ikawrakow/ik_llama.cpp
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CUDA: iq2_k_r4 GEMV

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Iwan Kawrakow
2025-05-26 16:13:54 +03:00
parent aaf6d34789
commit fa011c9017
5 changed files with 126 additions and 1 deletions
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1
ggml/src/ggml-cuda.cu
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@@ -3470,6 +3470,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_TYPE_IQ6_K:
case GGML_TYPE_IQ1_BN:
case GGML_TYPE_IQ2_BN:
case GGML_TYPE_IQ2_K_R4:
case GGML_TYPE_IQ3_K_R4:
case GGML_TYPE_IQ4_K_R4:
case GGML_TYPE_IQ5_K_R4:

61
ggml/src/ggml-cuda/convert.cu
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@@ -886,6 +886,53 @@ static __global__ void dequantize_block_iq5_k_r4(const void * __restrict__ vx, d
}
}
template<typename dst_t>
static __global__ void dequantize_block_iq2_k_r4(const void * __restrict__ vx, dst_t * __restrict__ yy, int64_t n_per_row, int64_t row_size) {
int64_t ii = blockIdx.x;
int64_t nblock = n_per_row/256;
int64_t row = ii/nblock;
int64_t row4 = row/4;
int64_t ir = row%4;
int64_t ibl = row4*nblock + ii%nblock;
const int tid = threadIdx.x;
const int il = tid/8; // 0...3
const int ib = tid%8; // 0...7
const block_iq2_k_r4 * x = (const block_iq2_k_r4 *)vx;
dst_t * y = yy + 256*ii + 32*ib;
const float d = __half2float(x[ibl].d[ir]);
int is = 8*ib + ir;
float dl1 = d * (((x[ibl].scales[is%32] >> 4*(is/32)) & 0xf) - 8);
is += 4;
float dl2 = d * (((x[ibl].scales[is%32] >> 4*(is/32)) & 0xf) - 8);
auto values1 = iq2nl_values + (((x[ibl].extra[ir+0] >> ib) & 1) << 2);
auto values2 = iq2nl_values + (((x[ibl].extra[ir+4] >> ib) & 1) << 2);
auto ql = x[ibl].qs + 32*ib + 4*ir;
if constexpr (std::is_same_v<dst_t, nv_bfloat16>) {
y[il+ 0] = __float2bfloat16(dl1 * values1[(ql[il+ 0] >> 0) & 3]);
y[il+ 4] = __float2bfloat16(dl1 * values1[(ql[il+ 0] >> 2) & 3]);
y[il+ 8] = __float2bfloat16(dl1 * values1[(ql[il+ 0] >> 4) & 3]);
y[il+12] = __float2bfloat16(dl1 * values1[(ql[il+ 0] >> 6) & 3]);
y[il+16] = __float2bfloat16(dl2 * values2[(ql[il+16] >> 0) & 3]);
y[il+20] = __float2bfloat16(dl2 * values2[(ql[il+16] >> 2) & 3]);
y[il+24] = __float2bfloat16(dl2 * values2[(ql[il+16] >> 4) & 3]);
y[il+28] = __float2bfloat16(dl2 * values2[(ql[il+16] >> 6) & 3]);
} else {
y[il+ 0] = dl1 * values1[(ql[il+ 0] >> 0) & 3];
y[il+ 4] = dl1 * values1[(ql[il+ 0] >> 2) & 3];
y[il+ 8] = dl1 * values1[(ql[il+ 0] >> 4) & 3];
y[il+12] = dl1 * values1[(ql[il+ 0] >> 6) & 3];
y[il+16] = dl2 * values2[(ql[il+16] >> 0) & 3];
y[il+20] = dl2 * values2[(ql[il+16] >> 2) & 3];
y[il+24] = dl2 * values2[(ql[il+16] >> 4) & 3];
y[il+28] = dl2 * values2[(ql[il+16] >> 6) & 3];
}
}
template<typename dst_t>
static __global__ void dequantize_block_iq3_k_r4(const void * __restrict__ vx, dst_t * __restrict__ yy, int64_t n_per_row, int64_t row_size) {
@@ -1353,6 +1400,14 @@ static void dequantize_row_iq3_k_r4_cuda(const void * vx, dst_t * y, const int64
dequantize_block_iq3_k_r4<<<nb, 32, 0, stream>>>(vx, y, n_per_row, row_size);
}
template<typename dst_t>
static void dequantize_row_iq2_k_r4_cuda(const void * vx, dst_t * y, const int64_t nrows, const int64_t n_per_row, cudaStream_t stream) {
const int64_t k = nrows * n_per_row;
const int64_t row_size = ggml_row_size(GGML_TYPE_IQ4_K, n_per_row);
const int nb = (k + QK_K - 1) / QK_K;
dequantize_block_iq2_k_r4<<<nb, 32, 0, stream>>>(vx, y, n_per_row, row_size);
}
template<typename dst_t>
static void dequantize_row_iq4_k_cuda(const void * vx, dst_t * y, const int64_t nrows, const int64_t n_per_row, cudaStream_t stream) {
const int64_t k = nrows * n_per_row;
@@ -1479,6 +1534,8 @@ to_bf16_cuda_t ggml_get_to_bf16_cuda(ggml_type type) {
return dequantize_row_iq5_k_cuda<nv_bfloat16>;
case GGML_TYPE_IQ6_K:
return dequantize_row_iq6_k_cuda<nv_bfloat16>;
case GGML_TYPE_IQ2_K_R4:
return dequantize_row_iq2_k_r4_cuda<nv_bfloat16>;
case GGML_TYPE_IQ3_K_R4:
return dequantize_row_iq3_k_r4_cuda<nv_bfloat16>;
case GGML_TYPE_IQ4_K_R4:
@@ -1567,6 +1624,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
return convert_unary_cuda<float>;
case GGML_TYPE_BF16:
return convert_from_bf16_cuda;
case GGML_TYPE_IQ2_K_R4:
return dequantize_row_iq2_k_r4_cuda;
case GGML_TYPE_IQ3_K_R4:
return dequantize_row_iq3_k_r4_cuda;
case GGML_TYPE_IQ4_K_R4:
@@ -1652,6 +1711,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
return convert_unary_cuda<half>;
case GGML_TYPE_BF16:
return convert_from_bf16_cuda;
case GGML_TYPE_IQ2_K_R4:
return dequantize_row_iq2_k_r4_cuda;
case GGML_TYPE_IQ3_K_R4:
return dequantize_row_iq3_k_r4_cuda;
case GGML_TYPE_IQ4_K_R4:

56
ggml/src/ggml-cuda/iqk_mmvq.cu
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@@ -8,6 +8,13 @@
typedef void (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs, float *);
template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ2_K_R4> {
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_R4> {
static constexpr int qk = QK_K;
@@ -494,7 +501,7 @@ __device__ __forceinline__ void vec_dot_iq3_k_r4_q8_1(
// 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 int8_t * s8 = (const int8_t *)scales;
int2 val1;
const int * q2 = (const int *)bq3->qs + 8*ib32 + 4*is;
const int * qh = (const int *)bq3->qh + 4*ib32;
@@ -520,6 +527,45 @@ __device__ __forceinline__ void vec_dot_iq3_k_r4_q8_1(
}
}
__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;
int2 val1;
const int * q2 = (const int *)bq2->qs + 8*ib32 + 4*is;
int aux32[2];
const uint8_t * aux8 = (const uint8_t *)aux32;
for (int i = 0; i < 4; ++i) {
auto values1 = iq2nl_values + (((bq2->extra[i+4*is] >> ib32) & 1) << 2);
int sumi1 = 0;
aux32[0] = ((q2[i] >> 0) & 0x03030303);
aux32[1] = ((q2[i] >> 2) & 0x03030303);
// TODO: int_from_table_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[1], sumi1));
aux32[0] = ((q2[i] >> 4) & 0x03030303);
aux32[1] = ((q2[i] >> 6) & 0x03030303);
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[2], ggml_cuda_dp4a(val1.y, q8[3], sumi1));
const float d = __half2float(bq2->d[i]) * d8;
result[i] += d * sumi1 * s8[i];
}
}
#define VDR_IQ6_K_Q8_1_MMVQ 4
#define VDR_IQ6_K_Q8_1_MMQ 4
@@ -973,6 +1019,14 @@ void mul_mat_vec_iq5_k_r4_q8_1_cuda(
iqk_mul_mat_vec_q_cuda<GGML_TYPE_IQ5_K_R4, 2, vec_dot_iq5_k_r4_q8_1, 4>(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_iq2_k_r4_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_IQ2_K_R4, 2, vec_dot_iq2_k_r4_q8_1, 4>(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_k_r4_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,

5
ggml/src/ggml-cuda/iqk_mmvq.cuh
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@@ -61,6 +61,11 @@ void mul_mat_vec_iq2_bn_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_iq2_k_r4_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_k_r4_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,

4
ggml/src/ggml-cuda/mmvq.cu
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@@ -542,6 +542,9 @@ static void ggml_cuda_op_mul_mat_vec_q_impl(ggml_backend_cuda_context & ctx, ggm
case GGML_TYPE_IQ3_S:
mul_mat_vec_iq3_s_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_K_R4:
mul_mat_vec_iq2_k_r4_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_K_R4:
mul_mat_vec_iq3_k_r4_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;
@@ -664,6 +667,7 @@ bool ggml_cuda_mmvq_type_supported(ggml_type src0_type) {
case GGML_TYPE_IQ5_KS:
case GGML_TYPE_IQ6_K:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_K_R4:
case GGML_TYPE_IQ3_K_R4:
case GGML_TYPE_IQ4_K_R4:
case GGML_TYPE_IQ5_K_R4: