ml2*minus2; - } -} - -#define VDR_IQ4_KS_Q8_1_MMVQ 4 -#define VDR_IQ4_KS_Q8_1_MMQ 4 - -__device__ __forceinline__ void vec_dot_iq4_ks_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - float scale = *(const float *)vbq; - const block_iq4_ks * bq4 = (const block_iq4_ks *)((const char *)vbq + sizeof(float)) + kbx; - - // iqs is 0...28 - const int ib32 = iqs/4; // Why iqs/4 ? - const int32_t * q8 = (const int *)bq8_1[ib32].qs; - const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32; - const float dl = scale * ((bq4->scales[ib32] & 254) - 127); - auto values = iq4k_values + ((bq4->scales[ib32] & 1) << 4); - int sumi = 0; - for (int j = 0; j < 4; ++j) { - auto v = get_int_from_table_16(q4[j], values); - sumi = ggml_cuda_dp4a(v.x, q8[j+0], sumi); - sumi = ggml_cuda_dp4a(v.y, q8[j+4], sumi); - } - *result += dl * __low2float(bq8_1[ib32].ds) * sumi; -} - -__device__ __forceinline__ void vec_dot_iq4_kt_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - constexpr uint32_t ka = 0xCBAC1FED; - constexpr uint32_t km = 0x3f3f3f3f; - - float scale = *(const float *)vbq; - const block_iq4_kt * bq4 = (const block_iq4_kt *)((const char *)vbq + sizeof(float)) + kbx; - - // iqs is 0...28 - const int ib32 = iqs/4; // Why iqs/4 ? - const int32_t * q8 = (const int *)bq8_1[ib32].qs; - //const int8_t * q8 = bq8_1[ib32].qs; - const int ls = (bq4->qs[ib32] & 0xff) >> 1; - const float dl = scale * (ls - 64); - const uint32_t idx0 = ((bq4->qs[ib32] & 1) << 15) + 4096; - auto ql = (const uint8_t *)(bq4->qs + 8); - auto qh = ql + 64; - ql += 8*ib32; - qh += 8*(ib32%4); - const int shift1 = 8 - 4*(ib32/4); - int sumi = 0; - for (int j = 0; j < 8; ++j) { - const uint32_t sh = bq4->qs[ib32] >> (8 + 3*j); - uint32_t val = ql[j] + ((qh[j] << shift1) & 0xf00) + ((sh & 7) << 12) + idx0; - int v4 = 0; - for (int k = 0; k < 4; ++k) { - val *= ka; - //int s = val & km; - //sumi += q8[4*j+k] * ggml_cuda_dp4a(s, 0x01010101, -126); - v4 |= (ggml_cuda_dp4a(val & km, 0x01010101, -126) & 0xff) << 8*k; - } - sumi = ggml_cuda_dp4a(v4, q8[j], sumi); - } - *result += dl * __low2float(bq8_1[ib32].ds) * sumi; -} - -__device__ __forceinline__ void vec_dot_iq1_kt_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - constexpr uint32_t ka = 0xCBAC1FED; - constexpr uint32_t km = 0x3f3f3f3f; - - float scale = *(const float *)vbq; - const block_iq1_kt * bq1 = (const block_iq1_kt *)((const char *)vbq + sizeof(float)) + kbx; - - // iqs is 0...28 - const int ib32 = iqs/4; - const int32_t * q8 = (const int *)bq8_1[ib32].qs; - const int ls = iq4k_values[bq1->sh[ib32] & 0xf]; - const float dl = scale * ls; - int sumi = 0; - for (int j = 0; j < 4; ++j) { - uint32_t val = bq1->ql[4*ib32+j] + 4096 + ((bq1->qh[4*(ib32%4)+j] << (8 - 4*(ib32/4))) & 0xf00) + ((bq1->sh[ib32] << (8 - j)) & 0x1000); - int v4 = 0; - for (int k = 0; k < 4; ++k) { - val *= ka; - v4 |= (ggml_cuda_dp4a(val & km, 0x01010101, -126) & 0xff) << 8*k; - } - sumi = ggml_cuda_dp4a(v4, q8[2*j+0], sumi); - v4 = 0; - for (int k = 0; k < 4; ++k) { - val *= ka; - v4 |= (ggml_cuda_dp4a(val & km, 0x01010101, -126) & 0xff) << 8*k; - } - sumi = ggml_cuda_dp4a(v4, q8[2*j+1], sumi); - } - *result += dl * __low2float(bq8_1[ib32].ds) * sumi; -} - -__device__ __forceinline__ void vec_dot_iq2_kt_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - constexpr uint32_t ka = 0xCBAC1FED; - constexpr uint32_t km = 0x3f3f3f3f; - - float scale = *(const float *)vbq; - const block_iq2_kt * bq2 = (const block_iq2_kt *)((const char *)vbq + sizeof(float)) + kbx; - - // iqs is 0...28 - const int ib32 = iqs/4; - const int32_t * q8 = (const int *)bq8_1[ib32].qs; - const int ls = iq4k_values[(bq2->scales[ib32%4] >> 4*(ib32/4)) & 0xf]; - const float dl = scale * ls * 1.05f; - auto ql = (const uint16_t *)bq2->ql; - int sumi = 0; - for (int j = 0; j < 4; ++j) { - uint32_t val = ql[4*ib32+j] + 4096; - int v4 = 0; - for (int k = 0; k < 4; ++k) { - val *= ka; - v4 |= (ggml_cuda_dp4a(val & km, 0x01010101, -126) & 0xff) << 8*k; - } - sumi = ggml_cuda_dp4a(v4, q8[2*j+0], sumi); - v4 = 0; - for (int k = 0; k < 4; ++k) { - val *= ka; - v4 |= (ggml_cuda_dp4a(val & km, 0x01010101, -126) & 0xff) << 8*k; - } - sumi = ggml_cuda_dp4a(v4, q8[2*j+1], sumi); - } - *result += dl * __low2float(bq8_1[ib32].ds) * sumi; -} - -__device__ __forceinline__ void vec_dot_iq3_kt_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - constexpr uint32_t ka = 0xCBAC1FED; - constexpr uint32_t km = 0x3f3f3f3f; - - float scale = *(const float *)vbq; - const block_iq3_kt * bq3 = (const block_iq3_kt *)((const char *)vbq + sizeof(float)) + kbx; - - // iqs is 0...28 - const int ib32 = iqs/4; - const int32_t * q8 = (const int *)bq8_1[ib32].qs; - const int ls = (bq3->scales[ib32%4] >> 4*(ib32/4)) & 0xf; - const float dl = scale * ls * 1.015f; - auto ql = (const uint16_t *)bq3->ql; - uint32_t mask = 0x01010101 << ib32; - const uint32_t * qh = (const uint32_t *)bq3->qh; - int sumi = 0; - for (int j = 0; j < 4; ++j) { - uint32_t val = ql[4*ib32+j] + 4096; - int v4 = 0; - for (int k = 0; k < 4; ++k) { - val *= ka; - int8_t q = std::abs(ggml_cuda_dp4a(val & km, 0x01010101, -126)); - v4 |= q << 8*k; - } - uint32_t signs = __vcmpne4(qh[2*j+0] & mask, 0); - v4 = __vsub4(v4 ^ signs, signs); - sumi = ggml_cuda_dp4a(v4, q8[2*j+0], sumi); - v4 = 0; - for (int k = 0; k < 4; ++k) { - val *= ka; - int8_t q = std::abs(ggml_cuda_dp4a(val & km, 0x01010101, -126)); - v4 |= q << 8*k; - } - signs = __vcmpne4(qh[2*j+1] & mask, 0); - v4 = __vsub4(v4 ^ signs, signs); - sumi = ggml_cuda_dp4a(v4, q8[2*j+1], sumi); - } - *result += dl * __low2float(bq8_1[ib32].ds) * sumi; -} - -#define VDR_IQ4_KSS_Q8_1_MMVQ 4 -#define VDR_IQ4_KSS_Q8_1_MMQ 4 - -__device__ __forceinline__ void vec_dot_iq4_kss_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - float scale = *(const float *)vbq; - const block_iq4_kss * bq4 = (const block_iq4_kss *)((const char *)vbq + sizeof(float)) + kbx; - - // iqs is 0...28 - const int ib32 = iqs/4; // Why iqs/4 ? - const int32_t * q8 = (const int *)bq8_1[ib32].qs; - const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32; - uint32_t s32 = (q4[0] & 0x00010001) | ((q4[1] & 0x00010001) << 2) | ((q4[2] & 0x00010001) << 4) | ((q4[3] & 0x00010001) << 6); - uint8_t ls = (s32 | (s32 >> 15)) & 0xff; - const float dl = scale * ((ls & 254) - 127); - auto values = iq4k_values + ((ls & 1) << 4); - int sumi = 0; - for (int j = 0; j < 4; ++j) { - uint32_t aux32 = q4[j] & 0xfffefffe; - aux32 ^= (aux32 >> 1); - auto v = get_int_from_table_16(aux32, values); - sumi = ggml_cuda_dp4a(v.x, q8[j+0], sumi); - sumi = ggml_cuda_dp4a(v.y, q8[j+4], sumi); - } - *result += dl * __low2float(bq8_1[ib32].ds) * sumi; -} - -#define VDR_IQ5_K_Q8_1_MMVQ 4 -#define VDR_IQ5_K_Q8_1_MMQ 4 - __device__ __forceinline__ int int_from_table(const uint8_t * a8, const uint8_t * values) { uint16_t v1 = values[a8[0]] | (values[a8[1]] << 8); uint16_t v2 = values[a8[2]] | (values[a8[3]] << 8); return v1 | (v2 << 16); } -__device__ __forceinline__ void vec_dot_iq5_k_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { +#define VDR_IQ4_K_Q8_1_MMVQ 4 +#define VDR_IQ4_K_Q8_1_MMQ 4 - const block_iq5_k * bq5 = (const block_iq5_k *) vbq + kbx; - const uint8_t * all_values = (const uint8_t *)iq5nl_values; +#define VDR_IQ4_KS_Q8_1_MMVQ 4 +#define VDR_IQ4_KS_Q8_1_MMQ 4 - int i4 = iqs/4; // 0...7. Blocks of 16 index is 4*(i4/2) + (i4%2) + (0 and 2) +#define VDR_IQ4_KSS_Q8_1_MMVQ 4 +#define VDR_IQ4_KSS_Q8_1_MMQ 4 - const int32_t * q8_1 = (const int *)bq8_1[2*(i4/2)+0].qs + 4*(i4%2); - const int32_t * q8_2 = (const int *)bq8_1[2*(i4/2)+1].qs + 4*(i4%2); - const uint32_t * q4 = (const uint32_t *)bq5->qs + 8*(i4/2) + 4*(i4%2); - const uint32_t * qh = (const uint32_t *)bq5->qh + 4*(i4%2); - const uint16_t extra = bq5->extra >> (4*(i4/2) + (i4%2)); - const uint8_t * values1 = all_values + 32*(extra & 1); - const uint8_t * values2 = all_values + 8*(extra & 4); - uint32_t aux32[2]; - const uint8_t * a8 = (const uint8_t *)aux32; - int v1, v2; - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < 4; ++j) { - uint32_t h = qh[j] >> 2*(i4/2); - aux32[0] = ((q4[j] >> 0) & 0x0f0f0f0f) | ((h << 4) & 0x10101010); - aux32[1] = ((q4[j] >> 4) & 0x0f0f0f0f) | ((h << 3) & 0x10101010); - v1 = int_from_table(a8+0, values1); - v2 = int_from_table(a8+4, values2); - sumi1 = ggml_cuda_dp4a(v1, q8_1[j], sumi1); - sumi2 = ggml_cuda_dp4a(v2, q8_2[j], sumi2); - } - const float d5 = __half2float(bq5->d); - const uint8_t sh = bq5->scales_h[i4/2] >> 2*(i4%2); - const int ls1 = (((bq5->scales_l[2*(i4/2)+0] >> 4*(i4%2)) & 0xf) | ((sh << 4) & 0x30)) - 32; - const int ls2 = (((bq5->scales_l[2*(i4/2)+1] >> 4*(i4%2)) & 0xf) | ((sh << 0) & 0x30)) - 32; - *result += d5 * (__low2float(bq8_1[2*(i4/2)+0].ds) * sumi1 * ls1 + __low2float(bq8_1[2*(i4/2)+1].ds) * sumi2 * ls2); -} - -__device__ __forceinline__ void vec_dot_iq5_k_r4_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - const block_iq5_k_r4 * bq5 = (const block_iq5_k_r4 *)vbq + kbx; - - // iqs is 0...28 in steps of 2 - const int ib16 = iqs/2; - const float d8 = __low2float(bq8_1[ib16/2].ds); - const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); - - int ib32 = ib16/2; - int is = ib16%2; - int scales; - const uint32_t * scales_l = (const uint32_t *)bq5->scales_l; - const uint32_t * scales_h = (const uint32_t *)bq5->scales_h; - scales = __vsub4(((scales_l[2*(ib32%4)+is] >> 4*(ib32/4)) & 0x0f0f0f0f) | (((scales_h[2*(ib32%2)+is] >> 2*(ib32/2)) & 0x03030303) << 4), 0x20202020); - const int8_t * s8 = (const int8_t *)&scales; - int2 val1; - const int * q4 = (const int *)bq5->qs + 16*ib32; - const int * qh = (const int *)bq5->qh + 4*ib32; - int aux32[2]; - const uint8_t * aux8 = (const uint8_t *)aux32; - for (int i = 0; i < 4; ++i) { - auto values1 = iq5nl_values + (((bq5->extra[i+4*is] >> ib32) & 1) << 5); - int sumi1 = 0; - aux32[0] = ((q4[i+4*is+0] >> 0) & 0x0f0f0f0f) | (((qh[i] >> (2*is+0)) & 0x01010101) << 4); - aux32[1] = ((q4[i+4*is+0] >> 4) & 0x0f0f0f0f) | (((qh[i] >> (2*is+1)) & 0x01010101) << 4); - val1.x = int_from_table(aux8+0, (const uint8_t *)values1); - val1.y = int_from_table(aux8+4, (const uint8_t *)values1); - sumi1 = ggml_cuda_dp4a(val1.x, q8[0], ggml_cuda_dp4a(val1.y, q8[2], sumi1)); - aux32[0] = ((q4[i+4*is+8] >> 0) & 0x0f0f0f0f) | (((qh[i] >> (2*is+4)) & 0x01010101) << 4); - aux32[1] = ((q4[i+4*is+8] >> 4) & 0x0f0f0f0f) | (((qh[i] >> (2*is+5)) & 0x01010101) << 4); - val1.x = int_from_table(aux8+0, (const uint8_t *)values1); - val1.y = int_from_table(aux8+4, (const uint8_t *)values1); - sumi1 = ggml_cuda_dp4a(val1.x, q8[1], ggml_cuda_dp4a(val1.y, q8[3], sumi1)); - const float d = __half2float(bq5->d[i]) * d8; - result[i] += d * sumi1 * s8[i]; - } -} - -__device__ __forceinline__ void vec_dot_iq5_ks_r4_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - const float * dptr = (const float *)vbq; - const block_iq5_ks_r4 * bq5 = (const block_iq5_ks_r4 *)(dptr + 4) + kbx; - - // iqs is 0...28 in steps of 2 - const int ib16 = iqs/2; - const float d8 = __low2float(bq8_1[ib16/2].ds); - const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); - - int ib32 = ib16/2; - int is = ib16%2; - const uint32_t * scales32 = (const uint32_t *)bq5->scales; - int scales = __vsub4(scales32[ib32] & 0xfefefefe, 0x7f7f7f7f); - const int8_t * s8 = (const int8_t *)&scales; - int2 val; - const int * q4 = (const int *)bq5->qs + 16*ib32; - const int * qh = (const int *)bq5->qh + 4*ib32; - int aux32[2]; - const uint8_t * aux8 = (const uint8_t *)aux32; - for (int i = 0; i < 4; ++i) { - auto values = iq5nl_values + ((bq5->scales[4*ib32+i] & 1) << 5); - int sumi = 0; - aux32[0] = ((q4[i+4*is+0] >> 0) & 0x0f0f0f0f) | (((qh[i] >> (2*is+0)) & 0x01010101) << 4); - aux32[1] = ((q4[i+4*is+0] >> 4) & 0x0f0f0f0f) | (((qh[i] >> (2*is+1)) & 0x01010101) << 4); - val.x = int_from_table(aux8+0, (const uint8_t *)values); - val.y = int_from_table(aux8+4, (const uint8_t *)values); - sumi = ggml_cuda_dp4a(val.x, q8[0], ggml_cuda_dp4a(val.y, q8[2], sumi)); - aux32[0] = ((q4[i+4*is+8] >> 0) & 0x0f0f0f0f) | (((qh[i] >> (2*is+4)) & 0x01010101) << 4); - aux32[1] = ((q4[i+4*is+8] >> 4) & 0x0f0f0f0f) | (((qh[i] >> (2*is+5)) & 0x01010101) << 4); - val.x = int_from_table(aux8+0, (const uint8_t *)values); - val.y = int_from_table(aux8+4, (const uint8_t *)values); - sumi = ggml_cuda_dp4a(val.x, q8[1], ggml_cuda_dp4a(val.y, q8[3], sumi)); - result[i] += dptr[i] * d8 * sumi * s8[i]; - } -} - -__device__ __forceinline__ void vec_dot_iq5_ks_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - float scale = *(const float *)vbq; - const block_iq5_ks * bq5 = (const block_iq5_ks *)((const char *)vbq + sizeof(float)) + kbx; - const uint8_t * all_values = (const uint8_t *)iq5nl_values; - - int i4 = iqs/4; // 0...7. Blocks of 16 index is 4*(i4/2) + (i4%2) + (0 and 2) - - const int32_t * q8_1 = (const int *)bq8_1[2*(i4/2)+0].qs + 4*(i4%2); - const int32_t * q8_2 = (const int *)bq8_1[2*(i4/2)+1].qs + 4*(i4%2); - const uint32_t * q4 = (const uint32_t *)bq5->qs + 8*(i4/2) + 4*(i4%2); - const uint32_t * qh = (const uint32_t *)bq5->qh + 4*(i4%2); - const uint8_t * values1 = all_values + ((bq5->scales[2*(i4/2)+0] & 1) << 5); - const uint8_t * values2 = all_values + ((bq5->scales[2*(i4/2)+1] & 1) << 5); - uint32_t aux32[2]; - const uint8_t * a8 = (const uint8_t *)aux32; - int v1, v2; - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < 4; ++j) { - uint32_t h = qh[j] >> 2*(i4/2); - aux32[0] = ((q4[j] >> 0) & 0x0f0f0f0f) | ((h << 4) & 0x10101010); - aux32[1] = ((q4[j] >> 4) & 0x0f0f0f0f) | ((h << 3) & 0x10101010); - v1 = int_from_table(a8+0, values1); - v2 = int_from_table(a8+4, values2); - sumi1 = ggml_cuda_dp4a(v1, q8_1[j], sumi1); - sumi2 = ggml_cuda_dp4a(v2, q8_2[j], sumi2); - } - const int ls1 = (bq5->scales[2*(i4/2)+0] & 254) - 127; - const int ls2 = (bq5->scales[2*(i4/2)+1] & 254) - 127; - *result += scale * (__low2float(bq8_1[2*(i4/2)+0].ds) * sumi1 * ls1 + __low2float(bq8_1[2*(i4/2)+1].ds) * sumi2 * ls2); -} - -__device__ __forceinline__ void vec_dot_iq3_k_r4_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - const block_iq3_k_r4 * bq3 = (const block_iq3_k_r4 *)vbq + kbx; - - // iqs is 0...30 in steps of 2 - const int ib16 = iqs/2; - const float d8 = __low2float(bq8_1[ib16/2].ds); - const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); - - int ib32 = ib16/2; - int is = ib16%2; - int scales[2]; - const uint32_t * scales_l = (const uint32_t *)bq3->scales_l; - const uint32_t * scales_h = (const uint32_t *)bq3->scales_h; - - scales[0] = (((scales_l[2*(ib32%4)+is] >> 4*(ib32/4)) & 0x0f0f0f0f) << 1) | 0x01010101; - scales[1] = (scales_h[is] >> ib32) & 0x01010101; - // This is not faster. Why? - //scales[1] = __vcmpeq4((scales_h[is] >> ib32) & 0x01010101, 0x01010101); - //scales[0] = __vsub4(scales[0] ^ scales[1], scales[1]); - const int8_t * s8 = (const int8_t *)scales; - const uint32_t * q2 = (const uint32_t *)bq3->qs + 8*ib32 + 4*is; - const uint32_t * qh = (const uint32_t *)bq3->qh + 4*ib32; - for (int i = 0; i < 4; ++i) { - uint32_t extra32 = uint32_t((bq3->extra[i+4*is] >> ib32) & 1) * 0x88888888; - - int sumi1 = 0; - uint32_t h = qh[i] >> 4*is; - uint32_t val1 = ((q2[i] >> 0) & 0x33333333) | extra32 | ((h << 2) & 0x04040404) | ((h << 4) & 0x40404040); - uint32_t val2 = ((q2[i] >> 2) & 0x33333333) | extra32 | ((h << 1) & 0x04040404) | ((h << 3) & 0x40404040); - int2 v1 = get_int_from_table_16(val1, iq3nl_values); - int2 v2 = get_int_from_table_16(val2, iq3nl_values); - sumi1 = ggml_cuda_dp4a(v1.x, q8[0], ggml_cuda_dp4a(v2.x, q8[1], sumi1)); - sumi1 = ggml_cuda_dp4a(v1.y, q8[2], ggml_cuda_dp4a(v2.y, q8[3], sumi1)); - const float d = __half2float(bq3->d[i]) * d8; - result[i] += d * sumi1 * s8[i] * (s8[i+4] ? -1 : 1); - } -} +#define VDR_IQ5_K_Q8_1_MMVQ 4 +#define VDR_IQ5_K_Q8_1_MMQ 4 #define VDR_IQ6_K_Q8_1_MMVQ 4 #define VDR_IQ6_K_Q8_1_MMQ 4 -__device__ __forceinline__ void vec_dot_iq6_k_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - const block_iq6_k * bq6 = (const block_iq6_k *) vbq + kbx; - const uint8_t * all_values = (const uint8_t *)iq6nl_values; - - int i4 = iqs/4; // 0...7. Blocks of 16 index is 4*(i4/2) + (i4%2) + (0 and 2) - // Blocks of 32 index is 2*(i4/2) + 0 or 1 - - const int32_t * q8_1 = (const int *)bq8_1[2*(i4/2)+0].qs + 4*(i4%2); - const int32_t * q8_2 = (const int *)bq8_1[2*(i4/2)+1].qs + 4*(i4%2); - const uint32_t * q4 = (const uint32_t *)bq6->qs + 8*(i4/2) + 4*(i4%2); - const uint32_t * qh = (const uint32_t *)bq6->qh + 8*(i4/4) + 4*(i4%2); - const uint16_t extra = bq6->extra >> (4*(i4/2) + (i4%2)); - const uint8_t * values1 = all_values + 64*(extra & 1); - const uint8_t * values2 = all_values + 16*(extra & 4); - uint32_t aux32[2]; - const uint8_t * a8 = (const uint8_t *)aux32; - int v1, v2; - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < 4; ++j) { - uint32_t h = qh[j] >> 4*((i4/2)%2); - aux32[0] = ((q4[j] >> 0) & 0x0f0f0f0f) | ((h << 4) & 0x30303030); - aux32[1] = ((q4[j] >> 4) & 0x0f0f0f0f) | ((h << 2) & 0x30303030); - v1 = int_from_table(a8+0, values1); - v2 = int_from_table(a8+4, values2); - sumi1 = ggml_cuda_dp4a(v1, q8_1[j], sumi1); - sumi2 = ggml_cuda_dp4a(v2, q8_2[j], sumi2); - } - const float d6 = __half2float(bq6->d); - *result += d6 * (__low2float(bq8_1[2*(i4/2)+0].ds) * sumi1 * bq6->scales[4*(i4/2)+(i4%2)] + __low2float(bq8_1[2*(i4/2)+1].ds) * sumi2 * bq6->scales[4*(i4/2)+(i4%2)+2]); -} - #define VDR_IQ2_K_Q8_1_MMVQ 4 #define VDR_IQ2_K_Q8_1_MMQ 4 -__device__ __forceinline__ void vec_dot_iq2_k_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - // iqs is 0, 4, 8, 12, 16, 20, 24, 28 - // we have 16 packed quants (when cast to int) - - int i4 = iqs/4; // 0...7. We will process q8 blocks 4*(i4/4), 4*(i4/4)+1, 4*(i4/4)+2, 4*(i4/4)+3 - const int32_t * q8_1 = (const int *)bq8_1[4*(i4/4)+0].qs + 2*(i4%4); - const int32_t * q8_2 = (const int *)bq8_1[4*(i4/4)+1].qs + 2*(i4%4); - const int32_t * q8_3 = (const int *)bq8_1[4*(i4/4)+2].qs + 2*(i4%4); - const int32_t * q8_4 = (const int *)bq8_1[4*(i4/4)+3].qs + 2*(i4%4); - - const block_iq2_k * bq2 = (const block_iq2_k *) vbq + kbx; - const uint32_t * q2 = (const uint32_t *)bq2->qs + 8*(i4/4) + 2*(i4%4); - const uint16_t extra = bq2->extra >> (8*(i4/4) + (i4%4)/2); - - const uint32_t * scales = (const uint32_t *)bq2->scales; - uint32_t s32 = __vsub4((scales[i4/4] >> 4*(((i4%4)/2)%2)) & 0x0f0f0f0f, 0x08080808); - const int8_t * s8 = (const int8_t *)&s32; - - // Block of 16: (32*(4*(i4/4)+k)+8*(i4%4))/16 = 8*(i4/4) + 2*k + (i4%4)/2 - // -> scales_l[4*(i4/4) + k] >> 4*(((i4%4)/2)%2) - -#ifdef __CUDA_ARCH__ - uint32_t extra32 = uint32_t(extra & 0xff) * 0x01010101; - uint32_t extra32_1 = (extra32 << 2) & 0x44444444; - uint32_t extra32_2 = (extra32 << 0) & 0x44444444; - - uint32_t val1, val2; - - val1 = ((q2[0] >> 0) & 0x33333333) | extra32_1; val2 = ((q2[1] >> 0) & 0x33333333) | extra32_1; - int2 v1 = get_int_from_table_8(val1, iq2nl_values); - int2 v2 = get_int_from_table_8(val2, iq2nl_values); - int sumi1 = ggml_cuda_dp4a(v2.x, q8_1[1], ggml_cuda_dp4a(v1.x, q8_1[0], 0)) * s8[0]; - int sumi3 = ggml_cuda_dp4a(v2.y, q8_3[1], ggml_cuda_dp4a(v1.y, q8_3[0], 0)) * s8[2]; - - val1 = ((q2[0] >> 2) & 0x33333333) | extra32_2; val2 = ((q2[1] >> 2) & 0x33333333) | extra32_2; - v1 = get_int_from_table_8(val1, iq2nl_values); - v2 = get_int_from_table_8(val2, iq2nl_values); - int sumi2 = ggml_cuda_dp4a(v2.x, q8_2[1], ggml_cuda_dp4a(v1.x, q8_2[0], 0)) * s8[1]; - int sumi4 = ggml_cuda_dp4a(v2.y, q8_4[1], ggml_cuda_dp4a(v1.y, q8_4[0], 0)) * s8[3]; - -#else - - const int * all_values = (const int *)iq2k_table; - const int * values; - - uint32_t val1 = q2[0], val2 = q2[1]; - - uint32_t aux32[2]; - int v1, v2; - - aux32[0] = ((val1 >> 0) & 0x03030303); aux32[1] = ((val2 >> 0) & 0x03030303); values = all_values + ((extra & 0x01) << 8); - v1 = int_from_table_4(aux32[0], values); - v2 = int_from_table_4(aux32[1], values); - int sumi1 = ggml_cuda_dp4a(v2, q8_1[1], ggml_cuda_dp4a(v1, q8_1[0], 0)) * s8[0]; - - aux32[0] = ((val1 >> 2) & 0x03030303); aux32[1] = ((val2 >> 2) & 0x03030303); values = all_values + ((extra & 0x04) << 6); - v1 = int_from_table_4(aux32[0], values); - v2 = int_from_table_4(aux32[1], values); - int sumi2 = ggml_cuda_dp4a(v2, q8_2[1], ggml_cuda_dp4a(v1, q8_2[0], 0)) * s8[1]; - - aux32[0] = ((val1 >> 4) & 0x03030303); aux32[1] = ((val2 >> 4) & 0x03030303); values = all_values + ((extra & 0x10) << 4); - v1 = int_from_table_4(aux32[0], values); - v2 = int_from_table_4(aux32[1], values); - int sumi3 = ggml_cuda_dp4a(v2, q8_3[1], ggml_cuda_dp4a(v1, q8_3[0], 0)) * s8[2]; - - aux32[0] = ((val1 >> 6) & 0x03030303); aux32[1] = ((val2 >> 6) & 0x03030303); values = all_values + ((extra & 0x40) << 2); - v1 = int_from_table_4(aux32[0], values); - v2 = int_from_table_4(aux32[1], values); - int sumi4 = ggml_cuda_dp4a(v2, q8_4[1], ggml_cuda_dp4a(v1, q8_4[0], 0)) * s8[3]; -#endif - - *result += __half2float(bq2->d) * (__low2float(bq8_1[4*(i4/4)+0].ds) * sumi1 - + __low2float(bq8_1[4*(i4/4)+1].ds) * sumi2 - + __low2float(bq8_1[4*(i4/4)+2].ds) * sumi3 - + __low2float(bq8_1[4*(i4/4)+3].ds) * sumi4); -} - #define VDR_IQ2_KS_Q8_1_MMVQ 4 #define VDR_IQ2_KS_Q8_1_MMQ 4 -__device__ __forceinline__ void vec_dot_iq2_ks_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - float scale = *(const half *)vbq; - const block_iq2_ks * bq2 = (const block_iq2_ks *)((const char *)vbq + sizeof(half)) + kbx; - - int i4 = iqs/4; // 0...7. We will process q8 blocks 4*(i4/4), 4*(i4/4)+1, 4*(i4/4)+2, 4*(i4/4)+3 - const int32_t * q8_1 = (const int *)bq8_1[4*(i4/4)+0].qs + 2*(i4%4); - const int32_t * q8_2 = (const int *)bq8_1[4*(i4/4)+1].qs + 2*(i4%4); - const int32_t * q8_3 = (const int *)bq8_1[4*(i4/4)+2].qs + 2*(i4%4); - const int32_t * q8_4 = (const int *)bq8_1[4*(i4/4)+3].qs + 2*(i4%4); - - const uint16_t * q2 = (const uint16_t *)bq2->qs + 16*(i4/4) + 4*(i4%4); - const uint16_t extra = bq2->extra >> 4*(i4/4); - - uint32_t val1 = q2[0] | (q2[1] << 16), val2 = q2[2] | (q2[3] << 16); - - int32_t scales32; - const uint16_t * scales16 = (const uint16_t *)bq2->scales; - scales32 = __vsub4((scales16[i4/4] | (scales16[i4/4] << 12)) & 0x0f0f0f0f, 0x10101010); - int8_t * s8 = (int8_t *)&scales32; - s8[0] += ((extra >> 4) & 0x10); - s8[1] += ((extra >> 6) & 0x10); - s8[2] += ((extra >> 5) & 0x10); - s8[3] += ((extra >> 7) & 0x10); - -#ifdef __CUDA_ARCH__ - - uint32_t extra32 = uint32_t(extra & 0xf) * 0x01010101; - - uint32_t this_extra = ((extra32 << 2) & 0x04040404) | ((extra32 << 4) & 0x40404040); - uint32_t idx1 = ((val1 >> 0) & 0x33333333) | this_extra; - uint32_t idx2 = ((val2 >> 0) & 0x33333333) | this_extra; - int2 v1 = get_int_from_table_8(idx1, iq2nl_values); - int2 v2 = get_int_from_table_8(idx2, iq2nl_values); - - int sumi1 = ggml_cuda_dp4a(v2.x, q8_1[1], ggml_cuda_dp4a(v1.x, q8_1[0], 0)) * s8[0]; - int sumi3 = ggml_cuda_dp4a(v2.y, q8_3[1], ggml_cuda_dp4a(v1.y, q8_3[0], 0)) * s8[1]; - - this_extra = ((extra32 << 1) & 0x04040404) | ((extra32 << 3) & 0x40404040); - idx1 = ((val1 >> 2) & 0x33333333) | this_extra; - idx2 = ((val2 >> 2) & 0x33333333) | this_extra; - v1 = get_int_from_table_8(idx1, iq2nl_values); - v2 = get_int_from_table_8(idx2, iq2nl_values); - - int sumi2 = ggml_cuda_dp4a(v2.x, q8_2[1], ggml_cuda_dp4a(v1.x, q8_2[0], 0)) * s8[2]; - int sumi4 = ggml_cuda_dp4a(v2.y, q8_4[1], ggml_cuda_dp4a(v1.y, q8_4[0], 0)) * s8[3]; - -#else - - uint32_t aux32[2]; - int v1, v2; - const int * all_values = (const int *)iq2k_table; - const int * values; - - aux32[0] = ((val1 >> 0) & 0x03030303); aux32[1] = ((val2 >> 0) & 0x03030303); values = all_values + ((extra & 0x01) << 8); - v1 = int_from_table_4(aux32[0], values); - v2 = int_from_table_4(aux32[1], values); - int sumi1 = ggml_cuda_dp4a(v2, q8_1[1], ggml_cuda_dp4a(v1, q8_1[0], 0)) * s8[0]; - - aux32[0] = ((val1 >> 2) & 0x03030303); aux32[1] = ((val2 >> 2) & 0x03030303); values = all_values + ((extra & 0x02) << 7); - v1 = int_from_table_4(aux32[0], values); - v2 = int_from_table_4(aux32[1], values); - int sumi2 = ggml_cuda_dp4a(v2, q8_2[1], ggml_cuda_dp4a(v1, q8_2[0], 0)) * s8[2]; - - aux32[0] = ((val1 >> 4) & 0x03030303); aux32[1] = ((val2 >> 4) & 0x03030303); values = all_values + ((extra & 0x04) << 6); - v1 = int_from_table_4(aux32[0], values); - v2 = int_from_table_4(aux32[1], values); - int sumi3 = ggml_cuda_dp4a(v2, q8_3[1], ggml_cuda_dp4a(v1, q8_3[0], 0)) * s8[1]; - - aux32[0] = ((val1 >> 6) & 0x03030303); aux32[1] = ((val2 >> 6) & 0x03030303); values = all_values + ((extra & 0x08) << 5); - v1 = int_from_table_4(aux32[0], values); - v2 = int_from_table_4(aux32[1], values); - int sumi4 = ggml_cuda_dp4a(v2, q8_4[1], ggml_cuda_dp4a(v1, q8_4[0], 0)) * s8[3]; -#endif - - *result += scale * (__low2float(bq8_1[4*(i4/4)+0].ds) * sumi1 - + __low2float(bq8_1[4*(i4/4)+1].ds) * sumi2 - + __low2float(bq8_1[4*(i4/4)+2].ds) * sumi3 - + __low2float(bq8_1[4*(i4/4)+3].ds) * sumi4); -} - -__device__ __forceinline__ void vec_dot_iq2_k_r4_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - const block_iq2_k_r4 * bq2 = (const block_iq2_k_r4 *)vbq + kbx; - - // iqs is 0...30 in steps of 2 - const int ib16 = iqs/2; - const float d8 = __low2float(bq8_1[ib16/2].ds); - const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); - - int ib32 = ib16/2; - int is = ib16%2; - const int * scales_l = (const int *)bq2->scales; - - int scales = __vsub4(((scales_l[2*(ib32%4)+is] >> 4*(ib32/4)) & 0x0f0f0f0f), 0x08080808); - const int8_t * s8 = (const int8_t *)&scales; - - const int * q2 = (const int *)bq2->qs + 8*ib32 + 4*is; - -#ifdef __CUDA_ARCH__ - -#pragma unroll - for (int i = 0; i < 4; ++i) { - uint32_t extra32 = uint32_t((bq2->extra[i+4*is] >> ib32) & 1) * 0x04040404; - extra32 |= (extra32 << 4); - uint32_t val1 = ((q2[i] >> 0) & 0x33333333) | extra32; - uint32_t val2 = ((q2[i] >> 2) & 0x33333333) | extra32; - int2 v1 = get_int_from_table_8(val1, iq2nl_values); - int2 v2 = get_int_from_table_8(val2, iq2nl_values); - int sumi = 0; - sumi = ggml_cuda_dp4a(v1.x, q8[0], ggml_cuda_dp4a(v2.x, q8[1], sumi)); - sumi = ggml_cuda_dp4a(v1.y, q8[2], ggml_cuda_dp4a(v2.y, q8[3], sumi)); - const float d = __half2float(bq2->d[i]) * d8; - result[i] += d * sumi * s8[i]; - } - -#else - const int * all_values = (const int *)iq2k_table; - int2 val1; - int aux32[2]; -#pragma unroll - for (int i = 0; i < 4; ++i) { - auto values1 = all_values + (((bq2->extra[i+4*is] >> ib32) & 1) << 8); - int sumi1 = 0; - aux32[0] = ((q2[i] >> 0) & 0x03030303); - aux32[1] = ((q2[i] >> 2) & 0x03030303); - val1.x = int_from_table_4(aux32[0], values1); - val1.y = int_from_table_4(aux32[1], values1); - sumi1 = ggml_cuda_dp4a(val1.x, q8[0], ggml_cuda_dp4a(val1.y, q8[1], sumi1)); - aux32[0] = ((q2[i] >> 4) & 0x03030303); - aux32[1] = ((q2[i] >> 6) & 0x03030303); - val1.x = int_from_table_4(aux32[0], values1); - val1.y = int_from_table_4(aux32[1], values1); - sumi1 = ggml_cuda_dp4a(val1.x, q8[2], ggml_cuda_dp4a(val1.y, q8[3], sumi1)); - const float d = __half2float(bq2->d[i]) * d8; - result[i] += d * sumi1 * s8[i]; - } -#endif -} - #define VDR_IQ3_K_Q8_1_MMVQ 4 #define VDR_IQ3_K_Q8_1_MMQ 4 -__device__ __forceinline__ void vec_dot_iq3_k_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iiqs, float * result) { - const block_iq3_k * bq3 = (const block_iq3_k *) vbq + kbx; - - int iqs = iiqs/4; - const int ib128 = iqs/4; // 0 or 1. 0 works on quants 0...127, 1 on quants 128...255 - // Each thread processes 8 quants in each of the 4 32-blocks - const int il8 = iqs%4; // 0...3. 0 works on quants 0...7, 1 on quants 8...15, 2 on 16...23, 3 on 24...31 - const int shift = 4*(il8/2); - - const uint16_t * ql = (const uint16_t *)bq3->qs + 16*ib128 + 4*il8; - const uint16_t * qh = (const uint16_t *)bq3->qh + 4*il8; - - uint32_t aux32; - const uint8_t * aux8 = (const uint8_t *)&aux32; - - const int hshift = 4*(1-ib128); - const uint16_t sh = bq3->scales_h >> (8*ib128 + il8/2); - - const uint8_t extra = bq3->extra >> (8*ib128 + il8/2); - uint32_t extra32 = uint32_t(extra) * 0x01010101; - uint32_t extra32_1 = ((extra32 << 3) & 0x08080808) | ((extra32 << 5) & 0x80808080); - uint32_t extra32_2 = ((extra32 << 2) & 0x08080808) | ((extra32 << 4) & 0x80808080); - - const int * q8; - int sumi[4] = {0, 0, 0, 0}; - for (int i = 0; i < 2; ++i) { - uint32_t vl = ql[2*i+0] | (ql[2*i+1] << 16); - uint32_t vh = ((qh[2*i+0] | (qh[2*i+1] << 16)) << hshift); - - uint32_t val1 = ((vl >> 0) & 0x33333333) | extra32_1 | ((vh >> 2) & 0x04040404) | ((vh >> 0) & 0x40404040); - uint32_t val2 = ((vl >> 2) & 0x33333333) | extra32_2 | ((vh >> 3) & 0x04040404) | ((vh >> 1) & 0x40404040); - int2 v1 = get_int_from_table_16(val1, iq3nl_values); - int2 v2 = get_int_from_table_16(val2, iq3nl_values); - - q8 = (const int *)bq8_1[4*ib128+0].qs + 2*il8; - sumi[0] = ggml_cuda_dp4a(v1.x, q8[i], sumi[0]); - - q8 += sizeof(block_q8_1)/4; - sumi[1] = ggml_cuda_dp4a(v2.x, q8[i], sumi[1]); - - q8 += sizeof(block_q8_1)/4; - sumi[2] = ggml_cuda_dp4a(v1.y, q8[i], sumi[2]); - - q8 += sizeof(block_q8_1)/4; - sumi[3] = ggml_cuda_dp4a(v2.y, q8[i], sumi[3]); - } - const float d = __half2float(bq3->d); - const uint16_t * sl16 = (const uint16_t *)bq3->scales_l + 2*ib128; - aux32 = ((((sl16[0] | (sl16[1] << 16)) >> shift) & 0x0f0f0f0f) << 1) | 0x01010101; - *result += d * (__low2float(bq8_1[4*ib128+0].ds) * aux8[0] * (sh & 0x01 ? -1 : 1) * sumi[0] + - __low2float(bq8_1[4*ib128+1].ds) * aux8[1] * (sh & 0x04 ? -1 : 1) * sumi[1] + - __low2float(bq8_1[4*ib128+2].ds) * aux8[2] * (sh & 0x10 ? -1 : 1) * sumi[2] + - __low2float(bq8_1[4*ib128+3].ds) * aux8[3] * (sh & 0x40 ? -1 : 1) * sumi[3]); - -} - -// TODO -__device__ __forceinline__ void vec_dot_iq2_kl_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iiqs, float * result) { - - float d = __half2float(*(const half *)vbq); - const block_iq2_kl * bq2 = (const block_iq2_kl *)((const char *)vbq + sizeof(half)) + kbx; - - int iqs = iiqs/4; - const int ib64 = iqs/2; // 0...3. 0 works on quants 0...63, 1 on quants 64...127, etc. - // Each thread processes 16 quants in each of the 2 32-blocks - const int il16 = iqs%2; // 0...3. 0 works on quants 0...7, 1 on quants 8...15, 2 on 16...23, 3 on 24...31 - - const uint16_t * ql = (const uint16_t *)bq2->qs + 8*ib64 + 4*il16; - const uint16_t * qh = (const uint16_t *)bq2->qh + 4*il16; - - int32_t aux32; - const uint8_t * aux8 = (const uint8_t *)&aux32; - - const int * q8l = (const int *)bq8_1[2*ib64+0].qs + 4*il16; - const int * q8h = (const int *)bq8_1[2*ib64+1].qs + 4*il16; - - int sumi1 = 0, sumi2 = 0; - int v1, v2; - for (int i = 0; i < 2; ++i) { - uint32_t vl = ql[2*i+0] | (ql[2*i+1] << 16); - uint32_t vh = (qh[2*i+0] | (qh[2*i+1] << 16)) >> 2*ib64; - - aux32 = (vl & 0x0f0f0f0f) | ((vh << 4) & 0x10101010); - v1 = iq2kl_values[aux8[0]] | (iq2kl_values[aux8[1]] << 16); - v2 = iq2kl_values[aux8[2]] | (iq2kl_values[aux8[3]] << 16); - sumi1 = ggml_cuda_dp4a(v1, q8l[2*i+0], ggml_cuda_dp4a(v2, q8l[2*i+1], sumi1)); - - aux32 = ((vl >> 4) & 0x0f0f0f0f) | ((vh << 3) & 0x10101010); - v1 = iq2kl_values[aux8[0]] | (iq2kl_values[aux8[1]] << 16); - v2 = iq2kl_values[aux8[2]] | (iq2kl_values[aux8[3]] << 16); - sumi2 = ggml_cuda_dp4a(v1, q8h[2*i+0], ggml_cuda_dp4a(v2, q8h[2*i+1], sumi2)); - } - - auto sh = bq2->scales_h >> 4*ib64; - int ls1 = int(((bq2->scales_l[(2*ib64+0)%4] >> 4*(ib64/2)) & 0xf) | ((sh << 4) & 0x30)) - 32; - int ls2 = int(((bq2->scales_l[(2*ib64+1)%4] >> 4*(ib64/2)) & 0xf) | ((sh << 2) & 0x30)) - 32; - - *result += d * (__low2float(bq8_1[2*ib64+0].ds) * ls1 * sumi1 + __low2float(bq8_1[2*ib64+1].ds) * ls2 * sumi2); - -} - -__device__ __forceinline__ void vec_dot_iq3_ks_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iiqs, float * result) { - - float d = __half2float(*(const half *)vbq); - const block_iq3_ks * bq3 = (const block_iq3_ks *)((const char *)vbq + sizeof(half)) + kbx; - - int iqs = iiqs/4; - const int ib128 = iqs/4; // 0 or 1. 0 works on quants 0...127, 1 on quants 128...255 - // Each thread processes 8 quants in each of the 4 32-blocks - const int il8 = iqs%4; // 0...3. 0 works on quants 0...7, 1 on quants 8...15, 2 on 16...23, 3 on 24...31 - - const uint16_t * ql = (const uint16_t *)bq3->qs + 16*ib128 + 4*il8; - const uint16_t * qh = (const uint16_t *)bq3->qh + 4*il8; - - uint16_t extra = bq3->extra >> 4*ib128; - uint32_t extra_v = uint32_t(extra >> 8) * 0x01010101; - - uint32_t extra32_1 = ((extra_v << 3) & 0x08080808) | ((extra_v << 5) & 0x80808080); - uint32_t extra32_2 = ((extra_v << 2) & 0x08080808) | ((extra_v << 4) & 0x80808080); - - const int * q8; - int sumi[4] = {0, 0, 0, 0}; - for (int i = 0; i < 2; ++i) { - uint32_t vl = ql[2*i+0] | (ql[2*i+1] << 16); - uint32_t vh = ((qh[2*i+0] | (qh[2*i+1] << 16)) >> 4*ib128); - - uint32_t val1 = ((vl >> 0) & 0x33333333) | extra32_1 | ((vh << 2) & 0x04040404) | ((vh << 4) & 0x40404040); - uint32_t val2 = ((vl >> 2) & 0x33333333) | extra32_2 | ((vh << 1) & 0x04040404) | ((vh << 3) & 0x40404040); - int2 v1 = get_int_from_table_16(val1, iq3nl_values); - int2 v2 = get_int_from_table_16(val2, iq3nl_values); - - q8 = (const int *)bq8_1[4*ib128+0].qs + 2*il8; - sumi[0] = ggml_cuda_dp4a(v1.x, q8[i], sumi[0]); - - q8 += sizeof(block_q8_1)/4; - sumi[1] = ggml_cuda_dp4a(v2.x, q8[i], sumi[1]); - - q8 += sizeof(block_q8_1)/4; - sumi[2] = ggml_cuda_dp4a(v1.y, q8[i], sumi[2]); - - q8 += sizeof(block_q8_1)/4; - sumi[3] = ggml_cuda_dp4a(v2.y, q8[i], sumi[3]); - } - const uint16_t * sl16 = (const uint16_t *)bq3->scales; - int32_t aux32 = __vsub4(((sl16[0] | (sl16[1] << 16)) >> 4*ib128) & 0x0f0f0f0f, 0x10101010); - const int8_t * a8 = (const int8_t *)&aux32; - *result += d * (__low2float(bq8_1[4*ib128+0].ds) * (a8[0] + ((extra << 4) & 0x10)) * sumi[0] + - __low2float(bq8_1[4*ib128+1].ds) * (a8[1] + ((extra << 3) & 0x10)) * sumi[1] + - __low2float(bq8_1[4*ib128+2].ds) * (a8[2] + ((extra << 2) & 0x10)) * sumi[2] + - __low2float(bq8_1[4*ib128+3].ds) * (a8[3] + ((extra << 1) & 0x10)) * sumi[3]); - -} - -__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 = __dp4a(val[0], q8[4*l+0], __dp4a(val[1], q8[4*l+1], __dp4a(val[2], q8[4*l+2], __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 -} - -__device__ __forceinline__ void vec_dot_iq2_bn_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { - - float scale = *(const float *)vbq; - const block_iq2_bn * bq2 = (const block_iq2_bn *)((const char *)vbq + sizeof(float)) + kbx; - - // iqs is 0 or 1 - -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - auto qs = (const int *)bq2->qs + 2*iqs; - auto q8l = (const int *)bq8_1[0].qs + 2*iqs; - auto q8h = (const int *)bq8_1[1].qs + 2*iqs; - int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0; - for (int j = 0; j < 2; ++j) { - int vl = qs[j]; - int vh = qs[j] >> 4; - sumi1 = __dp4a(vl & 0x03030303, q8l[j+0], sumi1); - sumi2 = __dp4a(vl & 0x0c0c0c0c, q8l[j+4], sumi2); - sumi3 = __dp4a(vh & 0x03030303, q8h[j+0], sumi3); - sumi4 = __dp4a(vh & 0x0c0c0c0c, q8h[j+4], sumi4); - } - auto d8l = __half22float2(bq8_1[0].ds); - auto d8h = __half22float2(bq8_1[1].ds); - *result += scale * (d8l.x * (sumi1 + 0.25f*sumi2) + d8h.x * (sumi3 + 0.25f * sumi4) - 0.5f*d8l.y - 0.5f*d8h.y); -#else - int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0; - auto q8l = bq8_1[0].qs + 8*iqs; - auto q8h = bq8_1[1].qs + 8*iqs; - auto qs = bq2->qs + 8*iqs; - for (int j = 0; j < 8; ++j) { - sumi1 += q8l[j+ 0] * (qs[j] & 0x03); - sumi2 += q8l[j+16] * (qs[j] & 0x0c); - sumi3 += q8h[j+ 0] * (qs[j] & 0x30); - sumi4 += q8h[j+16] * (qs[j] & 0xc0); - } - auto d8l = __half22float2(bq8_1[0].ds); - auto d8h = __half22float2(bq8_1[1].ds); - *result += scale * (d8l.x * (sumi1 + 0.25f*sumi2) + 0.0625f * d8h.x*(sumi3 + 0.25f*sumi4) - 0.5f*d8l.y - 0.5f*d8h.y); -#endif -} - } // namespace extern void mul_mat_vec_iq2_k_q8_1_cuda(const mmvq_args & args, cudaStream_t stream); diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_bn.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_bn.cu index aaa33f43..8c8184fe 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_bn.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_bn.cu @@ -1,5 +1,64 @@ #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 = __dp4a(val[0], q8[4*l+0], __dp4a(val[1], q8[4*l+1], __dp4a(val[2], q8[4*l+2], __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(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_kt.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_kt.cu index 9a056394..ef5a270b 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_kt.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_kt.cu @@ -1,5 +1,38 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq1_kt_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + constexpr uint32_t ka = 0xCBAC1FED; + constexpr uint32_t km = 0x3f3f3f3f; + + float scale = *(const float *)vbq; + const block_iq1_kt * bq1 = (const block_iq1_kt *)((const char *)vbq + sizeof(float)) + kbx; + + // iqs is 0...28 + const int ib32 = iqs/4; + const int32_t * q8 = (const int *)bq8_1[ib32].qs; + const int ls = iq4k_values[bq1->sh[ib32] & 0xf]; + const float dl = scale * ls; + int sumi = 0; + for (int j = 0; j < 4; ++j) { + uint32_t val = bq1->ql[4*ib32+j] + 4096 + ((bq1->qh[4*(ib32%4)+j] << (8 - 4*(ib32/4))) & 0xf00) + ((bq1->sh[ib32] << (8 - j)) & 0x1000); + int v4 = 0; + for (int k = 0; k < 4; ++k) { + val *= ka; + v4 |= (ggml_cuda_dp4a(val & km, 0x01010101, -126) & 0xff) << 8*k; + } + sumi = ggml_cuda_dp4a(v4, q8[2*j+0], sumi); + v4 = 0; + for (int k = 0; k < 4; ++k) { + val *= ka; + v4 |= (ggml_cuda_dp4a(val & km, 0x01010101, -126) & 0xff) << 8*k; + } + sumi = ggml_cuda_dp4a(v4, q8[2*j+1], sumi); + } + *result += dl * __low2float(bq8_1[ib32].ds) * sumi; +} + void mul_mat_vec_iq1_kt_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_m_r4.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_m_r4.cu index 9efec5e6..7567bd55 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_m_r4.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_m_r4.cu @@ -1,5 +1,37 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq1_m_r4_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + const half * dptr = (const half *)vbq; + const block_iq1_m_r4 * bq1 = (const block_iq1_m_r4 *)(dptr + 4) + kbx; + + // iqs is 0 or 2 + const float d8 = __low2float(bq8_1->ds); + const int32_t * q8 = (const int *)bq8_1->qs; + + int32_t grid32[2]; + const int * igrid = (const int *)grid32; + + int minus1 = ggml_cuda_dp4a(0x01010101, q8[4*(iqs/2)+0], ggml_cuda_dp4a(0x01010101, q8[4*(iqs/2)+1], 0)); + int minus2 = ggml_cuda_dp4a(0x01010101, q8[4*(iqs/2)+2], ggml_cuda_dp4a(0x01010101, q8[4*(iqs/2)+3], 0)); + + for (int i = 0; i < 4; ++i) { + float dl = __half2float(dptr[i])*((bq1->scales[i] >> 4*(iqs/2)) & 0xf) * d8; + float ml1 = dl * (bq1->qh[4*(iqs/2)+i] & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA); + float ml2 = dl * (bq1->qh[4*(iqs/2)+i] & 0x80 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA); + grid32[0] = iq1s_grid_gpu[bq1->qs[4*iqs+i] | ((bq1->qh[4*(iqs/2)+i] & 0x07) << 8)]; + grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; + grid32[0] &= 0x0f0f0f0f; + int sumi = ggml_cuda_dp4a(igrid[0], q8[4*(iqs/2)+0], ggml_cuda_dp4a(igrid[1], q8[4*(iqs/2)+1], 0)); + grid32[0] = iq1s_grid_gpu[bq1->qs[4*iqs+i+4] | ((bq1->qh[4*(iqs/2)+i] & 0x70) << 4)]; + grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; + grid32[0] &= 0x0f0f0f0f; + sumi = ggml_cuda_dp4a(igrid[0], q8[4*(iqs/2)+2], ggml_cuda_dp4a(igrid[1], q8[4*(iqs/2)+3], sumi)); + result[i] += dl * sumi + ml1 * minus1 + ml2*minus2; + } +} + void mul_mat_vec_iq1_m_r4_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_s_r4.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_s_r4.cu index 7b811041..e5cfd9a1 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_s_r4.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq1_s_r4.cu @@ -1,5 +1,36 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq1_s_r4_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + const half * dptr = (const half *)vbq; + const block_iq1_s_r4 * bq1 = (const block_iq1_s_r4 *)(dptr + 4) + kbx; + + // iqs is 0 or 2 + const float d8 = __low2float(bq8_1->ds); + const int32_t * q8 = (const int *)bq8_1->qs; + + int32_t grid32[2]; + const int * igrid = (const int *)grid32; + + int minus = 0; + for (int k = 0; k < 4; ++k) minus = ggml_cuda_dp4a(0x01010101, q8[4*(iqs/2)+k], minus); + + for (int i = 0; i < 4; ++i) { + float dl = __half2float(dptr[i])*(2*((bq1->qh[i] >> 12) & 7) + 1) * d8; + float ml = dl * (bq1->qh[i] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA); + grid32[0] = iq1s_grid_gpu[bq1->qs[4*iqs+i] | (((bq1->qh[i] >> 3*iqs) & 7) << 8)]; + grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; + grid32[0] &= 0x0f0f0f0f; + int sumi = ggml_cuda_dp4a(igrid[0], q8[4*(iqs/2)+0], ggml_cuda_dp4a(igrid[1], q8[4*(iqs/2)+1], 0)); + grid32[0] = iq1s_grid_gpu[bq1->qs[4*iqs+i+4] | (((bq1->qh[i] >> (3*iqs+3)) & 7) << 8)]; + grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; + grid32[0] &= 0x0f0f0f0f; + sumi = ggml_cuda_dp4a(igrid[0], q8[4*(iqs/2)+2], ggml_cuda_dp4a(igrid[1], q8[4*(iqs/2)+3], sumi)); + result[i] += dl * sumi + ml * minus; + } +} + void mul_mat_vec_iq1_s_r4_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_bn.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_bn.cu index 880d32e6..93bc5621 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_bn.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_bn.cu @@ -1,5 +1,46 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq2_bn_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + float scale = *(const float *)vbq; + const block_iq2_bn * bq2 = (const block_iq2_bn *)((const char *)vbq + sizeof(float)) + kbx; + + // iqs is 0 or 1 + +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + auto qs = (const int *)bq2->qs + 2*iqs; + auto q8l = (const int *)bq8_1[0].qs + 2*iqs; + auto q8h = (const int *)bq8_1[1].qs + 2*iqs; + int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0; + for (int j = 0; j < 2; ++j) { + int vl = qs[j]; + int vh = qs[j] >> 4; + sumi1 = __dp4a(vl & 0x03030303, q8l[j+0], sumi1); + sumi2 = __dp4a(vl & 0x0c0c0c0c, q8l[j+4], sumi2); + sumi3 = __dp4a(vh & 0x03030303, q8h[j+0], sumi3); + sumi4 = __dp4a(vh & 0x0c0c0c0c, q8h[j+4], sumi4); + } + auto d8l = __half22float2(bq8_1[0].ds); + auto d8h = __half22float2(bq8_1[1].ds); + *result += scale * (d8l.x * (sumi1 + 0.25f*sumi2) + d8h.x * (sumi3 + 0.25f * sumi4) - 0.5f*d8l.y - 0.5f*d8h.y); +#else + int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0; + auto q8l = bq8_1[0].qs + 8*iqs; + auto q8h = bq8_1[1].qs + 8*iqs; + auto qs = bq2->qs + 8*iqs; + for (int j = 0; j < 8; ++j) { + sumi1 += q8l[j+ 0] * (qs[j] & 0x03); + sumi2 += q8l[j+16] * (qs[j] & 0x0c); + sumi3 += q8h[j+ 0] * (qs[j] & 0x30); + sumi4 += q8h[j+16] * (qs[j] & 0xc0); + } + auto d8l = __half22float2(bq8_1[0].ds); + auto d8h = __half22float2(bq8_1[1].ds); + *result += scale * (d8l.x * (sumi1 + 0.25f*sumi2) + 0.0625f * d8h.x*(sumi3 + 0.25f*sumi4) - 0.5f*d8l.y - 0.5f*d8h.y); +#endif +} + void mul_mat_vec_iq2_bn_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_k.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_k.cu index 38487179..e0ab8131 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_k.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_k.cu @@ -1,5 +1,84 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq2_k_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + // iqs is 0, 4, 8, 12, 16, 20, 24, 28 + // we have 16 packed quants (when cast to int) + + int i4 = iqs/4; // 0...7. We will process q8 blocks 4*(i4/4), 4*(i4/4)+1, 4*(i4/4)+2, 4*(i4/4)+3 + const int32_t * q8_1 = (const int *)bq8_1[4*(i4/4)+0].qs + 2*(i4%4); + const int32_t * q8_2 = (const int *)bq8_1[4*(i4/4)+1].qs + 2*(i4%4); + const int32_t * q8_3 = (const int *)bq8_1[4*(i4/4)+2].qs + 2*(i4%4); + const int32_t * q8_4 = (const int *)bq8_1[4*(i4/4)+3].qs + 2*(i4%4); + + const block_iq2_k * bq2 = (const block_iq2_k *) vbq + kbx; + const uint32_t * q2 = (const uint32_t *)bq2->qs + 8*(i4/4) + 2*(i4%4); + const uint16_t extra = bq2->extra >> (8*(i4/4) + (i4%4)/2); + + const uint32_t * scales = (const uint32_t *)bq2->scales; + uint32_t s32 = __vsub4((scales[i4/4] >> 4*(((i4%4)/2)%2)) & 0x0f0f0f0f, 0x08080808); + const int8_t * s8 = (const int8_t *)&s32; + + // Block of 16: (32*(4*(i4/4)+k)+8*(i4%4))/16 = 8*(i4/4) + 2*k + (i4%4)/2 + // -> scales_l[4*(i4/4) + k] >> 4*(((i4%4)/2)%2) + +#ifdef __CUDA_ARCH__ + uint32_t extra32 = uint32_t(extra & 0xff) * 0x01010101; + uint32_t extra32_1 = (extra32 << 2) & 0x44444444; + uint32_t extra32_2 = (extra32 << 0) & 0x44444444; + + uint32_t val1, val2; + + val1 = ((q2[0] >> 0) & 0x33333333) | extra32_1; val2 = ((q2[1] >> 0) & 0x33333333) | extra32_1; + int2 v1 = get_int_from_table_8(val1, iq2nl_values); + int2 v2 = get_int_from_table_8(val2, iq2nl_values); + int sumi1 = ggml_cuda_dp4a(v2.x, q8_1[1], ggml_cuda_dp4a(v1.x, q8_1[0], 0)) * s8[0]; + int sumi3 = ggml_cuda_dp4a(v2.y, q8_3[1], ggml_cuda_dp4a(v1.y, q8_3[0], 0)) * s8[2]; + + val1 = ((q2[0] >> 2) & 0x33333333) | extra32_2; val2 = ((q2[1] >> 2) & 0x33333333) | extra32_2; + v1 = get_int_from_table_8(val1, iq2nl_values); + v2 = get_int_from_table_8(val2, iq2nl_values); + int sumi2 = ggml_cuda_dp4a(v2.x, q8_2[1], ggml_cuda_dp4a(v1.x, q8_2[0], 0)) * s8[1]; + int sumi4 = ggml_cuda_dp4a(v2.y, q8_4[1], ggml_cuda_dp4a(v1.y, q8_4[0], 0)) * s8[3]; + +#else + + const int * all_values = (const int *)iq2k_table; + const int * values; + + uint32_t val1 = q2[0], val2 = q2[1]; + + uint32_t aux32[2]; + int v1, v2; + + aux32[0] = ((val1 >> 0) & 0x03030303); aux32[1] = ((val2 >> 0) & 0x03030303); values = all_values + ((extra & 0x01) << 8); + v1 = int_from_table_4(aux32[0], values); + v2 = int_from_table_4(aux32[1], values); + int sumi1 = ggml_cuda_dp4a(v2, q8_1[1], ggml_cuda_dp4a(v1, q8_1[0], 0)) * s8[0]; + + aux32[0] = ((val1 >> 2) & 0x03030303); aux32[1] = ((val2 >> 2) & 0x03030303); values = all_values + ((extra & 0x04) << 6); + v1 = int_from_table_4(aux32[0], values); + v2 = int_from_table_4(aux32[1], values); + int sumi2 = ggml_cuda_dp4a(v2, q8_2[1], ggml_cuda_dp4a(v1, q8_2[0], 0)) * s8[1]; + + aux32[0] = ((val1 >> 4) & 0x03030303); aux32[1] = ((val2 >> 4) & 0x03030303); values = all_values + ((extra & 0x10) << 4); + v1 = int_from_table_4(aux32[0], values); + v2 = int_from_table_4(aux32[1], values); + int sumi3 = ggml_cuda_dp4a(v2, q8_3[1], ggml_cuda_dp4a(v1, q8_3[0], 0)) * s8[2]; + + aux32[0] = ((val1 >> 6) & 0x03030303); aux32[1] = ((val2 >> 6) & 0x03030303); values = all_values + ((extra & 0x40) << 2); + v1 = int_from_table_4(aux32[0], values); + v2 = int_from_table_4(aux32[1], values); + int sumi4 = ggml_cuda_dp4a(v2, q8_4[1], ggml_cuda_dp4a(v1, q8_4[0], 0)) * s8[3]; +#endif + + *result += __half2float(bq2->d) * (__low2float(bq8_1[4*(i4/4)+0].ds) * sumi1 + + __low2float(bq8_1[4*(i4/4)+1].ds) * sumi2 + + __low2float(bq8_1[4*(i4/4)+2].ds) * sumi3 + + __low2float(bq8_1[4*(i4/4)+3].ds) * sumi4); +} + void mul_mat_vec_iq2_k_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_k_r4.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_k_r4.cu index 24a1cc26..8790e532 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_k_r4.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_k_r4.cu @@ -1,5 +1,65 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq2_k_r4_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + const block_iq2_k_r4 * bq2 = (const block_iq2_k_r4 *)vbq + kbx; + + // iqs is 0...30 in steps of 2 + const int ib16 = iqs/2; + const float d8 = __low2float(bq8_1[ib16/2].ds); + const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); + + int ib32 = ib16/2; + int is = ib16%2; + const int * scales_l = (const int *)bq2->scales; + + int scales = __vsub4(((scales_l[2*(ib32%4)+is] >> 4*(ib32/4)) & 0x0f0f0f0f), 0x08080808); + const int8_t * s8 = (const int8_t *)&scales; + + const int * q2 = (const int *)bq2->qs + 8*ib32 + 4*is; + +#ifdef __CUDA_ARCH__ + +#pragma unroll + for (int i = 0; i < 4; ++i) { + uint32_t extra32 = uint32_t((bq2->extra[i+4*is] >> ib32) & 1) * 0x04040404; + extra32 |= (extra32 << 4); + uint32_t val1 = ((q2[i] >> 0) & 0x33333333) | extra32; + uint32_t val2 = ((q2[i] >> 2) & 0x33333333) | extra32; + int2 v1 = get_int_from_table_8(val1, iq2nl_values); + int2 v2 = get_int_from_table_8(val2, iq2nl_values); + int sumi = 0; + sumi = ggml_cuda_dp4a(v1.x, q8[0], ggml_cuda_dp4a(v2.x, q8[1], sumi)); + sumi = ggml_cuda_dp4a(v1.y, q8[2], ggml_cuda_dp4a(v2.y, q8[3], sumi)); + const float d = __half2float(bq2->d[i]) * d8; + result[i] += d * sumi * s8[i]; + } + +#else + const int * all_values = (const int *)iq2k_table; + int2 val1; + int aux32[2]; +#pragma unroll + for (int i = 0; i < 4; ++i) { + auto values1 = all_values + (((bq2->extra[i+4*is] >> ib32) & 1) << 8); + int sumi1 = 0; + aux32[0] = ((q2[i] >> 0) & 0x03030303); + aux32[1] = ((q2[i] >> 2) & 0x03030303); + val1.x = int_from_table_4(aux32[0], values1); + val1.y = int_from_table_4(aux32[1], values1); + sumi1 = ggml_cuda_dp4a(val1.x, q8[0], ggml_cuda_dp4a(val1.y, q8[1], sumi1)); + aux32[0] = ((q2[i] >> 4) & 0x03030303); + aux32[1] = ((q2[i] >> 6) & 0x03030303); + val1.x = int_from_table_4(aux32[0], values1); + val1.y = int_from_table_4(aux32[1], values1); + sumi1 = ggml_cuda_dp4a(val1.x, q8[2], ggml_cuda_dp4a(val1.y, q8[3], sumi1)); + const float d = __half2float(bq2->d[i]) * d8; + result[i] += d * sumi1 * s8[i]; + } +#endif +} + void mul_mat_vec_iq2_k_r4_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_kl.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_kl.cu index ac192f63..83769e4f 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_kl.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_kl.cu @@ -1,5 +1,50 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq2_kl_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iiqs, float * result) { + + float d = __half2float(*(const half *)vbq); + const block_iq2_kl * bq2 = (const block_iq2_kl *)((const char *)vbq + sizeof(half)) + kbx; + + int iqs = iiqs/4; + const int ib64 = iqs/2; // 0...3. 0 works on quants 0...63, 1 on quants 64...127, etc. + // Each thread processes 16 quants in each of the 2 32-blocks + const int il16 = iqs%2; // 0...3. 0 works on quants 0...7, 1 on quants 8...15, 2 on 16...23, 3 on 24...31 + + const uint16_t * ql = (const uint16_t *)bq2->qs + 8*ib64 + 4*il16; + const uint16_t * qh = (const uint16_t *)bq2->qh + 4*il16; + + int32_t aux32; + const uint8_t * aux8 = (const uint8_t *)&aux32; + + const int * q8l = (const int *)bq8_1[2*ib64+0].qs + 4*il16; + const int * q8h = (const int *)bq8_1[2*ib64+1].qs + 4*il16; + + int sumi1 = 0, sumi2 = 0; + int v1, v2; + for (int i = 0; i < 2; ++i) { + uint32_t vl = ql[2*i+0] | (ql[2*i+1] << 16); + uint32_t vh = (qh[2*i+0] | (qh[2*i+1] << 16)) >> 2*ib64; + + aux32 = (vl & 0x0f0f0f0f) | ((vh << 4) & 0x10101010); + v1 = iq2kl_values[aux8[0]] | (iq2kl_values[aux8[1]] << 16); + v2 = iq2kl_values[aux8[2]] | (iq2kl_values[aux8[3]] << 16); + sumi1 = ggml_cuda_dp4a(v1, q8l[2*i+0], ggml_cuda_dp4a(v2, q8l[2*i+1], sumi1)); + + aux32 = ((vl >> 4) & 0x0f0f0f0f) | ((vh << 3) & 0x10101010); + v1 = iq2kl_values[aux8[0]] | (iq2kl_values[aux8[1]] << 16); + v2 = iq2kl_values[aux8[2]] | (iq2kl_values[aux8[3]] << 16); + sumi2 = ggml_cuda_dp4a(v1, q8h[2*i+0], ggml_cuda_dp4a(v2, q8h[2*i+1], sumi2)); + } + + auto sh = bq2->scales_h >> 4*ib64; + int ls1 = int(((bq2->scales_l[(2*ib64+0)%4] >> 4*(ib64/2)) & 0xf) | ((sh << 4) & 0x30)) - 32; + int ls2 = int(((bq2->scales_l[(2*ib64+1)%4] >> 4*(ib64/2)) & 0xf) | ((sh << 2) & 0x30)) - 32; + + *result += d * (__low2float(bq8_1[2*ib64+0].ds) * ls1 * sumi1 + __low2float(bq8_1[2*ib64+1].ds) * ls2 * sumi2); + +} + void mul_mat_vec_iq2_kl_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_ks.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_ks.cu index 3a4e4aef..35168537 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_ks.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_ks.cu @@ -1,5 +1,86 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq2_ks_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + float scale = *(const half *)vbq; + const block_iq2_ks * bq2 = (const block_iq2_ks *)((const char *)vbq + sizeof(half)) + kbx; + + int i4 = iqs/4; // 0...7. We will process q8 blocks 4*(i4/4), 4*(i4/4)+1, 4*(i4/4)+2, 4*(i4/4)+3 + const int32_t * q8_1 = (const int *)bq8_1[4*(i4/4)+0].qs + 2*(i4%4); + const int32_t * q8_2 = (const int *)bq8_1[4*(i4/4)+1].qs + 2*(i4%4); + const int32_t * q8_3 = (const int *)bq8_1[4*(i4/4)+2].qs + 2*(i4%4); + const int32_t * q8_4 = (const int *)bq8_1[4*(i4/4)+3].qs + 2*(i4%4); + + const uint16_t * q2 = (const uint16_t *)bq2->qs + 16*(i4/4) + 4*(i4%4); + const uint16_t extra = bq2->extra >> 4*(i4/4); + + uint32_t val1 = q2[0] | (q2[1] << 16), val2 = q2[2] | (q2[3] << 16); + + int32_t scales32; + const uint16_t * scales16 = (const uint16_t *)bq2->scales; + scales32 = __vsub4((scales16[i4/4] | (scales16[i4/4] << 12)) & 0x0f0f0f0f, 0x10101010); + int8_t * s8 = (int8_t *)&scales32; + s8[0] += ((extra >> 4) & 0x10); + s8[1] += ((extra >> 6) & 0x10); + s8[2] += ((extra >> 5) & 0x10); + s8[3] += ((extra >> 7) & 0x10); + +#ifdef __CUDA_ARCH__ + + uint32_t extra32 = uint32_t(extra & 0xf) * 0x01010101; + + uint32_t this_extra = ((extra32 << 2) & 0x04040404) | ((extra32 << 4) & 0x40404040); + uint32_t idx1 = ((val1 >> 0) & 0x33333333) | this_extra; + uint32_t idx2 = ((val2 >> 0) & 0x33333333) | this_extra; + int2 v1 = get_int_from_table_8(idx1, iq2nl_values); + int2 v2 = get_int_from_table_8(idx2, iq2nl_values); + + int sumi1 = ggml_cuda_dp4a(v2.x, q8_1[1], ggml_cuda_dp4a(v1.x, q8_1[0], 0)) * s8[0]; + int sumi3 = ggml_cuda_dp4a(v2.y, q8_3[1], ggml_cuda_dp4a(v1.y, q8_3[0], 0)) * s8[1]; + + this_extra = ((extra32 << 1) & 0x04040404) | ((extra32 << 3) & 0x40404040); + idx1 = ((val1 >> 2) & 0x33333333) | this_extra; + idx2 = ((val2 >> 2) & 0x33333333) | this_extra; + v1 = get_int_from_table_8(idx1, iq2nl_values); + v2 = get_int_from_table_8(idx2, iq2nl_values); + + int sumi2 = ggml_cuda_dp4a(v2.x, q8_2[1], ggml_cuda_dp4a(v1.x, q8_2[0], 0)) * s8[2]; + int sumi4 = ggml_cuda_dp4a(v2.y, q8_4[1], ggml_cuda_dp4a(v1.y, q8_4[0], 0)) * s8[3]; + +#else + uint32_t aux32[2]; + int v1, v2; + const int * all_values = (const int *)iq2k_table; + const int * values; + + aux32[0] = ((val1 >> 0) & 0x03030303); aux32[1] = ((val2 >> 0) & 0x03030303); values = all_values + ((extra & 0x01) << 8); + v1 = int_from_table_4(aux32[0], values); + v2 = int_from_table_4(aux32[1], values); + int sumi1 = ggml_cuda_dp4a(v2, q8_1[1], ggml_cuda_dp4a(v1, q8_1[0], 0)) * s8[0]; + + aux32[0] = ((val1 >> 2) & 0x03030303); aux32[1] = ((val2 >> 2) & 0x03030303); values = all_values + ((extra & 0x02) << 7); + v1 = int_from_table_4(aux32[0], values); + v2 = int_from_table_4(aux32[1], values); + int sumi2 = ggml_cuda_dp4a(v2, q8_2[1], ggml_cuda_dp4a(v1, q8_2[0], 0)) * s8[2]; + + aux32[0] = ((val1 >> 4) & 0x03030303); aux32[1] = ((val2 >> 4) & 0x03030303); values = all_values + ((extra & 0x04) << 6); + v1 = int_from_table_4(aux32[0], values); + v2 = int_from_table_4(aux32[1], values); + int sumi3 = ggml_cuda_dp4a(v2, q8_3[1], ggml_cuda_dp4a(v1, q8_3[0], 0)) * s8[1]; + + aux32[0] = ((val1 >> 6) & 0x03030303); aux32[1] = ((val2 >> 6) & 0x03030303); values = all_values + ((extra & 0x08) << 5); + v1 = int_from_table_4(aux32[0], values); + v2 = int_from_table_4(aux32[1], values); + int sumi4 = ggml_cuda_dp4a(v2, q8_4[1], ggml_cuda_dp4a(v1, q8_4[0], 0)) * s8[3]; +#endif + + *result += scale * (__low2float(bq8_1[4*(i4/4)+0].ds) * sumi1 + + __low2float(bq8_1[4*(i4/4)+1].ds) * sumi2 + + __low2float(bq8_1[4*(i4/4)+2].ds) * sumi3 + + __low2float(bq8_1[4*(i4/4)+3].ds) * sumi4); +} + void mul_mat_vec_iq2_ks_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_kt.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_kt.cu index 4c27b40c..df3303c1 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_kt.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq2_kt.cu @@ -1,5 +1,39 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq2_kt_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + constexpr uint32_t ka = 0xCBAC1FED; + constexpr uint32_t km = 0x3f3f3f3f; + + float scale = *(const float *)vbq; + const block_iq2_kt * bq2 = (const block_iq2_kt *)((const char *)vbq + sizeof(float)) + kbx; + + // iqs is 0...28 + const int ib32 = iqs/4; + const int32_t * q8 = (const int *)bq8_1[ib32].qs; + const int ls = iq4k_values[(bq2->scales[ib32%4] >> 4*(ib32/4)) & 0xf]; + const float dl = scale * ls * 1.05f; + auto ql = (const uint16_t *)bq2->ql; + int sumi = 0; + for (int j = 0; j < 4; ++j) { + uint32_t val = ql[4*ib32+j] + 4096; + int v4 = 0; + for (int k = 0; k < 4; ++k) { + val *= ka; + v4 |= (ggml_cuda_dp4a(val & km, 0x01010101, -126) & 0xff) << 8*k; + } + sumi = ggml_cuda_dp4a(v4, q8[2*j+0], sumi); + v4 = 0; + for (int k = 0; k < 4; ++k) { + val *= ka; + v4 |= (ggml_cuda_dp4a(val & km, 0x01010101, -126) & 0xff) << 8*k; + } + sumi = ggml_cuda_dp4a(v4, q8[2*j+1], sumi); + } + *result += dl * __low2float(bq8_1[ib32].ds) * sumi; +} + void mul_mat_vec_iq2_kt_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_k.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_k.cu index abc50af0..e01df5f9 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_k.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_k.cu @@ -1,5 +1,62 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq3_k_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iiqs, float * result) { + const block_iq3_k * bq3 = (const block_iq3_k *) vbq + kbx; + + int iqs = iiqs/4; + const int ib128 = iqs/4; // 0 or 1. 0 works on quants 0...127, 1 on quants 128...255 + // Each thread processes 8 quants in each of the 4 32-blocks + const int il8 = iqs%4; // 0...3. 0 works on quants 0...7, 1 on quants 8...15, 2 on 16...23, 3 on 24...31 + const int shift = 4*(il8/2); + + const uint16_t * ql = (const uint16_t *)bq3->qs + 16*ib128 + 4*il8; + const uint16_t * qh = (const uint16_t *)bq3->qh + 4*il8; + + uint32_t aux32; + const uint8_t * aux8 = (const uint8_t *)&aux32; + + const int hshift = 4*(1-ib128); + const uint16_t sh = bq3->scales_h >> (8*ib128 + il8/2); + + const uint8_t extra = bq3->extra >> (8*ib128 + il8/2); + uint32_t extra32 = uint32_t(extra) * 0x01010101; + uint32_t extra32_1 = ((extra32 << 3) & 0x08080808) | ((extra32 << 5) & 0x80808080); + uint32_t extra32_2 = ((extra32 << 2) & 0x08080808) | ((extra32 << 4) & 0x80808080); + + const int * q8; + int sumi[4] = {0, 0, 0, 0}; + for (int i = 0; i < 2; ++i) { + uint32_t vl = ql[2*i+0] | (ql[2*i+1] << 16); + uint32_t vh = ((qh[2*i+0] | (qh[2*i+1] << 16)) << hshift); + + uint32_t val1 = ((vl >> 0) & 0x33333333) | extra32_1 | ((vh >> 2) & 0x04040404) | ((vh >> 0) & 0x40404040); + uint32_t val2 = ((vl >> 2) & 0x33333333) | extra32_2 | ((vh >> 3) & 0x04040404) | ((vh >> 1) & 0x40404040); + int2 v1 = get_int_from_table_16(val1, iq3nl_values); + int2 v2 = get_int_from_table_16(val2, iq3nl_values); + + q8 = (const int *)bq8_1[4*ib128+0].qs + 2*il8; + sumi[0] = ggml_cuda_dp4a(v1.x, q8[i], sumi[0]); + + q8 += sizeof(block_q8_1)/4; + sumi[1] = ggml_cuda_dp4a(v2.x, q8[i], sumi[1]); + + q8 += sizeof(block_q8_1)/4; + sumi[2] = ggml_cuda_dp4a(v1.y, q8[i], sumi[2]); + + q8 += sizeof(block_q8_1)/4; + sumi[3] = ggml_cuda_dp4a(v2.y, q8[i], sumi[3]); + } + const float d = __half2float(bq3->d); + const uint16_t * sl16 = (const uint16_t *)bq3->scales_l + 2*ib128; + aux32 = ((((sl16[0] | (sl16[1] << 16)) >> shift) & 0x0f0f0f0f) << 1) | 0x01010101; + *result += d * (__low2float(bq8_1[4*ib128+0].ds) * aux8[0] * (sh & 0x01 ? -1 : 1) * sumi[0] + + __low2float(bq8_1[4*ib128+1].ds) * aux8[1] * (sh & 0x04 ? -1 : 1) * sumi[1] + + __low2float(bq8_1[4*ib128+2].ds) * aux8[2] * (sh & 0x10 ? -1 : 1) * sumi[2] + + __low2float(bq8_1[4*ib128+3].ds) * aux8[3] * (sh & 0x40 ? -1 : 1) * sumi[3]); + +} + void mul_mat_vec_iq3_k_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_k_4.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_k_4.cu deleted file mode 100644 index 2b7a0b90..00000000 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_k_4.cu +++ /dev/null @@ -1,6 +0,0 @@ -#include "../iqk_mmvq_templates.cuh" - -void mul_mat_vec_iq3_k_r4_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { - iqk_mul_mat_vec_q_cuda(args, stream); -} - diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_k_r4.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_k_r4.cu new file mode 100644 index 00000000..00e7faa1 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_k_r4.cu @@ -0,0 +1,46 @@ +#include "../iqk_mmvq_templates.cuh" + +__device__ __forceinline__ void vec_dot_iq3_k_r4_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + const block_iq3_k_r4 * bq3 = (const block_iq3_k_r4 *)vbq + kbx; + + // iqs is 0...30 in steps of 2 + const int ib16 = iqs/2; + const float d8 = __low2float(bq8_1[ib16/2].ds); + const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); + + int ib32 = ib16/2; + int is = ib16%2; + int scales[2]; + const uint32_t * scales_l = (const uint32_t *)bq3->scales_l; + const uint32_t * scales_h = (const uint32_t *)bq3->scales_h; + + scales[0] = (((scales_l[2*(ib32%4)+is] >> 4*(ib32/4)) & 0x0f0f0f0f) << 1) | 0x01010101; + scales[1] = (scales_h[is] >> ib32) & 0x01010101; + // This is not faster. Why? + //scales[1] = __vcmpeq4((scales_h[is] >> ib32) & 0x01010101, 0x01010101); + //scales[0] = __vsub4(scales[0] ^ scales[1], scales[1]); + const int8_t * s8 = (const int8_t *)scales; + const uint32_t * q2 = (const uint32_t *)bq3->qs + 8*ib32 + 4*is; + const uint32_t * qh = (const uint32_t *)bq3->qh + 4*ib32; + for (int i = 0; i < 4; ++i) { + uint32_t extra32 = uint32_t((bq3->extra[i+4*is] >> ib32) & 1) * 0x88888888; + + int sumi1 = 0; + uint32_t h = qh[i] >> 4*is; + uint32_t val1 = ((q2[i] >> 0) & 0x33333333) | extra32 | ((h << 2) & 0x04040404) | ((h << 4) & 0x40404040); + uint32_t val2 = ((q2[i] >> 2) & 0x33333333) | extra32 | ((h << 1) & 0x04040404) | ((h << 3) & 0x40404040); + int2 v1 = get_int_from_table_16(val1, iq3nl_values); + int2 v2 = get_int_from_table_16(val2, iq3nl_values); + sumi1 = ggml_cuda_dp4a(v1.x, q8[0], ggml_cuda_dp4a(v2.x, q8[1], sumi1)); + sumi1 = ggml_cuda_dp4a(v1.y, q8[2], ggml_cuda_dp4a(v2.y, q8[3], sumi1)); + const float d = __half2float(bq3->d[i]) * d8; + result[i] += d * sumi1 * s8[i] * (s8[i+4] ? -1 : 1); + } +} + +void mul_mat_vec_iq3_k_r4_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { + iqk_mul_mat_vec_q_cuda(args, stream); +} + diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_ks.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_ks.cu index 327c807a..83f68a62 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_ks.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_ks.cu @@ -1,5 +1,58 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq3_ks_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iiqs, float * result) { + + float d = __half2float(*(const half *)vbq); + const block_iq3_ks * bq3 = (const block_iq3_ks *)((const char *)vbq + sizeof(half)) + kbx; + + int iqs = iiqs/4; + const int ib128 = iqs/4; // 0 or 1. 0 works on quants 0...127, 1 on quants 128...255 + // Each thread processes 8 quants in each of the 4 32-blocks + const int il8 = iqs%4; // 0...3. 0 works on quants 0...7, 1 on quants 8...15, 2 on 16...23, 3 on 24...31 + + const uint16_t * ql = (const uint16_t *)bq3->qs + 16*ib128 + 4*il8; + const uint16_t * qh = (const uint16_t *)bq3->qh + 4*il8; + + uint16_t extra = bq3->extra >> 4*ib128; + uint32_t extra_v = uint32_t(extra >> 8) * 0x01010101; + + uint32_t extra32_1 = ((extra_v << 3) & 0x08080808) | ((extra_v << 5) & 0x80808080); + uint32_t extra32_2 = ((extra_v << 2) & 0x08080808) | ((extra_v << 4) & 0x80808080); + + const int * q8; + int sumi[4] = {0, 0, 0, 0}; + for (int i = 0; i < 2; ++i) { + uint32_t vl = ql[2*i+0] | (ql[2*i+1] << 16); + uint32_t vh = ((qh[2*i+0] | (qh[2*i+1] << 16)) >> 4*ib128); + + uint32_t val1 = ((vl >> 0) & 0x33333333) | extra32_1 | ((vh << 2) & 0x04040404) | ((vh << 4) & 0x40404040); + uint32_t val2 = ((vl >> 2) & 0x33333333) | extra32_2 | ((vh << 1) & 0x04040404) | ((vh << 3) & 0x40404040); + int2 v1 = get_int_from_table_16(val1, iq3nl_values); + int2 v2 = get_int_from_table_16(val2, iq3nl_values); + + q8 = (const int *)bq8_1[4*ib128+0].qs + 2*il8; + sumi[0] = ggml_cuda_dp4a(v1.x, q8[i], sumi[0]); + + q8 += sizeof(block_q8_1)/4; + sumi[1] = ggml_cuda_dp4a(v2.x, q8[i], sumi[1]); + + q8 += sizeof(block_q8_1)/4; + sumi[2] = ggml_cuda_dp4a(v1.y, q8[i], sumi[2]); + + q8 += sizeof(block_q8_1)/4; + sumi[3] = ggml_cuda_dp4a(v2.y, q8[i], sumi[3]); + } + const uint16_t * sl16 = (const uint16_t *)bq3->scales; + int32_t aux32 = __vsub4(((sl16[0] | (sl16[1] << 16)) >> 4*ib128) & 0x0f0f0f0f, 0x10101010); + const int8_t * a8 = (const int8_t *)&aux32; + *result += d * (__low2float(bq8_1[4*ib128+0].ds) * (a8[0] + ((extra << 4) & 0x10)) * sumi[0] + + __low2float(bq8_1[4*ib128+1].ds) * (a8[1] + ((extra << 3) & 0x10)) * sumi[1] + + __low2float(bq8_1[4*ib128+2].ds) * (a8[2] + ((extra << 2) & 0x10)) * sumi[2] + + __low2float(bq8_1[4*ib128+3].ds) * (a8[3] + ((extra << 1) & 0x10)) * sumi[3]); + +} + void mul_mat_vec_iq3_ks_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_kt.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_kt.cu index d118793d..a4429e74 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_kt.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq3_kt.cu @@ -1,5 +1,47 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq3_kt_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + constexpr uint32_t ka = 0xCBAC1FED; + constexpr uint32_t km = 0x3f3f3f3f; + + float scale = *(const float *)vbq; + const block_iq3_kt * bq3 = (const block_iq3_kt *)((const char *)vbq + sizeof(float)) + kbx; + + // iqs is 0...28 + const int ib32 = iqs/4; + const int32_t * q8 = (const int *)bq8_1[ib32].qs; + const int ls = (bq3->scales[ib32%4] >> 4*(ib32/4)) & 0xf; + const float dl = scale * ls * 1.015f; + auto ql = (const uint16_t *)bq3->ql; + uint32_t mask = 0x01010101 << ib32; + const uint32_t * qh = (const uint32_t *)bq3->qh; + int sumi = 0; + for (int j = 0; j < 4; ++j) { + uint32_t val = ql[4*ib32+j] + 4096; + int v4 = 0; + for (int k = 0; k < 4; ++k) { + val *= ka; + int8_t q = std::abs(ggml_cuda_dp4a(val & km, 0x01010101, -126)); + v4 |= q << 8*k; + } + uint32_t signs = __vcmpne4(qh[2*j+0] & mask, 0); + v4 = __vsub4(v4 ^ signs, signs); + sumi = ggml_cuda_dp4a(v4, q8[2*j+0], sumi); + v4 = 0; + for (int k = 0; k < 4; ++k) { + val *= ka; + int8_t q = std::abs(ggml_cuda_dp4a(val & km, 0x01010101, -126)); + v4 |= q << 8*k; + } + signs = __vcmpne4(qh[2*j+1] & mask, 0); + v4 = __vsub4(v4 ^ signs, signs); + sumi = ggml_cuda_dp4a(v4, q8[2*j+1], sumi); + } + *result += dl * __low2float(bq8_1[ib32].ds) * sumi; +} + void mul_mat_vec_iq3_kt_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_k.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_k.cu index bdc98a30..f92e287d 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_k.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_k.cu @@ -1,5 +1,32 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq4_k_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + const block_iq4_k * bq4 = (const block_iq4_k *) vbq + kbx; + const uint8_t * all_values = (const uint8_t *)iq4k_values; + + // iqs is 0...28 + const int ib32 = iqs/4; + // Why iqs/4 ? + const int32_t * q8 = (const int *)bq8_1[ib32].qs; + const uint16_t * q4 = (const uint16_t *)bq4->qs + 8*ib32; + const uint16_t extra = bq4->extra >> 2*ib32; + int v1, v2; + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 4; ++j) { + const uint32_t aux32 = q4[2*j+0] | (q4[2*j+1] << 16); + get_int_from_table_16_shift(aux32, extra, all_values, v1, v2); + sumi1 = ggml_cuda_dp4a(v1, q8[j+0], sumi1); + sumi2 = ggml_cuda_dp4a(v2, q8[j+4], sumi2); + } + const float d = __half2float(bq4->d) * __low2float(bq8_1[ib32].ds); + const uint8_t sh = bq4->scales_h[ib32/2] >> 4*(ib32%2); + const int ls1 = ((bq4->scales_l[ib32] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int ls2 = ((bq4->scales_l[ib32] >> 4) | ((sh << 2) & 0x30)) - 32; + *result += d * (sumi1 * ls1 + sumi2 * ls2); +} + void mul_mat_vec_iq4_k_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_k_r4.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_k_r4.cu index ddd97433..fd776a73 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_k_r4.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_k_r4.cu @@ -1,5 +1,36 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq4_k_r4_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + const block_iq4_k_r4 * bq4 = (const block_iq4_k_r4 *)vbq + kbx; + + // iqs is 0...28 in steps of 2 + const int ib16 = iqs/2; + const float d8 = __low2float(bq8_1[ib16/2].ds); + const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); + + int ib32 = ib16/2; + int is = ib16%2; + int scales; + const uint32_t * scales_l = (const uint32_t *)bq4->scales_l; + const uint32_t * scales_h = (const uint32_t *)bq4->scales_h; + scales = __vsub4(((scales_l[2*(ib32%4)+is] >> 4*(ib32/4)) & 0x0f0f0f0f) | (((scales_h[2*(ib32%2)+is] >> 2*(ib32/2)) & 0x03030303) << 4), 0x20202020); + const int8_t * s8 = (const int8_t *)&scales; + int2 val1; + const int * q4 = (const int *)bq4->qs + 16*ib32; + for (int i = 0; i < 4; ++i) { + auto values1 = iq4k_values + (((bq4->extra[i+4*is] >> ib32) & 1) << 4); + int sumi1 = 0; + val1 = get_int_from_table_16(q4[i+4*is+0], values1); + sumi1 = ggml_cuda_dp4a(val1.x, q8[0], ggml_cuda_dp4a(val1.y, q8[2], sumi1)); + val1 = get_int_from_table_16(q4[i+4*is+8], values1); + sumi1 = ggml_cuda_dp4a(val1.x, q8[1], ggml_cuda_dp4a(val1.y, q8[3], sumi1)); + const float d = __half2float(bq4->d[i]) * d8; + result[i] += d * sumi1 * s8[i]; + } +} + void mul_mat_vec_iq4_k_r4_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_ks.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_ks.cu index 1ee885c1..7f226761 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_ks.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_ks.cu @@ -1,5 +1,26 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq4_ks_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + float scale = *(const float *)vbq; + const block_iq4_ks * bq4 = (const block_iq4_ks *)((const char *)vbq + sizeof(float)) + kbx; + + // iqs is 0...28 + const int ib32 = iqs/4; // Why iqs/4 ? + const int32_t * q8 = (const int *)bq8_1[ib32].qs; + const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32; + const float dl = scale * ((bq4->scales[ib32] & 254) - 127); + auto values = iq4k_values + ((bq4->scales[ib32] & 1) << 4); + int sumi = 0; + for (int j = 0; j < 4; ++j) { + auto v = get_int_from_table_16(q4[j], values); + sumi = ggml_cuda_dp4a(v.x, q8[j+0], sumi); + sumi = ggml_cuda_dp4a(v.y, q8[j+4], sumi); + } + *result += dl * __low2float(bq8_1[ib32].ds) * sumi; +} + void mul_mat_vec_iq4_ks_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_ks_r4.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_ks_r4.cu index 0a7a2933..4fe99165 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_ks_r4.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_ks_r4.cu @@ -1,5 +1,35 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq4_ks_r4_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + const float * dptr = (const float *)vbq; + const block_iq4_ks_r4 * bq4 = (const block_iq4_ks_r4 *)(dptr + 4) + kbx; + + // iqs is 0...28 in steps of 2 + const int ib16 = iqs/2; + const float d8 = __low2float(bq8_1[ib16/2].ds); + const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); + + int ib32 = ib16/2; + int is = ib16%2; + const uint32_t * scales32 = (const uint32_t *)bq4->scales; + int scales = __vsub4(scales32[ib32] & 0xfefefefe, 0x7f7f7f7f); + const int8_t * s8 = (const int8_t *)&scales; + int2 val; + const int * q4 = (const int *)bq4->qs + 16*ib32; + for (int i = 0; i < 4; ++i) { + auto values = iq4k_values + ((bq4->scales[4*ib32+i] & 1) << 4); + int sumi = 0; + val = get_int_from_table_16(q4[i+4*is+0], values); + sumi = ggml_cuda_dp4a(val.x, q8[0], ggml_cuda_dp4a(val.y, q8[2], sumi)); + val = get_int_from_table_16(q4[i+4*is+8], values); + sumi = ggml_cuda_dp4a(val.x, q8[1], ggml_cuda_dp4a(val.y, q8[3], sumi)); + const float d = dptr[i] * d8; + result[i] += d * sumi * s8[i]; + } +} + void mul_mat_vec_iq4_ks_r4_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_kss.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_kss.cu index 453893e8..a2eb6015 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_kss.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_kss.cu @@ -1,5 +1,30 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq4_kss_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + float scale = *(const float *)vbq; + const block_iq4_kss * bq4 = (const block_iq4_kss *)((const char *)vbq + sizeof(float)) + kbx; + + // iqs is 0...28 + const int ib32 = iqs/4; // Why iqs/4 ? + const int32_t * q8 = (const int *)bq8_1[ib32].qs; + const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32; + uint32_t s32 = (q4[0] & 0x00010001) | ((q4[1] & 0x00010001) << 2) | ((q4[2] & 0x00010001) << 4) | ((q4[3] & 0x00010001) << 6); + uint8_t ls = (s32 | (s32 >> 15)) & 0xff; + const float dl = scale * ((ls & 254) - 127); + auto values = iq4k_values + ((ls & 1) << 4); + int sumi = 0; + for (int j = 0; j < 4; ++j) { + uint32_t aux32 = q4[j] & 0xfffefffe; + aux32 ^= (aux32 >> 1); + auto v = get_int_from_table_16(aux32, values); + sumi = ggml_cuda_dp4a(v.x, q8[j+0], sumi); + sumi = ggml_cuda_dp4a(v.y, q8[j+4], sumi); + } + *result += dl * __low2float(bq8_1[ib32].ds) * sumi; +} + void mul_mat_vec_iq4_kss_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_kt.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_kt.cu index 111425fb..efb6365c 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_kt.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq4_kt.cu @@ -1,5 +1,42 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq4_kt_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + constexpr uint32_t ka = 0xCBAC1FED; + constexpr uint32_t km = 0x3f3f3f3f; + + float scale = *(const float *)vbq; + const block_iq4_kt * bq4 = (const block_iq4_kt *)((const char *)vbq + sizeof(float)) + kbx; + + // iqs is 0...28 + const int ib32 = iqs/4; // Why iqs/4 ? + const int32_t * q8 = (const int *)bq8_1[ib32].qs; + //const int8_t * q8 = bq8_1[ib32].qs; + const int ls = (bq4->qs[ib32] & 0xff) >> 1; + const float dl = scale * (ls - 64); + const uint32_t idx0 = ((bq4->qs[ib32] & 1) << 15) + 4096; + auto ql = (const uint8_t *)(bq4->qs + 8); + auto qh = ql + 64; + ql += 8*ib32; + qh += 8*(ib32%4); + const int shift1 = 8 - 4*(ib32/4); + int sumi = 0; + for (int j = 0; j < 8; ++j) { + const uint32_t sh = bq4->qs[ib32] >> (8 + 3*j); + uint32_t val = ql[j] + ((qh[j] << shift1) & 0xf00) + ((sh & 7) << 12) + idx0; + int v4 = 0; + for (int k = 0; k < 4; ++k) { + val *= ka; + //int s = val & km; + //sumi += q8[4*j+k] * ggml_cuda_dp4a(s, 0x01010101, -126); + v4 |= (ggml_cuda_dp4a(val & km, 0x01010101, -126) & 0xff) << 8*k; + } + sumi = ggml_cuda_dp4a(v4, q8[j], sumi); + } + *result += dl * __low2float(bq8_1[ib32].ds) * sumi; +} + void mul_mat_vec_iq4_kt_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_k.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_k.cu index a176b398..612692bc 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_k.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_k.cu @@ -1,5 +1,40 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq5_k_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + const block_iq5_k * bq5 = (const block_iq5_k *) vbq + kbx; + const uint8_t * all_values = (const uint8_t *)iq5nl_values; + + int i4 = iqs/4; // 0...7. Blocks of 16 index is 4*(i4/2) + (i4%2) + (0 and 2) + + const int32_t * q8_1 = (const int *)bq8_1[2*(i4/2)+0].qs + 4*(i4%2); + const int32_t * q8_2 = (const int *)bq8_1[2*(i4/2)+1].qs + 4*(i4%2); + const uint32_t * q4 = (const uint32_t *)bq5->qs + 8*(i4/2) + 4*(i4%2); + const uint32_t * qh = (const uint32_t *)bq5->qh + 4*(i4%2); + const uint16_t extra = bq5->extra >> (4*(i4/2) + (i4%2)); + const uint8_t * values1 = all_values + 32*(extra & 1); + const uint8_t * values2 = all_values + 8*(extra & 4); + uint32_t aux32[2]; + const uint8_t * a8 = (const uint8_t *)aux32; + int v1, v2; + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 4; ++j) { + uint32_t h = qh[j] >> 2*(i4/2); + aux32[0] = ((q4[j] >> 0) & 0x0f0f0f0f) | ((h << 4) & 0x10101010); + aux32[1] = ((q4[j] >> 4) & 0x0f0f0f0f) | ((h << 3) & 0x10101010); + v1 = int_from_table(a8+0, values1); + v2 = int_from_table(a8+4, values2); + sumi1 = ggml_cuda_dp4a(v1, q8_1[j], sumi1); + sumi2 = ggml_cuda_dp4a(v2, q8_2[j], sumi2); + } + const float d5 = __half2float(bq5->d); + const uint8_t sh = bq5->scales_h[i4/2] >> 2*(i4%2); + const int ls1 = (((bq5->scales_l[2*(i4/2)+0] >> 4*(i4%2)) & 0xf) | ((sh << 4) & 0x30)) - 32; + const int ls2 = (((bq5->scales_l[2*(i4/2)+1] >> 4*(i4%2)) & 0xf) | ((sh << 0) & 0x30)) - 32; + *result += d5 * (__low2float(bq8_1[2*(i4/2)+0].ds) * sumi1 * ls1 + __low2float(bq8_1[2*(i4/2)+1].ds) * sumi2 * ls2); +} + void mul_mat_vec_iq5_k_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_k_r4.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_k_r4.cu index 6779b55f..a0fee28c 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_k_r4.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_k_r4.cu @@ -1,5 +1,45 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq5_k_r4_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + const block_iq5_k_r4 * bq5 = (const block_iq5_k_r4 *)vbq + kbx; + + // iqs is 0...28 in steps of 2 + const int ib16 = iqs/2; + const float d8 = __low2float(bq8_1[ib16/2].ds); + const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); + + int ib32 = ib16/2; + int is = ib16%2; + int scales; + const uint32_t * scales_l = (const uint32_t *)bq5->scales_l; + const uint32_t * scales_h = (const uint32_t *)bq5->scales_h; + scales = __vsub4(((scales_l[2*(ib32%4)+is] >> 4*(ib32/4)) & 0x0f0f0f0f) | (((scales_h[2*(ib32%2)+is] >> 2*(ib32/2)) & 0x03030303) << 4), 0x20202020); + const int8_t * s8 = (const int8_t *)&scales; + int2 val1; + const int * q4 = (const int *)bq5->qs + 16*ib32; + const int * qh = (const int *)bq5->qh + 4*ib32; + int aux32[2]; + const uint8_t * aux8 = (const uint8_t *)aux32; + for (int i = 0; i < 4; ++i) { + auto values1 = iq5nl_values + (((bq5->extra[i+4*is] >> ib32) & 1) << 5); + int sumi1 = 0; + aux32[0] = ((q4[i+4*is+0] >> 0) & 0x0f0f0f0f) | (((qh[i] >> (2*is+0)) & 0x01010101) << 4); + aux32[1] = ((q4[i+4*is+0] >> 4) & 0x0f0f0f0f) | (((qh[i] >> (2*is+1)) & 0x01010101) << 4); + val1.x = int_from_table(aux8+0, (const uint8_t *)values1); + val1.y = int_from_table(aux8+4, (const uint8_t *)values1); + sumi1 = ggml_cuda_dp4a(val1.x, q8[0], ggml_cuda_dp4a(val1.y, q8[2], sumi1)); + aux32[0] = ((q4[i+4*is+8] >> 0) & 0x0f0f0f0f) | (((qh[i] >> (2*is+4)) & 0x01010101) << 4); + aux32[1] = ((q4[i+4*is+8] >> 4) & 0x0f0f0f0f) | (((qh[i] >> (2*is+5)) & 0x01010101) << 4); + val1.x = int_from_table(aux8+0, (const uint8_t *)values1); + val1.y = int_from_table(aux8+4, (const uint8_t *)values1); + sumi1 = ggml_cuda_dp4a(val1.x, q8[1], ggml_cuda_dp4a(val1.y, q8[3], sumi1)); + const float d = __half2float(bq5->d[i]) * d8; + result[i] += d * sumi1 * s8[i]; + } +} + void mul_mat_vec_iq5_k_r4_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_ks.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_ks.cu index b8479621..979b26be 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_ks.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_ks.cu @@ -1,5 +1,38 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq5_ks_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + float scale = *(const float *)vbq; + const block_iq5_ks * bq5 = (const block_iq5_ks *)((const char *)vbq + sizeof(float)) + kbx; + const uint8_t * all_values = (const uint8_t *)iq5nl_values; + + int i4 = iqs/4; // 0...7. Blocks of 16 index is 4*(i4/2) + (i4%2) + (0 and 2) + + const int32_t * q8_1 = (const int *)bq8_1[2*(i4/2)+0].qs + 4*(i4%2); + const int32_t * q8_2 = (const int *)bq8_1[2*(i4/2)+1].qs + 4*(i4%2); + const uint32_t * q4 = (const uint32_t *)bq5->qs + 8*(i4/2) + 4*(i4%2); + const uint32_t * qh = (const uint32_t *)bq5->qh + 4*(i4%2); + const uint8_t * values1 = all_values + ((bq5->scales[2*(i4/2)+0] & 1) << 5); + const uint8_t * values2 = all_values + ((bq5->scales[2*(i4/2)+1] & 1) << 5); + uint32_t aux32[2]; + const uint8_t * a8 = (const uint8_t *)aux32; + int v1, v2; + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 4; ++j) { + uint32_t h = qh[j] >> 2*(i4/2); + aux32[0] = ((q4[j] >> 0) & 0x0f0f0f0f) | ((h << 4) & 0x10101010); + aux32[1] = ((q4[j] >> 4) & 0x0f0f0f0f) | ((h << 3) & 0x10101010); + v1 = int_from_table(a8+0, values1); + v2 = int_from_table(a8+4, values2); + sumi1 = ggml_cuda_dp4a(v1, q8_1[j], sumi1); + sumi2 = ggml_cuda_dp4a(v2, q8_2[j], sumi2); + } + const int ls1 = (bq5->scales[2*(i4/2)+0] & 254) - 127; + const int ls2 = (bq5->scales[2*(i4/2)+1] & 254) - 127; + *result += scale * (__low2float(bq8_1[2*(i4/2)+0].ds) * sumi1 * ls1 + __low2float(bq8_1[2*(i4/2)+1].ds) * sumi2 * ls2); +} + void mul_mat_vec_iq5_ks_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_ks_r4.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_ks_r4.cu index b512be3b..baf625de 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_ks_r4.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq5_ks_r4.cu @@ -1,5 +1,43 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq5_ks_r4_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + const float * dptr = (const float *)vbq; + const block_iq5_ks_r4 * bq5 = (const block_iq5_ks_r4 *)(dptr + 4) + kbx; + + // iqs is 0...28 in steps of 2 + const int ib16 = iqs/2; + const float d8 = __low2float(bq8_1[ib16/2].ds); + const int32_t * q8 = (const int *)bq8_1[ib16/2].qs + 4*(ib16%2); + + int ib32 = ib16/2; + int is = ib16%2; + const uint32_t * scales32 = (const uint32_t *)bq5->scales; + int scales = __vsub4(scales32[ib32] & 0xfefefefe, 0x7f7f7f7f); + const int8_t * s8 = (const int8_t *)&scales; + int2 val; + const int * q4 = (const int *)bq5->qs + 16*ib32; + const int * qh = (const int *)bq5->qh + 4*ib32; + int aux32[2]; + const uint8_t * aux8 = (const uint8_t *)aux32; + for (int i = 0; i < 4; ++i) { + auto values = iq5nl_values + ((bq5->scales[4*ib32+i] & 1) << 5); + int sumi = 0; + aux32[0] = ((q4[i+4*is+0] >> 0) & 0x0f0f0f0f) | (((qh[i] >> (2*is+0)) & 0x01010101) << 4); + aux32[1] = ((q4[i+4*is+0] >> 4) & 0x0f0f0f0f) | (((qh[i] >> (2*is+1)) & 0x01010101) << 4); + val.x = int_from_table(aux8+0, (const uint8_t *)values); + val.y = int_from_table(aux8+4, (const uint8_t *)values); + sumi = ggml_cuda_dp4a(val.x, q8[0], ggml_cuda_dp4a(val.y, q8[2], sumi)); + aux32[0] = ((q4[i+4*is+8] >> 0) & 0x0f0f0f0f) | (((qh[i] >> (2*is+4)) & 0x01010101) << 4); + aux32[1] = ((q4[i+4*is+8] >> 4) & 0x0f0f0f0f) | (((qh[i] >> (2*is+5)) & 0x01010101) << 4); + val.x = int_from_table(aux8+0, (const uint8_t *)values); + val.y = int_from_table(aux8+4, (const uint8_t *)values); + sumi = ggml_cuda_dp4a(val.x, q8[1], ggml_cuda_dp4a(val.y, q8[3], sumi)); + result[i] += dptr[i] * d8 * sumi * s8[i]; + } +} + void mul_mat_vec_iq5_ks_r4_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); } diff --git a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq6_k.cu b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq6_k.cu index b1837496..bcb438b8 100644 --- a/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq6_k.cu +++ b/ggml/src/ggml-cuda/template-instances/mmvq-instance-iq6_k.cu @@ -1,5 +1,38 @@ #include "../iqk_mmvq_templates.cuh" +__device__ __forceinline__ void vec_dot_iq6_k_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float * result) { + + const block_iq6_k * bq6 = (const block_iq6_k *) vbq + kbx; + const uint8_t * all_values = (const uint8_t *)iq6nl_values; + + int i4 = iqs/4; // 0...7. Blocks of 16 index is 4*(i4/2) + (i4%2) + (0 and 2) + // Blocks of 32 index is 2*(i4/2) + 0 or 1 + + const int32_t * q8_1 = (const int *)bq8_1[2*(i4/2)+0].qs + 4*(i4%2); + const int32_t * q8_2 = (const int *)bq8_1[2*(i4/2)+1].qs + 4*(i4%2); + const uint32_t * q4 = (const uint32_t *)bq6->qs + 8*(i4/2) + 4*(i4%2); + const uint32_t * qh = (const uint32_t *)bq6->qh + 8*(i4/4) + 4*(i4%2); + const uint16_t extra = bq6->extra >> (4*(i4/2) + (i4%2)); + const uint8_t * values1 = all_values + 64*(extra & 1); + const uint8_t * values2 = all_values + 16*(extra & 4); + uint32_t aux32[2]; + const uint8_t * a8 = (const uint8_t *)aux32; + int v1, v2; + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 4; ++j) { + uint32_t h = qh[j] >> 4*((i4/2)%2); + aux32[0] = ((q4[j] >> 0) & 0x0f0f0f0f) | ((h << 4) & 0x30303030); + aux32[1] = ((q4[j] >> 4) & 0x0f0f0f0f) | ((h << 2) & 0x30303030); + v1 = int_from_table(a8+0, values1); + v2 = int_from_table(a8+4, values2); + sumi1 = ggml_cuda_dp4a(v1, q8_1[j], sumi1); + sumi2 = ggml_cuda_dp4a(v2, q8_2[j], sumi2); + } + const float d6 = __half2float(bq6->d); + *result += d6 * (__low2float(bq8_1[2*(i4/2)+0].ds) * sumi1 * bq6->scales[4*(i4/2)+(i4%2)] + __low2float(bq8_1[2*(i4/2)+1].ds) * sumi2 * bq6->scales[4*(i4/2)+(i4%2)+2]); +} + void mul_mat_vec_iq6_k_q8_1_cuda(const mmvq_args & args, cudaStream_t stream) { iqk_mul_mat_vec_q_cuda(args, stream); }