ikawrakow/ik_llama.cpp
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iq3_kt: use integer trellis + CUDA dequantize and MMVQ

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This commit is contained in:
Iwan Kawrakow
2025-06-16 16:57:16 +03:00
parent 6d38e43f1d
commit 32ff1f956f
6 changed files with 91 additions and 21 deletions
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21
ggml/src/ggml-cuda/common.cuh
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@@ -578,6 +578,20 @@ struct ggml_cuda_type_traits<GGML_TYPE_IQ2_KT> {
static constexpr int qi = QI4_XS;
};
template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ3_KT> {
static constexpr int qk = QK_K;
static constexpr int qr = QR4_XS;
static constexpr int qi = QI4_XS;
};
template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ4_KT> {
static constexpr int qk = QK_K;
static constexpr int qr = QR4_XS;
static constexpr int qi = QI4_XS;
};
template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ3_K> {
static constexpr int qk = QK_K;
@@ -648,13 +662,6 @@ struct ggml_cuda_type_traits<GGML_TYPE_IQ3_S> {
static constexpr int qi = QI3_S;
};
template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ4_KT> {
static constexpr int qk = QK_K;
static constexpr int qr = QR4_XS;
static constexpr int qi = QI4_XS;
};
//////////////////////
struct ggml_cuda_device_info {

4
ggml/src/ggml-cuda/convert.cu
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@@ -394,10 +394,10 @@ static __global__ void dequantize_block_iq3_kt(const void * __restrict__ vx, dst
dst_t * y = yy + ii*QK_K + 8*ib;
const uint16_t * ql = (const uint16_t *)x[i].ql;
uint32_t idx = ql[ib] + 4096;
const float dl = scale * ((x[i].scales[(ib/4)%4] >> 4*(ib/16)) & 0xf) * 31.75f * 1.01f; //1.015f;
const float dl = scale * ((x[i].scales[(ib/4)%4] >> 4*(ib/16)) & 0xf) * 1.01f; //1.015f;
uint8_t mask = 1 << (ib/4);
for (int j = 0; j < 8; ++j) {
y[j] = dl * std::abs(trellis_next(idx)) * (x[i].qh[(8*ib+j)%32] & mask ? -1.f : 1.f);
y[j] = dl * std::abs(trellis_next_int(idx)) * (x[i].qh[(8*ib+j)%32] & mask ? -1.f : 1.f);
}
}

50
ggml/src/ggml-cuda/iqk_mmvq.cu
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@@ -504,6 +504,48 @@ __device__ __forceinline__ void vec_dot_iq2_kt_q8_1(
*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
@@ -1304,6 +1346,14 @@ void mul_mat_vec_iq2_kt_q8_1_cuda(
iqk_mul_mat_vec_q_cuda<GGML_TYPE_IQ2_KT, VDR_IQ4_KS_Q8_1_MMVQ, vec_dot_iq2_kt_q8_1>(vx, vy, dst, ids_data, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, ne2, nb02, nb12, nb2, ids_nb0, stream);
}
void mul_mat_vec_iq3_kt_q8_1_cuda(
const void * vx, const void * vy, float * dst, const char * ids_data,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst,
const int ne2, const uint64_t nb02, const uint64_t nb12, const uint64_t nb2, int64_t ids_nb0, cudaStream_t stream) {
iqk_mul_mat_vec_q_cuda<GGML_TYPE_IQ3_KT, VDR_IQ4_KS_Q8_1_MMVQ, vec_dot_iq3_kt_q8_1>(vx, vy, dst, ids_data, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, ne2, nb02, nb12, nb2, ids_nb0, stream);
}
void mul_mat_vec_iq4_kss_q8_1_cuda(
const void * vx, const void * vy, float * dst, const char * ids_data,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst,

15
ggml/src/ggml-cuda/iqk_mmvq.cuh
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@@ -101,12 +101,17 @@ void mul_mat_vec_iq1_m_r4_q8_1_cuda(
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst,
const int ne2, const uint64_t nb02, const uint64_t nb12, const uint64_t nb2, const int64_t ids_nb0, cudaStream_t stream);
void mul_mat_vec_iq4_kt_q8_1_cuda(
const void * vx, const void * vy, float * dst, const char * ids_data,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst,
const int ne2, const uint64_t nb02, const uint64_t nb12, const uint64_t nb2, const int64_t ids_nb0, cudaStream_t stream);
void mul_mat_vec_iq2_kt_q8_1_cuda(
const void * vx, const void * vy, float * dst, const char * ids_data,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst,
const int ne2, const uint64_t nb02, const uint64_t nb12, const uint64_t nb2, const int64_t ids_nb0, cudaStream_t stream);
void mul_mat_vec_iq3_kt_q8_1_cuda(
const void * vx, const void * vy, float * dst, const char * ids_data,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst,
const int ne2, const uint64_t nb02, const uint64_t nb12, const uint64_t nb2, const int64_t ids_nb0, cudaStream_t stream);
void mul_mat_vec_iq4_kt_q8_1_cuda(
const void * vx, const void * vy, float * dst, const char * ids_data,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst,
const int ne2, const uint64_t nb02, const uint64_t nb12, const uint64_t nb2, const int64_t ids_nb0, cudaStream_t stream);

12
ggml/src/ggml-cuda/mmvq.cu
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@@ -527,12 +527,15 @@ static void ggml_cuda_op_mul_mat_vec_q_impl(ggml_backend_cuda_context & ctx, ggm
case GGML_TYPE_IQ4_KSS:
mul_mat_vec_iq4_kss_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ids_data, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, ne2, nb02, nb12, nb2, ids_nb0, stream);
break;
case GGML_TYPE_IQ4_KT:
mul_mat_vec_iq4_kt_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ids_data, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, ne2, nb02, nb12, nb2, ids_nb0, stream);
break;
case GGML_TYPE_IQ2_KT:
mul_mat_vec_iq2_kt_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ids_data, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, ne2, nb02, nb12, nb2, ids_nb0, stream);
break;
case GGML_TYPE_IQ3_KT:
mul_mat_vec_iq3_kt_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ids_data, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, ne2, nb02, nb12, nb2, ids_nb0, stream);
break;
case GGML_TYPE_IQ4_KT:
mul_mat_vec_iq4_kt_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ids_data, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, ne2, nb02, nb12, nb2, ids_nb0, stream);
break;
case GGML_TYPE_IQ2_KS:
mul_mat_vec_iq2_ks_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ids_data, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, ne2, nb02, nb12, nb2, ids_nb0, stream);
break;
@@ -693,8 +696,9 @@ bool ggml_cuda_mmvq_type_supported(ggml_type src0_type) {
case GGML_TYPE_IQ5_KS_R4:
case GGML_TYPE_IQ1_S_R4:
case GGML_TYPE_IQ1_M_R4:
case GGML_TYPE_IQ4_KT:
case GGML_TYPE_IQ2_KT:
case GGML_TYPE_IQ3_KT:
case GGML_TYPE_IQ4_KT:
return true;
default:
return false;

10
ggml/src/iqk/iqk_quantize.cpp
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@@ -7427,7 +7427,11 @@ public:
for (int k = 0; k < kGroupSize; ++k) {
x = ka*x;
s = x & 0x3f3f3f3f;
result[k] = scale*(i8[0] + i8[1] + i8[2] + i8[3] - 126.f);
if constexpr (is_abs) {
result[k] = scale*std::abs(i8[0] + i8[1] + i8[2] + i8[3] - 126.f);
} else {
result[k] = scale*(i8[0] + i8[1] + i8[2] + i8[3] - 126.f);
}
}
} else {
constexpr uint32_t ka = 89226354;
@@ -8289,7 +8293,7 @@ void vec_dot_iq2_kt_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx
namespace {
using QuantizerIQ3KT = QuantizerIQKT<32, 8, 16, true>;
using QuantizerIQ3KT = QuantizerIQKT<32, 8, 16, true, true>;
const QuantizerIQ3KT& iq3kt_quantizer() {
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
@@ -8500,7 +8504,7 @@ size_t quantize_iq3_kt(const float * src, void * dst, int64_t nrows, int64_t n_p
void dequantize_row_iq3_kt(const block_iq3_kt * x, float * y, int64_t k) {
#ifdef __AVX2__
if (iqk_dequantize_ktquants(GGML_TYPE_IQ3_KT, k, x, 0, y, 0, 1)) return;
//if (iqk_dequantize_ktquants(GGML_TYPE_IQ3_KT, k, x, 0, y, 0, 1)) return;
#endif
using Q = QuantizerIQ3KT;
constexpr int kNumGroups = Q::kSuperBlockSize/Q::kGroupSize;