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IQ1_M: 1.75 bpw quantization (#6302)

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* iq1_m: basics

* iq1_m: basics-2

* iq1_m: CUDA dequantize works

Very 1st shot I get PPL = 9.76 for LLaMA-v2-7B.

* iq1_m: separate shifts for each group of 8 in a block

We get
PPL(LLaMA-v2-7B ) = 9.2810
PPL(LLaMA-v2-13B) = 6.8105

Not bad, but slightly higher than
  sqrt(PPL(IQ1_S) * PPL(IQ2_XXS))
which is the expected outcome given that IQ1_M is
halfway between IQ1_S and IQ2_XXS in terms of bpw.
From this, we would expect
 PPL = 9.14 for LLaMA-v2-7B
 PPL = 6.63 for LLaMA-v2-13B

* iq1_m: go to 3-bit scales

There is slight increase in PPL, but the 0.0625 bpw reduction
in size is totally worth it.

We now have
PPL(LLaMA-v2-7B ) = 9.4469 at 1.96 bpw
PPL(LLaMA-v2-13B) = 6.8717 at 1.93 bpw
PPL(LLaMA-v2-70B) = 4.8568 at 1.85 bpw

* iq1_m: scalar dot product

* iq1_m: AVX2 dot product

* iq1_m: very slightly faster AVX2 dot product

* iq1_m: ARM_NEON dot product

Works, but very slow (10.5 t/s)

* iq1_m: Metal - dequantize works, dot product does not

* iq1_m: Metal now works

About the same performance as iq1_s.

* iq1_m: minor

* iq1_m: checking pure iq1_m quantization

It is pretty bad: PPL(LLaMA-v2-7B) = 34 if we quantize output.weight
with Q4_K.

* iiq1_m: slightly faster ARM_NEON dot product

10.5 t/s -> 11.65 t/s

* iq1_m: faster ARM_NEON dot product

11.65 t/s -> 14.9 t/s

* iq1_m: another minor ARM_NEON dot product improvement

14.9 -> 15.0 t/s

* iq1_m: small PPL improvement via super-block scale adjustment

After quantizing block scales redo the super-block scale fit.

PPL(LLaMA-v2-7B ) = 9.3346
PPL(LLaMA-v2-13B) = 6.8419
PPL(LLaMA-v2-70B) = 4.8294
PPL(Mistral-7B  ) = 8.1624

* iq1_m: adapt to CUDA refactoring

* iq1_m: remove unused variable

We have progressed to warnings being errors.

* iq1_m: add to backend-ops tests

* iq1_m: fix Windows ARM

* iq1_m: use common definition of iq1m_scale_t

* cuda: assert -> NO_DEVICE_CODE

* iq1_M: PR comments

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow 2024-03-26 15:21:27 +01:00 committed by GitHub
parent e097633f63
commit 55c1b2a3bb
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GPG Key ID: B5690EEEBB952194
16 changed files with 1006 additions and 125 deletions
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11
examples/quantize/quantize.cpp
View File

@ -26,6 +26,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", }, { "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", },
{ "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", }, { "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", },
{ "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", }, { "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", },
{ "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.75 bpw quantization", },
{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", }, { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", }, { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
{ "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", }, { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", },
@ -370,10 +371,12 @@ int main(int argc, char ** argv) {
if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && imatrix_data.empty()) { params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
fprintf(stderr, "\n===============================================================================================\n"); params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
fprintf(stderr, "Please do not use IQ1_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n"); params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) && imatrix_data.empty()) {
fprintf(stderr, "===============================================================================================\n\n\n"); fprintf(stderr, "\n==========================================================================================================\n");
fprintf(stderr, "Please do not use IQ1_S, IQ1_M, IQ2_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
fprintf(stderr, "==========================================================================================================\n\n\n");
return 1; return 1;
} }

15
ggml-common.h
View File

@ -377,6 +377,20 @@ typedef struct {
} block_iq1_s; } block_iq1_s;
static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding"); static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");
// 1.8125 bpw
typedef struct {
uint8_t qs[QK_K/8]; // grid index, low 8 bits
uint8_t qh[QK_K/16]; // grid index, high 3 bits + grid shift bit (for two groups of 8)
uint8_t scales[QK_K/32]; // 4-bit block scales
} block_iq1_m;
static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding");
// Used by IQ1_M quants
typedef union {
ggml_half f16;
uint16_t u16;
} iq1m_scale_t;
// Non-linear quants // Non-linear quants
#define QK4_NL 32 #define QK4_NL 32
typedef struct { typedef struct {
@ -1050,6 +1064,7 @@ GGML_TABLE_END()
#define NGRID_IQ1S 2048 #define NGRID_IQ1S 2048
#define IQ1S_DELTA 0.125f #define IQ1S_DELTA 0.125f
#define IQ1M_DELTA 0.125f
#if defined(GGML_COMMON_IMPL_C) #if defined(GGML_COMMON_IMPL_C)
GGML_TABLE_BEGIN(uint64_t, iq1s_grid, NGRID_IQ1S) GGML_TABLE_BEGIN(uint64_t, iq1s_grid, NGRID_IQ1S)
0xffffffffffffffff, 0xffffffffffffff01, 0xffffffffffff0000, 0xffffffffffff01ff, 0xffffffffffffffff, 0xffffffffffffff01, 0xffffffffffff0000, 0xffffffffffff01ff,

4
ggml-cuda.cu
View File

@ -615,6 +615,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
case GGML_TYPE_IQ2_S: case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ3_S:
@ -643,6 +644,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
case GGML_TYPE_IQ2_S: case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ3_S:
@ -2560,7 +2562,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
ggml_type a_type = a->type; 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 || 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_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S ||
a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) { a_type == GGML_TYPE_IQ1_M || a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) {
if (b->ne[1] == 1 && ggml_nrows(b) > 1) { if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
return false; return false;
} }

53
ggml-cuda/convert.cu
View File

@ -373,7 +373,7 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds
const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
#else #else
assert(false); NO_DEVICE_CODE;
#endif #endif
} }
@ -395,7 +395,7 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst
const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
#else #else
assert(false); NO_DEVICE_CODE;
#endif #endif
} }
@ -416,7 +416,7 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_
const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
#else #else
assert(false); NO_DEVICE_CODE;
#endif #endif
} }
@ -444,7 +444,7 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds
y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
} }
#else #else
assert(false); NO_DEVICE_CODE;
#endif #endif
} }
@ -470,7 +470,7 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_
y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
} }
#else #else
assert(false); NO_DEVICE_CODE;
#endif #endif
} }
@ -496,11 +496,42 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_
y[j] = d * (q[j] + delta); y[j] = d * (q[j] + delta);
} }
#else #else
assert(false); NO_DEVICE_CODE;
#endif #endif
} }
template<typename dst_t>
static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_t * __restrict__ yy) {
const int i = blockIdx.x;
const block_iq1_m * x = (const block_iq1_m *) vx;
const int tid = threadIdx.x;
#if QK_K == 256
const int il = tid/8; // 0...3
const int ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 8*il;
const uint16_t * sc = (const uint16_t *)x[i].scales;
iq1m_scale_t scale;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
const int ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)];
grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
grid32[0] &= 0x0f0f0f0f;
for (int j = 0; j < 8; ++j) {
y[j] = d * (q[j] + delta);
}
#else
NO_DEVICE_CODE;
#endif
}
template<typename dst_t> template<typename dst_t>
static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) { static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) {
@ -658,6 +689,12 @@ 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); dequantize_block_iq4_nl<<<nb, 32, 0, stream>>>(vx, y);
} }
template<typename dst_t>
static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
const int nb = k / QK_K;
dequantize_block_iq1_m<<<nb, 32, 0, stream>>>(vx, y);
}
template<typename dst_t> template<typename dst_t>
static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { 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; const int nb = (k + QK_K - 1) / QK_K;
@ -724,6 +761,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
return dequantize_row_iq3_xxs_cuda; return dequantize_row_iq3_xxs_cuda;
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
return dequantize_row_iq1_s_cuda; return dequantize_row_iq1_s_cuda;
case GGML_TYPE_IQ1_M:
return dequantize_row_iq1_m_cuda;
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_cuda; return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:
@ -769,6 +808,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
return dequantize_row_iq3_xxs_cuda; return dequantize_row_iq3_xxs_cuda;
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
return dequantize_row_iq1_s_cuda; return dequantize_row_iq1_s_cuda;
case GGML_TYPE_IQ1_M:
return dequantize_row_iq1_m_cuda;
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_cuda; return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:

11
ggml-cuda/mmvq.cu
View File

@ -282,6 +282,14 @@ static void mul_mat_vec_iq1_s_q8_1_cuda(
(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
} }
static void mul_mat_vec_iq1_m_q8_1_cuda(
const void * vx, const void * vy, float * dst,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
mul_mat_vec_q_cuda<QK_K, QI1_S, block_iq1_m, 1, vec_dot_iq1_m_q8_1>
(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
}
static void mul_mat_vec_iq4_nl_q8_1_cuda( static void mul_mat_vec_iq4_nl_q8_1_cuda(
const void * vx, const void * vy, float * dst, const void * vx, const void * vy, float * dst,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
@ -373,6 +381,9 @@ void ggml_cuda_op_mul_mat_vec_q(
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
mul_mat_vec_iq1_s_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); mul_mat_vec_iq1_s_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break; break;
case GGML_TYPE_IQ1_M:
mul_mat_vec_iq1_m_q8_1_cuda(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_NL: case GGML_TYPE_IQ4_NL:
mul_mat_vec_iq4_nl_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); mul_mat_vec_iq4_nl_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break; break;

112
ggml-cuda/vecdotq.cuh
View File

@ -961,8 +961,7 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
return d * (sumi1 + sumi2); return d * (sumi1 + sumi2);
#endif #endif
#else #else
assert(false); NO_DEVICE_CODE;
return 0.f;
#endif #endif
} }
@ -1001,13 +1000,11 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
#else #else
GGML_UNUSED(ksigns64); GGML_UNUSED(ksigns64);
assert(false); NO_DEVICE_CODE;
return 0.f;
#endif #endif
#else #else
GGML_UNUSED(ksigns64); GGML_UNUSED(ksigns64);
assert(false); NO_DEVICE_CODE;
return 0.f;
#endif #endif
} }
@ -1049,13 +1046,11 @@ static __device__ __forceinline__ float vec_dot_iq2_s_q8_1(
return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
#else #else
GGML_UNUSED(ksigns64); GGML_UNUSED(ksigns64);
assert(false); NO_DEVICE_CODE;
return 0.f;
#endif #endif
#else #else
GGML_UNUSED(ksigns64); GGML_UNUSED(ksigns64);
assert(false); NO_DEVICE_CODE;
return 0.f;
#endif #endif
} }
@ -1085,12 +1080,10 @@ static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1(
const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f; const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f;
return d * sumi; return d * sumi;
#else #else
assert(false); NO_DEVICE_CODE;
return 0.f;
#endif #endif
#else #else
assert(false); NO_DEVICE_CODE;
return 0.f;
#endif #endif
} }
@ -1119,12 +1112,10 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds); const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds);
return d * sumi; return d * sumi;
#else #else
assert(false); NO_DEVICE_CODE;
return 0.f;
#endif #endif
#else #else
assert(false); NO_DEVICE_CODE;
return 0.f;
#endif #endif
} }
@ -1159,8 +1150,50 @@ static __device__ __forceinline__ float vec_dot_iq1_s_q8_1(
const float m = d1q * __high2float(bq8_1[ib32].ds); const float m = d1q * __high2float(bq8_1[ib32].ds);
return d * sumi + m * delta; return d * sumi + m * delta;
#else #else
assert(false); NO_DEVICE_CODE;
return 0.f; #endif
}
static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if QK_K == 256
const block_iq1_m * bq1 = (const block_iq1_m *) vbq;
const int ib32 = iqs;
int sumi[2] = {0, 0};
float sumf[2] = {0.f, 0.f};
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
const int * q8 = (const int *)bq8_1[ib32].qs;
for (int l = 0; l < 4; ++l) {
const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 7) << 8)));
int grid0 = grid[0] & 0x0f0f0f0f;
int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
sumi[l/2] = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi[l/2]));
const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
const int sumy = __dp4a(q8[2*l+1], 0x01010101, __dp4a(q8[2*l+0], 0x01010101, 0));
sumf[l/2] += delta*sumy;
}
#else
const int8_t * q8 = bq8_1[ib32].qs;
for (int l = 0; l < 4; ++l) {
const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
int sumy = 0;
for (int j = 0; j < 4; ++j) {
sumi[l/2] += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4);
sumy += q8[j] + q8[j+4];
}
const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
sumf[l/2] += delta*sumy;
q8 += 8;
}
#endif
iq1m_scale_t scale;
const uint16_t * sc = (const uint16_t *)bq1->scales;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
const float d = (float)scale.f16 * __low2float (bq8_1[ib32].ds);
return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1));
#else
NO_DEVICE_CODE;
#endif #endif
} }
@ -1223,27 +1256,6 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq;
const uint8_t * values = (const uint8_t *)kvalues_iq4nl; 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 // iqs is 0...7
const int ib32 = iqs; const int ib32 = iqs;
const int32_t * q8 = (const int *)bq8_1[ib32].qs; const int32_t * q8 = (const int *)bq8_1[ib32].qs;
@ -1259,24 +1271,8 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
} }
return d * (sumi1 + 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 #else
assert(false); NO_DEVICE_CODE;
return 0.f;
#endif #endif
#else #else
return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs); return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs);

37
ggml-metal.m
View File

@ -64,6 +64,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,
GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,
@ -91,6 +92,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_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_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,
@ -114,6 +116,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, 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_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_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_MV_ID_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,
@ -134,6 +137,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_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_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,
@ -154,6 +158,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, 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_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_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_MUL_MM_ID_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_ROPE_F32, GGML_METAL_KERNEL_TYPE_ROPE_F32,
@ -490,6 +495,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true);
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_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_IQ1_S, get_rows_iq1_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, get_rows_iq1_m, 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_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_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_GET_ROWS_I32, get_rows_i32, true);
@ -517,6 +523,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction);
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_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_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_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_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_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_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction);
@ -540,6 +547,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction);
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_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_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_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_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_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_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm);
@ -560,6 +568,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm);
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_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_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_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_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_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_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm);
@ -580,6 +589,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm);
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_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_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_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_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_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_F32, rope_f32, true);
@ -1421,6 +1431,7 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32 ].pipeline; break; case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32 ].pipeline; break;
case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32 ].pipeline; break; 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_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break;
case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_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_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; 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"); default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
@ -1575,6 +1586,12 @@ static enum ggml_status ggml_metal_graph_compute(
nth1 = 16; nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline;
} break; } break;
case GGML_TYPE_IQ1_M:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32].pipeline;
} break;
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
{ {
nth0 = 4; nth0 = 4;
@ -1619,9 +1636,9 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder setBytes:&r2 length:sizeof(r2) atIndex:17]; [encoder setBytes:&r2 length:sizeof(r2) atIndex:17];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; [encoder setBytes:&r3 length:sizeof(r3) atIndex:18];
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 ||
src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K ||
src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ2_S) { src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} }
else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) {
@ -1743,6 +1760,7 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32 ].pipeline; break; case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32 ].pipeline; break;
case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32 ].pipeline; break; 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_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break;
case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_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_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; 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"); default: GGML_ASSERT(false && "MUL_MAT_ID not implemented");
@ -1900,6 +1918,12 @@ static enum ggml_status ggml_metal_graph_compute(
nth1 = 16; nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline;
} break; } break;
case GGML_TYPE_IQ1_M:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32].pipeline;
} break;
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
{ {
nth0 = 4; nth0 = 4;
@ -1960,9 +1984,9 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j]; [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j];
} }
if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || src2t == GGML_TYPE_Q5_0 ||
src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || src2t == GGML_TYPE_Q2_K ||
src2t == GGML_TYPE_Q2_K || src2t == GGML_TYPE_IQ1_S || src2t == GGML_TYPE_IQ2_S) { src2t == GGML_TYPE_IQ1_S || src2t == GGML_TYPE_IQ1_M || src2t == GGML_TYPE_IQ2_S) {
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} }
else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) { else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) {
@ -2024,6 +2048,7 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S ].pipeline; break; case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S ].pipeline; break;
case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S ].pipeline; break; 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_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break;
case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M ].pipeline; break;
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].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_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; case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break;

216
ggml-metal.metal
View File

@ -4456,6 +4456,104 @@ void kernel_mul_mv_iq1_s_f32_impl(
} }
} }
void kernel_mul_mv_iq1_m_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,
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 * N_SIMDGROUP + sgitg) * N_DST;
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_iq1_m * x = (device const block_iq1_m *) src0 + ib_row + offset0;
device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1;
float yl[32];
float sumf[N_DST]={0.f}, all_sum;
const int nb32 = nb * (QK_K / 32);
const int ix = tiisg;
device const float * y4 = y + 32 * ix;
iq1m_scale_t scale;
for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
float4 sumy = {0.f};
for (int i = 0; i < 8; ++i) {
yl[i+ 0] = y4[i+ 0]; sumy[0] += yl[i+ 0];
yl[i+ 8] = y4[i+ 8]; sumy[1] += yl[i+ 8];
yl[i+16] = y4[i+16]; sumy[2] += yl[i+16];
yl[i+24] = y4[i+24]; sumy[3] += yl[i+24];
}
const int ibl = ib32 / (QK_K / 32);
const int ib = ib32 % (QK_K / 32);
device const block_iq1_m * xr = x + ibl;
device const uint8_t * qs = xr->qs + 4 * ib;
device const uint8_t * qh = xr->qh + 2 * ib;
device const uint16_t * sc = (device const uint16_t *)xr->scales;
for (int row = 0; row < N_DST; row++) {
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
constant uint8_t * grid3 = (constant uint8_t *)(iq1s_grid_gpu + (qs[2] | ((qh[1] << 8) & 0x700)));
constant uint8_t * grid4 = (constant uint8_t *)(iq1s_grid_gpu + (qs[3] | ((qh[1] << 4) & 0x700)));
float2 sum = {0.f};
for (int j = 0; j < 4; ++j) {
sum[0] += yl[j+ 0] * (grid1[j] & 0xf) + yl[j+ 4] * (grid1[j] >> 4)
+ yl[j+ 8] * (grid2[j] & 0xf) + yl[j+12] * (grid2[j] >> 4);
sum[1] += yl[j+16] * (grid3[j] & 0xf) + yl[j+20] * (grid3[j] >> 4)
+ yl[j+24] * (grid4[j] & 0xf) + yl[j+28] * (grid4[j] >> 4);
}
const float delta1 = sumy[0] * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[1] * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
const float delta2 = sumy[2] * (qh[1] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[3] * (qh[1] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) +
(sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1));
sc += nb*sizeof(block_iq1_m)/2;
qs += nb*sizeof(block_iq1_m);
qh += nb*sizeof(block_iq1_m);
}
y4 += 32 * 32;
}
for (int row = 0; row < N_DST; ++row) {
all_sum = simd_sum(sumf[row]);
if (tiisg == 0) {
dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
}
}
}
void kernel_mul_mv_iq4_nl_f32_impl( void kernel_mul_mv_iq4_nl_f32_impl(
device const void * src0, device const void * src0,
device const float * src1, device const float * src1,
@ -4673,6 +4771,34 @@ kernel void kernel_mul_mv_iq1_s_f32(
kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
} }
[[host_name("kernel_mul_mv_iq1_m_f32")]]
kernel void kernel_mul_mv_iq1_m_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,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]]) {
kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
}
[[host_name("kernel_mul_mv_iq4_nl_f32")]] [[host_name("kernel_mul_mv_iq4_nl_f32")]]
kernel void kernel_mul_mv_iq4_nl_f32( kernel void kernel_mul_mv_iq4_nl_f32(
device const void * src0, device const void * src0,
@ -5146,6 +5272,30 @@ void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 &
} }
} }
template <typename type4x4>
void dequantize_iq1_m(device const block_iq1_m * 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;
iq1m_scale_t scale;
device const uint16_t * sc = (device const uint16_t *)xb->scales;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
const float d = scale.f16;
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
device const uint8_t * qh = xb->qh + 2*ib32 + il;
const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1);
const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
for (int i = 0; i < 4; ++i) {
reg[0][i] = dl * (grid1[i] & 0xf) + ml1;
reg[1][i] = dl * (grid1[i] >> 4) + ml1;
reg[2][i] = dl * (grid2[i] & 0xf) + ml2;
reg[3][i] = dl * (grid2[i] >> 4) + ml2;
}
}
template <typename type4x4> template <typename type4x4>
void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) { void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) {
device const uint16_t * q4 = (device const uint16_t *)xb->qs; device const uint16_t * q4 = (device const uint16_t *)xb->qs;
@ -5730,6 +5880,7 @@ template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_r
template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_t kernel_get_rows<block_iq3_s, QK_NL, dequantize_iq3_s>; template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_t kernel_get_rows<block_iq3_s, QK_NL, dequantize_iq3_s>;
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_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_iq1_s")]] kernel get_rows_t kernel_get_rows<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_get_rows_iq1_m")]] kernel get_rows_t kernel_get_rows<block_iq1_m, QK_NL, dequantize_iq1_m>;
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_nl")]] kernel get_rows_t kernel_get_rows<block_iq4_nl, 2, dequantize_iq4_nl>;
#if QK_K == 64 #if QK_K == 64
template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows<block_iq4_xs, 2, dequantize_iq4_xs>; template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows<block_iq4_xs, 2, dequantize_iq4_xs>;
@ -5778,6 +5929,7 @@ template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_m
template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq3_s, QK_NL, dequantize_iq3_s>; template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq3_s, QK_NL, dequantize_iq3_s>;
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_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_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_mul_mm_iq1_m_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq1_m, QK_NL, dequantize_iq1_m>;
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_nl_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_nl, 2, dequantize_iq4_nl>;
#if QK_K == 64 #if QK_K == 64
template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_nl, 2, dequantize_iq4_xs>; template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_nl, 2, dequantize_iq4_xs>;
@ -5838,6 +5990,7 @@ template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel
template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq3_s, QK_NL, dequantize_iq3_s>; template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq3_s, QK_NL, dequantize_iq3_s>;
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_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_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_iq1_m_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq1_m, QK_NL, dequantize_iq1_m>;
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_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_nl, 2, dequantize_iq4_nl>;
#if QK_K == 64 #if QK_K == 64
template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_xs, 2, dequantize_iq4_xs>; template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_xs, 2, dequantize_iq4_xs>;
@ -7005,6 +7158,69 @@ kernel void kernel_mul_mv_id_iq1_s_f32(
sgitg); sgitg);
} }
[[host_name("kernel_mul_mv_id_iq1_m_f32")]]
kernel void kernel_mul_mv_id_iq1_m_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,
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_iq1_m_f32_impl(
src0[id],
(device const float *) (src1 + bid*nb11),
dst + bid*ne0,
ne00,
ne01,
ne02,
ne10,
ne12,
ne0,
ne1,
r2,
r3,
tgpig,
tiisg,
sgitg);
}
[[host_name("kernel_mul_mv_id_iq4_nl_f32")]] [[host_name("kernel_mul_mv_id_iq4_nl_f32")]]
kernel void kernel_mul_mv_id_iq4_nl_f32( kernel void kernel_mul_mv_id_iq4_nl_f32(
device const char * ids, device const char * ids,

611
ggml-quants.c
View File

@ -3474,6 +3474,54 @@ void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, in
} }
} }
void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, int k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
float delta[4];
uint16_t idx[4];
iq1m_scale_t scale;
for (int i = 0; i < nb; i++) {
const uint16_t * sc = (const uint16_t *)x[i].scales;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
const float d = GGML_FP16_TO_FP32(scale.f16);
const uint8_t * qs = x[i].qs;
const uint8_t * qh = x[i].qh;
for (int ib = 0; ib < QK_K/32; ++ib) {
const float dl1 = d * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1);
const float dl2 = d * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1);
idx[0] = qs[0] | ((qh[0] << 8) & 0x700);
idx[1] = qs[1] | ((qh[0] << 4) & 0x700);
idx[2] = qs[2] | ((qh[1] << 8) & 0x700);
idx[3] = qs[3] | ((qh[1] << 4) & 0x700);
delta[0] = qh[0] & 0x08 ? -IQ1S_DELTA : IQ1S_DELTA;
delta[1] = qh[0] & 0x80 ? -IQ1S_DELTA : IQ1S_DELTA;
delta[2] = qh[1] & 0x08 ? -IQ1S_DELTA : IQ1S_DELTA;
delta[3] = qh[1] & 0x80 ? -IQ1S_DELTA : IQ1S_DELTA;
for (int l = 0; l < 2; ++l) {
const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
for (int j = 0; j < 8; ++j) {
y[j] = dl1 * (grid[j] + delta[l]);
}
y += 8;
}
for (int l = 2; l < 4; ++l) {
const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
for (int j = 0; j < 8; ++j) {
y[j] = dl2 * (grid[j] + delta[l]);
}
y += 8;
}
qs += 4;
qh += 2;
}
}
}
static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, int k) { void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, int k) {
@ -9695,6 +9743,206 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void
#endif #endif
} }
void ggml_vec_dot_iq1_m_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(n % QK_K == 0);
assert(nrc == 1);
UNUSED(nrc);
UNUSED(bx);
UNUSED(by);
UNUSED(bs);
const block_iq1_m * restrict x = vx;
const block_q8_K * restrict y = vy;
const int nb = n / QK_K;
iq1m_scale_t scale;
#if defined __ARM_NEON
const int32x4_t mask = vdupq_n_s32(0x7);
const int32x4_t mone = vdupq_n_s32(1);
const int32x4_t mzero = vdupq_n_s32(0);
ggml_int8x16x4_t deltas;
deltas.val[0] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(+1));
deltas.val[1] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(+1));
deltas.val[2] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(-1));
deltas.val[3] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(-1));
ggml_int8x16x4_t q1b;
ggml_int8x16x4_t q8b;
uint32_t aux32;
const uint8_t * aux8 = (const uint8_t *)&aux32;
float sumf = 0;
for (int i = 0; i < nb; ++i) {
const int8_t * q8 = y[i].qs;
const uint8_t * qs = x[i].qs;
const uint8_t * qh = x[i].qh;
const uint16_t * sc = (const uint16_t *)x[i].scales;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
int32x4_t sumi1 = mzero;
int32x4_t sumi2 = mzero;
for (int ib = 0; ib < QK_K/32; ib += 2) {
q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[0] << 8) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[0] << 4) & 0x700)))));
q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[1] << 8) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[1] << 4) & 0x700)))));
q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[2] << 8) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[2] << 4) & 0x700)))));
q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[3] << 8) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[3] << 4) & 0x700)))));
q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
const int32x4_t p1 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[0], q8b.val[0]), ggml_vdotq_s32(mzero, q1b.val[1], q8b.val[1]));
const int32x4_t p2 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[2], q8b.val[2]), ggml_vdotq_s32(mzero, q1b.val[3], q8b.val[3]));
const int32x4_t p12 = vpaddq_s32(p1, p2);
const uint32_t * qh32 = (const uint32_t *)qh; // we are 4-byte aligned, so we can do that
aux32 = ((qh32[0] >> 3) & 0x01010101) | ((qh32[0] >> 6) & 0x02020202);
const int32x4_t p3 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[0]], q8b.val[0]), ggml_vdotq_s32(mzero, deltas.val[aux8[1]], q8b.val[1]));
const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3]));
const int32x4_t p34 = vpaddq_s32(p3, p4);
int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9);
scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone);
sumi1 = vmlaq_s32(sumi1, scales_4, p12);
sumi2 = vmlaq_s32(sumi2, scales_4, p34);
qs += 8; qh += 4;
}
sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
}
*s = sumf;
#elif defined __AVX2__
const __m256i mask = _mm256_set1_epi16(0x7);
const __m256i mone = _mm256_set1_epi16(1);
__m256 accum1 = _mm256_setzero_ps();
__m256 accum2 = _mm256_setzero_ps();
for (int i = 0; i < nb; ++i) {
const int8_t * q8 = y[i].qs;
const uint8_t * qs = x[i].qs;
const uint8_t * qh = x[i].qh;
const uint16_t * sc = (const uint16_t *)x[i].scales;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
__m256i sumi1 = _mm256_setzero_si256();
__m256i sumi2 = _mm256_setzero_si256();
for (int ib = 0; ib < QK_K/32; ib += 2) {
const __m256i q1b_1 = _mm256_set_epi64x(
iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)],
iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)]
);
const __m256i q1b_2 = _mm256_set_epi64x(
iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)],
iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)]
);
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 dot1 = mul_add_epi8(q1b_1, q8b_1);
const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2);
const __m256i delta1 = _mm256_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101,
qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
const __m256i delta2 = _mm256_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101,
qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
const __m256i dot3 = mul_add_epi8(delta1, q8b_1);
const __m256i dot4 = mul_add_epi8(delta2, q8b_2);
__m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0));
__m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6));
scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone);
scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone);
const __m256i p1 = _mm256_madd_epi16(dot1, scale1);
const __m256i p2 = _mm256_madd_epi16(dot2, scale2);
const __m256i p3 = _mm256_madd_epi16(dot3, scale1);
const __m256i p4 = _mm256_madd_epi16(dot4, scale2);
sumi1 = _mm256_add_epi32(sumi1, _mm256_add_epi32(p1, p2));
sumi2 = _mm256_add_epi32(sumi2, _mm256_add_epi32(p3, p4));
qs += 8; qh += 4;
}
const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16));
accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1);
accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2);
}
*s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2);
#else
int sum1[2], sum2[2], delta[4];
float sumf = 0;
for (int i = 0; i < nb; i++) {
const int8_t * q8 = y[i].qs;
const uint8_t * qs = x[i].qs;
const uint8_t * qh = x[i].qh;
const uint16_t * sc = (const uint16_t *)x[i].scales;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
int sumi1 = 0, sumi2 = 0;
for (int ib = 0; ib < QK_K/32; ++ib) {
delta[0] = qh[0] & 0x08 ? -1 : 1;
delta[1] = qh[0] & 0x80 ? -1 : 1;
delta[2] = qh[1] & 0x08 ? -1 : 1;
delta[3] = qh[1] & 0x80 ? -1 : 1;
sum1[0] = sum1[1] = sum2[0] = sum2[1] = 0;
for (int l = 0; l < 4; ++l) {
const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((uint16_t)qh[l/2] << (8 - 4*(l%2))) & 0x700)));
int lsum1 = 0, lsum2 = 0;
for (int j = 0; j < 8; ++j) {
lsum1 += q8[j] * grid[j];
lsum2 += q8[j];
}
q8 += 8;
sum1[l/2] += lsum1;
sum2[l/2] += lsum2*delta[l];
}
const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1;
const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1;
sumi1 += sum1[0] * ls1 + sum1[1] * ls2;
sumi2 += sum2[0] * ls1 + sum2[1] * ls2;
qs += 4;
qh += 2;
}
sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
}
*s = sumf;
#endif
}
void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { void ggml_vec_dot_iq4_nl_q8_0(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); assert(nrc == 1);
UNUSED(nrc); UNUSED(nrc);
@ -9938,17 +10186,17 @@ static iq2_entry_t iq2_data[4] = {
}; };
static inline int iq2_data_index(enum ggml_type type) { static inline int iq2_data_index(enum ggml_type type) {
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S); GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
return type == GGML_TYPE_IQ2_XXS ? 0 : return type == GGML_TYPE_IQ2_XXS ? 0 :
type == GGML_TYPE_IQ2_XS ? 1 : type == GGML_TYPE_IQ2_XS ? 1 :
type == GGML_TYPE_IQ1_S ? 2 : 3; type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? 2 : 3;
} }
static inline int iq2_grid_size(enum ggml_type type) { static inline int iq2_grid_size(enum ggml_type type) {
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S); GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
return type == GGML_TYPE_IQ2_XXS ? 256 : return type == GGML_TYPE_IQ2_XXS ? 256 :
type == GGML_TYPE_IQ2_XS ? 512 : type == GGML_TYPE_IQ2_XS ? 512 :
type == GGML_TYPE_IQ1_S ? NGRID_IQ1S : 1024; type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? NGRID_IQ1S : 1024;
} }
static int iq2_compare_func(const void * left, const void * right) { static int iq2_compare_func(const void * left, const void * right) {
@ -10214,10 +10462,10 @@ void iq2xs_init_impl(enum ggml_type type) {
const int kmap_size = 43692; const int kmap_size = 43692;
//const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2; //const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2;
const int nwant = type == GGML_TYPE_IQ1_S ? 3 : type == GGML_TYPE_IQ2_S ? 1 : 2; const int nwant = type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? 3 : type == GGML_TYPE_IQ2_S ? 1 : 2;
const uint16_t * kgrid = type == GGML_TYPE_IQ2_XXS ? kgrid_2bit_256 : const uint16_t * kgrid = type == GGML_TYPE_IQ2_XXS ? kgrid_2bit_256 :
type == GGML_TYPE_IQ2_XS ? kgrid_2bit_512 : type == GGML_TYPE_IQ2_XS ? kgrid_2bit_512 :
type == GGML_TYPE_IQ1_S ? kgrid_1bit_2048 : kgrid_2bit_1024; type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? kgrid_1bit_2048 : kgrid_2bit_1024;
uint64_t * kgrid_q2xs; uint64_t * kgrid_q2xs;
int * kmap_q2xs; int * kmap_q2xs;
uint16_t * kneighbors_q2xs; uint16_t * kneighbors_q2xs;
@ -10314,7 +10562,7 @@ void iq2xs_init_impl(enum ggml_type type) {
} }
void iq2xs_free_impl(enum ggml_type type) { void iq2xs_free_impl(enum ggml_type type) {
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S); GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
const int gindex = iq2_data_index(type); const int gindex = iq2_data_index(type);
if (iq2_data[gindex].grid) { if (iq2_data[gindex].grid) {
free(iq2_data[gindex].grid); iq2_data[gindex].grid = NULL; free(iq2_data[gindex].grid); iq2_data[gindex].grid = NULL;
@ -11520,7 +11768,16 @@ static int iq1_sort_helper(const void * left, const void * right) {
} }
#define IQ1S_BLOCK_SIZE 32 #define IQ1S_BLOCK_SIZE 32
static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { #define IQ1M_BLOCK_SIZE 16
static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights,
float * scales,
float * weight,
float * sumx,
float * sumw,
float * pairs,
int8_t * L,
uint16_t * index,
int8_t * shifts) {
const int gindex = iq2_data_index(GGML_TYPE_IQ1_S); const int gindex = iq2_data_index(GGML_TYPE_IQ1_S);
@ -11534,22 +11791,17 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?"); GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(n%QK_K == 0); GGML_ASSERT(n%QK_K == 0);
block_iq1_s * y = vy;
const int nbl = n/QK_K; const int nbl = n/QK_K;
block_iq1_s * y = vy; const int block_size = IQ1S_BLOCK_SIZE;
const float x_p[3] = {-1 + IQ1S_DELTA, IQ1S_DELTA, 1 + IQ1S_DELTA}; const float x_p[3] = {-1 + IQ1S_DELTA, IQ1S_DELTA, 1 + IQ1S_DELTA};
const float x_m[3] = {-1 - IQ1S_DELTA, -IQ1S_DELTA, 1 - IQ1S_DELTA}; const float x_m[3] = {-1 - IQ1S_DELTA, -IQ1S_DELTA, 1 - IQ1S_DELTA};
float scales[QK_K/IQ1S_BLOCK_SIZE];
float weight[IQ1S_BLOCK_SIZE];
int8_t L[IQ1S_BLOCK_SIZE];
float sumx[IQ1S_BLOCK_SIZE+1];
float sumw[IQ1S_BLOCK_SIZE+1];
float pairs[2*IQ1S_BLOCK_SIZE];
int * idx = (int *)(pairs + 1); int * idx = (int *)(pairs + 1);
uint16_t index[IQ1S_BLOCK_SIZE/8];
int8_t shifts[QK_K/IQ1S_BLOCK_SIZE];
for (int ibl = 0; ibl < nbl; ++ibl) { for (int ibl = 0; ibl < nbl; ++ibl) {
@ -11564,15 +11816,15 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
float sigma2 = 2*sumx2/QK_K; float sigma2 = 2*sumx2/QK_K;
for (int ib = 0; ib < QK_K/IQ1S_BLOCK_SIZE; ++ib) { for (int ib = 0; ib < QK_K/block_size; ++ib) {
const float * xb = xbl + IQ1S_BLOCK_SIZE*ib; const float * xb = xbl + block_size*ib;
const float * qw = quant_weights + QK_K*ibl + IQ1S_BLOCK_SIZE*ib; const float * qw = quant_weights + QK_K*ibl + block_size*ib;
for (int i = 0; i < IQ1S_BLOCK_SIZE; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
float max = fabsf(xb[0]); float max = fabsf(xb[0]);
for (int i = 1; i < IQ1S_BLOCK_SIZE; ++i) max = MAX(max, fabsf(xb[i])); for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
if (!max) { if (!max) {
scales[ib] = 0; scales[ib] = 0;
memset(L, 1, IQ1S_BLOCK_SIZE); memset(L, 1, block_size);
continue; continue;
} }
// Here we solve exactly the sum of squared difference (SSD) weighted minimization problem. // Here we solve exactly the sum of squared difference (SSD) weighted minimization problem.
@ -11581,14 +11833,14 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
// in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and // in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and
// Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale // Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale
// for each possible and score for each split. // for each possible and score for each split.
for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) { for (int j = 0; j < block_size; ++j) {
pairs[2*j] = xb[j]; pairs[2*j] = xb[j];
idx[2*j] = j; idx[2*j] = j;
} }
qsort(pairs, IQ1S_BLOCK_SIZE, 2*sizeof(float), iq1_sort_helper); qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper);
{ {
sumx[0] = sumw[0] = 0; sumx[0] = sumw[0] = 0;
for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) { for (int j = 0; j < block_size; ++j) {
int i = idx[2*j]; int i = idx[2*j];
sumx[j+1] = sumx[j] + weight[i]*xb[i]; sumx[j+1] = sumx[j] + weight[i]*xb[i];
sumw[j+1] = sumw[j] + weight[i]; sumw[j+1] = sumw[j] + weight[i];
@ -11596,16 +11848,16 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
} }
float best_score = 0, scale = max; float best_score = 0, scale = max;
int besti1 = -1, besti2 = -1, best_shift = 0; int besti1 = -1, besti2 = -1, best_shift = 0;
for (int i1 = 0; i1 <= IQ1S_BLOCK_SIZE; ++i1) { for (int i1 = 0; i1 <= block_size; ++i1) {
for (int i2 = i1; i2 <= IQ1S_BLOCK_SIZE; ++i2) { for (int i2 = i1; i2 <= block_size; ++i2) {
float sumqx = (sumx[i1] - sumx[0])*x_p[0] + (sumx[i2] - sumx[i1])*x_p[1] + (sumx[IQ1S_BLOCK_SIZE] - sumx[i2])*x_p[2]; float sumqx = (sumx[i1] - sumx[0])*x_p[0] + (sumx[i2] - sumx[i1])*x_p[1] + (sumx[block_size] - sumx[i2])*x_p[2];
float sumq2 = (sumw[i1] - sumw[0])*x_p[0]*x_p[0] + (sumw[i2] - sumw[i1])*x_p[1]*x_p[1] + (sumw[IQ1S_BLOCK_SIZE] - sumw[i2])*x_p[2]*x_p[2]; float sumq2 = (sumw[i1] - sumw[0])*x_p[0]*x_p[0] + (sumw[i2] - sumw[i1])*x_p[1]*x_p[1] + (sumw[block_size] - sumw[i2])*x_p[2]*x_p[2];
if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) { if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
scale = sumqx/sumq2; best_score = scale*sumqx; scale = sumqx/sumq2; best_score = scale*sumqx;
besti1 = i1; besti2 = i2; best_shift = 1; besti1 = i1; besti2 = i2; best_shift = 1;
} }
sumqx = (sumx[i1] - sumx[0])*x_m[0] + (sumx[i2] - sumx[i1])*x_m[1] + (sumx[IQ1S_BLOCK_SIZE] - sumx[i2])*x_m[2]; sumqx = (sumx[i1] - sumx[0])*x_m[0] + (sumx[i2] - sumx[i1])*x_m[1] + (sumx[block_size] - sumx[i2])*x_m[2];
sumq2 = (sumw[i1] - sumw[0])*x_m[0]*x_m[0] + (sumw[i2] - sumw[i1])*x_m[1]*x_m[1] + (sumw[IQ1S_BLOCK_SIZE] - sumw[i2])*x_m[2]*x_m[2]; sumq2 = (sumw[i1] - sumw[0])*x_m[0]*x_m[0] + (sumw[i2] - sumw[i1])*x_m[1]*x_m[1] + (sumw[block_size] - sumw[i2])*x_m[2]*x_m[2];
if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) { if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
scale = sumqx/sumq2; best_score = scale*sumqx; scale = sumqx/sumq2; best_score = scale*sumqx;
besti1 = i1; besti2 = i2; best_shift = -1; besti1 = i1; besti2 = i2; best_shift = -1;
@ -11615,14 +11867,14 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_shift != 0); GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_shift != 0);
for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0; for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0;
for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1; for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1;
for (int j = besti2; j < IQ1S_BLOCK_SIZE; ++j) L[idx[2*j]] = 2; for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2;
if (scale < 0) { if (scale < 0) {
for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) L[j] = 2 - L[j]; for (int j = 0; j < block_size; ++j) L[j] = 2 - L[j];
scale = -scale; best_shift = -best_shift; scale = -scale; best_shift = -best_shift;
} }
bool all_on_grid = true; bool all_on_grid = true;
const float * xx = best_shift == 1 ? x_p : x_m; const float * xx = best_shift == 1 ? x_p : x_m;
for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) { for (int k = 0; k < block_size/8; ++k) {
uint16_t u = 0; uint16_t u = 0;
for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j); for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j);
int grid_index = kmap_q2xs[u]; int grid_index = kmap_q2xs[u];
@ -11636,7 +11888,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
} }
if (!all_on_grid) { if (!all_on_grid) {
float sumqx = 0, sumq2 = 0; float sumqx = 0, sumq2 = 0;
for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) { for (int k = 0; k < block_size/8; ++k) {
const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]); const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]);
for (int j = 0; j < 8; ++j) { for (int j = 0; j < 8; ++j) {
float w = weight[8*k + j]; float w = weight[8*k + j];
@ -11648,8 +11900,8 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
if (sumqx > 0 && sumq2 > 0) scale = sumqx/sumq2; if (sumqx > 0 && sumq2 > 0) scale = sumqx/sumq2;
} }
uint16_t h = 0; uint16_t h = 0;
for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) { for (int k = 0; k < block_size/8; ++k) {
y[ibl].qs[(IQ1S_BLOCK_SIZE/8)*ib + k] = index[k] & 255; y[ibl].qs[(block_size/8)*ib + k] = index[k] & 255;
h |= (index[k] >> 8) << 3*k; h |= (index[k] >> 8) << 3*k;
} }
y[ibl].qh[ib] = h; y[ibl].qh[ib] = h;
@ -11660,14 +11912,13 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
} }
if (!max_scale) { if (!max_scale) {
memset(y[ibl].qs, 0, QK_K/8);
continue; continue;
} }
float d = max_scale/15; float d = max_scale/15;
y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.085f is another fudge factor. Don't ask me why it is needed. y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.125f is another fudge factor. Don't ask me why it is needed.
float id = 1/d; float id = 1/d;
for (int ib = 0; ib < QK_K/IQ1S_BLOCK_SIZE; ++ib) { for (int ib = 0; ib < QK_K/block_size; ++ib) {
int l = nearest_int(0.5f*(id*scales[ib]-1)); int l = nearest_int(0.5f*(id*scales[ib]-1));
l = MAX(0, MIN(7, l)); l = MAX(0, MIN(7, l));
if (shifts[ib] == -1) l |= 8; if (shifts[ib] == -1) l |= 8;
@ -11678,16 +11929,292 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
GGML_ASSERT(n_per_row%QK_K == 0); GGML_ASSERT(n_per_row%QK_K == 0);
float scales[QK_K/IQ1S_BLOCK_SIZE];
float weight[IQ1S_BLOCK_SIZE];
int8_t L[IQ1S_BLOCK_SIZE];
float sumx[IQ1S_BLOCK_SIZE+1];
float sumw[IQ1S_BLOCK_SIZE+1];
float pairs[2*IQ1S_BLOCK_SIZE];
uint16_t index[IQ1S_BLOCK_SIZE/8];
int8_t shifts[QK_K/IQ1S_BLOCK_SIZE];
int nblock = n_per_row/QK_K; int nblock = n_per_row/QK_K;
char * qrow = (char *)dst; char * qrow = (char *)dst;
for (int row = 0; row < nrow; ++row) { for (int row = 0; row < nrow; ++row) {
quantize_row_iq1_s_impl(src, qrow, n_per_row, quant_weights); quantize_row_iq1_s_impl(src, qrow, n_per_row, quant_weights, scales, weight, sumx, sumw, pairs, L, index, shifts);
src += n_per_row; src += n_per_row;
qrow += nblock*sizeof(block_iq1_s); qrow += nblock*sizeof(block_iq1_s);
} }
return nrow * nblock * sizeof(block_iq1_s); return nrow * nblock * sizeof(block_iq1_s);
} }
static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights,
float * scales,
float * weight,
float * pairs,
int8_t * L,
uint16_t * index,
int8_t * shifts) {
const int gindex = iq2_data_index(GGML_TYPE_IQ1_M);
const uint64_t * kgrid_q2xs = iq2_data[gindex].grid;
const int * kmap_q2xs = iq2_data[gindex].map;
const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours;
//GGML_ASSERT(quant_weights && "missing quantization weights");
GGML_ASSERT(kgrid_q2xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(kmap_q2xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(n%QK_K == 0);
block_iq1_m * y = vy;
const int nbl = n/QK_K;
const int block_size = IQ1M_BLOCK_SIZE;
const float x_p[3] = {-1 + IQ1M_DELTA, IQ1M_DELTA, 1 + IQ1M_DELTA};
const float x_m[3] = {-1 - IQ1M_DELTA, -IQ1M_DELTA, 1 - IQ1M_DELTA};
const uint8_t masks[4] = {0x00, 0x80, 0x08, 0x88};
int * idx = (int *)(pairs + 1);
float sumqx[4], sumq2[4];
iq1m_scale_t s;
const float * xx;
for (int ibl = 0; ibl < nbl; ++ibl) {
//y[ibl].d = GGML_FP32_TO_FP16(0.f);
memset(y[ibl].qs, 0, QK_K/8);
memset(y[ibl].qh, 0, QK_K/16);
memset(y[ibl].scales, 0, QK_K/32);
float max_scale = 0;
const float * xbl = x + QK_K*ibl;
float sumx2 = 0;
for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
float sigma2 = 2*sumx2/QK_K;
for (int ib = 0; ib < QK_K/block_size; ++ib) {
const float * xb = xbl + block_size*ib;
if (quant_weights) {
const float * qw = quant_weights + QK_K*ibl + block_size*ib;
for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
} else {
for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
}
float max = fabsf(xb[0]);
for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
if (!max) {
scales[ib] = 0;
memset(L, 1, block_size);
continue;
}
// Here we solve exactly the sum of squared difference (SSD) weighted minimization problem.
// With just 3 allowed quant values (-1, 0, 1), we can search exhaustively for the two
// boundaries that split the weights xb[i] into 3 groups. To do so, we sort the weights
// in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and
// Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale
// for each possible and score for each split.
for (int j = 0; j < block_size; ++j) {
pairs[2*j] = xb[j];
idx[2*j] = j;
}
qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper);
float best_score = 0, scale = max;
int besti1 = -1, besti2 = -1, best_k = -1;
// 0: +, +
// 1: +, -
// 2: -, +
// 3: -, -
for (int i1 = 0; i1 <= block_size; ++i1) {
for (int i2 = i1; i2 <= block_size; ++i2) {
memset(sumqx, 0, 4*sizeof(float));
memset(sumq2, 0, 4*sizeof(float));
for (int j = 0; j < i1; ++j) {
int i = idx[2*j];
if (i < block_size/2) {
sumqx[0] += weight[i]*x_p[0]*xb[i];
sumqx[1] += weight[i]*x_p[0]*xb[i];
sumqx[2] += weight[i]*x_m[0]*xb[i];
sumqx[3] += weight[i]*x_m[0]*xb[i];
sumq2[0] += weight[i]*x_p[0]*x_p[0];
sumq2[1] += weight[i]*x_p[0]*x_p[0];
sumq2[2] += weight[i]*x_m[0]*x_m[0];
sumq2[3] += weight[i]*x_m[0]*x_m[0];
} else {
sumqx[0] += weight[i]*x_p[0]*xb[i];
sumqx[2] += weight[i]*x_p[0]*xb[i];
sumqx[1] += weight[i]*x_m[0]*xb[i];
sumqx[3] += weight[i]*x_m[0]*xb[i];
sumq2[0] += weight[i]*x_p[0]*x_p[0];
sumq2[2] += weight[i]*x_p[0]*x_p[0];
sumq2[1] += weight[i]*x_m[0]*x_m[0];
sumq2[3] += weight[i]*x_m[0]*x_m[0];
}
}
for (int j = i1; j < i2; ++j) {
int i = idx[2*j];
if (i < block_size/2) {
sumqx[0] += weight[i]*x_p[1]*xb[i];
sumqx[1] += weight[i]*x_p[1]*xb[i];
sumqx[2] += weight[i]*x_m[1]*xb[i];
sumqx[3] += weight[i]*x_m[1]*xb[i];
sumq2[0] += weight[i]*x_p[1]*x_p[1];
sumq2[1] += weight[i]*x_p[1]*x_p[1];
sumq2[2] += weight[i]*x_m[1]*x_m[1];
sumq2[3] += weight[i]*x_m[1]*x_m[1];
} else {
sumqx[0] += weight[i]*x_p[1]*xb[i];
sumqx[2] += weight[i]*x_p[1]*xb[i];
sumqx[1] += weight[i]*x_m[1]*xb[i];
sumqx[3] += weight[i]*x_m[1]*xb[i];
sumq2[0] += weight[i]*x_p[1]*x_p[1];
sumq2[2] += weight[i]*x_p[1]*x_p[1];
sumq2[1] += weight[i]*x_m[1]*x_m[1];
sumq2[3] += weight[i]*x_m[1]*x_m[1];
}
}
for (int j = i2; j < block_size; ++j) {
int i = idx[2*j];
if (i < block_size/2) {
sumqx[0] += weight[i]*x_p[2]*xb[i];
sumqx[1] += weight[i]*x_p[2]*xb[i];
sumqx[2] += weight[i]*x_m[2]*xb[i];
sumqx[3] += weight[i]*x_m[2]*xb[i];
sumq2[0] += weight[i]*x_p[2]*x_p[2];
sumq2[1] += weight[i]*x_p[2]*x_p[2];
sumq2[2] += weight[i]*x_m[2]*x_m[2];
sumq2[3] += weight[i]*x_m[2]*x_m[2];
} else {
sumqx[0] += weight[i]*x_p[2]*xb[i];
sumqx[2] += weight[i]*x_p[2]*xb[i];
sumqx[1] += weight[i]*x_m[2]*xb[i];
sumqx[3] += weight[i]*x_m[2]*xb[i];
sumq2[0] += weight[i]*x_p[2]*x_p[2];
sumq2[2] += weight[i]*x_p[2]*x_p[2];
sumq2[1] += weight[i]*x_m[2]*x_m[2];
sumq2[3] += weight[i]*x_m[2]*x_m[2];
}
}
for (int k = 0; k < 4; ++k) {
if (sumq2[k] > 0 && sumqx[k]*sumqx[k] > best_score*sumq2[k]) {
scale = sumqx[k]/sumq2[k]; best_score = scale*sumqx[k];
besti1 = i1; besti2 = i2; best_k = k;
}
}
}
}
GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_k >= 0);
for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0;
for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1;
for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2;
if (scale < 0) {
for (int j = 0; j < block_size; ++j) L[j] = 2 - L[j];
scale = -scale;
best_k = best_k == 0 ? 3 : best_k == 1 ? 2 : best_k == 2 ? 1 : 0;
}
bool all_on_grid = true;
for (int k = 0; k < block_size/8; ++k) {
if (k == 0) xx = best_k < 2 ? x_p : x_m;
else xx = best_k%2 == 0 ? x_p : x_m;
uint16_t u = 0;
for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j);
int grid_index = kmap_q2xs[u];
if (grid_index < 0) {
all_on_grid = false;
const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
grid_index = iq1_find_best_neighbour2(neighbours, kgrid_q2xs, xb + 8*k, weight + 8*k, scale, xx, L + 8*k, NGRID_IQ1S);
GGML_ASSERT(grid_index >= 0);
}
index[k] = grid_index;
}
if (!all_on_grid) {
float sumqx_f = 0, sumq2_f = 0;
for (int k = 0; k < block_size/8; ++k) {
if (k == 0) xx = best_k < 2 ? x_p : x_m;
else xx = best_k%2 == 0 ? x_p : x_m;
const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]);
for (int j = 0; j < 8; ++j) {
float w = weight[8*k + j];
float q = xx[(pg[j] - 1)/2];
sumqx_f += w*q*xb[8*k+j];
sumq2_f += w*q*q;
}
}
if (sumqx_f > 0 && sumq2_f > 0) scale = sumqx_f/sumq2_f;
}
y[ibl].qs[2*ib + 0] = index[0] & 255;
y[ibl].qs[2*ib + 1] = index[1] & 255;
y[ibl].qh[ib] = (index[0] >> 8) | ((index[1] >> 8) << 4);
GGML_ASSERT(scale >= 0);
scales[ib] = scale;
shifts[ib] = best_k;
max_scale = MAX(max_scale, scale);
}
if (!max_scale) {
continue;
}
uint16_t * sc = (uint16_t *)y[ibl].scales;
float d = max_scale/15;
float id = 1/d;
float sumqx_f = 0, sumq2_f = 0;
for (int ib = 0; ib < QK_K/block_size; ++ib) {
int l = nearest_int(0.5f*(id*scales[ib+0]-1));
l = MAX(0, MIN(7, l));
sc[ib/4] |= (l << 3*(ib%4));
y[ibl].qh[ib] |= masks[shifts[ib]];
const float * xb = xbl + block_size*ib;
if (quant_weights) {
const float * qw = quant_weights + QK_K*ibl + block_size*ib;
for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
} else {
for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
}
for (int k = 0; k < block_size/8; ++k) {
if (k == 0) xx = shifts[ib] < 2 ? x_p : x_m;
else xx = shifts[ib]%2 == 0 ? x_p : x_m;
const int8_t * pg = (const int8_t *)(kgrid_q2xs + y[ibl].qs[2*ib+k] + ((y[ibl].qh[ib] << (8 - 4*k)) & 0x700));
for (int j = 0; j < 8; ++j) {
float w = weight[8*k + j];
float q = xx[(pg[j] - 1)/2]*(2*l+1);
sumqx_f += w*q*xb[8*k+j];
sumq2_f += w*q*q;
}
}
}
if (sumq2_f > 0) d = sumqx_f/sumq2_f;
s.f16 = GGML_FP32_TO_FP16(d*1.1125f); // 1.1125f is another fudge factor. Don't ask me why it is needed.
sc[0] |= ((s.u16 & 0x000f) << 12);
sc[1] |= ((s.u16 & 0x00f0) << 8);
sc[2] |= ((s.u16 & 0x0f00) << 4);
sc[3] |= ((s.u16 & 0xf000) << 0);
}
}
size_t quantize_iq1_m(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
GGML_ASSERT(n_per_row%QK_K == 0);
float scales[QK_K/IQ1M_BLOCK_SIZE];
float weight[IQ1M_BLOCK_SIZE];
int8_t L[IQ1M_BLOCK_SIZE];
float pairs[2*IQ1M_BLOCK_SIZE];
uint16_t index[IQ1M_BLOCK_SIZE/8];
int8_t shifts[QK_K/IQ1M_BLOCK_SIZE];
int nblock = n_per_row/QK_K;
char * qrow = (char *)dst;
for (int row = 0; row < nrow; ++row) {
quantize_row_iq1_m_impl(src, qrow, n_per_row, quant_weights, scales, weight, pairs, L, index, shifts);
src += n_per_row;
qrow += nblock*sizeof(block_iq1_m);
}
return nrow * nblock * sizeof(block_iq1_m);
}
// ============================ 4-bit non-linear quants // ============================ 4-bit non-linear quants
static inline int best_index_int8(int n, const int8_t * val, float x) { static inline int best_index_int8(int n, const int8_t * val, float x) {

3
ggml-quants.h
View File

@ -72,6 +72,7 @@ void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_
void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); 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_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq1_m (const block_iq1_m * 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_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_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); void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
@ -94,6 +95,7 @@ void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
void ggml_vec_dot_iq2_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_iq2_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_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_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_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_iq1_m_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_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_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); 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);
@ -104,6 +106,7 @@ size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT ds
size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq1_m (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix); size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);

28
ggml.c
View File

@ -794,6 +794,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.vec_dot_type = GGML_TYPE_Q8_K, .vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1, .nrows = 1,
}, },
[GGML_TYPE_IQ1_M] = {
.type_name = "iq1_m",
.blck_size = QK_K,
.type_size = sizeof(block_iq1_m),
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq1_m,
.from_float = NULL,
.from_float_reference = NULL,
.vec_dot = ggml_vec_dot_iq1_m_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ4_NL] = { [GGML_TYPE_IQ4_NL] = {
.type_name = "iq4_nl", .type_name = "iq4_nl",
.blck_size = QK4_NL, .blck_size = QK4_NL,
@ -2539,6 +2551,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break; case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break;
case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break; 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_IQ1_S: wtype = GGML_TYPE_IQ1_S; break;
case GGML_FTYPE_MOSTLY_IQ1_M: wtype = GGML_TYPE_IQ1_M; break;
case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; 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_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break; case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
@ -8135,6 +8148,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ3_S:
@ -8417,6 +8431,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ3_S:
@ -8544,6 +8559,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ3_S:
@ -11447,6 +11463,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ3_S:
@ -11638,6 +11655,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ3_S:
@ -11861,6 +11879,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ3_S:
@ -12564,6 +12583,7 @@ static void ggml_compute_forward_alibi(
case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ3_S:
@ -12652,6 +12672,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ3_S:
@ -20306,7 +20327,8 @@ void ggml_quantize_init(enum ggml_type type) {
case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_S: case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ1_S: iq2xs_init_impl(type); break; case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M: iq2xs_init_impl(type); break;
case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break; case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break;
case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break; case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break;
default: // nothing default: // nothing
@ -20331,7 +20353,8 @@ bool ggml_quantize_requires_imatrix(enum ggml_type type) {
return return
type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XXS ||
type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XS ||
type == GGML_TYPE_IQ1_S; type == GGML_TYPE_IQ1_S;// ||
//type == GGML_TYPE_IQ1_M;
} }
size_t ggml_quantize_chunk( size_t ggml_quantize_chunk(
@ -20375,6 +20398,7 @@ size_t ggml_quantize_chunk(
case GGML_TYPE_IQ3_S: result = quantize_iq3_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ3_S: result = quantize_iq3_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ2_S: result = quantize_iq2_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ2_S: result = quantize_iq2_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ1_M: result = quantize_iq1_m (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
#if QK_K == 64 #if QK_K == 64
case GGML_TYPE_IQ4_XS: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ4_XS: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;

2
ggml.h
View File

@ -369,6 +369,7 @@ extern "C" {
GGML_TYPE_I32 = 26, GGML_TYPE_I32 = 26,
GGML_TYPE_I64 = 27, GGML_TYPE_I64 = 27,
GGML_TYPE_F64 = 28, GGML_TYPE_F64 = 28,
GGML_TYPE_IQ1_M = 29,
GGML_TYPE_COUNT, GGML_TYPE_COUNT,
}; };
@ -408,6 +409,7 @@ extern "C" {
GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ1_M = 23, // except 1d tensors
}; };
// available tensor operations: // available tensor operations:

1
gguf-py/gguf/constants.py
View File

@ -706,6 +706,7 @@ class GGMLQuantizationType(IntEnum):
I32 = 26 I32 = 26
I64 = 27 I64 = 27
F64 = 28 F64 = 28
IQ1_M = 29
class GGUFEndian(IntEnum): class GGUFEndian(IntEnum):

24
llama.cpp
View File

@ -3018,6 +3018,7 @@ struct llama_model_loader {
case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_S; break; case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_S; break;
case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break; 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_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break;
case GGML_TYPE_IQ1_M: ftype = LLAMA_FTYPE_MOSTLY_IQ1_M; break;
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; 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_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break; case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break;
@ -3413,6 +3414,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw"; case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw"; 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_IQ1_S :return "IQ1_S - 1.5625 bpw";
case LLAMA_FTYPE_MOSTLY_IQ1_M :return "IQ1_M - 1.75 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 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_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_S: return "IQ3_S - 3.4375 bpw";
@ -12447,7 +12449,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
new_type = GGML_TYPE_Q8_0; new_type = GGML_TYPE_Q8_0;
} }
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) { ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
new_type = GGML_TYPE_Q5_K; new_type = GGML_TYPE_Q5_K;
} }
else if (new_type != GGML_TYPE_Q8_0) { else if (new_type != GGML_TYPE_Q8_0) {
@ -12458,7 +12461,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
if (qs.params->token_embedding_type < GGML_TYPE_COUNT) { if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
new_type = qs.params->token_embedding_type; new_type = qs.params->token_embedding_type;
} else { } else {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) { if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
new_type = GGML_TYPE_Q2_K; new_type = GGML_TYPE_Q2_K;
} }
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) { else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
@ -12469,7 +12473,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
} }
} }
} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) { ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
if (name.find("attn_v.weight") != std::string::npos) { if (name.find("attn_v.weight") != std::string::npos) {
if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K; if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K; else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
@ -12488,7 +12492,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
if (qs.model.hparams.n_expert == 8) { if (qs.model.hparams.n_expert == 8) {
new_type = GGML_TYPE_Q5_K; new_type = GGML_TYPE_Q5_K;
} else { } else {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) new_type = GGML_TYPE_IQ2_XXS; if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
} }
} }
@ -12655,7 +12659,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K || 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_IQ4_XS || 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_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) { new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
new_type == GGML_TYPE_IQ1_M) {
int nx = tensor->ne[0]; int nx = tensor->ne[0];
int ny = tensor->ne[1]; int ny = tensor->ne[1];
if (nx % QK_K != 0) { if (nx % QK_K != 0) {
@ -12673,6 +12678,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_Q2_K: case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K: case GGML_TYPE_Q3_K:
case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break; case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
@ -12754,6 +12760,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break; case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break;
case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break; case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break; case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break;
case LLAMA_FTYPE_MOSTLY_IQ1_M: default_type = GGML_TYPE_IQ1_M; break;
case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break; case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break;
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break; case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break; case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break;
@ -12929,6 +12936,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (!params->pure && ggml_is_quantized(default_type)) { if (!params->pure && ggml_is_quantized(default_type)) {
new_type = llama_tensor_get_type(qs, new_type, tensor, ftype); new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
} }
else if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
new_type = params->token_embedding_type;
}
else if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
new_type = params->output_tensor_type;
}
// If we've decided to quantize to the same type the tensor is already // If we've decided to quantize to the same type the tensor is already
// in then there's nothing to do. // in then there's nothing to do.
@ -12961,6 +12974,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XS ||
new_type == GGML_TYPE_IQ2_S || new_type == GGML_TYPE_IQ2_S ||
new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ1_S ||
(new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight")) ||
(new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) { (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
LLAMA_LOG_ERROR("\n\n============================================================\n"); LLAMA_LOG_ERROR("\n\n============================================================\n");
LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name); LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);

1
llama.h
View File

@ -117,6 +117,7 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ1_M = 31, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
}; };

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

@ -1960,7 +1960,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
GGML_TYPE_Q4_K, GGML_TYPE_Q5_K, GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
GGML_TYPE_Q6_K, GGML_TYPE_Q6_K,
GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
}; };