Merge branch 'ggerganov:master' into refine-ggml-backend-subsystem

Makefile

@@ -571,6 +571,7 @@ ifdef LLAMA_HIP_UMA
 	MK_CPPFLAGS += -DGGML_HIP_UMA
 endif # LLAMA_HIP_UMA
 	MK_LDFLAGS  += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
+	MK_LDFLAGS  += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64
 	MK_LDFLAGS	+= -lhipblas -lamdhip64 -lrocblas
 	HIPFLAGS    += $(addprefix --offload-arch=,$(AMDGPU_TARGETS))
 	HIPFLAGS    += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)

README.md

@@ -396,7 +396,7 @@ On Mac and Linux, the homebrew package manager can be used via
 ```
 brew install llama.cpp
 ```
-The formula is automatically updated with new `llama.cpp` releases.
+The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668
 
 ### Metal Build
 

convert-hf-to-gguf.py

@@ -2840,7 +2840,12 @@ def main() -> None:
     hparams = Model.load_hparams(dir_model)
 
     with torch.inference_mode():
-        model_class = Model.from_model_architecture(hparams["architectures"][0])
+        try:
+            model_class = Model.from_model_architecture(hparams["architectures"][0])
+        except NotImplementedError:
+            logger.error(f"Model {hparams['architectures'][0]} is not supported")
+            sys.exit(1)
+
         model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file, args.no_lazy)
 
         logger.info("Set model parameters")

ggml-quants.c

@@ -6088,6 +6088,7 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
         const uint8_t * restrict q2 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
+
         const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0);
         const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4);
         const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4);
@@ -6807,6 +6808,8 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
     for (int i = 0; i < nb; ++i) {
 
         const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const uint8_t * restrict q3 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
         // Set up scales
         memcpy(aux, x[i].scales, 12);
         __m128i scales128 = lsx_set_w(
@@ -6830,8 +6833,6 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         int is  = 0;
         __m256i xvbit;
 
-        const uint8_t * restrict q3 = x[i].qs;
-        const int8_t  * restrict q8 = y[i].qs;
 
         for (int j = 0; j < QK_K/128; ++j) {
             // load low 2 bits
@@ -7404,6 +7405,9 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
     *s = vec_extract(vsumf0, 0);
 
 #elif defined __loongarch_asx
+    GGML_UNUSED(kmask1);
+    GGML_UNUSED(kmask2);
+    GGML_UNUSED(kmask3);
 
     const __m256i m4 = __lasx_xvreplgr2vr_b(0xF);
 
@@ -7416,6 +7420,11 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
 
         const uint8_t * restrict q4 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -7455,16 +7464,17 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
         __m256 vd = __lasx_xvreplfr2vr_s(d);
         acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc);
+
     }
 
     acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee));
     __m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0);
     acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1);
 
+
     ft_union fi;
     fi.i = __lsx_vpickve2gr_w(acc_m, 0);
     *s = hsum_float_8(acc) + fi.f ;
-
 #else
 
     const uint8_t * scales = (const uint8_t*)&utmp[0];
@@ -8002,6 +8012,9 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
     *s = vec_extract(vsumf0, 0);
 
 #elif defined __loongarch_asx
+    GGML_UNUSED(kmask1);
+    GGML_UNUSED(kmask2);
+    GGML_UNUSED(kmask3);
 
     const __m256i m4 = __lasx_xvreplgr2vr_b(0xF);
     const __m128i mzero = __lsx_vldi(0);
@@ -8020,6 +8033,11 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
 
         const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0]));
 
@@ -8069,10 +8087,12 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             p16_1 = lasx_madd_h(scale_1, p16_1);
 
             sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1));
+
         }
 
         __m256 vd = __lasx_xvreplfr2vr_s(d);
         acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc);
+
     }
 
     *s = hsum_float_8(acc) + summs;

ggml.c

@@ -1576,11 +1576,11 @@ do {                                                              \
 
 // F16 arithmetic is not supported by AVX, so we use F32 instead
 
-#define GGML_F32Cx8             __m256
+#define GGML_F32Cx8          __m256
 #define GGML_F32Cx8_ZERO    (__m256)__lasx_xvldi(0)
 #define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x))
 
-static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t *x) {
+static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) {
     float tmp[8];
 
     for (int i = 0; i < 8; i++) {
@@ -1589,13 +1589,14 @@ static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t *x) {
 
     return (__m256)__lasx_xvld(tmp, 0);
 }
-static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) {
+static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) {
     float arr[8];
 
     __lasx_xvst(y, arr, 0);
 
-    for (int i = 0; i < 8; i++)
+    for (int i = 0; i < 8; i++) {
         x[i] = GGML_FP32_TO_FP16(arr[i]);
+    }
 }
 #define GGML_F32Cx8_LOAD(x)     __lasx_f32cx8_load(x)
 #define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y)
@@ -1671,7 +1672,7 @@ static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) {
 #define GGML_F16_STEP 32
 #define GGML_F16_EPR  4
 
-static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) {
+static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) {
     float tmp[4];
 
     tmp[0] = GGML_FP16_TO_FP32(x[0]);
@@ -1682,7 +1683,7 @@ static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) {
     return __lsx_vld(tmp, 0);
 }
 
-static inline void __lsx_f16x4_store(ggml_fp16_t *x, __m128 y) {
+static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) {
     float arr[4];
 
     __lsx_vst(y, arr, 0);

scripts/compare-llama-bench.py

@@ -19,22 +19,22 @@
 
 # Properties by which to differentiate results per commit:
 KEY_PROPERTIES = [
-    "cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas",
-    "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads",
-    "type_k", "type_v", "no_kv_offload", "tensor_split", "n_prompt", "n_gen"
+    "cpu_info", "gpu_info", "n_gpu_layers", "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas",
+    "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "embeddings", "n_threads",
+    "type_k", "type_v", "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen"
 ]
 
 # Properties that are boolean and are converted to Yes/No for the table:
-BOOL_PROPERTIES = ["cuda", "opencl", "metal", "gpu_blas", "blas"]
+BOOL_PROPERTIES = ["cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "embeddings", "use_mmap", "no_kv_offload", "flash_attn"]
 
 # Header names for the table:
 PRETTY_NAMES = {
-    "cuda": "CUDA", "opencl": "OpenCL", "metal": "Metal", "gpu_blas": "GPU BLAS", "blas": "BLAS",
-    "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model",
-    "model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters",
-    "n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type",
-    "n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO",
-    "tensor_split": "Tensor split"
+    "cuda": "CUDA", "opencl": "OpenCL", "vulkan": "Vulkan", "kompute": "Kompute", "metal": "Metal", "sycl": "SYCL", "rpc": "RPC",
+    "gpu_blas": "GPU BLAS", "blas": "BLAS", "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model",
+    "model_size": "Model Size [GiB]", "model_n_params": "Num. of Par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size",
+    "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "n_gpu_layers": "GPU layers", "split_mode": "Split mode",
+    "main_gpu": "Main GPU", "no_kv_offload": "NKVO", "flash_attn": "FlashAttention", "tensor_split": "Tensor split",
+    "use_mmap": "Use mmap", "embeddings": "Embeddings",
 }
 
 DEFAULT_SHOW = ["model_type"]  # Always show these properties by default.

tests/CMakeLists.txt

@@ -129,8 +129,11 @@ llama_target_and_test(test-rope.cpp)
 llama_target_and_test(test-model-load-cancel.cpp  LABEL "model")
 llama_target_and_test(test-autorelease.cpp        LABEL "model")
 
-llama_target_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
-target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
+# TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
+if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
+    llama_target_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
+    target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
+endif()
 
 # dummy executable - not installed
 get_filename_component(TEST_TARGET test-c.c NAME_WE)