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IQ4_XS: a 4.25 bpw quantization (#5747)

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* Try IQ4_NL with blocks of 64 - does not look good

* iq4_xs: go to super-blocks of 256 and 6-bit scales for blocks of 32

* iq4_xs: CUDA works - 133.2 t/s

* iq4_xs: AVX2 dot product

* iq4_xs: ARM_NEON dot product

* iq4_nl: Metal implementation

As usual, Metal / Apple Silicon don't like my quants.

* iq3_xs: minor fix

* iq4_xs: shrink by using IQ3_S for attn_k and attn_q

* iq4_xs: revert using IQ3_S for attn_k and attn_v

PPL vs size is good, but CPU performance suffers: on M2 Max
TG-128 drops to 21.7 t/s from 28.8, and on a Ryzen-7950X
to 14.5 t/s from 15.8 t/s. On CUDA we have 135 t/s when
using IQ3_S vs 133 t/s with pure IQ4_XS.

* Fix CI

* iq4_xs: Added forgotten check for 256 divisibility

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow 2024-02-27 16:34:24 +02:00 committed by GitHub
parent c24a2a6e60
commit 0becb22ac0
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GPG Key ID: B5690EEEBB952194
11 changed files with 668 additions and 38 deletions
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3
examples/quantize/quantize.cpp
View File

@ -36,7 +36,8 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
{ "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", },
{ "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.25 bpw non-linear quantization", },
{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
{ "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
{ "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
{ "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", },
{ "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", },

119
ggml-cuda.cu
View File

@ -571,6 +571,18 @@ typedef struct {
} block_iq4_nl;
static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding");
// QR4_XS = 8 is very slightly faster than QR4_XS = 4
#define QR4_XS 8
#define QI4_XS (QK_K / (4*QR4_XS))
typedef struct {
half d;
uint16_t scales_h;
uint8_t scales_l[QK_K/64];
uint8_t qs[QK_K/2];
} block_iq4_xs;
static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
#define WARP_SIZE 32
#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
@ -2427,6 +2439,25 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst
}
template<typename dst_t>
static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
const int i = blockIdx.x;
const block_iq4_xs * x = (const block_iq4_xs *)vx;
const int tid = threadIdx.x;
const int il = tid/8; // 0...3
const int ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 4*il;
const uint8_t * q4 = x[i].qs + 16*ib + 4*il;
const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);
for (int j = 0; j < 4; ++j) {
y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
y[j+16] = d * kvalues_iq4nl[q4[j] >> 4];
}
}
static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
@ -5286,6 +5317,76 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
return d * (sumi1 + sumi2);
}
static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if QK_K == 256
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq;
const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
//// iqs is 0...7
//const int ib64 = iqs/2;
//const int il = iqs%2;
//const int32_t * q8_1 = (const int *)bq8_1[2*ib64+0].qs + 2*il;
//const int32_t * q8_2 = (const int *)bq8_1[2*ib64+1].qs + 2*il;
//const uint32_t * q4_1 = (const uint32_t *)bq4->qs + 8*ib64 + 2*il;
//const uint32_t * q4_2 = q4_1 + 4;
//const int8_t ls1 = (bq4->scales_l[ib64] & 0xf) | (((bq4->scales_h >> (4*ib64+0)) & 3) << 4);
//const int8_t ls2 = (bq4->scales_l[ib64] >> 4) | (((bq4->scales_h >> (4*ib64+2)) & 3) << 4);
//const float d1 = (float)bq4->d * (ls1 - 32) * __low2float(bq8_1[2*ib64+0].ds);
//const float d2 = (float)bq4->d * (ls2 - 32) * __low2float(bq8_1[2*ib64+1].ds);
//int v1, v2;
//int sumi1 = 0, sumi2 = 0;
//for (int j = 0; j < 2; ++j) {
// get_int_from_table_16(q4_1[j], values, v1, v2);
// sumi1 = __dp4a(v2, q8_1[j+4], __dp4a(v1, q8_1[j+0], sumi1));
// get_int_from_table_16(q4_2[j], values, v1, v2);
// sumi2 = __dp4a(v2, q8_2[j+4], __dp4a(v1, q8_2[j+0], sumi2));
//}
//return d1 * sumi1 + d2 * sumi2;
// iqs is 0...7
const int ib32 = iqs;
const int32_t * q8 = (const int *)bq8_1[ib32].qs;
const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds);
int v1, v2;
int sumi1 = 0, sumi2 = 0;
for (int j = 0; j < 4; ++j) {
get_int_from_table_16(q4[j], values, v1, v2);
sumi1 = __dp4a(v1, q8[j+0], sumi1);
sumi2 = __dp4a(v2, q8[j+4], sumi2);
}
return d * (sumi1 + sumi2);
//// iqs is 0...15
//const int ib32 = iqs/2;
//const int il = iqs%2;
//const int32_t * q8 = (const int *)bq8_1[ib32].qs + 2*il;
//const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32 + 2*il;
//const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
//const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds);
//int v1, v2;
//int sumi1 = 0, sumi2 = 0;
//for (int j = 0; j < 2; ++j) {
// get_int_from_table_16(q4[j], values, v1, v2);
// sumi1 = __dp4a(v1, q8[j+0], sumi1);
// sumi2 = __dp4a(v2, q8[j+4], sumi2);
//}
//return d * (sumi1 + sumi2);
#else
assert(false);
return 0.f;
#endif
#else
assert(false);
return 0.f;
#endif
}
template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
static __device__ __forceinline__ void mul_mat_q(
@ -7340,6 +7441,12 @@ static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k,
dequantize_block_iq4_nl<<<nb, 32, 0, stream>>>(vx, y);
}
template<typename dst_t>
static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
const int nb = (k + QK_K - 1) / QK_K;
dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
}
template <typename src_t, typename dst_t>
static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
@ -7385,6 +7492,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
return dequantize_row_iq1_s_cuda;
case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ4_XS:
return dequantize_row_iq4_xs_cuda;
case GGML_TYPE_IQ3_S:
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_F32:
@ -7428,6 +7537,8 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
return dequantize_row_iq1_s_cuda;
case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ4_XS:
return dequantize_row_iq4_xs_cuda;
case GGML_TYPE_IQ3_S:
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_F16:
@ -9176,6 +9287,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
return max_compute_capability >= CC_RDNA2 ? 128 : 64;
default:
@ -9203,6 +9315,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
return max_compute_capability >= CC_VOLTA ? 128 : 64;
case GGML_TYPE_Q6_K:
@ -9313,6 +9426,10 @@ static void ggml_cuda_op_mul_mat_vec_q(
mul_mat_vec_q_cuda<QK4_NL, QI4_NL, block_iq4_nl, VDR_Q4_0_Q8_1_MMVQ, vec_dot_iq4_nl_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_IQ4_XS:
mul_mat_vec_q_cuda<QK_K, QI4_XS, block_iq4_xs, 1, vec_dot_iq4_xs_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_IQ3_S:
mul_mat_vec_q_cuda<QK_K, QI3_XS, block_iq3_s, 1, vec_dot_iq3_s_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
@ -12041,7 +12158,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
ggml_type a_type = a->type;
if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS ||
a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S ||
a_type == GGML_TYPE_IQ2_S) {
a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) {
if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
return false;
}

29
ggml-metal.m
View File

@ -65,6 +65,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,
GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,
GGML_METAL_KERNEL_TYPE_RMS_NORM,
GGML_METAL_KERNEL_TYPE_GROUP_NORM,
@ -91,6 +92,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,
//GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,
@ -113,6 +115,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,
@ -132,6 +135,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32,
@ -151,6 +155,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_ROPE_F32,
GGML_METAL_KERNEL_TYPE_ROPE_F16,
GGML_METAL_KERNEL_TYPE_ALIBI_F32,
@ -466,6 +471,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction);
@ -492,6 +498,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction);
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction);
@ -514,6 +521,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm);
@ -533,6 +541,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm);
@ -552,6 +561,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true);
@ -1371,6 +1381,7 @@ static bool ggml_metal_graph_compute(
case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32 ].pipeline; break;
case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break;
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break;
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break;
default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
}
@ -1529,6 +1540,12 @@ static bool ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline;
} break;
case GGML_TYPE_IQ4_XS:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline;
} break;
default:
{
GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
@ -1576,7 +1593,7 @@ static bool ggml_metal_graph_compute(
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_IQ4_NL) {
else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) {
const int mem_size = 32*sizeof(float);
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@ -1678,6 +1695,7 @@ static bool ggml_metal_graph_compute(
case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32 ].pipeline; break;
case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break;
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break;
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break;
default: GGML_ASSERT(false && "MUL_MAT_ID not implemented");
}
@ -1839,6 +1857,12 @@ static bool ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline;
} break;
case GGML_TYPE_IQ4_XS:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline;
} break;
default:
{
GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t);
@ -1902,7 +1926,7 @@ static bool ggml_metal_graph_compute(
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src2t == GGML_TYPE_IQ4_NL) {
else if (src2t == GGML_TYPE_IQ4_NL || src2t == GGML_TYPE_IQ4_XS) {
const int mem_size = 32*sizeof(float);
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@ -1952,6 +1976,7 @@ static bool ggml_metal_graph_compute(
case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S ].pipeline; break;
case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break;
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break;
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break;
case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break;
default: GGML_ASSERT(false && "not implemented");
}

218
ggml-metal.metal
View File

@ -2560,6 +2560,13 @@ typedef struct {
uint8_t qs[QK4_NL/2];
} block_iq4_nl;
typedef struct {
half d;
uint16_t scales_h;
uint8_t scales_l[QK_K/64];
uint8_t qs[QK_K/2];
} block_iq4_xs;
//====================================== dot products =========================
void kernel_mul_mv_q2_K_f32_impl(
@ -5160,6 +5167,100 @@ void kernel_mul_mv_iq4_nl_f32_impl(
}
}
void kernel_mul_mv_iq4_xs_f32_impl(
device const void * src0,
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne10,
constant int64_t & ne12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint & r2,
constant uint & r3,
threadgroup float * shared_values [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]]) {
const int nb = ne00/QK_K;
const int r0 = tgpig.x;
const int r1 = tgpig.y;
const int im = tgpig.z;
const int first_row = (r0 * 2 + sgitg) * 2;
const int ib_row = first_row * nb;
const uint i12 = im%ne12;
const uint i13 = im/ne12;
const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
device const block_iq4_xs * x = (device const block_iq4_xs *) src0 + ib_row + offset0;
device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1;
const int ix = tiisg/16; // 0 or 1
const int it = tiisg%16; // 0...15
const int ib = it/2;
const int il = it%2;
shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16];
threadgroup_barrier(mem_flags::mem_threadgroup);
float4 yl[4];
float sumf[2]={0.f}, all_sum;
device const float * yb = y + ix * QK_K + ib * 32 + il * 8;
uint32_t aux32[2];
thread const uint8_t * q8 = (thread const uint8_t *)aux32;
float4 qf1, qf2;
for (int ibl = ix; ibl < nb; ibl += 2) {
device const float4 * y4 = (device const float4 *)yb;
yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5];
for (int row = 0; row < 2; ++row) {
device const block_iq4_xs & xb = x[row*nb + ibl];
device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il);
float4 acc1 = {0.f}, acc2 = {0.f};
aux32[0] = q4[0] & 0x0f0f0f0f;
aux32[1] = (q4[0] >> 4) & 0x0f0f0f0f;
qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
acc1 += yl[0] * qf1;
acc2 += yl[1] * qf2;
aux32[0] = q4[1] & 0x0f0f0f0f;
aux32[1] = (q4[1] >> 4) & 0x0f0f0f0f;
qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
acc1 += yl[2] * qf1;
acc2 += yl[3] * qf2;
acc1 += acc2;
const int ls = (((xb.scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((xb.scales_h >> 2*ib) & 3) << 4)) - 32;
sumf[row] += (float)xb.d * ls * (acc1[0] + acc1[1] + acc1[2] + acc1[3]);
}
yb += 2 * QK_K;
}
for (int row = 0; row < 2; ++row) {
all_sum = simd_sum(sumf[row]);
if (tiisg == 0) {
dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
}
}
}
[[host_name("kernel_mul_mv_iq1_s_f32")]]
kernel void kernel_mul_mv_iq1_s_f32(
device const void * src0,
@ -5217,6 +5318,35 @@ kernel void kernel_mul_mv_iq4_nl_f32(
kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
}
[[host_name("kernel_mul_mv_iq4_xs_f32")]]
kernel void kernel_mul_mv_iq4_xs_f32(
device const void * src0,
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint & r2,
constant uint & r3,
threadgroup float * shared_values [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]]) {
kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
}
//============================= templates and their specializations =============================
// NOTE: this is not dequantizing - we are simply fitting the template
@ -5638,6 +5768,26 @@ void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4
}
}
template <typename type4x4>
void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const int ib32 = il/2;
il = il%2;
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32;
const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4);
const float d = (float)xb->d * (ls - 32);
uint32_t aux32;
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
for (int i = 0; i < 4; ++i) {
aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f;
reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
}
}
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
kernel void kernel_get_rows(
device const void * src0,
@ -6184,6 +6334,7 @@ template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_t kernel_get_r
template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_t kernel_get_rows<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows<block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows<block_iq4_xs, QK_NL, dequantize_iq4_xs>;
//
// matrix-matrix multiplication
@ -6227,6 +6378,7 @@ template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_m
template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_xs, QK_NL, dequantize_iq4_xs>;
//
// indirect matrix-matrix multiplication
@ -6282,6 +6434,7 @@ template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mat_mm_id_t kernel
template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_xs, QK_NL, dequantize_iq4_xs>;
//
// matrix-vector multiplication
@ -7507,3 +7660,68 @@ kernel void kernel_mul_mv_id_iq4_nl_f32(
tiisg,
sgitg);
}
[[host_name("kernel_mul_mv_id_iq4_xs_f32")]]
kernel void kernel_mul_mv_id_iq4_xs_f32(
device const char * ids,
device const char * src1,
device float * dst,
constant uint64_t & nbi1,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant int64_t & ne13,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint64_t & nb1,
constant uint & r2,
constant uint & r3,
constant int & idx,
device const char * src00,
device const char * src01,
device const char * src02,
device const char * src03,
device const char * src04,
device const char * src05,
device const char * src06,
device const char * src07,
threadgroup float * shared_values [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiitg[[thread_index_in_threadgroup]],
uint tiisg[[thread_index_in_simdgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]]) {
device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
const int64_t bid = tgpig.z/(ne12*ne13);
tgpig.z = tgpig.z%(ne12*ne13);
const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
kernel_mul_mv_iq4_xs_f32_impl(
src0[id],
(device const float *) (src1 + bid*nb11),
dst + bid*ne0,
ne00,
ne01,
ne02,
ne10,
ne12,
ne0,
ne1,
r2,
r3,
shared_values,
tgpig,
tiisg,
sgitg);
}

259
ggml-quants.c
View File

@ -4225,6 +4225,29 @@ void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y,
}
}
void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
for (int i = 0; i < nb; i++) {
const uint8_t * qs = x[i].qs;
const float d = GGML_FP16_TO_FP32(x[i].d);
for (int ib = 0; ib < QK_K/32; ++ib) {
const int ls = ((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4);
const float dl = d * (ls - 32);
for (int j = 0; j < 16; ++j) {
y[j+ 0] = dl * kvalues_iq4nl[qs[j] & 0xf];
y[j+16] = dl * kvalues_iq4nl[qs[j] >> 4];
}
y += 32;
qs += 16;
}
}
}
//===================================== Q8_K ==============================================
void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) {
@ -9675,8 +9698,8 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void *
qs += 8;
vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16)));
vs.val[1] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
vs.val[0] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
vs.val[0] = vceqq_u8(vs.val[0], mask2);
vs.val[1] = vceqq_u8(vs.val[1], mask2);
@ -9684,8 +9707,8 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void *
q2s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[1]);
vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16)));
vs.val[1] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
vs.val[0] = vandq_u8(vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
vs.val[0] = vceqq_u8(vs.val[0], mask2);
vs.val[1] = vceqq_u8(vs.val[1], mask2);
@ -10425,6 +10448,134 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
#endif
}
void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
assert(nrc == 1);
UNUSED(nrc);
UNUSED(bx);
UNUSED(by);
UNUSED(bs);
assert(n % QK_K == 0);
const block_iq4_xs * restrict x = vx;
const block_q8_K * restrict y = vy;
const int nb = n / QK_K;
#if defined __ARM_NEON
const int8x16_t values = vld1q_s8(kvalues_iq4nl);
const uint8x16_t m4b = vdupq_n_u8(0x0f);
uint8x16x2_t q4bits;
int8x16x4_t q4b;
int8x16x4_t q8b;
int32x4_t prod_1, prod_2;
float sumf = 0;
for (int ibl = 0; ibl < nb; ++ibl) {
const int8_t * q8 = y[ibl].qs;
const uint8_t * q4 = x[ibl].qs;
uint16_t h = x[ibl].scales_h;
int sumi1 = 0, sumi2 = 0;
for (int ib = 0; ib < QK_K/64; ++ib) {
q4bits = ggml_vld1q_u8_x2(q4); q4 += 32;
q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b));
q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b));
q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4));
prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]);
prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
int ls1 = ((x[ibl].scales_l[ib] & 0xf) | ((h << 4) & 0x30)) - 32;
int ls2 = ((x[ibl].scales_l[ib] >> 4) | ((h << 2) & 0x30)) - 32;
h >>= 4;
sumi1 += vaddvq_s32(prod_1) * ls1;
sumi2 += vaddvq_s32(prod_2) * ls2;
}
sumf += GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d * (sumi1 + sumi2);
}
*s = sumf;
#elif defined __AVX2__
const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl);
const __m128i m4b = _mm_set1_epi8(0x0f);
__m256 accum = _mm256_setzero_ps();
for (int ibl = 0; ibl < nb; ++ibl) {
const uint8_t * qs = x[ibl].qs;
const int8_t * q8 = y[ibl].qs;
uint16_t sh = x[ibl].scales_h;
__m256i sumi1 = _mm256_setzero_si256();
__m256i sumi2 = _mm256_setzero_si256();
for (int ib = 0; ib < QK_K/32; ib += 2) {
const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)qs); qs += 16;
const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs); qs += 16;
const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
const __m256i q4b_1 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
_mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
const __m256i q4b_2 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
_mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)));
const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1);
const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32;
const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32;
sh >>= 4;
const __m256i p_1 = _mm256_madd_epi16(p16_1, _mm256_set1_epi16(ls1));
const __m256i p_2 = _mm256_madd_epi16(p16_2, _mm256_set1_epi16(ls2));
sumi1 = _mm256_add_epi32(p_1, sumi1);
sumi2 = _mm256_add_epi32(p_2, sumi2);
}
accum = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d),
_mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accum);
}
*s = hsum_float_8(accum);
#else
float sumf = 0;
for (int ibl = 0; ibl < nb; ++ibl) {
const float d4d8 = GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
uint16_t h = x[ibl].scales_h;
const uint8_t * qs = x[ibl].qs;
const int8_t * q8 = y[ibl].qs;
for (int ib = 0; ib < QK_K/32; ib += 2) {
const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30);
const uint8_t ls2 = (x[ibl].scales_l[ib/2] >> 4) | ((h << 2) & 0x30);
h >>= 4;
const float d1 = d4d8*(ls1 - 32);
const float d2 = d4d8*(ls2 - 32);
int sumi1 = 0, sumi2 = 0;
for (int j = 0; j < 16; ++j) {
sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4];
}
sumf += d1 * (sumi1 + sumi2);
qs += 16;
q8 += 32;
sumi1 = sumi2 = 0;
for (int j = 0; j < 16; ++j) {
sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4];
}
sumf += d2 * (sumi1 + sumi2);
qs += 16;
q8 += 32;
}
}
*s = sumf;
#endif
}
// ================================ IQ2 quantization =============================================
typedef struct {
@ -12021,23 +12172,23 @@ static inline int best_index_int8(int n, const int8_t * val, float x) {
return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
}
static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RESTRICT x,
ggml_fp16_t * dh, uint8_t * q4,
float * weight, uint8_t * L,
static void quantize_row_iq4_nl_impl(const int super_block_size, const int block_size, const float * GGML_RESTRICT x,
ggml_fp16_t * dh, uint8_t * q4, uint16_t * scales_h, uint8_t * scales_l,
float * scales, float * weight, uint8_t * L,
const int8_t * values,
const float * quant_weights) {
const int ntry = 7;
float sigma2 = 0;
for (int j = 0; j < QK4_NL; ++j) sigma2 += x[j]*x[j];
sigma2 *= 2.f/QK4_NL;
for (int j = 0; j < super_block_size; ++j) sigma2 += x[j]*x[j];
sigma2 *= 2.f/super_block_size;
const int nb = QK4_NL/block_size;
memset(q4, 0, super_block_size/2);
dh[0] = GGML_FP32_TO_FP16(0.f);
memset(q4, 0, QK4_NL/2);
for (int ib = 0; ib < nb; ++ib) {
dh[ib] = GGML_FP32_TO_FP16(0.f);
float max_scale = 0, amax_scale = 0;
for (int ib = 0; ib < super_block_size/block_size; ++ib) {
const float * xb = x + ib*block_size;
if (quant_weights) {
const float * qw = quant_weights + ib*block_size;
@ -12053,6 +12204,7 @@ static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RE
}
}
if (!amax) {
scales[ib] = 0;
continue;
}
float d = -max/values[0];
@ -12066,7 +12218,6 @@ static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RE
sumqx += w*q*xb[j];
sumq2 += w*q*q;
}
float best_id = id;
d = sumqx/sumq2;
float best = d*sumqx;
for (int itry = -ntry; itry <= ntry; ++itry) {
@ -12082,15 +12233,47 @@ static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RE
}
if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
d = sumqx/sumq2; best = d * sumqx;
best_id = id;
}
}
dh[ib] = GGML_FP32_TO_FP16(d);
scales[ib] = d;
float abs_d = fabsf(d);
if (abs_d > amax_scale) {
amax_scale = abs_d; max_scale = d;
}
}
if (super_block_size/block_size > 1) {
int nb = super_block_size/block_size;
memset(scales_h, 0, ((nb+7)/8)*sizeof(uint16_t));
float d = -max_scale/32;
dh[0] = GGML_FP32_TO_FP16(d);
float id = d ? 1/d : 0.f;
for (int ib = 0; ib < super_block_size/block_size; ++ib) {
int l = nearest_int(id*scales[ib]);
l = MAX(-32, MIN(31, l));
float dl = d * l;
float idl = dl ? 1/dl : 0.f;
uint8_t * Lb = L + ib*block_size;
const float * xb = x + ib*block_size;
for (int j = 0; j < block_size; ++j) {
L[ib*block_size + j] = best_index_int8(16, values, best_id*xb[j]);
Lb[j] = best_index_int8(16, values, idl*xb[j]);
}
l += 32;
uint8_t l_l = l & 0xf;
uint8_t l_h = l >> 4;
if (ib%2 == 0) scales_l[ib/2] = l_l;
else scales_l[ib/2] |= (l_l << 4);
scales_h[ib/8] |= (l_h << 2*(ib%8));
}
} else {
dh[0] = GGML_FP32_TO_FP16(scales[0]);
float id = scales[0] ? 1/scales[0] : 0;
for (int j = 0; j < super_block_size; ++j) {
L[j] = best_index_int8(16, values, id*x[j]);
}
}
for (int i = 0; i < QK4_NL/32; ++i) {
for (int i = 0; i < super_block_size/32; ++i) {
for (int j = 0; j < 16; ++j) {
q4[16*i + j] = L[32*i + j] | (L[32*i + 16 + j] << 4);
}
@ -12103,12 +12286,16 @@ size_t quantize_iq4_nl(const float * src, void * dst, int nrow, int n_per_row, i
int nblock = n_per_row/QK4_NL;
char * qrow = (char *)dst;
uint8_t L[QK4_NL];
float weight[32];
float weight[QK4_NL];
uint16_t unused_h;
uint8_t * unused_l = NULL;
float scale;
for (int row = 0; row < nrow; ++row) {
block_iq4_nl * iq4 = (block_iq4_nl *)qrow;
for (int ibl = 0; ibl < nblock; ++ibl) {
const float * qw = quant_weights ? quant_weights + QK4_NL*ibl : NULL;
quantize_row_iq4_nl_impl(32, src + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, weight, L, kvalues_iq4nl, qw);
quantize_row_iq4_nl_impl(QK4_NL, 32, src + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, &unused_h, unused_l,
&scale, weight, L, kvalues_iq4nl, qw);
}
src += n_per_row;
qrow += nblock*sizeof(block_iq4_nl);
@ -12127,6 +12314,38 @@ void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * rest
quantize_iq4_nl(x, y, 1, k, NULL, NULL);
}
size_t quantize_iq4_xs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
(void)hist;
GGML_ASSERT(n_per_row%QK_K == 0);
int nblock = n_per_row/QK_K;
char * qrow = (char *)dst;
uint8_t L[QK_K];
float weight[32];
float scales[QK_K/32];
for (int row = 0; row < nrow; ++row) {
block_iq4_xs * iq4 = (block_iq4_xs *)qrow;
for (int ibl = 0; ibl < nblock; ++ibl) {
const float * qw = quant_weights ? quant_weights + QK_K*ibl : NULL;
quantize_row_iq4_nl_impl(QK_K, 32, src + QK_K*ibl, &iq4[ibl].d, iq4[ibl].qs, &iq4[ibl].scales_h, iq4[ibl].scales_l,
scales, weight, L, kvalues_iq4nl, qw);
}
src += n_per_row;
qrow += nblock*sizeof(block_iq4_xs);
}
return nrow * nblock * sizeof(block_iq4_xs);
}
void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int k) {
assert(k % QK_K == 0);
block_iq4_xs * restrict y = vy;
quantize_row_iq4_xs_reference(x, y, k);
}
void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int k) {
assert(k % QK_K == 0);
quantize_iq4_xs(x, y, 1, k, NULL, NULL);
}
// =============================== 2.5625 bpw
static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {

13
ggml-quants.h
View File

@ -230,6 +230,14 @@ typedef struct {
} block_iq4_nl;
static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding");
typedef struct {
ggml_fp16_t d;
uint16_t scales_h;
uint8_t scales_l[QK_K/64];
uint8_t qs[QK_K/2];
} block_iq4_xs;
static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
#ifdef __cplusplus
extern "C" {
#endif
@ -250,6 +258,7 @@ void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGM
void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k);
void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k);
void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int k);
void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int k);
void quantize_row_iq3_s_reference (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int k);
void quantize_row_iq2_s_reference (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int k);
@ -268,6 +277,7 @@ void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq3_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq2_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
@ -291,6 +301,7 @@ void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_
void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
// Dot product
@ -311,6 +322,7 @@ void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
//
@ -322,6 +334,7 @@ size_t quantize_iq2_s (const float * src, void * dst, int nrows, int n_per_row,
size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq1_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq4_nl (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq4_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq3_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);

30
ggml.c
View File

@ -726,6 +726,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
},
[GGML_TYPE_IQ4_XS] = {
.type_name = "iq4_xs",
.blck_size = QK_K,
.type_size = sizeof(block_iq4_xs),
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq4_xs,
.from_float = quantize_row_iq4_xs,
.from_float_reference = (ggml_from_float_t)quantize_row_iq4_xs_reference,
.vec_dot = ggml_vec_dot_iq4_xs_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_Q8_K] = {
.type_name = "q8_K",
.blck_size = QK_K,
@ -2328,6 +2340,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break;
case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break;
case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break;
case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
case GGML_FTYPE_MOSTLY_IQ2_S: wtype = GGML_TYPE_IQ2_S; break;
case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break;
@ -7764,6 +7777,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
{
@ -8045,6 +8059,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
{
@ -8171,6 +8186,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
default:
@ -11071,6 +11087,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
{
@ -11261,6 +11278,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
default:
@ -11465,6 +11483,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
{
@ -12167,6 +12186,7 @@ static void ggml_compute_forward_alibi(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q8_K:
@ -12252,6 +12272,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q8_K:
@ -19817,6 +19838,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
result = quantize_iq4_nl(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
GGML_ASSERT(result == row_size * nrows);
} break;
case GGML_TYPE_IQ4_XS:
{
GGML_ASSERT(start % QK4_NL == 0);
GGML_ASSERT(start % n_per_row == 0);
size_t start_row = start / n_per_row;
size_t row_size = ggml_row_size(type, n_per_row);
result = quantize_iq4_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
GGML_ASSERT(result == row_size * nrows);
} break;
case GGML_TYPE_F16:
{
size_t elemsize = sizeof(ggml_fp16_t);

2
ggml.h
View File

@ -352,6 +352,7 @@ extern "C" {
GGML_TYPE_IQ4_NL = 20,
GGML_TYPE_IQ3_S = 21,
GGML_TYPE_IQ2_S = 22,
GGML_TYPE_IQ4_XS = 23,
GGML_TYPE_I8,
GGML_TYPE_I16,
GGML_TYPE_I32,
@ -393,6 +394,7 @@ extern "C" {
GGML_FTYPE_MOSTLY_IQ4_NL = 19, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors
};
// available tensor operations:

16
llama.cpp
View File

@ -2584,6 +2584,7 @@ struct llama_model_loader {
case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break;
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break;
default:
{
@ -2941,6 +2942,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw";
case LLAMA_FTYPE_MOSTLY_IQ1_S :return "IQ1_S - 1.5625 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_M: return "IQ3_S mix - 3.66 bpw";
@ -10871,7 +10873,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL && qs.model.hparams.n_gqa() >= 4) {
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) {
new_type = GGML_TYPE_Q5_K;
}
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
@ -10940,8 +10942,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
}
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL && !qs.has_imatrix) {
if (i_layer < n_layer/8) new_type = GGML_TYPE_Q5_K;
else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) {
new_type = GGML_TYPE_Q5_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
@ -10961,7 +10963,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL ||
ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) {
new_type = GGML_TYPE_Q5_K;
}
} else {
@ -11012,7 +11014,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
//}
bool convert_incompatible_tensor = false;
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K ||
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS ||
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
new_type == GGML_TYPE_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || new_type == GGML_TYPE_IQ3_S) {
int nx = tensor->ne[0];
@ -11033,7 +11035,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K: new_type = GGML_TYPE_IQ4_NL; break;
case GGML_TYPE_Q3_K:
case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break;
case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
@ -11078,6 +11081,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_IQ3_XXS: quantized_type = GGML_TYPE_IQ3_XXS; break;
case LLAMA_FTYPE_MOSTLY_IQ1_S: quantized_type = GGML_TYPE_IQ1_S; break;
case LLAMA_FTYPE_MOSTLY_IQ4_NL: quantized_type = GGML_TYPE_IQ4_NL; break;
case LLAMA_FTYPE_MOSTLY_IQ4_XS: quantized_type = GGML_TYPE_IQ4_XS; break;
case LLAMA_FTYPE_MOSTLY_IQ3_S: quantized_type = GGML_TYPE_IQ3_S; break;
case LLAMA_FTYPE_MOSTLY_IQ3_M: quantized_type = GGML_TYPE_IQ3_S; break;

1
llama.h
View File

@ -115,6 +115,7 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};

2
tests/test-backend-ops.cpp
View File

@ -1918,7 +1918,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
GGML_TYPE_Q6_K,
GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S,
GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S,
GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
};
// unary ops