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-rw-r--r--examples/quantize/quantize.cpp1
-rw-r--r--ggml/include/ggml.h10
-rw-r--r--ggml/src/ggml-common.h15
-rw-r--r--ggml/src/ggml-cuda.cu3
-rw-r--r--ggml/src/ggml-cuda/common.cuh7
-rw-r--r--ggml/src/ggml-cuda/convert.cu57
-rw-r--r--ggml/src/ggml-cuda/iqk_mmvq.cu23
-rw-r--r--ggml/src/ggml-cuda/iqk_mmvq.cuh4
-rw-r--r--ggml/src/ggml-cuda/mmvq.cu9
-rw-r--r--ggml/src/ggml-quants.c1
-rw-r--r--ggml/src/ggml.c21
-rw-r--r--ggml/src/iqk/iqk_quantize.cpp268
-rw-r--r--ggml/src/iqk/iqk_quantize.h6
-rw-r--r--include/llama.h5
-rw-r--r--src/llama.cpp14
15 files changed, 416 insertions, 28 deletions
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index 17e87e53..0b4c3444 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -41,6 +41,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
{ "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
{ "IQ2_K", LLAMA_FTYPE_MOSTLY_IQ2_K, " 2.375 bpw non-linear quantization",},
+ { "IQ3_K", LLAMA_FTYPE_MOSTLY_IQ3_K, " 3.44 bpw non-linear quantization", },
{ "IQ4_K", LLAMA_FTYPE_MOSTLY_IQ4_K, " 4.5 bpw non-linear quantization", },
{ "IQ5_K", LLAMA_FTYPE_MOSTLY_IQ5_K, " 5.5 bpw non-linear quantization", },
{ "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index b7585ad6..94ffae7e 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -390,8 +390,9 @@ extern "C" {
GGML_TYPE_IQ2_BN = 35,
GGML_TYPE_Q8_K64 = 36,
GGML_TYPE_IQ2_K = 37,
- GGML_TYPE_IQ4_K = 38,
- GGML_TYPE_IQ5_K = 39,
+ GGML_TYPE_IQ3_K = 38,
+ GGML_TYPE_IQ4_K = 39,
+ GGML_TYPE_IQ5_K = 40,
GGML_TYPE_COUNT,
};
@@ -439,8 +440,9 @@ extern "C" {
GGML_FTYPE_MOSTLY_IQ1_BN = 28, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ2_BN = 29, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ2_K = 30, // except 1d tensors
- GGML_FTYPE_MOSTLY_IQ4_K = 31, // except 1d tensors
- GGML_FTYPE_MOSTLY_IQ5_K = 32, // except 1d tensors
+ GGML_FTYPE_MOSTLY_IQ3_K = 31, // except 1d tensors
+ GGML_FTYPE_MOSTLY_IQ4_K = 32, // except 1d tensors
+ GGML_FTYPE_MOSTLY_IQ5_K = 33, // except 1d tensors
};
// available tensor operations:
diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h
index 7da27794..423797b6 100644
--- a/ggml/src/ggml-common.h
+++ b/ggml/src/ggml-common.h
@@ -459,6 +459,16 @@ static_assert(sizeof(block_iq2_k) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K
typedef struct {
ggml_half d;
uint16_t extra;
+ uint16_t scales_h;
+ uint8_t scales_l[QK_K/32];
+ uint8_t qs[QK_K/4];
+ uint8_t qh[QK_K/8];
+} block_iq3_k;
+static_assert(sizeof(block_iq3_k) == sizeof(ggml_half) + 2*sizeof(uint16_t) + QK_K/32 + QK_K/4 + QK_K/8, "wrong iq3_k block size/padding");
+
+typedef struct {
+ ggml_half d;
+ uint16_t extra;
uint8_t scales_h[QK_K/64];
uint8_t scales_l[QK_K/32];
uint8_t qs[QK_K/2];
@@ -1911,6 +1921,11 @@ GGML_TABLE_BEGIN(int8_t, iq2nl_values, 8)
-31, -13, 1, 17, -26, -8, 6, 22
GGML_TABLE_END()
+GGML_TABLE_BEGIN(int8_t, iq3nl_values, 16)
+ -63, -40, -23, -10, 1, 13, 28, 47,
+ -59, -36, -19, -6, 5, 17, 32, 51,
+GGML_TABLE_END()
+
GGML_TABLE_BEGIN(int8_t, iq4k_values, 32)
-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113,
-123, -100, -79, -61, -45, -31, -18, -6, 5, 17, 29, 42, 57, 73, 93, 117
diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu
index ba9d89aa..d34aa386 100644
--- a/ggml/src/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda.cu
@@ -2753,9 +2753,10 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
+ case GGML_TYPE_IQ2_K:
+ case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
case GGML_TYPE_IQ5_K:
- case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ1_BN:
case GGML_TYPE_IQ2_BN:
return true;
diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 516e74d8..fbc52aa9 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -677,6 +677,13 @@ struct ggml_cuda_type_traits<GGML_TYPE_IQ2_K> {
};
template<>
+struct ggml_cuda_type_traits<GGML_TYPE_IQ3_K> {
+ static constexpr int qk = QK_K;
+ static constexpr int qr = QR4_XS;
+ static constexpr int qi = QI4_XS;
+};
+
+template<>
struct ggml_cuda_type_traits<GGML_TYPE_IQ4_K> {
static constexpr int qk = QK_K;
static constexpr int qr = QR4_XS;
diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu
index f388e9f3..ed7e4bd0 100644
--- a/ggml/src/ggml-cuda/convert.cu
+++ b/ggml/src/ggml-cuda/convert.cu
@@ -595,6 +595,33 @@ static __global__ void dequantize_block_iq2_k(const void * __restrict__ vx, dst_
}
}
+template<typename dst_t>
+static __global__ void dequantize_block_iq3_k(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+ const int i = blockIdx.x;
+ const block_iq3_k * x = (const block_iq3_k *) vx;
+
+ const int tid = threadIdx.x;
+ int ib128 = tid/16; // 0 or 1
+ int il = tid%16; // 0...15
+ dst_t * y = yy + i*QK_K + 128*ib128 + 2*il;
+ const float d = (float)x[i].d * 1.01f; //1.0125f;
+ const uint16_t sh = x[i].scales_h >> (8*ib128 + (il/8));
+ const float dl1 = d * ((2*((x[i].scales_l[4*ib128+0] >> 4*(il/8)) & 0xf) + 1) * ((sh & 0x01) ? -1 : 1));
+ const float dl2 = d * ((2*((x[i].scales_l[4*ib128+1] >> 4*(il/8)) & 0xf) + 1) * ((sh & 0x04) ? -1 : 1));
+ const float dl3 = d * ((2*((x[i].scales_l[4*ib128+2] >> 4*(il/8)) & 0xf) + 1) * ((sh & 0x10) ? -1 : 1));
+ const float dl4 = d * ((2*((x[i].scales_l[4*ib128+3] >> 4*(il/8)) & 0xf) + 1) * ((sh & 0x40) ? -1 : 1));
+ const uint8_t * qs = x[i].qs + 32*ib128 + 2*il;
+ const uint8_t * qh = x[i].qh + 2*il;
+ const int16_t extra = x[i].extra >> (8*ib128 + (il/8));
+ for (int j = 0; j < 2; ++j) {
+ const uint8_t h = qh[j] >> (4*(ib128%2));
+ y[j+ 0] = dl1 * iq3nl_values[(((qs[j] >> 0) & 0x03) | ((h & 0x01) << 2)) + ((extra << 3) & 8)];
+ y[j+32] = dl2 * iq3nl_values[(((qs[j] >> 2) & 0x03) | ((h & 0x02) << 1)) + ((extra << 1) & 8)];
+ y[j+64] = dl3 * iq3nl_values[(((qs[j] >> 4) & 0x03) | ((h & 0x04) >> 0)) + ((extra >> 1) & 8)];
+ y[j+96] = dl4 * iq3nl_values[(((qs[j] >> 6) & 0x03) | ((h & 0x08) >> 1)) + ((extra >> 3) & 8)];
+ }
+}
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) {
@@ -726,21 +753,27 @@ static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t
}
template<typename dst_t>
-static void dequantize_row_iq4_k_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+static void dequantize_row_iq2_k_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
const int nb = (k + QK_K - 1) / QK_K;
- dequantize_block_iq4_k<<<nb, 32, 0, stream>>>(vx, y);
+ dequantize_block_iq2_k<<<nb, 32, 0, stream>>>(vx, y);
}
template<typename dst_t>
-static void dequantize_row_iq5_k_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+static void dequantize_row_iq3_k_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
const int nb = (k + QK_K - 1) / QK_K;
- dequantize_block_iq5_k<<<nb, 32, 0, stream>>>(vx, y);
+ dequantize_block_iq3_k<<<nb, 32, 0, stream>>>(vx, y);
}
template<typename dst_t>
-static void dequantize_row_iq2_k_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+static void dequantize_row_iq4_k_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
const int nb = (k + QK_K - 1) / QK_K;
- dequantize_block_iq2_k<<<nb, 32, 0, stream>>>(vx, y);
+ dequantize_block_iq4_k<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq5_k_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+ const int nb = (k + QK_K - 1) / QK_K;
+ dequantize_block_iq5_k<<<nb, 32, 0, stream>>>(vx, y);
}
template <typename src_t, typename dst_t>
@@ -807,12 +840,14 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ4_XS:
return dequantize_row_iq4_xs_cuda;
+ case GGML_TYPE_IQ2_K:
+ return dequantize_row_iq2_k_cuda;
+ case GGML_TYPE_IQ3_K:
+ return dequantize_row_iq3_k_cuda;
case GGML_TYPE_IQ4_K:
return dequantize_row_iq4_k_cuda;
case GGML_TYPE_IQ5_K:
return dequantize_row_iq5_k_cuda;
- case GGML_TYPE_IQ2_K:
- return dequantize_row_iq2_k_cuda;
case GGML_TYPE_IQ3_S:
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_F32:
@@ -864,12 +899,14 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ4_XS:
return dequantize_row_iq4_xs_cuda;
+ case GGML_TYPE_IQ2_K:
+ return dequantize_row_iq2_k_cuda;
+ case GGML_TYPE_IQ3_K:
+ return dequantize_row_iq3_k_cuda;
case GGML_TYPE_IQ4_K:
return dequantize_row_iq4_k_cuda;
case GGML_TYPE_IQ5_K:
return dequantize_row_iq5_k_cuda;
- case GGML_TYPE_IQ2_K:
- return dequantize_row_iq2_k_cuda;
case GGML_TYPE_IQ3_S:
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_F16:
diff --git a/ggml/src/ggml-cuda/iqk_mmvq.cu b/ggml/src/ggml-cuda/iqk_mmvq.cu
index 3c2277ed..bf7b2aa7 100644
--- a/ggml/src/ggml-cuda/iqk_mmvq.cu
+++ b/ggml/src/ggml-cuda/iqk_mmvq.cu
@@ -245,9 +245,6 @@ __device__ __forceinline__ float vec_dot_iq5_k_q8_1(
return d5 * (__low2float(bq8_1[2*(i4/2)+0].ds) * sumi1 * ls1 + __low2float(bq8_1[2*(i4/2)+1].ds) * sumi2 * ls2);
}
-#define VDR_IQ2_K_Q8_1_MMVQ 4
-#define VDR_IQ2_K_Q8_1_MMQ 4
-
static const __device__ uint32_t iq2k_table[512] = {
0xe1e1e1e1, 0xe1e1e1f3, 0xe1e1e101, 0xe1e1e111, 0xe1e1f3e1, 0xe1e1f3f3, 0xe1e1f301, 0xe1e1f311,
0xe1e101e1, 0xe1e101f3, 0xe1e10101, 0xe1e10111, 0xe1e111e1, 0xe1e111f3, 0xe1e11101, 0xe1e11111,
@@ -319,6 +316,9 @@ __device__ __forceinline__ int int_from_table_4(const uint8_t * a8, const int *
return values[a8[0] | (a8[1] << 2) | (a8[2] << 4) | (a8[3] << 6)];
}
+#define VDR_IQ2_K_Q8_1_MMVQ 4
+#define VDR_IQ2_K_Q8_1_MMQ 4
+
__device__ __forceinline__ float vec_dot_iq2_k_q8_1(
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
@@ -378,8 +378,18 @@ __device__ __forceinline__ float vec_dot_iq2_k_q8_1(
}
+#define VDR_IQ3_K_Q8_1_MMVQ 4
+#define VDR_IQ3_K_Q8_1_MMQ 4
+
+// TODO
+__device__ __forceinline__ float vec_dot_iq3_k_q8_1(
+ const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+ return 0;
+
}
+} // namespace
+
void mul_mat_vec_iq2_k_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) {
@@ -387,6 +397,13 @@ void mul_mat_vec_iq2_k_q8_1_cuda(
iqk_mul_mat_vec_q_cuda<GGML_TYPE_IQ2_K, VDR_IQ2_K_Q8_1_MMVQ, vec_dot_iq2_k_q8_1>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
}
+void mul_mat_vec_iq3_k_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) {
+
+ iqk_mul_mat_vec_q_cuda<GGML_TYPE_IQ3_K, VDR_IQ3_K_Q8_1_MMVQ, vec_dot_iq3_k_q8_1>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
void mul_mat_vec_iq4_k_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) {
diff --git a/ggml/src/ggml-cuda/iqk_mmvq.cuh b/ggml/src/ggml-cuda/iqk_mmvq.cuh
index 14e5c1c7..9a33af0d 100644
--- a/ggml/src/ggml-cuda/iqk_mmvq.cuh
+++ b/ggml/src/ggml-cuda/iqk_mmvq.cuh
@@ -4,6 +4,10 @@ void mul_mat_vec_iq2_k_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);
+void mul_mat_vec_iq3_k_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);
+
void mul_mat_vec_iq4_k_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);
diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu
index 93c8ac29..56bf3ebe 100644
--- a/ggml/src/ggml-cuda/mmvq.cu
+++ b/ggml/src/ggml-cuda/mmvq.cu
@@ -432,15 +432,18 @@ void ggml_cuda_op_mul_mat_vec_q(
case GGML_TYPE_IQ4_XS:
mul_mat_vec_iq4_xs_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_IQ2_K:
+ mul_mat_vec_iq2_k_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_IQ3_K:
+ mul_mat_vec_iq3_k_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_K:
mul_mat_vec_iq4_k_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_IQ5_K:
mul_mat_vec_iq5_k_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_IQ2_K:
- mul_mat_vec_iq2_k_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_IQ3_S:
mul_mat_vec_iq3_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;
diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index 4b3bf361..c2c66f38 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -14948,6 +14948,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb);
} break;
case GGML_TYPE_IQ2_K: break;
+ case GGML_TYPE_IQ3_K: break;
case GGML_TYPE_IQ4_K: break;
case GGML_TYPE_IQ5_K: break;
case GGML_TYPE_Q4_0_4_4:
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index f873e49a..4ce9948d 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -992,6 +992,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
+ [GGML_TYPE_IQ3_K] = {
+ .type_name = "iq3_k",
+ .blck_size = QK_K,
+ .type_size = sizeof(block_iq3_k),
+ .is_quantized = true,
+ .to_float = (ggml_to_float_t) dequantize_row_iq3_k,
+ .from_float = quantize_row_iq3_k,
+ .from_float_ref = (ggml_from_float_t)quantize_row_iq3_k_ref,
+ .vec_dot = vec_dot_iq3_k_q8_k,
+ .vec_dot_type = GGML_TYPE_Q8_K,
+ .nrows = 1,
+ },
[GGML_TYPE_IQ4_K] = {
.type_name = "iq4_k",
.blck_size = QK_K,
@@ -3366,6 +3378,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
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_IQ2_K: wtype = GGML_TYPE_IQ2_K; break;
+ case GGML_FTYPE_MOSTLY_IQ3_K: wtype = GGML_TYPE_IQ3_K; break;
case GGML_FTYPE_MOSTLY_IQ4_K: wtype = GGML_TYPE_IQ4_K; break;
case GGML_FTYPE_MOSTLY_IQ5_K: wtype = GGML_TYPE_IQ5_K; break;
case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
@@ -9618,6 +9631,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ2_K:
+ case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
case GGML_TYPE_IQ5_K:
case GGML_TYPE_IQ3_S:
@@ -10001,6 +10015,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ2_K:
+ case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
case GGML_TYPE_IQ5_K:
case GGML_TYPE_IQ3_S:
@@ -10134,6 +10149,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ2_K:
+ case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
case GGML_TYPE_IQ5_K:
case GGML_TYPE_IQ3_S:
@@ -13056,6 +13072,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ2_K:
+ case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
case GGML_TYPE_IQ5_K:
case GGML_TYPE_IQ3_S:
@@ -13249,6 +13266,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ2_K:
+ case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
case GGML_TYPE_IQ5_K:
case GGML_TYPE_IQ3_S:
@@ -13516,6 +13534,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ2_K:
+ case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
case GGML_TYPE_IQ5_K:
case GGML_TYPE_IQ3_S:
@@ -14110,6 +14129,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ2_K:
+ case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
case GGML_TYPE_IQ5_K:
case GGML_TYPE_IQ3_S:
@@ -20848,6 +20868,7 @@ size_t ggml_quantize_chunk(
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_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ2_K: result = quantize_iq2_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+ case GGML_TYPE_IQ3_K: result = quantize_iq3_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_K: result = quantize_iq4_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ5_K: result = quantize_iq5_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_Q4_0_4_4: result = quantize_q4_0_4x4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
diff --git a/ggml/src/iqk/iqk_quantize.cpp b/ggml/src/iqk/iqk_quantize.cpp
index 9c502f07..24076467 100644
--- a/ggml/src/iqk/iqk_quantize.cpp
+++ b/ggml/src/iqk/iqk_quantize.cpp
@@ -629,6 +629,274 @@ void vec_dot_iq2_k_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void *
}
//
+// ============================================== iq3_k
+//
+namespace {
+static int8_t iq3nl_index[69] = {
+ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,
+ 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+ 5, 5, 5, 5, 5
+};
+static inline int best_index_iq3nl(const int8_t * values, float x) {
+ int index = x < values[1] ? 0 : x >= values[6] ? 6 : iq3nl_index[(int)x - values[1]];
+ return x - values[index] < values[index+1] - x ? index : index+1;
+}
+
+static void quantize_row_iq3_k_impl(const float * x, void * vy, int n_per_row, const float * quant_weights) {
+
+ const int ntry = 5;
+
+ block_iq3_k * y = (block_iq3_k *)vy;
+
+ float scales[QK_K/16];
+ float weight[16];
+
+ const int8_t * shifted_values = iq3nl_values + 8;
+
+ for (int ibl = 0; ibl < n_per_row/QK_K; ++ibl) {
+
+ memset(&y[ibl], 0, sizeof(block_iq3_k));
+ y[ibl].d = GGML_FP32_TO_FP16(0.f);
+
+ const float * xbl = x + ibl*QK_K;
+ float sumx2 = 0;
+ for (int j = 0; j < QK_K; ++j) sumx2 += xbl[j]*xbl[j];
+ const float sigma2 = sumx2/QK_K;
+
+ uint16_t extra = 0;
+
+ float max_abs_scale = 0;
+
+ for (int ib = 0; ib < QK_K/16; ++ib) {
+ const float * xb = xbl + 16*ib;
+ if (quant_weights) {
+ const float * qw = quant_weights + ibl*QK_K + ib*16;
+ for (int j = 0; j < 16; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
+ } else {
+ for (int j = 0; j < 16; ++j) weight[j] = 0.25f*sigma2 + xb[j]*xb[j];
+ }
+ float amax = 0, max = 0;
+ for (int j = 0; j < 16; ++j) {
+ float ax = fabsf(xb[j]);
+ if (ax > amax) {
+ amax = ax; max = xb[j];
+ }
+ }
+ if (!amax) {
+ scales[ib] = 0;
+ continue;
+ }
+ float d = ntry > 0 ? -max/iq3nl_values[0] : max/iq3nl_values[0];
+ float id = 1/d;
+ float sumqx_p = 0, sumq2_p = 0;
+ float sumqx_m = 0, sumq2_m = 0;
+ for (int j = 0; j < 16; ++j) {
+ float w = weight[j];
+ float al = id*xb[j];
+ int l = best_index_iq3nl(iq3nl_values, al);
+ float q = iq3nl_values[l];
+ sumqx_p += w*q*xb[j];
+ sumq2_p += w*q*q;
+ l = best_index_iq3nl(iq3nl_values, -al);
+ q = iq3nl_values[l];
+ sumqx_m += w*q*xb[j];
+ sumq2_m += w*q*q;
+ }
+ d = sumqx_p/sumq2_p;
+ float best = d*sumqx_p;
+ if (sumq2_m > 0 && sumqx_m*sumqx_m > best*sumq2_m) {
+ d = sumqx_m/sumq2_m; best = d*sumqx_m;
+ }
+ bool is_shifted = false;
+ for (int itry = -ntry; itry <= ntry; ++itry) {
+ id = (itry + iq3nl_values[0])/max;
+ sumqx_p = sumq2_p = 0;
+ sumqx_m = sumq2_m = 0;
+ for (int j = 0; j < 16; ++j) {
+ float w = weight[j];
+ float al = id*xb[j];
+ int l = best_index_iq3nl(iq3nl_values, al);
+ float q = iq3nl_values[l];
+ sumqx_p += w*q*xb[j];
+ sumq2_p += w*q*q;
+ l = best_index_iq3nl(iq3nl_values, -al);
+ q = iq3nl_values[l];
+ sumqx_m += w*q*xb[j];
+ sumq2_m += w*q*q;
+ }
+ if (sumq2_p > 0 && sumqx_p*sumqx_p > best*sumq2_p) {
+ d = sumqx_p/sumq2_p; best = d * sumqx_p; is_shifted = false;
+ }
+ if (sumq2_m > 0 && sumqx_m*sumqx_m > best*sumq2_m) {
+ d = sumqx_m/sumq2_m; best = d * sumqx_m; is_shifted = false;
+ }
+ id = (itry + shifted_values[0])/max;
+ sumqx_p = sumq2_p = 0;
+ sumqx_m = sumq2_m = 0;
+ for (int j = 0; j < 16; ++j) {
+ float w = weight[j];
+ float al = id*xb[j];
+ int l = best_index_iq3nl(shifted_values, al);
+ float q = shifted_values[l];
+ sumqx_p += w*q*xb[j];
+ sumq2_p += w*q*q;
+ l = best_index_iq3nl(shifted_values, -al);
+ q = shifted_values[l];
+ sumqx_m += w*q*xb[j];
+ sumq2_m += w*q*q;
+ }
+ if (sumq2_p > 0 && sumqx_p*sumqx_p > best*sumq2_p) {
+ d = sumqx_p/sumq2_p; best = d * sumqx_p; is_shifted = true;
+ }
+ if (sumq2_m > 0 && sumqx_m*sumqx_m > best*sumq2_m) {
+ d = sumqx_m/sumq2_m; best = d * sumqx_m; is_shifted = true;
+ }
+ }
+ if (d) {
+ const int8_t * block_values = is_shifted ? shifted_values : iq3nl_values;
+ float sumqx = 0, sumq2 = 0;
+ id = 1/d;
+ for (int j = 0; j < 16; ++j) {
+ float w = weight[j];
+ float al = id*xb[j];
+ int l = best_index_iq3nl(block_values, al);
+ float q = block_values[l];
+ sumqx += w*q*xb[j];
+ sumq2 += w*q*q;
+ }
+ if (sumq2 > 0) d = sumqx/sumq2;
+ }
+ scales[ib] = d;
+
+ if (is_shifted) extra |= (1 << ib);
+
+ float abs_scale = fabsf(scales[ib]);
+ max_abs_scale = MAX(max_abs_scale, abs_scale);
+ }
+
+ if (!max_abs_scale) continue;
+
+ float d = max_abs_scale/31;
+ y[ibl].d = GGML_FP32_TO_FP16(d);
+ y[ibl].extra = extra;
+ float id = 1/d;
+
+ float sumqx = 0, sumq2 = 0;
+ for (int ib = 0; ib < QK_K/16; ++ib) {
+ int ls = nearest_int(0.5f*(id*fabsf(scales[ib])-1));
+ ls = MAX(0, MIN(15, ls));
+ y[ibl].scales_l[ib/2] |= (ls << 4*(ib%2));
+ if (scales[ib] < 0) y[ibl].scales_h |= (1 << ib);
+ ls = (2*ls + 1) * (scales[ib] < 0 ? -1 : 1);
+ float dl = d * ls;
+ if (dl) {
+ const int8_t * block_values = y[ibl].extra & (1 << ib) ? shifted_values : iq3nl_values;
+ const float * xb = xbl + 16*ib;
+ if (quant_weights) {
+ const float * qw = quant_weights + ibl*QK_K + ib*16;
+ for (int j = 0; j < 16; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
+ } else {
+ for (int j = 0; j < 16; ++j) weight[j] = 0.25f*sigma2 + xb[j]*xb[j];
+ }
+ float idl = 1/dl;
+ int ib32 = ib/2;
+ int offset = 16*(ib%2);
+ uint8_t * qs = y[ibl].qs + 32*(ib32/4) + offset;
+ uint8_t * qh = y[ibl].qh + 32*(ib32/8) + offset;
+ for (int j = 0; j < 16; ++j) {
+ const float al = idl*xb[j];
+ int ibest = best_index_iq3nl(block_values, al);
+ qs[j] |= ((ibest & 3) << 2*(ib32%4));
+ qh[j] |= ((ibest >> 2) << (ib32%8));
+ float w = weight[j];
+ float q = block_values[ibest]*ls;
+ sumqx += w*q*xb[j];
+ sumq2 += w*q*q;
+ }
+ }
+ }
+ if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(sumqx/sumq2);
+
+ }
+}
+
+}
+
+void quantize_row_iq3_k_ref(const float * x, block_iq3_k * y, int64_t k) {
+ assert(k % QK_K == 0);
+ quantize_iq3_k(x, (void *)y, 1, k, nullptr);
+}
+
+void quantize_row_iq3_k(const float * x, void * vy, int64_t k) {
+ assert(k % QK_K == 0);
+ block_iq3_k * y = (block_iq3_k *)vy;
+ quantize_row_iq3_k_ref(x, y, k);
+}
+
+size_t quantize_iq3_k(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
+ GGML_ASSERT(n_per_row%QK_K == 0);
+ int nblock = n_per_row/QK_K;
+ char * qrow = (char *)dst;
+ for (int64_t row = 0; row < nrows; ++row) {
+ quantize_row_iq3_k_impl(src, (void *)qrow, n_per_row, imatrix);
+ src += n_per_row;
+ qrow += nblock*sizeof(block_iq3_k);
+ }
+ return nrows * nblock * sizeof(block_iq3_k);
+}
+
+void dequantize_row_iq3_k(const block_iq3_k * x, float * y, int64_t k) {
+ assert(k % QK_K == 0);
+ const int nb = k / QK_K;
+
+ for (int i = 0; i < nb; i++) {
+
+ const float d = GGML_FP16_TO_FP32(x[i].d);
+ const uint8_t * qs = x[i].qs;
+ const uint8_t * qh = x[i].qh;
+
+ uint16_t sh = x[i].scales_h;
+ uint16_t extra = x[i].extra;
+
+ for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+ float dl1 = d * ((2*(x[i].scales_l[ib32] & 0xf) + 1) * ((sh & 1) ? -1 : 1));
+ float dl2 = d * ((2*(x[i].scales_l[ib32] >> 4) + 1) * ((sh & 2) ? -1 : 1));
+ sh >>= 2;
+ const int8_t * values1 = extra & 1 ? iq3nl_values + 8 : iq3nl_values;
+ const int8_t * values2 = extra & 2 ? iq3nl_values + 8 : iq3nl_values;
+ extra >>= 2;
+ int shift_l = 2*(ib32%4);
+ int shift_h = ib32%8;
+ for (int j = 0; j < 16; ++j) {
+ y[j+ 0] = dl1 * values1[((qs[j+ 0] >> shift_l) & 3) | (((qh[j+ 0] >> shift_h) & 1) << 2)];
+ y[j+16] = dl2 * values2[((qs[j+16] >> shift_l) & 3) | (((qh[j+16] >> shift_h) & 1) << 2)];
+ }
+ y += 32;
+ if (shift_l == 6) qs += 32;
+ }
+
+ }
+}
+
+void vec_dot_iq3_k_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) {
+ assert(n % QK_K == 0);
+ assert(nrc == 1);
+ GGML_UNUSED(nrc);
+ GGML_UNUSED(bx);
+ GGML_UNUSED(by);
+ GGML_UNUSED(bs);
+
+ if (iqk_mul_mat(1, 1, n, GGML_TYPE_IQ3_K, vx, 0, GGML_TYPE_Q8_K, vy, 0, s, 0, 0, 1)) {
+ return;
+ }
+
+ const int nb = n / QK_K;
+
+ const block_iq2_k * x = (const block_iq2_k *)vx;
+ const block_q8_K * y = (const block_q8_K *)vy;
+}
+
+//
// ============================================== iq4_K
//
void dequantize_row_iq4_k(const block_iq4_k * x, float * y, int64_t k) {
diff --git a/ggml/src/iqk/iqk_quantize.h b/ggml/src/iqk/iqk_quantize.h
index b8b03169..0295eb99 100644
--- a/ggml/src/iqk/iqk_quantize.h
+++ b/ggml/src/iqk/iqk_quantize.h
@@ -19,6 +19,12 @@ size_t quantize_iq2_k(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst,
void dequantize_row_iq2_k(const block_iq2_k * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void vec_dot_iq2_k_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 quantize_row_iq3_k_ref(const float * GGML_RESTRICT x, block_iq3_k * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq3_k(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+size_t quantize_iq3_k(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+void dequantize_row_iq3_k(const block_iq3_k * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void vec_dot_iq3_k_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 quantize_row_iq4_k_ref(const float * GGML_RESTRICT x, block_iq4_k * GGML_RESTRICT y, int64_t k);
void quantize_row_iq4_k(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
size_t quantize_iq4_k(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
diff --git a/include/llama.h b/include/llama.h
index 7bccd4bb..88d82958 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -171,8 +171,9 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ1_BN = 36, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_BN = 37, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_K = 38, // except 1d tensors
- LLAMA_FTYPE_MOSTLY_IQ4_K = 39, // except 1d tensors
- LLAMA_FTYPE_MOSTLY_IQ5_K = 40, // except 1d tensors
+ LLAMA_FTYPE_MOSTLY_IQ3_K = 39, // except 1d tensors
+ LLAMA_FTYPE_MOSTLY_IQ4_K = 40, // except 1d tensors
+ LLAMA_FTYPE_MOSTLY_IQ5_K = 41, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};
diff --git a/src/llama.cpp b/src/llama.cpp
index 4e7e4a6c..2caaf7d0 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -3762,6 +3762,7 @@ struct llama_model_loader {
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_IQ2_K: ftype = LLAMA_FTYPE_MOSTLY_IQ2_K; break;
+ case GGML_TYPE_IQ3_K: ftype = LLAMA_FTYPE_MOSTLY_IQ3_K; break;
case GGML_TYPE_IQ4_K: ftype = LLAMA_FTYPE_MOSTLY_IQ4_K; break;
case GGML_TYPE_IQ5_K: ftype = LLAMA_FTYPE_MOSTLY_IQ5_K; break;
case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break;
@@ -4460,6 +4461,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
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_IQ2_K: return "IQ2_K - 2.375 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ3_K: return "IQ3_K - 3.4325 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_K: return "IQ4_K - 4.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ5_K: return "IQ5_K - 5.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw";
@@ -15477,8 +15479,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
}
- else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 4) {
- new_type = GGML_TYPE_Q4_K;
+ else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K) && qs.model.hparams.n_gqa() >= 4) {
+ new_type = GGML_TYPE_IQ4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
new_type = GGML_TYPE_Q4_K;
@@ -15578,12 +15580,12 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
++qs.i_ffn_down;
} else if (name.find("attn_output.weight") != std::string::npos) {
if (arch != LLM_ARCH_FALCON) {
- if (qs.model.hparams.n_expert == 8) {
+ if (qs.model.hparams.n_expert >= 8) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL ||
ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_K ||
- ftype == LLAMA_FTYPE_MOSTLY_IQ2_K) {
+ ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K) {
new_type = GGML_TYPE_Q5_K;
}
} else {
@@ -15638,7 +15640,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_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 || new_type == GGML_TYPE_IQ4_K || new_type == GGML_TYPE_IQ2_K ||
- new_type == GGML_TYPE_IQ5_K) {
+ new_type == GGML_TYPE_IQ5_K || new_type == GGML_TYPE_IQ3_K) {
int nx = tensor->ne[0];
int ny = tensor->ne[1];
if (nx % QK_K != 0) {
@@ -15666,6 +15668,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_IQ2_K:
+ case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
case GGML_TYPE_IQ4_K:
case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
@@ -15773,6 +15776,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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_IQ2_K: default_type = GGML_TYPE_IQ2_K; break;
+ case LLAMA_FTYPE_MOSTLY_IQ3_K: default_type = GGML_TYPE_IQ3_K; break;
case LLAMA_FTYPE_MOSTLY_IQ4_K: default_type = GGML_TYPE_IQ4_K; break;
case LLAMA_FTYPE_MOSTLY_IQ5_K: default_type = GGML_TYPE_IQ5_K; break;
case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break;
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-16 03:51:16 +0000