llama : initial ggml-backend integration (#4520)

* llama : initial ggml-backend integration

* add ggml-metal

* cuda backend can be used though ggml-backend with LLAMA_GGML_BACKEND_CUDA_TEST
access all tensor data with ggml_backend_tensor_get/set

* add ggml_backend_buffer_clear
zero-init KV cache buffer

* add ggml_backend_buffer_is_hos, used to avoid copies if possible when accesing tensor data

* disable gpu backends with ngl 0

* more accurate mlock

* unmap offloaded part of the model

* use posix_fadvise64(.., POSIX_FADV_SEQUENTIAL) to improve performance with mmap

* update quantize and lora

* update session copy/set to use ggml-backend

ggml-ci

* use posix_fadvise instead of posix_fadvise64

* ggml_backend_alloc_ctx_tensors_from_buft : remove old print

* llama_mmap::align_offset : use pointers instead of references for out parameters

* restore progress_callback behavior

* move final progress_callback call to load_all_data

* cuda : fix fprintf format string (minor)

* do not offload scales

* llama_mmap : avoid unmapping the same fragments again in the destructor

* remove unnecessary unmap

* metal : add default log function that prints to stderr, cleanup code

ggml-ci

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

.editorconfig

@@ -15,8 +15,14 @@ indent_size = 4
 [Makefile]
 indent_style = tab
 
+[scripts/*.mk]
+indent_style = tab
+
 [prompts/*.txt]
 insert_final_newline = unset
 
 [examples/server/public/*]
 indent_size = 2
+
+[examples/llama.swiftui/llama.swiftui.xcodeproj/*]
+indent_style = tab

CMakeLists.txt

@@ -91,6 +91,7 @@ set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for
 set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
                                              "llama: max. batch size for using peer access")
 option(LLAMA_HIPBLAS                         "llama: use hipBLAS"                               OFF)
+option(LLAMA_HIP_UMA                         "llama: use HIP unified memory architecture"       OFF)
 option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
 option(LLAMA_METAL                           "llama: use Metal"                                 ${LLAMA_METAL_DEFAULT})
 option(LLAMA_METAL_NDEBUG                    "llama: disable Metal debugging"                   OFF)
@@ -291,7 +292,12 @@ if (LLAMA_CUBLAS)
         add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${LLAMA_CUDA_PEER_MAX_BATCH_SIZE})
 
         if (LLAMA_STATIC)
-            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
+            if (WIN32)
+                # As of 12.3.1 CUDA Tookit for Windows does not offer a static cublas library
+                set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
+            else ()
+                set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
+            endif()
         else()
             set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
         endif()
@@ -372,6 +378,9 @@ if (LLAMA_HIPBLAS)
     if (${hipblas_FOUND} AND ${hip_FOUND})
         message(STATUS "HIP and hipBLAS found")
         add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS)
+        if (LLAMA_HIP_UMA)
+            add_compile_definitions(GGML_HIP_UMA)
+        endif()
         add_library(ggml-rocm OBJECT ggml-cuda.cu ggml-cuda.h)
         if (BUILD_SHARED_LIBS)
             set_target_properties(ggml-rocm PROPERTIES POSITION_INDEPENDENT_CODE ON)
@@ -397,57 +406,102 @@ if (LLAMA_HIPBLAS)
     endif()
 endif()
 
-if (LLAMA_ALL_WARNINGS)
-    if (NOT MSVC)
-        set(warning_flags -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
-        set(c_flags -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int -Werror=implicit-function-declaration)
-        set(cxx_flags -Wmissing-declarations -Wmissing-noreturn)
-        set(host_cxx_flags "")
+function(get_flags CCID CCVER)
+    set(C_FLAGS "")
+    set(CXX_FLAGS "")
 
-        if (CMAKE_C_COMPILER_ID MATCHES "Clang")
-            set(warning_flags ${warning_flags} -Wunreachable-code-break -Wunreachable-code-return)
-            set(host_cxx_flags ${host_cxx_flags} -Wmissing-prototypes -Wextra-semi)
+    if (CCID MATCHES "Clang")
+        set(C_FLAGS   -Wunreachable-code-break -Wunreachable-code-return)
+        set(CXX_FLAGS -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi)
 
-            if (
-                (CMAKE_C_COMPILER_ID STREQUAL "Clang"      AND CMAKE_C_COMPILER_VERSION VERSION_GREATER_EQUAL 3.8.0) OR
-                (CMAKE_C_COMPILER_ID STREQUAL "AppleClang" AND CMAKE_C_COMPILER_VERSION VERSION_GREATER_EQUAL 7.3.0)
-            )
-                set(c_flags ${c_flags} -Wdouble-promotion)
-            endif()
-        elseif (CMAKE_C_COMPILER_ID STREQUAL "GNU")
-            set(c_flags ${c_flags} -Wdouble-promotion)
-            set(host_cxx_flags ${host_cxx_flags} -Wno-array-bounds)
-
-            if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 7.1.0)
-                set(host_cxx_flags ${host_cxx_flags} -Wno-format-truncation)
-            endif()
-            if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 8.1.0)
-                set(host_cxx_flags ${host_cxx_flags} -Wextra-semi)
-            endif()
+        if (
+            (CCID STREQUAL "Clang"      AND CCVER VERSION_GREATER_EQUAL 3.8.0) OR
+            (CCID STREQUAL "AppleClang" AND CCVER VERSION_GREATER_EQUAL 7.3.0)
+        )
+            set(C_FLAGS ${C_FLAGS} -Wdouble-promotion)
+        endif()
+    elseif (CCID STREQUAL "GNU")
+        set(C_FLAGS   -Wdouble-promotion)
+        set(CXX_FLAGS -Wno-array-bounds)
+
+        if (CCVER VERSION_GREATER_EQUAL 7.1.0)
+            set(CXX_FLAGS ${CXX_FLAGS} -Wno-format-truncation)
+        endif()
+        if (CCVER VERSION_GREATER_EQUAL 8.1.0)
+            set(CXX_FLAGS ${CXX_FLAGS} -Wextra-semi)
         endif()
-    else()
-        # todo : msvc
     endif()
 
-    set(c_flags   ${c_flags}   ${warning_flags})
-    set(cxx_flags ${cxx_flags} ${warning_flags})
-    add_compile_options("$<$<COMPILE_LANGUAGE:C>:${c_flags}>"
-                        "$<$<COMPILE_LANGUAGE:CXX>:${cxx_flags}>"
-                        "$<$<COMPILE_LANGUAGE:CXX>:${host_cxx_flags}>")
+    set(GF_C_FLAGS   ${C_FLAGS}   PARENT_SCOPE)
+    set(GF_CXX_FLAGS ${CXX_FLAGS} PARENT_SCOPE)
+endfunction()
 
+if (LLAMA_ALL_WARNINGS)
+    if (NOT MSVC)
+        set(WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
+        set(C_FLAGS       -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes
+                          -Werror=implicit-int -Werror=implicit-function-declaration)
+        set(CXX_FLAGS     -Wmissing-declarations -Wmissing-noreturn)
+
+        set(C_FLAGS   ${WARNING_FLAGS} ${C_FLAGS})
+        set(CXX_FLAGS ${WARNING_FLAGS} ${CXX_FLAGS})
+
+        get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION})
+
+        add_compile_options("$<$<COMPILE_LANGUAGE:C>:${C_FLAGS};${GF_C_FLAGS}>"
+                            "$<$<COMPILE_LANGUAGE:CXX>:${CXX_FLAGS};${GF_CXX_FLAGS}>")
+    else()
+        # todo : msvc
+        set(C_FLAGS   "")
+        set(CXX_FLAGS "")
+    endif()
 endif()
 
-if (NOT MSVC)
-    set(cuda_flags -Wno-pedantic)
-endif()
-set(cuda_flags ${cxx_flags} -use_fast_math ${cuda_flags})
+if (LLAMA_CUBLAS)
+    set(CUDA_FLAGS ${CXX_FLAGS} -use_fast_math)
+    if (NOT MSVC)
+        set(CUDA_FLAGS ${CUDA_FLAGS} -Wno-pedantic)
+    endif()
 
-list(JOIN host_cxx_flags " " cuda_host_flags)  # pass host compiler flags as a single argument
-if (NOT cuda_host_flags STREQUAL "")
-    set(cuda_flags ${cuda_flags} -Xcompiler ${cuda_host_flags})
-endif()
+    if (LLAMA_ALL_WARNINGS AND NOT MSVC)
+        set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c)
+        if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "")
+            set(NVCC_CMD ${NVCC_CMD} -ccbin ${CMAKE_CUDA_HOST_COMPILER})
+        endif()
 
-add_compile_options("$<$<COMPILE_LANGUAGE:CUDA>:${cuda_flags}>")
+        execute_process(
+            COMMAND ${NVCC_CMD} -Xcompiler --version
+            OUTPUT_VARIABLE CUDA_CCFULLVER
+            ERROR_QUIET
+        )
+
+        if (NOT CUDA_CCFULLVER MATCHES clang)
+            set(CUDA_CCID "GNU")
+            execute_process(
+                COMMAND ${NVCC_CMD} -Xcompiler "-dumpfullversion -dumpversion"
+                OUTPUT_VARIABLE CUDA_CCVER
+                ERROR_QUIET
+            )
+        else()
+            if (CUDA_CCFULLVER MATCHES Apple)
+                set(CUDA_CCID "AppleClang")
+            else()
+                set(CUDA_CCID "Clang")
+            endif()
+            string(REGEX REPLACE "^.* version ([0-9.]*).*$" "\\1" CUDA_CCVER ${CUDA_CCFULLVER})
+        endif()
+
+        message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}")
+
+        get_flags(${CUDA_CCID} ${CUDA_CCVER})
+        list(JOIN GF_CXX_FLAGS " " CUDA_CXX_FLAGS)  # pass host compiler flags as a single argument
+        if (NOT CUDA_CXX_FLAGS STREQUAL "")
+            set(CUDA_FLAGS ${CUDA_FLAGS} -Xcompiler ${CUDA_CXX_FLAGS})
+        endif()
+    endif()
+
+    add_compile_options("$<$<COMPILE_LANGUAGE:CUDA>:${CUDA_FLAGS}>")
+endif()
 
 if (WIN32)
     add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
@@ -471,6 +525,7 @@ endif()
 execute_process(
     COMMAND ${CMAKE_C_COMPILER} ${CMAKE_EXE_LINKER_FLAGS} -Wl,-v
     ERROR_VARIABLE output
+    OUTPUT_QUIET
 )
 if (output MATCHES "dyld-1015\.7")
     add_compile_definitions(HAVE_BUGGY_APPLE_LINKER)
@@ -593,6 +648,11 @@ else()
     message(STATUS "Unknown architecture")
 endif()
 
+if (MINGW)
+    # Target Windows 8 for PrefetchVirtualMemory
+    add_compile_definitions(_WIN32_WINNT=0x602)
+endif()
+
 #
 # POSIX conformance
 #

Makefile

@@ -26,20 +26,6 @@ ifndef UNAME_M
 UNAME_M := $(shell uname -m)
 endif
 
-ifeq '' '$(findstring clang,$(shell $(CC) --version))'
-	CC_IS_GCC=1
-	CC_VER := $(shell $(CC) -dumpfullversion -dumpversion | awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
-else
-	CC_IS_CLANG=1
-	ifeq '' '$(findstring Apple,$(shell $(CC) --version))'
-		CC_IS_LLVM_CLANG=1
-	else
-		CC_IS_APPLE_CLANG=1
-	endif
-	CC_VER := $(shell $(CC) --version | sed -n 's/^.* version \([0-9.]*\).*$$/\1/p' \
-				| awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
-endif
-
 # Mac OS + Arm can report x86_64
 # ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
 ifeq ($(UNAME_S),Darwin)
@@ -79,7 +65,7 @@ test: $(TEST_TARGETS)
 			./$$test_target; \
 		fi; \
 		if [ $$? -ne 0 ]; then \
-			printf 'Test $$test_target FAILED!\n\n' $$test_target; \
+			printf 'Test %s FAILED!\n\n' $$test_target; \
 			failures=$$(( failures + 1 )); \
 		else \
 			printf 'Test %s passed.\n\n' $$test_target; \
@@ -121,12 +107,12 @@ MK_CXXFLAGS = -std=c++11 -fPIC
 
 # -Ofast tends to produce faster code, but may not be available for some compilers.
 ifdef LLAMA_FAST
-MK_CFLAGS        += -Ofast
-MK_HOST_CXXFLAGS += -Ofast
-MK_CUDA_CXXFLAGS += -O3
+MK_CFLAGS     += -Ofast
+HOST_CXXFLAGS += -Ofast
+MK_NVCCFLAGS  += -O3
 else
-MK_CFLAGS        += -O3
-MK_CXXFLAGS      += -O3
+MK_CFLAGS     += -O3
+MK_CXXFLAGS   += -O3
 endif
 
 # clock_gettime came in POSIX.1b (1993)
@@ -220,30 +206,6 @@ MK_CFLAGS    += $(WARN_FLAGS) -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmis
 				-Werror=implicit-function-declaration
 MK_CXXFLAGS  += $(WARN_FLAGS) -Wmissing-declarations -Wmissing-noreturn
 
-ifeq ($(CC_IS_CLANG), 1)
-	# clang options
-	MK_CFLAGS        += -Wunreachable-code-break -Wunreachable-code-return
-	MK_HOST_CXXFLAGS += -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi
-
-	ifneq '' '$(and $(CC_IS_LLVM_CLANG),$(filter 1,$(shell expr $(CC_VER) \>= 030800)))'
-		MK_CFLAGS += -Wdouble-promotion
-	endif
-	ifneq '' '$(and $(CC_IS_APPLE_CLANG),$(filter 1,$(shell expr $(CC_VER) \>= 070300)))'
-		MK_CFLAGS += -Wdouble-promotion
-	endif
-else
-	# gcc options
-	MK_CFLAGS        += -Wdouble-promotion
-	MK_HOST_CXXFLAGS += -Wno-array-bounds
-
-	ifeq ($(shell expr $(CC_VER) \>= 070100), 1)
-		MK_HOST_CXXFLAGS += -Wno-format-truncation
-	endif
-	ifeq ($(shell expr $(CC_VER) \>= 080100), 1)
-		MK_HOST_CXXFLAGS += -Wextra-semi
-	endif
-endif
-
 # this version of Apple ld64 is buggy
 ifneq '' '$(findstring dyld-1015.7,$(shell $(CC) $(LDFLAGS) -Wl,-v 2>&1))'
 	MK_CPPFLAGS += -DHAVE_BUGGY_APPLE_LINKER
@@ -294,8 +256,8 @@ ifndef RISCV
 
 ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
 	# Use all CPU extensions that are available:
-	MK_CFLAGS   += -march=native -mtune=native
-	MK_HOST_CXXFLAGS += -march=native -mtune=native
+	MK_CFLAGS     += -march=native -mtune=native
+	HOST_CXXFLAGS += -march=native -mtune=native
 
 	# Usage AVX-only
 	#MK_CFLAGS   += -mfma -mf16c -mavx
@@ -306,12 +268,15 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
 	#MK_CXXFLAGS += -mssse3
 endif
 
-# The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves.
-# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412
-# https://github.com/ggerganov/llama.cpp/issues/2922
 ifneq '' '$(findstring mingw,$(shell $(CC) -dumpmachine))'
+	# The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves.
+	# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412
+	# https://github.com/ggerganov/llama.cpp/issues/2922
 	MK_CFLAGS   += -Xassembler -muse-unaligned-vector-move
 	MK_CXXFLAGS += -Xassembler -muse-unaligned-vector-move
+
+	# Target Windows 8 for PrefetchVirtualMemory
+	MK_CPPFLAGS += -D_WIN32_WINNT=0x602
 endif
 
 ifneq ($(filter aarch64%,$(UNAME_M)),)
@@ -395,61 +360,64 @@ ifdef LLAMA_CUBLAS
 	MK_CPPFLAGS  += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
 	MK_LDFLAGS   += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
 	OBJS         += ggml-cuda.o
-	NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math
+	MK_NVCCFLAGS  = --forward-unknown-to-host-compiler -use_fast_math
+
+ifdef LLAMA_DEBUG
+	MK_NVCCFLAGS += -lineinfo
+endif
+
 ifdef LLAMA_CUDA_NVCC
 	NVCC = $(LLAMA_CUDA_NVCC)
 else
 	NVCC = nvcc
 endif #LLAMA_CUDA_NVCC
 ifdef CUDA_DOCKER_ARCH
-	NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
-else ifdef CUDA_POWER_ARCH
-	NVCCFLAGS +=
-else
-	NVCCFLAGS += -arch=native
+	MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
+else ifndef CUDA_POWER_ARCH
+	MK_NVCCFLAGS += -arch=native
 endif # CUDA_DOCKER_ARCH
 ifdef LLAMA_CUDA_FORCE_DMMV
-	NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
+	MK_NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
 endif # LLAMA_CUDA_FORCE_DMMV
 ifdef LLAMA_CUDA_FORCE_MMQ
-	NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ
+	MK_NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ
 endif # LLAMA_CUDA_FORCE_MMQ
 ifdef LLAMA_CUDA_DMMV_X
-	NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
+	MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
 else
-	NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
+	MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
 endif # LLAMA_CUDA_DMMV_X
 ifdef LLAMA_CUDA_MMV_Y
-	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
+	MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
 else ifdef LLAMA_CUDA_DMMV_Y
-	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility
+	MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility
 else
-	NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
+	MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
 endif # LLAMA_CUDA_MMV_Y
 ifdef LLAMA_CUDA_F16
-	NVCCFLAGS += -DGGML_CUDA_F16
+	MK_NVCCFLAGS += -DGGML_CUDA_F16
 endif # LLAMA_CUDA_F16
 ifdef LLAMA_CUDA_DMMV_F16
-	NVCCFLAGS += -DGGML_CUDA_F16
+	MK_NVCCFLAGS += -DGGML_CUDA_F16
 endif # LLAMA_CUDA_DMMV_F16
 ifdef LLAMA_CUDA_KQUANTS_ITER
-	NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
+	MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
 else
-	NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
+	MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
 endif
 ifdef LLAMA_CUDA_PEER_MAX_BATCH_SIZE
-	NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(LLAMA_CUDA_PEER_MAX_BATCH_SIZE)
+	MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(LLAMA_CUDA_PEER_MAX_BATCH_SIZE)
 else
-	NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128
+	MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128
 endif # LLAMA_CUDA_PEER_MAX_BATCH_SIZE
 #ifdef LLAMA_CUDA_CUBLAS
-#	NVCCFLAGS += -DGGML_CUDA_CUBLAS
+#	MK_NVCCFLAGS += -DGGML_CUDA_CUBLAS
 #endif # LLAMA_CUDA_CUBLAS
 ifdef LLAMA_CUDA_CCBIN
-	NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
+	MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
 endif
 ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
-	$(NVCC) $(NVCCFLAGS) -c $< -o $@
+	$(NVCC) $(BASE_CXXFLAGS) $(NVCCFLAGS) -Wno-pedantic -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
 endif # LLAMA_CUBLAS
 
 ifdef LLAMA_CLBLAST
@@ -471,9 +439,15 @@ ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
 endif # LLAMA_CLBLAST
 
 ifdef LLAMA_HIPBLAS
-	ROCM_PATH	?= /opt/rocm
-	HIPCC	    ?= $(ROCM_PATH)/bin/hipcc
-	GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
+
+	ifeq ($(wildcard /opt/rocm),)
+		ROCM_PATH	?= /usr
+		GPU_TARGETS ?= $(shell $(shell which amdgpu-arch))
+	else
+		ROCM_PATH	?= /opt/rocm
+		GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
+	endif
+	HIPCC                   ?= $(ROCM_PATH)/bin/hipcc
 	LLAMA_CUDA_DMMV_X       ?= 32
 	LLAMA_CUDA_MMV_Y        ?= 1
 	LLAMA_CUDA_KQUANTS_ITER ?= 2
@@ -511,16 +485,22 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h
 	$(CC) $(CFLAGS) -c $< -o $@
 endif # LLAMA_MPI
 
-# combine build flags with cmdline overrides
-override CFLAGS        := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS)
-override CXXFLAGS      := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
-override CUDA_CXXFLAGS := $(MK_CUDA_CXXFLAGS) $(CUDA_CXXFLAGS)
-override HOST_CXXFLAGS := $(MK_HOST_CXXFLAGS) $(HOST_CXXFLAGS)
-override LDFLAGS       := $(MK_LDFLAGS) $(LDFLAGS)
+GF_CC := $(CC)
+include scripts/get-flags.mk
 
-# save CXXFLAGS before we add host-only options
-NVCCFLAGS := $(NVCCFLAGS) $(CXXFLAGS) $(CUDA_CXXFLAGS) -Wno-pedantic -Xcompiler "$(HOST_CXXFLAGS)"
-override CXXFLAGS += $(HOST_CXXFLAGS)
+# combine build flags with cmdline overrides
+override CFLAGS    := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(GF_CFLAGS) $(CFLAGS)
+BASE_CXXFLAGS      := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
+override CXXFLAGS  := $(BASE_CXXFLAGS) $(HOST_CXXFLAGS) $(GF_CXXFLAGS)
+override NVCCFLAGS := $(MK_NVCCFLAGS) $(NVCCFLAGS)
+override LDFLAGS   := $(MK_LDFLAGS) $(LDFLAGS)
+
+# identify CUDA host compiler
+ifdef LLAMA_CUBLAS
+GF_CC := $(NVCC) $(NVCCFLAGS) 2>/dev/null .c -Xcompiler
+include scripts/get-flags.mk
+CUDA_CXXFLAGS := $(GF_CXXFLAGS)
+endif
 
 #
 # Print build information
@@ -730,16 +710,16 @@ tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS)
 tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS)

README.md

@@ -10,10 +10,11 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 ### Hot topics
 
-- **llama.h API change for handling KV cache offloading and data type: https://github.com/ggerganov/llama.cpp/pull/4309**
-- Using `llama.cpp` with AWS instances: https://github.com/ggerganov/llama.cpp/discussions/4225
+- Collecting Apple Silicon performance stats:
+  - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167
+  - A-series: https://github.com/ggerganov/llama.cpp/discussions/4508
+- Added Mixtral support: https://github.com/ggerganov/llama.cpp/pull/4406
 - Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
-- Collecting Apple Silicon performance stats: https://github.com/ggerganov/llama.cpp/discussions/4167
 
 ----
 
@@ -96,7 +97,18 @@ as the main playground for developing new features for the [ggml](https://github
 - [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410)
 - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417)
 - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553)
+- [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
 - [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586)
+- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
+- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
+- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
+
+**Multimodal models:**
+
+- [x] [Llava 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e)
+- [x] [Bakllava](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
+- [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
+- [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
 
 
 **Bindings:**
@@ -420,14 +432,15 @@ Building the program with BLAS support may lead to some performance improvements
     ```bash
     make LLAMA_HIPBLAS=1
     ```
-  - Using `CMake` for Linux:
+  - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
     ```bash
-    mkdir build
-    cd build
-    CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ cmake .. -DLLAMA_HIPBLAS=ON
-    cmake --build .
+    CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \
+        cmake -H. -Bbuild -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
+        && cmake --build build -- -j 16
     ```
-  - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS):
+    On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON"`.
+    However, this hurts performance for non-integrated GPUs.
+  - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
     ```bash
     set PATH=%HIP_PATH%\bin;%PATH%
     mkdir build
@@ -436,10 +449,11 @@ Building the program with BLAS support may lead to some performance improvements
     cmake --build .
     ```
     Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
+    Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`.
 
 
   The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
-  If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3.
+  If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
   The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
 
   | Option                  | Legal values           | Default | Description |
@@ -970,6 +984,8 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /
 - There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
 - Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
 - See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
+- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
+- Matrix multiplication is unconventional: [`z = ggml_mul_mat(ctx, x, y)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means `zT = x @ yT`
 
 ### Docs
 

common/common.cpp

@@ -656,6 +656,10 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
         } else if (arg == "-h" || arg == "--help") {
             return false;
 
+        } else if (arg == "--version") {
+            fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+            fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
+            exit(0);
         } else if (arg == "--random-prompt") {
             params.random_prompt = true;
         } else if (arg == "--in-prefix-bos") {
@@ -794,6 +798,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("\n");
     printf("options:\n");
     printf("  -h, --help            show this help message and exit\n");
+    printf("      --version         show version and build info\n");
     printf("  -i, --interactive     run in interactive mode\n");
     printf("  --interactive-first   run in interactive mode and wait for input right away\n");
     printf("  -ins, --instruct      run in instruction mode (use with Alpaca models)\n");
@@ -915,7 +920,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  -m FNAME, --model FNAME\n");
     printf("                        model path (default: %s)\n", params.model.c_str());
     printf("  -md FNAME, --model-draft FNAME\n");
-    printf("                        draft model for speculative decoding (default: %s)\n", params.model.c_str());
+    printf("                        draft model for speculative decoding\n");
     printf("  -ld LOGDIR, --logdir LOGDIR\n");
     printf("                        path under which to save YAML logs (no logging if unset)\n");
     printf("  --override-kv KEY=TYPE:VALUE\n");

common/log.h

@@ -61,13 +61,13 @@
 //  #define LOG_TARGET stderr
 //  #include "log.h"
 //
-//  The log target can also be redirected to a diffrent function
+//  The log target can also be redirected to a different function
 //  like so:
 //
-//  #define LOG_TARGET log_handler_diffrent()
+//  #define LOG_TARGET log_handler_different()
 //  #include "log.h"
 //
-//  FILE* log_handler_diffrent()
+//  FILE* log_handler_different()
 //  {
 //      return stderr;
 //  }
@@ -421,7 +421,7 @@ inline FILE *log_handler2_impl(bool change = false, LogTriState append = LogTriS
 
 // Disables logs entirely at runtime.
 //  Makes LOG() and LOG_TEE() produce no output,
-//  untill enabled back.
+//  until enabled back.
 #define log_disable() log_disable_impl()
 
 // INTERNAL, DO NOT USE

common/train.cpp

@@ -71,7 +71,7 @@ void free_random_uniform_distribution(struct random_uniform_distribution * rnd)
 
 struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct random_normal_distribution * rnd) {
     float scale = 1.0f; // xavier
-    switch (tensor->n_dims) {
+    switch (ggml_n_dims(tensor)) {
         case 1:
             scale /= sqrtf((float) tensor->ne[0]);
             for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
@@ -119,7 +119,7 @@ struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct
 }
 
 struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd) {
-    switch (tensor->n_dims) {
+    switch (ggml_n_dims(tensor)) {
         case 1:
             for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
                 float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]);
@@ -183,25 +183,27 @@ float fclamp(const float v, const float min, const float max) {
 }
 
 void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) {
-    GGML_ASSERT(tensor->n_dims == 1);
     GGML_ASSERT(tensor->ne[0] == ne0);
+    GGML_ASSERT(tensor->ne[1] == 1);
+    GGML_ASSERT(tensor->ne[2] == 1);
+    GGML_ASSERT(tensor->ne[3] == 1);
 }
 
 void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1) {
-    GGML_ASSERT(tensor->n_dims == 2);
     GGML_ASSERT(tensor->ne[0] == ne0);
     GGML_ASSERT(tensor->ne[1] == ne1);
+    GGML_ASSERT(tensor->ne[2] == 1);
+    GGML_ASSERT(tensor->ne[3] == 1);
 }
 
 void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2) {
-    GGML_ASSERT(tensor->n_dims == 3);
     GGML_ASSERT(tensor->ne[0] == ne0);
     GGML_ASSERT(tensor->ne[1] == ne1);
     GGML_ASSERT(tensor->ne[2] == ne2);
+    GGML_ASSERT(tensor->ne[3] == 1);
 }
 
 void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {
-    GGML_ASSERT(tensor->n_dims == 4);
     GGML_ASSERT(tensor->ne[0] == ne0);
     GGML_ASSERT(tensor->ne[1] == ne1);
     GGML_ASSERT(tensor->ne[2] == ne2);
@@ -225,8 +227,8 @@ int64_t get_example_targets_batch(
     bool                   sample_random_offsets
 ) {
     GGML_ASSERT(samples_count > 0);
-    GGML_ASSERT(tokens_input->n_dims  == 2);
-    GGML_ASSERT(target_probs->n_dims  == 3);
+    GGML_ASSERT(ggml_is_matrix(tokens_input));
+    GGML_ASSERT(ggml_is_3d(target_probs));
     int64_t n_vocab  = target_probs->ne[0];
     int64_t n_tokens = tokens_input->ne[0];
     int64_t n_batch  = tokens_input->ne[1];

convert-hf-to-gguf.py

@@ -77,8 +77,18 @@ class Model:
             self.gguf_writer.add_embedding_length(n_embd)
         if (n_ff := self.hparams.get("intermediate_size")) is not None:
             self.gguf_writer.add_feed_forward_length(n_ff)
-        if (n_head := self.hparams.get("num_attention_head")) is not None:
+        if (n_head := self.hparams.get("num_attention_heads")) is not None:
             self.gguf_writer.add_head_count(n_head)
+        if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
+            self.gguf_writer.add_head_count_kv(n_head_kv)
+
+        if (n_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
+            self.gguf_writer.add_layer_norm_rms_eps(n_rms_eps)
+        if (n_experts := self.hparams.get("num_local_experts")) is not None:
+            self.gguf_writer.add_expert_count(n_experts)
+        if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
+            self.gguf_writer.add_expert_used_count(n_experts_used)
+
         self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
 
     def write_tensors(self):
@@ -170,6 +180,10 @@ class Model:
             return StableLMModel
         if model_architecture == "QWenLMHeadModel":
             return QwenModel
+        if model_architecture == "MixtralForCausalLM":
+            return MixtralModel
+        if model_architecture == "PhiForCausalLM":
+            return Phi2Model
         return Model
 
     def _is_model_safetensors(self) -> bool:
@@ -207,6 +221,10 @@ class Model:
             return gguf.MODEL_ARCH.STABLELM
         if arch == "QWenLMHeadModel":
             return gguf.MODEL_ARCH.QWEN
+        if arch == "MixtralForCausalLM":
+            return gguf.MODEL_ARCH.LLAMA
+        if arch == "PhiForCausalLM":
+            return gguf.MODEL_ARCH.PHI2
 
         raise NotImplementedError(f'Architecture "{arch}" not supported!')
 
@@ -837,6 +855,11 @@ class StableLMModel(Model):
         self.gguf_writer.add_layer_norm_eps(1e-5)
 
 
+class MixtralModel(Model):
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+
 class QwenModel(Model):
     @staticmethod
     def token_bytes_to_string(b):
@@ -961,6 +984,24 @@ class QwenModel(Model):
             print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
             self.gguf_writer.add_tensor(new_name, data)
 
+
+class Phi2Model(Model):
+    def set_gguf_parameters(self):
+        block_count = self.hparams["n_layer"]
+
+        self.gguf_writer.add_name("Phi2")
+        self.gguf_writer.add_context_length(self.hparams["n_positions"])
+        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
+        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_head_count(self.hparams["n_head"])
+        self.gguf_writer.add_head_count_kv(self.hparams["n_head"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"])
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_add_bos_token(False)
+
+
 ###### CONVERSION LOGIC ######
 
 

convert-lora-to-ggml.py

@@ -3,7 +3,6 @@ from __future__ import annotations
 
 import json
 import os
-import re
 import struct
 import sys
 from typing import Any, BinaryIO, Sequence
@@ -11,43 +10,15 @@ from typing import Any, BinaryIO, Sequence
 import numpy as np
 import torch
 
+from pathlib import Path
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+
 NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
 
 
-HF_SUBLAYER_TO_GGML = {
-    "self_attn.q_proj": "attn_q",
-    "self_attn.k_proj": "attn_k",
-    "self_attn.v_proj": "attn_v",
-    "self_attn.o_proj": "attn_output",
-    "mlp.gate_proj": "ffn_gate",
-    "mlp.down_proj": "ffn_down",
-    "mlp.up_proj": "ffn_up",
-    "input_layernorm": "attn_norm",
-    "post_attention_layernorm": "ffn_norm",
-}
-
-
-def translate_tensor_name(t: str) -> str:
-    match = re.match(r".*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight", t)
-    if match:
-        nn = match.group(1)
-        sub_layer = match.group(2)
-        lora_type = match.group(3)
-
-        sub_layer_renamed = HF_SUBLAYER_TO_GGML.get(sub_layer)
-        if sub_layer_renamed is None:
-            print(f"Error: unrecognized sub-layer {sub_layer} in tensor {t}")
-            sys.exit(1)
-
-        output_string = (
-            f"blk.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
-        )
-        return output_string
-    else:
-        print(f"Error: unrecognized tensor {t}")
-        sys.exit(1)
-
-
 def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
     fout.write(b"ggla"[::-1])  # magic (ggml lora)
     fout.write(struct.pack("i", 1))  # file version
@@ -61,9 +32,7 @@ def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
     fout.write(struct.pack("i", int(params["lora_alpha"])))
 
 
-def write_tensor_header(
-    self, name: str, shape: Sequence[int], data_type: np.dtype[Any]
-) -> None:
+def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None:
     sname = name.encode("utf-8")
     fout.write(
         struct.pack(
@@ -78,11 +47,12 @@ def write_tensor_header(
     fout.seek((fout.tell() + 31) & -32)
 
 
-if len(sys.argv) != 2:
-    print(f"Usage: python {sys.argv[0]} <path>")
+if len(sys.argv) < 2:
+    print(f"Usage: python {sys.argv[0]} <path> [arch]")
     print(
         "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
     )
+    print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
     sys.exit(1)
 
 input_json = os.path.join(sys.argv[1], "adapter_config.json")
@@ -90,6 +60,14 @@ input_model = os.path.join(sys.argv[1], "adapter_model.bin")
 output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
 
 model = torch.load(input_model, map_location="cpu")
+arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
+
+if arch_name not in gguf.MODEL_ARCH_NAMES.values():
+    print(f"Error: unsupported architecture {arch_name}")
+    sys.exit(1)
+
+arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
+name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
 
 with open(input_json, "r") as f:
     params = json.load(f)
@@ -117,6 +95,7 @@ with open(output_path, "wb") as fout:
 
     write_file_header(fout, params)
     for k, v in model.items():
+        orig_k = k
         if k.endswith(".default.weight"):
             k = k.replace(".default.weight", ".weight")
         if k in ["llama_proj.weight", "llama_proj.bias"]:
@@ -129,7 +108,32 @@ with open(output_path, "wb") as fout:
             v = v.float()
 
         t = v.detach().numpy()
-        tname = translate_tensor_name(k)
+
+        prefix = "base_model.model."
+        if k.startswith(prefix):
+            k = k[len(prefix) :]
+
+        lora_suffixes = (".lora_A.weight", ".lora_B.weight")
+        if k.endswith(lora_suffixes):
+            suffix = k[-len(lora_suffixes[0]):]
+            k = k[: -len(lora_suffixes[0])]
+        else:
+            print(f"Error: unrecognized tensor name {orig_k}")
+            sys.exit(1)
+
+        tname = name_map.get_name(k)
+        if tname is None:
+            print(f"Error: could not map tensor name {orig_k}")
+            print(" Note: the arch parameter must be specified if the model is not llama")
+            sys.exit(1)
+
+        if suffix == ".lora_A.weight":
+            tname += ".weight.loraA"
+        elif suffix == ".lora_B.weight":
+            tname += ".weight.loraB"
+        else:
+            assert False
+
         print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
         write_tensor_header(fout, tname, t.shape, t.dtype)
         t.tofile(fout)

convert.py

@@ -10,6 +10,7 @@ import itertools
 import json
 import math
 import mmap
+import os
 import pickle
 import re
 import signal
@@ -18,15 +19,15 @@ import sys
 import time
 import zipfile
 from abc import ABCMeta, abstractmethod
+from collections import OrderedDict
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
 from dataclasses import dataclass
 from pathlib import Path
-from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar
+from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, Optional, TypeVar, cast
 
 import numpy as np
 from sentencepiece import SentencePieceProcessor
 
-import os
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
@@ -42,6 +43,7 @@ NDArray: TypeAlias = 'np.ndarray[Any, Any]'
 ARCH = gguf.MODEL_ARCH.LLAMA
 
 DEFAULT_CONCURRENCY = 8
+
 #
 # data types
 #
@@ -62,10 +64,10 @@ class UnquantizedDataType(DataType):
     pass
 
 
-DT_F16  = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
-DT_F32  = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
-DT_I32  = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
-DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
+DT_F16  = UnquantizedDataType('F16',  dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
+DT_F32  = UnquantizedDataType('F32',  dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
+DT_I32  = UnquantizedDataType('I32',  dtype = np.dtype(np.int16),   valid_conversions = [])
+DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16),  valid_conversions = ['F32', 'F16', 'Q8_0'])
 
 
 @dataclass(frozen=True)
@@ -151,14 +153,16 @@ GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
 
 @dataclass
 class Params:
-    n_vocab:    int
-    n_embd:     int
-    n_layer:    int
-    n_ctx:      int
-    n_ff:       int
-    n_head:     int
-    n_head_kv:  int
-    f_norm_eps: float
+    n_vocab:        int
+    n_embd:         int
+    n_layer:        int
+    n_ctx:          int
+    n_ff:           int
+    n_head:         int
+    n_head_kv:      int
+    n_experts:      int | None = None
+    n_experts_used: int | None = None
+    f_norm_eps:     float | None = None
 
     rope_scaling_type: gguf.RopeScalingType | None = None
     f_rope_freq_base: float | None = None
@@ -233,6 +237,13 @@ class Params:
             raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
                             "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
 
+        n_experts      = None
+        n_experts_used = None
+
+        if "num_local_experts" in config:
+            n_experts = config["num_local_experts"]
+            n_experts_used = config["num_experts_per_tok"]
+
         return Params(
             n_vocab           = config["vocab_size"],
             n_embd            = config["hidden_size"],
@@ -241,6 +252,8 @@ class Params:
             n_ff              = config["intermediate_size"],
             n_head            = (n_head := config["num_attention_heads"]),
             n_head_kv         = config.get("num_key_value_heads", n_head),
+            n_experts         = n_experts,
+            n_experts_used    = n_experts_used,
             f_norm_eps        = config["rms_norm_eps"],
             f_rope_freq_base  = config.get("rope_theta"),
             rope_scaling_type = rope_scaling_type,
@@ -255,8 +268,15 @@ class Params:
     def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
         config = json.load(open(config_path))
 
+        n_experts      = None
+        n_experts_used = None
+        f_rope_freq_base = None
+
         # hack to determine LLaMA v1 vs v2 vs CodeLlama
-        if config.get("rope_theta") == 1000000:
+        if config.get("moe"):
+            # Mixtral
+            n_ctx = 32768
+        elif config.get("rope_theta") == 1000000:
             # CodeLlama
             n_ctx = 16384
         elif config["norm_eps"] == 1e-05:
@@ -266,16 +286,27 @@ class Params:
             # LLaMA v1
             n_ctx = 2048
 
+        if "layers.0.feed_forward.w1.weight" in model:
+            n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]
+
+        if config.get("moe"):
+            n_ff = model["layers.0.feed_forward.experts.0.w1.weight"].shape[0]
+            n_experts      = config["moe"]["num_experts"]
+            n_experts_used = config["moe"]["num_experts_per_tok"]
+            f_rope_freq_base = 1e6
+
         return Params(
             n_vocab          = model["tok_embeddings.weight"].shape[0],
             n_embd           = config["dim"],
             n_layer          = config["n_layers"],
             n_ctx            = n_ctx,
-            n_ff             = model["layers.0.feed_forward.w1.weight"].shape[0],
+            n_ff             = n_ff,
             n_head           = (n_head := config["n_heads"]),
             n_head_kv        = config.get("n_kv_heads", n_head),
+            n_experts        = n_experts,
+            n_experts_used   = n_experts_used,
             f_norm_eps       = config["norm_eps"],
-            f_rope_freq_base = config.get("rope_theta"),
+            f_rope_freq_base = config.get("rope_theta", f_rope_freq_base),
         )
 
     @staticmethod
@@ -297,127 +328,138 @@ class Params:
         return params
 
 
-#
-# vocab
-#
+class VocabLoader:
+    def __init__(self, params: Params, fname_tokenizer: Path) -> None:
+        try:
+            from transformers import AutoTokenizer
+        except ImportError as e:
+            raise ImportError(
+                "To use VocabLoader, please install the `transformers` package. "
+                "You can install it with `pip install transformers`."
+            ) from e
 
-class BpeVocab:
-    def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
-        self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
-        added_tokens: dict[str, int]
-        if fname_added_tokens is not None:
-            # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
-            added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
+        try:
+            self.tokenizer = AutoTokenizer.from_pretrained(str(fname_tokenizer), trust_remote_code=True)
+        except ValueError:
+            self.tokenizer = AutoTokenizer.from_pretrained(str(fname_tokenizer), use_fast=False, trust_remote_code=True)
+
+        self.added_tokens_dict: OrderedDict[str, int] = OrderedDict()
+
+        for tok, tokidx in sorted(self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]):
+            if tokidx >= params.n_vocab or tokidx < self.tokenizer.vocab_size:
+                continue
+
+            self.added_tokens_dict[tok] = tokidx
+
+        self.unk_token_id: int = self.tokenizer.unk_token_id
+        self.specials: dict[str, int] = {
+            tok: self.tokenizer.get_vocab()[tok]
+            for tok in self.tokenizer.all_special_tokens
+        }
+        self.special_ids: set[int] = set(self.tokenizer.all_special_ids)
+        self.vocab_size_base: int = self.tokenizer.vocab_size
+        self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_dict)
+        self.fname_tokenizer: Path = fname_tokenizer
+
+        vocab_file = "tokenizer.model"
+        path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file)
+        if path_candidate is not None:
+            self.spm = SentencePieceProcessor(str(path_candidate))
+            print(self.spm.vocab_size(), self.vocab_size_base)
         else:
-            # Fall back to trying to find the added tokens in tokenizer.json
-            tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json'
-            if not tokenizer_json_file.is_file():
-                added_tokens = {}
-            else:
-                tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8"))
-                added_tokens = dict(
-                    (item['content'], item['id'])
-                    for item in tokenizer_json.get('added_tokens', [])
-                    # Added tokens here can be duplicates of the main vocabulary.
-                    if item['content'] not in self.bpe_tokenizer)
+            self.spm = None
 
-        vocab_size: int = len(self.bpe_tokenizer)
-        expected_ids    = list(range(vocab_size, vocab_size + len(added_tokens)))
-        actual_ids      = sorted(added_tokens.values())
-        if expected_ids != actual_ids:
-            expected_end_id = vocab_size + len(actual_ids) - 1
-            raise Exception(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}")
+    def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        tokenizer = self.tokenizer
+        reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.get_vocab().items()}
+        added_tokens_ids = set(self.added_tokens_dict.values())
 
-        items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
-        self.added_tokens_list    = [text for (text, idx) in items]
-        self.vocab_size_base: int = vocab_size
-        self.vocab_size: int      = self.vocab_size_base + len(self.added_tokens_list)
-        self.fname_tokenizer      = fname_tokenizer
-        self.fname_added_tokens   = fname_added_tokens
+        for i in range(self.vocab_size_base):
+            if i in added_tokens_ids:
+                continue
 
-    def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        tokenizer = self.bpe_tokenizer
-        reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}
+            text = reverse_vocab[i].encode("utf-8")
+            yield text, self.get_token_score(i), self.get_token_type(i)
 
-        for i, _ in enumerate(tokenizer):
-            yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
+    def get_token_type(self, token_id: int) -> gguf.TokenType:
+        toktype = gguf.TokenType.NORMAL
 
-    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        for text in self.added_tokens_list:
-            score = -1000.0
-            yield text.encode("utf-8"), score, gguf.TokenType.CONTROL
-
-    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        yield from self.bpe_tokens()
-        yield from self.added_tokens()
-
-    def __repr__(self) -> str:
-        return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
-
-
-class SentencePieceVocab:
-    def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
-        self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
-        added_tokens: dict[str, int]
-        if fname_added_tokens is not None:
-            added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
-        else:
-            added_tokens = {}
-
-        vocab_size: int = self.sentencepiece_tokenizer.vocab_size()
-
-        new_tokens       = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
-        expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
-        actual_new_ids   = sorted(new_tokens.keys())
-
-        if expected_new_ids != actual_new_ids:
-            raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
-
-        # Token pieces that were added to the base vocabulary.
-        self.added_tokens_list  = [new_tokens[id] for id in actual_new_ids]
-        self.vocab_size_base    = vocab_size
-        self.vocab_size         = self.vocab_size_base + len(self.added_tokens_list)
-        self.fname_tokenizer    = fname_tokenizer
-        self.fname_added_tokens = fname_added_tokens
-
-    def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        tokenizer = self.sentencepiece_tokenizer
-        for i in range(tokenizer.vocab_size()):
-            piece = tokenizer.id_to_piece(i)
-            text: bytes = piece.encode("utf-8")
-            score: float = tokenizer.get_score(i)
-
-            toktype = gguf.TokenType.NORMAL
-            if tokenizer.is_unknown(i):
+        if self.spm is not None and token_id < self.spm.vocab_size():
+            if self.spm.is_unknown(token_id):
                 toktype = gguf.TokenType.UNKNOWN
-            if tokenizer.is_control(i):
+            if self.spm.is_control(token_id):
+                toktype = gguf.TokenType.CONTROL
+            if self.spm.is_unused(token_id):
+                toktype = gguf.TokenType.UNUSED
+            if self.spm.is_byte(token_id):
+                toktype = gguf.TokenType.BYTE
+        else:
+            if token_id == self.unk_token_id:
+                toktype = gguf.TokenType.UNKNOWN
+            if token_id in self.special_ids:
                 toktype = gguf.TokenType.CONTROL
 
-            # NOTE: I think added_tokens are user defined.
-            # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
-            # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
+        return toktype
 
-            if tokenizer.is_unused(i):
-                toktype = gguf.TokenType.UNUSED
-            if tokenizer.is_byte(i):
-                toktype = gguf.TokenType.BYTE
-
-            yield text, score, toktype
+    def get_token_score(self, token_id: int) -> float:
+        if self.spm is not None and token_id < self.spm.vocab_size():
+            return cast(float, self.spm.get_score(token_id))
+        return 0.0
 
     def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        for text in self.added_tokens_list:
-            score = -1000.0
-            yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED
+
+        for text in self.added_tokens_dict:
+            if text in self.specials:
+
+                toktype = self.get_token_type(self.specials[text])
+                score = self.get_token_score(self.specials[text])
+
+            else:
+                toktype = gguf.TokenType.USER_DEFINED
+                score = -1000.0
+
+            yield text.encode("utf-8"), score, toktype
+
+    def has_newline_token(self) -> bool:
+        return '<0x0A>' in self.tokenizer.vocab or '\n' in self.tokenizer.vocab
 
     def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        yield from self.sentencepiece_tokens()
+        yield from self.hf_tokens()
         yield from self.added_tokens()
 
+    def get_vocab_type(self) -> str:
+        path_candidates = []
+        vocab_file = "tokenizer.model"
+        path_candidates.append(vocab_file)
+        path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file)
+        if path_candidate is not None:
+            return "llama"
+
+        vocab_file = "vocab.json"
+        path_candidates.append(vocab_file)
+        path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file)
+        if path_candidate is not None:
+            return "gpt2"
+
+        vocab_file = "tokenizer.json"
+        path_candidates.append(vocab_file)
+        path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file)
+        if path_candidate:
+            if not self.has_newline_token():
+                return "gpt2"
+            return "llama"
+
+        raise FileNotFoundError(
+            f"Could not find {path_candidates} in {self.fname_tokenizer} or its parent; "
+            "if it's in another directory, pass the directory as --vocab-dir"
+        )
+
     def __repr__(self) -> str:
-        return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
+        return f"<VocabLoader with {self.vocab_size_base} base tokens and {len(self.added_tokens_dict)} added tokens>"
 
 
-Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab'
+Vocab: TypeAlias = 'VocabLoader'
+
 
 #
 # data loading
@@ -585,7 +627,7 @@ def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:
 
     if any("model.embed_tokens.weight" in mp.model for mp in models_plus):
         # Transformers models put different tensors in different files, but
-        # don't split indivdual tensors between files.
+        # don't split individual tensors between files.
         model: LazyModel = {}
         for mp in models_plus:
             model.update(mp.model)
@@ -678,7 +720,7 @@ class LazyUnpickler(pickle.Unpickler):
         return func(*args)
 
     CLASSES: dict[tuple[str, str], Any] = {
-        # getattr used here as a workaround for mypy not being smart enough to detrmine
+        # getattr used here as a workaround for mypy not being smart enough to determine
         # the staticmethods have a __func__ attribute.
         ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),
         ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'),
@@ -794,20 +836,27 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
             yield result
 
 
-def check_vocab_size(params: Params, vocab: Vocab) -> None:
+def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> None:
     if params.n_vocab != vocab.vocab_size:
-        assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
-        if params.n_vocab == vocab.vocab_size_base:
+        if params.n_vocab == vocab.vocab_size:
             print("Ignoring added_tokens.json since model matches vocab size without it.")
-            vocab.added_tokens_list = []
-            vocab.vocab_size = vocab.vocab_size_base
+            vocab.added_tokens_dict = OrderedDict()
+            vocab.vocab_size = vocab.vocab_size
+            return
+
+        if pad_vocab and params.n_vocab > vocab.vocab_size:
+            pad_count = params.n_vocab - vocab.vocab_size
+            print(f'Padding vocab with {pad_count} token(s) - <dummy00001> through <dummy{pad_count:05}>')
+            for i in range(1, (params.n_vocab - vocab.vocab_size) + 1):
+                vocab.added_tokens_dict[f'<dummy{i:05}>'] = -1
+            vocab.vocab_size = params.n_vocab
             return
         msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}"
-        if vocab.fname_added_tokens is not None:
-            msg += f" combined with {vocab.fname_added_tokens}"
         msg += f" has {vocab.vocab_size})."
-        if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20 and vocab.fname_added_tokens is None:
+        if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20:
             msg += f"  Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})."
+        if vocab.vocab_size < params.n_vocab:
+            msg += " Possibly try using the --padvocab option."
         raise Exception(msg)
 
 
@@ -832,7 +881,17 @@ class OutputFile:
         self.gguf.add_rope_dimension_count(params.n_embd // params.n_head)
         self.gguf.add_head_count          (params.n_head)
         self.gguf.add_head_count_kv       (params.n_head_kv)
-        self.gguf.add_layer_norm_rms_eps  (params.f_norm_eps)
+
+        if params.n_experts:
+            self.gguf.add_expert_count(params.n_experts)
+
+        if params.n_experts_used:
+            self.gguf.add_expert_used_count(params.n_experts_used)
+
+        if params.f_norm_eps:
+            self.gguf.add_layer_norm_rms_eps(params.f_norm_eps)
+        else:
+            raise ValueError('f_norm_eps is None')
 
         if params.f_rope_freq_base is not None:
             self.gguf.add_rope_freq_base(params.f_rope_freq_base)
@@ -861,12 +920,8 @@ class OutputFile:
             scores.append(score)
             toktypes.append(toktype)
 
-        if isinstance(vocab, SentencePieceVocab):
-            self.gguf.add_tokenizer_model("llama")
-        elif isinstance(vocab, BpeVocab):
-            self.gguf.add_tokenizer_model("gpt2")
-        else:
-            raise ValueError('Unknown vocab type: Not BpeVocab or SentencePieceVocab')
+        vocab_type = vocab.get_vocab_type()
+        self.gguf.add_tokenizer_model(vocab_type)
         self.gguf.add_token_list(tokens)
         self.gguf.add_token_scores(scores)
         self.gguf.add_token_types(toktypes)
@@ -892,8 +947,12 @@ class OutputFile:
         self.gguf.close()
 
     @staticmethod
-    def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
-        check_vocab_size(params, vocab)
+    def write_vocab_only(
+        fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab,
+        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
+        pad_vocab: bool = False,
+    ) -> None:
+        check_vocab_size(params, vocab, pad_vocab = pad_vocab)
 
         of = OutputFile(fname_out, endianess=endianess)
 
@@ -920,8 +979,13 @@ class OutputFile:
         return dt.quantize(arr)
 
     @staticmethod
-    def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
-        check_vocab_size(params, vocab)
+    def write_all(
+        fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab,
+        concurrency: int = DEFAULT_CONCURRENCY,
+        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
+        pad_vocab: bool = False,
+    ) -> None:
+        check_vocab_size(params, vocab, pad_vocab = pad_vocab)
 
         of = OutputFile(fname_out, endianess=endianess)
 
@@ -956,7 +1020,7 @@ class OutputFile:
 
 
 def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
-    wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) +".weight"].data_type
+    wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type
 
     if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
         return GGMLFileType.AllF32
@@ -1079,35 +1143,17 @@ def load_some_model(path: Path) -> ModelPlus:
     return model_plus
 
 
-def load_vocab(path: Path, vocabtype: str | None) -> Vocab:
-    # Be extra-friendly and accept either a file or a directory.  Also, if it's
-    # a directory, it might be the model directory, and tokenizer.model might
-    # be in the parent of that.
-    if path.is_dir():
-        vocab_file = "tokenizer.model"
-        if vocabtype == 'bpe':
-            vocab_file = "vocab.json"
-        path2 = path / vocab_file
-        # Use `.parent` instead of /.. to handle the symlink case better.
-        path3 = path.parent / vocab_file
-        if path2.exists():
-            path = path2
-        elif path3.exists():
-            path = path3
-        else:
-            raise FileNotFoundError(
-                f"Could not find {vocab_file} in {path} or its parent; "
-                "if it's in another directory, pass the directory as --vocab-dir")
+def find_vocab_file_path(path: Path, vocab_file: str) -> Optional[Path]:
+    path2 = path / vocab_file
+    # Use `.parent` instead of /.. to handle the symlink case better.
+    path3 = path.parent / vocab_file
 
-    print(f"Loading vocab file '{path}', type '{vocabtype}'")
+    if path2.exists():
+        return path2
+    if path3.exists():
+        return path3
 
-    added_tokens_path = path.parent / "added_tokens.json"
-    if vocabtype == "bpe":
-        return BpeVocab(path, added_tokens_path if added_tokens_path.exists() else None)
-    elif vocabtype == "spm":
-        return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None)
-    else:
-        raise ValueError(f"Unsupported vocabulary type {vocabtype}")
+    return None
 
 
 def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path:
@@ -1145,11 +1191,11 @@ def main(args_in: list[str] | None = None) -> None:
     parser.add_argument("--outtype",     choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
     parser.add_argument("--vocab-dir",   type=Path,              help="directory containing tokenizer.model, if separate from model file")
     parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
-    parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.pth, *.pt, *.bin, *.safetensors)")
-    parser.add_argument("--vocabtype",   choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
+    parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
     parser.add_argument("--ctx",         type=int,               help="model training context (default: based on input)")
     parser.add_argument("--concurrency", type=int,               help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)
     parser.add_argument("--bigendian",   action="store_true",    help="model is executed on big endian machine")
+    parser.add_argument("--padvocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides")
 
     args = parser.parse_args(args_in)
     if args.dump_single:
@@ -1192,12 +1238,13 @@ def main(args_in: list[str] | None = None) -> None:
         if not args.outfile:
             raise ValueError("need --outfile if using --vocab-only")
         # FIXME: Try to respect vocab_dir somehow?
-        vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)
+        vocab = VocabLoader(params, args.vocab_dir or args.model)
         special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
-                                          load_merges = args.vocabtype == 'bpe',
+                                          load_merges = True,
                                           n_vocab = vocab.vocab_size)
         outfile = args.outfile
-        OutputFile.write_vocab_only(outfile, params, vocab, special_vocab)
+        OutputFile.write_vocab_only(outfile, params, vocab, special_vocab,
+                                    endianess = endianess, pad_vocab = args.padvocab)
         print(f"Wrote {outfile}")
         return
 
@@ -1205,12 +1252,15 @@ def main(args_in: list[str] | None = None) -> None:
         vocab = model_plus.vocab
     else:
         vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
-        vocab = load_vocab(vocab_dir, args.vocabtype)
+        vocab = VocabLoader(params, vocab_dir)
+
     # FIXME: Try to respect vocab_dir somehow?
+    print(f"Vocab info: {vocab}")
     special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
-                                      load_merges = args.vocabtype == 'bpe',
+                                      load_merges = True,
                                       n_vocab = vocab.vocab_size)
 
+    print(f"Special vocab info: {special_vocab}")
     model   = model_plus.model
     model   = convert_model_names(model, params)
     ftype   = pick_output_type(model, args.outtype)
@@ -1220,7 +1270,8 @@ def main(args_in: list[str] | None = None) -> None:
     params.ftype = ftype
     print(f"Writing {outfile}, format {ftype}")
 
-    OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency, endianess=endianess)
+    OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab,
+                         concurrency = args.concurrency, endianess = endianess, pad_vocab = args.padvocab)
     print(f"Wrote {outfile}")
 
 

examples/baby-llama/baby-llama.cpp

@@ -1258,9 +1258,9 @@ static struct ggml_tensor * forward_lora(
 }
 
 static void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) {
-    assert(logits->n_dims == 2);
-    assert(probs->n_dims == 2);
-    assert(best_samples->n_dims == 1);
+    assert(ggml_is_matrix(logits));
+    assert(ggml_is_matrix(probs));
+    assert(ggml_is_vector(best_samples));
     assert(logits->ne[1] == best_samples->ne[0]);
     assert(logits->ne[0] == probs->ne[0]);
     assert(logits->ne[1] == probs->ne[1]);
@@ -1292,9 +1292,9 @@ static void sample_softmax_batch(
     struct ggml_context * ctx, struct ggml_tensor * logits, struct ggml_tensor * probs,
     struct ggml_tensor * best_samples
 ) {
-    GGML_ASSERT(best_samples->n_dims == 2);
-    GGML_ASSERT(logits->n_dims == 3);
-    GGML_ASSERT(probs->n_dims == 3);
+    GGML_ASSERT(ggml_is_matrix(best_samples));
+    GGML_ASSERT(ggml_is_3d(logits));
+    GGML_ASSERT(ggml_is_3d(probs));
     int n_tokens = best_samples->ne[0];
     int n_batch  = best_samples->ne[1];
     int n_vocab  = logits->ne[0];
@@ -1334,7 +1334,7 @@ static void print_row(struct ggml_tensor * probs, int i) {
 }
 
 static void print_matrix(struct ggml_tensor * probs) {
-    assert(probs->n_dims == 2);
+    assert(ggml_is_matrix(probs));
     for (int i = 0; i < probs->ne[1]; ++i) {
         for (int k = 0; k < probs->ne[0]; ++k) {
             float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);
@@ -1386,8 +1386,8 @@ static void get_example_targets(int example_id, struct ggml_tensor * tokens_inpu
 static void get_example_targets_batch(
     struct ggml_context * ctx, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets
 ) {
-    GGML_ASSERT(tokens_input->n_dims == 2);
-    GGML_ASSERT(     targets->n_dims == 3);
+    GGML_ASSERT(ggml_is_matrix(tokens_input));
+    GGML_ASSERT(ggml_is_3d(targets));
     int n_tokens = tokens_input->ne[0];
     int n_batch  = tokens_input->ne[1];
     GGML_ASSERT(n_tokens == targets->ne[1]);

examples/benchmark/benchmark-matmult.cpp

@@ -129,13 +129,13 @@ int main(int argc, char ** argv)  {
     const ggml_type qtype = GGML_TYPE_Q4_1;
 
     size_t ctx_size = 0;
-    ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);
-    ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);
-    ctx_size += sizex*sizez*ggml_type_sizef(GGML_TYPE_F32);
-    ctx_size += sizex*sizey*ggml_type_sizef(qtype);
-    ctx_size += sizex*sizey*ggml_type_sizef(qtype);
-    ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
-    ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
+    ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey);
+    ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey);
+    ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizez);
+    ctx_size += ggml_row_size(qtype,         sizex*sizey);
+    ctx_size += ggml_row_size(qtype,         sizex*sizey);
+    ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); // BLAS
+    ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); // BLAS
     ctx_size += 1024*1024*16;
 
     printf("Allocating Memory of size %zi bytes, %zi MB\n",ctx_size, (ctx_size/1024/1024));

examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp

@@ -427,7 +427,7 @@ static void print_row(struct ggml_tensor * probs, int i) {
 }
 
 static void print_matrix(struct ggml_tensor * probs) {
-    assert(probs->n_dims == 2);
+    assert(ggml_is_matrix(probs));
     for (int i = 0; i < probs->ne[1]; ++i) {
         for (int k = 0; k < probs->ne[0]; ++k) {
             float p = get_f32_2d(probs, k, i);
@@ -639,7 +639,7 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab
 
 static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) {
     int ct;
-    switch (gg_weights->n_dims){
+    switch (ggml_n_dims(gg_weights)) {
         case 1:
             ct = 0;
             for (int i0 = 0; i0 < gg_weights->ne[0]; i0++){

examples/finetune/finetune.cpp

@@ -1110,7 +1110,7 @@ static void write_tensor(struct llama_file * file, struct ggml_tensor * tensor,
         name = ggml_get_name(tensor);
     }
     uint32_t name_len = strlen(name);
-    uint32_t nd = tensor->n_dims;
+    uint32_t nd = ggml_n_dims(tensor);
     uint32_t ne[4] = { (uint32_t)tensor->ne[0],
                        (uint32_t)tensor->ne[1],
                        (uint32_t)tensor->ne[2],
@@ -1620,8 +1620,6 @@ int main(int argc, char ** argv) {
     opt->params.adam.gclip              = params.common.adam_gclip;
     opt->params.adam.eps_f              = params.common.adam_eps_f;
 
-    ggml_allocr * alloc = NULL;
-
     printf("%s: init model\n", __func__);
     bool existed = load_checkpoint_lora_file(params.common.fn_checkpoint_in, &model, &lora, train);
 
@@ -1725,10 +1723,9 @@ int main(int argc, char ** argv) {
 
     // allocate input tensors
     mem_input_data.resize(max_input_size);
-    alloc = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
-    ggml_allocr_alloc(alloc, tokens_input);
-    ggml_allocr_alloc(alloc, target_probs);
-    ggml_allocr_free(alloc);
+    ggml_allocr_t alloc_inps = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
+    ggml_allocr_alloc(alloc_inps, tokens_input);
+    ggml_allocr_alloc(alloc_inps, target_probs);
 
     // context for compute tensors without their data
     const size_t estimated_compute_size_wo_data = (
@@ -1755,7 +1752,7 @@ int main(int argc, char ** argv) {
     // find best evaluation order
     for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
         ctx_compute = ggml_init(ctx_compute_params);
-        alloc = ggml_allocr_new_measure(tensor_alignment);
+        ggml_allocr_t alloc = ggml_allocr_new_measure(tensor_alignment);
         gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
         gf->order = (enum ggml_cgraph_eval_order) order;
         gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
@@ -1788,7 +1785,7 @@ int main(int argc, char ** argv) {
     // allocate compute tensors
     mem_compute_data.resize(max_compute_size);
     ctx_compute = ggml_init(ctx_compute_params);
-    alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
+    ggml_allocr_t alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
     gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
     gf->order = best_order;
     gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
@@ -1804,6 +1801,8 @@ int main(int argc, char ** argv) {
         params.common.use_checkpointing
     );
     ggml_allocr_free(alloc);
+    ggml_allocr_free(alloc_inps);
+
 
     // tokenize data
     std::vector<llama_token> train_tokens;

examples/gguf/gguf.cpp

@@ -195,7 +195,7 @@ static bool gguf_ex_read_1(const std::string & fname) {
 
             struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
 
-            printf("%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, cur->n_dims, cur->name, cur->data);
+            printf("%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, ggml_n_dims(cur), cur->name, cur->data);
 
             // print first 10 elements
             const float * data = (const float *) cur->data;

examples/llama.swiftui/.gitignore

@@ -1 +1,2 @@
 xcuserdata
+xcshareddata

examples/llama.swiftui/llama.cpp.swift/LibLlama.swift

@@ -6,16 +6,34 @@ enum LlamaError: Error {
     case couldNotInitializeContext
 }
 
+func llama_batch_clear(_ batch: inout llama_batch) {
+    batch.n_tokens = 0
+}
+
+func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama_pos, _ seq_ids: [llama_seq_id], _ logits: Bool) {
+    batch.token   [Int(batch.n_tokens)] = id
+    batch.pos     [Int(batch.n_tokens)] = pos
+    batch.n_seq_id[Int(batch.n_tokens)] = Int32(seq_ids.count)
+    for i in 0..<seq_ids.count {
+        batch.seq_id[Int(batch.n_tokens)]![Int(i)] = seq_ids[i]
+    }
+    batch.logits  [Int(batch.n_tokens)] = logits ? 1 : 0
+
+    batch.n_tokens += 1
+}
+
 actor LlamaContext {
     private var model: OpaquePointer
     private var context: OpaquePointer
     private var batch: llama_batch
     private var tokens_list: [llama_token]
+
     /// This variable is used to store temporarily invalid cchars
     private var temporary_invalid_cchars: [CChar]
 
-    var n_len: Int32 = 512
+    var n_len: Int32 = 64
     var n_cur: Int32 = 0
+
     var n_decode: Int32 = 0
 
     init(model: OpaquePointer, context: OpaquePointer) {
@@ -27,25 +45,34 @@ actor LlamaContext {
     }
 
     deinit {
+        llama_batch_free(batch)
         llama_free(context)
         llama_free_model(model)
         llama_backend_free()
     }
 
-    static func createContext(path: String) throws -> LlamaContext {
+    static func create_context(path: String) throws -> LlamaContext {
         llama_backend_init(false)
-        let model_params = llama_model_default_params()
+        var model_params = llama_model_default_params()
 
+#if targetEnvironment(simulator)
+        model_params.n_gpu_layers = 0
+        print("Running on simulator, force use n_gpu_layers = 0")
+#endif
         let model = llama_load_model_from_file(path, model_params)
         guard let model else {
             print("Could not load model at \(path)")
             throw LlamaError.couldNotInitializeContext
         }
+
+        let n_threads = max(1, min(8, ProcessInfo.processInfo.processorCount - 2))
+        print("Using \(n_threads) threads")
+
         var ctx_params = llama_context_default_params()
-        ctx_params.seed = 1234
+        ctx_params.seed  = 1234
         ctx_params.n_ctx = 2048
-        ctx_params.n_threads = 8
-        ctx_params.n_threads_batch = 8
+        ctx_params.n_threads       = UInt32(n_threads)
+        ctx_params.n_threads_batch = UInt32(n_threads)
 
         let context = llama_new_context_with_model(model, ctx_params)
         guard let context else {
@@ -56,6 +83,26 @@ actor LlamaContext {
         return LlamaContext(model: model, context: context)
     }
 
+    func model_info() -> String {
+        let result = UnsafeMutablePointer<Int8>.allocate(capacity: 256)
+        result.initialize(repeating: Int8(0), count: 256)
+        defer {
+            result.deallocate()
+        }
+
+        // TODO: this is probably very stupid way to get the string from C
+
+        let nChars = llama_model_desc(model, result, 256)
+        let bufferPointer = UnsafeBufferPointer(start: result, count: Int(nChars))
+
+        var SwiftString = ""
+        for char in bufferPointer {
+            SwiftString.append(Character(UnicodeScalar(UInt8(char))))
+        }
+
+        return SwiftString
+    }
+
     func get_n_tokens() -> Int32 {
         return batch.n_tokens;
     }
@@ -79,16 +126,11 @@ actor LlamaContext {
             print(String(cString: token_to_piece(token: id) + [0]))
         }
 
-        // batch = llama_batch_init(512, 0) // done in init()
-        batch.n_tokens = Int32(tokens_list.count)
+        llama_batch_clear(&batch)
 
-        for i1 in 0..<batch.n_tokens {
+        for i1 in 0..<tokens_list.count {
             let i = Int(i1)
-            batch.token[i] = tokens_list[i]
-            batch.pos[i] = i1
-            batch.n_seq_id[Int(i)] = 1
-            batch.seq_id[Int(i)]![0] = 0
-            batch.logits[i] = 0
+            llama_batch_add(&batch, tokens_list[i], Int32(i), [0], false)
         }
         batch.logits[Int(batch.n_tokens) - 1] = 1 // true
 
@@ -141,18 +183,11 @@ actor LlamaContext {
         print(new_token_str)
         // tokens_list.append(new_token_id)
 
-        batch.n_tokens = 0
-
-        batch.token[Int(batch.n_tokens)] = new_token_id
-        batch.pos[Int(batch.n_tokens)] = n_cur
-        batch.n_seq_id[Int(batch.n_tokens)] = 1
-        batch.seq_id[Int(batch.n_tokens)]![0] = 0
-        batch.logits[Int(batch.n_tokens)] = 1 // true
-        batch.n_tokens += 1
+        llama_batch_clear(&batch)
+        llama_batch_add(&batch, new_token_id, n_cur, [0], true)
 
         n_decode += 1
-
-        n_cur += 1
+        n_cur    += 1
 
         if llama_decode(context, batch) != 0 {
             print("failed to evaluate llama!")
@@ -161,14 +196,111 @@ actor LlamaContext {
         return new_token_str
     }
 
+    func bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) -> String {
+        var pp_avg: Double = 0
+        var tg_avg: Double = 0
+
+        var pp_std: Double = 0
+        var tg_std: Double = 0
+
+        for _ in 0..<nr {
+            // bench prompt processing
+
+            llama_batch_clear(&batch)
+
+            let n_tokens = pp
+
+            for i in 0..<n_tokens {
+                llama_batch_add(&batch, 0, Int32(i), [0], false)
+            }
+            batch.logits[Int(batch.n_tokens) - 1] = 1 // true
+
+            llama_kv_cache_clear(context)
+
+            let t_pp_start = ggml_time_us()
+
+            if llama_decode(context, batch) != 0 {
+                print("llama_decode() failed during prompt")
+            }
+
+            let t_pp_end = ggml_time_us()
+
+            // bench text generation
+
+            llama_kv_cache_clear(context)
+
+            let t_tg_start = ggml_time_us()
+
+            for i in 0..<tg {
+                llama_batch_clear(&batch)
+
+                for j in 0..<pl {
+                    llama_batch_add(&batch, 0, Int32(i), [Int32(j)], true)
+                }
+
+                if llama_decode(context, batch) != 0 {
+                    print("llama_decode() failed during text generation")
+                }
+            }
+
+            let t_tg_end = ggml_time_us()
+
+            llama_kv_cache_clear(context)
+
+            let t_pp = Double(t_pp_end - t_pp_start) / 1000000.0
+            let t_tg = Double(t_tg_end - t_tg_start) / 1000000.0
+
+            let speed_pp = Double(pp)    / t_pp
+            let speed_tg = Double(pl*tg) / t_tg
+
+            pp_avg += speed_pp
+            tg_avg += speed_tg
+
+            pp_std += speed_pp * speed_pp
+            tg_std += speed_tg * speed_tg
+
+            print("pp \(speed_pp) t/s, tg \(speed_tg) t/s")
+        }
+
+        pp_avg /= Double(nr)
+        tg_avg /= Double(nr)
+
+        if nr > 1 {
+            pp_std = sqrt(pp_std / Double(nr - 1) - pp_avg * pp_avg * Double(nr) / Double(nr - 1))
+            tg_std = sqrt(tg_std / Double(nr - 1) - tg_avg * tg_avg * Double(nr) / Double(nr - 1))
+        } else {
+            pp_std = 0
+            tg_std = 0
+        }
+
+        let model_desc     = model_info();
+        let model_size     = String(format: "%.2f GiB", Double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0);
+        let model_n_params = String(format: "%.2f B", Double(llama_model_n_params(model)) / 1e9);
+        let backend        = "Metal";
+        let pp_avg_str     = String(format: "%.2f", pp_avg);
+        let tg_avg_str     = String(format: "%.2f", tg_avg);
+        let pp_std_str     = String(format: "%.2f", pp_std);
+        let tg_std_str     = String(format: "%.2f", tg_std);
+
+        var result = ""
+
+        result += String("| model | size | params | backend | test | t/s |\n")
+        result += String("| --- | --- | --- | --- | --- | --- |\n")
+        result += String("| \(model_desc) | \(model_size) | \(model_n_params) | \(backend) | pp \(pp) | \(pp_avg_str) ± \(pp_std_str) |\n")
+        result += String("| \(model_desc) | \(model_size) | \(model_n_params) | \(backend) | tg \(tg) | \(tg_avg_str) ± \(tg_std_str) |\n")
+
+        return result;
+    }
+
     func clear() {
         tokens_list.removeAll()
         temporary_invalid_cchars.removeAll()
+        llama_kv_cache_clear(context)
     }
 
     private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
         let utf8Count = text.utf8.count
-        let n_tokens = utf8Count + (add_bos ? 1 : 0)
+        let n_tokens = utf8Count + (add_bos ? 1 : 0) + 1
         let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
         let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)
 

examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj

@@ -1,481 +1,483 @@
 // !$*UTF8*$!
 {
-    archiveVersion = 1;
-    classes = {
-    };
-    objectVersion = 56;
-    objects = {
+	archiveVersion = 1;
+	classes = {
+	};
+	objectVersion = 56;
+	objects = {
 
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+				DEBUG_INFORMATION_FORMAT = dwarf;
+				ENABLE_STRICT_OBJC_MSGSEND = YES;
+				ENABLE_TESTABILITY = YES;
+				ENABLE_USER_SCRIPT_SANDBOXING = YES;
+				GCC_C_LANGUAGE_STANDARD = gnu17;
+				GCC_DYNAMIC_NO_PIC = NO;
+				GCC_NO_COMMON_BLOCKS = YES;
+				GCC_OPTIMIZATION_LEVEL = 0;
+				GCC_PREPROCESSOR_DEFINITIONS = (
+					"DEBUG=1",
+					"$(inherited)",
+				);
+				GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
+				GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
+				GCC_WARN_UNDECLARED_SELECTOR = YES;
+				GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
+				GCC_WARN_UNUSED_FUNCTION = YES;
+				GCC_WARN_UNUSED_VARIABLE = YES;
+				IPHONEOS_DEPLOYMENT_TARGET = 17.0;
+				LOCALIZATION_PREFERS_STRING_CATALOGS = YES;
+				MTL_ENABLE_DEBUG_INFO = INCLUDE_SOURCE;
+				MTL_FAST_MATH = YES;
+				ONLY_ACTIVE_ARCH = YES;
+				SDKROOT = iphoneos;
+				SWIFT_ACTIVE_COMPILATION_CONDITIONS = "DEBUG $(inherited)";
+				SWIFT_OPTIMIZATION_LEVEL = "-Onone";
+			};
+			name = Debug;
+		};
+		8A1C83802AC328BE0096AF73 /* Release */ = {
+			isa = XCBuildConfiguration;
+			buildSettings = {
+				ALWAYS_SEARCH_USER_PATHS = NO;
+				ASSETCATALOG_COMPILER_GENERATE_SWIFT_ASSET_SYMBOL_EXTENSIONS = YES;
+				CLANG_ANALYZER_NONNULL = YES;
+				CLANG_ANALYZER_NUMBER_OBJECT_CONVERSION = YES_AGGRESSIVE;
+				CLANG_CXX_LANGUAGE_STANDARD = "gnu++20";
+				CLANG_ENABLE_MODULES = YES;
+				CLANG_ENABLE_OBJC_ARC = YES;
+				CLANG_ENABLE_OBJC_WEAK = YES;
+				CLANG_WARN_BLOCK_CAPTURE_AUTORELEASING = YES;
+				CLANG_WARN_BOOL_CONVERSION = YES;
+				CLANG_WARN_COMMA = YES;
+				CLANG_WARN_CONSTANT_CONVERSION = YES;
+				CLANG_WARN_DEPRECATED_OBJC_IMPLEMENTATIONS = YES;
+				CLANG_WARN_DIRECT_OBJC_ISA_USAGE = YES_ERROR;
+				CLANG_WARN_DOCUMENTATION_COMMENTS = YES;
+				CLANG_WARN_EMPTY_BODY = YES;
+				CLANG_WARN_ENUM_CONVERSION = YES;
+				CLANG_WARN_INFINITE_RECURSION = YES;
+				CLANG_WARN_INT_CONVERSION = YES;
+				CLANG_WARN_NON_LITERAL_NULL_CONVERSION = YES;
+				CLANG_WARN_OBJC_IMPLICIT_RETAIN_SELF = YES;
+				CLANG_WARN_OBJC_LITERAL_CONVERSION = YES;
+				CLANG_WARN_OBJC_ROOT_CLASS = YES_ERROR;
+				CLANG_WARN_QUOTED_INCLUDE_IN_FRAMEWORK_HEADER = YES;
+				CLANG_WARN_RANGE_LOOP_ANALYSIS = YES;
+				CLANG_WARN_STRICT_PROTOTYPES = YES;
+				CLANG_WARN_SUSPICIOUS_MOVE = YES;
+				CLANG_WARN_UNGUARDED_AVAILABILITY = YES_AGGRESSIVE;
+				CLANG_WARN_UNREACHABLE_CODE = YES;
+				CLANG_WARN__DUPLICATE_METHOD_MATCH = YES;
+				COPY_PHASE_STRIP = NO;
+				DEBUG_INFORMATION_FORMAT = "dwarf-with-dsym";
+				ENABLE_NS_ASSERTIONS = NO;
+				ENABLE_STRICT_OBJC_MSGSEND = YES;
+				ENABLE_USER_SCRIPT_SANDBOXING = YES;
+				GCC_C_LANGUAGE_STANDARD = gnu17;
+				GCC_NO_COMMON_BLOCKS = YES;
+				GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
+				GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
+				GCC_WARN_UNDECLARED_SELECTOR = YES;
+				GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
+				GCC_WARN_UNUSED_FUNCTION = YES;
+				GCC_WARN_UNUSED_VARIABLE = YES;
+				IPHONEOS_DEPLOYMENT_TARGET = 17.0;
+				LOCALIZATION_PREFERS_STRING_CATALOGS = YES;
+				MTL_ENABLE_DEBUG_INFO = NO;
+				MTL_FAST_MATH = YES;
+				SDKROOT = iphoneos;
+				SWIFT_COMPILATION_MODE = wholemodule;
+				VALIDATE_PRODUCT = YES;
+			};
+			name = Release;
+		};
+		8A1C83822AC328BE0096AF73 /* Debug */ = {
+			isa = XCBuildConfiguration;
+			buildSettings = {
+				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
+				ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
+				CLANG_ENABLE_MODULES = YES;
+				CODE_SIGN_STYLE = Automatic;
+				CURRENT_PROJECT_VERSION = 1;
+				DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
+				DEVELOPMENT_TEAM = STLSG3FG8Q;
+				ENABLE_PREVIEWS = YES;
+				GENERATE_INFOPLIST_FILE = YES;
+				INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES;
+				INFOPLIST_KEY_UIApplicationSupportsIndirectInputEvents = YES;
+				INFOPLIST_KEY_UILaunchScreen_Generation = YES;
+				INFOPLIST_KEY_UISupportedInterfaceOrientations_iPad = "UIInterfaceOrientationPortrait UIInterfaceOrientationPortraitUpsideDown UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+				INFOPLIST_KEY_UISupportedInterfaceOrientations_iPhone = "UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+				IPHONEOS_DEPLOYMENT_TARGET = 16.0;
+				LD_RUNPATH_SEARCH_PATHS = (
+					"$(inherited)",
+					"@executable_path/Frameworks",
+				);
+				MARKETING_VERSION = 1.0;
+				PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
+				PRODUCT_NAME = "$(TARGET_NAME)";
+				SWIFT_EMIT_LOC_STRINGS = YES;
+				SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
+				SWIFT_OPTIMIZATION_LEVEL = "-Onone";
+				SWIFT_VERSION = 5.0;
+				TARGETED_DEVICE_FAMILY = "1,2";
+			};
+			name = Debug;
+		};
+		8A1C83832AC328BE0096AF73 /* Release */ = {
+			isa = XCBuildConfiguration;
+			buildSettings = {
+				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
+				ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
+				CLANG_ENABLE_MODULES = YES;
+				CODE_SIGN_STYLE = Automatic;
+				CURRENT_PROJECT_VERSION = 1;
+				DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
+				DEVELOPMENT_TEAM = STLSG3FG8Q;
+				ENABLE_PREVIEWS = YES;
+				GENERATE_INFOPLIST_FILE = YES;
+				INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES;
+				INFOPLIST_KEY_UIApplicationSupportsIndirectInputEvents = YES;
+				INFOPLIST_KEY_UILaunchScreen_Generation = YES;
+				INFOPLIST_KEY_UISupportedInterfaceOrientations_iPad = "UIInterfaceOrientationPortrait UIInterfaceOrientationPortraitUpsideDown UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+				INFOPLIST_KEY_UISupportedInterfaceOrientations_iPhone = "UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+				IPHONEOS_DEPLOYMENT_TARGET = 16.0;
+				LD_RUNPATH_SEARCH_PATHS = (
+					"$(inherited)",
+					"@executable_path/Frameworks",
+				);
+				MARKETING_VERSION = 1.0;
+				PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
+				PRODUCT_NAME = "$(TARGET_NAME)";
+				SWIFT_EMIT_LOC_STRINGS = YES;
+				SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
+				SWIFT_VERSION = 5.0;
+				TARGETED_DEVICE_FAMILY = "1,2";
+			};
+			name = Release;
+		};
 /* End XCBuildConfiguration section */
 
 /* Begin XCConfigurationList section */
-        8A1C836E2AC328BD0096AF73 /* Build configuration list for PBXProject "llama.swiftui" */ = {
-            isa = XCConfigurationList;
-            buildConfigurations = (
-                8A1C837F2AC328BE0096AF73 /* Debug */,
-                8A1C83802AC328BE0096AF73 /* Release */,
-            );
-            defaultConfigurationIsVisible = 0;
-            defaultConfigurationName = Release;
-        };
-        8A1C83812AC328BE0096AF73 /* Build configuration list for PBXNativeTarget "llama.swiftui" */ = {
-            isa = XCConfigurationList;
-            buildConfigurations = (
-                8A1C83822AC328BE0096AF73 /* Debug */,
-                8A1C83832AC328BE0096AF73 /* Release */,
-            );
-            defaultConfigurationIsVisible = 0;
-            defaultConfigurationName = Release;
-        };
+		8A1C836E2AC328BD0096AF73 /* Build configuration list for PBXProject "llama.swiftui" */ = {
+			isa = XCConfigurationList;
+			buildConfigurations = (
+				8A1C837F2AC328BE0096AF73 /* Debug */,
+				8A1C83802AC328BE0096AF73 /* Release */,
+			);
+			defaultConfigurationIsVisible = 0;
+			defaultConfigurationName = Release;
+		};
+		8A1C83812AC328BE0096AF73 /* Build configuration list for PBXNativeTarget "llama.swiftui" */ = {
+			isa = XCConfigurationList;
+			buildConfigurations = (
+				8A1C83822AC328BE0096AF73 /* Debug */,
+				8A1C83832AC328BE0096AF73 /* Release */,
+			);
+			defaultConfigurationIsVisible = 0;
+			defaultConfigurationName = Release;
+		};
 /* End XCConfigurationList section */
-    };
-    rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
+	};
+	rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
 }

examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift

@@ -3,24 +3,26 @@ import Foundation
 @MainActor
 class LlamaState: ObservableObject {
     @Published var messageLog = ""
+    @Published var cacheCleared = false
 
     private var llamaContext: LlamaContext?
-    private var modelUrl: URL? {
-        Bundle.main.url(forResource: "q8_0", withExtension: "gguf", subdirectory: "models")
+    private var defaultModelUrl: URL? {
+        Bundle.main.url(forResource: "ggml-model", withExtension: "gguf", subdirectory: "models")
         // Bundle.main.url(forResource: "llama-2-7b-chat", withExtension: "Q2_K.gguf", subdirectory: "models")
     }
+
     init() {
         do {
-            try loadModel()
+            try loadModel(modelUrl: defaultModelUrl)
         } catch {
             messageLog += "Error!\n"
         }
     }
 
-    private func loadModel() throws {
+    func loadModel(modelUrl: URL?) throws {
         messageLog += "Loading model...\n"
         if let modelUrl {
-            llamaContext = try LlamaContext.createContext(path: modelUrl.path())
+            llamaContext = try LlamaContext.create_context(path: modelUrl.path())
             messageLog += "Loaded model \(modelUrl.lastPathComponent)\n"
         } else {
             messageLog += "Could not locate model\n"
@@ -31,7 +33,7 @@ class LlamaState: ObservableObject {
         guard let llamaContext else {
             return
         }
-        messageLog += "Attempting to complete text...\n"
+
         await llamaContext.completion_init(text: text)
         messageLog += "\(text)"
 
@@ -42,4 +44,42 @@ class LlamaState: ObservableObject {
         await llamaContext.clear()
         messageLog += "\n\ndone\n"
     }
+
+    func bench() async {
+        guard let llamaContext else {
+            return
+        }
+
+        messageLog += "\n"
+        messageLog += "Running benchmark...\n"
+        messageLog += "Model info: "
+        messageLog += await llamaContext.model_info() + "\n"
+
+        let t_start = DispatchTime.now().uptimeNanoseconds
+        await llamaContext.bench(pp: 8, tg: 4, pl: 1) // heat up
+        let t_end = DispatchTime.now().uptimeNanoseconds
+
+        let t_heat = Double(t_end - t_start) / 1_000_000_000.0
+        messageLog += "Heat up time: \(t_heat) seconds, please wait...\n"
+
+        // if more than 5 seconds, then we're probably running on a slow device
+        if t_heat > 5.0 {
+            messageLog += "Heat up time is too long, aborting benchmark\n"
+            return
+        }
+
+        let result = await llamaContext.bench(pp: 512, tg: 128, pl: 1, nr: 3)
+
+        messageLog += "\(result)"
+        messageLog += "\n"
+    }
+
+    func clear() async {
+        guard let llamaContext else {
+            return
+        }
+
+        await llamaContext.clear()
+        messageLog = ""
+    }
 }

examples/llama.swiftui/llama.swiftui/UI/ContentView.swift

@@ -5,24 +5,132 @@ struct ContentView: View {
 
     @State private var multiLineText = ""
 
+    private static func cleanupModelCaches() {
+        // Delete all models (*.gguf)
+        let fileManager = FileManager.default
+        let documentsUrl =  FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0]
+        do {
+            let fileURLs = try fileManager.contentsOfDirectory(at: documentsUrl, includingPropertiesForKeys: nil)
+            for fileURL in fileURLs {
+                if fileURL.pathExtension == "gguf" {
+                    try fileManager.removeItem(at: fileURL)
+                }
+            }
+        } catch {
+            print("Error while enumerating files \(documentsUrl.path): \(error.localizedDescription)")
+        }
+    }
+
     var body: some View {
         VStack {
-            ScrollView(.vertical) {
+            ScrollView(.vertical, showsIndicators: true) {
                 Text(llamaState.messageLog)
+                .font(.system(size: 12))
+                .frame(maxWidth: .infinity, alignment: .leading)
+                .padding()
+                .onTapGesture {
+                    UIApplication.shared.sendAction(#selector(UIResponder.resignFirstResponder), to: nil, from: nil, for: nil)
+                }
             }
 
             TextEditor(text: $multiLineText)
-                .frame(height: 200)
+                .frame(height: 80)
                 .padding()
                 .border(Color.gray, width: 0.5)
-            Button(action: {
-                sendText()
-            }) {
-                Text("Send")
-                    .padding()
-                    .background(Color.blue)
-                    .foregroundColor(.white)
-                    .cornerRadius(8)
+
+            HStack {
+                Button("Send") {
+                    sendText()
+                }
+                .padding(8)
+                .background(Color.blue)
+                .foregroundColor(.white)
+                .cornerRadius(8)
+
+                Button("Bench") {
+                    bench()
+                }
+                .padding(8)
+                .background(Color.blue)
+                .foregroundColor(.white)
+                .cornerRadius(8)
+
+                Button("Clear") {
+                    clear()
+                }
+                .padding(8)
+                .background(Color.blue)
+                .foregroundColor(.white)
+                .cornerRadius(8)
+
+                Button("Copy") {
+                    UIPasteboard.general.string = llamaState.messageLog
+                }
+                .padding(8)
+                .background(Color.blue)
+                .foregroundColor(.white)
+                .cornerRadius(8)
+            }
+
+            VStack {
+                DownloadButton(
+                    llamaState: llamaState,
+                    modelName: "TinyLlama-1.1B (Q4_0, 0.6 GiB)",
+                    modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",
+                    filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf"
+                )
+                .font(.system(size: 12))
+                .padding(.top, 4)
+                .frame(maxWidth: .infinity, alignment: .leading)
+
+                DownloadButton(
+                    llamaState: llamaState,
+                    modelName: "TinyLlama-1.1B (Q8_0, 1.1 GiB)",
+                    modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf?download=true",
+                    filename: "tinyllama-1.1b-1t-openorca.Q8_0.gguf"
+                )
+                .font(.system(size: 12))
+
+                DownloadButton(
+                    llamaState: llamaState,
+                    modelName: "TinyLlama-1.1B (F16, 2.2 GiB)",
+                    modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true",
+                    filename: "tinyllama-1.1b-f16.gguf"
+                )
+                .font(.system(size: 12))
+                .frame(maxWidth: .infinity, alignment: .leading)
+
+                DownloadButton(
+                    llamaState: llamaState,
+                    modelName: "Phi-2.7B (Q4_0, 1.6 GiB)",
+                    modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true",
+                    filename: "phi-2-q4_0.gguf"
+                )
+                .font(.system(size: 12))
+
+                DownloadButton(
+                    llamaState: llamaState,
+                    modelName: "Phi-2.7B (Q8_0, 2.8 GiB)",
+                    modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true",
+                    filename: "phi-2-q8_0.gguf"
+                )
+                .font(.system(size: 12))
+                .frame(maxWidth: .infinity, alignment: .leading)
+
+                DownloadButton(
+                    llamaState: llamaState,
+                    modelName: "Mistral-7B-v0.1 (Q4_0, 3.8 GiB)",
+                    modelUrl: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true",
+                    filename: "mistral-7b-v0.1.Q4_0.gguf"
+                )
+                .font(.system(size: 12))
+
+                Button("Clear downloaded models") {
+                    ContentView.cleanupModelCaches()
+                    llamaState.cacheCleared = true
+                }
+                .padding(8)
+                .font(.system(size: 12))
             }
         }
         .padding()
@@ -34,9 +142,20 @@ struct ContentView: View {
             multiLineText = ""
         }
     }
+
+    func bench() {
+        Task {
+            await llamaState.bench()
+        }
+    }
+
+    func clear() {
+        Task {
+            await llamaState.clear()
+        }
+    }
 }
-/*
-#Preview {
-    ContentView()
-}
-*/
+
+//#Preview {
+//    ContentView()
+//}

examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift

@@ -0,0 +1,122 @@
+import SwiftUI
+
+struct DownloadButton: View {
+    @ObservedObject private var llamaState: LlamaState
+    private var modelName: String
+    private var modelUrl: String
+    private var filename: String
+
+    @State private var status: String
+
+    @State private var downloadTask: URLSessionDownloadTask?
+    @State private var progress = 0.0
+    @State private var observation: NSKeyValueObservation?
+
+    private static func getFileURL(filename: String) -> URL {
+        FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0].appendingPathComponent(filename)
+    }
+
+    private func checkFileExistenceAndUpdateStatus() {
+    }
+
+    init(llamaState: LlamaState, modelName: String, modelUrl: String, filename: String) {
+        self.llamaState = llamaState
+        self.modelName = modelName
+        self.modelUrl = modelUrl
+        self.filename = filename
+
+        let fileURL = DownloadButton.getFileURL(filename: filename)
+        status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download"
+    }
+
+    private func download() {
+        status = "downloading"
+        print("Downloading model \(modelName) from \(modelUrl)")
+        guard let url = URL(string: modelUrl) else { return }
+        let fileURL = DownloadButton.getFileURL(filename: filename)
+
+        downloadTask = URLSession.shared.downloadTask(with: url) { temporaryURL, response, error in
+            if let error = error {
+                print("Error: \(error.localizedDescription)")
+                return
+            }
+
+            guard let response = response as? HTTPURLResponse, (200...299).contains(response.statusCode) else {
+                print("Server error!")
+                return
+            }
+
+            do {
+                if let temporaryURL = temporaryURL {
+                    try FileManager.default.copyItem(at: temporaryURL, to: fileURL)
+                    print("Writing to \(filename) completed")
+
+                    llamaState.cacheCleared = false
+
+                    status = "downloaded"
+                }
+            } catch let err {
+                print("Error: \(err.localizedDescription)")
+            }
+        }
+
+        observation = downloadTask?.progress.observe(\.fractionCompleted) { progress, _ in
+            self.progress = progress.fractionCompleted
+        }
+
+        downloadTask?.resume()
+    }
+
+    var body: some View {
+        VStack {
+            if status == "download" {
+                Button(action: download) {
+                    Text("Download " + modelName)
+                }
+            } else if status == "downloading" {
+                Button(action: {
+                    downloadTask?.cancel()
+                    status = "download"
+                }) {
+                    Text("\(modelName) (Downloading \(Int(progress * 100))%)")
+                }
+            } else if status == "downloaded" {
+                Button(action: {
+                    let fileURL = DownloadButton.getFileURL(filename: filename)
+                    if !FileManager.default.fileExists(atPath: fileURL.path) {
+                        download()
+                        return
+                    }
+                    do {
+                        try llamaState.loadModel(modelUrl: fileURL)
+                    } catch let err {
+                        print("Error: \(err.localizedDescription)")
+                    }
+                }) {
+                    Text("\(modelName) (Downloaded)")
+                }
+            } else {
+                Text("Unknown status")
+            }
+        }
+        .onDisappear() {
+            downloadTask?.cancel()
+        }
+        .onChange(of: llamaState.cacheCleared) { newValue in
+            if newValue {
+                downloadTask?.cancel()
+                let fileURL = DownloadButton.getFileURL(filename: filename)
+                status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download"
+            }
+        }
+    }
+}
+
+// #Preview {
+//    DownloadButton(
+//        llamaState: LlamaState(),
+//        modelName: "TheBloke / TinyLlama-1.1B-1T-OpenOrca-GGUF (Q4_0)",
+//        modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",
+//        filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf"
+//    )
+// }

examples/llava/clip.cpp

@@ -514,7 +514,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             ctx_size += padded_size;
             if (verbosity >= 3) {
                 printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, padded_size=%zu, offset=%zu\n", __func__, i,
-                       cur->n_dims, cur->name, tensor_size, padded_size, offset);
+                       ggml_n_dims(cur), cur->name, tensor_size, padded_size, offset);
             }
         }
     }
@@ -739,7 +739,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
         temp->ny = longer_side;
         temp->size = 3 * longer_side * longer_side;
         temp->data = new uint8_t[temp->size]();
-        uint8_t bc[3] = {122, 116, 104}; // bakground color in RGB from LLaVA
+        uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA
 
         // fill with background color
         for (size_t i = 0; i < temp->size; i++) {
@@ -962,7 +962,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
         }
 
         // quantize only 2D tensors
-        quantize &= (cur->n_dims == 2);
+        quantize &= (ggml_n_dims(cur) == 2);
 
         if (quantize) {
             new_type = type;
@@ -1035,7 +1035,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
             fout.put(0);
         }
 
-        printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), cur->n_dims, quantize,
+        printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
                orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
     }
 

examples/llava/convert-image-encoder-to-gguf.py

@@ -51,7 +51,7 @@ def bytes_to_unicode():
     The reversible bpe codes work on unicode strings.
     This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
     When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    This is a significant percentage of your normal, say, 32K bpe vocab.
     To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
     And avoids mapping to whitespace/control characters the bpe code barfs on.
     """

examples/lookahead/README.md

@@ -1,6 +1,6 @@
 # llama.cpp/examples/lookahead
 
-Demonstartion of lookahead decoding technique:
+Demonstration of lookahead decoding technique:
 
 https://lmsys.org/blog/2023-11-21-lookahead-decoding/
 

examples/server/README.md

@@ -222,7 +222,7 @@ node index.js
 
     `content`: Set the text to process.
 
-    **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream.
+-   **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream.
 
     *Options:*
 

examples/server/json.hpp

@@ -11227,7 +11227,7 @@ class binary_reader
                 }
                 if (is_ndarray) // ndarray dimensional vector can only contain integers, and can not embed another array
                 {
-                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read, exception_message(input_format, "ndarray dimentional vector is not allowed", "size"), nullptr));
+                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read, exception_message(input_format, "ndarray dimensional vector is not allowed", "size"), nullptr));
                 }
                 std::vector<size_t> dim;
                 if (JSON_HEDLEY_UNLIKELY(!get_ubjson_ndarray_size(dim)))

examples/server/public/completion.js

@@ -34,7 +34,8 @@ export async function* llama(prompt, params = {}, config = {}) {
     headers: {
       'Connection': 'keep-alive',
       'Content-Type': 'application/json',
-      'Accept': 'text/event-stream'
+      'Accept': 'text/event-stream',
+      ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {})
     },
     signal: controller.signal,
   });
@@ -114,7 +115,7 @@ export async function* llama(prompt, params = {}, config = {}) {
   return content;
 }
 
-// Call llama, return an event target that you can subcribe to
+// Call llama, return an event target that you can subscribe to
 //
 // Example:
 //

examples/server/public/index.html

@@ -223,7 +223,7 @@
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
       top_k: 40, // <= 0 to use vocab size
-      top_p: 0.5, // 1.0 = disabled
+      top_p: 0.95, // 1.0 = disabled
       min_p: 0.05, // 0 = disabled
       tfs_z: 1.0, // 1.0 = disabled
       typical_p: 1.0, // 1.0 = disabled
@@ -235,10 +235,11 @@
       grammar: '',
       n_probs: 0, // no completion_probabilities,
       image_data: [],
-      cache_prompt: true
+      cache_prompt: true,
+      api_key: ''
     })
 
-    /* START: Support for storing prompt templates and parameters in borwser LocalStorage */
+    /* START: Support for storing prompt templates and parameters in browsers LocalStorage */
 
     const local_storage_storageKey = "llamacpp_server_local_storage";
 
@@ -282,7 +283,7 @@
     let importedTemplates = local_storage_getDataAsObject('user_templates')
 
     if (importedTemplates) {
-      // saved templates were successfuly imported.
+      // saved templates were successfully imported.
 
       console.log('Processing saved templates and updating default template')
       params.value = { ...params.value, image_data: [] };
@@ -303,7 +304,7 @@
     }
 
     function userTemplateResetToDefault() {
-      console.log('Reseting themplate to default')
+      console.log('Resetting template to default')
       selectedUserTemplate.value.name = 'default';
       selectedUserTemplate.value.data = savedUserTemplates.value['default'];
     }
@@ -762,7 +763,7 @@
 
           <fieldset class="two">
             ${IntField({ label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict })}
-            ${FloatField({ label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
+            ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
@@ -790,6 +791,10 @@
             <fieldset>
               ${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
             </fieldset>
+            <fieldset>
+              <label for="api_key">API Key</label>
+              <input type="text" name="api_key" value="${params.value.api_key}" placeholder="Enter API key" oninput=${updateParams} />
+            </fieldset>
           </details>
         </form>
       `

examples/server/server.cpp

@@ -10,7 +10,8 @@
 // crash the server in debug mode, otherwise send an http 500 error
 #define CPPHTTPLIB_NO_EXCEPTIONS 1
 #endif
-
+// increase max payload length to allow use of larger context size
+#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
 #include "httplib.h"
 #include "json.hpp"
 
@@ -36,6 +37,7 @@ using json = nlohmann::json;
 struct server_params
 {
     std::string hostname = "127.0.0.1";
+    std::string api_key;
     std::string public_path = "examples/server/public";
     int32_t port = 8080;
     int32_t read_timeout = 600;
@@ -376,7 +378,6 @@ struct llama_client_slot
 
     int32_t num_prompt_tokens           = 0;
     int32_t num_prompt_tokens_processed = 0;
-    int32_t multibyte_pending           = 0;
 
     json prompt;
     std::string generated_text;
@@ -425,7 +426,6 @@ struct llama_client_slot
         stopped_word           = false;
         stopped_limit          = false;
         stopping_word          = "";
-        multibyte_pending      = 0;
         n_past                 = 0;
         sent_count             = 0;
         sent_token_probs_index = 0;
@@ -992,35 +992,36 @@ struct llama_server_context
         slot.generated_text += token_str;
         slot.has_next_token = true;
 
-        if (slot.multibyte_pending > 0)
+        // check if there is incomplete UTF-8 character at the end
+        bool incomplete = false;
+        for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
         {
-            slot.multibyte_pending -= token_str.size();
-        }
-        else if (token_str.size() == 1)
-        {
-            const char c = token_str[0];
-            // 2-byte characters: 110xxxxx 10xxxxxx
+            unsigned char c = slot.generated_text[slot.generated_text.size() - i];
+            if ((c & 0xC0) == 0x80)
+            {
+                // continuation byte: 10xxxxxx
+                continue;
+            }
             if ((c & 0xE0) == 0xC0)
             {
-                slot.multibyte_pending = 1;
-                // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
+                // 2-byte character: 110xxxxx ...
+                incomplete = i < 2;
             }
             else if ((c & 0xF0) == 0xE0)
             {
-                slot.multibyte_pending = 2;
-                // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
+                // 3-byte character: 1110xxxx ...
+                incomplete = i < 3;
             }
             else if ((c & 0xF8) == 0xF0)
             {
-                slot.multibyte_pending = 3;
-            }
-            else
-            {
-                slot.multibyte_pending = 0;
+                // 4-byte character: 11110xxx ...
+                incomplete = i < 4;
             }
+            // else 1-byte character or invalid byte
+            break;
         }
 
-        if (slot.multibyte_pending == 0)
+        if (!incomplete)
         {
             size_t pos = std::min(slot.sent_count, slot.generated_text.size());
             const std::string str_test = slot.generated_text.substr(pos);
@@ -1055,7 +1056,7 @@ struct llama_server_context
             }
         }
 
-        if (slot.multibyte_pending > 0 && !slot.has_next_token)
+        if (incomplete)
         {
             slot.has_next_token = true;
         }
@@ -1954,6 +1955,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("  --host                ip address to listen (default  (default: %s)\n", sparams.hostname.c_str());
     printf("  --port PORT           port to listen (default  (default: %d)\n", sparams.port);
     printf("  --path PUBLIC_PATH    path from which to serve static files (default %s)\n", sparams.public_path.c_str());
+    printf("  --api-key API_KEY     optional api key to enhance server security. If set, requests must include this key for access.\n");
     printf("  -to N, --timeout N    server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
     printf("  --embedding           enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
     printf("  -np N, --parallel N   number of slots for process requests (default: %d)\n", params.n_parallel);
@@ -2003,6 +2005,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             }
             sparams.public_path = argv[i];
         }
+        else if (arg == "--api-key")
+        {
+            if (++i >= argc)
+            {
+                invalid_param = true;
+                break;
+            }
+            sparams.api_key = argv[i];
+        }
         else if (arg == "--timeout" || arg == "-to")
         {
             if (++i >= argc)
@@ -2382,6 +2393,7 @@ json oaicompat_completion_params_parse(
     llama_params["__oaicompat"] = true;
 
     // Map OpenAI parameters to llama.cpp parameters
+    llama_params["model"]             = json_value(body, "model", std::string("uknown"));
     llama_params["prompt"]            = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
     llama_params["cache_prompt"]      = json_value(body, "cache_prompt", false);
     llama_params["temperature"]       = json_value(body, "temperature", 0.8);
@@ -2402,7 +2414,7 @@ json oaicompat_completion_params_parse(
     llama_params["ignore_eos"]        = json_value(body, "ignore_eos", false);
     llama_params["tfs_z"]             = json_value(body, "tfs_z", 0.0);
 
-    if (llama_params.count("grammar") != 0) {
+    if (body.count("grammar") != 0) {
         llama_params["grammar"] = json_value(body, "grammar", json::object());
     }
 
@@ -2633,6 +2645,9 @@ static void append_to_generated_text_from_generated_token_probs(llama_server_con
 
 int main(int argc, char **argv)
 {
+#if SERVER_VERBOSE != 1
+    log_disable();
+#endif
     // own arguments required by this example
     gpt_params params;
     server_params sparams;
@@ -2669,6 +2684,32 @@ int main(int argc, char **argv)
 
     httplib::Server svr;
 
+    // Middleware for API key validation
+    auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
+        // If API key is not set, skip validation
+        if (sparams.api_key.empty()) {
+            return true;
+        }
+
+        // Check for API key in the header
+        auto auth_header = req.get_header_value("Authorization");
+        std::string prefix = "Bearer ";
+        if (auth_header.substr(0, prefix.size()) == prefix) {
+            std::string received_api_key = auth_header.substr(prefix.size());
+            if (received_api_key == sparams.api_key) {
+                return true; // API key is valid
+            }
+        }
+
+        // API key is invalid or not provided
+        res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8");
+        res.status = 401; // Unauthorized
+
+        LOG_WARNING("Unauthorized: Invalid API Key", {});
+
+        return false;
+    };
+
     svr.set_default_headers({{"Server", "llama.cpp"},
                              {"Access-Control-Allow-Origin", "*"},
                              {"Access-Control-Allow-Headers", "content-type"}});
@@ -2676,28 +2717,28 @@ int main(int argc, char **argv)
     // this is only called if no index.html is found in the public --path
     svr.Get("/", [](const httplib::Request &, httplib::Response &res)
             {
-                res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html");
+                res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html; charset=utf-8");
                 return false;
             });
 
     // this is only called if no index.js is found in the public --path
     svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res)
             {
-                res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript");
+                res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript; charset=utf-8");
                 return false;
             });
 
     // this is only called if no index.html is found in the public --path
     svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res)
             {
-                res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript");
+                res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript; charset=utf-8");
                 return false;
             });
 
     // this is only called if no index.html is found in the public --path
     svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res)
             {
-                res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript");
+                res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8");
                 return false;
             });
 
@@ -2708,23 +2749,26 @@ int main(int argc, char **argv)
                     { "user_name",      llama.name_user.c_str() },
                     { "assistant_name", llama.name_assistant.c_str() }
                 };
-                res.set_content(data.dump(), "application/json");
+                res.set_content(data.dump(), "application/json; charset=utf-8");
             });
 
-    svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res)
+    svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
             {
+                if (!validate_api_key(req, res)) {
+                    return;
+                }
                 json data = json::parse(req.body);
                 const int task_id = llama.request_completion(data, false, false, -1);
                 if (!json_value(data, "stream", false)) {
                     std::string completion_text;
                     task_result result = llama.next_result(task_id);
                     if (!result.error && result.stop) {
-                        res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json");
+                        res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
                     }
                     else
                     {
                         res.status = 404;
-                        res.set_content(result.result_json["content"], "text/plain");
+                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
                         return;
                     }
                 } else {
@@ -2795,12 +2839,15 @@ int main(int argc, char **argv)
                     }}
                 };
 
-                res.set_content(models.dump(), "application/json");
+                res.set_content(models.dump(), "application/json; charset=utf-8");
             });
 
     // TODO: add mount point without "/v1" prefix -- how?
-    svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req, httplib::Response &res)
+    svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
             {
+                if (!validate_api_key(req, res)) {
+                    return;
+                }
                 json data = oaicompat_completion_params_parse(json::parse(req.body));
 
                 const int task_id = llama.request_completion(data, false, false, -1);
@@ -2814,10 +2861,10 @@ int main(int argc, char **argv)
 
                         res.set_content(oaicompat_result.dump(-1, ' ', false,
                                             json::error_handler_t::replace),
-                                            "application/json");
+                                            "application/json; charset=utf-8");
                     } else {
                         res.status = 500;
-                        res.set_content(result.result_json["content"], "text/plain");
+                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
                         return;
                     }
                 } else {
@@ -2869,8 +2916,11 @@ int main(int argc, char **argv)
                 }
             });
 
-    svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
+    svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
             {
+                if (!validate_api_key(req, res)) {
+                    return;
+                }
                 json data = json::parse(req.body);
                 const int task_id = llama.request_completion(data, true, false, -1);
                 if (!json_value(data, "stream", false)) {
@@ -2878,12 +2928,12 @@ int main(int argc, char **argv)
                     task_result result = llama.next_result(task_id);
                     if (!result.error && result.stop)
                     {
-                        res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json");
+                        res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
                     }
                     else
                     {
                         res.status = 404;
-                        res.set_content(result.result_json["content"], "text/plain");
+                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
                         return;
                     }
                 } else {
@@ -2932,11 +2982,11 @@ int main(int argc, char **argv)
     svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res)
             {
                 const json data = llama.get_model_props();
-                return res.set_content(data.dump(), "application/json");
+                return res.set_content(data.dump(), "application/json; charset=utf-8");
             });
 
     svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
-                { return res.set_content("", "application/json"); });
+                { return res.set_content("", "application/json; charset=utf-8"); });
 
     svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res)
             {
@@ -2947,7 +2997,7 @@ int main(int argc, char **argv)
                     tokens = llama.tokenize(body["content"], false);
                 }
                 const json data = format_tokenizer_response(tokens);
-                return res.set_content(data.dump(), "application/json");
+                return res.set_content(data.dump(), "application/json; charset=utf-8");
             });
 
     svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res)
@@ -2961,7 +3011,7 @@ int main(int argc, char **argv)
                 }
 
                 const json data = format_detokenized_response(content);
-                return res.set_content(data.dump(), "application/json");
+                return res.set_content(data.dump(), "application/json; charset=utf-8");
             });
 
     svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res)
@@ -2978,7 +3028,7 @@ int main(int argc, char **argv)
                 }
                 const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true, -1);
                 task_result result = llama.next_result(task_id);
-                return res.set_content(result.result_json.dump(), "application/json");
+                return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
             });
 
     svr.set_logger(log_server_request);
@@ -2999,19 +3049,23 @@ int main(int argc, char **argv)
                 {
                     snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
                 }
-                res.set_content(buf, "text/plain");
+                res.set_content(buf, "text/plain; charset=utf-8");
                 res.status = 500;
             });
 
     svr.set_error_handler([](const httplib::Request &, httplib::Response &res)
             {
+                if (res.status == 401)
+                {
+                    res.set_content("Unauthorized", "text/plain; charset=utf-8");
+                }
                 if (res.status == 400)
                 {
-                    res.set_content("Invalid request", "text/plain");
+                    res.set_content("Invalid request", "text/plain; charset=utf-8");
                 }
-                else if (res.status != 500)
+                else if (res.status == 404)
                 {
-                    res.set_content("File Not Found", "text/plain");
+                    res.set_content("File Not Found", "text/plain; charset=utf-8");
                     res.status = 404;
                 }
             });
@@ -3032,11 +3086,15 @@ int main(int argc, char **argv)
     // to make it ctrl+clickable:
     LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
 
-    LOG_INFO("HTTP server listening", {
-                                          {"hostname", sparams.hostname},
-                                          {"port", sparams.port},
-                                      });
+    std::unordered_map<std::string, std::string> log_data;
+    log_data["hostname"] = sparams.hostname;
+    log_data["port"] = std::to_string(sparams.port);
 
+    if (!sparams.api_key.empty()) {
+        log_data["api_key"] = "api_key: ****" + sparams.api_key.substr(sparams.api_key.length() - 4);
+    }
+
+    LOG_INFO("HTTP server listening", log_data);
     // run the HTTP server in a thread - see comment below
     std::thread t([&]()
             {

examples/speculative/README.md

@@ -1,6 +1,6 @@
 # llama.cpp/examples/speculative
 
-Demonstartion of speculative decoding and tree-based speculative decoding techniques
+Demonstration of speculative decoding and tree-based speculative decoding techniques
 
 More info:
 

examples/speculative/speculative.cpp

@@ -428,7 +428,7 @@ int main(int argc, char ** argv) {
             ++n_past_tgt;
         }
 
-        // the first token is always proposed by the traget model before the speculation loop so we erase it here
+        // the first token is always proposed by the target model before the speculation loop so we erase it here
         for (int s = 0; s < n_seq_dft; ++s) {
             if (!drafts[s].active) {
                 continue;

ggml-alloc.c

@@ -449,11 +449,10 @@ static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool upd
     if (update_backend) {
         view->backend = view->view_src->backend;
     }
-    view->buffer  = view->view_src->buffer;
+    // views are initialized in the alloc buffer rather than the view_src buffer
+    view->buffer  = alloc->buffer;
     view->data    = (char *)view->view_src->data + view->view_offs;
 
-    // FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend
-    // due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras
     assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->buft == alloc->buffer->buft);
 
     if (!alloc->measure) {
@@ -736,6 +735,10 @@ void ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n) {
 }
 
 void ggml_allocr_free(ggml_allocr_t alloc) {
+    if (alloc == NULL) {
+        return;
+    }
+
     ggml_gallocr_free(alloc->galloc);
     ggml_tallocr_free(alloc->talloc);
     free(alloc);
@@ -775,7 +778,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
     }
 
     if (nbytes == 0) {
-        fprintf(stderr, "%s: no tensors to allocate\n", __func__);
+        // all the tensors in the context are already allocated
         return NULL;
     }
 
@@ -789,6 +792,11 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
             } else {
                 ggml_backend_view_init(buffer, t);
             }
+        } else {
+            if (t->view_src != NULL) {
+                // view of a pre-allocated tensor
+                ggml_backend_view_init(buffer, t);
+            }
         }
     }
 

ggml-alloc.h

@@ -43,7 +43,7 @@ GGML_API size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph
 // ggml-backend v2 API
 //
 
-// Seperate tensor and graph allocator objects
+// Separate tensor and graph allocator objects
 // This is necessary for multi-backend allocation because the graph allocator needs to use multiple tensor allocators
 // The original API is kept as a wrapper around the new API
 

ggml-backend-impl.h

@@ -20,6 +20,9 @@ extern "C" {
         size_t                (*get_alignment)   (ggml_backend_buffer_type_t buft); // tensor alignment
         size_t                (*get_alloc_size)  (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
         bool                  (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
+        // check if tensor data is in host memory
+        // should be equivalent to supports_backend(buft, ggml_backend_cpu_init())
+        bool                  (*is_host)         (ggml_backend_buffer_type_t buft);
     };
 
     struct ggml_backend_buffer_type {
@@ -31,15 +34,16 @@ extern "C" {
     typedef void * ggml_backend_buffer_context_t;
 
     struct ggml_backend_buffer_i {
-        void     (*free_buffer)(ggml_backend_buffer_t buffer);
+        void   (*free_buffer)    (ggml_backend_buffer_t buffer);
         //void     (*reset)      (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
-        void *   (*get_base)   (ggml_backend_buffer_t buffer);
-        void     (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-        void     (*set_tensor) (ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-        void     (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        void * (*get_base)       (ggml_backend_buffer_t buffer);
+        void   (*init_tensor)    (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+        void   (*set_tensor)     (ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+        void   (*get_tensor)     (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
         // (optional) copy tensor between different buffer-type, allow for single-copy tranfers
-        void (*cpy_tensor_from)(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
-        void (*cpy_tensor_to)  (ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
+        void   (*cpy_tensor_from)(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
+        void   (*cpy_tensor_to)  (ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
+        void   (*clear)          (ggml_backend_buffer_t buffer, uint8_t value);
     };
 
     struct ggml_backend_buffer {
@@ -78,7 +82,7 @@ extern "C" {
         void (*cpy_tensor_from_async)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
         void (*cpy_tensor_to_async)  (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
 
-        void (*synchronize)     (ggml_backend_t backend);
+        void (*synchronize)(ggml_backend_t backend);
 
         // compute graph with a plan
         ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);

ggml-backend.c

@@ -35,6 +35,13 @@ bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_ba
     return buft->iface.supports_backend(buft, backend);
 }
 
+bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
+    if (buft->iface.is_host) {
+        return buft->iface.is_host(buft);
+    }
+    return false;
+}
+
 // backend buffer
 
 ggml_backend_buffer_t ggml_backend_buffer_init(
@@ -94,6 +101,14 @@ size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct g
     return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type(buffer), tensor);
 }
 
+void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    buffer->iface.clear(buffer, value);
+}
+
+bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) {
+    return ggml_backend_buft_is_host(ggml_backend_buffer_type(buffer));
+}
+
 ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer) {
     return buffer->buft;
 }
@@ -378,7 +393,6 @@ static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
 
 static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     free(buffer->context);
-    GGML_UNUSED(buffer);
 }
 
 static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
@@ -411,6 +425,10 @@ static void ggml_backend_cpu_buffer_cpy_tensor_to(ggml_backend_buffer_t buffer,
     GGML_UNUSED(buffer);
 }
 
+static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    memset(buffer->context, value, buffer->size);
+}
+
 static struct ggml_backend_buffer_i cpu_backend_buffer_i = {
     /* .free_buffer     = */ ggml_backend_cpu_buffer_free_buffer,
     /* .get_base        = */ ggml_backend_cpu_buffer_get_base,
@@ -419,6 +437,7 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i = {
     /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
     /* .cpy_tensor_from = */ ggml_backend_cpu_buffer_cpy_tensor_from,
     /* .cpy_tensor_to   = */ ggml_backend_cpu_buffer_cpy_tensor_to,
+    /* .clear           = */ ggml_backend_cpu_buffer_clear,
 };
 
 // for buffers from ptr, free is not called
@@ -430,6 +449,7 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
     /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
     /* .cpy_tensor_from = */ ggml_backend_cpu_buffer_cpy_tensor_from,
     /* .cpy_tensor_to   = */ ggml_backend_cpu_buffer_cpy_tensor_to,
+    /* .clear           = */ ggml_backend_cpu_buffer_clear,
 };
 
 static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512
@@ -455,20 +475,70 @@ static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_ty
     GGML_UNUSED(buft);
 }
 
+static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+    return true;
+
+    GGML_UNUSED(buft);
+}
+
 ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
-    static struct ggml_backend_buffer_type ggml_backend_buffer_type_cpu = {
+    static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = {
         /* .iface = */ {
             /* .alloc_buffer     = */ ggml_backend_cpu_buffer_type_alloc_buffer,
             /* .get_alignment    = */ ggml_backend_cpu_buffer_type_get_alignment,
             /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
             /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend,
+            /* .is_host          = */ ggml_backend_cpu_buffer_type_is_host,
         },
         /* .context = */ NULL,
     };
 
-    return &ggml_backend_buffer_type_cpu;
+    return &ggml_backend_cpu_buffer_type;
 }
 
+#ifdef GGML_USE_CPU_HBM
+
+// buffer type HBM
+
+#include <hbwmalloc.h>
+
+static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    hbw_free(buffer->context);
+}
+
+static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    //void * ptr = hbw_malloc(size);
+    void * ptr;
+    int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size);
+    if (result != 0) {
+        fprintf(stderr, "failed to allocate HBM buffer of size %zu\n", size);
+        return NULL;
+    }
+
+    // FIXME: this is a hack to avoid having to implement a new buffer type
+    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
+    buffer->buft = buft;
+    buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer;
+
+    return buffer;
+}
+
+ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type() {
+    static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = {
+        /* .iface    = */ {
+            /* .alloc_buffer     = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_cpu_buffer_type_get_alignment,
+            /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
+            /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend,
+            /* .is_host          = */ ggml_backend_cpu_buffer_type_is_host,
+        },
+        /* .context  = */ NULL,
+    };
+
+    return &ggml_backend_cpu_buffer_type_hbm;
+}
+#endif
+
 struct ggml_backend_cpu_context {
     int n_threads;
     void * work_data;
@@ -505,7 +575,7 @@ static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend
     struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu));
 
     cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
-    cpu_plan->cgraph = *cgraph;
+    cpu_plan->cgraph = *cgraph; // FIXME: deep copy
 
     if (cpu_plan->cplan.work_size > 0) {
         cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size);
@@ -1180,7 +1250,7 @@ void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml
 // utils
 void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
     GGML_ASSERT(tensor->buffer == NULL);
-    GGML_ASSERT(tensor->data == NULL);
+    //GGML_ASSERT(tensor->data == NULL); // views of pre-allocted tensors may have the data set, but still need to be initialized
     GGML_ASSERT(tensor->view_src != NULL);
     GGML_ASSERT(tensor->view_src->buffer != NULL);
     GGML_ASSERT(tensor->view_src->data != NULL);

ggml-backend.h

@@ -21,6 +21,7 @@ extern "C" {
     GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
     GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
     GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
+    GGML_API bool ggml_backend_buft_is_host         (ggml_backend_buffer_type_t buft);
 
     // buffer
     GGML_API void   ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
@@ -29,6 +30,8 @@ extern "C" {
     GGML_API void   ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
     GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
     GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API void   ggml_backend_buffer_clear         (ggml_backend_buffer_t buffer, uint8_t value);
+    GGML_API bool   ggml_backend_buffer_is_host       (ggml_backend_buffer_t buffer);
     GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer);
 
     //
@@ -76,6 +79,10 @@ extern "C" {
 
     GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
 
+#ifdef GGML_USE_CPU_HBM
+    GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
+#endif
+
     //
     // Backend registry
     //

ggml-metal.h

@@ -98,7 +98,10 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void);
 
 GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
 
+GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
+
 GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
+
 GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
 
 // helper to check if the device supports a specific family

ggml-quants.c

@@ -3114,7 +3114,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
 
     size_t vl = __riscv_vsetvl_e8m1(qk/2);
 
-    // These tempory registers are for masking and shift operations
+    // These temporary registers are for masking and shift operations
     vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl);
     vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl);
 
@@ -4757,7 +4757,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 
             vl = 16;
 
-            // retreive lane to multiply with scale
+            // retrieve lane to multiply with scale
             vint32m2_t aux0_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 0), (scale[0]), vl);
             vint32m2_t aux0_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 1), (scale[1]), vl);
             vint32m2_t aux1_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 0), (scale[2]), vl);

ggml.h

@@ -215,9 +215,9 @@
 #define GGML_QNT_VERSION_FACTOR 1000 // do not change this
 
 #define GGML_MAX_DIMS           4
-#define GGML_MAX_PARAMS         1024
+#define GGML_MAX_PARAMS         2048
 #define GGML_MAX_CONTEXTS       64
-#define GGML_MAX_SRC            6
+#define GGML_MAX_SRC            10
 #define GGML_MAX_NAME           64
 #define GGML_MAX_OP_PARAMS      64
 #define GGML_DEFAULT_N_THREADS  4
@@ -303,7 +303,7 @@ extern "C" {
 
 #if defined(__ARM_NEON) && defined(__CUDACC__)
     typedef half ggml_fp16_t;
-#elif defined(__ARM_NEON)
+#elif defined(__ARM_NEON) && !defined(_MSC_VER)
     typedef __fp16 ggml_fp16_t;
 #else
     typedef uint16_t ggml_fp16_t;
@@ -343,6 +343,12 @@ extern "C" {
         GGML_TYPE_COUNT,
     };
 
+    // precision
+    enum ggml_prec {
+        GGML_PREC_DEFAULT,
+        GGML_PREC_F32,
+    };
+
     enum ggml_backend_type {
         GGML_BACKEND_CPU = 0,
         GGML_BACKEND_GPU = 10,
@@ -423,7 +429,9 @@ extern "C" {
         GGML_OP_POOL_1D,
         GGML_OP_POOL_2D,
         GGML_OP_UPSCALE, // nearest interpolate
+        GGML_OP_PAD,
         GGML_OP_ARGSORT,
+        GGML_OP_LEAKY_RELU,
 
         GGML_OP_FLASH_ATTN,
         GGML_OP_FLASH_FF,
@@ -463,7 +471,6 @@ extern "C" {
         GGML_UNARY_OP_GELU,
         GGML_UNARY_OP_GELU_QUICK,
         GGML_UNARY_OP_SILU,
-        GGML_UNARY_OP_LEAKY,
 
         GGML_UNARY_OP_COUNT,
     };
@@ -501,7 +508,6 @@ extern "C" {
 
         struct ggml_backend_buffer * buffer;
 
-        int     n_dims;
         int64_t ne[GGML_MAX_DIMS]; // number of elements
         size_t  nb[GGML_MAX_DIMS]; // stride in bytes:
                                    // nb[0] = ggml_type_size(type)
@@ -533,7 +539,7 @@ extern "C" {
 
         void * extra; // extra things e.g. for ggml-cuda.cu
 
-        char padding[12];
+        char padding[8];
     };
 
     static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
@@ -638,11 +644,14 @@ extern "C" {
     GGML_API int64_t ggml_nrows       (const struct ggml_tensor * tensor);
     GGML_API size_t  ggml_nbytes      (const struct ggml_tensor * tensor);
     GGML_API size_t  ggml_nbytes_pad  (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
-    GGML_API size_t  ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);
 
-    GGML_API int     ggml_blck_size (enum ggml_type type);
-    GGML_API size_t  ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
-    GGML_API float   ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
+    GGML_API int    ggml_blck_size(enum ggml_type type);
+    GGML_API size_t ggml_type_size(enum ggml_type type);             // size in bytes for all elements in a block
+    GGML_API size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
+
+    GGML_DEPRECATED(
+    GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
+    "use ggml_row_size() instead");
 
     GGML_API const char * ggml_type_name(enum ggml_type type);
     GGML_API const char * ggml_op_name  (enum ggml_op   op);
@@ -661,6 +670,11 @@ extern "C" {
     GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
     GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
     GGML_API bool ggml_is_permuted  (const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_scalar    (const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_vector    (const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_matrix    (const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_3d        (const struct ggml_tensor * tensor);
+    GGML_API int  ggml_n_dims       (const struct ggml_tensor * tensor); // returns 1 for scalars
 
     GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
 
@@ -721,8 +735,8 @@ extern "C" {
     GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src);
 
     // Context tensor enumeration and lookup
-    GGML_API struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx);
-    GGML_API struct ggml_tensor * ggml_get_next_tensor (struct ggml_context * ctx, struct ggml_tensor * tensor);
+    GGML_API struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx);
+    GGML_API struct ggml_tensor * ggml_get_next_tensor (const struct ggml_context * ctx, struct ggml_tensor * tensor);
     GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
 
     GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
@@ -793,6 +807,9 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
+    // dst = a
+    // view(dst, nb1, nb2, nb3, offset) += b
+    // return dst
     GGML_API struct ggml_tensor * ggml_acc(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -957,15 +974,14 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
-    GGML_API struct ggml_tensor * ggml_leaky(
+    GGML_API struct ggml_tensor * ggml_leaky_relu(
             struct ggml_context * ctx,
-            struct ggml_tensor  * a);
+            struct ggml_tensor  * a, float negative_slope, bool inplace);
 
     GGML_API struct ggml_tensor * ggml_relu_inplace(
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
-    // TODO: double-check this computation is correct
     GGML_API struct ggml_tensor * ggml_gelu(
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
@@ -1047,11 +1063,18 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
+    // change the precision of a matrix multiplication
+    // set to GGML_PREC_F32 for higher precision (useful for phi-2)
+    GGML_API void ggml_mul_mat_set_prec(
+            struct ggml_tensor * a,
+            enum ggml_prec       prec);
+
     // indirect matrix multiplication
     //  ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b)
     GGML_API struct ggml_tensor * ggml_mul_mat_id(
             struct ggml_context * ctx,
-            struct ggml_tensor  * as[],
+            struct ggml_tensor  * const as[],
+            int                   n_as,
             struct ggml_tensor  * ids,
             int                   id,
             struct ggml_tensor  * b);
@@ -1263,6 +1286,7 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
+    // supports 3D: a->ne[2] == b->ne[1]
     GGML_API struct ggml_tensor * ggml_get_rows(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -1549,6 +1573,15 @@ extern "C" {
             struct ggml_tensor  * a,
             int                   scale_factor);
 
+    // pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0]
+    GGML_API struct ggml_tensor * ggml_pad(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                  p0,
+            int                  p1,
+            int                  p2,
+            int                  p3);
+
     // sort rows
     enum ggml_sort_order {
         GGML_SORT_ASC,
@@ -2102,10 +2135,11 @@ extern "C" {
     GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id);
     GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int key_id, int i);
 
-    GGML_API int    gguf_get_n_tensors    (const struct gguf_context * ctx);
-    GGML_API int    gguf_find_tensor      (const struct gguf_context * ctx, const char * name);
-    GGML_API size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i);
-    GGML_API char * gguf_get_tensor_name  (const struct gguf_context * ctx, int i);
+    GGML_API int            gguf_get_n_tensors    (const struct gguf_context * ctx);
+    GGML_API int            gguf_find_tensor      (const struct gguf_context * ctx, const char * name);
+    GGML_API size_t         gguf_get_tensor_offset(const struct gguf_context * ctx, int i);
+    GGML_API char *         gguf_get_tensor_name  (const struct gguf_context * ctx, int i);
+    GGML_API enum ggml_type gguf_get_tensor_type  (const struct gguf_context * ctx, int i);
 
     // overrides existing values or adds a new one
     GGML_API void gguf_set_val_u8  (struct gguf_context * ctx, const char * key, uint8_t  val);

gguf-py/README.md

@@ -61,7 +61,7 @@ If you want to publish the package manually for any reason, you need to have `tw
 pip install build twine
 ```
 
-Then, folow these steps to release a new version:
+Then, follow these steps to release a new version:
 
 1. Bump the version in `pyproject.toml`.
 2. Build the package:

gguf-py/gguf/constants.py

@@ -38,6 +38,8 @@ class Keys:
         FEED_FORWARD_LENGTH   = "{arch}.feed_forward_length"
         USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
         TENSOR_DATA_LAYOUT    = "{arch}.tensor_data_layout"
+        EXPERT_COUNT          = "{arch}.expert_count"
+        EXPERT_USED_COUNT     = "{arch}.expert_used_count"
 
     class Attention:
         HEAD_COUNT        = "{arch}.attention.head_count"
@@ -93,6 +95,7 @@ class MODEL_ARCH(IntEnum):
     BLOOM     = auto()
     STABLELM  = auto()
     QWEN      = auto()
+    PHI2      = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -111,10 +114,14 @@ class MODEL_TENSOR(IntEnum):
     ATTN_NORM       = auto()
     ATTN_NORM_2     = auto()
     ATTN_ROT_EMBD   = auto()
+    FFN_GATE_INP    = auto()
+    FFN_NORM        = auto()
     FFN_GATE        = auto()
     FFN_DOWN        = auto()
     FFN_UP          = auto()
-    FFN_NORM        = auto()
+    FFN_GATE_EXP    = auto()
+    FFN_DOWN_EXP    = auto()
+    FFN_UP_EXP      = auto()
     ATTN_Q_NORM     = auto()
     ATTN_K_NORM     = auto()
 
@@ -134,6 +141,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.BLOOM:          "bloom",
     MODEL_ARCH.STABLELM:       "stablelm",
     MODEL_ARCH.QWEN:           "qwen",
+    MODEL_ARCH.PHI2:           "phi2",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -154,10 +162,14 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ATTN_ROT_EMBD:   "blk.{bid}.attn_rot_embd",
     MODEL_TENSOR.ATTN_Q_NORM:     "blk.{bid}.attn_q_norm",
     MODEL_TENSOR.ATTN_K_NORM:     "blk.{bid}.attn_k_norm",
+    MODEL_TENSOR.FFN_GATE_INP:    "blk.{bid}.ffn_gate_inp",
     MODEL_TENSOR.FFN_NORM:        "blk.{bid}.ffn_norm",
     MODEL_TENSOR.FFN_GATE:        "blk.{bid}.ffn_gate",
     MODEL_TENSOR.FFN_DOWN:        "blk.{bid}.ffn_down",
     MODEL_TENSOR.FFN_UP:          "blk.{bid}.ffn_up",
+    MODEL_TENSOR.FFN_GATE_EXP:    "blk.{bid}.ffn_gate.{xid}",
+    MODEL_TENSOR.FFN_DOWN_EXP:    "blk.{bid}.ffn_down.{xid}",
+    MODEL_TENSOR.FFN_UP_EXP:      "blk.{bid}.ffn_up.{xid}",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -172,10 +184,14 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ATTN_V,
         MODEL_TENSOR.ATTN_OUT,
         MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_GATE_INP,
         MODEL_TENSOR.FFN_NORM,
         MODEL_TENSOR.FFN_GATE,
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
     ],
     MODEL_ARCH.GPTNEOX: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -336,6 +352,17 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
     MODEL_ARCH.GPT2: [
         # TODO
     ],
+    MODEL_ARCH.PHI2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ]
     # TODO
 }
 

gguf-py/gguf/gguf_writer.py

@@ -339,6 +339,12 @@ class GGUFWriter:
     def add_clamp_kqv(self, value: float) -> None:
         self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
 
+    def add_expert_count(self, count: int) -> None:
+        self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count)
+
+    def add_expert_used_count(self, count: int) -> None:
+        self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count)
+
     def add_layer_norm_eps(self, value: float) -> None:
         self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)
 

gguf-py/gguf/tensor_mapping.py

@@ -17,6 +17,7 @@ class TensorNameMap:
             "tok_embeddings",                            # llama-pth
             "embeddings.word_embeddings",                # bert
             "language_model.embedding.word_embeddings",  # persimmon
+            "transformer.embd.wte",                      # phi2
         ),
 
         # Token type embeddings
@@ -41,6 +42,7 @@ class TensorNameMap:
             "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen
             "output",                    # llama-pth bloom
             "word_embeddings_for_head",  # persimmon
+            "lm_head.linear",            # phi2
         ),
 
         # Output norm
@@ -53,6 +55,7 @@ class TensorNameMap:
             "transformer.norm_f",                      # mpt
             "ln_f",                                    # refact bloom qwen
             "language_model.encoder.final_layernorm",  # persimmon
+            "lm_head.ln",                              # phi2
         ),
 
         # Rope frequencies
@@ -75,6 +78,7 @@ class TensorNameMap:
             "encoder.layer.{bid}.attention.output.LayerNorm",       # bert
             "language_model.encoder.layers.{bid}.input_layernorm",  # persimmon
             "model.layers.{bid}.ln1",                               # yi
+            "transformer.h.{bid}.ln",                               # phi2
         ),
 
         # Attention norm 2
@@ -90,6 +94,7 @@ class TensorNameMap:
             "transformer.h.{bid}.self_attention.query_key_value",                  # falcon
             "h.{bid}.self_attention.query_key_value",                              # bloom
             "language_model.encoder.layers.{bid}.self_attention.query_key_value",  # persimmon
+            "transformer.h.{bid}.mixer.Wqkv",                                      # phi2
         ),
 
         # Attention query
@@ -128,6 +133,7 @@ class TensorNameMap:
             "encoder.layer.{bid}.attention.output.dense",                # bert
             "transformer.h.{bid}.attn.out_proj",                         # gpt-j
             "language_model.encoder.layers.{bid}.self_attention.dense",  # persimmon
+            "transformer.h.{bid}.mixer.out_proj",                        # phi2
         ),
 
         # Rotary embeddings
@@ -149,6 +155,11 @@ class TensorNameMap:
             "model.layers.{bid}.ln2",                                        # yi
         ),
 
+        MODEL_TENSOR.FFN_GATE_INP: (
+            "layers.{bid}.feed_forward.gate",           # mixtral
+            "model.layers.{bid}.block_sparse_moe.gate", # mixtral
+        ),
+
         # Feed-forward up
         MODEL_TENSOR.FFN_UP: (
             "gpt_neox.layers.{bid}.mlp.dense_h_to_4h",                # gptneox
@@ -162,13 +173,24 @@ class TensorNameMap:
             "transformer.h.{bid}.mlp.fc_in",                          # gpt-j
             "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h",  # persimmon
             "transformer.h.{bid}.mlp.w1",                             # qwen
+            "transformer.h.{bid}.mlp.fc1",                            # phi2
+        ),
+
+        MODEL_TENSOR.FFN_UP_EXP: (
+            "layers.{bid}.feed_forward.experts.{xid}.w3",           # mixtral
+            "model.layers.{bid}.block_sparse_moe.experts.{xid}.w3", # mixtral
         ),
 
         # Feed-forward gate
         MODEL_TENSOR.FFN_GATE: (
-            "model.layers.{bid}.mlp.gate_proj",  # llama-hf refact
-            "layers.{bid}.feed_forward.w1",      # llama-pth
-            "transformer.h.{bid}.mlp.w2",        # qwen
+            "model.layers.{bid}.mlp.gate_proj",           # llama-hf refact
+            "layers.{bid}.feed_forward.w1",               # llama-pth
+            "transformer.h.{bid}.mlp.w2",                 # qwen
+        ),
+
+        MODEL_TENSOR.FFN_GATE_EXP: (
+            "layers.{bid}.feed_forward.experts.{xid}.w1",           # mixtral
+            "model.layers.{bid}.block_sparse_moe.experts.{xid}.w1", # mixtral
         ),
 
         # Feed-forward down
@@ -183,6 +205,12 @@ class TensorNameMap:
             "encoder.layer.{bid}.output.dense",                       # bert
             "transformer.h.{bid}.mlp.fc_out",                         # gpt-j
             "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h",  # persimmon
+            "transformer.h.{bid}.mlp.fc2",                            # phi2
+        ),
+
+        MODEL_TENSOR.FFN_DOWN_EXP: (
+            "layers.{bid}.feed_forward.experts.{xid}.w2",           # mixtral
+            "model.layers.{bid}.block_sparse_moe.experts.{xid}.w2", # mixtral
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (
@@ -213,11 +241,14 @@ class TensorNameMap:
             for tensor, keys in self.block_mappings_cfg.items():
                 if tensor not in MODEL_TENSORS[arch]:
                     continue
-                tensor_name = TENSOR_NAMES[tensor].format(bid = bid)
-                self.mapping[tensor_name] = (tensor, tensor_name)
-                for key in keys:
-                    key = key.format(bid = bid)
-                    self.mapping[key] = (tensor, tensor_name)
+                # TODO: make this configurable
+                n_experts = 8
+                for xid in range(n_experts):
+                    tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid)
+                    self.mapping[tensor_name] = (tensor, tensor_name)
+                    for key in keys:
+                        key = key.format(bid = bid, xid = xid)
+                        self.mapping[key] = (tensor, tensor_name)
 
     def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None:
         result = self.mapping.get(key)

gguf-py/gguf/vocab.py

@@ -84,7 +84,7 @@ class SpecialVocab:
         merges_file = path / 'merges.txt'
         if not merges_file.is_file():
             return False
-        with open(merges_file, 'r') as fp:
+        with open(merges_file, 'r', encoding = 'utf-8') as fp:
             first_line = next(fp, '').strip()
             if not first_line.startswith('#'):
                 fp.seek(0)
@@ -109,8 +109,10 @@ class SpecialVocab:
         return True
 
     def _set_special_token(self, typ: str, tid: Any) -> None:
-        if not isinstance(tid, int) or tid < 0:
+        if not isinstance(tid, int):
             return
+        if tid < 0:
+            raise ValueError(f'invalid value for special token type {typ}: {tid}')
         if self.n_vocab is None or tid < self.n_vocab:
             if typ in self.special_token_ids:
                 return

gguf-py/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.6.0"
+version = "0.7.0"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [

llama.h

@@ -39,6 +39,7 @@
 
 #define LLAMA_MAX_RNG_STATE (64*1024)
 
+#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
 #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
 
 #define LLAMA_SESSION_MAGIC   LLAMA_FILE_MAGIC_GGSN
@@ -216,7 +217,7 @@ extern "C" {
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
         bool mul_mat_q;   // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
-        bool logits_all;  // the llama_eval() call computes all logits, not just the last one
+        bool logits_all;  // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
         bool embedding;   // embedding mode only
         bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
     };
@@ -313,7 +314,9 @@ extern "C" {
 
     LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
 
-    LLAMA_API int llama_n_ctx      (const struct llama_context * ctx);
+    // TODO: become more consistent with returned int types across the API
+    LLAMA_API uint32_t llama_n_ctx      (const struct llama_context * ctx);
+    LLAMA_API uint32_t llama_n_batch    (const struct llama_context * ctx);
 
     LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
 

requirements.txt

@@ -1,3 +1,5 @@
 numpy==1.24.4
 sentencepiece==0.1.98
+transformers>=4.34.0
 gguf>=0.1.0
+protobuf>=4.21.0

scripts/get-flags.mk

@@ -0,0 +1,38 @@
+ifeq '' '$(findstring clang,$(shell $(GF_CC) --version))'
+	GF_CC_IS_GCC = 1
+	GF_CC_VER := $(shell { $(GF_CC) -dumpfullversion 2>/dev/null || $(GF_CC) -dumpversion; } | awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
+else
+	GF_CC_IS_CLANG = 1
+	ifeq '' '$(findstring Apple,$(shell $(GF_CC) --version))'
+		GF_CC_IS_LLVM_CLANG = 1
+	else
+		GF_CC_IS_APPLE_CLANG = 1
+	endif
+	GF_CC_VER := \
+		$(shell $(GF_CC) --version | sed -n 's/^.* version \([0-9.]*\).*$$/\1/p' \
+		| awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
+endif
+
+ifeq ($(GF_CC_IS_CLANG), 1)
+	# clang options
+	GF_CFLAGS   = -Wunreachable-code-break -Wunreachable-code-return
+	GF_CXXFLAGS = -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi
+
+	ifneq '' '$(and $(GF_CC_IS_LLVM_CLANG),$(filter 1,$(shell expr $(GF_CC_VER) \>= 030800)))'
+		GF_CFLAGS += -Wdouble-promotion
+	endif
+	ifneq '' '$(and $(GF_CC_IS_APPLE_CLANG),$(filter 1,$(shell expr $(GF_CC_VER) \>= 070300)))'
+		GF_CFLAGS += -Wdouble-promotion
+	endif
+else
+	# gcc options
+	GF_CFLAGS   = -Wdouble-promotion
+	GF_CXXFLAGS = -Wno-array-bounds
+
+	ifeq ($(shell expr $(GF_CC_VER) \>= 070100), 1)
+		GF_CXXFLAGS += -Wno-format-truncation
+	endif
+	ifeq ($(shell expr $(GF_CC_VER) \>= 080100), 1)
+		GF_CXXFLAGS += -Wextra-semi
+	endif
+endif

tests/test-backend-ops.cpp

@@ -20,8 +20,6 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
     size_t size = ggml_nelements(tensor);
     std::vector<float> data(size);
 
-    std::random_device rd;
-
 #if 0
     std::default_random_engine generator(rd());
     std::uniform_real_distribution<float> distribution(min, max);
@@ -31,6 +29,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
     }
 #endif
     auto init_thread = [&](size_t start, size_t end) {
+        std::random_device rd;
         std::default_random_engine generator(rd());
         std::uniform_real_distribution<float> distribution(min, max);
 
@@ -51,11 +50,11 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
         t.join();
     }
 
-    if (tensor->type == GGML_TYPE_F32) {
+    if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
         ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float));
     } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16) {
         GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0);
-        std::vector<uint8_t> dataq(ggml_type_size(tensor->type)*size/ggml_blck_size(tensor->type));
+        std::vector<uint8_t> dataq(ggml_row_size(tensor->type, size));
         int64_t hist[16];
         ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size, hist);
         ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
@@ -71,23 +70,29 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
     std::vector<uint8_t> buf(ggml_nbytes(t));
     ggml_backend_tensor_get(t, buf.data(), 0, ggml_nbytes(t));
 
+    ggml_type_traits_t tt = ggml_internal_get_type_traits(t->type);
+    size_t bs = ggml_blck_size(t->type);
+    std::vector<float> vq(ggml_blck_size(t->type));
+    bool quantized = ggml_is_quantized(t->type);
+
     // access elements by index to avoid gaps in views
     for (int64_t i3 = 0; i3 < t->ne[3]; i3++) {
         for (int64_t i2 = 0; i2 < t->ne[2]; i2++) {
             for (int64_t i1 = 0; i1 < t->ne[1]; i1++) {
-                for (int64_t i0 = 0; i0 < t->ne[0]; i0++) {
-                    size_t i = i3*t->nb[3] + i2*t->nb[2] + i1*t->nb[1] + i0*t->nb[0];
-                    float v;
+                for (int64_t i0 = 0; i0 < t->ne[0]; i0 += bs) {
+                    size_t i = i3*t->nb[3] + i2*t->nb[2] + i1*t->nb[1] + i0/bs*t->nb[0];
                     if (t->type == GGML_TYPE_F16) {
-                        v = (float) ggml_fp16_to_fp32(*(ggml_fp16_t*)&buf[i]);
+                        tv.push_back(ggml_fp16_to_fp32(*(ggml_fp16_t*)&buf[i]));
                     } else if (t->type == GGML_TYPE_F32) {
-                        v = *(float *) &buf[i];
+                        tv.push_back(*(float *) &buf[i]);
                     } else if (t->type == GGML_TYPE_I32) {
-                        v = *(int32_t *) &buf[i];
+                        tv.push_back((float)*(int32_t *) &buf[i]);
+                    } else if (quantized) {
+                        tt.to_float(&buf[i], vq.data(), bs);
+                        tv.insert(tv.end(), vq.begin(), vq.end());
                     } else {
                         GGML_ASSERT(false);
                     }
-                    tv.push_back(v);
                 }
             }
         }
@@ -230,9 +235,18 @@ static bool ggml_is_view_op(enum ggml_op op) {
     return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE;
 }
 
+enum test_mode {
+    MODE_TEST,
+    MODE_PERF,
+};
+
 struct test_case {
     virtual ~test_case() {}
 
+    virtual std::string op_desc(ggml_tensor * t) {
+        return ggml_op_desc(t);
+    }
+
     virtual std::string vars() {
         return "";
     }
@@ -240,7 +254,7 @@ struct test_case {
     virtual ggml_tensor * build_graph(ggml_context * ctx) = 0;
 
     virtual double max_nmse_err() {
-        return 1e-6;
+        return 1e-7;
     }
 
     virtual void initialize_tensors(ggml_context * ctx) {
@@ -260,7 +274,58 @@ struct test_case {
         return size;
     }
 
+    ggml_cgraph * gf = nullptr;
+
+    static const int sentinel_size = 1024;
+
+    test_mode mode;
+
+    std::vector<ggml_tensor *> sentinels;
+
+    void add_sentinel(ggml_context * ctx) {
+        if (mode == MODE_PERF) {
+            return;
+        }
+        ggml_tensor * sentinel = ::ggml_new_tensor_1d(ctx, GGML_TYPE_F32, sentinel_size);
+        ggml_format_name(sentinel, "sent_%zu", sentinels.size());
+        sentinels.push_back(sentinel);
+    }
+
+    // hijack ggml_new_tensor to add sentinels after each tensor to check for overflows in the backend
+
+    ggml_tensor * ggml_new_tensor(ggml_context * ctx, ggml_type type, int n_dims, const int64_t * ne) {
+        ggml_tensor * t = ::ggml_new_tensor(ctx, type, n_dims, ne);
+        add_sentinel(ctx);
+        return t;
+    }
+
+    ggml_tensor * ggml_new_tensor_1d(ggml_context * ctx, ggml_type type, int64_t ne0) {
+        ggml_tensor * t = ::ggml_new_tensor_1d(ctx, type, ne0);
+        add_sentinel(ctx);
+        return t;
+    }
+
+    ggml_tensor * ggml_new_tensor_2d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1) {
+        ggml_tensor * t = ::ggml_new_tensor_2d(ctx, type, ne0, ne1);
+        add_sentinel(ctx);
+        return t;
+    }
+
+    ggml_tensor * ggml_new_tensor_3d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2) {
+        ggml_tensor * t = ::ggml_new_tensor_3d(ctx, type, ne0, ne1, ne2);
+        add_sentinel(ctx);
+        return t;
+    }
+
+    ggml_tensor * ggml_new_tensor_4d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {
+        ggml_tensor * t = ::ggml_new_tensor_4d(ctx, type, ne0, ne1, ne2, ne3);
+        add_sentinel(ctx);
+        return t;
+    }
+
     bool eval(ggml_backend_t backend1, ggml_backend_t backend2, const char * op_name) {
+        mode = MODE_TEST;
+
         ggml_init_params params = {
             /* .mem_size = */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
             /* .mem_base = */ NULL,
@@ -268,15 +333,20 @@ struct test_case {
         };
         ggml_context * ctx = ggml_init(params);
 
+        gf = ggml_new_graph(ctx);
+
+        // pre-graph sentinel
+        add_sentinel(ctx);
+
         ggml_tensor * out = build_graph(ctx);
 
-        if (op_name != nullptr && strcmp(ggml_op_desc(out), op_name) != 0) {
-            //printf("  %s: skipping\n", ggml_op_desc(out));
+        if (op_name != nullptr && op_desc(out) != op_name) {
+            //printf("  %s: skipping\n", op_desc(out).c_str());
             ggml_free(ctx);
             return true;
         }
 
-        printf("  %s(%s): ", ggml_op_desc(out), vars().c_str());
+        printf("  %s(%s): ", op_desc(out).c_str(), vars().c_str());
         fflush(stdout);
 
         // check if backends support op
@@ -288,13 +358,20 @@ struct test_case {
             }
         }
 
+        // post-graph sentinel
+        add_sentinel(ctx);
+
         // allocate
         ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend1);
 
         // build graph
-        ggml_cgraph * gf = ggml_new_graph(ctx);
         ggml_build_forward_expand(gf, out);
 
+        // add sentinels as graph nodes so that they are checked in the callback
+        for (ggml_tensor * sentinel : sentinels) {
+            gf->nodes[gf->n_nodes++] = sentinel;
+        }
+
         // randomize tensors
         initialize_tensors(ctx);
 
@@ -310,14 +387,29 @@ struct test_case {
         };
 
         auto callback = [](int index, ggml_tensor * t1, ggml_tensor * t2, void * user_data) -> bool {
+            callback_userdata * ud = (callback_userdata *) user_data;
+
+            if (t1->op == GGML_OP_NONE) {
+                // sentinels must be unchanged
+                std::vector<uint8_t> t1_data(ggml_nbytes(t1));
+                std::vector<uint8_t> t2_data(ggml_nbytes(t2));
+                ggml_backend_tensor_get(t1, t1_data.data(), 0, ggml_nbytes(t1));
+                ggml_backend_tensor_get(t2, t2_data.data(), 0, ggml_nbytes(t2));
+
+                if (memcmp(t1_data.data(), t2_data.data(), ggml_nbytes(t1)) != 0) {
+                    printf("sentinel mismatch: %s ", t1->name);
+                    ud->ok = false;
+                    return true;
+                }
+            }
+
             std::vector<float> f1 = tensor_to_float(t1);
             std::vector<float> f2 = tensor_to_float(t2);
-            callback_userdata * ud = (callback_userdata *) user_data;
 
             for (size_t i = 0; i < f1.size(); i++) {
                 // check for nans
                 if (std::isnan(f1[i]) || std::isnan(f2[i])) {
-                    printf("NaN at index %zu ", i);
+                    printf("[%s] NaN at index %zu (%f %f) ", ggml_op_desc(t1), i, f1[i], f2[i]);
                     ud->ok = false;
                     return true;
                 }
@@ -325,12 +417,12 @@ struct test_case {
                 if (isinf_or_max(f1[i]) || isinf_or_max(f2[i])) {
                     if (isinf_or_max(f1[i]) && isinf_or_max(f2[i])) {
                         if (std::signbit(f1[i]) != std::signbit(f2[i])) {
-                            printf("inf sign mismatch: %f %f ", f1[i], f2[i]);
+                            printf("[%s] inf sign mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]);
                             ud->ok = false;
                             return true;
                         }
                     } else {
-                        printf("inf mismatch: %f %f ", f1[i], f2[i]);
+                        printf("[%s] inf mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]);
                         ud->ok = false;
                         return true;
                     }
@@ -339,10 +431,17 @@ struct test_case {
 
             double err = nmse(f1.data(), f2.data(), f1.size());
             if (err > ud->max_err) {
-                printf("NMSE = %f ", err);
+                printf("[%s] NMSE = %f ", ggml_op_desc(t1), err);
+                //for (int i = 0; i < f1.size(); i++) {
+                //    printf("%5d %9.6f %9.6f, diff = %9.6f\n", i, f1[i], f2[i], f1[i] - f2[i]);
+                //}
+                //printf("\n");
+                //exit(1);
                 ud->ok = false;
             }
             return true;
+
+            GGML_UNUSED(index);
         };
 
         ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud);
@@ -361,6 +460,8 @@ struct test_case {
     }
 
     bool eval_perf(ggml_backend_t backend, const char * op_name) {
+        mode = MODE_PERF;
+
         static const size_t graph_nodes = 8192;
 
         ggml_init_params params = {
@@ -372,13 +473,13 @@ struct test_case {
 
         ggml_tensor * out = build_graph(ctx);
 
-        if (op_name != nullptr && strcmp(ggml_op_desc(out), op_name) != 0) {
-            //printf("  %s: skipping\n", ggml_op_desc(out));
+        if (op_name != nullptr && op_desc(out) != op_name) {
+            //printf("  %s: skipping\n", op_desc(out).c_str());
             ggml_free(ctx);
             return true;
         }
 
-        int len = printf("  %s(%s): ", ggml_op_desc(out), vars().c_str());
+        int len = printf("  %s(%s): ", op_desc(out).c_str(), vars().c_str());
         fflush(stdout);
 
         // check if backends support op
@@ -430,8 +531,9 @@ struct test_case {
             return size;
         };
         for (int i = 0; i < gf->n_nodes; i++) {
-            if (ggml_is_view_op(gf->nodes[i]->op) || gf->nodes[i] == out)
+            if (ggml_is_view_op(gf->nodes[i]->op) || gf->nodes[i] == out) {
                 continue;
+            }
             mem += tensor_op_size(gf->nodes[i]);
         }
 
@@ -486,17 +588,22 @@ struct test_get_rows : public test_case {
     const int n; // cols
     const int m; // rows
     const int r; // rows to get
+    const int b; // batch size
+    const bool v; // view (non-contiguous src1)
 
     std::string vars() override {
-        return VARS_TO_STR4(type, n, m, r);
+        return VARS_TO_STR6(type, n, m, r, b, v);
     }
 
-    test_get_rows(ggml_type type = GGML_TYPE_F32, int n = 10, int m = 5, int r = 3)
-        : type(type), n(n), m(m), r(r) {}
+    test_get_rows(ggml_type type = GGML_TYPE_F32, int n = 10, int m = 5, int r = 3, int b = 1, bool v = false)
+        : type(type), n(n), m(m), r(r), b(b), v(v) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * in = ggml_new_tensor_2d(ctx, type, n, m);
-        ggml_tensor * rows = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, r);
+        ggml_tensor * in = ggml_new_tensor_3d(ctx, type, n, m, b);
+        ggml_tensor * rows = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, r, b);
+        if (v) {
+            rows = ggml_view_2d(ctx, rows, r/2, b, rows->nb[1], 0);
+        }
         ggml_tensor * out = ggml_get_rows(ctx, in, rows);
         return out;
     }
@@ -504,12 +611,13 @@ struct test_get_rows : public test_case {
     void initialize_tensors(ggml_context * ctx) override {
         for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
             if (t->type == GGML_TYPE_I32) {
+                if (ggml_is_view_op(t->op)) { continue; }
                 // rows
-                std::vector<int> data(r);
-                for (int i = 0; i < r; i++) {
+                std::vector<int> data(r*b);
+                for (int i = 0; i < r*b; i++) {
                     data[i] = rand() % m;
                 }
-                ggml_backend_tensor_set(t, data.data(), 0, r * sizeof(int));
+                ggml_backend_tensor_set(t, data.data(), 0, r * b * sizeof(int));
             } else {
                 init_tensor_uniform(t);
             }
@@ -770,11 +878,10 @@ struct test_mul_mat_id : public test_case {
     const int64_t m;
     const int64_t n;
     const int64_t k;
-    const std::array<int64_t, 2> bs; // dims 3 and 4
-    const std::array<int64_t, 2> nr; // repeat in dims 3 and 4
+    const bool v; // view (non-contiguous ids)
 
     std::string vars() override {
-        return VARS_TO_STR9(type_a, type_b, n_mats, id, m, n, k, bs, nr);
+        return VARS_TO_STR8(type_a, type_b, n_mats, id, m, n, k, v);
     }
 
     double max_nmse_err() override {
@@ -782,7 +889,7 @@ struct test_mul_mat_id : public test_case {
     }
 
     size_t op_size(ggml_tensor * t) override {
-        size_t a = ggml_nbytes(t->src[2]) * n * nr[0] * nr[1];
+        size_t a = ggml_nbytes(t->src[2]) * n;
         size_t b = ggml_nbytes(t->src[1]) * m;
         size_t c  = ggml_nbytes(t);
         return a + b + c;
@@ -792,35 +899,41 @@ struct test_mul_mat_id : public test_case {
 
     test_mul_mat_id(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
             int n_mats = 2, int id = 0,
-            int64_t m = 32, int64_t n = 32, int64_t k = 32,
-            std::array<int64_t, 2> bs = {10, 10},
-            std::array<int64_t, 2> nr = {2, 2})
+            int64_t m = 32, int64_t n = 32, int64_t k = 32, bool v = false)
         : type_a(type_a), type_b(type_b), n_mats(n_mats), id(id),
-            m(m), n(n), k(k), bs(bs), nr(nr) {}
+            m(m), n(n), k(k), v(v) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         // C^T = A * B^T: (k, m) * (k, n) => (m, n)
         std::vector<ggml_tensor *> mats;
         for (int i = 0; i < n_mats; i++) {
-            ggml_tensor * a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0], bs[1]);
+            ggml_tensor * a = ggml_new_tensor_2d(ctx, type_a, k, m);
             mats.push_back(a);
         }
-        ggml_tensor * ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_mats);
-        ggml_tensor * b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
-        ggml_tensor * out = ggml_mul_mat_id(ctx, mats.data(), ids, id, b);
+        ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, n);
+        if (v) {
+            ids = ggml_view_2d(ctx, ids, n_mats/2, ids->ne[1], ids->nb[1], 0);
+        }
+        ggml_tensor * b = ggml_new_tensor_2d(ctx, type_b, k, n);
+        ggml_tensor * out = ggml_mul_mat_id(ctx, mats.data(), n_mats, ids, v ? id/2 : id, b);
         return out;
     }
 
     void initialize_tensors(ggml_context * ctx) override {
+        std::random_device rd;
+        std::default_random_engine rng(rd());
         for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
             if (t->type == GGML_TYPE_I32) {
+                if (ggml_is_view_op(t->op)) { continue; }
                 // ids
-                std::vector<int> data(n_mats);
-                for (int i = 0; i < n_mats; i++) {
-                    data[i] = i;
+                for (int64_t r = 0; r < ggml_nrows(t); r++) {
+                    std::vector<int32_t> data(t->ne[0]);
+                    for (int i = 0; i < t->ne[0]; i++) {
+                        data[i] = i % n_mats;
+                    }
+                    std::shuffle(data.begin(), data.end(), rng);
+                    ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t));
                 }
-                std::shuffle(data.begin(), data.end(), std::default_random_engine(std::random_device()()));
-                ggml_backend_tensor_set(t, data.data(), 0, n_mats * sizeof(int));
             } else {
                 init_tensor_uniform(t);
             }
@@ -1109,22 +1222,227 @@ struct test_sum_rows : public test_case {
     }
 };
 
-enum test_mode {
-    MODE_TEST,
-    MODE_PERF,
+// GGML_OP_UPSCALE
+struct test_upscale : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+    const int32_t scale_factor;
+
+    std::string vars() override {
+        return VARS_TO_STR3(type, ne, scale_factor);
+    }
+
+    test_upscale(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {512, 512, 3, 1},
+            int32_t scale_factor = 2)
+        : type(type), ne(ne), scale_factor(scale_factor) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_tensor * out = ggml_upscale(ctx, a, scale_factor);
+        return out;
+    }
+};
+
+// GGML_OP_GROUP_NORM
+struct test_group_norm : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+    const int32_t num_groups;
+
+    std::string vars() override {
+        return VARS_TO_STR3(type, ne, num_groups);
+    }
+
+    test_group_norm(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {64, 64, 320, 1},
+            int32_t num_groups = 32)
+        : type(type), ne(ne), num_groups(num_groups) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_tensor * out = ggml_group_norm(ctx, a, num_groups);
+        return out;
+    }
+};
+
+// GGML_OP_ACC
+struct test_acc : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne_a;
+    const std::array<int64_t, 4> ne_b;
+
+    std::string vars() override {
+        return VARS_TO_STR3(type, ne_a, ne_b);
+    }
+
+    test_acc(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne_a = {1024, 577, 1, 1},
+            std::array<int64_t, 4> ne_b = {1024, 576, 1, 1})
+        : type(type), ne_a(ne_a), ne_b(ne_b) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data());
+        ggml_tensor * out = ggml_acc(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], b->nb[1]);
+        return out;
+    }
+};
+
+// GGML_OP_PAD
+struct test_pad : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne_a;
+    const int pad_0;
+    const int pad_1;
+
+    std::string vars() override {
+        return VARS_TO_STR4(type, ne_a, pad_0, pad_1);
+    }
+
+    test_pad(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne_a = {512, 512, 1, 1},
+            int pad_0 = 1, int pad_1 = 1)
+        : type(type), ne_a(ne_a), pad_0(pad_0), pad_1(pad_1)  {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        ggml_tensor * out = ggml_pad(ctx, a, pad_0, pad_1, 0, 0);
+        return out;
+    }
+};
+
+// GGML_OP_LEAKY_RELU
+struct test_leaky_relu : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne_a;
+    const float negative_slope;
+
+    std::string vars() override {
+        return VARS_TO_STR3(type, ne_a, negative_slope);
+    }
+
+    test_leaky_relu(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne_a = {10, 10, 10, 10},
+            float negative_slope = 0.1f)
+        : type(type), ne_a(ne_a), negative_slope(negative_slope)  {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        ggml_tensor * out = ggml_leaky_relu(ctx, a, negative_slope, true);
+        return out;
+    }
+};
+
+// Mixtral MOE
+struct test_moe : public test_case {
+    const int n_experts;
+    const int n_experts_per_tok;
+    const int n_tokens;
+    const int n_embd;
+    const int n_ff;
+
+    std::string op_desc(ggml_tensor * t) override {
+        return "MOE";
+
+        GGML_UNUSED(t);
+    }
+
+    std::string vars() override {
+        return VARS_TO_STR5(n_experts, n_experts_per_tok, n_tokens, n_embd, n_ff);
+    }
+
+    test_moe(int n_experts = 8, int n_experts_per_tok = 2, int n_tokens = 1, int n_embd = 4096, int n_ff = 14336)
+        : n_experts(n_experts), n_experts_per_tok(n_experts_per_tok), n_tokens(n_tokens), n_embd(n_embd), n_ff(n_ff) {
+    }
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * ffn_gate_inp = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_experts);
+
+        std::vector<ggml_tensor *> ffn_up_exp(n_experts);
+        std::vector<ggml_tensor *> ffn_gate_exp(n_experts);
+        std::vector<ggml_tensor *> ffn_down_exp(n_experts);
+
+        for (int i = 0; i < n_experts; ++i) {
+            ffn_up_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
+            ffn_gate_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
+            ffn_down_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
+        }
+
+        ggml_tensor * cur = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens);
+
+        ggml_tensor * logits = ggml_mul_mat(ctx, ffn_gate_inp, cur);
+        ggml_tensor * probs = ggml_soft_max_ext(ctx, logits, nullptr, 1.0f/sqrtf(n_embd));
+
+        // select experts
+        ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_experts_per_tok);
+
+        ggml_tensor * weights = ggml_get_rows(ctx,
+                ggml_reshape_3d(ctx, probs, 1, n_experts, n_tokens), selected_experts);
+
+        weights = ggml_reshape_2d(ctx, weights, n_experts_per_tok, n_tokens);
+
+        ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights);
+
+        weights = ggml_div(ctx, weights, weights_sum);
+
+        // compute expert outputs
+        ggml_tensor * moe_out = nullptr;
+
+        for (int i = 0; i < n_experts_per_tok; ++i) {
+            ggml_tensor * cur_expert;
+
+            ggml_tensor * cur_up = ggml_mul_mat_id(ctx, ffn_up_exp.data(), n_experts, selected_experts, i, cur);
+
+            ggml_tensor * cur_gate = ggml_mul_mat_id(ctx, ffn_gate_exp.data(), n_experts, selected_experts, i, cur);
+
+            cur_gate = ggml_silu(ctx, cur_gate);
+
+            cur_expert = ggml_mul(ctx, cur_up, cur_gate);
+
+            cur_expert = ggml_mul_mat_id(ctx, ffn_down_exp.data(), n_experts, selected_experts, i, cur_expert);
+
+            cur_expert = ggml_mul(ctx, cur_expert,
+                    ggml_view_2d(ctx, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
+
+            if (i == 0) {
+                moe_out = cur_expert;
+            } else {
+                moe_out = ggml_add(ctx, moe_out, cur_expert);
+            }
+        }
+
+        cur = moe_out;
+
+        return cur;
+    }
 };
 
 static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_name) {
     std::vector<std::unique_ptr<test_case>> test_cases;
 
+    const ggml_type all_types[] = {
+        GGML_TYPE_F32, GGML_TYPE_F16,
+        GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
+        GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
+        GGML_TYPE_Q8_0,
+        GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
+        GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
+        GGML_TYPE_Q6_K
+    };
+
     // unary ops
     for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
         test_cases.emplace_back(new test_unary((ggml_unary_op) op));
     }
 
-    for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
-        test_cases.emplace_back(new test_get_rows(type, 10, 5, 3));
-        test_cases.emplace_back(new test_get_rows(type, 16, 5, 3));
+    test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, false));
+    for (ggml_type type : all_types) {
+        for (int b : {1, 7}) {
+            for (bool v : {false, true}) {
+                test_cases.emplace_back(new test_get_rows(type, 256, 5, 4, b, v));
+            }
+        }
     }
 
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));
@@ -1134,7 +1452,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 2}));
 
     test_cases.emplace_back(new test_dup());
-    test_cases.emplace_back(new test_cpy());
+
+    for (ggml_type type : all_types) {
+       test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, type, {256, 10, 10, 1}));
+    }
+
     test_cases.emplace_back(new test_cont());
 
     auto add_test_bin_bcast = [&](ggml_type type, std::array<int64_t, 4> ne, std::array<int, 4> nr) {
@@ -1144,6 +1466,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     };
 
     add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 8, 1}, {1, 1, 1, 1});
+    add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1, 1}, {32, 1, 1, 1});
     add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 320, 320}, {1, 1, 1, 1});
     add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 1, 1}, {1, 1, 1, 1});
     add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 1}, {1, 1, 1, 1});
@@ -1170,8 +1493,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 640, 1}, {32, 32, 1, 1});
     add_test_bin_bcast(GGML_TYPE_F32, {5120, 1, 1, 1}, {1, 256, 1, 1});
     add_test_bin_bcast(GGML_TYPE_F32, {640, 1, 1, 1}, {1, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {1, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {2, 1, 1, 1});
+    //add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {1, 1, 1, 1});
+    //add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {2, 1, 1, 1});
 
     test_cases.emplace_back(new test_scale());
 
@@ -1180,16 +1503,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps));
     }
 
-    const ggml_type all_types[] = {
-        GGML_TYPE_F32, GGML_TYPE_F16,
-        GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
-        GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
-        GGML_TYPE_Q8_0,
-        GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
-        GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
-        GGML_TYPE_Q6_K
-    };
-
     for (ggml_type type_a : all_types) {
         for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
             // FIXME: CPU crashes on f16xf16
@@ -1213,9 +1526,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
 
     for (ggml_type type_a : all_types) {
         for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
-            for (int n_mats : {1, 2, 4}) {
+            for (int n_mats : {2, 4, 8}) {
                 for (int id = 0; id < n_mats; id++) {
-                    test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, id, 16, 16, 256, {1, 1}, {1, 1}));
+                    for (bool v : {false, true}) {
+                        test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, id, 16, 16, 256, v));
+                    }
                 }
             }
         }
@@ -1240,6 +1555,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         test_cases.emplace_back(new test_rope(type, { 64,   8, 10, 1},  64, 2, 512)); // neox (falcon 40B)
         test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1},  64, 2, 512)); // neox (falcon 40B)
         test_cases.emplace_back(new test_rope(type, { 80,  32, 10, 1},  20, 2, 512)); // neox (stablelm)
+        test_cases.emplace_back(new test_rope(type, { 80,  32, 10, 1},  32, 2, 512)); // neox (phi-2)
     }
 
     test_cases.emplace_back(new test_alibi());
@@ -1247,10 +1563,22 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_concat());
 
     for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) {
+        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16, 10, 10, 10}, order));
     }
 
     test_cases.emplace_back(new test_sum_rows());
+    test_cases.emplace_back(new test_upscale());
+    test_cases.emplace_back(new test_group_norm());
+    test_cases.emplace_back(new test_acc());
+    test_cases.emplace_back(new test_pad());
+    test_cases.emplace_back(new test_leaky_relu());
+
+#if !defined(__SANITIZE_THREAD__)
+    // FIXME: these tests use too much memory with thread sanitizer
+    test_cases.emplace_back(new test_moe(8, 2, 1, 4096, 8*1024));
+    //test_cases.emplace_back(new test_moe(8, 2, 8, 4096, 14336));
+#endif
 
     // run tests
     if (mode == MODE_TEST) {
@@ -1267,14 +1595,17 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         ggml_backend_free(backend_cpu);
 
         return n_ok == test_cases.size();
-    } else if (mode == MODE_PERF) {
+    }
+
+    if (mode == MODE_PERF) {
         for (auto & test : test_cases) {
             test->eval_perf(backend, op_name);
         }
         return true;
-    } else {
-        GGML_ASSERT(false);
     }
+
+    GGML_ASSERT(false);
+    return false;
 }
 
 static void usage(char ** argv) {
@@ -1347,11 +1678,12 @@ int main(int argc, char ** argv) {
     }
 
     printf("%zu/%zu backends passed\n", n_ok, ggml_backend_reg_get_count());
+
     if (n_ok != ggml_backend_reg_get_count()) {
         printf("\033[1;31mFAIL\033[0m\n");
         return 1;
-    } else {
-        printf("\033[1;32mOK\033[0m\n");
-        return 0;
     }
+
+    printf("\033[1;32mOK\033[0m\n");
+    return 0;
 }

tests/test-grad0.cpp

@@ -1,4 +1,4 @@
-#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
+#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows
 #include "ggml.h"
 
 #include <cmath>

tests/test-quantize-perf.cpp

@@ -117,7 +117,7 @@ static void usage(char * argv[]) {
     printf("  --size SIZE           set test size, divisible by 32 (L1_SIZE:%d)\n", L1_SIZE);
     printf("  -3                    use size as L1, L2, L3 sizes (L1:%d L2:%d L3:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE);
     printf("  -4                    use size as L1, L2, L3, MEM sizes (L1:%d L2:%d L3:%d MEM:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE, MEM_SIZE);
-    printf("  --op OP               set test opration as quantize_row_q_reference, quantize_row_q, dequantize_row_q,\n");
+    printf("  --op OP               set test operation as quantize_row_q_reference, quantize_row_q, dequantize_row_q,\n");
     printf("                        quantize_row_q_dot, vec_dot_q (all)\n");
     printf("  --type TYPE           set test type as");
     for (int i = 0; i < GGML_TYPE_COUNT; i++) {
@@ -202,7 +202,7 @@ int main(int argc, char * argv[]) {
             }
             int alignment = std::stoi(argv[i]);
             if (alignment < 0 || alignment > MAX_ALIGNMENT) {
-            fprintf(stderr, "error: aligment-offset must be less than %d\n", MAX_ALIGNMENT);
+            fprintf(stderr, "error: alignment-offset must be less than %d\n", MAX_ALIGNMENT);
                 invalid_param = true;
                 break;
             }
@@ -286,7 +286,7 @@ int main(int argc, char * argv[]) {
                         qfns.from_float_reference(test_data1, test_q1, size);
                         return test_q1[0];
                     };
-                    size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
+                    size_t quantized_size = ggml_row_size(type, size);
                     benchmark_function(size, quantized_size, iterations, quantize_fn);
                 }
                 printf("\n");
@@ -300,7 +300,7 @@ int main(int argc, char * argv[]) {
                         qfns.from_float(test_data1, test_q1, size);
                         return test_q1[0];
                     };
-                    size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
+                    size_t quantized_size = ggml_row_size(type, size);
                     benchmark_function(size, quantized_size, iterations, quantize_fn);
                 }
                 printf("\n");
@@ -315,7 +315,7 @@ int main(int argc, char * argv[]) {
                         qfns.to_float(test_q1, test_out, size);
                         return test_out[0];
                     };
-                    size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
+                    size_t quantized_size = ggml_row_size(type, size);
                     benchmark_function(size, quantized_size, iterations, quantize_fn);
                 }
                 printf("\n");
@@ -330,7 +330,7 @@ int main(int argc, char * argv[]) {
                         vdot.from_float(test_data1, test_q1, size);
                         return test_q1[0];
                     };
-                    size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
+                    size_t quantized_size = ggml_row_size(type, size);
                     benchmark_function(size, quantized_size, iterations, quantize_fn);
                 }
                 printf("\n");
@@ -347,7 +347,7 @@ int main(int argc, char * argv[]) {
                         qfns.vec_dot(size, &result, test_q1, test_q2);
                         return result;
                     };
-                    size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
+                    size_t quantized_size = ggml_row_size(type, size);
                     benchmark_function(size, quantized_size, iterations, quantize_fn);
                 }
                 printf("\n");