bitnet : pad tensors to 256

convert-hf-to-gguf.py

@@ -1397,6 +1397,49 @@ class LlamaModel(Model):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@Model.register("BitnetForCausalLM")
+class BitnetModel(Model):
+    model_arch = gguf.MODEL_ARCH.BITNET
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+        self.gguf_writer.add_rope_scaling_factor(1.0)
+
+    def weight_quant(self, weight):
+        dtype = weight.dtype
+        weight = weight.float()
+        s = 1 / weight.abs().mean().clamp(min=1e-5)
+        result = (weight * s).round().clamp(-1, 1) / s
+        return result.type(dtype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # transform weight into 1/0/-1 (in fp32)
+        if name.endswith(("q_proj.weight", "k_proj.weight", "v_proj.weight",
+                          "down_proj.weight", "up_proj.weight", "gate_proj.weight",
+                          "o_proj.weight")):
+            data_torch = self.weight_quant(data_torch)
+
+        # pad 1D tensors
+        # TODO: is padding with 0s an invariant, or do we also need some scaling factor?
+        if name.endswith(("input_layernorm.weight", "post_attention_layernorm.weight", "model.norm.weight")):
+            data_torch = torch.nn.functional.pad(data_torch, (0, 256 - data_torch.size(0) % 256), mode='constant', value=0)
+            logger.info(f"pad {name} to {data_torch.size()}")
+
+        # pad 2D tensors
+        # TODO: double-check that this is the correct way to pad the rows
+        if name.endswith(("embed_tokens.weight", "q_proj.weight", "k_proj.weight", "v_proj.weight",
+                          "down_proj.weight", "up_proj.weight", "gate_proj.weight",
+                          "o_proj.weight")):
+            data_torch = torch.nn.functional.pad(data_torch, (0, 256 - data_torch.size(1) % 256), mode='constant', value=0)
+            logger.info(f"pad {name} to {data_torch.size()}")
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
 @Model.register("GrokForCausalLM")
 class GrokModel(Model):
     model_arch = gguf.MODEL_ARCH.GROK

examples/quantize/quantize.cpp

@@ -26,6 +26,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "IQ2_M",  LLAMA_FTYPE_MOSTLY_IQ2_M,  " 2.7  bpw quantization",            },
     { "IQ1_S",  LLAMA_FTYPE_MOSTLY_IQ1_S,  " 1.56 bpw quantization",            },
     { "IQ1_M",  LLAMA_FTYPE_MOSTLY_IQ1_M,  " 1.75 bpw quantization",            },
+    { "I2_S",   LLAMA_FTYPE_MOSTLY_I2_S,   " 2    bpw per-tensor quantization", },
     { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
     { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
     { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization",            },

ggml-common.h

@@ -1022,6 +1022,73 @@ GGML_TABLE_BEGIN(uint32_t, iq3s_grid, 512)
     0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101,
 GGML_TABLE_END()
 
+GGML_TABLE_BEGIN(uint32_t, i2s_i8s, 256)
+    0x00000000, 0x01000000, 0x00000000, 0xff000000,
+    0x00010000, 0x01010000, 0x00010000, 0xff010000,
+    0x00000000, 0x01000000, 0x00000000, 0xff000000,
+    0x00ff0000, 0x01ff0000, 0x00ff0000, 0xffff0000,
+    0x00000100, 0x01000100, 0x00000100, 0xff000100,
+    0x00010100, 0x01010100, 0x00010100, 0xff010100,
+    0x00000100, 0x01000100, 0x00000100, 0xff000100,
+    0x00ff0100, 0x01ff0100, 0x00ff0100, 0xffff0100,
+    0x00000000, 0x01000000, 0x00000000, 0xff000000,
+    0x00010000, 0x01010000, 0x00010000, 0xff010000,
+    0x00000000, 0x01000000, 0x00000000, 0xff000000,
+    0x00ff0000, 0x01ff0000, 0x00ff0000, 0xffff0000,
+    0x0000ff00, 0x0100ff00, 0x0000ff00, 0xff00ff00,
+    0x0001ff00, 0x0101ff00, 0x0001ff00, 0xff01ff00,
+    0x0000ff00, 0x0100ff00, 0x0000ff00, 0xff00ff00,
+    0x00ffff00, 0x01ffff00, 0x00ffff00, 0xffffff00,
+    0x00000001, 0x01000001, 0x00000001, 0xff000001,
+    0x00010001, 0x01010001, 0x00010001, 0xff010001,
+    0x00000001, 0x01000001, 0x00000001, 0xff000001,
+    0x00ff0001, 0x01ff0001, 0x00ff0001, 0xffff0001,
+    0x00000101, 0x01000101, 0x00000101, 0xff000101,
+    0x00010101, 0x01010101, 0x00010101, 0xff010101,
+    0x00000101, 0x01000101, 0x00000101, 0xff000101,
+    0x00ff0101, 0x01ff0101, 0x00ff0101, 0xffff0101,
+    0x00000001, 0x01000001, 0x00000001, 0xff000001,
+    0x00010001, 0x01010001, 0x00010001, 0xff010001,
+    0x00000001, 0x01000001, 0x00000001, 0xff000001,
+    0x00ff0001, 0x01ff0001, 0x00ff0001, 0xffff0001,
+    0x0000ff01, 0x0100ff01, 0x0000ff01, 0xff00ff01,
+    0x0001ff01, 0x0101ff01, 0x0001ff01, 0xff01ff01,
+    0x0000ff01, 0x0100ff01, 0x0000ff01, 0xff00ff01,
+    0x00ffff01, 0x01ffff01, 0x00ffff01, 0xffffff01,
+    0x00000000, 0x01000000, 0x00000000, 0xff000000,
+    0x00010000, 0x01010000, 0x00010000, 0xff010000,
+    0x00000000, 0x01000000, 0x00000000, 0xff000000,
+    0x00ff0000, 0x01ff0000, 0x00ff0000, 0xffff0000,
+    0x00000100, 0x01000100, 0x00000100, 0xff000100,
+    0x00010100, 0x01010100, 0x00010100, 0xff010100,
+    0x00000100, 0x01000100, 0x00000100, 0xff000100,
+    0x00ff0100, 0x01ff0100, 0x00ff0100, 0xffff0100,
+    0x00000000, 0x01000000, 0x00000000, 0xff000000,
+    0x00010000, 0x01010000, 0x00010000, 0xff010000,
+    0x00000000, 0x01000000, 0x00000000, 0xff000000,
+    0x00ff0000, 0x01ff0000, 0x00ff0000, 0xffff0000,
+    0x0000ff00, 0x0100ff00, 0x0000ff00, 0xff00ff00,
+    0x0001ff00, 0x0101ff00, 0x0001ff00, 0xff01ff00,
+    0x0000ff00, 0x0100ff00, 0x0000ff00, 0xff00ff00,
+    0x00ffff00, 0x01ffff00, 0x00ffff00, 0xffffff00,
+    0x000000ff, 0x010000ff, 0x000000ff, 0xff0000ff,
+    0x000100ff, 0x010100ff, 0x000100ff, 0xff0100ff,
+    0x000000ff, 0x010000ff, 0x000000ff, 0xff0000ff,
+    0x00ff00ff, 0x01ff00ff, 0x00ff00ff, 0xffff00ff,
+    0x000001ff, 0x010001ff, 0x000001ff, 0xff0001ff,
+    0x000101ff, 0x010101ff, 0x000101ff, 0xff0101ff,
+    0x000001ff, 0x010001ff, 0x000001ff, 0xff0001ff,
+    0x00ff01ff, 0x01ff01ff, 0x00ff01ff, 0xffff01ff,
+    0x000000ff, 0x010000ff, 0x000000ff, 0xff0000ff,
+    0x000100ff, 0x010100ff, 0x000100ff, 0xff0100ff,
+    0x000000ff, 0x010000ff, 0x000000ff, 0xff0000ff,
+    0x00ff00ff, 0x01ff00ff, 0x00ff00ff, 0xffff00ff,
+    0x0000ffff, 0x0100ffff, 0x0000ffff, 0xff00ffff,
+    0x0001ffff, 0x0101ffff, 0x0001ffff, 0xff01ffff,
+    0x0000ffff, 0x0100ffff, 0x0000ffff, 0xff00ffff,
+    0x00ffffff, 0x01ffffff, 0x00ffffff, 0xffffffff,
+GGML_TABLE_END()
+
 #define NGRID_IQ1S 2048
 #define IQ1S_DELTA 0.125f
 #define IQ1M_DELTA 0.125f

ggml-quants.c

@@ -659,6 +659,24 @@ static inline __m128i packNibbles( __m256i bytes ) {
 }
 #endif  //__loongarch_asx
 
+void quantize_row_i8_s(const float * x, void * y, int64_t n, float* act_scales) {
+    int8_t* dst = (int8_t*)y;
+    double min = 0.00001;
+    double max = min;
+    for (int i = 0; i < n; ++i) {
+        max = MAX(max, (double)fabs((double)x[i]));
+    }
+    float s = 127 / max;
+    act_scales[0] = s;
+    float temp;
+    for (int i = 0; i < n; ++i) {
+        temp = round((double)(x[i] * s));
+        if (temp >  127) temp = 127;
+        if (temp < -128) temp = -128;
+        dst[i] = (int8_t)(temp);
+    }
+}
+
 // reference implementation for deterministic creation of model files
 void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int64_t k) {
     static const int qk = QK4_0;
@@ -3306,6 +3324,50 @@ size_t quantize_q8_0(const float * restrict src, void * restrict dst, int64_t nr
     return nrow * row_size;
 }
 
+size_t quantize_i2_s(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    // 2 bits per weight
+    UNUSED(quant_weights);
+
+    size_t row_size = ggml_row_size(GGML_TYPE_I2_S, n_per_row);
+
+    int n = nrow * n_per_row;
+
+    // f32 -> q8
+    double max = 0;
+    for (int i = 0; i < n; ++i) {
+        max = MAX(max, (double)fabs((double)src[i]));
+    }
+    double i2_scale = max;
+
+    uint8_t* q8 = (uint8_t*)dst;
+    for (int i=0; i<n; i++) {
+        if (fabs((double)(src[i])) < 1e-6) {
+            q8[i] = 0;
+            continue;
+        }
+        q8[i] = (double)src[i] * i2_scale > 0 ? 1 : 3;
+    }
+
+    // q8 -> 0, 1, 3
+    //       |  |  |
+    //       0, 1,-1
+
+    uint8_t* i2_weight = (uint8_t*)dst;
+    for (int i=0; i<n; i++) {
+        int group_idx = i / 4;
+        int group_pos = i % 4;
+        uint8_t temp = (q8[i] << (6 - 2 * group_pos));
+        q8[i] = 0;
+        i2_weight[group_idx] |= temp;
+    }
+
+    float* scale_ptr = (float*)((char*)i2_weight + n / 4);
+    scale_ptr[0] = i2_scale;
+
+    // 32B for alignment
+    return nrow * row_size / 4 + 32;
+}
+
 // ====================== "True" 2-bit (de)-quantization
 
 void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int64_t k) {
@@ -3726,6 +3788,86 @@ static inline __m128i get_scale_shuffle(int i) {
 }
 #endif
 
+//====================================== I2 ===============================================
+
+void ggml_vec_dot_i2_i8_s(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    const uint8_t *    restrict x = vx;
+    const int8_t  *    restrict y = vy;
+
+    UNUSED(bs);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(nrc);
+
+#if defined(__AVX2__)
+    __m256i accu = _mm256_setzero_si256();
+
+    // max group_size is 128 (2^8)
+    // limited by 8640 to 2 (8640 % (2 * 32) == 0)
+    int group_num = 2;
+
+    for (int i=0; i < n / (group_num * 32); i++){
+        __m256i laccu = _mm256_setzero_si256();
+        __m256i haccu = _mm256_setzero_si256();
+
+        for (int j=0; j < group_num; j++) {
+            __m256i xq8 = _mm256_set_epi32(
+                (int)i2s_i8s[x[i * group_num * 8 + j * 8 + 7]],
+                (int)i2s_i8s[x[i * group_num * 8 + j * 8 + 6]],
+                (int)i2s_i8s[x[i * group_num * 8 + j * 8 + 5]],
+                (int)i2s_i8s[x[i * group_num * 8 + j * 8 + 4]],
+                (int)i2s_i8s[x[i * group_num * 8 + j * 8 + 3]],
+                (int)i2s_i8s[x[i * group_num * 8 + j * 8 + 2]],
+                (int)i2s_i8s[x[i * group_num * 8 + j * 8 + 1]],
+                (int)i2s_i8s[x[i * group_num * 8 + j * 8 + 0]]
+            );
+
+            __m256i yq8 = _mm256_loadu_si256((const __m256i*)(y + i * group_num * 32 + j * 32));
+
+            __m128i hxq8 = _mm256_castsi256_si128(xq8);
+            __m128i lxq8 = _mm256_extractf128_si256(xq8, 1);
+            __m128i hyq8 = _mm256_castsi256_si128(yq8);
+            __m128i lyq8 = _mm256_extractf128_si256(yq8, 1);
+
+            __m256i hxq16 = _mm256_cvtepi8_epi16(hxq8);
+            __m256i lxq16 = _mm256_cvtepi8_epi16(lxq8);
+            __m256i hyq16 = _mm256_cvtepi8_epi16(hyq8);
+            __m256i lyq16 = _mm256_cvtepi8_epi16(lyq8);
+
+            __m256i hzq16 = _mm256_sign_epi16(hyq16, hxq16);
+            __m256i lzq16 = _mm256_sign_epi16(lyq16, lxq16);
+
+            haccu = _mm256_add_epi16(haccu, hzq16);
+            laccu = _mm256_add_epi16(laccu, lzq16);
+        }
+
+        __m256i hhzq32 = _mm256_cvtepi16_epi32(_mm256_castsi256_si128(haccu));
+        __m256i hlzq32 = _mm256_cvtepi16_epi32(_mm256_extractf128_si256(haccu, 1));
+        __m256i llzq32 = _mm256_cvtepi16_epi32(_mm256_castsi256_si128(laccu));
+        __m256i lhzq32 = _mm256_cvtepi16_epi32(_mm256_extractf128_si256(laccu, 1));
+
+        accu = _mm256_add_epi32(accu, hhzq32);
+        accu = _mm256_add_epi32(accu, hlzq32);
+        accu = _mm256_add_epi32(accu, llzq32);
+        accu = _mm256_add_epi32(accu, lhzq32);
+    }
+    int sumi = hsum_i32_8(accu);
+    *s = (float)sumi;
+#else
+
+    int sumi = 0;
+
+    for (int i = 0; i < n / 4; i++) {
+        const int8_t* weight = (const int8_t *)(i2s_i8s + x[i]);
+        sumi += (int)y[i*4+0] * weight[0];
+        sumi += (int)y[i*4+1] * weight[1];
+        sumi += (int)y[i*4+2] * weight[2];
+        sumi += (int)y[i*4+3] * weight[3];
+    }
+    *s = (float)sumi;
+#endif
+}
+
 void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;
@@ -14367,6 +14509,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
         case GGML_TYPE_I64:
+        case GGML_TYPE_I2_S:
             // nothing to validate
             break;
         default:

ggml-quants.h

@@ -51,6 +51,7 @@ void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y,
 void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_iq3_s  (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_iq2_s  (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_i8_s   (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k, float* n);
 
 // Dequantization
 void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
@@ -99,6 +100,7 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const
 void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_iq3_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_i2_i8_s     (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);
 
 // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
 size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
@@ -121,6 +123,7 @@ size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst,
 size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_i2_s(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 
 void iq2xs_init_impl(enum ggml_type type);
 void iq2xs_free_impl(enum ggml_type type);

ggml.c

@@ -908,6 +908,21 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_bf16,
         .vec_dot_type             = GGML_TYPE_BF16,
         .nrows                    = 1,
+    },
+    [GGML_TYPE_I2_S] = {
+        .type_name                = "i2_s",
+        .blck_size                = 1,
+        .type_size                = sizeof(int8_t),
+        .is_quantized             = true,
+        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_i2_i8_s,
+        .vec_dot_type             = GGML_TYPE_I8_S,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_I8_S] = {
+        .type_name                = "i8_s",
+        .blck_size                = 1,
+        .type_size                = sizeof(int8_t),
+        .is_quantized             = true,
     }
 };
 
@@ -3056,6 +3071,9 @@ GGML_CALL size_t ggml_nbytes(const struct ggml_tensor * tensor) {
         for (int i = 0; i < GGML_MAX_DIMS; ++i) {
             nbytes += (tensor->ne[i] - 1)*tensor->nb[i];
         }
+        if(tensor->type == GGML_TYPE_I2_S){
+            nbytes = nbytes / 4 + 32;
+        }
     }
     else {
         nbytes = tensor->ne[0]*tensor->nb[0]/blck_size;
@@ -12271,6 +12289,10 @@ static void ggml_compute_forward_mul_mat_one_chunk(
     // 16 * 2, accounting for mmla kernels
     float tmp[32];
 
+    // for per-tensor quant
+    const float * scale      = (float * )((uint8_t*) (src0->data) + (ne00 * ne01 / 4));
+    const float * act_scales = (const float*) ((const char *) wdata + (ne11 * ne10));
+
     for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) {
         for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) {
             for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) {
@@ -12303,7 +12325,12 @@ static void ggml_compute_forward_mul_mat_one_chunk(
                 //}
 
                 for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) {
-                    vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot);
+                    if (src0->type == GGML_TYPE_I2_S) {
+                        vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01 / 4, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot);
+                        tmp[ir0 - iir0] = tmp[ir0 - iir0] / (act_scales[i11]) * (*scale);
+                    } else {
+                        vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot);
+                    }
                 }
 
                 for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) {
@@ -12467,8 +12494,13 @@ UseGgmlGemm1:;
             for (int64_t i13 = 0; i13 < ne13; ++i13) {
                 for (int64_t i12 = 0; i12 < ne12; ++i12) {
                     for (int64_t i11 = 0; i11 < ne11; ++i11) {
-                        from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
+                        if (src0->type == GGML_TYPE_I2_S) {
-                        wdata += row_size;
+                            float* act_scales = (float*) ((char *) wdata + (ne11 * ne10));
+                            quantize_row_i8_s((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (char *) wdata + ((i11*nb11 + i12*nb12 + i13*nb13) / 4), ne10, act_scales + i11);
+                        } else {
+                            from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
+                            wdata += row_size;
+                        }
                     }
                 }
             }
@@ -14183,6 +14215,8 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_I32:
         case GGML_TYPE_I64:
         case GGML_TYPE_F64:
+        case GGML_TYPE_I2_S:
+        case GGML_TYPE_I8_S:
         case GGML_TYPE_COUNT:
             {
                 GGML_ASSERT(false);
@@ -21325,6 +21359,7 @@ size_t ggml_quantize_chunk(
         case GGML_TYPE_IQ1_M:   result = quantize_iq1_m  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_NL:  result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_XS:  result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_I2_S:    result = quantize_i2_s   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_F16:
             {
                 size_t elemsize = sizeof(ggml_fp16_t);
@@ -21347,7 +21382,11 @@ size_t ggml_quantize_chunk(
             assert(false);
     }
 
-    GGML_ASSERT(result == nrows * row_size);
+    if (type == GGML_TYPE_I2_S) {
+        result = nrows * row_size / 4 + 32;
+    } else {
+        GGML_ASSERT(result == nrows * row_size);
+    }
 
     return result;
 }

ggml.h

@@ -377,6 +377,8 @@ extern "C" {
         GGML_TYPE_F64     = 28,
         GGML_TYPE_IQ1_M   = 29,
         GGML_TYPE_BF16    = 30,
+        GGML_TYPE_I2_S    = 31,
+        GGML_TYPE_I8_S    = 32,
         GGML_TYPE_COUNT,
     };
 

gguf-py/gguf/constants.py

@@ -148,6 +148,7 @@ class MODEL_ARCH(IntEnum):
     OLMO       = auto()
     ARCTIC     = auto()
     DEEPSEEK2  = auto()
+    BITNET     = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -199,6 +200,8 @@ class MODEL_TENSOR(IntEnum):
     ATTN_KV_B          = auto()
     ATTN_Q_A_NORM      = auto()
     ATTN_KV_A_NORM     = auto()
+    FFN_SUB_NORM       = auto()
+    ATTN_SUB_NORM      = auto()
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -236,6 +239,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.OLMO:           "olmo",
     MODEL_ARCH.ARCTIC:         "arctic",
     MODEL_ARCH.DEEPSEEK2:      "deepseek2",
+    MODEL_ARCH.BITNET:         "bitnet",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -287,6 +291,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ATTN_KV_B:          "blk.{bid}.attn_kv_b",
     MODEL_TENSOR.ATTN_Q_A_NORM:      "blk.{bid}.attn_q_a_norm",
     MODEL_TENSOR.ATTN_KV_A_NORM:     "blk.{bid}.attn_kv_a_norm",
+    MODEL_TENSOR.ATTN_SUB_NORM:      "blk.{bid}.attn_sub_norm",
+    MODEL_TENSOR.FFN_SUB_NORM:       "blk.{bid}.ffn_sub_norm",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -807,6 +813,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_SHEXP,
         MODEL_TENSOR.FFN_UP_SHEXP,
     ],
+    MODEL_ARCH.BITNET: [
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ATTN_SUB_NORM,
+        MODEL_TENSOR.FFN_SUB_NORM,
+    ],
     # TODO
 }
 

gguf-py/gguf/tensor_mapping.py

@@ -413,6 +413,14 @@ class TensorNameMap:
         MODEL_TENSOR.ATTN_KV_A_NORM: (
             "model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2
         ),
+
+        MODEL_TENSOR.ATTN_SUB_NORM: (
+            "model.layers.{bid}.self_attn.inner_attn_ln",  # bitnet
+        ),
+
+        MODEL_TENSOR.FFN_SUB_NORM: (
+            "model.layers.{bid}.mlp.ffn_layernorm",  # bitnet
+        ),
     }
 
     # architecture-specific block mappings

llama.cpp

@@ -221,6 +221,7 @@ enum llm_arch {
     LLM_ARCH_OLMO,
     LLM_ARCH_ARCTIC,
     LLM_ARCH_DEEPSEEK2,
+    LLM_ARCH_BITNET,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -259,6 +260,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_OLMO,            "olmo"         },
     { LLM_ARCH_ARCTIC,          "arctic"       },
     { LLM_ARCH_DEEPSEEK2,       "deepseek2"    },
+    { LLM_ARCH_BITNET,          "bitnet"       },
     { LLM_ARCH_UNKNOWN,         "(unknown)"    },
 };
 
@@ -494,6 +496,8 @@ enum llm_tensor {
     LLM_TENSOR_ATTN_KV_B,
     LLM_TENSOR_ATTN_Q_A_NORM,
     LLM_TENSOR_ATTN_KV_A_NORM,
+    LLM_TENSOR_ATTN_SUB_NORM,
+    LLM_TENSOR_FFN_SUB_NORM,
 };
 
 static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@@ -1107,6 +1111,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
         },
     },
+    {
+        LLM_ARCH_BITNET,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_SUB_NORM,   "blk.%d.attn_sub_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_SUB_NORM,    "blk.%d.ffn_sub_norm" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -1984,6 +2006,8 @@ struct llama_layer {
     struct ggml_tensor * attn_out_norm_b;
     struct ggml_tensor * attn_q_a_norm;
     struct ggml_tensor * attn_kv_a_norm;
+    struct ggml_tensor * attn_sub_norm;
+    struct ggml_tensor * ffn_sub_norm;
 
     // attention
     struct ggml_tensor * wq;
@@ -1997,9 +2021,9 @@ struct llama_layer {
     struct ggml_tensor * wkv_b;
 
     // attention bias
-    struct ggml_tensor * bq;
+    struct ggml_tensor * bq = nullptr;
-    struct ggml_tensor * bk;
+    struct ggml_tensor * bk = nullptr;
-    struct ggml_tensor * bv;
+    struct ggml_tensor * bv = nullptr;
     struct ggml_tensor * bo;
     struct ggml_tensor * bqkv;
 
@@ -4492,6 +4516,15 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_BITNET:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 26: model.type = e_model::MODEL_3B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         default: (void)0;
     }
 
@@ -6405,6 +6438,44 @@ static bool llm_load_tensors(
                         }
                     }
                 } break;
+            case LLM_ARCH_BITNET:
+                {
+                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff_pad = GGML_PAD(n_ff, 256);
+
+                    const int64_t n_embd_pad = GGML_PAD(n_embd, 256);
+
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd_pad, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd_pad});
+                    }
+
+                    model.layers.resize(n_layer);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm     = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,     "weight", i), {n_embd_pad});
+                        layer.attn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd_pad, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd_pad, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd_pad, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_pad, n_embd});
+
+                        layer.ffn_norm     = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM,     "weight", i), {n_embd_pad});
+                        layer.ffn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff});
+
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd_pad, n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff_pad,   n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd_pad, n_ff});
+                    }
+                } break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -11449,6 +11520,223 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_bitnet() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                // B1.K
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                // B1.V
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                llm_build_kv_store(ctx0, hparams, cparams, kv_self, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+
+                const int64_t n_ctx                 = cparams.n_ctx;
+                const int64_t n_head                = hparams.n_head;
+                const int64_t n_head_kv             = hparams.n_head_kv;
+                const int64_t n_embd_head_k         = hparams.n_embd_head_k;
+                const int64_t n_embd_k_gqa          = hparams.n_embd_k_gqa();
+                const int64_t n_embd_head_v         = hparams.n_embd_head_v;
+                const int64_t n_embd_v_gqa          = hparams.n_embd_v_gqa();
+
+                struct ggml_tensor *        q_cur   = Qcur;
+                struct ggml_tensor *        kq_mask = KQ_mask;
+                float                      kq_scale = 1.0f/sqrtf(float(n_embd_head));
+                struct ggml_tensor *  attn_sub_norm = model.layers[il].attn_sub_norm;
+                struct ggml_cgraph *          graph = gf;
+                struct ggml_tensor *             wo = model.layers[il].wo;
+                struct ggml_tensor *       cur_attn;
+                struct ggml_tensor *              q = ggml_permute(ctx0, q_cur, 0, 2, 1, 3);
+                cb(q, "q", il);
+
+                struct ggml_tensor * k =
+                    ggml_view_3d(ctx0, kv_self.k_l[il],
+                            n_embd_head_k, n_kv, n_head_kv,
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
+                            0);
+                cb(k, "k", il);
+
+                if (cparams.flash_attn) {
+
+                    // split cached v into n_head heads (not transposed)
+                    struct ggml_tensor * v =
+                        ggml_view_3d(ctx0, kv_self.v_l[il],
+                                n_embd_head_v, n_kv, n_head_kv,
+                                ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
+                                ggml_row_size(kv_self.v_l[il]->type, n_embd_head_v),
+                                0);
+                    cb(v, "v", il);
+
+                    cur_attn = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias);
+
+                    cur_attn = ggml_reshape_2d(ctx0, cur, n_embd_head_v*n_head, n_tokens);
+                } else {
+                    struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+                    cb(kq, "kq", il);
+
+                    kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias);
+                    cb(kq, "kq_soft_max_ext", il);
+
+                    GGML_ASSERT(kv_self.size == n_ctx);
+
+                    // split cached v into n_head heads
+                    struct ggml_tensor * v =
+                        ggml_view_3d(ctx0, kv_self.v_l[il],
+                                n_kv, n_embd_head_v, n_head_kv,
+                                ggml_element_size(kv_self.v_l[il])*n_ctx,
+                                ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head_v,
+                                0);
+                    cb(v, "v", il);
+
+                    struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
+                    cb(kqv, "kqv", il);
+
+                    struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+                    cb(kqv_merged, "kqv_merged", il);
+
+                    cur_attn = ggml_cont_2d(ctx0, kqv_merged, n_embd_head_v*n_head, n_tokens);
+                    cb(cur_attn, "kqv_merged_cont", il);
+                }
+
+                cur_attn = llm_build_norm(ctx0, cur_attn, hparams,
+                        attn_sub_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur_attn, "attn_sub_norm", il);
+
+                ggml_build_forward_expand(graph, cur_attn);
+
+                cur_attn = ggml_pad(ctx0, cur_attn, (256 - cur_attn->ne[0] % 256) % 256, 0, 0, 0);
+                cur = ggml_mul_mat(ctx0, wo, cur_attn);
+
+                cb(cur, "kqv_out", il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            cur = ggml_pad(ctx0, cur, (256 - cur->ne[0] % 256) % 256, 0, 0, 0);
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward forward
+            if (model.layers[il].ffn_gate_inp == nullptr) {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                struct ggml_tensor * tmp = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur);
+
+                cb(tmp, "ffn_up", il);
+
+                cur = ggml_mul_mat(ctx0, model.layers[il].ffn_gate, cur);
+
+                cb(cur, "ffn_gate", il);
+
+
+                cur = ggml_silu(ctx0, cur);
+                cb(cur, "ffn_silu", il);
+
+                cur = ggml_mul(ctx0, cur, tmp);
+                cb(cur, "ffn_gate_par", il);
+
+                cur = llm_build_norm(ctx0, cur, hparams,
+                                model.layers[il].ffn_sub_norm, NULL,
+                                LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_sub_norm", il);
+
+                cur = ggml_pad(ctx0, cur, (256 - cur->ne[0] % 256) % 256, 0, 0, 0);
+
+                cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down, cur);
+                cb(cur, "ffn_down", il);
+            }
+
+            cur = ggml_pad(ctx0, cur, (256 - cur->ne[0] % 256) % 256, 0, 0, 0);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+        return gf;
+    }
+
 };
 
 static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@@ -11671,6 +11959,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_deepseek2();
             } break;
+        case LLM_ARCH_BITNET:
+            {
+                result = llm.build_bitnet();
+            } break;
         default:
             GGML_ASSERT(false);
     }
@@ -15172,6 +15464,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         case LLAMA_FTYPE_MOSTLY_F16:  default_type = GGML_TYPE_F16;  break;
         case LLAMA_FTYPE_MOSTLY_BF16: default_type = GGML_TYPE_BF16; break;
         case LLAMA_FTYPE_ALL_F32:     default_type = GGML_TYPE_F32;  break;
+        case LLAMA_FTYPE_MOSTLY_I2_S: default_type = GGML_TYPE_I2_S; break;
 
         // K-quants
         case LLAMA_FTYPE_MOSTLY_Q2_K_S:
@@ -16440,6 +16733,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_BERT:
         case LLM_ARCH_NOMIC_BERT:
         case LLM_ARCH_STABLELM:
+        case LLM_ARCH_BITNET:
         case LLM_ARCH_QWEN:
         case LLM_ARCH_QWEN2:
         case LLM_ARCH_QWEN2MOE:

llama.h

@@ -156,6 +156,7 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_IQ4_XS        = 30, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ1_M         = 31, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_BF16          = 32, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_I2_S          = 33,
 
         LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };