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-rw-r--r--examples/quantize/quantize.cpp2
-rw-r--r--ggml/include/ggml.h2
-rw-r--r--ggml/src/ggml-common.h6
-rw-r--r--ggml/src/ggml-cuda.cu1
-rw-r--r--ggml/src/ggml-cuda/common.cuh7
-rw-r--r--ggml/src/ggml-cuda/convert.cu35
-rw-r--r--ggml/src/ggml-cuda/iqk_mmvq.cu33
-rw-r--r--ggml/src/ggml-cuda/iqk_mmvq.cuh4
-rw-r--r--ggml/src/ggml-cuda/mmvq.cu3
-rw-r--r--ggml/src/ggml-metal.m29
-rw-r--r--ggml/src/ggml-metal.metal160
-rw-r--r--ggml/src/ggml-quants.c3
-rw-r--r--ggml/src/ggml.c29
-rw-r--r--ggml/src/iqk/iqk_mul_mat.cpp143
-rw-r--r--ggml/src/iqk/iqk_quantize.cpp253
-rw-r--r--ggml/src/iqk/iqk_quantize.h6
-rw-r--r--include/llama.h2
-rw-r--r--src/llama.cpp29
18 files changed, 719 insertions, 28 deletions
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index 2b240299..3cc19f70 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -43,8 +43,10 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
{ "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
+ { "IQ4_KS", LLAMA_FTYPE_MOSTLY_IQ4_KS, " 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", },
+ { "IQ3_KL", LLAMA_FTYPE_MOSTLY_IQ3_KL, " 4 bpw non-linear quantization mix",},
{ "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", },
{ "IQ6_K", LLAMA_FTYPE_MOSTLY_IQ6_K, " 6.6 bpw non-linear quantization", },
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 13aaeafb..3054dabd 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -403,6 +403,7 @@ extern "C" {
GGML_TYPE_IQ6_K = 141,
GGML_TYPE_IQ2_TN = 142,
GGML_TYPE_IQ1_TN = 143,
+ GGML_TYPE_IQ4_KS = 144,
GGML_TYPE_COUNT,
};
@@ -458,6 +459,7 @@ extern "C" {
GGML_FTYPE_MOSTLY_IQ6_K = 134, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ2_TN = 135, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ1_TN = 136, // except 1d tensors
+ GGML_FTYPE_MOSTLY_IQ4_KS = 137, // except 1d tensors
};
// available tensor operations:
diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h
index 02ecf071..7eaf7437 100644
--- a/ggml/src/ggml-common.h
+++ b/ggml/src/ggml-common.h
@@ -442,6 +442,12 @@ typedef struct {
static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
typedef struct {
+ uint8_t scales[QK_K/32];
+ uint8_t qs[QK_K/2];
+} block_iq4_ks;
+static_assert(sizeof(block_iq4_ks) == QK_K/32 + QK_K/2, "wrong iq4_ks block size/padding");
+
+typedef struct {
ggml_half d;
uint16_t extra;
uint8_t scales[QK_K/32];
diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu
index 871d4007..0657252d 100644
--- a/ggml/src/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda.cu
@@ -2828,6 +2828,7 @@ 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_IQ4_KS:
case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index d7e9c529..c00cef29 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -530,6 +530,13 @@ struct ggml_cuda_type_traits<GGML_TYPE_IQ4_K> {
};
template<>
+struct ggml_cuda_type_traits<GGML_TYPE_IQ4_KS> {
+ 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_IQ5_K> {
static constexpr int qk = QK_K;
static constexpr int qr = QR5_XS;
diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu
index 28b2415b..62dd52a2 100644
--- a/ggml/src/ggml-cuda/convert.cu
+++ b/ggml/src/ggml-cuda/convert.cu
@@ -616,6 +616,29 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst
}
template<typename dst_t>
+static __global__ void dequantize_block_iq4_ks(const void * __restrict__ vx, dst_t * __restrict__ yy, int64_t n_per_row, int64_t row_size) {
+
+ int64_t ii = blockIdx.x;
+ int64_t row = (QK_K * ii) / n_per_row;
+ const char * cx = (const char *)vx + row * row_size;
+ float scale = *(const float *)cx;
+ const block_iq4_ks * x = (const block_iq4_ks *)(cx + sizeof(float));
+ const int64_t i = ii - (row*n_per_row)/QK_K;
+
+ const int64_t tid = threadIdx.x;
+ const int64_t il = tid/8; // 0...3
+ const int64_t ib = tid%8; // 0...7
+ dst_t * y = yy + ii*QK_K + 32*ib + 4*il;
+ const uint8_t * q4 = x[i].qs + 16*ib + 4*il;
+ const float d = scale * ((x[i].scales[ib] & 254) - 127);
+ const int8_t * values = iq4k_values + ((x[i].scales[ib] & 1) << 4);
+ for (int j = 0; j < 4; ++j) {
+ y[j+ 0] = d * values[q4[j] & 0xf];
+ y[j+16] = d * values[q4[j] >> 4];
+ }
+}
+
+template<typename dst_t>
static __global__ void dequantize_block_iq4_k(const void * __restrict__ vx, dst_t * __restrict__ yy) {
const int64_t i = blockIdx.x;
const block_iq4_k * x = (const block_iq4_k *)vx;
@@ -922,6 +945,14 @@ 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_ks_cuda(const void * vx, dst_t * y, const int64_t nrows, const int64_t n_per_row, cudaStream_t stream) {
+ const int64_t k = nrows * n_per_row;
+ const int64_t row_size = ggml_row_size(GGML_TYPE_IQ4_KS, n_per_row);
+ const int nb = (k + QK_K - 1) / QK_K;
+ dequantize_block_iq4_ks<<<nb, 32, 0, stream>>>(vx, y, n_per_row, row_size);
+}
+
+template<typename dst_t>
static void dequantize_row_iq2_k_cuda(const void * vx, dst_t * y, const int64_t nrows, const int64_t n_per_row, cudaStream_t stream) {
const int64_t k = nrows * n_per_row;
const int nb = (k + QK_K - 1) / QK_K;
@@ -1083,6 +1114,8 @@ 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_IQ4_KS:
+ return dequantize_row_iq4_ks_cuda;
case GGML_TYPE_IQ2_K:
return dequantize_row_iq2_k_cuda;
case GGML_TYPE_IQ3_K:
@@ -1152,6 +1185,8 @@ 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_IQ4_KS:
+ return dequantize_row_iq4_ks_cuda;
case GGML_TYPE_IQ2_K:
return dequantize_row_iq2_k_cuda;
case GGML_TYPE_IQ3_K:
diff --git a/ggml/src/ggml-cuda/iqk_mmvq.cu b/ggml/src/ggml-cuda/iqk_mmvq.cu
index b2c32c0c..a1f2d28c 100644
--- a/ggml/src/ggml-cuda/iqk_mmvq.cu
+++ b/ggml/src/ggml-cuda/iqk_mmvq.cu
@@ -214,6 +214,32 @@ __device__ __forceinline__ float vec_dot_iq4_k_q8_1(
return d * (sumi1 * ls1 + sumi2 * ls2);
}
+#define VDR_IQ4_KS_Q8_1_MMVQ 4
+#define VDR_IQ4_KS_Q8_1_MMQ 4
+
+// TODO
+__device__ __forceinline__ float vec_dot_iq4_ks_q8_1(
+ const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+ float scale = *(const float *)vbq;
+ const block_iq4_ks * bq4 = (const block_iq4_ks *)((const char *)vbq + sizeof(float)) + kbx;
+ const uint8_t * all_values = (const uint8_t *)iq4k_values;
+
+ // iqs is 0...28
+ const int ib32 = iqs/4; // Why iqs/4 ?
+ const int32_t * q8 = (const int *)bq8_1[ib32].qs;
+ const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
+ const float dl = scale * ((bq4->scales[ib32] & 254) - 127);
+ int v1, v2;
+ int sumi = 0;
+ for (int j = 0; j < 4; ++j) {
+ get_int_from_table_16_shift(q4[j], bq4->scales[ib32] & 1, all_values, v1, v2);
+ sumi = ggml_cuda_dp4a(v1, q8[j+0], sumi);
+ sumi = ggml_cuda_dp4a(v2, q8[j+4], sumi);
+ }
+ return dl * __low2float(bq8_1[ib32].ds) * sumi;
+}
+
#define VDR_IQ5_K_Q8_1_MMVQ 4
#define VDR_IQ5_K_Q8_1_MMQ 4
@@ -612,6 +638,13 @@ void mul_mat_vec_iq4_k_q8_1_cuda(
iqk_mul_mat_vec_q_cuda<GGML_TYPE_IQ4_K, VDR_IQ4_K_Q8_1_MMVQ, vec_dot_iq4_k_q8_1>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
}
+void mul_mat_vec_iq4_ks_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_IQ4_KS, VDR_IQ4_KS_Q8_1_MMVQ, vec_dot_iq4_ks_q8_1>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
void mul_mat_vec_iq5_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 7fb76ff6..8d76be1d 100644
--- a/ggml/src/ggml-cuda/iqk_mmvq.cuh
+++ b/ggml/src/ggml-cuda/iqk_mmvq.cuh
@@ -28,3 +28,7 @@ void mul_mat_vec_iq1_tn_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_ks_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 15e8fb5a..8e3c4aa4 100644
--- a/ggml/src/ggml-cuda/mmvq.cu
+++ b/ggml/src/ggml-cuda/mmvq.cu
@@ -459,6 +459,9 @@ void ggml_cuda_op_mul_mat_vec_q(
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_IQ4_KS:
+ mul_mat_vec_iq4_ks_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;
diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m
index 4badc7a7..a326a36f 100644
--- a/ggml/src/ggml-metal.m
+++ b/ggml/src/ggml-metal.m
@@ -106,6 +106,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_TN,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,
+ GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_KS,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_K,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_K,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_K,
@@ -147,6 +148,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_TN_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_KS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_K_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_K_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_K_F32,
@@ -182,6 +184,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_TN_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_KS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_K_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_K_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_K_F32,
@@ -214,6 +217,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_TN_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_KS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_K_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_K_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_K_F32,
@@ -246,6 +250,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_TN_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_KS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_K_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_K_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_K_F32,
@@ -639,6 +644,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_TN, get_rows_iq2_tn, 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_KS, get_rows_iq4_ks, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_K, get_rows_iq2_k, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_K, get_rows_iq3_k, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_K, get_rows_iq4_k, true);
@@ -680,6 +686,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_TN_F32, mul_mv_iq2_tn_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_KS_F32, mul_mv_iq4_ks_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_K_F32, mul_mv_iq2_k_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_K_F32, mul_mv_iq3_k_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_K_F32, mul_mv_iq4_k_f32, ctx->support_simdgroup_reduction);
@@ -715,6 +722,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_TN_F32, mul_mv_id_iq2_tn_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_KS_F32, mul_mv_id_iq4_ks_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_K_F32, mul_mv_id_iq2_k_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_K_F32, mul_mv_id_iq3_k_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_K_F32, mul_mv_id_iq4_k_f32, ctx->support_simdgroup_reduction);
@@ -747,6 +755,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_TN_F32, mul_mm_iq2_tn_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_KS_F32, mul_mm_iq4_ks_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_K_F32, mul_mm_iq2_k_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_K_F32, mul_mm_iq3_k_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_K_F32, mul_mm_iq4_k_f32, ctx->support_simdgroup_mm);
@@ -779,6 +788,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_TN_F32, mul_mm_id_iq2_tn_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_KS_F32, mul_mm_id_iq4_ks_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_K_F32, mul_mm_id_iq2_k_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_K_F32, mul_mm_id_iq3_k_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_K_F32, mul_mm_id_iq4_k_f32, ctx->support_simdgroup_mm);
@@ -1976,6 +1986,7 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_TYPE_IQ2_TN: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_TN_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_KS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_KS_F32 ].pipeline; break;
case GGML_TYPE_IQ2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_K_F32 ].pipeline; break;
case GGML_TYPE_IQ3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_K_F32 ].pipeline; break;
case GGML_TYPE_IQ4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_K_F32 ].pipeline; break;
@@ -2194,6 +2205,12 @@ static enum ggml_status ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline;
} break;
+ case GGML_TYPE_IQ4_KS:
+ {
+ nth0 = 4;
+ nth1 = 16;
+ pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_KS_F32].pipeline;
+ } break;
case GGML_TYPE_IQ2_K:
{
nth0 = 4;
@@ -2270,7 +2287,7 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS || src0t == GGML_TYPE_IQ4_K ||
- src0t == GGML_TYPE_IQ5_K || src0t == GGML_TYPE_IQ6_K) {
+ src0t == GGML_TYPE_IQ5_K || src0t == GGML_TYPE_IQ6_K || src0t == GGML_TYPE_IQ4_KS) {
const int mem_size = src0t == GGML_TYPE_IQ6_K ? 128*sizeof(float) : GGML_TYPE_IQ5_K ? 64*sizeof(float) : 32*sizeof(float);
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@@ -2365,6 +2382,7 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_TYPE_IQ2_TN: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_TN_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_KS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_KS_F32 ].pipeline; break;
case GGML_TYPE_IQ2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_K_F32 ].pipeline; break;
case GGML_TYPE_IQ3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_K_F32 ].pipeline; break;
case GGML_TYPE_IQ4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_K_F32 ].pipeline; break;
@@ -2571,6 +2589,12 @@ static enum ggml_status ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline;
} break;
+ case GGML_TYPE_IQ4_KS:
+ {
+ nth0 = 4;
+ nth1 = 16;
+ pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_KS_F32].pipeline;
+ } break;
case GGML_TYPE_IQ2_K:
{
nth0 = 4;
@@ -2658,7 +2682,7 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS || src0t == GGML_TYPE_IQ4_K ||
- src0t == GGML_TYPE_IQ5_K || src0t == GGML_TYPE_IQ6_K) {
+ src0t == GGML_TYPE_IQ5_K || src0t == GGML_TYPE_IQ6_K || src0t == GGML_TYPE_IQ4_KS) {
const int mem_size = src0t == GGML_TYPE_IQ6_K ? 128*sizeof(float) : GGML_TYPE_IQ5_K ? 64*sizeof(float) : 32*sizeof(float);
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@@ -2711,6 +2735,7 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_TYPE_IQ2_TN: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_TN ].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_KS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_KS ].pipeline; break;
case GGML_TYPE_IQ2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_K ].pipeline; break;
case GGML_TYPE_IQ3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_K ].pipeline; break;
case GGML_TYPE_IQ4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_K ].pipeline; break;
diff --git a/ggml/src/ggml-metal.metal b/ggml/src/ggml-metal.metal
index 4dbfa089..ea0cda99 100644
--- a/ggml/src/ggml-metal.metal
+++ b/ggml/src/ggml-metal.metal
@@ -3990,7 +3990,6 @@ void kernel_mul_mv_iq2_tn_f32_impl(
const int it = tiisg%8; // 0...7
const int iq = it/4; // 0 or 1
const int ir = it%4; // 0...3
- const int is = (8*ir)/16;// 0 or 1
device const float * y4 = y + ix * QK_K + 128 * iq + 8 * ir;
@@ -5614,8 +5613,6 @@ void kernel_mul_mv_iq1_bn_f32_impl(
device const float * y4 = (device const float *)y + 32 * ix + 16 * ir;
- uint32_t aux32[2];
-
const float values[3] = {-1.f, 0.f, 1.f};
constexpr uint8_t k_mult[5] = {81, 27, 9, 3, 1};
@@ -5717,8 +5714,6 @@ void kernel_mul_mv_iq1_tn_f32_impl(
device const float * y4 = (device const float *)y + 32 * ix + 16 * ir;
- uint32_t aux32[2];
-
const float values[3] = {-1.f, 0.f, 1.f};
constexpr uint8_t k_mult[5] = {81, 27, 9, 3, 1};
@@ -6040,6 +6035,108 @@ void kernel_mul_mv_iq4_xs_f32_impl(
}
}
+void kernel_mul_mv_iq4_ks_f32_impl(
+ device const void * src0,
+ device const float * src1,
+ device float * dst,
+ int64_t ne00,
+ int64_t ne01,
+ int64_t ne02,
+ int64_t ne10,
+ int64_t ne12,
+ int64_t ne0,
+ int64_t ne1,
+ uint r2,
+ uint r3,
+ threadgroup int8_t * shared_values_i8,
+ uint3 tgpig,
+ uint tiisg,
+ uint sgitg) {
+
+ threadgroup float * shared_values = (threadgroup float *)shared_values_i8;
+ const int nb = ne00/QK_K;
+ const int r0 = tgpig.x;
+ const int r1 = tgpig.y;
+ const int im = tgpig.z;
+ const int first_row = (r0 * 2 + sgitg) * 2;
+
+ const uint i12 = im%ne12;
+ const uint i13 = im/ne12;
+
+ const uint row_size = 4 + nb*sizeof(block_iq4_ks);
+ const uint offset0 = (i12/r2)*ne01 + (i13/r3)*(ne01*ne02);
+ device const char * cx = (device const char *)src0 + (first_row + offset0)*row_size;
+ device const float * y = (device const float *)src1 + r1*ne10 + im*ne00*ne1;
+
+ const int ix = tiisg/16; // 0 or 1
+ const int it = tiisg%16; // 0...15
+ const int ib = it/2;
+ const int il = it%2;
+
+ shared_values[tiisg] = kvalues_iq4k_f[tiisg];
+ threadgroup_barrier(mem_flags::mem_threadgroup);
+
+ float4 yl[4];
+ float2 sumf = 0.f;
+
+ device const float * yb = y + ix * QK_K + ib * 32 + il * 8;
+
+ uint32_t aux32[2];
+ thread const uint8_t * q8 = (thread const uint8_t *)aux32;
+
+ float4 qf1, qf2;
+
+ for (int ibl = ix; ibl < nb; ibl += 2) {
+
+ device const float4 * y4 = (device const float4 *)yb;
+ yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5];
+
+ device const float * dptr = (device const float *)cx;
+
+ for (int row = 0; row < 2; ++row) {
+
+ //device const float * dptr = (device const float *)(cx + row*row_size);
+ const float d = *dptr;
+ device const block_iq4_ks * x = (device const block_iq4_ks *)(dptr + 1);
+ device const block_iq4_ks & xb = x[ibl];
+ device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il);
+
+ threadgroup const float * block_values = shared_values + ((xb.scales[ib] & 1) << 4);
+
+ float4 acc1 = {0.f}, acc2 = {0.f};
+
+ aux32[0] = q4[0] & 0x0f0f0f0f;
+ aux32[1] = (q4[0] >> 4) & 0x0f0f0f0f;
+ qf1 = {block_values[q8[0]], block_values[q8[1]], block_values[q8[2]], block_values[q8[3]]};
+ qf2 = {block_values[q8[4]], block_values[q8[5]], block_values[q8[6]], block_values[q8[7]]};
+ acc1 += yl[0] * qf1;
+ acc2 += yl[1] * qf2;
+
+ aux32[0] = q4[1] & 0x0f0f0f0f;
+ aux32[1] = (q4[1] >> 4) & 0x0f0f0f0f;
+ qf1 = {block_values[q8[0]], block_values[q8[1]], block_values[q8[2]], block_values[q8[3]]};
+ qf2 = {block_values[q8[4]], block_values[q8[5]], block_values[q8[6]], block_values[q8[7]]};
+ acc1 += yl[2] * qf1;
+ acc2 += yl[3] * qf2;
+
+ acc1 += acc2;
+
+ const int ls = (xb.scales[ib] & 254) - 127;
+ sumf[row] += d * ls * (acc1[0] + acc1[1] + acc1[2] + acc1[3]);
+
+ dptr += row_size/4;
+
+ }
+
+ yb += 2 * QK_K;
+ }
+
+ sumf = simd_sum(sumf);
+ if (tiisg < 2) {
+ dst[r1*ne0 + im*ne0*ne1 + first_row + tiisg] = sumf[tiisg];
+ }
+}
+
void kernel_mul_mv_iq2_k_f32_impl(
device const void * src0,
device const float * src1,
@@ -6808,6 +6905,35 @@ kernel void kernel_mul_mv_iq4_xs_f32(
kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
}
+[[host_name("kernel_mul_mv_iq4_ks_f32")]]
+kernel void kernel_mul_mv_iq4_ks_f32(
+ device const void * src0,
+ device const float * src1,
+ device float * dst,
+ constant int64_t & ne00,
+ constant int64_t & ne01,
+ constant int64_t & ne02,
+ constant uint64_t & nb00,
+ constant uint64_t & nb01,
+ constant uint64_t & nb02,
+ constant int64_t & ne10,
+ constant int64_t & ne11,
+ constant int64_t & ne12,
+ constant uint64_t & nb10,
+ constant uint64_t & nb11,
+ constant uint64_t & nb12,
+ constant int64_t & ne0,
+ constant int64_t & ne1,
+ constant uint & r2,
+ constant uint & r3,
+ threadgroup int8_t * shared_values [[threadgroup(0)]],
+ uint3 tgpig[[threadgroup_position_in_grid]],
+ uint tiisg[[thread_index_in_simdgroup]],
+ uint sgitg[[simdgroup_index_in_threadgroup]]) {
+
+ kernel_mul_mv_iq4_ks_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+}
+
[[host_name("kernel_mul_mv_iq4_k_f32")]]
kernel void kernel_mul_mv_iq4_k_f32(
device const void * src0,
@@ -7405,6 +7531,26 @@ void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4
}
template <typename type4x4>
+void dequantize_iq4_ks(device const block_iq4_ks * xb, short il, thread type4x4 & reg) {
+ // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+ const int ib32 = il/2;
+ il = il%2;
+ // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
+ device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32;
+ const float ls = (xb->scales[ib32] & 254) - 127;
+ constant float * values = kvalues_iq4k_f + ((xb->scales[ib32] & 1) << 4);
+ uint32_t aux32;
+ thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
+ for (int i = 0; i < 4; ++i) {
+ aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f;
+ reg[i][0] = ls * values[q8[0]];
+ reg[i][1] = ls * values[q8[1]];
+ reg[i][2] = ls * values[q8[2]];
+ reg[i][3] = ls * values[q8[3]];
+ }
+}
+
+template <typename type4x4>
void dequantize_iq2_k(device const block_iq2_k * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256
device const uint32_t * q32 = (device const uint32_t *)xb->qs + 8*(il/8) + 4*(il&1);
@@ -8047,6 +8193,7 @@ template [[host_name("kernel_get_rows_iq1_bn")]] kernel get_rows_q_t kernel_get
template [[host_name("kernel_get_rows_iq2_bn")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_bn, 4, dequantize_iq2_bn>;
template [[host_name("kernel_get_rows_iq1_tn")]] kernel get_rows_q_t kernel_get_rows_q2<DequantizerRS<float4x4, block_iq1_bn, half, 4, dequantize_iq1_bn>>;
template [[host_name("kernel_get_rows_iq2_tn")]] kernel get_rows_q_t kernel_get_rows_q2<DequantizerRS<float4x4, block_iq2_tn, float, 16, dequantize_iq2_tn>>;
+template [[host_name("kernel_get_rows_iq4_ks")]] kernel get_rows_q_t kernel_get_rows_q2<DequantizerRS<float4x4, block_iq4_ks, float, 16, dequantize_iq4_ks>>;
//
// matrix-matrix multiplication
@@ -8089,6 +8236,7 @@ template [[host_name("kernel_mul_mm_iq1_bn_f32")]] kernel mat_mm_t kernel_mul_m
template [[host_name("kernel_mul_mm_iq2_bn_f32")]] kernel mat_mm_t kernel_mul_mm<half, simdgroup_half8x8, DD<block_iq2_bn, 4, dequantize_iq2_bn>>;
template [[host_name("kernel_mul_mm_iq1_tn_f32")]] kernel mat_mm_t kernel_mul_mm<half, simdgroup_half8x8, DequantizerRS<half4x4, block_iq1_bn, half, 4, dequantize_iq1_bn>>;
template [[host_name("kernel_mul_mm_iq2_tn_f32")]] kernel mat_mm_t kernel_mul_mm<half, simdgroup_half8x8, DequantizerRS<half4x4, block_iq2_tn, float, 16, dequantize_iq2_tn>>;
+template [[host_name("kernel_mul_mm_iq4_ks_f32")]] kernel mat_mm_t kernel_mul_mm<half, simdgroup_half8x8, DequantizerRS<half4x4, block_iq4_ks, float, 16, dequantize_iq4_ks>>;
//
// indirect matrix-matrix multiplication
@@ -8128,6 +8276,7 @@ template [[host_name("kernel_mul_mm_id_iq5_k_f32")]] kernel mat_mm_id_t kernel
template [[host_name("kernel_mul_mm_id_iq6_k_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<DD<block_iq6_k, QK_NL, dequantize_iq6_k>>;
template [[host_name("kernel_mul_mm_id_iq1_tn_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<DequantizerRS<half4x4, block_iq1_bn, half, 4, dequantize_iq1_bn>>;
template [[host_name("kernel_mul_mm_id_iq2_tn_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<DequantizerRS<half4x4, block_iq2_tn, float, 16, dequantize_iq2_tn>>;
+template [[host_name("kernel_mul_mm_id_iq4_ks_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<DequantizerRS<half4x4, block_iq4_ks, float, 16, dequantize_iq4_ks>>;
//
// matrix-vector multiplication
@@ -8343,6 +8492,7 @@ template [[host_name("kernel_mul_mv_id_iq3_s_f32")]] kernel kernel_mul_mv_id_t
template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_s_f32_impl>>;
template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_nl_f32_impl>>;
template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_xs_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_iq4_ks_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_ks_f32_impl>>;
template [[host_name("kernel_mul_mv_id_iq2_k_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_k_f32_impl>>;
template [[host_name("kernel_mul_mv_id_iq3_k_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq3_k_f32_impl>>;
template [[host_name("kernel_mul_mv_id_iq4_k_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_k_f32_impl>>;
diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index f5fff22e..40978ac0 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -14947,7 +14947,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
return false;
}
- if (type != GGML_TYPE_IQ2_TN && type != GGML_TYPE_IQ1_TN && nbytes % ggml_type_size(type) != 0) {
+ if (type != GGML_TYPE_IQ2_TN && type != GGML_TYPE_IQ1_TN && type != GGML_TYPE_IQ4_KS && nbytes % ggml_type_size(type) != 0) {
fprintf(stderr, "%s: invalid size %zu for type %s (type size = %zu)\n", __func__, nbytes, ggml_type_name(type), ggml_type_size(type));
return false;
}
@@ -15166,6 +15166,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
case GGML_TYPE_IQ6_K: break;
case GGML_TYPE_IQ2_TN: break;
case GGML_TYPE_IQ1_TN: break;
+ case GGML_TYPE_IQ4_KS: break;
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
{
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 7ad666eb..97fa81b1 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -1087,6 +1087,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.nrows = 1,
.row_meta_size = 0,
},
+ [GGML_TYPE_IQ4_KS] = {
+ .type_name = "iq4_ks",
+ .blck_size = QK_K,
+ .type_size = sizeof(block_iq4_ks),
+ .is_quantized = true,
+ .to_float = (ggml_to_float_t) dequantize_row_iq4_ks,
+ .from_float = quantize_row_iq4_ks,
+ .from_float_ref = (ggml_from_float_t)quantize_row_iq4_ks_ref,
+ .vec_dot = vec_dot_iq4_ks_q8_k,
+ .vec_dot_type = GGML_TYPE_Q8_K,
+ .nrows = 1,
+ .row_meta_size = 4,
+ },
[GGML_TYPE_Q8_K] = {
.type_name = "q8_K",
.blck_size = QK_K,
@@ -3891,6 +3904,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ1_TN: wtype = GGML_TYPE_IQ1_TN; 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_KS: wtype = GGML_TYPE_IQ4_KS; 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;
@@ -10390,6 +10404,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ1_TN:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
+ case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
@@ -10778,6 +10793,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ1_TN:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
+ case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
@@ -10916,6 +10932,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ1_TN:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
+ case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
@@ -13262,7 +13279,7 @@ static void ggml_compute_forward_mul_mat_one_chunk(
return;
}
- const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
+ const void * wdata = (src1->type == vec_dot_type) ? src1->data : (char *)params->wdata + params->wsize - params->qsize + GGML_MAX_NAME;
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
assert(ne12 % ne02 == 0);
@@ -13517,6 +13534,11 @@ IQK_MulMat_Not_Available2:;
UseGgmlGemm2:;
#endif
+ if (ith == 0) {
+ atomic_store(&params->shared->current_chunk, nth);
+ }
+ ggml_barrier(params->shared);
+
// This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers)
const int64_t nr0 = ne0;
@@ -14095,6 +14117,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ1_TN:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
+ case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
@@ -14473,6 +14496,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ1_TN:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
+ case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
@@ -14745,6 +14769,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ1_TN:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
+ case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
@@ -15344,6 +15369,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ1_TN:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
+ case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ4_K:
@@ -22160,6 +22186,7 @@ size_t ggml_quantize_chunk(
case GGML_TYPE_IQ1_TN: result = quantize_iq1_tn (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_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+ case GGML_TYPE_IQ4_KS: result = quantize_iq4_ks (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;
diff --git a/ggml/src/iqk/iqk_mul_mat.cpp b/ggml/src/iqk/iqk_mul_mat.cpp
index 72f1c85b..dc457c2f 100644
--- a/ggml/src/iqk/iqk_mul_mat.cpp
+++ b/ggml/src/iqk/iqk_mul_mat.cpp
@@ -635,8 +635,10 @@ struct DequantizerIQ4XS final : public BaseDequantizer<block_iq4_xs> {
s8k.accum_mins(scales128, q8, i, -128.f*d, accd);
auto scales256 = MM256_SET_M128I(scales128, scales128);
auto all_scales = _mm512_inserti32x8(_mm512_castsi256_si512(scales256), scales256, 1);
- scales[0] = _mm512_shuffle_epi8(all_scales, s8k.shuffles512[0]);
- scales[1] = _mm512_shuffle_epi8(all_scales, s8k.shuffles512[1]);
+ scales[0] = _mm512_shuffle_epi8(all_scales, shuffles[0]);
+ scales[1] = _mm512_shuffle_epi8(all_scales, shuffles[1]);
+ scales[2] = _mm512_shuffle_epi8(all_scales, shuffles[2]);
+ scales[3] = _mm512_shuffle_epi8(all_scales, shuffles[3]);
}
inline void prepare(const uint8_t * q4) {
bits.prepare64(q4);
@@ -652,11 +654,17 @@ struct DequantizerIQ4XS final : public BaseDequantizer<block_iq4_xs> {
}
Q4Bits bits;
- Scales8K s8k;
+ Scales8KBase s8k;
ScaleIQ4XS siq4;
const __m512i values;
const __m512i permute1 = _mm512_set_epi64(11, 10, 3, 2, 9, 8, 1, 0);
const __m512i permute2 = _mm512_set_epi64(15, 14, 7, 6, 13, 12, 5, 4);
+ const __m512i shuffles[4] = {
+ _mm512_inserti32x8(_mm512_set1_epi16(0x0100), _mm256_set1_epi16(0x0302), 1),
+ _mm512_inserti32x8(_mm512_set1_epi16(0x0504), _mm256_set1_epi16(0x0706), 1),
+ _mm512_inserti32x8(_mm512_set1_epi16(0x0908), _mm256_set1_epi16(0x0b0a), 1),
+ _mm512_inserti32x8(_mm512_set1_epi16(0x0d0c), _mm256_set1_epi16(0x0f0e), 1),
+ };
};
struct HighBit5 {
@@ -1099,6 +1107,54 @@ struct DequantizerIQ6K final : public BaseDequantizer<block_iq6_k> {
const __m512i permute2 = _mm512_set_epi64(15, 14, 13, 12, 7, 6, 5, 4);
};
+struct DequantizerIQ4XXS final : public BaseDequantizer<block_iq4_ks, true> {
+ DequantizerIQ4XXS(const void * vx, size_t bx) : BaseDequantizer(vx, bx), values(load_iq4nl_values_512()) {}
+ template <typename Q8>
+ inline void new_block(int i, const Q8& q8, __m256 * accm, __m512i * scales) {
+ auto scales128 = _mm_cvtepu8_epi16(_mm_loadl_epi64((const __m128i *)x[i].scales));
+ auto shifts = _mm_and_si128(_mm_cmpeq_epi16(_mm_and_si128(scales128, m1), m1), m4);
+ scales128 = _mm_add_epi16(_mm_and_si128(scales128, mask), m127);
+ auto scales_s = _mm_mullo_epi16(scales128, _mm_add_epi16(m128, shifts));
+ s8k.accum_mins(scales_s, q8, i, d, accm);
+ auto scales256 = MM256_SET_M128I(scales128, scales128);
+ auto all_scales = _mm512_inserti32x8(_mm512_castsi256_si512(scales256), scales256, 1);
+ scales[0] = _mm512_shuffle_epi8(all_scales, shuffles[0]);
+ scales[1] = _mm512_shuffle_epi8(all_scales, shuffles[1]);
+ scales[2] = _mm512_shuffle_epi8(all_scales, shuffles[2]);
+ scales[3] = _mm512_shuffle_epi8(all_scales, shuffles[3]);
+ prepare(x[i].qs);
+ }
+ inline void prepare(const uint8_t * q4) {
+ bits.prepare64(q4);
+ // We now have in bits.valuse[0]: 0...15, 32...47, 64...79, 96...111
+ // bits.valuse[1]: 16..31, 48...63, 80...95, 112..127
+ // etc.
+ auto tmp = _mm512_permutex2var_epi64(bits.values[0], permute1, bits.values[1]);
+ bits.values[1] = _mm512_shuffle_epi8(values, _mm512_permutex2var_epi64(bits.values[0], permute2, bits.values[1]));
+ bits.values[0] = _mm512_shuffle_epi8(values, tmp);
+ tmp = _mm512_permutex2var_epi64(bits.values[2], permute1, bits.values[3]);
+ bits.values[3] = _mm512_shuffle_epi8(values, _mm512_permutex2var_epi64(bits.values[2], permute2, bits.values[3]));
+ bits.values[2] = _mm512_shuffle_epi8(values, tmp);
+ }
+
+ Q4Bits bits;
+ Scales8KBase s8k;
+ const __m512i values;
+ const __m512i permute1 = _mm512_set_epi64(11, 10, 3, 2, 9, 8, 1, 0);
+ const __m512i permute2 = _mm512_set_epi64(15, 14, 7, 6, 13, 12, 5, 4);
+ const __m128i mask = _mm_set1_epi16(254);
+ const __m128i m127 = _mm_set1_epi16(-127);
+ const __m128i m128 = _mm_set1_epi16(-128);
+ const __m128i m1 = _mm_set1_epi16(1);
+ const __m128i m4 = _mm_set1_epi16(4);
+ const __m512i shuffles[4] = {
+ _mm512_inserti32x8(_mm512_set1_epi16(0x0100), _mm256_set1_epi16(0x0302), 1),
+ _mm512_inserti32x8(_mm512_set1_epi16(0x0504), _mm256_set1_epi16(0x0706), 1),
+ _mm512_inserti32x8(_mm512_set1_epi16(0x0908), _mm256_set1_epi16(0x0b0a), 1),
+ _mm512_inserti32x8(_mm512_set1_epi16(0x0d0c), _mm256_set1_epi16(0x0f0e), 1),
+ };
+};
+
template <typename Q8>
inline void compute_block(int iy, int i, float d, const Q8& q8, const __m512i * values, const __m512i * scales, __m512 * accd) {
const __m512i p1 = _mm512_dpbusd_epi32(_mm512_setzero_si512(), values[0], q8.load_quants64(iy, i, 0));
@@ -1455,16 +1511,6 @@ struct IQXKScales {
inline void process(int i, float d, uint16_t extra, __m128i scales8, const Q8& q8, __m256 * accm, __m256i * scales) const {
auto scales16 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales8, hshuff));
process(i, d, extra, scales16, q8, accm, scales);
- //auto extra128 = _mm_set1_epi16(extra);
- //extra128 = _mm_cmpeq_epi8(_mm_and_si128(extra128, emask), emask);
- //extra128 = _mm_and_si128(extra128, eshift);
- //extra128 = _mm_shuffle_epi8(extra128, eshuffle);
- //auto scales_s = _mm256_mullo_epi16(scales16, _mm256_add_epi16(min, _mm256_cvtepi8_epi16(extra128)));
- //for (int iy = 0; iy < Q8::nrc_y; ++iy) {
- // const __m256i prod = _mm256_madd_epi16(scales_s, q8.load_bsums(iy, i));
- // accm[iy] = _mm256_fmadd_ps(_mm256_set1_ps(d * q8.scale(iy, i)), _mm256_cvtepi32_ps(prod), accm[iy]);
- //}
- //prepare_scales_16(scales16, scales);
}
template <typename Q8>
inline void process(int i, float d, uint16_t extra, __m256i scales16, const Q8& q8, __m256 * accm, __m256i * scales) const {
@@ -1694,6 +1740,37 @@ struct DequantizerIQ6K final : public BaseDequantizer<block_iq6_k> {
const __m256i mh = _mm256_set1_epi8(-128); // to avoid stupid warning about 0x80 overflowing
};
+struct DequantizerIQ4XXS final : public BaseDequantizer<block_iq4_ks, true> {
+ DequantizerIQ4XXS(const void * vx, size_t bx) : BaseDequantizer(vx, bx), values(load_iq4nl_values_256()) {}
+ template <typename Q8>
+ inline __m256i new_block(int i, const Q8& q8, __m256 * accd) {
+ auto scales128 = _mm_cvtepu8_epi16(_mm_loadl_epi64((const __m128i *)x[i].scales));
+ auto shifts = _mm_and_si128(_mm_cmpeq_epi16(_mm_and_si128(scales128, m1), m1), m4);
+ scales128 = _mm_add_epi16(_mm_and_si128(scales128, mask), m127);
+ auto scales_s = _mm_mullo_epi16(scales128, _mm_add_epi16(m128, shifts));
+ s8k.accum_mins(scales_s, q8, i, d, accd);
+ return MM256_SET_M128I(scales128, scales128);
+ }
+ inline void prepare(int i, int j) {
+ bits.prepare16(x[i].qs, j);
+ bits.values[0] = _mm256_shuffle_epi8(values, bits.values[0]);
+ bits.values[1] = _mm256_shuffle_epi8(values, bits.values[1]);
+ bits.values[2] = _mm256_shuffle_epi8(values, bits.values[2]);
+ bits.values[3] = _mm256_shuffle_epi8(values, bits.values[3]);
+ }
+
+ Q4Bits bits;
+ Scales8KBase s8k;
+ const __m256i values;
+ const __m128i mask = _mm_set1_epi16(254);
+ const __m128i m127 = _mm_set1_epi16(-127);
+ const __m128i m128 = _mm_set1_epi16(-128);
+ const __m128i m1 = _mm_set1_epi16(1);
+ const __m128i m4 = _mm_set1_epi16(4);
+ const __m256i shuff1 = _mm256_set_epi64x(0x0706070605040504, 0x0302030201000100, 0x0706070605040504, 0x0302030201000100);
+ const __m256i shuff2 = _mm256_set_epi64x(0x0f0e0f0e0d0c0d0c, 0x0b0a0b0a09080908, 0x0f0e0f0e0d0c0d0c, 0x0b0a0b0a09080908);
+};
+
struct DequantizerQ5K final : public BaseDequantizer<block_q5_K> {
DequantizerQ5K(const void * vx, size_t bx) : BaseDequantizer(vx, bx) {}
template <typename Q8>
@@ -3672,7 +3749,9 @@ template <typename Dequantizer> void MulMat::set_functions(MulMat& m) {
if constexpr (std::is_same_v<Dequantizer, DequantizerIQ6K> ||
std::is_same_v<Dequantizer, DequantizerIQ5K> ||
std::is_same_v<Dequantizer, DequantizerIQ4K> ||
- std::is_same_v<Dequantizer, DequantizerIQ3K>) {
+ std::is_same_v<Dequantizer, DequantizerIQ3K> ||
+ std::is_same_v<Dequantizer, DequantizerIQ4XS>||
+ std::is_same_v<Dequantizer, DequantizerIQ4XXS>) {
m.funcs[0] = mul_mat_iqX_k_q8_K_AVX512<Dequantizer, 1>;
m.funcs[1] = mul_mat_iqX_k_q8_K_AVX512<Dequantizer, 2>;
m.funcs[2] = mul_mat_iqX_k_q8_K_AVX512<Dequantizer, 3>;
@@ -3832,6 +3911,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
assert (ne00 % QK_K == 0);
MulMat::set_functions<DequantizerIQ4XS>(mm);
break;
+ case GGML_TYPE_IQ4_KS:
+ assert (ne00 % QK_K == 0);
+ MulMat::set_functions<DequantizerIQ4XXS>(mm);
+ break;
case GGML_TYPE_IQ2_K:
assert (ne00 % QK_K == 0);
MulMat::set_functions<DequantizerIQ2K>(mm);
@@ -4726,6 +4809,35 @@ struct DequantizerIQ4XS final : public BaseDequantizer<block_iq4_xs> {
};
+struct DequantizerIQ4XXS final : public BaseDequantizer<block_iq4_ks, true> {
+
+ DequantizerIQ4XXS(const void * vx, size_t bx, int nrc) : BaseDequantizer(vx, bx, nrc), values(vld1q_s8_x2(iq4k_values)) {}
+
+ constexpr static int num_blocks() { return 8; }
+ constexpr static bool should_scale_quants() { return false; }
+
+ template <typename Q8>
+ inline int32x4x2_t new_block(int i, const Q8& q8, float32x4_t * acc) {
+ (void)q8;
+ (void)acc;
+ auto scales16 = vaddq_s16(vreinterpretq_s16_u16(vandq_u16(vmovl_u8(vld1_u8(x[i].scales)), mask)), m127);
+ int32x4x2_t scales = {vmovl_s16(vget_low_s16(scales16)), vmovl_s16(vget_high_s16(scales16))};
+ return scales;
+ }
+ inline void prepare(int i, int j) {
+ bits.prepare16(x[i].qs+64*j);
+ for (int k = 0; k < 4; ++k) {
+ bits.b1.val[k] = vreinterpretq_u8_s8(vqtbl1q_s8(values.val[x[i].scales[4*j+k] & 1], bits.b1.val[k]));
+ bits.b2.val[k] = vreinterpretq_u8_s8(vqtbl1q_s8(values.val[x[i].scales[4*j+k] & 1], bits.b2.val[k]));
+ }
+ }
+
+ Q4bits bits;
+ const int8x16x2_t values;
+ const uint16x8_t mask = vdupq_n_u16(254);
+ const int16x8_t m127 = vdupq_n_s16(-127);
+};
+
struct SimpleBits {
uint8x16x4_t b1;
uint8x16x4_t b2;
@@ -6458,6 +6570,9 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
case GGML_TYPE_IQ4_XS:
MulMat::set_functions<DequantizerIQ4XS>(m);
break;
+ case GGML_TYPE_IQ4_KS:
+ MulMat::set_functions<DequantizerIQ4XXS>(m);
+ break;
case GGML_TYPE_IQ4_K:
MulMat::set_functions<DequantizerIQ4K>(m);
break;
diff --git a/ggml/src/iqk/iqk_quantize.cpp b/ggml/src/iqk/iqk_quantize.cpp
index 3ff6b4da..430b629f 100644
--- a/ggml/src/iqk/iqk_quantize.cpp
+++ b/ggml/src/iqk/iqk_quantize.cpp
@@ -2166,3 +2166,256 @@ void iqk_quantize_row_q8_K(const float * x, void * vy, int64_t k) {
#endif
}
+
+namespace {
+static void quantize_row_iq4_k_impl_bs128(const int super_block_size, const int block_size,
+ int n_per_row, const float * x, char * cy,
+ float * all_scales, float * weight,
+ const int8_t * values,
+ const float * quant_weights,
+ const int ntry) {
+
+ //GGML_ASSERT(super_block_size == 256 && block_size == 128);
+
+ float * dptr = (float *)cy;
+ block_iq4_ks * y = (block_iq4_ks *)(dptr + 1);
+
+ const int8_t * shifted_values = values + 16;
+
+ float amax_scale = 0;
+
+ for (int ibl = 0; ibl < n_per_row/super_block_size; ++ibl) {
+ memset(&y[ibl], 0, sizeof(block_iq4_ks));
+ const float * xbl = x + ibl*super_block_size;
+ auto scales = all_scales + ibl*(super_block_size/block_size);
+ float sigma2 = 0;
+ for (int j = 0; j < super_block_size; ++j) sigma2 += xbl[j]*xbl[j];
+ sigma2 *= 2.f/super_block_size;
+ for (int ib = 0; ib < super_block_size/block_size; ++ib) {
+ const float * xb = xbl + ib*block_size;
+ if (quant_weights) {
+ const float * qw = quant_weights + ibl*super_block_size + ib*block_size;
+ for (int j = 0; j < block_size; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
+ } else {
+ for (int j = 0; j < block_size; ++j) weight[j] = xb[j]*xb[j];
+ }
+ float amax = 0, max = 0;
+ for (int j = 0; j < block_size; ++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/values[0] : max/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 < block_size; ++j) {
+ float w = weight[j];
+ float al = id*xb[j];
+ int l = best_index_iq4nl(values, al);
+ float q = values[l];
+ sumqx_p += w*q*xb[j];
+ sumq2_p += w*q*q;
+ l = best_index_iq4nl(values, -al);
+ q = values[l];
+ sumqx_m += w*q*xb[j];
+ sumq2_m += w*q*q;
+ }
+ d = sumqx_p/sumq2_p;
+ bool is_shifted = false;
+ 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;
+ }
+ for (int itry = -ntry; itry <= ntry; ++itry) {
+ id = (itry + values[0])/max;
+ sumqx_p = sumq2_p = 0;
+ sumqx_m = sumq2_m = 0;
+ for (int j = 0; j < block_size; ++j) {
+ float w = weight[j];
+ float al = id*xb[j];
+ int l = best_index_iq4nl(values, al);
+ float q = values[l];
+ sumqx_p += w*q*xb[j];
+ sumq2_p += w*q*q;
+ l = best_index_iq4nl(values, -al);
+ q = 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 < block_size; ++j) {
+ float w = weight[j];
+ float al = id*xb[j];
+ int l = best_index_iq4nl(shifted_values, al);
+ float q = shifted_values[l];
+ sumqx_p += w*q*xb[j];
+ sumq2_p += w*q*q;
+ l = best_index_iq4nl(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 (is_shifted) y[ibl].scales[ib] = 0x01;
+ scales[ib] = d;
+ amax_scale = std::max(amax_scale, std::abs(d));
+ }
+ }
+ float d = amax_scale/127;
+ *dptr = d;
+ if (!d) return;
+ float id = d ? 1/d : 0.f;
+ float sumqx = 0, sumq2 = 0;
+ //float mse = 0;
+ for (int ibl = 0; ibl < n_per_row/super_block_size; ++ibl) {
+ const float * xbl = x + ibl*super_block_size;
+ float sigma2 = 0;
+ for (int j = 0; j < super_block_size; ++j) sigma2 += xbl[j]*xbl[j];
+ sigma2 *= 2.f/super_block_size;
+ auto scales = all_scales + (super_block_size/block_size)*ibl;
+ for (int ib = 0; ib < super_block_size/block_size; ++ib) {
+ const int8_t * block_values = y[ibl].scales[ib] & 0x01 ? shifted_values : values;
+ int l = nearest_int(0.5f*(id*scales[ib]+127.f));
+ l = std::max(0, std::min(127, l)) << 1;
+ //printf("d = %g, id = %g, scales = %g, l = %d, dl = %g\n", d, id, scales[ib], l, d*(l - 127));
+ y[ibl].scales[ib] |= l;
+ l -= 127;
+ float dl = d * l;
+ float idl = dl ? 1/dl : 0.f;
+ const float * xb = xbl + ib*block_size;
+ if (quant_weights) {
+ const float * qw = quant_weights + ibl*super_block_size + ib*block_size;
+ for (int j = 0; j < block_size; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
+ } else {
+ for (int j = 0; j < block_size; ++j) weight[j] = xb[j]*xb[j];
+ }
+ auto qs = y[ibl].qs + ib*(block_size/2);
+ for (int j = 0; j < block_size/2; ++j) {
+ uint8_t i1 = best_index_iq4nl(block_values, idl*xb[j]);
+ uint8_t i2 = best_index_iq4nl(block_values, idl*xb[j+block_size/2]);
+ qs[j] = i1 | (i2 << 4);
+ float w1 = weight[j];
+ float w2 = weight[j+block_size/2];
+ float q1 = block_values[i1]*l;
+ float q2 = block_values[i2]*l;
+ sumqx += w1*q1*xb[j] + w2*q2*xb[j+block_size/2];
+ sumq2 += w1*q1*q1 + w2*q2*q2;
+ //float diff = xb[j] - d*q1; mse += diff*diff;
+ //diff = xb[j+block_size/2] - d*q2; mse += diff*diff;
+ }
+ }
+ }
+ //printf("rmse = %g\n", sqrt(mse/n_per_row));
+ if (sumq2 > 0) *dptr = sumqx/sumq2;
+}
+}
+
+void quantize_row_iq4_ks_ref(const float * x, block_iq4_ks * y, int64_t k) {
+ quantize_iq4_ks(x, (void *)y, 1, k, nullptr);
+}
+
+void quantize_row_iq4_ks(const float * x, void * y, int64_t k) {
+ quantize_iq4_ks(x, (void *)y, 1, k, nullptr);
+}
+
+size_t quantize_iq4_ks(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
+ //printf("============ %s(%d, %d)\n", __func__, int(nrows), int(n_per_row));
+ constexpr int kBlockSize = 32; //128;
+ GGML_ASSERT(n_per_row%QK_K == 0);
+ auto row_size = ggml_row_size(GGML_TYPE_IQ4_KS, n_per_row);
+ char * qrow = (char *)dst;
+ float weight[kBlockSize];
+ std::vector<float> all_scales(n_per_row/kBlockSize);
+ for (int64_t row = 0; row < nrows; ++row) {
+ quantize_row_iq4_k_impl_bs128(QK_K, kBlockSize, n_per_row, src, qrow, all_scales.data(), weight, iq4k_values, imatrix, 7);
+ src += n_per_row;
+ qrow += row_size;
+ }
+ return nrows * row_size;
+}
+
+void dequantize_row_iq4_ks(const block_iq4_ks * x, float * y, int64_t k) {
+ constexpr int kBlockSize = 32; //128;
+ GGML_ASSERT(k%QK_K == 0);
+ const float * dptr = (const float *)x;
+ float d = *dptr;
+ x = (const block_iq4_ks *)(dptr + 1);
+ int nblock = k/QK_K;
+ for (int ibl = 0; ibl < nblock; ++ibl) {
+ auto qs = x[ibl].qs;
+ for (int ib = 0; ib < QK_K/kBlockSize; ++ib) {
+ float dl = d * ((int)(x[ibl].scales[ib] & 254) - 127);
+ const int8_t * values = iq4k_values + ((x[ibl].scales[ib] & 1) << 4);
+ for (int j = 0; j < kBlockSize/2; ++j) {
+ y[j ] = dl * values[qs[j] & 0xf];
+ y[j+kBlockSize/2] = dl * values[qs[j] >> 4];
+ }
+ y += kBlockSize;
+ qs += kBlockSize/2;
+ }
+ }
+}
+
+void vec_dot_iq4_ks_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
+ constexpr int kBlockSize = 32;
+#if GGML_USE_IQK_MULMAT
+ if (iqk_mul_mat(1, 1, n, GGML_TYPE_IQ4_KS, vx, 0, GGML_TYPE_Q8_K, vy, 0, s, 0, 0, 1)) {
+ return;
+ }
+#endif
+ GGML_ASSERT(n%QK_K == 0);
+ GGML_ASSERT(nrc == 1);
+ GGML_UNUSED(bs);
+ GGML_UNUSED(bx);
+ GGML_UNUSED(by);
+ const float * dptr = (const float *)vx;
+ const float d = *dptr;
+ //printf("%s: n = %d, d = %g\n", __func__, n, d);
+ const block_iq4_ks * x = (const block_iq4_ks *)(dptr + 1);
+ const block_q8_K * y = (const block_q8_K *)vy;
+ int nblock = n/QK_K;
+ float sumf = 0;
+ for (int ibl = 0; ibl < nblock; ++ibl) {
+ //int sumi = 0;
+ auto qy = y[ibl].qs;
+ auto qx = x[ibl].qs;
+ float db = d * y[ibl].d;
+ for (int ib = 0; ib < QK_K/kBlockSize; ++ib) {
+ float dl = db * ((x[ibl].scales[ib] & 254) - 127);
+ //int ls = (x[ibl].scales[ib] & 254) - 127;
+ const int8_t * values = iq4k_values + ((x[ibl].scales[ib] & 1) << 4);
+ int suml = 0;
+ for (int j = 0; j < kBlockSize/2; ++j) {
+ suml += qy[j ] * values[qx[j] & 0xf]
+ + qy[j + kBlockSize/2] * values[qx[j] >> 4];
+ }
+ sumf += dl * suml;
+ //sumi += ls * suml;
+ qy += kBlockSize;
+ qx += kBlockSize/2;
+ }
+ //sumf += d * y[ibl].d * sumi;
+ }
+ *s = sumf;
+}
+
diff --git a/ggml/src/iqk/iqk_quantize.h b/ggml/src/iqk/iqk_quantize.h
index e5c16fc9..a3623963 100644
--- a/ggml/src/iqk/iqk_quantize.h
+++ b/ggml/src/iqk/iqk_quantize.h
@@ -55,6 +55,12 @@ size_t quantize_iq1_tn(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst
void dequantize_row_iq1_tn(const block_iq1_tn * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void vec_dot_iq1_tn_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_ks_ref(const float * GGML_RESTRICT x, block_iq4_ks * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_ks(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+size_t quantize_iq4_ks(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+void dequantize_row_iq4_ks(const block_iq4_ks * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void vec_dot_iq4_ks_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 iqk_quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
#ifdef __cplusplus
diff --git a/include/llama.h b/include/llama.h
index 43c0091e..9fb4af53 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -177,6 +177,8 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ6_K = 142, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_TN = 143, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ1_TN = 144, // except 1d tensors
+ LLAMA_FTYPE_MOSTLY_IQ4_KS = 145, // except 1d tensors
+ LLAMA_FTYPE_MOSTLY_IQ3_KL = 146, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};
diff --git a/src/llama.cpp b/src/llama.cpp
index 9ed109c6..80104303 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -3793,6 +3793,7 @@ struct llama_model_loader {
case GGML_TYPE_IQ2_TN: ftype = LLAMA_FTYPE_MOSTLY_IQ2_TN; 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_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS; 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;
@@ -4494,8 +4495,10 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
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_XS: return "IQ4_XS - 4.25 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ4_KS: return "IQ4_KS - 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_IQ3_KL: return "IQ3_KL - 4 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_IQ6_K: return "IQ6_K - 6.6 bpw";
@@ -15623,7 +15626,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
ftype == LLAMA_FTYPE_MOSTLY_IQ1_M || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K) {
new_type = !qs.has_output ? GGML_TYPE_IQ4_K : GGML_TYPE_Q5_K;
}
- else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_output) {
+ else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS) && !qs.has_output) {
new_type = GGML_TYPE_IQ5_K;
}
else if (new_type != GGML_TYPE_Q8_0 && new_type != GGML_TYPE_IQ6_K) {
@@ -15697,12 +15700,15 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ3_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 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K) && qs.model.hparams.n_gqa() >= 2) {
+ else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 2) {
new_type = GGML_TYPE_IQ4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_K && qs.model.hparams.n_gqa() >= 2) {
new_type = GGML_TYPE_IQ4_K;
}
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_KL) {
+ new_type = qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ5_K : GGML_TYPE_IQ4_K;
+ }
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
new_type = qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ5_K : GGML_TYPE_IQ4_K;
}
@@ -15710,7 +15716,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
- else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 2) {
+ else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS) && qs.model.hparams.n_gqa() >= 2) {
new_type = GGML_TYPE_IQ5_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ4_K && qs.model.hparams.n_gqa() >= 2) {
@@ -15779,6 +15785,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
}
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_KL) {
+ new_type = use_more_bits(i_layer, n_layer) ? GGML_TYPE_IQ4_KS : GGML_TYPE_IQ3_K;
+ }
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
if (arch == LLM_ARCH_FALCON) {
new_type = i_layer < n_layer/16 ? GGML_TYPE_Q6_K :
@@ -15787,7 +15796,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
}
}
- else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) {
+ else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS) && !qs.has_imatrix) {
new_type = GGML_TYPE_Q5_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
@@ -15819,6 +15828,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M ) new_type = GGML_TYPE_IQ4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_K ) new_type = GGML_TYPE_IQ3_K;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_KL ) new_type = GGML_TYPE_IQ4_KS;
}
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
@@ -15838,6 +15848,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
new_type = GGML_TYPE_IQ3_XXS;
}
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_KL && use_more_bits(i_layer, n_layer)) {
+ new_type = GGML_TYPE_IQ4_KS;
+ }
++qs.i_ffn_gate;
}
else if (name.find("ffn_up") != std::string::npos) {
@@ -15846,6 +15859,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
new_type = GGML_TYPE_IQ3_XXS;
}
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_KL && use_more_bits(i_layer, n_layer)) {
+ new_type = GGML_TYPE_IQ4_KS;
+ }
++qs.i_ffn_up;
}
@@ -15867,7 +15883,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
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_IQ3_K || new_type == GGML_TYPE_IQ2_TN ||
- new_type == GGML_TYPE_IQ6_K || new_type == GGML_TYPE_IQ1_TN) {
+ new_type == GGML_TYPE_IQ6_K || new_type == GGML_TYPE_IQ1_TN || new_type == GGML_TYPE_IQ4_KS) {
int nx = tensor->ne[0];
int ny = tensor->ne[1];
if (nx % QK_K != 0) {
@@ -15898,6 +15914,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
case GGML_TYPE_Q3_K:
case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ3_K:
+ case GGML_TYPE_IQ4_KS:
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;
@@ -16008,8 +16025,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_IQ2_TN: default_type = GGML_TYPE_IQ2_TN; 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_KS: default_type = GGML_TYPE_IQ4_KS; 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_IQ3_KL: 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_IQ6_K: default_type = GGML_TYPE_IQ6_K; break;
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-15 22:07:13 +0000