cuda : fix conflict with std::swap (#6186)

* Add MobileVLM_V2 backup

* Update MobileVLM-README.md

* Update examples/llava/MobileVLM-README.md

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

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

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

* clip :  fix whitespace

* fix deifinition mistake in clip.cpp

---------

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

.github/workflows/build.yml

@@ -48,6 +48,28 @@ jobs:
           CC=gcc-8 make tests -j $(nproc)
           make test -j $(nproc)
 
+  ubuntu-focal-make-curl:
+    runs-on: ubuntu-20.04
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v3
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential gcc-8 libcurl4-openssl-dev
+
+      - name: Build
+        id: make_build
+        env:
+          LLAMA_FATAL_WARNINGS: 1
+          LLAMA_CURL: 1
+        run: |
+          CC=gcc-8 make -j $(nproc)
+
   ubuntu-latest-cmake:
     runs-on: ubuntu-latest
 
@@ -76,40 +98,40 @@ jobs:
           cd build
           ctest -L main --verbose --timeout 900
 
-  ubuntu-latest-cmake-sanitizer:
-    runs-on: ubuntu-latest
-
-    continue-on-error: true
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, THREAD, UNDEFINED]
-        build_type: [Debug, Release]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v3
-
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential
-
-      - name: Build
-        id: cmake_build
-        run: |
-          mkdir build
-          cd build
-          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
-          cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
-
-      - name: Test
-        id: cmake_test
-        run: |
-          cd build
-          ctest -L main --verbose --timeout 900
+#  ubuntu-latest-cmake-sanitizer:
+#    runs-on: ubuntu-latest
+#
+#    continue-on-error: true
+#
+#    strategy:
+#      matrix:
+#        sanitizer: [ADDRESS, THREAD, UNDEFINED]
+#        build_type: [Debug, Release]
+#
+#    steps:
+#      - name: Clone
+#        id: checkout
+#        uses: actions/checkout@v3
+#
+#      - name: Dependencies
+#        id: depends
+#        run: |
+#          sudo apt-get update
+#          sudo apt-get install build-essential
+#
+#      - name: Build
+#        id: cmake_build
+#        run: |
+#          mkdir build
+#          cd build
+#          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
+#          cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
+#
+#      - name: Test
+#        id: cmake_test
+#        run: |
+#          cd build
+#          ctest -L main --verbose --timeout 900
 
   ubuntu-latest-cmake-mpi:
     runs-on: ubuntu-latest
@@ -333,6 +355,7 @@ jobs:
           mkdir build
           cd build
           cmake -G Xcode .. \
+            -DLLAMA_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
             -DLLAMA_BUILD_SERVER=OFF \
@@ -361,6 +384,7 @@ jobs:
           mkdir build
           cd build
           cmake -G Xcode .. \
+            -DLLAMA_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
             -DLLAMA_BUILD_SERVER=OFF \
@@ -425,6 +449,8 @@ jobs:
             defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON'
           - build: 'vulkan'
             defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON'
+          - build: 'arm64'
+            defines: '-A ARM64 -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
 
     steps:
       - name: Clone
@@ -520,7 +546,7 @@ jobs:
       - name: Test
         id: cmake_test
         # not all machines have native AVX-512
-        if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }}
+        if: ${{ matrix.build != 'arm64' && matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }}
         run: |
           cd build
           ctest -L main -C Release --verbose --timeout 900

.github/workflows/close-issue.yml

@@ -0,0 +1,23 @@
+name: Close inactive issues
+on:
+  schedule:
+    - cron: "42 0 * * *"
+
+jobs:
+  close-issues:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+      - uses: actions/stale@v5
+        with:
+          exempt-issue-labels: "refactor,help wanted,good first issue,research"
+          days-before-issue-stale: 30
+          days-before-issue-close: 14
+          stale-issue-label: "stale"
+          close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale."
+          days-before-pr-stale: -1
+          days-before-pr-close: -1
+          operations-per-run: 1000
+          repo-token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/server.yml

@@ -24,18 +24,15 @@ jobs:
 
     strategy:
       matrix:
-        sanitizer: [ADDRESS, THREAD, UNDEFINED]
-        build_type: [Debug, Release]
+        # TODO: temporary disabled due to linux kernel issues
+        #sanitizer: [ADDRESS, THREAD, UNDEFINED]
+        sanitizer: [UNDEFINED]
+        build_type: [Debug]
         include:
           - build_type: Release
             sanitizer: ""
-        exclude:
-          - build_type: Release
-            sanitizer: ADDRESS
-          - build_type: Release
-            sanitizer: THREAD
-          - build_type: Release
-            sanitizer: UNDEFINED
+            disabled_on_pr: true
+      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
 
     container:
       image: ubuntu:latest
@@ -60,7 +57,8 @@ jobs:
             cmake \
             python3-pip \
             wget \
-            language-pack-en
+            language-pack-en \
+            libcurl4-openssl-dev
 
       - name: Build
         id: cmake_build
@@ -70,6 +68,7 @@ jobs:
           cmake .. \
               -DLLAMA_NATIVE=OFF \
               -DLLAMA_BUILD_SERVER=ON \
+              -DLLAMA_CURL=ON \
               -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
               -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
           cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server
@@ -81,13 +80,14 @@ jobs:
 
       - name: Tests
         id: server_integration_tests
+        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
         run: |
           cd examples/server/tests
           PORT=8888 ./tests.sh
 
       - name: Slow tests
         id: server_integration_tests_slow
-        if: ${{ github.event.schedule != '' && matrix.build_type == 'Release' || github.event.inputs.slow_tests == 'true' }}
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
         run: |
           cd examples/server/tests
           PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow
@@ -103,12 +103,21 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: libCURL
+        id: get_libcurl
+        env:
+          CURL_VERSION: 8.6.0_6
+        run: |
+          curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip"
+          mkdir $env:RUNNER_TEMP/libcurl
+          tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl
+
       - name: Build
         id: cmake_build
         run: |
           mkdir build
           cd build
-          cmake ..  -DLLAMA_BUILD_SERVER=ON -DCMAKE_BUILD_TYPE=Release ;
+          cmake .. -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
           cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
 
       - name: Python setup
@@ -122,15 +131,21 @@ jobs:
         run: |
           pip install -r examples/server/tests/requirements.txt
 
+      - name: Copy Libcurl
+        id: prepare_libcurl
+        run: |
+          cp $env:RUNNER_TEMP/libcurl/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll
+
       - name: Tests
         id: server_integration_tests
+        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
         run: |
           cd examples/server/tests
           behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
 
       - name: Slow tests
         id: server_integration_tests_slow
-        if: ${{ github.event.schedule != '' || github.event.inputs.slow_tests == 'true' }}
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
         run: |
           cd examples/server/tests
           behave.exe --stop --no-skipped --no-capture --tags slow

.github/workflows/tidy-post.yml

@@ -1,20 +0,0 @@
-name: clang-tidy review post comments
-
-on:
-  workflow_dispatch:
-    workflows: ["clang-tidy-review"]
-    types:
-      - completed
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-
-    steps:
-      - uses: ZedThree/clang-tidy-review/post@v0.13.0
-        # lgtm_comment_body, max_comments, and annotations need to be set on the posting workflow in a split setup
-        with:
-          # adjust options as necessary
-          lgtm_comment_body: ''
-          annotations: false
-          max_comments: 25

.github/workflows/tidy-review.yml

@@ -1,23 +0,0 @@
-name: clang-tidy-review
-
-on:
-  pull_request:
-    branches:
-      - master
-
-jobs:
-  clang-tidy-review:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v3
-
-    - uses: ZedThree/clang-tidy-review@v0.13.0
-      id: review
-      with:
-        lgtm_comment_body: ''
-        build_dir: build
-        cmake_command: cmake . -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=on
-        split_workflow: true
-
-    - uses: ZedThree/clang-tidy-review/upload@v0.13.0

.gitignore

@@ -12,6 +12,8 @@
 *.dot
 *.bat
 *.metallib
+*.etag
+*.lastModified
 .DS_Store
 .build/
 .cache/
@@ -25,6 +27,8 @@
 .vscode/
 .idea/
 
+ggml-metal-embed.metal
+
 lcov-report/
 gcovr-report/
 

CMakeLists.txt

@@ -99,6 +99,7 @@ option(LLAMA_CUDA_F16                        "llama: use 16 bit floats for some
 set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
 set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
                                              "llama: max. batch size for using peer access")
+option(LLAMA_CURL                            "llama: use libcurl to download model from an URL" OFF)
 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)
@@ -116,7 +117,9 @@ option(LLAMA_MPI                             "llama: use MPI"
 option(LLAMA_QKK_64                          "llama: use super-block size of 64 for k-quants"   OFF)
 option(LLAMA_SYCL                            "llama: use SYCL"                                  OFF)
 option(LLAMA_SYCL_F16                        "llama: use 16 bit floats for sycl calculations"   OFF)
+set(LLAMA_SYCL_TARGET   "INTEL" CACHE STRING "llama: sycl target device")
 option(LLAMA_CPU_HBM                         "llama: use memkind for CPU HBM"                   OFF)
+set(LLAMA_SCHED_MAX_COPIES  "4" CACHE STRING "llama: max input copies for pipeline parallelism")
 
 option(LLAMA_BUILD_TESTS                     "llama: build tests"    ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES                  "llama: build examples" ${LLAMA_STANDALONE})
@@ -146,6 +149,8 @@ set(THREADS_PREFER_PTHREAD_FLAG ON)
 find_package(Threads REQUIRED)
 include(CheckCXXCompilerFlag)
 
+add_compile_definitions(GGML_SCHED_MAX_COPIES=${LLAMA_SCHED_MAX_COPIES})
+
 # enable libstdc++ assertions for debug builds
 if (CMAKE_SYSTEM_NAME MATCHES "Linux")
     add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
@@ -196,9 +201,6 @@ if (LLAMA_METAL)
         add_compile_definitions(GGML_METAL_NDEBUG)
     endif()
 
-    # get full path to the file
-    #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
-
     # copy ggml-common.h and ggml-metal.metal to bin directory
     configure_file(ggml-common.h    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h    COPYONLY)
     configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
@@ -207,53 +209,62 @@ if (LLAMA_METAL)
         enable_language(ASM)
         add_compile_definitions(GGML_METAL_EMBED_LIBRARY)
 
-        set(METALLIB_SOURCE "${CMAKE_SOURCE_DIR}/ggml-metal.metal")
+        set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/ggml-common.h")
+        set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
+
         file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated")
-        set(EMBED_METALLIB_ASSEMBLY "${CMAKE_BINARY_DIR}/autogenerated/ggml-embed-metallib.s")
+
+        # merge ggml-common.h and ggml-metal.metal into a single file
+        set(METALLIB_EMBED_ASM    "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s")
+        set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal")
 
         add_custom_command(
-            OUTPUT ${EMBED_METALLIB_ASSEMBLY}
-            COMMAND echo ".section __DATA,__ggml_metallib" > ${EMBED_METALLIB_ASSEMBLY}
-            COMMAND echo ".globl _ggml_metallib_start" >> ${EMBED_METALLIB_ASSEMBLY}
-            COMMAND echo "_ggml_metallib_start:" >> ${EMBED_METALLIB_ASSEMBLY}
-            COMMAND echo ".incbin \\\"${METALLIB_SOURCE}\\\"" >> ${EMBED_METALLIB_ASSEMBLY}
-            COMMAND echo ".globl _ggml_metallib_end" >> ${EMBED_METALLIB_ASSEMBLY}
-            COMMAND echo "_ggml_metallib_end:" >> ${EMBED_METALLIB_ASSEMBLY}
-            DEPENDS ${METALLIB_SOURCE}
+            OUTPUT ${METALLIB_EMBED_ASM}
+            COMMAND echo "Embedding Metal library"
+            COMMAND sed -e '/\#include \"ggml-common.h\"/r ${METALLIB_COMMON}' -e '/\#include \"ggml-common.h\"/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED}
+            COMMAND echo ".section __DATA,__ggml_metallib"          >  ${METALLIB_EMBED_ASM}
+            COMMAND echo ".globl _ggml_metallib_start"              >> ${METALLIB_EMBED_ASM}
+            COMMAND echo "_ggml_metallib_start:"                    >> ${METALLIB_EMBED_ASM}
+            COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM}
+            COMMAND echo ".globl _ggml_metallib_end"                >> ${METALLIB_EMBED_ASM}
+            COMMAND echo "_ggml_metallib_end:"                      >> ${METALLIB_EMBED_ASM}
+            DEPENDS ggml-metal.metal ggml-common.h
             COMMENT "Generate assembly for embedded Metal library"
         )
 
-        set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${EMBED_METALLIB_ASSEMBLY})
-    endif()
-
-    if (LLAMA_METAL_SHADER_DEBUG)
-        # custom command to do the following:
-        #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
-        #   xcrun -sdk macosx metallib                   ggml-metal.air   -o default.metallib
-        #
-        # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
-        #       disabling fast math is needed in order to pass tests/test-backend-ops
-        # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
-        # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
-        #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
-        set(XC_FLAGS -fno-fast-math -fno-inline -g)
-        if (LLAMA_QKK_64)
-            set(XC_FLAGS ${XC_FLAGS} -DQK_K=64)
+        set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${METALLIB_EMBED_ASM})
+    else()
+        if (LLAMA_METAL_SHADER_DEBUG)
+            # custom command to do the following:
+            #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
+            #   xcrun -sdk macosx metallib                   ggml-metal.air   -o default.metallib
+            #
+            # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
+            #       disabling fast math is needed in order to pass tests/test-backend-ops
+            # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
+            # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
+            #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
+            set(XC_FLAGS -fno-fast-math -fno-inline -g)
+        else()
+            set(XC_FLAGS -O3)
         endif()
 
         add_custom_command(
             OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
             COMMAND xcrun -sdk macosx metal    ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
             COMMAND xcrun -sdk macosx metallib                ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air   -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-            DEPENDS ggml-metal.metal
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
+            DEPENDS ggml-metal.metal ggml-common.h
             COMMENT "Compiling Metal kernels"
-        )
+            )
 
         add_custom_target(
             ggml-metal ALL
             DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-        )
-    endif()
+            )
+    endif() # LLAMA_METAL_EMBED_LIBRARY
 
     set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
         ${FOUNDATION_LIBRARY}
@@ -534,6 +545,10 @@ if (LLAMA_HIPBLAS)
 endif()
 
 if (LLAMA_SYCL)
+    if (NOT LLAMA_SYCL_TARGET MATCHES "^(INTEL|NVIDIA)$")
+        message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL or NVIDIA")
+    endif()
+
     if ( NOT DEFINED ENV{ONEAPI_ROOT})
         message(FATAL_ERROR "Not detect ENV {ONEAPI_ROOT}, please install oneAPI & source it, like: source /opt/intel/oneapi/setvars.sh")
     endif()
@@ -555,6 +570,9 @@ if (LLAMA_SYCL)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib")
+    if (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
+    endif()
 
     set(GGML_HEADERS_SYCL ggml-sycl.h)
     set(GGML_SOURCES_SYCL ggml-sycl.cpp)
@@ -562,7 +580,11 @@ if (LLAMA_SYCL)
     if (WIN32)
         set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib)
     else()
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
+        if (LLAMA_SYCL_TARGET STREQUAL "INTEL")
+            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
+        elseif (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
+            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl pthread m dl onemkl)
+        endif()
     endif()
 endif()
 
@@ -1129,6 +1151,8 @@ endif()
 add_library(llama
             llama.cpp
             llama.h
+            unicode.h
+            unicode.cpp
             )
 
 target_include_directories(llama PUBLIC .)

Makefile

@@ -167,6 +167,10 @@ ifeq ($(UNAME_S),OpenBSD)
 	MK_CPPFLAGS += -D_BSD_SOURCE
 endif
 
+ifdef LLAMA_SCHED_MAX_COPIES
+	MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(LLAMA_SCHED_MAX_COPIES)
+endif
+
 ifdef LLAMA_DEBUG
 	MK_CFLAGS   += -O0 -g
 	MK_CXXFLAGS += -O0 -g
@@ -549,19 +553,20 @@ endif
 endif # LLAMA_METAL
 
 ifdef LLAMA_METAL
-ggml-metal.o: ggml-metal.m ggml-metal.h
+ggml-metal.o: ggml-metal.m ggml-metal.h ggml.h
 	$(CC) $(CFLAGS) -c $< -o $@
 
 ifdef LLAMA_METAL_EMBED_LIBRARY
-ggml-metal-embed.o: ggml-metal.metal
+ggml-metal-embed.o: ggml-metal.metal ggml-common.h
 	@echo "Embedding Metal library"
+	@sed -e '/#include "ggml-common.h"/r ggml-common.h' -e '/#include "ggml-common.h"/d' < ggml-metal.metal > ggml-metal-embed.metal
 	$(eval TEMP_ASSEMBLY=$(shell mktemp))
-	@echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY)
-	@echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY)
-	@echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY)
-	@echo ".incbin \"$<\"" >> $(TEMP_ASSEMBLY)
-	@echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY)
-	@echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY)
+	@echo ".section __DATA, __ggml_metallib"   >  $(TEMP_ASSEMBLY)
+	@echo ".globl _ggml_metallib_start"        >> $(TEMP_ASSEMBLY)
+	@echo "_ggml_metallib_start:"              >> $(TEMP_ASSEMBLY)
+	@echo ".incbin \"ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)
+	@echo ".globl _ggml_metallib_end"          >> $(TEMP_ASSEMBLY)
+	@echo "_ggml_metallib_end:"                >> $(TEMP_ASSEMBLY)
 	@$(AS) $(TEMP_ASSEMBLY) -o $@
 	@rm -f ${TEMP_ASSEMBLY}
 endif
@@ -590,6 +595,11 @@ include scripts/get-flags.mk
 CUDA_CXXFLAGS := $(BASE_CXXFLAGS) $(GF_CXXFLAGS) -Wno-pedantic
 endif
 
+ifdef LLAMA_CURL
+override CXXFLAGS := $(CXXFLAGS) -DLLAMA_USE_CURL
+override LDFLAGS  := $(LDFLAGS) -lcurl
+endif
+
 #
 # Print build information
 #
@@ -633,9 +643,12 @@ ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h
 ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h
 	$(CC) $(CFLAGS)    -c $< -o $@
 
-OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o
+unicode.o: unicode.cpp unicode.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
+OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o
+
+llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 COMMON_H_DEPS = common/common.h common/sampling.h common/log.h
@@ -740,6 +753,10 @@ gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
+gguf-split: examples/gguf-split/gguf-split.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
 train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

Package.swift

@@ -31,6 +31,7 @@ let package = Package(
             sources: [
                 "ggml.c",
                 "llama.cpp",
+                "unicode.cpp",
                 "ggml-alloc.c",
                 "ggml-backend.c",
                 "ggml-quants.c",

README-sycl.md

@@ -29,6 +29,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
 ## News
 
 - 2024.3
+  - New base line is ready: [tag b2437](https://github.com/ggerganov/llama.cpp/tree/b2437).
   - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing.
   - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE.
   - Support detecting all GPUs with level-zero and same top **Max compute units**.
@@ -73,6 +74,29 @@ For iGPU, please make sure the shared memory from host memory is enough. For lla
 
 For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+.
 
+## Nvidia GPU
+
+### Verified
+
+|Intel GPU| Status | Verified Model|
+|-|-|-|
+|Ampere Series| Support| A100|
+
+### oneMKL for CUDA
+
+The current oneMKL release does not contain the oneMKL cuBlas backend.
+As a result for Nvidia GPU's oneMKL must be built from source.
+
+```
+git clone https://github.com/oneapi-src/oneMKL
+cd oneMKL
+mkdir build
+cd build
+cmake -G Ninja .. -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON
+ninja
+// Add paths as necessary
+```
+
 ## Docker
 
 Note:
@@ -186,6 +210,9 @@ source /opt/intel/oneapi/setvars.sh
 # Or, for FP32:
 cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
+# For Nvidia GPUs
+cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+
 # Build example/main only
 #cmake --build . --config Release --target main
 
@@ -228,16 +255,16 @@ Run without parameter:
 Check the ID in startup log, like:
 
 ```
-found 4 SYCL devices:
-  Device 0: Intel(R) Arc(TM) A770 Graphics,	compute capability 1.3,
-    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
-  Device 1: Intel(R) FPGA Emulation Device,	compute capability 1.2,
-    max compute_units 24,	max work group size 67108864,	max sub group size 64,	global mem size 67065057280
-  Device 2: 13th Gen Intel(R) Core(TM) i7-13700K,	compute capability 3.0,
-    max compute_units 24,	max work group size 8192,	max sub group size 64,	global mem size 67065057280
-  Device 3: Intel(R) Arc(TM) A770 Graphics,	compute capability 3.0,
-    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
-
+found 6 SYCL devices:
+|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
+|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
+|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
+| 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
+| 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
+| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
+| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
+| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
+| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|
 ```
 
 |Attribute|Note|
@@ -245,12 +272,35 @@ found 4 SYCL devices:
 |compute capability 1.3|Level-zero running time, recommended |
 |compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
 
-4. Set device ID and execute llama.cpp
+4. Device selection and execution of llama.cpp
 
-Set device ID = 0 by **GGML_SYCL_DEVICE=0**
+There are two device selection modes:
+
+- Single device: Use one device assigned by user.
+- Multiple devices: Automatically choose the devices with the same biggest Max compute units.
+
+|Device selection|Parameter|
+|-|-|
+|Single device|--split-mode none --main-gpu DEVICE_ID |
+|Multiple devices|--split-mode layer (default)|
+
+Examples:
+
+- Use device 0:
 
 ```sh
-GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
+```
+or run by script:
+
+```sh
+./examples/sycl/run_llama2.sh 0
+```
+
+- Use multiple devices:
+
+```sh
+ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
 ```
 or run by script:
 
@@ -263,12 +313,18 @@ Note:
 - By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue.
 
 
-5. Check the device ID in output
+5. Verify the device ID in output
+
+Verify to see if the selected GPU is shown in the output, like:
 
-Like:
 ```
-Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
+detect 1 SYCL GPUs: [0] with top Max compute units:512
 ```
+Or
+```
+use 1 SYCL GPUs: [0] with Max compute units:512
+```
+
 
 ## Windows
 
@@ -329,7 +385,7 @@ a. Download & install cmake for Windows: https://cmake.org/download/
 
 b. Download & install mingw-w64 make for Windows provided by w64devkit
 
-- Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
+- Download the 1.19.0 version of [w64devkit](https://github.com/skeeto/w64devkit/releases/download/v1.19.0/w64devkit-1.19.0.zip).
 
 - Extract `w64devkit` on your pc.
 
@@ -404,15 +460,16 @@ build\bin\main.exe
 Check the ID in startup log, like:
 
 ```
-found 4 SYCL devices:
-  Device 0: Intel(R) Arc(TM) A770 Graphics,	compute capability 1.3,
-    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
-  Device 1: Intel(R) FPGA Emulation Device,	compute capability 1.2,
-    max compute_units 24,	max work group size 67108864,	max sub group size 64,	global mem size 67065057280
-  Device 2: 13th Gen Intel(R) Core(TM) i7-13700K,	compute capability 3.0,
-    max compute_units 24,	max work group size 8192,	max sub group size 64,	global mem size 67065057280
-  Device 3: Intel(R) Arc(TM) A770 Graphics,	compute capability 3.0,
-    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+found 6 SYCL devices:
+|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
+|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
+|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
+| 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
+| 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
+| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
+| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
+| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
+| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|
 
 ```
 
@@ -421,13 +478,31 @@ found 4 SYCL devices:
 |compute capability 1.3|Level-zero running time, recommended |
 |compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
 
-4. Set device ID and execute llama.cpp
 
-Set device ID = 0 by **set GGML_SYCL_DEVICE=0**
+4. Device selection and execution of llama.cpp
+
+There are two device selection modes:
+
+- Single device: Use one device assigned by user.
+- Multiple devices: Automatically choose the devices with the same biggest Max compute units.
+
+|Device selection|Parameter|
+|-|-|
+|Single device|--split-mode none --main-gpu DEVICE_ID |
+|Multiple devices|--split-mode layer (default)|
+
+Examples:
+
+- Use device 0:
 
 ```
-set GGML_SYCL_DEVICE=0
-build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0
+build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0
+```
+
+- Use multiple devices:
+
+```
+build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer
 ```
 or run by script:
 
@@ -440,11 +515,17 @@ Note:
 - By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue.
 
 
-5. Check the device ID in output
 
-Like:
+5. Verify the device ID in output
+
+Verify to see if the selected GPU is shown in the output, like:
+
 ```
-Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
+detect 1 SYCL GPUs: [0] with top Max compute units:512
+```
+Or
+```
+use 1 SYCL GPUs: [0] with Max compute units:512
 ```
 
 ## Environment Variable
@@ -463,7 +544,6 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
 
 |Name|Value|Function|
 |-|-|-|
-|GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output|
 |GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG|
 |ZES_ENABLE_SYSMAN| 0 (default) or 1|Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer|
 
@@ -481,6 +561,9 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
 
 ## Q&A
 
+Note: please add prefix **[SYCL]** in issue title, so that we will check it as soon as possible.
+
+
 - Error:  `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
 
   Miss to enable oneAPI running environment.
@@ -512,4 +595,4 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
 
 ## Todo
 
-- Support multiple cards.
+- Support row layer split for multiple card runs.

README.md

@@ -8,19 +8,18 @@
 
 Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
 
-> [!IMPORTANT]
-> **Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962**
->
-> Vote for which quantization type provides better responses, all other parameters being the same.
-
 ### Recent API changes
 
-- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_max_seq()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328
+- [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
+- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328
 - [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796
 - [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849
 
 ### Hot topics
 
+- Multi-GPU pipeline parallelizm support https://github.com/ggerganov/llama.cpp/pull/6017
+- Looking for contributions to add Deepseek support: https://github.com/ggerganov/llama.cpp/issues/5981
+- Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962
 - Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328
 
 ----
@@ -113,6 +112,7 @@ Typically finetunes of the base models below are supported as well.
 - [x] [CodeShell](https://github.com/WisdomShell/codeshell)
 - [x] [Gemma](https://ai.google.dev/gemma)
 - [x] [Mamba](https://github.com/state-spaces/mamba)
+- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
 
 **Multimodal models:**
 
@@ -134,6 +134,7 @@ Typically finetunes of the base models below are supported as well.
 - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
 - JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
 - JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
+- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama)
 - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
 - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
 - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
@@ -905,6 +906,9 @@ First, install the essential packages for termux:
 pkg install clang wget git cmake
 ```
 Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake:
+
+You can execute the following commands on your computer to avoid downloading the NDK to your mobile. Of course, you can also do this in Termux.
+
 ```
 $ mkdir build-android
 $ cd build-android
@@ -913,7 +917,28 @@ $ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROI
 $ make
 ```
 Install [termux](https://termux.dev/) on your device and run `termux-setup-storage` to get access to your SD card.
-Finally, copy the `llama` binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone:
+Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
+
+(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
+```
+$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
+$cd /data/data/com.termux/files/home/bin
+$chmod +x ./*
+```
+
+Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
+
+```
+$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/
+```
+
+Now, you can start chatting:
+```
+$cd /data/data/com.termux/files/home/bin
+$./main -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
+```
+
+Here is a demo of an interactive session running on Pixel 5 phone:
 
 https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
 

build.zig

@@ -115,6 +115,7 @@ pub fn build(b: *std.build.Builder) !void {
     const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c");
     const ggml_backend = make.obj("ggml-backend", "ggml-backend.c");
     const ggml_quants = make.obj("ggml-quants", "ggml-quants.c");
+    const unicode = make.obj("unicode", "unicode.cpp");
     const llama = make.obj("llama", "llama.cpp");
     const buildinfo = make.obj("common", "common/build-info.cpp");
     const common = make.obj("common", "common/common.cpp");
@@ -125,14 +126,14 @@ pub fn build(b: *std.build.Builder) !void {
     const clip = make.obj("clip", "examples/llava/clip.cpp");
     const llava = make.obj("llava", "examples/llava/llava.cpp");
 
-    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser });
-    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
-    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
-    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
-    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
-    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
+    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, sampling, console, grammar_parser });
+    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
+    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
+    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
+    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, train });
+    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, train });
 
-    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip, llava });
+    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, sampling, grammar_parser, clip, llava });
     if (server.target.isWindows()) {
         server.linkSystemLibrary("ws2_32");
     }

common/CMakeLists.txt

@@ -68,6 +68,17 @@ if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()
 
+set(LLAMA_COMMON_EXTRA_LIBS build_info)
+
+# Use curl to download model url
+if (LLAMA_CURL)
+    find_package(CURL REQUIRED)
+    add_definitions(-DLLAMA_USE_CURL)
+    include_directories(${CURL_INCLUDE_DIRS})
+    find_library(CURL_LIBRARY curl REQUIRED)
+    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
+endif ()
+
 target_include_directories(${TARGET} PUBLIC .)
 target_compile_features(${TARGET} PUBLIC cxx_std_11)
-target_link_libraries(${TARGET} PRIVATE build_info PUBLIC llama)
+target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama)

common/common.h

@@ -37,10 +37,13 @@ extern char const *LLAMA_COMMIT;
 extern char const *LLAMA_COMPILER;
 extern char const *LLAMA_BUILD_TARGET;
 
+struct llama_control_vector_load_info;
+
+int32_t get_num_physical_cores();
+
 //
 // CLI argument parsing
 //
-int32_t get_num_physical_cores();
 
 struct gpt_params {
     uint32_t seed                 = LLAMA_DEFAULT_SEED; // RNG seed
@@ -51,7 +54,8 @@ struct gpt_params {
     int32_t n_threads_batch_draft = -1;
     int32_t n_predict             = -1;    // new tokens to predict
     int32_t n_ctx                 = 512;   // context size
-    int32_t n_batch               = 512;   // batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_batch               = 2048;  // logical batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_ubatch              = 512;   // physical batch size for prompt processing (must be >=32 to use BLAS)
     int32_t n_keep                = 0;     // number of tokens to keep from initial prompt
     int32_t n_draft               = 5;     // number of tokens to draft during speculative decoding
     int32_t n_chunks              = -1;    // max number of chunks to process (-1 = unlimited)
@@ -85,6 +89,7 @@ struct gpt_params {
     struct llama_sampling_params sparams;
 
     std::string model             = "models/7B/ggml-model-f16.gguf"; // model path
+    std::string model_url         = ""; // model url to download
     std::string model_draft       = "";                              // draft model for speculative decoding
     std::string model_alias       = "unknown"; // model alias
     std::string prompt            = "";
@@ -102,6 +107,11 @@ struct gpt_params {
     std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
     std::string lora_base  = "";                              // base model path for the lora adapter
 
+    std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
+
+    int32_t control_vector_layer_start = -1; // layer range for control vector
+    int32_t control_vector_layer_end   = -1; // layer range for control vector
+
     int  ppl_stride        = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
     int  ppl_output_type   = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
                                     //                                       (which is more convenient to use for plotting)
@@ -182,6 +192,9 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
 struct llama_model_params   llama_model_params_from_gpt_params  (const gpt_params & params);
 struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
 
+struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model,
+                                                         struct llama_model_params     params);
+
 // Batch utils
 
 void llama_batch_clear(struct llama_batch & batch);
@@ -267,3 +280,25 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40
 
 void llama_embd_normalize(const float * inp, float * out, int n);
 
+float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n);
+
+//
+// Control vector utils
+//
+
+struct llama_control_vector_data {
+    int n_embd;
+
+    // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
+    std::vector<float> data;
+};
+
+struct llama_control_vector_load_info {
+    float strength;
+
+    std::string fname;
+};
+
+// Load control vectors, scale each by strength, and add them together.
+// On error, returns {-1, empty}
+llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos);

common/sampling.cpp

@@ -17,6 +17,13 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
             return nullptr;
         }
 
+        // Ensure that there is a "root" node.
+        if (result->parsed_grammar.symbol_ids.find("root") == result->parsed_grammar.symbol_ids.end()) {
+            fprintf(stderr, "%s: grammar does not contain a 'root' symbol\n", __func__);
+            delete result;
+            return nullptr;
+        }
+
         std::vector<const llama_grammar_element *> grammar_rules(result->parsed_grammar.c_rules());
 
         result->grammar = llama_grammar_init(

common/sampling.h

@@ -32,13 +32,13 @@ typedef struct llama_sampling_params {
     float       dynatemp_range        = 0.00f;    // 0.0 = disabled
     float       dynatemp_exponent     = 1.00f;    // controls how entropy maps to temperature in dynamic temperature sampler
     int32_t     penalty_last_n        = 64;       // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float       penalty_repeat        = 1.10f;    // 1.0 = disabled
+    float       penalty_repeat        = 1.00f;    // 1.0 = disabled
     float       penalty_freq          = 0.00f;    // 0.0 = disabled
     float       penalty_present       = 0.00f;    // 0.0 = disabled
     int32_t     mirostat              = 0;        // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
     float       mirostat_tau          = 5.00f;    // target entropy
     float       mirostat_eta          = 0.10f;    // learning rate
-    bool        penalize_nl           = true;     // consider newlines as a repeatable token
+    bool        penalize_nl           = false;     // consider newlines as a repeatable token
 
     std::vector<llama_sampler_type> samplers_sequence = {
         llama_sampler_type::TOP_K,

convert-hf-to-gguf.py

@@ -1634,7 +1634,7 @@ in chat mode so that the conversation can end normally.")
                 self.post_write_tensors(tensor_map, name, data_torch)
 
 
-@Model.register("BertModel")
+@Model.register("BertModel", "CamembertModel")
 class BertModel(Model):
     model_arch = gguf.MODEL_ARCH.BERT
 
@@ -1965,6 +1965,23 @@ class MambaModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
+@Model.register("CohereForCausalLM")
+class CommandR2Model(Model):
+    model_arch = gguf.MODEL_ARCH.COMMAND_R
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # max_position_embeddings = 8192 in config.json but model was actually
+        # trained on 128k context length
+        self.hparams["max_position_embeddings"] = self.hparams["model_max_length"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+
 ###### CONVERSION LOGIC ######
 
 

convert.py

@@ -332,6 +332,9 @@ class Params:
 #
 
 class BpeVocab:
+    tokenizer_model = "gpt2"
+    name = "bpe"
+
     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())
         if isinstance(self.bpe_tokenizer.get('model'), dict):
@@ -390,6 +393,9 @@ class BpeVocab:
 
 
 class SentencePieceVocab:
+    tokenizer_model = "llama"
+    name = "spm"
+
     def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
         self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
         added_tokens: dict[str, int]
@@ -453,6 +459,9 @@ class SentencePieceVocab:
 
 
 class HfVocab:
+    tokenizer_model = "llama"
+    name = "hfft"
+
     def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None = None) -> None:
         try:
             from transformers import AutoTokenizer
@@ -553,7 +562,15 @@ class HfVocab:
         return f"<HfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
 
 
-Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab"
+class NoVocab:
+    tokenizer_model = "no_vocab"
+    name = "no_vocab"
+
+    def __repr__(self) -> str:
+        return "<NoVocab for a model without integrated vocabulary>"
+
+
+Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab | NoVocab"
 
 
 #
@@ -935,8 +952,10 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N
     # Handle special case where the model's vocab size is not set
     if params.n_vocab == -1:
         raise ValueError(
-            f"The model's vocab size is set to -1 in params.json. Please update it manually. Maybe {vocab.vocab_size}?"
+            f"The model's vocab size is set to -1 in params.json. Please update it manually.{f' Maybe {vocab.vocab_size}?' if hasattr(vocab, 'vocab_size') else ''}"
         )
+    if isinstance(vocab, NoVocab):
+        return  # model has no vocab
 
     # Check for a vocab size mismatch
     if params.n_vocab == vocab.vocab_size:
@@ -977,6 +996,7 @@ class OutputFile:
             name = str(params.path_model.parent).split('/')[-1]
 
         self.gguf.add_name                (name)
+        self.gguf.add_vocab_size          (params.n_vocab)
         self.gguf.add_context_length      (params.n_ctx)
         self.gguf.add_embedding_length    (params.n_embd)
         self.gguf.add_block_count         (params.n_layer)
@@ -1013,21 +1033,9 @@ class OutputFile:
         if params.ftype is not None:
             self.gguf.add_file_type(params.ftype)
 
-    def handle_tokenizer_model(self, vocab: Vocab) -> str:
-        # Map the vocab types to the supported tokenizer models
-        tokenizer_model = {
-            SentencePieceVocab: "llama",
-            HfVocab: "llama",
-            BpeVocab: "gpt2",
-        }.get(type(vocab))
-
-        # Block if vocab type is not predefined
-        if tokenizer_model is None:
-            raise ValueError("Unknown vocab type: Not supported")
-
-        return tokenizer_model
-
     def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]:
+        assert not isinstance(vocab, NoVocab)
+
         tokens = []
         scores = []
         toktypes = []
@@ -1043,11 +1051,8 @@ class OutputFile:
         return tokens, scores, toktypes
 
     def add_meta_vocab(self, vocab: Vocab) -> None:
-        # Handle the tokenizer model
-        tokenizer_model = self.handle_tokenizer_model(vocab)
-
         # Ensure that tokenizer_model is added to the GGUF model
-        self.gguf.add_tokenizer_model(tokenizer_model)
+        self.gguf.add_tokenizer_model(vocab.tokenizer_model)
 
         # Extract model vocabulary for model conversion
         tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab)
@@ -1074,6 +1079,26 @@ class OutputFile:
     def write_tensor_info(self) -> None:
         self.gguf.write_ti_data_to_file()
 
+    def write_tensor_data(self, ftype: GGMLFileType, model: LazyModel, concurrency: int) -> None:
+        ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency=concurrency)
+        if ftype == GGMLFileType.MostlyQ8_0:
+            ndarrays = bounded_parallel_map(
+                OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
+                use_processpool_executor=True,
+            )
+        else:
+            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
+
+        start = time.time()
+        for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
+            elapsed = time.time() - start
+            size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
+            padi = len(str(len(model)))
+            print(
+                f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
+            )
+            self.gguf.write_tensor_data(ndarray)
+
     def close(self) -> None:
         self.gguf.close()
 
@@ -1082,7 +1107,7 @@ class OutputFile:
         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)
+        check_vocab_size(params, vocab, pad_vocab=pad_vocab)
 
         of = OutputFile(fname_out, endianess=endianess)
 
@@ -1120,8 +1145,11 @@ class OutputFile:
 
         # meta data
         of.add_meta_arch(params)
-        of.add_meta_vocab(vocab)
-        of.add_meta_special_vocab(svocab)
+        if isinstance(vocab, NoVocab):
+            of.gguf.add_tokenizer_model(vocab.tokenizer_model)
+        else:
+            of.add_meta_vocab(vocab)
+            of.add_meta_special_vocab(svocab)
 
         # tensor info
         for name, lazy_tensor in model.items():
@@ -1131,24 +1159,7 @@ class OutputFile:
         of.write_tensor_info()
 
         # tensor data
-        ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)
-        if ftype == GGMLFileType.MostlyQ8_0:
-            ndarrays = bounded_parallel_map(
-                OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
-                use_processpool_executor=True,
-            )
-        else:
-            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
-
-        start = time.time()
-        for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
-            elapsed = time.time() - start
-            size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
-            padi = len(str(len(model)))
-            print(
-                f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
-            )
-            of.gguf.write_tensor_data(ndarray)
+        of.write_tensor_data(ftype, model, concurrency)
 
         of.close()
 
@@ -1156,9 +1167,9 @@ 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
 
-    if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
+    if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)):
         return GGMLFileType.AllF32
-    if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
+    if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16):
         return GGMLFileType.MostlyF16
     if output_type_str == "q8_0":
         return GGMLFileType.MostlyQ8_0
@@ -1309,8 +1320,8 @@ class VocabFactory:
                 return vtype, path
         raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}")
 
-    def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: Path) -> gguf.SpecialVocab:
-        load_merges = vocabtype == "bpe"
+    def _create_special_vocab(self, vocab: Vocab, model_parent_path: Path) -> gguf.SpecialVocab:
+        load_merges = vocab.name == "bpe"
         n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None
         return gguf.SpecialVocab(
             model_parent_path,
@@ -1319,30 +1330,34 @@ class VocabFactory:
             n_vocab=n_vocab,
         )
 
-    def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
+    def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab:
         vocab_type, path = self._select_file(vocab_types)
         print(f"Loading vocab file {path!r}, type {vocab_type!r}")
 
         added_tokens_path = path.parent / "added_tokens.json"
-        vocab: Vocab
         if vocab_type == "bpe":
-            vocab = BpeVocab(
+            return BpeVocab(
                 path, added_tokens_path if added_tokens_path.exists() else None
             )
-        elif vocab_type == "spm":
-            vocab = SentencePieceVocab(
+        if vocab_type == "spm":
+            return SentencePieceVocab(
                 path, added_tokens_path if added_tokens_path.exists() else None
             )
-        elif vocab_type == "hfft":
-            vocab = HfVocab(
+        if vocab_type == "hfft":
+            return HfVocab(
                 path.parent, added_tokens_path if added_tokens_path.exists() else None
             )
+        raise ValueError(vocab_type)
+
+    def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
+        vocab: Vocab
+        if len(vocab_types) == 1 and "no_vocab" in vocab_types:
+            vocab = NoVocab()
         else:
-            raise ValueError(vocab_type)
+            vocab = self._create_vocab_by_path(vocab_types)
         # FIXME: Respect --vocab-dir?
         special_vocab = self._create_special_vocab(
             vocab,
-            vocab_type,
             model_parent_path,
         )
         return vocab, special_vocab
@@ -1380,6 +1395,7 @@ def main(args_in: list[str] | None = None) -> None:
     parser.add_argument("--dump",         action="store_true",    help="don't convert, just show what's in the model")
     parser.add_argument("--dump-single",  action="store_true",    help="don't convert, just show what's in a single model file")
     parser.add_argument("--vocab-only",   action="store_true",    help="extract only the vocab")
+    parser.add_argument("--no-vocab",     action="store_true",    help="store model without the vocab")
     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("--vocab-type",                           help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft")
@@ -1392,6 +1408,10 @@ def main(args_in: list[str] | None = None) -> None:
     parser.add_argument("--skip-unknown", action="store_true",    help="skip unknown tensor names instead of failing")
 
     args = parser.parse_args(args_in)
+    if args.no_vocab:
+        if args.vocab_only:
+            raise ValueError("no need to specify --vocab-only if using --no-vocab")
+        args.vocab_type = "no_vocab"
 
     if args.dump_single:
         model_plus = lazy_load_file(args.model)
@@ -1442,7 +1462,7 @@ def main(args_in: list[str] | None = None) -> None:
         print(f"Wrote {outfile}")
         return
 
-    if model_plus.vocab is not None and args.vocab_dir is None:
+    if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab:
         vocab = model_plus.vocab
 
     print(f"Vocab info: {vocab}")

examples/CMakeLists.txt

@@ -21,6 +21,7 @@ else()
     add_subdirectory(embedding)
     add_subdirectory(finetune)
     add_subdirectory(gritlm)
+    add_subdirectory(gguf-split)
     add_subdirectory(infill)
     add_subdirectory(llama-bench)
     add_subdirectory(llava)

examples/batched-bench/batched-bench.cpp

@@ -106,7 +106,7 @@ int main(int argc, char ** argv) {
     ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
     // ensure enough sequences are available
-    ctx_params.n_parallel = *std::max_element(n_pl.begin(), n_pl.end());
+    ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end());
 
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
@@ -138,6 +138,8 @@ int main(int argc, char ** argv) {
                 LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
                 return false;
             }
+
+            llama_synchronize(ctx);
         }
 
         return true;

examples/batched/batched.cpp

@@ -80,7 +80,7 @@ int main(int argc, char ** argv) {
     ctx_params.seed  = 1234;
     ctx_params.n_ctx = n_kv_req;
     ctx_params.n_batch = std::max(n_len, n_parallel);
-    ctx_params.n_parallel      = n_parallel;
+    ctx_params.n_seq_max       = n_parallel;
     ctx_params.n_threads       = params.n_threads;
     ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 

examples/embedding/embedding.cpp

@@ -107,18 +107,25 @@ int main(int argc, char ** argv) {
 
     // max batch size
     const uint64_t n_batch = params.n_batch;
-    GGML_ASSERT(params.n_batch == params.n_ctx);
+    GGML_ASSERT(params.n_batch >= params.n_ctx);
 
     // tokenize the prompts and trim
     std::vector<std::vector<int32_t>> inputs;
     for (const auto & prompt : prompts) {
-        auto inp = ::llama_tokenize(ctx, prompt, true);
+        auto inp = ::llama_tokenize(ctx, prompt, true, false);
         if (inp.size() > n_batch) {
             inp.resize(n_batch);
         }
         inputs.push_back(inp);
     }
 
+    // add eos if not present
+    for (auto & inp : inputs) {
+        if (inp.empty() || inp.back() != llama_token_eos(model)) {
+            inp.push_back(llama_token_eos(model));
+        }
+    }
+
     // tokenization stats
     if (params.verbose_prompt) {
         for (int i = 0; i < (int) inputs.size(); i++) {
@@ -167,15 +174,26 @@ int main(int argc, char ** argv) {
     float * out = emb + p * n_embd;
     batch_decode(ctx, batch, out, s, n_embd);
 
-    // print first 3 embeddings
-    for (int j = 0; j < std::min(3, n_prompts); j++) {
-        fprintf(stderr, "embedding %d: ", j);
-        for (int i = 0; i < n_embd; i++) {
-            fprintf(stderr, "%f ", emb[j * n_embd + i]);
+    // print the first part of the embeddings
+    fprintf(stdout, "\n");
+    for (int j = 0; j < n_prompts; j++) {
+        fprintf(stdout, "embedding %d: ", j);
+        for (int i = 0; i < std::min(16, n_embd); i++) {
+            fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
         }
-        fprintf(stderr, "\n\n");
+        fprintf(stdout, "\n");
+    }
+
+    // print cosine similarity matrix
+    fprintf(stdout, "\n");
+    printf("cosine similarity matrix:\n\n");
+    for (int i = 0; i < n_prompts; i++) {
+        for (int j = 0; j < n_prompts; j++) {
+            float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
+            fprintf(stdout, "%6.2f ", sim);
+        }
+        fprintf(stdout, "\n");
     }
-    fprintf(stderr, "\n");
 
     // clean up
     llama_print_timings(ctx);

examples/gguf-split/CMakeLists.txt

@@ -0,0 +1,5 @@
+set(TARGET gguf-split)
+add_executable(${TARGET} gguf-split.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/gguf-split/README.md

@@ -0,0 +1,9 @@
+## GGUF split Example
+
+CLI to split / merge GGUF files.
+
+**Command line options:**
+
+- `--split`: split GGUF to multiple GGUF, default operation.
+- `--split-max-tensors`: maximum tensors in each split: default(128)
+- `--merge`: merge multiple GGUF to a single GGUF.

examples/gguf-split/gguf-split.cpp

@@ -0,0 +1,489 @@
+#include "llama.h"
+#include "ggml.h"
+#include "common.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+#include <cstdlib>
+#include <fstream>
+#include <ios>
+#include <string>
+#include <vector>
+
+#include <stdio.h>
+#include <fcntl.h>
+#include <string.h>
+
+enum split_operation : uint8_t {
+    SPLIT_OP_SPLIT,
+    SPLIT_OP_MERGE,
+};
+
+static const char * const LLM_KV_GENERAL_SPLIT_I_SPLIT = "general.split";
+static const char * const LLM_KV_GENERAL_SPLIT_N_SPLIT = "general.split_count";
+
+static const int SPLIT_FILENAME_MAX = 256;
+
+static const char * const SPLIT_FILENAME_FORMAT = "%s-%05d-of-%05d.gguf";
+
+struct split_params {
+    split_operation operation = SPLIT_OP_SPLIT;
+    int n_split_tensors = 128;
+    std::string input;
+    std::string output;
+};
+
+static void split_print_usage(const char * executable) {
+    const split_params default_params;
+    printf("\n");
+    printf("usage: %s [options] GGUF_IN GGUF_OUT\n", executable);
+    printf("\n");
+    printf("Apply a GGUF operation on IN to OUT.");
+    printf("\n");
+    printf("options:\n");
+    printf("  -h, --help            show this help message and exit\n");
+    printf("  --version             show version and build info\n");
+    printf("  --split               split GGUF to multiple GGUF (default)\n");
+    printf("  --split-max-tensors   max tensors in each split: default(%d)\n", default_params.n_split_tensors);
+    printf("  --merge               merge multiple GGUF to a single GGUF\n");
+    printf("\n");
+}
+
+static bool split_params_parse_ex(int argc, const char ** argv, split_params & params) {
+    std::string arg;
+    const std::string arg_prefix = "--";
+    bool invalid_param = false;
+
+    int arg_idx = 1;
+    for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
+        arg = argv[arg_idx];
+        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+            std::replace(arg.begin(), arg.end(), '_', '-');
+        }
+
+        bool arg_found = false;
+        if (arg == "-h" || arg == "--help") {
+            split_print_usage(argv[0]);
+            exit(0);
+        }
+        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);
+        }
+
+        if (arg == "--merge") {
+            arg_found = true;
+            params.operation = SPLIT_OP_MERGE;
+        }
+        if (arg == "--split") {
+            arg_found = true;
+            params.operation = SPLIT_OP_SPLIT;
+        }
+        if (arg == "--split-max-tensors") {
+            if (++arg_idx >= argc) {
+                invalid_param = true;
+                break;
+            }
+            arg_found = true;
+            params.n_split_tensors = atoi(argv[arg_idx]);
+        }
+
+        if (!arg_found) {
+            throw std::invalid_argument("error: unknown argument: " + arg);
+        }
+    }
+
+    if (invalid_param) {
+        throw std::invalid_argument("error: invalid parameter for argument: " + arg);
+    }
+
+    if (argc - arg_idx < 2) {
+        printf("%s: bad arguments\n", argv[0]);
+        split_print_usage(argv[0]);
+        return false;
+    }
+
+    params.input = argv[arg_idx++];
+    params.output = argv[arg_idx++];
+
+    return true;
+}
+
+static bool split_params_parse(int argc, const char ** argv, split_params & params) {
+    bool result = true;
+    try {
+        if (!split_params_parse_ex(argc, argv, params)) {
+            split_print_usage(argv[0]);
+            exit(1);
+        }
+    }
+    catch (const std::invalid_argument & ex) {
+        fprintf(stderr, "%s\n", ex.what());
+        split_print_usage(argv[0]);
+        exit(1);
+    }
+    return result;
+}
+
+static void zeros(std::ofstream & file, size_t n) {
+    char zero = 0;
+    for (size_t i = 0; i < n; ++i) {
+        file.write(&zero, 1);
+    }
+}
+
+static std::string split_file_name(const std::string & path, int i_split, int n_split) {
+    char f_split[SPLIT_FILENAME_MAX] = {0};
+    snprintf(f_split, sizeof(f_split), SPLIT_FILENAME_FORMAT, path.c_str(), i_split + 1, n_split);
+    return std::string(f_split);
+}
+
+struct split_strategy {
+    const split_params params;
+    std::ifstream & f_input;
+    struct gguf_context * ctx_gguf;
+    struct ggml_context * ctx_meta = NULL;
+    const int n_tensors;
+
+    const int n_split;
+    int i_split = 0;
+
+    int i_tensor = 0;
+
+    std::vector<uint8_t> read_data;
+
+    struct gguf_context * ctx_out;
+    std::ofstream fout;
+
+    split_strategy(const split_params & params,
+            std::ifstream & f_input,
+            struct gguf_context * ctx_gguf,
+            struct ggml_context * ctx_meta) :
+        params(params),
+        f_input(f_input),
+        ctx_gguf(ctx_gguf),
+        ctx_meta(ctx_meta),
+        n_tensors(gguf_get_n_tensors(ctx_gguf)),
+        n_split(std::ceil(1. * n_tensors / params.n_split_tensors)) {
+        }
+
+    bool should_split() const {
+        return i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0;
+    }
+
+    void split_start() {
+        ctx_out = gguf_init_empty();
+
+        // Save all metadata in first split only
+        if (i_split == 0) {
+            gguf_set_kv(ctx_out, ctx_gguf);
+        }
+        gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_I_SPLIT, i_split);
+        gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_N_SPLIT, n_split);
+
+        // populate the original tensors, so we get an initial metadata
+        for (int i = i_split * params.n_split_tensors; i < n_tensors && i < (i_split + 1) * params.n_split_tensors; ++i) {
+            struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
+            gguf_add_tensor(ctx_out, meta);
+        }
+
+        auto split_name = split_file_name(params.output, i_split, n_split);
+
+        fprintf(stderr, "%s: %s ...", __func__, split_name.c_str());
+        fout = std::ofstream(split_name, std::ios::binary);
+        fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+
+        auto meta_size = gguf_get_meta_size(ctx_out);
+
+        // placeholder for the meta data
+        ::zeros(fout, meta_size);
+
+        i_split++;
+    }
+
+    void next_tensor() {
+        const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
+        struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
+        auto n_bytes = ggml_nbytes(t);
+
+        if (read_data.size() < n_bytes) {
+            read_data.resize(n_bytes);
+        }
+
+        auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor);
+        f_input.seekg(offset);
+        f_input.read((char *)read_data.data(), n_bytes);
+
+        t->data = read_data.data();
+
+        // write tensor data + padding
+        fout.write((const char *)t->data, n_bytes);
+        zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
+
+        i_tensor++;
+    }
+
+    void split_end() {
+        // go back to beginning of file and write the updated metadata
+        fout.seekp(0);
+        std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
+        gguf_get_meta_data(ctx_out, data.data());
+        fout.write((const char *)data.data(), data.size());
+
+        fout.close();
+        gguf_free(ctx_out);
+
+        fprintf(stderr, "\033[3Ddone\n");
+    }
+};
+
+static void gguf_split(const split_params & split_params) {
+    struct ggml_context * ctx_meta = NULL;
+
+    struct gguf_init_params params = {
+        /*.no_alloc = */ true,
+        /*.ctx      = */ &ctx_meta,
+    };
+
+    std::ifstream f_input(split_params.input.c_str(), std::ios::binary);
+    if (!f_input.is_open()) {
+        fprintf(stderr, "%s:  failed to open input GGUF from %s\n", __func__, split_params.input.c_str());
+        exit(1);
+    }
+
+    auto * ctx_gguf = gguf_init_from_file(split_params.input.c_str(), params);
+    if (!ctx_gguf) {
+        fprintf(stderr, "%s:  failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
+        exit(1);
+    }
+
+    split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta);
+    fprintf(stderr, "%s: %s -> %s (%d tensors per file)\n",
+            __func__, split_params.input.c_str(),
+            split_file_name(split_params.output, strategy.i_split, strategy.n_split).c_str(),
+            split_params.n_split_tensors);
+
+    strategy.split_start();
+
+    while (strategy.i_tensor < strategy.n_tensors) {
+        strategy.next_tensor();
+        if (strategy.should_split()) {
+            strategy.split_end();
+            strategy.split_start();
+        }
+    }
+    strategy.split_end();
+
+    gguf_free(ctx_gguf);
+    f_input.close();
+
+    fprintf(stderr, "%s: %d gguf split written with a total of %d tensors.\n",
+            __func__, strategy.n_split, strategy.n_tensors);
+}
+
+static void gguf_merge(const split_params & split_params) {
+    fprintf(stderr, "%s: %s -> %s\n",
+            __func__, split_params.input.c_str(),
+            split_params.output.c_str());
+    int n_split = 1;
+    int total_tensors = 0;
+
+    auto * ctx_out = gguf_init_empty();
+    std::ofstream fout(split_params.output.c_str(), std::ios::binary);
+    fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+
+    std::vector<uint8_t> read_data;
+    std::vector<ggml_context *> ctx_metas;
+    std::vector<gguf_context *> ctx_ggufs;
+
+    std::string split_prefix;
+
+    // First pass to find KV and tensors metadata
+    for (int i_split = 0; i_split < n_split; i_split++) {
+        struct ggml_context * ctx_meta = NULL;
+
+        struct gguf_init_params params = {
+            /*.no_alloc = */ true,
+            /*.ctx      = */ &ctx_meta,
+        };
+
+        auto split_name = split_params.input;
+        if (i_split > 0) {
+            split_name = split_file_name(split_prefix, i_split, n_split);
+        }
+        fprintf(stderr, "%s: reading metadata %s ...", __func__, split_name.c_str());
+
+        auto * ctx_gguf = gguf_init_from_file(split_name.c_str(), params);
+        if (!ctx_gguf) {
+            fprintf(stderr, "\n%s:  failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
+            exit(1);
+        }
+        ctx_ggufs.push_back(ctx_gguf);
+        ctx_metas.push_back(ctx_meta);
+
+        if (i_split == 0) {
+            auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_GENERAL_SPLIT_N_SPLIT);
+            if (key_n_split < 0) {
+                fprintf(stderr,
+                        "\n%s: input file does not contain %s metadata\n",
+                        __func__,
+                        LLM_KV_GENERAL_SPLIT_N_SPLIT);
+                gguf_free(ctx_gguf);
+                gguf_free(ctx_out);
+                fout.close();
+                exit(1);
+            }
+
+            n_split = gguf_get_val_u8(ctx_gguf, key_n_split);
+            if (n_split < 1) {
+                fprintf(stderr,
+                        "\n%s: input file does not contain a valid split count %d\n",
+                        __func__,
+                        n_split);
+                gguf_free(ctx_gguf);
+                gguf_free(ctx_out);
+                fout.close();
+                exit(1);
+            }
+
+            // Do not trigger merge if we try to merge again the output
+            gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_N_SPLIT, 0);
+
+            // Set metadata from the first split
+            gguf_set_kv(ctx_out, ctx_gguf);
+        }
+
+        // Verify the file naming
+        {
+            int i_split_file = 0;
+            int n_split_file = 0;
+            const char * i_split_format = "-00000-of-00000.gguf";
+
+            if (split_name.size() < strlen(i_split_format)) {
+                fprintf(stderr, "\n%s: unexpected input file name: %s\n", __func__, split_params.input.c_str());
+                for (auto * _ctx_gguf : ctx_ggufs) {
+                    gguf_free(_ctx_gguf);
+                }
+                gguf_free(ctx_out);
+                fout.close();
+                exit(1);
+            }
+
+            split_prefix = split_name.substr(0, split_name.size() - strlen(i_split_format));
+
+            const char * split_name_c_str = split_name.c_str();
+            int n_part = sscanf(&split_name_c_str[0] + split_prefix.size(), "-%d-of-%d", &i_split_file, &n_split_file);
+
+            if (n_part != 2 || i_split_file - 1 != i_split || n_split_file != n_split) {
+                fprintf(stderr, "\n%s: unexpected input file name: %s"
+                                " i_split=%d i_split_file=%d"
+                                " n_split=%d n_split_file=%d\n", __func__,
+                        split_params.input.c_str(),
+                        i_split, i_split_file,
+                        n_split, n_split_file);
+                for (auto * _ctx_gguf : ctx_ggufs) {
+                    gguf_free(_ctx_gguf);
+                }
+                gguf_free(ctx_out);
+                fout.close();
+                exit(1);
+            }
+        }
+
+        auto n_tensors = gguf_get_n_tensors(ctx_gguf);
+        for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
+            const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
+            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
+            gguf_add_tensor(ctx_out, t);
+        }
+        total_tensors += n_tensors;
+
+        fprintf(stderr, "\033[3Ddone\n");
+    }
+
+    // placeholder for the meta data
+    {
+        auto meta_size = gguf_get_meta_size(ctx_out);
+        ::zeros(fout, meta_size);
+    }
+
+    // Write tensors data
+    for (int i_split = 0; i_split < n_split; i_split++) {
+        auto split_name = split_file_name(split_prefix, i_split, n_split);
+        std::ifstream f_input(split_name.c_str(), std::ios::binary);
+        if (!f_input.is_open()) {
+            fprintf(stderr, "%s:  failed to open input GGUF from %s\n", __func__, split_name.c_str());
+            for (auto * _ctx_gguf : ctx_ggufs) {
+                gguf_free(_ctx_gguf);
+            }
+            gguf_free(ctx_out);
+            fout.close();
+            exit(1);
+        }
+        fprintf(stderr, "%s: writing tensors %s ...", __func__, split_name.c_str());
+
+        auto * ctx_gguf = ctx_ggufs[i_split];
+        auto * ctx_meta = ctx_metas[i_split];
+
+        auto n_tensors = gguf_get_n_tensors(ctx_gguf);
+        for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
+            const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
+            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
+
+            auto n_bytes = ggml_nbytes(t);
+
+            if (read_data.size() < n_bytes) {
+                read_data.resize(n_bytes);
+            }
+
+            auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor);
+            f_input.seekg(offset);
+            f_input.read((char *)read_data.data(), n_bytes);
+
+            // write tensor data + padding
+            fout.write((const char *)read_data.data(), n_bytes);
+            zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
+        }
+
+        gguf_free(ctx_gguf);
+        ggml_free(ctx_meta);
+        f_input.close();
+        fprintf(stderr, "\033[3Ddone\n");
+    }
+
+    {
+        // go back to beginning of file and write the updated metadata
+        fout.seekp(0);
+        std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
+        gguf_get_meta_data(ctx_out, data.data());
+        fout.write((const char *)data.data(), data.size());
+
+        fout.close();
+        gguf_free(ctx_out);
+    }
+
+    fprintf(stderr, "%s: %s merged from %d split with %d tensors.\n",
+            __func__, split_params.output.c_str(), n_split, total_tensors);
+}
+
+int main(int argc, const char ** argv) {
+    if (argc < 3) {
+        split_print_usage(argv[0]);
+    }
+
+    split_params params;
+    split_params_parse(argc, argv, params);
+
+    switch (params.operation) {
+        case SPLIT_OP_SPLIT: gguf_split(params);
+            break;
+        case SPLIT_OP_MERGE: gguf_merge(params);
+            break;
+        default:split_print_usage(argv[0]);
+            exit(1);
+    }
+
+    return 0;
+}

examples/gguf/gguf.cpp

@@ -211,6 +211,7 @@ static bool gguf_ex_read_1(const std::string & fname) {
                 for (int j = 0; j < ggml_nelements(cur); ++j) {
                     if (data[j] != 100 + i) {
                         fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]);
+                        gguf_free(ctx);
                         return false;
                     }
                 }

examples/gritlm/README.md

@@ -0,0 +1,62 @@
+## Generative Representational Instruction Tuning (GRIT) Example
+[gritlm] a model which can generate embeddings as well as "normal" text
+generation depending on the instructions in the prompt.
+
+* Paper: https://arxiv.org/pdf/2402.09906.pdf
+
+### Retrieval-Augmented Generation (RAG) use case
+One use case for `gritlm` is to use it with RAG. If we recall how RAG works is
+that we take documents that we want to use as context, to ground the large
+language model (LLM), and we create token embeddings for them. We then store
+these token embeddings in a vector database.
+
+When we perform a query, prompt the LLM, we will first create token embeddings
+for the query and then search the vector database to retrieve the most
+similar vectors, and return those documents so they can be passed to the LLM as
+context. Then the query and the context will be passed to the LLM which will
+have to _again_ create token embeddings for the query. But because gritlm is used
+the first query can be cached and the second query tokenization generation does
+not have to be performed at all.
+
+### Running the example
+Download a Grit model:
+```console
+$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf
+```
+
+Run the example using the downloaded model:
+```console
+$ ./gritlm -m gritlm-7b_q4_1.gguf
+
+Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605
+Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103
+Cosine similarity between "Generative Representational Instruction Tuning" and "A purely peer-to-peer version of electronic cash w" is: 0.112
+Cosine similarity between "Generative Representational Instruction Tuning" and "All text-based language problems can be reduced to" is: 0.547
+
+Oh, brave adventurer, who dared to climb
+The lofty peak of Mt. Fuji in the night,
+When shadows lurk and ghosts do roam,
+And darkness reigns, a fearsome sight.
+
+Thou didst set out, with heart aglow,
+To conquer this mountain, so high,
+And reach the summit, where the stars do glow,
+And the moon shines bright, up in the sky.
+
+Through the mist and fog, thou didst press on,
+With steadfast courage, and a steadfast will,
+Through the darkness, thou didst not be gone,
+But didst climb on, with a steadfast skill.
+
+At last, thou didst reach the summit's crest,
+And gazed upon the world below,
+And saw the beauty of the night's best,
+And felt the peace, that only nature knows.
+
+Oh, brave adventurer, who dared to climb
+The lofty peak of Mt. Fuji in the night,
+Thou art a hero, in the eyes of all,
+For thou didst conquer this mountain, so bright.
+```
+
+[gritlm]: https://github.com/ContextualAI/gritlm

examples/gritlm/gritlm.cpp

@@ -6,22 +6,6 @@
 
 // #define GRIT_DEBUG
 
-static float dot_product(const std::vector<float> & v1, const std::vector<float> & v2) {
-    float dot = 0.0f;
-    for (uint64_t i = 0; i < v1.size(); ++i) {
-        dot += v1[i] * v2[i];
-    }
-    return dot;
-}
-
-static float norm(const std::vector<float> & v) {
-    return std::sqrt(dot_product(v, v));
-}
-
-static float cosine_similarity(const std::vector<float> & v1, const std::vector<float> & v2) {
-    return dot_product(v1, v2) / (norm(v1) * norm(v2));
-}
-
 static std::vector<std::vector<float>> encode(llama_context * ctx, const std::vector<std::string> & sentences, const std::string & instruction) {
     std::vector<std::vector<float>> result;
 
@@ -203,10 +187,12 @@ int main(int argc, char * argv[]) {
         const std::vector<std::vector<float>> d_rep = encode(ctx, documents, gritlm_instruction(""));
         const std::vector<std::vector<float>> q_rep = encode(ctx, queries,   gritlm_instruction(instruction));
 
-        const float cosine_sim_q0_d0 = cosine_similarity(q_rep[0], d_rep[0]);
-        const float cosine_sim_q0_d1 = cosine_similarity(q_rep[0], d_rep[1]);
-        const float cosine_sim_q1_d0 = cosine_similarity(q_rep[1], d_rep[0]);
-        const float cosine_sim_q1_d1 = cosine_similarity(q_rep[1], d_rep[1]);
+        const int n_embd = llama_n_embd(mdl);
+
+        const float cosine_sim_q0_d0 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
+        const float cosine_sim_q0_d1 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
+        const float cosine_sim_q1_d0 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd);
+        const float cosine_sim_q1_d1 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd);
 
         std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0);
         std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1);

examples/imatrix/imatrix.cpp

@@ -56,13 +56,31 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
     const struct ggml_tensor * src0 = t->src[0];
     const struct ggml_tensor * src1 = t->src[1];
 
+    std::string wname;
+    {
+        // remove any prefix and suffixes from the name
+        // CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
+        const char * p = strchr(src0->name, '#');
+        if (p != NULL) {
+            p = p + 1;
+            const char * q = strchr(p, '#');
+            if (q != NULL) {
+                wname = std::string(p, q - p);
+            } else {
+                wname = p;
+            }
+        } else {
+            wname = src0->name;
+        }
+    }
+
     // when ask is true, the scheduler wants to know if we are interested in data from this tensor
     // if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
     if (ask) {
         if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications
         if (t->op != GGML_OP_MUL_MAT) return false;
         if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
-        if (!(strncmp(src0->name, "blk.", 4) == 0 || (m_params.collect_output_weight && strcmp(src0->name, "output.weight") == 0))) return false;
+        if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false;
         return true;
     }
 
@@ -94,12 +112,12 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
         // this is necessary to guarantee equal number of "ncall" for each tensor
         for (int ex = 0; ex < n_as; ++ex) {
             src0 = t->src[2 + ex];
-            auto& e = m_stats[src0->name];
+            auto& e = m_stats[wname];
             if (e.values.empty()) {
                 e.values.resize(src1->ne[0], 0);
             }
             else if (e.values.size() != (size_t)src1->ne[0]) {
-                fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]);
+                fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
                 exit(1); //GGML_ASSERT(false);
             }
             // NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
@@ -107,7 +125,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
             //if (idx == t->src[0]->ne[0] - 1) ++e.ncall;
             ++e.ncall;
             if (m_params.verbosity > 1) {
-                printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
+                printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
             }
             for (int row = 0; row < (int)src1->ne[1]; ++row) {
                 const int excur = m_ids[row*n_as + idx];
@@ -129,17 +147,17 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
             }
         }
     } else {
-        auto& e = m_stats[src0->name];
+        auto& e = m_stats[wname];
         if (e.values.empty()) {
             e.values.resize(src1->ne[0], 0);
         }
         else if (e.values.size() != (size_t)src1->ne[0]) {
-            fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]);
+            fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
             exit(1); //GGML_ASSERT(false);
         }
         ++e.ncall;
         if (m_params.verbosity > 1) {
-            printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
+            printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
         }
         for (int row = 0; row < (int)src1->ne[1]; ++row) {
             const float * x = data + row * src1->ne[0];

examples/llama-bench/llama-bench.cpp

@@ -8,6 +8,7 @@
 #include <cstdio>
 #include <cstring>
 #include <ctime>
+#include <cstdlib>
 #include <iterator>
 #include <map>
 #include <numeric>
@@ -103,6 +104,7 @@ static std::string get_cpu_info() {
                 }
             }
         }
+        fclose(f);
     }
 #endif
     // TODO: other platforms
@@ -112,10 +114,10 @@ static std::string get_cpu_info() {
 static std::string get_gpu_info() {
     std::string id;
 #ifdef GGML_USE_CUBLAS
-    int count = ggml_cuda_get_device_count();
+    int count = ggml_backend_cuda_get_device_count();
     for (int i = 0; i < count; i++) {
         char buf[128];
-        ggml_cuda_get_device_description(i, buf, sizeof(buf));
+        ggml_backend_cuda_get_device_description(i, buf, sizeof(buf));
         id += buf;
         if (i < count - 1) {
             id += "/";
@@ -164,6 +166,7 @@ struct cmd_params {
     std::vector<int> n_prompt;
     std::vector<int> n_gen;
     std::vector<int> n_batch;
+    std::vector<int> n_ubatch;
     std::vector<ggml_type> type_k;
     std::vector<ggml_type> type_v;
     std::vector<int> n_threads;
@@ -183,7 +186,8 @@ static const cmd_params cmd_params_defaults = {
     /* model         */ {"models/7B/ggml-model-q4_0.gguf"},
     /* n_prompt      */ {512},
     /* n_gen         */ {128},
-    /* n_batch       */ {512},
+    /* n_batch       */ {2048},
+    /* n_ubatch      */ {512},
     /* type_k        */ {GGML_TYPE_F16},
     /* type_v        */ {GGML_TYPE_F16},
     /* n_threads     */ {get_num_physical_cores()},
@@ -208,6 +212,7 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -p, --n-prompt <n>                  (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
     printf("  -n, --n-gen <n>                     (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
     printf("  -b, --batch-size <n>                (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
+    printf("  -ub N, --ubatch-size <n>            (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str());
     printf("  -ctk <t>, --cache-type-k <t>        (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
     printf("  -ctv <t>, --cache-type-v <t>        (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
     printf("  -t, --threads <n>                   (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
@@ -217,7 +222,7 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -nkvo, --no-kv-offload <0|1>        (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
     printf("  -mmp, --mmap <0|1>                  (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
     printf("  -embd, --embeddings <0|1>           (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
-    printf("  -ts, --tensor_split <ts0/ts1/..>    (default: 0)\n");
+    printf("  -ts, --tensor-split <ts0/ts1/..>    (default: 0)\n");
     printf("  -r, --repetitions <n>               (default: %d)\n", cmd_params_defaults.reps);
     printf("  -o, --output <csv|json|md|sql>      (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
     printf("  -v, --verbose                       (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
@@ -297,6 +302,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             }
             auto p = split<int>(argv[i], split_delim);
             params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
+        } else if (arg == "-ub" || arg == "--ubatch-size") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<int>(argv[i], split_delim);
+            params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end());
         } else if (arg == "-ctk" || arg == "--cache-type-k") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -455,6 +467,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
     if (params.n_prompt.empty())     { params.n_prompt = cmd_params_defaults.n_prompt; }
     if (params.n_gen.empty())        { params.n_gen = cmd_params_defaults.n_gen; }
     if (params.n_batch.empty())      { params.n_batch = cmd_params_defaults.n_batch; }
+    if (params.n_ubatch.empty())     { params.n_ubatch = cmd_params_defaults.n_ubatch; }
     if (params.type_k.empty())       { params.type_k = cmd_params_defaults.type_k; }
     if (params.type_v.empty())       { params.type_v = cmd_params_defaults.type_v; }
     if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
@@ -474,6 +487,7 @@ struct cmd_params_instance {
     int n_prompt;
     int n_gen;
     int n_batch;
+    int n_ubatch;
     ggml_type type_k;
     ggml_type type_v;
     int n_threads;
@@ -511,6 +525,7 @@ struct cmd_params_instance {
 
         cparams.n_ctx = n_prompt + n_gen;
         cparams.n_batch = n_batch;
+        cparams.n_ubatch = n_ubatch;
         cparams.type_k = type_k;
         cparams.type_v = type_v;
         cparams.offload_kqv = !no_kv_offload;
@@ -532,6 +547,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
     for (const auto & mmp : params.use_mmap)
     for (const auto & embd : params.embeddings)
     for (const auto & nb : params.n_batch)
+    for (const auto & nub : params.n_ubatch)
     for (const auto & tk : params.type_k)
     for (const auto & tv : params.type_v)
     for (const auto & nkvo : params.no_kv_offload)
@@ -545,6 +561,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_prompt     = */ n_prompt,
                 /* .n_gen        = */ 0,
                 /* .n_batch      = */ nb,
+                /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
                 /* .n_threads    = */ nt,
@@ -568,6 +585,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_prompt     = */ 0,
                 /* .n_gen        = */ n_gen,
                 /* .n_batch      = */ nb,
+                /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
                 /* .n_threads    = */ nt,
@@ -604,6 +622,7 @@ struct test {
     uint64_t model_size;
     uint64_t model_n_params;
     int n_batch;
+    int n_ubatch;
     int n_threads;
     ggml_type type_k;
     ggml_type type_v;
@@ -627,6 +646,7 @@ struct test {
         model_size = llama_model_size(lmodel);
         model_n_params = llama_model_n_params(lmodel);
         n_batch = inst.n_batch;
+        n_ubatch = inst.n_ubatch;
         n_threads = inst.n_threads;
         type_k = inst.type_k;
         type_v = inst.type_v;
@@ -705,7 +725,8 @@ struct test {
             "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas",
             "cpu_info", "gpu_info",
             "model_filename", "model_type", "model_size", "model_n_params",
-            "n_batch", "n_threads", "type_k", "type_v",
+            "n_batch", "n_ubatch",
+            "n_threads", "type_k", "type_v",
             "n_gpu_layers", "split_mode",
             "main_gpu", "no_kv_offload",
             "tensor_split", "use_mmap", "embeddings",
@@ -719,7 +740,8 @@ struct test {
     enum field_type {STRING, BOOL, INT, FLOAT};
 
     static field_type get_field_type(const std::string & field) {
-        if (field == "build_number" || field == "n_batch" || field == "n_threads" ||
+        if (field == "build_number" || field == "n_batch" || field == "n_ubatch" ||
+            field == "n_threads" ||
             field == "model_size" || field == "model_n_params" ||
             field == "n_gpu_layers" || field == "main_gpu" ||
             field == "n_prompt" || field == "n_gen" ||
@@ -759,7 +781,8 @@ struct test {
             std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas),
             cpu_info, gpu_info,
             model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
-            std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
+            std::to_string(n_batch), std::to_string(n_ubatch),
+            std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
             std::to_string(n_gpu_layers), split_mode_str(split_mode),
             std::to_string(main_gpu), std::to_string(no_kv_offload),
             tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
@@ -957,6 +980,9 @@ struct markdown_printer : public printer {
         if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
             fields.emplace_back("n_batch");
         }
+        if (params.n_ubatch.size() > 1 || params.n_ubatch != cmd_params_defaults.n_ubatch) {
+            fields.emplace_back("n_ubatch");
+        }
         if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) {
             fields.emplace_back("type_k");
         }
@@ -1096,25 +1122,40 @@ struct sql_printer : public printer {
 };
 
 static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
-    std::vector<llama_token> tokens(n_batch, llama_token_bos(llama_get_model(ctx)));
-    int n_processed = 0;
-
     llama_set_n_threads(ctx, n_threads, n_threads);
 
+    const llama_model * model = llama_get_model(ctx);
+    const int32_t n_vocab = llama_n_vocab(model);
+
+    std::vector<llama_token> tokens(n_batch);
+
+    int n_processed = 0;
+
     while (n_processed < n_prompt) {
         int n_tokens = std::min(n_prompt - n_processed, n_batch);
+        tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
+        for (int i = 1; i < n_tokens; i++) {
+            tokens[i] = std::rand() % n_vocab;
+        }
         llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));
         n_processed += n_tokens;
     }
+
+    llama_synchronize(ctx);
 }
 
 static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
-    llama_token token = llama_token_bos(llama_get_model(ctx));
-
     llama_set_n_threads(ctx, n_threads, n_threads);
 
+    const llama_model * model = llama_get_model(ctx);
+    const int32_t n_vocab = llama_n_vocab(model);
+
+    llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
+
     for (int i = 0; i < n_gen; i++) {
         llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));
+        llama_synchronize(ctx);
+        token = std::rand() % n_vocab;
     }
 }
 
@@ -1203,7 +1244,8 @@ int main(int argc, char ** argv) {
 
         // warmup run
         if (t.n_prompt > 0) {
-            test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads);
+            //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
+            test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
         }
         if (t.n_gen > 0) {
             test_gen(ctx, 1, 0, t.n_threads);
@@ -1219,6 +1261,7 @@ int main(int argc, char ** argv) {
             if (t.n_gen > 0) {
                 test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
             }
+
             uint64_t t_ns = get_time_ns() - t_start;
             t.samples_ns.push_back(t_ns);
         }

examples/llama.android/app/src/main/cpp/llama-android.cpp

@@ -33,6 +33,45 @@ jclass la_int_var;
 jmethodID la_int_var_value;
 jmethodID la_int_var_inc;
 
+std::string cached_token_chars;
+
+bool is_valid_utf8(const char * string) {
+    if (!string) {
+        return true;
+    }
+
+    const unsigned char * bytes = (const unsigned char *)string;
+    int num;
+
+    while (*bytes != 0x00) {
+        if ((*bytes & 0x80) == 0x00) {
+            // U+0000 to U+007F
+            num = 1;
+        } else if ((*bytes & 0xE0) == 0xC0) {
+            // U+0080 to U+07FF
+            num = 2;
+        } else if ((*bytes & 0xF0) == 0xE0) {
+            // U+0800 to U+FFFF
+            num = 3;
+        } else if ((*bytes & 0xF8) == 0xF0) {
+            // U+10000 to U+10FFFF
+            num = 4;
+        } else {
+            return false;
+        }
+
+        bytes += 1;
+        for (int i = 1; i < num; ++i) {
+            if ((*bytes & 0xC0) != 0x80) {
+                return false;
+            }
+            bytes += 1;
+        }
+    }
+
+    return true;
+}
+
 static void log_callback(ggml_log_level level, const char * fmt, void * data) {
     if (level == GGML_LOG_LEVEL_ERROR)     __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data);
     else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data);
@@ -295,6 +334,8 @@ Java_com_example_llama_Llm_completion_1init(
         jint n_len
     ) {
 
+    cached_token_chars.clear();
+
     const auto text = env->GetStringUTFChars(jtext, 0);
     const auto context = reinterpret_cast<llama_context *>(context_pointer);
     const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
@@ -372,8 +413,16 @@ Java_com_example_llama_Llm_completion_1loop(
     }
 
     auto new_token_chars = llama_token_to_piece(context, new_token_id);
-    LOGi("new_token_chars: `%s`", new_token_chars.c_str());
-    auto new_token = env->NewStringUTF(new_token_chars.c_str());
+    cached_token_chars += new_token_chars;
+
+    jstring new_token = nullptr;
+    if (is_valid_utf8(cached_token_chars.c_str())) {
+        new_token = env->NewStringUTF(cached_token_chars.c_str());
+        LOGi("cached: %s, new_token_chars: `%s`, id: %d", cached_token_chars.c_str(), new_token_chars.c_str(), new_token_id);
+        cached_token_chars.clear();
+    } else {
+        new_token = env->NewStringUTF("");
+    }
 
     llama_batch_clear(*batch);
     llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true);

examples/llama.android/app/src/main/java/com/example/llama/Llm.kt

@@ -71,7 +71,7 @@ class Llm {
         batch: Long,
         nLen: Int,
         ncur: IntVar
-    ): String
+    ): String?
 
     private external fun kv_cache_clear(context: Long)
 
@@ -115,7 +115,7 @@ class Llm {
                 val ncur = IntVar(completion_init(state.context, state.batch, message, nlen))
                 while (ncur.value <= nlen) {
                     val str = completion_loop(state.context, state.batch, nlen, ncur)
-                    if (str.isEmpty()) {
+                    if (str == null) {
                         break
                     }
                     emit(str)

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

@@ -221,6 +221,7 @@ actor LlamaContext {
             if llama_decode(context, batch) != 0 {
                 print("llama_decode() failed during prompt")
             }
+            llama_synchronize(context)
 
             let t_pp_end = ggml_time_us()
 
@@ -240,6 +241,7 @@ actor LlamaContext {
                 if llama_decode(context, batch) != 0 {
                     print("llama_decode() failed during text generation")
                 }
+                llama_synchronize(context)
             }
 
             let t_tg_end = ggml_time_us()

examples/llava/MobileVLM-README.md

@@ -1,11 +1,13 @@
 # MobileVLM
 
-Currently this implementation supports [MobileVLM-v1.7](https://huggingface.co/mtgv/MobileVLM-1.7B) variants.
+Currently this implementation supports [MobileVLM-1.7B](https://huggingface.co/mtgv/MobileVLM-1.7B) / [MobileVLM_V2-1.7B](https://huggingface.co/mtgv/MobileVLM_V2-1.7B) variants.
 
 for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com/Meituan-AutoML/MobileVLM)
 
 The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava.
 
+Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion  is a little different. Therefore, using MobiVLM as an example, the different conversion step will be shown.
+
 ## Usage
 Build with cmake or run `make llava-cli` to build it.
 
@@ -34,7 +36,7 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
 python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B
 ```
 
-3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` to convert the LLaVA image encoder to GGUF:
+3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** the arg is `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
 
 ```sh
 python ./examples/llava/convert-image-encoder-to-gguf \
@@ -44,6 +46,14 @@ python ./examples/llava/convert-image-encoder-to-gguf \
     --projector-type ldp
 ```
 
+```sh
+python ./examples/llava/convert-image-encoder-to-gguf \
+    -m path/to/clip-vit-large-patch14-336 \
+    --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \
+    --output-dir path/to/MobileVLM-1.7B_V2 \
+    --projector-type ldpv2
+```
+
 4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
 
 ```sh

examples/llava/README.md

@@ -63,12 +63,20 @@ Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` director
 ```console
 git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
 ```
-2) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
+
+2) Install the required Python packages:
+
+```sh
+pip install -r examples/llava/requirements.txt
+```
+
+3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
 ```console
 python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/
 ```
 - you will find a llava.projector and a llava.clip file in your model directory
-3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory:
+
+4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory:
 ```console
 mkdir vit
 cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin
@@ -76,18 +84,18 @@ cp ../llava-v1.6-vicuna-7b/llava.projector vit/
 curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json
 ```
 
-4) Create the visual gguf model:
+5) Create the visual gguf model:
 ```console
 python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
 ```
 - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP
 
-5) Then convert the model to gguf format:
+6) Then convert the model to gguf format:
 ```console
 python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown
 ```
 
-6) And finally we can run the llava-cli using the 1.6 model version:
+7) And finally we can run the llava-cli using the 1.6 model version:
 ```console
 ./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
 ```

examples/llava/clip.cpp

@@ -119,6 +119,7 @@ static std::string format(const char * fmt, ...) {
 #define TN_LLAVA_PROJ      "mm.%d.%s"
 #define TN_MVLM_PROJ_MLP   "mm.model.mlp.%d.%s"
 #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
+#define TN_MVLM_PROJ_PEG   "mm.model.peg.%d.%s"
 #define TN_IMAGE_NEWLINE   "model.image_newline"
 
 
@@ -126,12 +127,14 @@ enum projector_type {
     PROJECTOR_TYPE_MLP,
     PROJECTOR_TYPE_MLP_NORM,
     PROJECTOR_TYPE_LDP,
+    PROJECTOR_TYPE_LDPV2,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
 static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_MLP, "mlp" },
     { PROJECTOR_TYPE_LDP, "ldp" },
+    { PROJECTOR_TYPE_LDPV2, "ldpv2"},
 };
 
 
@@ -475,6 +478,14 @@ struct clip_vision_model {
     struct ggml_tensor * mm_model_block_2_block_2_0_w;
     struct ggml_tensor * mm_model_block_2_block_2_1_w;
     struct ggml_tensor * mm_model_block_2_block_2_1_b;
+
+    // MobileVLM_V2 projection
+    struct ggml_tensor * mm_model_mlp_0_w;
+    struct ggml_tensor * mm_model_mlp_0_b;
+    struct ggml_tensor * mm_model_mlp_2_w;
+    struct ggml_tensor * mm_model_mlp_2_b;
+    struct ggml_tensor * mm_model_peg_0_w;
+    struct ggml_tensor * mm_model_peg_0_b;
 };
 
 struct clip_ctx {
@@ -497,7 +508,6 @@ struct clip_ctx {
 
     // memory buffers to evaluate the model
     ggml_backend_buffer_t params_buffer  = NULL;
-    ggml_backend_buffer_t compute_buffer = NULL;
 
     ggml_backend_t backend       = NULL;
     ggml_gallocr_t compute_alloc = NULL;
@@ -808,6 +818,29 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             }
             embeddings = block_1;
         }
+        else if (ctx->proj_type == PROJECTOR_TYPE_LDPV2)
+        {
+            int n_patch = 24;
+            struct ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
+            mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b);
+            mlp_0 = ggml_gelu(ctx0, mlp_0);
+            struct ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0);
+            mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b);
+            // mlp_2 ne = [2048, 576, 1, 1]
+            // // AVG Pool Layer 2*2, strides = 2
+            mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3));
+            // mlp_2 ne = [576, 2048, 1, 1]
+            mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]);
+            // mlp_2 ne [24, 24, 2048, 1]
+            mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
+            // weight ne = [3, 3, 2048, 1]
+            struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
+            peg_0 = ggml_add(ctx0, peg_0, mlp_2);
+            peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
+            peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
+            peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]);
+            embeddings = peg_0;
+        }
         else {
             GGML_ASSERT(false);
         }
@@ -995,6 +1028,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         if (!new_clip->ctx_data) {
             fprintf(stderr, "%s: ggml_init() failed\n", __func__);
             clip_free(new_clip);
+            gguf_free(ctx);
             return nullptr;
         }
 
@@ -1002,6 +1036,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         if (!fin) {
             printf("cannot open model file for loading tensors\n");
             clip_free(new_clip);
+            gguf_free(ctx);
             return nullptr;
         }
 
@@ -1023,6 +1058,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             if (!fin) {
                 printf("%s: failed to seek for tensor %s\n", __func__, name);
                 clip_free(new_clip);
+                gguf_free(ctx);
                 return nullptr;
             }
             int num_bytes = ggml_nbytes(cur);
@@ -1175,7 +1211,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             vision_model.mm_model_block_2_block_2_0_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight"));
             vision_model.mm_model_block_2_block_2_1_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight"));
             vision_model.mm_model_block_2_block_2_1_b   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias"));
-        } else {
+        }
+        else if (new_clip->proj_type == PROJECTOR_TYPE_LDPV2)
+        {
+            // MobilVLM_V2 projection
+            vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "weight"));
+            vision_model.mm_model_mlp_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "bias"));
+            vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "weight"));
+            vision_model.mm_model_mlp_2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "bias"));
+            vision_model.mm_model_peg_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "weight"));
+            vision_model.mm_model_peg_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "bias"));
+        }
+        else {
             std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type];
             throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
         }
@@ -1232,16 +1279,16 @@ struct clip_image_f32 * clip_image_f32_init() {
 
 void clip_image_u8_free(struct clip_image_u8  * img) { delete img; }
 void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
-void clip_image_u8_batch_free(struct clip_image_u8_batch  & batch) {
-    if (batch.size > 0) {
-        delete[] batch.data;
-        batch.size = 0;
+void clip_image_u8_batch_free(struct clip_image_u8_batch  * batch) {
+    if (batch->size > 0) {
+        delete[] batch->data;
+        batch->size = 0;
     }
 }
-void clip_image_f32_batch_free(struct clip_image_f32_batch  & batch) {
-    if (batch.size > 0) {
-        delete[] batch.data;
-        batch.size = 0;
+void clip_image_f32_batch_free(struct clip_image_f32_batch  * batch) {
+    if (batch->size > 0) {
+        delete[] batch->data;
+        batch->size = 0;
     }
 }
 
@@ -1494,7 +1541,7 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
 
 // returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
 // res_imgs memory is being allocated here, previous allocations will be freed if found
-bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs) {
+bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
     bool pad_to_square = true;
     if (!ctx->has_vision_encoder) {
         printf("This gguf file seems to have no vision encoder\n");
@@ -1506,11 +1553,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
         pad_to_square = false;
     }
     // free the previous res_imgs if any set
-    if (res_imgs.size > 0) {
+    if (res_imgs->size > 0) {
         clip_image_f32_batch_free(res_imgs);
     }
-    res_imgs.data = nullptr;
-    res_imgs.size = 0;
+    res_imgs->data = nullptr;
+    res_imgs->size = 0;
 
     // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
     // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
@@ -1565,11 +1612,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
             bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
             patches.insert(patches.begin(), image_original_resize);
             // clip_image_f32_batch_init(patches.size());
-            res_imgs.size = patches.size();
-            res_imgs.data = new clip_image_f32[res_imgs.size];
+            res_imgs->size = patches.size();
+            res_imgs->data = new clip_image_f32[res_imgs->size];
             int num=0;
             for (auto& patch : patches) {
-                normalize_image_u8_to_f32(patch, &res_imgs.data[num], ctx->image_mean, ctx->image_std);
+                normalize_image_u8_to_f32(patch, &res_imgs->data[num], ctx->image_mean, ctx->image_std);
                 num++;
             }
 
@@ -1657,9 +1704,9 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
     // }
     // res_imgs.push_back(res);
 
-    res_imgs.size = 1;
-    res_imgs.data = new clip_image_f32[res_imgs.size];
-    res_imgs.data[0] = *res;
+    res_imgs->size = 1;
+    res_imgs->data = new clip_image_f32[res_imgs->size];
+    res_imgs->data[0] = *res;
     clip_image_f32_free(res);
 
     return true;
@@ -1673,6 +1720,9 @@ void clip_free(clip_ctx * ctx) {
     ggml_free(ctx->ctx_data);
     gguf_free(ctx->ctx_gguf);
 
+    ggml_backend_buffer_free(ctx->params_buffer);
+    ggml_backend_free(ctx->backend);
+    ggml_gallocr_free(ctx->compute_alloc);
     delete ctx;
 }
 
@@ -1908,6 +1958,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
                 break;
             default:
                 printf("Please use an input file in f32 or f16\n");
+                gguf_free(ctx_out);
                 return false;
             }
 
@@ -1960,6 +2011,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
     if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
         return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0];
     }
+    if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
+        return ctx->vision_model.mm_model_peg_0_b->ne[0];
+    }
     if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
         return ctx->vision_model.mm_2_b->ne[0];
     }

examples/llava/clip.h

@@ -60,8 +60,8 @@ CLIP_API struct clip_image_f32 * clip_image_f32_init();
 
 CLIP_API void clip_image_u8_free (struct clip_image_u8  * img);
 CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
-CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch  & batch);
-CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch & batch);
+CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch  * batch);
+CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
 
 CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
 
@@ -69,7 +69,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8
 CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
 
 /** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */
-CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs );
+CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
 
 CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
 

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

@@ -1,6 +1,7 @@
 import argparse
 import os
 import json
+import re
 
 import torch
 import numpy as np
@@ -38,9 +39,11 @@ def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: b
 def get_tensor_name(name: str) -> str:
     if "projection" in name:
         return name
-
     if "mm_projector" in name:
-        return name.replace("model.mm_projector", "mm")
+        name = name.replace("model.mm_projector", "mm")
+        name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
+        name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
+        return name
 
     return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
 
@@ -83,7 +86,7 @@ ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
 ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
                 help="The clip model is from openclip (for ViT-SO400M type))")
 ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
-ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp", choices=["mlp", "ldp"], default="mlp")
+ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp")
 ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
 # Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
 # Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5

examples/llava/llava.cpp

@@ -223,7 +223,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
     clip_image_f32_batch img_res_v;
     img_res_v.size = 0;
     img_res_v.data = nullptr;
-    if (!clip_image_preprocess(ctx_clip, img, img_res_v)) {
+    if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
         fprintf(stderr, "%s: unable to preprocess image\n", __func__);
         delete[] img_res_v.data;
         return false;

examples/llava/llava.h

@@ -29,9 +29,9 @@ struct llava_image_embed {
 };
 
 /** sanity check for clip <-> llava embed size match */
-LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip);
+LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip);
 
-LLAVA_API bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
+LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
 
 /** build an image embed from image file bytes */
 LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);

examples/main/README.md

@@ -67,6 +67,7 @@ main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt
 In this section, we cover the most commonly used options for running the `main` program with the LLaMA models:
 
 -   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
+-   `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf).
 -   `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
 -   `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models.
 -   `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.

examples/main/main.cpp

@@ -878,6 +878,7 @@ int main(int argc, char ** argv) {
                     const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
                     const auto line_inp = ::llama_tokenize(ctx, buffer,              false, false);
                     const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
+
                     LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
 
                     embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());

examples/perplexity/perplexity.cpp

@@ -589,9 +589,10 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
             }
         }
 
-        const auto t_end = std::chrono::high_resolution_clock::now();
 
         if (i == 0) {
+            llama_synchronize(ctx);
+            const auto t_end = std::chrono::high_resolution_clock::now();
             const float t_total = std::chrono::duration<float>(t_end - t_start).count();
             fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
             int total_seconds = (int)(t_total*n_chunk/n_seq);
@@ -841,7 +842,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     const int n_batch = params.n_batch;
 
     const int max_tasks_per_batch = 32;
-    const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_max_seq(ctx));
+    const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
 
     llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
 
@@ -1118,7 +1119,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
     const int n_batch = params.n_batch;
 
     const int max_tasks_per_batch = 128;
-    const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_max_seq(ctx));
+    const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
 
     llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
 
@@ -1470,7 +1471,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
     const int n_batch = params.n_batch;
 
     const int max_tasks_per_batch = 32;
-    const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_max_seq(ctx));
+    const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
 
     llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
 

examples/server/README.md

@@ -20,6 +20,7 @@ The project is under active development, and we are [looking for feedback and co
 - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation.
 - `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`)
 - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
+- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf).
 - `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
 - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
 - `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
@@ -123,10 +124,10 @@ You can consume the endpoints with Postman or NodeJS with axios library. You can
 ### Docker
 
 ```bash
-docker run -p 8080:8080 -v /path/to/models:/models ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
+docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
 
 # or, with CUDA:
-docker run -p 8080:8080 -v /path/to/models:/models --gpus all ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
+docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
 ```
 
 ## Testing with CURL
@@ -556,9 +557,51 @@ Run with bash:
 bash chat.sh
 ```
 
-### API like OAI
+### OAI-like API
 
-The HTTP server supports OAI-like API
+The HTTP server supports OAI-like API: https://github.com/openai/openai-openapi
+
+### API errors
+
+Server returns error in the same format as OAI: https://github.com/openai/openai-openapi
+
+Example of an error:
+
+```json
+{
+    "error": {
+        "code": 401,
+        "message": "Invalid API Key",
+        "type": "authentication_error"
+    }
+}
+```
+
+Apart from error types supported by OAI, we also have custom types that are specific to functionalities of llama.cpp:
+
+**When /metrics or /slots endpoint is disabled**
+
+```json
+{
+    "error": {
+        "code": 501,
+        "message": "This server does not support metrics endpoint.",
+        "type": "not_supported_error"
+    }
+}
+```
+
+**When the server receives invalid grammar via */completions endpoint**
+
+```json
+{
+    "error": {
+        "code": 400,
+        "message": "Failed to parse grammar",
+        "type": "invalid_request_error"
+    }
+}
+```
 
 ### Extending or building alternative Web Front End
 

examples/server/completion.js.hpp

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+  0x3d, 0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, 0x29, 0x20, 0x3d,
+  0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x67,
   0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65,
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+  0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20,
+  0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x72, 0x6f, 0x70,
+  0x73, 0x20, 0x3d, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, 0x66, 0x65,
+  0x74, 0x63, 0x68, 0x28, 0x22, 0x2f, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x22,
+  0x29, 0x2e, 0x74, 0x68, 0x65, 0x6e, 0x28, 0x72, 0x20, 0x3d, 0x3e, 0x20,
+  0x72, 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x28, 0x29, 0x29, 0x3b, 0x0a, 0x20,
+  0x20, 0x20, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f,
+  0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d,
+  0x20, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x64, 0x65, 0x66, 0x61, 0x75,
+  0x6c, 0x74, 0x5f, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f,
+  0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a,
+  0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e,
+  0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f,
+  0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a
 };
-unsigned int completion_js_len = 5782;
+unsigned int completion_js_len = 5796;

examples/server/public/completion.js

@@ -96,18 +96,18 @@ export async function* llama(prompt, params = {}, config = {}) {
             }
           }
           if (result.error) {
-            result.error = JSON.parse(result.error);
-            if (result.error.content.includes('slot unavailable')) {
-              // Throw an error to be caught by upstream callers
-              throw new Error('slot unavailable');
-            } else {
-              console.error(`llama.cpp error: ${result.error.content}`);
+            try {
+              result.error = JSON.parse(result.error);
+              if (result.error.message.includes('slot unavailable')) {
+                // Throw an error to be caught by upstream callers
+                throw new Error('slot unavailable');
+              } else {
+                console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`);
+              }
+            } catch(e) {
+              console.error(`llama.cpp error ${result.error}`)
             }
           }
-          if (result.error) {
-            result.error = JSON.parse(result.error);
-            console.error(`llama.cpp error: ${result.error.content}`);
-          }
         }
       }
     }

examples/server/server.cpp

@@ -147,7 +147,7 @@ struct server_slot {
     int32_t n_decoded   = 0;
     int32_t n_remaining = -1;
     int32_t i_batch     = -1;
-    int32_t n_predict   = -1;
+    int32_t n_predict   = -1; // TODO: disambiguate from params.n_predict
 
     int32_t n_prompt_tokens           = 0;
     int32_t n_prompt_tokens_processed = 0;
@@ -396,7 +396,7 @@ struct server_queue {
     // callback functions
     std::function<void(server_task       &)> callback_new_task;
     std::function<void(server_task_multi &)> callback_finish_multitask;
-    std::function<void(void)>                callback_run_slots;
+    std::function<void(void)>                callback_update_slots;
 
     // Add a new task to the end of the queue
     int post(server_task task) {
@@ -435,8 +435,8 @@ struct server_queue {
     }
 
     // Register the function to be called when all slots data is ready to be processed
-    void on_run_slots(std::function<void(void)> callback) {
-        callback_run_slots = std::move(callback);
+    void on_update_slots(std::function<void(void)> callback) {
+        callback_update_slots = std::move(callback);
     }
 
     // Call when the state of one slot is changed
@@ -461,7 +461,7 @@ struct server_queue {
      * - Wait until a new task arrives
      * - Process the task (i.e. maybe copy data into slot)
      * - Check if multitask is finished
-     * - Run all slots
+     * - Update all slots
      */
     void start_loop() {
         running = true;
@@ -499,9 +499,9 @@ struct server_queue {
             }
 
             // all tasks in the current loop is processed, slots data is now ready
-            LOG_VERBOSE("callback_run_slots", {});
+            LOG_VERBOSE("callback_update_slots", {});
 
-            callback_run_slots();
+            callback_update_slots();
 
             LOG_VERBOSE("wait for new task", {});
             {
@@ -739,7 +739,13 @@ struct server_context {
         default_generation_settings_for_props = get_formated_generation(slots.front());
         default_generation_settings_for_props["seed"] = -1;
 
-        batch = llama_batch_init(n_ctx, 0, params.n_parallel);
+        // the update_slots() logic will always submit a maximum of n_batch tokens
+        // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
+        {
+            const int32_t n_batch = llama_n_batch(ctx);
+
+            batch = llama_batch_init(n_batch, 0, params.n_parallel);
+        }
 
         metrics.init();
     }
@@ -805,9 +811,10 @@ struct server_context {
         return last_used;
     }
 
-    bool launch_slot_with_data(server_slot & slot, json data) const {
+    bool launch_slot_with_task(server_slot & slot, const server_task & task) {
         slot_params default_params;
         llama_sampling_params default_sparams;
+        auto & data = task.data;
 
         if (data.count("__oaicompat") != 0) {
             slot.oaicompat = true;
@@ -864,10 +871,15 @@ struct server_context {
         {
             const auto & prompt = data.find("prompt");
             if (prompt == data.end()) {
-                slot.prompt = "";
+                send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST);
+                return false;
             } else {
                 slot.prompt = *prompt;
             }
+            if (slot.prompt.is_array() && slot.prompt.size() == 0) {
+                send_error(task, "\"prompt\" cannot be an empty array", ERROR_TYPE_INVALID_REQUEST);
+                return false;
+            }
         }
 
         // penalize user-provided tokens
@@ -926,6 +938,7 @@ struct server_context {
             if (logit_bias != data.end() && logit_bias->is_array()) {
                 const int n_vocab = llama_n_vocab(model);
                 for (const auto & el : *logit_bias) {
+                    // TODO: we may want to throw errors here, in case "el" is incorrect
                     if (el.is_array() && el.size() == 2) {
                         float bias;
                         if (el[1].is_number()) {
@@ -985,6 +998,11 @@ struct server_context {
                 llama_sampling_free(slot.ctx_sampling);
             }
             slot.ctx_sampling = llama_sampling_init(slot.sparams);
+            if (slot.ctx_sampling == nullptr) {
+                // for now, the only error that may happen here is invalid grammar
+                send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
+                return false;
+            }
             llama_set_rng_seed(ctx, slot.params.seed);
         }
 
@@ -1024,8 +1042,10 @@ struct server_context {
                 llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
             }
 
-            for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) {
-                const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i));
+            const int32_t n_batch = llama_n_batch(ctx);
+
+            for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
+                const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i);
                 llama_batch batch_view = {
                     n_tokens,
                     batch.token    + i,
@@ -1214,7 +1234,7 @@ struct server_context {
             {"mirostat_eta",              slot.sparams.mirostat_eta},
             {"penalize_nl",               slot.sparams.penalize_nl},
             {"stop",                      slot.params.antiprompt},
-            {"n_predict",                 slot.params.n_predict},
+            {"n_predict",                 slot.params.n_predict}, // TODO: fix duplicate key n_predict
             {"n_keep",                    params.n_keep},
             {"ignore_eos",                ignore_eos},
             {"stream",                    slot.params.stream},
@@ -1226,15 +1246,23 @@ struct server_context {
         };
     }
 
-    void send_error(const server_task & task, const std::string & error) {
-        LOG_TEE("task %i - error: %s\n", task.id, error.c_str());
+    void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
+        send_error(task.id, task.id_multi, error, type);
+    }
+
+    void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
+        send_error(slot.id_task, slot.id_multi, error, type);
+    }
+
+    void send_error(const int id_task, const int id_multi, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
+        LOG_TEE("task %i - error: %s\n", id_task, error.c_str());
 
         server_task_result res;
-        res.id       = task.id;
-        res.id_multi = task.id_multi;
+        res.id       = id_task;
+        res.id_multi = id_multi;
         res.stop     = false;
         res.error    = true;
-        res.data     = { { "content", error } };
+        res.data     = format_error_response(error, type);
 
         queue_results.send(res);
     }
@@ -1468,9 +1496,8 @@ struct server_context {
                     slot->infill    = task.infill;
                     slot->embedding = task.embedding;
 
-                    if (!launch_slot_with_data(*slot, task.data)) {
-                        // send error result
-                        send_error(task, "internal_error");
+                    if (!launch_slot_with_task(*slot, task)) {
+                        LOG_ERROR("error while launching slot", task.data);
                         break;
                     }
                 } break;
@@ -1587,7 +1614,7 @@ struct server_context {
         queue_results.send(result);
     }
 
-    bool update_slots() {
+    void update_slots() {
         if (system_need_update) {
             system_prompt_update();
         }
@@ -1630,7 +1657,7 @@ struct server_context {
                     kv_cache_clear();
                 }
 
-                return true;
+                return;
             }
         }
 
@@ -1719,7 +1746,8 @@ struct server_context {
         }
 
         // process in chunks of params.n_batch
-        int32_t n_batch = params.n_batch;
+        int32_t n_batch = llama_n_batch(ctx);
+        int32_t n_ubatch = llama_n_ubatch(ctx);
 
         // next, batch any pending prompts without exceeding n_batch
         if (params.cont_batching || batch.n_tokens == 0) {
@@ -1792,7 +1820,7 @@ struct server_context {
 
                         if (slot.embedding) {
                             // this prompt is too large to process - discard it
-                            if (slot.n_prompt_tokens > n_batch) {
+                            if (slot.n_prompt_tokens > n_ubatch) {
                                 slot.state = SLOT_STATE_PROCESSING;
                                 slot.command = SLOT_COMMAND_NONE;
                                 slot.release();
@@ -1975,8 +2003,7 @@ struct server_context {
 
         if (batch.n_tokens == 0) {
             LOG_VERBOSE("no tokens to decode", {});
-
-            return true;
+            return;
         }
 
         LOG_VERBOSE("decoding batch", {
@@ -2033,7 +2060,13 @@ struct server_context {
                 if (n_batch == 1 || ret < 0) {
                     // if you get here, it means the KV cache is full - try increasing it via the context size
                     LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
-                    return false;
+                    for (auto & slot : slots) {
+                        slot.state = SLOT_STATE_PROCESSING;
+                        slot.command = SLOT_COMMAND_NONE;
+                        slot.release();
+                        send_error(slot, "Input prompt is too big compared to KV size. Please try increasing KV size.");
+                    }
+                    break; // break loop of n_batch
                 }
 
                 LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);
@@ -2042,12 +2075,12 @@ struct server_context {
                 n_batch /= 2;
                 i -= n_batch;
 
-                continue;
+                continue; // continue loop of n_batch
             }
 
             for (auto & slot : slots) {
                 if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
-                    continue;
+                    continue; // continue loop of slots
                 }
 
                 // prompt evaluated for embedding
@@ -2055,7 +2088,7 @@ struct server_context {
                     send_embedding(slot, batch_view);
                     slot.release();
                     slot.i_batch = -1;
-                    continue;
+                    continue; // continue loop of slots
                 }
 
                 completion_token_output result;
@@ -2097,9 +2130,7 @@ struct server_context {
             }
         }
 
-        LOG_VERBOSE("slots updated", {});
-
-        return true;
+        LOG_VERBOSE("run slots completed", {});
     }
 
     json model_meta() const {
@@ -2135,7 +2166,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
     printf("  --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
     printf("  -dt N, --defrag-thold N\n");
     printf("                            KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
-    printf("  -b N, --batch-size N      batch size for prompt processing (default: %d)\n", params.n_batch);
+    printf("  -b N, --batch-size N      logical maximum batch size (default: %d)\n", params.n_batch);
+    printf("  -ub N, --ubatch-size N    physical maximum batch size (default: %d)\n", params.n_ubatch);
     printf("  --memory-f32              use f32 instead of f16 for memory key+value (default: disabled)\n");
     printf("                            not recommended: doubles context memory required and no measurable increase in quality\n");
     if (llama_supports_mlock()) {
@@ -2163,6 +2195,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
     }
     printf("  -m FNAME, --model FNAME\n");
     printf("                            model path (default: %s)\n", params.model.c_str());
+    printf("  -mu MODEL_URL, --model-url MODEL_URL\n");
+    printf("                            model download url (default: %s)\n", params.model_url.c_str());
     printf("  -a ALIAS, --alias ALIAS\n");
     printf("                            set an alias for the model, will be added as `model` field in completion response\n");
     printf("  --lora FNAME              apply LoRA adapter (implies --no-mmap)\n");
@@ -2285,6 +2319,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
                 break;
             }
             params.model = argv[i];
+        } else if (arg == "-mu" || arg == "--model-url") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.model_url = argv[i];
         } else if (arg == "-a" || arg == "--alias") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -2402,6 +2442,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
                 break;
             }
             params.n_batch = std::stoi(argv[i]);
+        } else if (arg == "-ub" || arg == "--ubatch-size") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_ubatch = std::stoi(argv[i]);
         } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -2741,36 +2787,37 @@ int main(int argc, char ** argv) {
         res.set_header("Access-Control-Allow-Credentials", "true");
         res.set_header("Access-Control-Allow-Methods",     "POST");
         res.set_header("Access-Control-Allow-Headers",     "*");
+        return res.set_content("", "application/json; charset=utf-8");
     });
 
     svr->set_logger(log_server_request);
 
-    svr->set_exception_handler([](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) {
-        const char fmt[] = "500 Internal Server Error\n%s";
+    auto res_error = [](httplib::Response & res, json error_data) {
+        json final_response {{"error", error_data}};
+        res.set_content(final_response.dump(), "application/json; charset=utf-8");
+        res.status = json_value(error_data, "code", 500);
+    };
 
-        char buf[BUFSIZ];
+    svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) {
+        std::string message;
         try {
             std::rethrow_exception(std::move(ep));
-        } catch (std::exception &e) {
-            snprintf(buf, sizeof(buf), fmt, e.what());
+        } catch (std::exception & e) {
+            message = e.what();
         } catch (...) {
-            snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
+            message = "Unknown Exception";
         }
 
-        res.set_content(buf, "text/plain; charset=utf-8");
-        res.status = 500;
+        json formatted_error = format_error_response(message, ERROR_TYPE_SERVER);
+        LOG_VERBOSE("Got exception", formatted_error);
+        res_error(res, formatted_error);
     });
 
-    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; charset=utf-8");
-        }
+    svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) {
         if (res.status == 404) {
-            res.set_content("File Not Found", "text/plain; charset=utf-8");
+            res_error(res, format_error_response("File Not Found", ERROR_TYPE_NOT_FOUND));
         }
+        // for other error codes, we skip processing here because it's already done by res_error()
     });
 
     // set timeouts and change hostname and port
@@ -2835,7 +2882,7 @@ int main(int argc, char ** argv) {
     // Middlewares
     //
 
-    auto middleware_validate_api_key = [&sparams](const httplib::Request & req, httplib::Response & res) {
+    auto middleware_validate_api_key = [&sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
         // TODO: should we apply API key to all endpoints, including "/health" and "/models"?
         static const std::set<std::string> protected_endpoints = {
             "/props",
@@ -2876,8 +2923,7 @@ int main(int argc, char ** argv) {
         // API key is invalid or not provided
         // TODO: make another middleware for CORS related logic
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-        res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8");
-        res.status = 401; // Unauthorized
+        res_error(res, format_error_response("Invalid API Key", ERROR_TYPE_AUTHENTICATION));
 
         LOG_WARNING("Unauthorized: Invalid API Key", {});
 
@@ -2940,21 +2986,18 @@ int main(int argc, char ** argv) {
                 }
             case SERVER_STATE_LOADING_MODEL:
                 {
-                    res.set_content(R"({"status": "loading model"})", "application/json");
-                    res.status = 503; // HTTP Service Unavailable
+                    res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
                 } break;
             case SERVER_STATE_ERROR:
                 {
-                    res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json");
-                    res.status = 500; // HTTP Internal Server Error
+                    res_error(res, format_error_response("Model failed to load", ERROR_TYPE_SERVER));
                 } break;
         }
     };
 
     const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) {
         if (!sparams.slots_endpoint) {
-            res.status = 501;
-            res.set_content("This server does not support slots endpoint.", "text/plain; charset=utf-8");
+            res_error(res, format_error_response("This server does not support slots endpoint.", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
@@ -2978,8 +3021,7 @@ int main(int argc, char ** argv) {
 
     const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) {
         if (!sparams.metrics_endpoint) {
-            res.status = 501;
-            res.set_content("This server does not support metrics endpoint.", "text/plain; charset=utf-8");
+            res_error(res, format_error_response("This server does not support metrics endpoint.", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
@@ -3090,7 +3132,7 @@ int main(int argc, char ** argv) {
         res.set_content(data.dump(), "application/json; charset=utf-8");
     };
 
-    const auto handle_completions = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_completions = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
 
         json data = json::parse(req.body);
@@ -3105,8 +3147,7 @@ int main(int argc, char ** argv) {
             if (!result.error && result.stop) {
                 res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
             } else {
-                res.status = 500;
-                res.set_content(result.data["content"], "text/plain; charset=utf-8");
+                res_error(res, result.data);
             }
 
             ctx_server.queue_results.remove_waiting_task_id(id_task);
@@ -3186,7 +3227,7 @@ int main(int argc, char ** argv) {
         res.set_content(models.dump(), "application/json; charset=utf-8");
     };
 
-    const auto handle_chat_completions = [&ctx_server, &sparams](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_chat_completions = [&ctx_server, &sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
         json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template);
 
@@ -3195,24 +3236,24 @@ int main(int argc, char ** argv) {
         ctx_server.queue_results.add_waiting_task_id(id_task);
         ctx_server.request_completion(id_task, -1, data, false, false);
 
+        const auto completion_id = gen_chatcmplid();
         if (!json_value(data, "stream", false)) {
             server_task_result result = ctx_server.queue_results.recv(id_task);
 
             if (!result.error && result.stop) {
-                json result_oai = format_final_response_oaicompat(data, result.data);
+                json result_oai = format_final_response_oaicompat(data, result.data, completion_id);
 
                 res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
             } else {
-                res.status = 500;
-                res.set_content(result.data["content"], "text/plain; charset=utf-8");
+                res_error(res, result.data);
             }
             ctx_server.queue_results.remove_waiting_task_id(id_task);
         } else {
-            const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
+            const auto chunked_content_provider = [id_task, &ctx_server, completion_id](size_t, httplib::DataSink & sink) {
                 while (true) {
                     server_task_result result = ctx_server.queue_results.recv(id_task);
                     if (!result.error) {
-                        std::vector<json> result_array = format_partial_response_oaicompat(result.data);
+                        std::vector<json> result_array = format_partial_response_oaicompat(result.data, completion_id);
 
                         for (auto it = result_array.begin(); it != result_array.end(); ++it) {
                             if (!it->empty()) {
@@ -3258,7 +3299,7 @@ int main(int argc, char ** argv) {
         }
     };
 
-    const auto handle_infill = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_infill = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
 
         json data = json::parse(req.body);
@@ -3273,8 +3314,7 @@ int main(int argc, char ** argv) {
             if (!result.error && result.stop) {
                 res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
             } else {
-                res.status = 404;
-                res.set_content(result.data["content"], "text/plain; charset=utf-8");
+                res_error(res, result.data);
             }
 
             ctx_server.queue_results.remove_waiting_task_id(id_task);
@@ -3345,7 +3385,7 @@ int main(int argc, char ** argv) {
         return res.set_content(data.dump(), "application/json; charset=utf-8");
     };
 
-    const auto handle_embeddings = [&params, &ctx_server](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_embeddings = [&params, &ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
         if (!params.embedding) {
             res.status = 501;
@@ -3356,65 +3396,58 @@ int main(int argc, char ** argv) {
         const json body = json::parse(req.body);
         bool is_openai = false;
 
-        // an input prompt can string or a list of tokens (integer)
-        std::vector<json> prompts;
+        // an input prompt can be a string or a list of tokens (integer)
+        json prompt;
         if (body.count("input") != 0) {
             is_openai = true;
-            if (body["input"].is_array()) {
-                // support multiple prompts
-                for (const json & elem : body["input"]) {
-                    prompts.push_back(elem);
-                }
-            } else {
-                // single input prompt
-                prompts.push_back(body["input"]);
-            }
+            prompt = body["input"];
         } else if (body.count("content") != 0) {
-            // only support single prompt here
-            std::string content = body["content"];
-            prompts.push_back(content);
+            // with "content", we only support single prompt
+            prompt = std::vector<std::string>{body["content"]};
         } else {
-            // TODO @ngxson : should return an error here
-            prompts.push_back("");
+            res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
+            return;
         }
 
-        // process all prompts
-        json responses = json::array();
-        for (auto & prompt : prompts) {
-            // TODO @ngxson : maybe support multitask for this endpoint?
-            // create and queue the task
+        // create and queue the task
+        json responses;
+        {
             const int id_task = ctx_server.queue_tasks.get_new_id();
-
             ctx_server.queue_results.add_waiting_task_id(id_task);
-            ctx_server.request_completion(id_task, -1, { {"prompt", prompt}, { "n_predict", 0}}, false, true);
+            ctx_server.request_completion(id_task, -1, {{"prompt", prompt}}, false, true);
 
             // get the result
             server_task_result result = ctx_server.queue_results.recv(id_task);
             ctx_server.queue_results.remove_waiting_task_id(id_task);
-
-            // append to the responses
-            responses.push_back(result.data);
+            if (!result.error) {
+                if (result.data.count("results")) {
+                    // result for multi-task
+                    responses = result.data["results"];
+                } else {
+                    // result for single task
+                    responses = std::vector<json>{result.data};
+                }
+            } else {
+                // error received, ignore everything else
+                res_error(res, result.data);
+                return;
+            }
         }
 
         // write JSON response
-        json root;
-        if (is_openai) {
-            json res_oai = json::array();
-            int i = 0;
-            for (auto & elem : responses) {
-                res_oai.push_back(json{
-                    {"embedding", json_value(elem, "embedding", json::array())},
-                    {"index",     i++},
-                    {"object",    "embedding"}
-                });
-            }
-            root = format_embeddings_response_oaicompat(body, res_oai);
-        } else {
-            root = responses[0];
-        }
+        json root = is_openai
+            ? format_embeddings_response_oaicompat(body, responses)
+            : responses[0];
         return res.set_content(root.dump(), "application/json; charset=utf-8");
     };
 
+    auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) {
+        return [content, len, mime_type](const httplib::Request &, httplib::Response & res) {
+            res.set_content(reinterpret_cast<const char*>(content), len, mime_type);
+            return false;
+        };
+    };
+
     //
     // Router
     //
@@ -3426,17 +3459,6 @@ int main(int argc, char ** argv) {
     }
 
     // using embedded static files
-    auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) {
-        return [content, len, mime_type](const httplib::Request &, httplib::Response & res) {
-            res.set_content(reinterpret_cast<const char*>(content), len, mime_type);
-            return false;
-        };
-    };
-
-    svr->Options(R"(/.*)", [](const httplib::Request &, httplib::Response & res) {
-        // TODO @ngxson : I have no idea what it is... maybe this is redundant?
-        return res.set_content("", "application/json; charset=utf-8");
-    });
     svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8"));
     svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8"));
     svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8"));
@@ -3487,7 +3509,7 @@ int main(int argc, char ** argv) {
         &server_context::process_single_task, &ctx_server, std::placeholders::_1));
     ctx_server.queue_tasks.on_finish_multitask(std::bind(
         &server_context::on_finish_multitask, &ctx_server, std::placeholders::_1));
-    ctx_server.queue_tasks.on_run_slots(std::bind(
+    ctx_server.queue_tasks.on_update_slots(std::bind(
         &server_context::update_slots, &ctx_server));
     ctx_server.queue_results.on_multitask_update(std::bind(
         &server_queue::update_multitask,

examples/server/tests/README.md

@@ -57,7 +57,7 @@ Feature or Scenario must be annotated with `@llama.cpp` to be included in the de
 To run a scenario annotated with `@bug`, start:
 
 ```shell
-DEBUG=ON ./tests.sh --no-skipped --tags bug
+DEBUG=ON ./tests.sh --no-skipped --tags bug --stop
 ```
 
 After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated.

examples/server/tests/features/embeddings.feature

@@ -4,11 +4,13 @@ Feature: llama.cpp server
 
   Background: Server startup
     Given a server listening on localhost:8080
-    And   a model file bert-bge-small/ggml-model-f16.gguf from HF repo ggml-org/models
+    And   a model url https://huggingface.co/ggml-org/models/resolve/main/bert-bge-small/ggml-model-f16.gguf
+    And   a model file ggml-model-f16.gguf
     And   a model alias bert-bge-small
     And   42 as server seed
     And   2 slots
     And   1024 as batch size
+    And   1024 as ubatch size
     And   2048 KV cache size
     And   embeddings extraction
     Then  the server is starting

examples/server/tests/features/environment.py

@@ -1,17 +1,18 @@
-import errno
 import os
-import socket
-import subprocess
-import time
-from contextlib import closing
 import signal
+import socket
+import sys
+import time
+import traceback
+from contextlib import closing
+from subprocess import TimeoutExpired
 
 
 def before_scenario(context, scenario):
     context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON'
     if context.debug:
-        print("DEBUG=ON\n")
-    print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m\n")
+        print("DEBUG=ON")
+    print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m")
     port = 8080
     if 'PORT' in os.environ:
         port = int(os.environ['PORT'])
@@ -20,58 +21,45 @@ def before_scenario(context, scenario):
 
 
 def after_scenario(context, scenario):
-    if context.server_process is None:
-        return
-    if scenario.status == "failed":
-        if 'GITHUB_ACTIONS' in os.environ:
-            print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n\n")
-            if os.path.isfile('llama.log'):
-                with closing(open('llama.log', 'r')) as f:
-                    for line in f:
-                        print(line)
-        if not is_server_listening(context.server_fqdn, context.server_port):
-            print("\x1b[33;101mERROR: Server stopped listening\x1b[0m\n")
+    try:
+        if 'server_process' not in context or context.server_process is None:
+            return
+        if scenario.status == "failed":
+            if 'GITHUB_ACTIONS' in os.environ:
+                print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n")
+                if os.path.isfile('llama.log'):
+                    with closing(open('llama.log', 'r')) as f:
+                        for line in f:
+                            print(line)
+            if not is_server_listening(context.server_fqdn, context.server_port):
+                print("\x1b[33;101mERROR: Server stopped listening\x1b[0m")
 
-    if not pid_exists(context.server_process.pid):
-        assert False, f"Server not running pid={context.server_process.pid} ..."
+        if context.server_process.poll() is not None:
+            assert False, f"Server not running pid={context.server_process.pid} ..."
 
-    server_graceful_shutdown(context)
+        server_graceful_shutdown(context)  # SIGINT
 
-    # Wait few for socket to free up
-    time.sleep(0.05)
+        try:
+            context.server_process.wait(0.5)
+        except TimeoutExpired:
+            print(f"server still alive after 500ms, force-killing pid={context.server_process.pid} ...")
+            context.server_process.kill()  # SIGKILL
+            context.server_process.wait()
 
-    attempts = 0
-    while pid_exists(context.server_process.pid) or is_server_listening(context.server_fqdn, context.server_port):
-        server_kill(context)
-        time.sleep(0.1)
-        attempts += 1
-        if attempts > 5:
-            server_kill_hard(context)
+        while is_server_listening(context.server_fqdn, context.server_port):
+            time.sleep(0.1)
+    except Exception:
+        print("ignoring error in after_scenario:")
+        traceback.print_exc(file=sys.stdout)
 
 
 def server_graceful_shutdown(context):
-    print(f"shutting down server pid={context.server_process.pid} ...\n")
+    print(f"shutting down server pid={context.server_process.pid} ...")
     if os.name == 'nt':
-        os.kill(context.server_process.pid, signal.CTRL_C_EVENT)
+        interrupt = signal.CTRL_C_EVENT
     else:
-        os.kill(context.server_process.pid, signal.SIGINT)
-
-
-def server_kill(context):
-    print(f"killing server pid={context.server_process.pid} ...\n")
-    context.server_process.kill()
-
-
-def server_kill_hard(context):
-    pid = context.server_process.pid
-    path = context.server_path
-
-    print(f"Server dangling exits, hard killing force {pid}={path}...\n")
-    if os.name == 'nt':
-        process = subprocess.check_output(['taskkill', '/F', '/pid', str(pid)]).decode()
-        print(process)
-    else:
-        os.kill(-pid, signal.SIGKILL)
+        interrupt = signal.SIGINT
+    context.server_process.send_signal(interrupt)
 
 
 def is_server_listening(server_fqdn, server_port):
@@ -79,22 +67,5 @@ def is_server_listening(server_fqdn, server_port):
         result = sock.connect_ex((server_fqdn, server_port))
         _is_server_listening = result == 0
         if _is_server_listening:
-            print(f"server is listening on {server_fqdn}:{server_port}...\n")
+            print(f"server is listening on {server_fqdn}:{server_port}...")
         return _is_server_listening
-
-
-def pid_exists(pid):
-    """Check whether pid exists in the current process table."""
-    if pid < 0:
-        return False
-    if os.name == 'nt':
-        output = subprocess.check_output(['TASKLIST', '/FI', f'pid eq {pid}']).decode()
-        print(output)
-        return "No tasks are running" not in output
-    else:
-        try:
-            os.kill(pid, 0)
-        except OSError as e:
-            return e.errno == errno.EPERM
-        else:
-            return True

examples/server/tests/features/server.feature

@@ -4,7 +4,8 @@ Feature: llama.cpp server
 
   Background: Server startup
     Given a server listening on localhost:8080
-    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    And   a model url https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories260K.gguf
+    And   a model file stories260K.gguf
     And   a model alias tinyllama-2
     And   42 as server seed
       # KV Cache corresponds to the total amount of tokens
@@ -34,9 +35,9 @@ Feature: llama.cpp server
     And   metric llamacpp:tokens_predicted is <n_predicted>
 
     Examples: Prompts
-      | prompt                                                                    | n_predict | re_content                    | n_prompt | n_predicted | truncated |
-      | I believe the meaning of life is                                          | 8         | (read\|going)+                | 18       | 8           | not       |
-      | Write a joke about AI from a very long prompt which will not be truncated | 256       | (princesses\|everyone\|kids)+ | 46       | 64          | not       |
+      | prompt                                                                    | n_predict | re_content                                  | n_prompt | n_predicted | truncated |
+      | I believe the meaning of life is                                          | 8         | (read\|going)+                              | 18       | 8           | not       |
+      | Write a joke about AI from a very long prompt which will not be truncated | 256       | (princesses\|everyone\|kids\|Anna\|forest)+ | 46       | 64          | not       |
 
   Scenario: Completion prompt truncated
     Given a prompt:
@@ -47,7 +48,7 @@ Feature: llama.cpp server
     Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
     """
     And   a completion request with no api error
-    Then  64 tokens are predicted matching fun|Annaks|popcorns|pictry
+    Then  64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl
     And   the completion is  truncated
     And   109 prompt tokens are processed
 
@@ -64,9 +65,9 @@ Feature: llama.cpp server
     And   the completion is <truncated> truncated
 
     Examples: Prompts
-      | model        | system_prompt               | user_prompt                          | max_tokens | re_content             | n_prompt | n_predicted | enable_streaming | truncated |
-      | llama-2      | Book                        | What is the best book                | 8          | (Here\|what)+          | 77       | 8           | disabled         | not       |
-      | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128        | (thanks\|happy\|bird)+ | -1       | 64          | enabled          |           |
+      | model        | system_prompt               | user_prompt                          | max_tokens | re_content                        | n_prompt | n_predicted | enable_streaming | truncated |
+      | llama-2      | Book                        | What is the best book                | 8          | (Here\|what)+                     | 77       | 8           | disabled         | not       |
+      | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128        | (thanks\|happy\|bird\|Annabyear)+ | -1       | 64          | enabled          |           |
 
 
   Scenario: Tokenize / Detokenize

examples/server/tests/features/steps/steps.py

@@ -5,6 +5,8 @@ import os
 import re
 import socket
 import subprocess
+import sys
+import threading
 import time
 from contextlib import closing
 from re import RegexFlag
@@ -22,21 +24,27 @@ from prometheus_client import parser
 def step_server_config(context, server_fqdn, server_port):
     context.server_fqdn = server_fqdn
     context.server_port = int(server_port)
+    context.n_gpu_layer = None
     if 'PORT' in os.environ:
         context.server_port = int(os.environ['PORT'])
         print(f"$PORT set, overriding server port with to {context.server_port}")
     if 'FQDN' in os.environ:
         context.server_fqdn = os.environ['FQDN']
         print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}")
+    if 'N_GPU_LAYERS' in os.environ:
+        context.n_gpu_layer = int(os.environ['N_GPU_LAYERS'])
+        print(f"$N_GPU_LAYERS set, overriding n_gpu_layer with to {context.n_gpu_layer}")
 
     context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
 
     context.model_alias = None
+    context.model_file = None
+    context.model_url = None
     context.n_batch = None
+    context.n_ubatch = None
     context.n_ctx = None
     context.n_ga = None
     context.n_ga_w = None
-    context.n_gpu_layer = None
     context.n_predict = None
     context.n_prompts = 0
     context.n_server_predict = None
@@ -61,7 +69,17 @@ def step_server_config(context, server_fqdn, server_port):
 def step_download_hf_model(context, hf_file, hf_repo):
     context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file)
     if context.debug:
-        print(f"model file: {context.model_file}\n")
+        print(f"model file: {context.model_file}")
+
+
+@step('a model file {model_file}')
+def step_model_file(context, model_file):
+    context.model_file = model_file
+
+
+@step('a model url {model_url}')
+def step_model_url(context, model_url):
+    context.model_url = model_url
 
 
 @step('a model alias {model_alias}')
@@ -118,14 +136,21 @@ def step_server_metrics(context):
 def step_start_server(context):
     start_server_background(context)
     attempts = 0
+    max_attempts = 20
+    if 'GITHUB_ACTIONS' in os.environ:
+        max_attempts *= 2
+
+    addrs = socket.getaddrinfo(context.server_fqdn, context.server_port, type=socket.SOCK_STREAM)
+    family, typ, proto, _, sockaddr = addrs[0]
+
     while True:
-        with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
-            result = sock.connect_ex((context.server_fqdn, context.server_port))
+        with closing(socket.socket(family, typ, proto)) as sock:
+            result = sock.connect_ex(sockaddr)
             if result == 0:
                 print("\x1b[33;46mserver started!\x1b[0m")
                 return
             attempts += 1
-            if attempts > 20:
+            if attempts > max_attempts:
                 assert False, "server not started"
             print(f"waiting for server to start, connect error code = {result}...")
             time.sleep(0.1)
@@ -136,7 +161,8 @@ def step_start_server(context):
 async def step_wait_for_the_server_to_be_started(context, expecting_status):
     match expecting_status:
         case 'healthy':
-            await wait_for_health_status(context, context.base_url, 200, 'ok')
+            await wait_for_health_status(context, context.base_url, 200, 'ok',
+                                         timeout=30)
 
         case 'ready' | 'idle':
             await wait_for_health_status(context, context.base_url, 200, 'ok',
@@ -189,7 +215,7 @@ async def step_request_completion(context, api_error):
                                           user_api_key=context.user_api_key)
     context.tasks_result.append(completion)
     if context.debug:
-        print(f"Completion response: {completion}\n")
+        print(f"Completion response: {completion}")
     if expect_api_error:
         assert completion == 401, f"completion must be an 401 status code: {completion}"
 
@@ -278,6 +304,11 @@ def step_n_batch(context, n_batch):
     context.n_batch = n_batch
 
 
+@step('{n_ubatch:d} as ubatch size')
+def step_n_ubatch(context, n_ubatch):
+    context.n_ubatch = n_ubatch
+
+
 @step('{seed:d} as seed')
 def step_seed(context, seed):
     context.seed = seed
@@ -329,7 +360,7 @@ def step_prompt_passkey(context, passkey, i_pos):
         prompt += context.prompt_junk_suffix
     if context.debug:
         passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m"
-        print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```\n")
+        print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```")
     context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix)
     context.n_prompts = len(context.prompts)
 
@@ -338,7 +369,7 @@ def step_prompt_passkey(context, passkey, i_pos):
 @async_run_until_complete
 async def step_oai_chat_completions(context, api_error):
     if context.debug:
-        print(f"Submitting OAI compatible completions request...\n")
+        print(f"Submitting OAI compatible completions request...")
     expect_api_error = api_error == 'raised'
     completion = await oai_chat_completions(context.prompts.pop(),
                                             context.system_prompt,
@@ -483,12 +514,12 @@ async def step_all_embeddings_are_the_same(context):
             embedding1 = np.array(embeddings[i])
             embedding2 = np.array(embeddings[j])
             if context.debug:
-                print(f"embedding1: {embedding1[-8:]}\n")
-                print(f"embedding2: {embedding2[-8:]}\n")
+                print(f"embedding1: {embedding1[-8:]}")
+                print(f"embedding2: {embedding2[-8:]}")
             similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
             msg = f"Similarity between {i} and {j}: {similarity:.10f}"
             if context.debug:
-                print(f"{msg}\n")
+                print(f"{msg}")
             assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg
 
 
@@ -605,7 +636,7 @@ async def step_prometheus_metrics_exported(context):
             metrics_raw = await metrics_response.text()
             metric_exported = False
             if context.debug:
-                print(f"/metrics answer:\n{metrics_raw}\n")
+                print(f"/metrics answer:\n{metrics_raw}")
             context.metrics = {}
             for metric in parser.text_string_to_metric_families(metrics_raw):
                 match metric.name:
@@ -801,7 +832,7 @@ async def oai_chat_completions(user_prompt,
                 stream=enable_streaming,
                 seed=seed
             )
-        except openai.error.APIError as e:
+        except openai.error.AuthenticationError as e:
             if expect_api_error is not None and expect_api_error:
                 return 401
             else:
@@ -907,7 +938,7 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
             last_match = end
         highlighted += content[last_match:]
         if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
-          print(f"Checking completion response: {highlighted}\n")
+          print(f"Checking completion response: {highlighted}")
         assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```'
     if expected_predicted_n and expected_predicted_n > 0:
         assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
@@ -917,7 +948,7 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
 async def gather_tasks_results(context):
     n_tasks = len(context.concurrent_tasks)
     if context.debug:
-        print(f"Waiting for all {n_tasks} tasks results...\n")
+        print(f"Waiting for all {n_tasks} tasks results...")
     for task_no in range(n_tasks):
         context.tasks_result.append(await context.concurrent_tasks.pop())
     n_completions = len(context.tasks_result)
@@ -934,9 +965,12 @@ async def wait_for_health_status(context,
                                  slots_processing=None,
                                  expected_slots=None):
     if context.debug:
-        print(f"Starting checking for health for expected_health_status={expected_health_status}\n")
+        print(f"Starting checking for health for expected_health_status={expected_health_status}")
     interval = 0.5
     counter = 0
+    if 'GITHUB_ACTIONS' in os.environ:
+        timeout *= 2
+
     async with aiohttp.ClientSession() as session:
         while True:
             async with await session.get(f'{base_url}/health', params=params) as health_response:
@@ -1020,15 +1054,18 @@ def start_server_background(context):
     if 'LLAMA_SERVER_BIN_PATH' in os.environ:
         context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
     server_listen_addr = context.server_fqdn
-    if os.name == 'nt':
-        server_listen_addr = '0.0.0.0'
     server_args = [
         '--host', server_listen_addr,
         '--port', context.server_port,
-        '--model', context.model_file
     ]
+    if context.model_file:
+        server_args.extend(['--model', context.model_file])
+    if context.model_url:
+        server_args.extend(['--model-url', context.model_url])
     if context.n_batch:
         server_args.extend(['--batch-size', context.n_batch])
+    if context.n_ubatch:
+        server_args.extend(['--ubatch-size', context.n_ubatch])
     if context.n_gpu_layer:
         server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
     if context.server_continuous_batching:
@@ -1055,7 +1092,7 @@ def start_server_background(context):
         server_args.append('--verbose')
     if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
         server_args.extend(['--log-format', "text"])
-    print(f"starting server with: {context.server_path} {server_args}\n")
+    print(f"starting server with: {context.server_path} {server_args}")
     flags = 0
     if 'nt' == os.name:
         flags |= subprocess.DETACHED_PROCESS
@@ -1064,8 +1101,23 @@ def start_server_background(context):
 
     pkwargs = {
         'creationflags': flags,
+        'stdout': subprocess.PIPE,
+        'stderr': subprocess.PIPE
     }
     context.server_process = subprocess.Popen(
         [str(arg) for arg in [context.server_path, *server_args]],
         **pkwargs)
+
+    def log_stdout(process):
+        for line in iter(process.stdout.readline, b''):
+            print(line.decode('utf-8'), end='')
+    thread_stdout = threading.Thread(target=log_stdout, args=(context.server_process,))
+    thread_stdout.start()
+
+    def log_stderr(process):
+        for line in iter(process.stderr.readline, b''):
+            print(line.decode('utf-8'), end='', file=sys.stderr)
+    thread_stderr = threading.Thread(target=log_stderr, args=(context.server_process,))
+    thread_stderr.start()
+
     print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")

examples/server/utils.hpp

@@ -14,6 +14,17 @@
 
 using json = nlohmann::json;
 
+// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
+enum error_type {
+    ERROR_TYPE_INVALID_REQUEST,
+    ERROR_TYPE_AUTHENTICATION,
+    ERROR_TYPE_SERVER,
+    ERROR_TYPE_NOT_FOUND,
+    ERROR_TYPE_PERMISSION,
+    ERROR_TYPE_UNAVAILABLE, // custom error
+    ERROR_TYPE_NOT_SUPPORTED, // custom error
+};
+
 extern bool server_verbose;
 extern bool server_log_json;
 
@@ -360,6 +371,7 @@ static json oaicompat_completion_params_parse(
     llama_params["repeat_last_n"]     = json_value(body,   "repeat_last_n",     default_sparams.penalty_last_n);
     llama_params["ignore_eos"]        = json_value(body,   "ignore_eos",        false);
     llama_params["tfs_z"]             = json_value(body,   "tfs_z",             default_sparams.tfs_z);
+    llama_params["n_keep"]            = json_value(body,   "n_keep",            0);
 
     if (body.count("grammar") != 0) {
         llama_params["grammar"] = json_value(body, "grammar", json::object());
@@ -378,7 +390,7 @@ static json oaicompat_completion_params_parse(
     return llama_params;
 }
 
-static json format_final_response_oaicompat(const json & request, json result, bool streaming = false) {
+static json format_final_response_oaicompat(const json & request, json result, const std::string & completion_id, bool streaming = false) {
     bool stopped_word        = result.count("stopped_word") != 0;
     bool stopped_eos         = json_value(result, "stopped_eos", false);
     int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
@@ -412,7 +424,7 @@ static json format_final_response_oaicompat(const json & request, json result, b
             {"prompt_tokens",     num_prompt_tokens},
             {"total_tokens",      num_tokens_predicted + num_prompt_tokens}
         }},
-        {"id", gen_chatcmplid()}
+        {"id", completion_id}
     };
 
     if (server_verbose) {
@@ -427,7 +439,7 @@ static json format_final_response_oaicompat(const json & request, json result, b
 }
 
 // return value is vector as there is one case where we might need to generate two responses
-static std::vector<json> format_partial_response_oaicompat(json result) {
+static std::vector<json> format_partial_response_oaicompat(json result, const std::string & completion_id) {
     if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
         return std::vector<json>({result});
     }
@@ -471,7 +483,7 @@ static std::vector<json> format_partial_response_oaicompat(json result) {
                                             {"role", "assistant"}
                                         }}}})},
                             {"created", t},
-                            {"id", gen_chatcmplid()},
+                            {"id", completion_id},
                             {"model", modelname},
                             {"object", "chat.completion.chunk"}};
 
@@ -482,7 +494,7 @@ static std::vector<json> format_partial_response_oaicompat(json result) {
                                                             {"content", content}}}
                                                             }})},
                             {"created", t},
-                            {"id", gen_chatcmplid()},
+                            {"id", completion_id},
                             {"model", modelname},
                             {"object", "chat.completion.chunk"}};
 
@@ -509,7 +521,7 @@ static std::vector<json> format_partial_response_oaicompat(json result) {
     json ret = json {
         {"choices", choices},
         {"created", t},
-        {"id",      gen_chatcmplid()},
+        {"id",      completion_id},
         {"model",   modelname},
         {"object",  "chat.completion.chunk"}
     };
@@ -518,6 +530,16 @@ static std::vector<json> format_partial_response_oaicompat(json result) {
 }
 
 static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
+    json data = json::array();
+    int i = 0;
+    for (auto & elem : embeddings) {
+        data.push_back(json{
+            {"embedding", json_value(elem, "embedding", json::array())},
+            {"index",     i++},
+            {"object",    "embedding"}
+        });
+    }
+
     json res = json {
         {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
         {"object", "list"},
@@ -525,7 +547,7 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
             {"prompt_tokens", 0},
             {"total_tokens", 0}
         }},
-        {"data", embeddings}
+        {"data", data}
     };
 
     return res;
@@ -542,3 +564,43 @@ static json format_detokenized_response(const std::string & content) {
         {"content", content}
     };
 }
+
+static json format_error_response(const std::string & message, const enum error_type type) {
+    std::string type_str;
+    int code = 500;
+    switch (type) {
+        case ERROR_TYPE_INVALID_REQUEST:
+            type_str = "invalid_request_error";
+            code = 400;
+            break;
+        case ERROR_TYPE_AUTHENTICATION:
+            type_str = "authentication_error";
+            code = 401;
+            break;
+        case ERROR_TYPE_NOT_FOUND:
+            type_str = "not_found_error";
+            code = 404;
+            break;
+        case ERROR_TYPE_SERVER:
+            type_str = "server_error";
+            code = 500;
+            break;
+        case ERROR_TYPE_PERMISSION:
+            type_str = "permission_error";
+            code = 403;
+            break;
+        case ERROR_TYPE_NOT_SUPPORTED:
+            type_str = "not_supported_error";
+            code = 501;
+            break;
+        case ERROR_TYPE_UNAVAILABLE:
+            type_str = "unavailable_error";
+            code = 503;
+            break;
+    }
+    return json {
+        {"code", code},
+        {"message", message},
+        {"type", type_str},
+    };
+}

examples/sycl/build.sh

@@ -13,8 +13,11 @@ source /opt/intel/oneapi/setvars.sh
 #for FP32
 cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
-#build example/main only
+#build example/main
 #cmake --build . --config Release --target main
 
+#build example/llama-bench
+#cmake --build . --config Release --target llama-bench
+
 #build all binary
 cmake --build . --config Release -v

examples/sycl/run-llama2.sh

@@ -9,18 +9,28 @@ source /opt/intel/oneapi/setvars.sh
 
 if [ $# -gt 0 ]; then
     GGML_SYCL_DEVICE=$1
+    GGML_SYCL_SINGLE_GPU=1
 else
     GGML_SYCL_DEVICE=0
 fi
-echo "use $GGML_SYCL_DEVICE as main GPU"
+
 #export GGML_SYCL_DEBUG=1
 
 
 #ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
 
-#use all GPUs with same max compute units
-ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
+if [ $GGML_SYCL_SINGLE_GPU -eq 1 ]; then
+    echo "use $GGML_SYCL_DEVICE as main GPU"
+    #use signle GPU only
+    ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
+else
+    #use multiple GPUs with same max compute units
+    ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
+fi
 
 #use main GPU only
 #ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
 
+#use multiple GPUs with same max compute units
+#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
+

examples/sycl/win-run-llama2.bat

@@ -6,8 +6,6 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
 
 
-set GGML_SYCL_DEVICE=0
-rem set GGML_SYCL_DEBUG=1
 .\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0
 
 

examples/train-text-from-scratch/train-text-from-scratch.cpp

@@ -711,6 +711,7 @@ static bool load_checkpoint_file(const char * filename, struct my_llama_model *
 
     load_checkpoint_gguf(fctx, f_ggml_ctx, model, train);
 
+    gguf_free(fctx);
     return true;
 }
 

flake.lock

@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1709703039,
-        "narHash": "sha256-6hqgQ8OK6gsMu1VtcGKBxKQInRLHtzulDo9Z5jxHEFY=",
+        "lastModified": 1710451336,
+        "narHash": "sha256-pP86Pcfu3BrAvRO7R64x7hs+GaQrjFes+mEPowCfkxY=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "9df3e30ce24fd28c7b3e2de0d986769db5d6225d",
+        "rev": "d691274a972b3165335d261cc4671335f5c67de9",
         "type": "github"
       },
       "original": {

ggml-alloc.c

@@ -61,7 +61,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
     }
 }
 
-// TODO: GGML_PAD ?
 static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
     assert(alignment && !(alignment & (alignment - 1))); // power of 2
     size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
@@ -69,25 +68,14 @@ static size_t aligned_offset(const void * buffer, size_t offset, size_t alignmen
 }
 
 // tallocr
-struct ggml_tallocr {
-    ggml_backend_buffer_t buffer;
-    void * base;
-    size_t alignment;
-    size_t offset;
-};
-
-ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) {
-    ggml_tallocr_t talloc = malloc(sizeof(struct ggml_tallocr));
-    if (talloc == NULL) {
-        return NULL;
-    }
 
+struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer) {
     void * base = ggml_backend_buffer_get_base(buffer);
     size_t align = ggml_backend_buffer_get_alignment(buffer);
 
     assert(align && !(align & (align - 1))); // power of 2
 
-    *talloc = (struct ggml_tallocr) {
+    struct ggml_tallocr talloc = (struct ggml_tallocr) {
         /*.buffer    = */ buffer,
         /*.base      = */ base,
         /*.alignment = */ align,
@@ -96,11 +84,7 @@ ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) {
     return talloc;
 }
 
-void ggml_tallocr_free(ggml_tallocr_t talloc) {
-    free(talloc);
-}
-
-void ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor) {
+void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) {
     size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor);
     size = GGML_PAD(size, talloc->alignment);
 
@@ -354,12 +338,16 @@ struct hash_node {
     bool allocated;
 };
 
-//
 struct tensor_alloc {
     size_t offset;
     size_t size_max; // 0 = pre-allocated, unused, or view
 };
 
+struct leaf_alloc {
+    int buffer_id;
+    struct tensor_alloc leaf;
+};
+
 struct node_alloc {
     int buffer_id;
     struct tensor_alloc dst;
@@ -378,7 +366,7 @@ struct ggml_gallocr {
     struct node_alloc * node_allocs; // [n_nodes]
     int n_nodes;
 
-    struct tensor_alloc * leaf_allocs; // [n_leafs]
+    struct leaf_alloc * leaf_allocs; // [n_leafs]
     int n_leafs;
 };
 
@@ -543,17 +531,28 @@ static int get_node_buffer_id(const int * node_buffer_ids, int i) {
     return node_buffer_ids ? node_buffer_ids[i] : 0;
 }
 
-static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) {
+static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
     // clear hash tables
     memset(galloc->hash_set.keys, 0, galloc->hash_set.size * sizeof(struct ggml_tensor *));
     memset(galloc->hash_values,   0, galloc->hash_set.size * sizeof(struct hash_node));
 
+    // allocate leafs
+    // these may be tensors that the application is not using in the graph, but may still want to allocate for other purposes
+    for (int i = 0; i < graph->n_leafs; i++) {
+        struct ggml_tensor * leaf = graph->leafs[i];
+        ggml_gallocr_allocate_node(galloc, leaf, get_node_buffer_id(leaf_buffer_ids, i));
+    }
+
     // count number of children and views
-    // allocate all graph inputs and leafs first to avoid overwriting them
+    // allocate other graph inputs and leafs first to avoid overwriting them
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
 
-        if (ggml_is_view(node)) {
+        // TODO: better way to add external dependencies
+        // GGML_OP_NONE does not appear normally in the graph nodes, but is used by ggml-backend to add dependencies to
+        // control when some tensors are allocated and freed. in this case, the dependencies are in `src`, but the node
+        // itself is never used and should not be considered a dependency
+        if (ggml_is_view(node) && node->op != GGML_OP_NONE) {
             struct ggml_tensor * view_src = node->view_src;
             ggml_gallocr_hash_get(galloc, view_src)->n_views += 1;
         }
@@ -570,26 +569,13 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
 
             ggml_gallocr_hash_get(galloc, src)->n_children += 1;
 
-            // allocate explicit inputs and leafs
-            if (src->flags & GGML_TENSOR_FLAG_INPUT || src->op == GGML_OP_NONE) {
+            // allocate explicit inputs
+            if (src->flags & GGML_TENSOR_FLAG_INPUT) {
                 ggml_gallocr_allocate_node(galloc, src, get_node_buffer_id(node_buffer_ids, i));
             }
         }
     }
 
-    // allocate the remaining leafs that are unused on the graph
-    // these are effectively static tensors that the application is not using in the graph, but may still want to allocate for other purposes
-    for (int i = 0; i < graph->n_leafs; i++) {
-        struct ggml_tensor * leaf = graph->leafs[i];
-        struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
-
-        if (hn->n_children == 0) {
-            assert(!hn->allocated);
-            // since buffer ids are only given for nodes, these leafs are always allocated in the first buffer
-            ggml_gallocr_allocate_node(galloc, leaf, 0);
-        }
-    }
-
     // allocate tensors
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
@@ -652,7 +638,7 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
     }
 }
 
-bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) {
+bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
     size_t hash_size = graph->visited_hash_table.size;
 
     // initialize hash table
@@ -676,7 +662,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
     }
 
     // allocate in hash table
-    ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids);
+    ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids);
 
     // set the node_allocs from the hash table
     if (galloc->n_nodes < graph->n_nodes) {
@@ -711,15 +697,16 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
     }
     if (galloc->n_leafs < graph->n_leafs) {
         free(galloc->leaf_allocs);
-        galloc->leaf_allocs = calloc(sizeof(struct tensor_alloc), graph->n_leafs);
+        galloc->leaf_allocs = calloc(sizeof(galloc->leaf_allocs[0]), graph->n_leafs);
         GGML_ASSERT(galloc->leaf_allocs != NULL);
     }
     galloc->n_leafs = graph->n_leafs;
     for (int i = 0; i < graph->n_leafs; i++) {
         struct ggml_tensor * leaf = graph->leafs[i];
         struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
-        galloc->leaf_allocs[i].offset = hn->offset;
-        galloc->leaf_allocs[i].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
+        galloc->leaf_allocs[i].buffer_id = hn->buffer_id;
+        galloc->leaf_allocs[i].leaf.offset = hn->offset;
+        galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
     }
 
     // reallocate buffers if needed
@@ -727,7 +714,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
         size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0;
         size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]);
 
-        if (new_size > cur_size) {
+        // even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
+        if (new_size > cur_size || galloc->buffers[i] == NULL) {
 #ifndef NDEBUG
             fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
 #endif
@@ -744,30 +732,30 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
 }
 
 bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
-    return ggml_gallocr_reserve_n(galloc, graph, NULL);
+    return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL);
 }
 
-static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, struct tensor_alloc * tensor_alloc) {
-    assert(node->data || node->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max);
+static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, int buffer_id, struct tensor_alloc * tensor_alloc) {
+    assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
 
-    if (node->view_src != NULL) {
-        if (node->buffer == NULL) {
+    if (tensor->view_src != NULL) {
+        if (tensor->buffer == NULL) {
             assert(tensor_alloc->offset == SIZE_MAX);
-            if (node->view_src->buffer == NULL) {
+            if (tensor->view_src->buffer == NULL) {
                 // this tensor was allocated without ggml-backend
                 return;
             }
-            ggml_backend_view_init(galloc->buffers[buffer_id], node);
+            ggml_backend_view_init(galloc->buffers[buffer_id], tensor);
         }
     } else {
-        if (node->data == NULL) {
+        if (tensor->data == NULL) {
             assert(tensor_alloc->offset != SIZE_MAX);
-            assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max);
+            assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
             void * base = ggml_backend_buffer_get_base(galloc->buffers[buffer_id]);
             void * addr = (char *)base + tensor_alloc->offset;
-            ggml_backend_tensor_alloc(galloc->buffers[buffer_id], node, addr);
+            ggml_backend_tensor_alloc(galloc->buffers[buffer_id], tensor, addr);
         } else {
-            if (node->buffer == NULL) {
+            if (tensor->buffer == NULL) {
                 // this tensor was allocated without ggml-backend
                 return;
             }
@@ -843,13 +831,18 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
 
     // reset buffers
     for (int i = 0; i < galloc->n_buffers; i++) {
-        // zero size buffers are not allocated
         if (galloc->buffers[i] != NULL) {
             ggml_backend_buffer_reset(galloc->buffers[i]);
         }
     }
 
     // allocate the graph tensors from the previous assignments
+    // leafs
+    for (int i = 0; i < graph->n_leafs; i++) {
+        struct ggml_tensor * leaf = graph->leafs[i];
+        struct leaf_alloc * leaf_alloc = &galloc->leaf_allocs[i];
+        ggml_gallocr_init_tensor(galloc, leaf, leaf_alloc->buffer_id, &leaf_alloc->leaf);
+    }
     // nodes
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
@@ -863,12 +856,6 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
         }
         ggml_gallocr_init_tensor(galloc, node, node_alloc->buffer_id, &node_alloc->dst);
     }
-    // leafs
-    for (int i = 0; i < graph->n_leafs; i++) {
-        struct ggml_tensor * leaf = graph->leafs[i];
-        struct tensor_alloc * leaf_alloc = &galloc->leaf_allocs[i];
-        ggml_gallocr_init_tensor(galloc, leaf, 0, leaf_alloc);
-    }
 
     return true;
 }
@@ -900,12 +887,12 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
         return false;
     }
 
-    struct ggml_tallocr * tallocr = ggml_tallocr_new(buffer);
+    struct ggml_tallocr tallocr = ggml_tallocr_new(buffer);
 
     for (struct ggml_tensor * t = first; t != last; t = ggml_get_next_tensor(ctx, t)) {
         if (t->data == NULL) {
             if (t->view_src == NULL) {
-                ggml_tallocr_alloc(tallocr, t);
+                ggml_tallocr_alloc(&tallocr, t);
             } else if (t->buffer == NULL) {
                 ggml_backend_view_init(buffer, t);
             }
@@ -917,8 +904,6 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
         }
     }
 
-    ggml_tallocr_free(tallocr);
-
     *buffers = realloc(*buffers, sizeof(ggml_backend_buffer_t) * (*n_buffers + 1));
     (*buffers)[(*n_buffers)++] = buffer;
 

ggml-alloc.h

@@ -11,11 +11,15 @@ typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
 typedef struct ggml_backend * ggml_backend_t;
 
 // Tensor allocator
-typedef struct ggml_tallocr * ggml_tallocr_t;
+struct ggml_tallocr {
+    ggml_backend_buffer_t buffer;
+    void * base;
+    size_t alignment;
+    size_t offset;
+};
 
-GGML_API ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer);
-GGML_API void           ggml_tallocr_free(ggml_tallocr_t talloc);
-GGML_API void           ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor);
+GGML_API struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer);
+GGML_API void                ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor);
 
 // Graph allocator
 /*
@@ -50,7 +54,11 @@ GGML_API void           ggml_gallocr_free(ggml_gallocr_t galloc);
 // not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed
 // returns false if the buffer allocation failed
 GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
-GGML_API bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids);
+GGML_API bool ggml_gallocr_reserve_n(
+    ggml_gallocr_t galloc,
+    struct ggml_cgraph * graph,
+    const int * node_buffer_ids,
+    const int * leaf_buffer_ids);
 
 // automatic reallocation if the topology changes when using a single buffer
 // returns false if using multiple buffers and a re-allocation is needed (call ggml_gallocr_reserve_n first to set the node buffers)

ggml-backend-impl.h

@@ -86,12 +86,12 @@ extern "C" {
         // (optional) asynchronous tensor data access
         void (*GGML_CALL set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
         void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
-        bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst);
+        bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
 
         // (optional) complete all pending operations
         void (*GGML_CALL synchronize)(ggml_backend_t backend);
 
-        // create a plan for ggml_cgraph and free it
+        // compute graph with a plan (not used currently)
         ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
         void                      (*GGML_CALL graph_plan_free)   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
@@ -102,16 +102,32 @@ extern "C" {
 
         // check if the backend supports an operation
         bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+
+        // check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
+        // these should be expensive operations with large batch sizes that may benefit from running on this backend
+        // even if the weight has to be copied from the CPU temporarily
+        bool (*GGML_CALL offload_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+
+        // (optional) event synchronization
+        ggml_backend_event_t (*GGML_CALL event_new)         (ggml_backend_t backend);
+        void                 (*GGML_CALL event_free)        (ggml_backend_event_t event);
+        void                 (*GGML_CALL event_record)      (ggml_backend_event_t event);
+        void                 (*GGML_CALL event_wait)        (ggml_backend_t backend, ggml_backend_event_t event);
+        void                 (*GGML_CALL event_synchronize) (ggml_backend_event_t event);
     };
 
     struct ggml_backend {
         ggml_guid_t guid;
 
         struct ggml_backend_i iface;
-
         ggml_backend_context_t context;
     };
 
+    struct ggml_backend_event {
+        ggml_backend_t backend;
+        void * context;
+    };
+
     //
     // Backend registry
     //

ggml-backend.h

@@ -9,6 +9,7 @@ extern "C" {
 
     typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
     typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
+    typedef struct ggml_backend_event * ggml_backend_event_t;
     typedef struct ggml_backend * ggml_backend_t;
     typedef void * ggml_backend_graph_plan_t;
 
@@ -69,14 +70,27 @@ extern "C" {
     GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph);
     GGML_API void                      ggml_backend_graph_plan_free  (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
-    GGML_API enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-    GGML_API enum ggml_status ggml_backend_graph_compute     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
-
+    GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+    GGML_API enum ggml_status ggml_backend_graph_compute      (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
     GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
+    GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op);
 
     // tensor copy between different backends
     GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
-    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); // automatic fallback to sync copy
+
+    // asynchronous copy
+    // the copy is performed after all the currently queued operations in backend_src
+    // backend_dst will wait for the copy to complete before performing other operations
+    // automatic fallback to sync copy if async is not supported
+    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
+
+    // events
+    GGML_API ggml_backend_event_t   ggml_backend_event_new        (ggml_backend_t backend);
+    GGML_API void                   ggml_backend_event_free       (ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_record     (ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_synchronize(ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_wait       (ggml_backend_t backend, ggml_backend_event_t event); // wait async on event
 
     //
     // CPU backend
@@ -123,27 +137,31 @@ extern "C" {
     /*
       Example usage:
 
-        sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends);
-        // sched is initialized with measure allocators and cannot be used until allocated with a measure graph
+        // operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be asigned
+        // preferrably to run on the same backend as the buffer
+        ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
 
-        // initialize buffers from a measure graph
-        measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed
+        sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
 
-        // in build_graph:
-        build_graph(...) {
-            // manually assign nodes to a backend (optional, should not be needed in most cases)
-            struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
-            ggml_backend_sched_set_node_backend(sched, node, backend_gpu);
-        }
+        // initialize buffers from a max size graph (optional)
+        reserve_graph = build_graph(sched, max_batch_size);
 
-        // allocate backend buffers from measure graph
-        ggml_backend_sched_init_measure(sched, measure_graph);
+        // manually assign nodes to a backend (optional, should not be needed in most cases)
+        struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
+        ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
 
-        // the scheduler is now ready to compute graphs
+        ggml_backend_sched_reserve(sched, reserve_graph);
 
         // compute
         graph = build_graph(sched);
         ggml_backend_sched_graph_compute(sched, graph);
+
+        // if there are graph inputs:
+        ggml_backend_sched_reset(sched);
+        ggml_backend_sched_alloc_graph(sched, graph);
+        ggml_backend_tensor_set(input_tensor, ...);
+        ggml_backend_sched_graph_compute(sched, graph);
+    }
     */
 
     struct ggml_backend_sched;
@@ -158,20 +176,26 @@ extern "C" {
     typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
 
     // Initialize a backend scheduler
-    GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size);
+    GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
     GGML_API void                 ggml_backend_sched_free(ggml_backend_sched_t sched);
+
     // Initialize backend buffers from a measure graph
     GGML_API bool                 ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
+
     // Get the number of splits of the last graph
     GGML_API int                  ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
+    GGML_API int                  ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
 
     GGML_API size_t               ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
 
-    GGML_API void                 ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
-    GGML_API ggml_backend_t       ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
+    GGML_API void                 ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
+    GGML_API ggml_backend_t       ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
 
     // Allocate and compute graph on the backend scheduler
+    GGML_API bool                 ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
     GGML_API enum ggml_status     ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API enum ggml_status     ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API void                 ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
 
     // Reset all assignments and allocators - must be called before changing the node backends
     GGML_API void                 ggml_backend_sched_reset(ggml_backend_sched_t sched);

ggml-cuda.h

@@ -17,29 +17,17 @@ extern "C" {
 
 #define GGML_CUDA_MAX_DEVICES       16
 
-// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`.
-GGML_API GGML_CALL void   ggml_init_cublas(void);
-
-// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`.
-GGML_API GGML_CALL bool   ggml_cublas_loaded(void);
-
-GGML_API GGML_CALL void * ggml_cuda_host_malloc(size_t size);
-GGML_API GGML_CALL void   ggml_cuda_host_free(void * ptr);
-
-GGML_API GGML_CALL bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-GGML_API GGML_CALL bool   ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
-
-GGML_API GGML_CALL int    ggml_cuda_get_device_count(void);
-GGML_API GGML_CALL void   ggml_cuda_get_device_description(int device, char * description, size_t description_size);
-
 // backend API
 GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device);
 
 GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend);
 
+// device buffer
 GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
+
 // split tensor buffer that splits matrices by rows across multiple devices
 GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
+
 // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
 GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
 
@@ -47,6 +35,9 @@ GGML_API GGML_CALL int  ggml_backend_cuda_get_device_count(void);
 GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
 GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
 
+GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
+GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer);
+
 #ifdef  __cplusplus
 }
 #endif

ggml-impl.h

@@ -53,26 +53,30 @@ extern "C" {
 //
 #include <arm_neon.h>
 
+typedef __fp16 ggml_fp16_internal_t;
+
 #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
 #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
 
 #define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
 
 static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
-    __fp16 tmp;
+    ggml_fp16_internal_t tmp;
     memcpy(&tmp, &h, sizeof(ggml_fp16_t));
     return (float)tmp;
 }
 
 static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
     ggml_fp16_t res;
-    __fp16 tmp = f;
+    ggml_fp16_internal_t tmp = f;
     memcpy(&res, &tmp, sizeof(ggml_fp16_t));
     return res;
 }
 
 #else
 
+typedef uint16_t ggml_fp16_internal_t;
+
 #ifdef __wasm_simd128__
 #include <wasm_simd128.h>
 #else

ggml-kompute.cpp

@@ -1951,6 +1951,12 @@ static struct ggml_backend_i kompute_backend_i = {
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_kompute_graph_compute,
     /* .supports_op             = */ ggml_backend_kompute_supports_op,
+    /* .offload_op              = */ NULL,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
 };
 
 static ggml_guid_t ggml_backend_kompute_guid() {

ggml-metal.m

@@ -280,6 +280,11 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
     id<MTLLibrary> metal_library;
 
     // load library
+    //
+    // - first check if the library is embedded
+    // - then check if the library is in the bundle
+    // - if not found, load the source and compile it
+    // - if that fails, return NULL
     {
         NSBundle * bundle = nil;
 #ifdef SWIFT_PACKAGE
@@ -287,12 +292,21 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
 #else
         bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
 #endif
+
         NSError * error = nil;
-        NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"];
-        if (libPath != nil) {
+
+#if GGML_METAL_EMBED_LIBRARY
+        const bool try_metallib = false;
+#else
+        const bool try_metallib = true;
+#endif
+
+        NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
+        if (try_metallib && path_lib != nil) {
             // pre-compiled library found
-            NSURL * libURL = [NSURL fileURLWithPath:libPath];
-            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]);
+            NSURL * libURL = [NSURL fileURLWithPath:path_lib];
+            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
+
             metal_library = [ctx->device newLibraryWithURL:libURL error:&error];
             if (error) {
                 GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
@@ -305,38 +319,41 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
             extern const char ggml_metallib_start[];
             extern const char ggml_metallib_end[];
 
-            NSString * src  = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
+            NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
 #else
             GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
 
-            NSString * sourcePath;
-            NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
+            NSString * path_source;
+            NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
 
-            GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, ggmlMetalPathResources ? [ggmlMetalPathResources UTF8String] : "nil");
+            GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");
 
-            if (ggmlMetalPathResources) {
-                sourcePath = [ggmlMetalPathResources stringByAppendingPathComponent:@"ggml-metal.metal"];
+            if (path_resource) {
+                path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"];
             } else {
-                sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
+                path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
             }
-            if (sourcePath == nil) {
+
+            if (path_source == nil) {
                 GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
-                sourcePath = @"ggml-metal.metal";
+                path_source = @"ggml-metal.metal";
             }
-            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [sourcePath UTF8String]);
-            NSString * src = [NSString stringWithContentsOfFile:sourcePath encoding:NSUTF8StringEncoding error:&error];
+
+            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
+
+            NSString * src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
             if (error) {
                 GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
                 return NULL;
             }
-#endif
+#endif // GGML_METAL_EMBED_LIBRARY
 
             @autoreleasepool {
                 // dictionary of preprocessor macros
                 NSMutableDictionary * prep = [NSMutableDictionary dictionary];
 
 #ifdef GGML_QKK_64
-                prep[@"QK_K"] = @(64);
+                prep[@"GGML_QKK_64"] = @(1);
 #endif
 
                 MTLCompileOptions* options = [MTLCompileOptions new];
@@ -1642,8 +1659,8 @@ static enum ggml_status ggml_metal_graph_compute(
                         // TODO: make this more general
                         GGML_ASSERT(n_as <= 8);
 
-                        // max size of the src1ids array in the kernel stack
-                        GGML_ASSERT(ne11 <= 512);
+                        // max size of the src1ids array in the kernel shared buffer
+                        GGML_ASSERT(ne11 <= 4096);
 
                         const int64_t  ne20 = src2 ? src2->ne[0] : 0;
                         const int64_t  ne21 = src2 ? src2->ne[1] : 0;
@@ -1741,7 +1758,7 @@ static enum ggml_status ggml_metal_graph_compute(
                                 [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j];
                             }
 
-                            [encoder setThreadgroupMemoryLength:8192 atIndex:0];
+                            [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + 2*ne11, 16) atIndex:0];
 
                             [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                         } else {
@@ -2820,6 +2837,12 @@ static struct ggml_backend_i ggml_backend_metal_i = {
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_metal_graph_compute,
     /* .supports_op             = */ ggml_backend_metal_supports_op,
+    /* .offload_op              = */ NULL,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
 };
 
 void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) {

ggml-metal.metal

@@ -1,49 +1,15 @@
-#include <metal_stdlib>
-
+#define GGML_COMMON_DECL_METAL
 #define GGML_COMMON_IMPL_METAL
 #include "ggml-common.h"
 
+#include <metal_stdlib>
+
 using namespace metal;
 
 #define MAX(x, y) ((x) > (y) ? (x) : (y))
 #define MIN(x, y) ((x) < (y) ? (x) : (y))
 #define SWAP(x, y) { auto tmp = (x); (x) = (y); (y) = tmp; }
 
-#define QK4_0 32
-#define QR4_0 2
-typedef struct {
-    half    d;             // delta
-    uint8_t qs[QK4_0 / 2]; // nibbles / quants
-} block_q4_0;
-
-#define QK4_1 32
-typedef struct {
-    half d;                 // delta
-    half m;                 // min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-
-#define QK5_0 32
-typedef struct {
-    half d;                // delta
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2]; // nibbles / quants
-} block_q5_0;
-
-#define QK5_1 32
-typedef struct {
-    half d;                 // delta
-    half m;                 // min
-    uint8_t qh[4];          // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2];  // nibbles / quants
-} block_q5_1;
-
-#define QK8_0 32
-typedef struct {
-    half    d;         // delta
-    int8_t  qs[QK8_0]; // quants
-} block_q8_0;
-
 #define N_SIMDWIDTH 32 // assuming SIMD group size is 32
 
 enum ggml_sort_order {
@@ -2478,147 +2444,6 @@ kernel void kernel_concat(
     }
 }
 
-//============================================ k-quants ======================================================
-
-#ifndef QK_K
-#define QK_K 256
-#else
-static_assert(QK_K == 256 || QK_K == 64, "QK_K must be 256 or 64");
-#endif
-
-#if QK_K == 256
-#define K_SCALE_SIZE 12
-#else
-#define K_SCALE_SIZE 4
-#endif
-
-typedef struct {
-    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
-    uint8_t qs[QK_K/4];      // quants
-    half d;           // super-block scale for quantized scales
-    half dmin;        // super-block scale for quantized mins
-} block_q2_K;
-// 84 bytes / block
-
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-#if QK_K == 64
-    uint8_t scales[2];
-#else
-    uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
-#endif
-    half d;             // super-block scale
-} block_q3_K;
-
-#if QK_K == 64
-typedef struct {
-    half    d[2];          // super-block scales/mins
-    uint8_t scales[2];
-    uint8_t qs[QK_K/2];    // 4-bit quants
-} block_q4_K;
-#else
-typedef struct {
-    half d;             // super-block scale for quantized scales
-    half dmin;          // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-#endif
-
-#if QK_K == 64
-typedef struct {
-    half  d;                     // super-block scales/mins
-    int8_t  scales[QK_K/16];     // 8-bit block scales
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-#else
-typedef struct {
-    half d;                      // super-block scale for quantized scales
-    half dmin;                   // super-block scale for quantized mins
-    uint8_t scales[3*QK_K/64];   // scales and mins, quantized with 6 bits
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-// 176 bytes / block
-#endif
-
-typedef struct {
-    uint8_t ql[QK_K/2];      // quants, lower 4 bits
-    uint8_t qh[QK_K/4];      // quants, upper 2 bits
-    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
-    half d;                  // super-block scale
-} block_q6_K;
-// 210 bytes / block
-
-typedef struct {
-    half d;
-    uint16_t qs[QK_K/8];
-} block_iq2_xxs;
-// 66 bytes / block for QK_K = 256, so 2.0625 bpw
-
-typedef struct {
-    half d;
-    uint16_t qs[QK_K/8];
-    uint8_t  scales[QK_K/32];
-} block_iq2_xs;
-// 74 bytes / block for QK_K = 256, so 2.3125 bpw
-
-// 2.5625 bpw quants
-typedef struct {
-    half d;
-    uint8_t qs[QK_K/4];
-    uint8_t qh[QK_K/32];
-    uint8_t scales[QK_K/32];
-} block_iq2_s;
-
-typedef struct {
-    half d;
-    uint8_t qs[3*QK_K/8];
-} block_iq3_xxs;
-// 98 bytes / block for QK_K = 256, so 3.0625 bpw
-
-// 3.4375 bpw
-#if QK_K == 64
-#define IQ3S_N_SCALE 2
-#else
-#define IQ3S_N_SCALE QK_K/64
-#endif
-typedef struct {
-    half d;
-    uint8_t qs[QK_K/4];
-    uint8_t qh[QK_K/32];
-    uint8_t signs[QK_K/8];
-    uint8_t scales[IQ3S_N_SCALE];
-} block_iq3_s;
-
-typedef struct {
-    half d;
-    uint8_t qs[QK_K/8];
-    uint8_t scales[QK_K/16];
-} block_iq1_s;
-
-// Non-linear quants
-#define QK4_NL 32
-typedef struct {
-    half    d;
-    uint8_t qs[QK4_NL/2];
-} block_iq4_nl;
-
-#if QK_K == 64
-#define block_iq4_xs block_iq4_nl
-#else
-typedef struct {
-    half d;
-    uint16_t scales_h;
-    uint8_t  scales_l[QK_K/64];
-    uint8_t  qs[QK_K/2];
-} block_iq4_xs;
-#endif
-
-//====================================== dot products =========================
-
 void kernel_mul_mv_q2_K_f32_impl(
         device const  void * src0,
         device const float * src1,
@@ -4338,48 +4163,53 @@ void kernel_mul_mv_iq1_s_f32_impl(
     device const block_iq1_s * x = (device const block_iq1_s *) src0 + ib_row + offset0;
     device const float       * y = (device const float       *) src1 + r1*ne10 + im*ne00*ne1;
 
-    float yl[16];
+    float yl[32];
     float sumf[N_DST]={0.f}, all_sum;
 
     const int nb32 = nb * (QK_K / 32);
 
-    const int ix = tiisg/2;
-    const int il = tiisg%2;
+    const int ix = tiisg;
 
-    device const float * y4 = y + 32 * ix + 16 * il;
+    device const float * y4 = y + 32 * ix;
 
-    for (int ib32 = ix; ib32 < nb32; ib32 += 16) {
+    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
 
-        for (int i = 0; i < 16; ++i) {
+        float sumy = 0;
+        for (int i = 0; i < 32; ++i) {
             yl[i] = y4[i];
+            sumy += yl[i];
         }
 
         const int ibl = ib32 / (QK_K / 32);
         const int ib  = ib32 % (QK_K / 32);
 
         device const block_iq1_s * xr = x + ibl;
-        device const uint8_t * qs = xr->qs + 4 * ib + 2 * il;
-        device const uint8_t * sc = xr->scales + 2 * ib + il;
-        device const half    * dh = &xr->d;
+        device const uint8_t  * qs = xr->qs + 4 * ib;
+        device const uint16_t * qh = xr->qh + ib;
+        device const half     * dh = &xr->d;
 
         for (int row = 0; row < N_DST; row++) {
 
-            constant int8_t * grid1 = (constant int8_t *)(iq1s_grid + (qs[0] | ((sc[0] & 0x08) << 5)));
-            constant int8_t * grid2 = (constant int8_t *)(iq1s_grid + (qs[1] | ((sc[0] & 0x80) << 1)));
+            constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
+            constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 5) & 0x700)));
+            constant uint8_t * grid3 = (constant uint8_t *)(iq1s_grid_gpu + (qs[2] | ((qh[0] << 2) & 0x700)));
+            constant uint8_t * grid4 = (constant uint8_t *)(iq1s_grid_gpu + (qs[3] | ((qh[0] >> 1) & 0x700)));
 
-            float2 sum = {0};
-            for (int j = 0; j < 8; ++j) {
-                sum[0] += yl[j+ 0] * grid1[j];
-                sum[1] += yl[j+ 8] * grid2[j];
+            float sum = 0;
+            for (int j = 0; j < 4; ++j) {
+                sum += yl[j+ 0] * (grid1[j] & 0xf) + yl[j+ 4] * (grid1[j] >> 4)
+                     + yl[j+ 8] * (grid2[j] & 0xf) + yl[j+12] * (grid2[j] >> 4)
+                     + yl[j+16] * (grid3[j] & 0xf) + yl[j+20] * (grid3[j] >> 4)
+                     + yl[j+24] * (grid4[j] & 0xf) + yl[j+28] * (grid4[j] >> 4);
             }
-            sumf[row] += (float)dh[0] * (sum[0] * (2*(sc[0] & 7) + 1) + sum[1] * (2*((sc[0] >> 4) & 7) + 1));
+            sumf[row] += (float)dh[0] * (sum + sumy * (qh[0] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA)) * (2*((qh[0] >> 12) & 7) + 1);
 
             dh += nb*sizeof(block_iq1_s)/2;
             qs += nb*sizeof(block_iq1_s);
-            sc += nb*sizeof(block_iq1_s);
+            qh += nb*sizeof(block_iq1_s)/2;
         }
 
-        y4 += 16 * 32;
+        y4 += 32 * 32;
     }
 
     for (int row = 0; row < N_DST; ++row) {
@@ -5066,16 +4896,21 @@ void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 &
 template <typename type4x4>
 void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) {
     // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const int ib32 = il/2;
+    il = il%2;
     const float d = xb->d;
-    device const uint8_t * qs = xb->qs + 2*il;
-    device const uint8_t * sc = xb->scales + il;
-    const float dl1 = d * (2*(sc[0] & 7) + 1);
-    const float dl2 = d * (2*((sc[0] >> 4) & 7) + 1);
-    constant int8_t * grid1 = (constant int8_t *)(iq1s_grid + (qs[0] | ((sc[0] & 0x08) << 5)));
-    constant int8_t * grid2 = (constant int8_t *)(iq1s_grid + (qs[1] | ((sc[0] & 0x80) << 1)));
-    for (int i = 0; i < 8; ++i) {
-        reg[i/4+0][i%4] = dl1 * grid1[i];
-        reg[i/4+2][i%4] = dl2 * grid2[i];
+    device const uint8_t  * qs = xb->qs + 4*ib32 + 2*il;
+    device const uint16_t * qh = xb->qh;
+    const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1);
+    const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA);
+    const uint16_t h = qh[ib32] >> 6*il;
+    constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700)));
+    constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700)));
+    for (int i = 0; i < 4; ++i) {
+        reg[0][i] = dl * (grid1[i] & 0xf) + ml;
+        reg[1][i] = dl * (grid1[i] >>  4) + ml;
+        reg[2][i] = dl * (grid2[i] & 0xf) + ml;
+        reg[3][i] = dl * (grid2[i] >>  4) + ml;
     }
 }
 
@@ -5386,7 +5221,7 @@ template<typename block_q, short nl, void (*dequantize_func)(device const block_
 void kernel_mul_mm_id_impl(
         device const  uchar * src0,
         device const  uchar * src1,
-        thread        short * src1ids,
+        threadgroup   short * src1ids,
         device        float * dst,
         constant    int64_t & ne00,
         constant    int64_t & ne02,
@@ -5589,9 +5424,9 @@ kernel void kernel_mul_mm_id(
     tgpig.z = tgpig.z%(ne12*ne13);
 
     // row indices of src1 for expert id
-    int64_t _ne1 = 0;
-    short src1ids[512];
+    threadgroup short * src1ids = (threadgroup short *)(shared_memory + 8192);
 
+    int64_t _ne1 = 0;
     for (int64_t i1 = 0; i1 < ne1; i1++) {
         if (((device int32_t *) (ids + i1*nbi1))[idx] == id) {
             src1ids[_ne1++] = i1;

ggml-quants.h

@@ -1,248 +1,12 @@
 #pragma once
 
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+
+#include "ggml.h"
+
 // GGML internal header
 
-#include "ggml-impl.h"
-
-#include <stdint.h>
-#include <stddef.h>
-
-#define QK4_0 32
-typedef struct {
-    ggml_fp16_t d;          // delta
-    uint8_t qs[QK4_0 / 2];  // nibbles / quants
-} block_q4_0;
-static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
-
-#define QK4_1 32
-typedef struct {
-    ggml_fp16_t d;          // delta
-    ggml_fp16_t m;          // min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
-
-#define QK5_0 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2]; // nibbles / quants
-} block_q5_0;
-static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
-
-#define QK5_1 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    ggml_fp16_t m;         // min
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2]; // nibbles / quants
-} block_q5_1;
-static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
-
-#define QK8_0 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    int8_t  qs[QK8_0];     // quants
-} block_q8_0;
-static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
-
-#define QK8_1 32
-typedef struct {
-    float d;               // delta
-    float s;               // d * sum(qs[i])
-    int8_t  qs[QK8_1];     // quants
-} block_q8_1;
-static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
-
-//
-// Super-block quantization structures
-//
-
-// Super-block size
-#ifdef GGML_QKK_64
-#define QK_K 64
-#define K_SCALE_SIZE 4
-#else
-#define QK_K 256
-#define K_SCALE_SIZE 12
-#endif
-
-// 2-bit quantization
-// weight is represented as x = a * q + b
-// 16 blocks of 16 elements each
-// Effectively 2.625 bits per weight
-typedef struct {
-    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
-    uint8_t qs[QK_K/4];      // quants
-    ggml_fp16_t d;           // super-block scale for quantized scales
-    ggml_fp16_t dmin;        // super-block scale for quantized mins
-} block_q2_K;
-static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
-
-// 3-bit quantization
-// weight is represented as x = a * q
-// 16 blocks of 16 elements each
-// Effectively 3.4375 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-    uint8_t scales[2];
-    ggml_fp16_t d;             // super-block scale
-} block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
-#else
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-    uint8_t scales[12];        // scales, quantized with 6 bits
-    ggml_fp16_t d;             // super-block scale
-} block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
-#endif
-
-// 4-bit quantization
-// 8 blocks of 32 elements each
-// weight is represented as x = a * q + b
-// Effectively 4.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    ggml_fp16_t d[2];          // super-block scales/mins
-    uint8_t scales[2];         // 4-bit block scales/mins
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
-#else
-typedef struct {
-    ggml_fp16_t d;             // super-block scale for quantized scales
-    ggml_fp16_t dmin;          // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
-#endif
-
-// 5-bit quantization
-// 8 blocks of 32 elements each
-// weight is represented as x = a * q + b
-// Effectively 5.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    ggml_fp16_t d;               // super-block scale
-    int8_t  scales[QK_K/16];     // 8-bit block scales
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
-#else
-typedef struct {
-    ggml_fp16_t d;               // super-block scale for quantized scales
-    ggml_fp16_t dmin;            // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE];   // scales and mins, quantized with 6 bits
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
-#endif
-
-// 6-bit quantization
-// weight is represented as x = a * q
-// 16 blocks of 16 elements each
-// Effectively 6.5625 bits per weight
-typedef struct {
-    uint8_t ql[QK_K/2];      // quants, lower 4 bits
-    uint8_t qh[QK_K/4];      // quants, upper 2 bits
-    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
-    ggml_fp16_t d;           // super-block scale
-} block_q6_K;
-static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
-
-// This is only used for intermediate quantization and dot products
-typedef struct {
-    float   d;              // delta
-    int8_t  qs[QK_K];       // quants
-    int16_t bsums[QK_K/16]; // sum of quants in groups of 16
-} block_q8_K;
-static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
-
-// (Almost) "true" 2-bit quantization.
-// Due to the need to use blocks as per ggml design, it ends up using
-// 2.0625 bpw because of the 16-bit scale for each block of 256.
-typedef struct {
-    ggml_fp16_t d;
-    uint16_t qs[QK_K/8];
-} block_iq2_xxs;
-static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding");
-
-// 2.3125 bpw quants
-typedef struct {
-    ggml_fp16_t d;
-    uint16_t qs[QK_K/8];
-    uint8_t  scales[QK_K/32];
-} block_iq2_xs;
-static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
-
-// 2.5625 bpw quants
-typedef struct {
-    ggml_fp16_t d;
-    uint8_t qs[QK_K/4];
-    uint8_t qh[QK_K/32];
-    uint8_t scales[QK_K/32];
-} block_iq2_s;
-static_assert(sizeof(block_iq2_s) == sizeof(ggml_fp16_t) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding");
-
-// (Almost) "true" 3-bit quantization.
-// Due to the need to use blocks as per ggml design, it ends up using
-// 3.0625 bpw because of the 16-bit scale for each block of 256.
-typedef struct {
-    ggml_fp16_t d;
-    uint8_t qs[3*QK_K/8];
-} block_iq3_xxs;
-static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
-
-// 3.4375 bpw
-#if QK_K == 64
-#define IQ3S_N_SCALE 2
-#else
-#define IQ3S_N_SCALE QK_K/64
-#endif
-typedef struct {
-    ggml_fp16_t d;
-    uint8_t qs[QK_K/4];
-    uint8_t qh[QK_K/32];
-    uint8_t signs[QK_K/8];
-    uint8_t scales[IQ3S_N_SCALE];
-} block_iq3_s;
-static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding");
-
-typedef struct {
-    ggml_fp16_t d;
-    uint8_t qs[QK_K/8];
-    uint8_t scales[QK_K/16];
-} block_iq1_s;
-static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");
-
-// Non-linear quants
-#define QK4_NL 32
-typedef struct {
-    ggml_fp16_t d;
-    uint8_t qs[QK4_NL/2];
-} block_iq4_nl;
-static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding");
-
-#if QK_K == 64
-#define block_iq4_xs block_iq4_nl
-//typedef struct block_iq4_nl block_iq4_xs;
-#else
-typedef struct {
-    ggml_fp16_t d;
-    uint16_t scales_h;
-    uint8_t  scales_l[QK_K/64];
-    uint8_t  qs[QK_K/2];
-} block_iq4_xs;
-static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
-#endif
-
 #ifdef __cplusplus
 extern "C" {
 #endif

ggml-sycl.h

@@ -13,7 +13,7 @@
 extern "C" {
 #endif
 
-#define GGML_SYCL_MAX_DEVICES       16
+#define GGML_SYCL_MAX_DEVICES       48
 #define GGML_SYCL_NAME "SYCL"
 
 GGML_API void   ggml_init_sycl(void);
@@ -29,6 +29,11 @@ GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_typ
 GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total);
 GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id);
 
+// TODO: these are temporary
+//       ref: https://github.com/ggerganov/llama.cpp/pull/6022#issuecomment-1992615670
+GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index);
+GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id);
+GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode();
 #ifdef  __cplusplus
 }
 #endif

ggml-vulkan.cpp

@@ -710,6 +710,12 @@ static uint32_t ggml_vk_find_queue_family_index(std::vector<vk::QueueFamilyPrope
         }
     }
 
+    // All commands that are allowed on a queue that supports transfer operations are also allowed on a queue that supports either graphics or compute operations.
+    // Thus, if the capabilities of a queue family include VK_QUEUE_GRAPHICS_BIT or VK_QUEUE_COMPUTE_BIT, then reporting the VK_QUEUE_TRANSFER_BIT capability separately for that queue family is optional.
+    if (compute_index >= 0) {
+        return compute_index;
+    }
+
     std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl;
 
     for(auto &q_family : queue_family_props) {
@@ -5693,6 +5699,12 @@ static ggml_backend_i ggml_backend_vk_interface = {
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_vk_graph_compute,
     /* .supports_op             = */ ggml_backend_vk_supports_op,
+    /* .offload_op              = */ NULL,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
 };
 
 static ggml_guid_t ggml_backend_vk_guid() {

ggml.c

@@ -282,8 +282,6 @@ inline static void * ggml_calloc(size_t num, size_t size) {
 #else
 #include <cblas.h>
 #endif
-#elif defined(GGML_USE_CUBLAS)
-#include "ggml-cuda.h"
 #elif defined(GGML_USE_CLBLAST)
 #include "ggml-opencl.h"
 #elif defined(GGML_USE_VULKAN)
@@ -470,6 +468,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .type_size                = sizeof(int32_t),
         .is_quantized             = false,
     },
+    [GGML_TYPE_I64] = {
+        .type_name                = "i64",
+        .blck_size                = 1,
+        .type_size                = sizeof(int64_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_F64] = {
+        .type_name                = "f64",
+        .blck_size                = 1,
+        .type_size                = sizeof(double),
+        .is_quantized             = false,
+        .nrows                    = 1,
+    },
     [GGML_TYPE_F32] = {
         .type_name                = "f32",
         .blck_size                = 1,
@@ -857,7 +868,7 @@ inline static float vaddvq_f32(float32x4_t v) {
     #define GGML_F16x8              float16x8_t
     #define GGML_F16x8_ZERO         vdupq_n_f16(0.0f)
     #define GGML_F16x8_SET1(x)      vdupq_n_f16(x)
-    #define GGML_F16x8_LOAD(x)      vld1q_f16((const __fp16 *)(x))
+    #define GGML_F16x8_LOAD(x)      vld1q_f16((const ggml_fp16_internal_t *)(x))
     #define GGML_F16x8_STORE        vst1q_f16
     #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c)
     #define GGML_F16x8_ADD          vaddq_f16
@@ -900,7 +911,7 @@ inline static float vaddvq_f32(float32x4_t v) {
     #define GGML_F32Cx4              float32x4_t
     #define GGML_F32Cx4_ZERO         vdupq_n_f32(0.0f)
     #define GGML_F32Cx4_SET1(x)      vdupq_n_f32(x)
-    #define GGML_F32Cx4_LOAD(x)      vcvt_f32_f16(vld1_f16((const __fp16 *)(x)))
+    #define GGML_F32Cx4_LOAD(x)      vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x)))
     #define GGML_F32Cx4_STORE(x, y)  vst1_f16(x, vcvt_f16_f32(y))
     #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c)
     #define GGML_F32Cx4_ADD          vaddq_f32
@@ -918,6 +929,101 @@ inline static float vaddvq_f32(float32x4_t v) {
     #define GGML_F16_VEC_REDUCE         GGML_F32Cx4_REDUCE
 #endif
 
+#elif defined(__AVX512F__)
+
+#define GGML_SIMD
+
+// F32 AVX512
+
+#define GGML_F32_STEP 64
+#define GGML_F32_EPR  16
+
+#define GGML_F32x16         __m512
+#define GGML_F32x16_ZERO    _mm512_setzero_ps()
+#define GGML_F32x16_SET1(x) _mm512_set1_ps(x)
+#define GGML_F32x16_LOAD    _mm512_loadu_ps
+#define GGML_F32x16_STORE   _mm512_storeu_ps
+// _mm512_fmadd_ps is defined in AVX512F so no guard is required
+#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a)
+#define GGML_F32x16_ADD     _mm512_add_ps
+#define GGML_F32x16_MUL     _mm512_mul_ps
+#define GGML_F32x16_REDUCE(res, x)                                    \
+do {                                                                  \
+    int offset = GGML_F32_ARR >> 1;                                   \
+    for (int i = 0; i < offset; ++i) {                                \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                      \
+    }                                                                 \
+    offset >>= 1;                                                     \
+    for (int i = 0; i < offset; ++i) {                                \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                      \
+    }                                                                 \
+    offset >>= 1;                                                     \
+    for (int i = 0; i < offset; ++i) {                                \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                      \
+    }                                                                 \
+    res = _mm512_reduce_add_ps(x[0]);                                 \
+} while (0)
+
+// TODO: is this optimal ?
+
+#define GGML_F32_VEC        GGML_F32x16
+#define GGML_F32_VEC_ZERO   GGML_F32x16_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x16_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x16_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x16_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x16_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x16_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x16_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE
+
+// F16 AVX512
+
+// F16 AVX
+
+#define GGML_F16_STEP 64
+#define GGML_F16_EPR  16
+
+// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead
+
+#define GGML_F32Cx16             __m512
+#define GGML_F32Cx16_ZERO        _mm512_setzero_ps()
+#define GGML_F32Cx16_SET1(x)     _mm512_set1_ps(x)
+
+// unlike  _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F
+// so F16C guard isn't required
+#define GGML_F32Cx16_LOAD(x)     _mm512_cvtph_ps(_mm256_loadu_si256((__m256i *)(x)))
+#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0))
+
+#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a)
+#define GGML_F32Cx16_ADD         _mm512_add_ps
+#define GGML_F32Cx16_MUL         _mm512_mul_ps
+#define GGML_F32Cx16_REDUCE(res, x)                               \
+do {                                                              \
+    int offset = GGML_F32_ARR >> 1;                               \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    res = _mm512_reduce_add_ps(x[0]);                             \
+} while (0)
+
+#define GGML_F16_VEC                GGML_F32Cx16
+#define GGML_F16_VEC_ZERO           GGML_F32Cx16_ZERO
+#define GGML_F16_VEC_SET1           GGML_F32Cx16_SET1
+#define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx16_LOAD(p)
+#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i])
+#define GGML_F16_VEC_FMA            GGML_F32Cx16_FMA
+#define GGML_F16_VEC_ADD            GGML_F32Cx16_ADD
+#define GGML_F16_VEC_MUL            GGML_F32Cx16_MUL
+#define GGML_F16_VEC_REDUCE         GGML_F32Cx16_REDUCE
+
 #elif defined(__AVX__)
 
 #define GGML_SIMD
@@ -2532,9 +2638,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
             GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
         }
 
-#if defined(GGML_USE_CUBLAS)
-        ggml_init_cublas();
-#elif defined(GGML_USE_CLBLAST)
+#if defined(GGML_USE_CLBLAST)
         ggml_cl_init();
 #elif defined(GGML_USE_VULKAN)
         ggml_vk_init_cpu_assist();
@@ -10997,7 +11101,6 @@ static void ggml_compute_forward_out_prod_f32(
     // nb01 >= nb00 - src0 is not transposed
     //   compute by src0 rows
 
-    // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod
     // TODO: #if defined(GGML_USE_CLBLAST)
 
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
@@ -11197,7 +11300,6 @@ static void ggml_compute_forward_out_prod_q_f32(
     // nb01 >= nb00 - src0 is not transposed
     //   compute by src0 rows
 
-    // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod
     // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
 
     if (params->type == GGML_TASK_TYPE_INIT) {
@@ -11560,8 +11662,6 @@ static void ggml_compute_forward_get_rows_q(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    assert(params->ith == 0);
-
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
@@ -11569,7 +11669,7 @@ static void ggml_compute_forward_get_rows_q(
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
-    const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
+    const int64_t nr = ggml_nelements(src1);
 
     const enum ggml_type type = src0->type;
     ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
@@ -11579,17 +11679,25 @@ static void ggml_compute_forward_get_rows_q(
     assert(nb00 == ggml_type_size(type));
     assert(ggml_nrows(dst) == nr);
 
-    // TODO: multi-thread
-    for (int64_t i12 = 0; i12 < ne12; ++i12) {
-        for (int64_t i11 = 0; i11 < ne11; ++i11) {
-            for (int64_t i10 = 0; i10 < ne10; ++i10) {
-                const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+    const int ith = params->ith;
+    const int nth = params->nth;
 
-                dequantize_row_q(
-                        (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
-                             (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
-            }
-        }
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i12 = i/(ne11*ne10);
+        const int64_t i11 = (i - i12*ne11*ne10)/ne10;
+        const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
+        const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+
+        dequantize_row_q(
+                (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
+                     (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
     }
 }
 
@@ -11600,8 +11708,6 @@ static void ggml_compute_forward_get_rows_f16(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    assert(params->ith == 0);
-
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
@@ -11609,24 +11715,32 @@ static void ggml_compute_forward_get_rows_f16(
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
-    const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
+    const int64_t nr = ggml_nelements(src1);
 
     assert(ne0  == nc);
     assert(ne02 == ne11);
     assert(nb00 == sizeof(ggml_fp16_t));
     assert(ggml_nrows(dst) == nr);
 
-    // TODO: multi-thread
-    for (int64_t i12 = 0; i12 < ne12; ++i12) {
-        for (int64_t i11 = 0; i11 < ne11; ++i11) {
-            for (int64_t i10 = 0; i10 < ne10; ++i10) {
-                const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+    const int ith = params->ith;
+    const int nth = params->nth;
 
-                ggml_fp16_to_fp32_row(
-                        (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
-                             (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
-            }
-        }
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i12 = i/(ne11*ne10);
+        const int64_t i11 = (i - i12*ne11*ne10)/ne10;
+        const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
+        const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+
+        ggml_fp16_to_fp32_row(
+                (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
+                     (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
     }
 }
 
@@ -11637,8 +11751,6 @@ static void ggml_compute_forward_get_rows_f32(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    assert(params->ith == 0);
-
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
@@ -11646,24 +11758,32 @@ static void ggml_compute_forward_get_rows_f32(
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
-    const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
+    const int64_t nr = ggml_nelements(src1);
 
     assert(ne0  == nc);
     assert(ne02 == ne11);
     assert(nb00 == sizeof(float));
     assert(ggml_nrows(dst) == nr);
 
-    // TODO: multi-thread
-    for (int64_t i12 = 0; i12 < ne12; ++i12) {
-        for (int64_t i11 = 0; i11 < ne11; ++i11) {
-            for (int64_t i10 = 0; i10 < ne10; ++i10) {
-                const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+    const int ith = params->ith;
+    const int nth = params->nth;
 
-                ggml_vec_cpy_f32(nc,
-                        (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3),
-                        (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03));
-            }
-        }
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i12 = i/(ne11*ne10);
+        const int64_t i11 = (i - i12*ne11*ne10)/ne10;
+        const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
+        const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+
+        ggml_vec_cpy_f32(nc,
+                (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3),
+                (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03));
     }
 }
 
@@ -12400,6 +12520,8 @@ static void ggml_compute_forward_alibi(
         case GGML_TYPE_I8:
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
+        case GGML_TYPE_I64:
+        case GGML_TYPE_F64:
         case GGML_TYPE_COUNT:
             {
                 GGML_ASSERT(false);
@@ -12486,6 +12608,8 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_I8:
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
+        case GGML_TYPE_I64:
+        case GGML_TYPE_F64:
         case GGML_TYPE_COUNT:
             {
                 GGML_ASSERT(false);
@@ -15921,14 +16045,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
         return;
     }
 
-#ifdef GGML_USE_CUBLAS
-    bool skip_cpu = ggml_cuda_compute_forward(params, tensor);
-    if (skip_cpu) {
-        return;
-    }
-    GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU);
-    GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU);
-#elif defined(GGML_USE_VULKAN)
+#if defined(GGML_USE_VULKAN)
     const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor);
 #ifdef GGML_VULKAN_CHECK_RESULTS
     if (skip_cpu) {
@@ -15940,7 +16057,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
     }
     GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU);
     GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU);
-#endif // GGML_USE_CUBLAS
+#endif // GGML_USE_VULKAN
 
 #ifdef GGML_USE_SYCL
     bool skip_cpu = ggml_sycl_compute_forward(params, tensor);
@@ -17796,7 +17913,7 @@ static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const
     node->perf_time_us += time_us_cur;
 }
 
-static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
+static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_threads) {
     int n_tasks = 0;
 
     switch (node->op) {
@@ -17877,6 +17994,12 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             {
                 n_tasks = n_threads;
             } break;
+        case GGML_OP_GET_ROWS:
+            {
+                // FIXME: the cost of launching additional threads decreases performance with GPU offloading
+                //n_tasks = MIN(n_threads, ggml_nelements(node->src[1]));
+                n_tasks = MIN(n_cur_threads, ggml_nelements(node->src[1]));
+            } break;
         case GGML_OP_SCALE:
         case GGML_OP_SET:
         case GGML_OP_CONT:
@@ -17884,7 +18007,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
-        case GGML_OP_GET_ROWS:
         case GGML_OP_GET_ROWS_BACK:
         case GGML_OP_DIAG:
             {
@@ -18102,7 +18224,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
                 /* FINALIZE */
                 struct ggml_tensor * node = cgraph->nodes[node_n];
                 if (GGML_OP_HAS_FINALIZE[node->op]) {
-                    params.nth = ggml_get_n_tasks(node, n_threads);
+                    params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
                     ggml_compute_forward(&params, node);
                 }
                 ggml_graph_compute_perf_stats_node(node, state->shared);
@@ -18112,7 +18234,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
             while (++node_n < cgraph->n_nodes) {
                 GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes);
                 struct ggml_tensor * node = cgraph->nodes[node_n];
-                const int n_tasks = ggml_get_n_tasks(node, n_threads);
+                const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
 
                 state->shared->perf_node_start_cycles  = ggml_perf_cycles();
                 state->shared->perf_node_start_time_us = ggml_perf_time_us();
@@ -18160,7 +18282,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
 
         /* INIT & COMPUTE */
         struct ggml_tensor * node = cgraph->nodes[node_n];
-        const int n_tasks = ggml_get_n_tasks(node, n_threads);
+        const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
 
         struct ggml_compute_params params = {
             /*.type  =*/ GGML_TASK_TYPE_INIT,
@@ -18225,7 +18347,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
     for (int i = 0; i < cgraph->n_nodes; i++) {
         struct ggml_tensor * node = cgraph->nodes[i];
 
-        const int n_tasks = ggml_get_n_tasks(node, n_threads);
+        const int n_tasks = ggml_get_n_tasks(node, n_threads, 1);
 
         max_tasks = MAX(max_tasks, n_tasks);
 

ggml.h

@@ -337,24 +337,24 @@ extern "C" {
     struct ggml_object;
     struct ggml_context;
 
+    // NOTE: always add types at the end of the enum to keep backward compatibility
     enum ggml_type {
-        GGML_TYPE_F32  = 0,
-        GGML_TYPE_F16  = 1,
-        GGML_TYPE_Q4_0 = 2,
-        GGML_TYPE_Q4_1 = 3,
+        GGML_TYPE_F32     = 0,
+        GGML_TYPE_F16     = 1,
+        GGML_TYPE_Q4_0    = 2,
+        GGML_TYPE_Q4_1    = 3,
         // GGML_TYPE_Q4_2 = 4, support has been removed
-        // GGML_TYPE_Q4_3 (5) support has been removed
-        GGML_TYPE_Q5_0 = 6,
-        GGML_TYPE_Q5_1 = 7,
-        GGML_TYPE_Q8_0 = 8,
-        GGML_TYPE_Q8_1 = 9,
-        // k-quantizations
-        GGML_TYPE_Q2_K = 10,
-        GGML_TYPE_Q3_K = 11,
-        GGML_TYPE_Q4_K = 12,
-        GGML_TYPE_Q5_K = 13,
-        GGML_TYPE_Q6_K = 14,
-        GGML_TYPE_Q8_K = 15,
+        // GGML_TYPE_Q4_3 = 5, support has been removed
+        GGML_TYPE_Q5_0    = 6,
+        GGML_TYPE_Q5_1    = 7,
+        GGML_TYPE_Q8_0    = 8,
+        GGML_TYPE_Q8_1    = 9,
+        GGML_TYPE_Q2_K    = 10,
+        GGML_TYPE_Q3_K    = 11,
+        GGML_TYPE_Q4_K    = 12,
+        GGML_TYPE_Q5_K    = 13,
+        GGML_TYPE_Q6_K    = 14,
+        GGML_TYPE_Q8_K    = 15,
         GGML_TYPE_IQ2_XXS = 16,
         GGML_TYPE_IQ2_XS  = 17,
         GGML_TYPE_IQ3_XXS = 18,
@@ -363,9 +363,11 @@ extern "C" {
         GGML_TYPE_IQ3_S   = 21,
         GGML_TYPE_IQ2_S   = 22,
         GGML_TYPE_IQ4_XS  = 23,
-        GGML_TYPE_I8,
-        GGML_TYPE_I16,
-        GGML_TYPE_I32,
+        GGML_TYPE_I8      = 24,
+        GGML_TYPE_I16     = 25,
+        GGML_TYPE_I32     = 26,
+        GGML_TYPE_I64     = 27,
+        GGML_TYPE_F64     = 28,
         GGML_TYPE_COUNT,
     };
 
@@ -383,20 +385,20 @@ extern "C" {
 
     // model file types
     enum ggml_ftype {
-        GGML_FTYPE_UNKNOWN     = -1,
-        GGML_FTYPE_ALL_F32     = 0,
-        GGML_FTYPE_MOSTLY_F16  = 1,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_0 = 2,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_1 = 3,  // except 1d tensors
+        GGML_FTYPE_UNKNOWN        = -1,
+        GGML_FTYPE_ALL_F32        = 0,
+        GGML_FTYPE_MOSTLY_F16     = 1,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0    = 2,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_1    = 3,  // except 1d tensors
         GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
-        GGML_FTYPE_MOSTLY_Q8_0 = 7,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_0 = 8,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_1 = 9,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q8_0    = 7,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_0    = 8,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_1    = 9,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q2_K    = 10, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q3_K    = 11, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_K    = 12, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_K    = 13, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q6_K    = 14, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ2_XS  = 16, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors

gguf-py/gguf/constants.py

@@ -32,6 +32,7 @@ class Keys:
         FILE_TYPE            = "general.file_type"
 
     class LLM:
+        VOCAB_SIZE            = "{arch}.vocab_size"
         CONTEXT_LENGTH        = "{arch}.context_length"
         EMBEDDING_LENGTH      = "{arch}.embedding_length"
         BLOCK_COUNT           = "{arch}.block_count"
@@ -41,6 +42,7 @@ class Keys:
         EXPERT_COUNT          = "{arch}.expert_count"
         EXPERT_USED_COUNT     = "{arch}.expert_used_count"
         POOLING_TYPE          = "{arch}.pooling_type"
+        LOGIT_SCALE           = "{arch}.logit_scale"
 
     class Attention:
         HEAD_COUNT        = "{arch}.attention.head_count"
@@ -120,6 +122,7 @@ class MODEL_ARCH(IntEnum):
     GEMMA      = auto()
     STARCODER2 = auto()
     MAMBA      = auto()
+    COMMAND_R  = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -186,6 +189,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.GEMMA:          "gemma",
     MODEL_ARCH.STARCODER2:     "starcoder2",
     MODEL_ARCH.MAMBA:          "mamba",
+    MODEL_ARCH.COMMAND_R:      "command-r",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -578,6 +582,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.SSM_D,
         MODEL_TENSOR.SSM_OUT,
     ],
+    MODEL_ARCH.COMMAND_R: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     # TODO
 }
 
@@ -661,6 +677,11 @@ class GGMLQuantizationType(IntEnum):
     IQ3_S   = 21
     IQ2_S   = 22
     IQ4_XS  = 23
+    I8      = 24
+    I16     = 25
+    I32     = 26
+    I64     = 27
+    F64     = 28
 
 
 class GGUFEndian(IntEnum):
@@ -727,6 +748,11 @@ GGML_QUANT_SIZES = {
     GGMLQuantizationType.IQ3_S:   (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4),
     GGMLQuantizationType.IQ2_S:   (256, 2 + QK_K // 4 + QK_K // 16),
     GGMLQuantizationType.IQ4_XS:  (256, 2 + 2 + QK_K // 2 + QK_K // 64),
+    GGMLQuantizationType.I8:      (1, 1),
+    GGMLQuantizationType.I16:     (1, 2),
+    GGMLQuantizationType.I32:     (1, 4),
+    GGMLQuantizationType.I64:     (1, 8),
+    GGMLQuantizationType.F64:     (1, 8),
 }
 
 
@@ -746,6 +772,7 @@ KEY_GENERAL_SOURCE_HF_REPO       = Keys.General.SOURCE_HF_REPO
 KEY_GENERAL_FILE_TYPE            = Keys.General.FILE_TYPE
 
 # LLM
+KEY_VOCAB_SIZE            = Keys.LLM.VOCAB_SIZE
 KEY_CONTEXT_LENGTH        = Keys.LLM.CONTEXT_LENGTH
 KEY_EMBEDDING_LENGTH      = Keys.LLM.EMBEDDING_LENGTH
 KEY_BLOCK_COUNT           = Keys.LLM.BLOCK_COUNT

gguf-py/gguf/gguf_reader.py

@@ -242,12 +242,27 @@ class GGUFReader:
             n_bytes = n_elems * type_size // block_size
             data_offs = int(start_offs + offset_tensor[0])
             item_type: npt.DTypeLike
-            if ggml_type == GGMLQuantizationType.F32:
-                item_count = n_elems
-                item_type = np.float32
-            elif ggml_type == GGMLQuantizationType.F16:
+            if ggml_type == GGMLQuantizationType.F16:
                 item_count = n_elems
                 item_type = np.float16
+            elif ggml_type == GGMLQuantizationType.F32:
+                item_count = n_elems
+                item_type = np.float32
+            elif ggml_type == GGMLQuantizationType.F64:
+                item_count = n_elems
+                item_type = np.float64
+            elif ggml_type == GGMLQuantizationType.I8:
+                item_count = n_elems
+                item_type = np.int8
+            elif ggml_type == GGMLQuantizationType.I16:
+                item_count = n_elems
+                item_type = np.int16
+            elif ggml_type == GGMLQuantizationType.I32:
+                item_count = n_elems
+                item_type = np.int32
+            elif ggml_type == GGMLQuantizationType.I64:
+                item_count = n_elems
+                item_type = np.int64
             else:
                 item_count = n_bytes
                 item_type = np.uint8

gguf-py/gguf/gguf_writer.py

@@ -196,9 +196,6 @@ class GGUFWriter:
         if self.state is not WriterState.EMPTY:
             raise ValueError(f'Expected output file to be empty, got {self.state}')
 
-        if raw_dtype is None and tensor_dtype not in (np.float32, np.float16):
-            raise ValueError("Only F32 and F16 tensors are supported for now")
-
         encoded_name = name.encode("utf8")
         self.ti_data += self._pack("Q", len(encoded_name))
         self.ti_data += encoded_name
@@ -207,7 +204,22 @@ class GGUFWriter:
         for i in range(n_dims):
             self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
         if raw_dtype is None:
-            dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
+            if tensor_dtype == np.float16:
+                dtype = GGMLQuantizationType.F16
+            elif tensor_dtype == np.float32:
+                dtype = GGMLQuantizationType.F32
+            elif tensor_dtype == np.float64:
+                dtype = GGMLQuantizationType.F64
+            elif tensor_dtype == np.int8:
+                dtype = GGMLQuantizationType.I8
+            elif tensor_dtype == np.int16:
+                dtype = GGMLQuantizationType.I16
+            elif tensor_dtype == np.int32:
+                dtype = GGMLQuantizationType.I32
+            elif tensor_dtype == np.int64:
+                dtype = GGMLQuantizationType.I64
+            else:
+                raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now")
         else:
             dtype = raw_dtype
         self.ti_data += self._pack("I", dtype)
@@ -313,6 +325,9 @@ class GGUFWriter:
         self.data_alignment = alignment
         self.add_uint32(Keys.General.ALIGNMENT, alignment)
 
+    def add_vocab_size(self, size: int) -> None:
+        self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size)
+
     def add_context_length(self, length: int) -> None:
         self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length)
 
@@ -346,6 +361,9 @@ class GGUFWriter:
     def add_clamp_kqv(self, value: float) -> None:
         self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
 
+    def add_logit_scale(self, value: float) -> None:
+        self.add_float32(Keys.LLM.LOGIT_SCALE.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)
 

gguf-py/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.7.0"
+version = "0.8.0"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [

llama.h

@@ -59,9 +59,10 @@ extern "C" {
     typedef int32_t llama_seq_id;
 
     enum llama_vocab_type {
-        LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
-        LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
-        LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece
+        LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
+        LLAMA_VOCAB_TYPE_SPM  = 1, // SentencePiece
+        LLAMA_VOCAB_TYPE_BPE  = 2, // Byte Pair Encoding
+        LLAMA_VOCAB_TYPE_WPM  = 3, // WordPiece
     };
 
     // note: these values should be synchronized with ggml_rope
@@ -234,8 +235,9 @@ extern "C" {
     struct llama_context_params {
         uint32_t seed;              // RNG seed, -1 for random
         uint32_t n_ctx;             // text context, 0 = from model
-        uint32_t n_batch;           // prompt processing maximum batch size
-        uint32_t n_parallel;        // number of parallel sequences (i.e. distinct states for recurrent models)
+        uint32_t n_batch;           // logical maximum batch size that can be submitted to llama_decode
+        uint32_t n_ubatch;          // physical maximum batch size
+        uint32_t n_seq_max;         // max number of sequences (i.e. distinct states for recurrent models)
         uint32_t n_threads;         // number of threads to use for generation
         uint32_t n_threads_batch;   // number of threads to use for batch processing
 
@@ -278,7 +280,7 @@ extern "C" {
         bool allow_requantize;       // allow quantizing non-f32/f16 tensors
         bool quantize_output_tensor; // quantize output.weight
         bool only_copy;              // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
-        bool pure;                   // disable k-quant mixtures and quantize all tensors to the same type
+        bool pure;                   // quantize all tensors to the default type
         void * imatrix;              // pointer to importance matrix data
     } llama_model_quantize_params;
 
@@ -377,7 +379,8 @@ extern "C" {
 
     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 uint32_t llama_n_max_seq  (const struct llama_context * ctx);
+    LLAMA_API uint32_t llama_n_ubatch   (const struct llama_context * ctx);
+    LLAMA_API uint32_t llama_n_seq_max  (const struct llama_context * ctx);
 
     LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
     LLAMA_API enum llama_rope_type  llama_rope_type (const struct llama_model * model);
@@ -385,6 +388,7 @@ extern "C" {
     LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
     LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
     LLAMA_API int32_t llama_n_embd     (const struct llama_model * model);
+    LLAMA_API int32_t llama_n_layer    (const struct llama_model * model);
 
     // Get the model's RoPE frequency scaling factor
     LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
@@ -432,10 +436,24 @@ extern "C" {
     // Returns 0 on success
     LLAMA_API int32_t llama_model_apply_lora_from_file(
             const struct llama_model * model,
-                      const char * path_lora,
-                           float   scale,
-                      const char * path_base_model,
-                         int32_t   n_threads);
+                          const char * path_lora,
+                               float   scale,
+                          const char * path_base_model,
+                             int32_t   n_threads);
+
+    // Apply a loaded control vector to a llama_context, or if data is NULL, clear
+    // the currently loaded vector.
+    // n_embd should be the size of a single layer's control, and data should point
+    // to an n_embd x n_layers buffer starting from layer 1.
+    // il_start and il_end are the layer range the vector should apply to (both inclusive)
+    // See llama_control_vector_load in common to load a control vector.
+    LLAMA_API int32_t llama_control_vector_apply(
+            struct llama_context * lctx,
+                     const float * data,
+                          size_t   len,
+                         int32_t   n_embd,
+                         int32_t   il_start,
+                         int32_t   il_end);
 
     //
     // KV cache
@@ -456,7 +474,7 @@ extern "C" {
         // Maximum number of sequences that can exist in a cell. It's not an error
         // if there are more sequences in a cell than this value, however they will
         // not be visible in the view cells_sequences.
-        int32_t n_max_seq;
+        int32_t n_seq_max;
 
         // Number of tokens in the cache. For example, if there are two populated
         // cells, the first with 1 sequence id in it and the second with 2 sequence
@@ -476,12 +494,12 @@ extern "C" {
         // Information for an individual cell.
         struct llama_kv_cache_view_cell * cells;
 
-        // The sequences for each cell. There will be n_max_seq items per cell.
+        // The sequences for each cell. There will be n_seq_max items per cell.
         llama_seq_id * cells_sequences;
     };
 
     // Create an empty KV cache view. (use only for debugging purposes)
-    LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq);
+    LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max);
 
     // Free a KV cache view. (use only for debugging purposes)
     LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
@@ -650,6 +668,11 @@ extern "C" {
     // Set abort callback
     LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
 
+    // Wait until all computations are finished
+    // This is automatically done when using one of the functions below to obtain the computation results
+    // and is not necessary to call it explicitly in most cases
+    LLAMA_API void llama_synchronize(struct llama_context * ctx);
+
     // Token logits obtained from the last call to llama_decode()
     // The logits for the last token are stored in the last row
     // Logits for which llama_batch.logits[i] == 0 are undefined
@@ -708,7 +731,7 @@ extern "C" {
 
     /// @details Convert the provided text into tokens.
     /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
-    /// @return Returns the number of tokens on success, no more than n_max_tokens
+    /// @return Returns the number of tokens on success, no more than n_tokens_max
     /// @return Returns a negative number on failure - the number of tokens that would have been returned
     /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
     ///                Does not insert a leading space.
@@ -717,7 +740,7 @@ extern "C" {
                       const char * text,
                          int32_t   text_len,
                      llama_token * tokens,
-                         int32_t   n_max_tokens,
+                         int32_t   n_tokens_max,
                             bool   add_bos,
                             bool   special);
 

scripts/sync-ggml.last

@@ -1 +1 @@
-8695910a39102609073d0e099aa7c97d6bcb3bf9
+43a6d4af1971ee2912ff7bc2404011ff327b6a60

tests/test-backend-ops.cpp

@@ -2222,8 +2222,8 @@ static void usage(char ** argv) {
 
 int main(int argc, char ** argv) {
     test_mode mode = MODE_TEST;
-    const char * op_name = NULL;
-    const char * backend = NULL;
+    const char * op_name_filter = NULL;
+    const char * backend_filter = NULL;
 
     for (int i = 1; i < argc; i++) {
         if (strcmp(argv[i], "test") == 0) {
@@ -2232,14 +2232,14 @@ int main(int argc, char ** argv) {
             mode = MODE_PERF;
         } else if (strcmp(argv[i], "-o") == 0) {
             if (i + 1 < argc) {
-                op_name = argv[++i];
+                op_name_filter = argv[++i];
             } else {
                 usage(argv);
                 return 1;
             }
         } else if (strcmp(argv[i], "-b") == 0) {
             if (i + 1 < argc) {
-                backend = argv[++i];
+                backend_filter = argv[++i];
             } else {
                 usage(argv);
                 return 1;
@@ -2258,7 +2258,7 @@ int main(int argc, char ** argv) {
     for (size_t i = 0; i < ggml_backend_reg_get_count(); i++) {
         printf("Backend %zu/%zu (%s)\n", i + 1, ggml_backend_reg_get_count(), ggml_backend_reg_get_name(i));
 
-        if (backend != NULL && strcmp(backend, ggml_backend_reg_get_name(i)) != 0) {
+        if (backend_filter != NULL && strcmp(backend_filter, ggml_backend_reg_get_name(i)) != 0) {
             printf("  Skipping\n");
             n_ok++;
             continue;
@@ -2266,9 +2266,17 @@ int main(int argc, char ** argv) {
 
         ggml_backend_t backend = ggml_backend_reg_init_backend(i, NULL);
         GGML_ASSERT(backend != NULL);
+
+        if (backend_filter == NULL && ggml_backend_is_cpu(backend)) {
+            printf("  Skipping CPU backend\n");
+            ggml_backend_free(backend);
+            n_ok++;
+            continue;
+        }
+
         printf("  Backend name: %s\n", ggml_backend_name(backend));
 
-        bool ok = test_backend(backend, mode, op_name);
+        bool ok = test_backend(backend, mode, op_name_filter);
 
         printf("  Backend %s: ", ggml_backend_name(backend));
         if (ok) {

tests/test-chat-template.cpp

@@ -31,6 +31,8 @@ int main(void) {
         "{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}",
         // google/gemma-7b-it
         "{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\\n' + message['content'] | trim + '<end_of_turn>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}",
+        // OrionStarAI/Orion-14B-Chat
+        "{% for message in messages %}{% if loop.first %}{{ bos_token }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\\n\\nAssistant: ' + eos_token }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}",
     };
     std::vector<std::string> expected_output = {
         // teknium/OpenHermes-2.5-Mistral-7B
@@ -45,6 +47,8 @@ int main(void) {
         "system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n   I am an assistant   </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
         // google/gemma-7b-it
         "<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
+        // OrionStarAI/Orion-14B-Chat
+        "Human: You are a helpful assistant\n\nHello\n\nAssistant: </s>Hi there</s>Human: Who are you\n\nAssistant: </s>   I am an assistant   </s>Human: Another question\n\nAssistant: </s>",
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;

tests/test-tokenizer-1-bpe.cpp

@@ -64,7 +64,7 @@ int main(int argc, char **argv) {
     for (int i = 0; i < n_vocab; ++i) {
         std::string str = llama_detokenize_bpe(ctx, std::vector<int>(1, i));
         try {
-            auto cps = codepoints_from_utf8(str);
+            auto cps = unicode_cpts_from_utf8(str);
             std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
             std::string check = llama_detokenize_bpe(ctx, tokens);
             if (check != str) {
@@ -97,7 +97,7 @@ int main(int argc, char **argv) {
                         continue;
                     }
 
-                    std::string str = codepoint_to_utf8(cp);
+                    std::string str = unicode_cpt_to_utf8(cp);
                     std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
                     std::string check = llama_detokenize_bpe(ctx, tokens);
                     if (cp != 9601 && str != check) {

tests/test-tokenizer-1-llama.cpp

@@ -85,7 +85,7 @@ int main(int argc, char **argv) {
                         continue;
                     }
 
-                    std::string str = codepoint_to_utf8(cp);
+                    std::string str = unicode_cpt_to_utf8(cp);
                     std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
                     std::string check = llama_detokenize_spm(ctx, tokens);
                     if (cp != 9601 && str != check) {

unicode.h

@@ -1,784 +1,26 @@
-#pragma once
+#pragma once
 
-#include <cassert>
-#include <map>
-#include <stdexcept>
+#include <cstdint>
 #include <string>
-#include <unordered_map>
 #include <vector>
 
-static const std::vector<std::pair<uint32_t, uint32_t>> digit_ranges = {
-{0x30, 0x39}, {0xB2, 0xB3}, {0xB9, 0xB9}, {0x660, 0x669}, {0x6F0, 0x6F9}, {0x7C0, 0x7C9}, {0x966, 0x96F}, {0x9E6, 0x9EF}, {0xA66, 0xA6F}, {0xAE6, 0xAEF}, {0xB66, 0xB6F}, {0xBE6, 0xBEF}, {0xC66, 0xC6F},
-{0xCE6, 0xCEF}, {0xD66, 0xD6F}, {0xDE6, 0xDEF}, {0xE50, 0xE59}, {0xED0, 0xED9}, {0xF20, 0xF29}, {0x1040, 0x1049}, {0x1090, 0x1099}, {0x1369, 0x1371}, {0x17E0, 0x17E9}, {0x1810, 0x1819}, {0x1946, 0x194F},
-{0x19D0, 0x19DA}, {0x1A80, 0x1A89}, {0x1A90, 0x1A99}, {0x1B50, 0x1B59}, {0x1BB0, 0x1BB9}, {0x1C40, 0x1C49}, {0x1C50, 0x1C59}, {0x2070, 0x2070}, {0x2074, 0x2079}, {0x2080, 0x2089}, {0x2460, 0x2468},
-{0x2474, 0x247C}, {0x2488, 0x2490}, {0x24EA, 0x24EA}, {0x24F5, 0x24FD}, {0x24FF, 0x24FF}, {0x2776, 0x277E}, {0x2780, 0x2788}, {0x278A, 0x2792}, {0xA620, 0xA629}, {0xA8D0, 0xA8D9}, {0xA900, 0xA909},
-{0xA9D0, 0xA9D9}, {0xA9F0, 0xA9F9}, {0xAA50, 0xAA59}, {0xABF0, 0xABF9}, {0xFF10, 0xFF19}, {0x104A0, 0x104A9}, {0x10A40, 0x10A43}, {0x10D30, 0x10D39}, {0x10E60, 0x10E68}, {0x11052, 0x1105A},
-{0x11066, 0x1106F}, {0x110F0, 0x110F9}, {0x11136, 0x1113F}, {0x111D0, 0x111D9}, {0x112F0, 0x112F9}, {0x11450, 0x11459}, {0x114D0, 0x114D9}, {0x11650, 0x11659}, {0x116C0, 0x116C9}, {0x11730, 0x11739},
-{0x118E0, 0x118E9}, {0x11950, 0x11959}, {0x11C50, 0x11C59}, {0x11D50, 0x11D59}, {0x11DA0, 0x11DA9}, {0x16A60, 0x16A69}, {0x16B50, 0x16B59}, {0x1D7CE, 0x1D7FF}, {0x1E140, 0x1E149}, {0x1E2F0, 0x1E2F9},
-{0x1E950, 0x1E959}, {0x1F100, 0x1F10A}, {0x1FBF0, 0x1FBF9},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> letter_ranges = {
-{0x41, 0x5A}, {0x61, 0x7A}, {0xAA, 0xAA}, {0xB5, 0xB5}, {0xBA, 0xBA}, {0xC0, 0xD6}, {0xD8, 0xF6}, {0xF8, 0x2C1}, {0x2C6, 0x2D1}, {0x2E0, 0x2E4}, {0x2EC, 0x2EC}, {0x2EE, 0x2EE}, {0x370, 0x374},
-{0x376, 0x377}, {0x37A, 0x37D}, {0x37F, 0x37F}, {0x386, 0x386}, {0x388, 0x38A}, {0x38C, 0x38C}, {0x38E, 0x3A1}, {0x3A3, 0x3F5}, {0x3F7, 0x481}, {0x48A, 0x52F}, {0x531, 0x556}, {0x559, 0x559},
-{0x560, 0x588}, {0x5D0, 0x5EA}, {0x5EF, 0x5F2}, {0x620, 0x64A}, {0x66E, 0x66F}, {0x671, 0x6D3}, {0x6D5, 0x6D5}, {0x6E5, 0x6E6}, {0x6EE, 0x6EF}, {0x6FA, 0x6FC}, {0x6FF, 0x6FF}, {0x710, 0x710},
-{0x712, 0x72F}, {0x74D, 0x7A5}, {0x7B1, 0x7B1}, {0x7CA, 0x7EA}, {0x7F4, 0x7F5}, {0x7FA, 0x7FA}, {0x800, 0x815}, {0x81A, 0x81A}, {0x824, 0x824}, {0x828, 0x828}, {0x840, 0x858}, {0x860, 0x86A},
-{0x8A0, 0x8B4}, {0x8B6, 0x8C7}, {0x904, 0x939}, {0x93D, 0x93D}, {0x950, 0x950}, {0x958, 0x961}, {0x971, 0x980}, {0x985, 0x98C}, {0x98F, 0x990}, {0x993, 0x9A8}, {0x9AA, 0x9B0}, {0x9B2, 0x9B2},
-{0x9B6, 0x9B9}, {0x9BD, 0x9BD}, {0x9CE, 0x9CE}, {0x9DC, 0x9DD}, {0x9DF, 0x9E1}, {0x9F0, 0x9F1}, {0x9FC, 0x9FC}, {0xA05, 0xA0A}, {0xA0F, 0xA10}, {0xA13, 0xA28}, {0xA2A, 0xA30}, {0xA32, 0xA33},
-{0xA35, 0xA36}, {0xA38, 0xA39}, {0xA59, 0xA5C}, {0xA5E, 0xA5E}, {0xA72, 0xA74}, {0xA85, 0xA8D}, {0xA8F, 0xA91}, {0xA93, 0xAA8}, {0xAAA, 0xAB0}, {0xAB2, 0xAB3}, {0xAB5, 0xAB9}, {0xABD, 0xABD},
-{0xAD0, 0xAD0}, {0xAE0, 0xAE1}, {0xAF9, 0xAF9}, {0xB05, 0xB0C}, {0xB0F, 0xB10}, {0xB13, 0xB28}, {0xB2A, 0xB30}, {0xB32, 0xB33}, {0xB35, 0xB39}, {0xB3D, 0xB3D}, {0xB5C, 0xB5D}, {0xB5F, 0xB61},
-{0xB71, 0xB71}, {0xB83, 0xB83}, {0xB85, 0xB8A}, {0xB8E, 0xB90}, {0xB92, 0xB95}, {0xB99, 0xB9A}, {0xB9C, 0xB9C}, {0xB9E, 0xB9F}, {0xBA3, 0xBA4}, {0xBA8, 0xBAA}, {0xBAE, 0xBB9}, {0xBD0, 0xBD0},
-{0xC05, 0xC0C}, {0xC0E, 0xC10}, {0xC12, 0xC28}, {0xC2A, 0xC39}, {0xC3D, 0xC3D}, {0xC58, 0xC5A}, {0xC60, 0xC61}, {0xC80, 0xC80}, {0xC85, 0xC8C}, {0xC8E, 0xC90}, {0xC92, 0xCA8}, {0xCAA, 0xCB3},
-{0xCB5, 0xCB9}, {0xCBD, 0xCBD}, {0xCDE, 0xCDE}, {0xCE0, 0xCE1}, {0xCF1, 0xCF2}, {0xD04, 0xD0C}, {0xD0E, 0xD10}, {0xD12, 0xD3A}, {0xD3D, 0xD3D}, {0xD4E, 0xD4E}, {0xD54, 0xD56}, {0xD5F, 0xD61},
-{0xD7A, 0xD7F}, {0xD85, 0xD96}, {0xD9A, 0xDB1}, {0xDB3, 0xDBB}, {0xDBD, 0xDBD}, {0xDC0, 0xDC6}, {0xE01, 0xE30}, {0xE32, 0xE33}, {0xE40, 0xE46}, {0xE81, 0xE82}, {0xE84, 0xE84}, {0xE86, 0xE8A},
-{0xE8C, 0xEA3}, {0xEA5, 0xEA5}, {0xEA7, 0xEB0}, {0xEB2, 0xEB3}, {0xEBD, 0xEBD}, {0xEC0, 0xEC4}, {0xEC6, 0xEC6}, {0xEDC, 0xEDF}, {0xF00, 0xF00}, {0xF40, 0xF47}, {0xF49, 0xF6C}, {0xF88, 0xF8C},
-{0x1000, 0x102A}, {0x103F, 0x103F}, {0x1050, 0x1055}, {0x105A, 0x105D}, {0x1061, 0x1061}, {0x1065, 0x1066}, {0x106E, 0x1070}, {0x1075, 0x1081}, {0x108E, 0x108E}, {0x10A0, 0x10C5}, {0x10C7, 0x10C7},
-{0x10CD, 0x10CD}, {0x10D0, 0x10FA}, {0x10FC, 0x1248}, {0x124A, 0x124D}, {0x1250, 0x1256}, {0x1258, 0x1258}, {0x125A, 0x125D}, {0x1260, 0x1288}, {0x128A, 0x128D}, {0x1290, 0x12B0}, {0x12B2, 0x12B5},
-{0x12B8, 0x12BE}, {0x12C0, 0x12C0}, {0x12C2, 0x12C5}, {0x12C8, 0x12D6}, {0x12D8, 0x1310}, {0x1312, 0x1315}, {0x1318, 0x135A}, {0x1380, 0x138F}, {0x13A0, 0x13F5}, {0x13F8, 0x13FD}, {0x1401, 0x166C},
-{0x166F, 0x167F}, {0x1681, 0x169A}, {0x16A0, 0x16EA}, {0x16F1, 0x16F8}, {0x1700, 0x170C}, {0x170E, 0x1711}, {0x1720, 0x1731}, {0x1740, 0x1751}, {0x1760, 0x176C}, {0x176E, 0x1770}, {0x1780, 0x17B3},
-{0x17D7, 0x17D7}, {0x17DC, 0x17DC}, {0x1820, 0x1878}, {0x1880, 0x1884}, {0x1887, 0x18A8}, {0x18AA, 0x18AA}, {0x18B0, 0x18F5}, {0x1900, 0x191E}, {0x1950, 0x196D}, {0x1970, 0x1974}, {0x1980, 0x19AB},
-{0x19B0, 0x19C9}, {0x1A00, 0x1A16}, {0x1A20, 0x1A54}, {0x1AA7, 0x1AA7}, {0x1B05, 0x1B33}, {0x1B45, 0x1B4B}, {0x1B83, 0x1BA0}, {0x1BAE, 0x1BAF}, {0x1BBA, 0x1BE5}, {0x1C00, 0x1C23}, {0x1C4D, 0x1C4F},
-{0x1C5A, 0x1C7D}, {0x1C80, 0x1C88}, {0x1C90, 0x1CBA}, {0x1CBD, 0x1CBF}, {0x1CE9, 0x1CEC}, {0x1CEE, 0x1CF3}, {0x1CF5, 0x1CF6}, {0x1CFA, 0x1CFA}, {0x1D00, 0x1DBF}, {0x1E00, 0x1F15}, {0x1F18, 0x1F1D},
-{0x1F20, 0x1F45}, {0x1F48, 0x1F4D}, {0x1F50, 0x1F57}, {0x1F59, 0x1F59}, {0x1F5B, 0x1F5B}, {0x1F5D, 0x1F5D}, {0x1F5F, 0x1F7D}, {0x1F80, 0x1FB4}, {0x1FB6, 0x1FBC}, {0x1FBE, 0x1FBE}, {0x1FC2, 0x1FC4},
-{0x1FC6, 0x1FCC}, {0x1FD0, 0x1FD3}, {0x1FD6, 0x1FDB}, {0x1FE0, 0x1FEC}, {0x1FF2, 0x1FF4}, {0x1FF6, 0x1FFC}, {0x2071, 0x2071}, {0x207F, 0x207F}, {0x2090, 0x209C}, {0x2102, 0x2102}, {0x2107, 0x2107},
-{0x210A, 0x2113}, {0x2115, 0x2115}, {0x2119, 0x211D}, {0x2124, 0x2124}, {0x2126, 0x2126}, {0x2128, 0x2128}, {0x212A, 0x212D}, {0x212F, 0x2139}, {0x213C, 0x213F}, {0x2145, 0x2149}, {0x214E, 0x214E},
-{0x2183, 0x2184}, {0x2C00, 0x2C2E}, {0x2C30, 0x2C5E}, {0x2C60, 0x2CE4}, {0x2CEB, 0x2CEE}, {0x2CF2, 0x2CF3}, {0x2D00, 0x2D25}, {0x2D27, 0x2D27}, {0x2D2D, 0x2D2D}, {0x2D30, 0x2D67}, {0x2D6F, 0x2D6F},
-{0x2D80, 0x2D96}, {0x2DA0, 0x2DA6}, {0x2DA8, 0x2DAE}, {0x2DB0, 0x2DB6}, {0x2DB8, 0x2DBE}, {0x2DC0, 0x2DC6}, {0x2DC8, 0x2DCE}, {0x2DD0, 0x2DD6}, {0x2DD8, 0x2DDE}, {0x2E2F, 0x2E2F}, {0x3005, 0x3006},
-{0x3031, 0x3035}, {0x303B, 0x303C}, {0x3041, 0x3096}, {0x309D, 0x309F}, {0x30A1, 0x30FA}, {0x30FC, 0x30FF}, {0x3105, 0x312F}, {0x3131, 0x318E}, {0x31A0, 0x31BF}, {0x31F0, 0x31FF}, {0x3400, 0x4DBF},
-{0x4E00, 0x9FFC}, {0xA000, 0xA48C}, {0xA4D0, 0xA4FD}, {0xA500, 0xA60C}, {0xA610, 0xA61F}, {0xA62A, 0xA62B}, {0xA640, 0xA66E}, {0xA67F, 0xA69D}, {0xA6A0, 0xA6E5}, {0xA717, 0xA71F}, {0xA722, 0xA788},
-{0xA78B, 0xA7BF}, {0xA7C2, 0xA7CA}, {0xA7F5, 0xA801}, {0xA803, 0xA805}, {0xA807, 0xA80A}, {0xA80C, 0xA822}, {0xA840, 0xA873}, {0xA882, 0xA8B3}, {0xA8F2, 0xA8F7}, {0xA8FB, 0xA8FB}, {0xA8FD, 0xA8FE},
-{0xA90A, 0xA925}, {0xA930, 0xA946}, {0xA960, 0xA97C}, {0xA984, 0xA9B2}, {0xA9CF, 0xA9CF}, {0xA9E0, 0xA9E4}, {0xA9E6, 0xA9EF}, {0xA9FA, 0xA9FE}, {0xAA00, 0xAA28}, {0xAA40, 0xAA42}, {0xAA44, 0xAA4B},
-{0xAA60, 0xAA76}, {0xAA7A, 0xAA7A}, {0xAA7E, 0xAAAF}, {0xAAB1, 0xAAB1}, {0xAAB5, 0xAAB6}, {0xAAB9, 0xAABD}, {0xAAC0, 0xAAC0}, {0xAAC2, 0xAAC2}, {0xAADB, 0xAADD}, {0xAAE0, 0xAAEA}, {0xAAF2, 0xAAF4},
-{0xAB01, 0xAB06}, {0xAB09, 0xAB0E}, {0xAB11, 0xAB16}, {0xAB20, 0xAB26}, {0xAB28, 0xAB2E}, {0xAB30, 0xAB5A}, {0xAB5C, 0xAB69}, {0xAB70, 0xABE2}, {0xAC00, 0xD7A3}, {0xD7B0, 0xD7C6}, {0xD7CB, 0xD7FB},
-{0xF900, 0xFA6D}, {0xFA70, 0xFAD9}, {0xFB00, 0xFB06}, {0xFB13, 0xFB17}, {0xFB1D, 0xFB1D}, {0xFB1F, 0xFB28}, {0xFB2A, 0xFB36}, {0xFB38, 0xFB3C}, {0xFB3E, 0xFB3E}, {0xFB40, 0xFB41}, {0xFB43, 0xFB44},
-{0xFB46, 0xFBB1}, {0xFBD3, 0xFD3D}, {0xFD50, 0xFD8F}, {0xFD92, 0xFDC7}, {0xFDF0, 0xFDFB}, {0xFE70, 0xFE74}, {0xFE76, 0xFEFC}, {0xFF21, 0xFF3A}, {0xFF41, 0xFF5A}, {0xFF66, 0xFFBE}, {0xFFC2, 0xFFC7},
-{0xFFCA, 0xFFCF}, {0xFFD2, 0xFFD7}, {0xFFDA, 0xFFDC}, {0x10000, 0x1000B}, {0x1000D, 0x10026}, {0x10028, 0x1003A}, {0x1003C, 0x1003D}, {0x1003F, 0x1004D}, {0x10050, 0x1005D}, {0x10080, 0x100FA},
-{0x10280, 0x1029C}, {0x102A0, 0x102D0}, {0x10300, 0x1031F}, {0x1032D, 0x10340}, {0x10342, 0x10349}, {0x10350, 0x10375}, {0x10380, 0x1039D}, {0x103A0, 0x103C3}, {0x103C8, 0x103CF}, {0x10400, 0x1049D},
-{0x104B0, 0x104D3}, {0x104D8, 0x104FB}, {0x10500, 0x10527}, {0x10530, 0x10563}, {0x10600, 0x10736}, {0x10740, 0x10755}, {0x10760, 0x10767}, {0x10800, 0x10805}, {0x10808, 0x10808}, {0x1080A, 0x10835},
-{0x10837, 0x10838}, {0x1083C, 0x1083C}, {0x1083F, 0x10855}, {0x10860, 0x10876}, {0x10880, 0x1089E}, {0x108E0, 0x108F2}, {0x108F4, 0x108F5}, {0x10900, 0x10915}, {0x10920, 0x10939}, {0x10980, 0x109B7},
-{0x109BE, 0x109BF}, {0x10A00, 0x10A00}, {0x10A10, 0x10A13}, {0x10A15, 0x10A17}, {0x10A19, 0x10A35}, {0x10A60, 0x10A7C}, {0x10A80, 0x10A9C}, {0x10AC0, 0x10AC7}, {0x10AC9, 0x10AE4}, {0x10B00, 0x10B35},
-{0x10B40, 0x10B55}, {0x10B60, 0x10B72}, {0x10B80, 0x10B91}, {0x10C00, 0x10C48}, {0x10C80, 0x10CB2}, {0x10CC0, 0x10CF2}, {0x10D00, 0x10D23}, {0x10E80, 0x10EA9}, {0x10EB0, 0x10EB1}, {0x10F00, 0x10F1C},
-{0x10F27, 0x10F27}, {0x10F30, 0x10F45}, {0x10FB0, 0x10FC4}, {0x10FE0, 0x10FF6}, {0x11003, 0x11037}, {0x11083, 0x110AF}, {0x110D0, 0x110E8}, {0x11103, 0x11126}, {0x11144, 0x11144}, {0x11147, 0x11147},
-{0x11150, 0x11172}, {0x11176, 0x11176}, {0x11183, 0x111B2}, {0x111C1, 0x111C4}, {0x111DA, 0x111DA}, {0x111DC, 0x111DC}, {0x11200, 0x11211}, {0x11213, 0x1122B}, {0x11280, 0x11286}, {0x11288, 0x11288},
-{0x1128A, 0x1128D}, {0x1128F, 0x1129D}, {0x1129F, 0x112A8}, {0x112B0, 0x112DE}, {0x11305, 0x1130C}, {0x1130F, 0x11310}, {0x11313, 0x11328}, {0x1132A, 0x11330}, {0x11332, 0x11333}, {0x11335, 0x11339},
-{0x1133D, 0x1133D}, {0x11350, 0x11350}, {0x1135D, 0x11361}, {0x11400, 0x11434}, {0x11447, 0x1144A}, {0x1145F, 0x11461}, {0x11480, 0x114AF}, {0x114C4, 0x114C5}, {0x114C7, 0x114C7}, {0x11580, 0x115AE},
-{0x115D8, 0x115DB}, {0x11600, 0x1162F}, {0x11644, 0x11644}, {0x11680, 0x116AA}, {0x116B8, 0x116B8}, {0x11700, 0x1171A}, {0x11800, 0x1182B}, {0x118A0, 0x118DF}, {0x118FF, 0x11906}, {0x11909, 0x11909},
-{0x1190C, 0x11913}, {0x11915, 0x11916}, {0x11918, 0x1192F}, {0x1193F, 0x1193F}, {0x11941, 0x11941}, {0x119A0, 0x119A7}, {0x119AA, 0x119D0}, {0x119E1, 0x119E1}, {0x119E3, 0x119E3}, {0x11A00, 0x11A00},
-{0x11A0B, 0x11A32}, {0x11A3A, 0x11A3A}, {0x11A50, 0x11A50}, {0x11A5C, 0x11A89}, {0x11A9D, 0x11A9D}, {0x11AC0, 0x11AF8}, {0x11C00, 0x11C08}, {0x11C0A, 0x11C2E}, {0x11C40, 0x11C40}, {0x11C72, 0x11C8F},
-{0x11D00, 0x11D06}, {0x11D08, 0x11D09}, {0x11D0B, 0x11D30}, {0x11D46, 0x11D46}, {0x11D60, 0x11D65}, {0x11D67, 0x11D68}, {0x11D6A, 0x11D89}, {0x11D98, 0x11D98}, {0x11EE0, 0x11EF2}, {0x11FB0, 0x11FB0},
-{0x12000, 0x12399}, {0x12480, 0x12543}, {0x13000, 0x1342E}, {0x14400, 0x14646}, {0x16800, 0x16A38}, {0x16A40, 0x16A5E}, {0x16AD0, 0x16AED}, {0x16B00, 0x16B2F}, {0x16B40, 0x16B43}, {0x16B63, 0x16B77},
-{0x16B7D, 0x16B8F}, {0x16E40, 0x16E7F}, {0x16F00, 0x16F4A}, {0x16F50, 0x16F50}, {0x16F93, 0x16F9F}, {0x16FE0, 0x16FE1}, {0x16FE3, 0x16FE3}, {0x17000, 0x187F7}, {0x18800, 0x18CD5}, {0x18D00, 0x18D08},
-{0x1B000, 0x1B11E}, {0x1B150, 0x1B152}, {0x1B164, 0x1B167}, {0x1B170, 0x1B2FB}, {0x1BC00, 0x1BC6A}, {0x1BC70, 0x1BC7C}, {0x1BC80, 0x1BC88}, {0x1BC90, 0x1BC99}, {0x1D400, 0x1D454}, {0x1D456, 0x1D49C},
-{0x1D49E, 0x1D49F}, {0x1D4A2, 0x1D4A2}, {0x1D4A5, 0x1D4A6}, {0x1D4A9, 0x1D4AC}, {0x1D4AE, 0x1D4B9}, {0x1D4BB, 0x1D4BB}, {0x1D4BD, 0x1D4C3}, {0x1D4C5, 0x1D505}, {0x1D507, 0x1D50A}, {0x1D50D, 0x1D514},
-{0x1D516, 0x1D51C}, {0x1D51E, 0x1D539}, {0x1D53B, 0x1D53E}, {0x1D540, 0x1D544}, {0x1D546, 0x1D546}, {0x1D54A, 0x1D550}, {0x1D552, 0x1D6A5}, {0x1D6A8, 0x1D6C0}, {0x1D6C2, 0x1D6DA}, {0x1D6DC, 0x1D6FA},
-{0x1D6FC, 0x1D714}, {0x1D716, 0x1D734}, {0x1D736, 0x1D74E}, {0x1D750, 0x1D76E}, {0x1D770, 0x1D788}, {0x1D78A, 0x1D7A8}, {0x1D7AA, 0x1D7C2}, {0x1D7C4, 0x1D7CB}, {0x1E100, 0x1E12C}, {0x1E137, 0x1E13D},
-{0x1E14E, 0x1E14E}, {0x1E2C0, 0x1E2EB}, {0x1E800, 0x1E8C4}, {0x1E900, 0x1E943}, {0x1E94B, 0x1E94B}, {0x1EE00, 0x1EE03}, {0x1EE05, 0x1EE1F}, {0x1EE21, 0x1EE22}, {0x1EE24, 0x1EE24}, {0x1EE27, 0x1EE27},
-{0x1EE29, 0x1EE32}, {0x1EE34, 0x1EE37}, {0x1EE39, 0x1EE39}, {0x1EE3B, 0x1EE3B}, {0x1EE42, 0x1EE42}, {0x1EE47, 0x1EE47}, {0x1EE49, 0x1EE49}, {0x1EE4B, 0x1EE4B}, {0x1EE4D, 0x1EE4F}, {0x1EE51, 0x1EE52},
-{0x1EE54, 0x1EE54}, {0x1EE57, 0x1EE57}, {0x1EE59, 0x1EE59}, {0x1EE5B, 0x1EE5B}, {0x1EE5D, 0x1EE5D}, {0x1EE5F, 0x1EE5F}, {0x1EE61, 0x1EE62}, {0x1EE64, 0x1EE64}, {0x1EE67, 0x1EE6A}, {0x1EE6C, 0x1EE72},
-{0x1EE74, 0x1EE77}, {0x1EE79, 0x1EE7C}, {0x1EE7E, 0x1EE7E}, {0x1EE80, 0x1EE89}, {0x1EE8B, 0x1EE9B}, {0x1EEA1, 0x1EEA3}, {0x1EEA5, 0x1EEA9}, {0x1EEAB, 0x1EEBB}, {0x20000, 0x2A6DD}, {0x2A700, 0x2B734},
-{0x2B740, 0x2B81D}, {0x2B820, 0x2CEA1}, {0x2CEB0, 0x2EBE0}, {0x2F800, 0x2FA1D}, {0x30000, 0x3134A},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> whitespace_ranges = {
-{0x9, 0xD}, {0x1C, 0x20}, {0x85, 0x85}, {0xA0, 0xA0}, {0x1680, 0x1680}, {0x2000, 0x200A}, {0x2028, 0x2029}, {0x202F, 0x202F}, {0x205F, 0x205F}, {0x3000, 0x3000},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> accent_mark_ranges = {
-{0x300, 0x36F}, {0x483, 0x489}, {0x591, 0x5BD}, {0x5BF, 0x5BF}, {0x5C1, 0x5C2}, {0x5C4, 0x5C5}, {0x5C7, 0x5C7}, {0x610, 0x61A}, {0x64B, 0x65F}, {0x670, 0x670}, {0x6D6, 0x6DC}, {0x6DF, 0x6E4},
-{0x6E7, 0x6E8}, {0x6EA, 0x6ED}, {0x711, 0x711}, {0x730, 0x74A}, {0x7A6, 0x7B0}, {0x7EB, 0x7F3}, {0x7FD, 0x7FD}, {0x816, 0x819}, {0x81B, 0x823}, {0x825, 0x827}, {0x829, 0x82D}, {0x859, 0x85B},
-{0x8D3, 0x8E1}, {0x8E3, 0x903}, {0x93A, 0x93C}, {0x93E, 0x94F}, {0x951, 0x957}, {0x962, 0x963}, {0x981, 0x983}, {0x9BC, 0x9BC}, {0x9BE, 0x9C4}, {0x9C7, 0x9C8}, {0x9CB, 0x9CD}, {0x9D7, 0x9D7},
-{0x9E2, 0x9E3}, {0x9FE, 0x9FE}, {0xA01, 0xA03}, {0xA3C, 0xA3C}, {0xA3E, 0xA42}, {0xA47, 0xA48}, {0xA4B, 0xA4D}, {0xA51, 0xA51}, {0xA70, 0xA71}, {0xA75, 0xA75}, {0xA81, 0xA83}, {0xABC, 0xABC},
-{0xABE, 0xAC5}, {0xAC7, 0xAC9}, {0xACB, 0xACD}, {0xAE2, 0xAE3}, {0xAFA, 0xAFF}, {0xB01, 0xB03}, {0xB3C, 0xB3C}, {0xB3E, 0xB44}, {0xB47, 0xB48}, {0xB4B, 0xB4D}, {0xB55, 0xB57}, {0xB62, 0xB63},
-{0xB82, 0xB82}, {0xBBE, 0xBC2}, {0xBC6, 0xBC8}, {0xBCA, 0xBCD}, {0xBD7, 0xBD7}, {0xC00, 0xC04}, {0xC3E, 0xC44}, {0xC46, 0xC48}, {0xC4A, 0xC4D}, {0xC55, 0xC56}, {0xC62, 0xC63}, {0xC81, 0xC83},
-{0xCBC, 0xCBC}, {0xCBE, 0xCC4}, {0xCC6, 0xCC8}, {0xCCA, 0xCCD}, {0xCD5, 0xCD6}, {0xCE2, 0xCE3}, {0xD00, 0xD03}, {0xD3B, 0xD3C}, {0xD3E, 0xD44}, {0xD46, 0xD48}, {0xD4A, 0xD4D}, {0xD57, 0xD57},
-{0xD62, 0xD63}, {0xD81, 0xD83}, {0xDCA, 0xDCA}, {0xDCF, 0xDD4}, {0xDD6, 0xDD6}, {0xDD8, 0xDDF}, {0xDF2, 0xDF3}, {0xE31, 0xE31}, {0xE34, 0xE3A}, {0xE47, 0xE4E}, {0xEB1, 0xEB1}, {0xEB4, 0xEBC},
-{0xEC8, 0xECD}, {0xF18, 0xF19}, {0xF35, 0xF35}, {0xF37, 0xF37}, {0xF39, 0xF39}, {0xF3E, 0xF3F}, {0xF71, 0xF84}, {0xF86, 0xF87}, {0xF8D, 0xF97}, {0xF99, 0xFBC}, {0xFC6, 0xFC6}, {0x102B, 0x103E},
-{0x1056, 0x1059}, {0x105E, 0x1060}, {0x1062, 0x1064}, {0x1067, 0x106D}, {0x1071, 0x1074}, {0x1082, 0x108D}, {0x108F, 0x108F}, {0x109A, 0x109D}, {0x135D, 0x135F}, {0x1712, 0x1714}, {0x1732, 0x1734},
-{0x1752, 0x1753}, {0x1772, 0x1773}, {0x17B4, 0x17D3}, {0x17DD, 0x17DD}, {0x180B, 0x180D}, {0x1885, 0x1886}, {0x18A9, 0x18A9}, {0x1920, 0x192B}, {0x1930, 0x193B}, {0x1A17, 0x1A1B}, {0x1A55, 0x1A5E},
-{0x1A60, 0x1A7C}, {0x1A7F, 0x1A7F}, {0x1AB0, 0x1AC0}, {0x1B00, 0x1B04}, {0x1B34, 0x1B44}, {0x1B6B, 0x1B73}, {0x1B80, 0x1B82}, {0x1BA1, 0x1BAD}, {0x1BE6, 0x1BF3}, {0x1C24, 0x1C37}, {0x1CD0, 0x1CD2},
-{0x1CD4, 0x1CE8}, {0x1CED, 0x1CED}, {0x1CF4, 0x1CF4}, {0x1CF7, 0x1CF9}, {0x1DC0, 0x1DF9}, {0x1DFB, 0x1DFF}, {0x20D0, 0x20F0}, {0x2CEF, 0x2CF1}, {0x2D7F, 0x2D7F}, {0x2DE0, 0x2DFF}, {0x302A, 0x302F},
-{0x3099, 0x309A}, {0xA66F, 0xA672}, {0xA674, 0xA67D}, {0xA69E, 0xA69F}, {0xA6F0, 0xA6F1}, {0xA802, 0xA802}, {0xA806, 0xA806}, {0xA80B, 0xA80B}, {0xA823, 0xA827}, {0xA82C, 0xA82C}, {0xA880, 0xA881},
-{0xA8B4, 0xA8C5}, {0xA8E0, 0xA8F1}, {0xA8FF, 0xA8FF}, {0xA926, 0xA92D}, {0xA947, 0xA953}, {0xA980, 0xA983}, {0xA9B3, 0xA9C0}, {0xA9E5, 0xA9E5}, {0xAA29, 0xAA36}, {0xAA43, 0xAA43}, {0xAA4C, 0xAA4D},
-{0xAA7B, 0xAA7D}, {0xAAB0, 0xAAB0}, {0xAAB2, 0xAAB4}, {0xAAB7, 0xAAB8}, {0xAABE, 0xAABF}, {0xAAC1, 0xAAC1}, {0xAAEB, 0xAAEF}, {0xAAF5, 0xAAF6}, {0xABE3, 0xABEA}, {0xABEC, 0xABED}, {0xFB1E, 0xFB1E},
-{0xFE00, 0xFE0F}, {0xFE20, 0xFE2F}, {0x101FD, 0x101FD}, {0x102E0, 0x102E0}, {0x10376, 0x1037A}, {0x10A01, 0x10A03}, {0x10A05, 0x10A06}, {0x10A0C, 0x10A0F}, {0x10A38, 0x10A3A}, {0x10A3F, 0x10A3F},
-{0x10AE5, 0x10AE6}, {0x10D24, 0x10D27}, {0x10EAB, 0x10EAC}, {0x10F46, 0x10F50}, {0x11000, 0x11002}, {0x11038, 0x11046}, {0x1107F, 0x11082}, {0x110B0, 0x110BA}, {0x11100, 0x11102}, {0x11127, 0x11134},
-{0x11145, 0x11146}, {0x11173, 0x11173}, {0x11180, 0x11182}, {0x111B3, 0x111C0}, {0x111C9, 0x111CC}, {0x111CE, 0x111CF}, {0x1122C, 0x11237}, {0x1123E, 0x1123E}, {0x112DF, 0x112EA}, {0x11300, 0x11303},
-{0x1133B, 0x1133C}, {0x1133E, 0x11344}, {0x11347, 0x11348}, {0x1134B, 0x1134D}, {0x11357, 0x11357}, {0x11362, 0x11363}, {0x11366, 0x1136C}, {0x11370, 0x11374}, {0x11435, 0x11446}, {0x1145E, 0x1145E},
-{0x114B0, 0x114C3}, {0x115AF, 0x115B5}, {0x115B8, 0x115C0}, {0x115DC, 0x115DD}, {0x11630, 0x11640}, {0x116AB, 0x116B7}, {0x1171D, 0x1172B}, {0x1182C, 0x1183A}, {0x11930, 0x11935}, {0x11937, 0x11938},
-{0x1193B, 0x1193E}, {0x11940, 0x11940}, {0x11942, 0x11943}, {0x119D1, 0x119D7}, {0x119DA, 0x119E0}, {0x119E4, 0x119E4}, {0x11A01, 0x11A0A}, {0x11A33, 0x11A39}, {0x11A3B, 0x11A3E}, {0x11A47, 0x11A47},
-{0x11A51, 0x11A5B}, {0x11A8A, 0x11A99}, {0x11C2F, 0x11C36}, {0x11C38, 0x11C3F}, {0x11C92, 0x11CA7}, {0x11CA9, 0x11CB6}, {0x11D31, 0x11D36}, {0x11D3A, 0x11D3A}, {0x11D3C, 0x11D3D}, {0x11D3F, 0x11D45},
-{0x11D47, 0x11D47}, {0x11D8A, 0x11D8E}, {0x11D90, 0x11D91}, {0x11D93, 0x11D97}, {0x11EF3, 0x11EF6}, {0x16AF0, 0x16AF4}, {0x16B30, 0x16B36}, {0x16F4F, 0x16F4F}, {0x16F51, 0x16F87}, {0x16F8F, 0x16F92},
-{0x16FE4, 0x16FE4}, {0x16FF0, 0x16FF1}, {0x1BC9D, 0x1BC9E}, {0x1D165, 0x1D169}, {0x1D16D, 0x1D172}, {0x1D17B, 0x1D182}, {0x1D185, 0x1D18B}, {0x1D1AA, 0x1D1AD}, {0x1D242, 0x1D244}, {0x1DA00, 0x1DA36},
-{0x1DA3B, 0x1DA6C}, {0x1DA75, 0x1DA75}, {0x1DA84, 0x1DA84}, {0x1DA9B, 0x1DA9F}, {0x1DAA1, 0x1DAAF}, {0x1E000, 0x1E006}, {0x1E008, 0x1E018}, {0x1E01B, 0x1E021}, {0x1E023, 0x1E024}, {0x1E026, 0x1E02A},
-{0x1E130, 0x1E136}, {0x1E2EC, 0x1E2EF}, {0x1E8D0, 0x1E8D6}, {0x1E944, 0x1E94A}, {0xE0100, 0xE01EF},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> punctuation_ranges = {
-{0x21, 0x23}, {0x25, 0x2A}, {0x2C, 0x2F}, {0x3A, 0x3B}, {0x3F, 0x40}, {0x5B, 0x5D}, {0x5F, 0x5F}, {0x7B, 0x7B}, {0x7D, 0x7D}, {0xA1, 0xA1}, {0xA7, 0xA7}, {0xAB, 0xAB}, {0xB6, 0xB7}, {0xBB, 0xBB},
-{0xBF, 0xBF}, {0x37E, 0x37E}, {0x387, 0x387}, {0x55A, 0x55F}, {0x589, 0x58A}, {0x5BE, 0x5BE}, {0x5C0, 0x5C0}, {0x5C3, 0x5C3}, {0x5C6, 0x5C6}, {0x5F3, 0x5F4}, {0x609, 0x60A}, {0x60C, 0x60D},
-{0x61B, 0x61B}, {0x61E, 0x61F}, {0x66A, 0x66D}, {0x6D4, 0x6D4}, {0x700, 0x70D}, {0x7F7, 0x7F9}, {0x830, 0x83E}, {0x85E, 0x85E}, {0x964, 0x965}, {0x970, 0x970}, {0x9FD, 0x9FD}, {0xA76, 0xA76},
-{0xAF0, 0xAF0}, {0xC77, 0xC77}, {0xC84, 0xC84}, {0xDF4, 0xDF4}, {0xE4F, 0xE4F}, {0xE5A, 0xE5B}, {0xF04, 0xF12}, {0xF14, 0xF14}, {0xF3A, 0xF3D}, {0xF85, 0xF85}, {0xFD0, 0xFD4}, {0xFD9, 0xFDA},
-{0x104A, 0x104F}, {0x10FB, 0x10FB}, {0x1360, 0x1368}, {0x1400, 0x1400}, {0x166E, 0x166E}, {0x169B, 0x169C}, {0x16EB, 0x16ED}, {0x1735, 0x1736}, {0x17D4, 0x17D6}, {0x17D8, 0x17DA}, {0x1800, 0x180A},
-{0x1944, 0x1945}, {0x1A1E, 0x1A1F}, {0x1AA0, 0x1AA6}, {0x1AA8, 0x1AAD}, {0x1B5A, 0x1B60}, {0x1BFC, 0x1BFF}, {0x1C3B, 0x1C3F}, {0x1C7E, 0x1C7F}, {0x1CC0, 0x1CC7}, {0x1CD3, 0x1CD3}, {0x2010, 0x2027},
-{0x2030, 0x2043}, {0x2045, 0x2051}, {0x2053, 0x205E}, {0x207D, 0x207E}, {0x208D, 0x208E}, {0x2308, 0x230B}, {0x2329, 0x232A}, {0x2768, 0x2775}, {0x27C5, 0x27C6}, {0x27E6, 0x27EF}, {0x2983, 0x2998},
-{0x29D8, 0x29DB}, {0x29FC, 0x29FD}, {0x2CF9, 0x2CFC}, {0x2CFE, 0x2CFF}, {0x2D70, 0x2D70}, {0x2E00, 0x2E2E}, {0x2E30, 0x2E4F}, {0x2E52, 0x2E52}, {0x3001, 0x3003}, {0x3008, 0x3011}, {0x3014, 0x301F},
-{0x3030, 0x3030}, {0x303D, 0x303D}, {0x30A0, 0x30A0}, {0x30FB, 0x30FB}, {0xA4FE, 0xA4FF}, {0xA60D, 0xA60F}, {0xA673, 0xA673}, {0xA67E, 0xA67E}, {0xA6F2, 0xA6F7}, {0xA874, 0xA877}, {0xA8CE, 0xA8CF},
-{0xA8F8, 0xA8FA}, {0xA8FC, 0xA8FC}, {0xA92E, 0xA92F}, {0xA95F, 0xA95F}, {0xA9C1, 0xA9CD}, {0xA9DE, 0xA9DF}, {0xAA5C, 0xAA5F}, {0xAADE, 0xAADF}, {0xAAF0, 0xAAF1}, {0xABEB, 0xABEB}, {0xFD3E, 0xFD3F},
-{0xFE10, 0xFE19}, {0xFE30, 0xFE52}, {0xFE54, 0xFE61}, {0xFE63, 0xFE63}, {0xFE68, 0xFE68}, {0xFE6A, 0xFE6B}, {0xFF01, 0xFF03}, {0xFF05, 0xFF0A}, {0xFF0C, 0xFF0F}, {0xFF1A, 0xFF1B}, {0xFF1F, 0xFF20},
-{0xFF3B, 0xFF3D}, {0xFF3F, 0xFF3F}, {0xFF5B, 0xFF5B}, {0xFF5D, 0xFF5D}, {0xFF5F, 0xFF65}, {0x10100, 0x10102}, {0x1039F, 0x1039F}, {0x103D0, 0x103D0}, {0x1056F, 0x1056F}, {0x10857, 0x10857},
-{0x1091F, 0x1091F}, {0x1093F, 0x1093F}, {0x10A50, 0x10A58}, {0x10A7F, 0x10A7F}, {0x10AF0, 0x10AF6}, {0x10B39, 0x10B3F}, {0x10B99, 0x10B9C}, {0x10EAD, 0x10EAD}, {0x10F55, 0x10F59}, {0x11047, 0x1104D},
-{0x110BB, 0x110BC}, {0x110BE, 0x110C1}, {0x11140, 0x11143}, {0x11174, 0x11175}, {0x111C5, 0x111C8}, {0x111CD, 0x111CD}, {0x111DB, 0x111DB}, {0x111DD, 0x111DF}, {0x11238, 0x1123D}, {0x112A9, 0x112A9},
-{0x1144B, 0x1144F}, {0x1145A, 0x1145B}, {0x1145D, 0x1145D}, {0x114C6, 0x114C6}, {0x115C1, 0x115D7}, {0x11641, 0x11643}, {0x11660, 0x1166C}, {0x1173C, 0x1173E}, {0x1183B, 0x1183B}, {0x11944, 0x11946},
-{0x119E2, 0x119E2}, {0x11A3F, 0x11A46}, {0x11A9A, 0x11A9C}, {0x11A9E, 0x11AA2}, {0x11C41, 0x11C45}, {0x11C70, 0x11C71}, {0x11EF7, 0x11EF8}, {0x11FFF, 0x11FFF}, {0x12470, 0x12474}, {0x16A6E, 0x16A6F},
-{0x16AF5, 0x16AF5}, {0x16B37, 0x16B3B}, {0x16B44, 0x16B44}, {0x16E97, 0x16E9A}, {0x16FE2, 0x16FE2}, {0x1BC9F, 0x1BC9F}, {0x1DA87, 0x1DA8B}, {0x1E95E, 0x1E95F},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> symbol_ranges = {
-{0x24, 0x24}, {0x2B, 0x2B}, {0x3C, 0x3E}, {0x5E, 0x5E}, {0x60, 0x60}, {0x7C, 0x7C}, {0x7E, 0x7E}, {0xA2, 0xA6}, {0xA8, 0xA9}, {0xAC, 0xAC}, {0xAE, 0xB1}, {0xB4, 0xB4}, {0xB8, 0xB8}, {0xD7, 0xD7},
-{0xF7, 0xF7}, {0x2C2, 0x2C5}, {0x2D2, 0x2DF}, {0x2E5, 0x2EB}, {0x2ED, 0x2ED}, {0x2EF, 0x2FF}, {0x375, 0x375}, {0x384, 0x385}, {0x3F6, 0x3F6}, {0x482, 0x482}, {0x58D, 0x58F}, {0x606, 0x608},
-{0x60B, 0x60B}, {0x60E, 0x60F}, {0x6DE, 0x6DE}, {0x6E9, 0x6E9}, {0x6FD, 0x6FE}, {0x7F6, 0x7F6}, {0x7FE, 0x7FF}, {0x9F2, 0x9F3}, {0x9FA, 0x9FB}, {0xAF1, 0xAF1}, {0xB70, 0xB70}, {0xBF3, 0xBFA},
-{0xC7F, 0xC7F}, {0xD4F, 0xD4F}, {0xD79, 0xD79}, {0xE3F, 0xE3F}, {0xF01, 0xF03}, {0xF13, 0xF13}, {0xF15, 0xF17}, {0xF1A, 0xF1F}, {0xF34, 0xF34}, {0xF36, 0xF36}, {0xF38, 0xF38}, {0xFBE, 0xFC5},
-{0xFC7, 0xFCC}, {0xFCE, 0xFCF}, {0xFD5, 0xFD8}, {0x109E, 0x109F}, {0x1390, 0x1399}, {0x166D, 0x166D}, {0x17DB, 0x17DB}, {0x1940, 0x1940}, {0x19DE, 0x19FF}, {0x1B61, 0x1B6A}, {0x1B74, 0x1B7C},
-{0x1FBD, 0x1FBD}, {0x1FBF, 0x1FC1}, {0x1FCD, 0x1FCF}, {0x1FDD, 0x1FDF}, {0x1FED, 0x1FEF}, {0x1FFD, 0x1FFE}, {0x2044, 0x2044}, {0x2052, 0x2052}, {0x207A, 0x207C}, {0x208A, 0x208C}, {0x20A0, 0x20BF},
-{0x2100, 0x2101}, {0x2103, 0x2106}, {0x2108, 0x2109}, {0x2114, 0x2114}, {0x2116, 0x2118}, {0x211E, 0x2123}, {0x2125, 0x2125}, {0x2127, 0x2127}, {0x2129, 0x2129}, {0x212E, 0x212E}, {0x213A, 0x213B},
-{0x2140, 0x2144}, {0x214A, 0x214D}, {0x214F, 0x214F}, {0x218A, 0x218B}, {0x2190, 0x2307}, {0x230C, 0x2328}, {0x232B, 0x2426}, {0x2440, 0x244A}, {0x249C, 0x24E9}, {0x2500, 0x2767}, {0x2794, 0x27C4},
-{0x27C7, 0x27E5}, {0x27F0, 0x2982}, {0x2999, 0x29D7}, {0x29DC, 0x29FB}, {0x29FE, 0x2B73}, {0x2B76, 0x2B95}, {0x2B97, 0x2BFF}, {0x2CE5, 0x2CEA}, {0x2E50, 0x2E51}, {0x2E80, 0x2E99}, {0x2E9B, 0x2EF3},
-{0x2F00, 0x2FD5}, {0x2FF0, 0x2FFB}, {0x3004, 0x3004}, {0x3012, 0x3013}, {0x3020, 0x3020}, {0x3036, 0x3037}, {0x303E, 0x303F}, {0x309B, 0x309C}, {0x3190, 0x3191}, {0x3196, 0x319F}, {0x31C0, 0x31E3},
-{0x3200, 0x321E}, {0x322A, 0x3247}, {0x3250, 0x3250}, {0x3260, 0x327F}, {0x328A, 0x32B0}, {0x32C0, 0x33FF}, {0x4DC0, 0x4DFF}, {0xA490, 0xA4C6}, {0xA700, 0xA716}, {0xA720, 0xA721}, {0xA789, 0xA78A},
-{0xA828, 0xA82B}, {0xA836, 0xA839}, {0xAA77, 0xAA79}, {0xAB5B, 0xAB5B}, {0xAB6A, 0xAB6B}, {0xFB29, 0xFB29}, {0xFBB2, 0xFBC1}, {0xFDFC, 0xFDFD}, {0xFE62, 0xFE62}, {0xFE64, 0xFE66}, {0xFE69, 0xFE69},
-{0xFF04, 0xFF04}, {0xFF0B, 0xFF0B}, {0xFF1C, 0xFF1E}, {0xFF3E, 0xFF3E}, {0xFF40, 0xFF40}, {0xFF5C, 0xFF5C}, {0xFF5E, 0xFF5E}, {0xFFE0, 0xFFE6}, {0xFFE8, 0xFFEE}, {0xFFFC, 0xFFFD}, {0x10137, 0x1013F},
-{0x10179, 0x10189}, {0x1018C, 0x1018E}, {0x10190, 0x1019C}, {0x101A0, 0x101A0}, {0x101D0, 0x101FC}, {0x10877, 0x10878}, {0x10AC8, 0x10AC8}, {0x1173F, 0x1173F}, {0x11FD5, 0x11FF1}, {0x16B3C, 0x16B3F},
-{0x16B45, 0x16B45}, {0x1BC9C, 0x1BC9C}, {0x1D000, 0x1D0F5}, {0x1D100, 0x1D126}, {0x1D129, 0x1D164}, {0x1D16A, 0x1D16C}, {0x1D183, 0x1D184}, {0x1D18C, 0x1D1A9}, {0x1D1AE, 0x1D1E8}, {0x1D200, 0x1D241},
-{0x1D245, 0x1D245}, {0x1D300, 0x1D356}, {0x1D6C1, 0x1D6C1}, {0x1D6DB, 0x1D6DB}, {0x1D6FB, 0x1D6FB}, {0x1D715, 0x1D715}, {0x1D735, 0x1D735}, {0x1D74F, 0x1D74F}, {0x1D76F, 0x1D76F}, {0x1D789, 0x1D789},
-{0x1D7A9, 0x1D7A9}, {0x1D7C3, 0x1D7C3}, {0x1D800, 0x1D9FF}, {0x1DA37, 0x1DA3A}, {0x1DA6D, 0x1DA74}, {0x1DA76, 0x1DA83}, {0x1DA85, 0x1DA86}, {0x1E14F, 0x1E14F}, {0x1E2FF, 0x1E2FF}, {0x1ECAC, 0x1ECAC},
-{0x1ECB0, 0x1ECB0}, {0x1ED2E, 0x1ED2E}, {0x1EEF0, 0x1EEF1}, {0x1F000, 0x1F02B}, {0x1F030, 0x1F093}, {0x1F0A0, 0x1F0AE}, {0x1F0B1, 0x1F0BF}, {0x1F0C1, 0x1F0CF}, {0x1F0D1, 0x1F0F5}, {0x1F10D, 0x1F1AD},
-{0x1F1E6, 0x1F202}, {0x1F210, 0x1F23B}, {0x1F240, 0x1F248}, {0x1F250, 0x1F251}, {0x1F260, 0x1F265}, {0x1F300, 0x1F6D7}, {0x1F6E0, 0x1F6EC}, {0x1F6F0, 0x1F6FC}, {0x1F700, 0x1F773}, {0x1F780, 0x1F7D8},
-{0x1F7E0, 0x1F7EB}, {0x1F800, 0x1F80B}, {0x1F810, 0x1F847}, {0x1F850, 0x1F859}, {0x1F860, 0x1F887}, {0x1F890, 0x1F8AD}, {0x1F8B0, 0x1F8B1}, {0x1F900, 0x1F978}, {0x1F97A, 0x1F9CB}, {0x1F9CD, 0x1FA53},
-{0x1FA60, 0x1FA6D}, {0x1FA70, 0x1FA74}, {0x1FA78, 0x1FA7A}, {0x1FA80, 0x1FA86}, {0x1FA90, 0x1FAA8}, {0x1FAB0, 0x1FAB6}, {0x1FAC0, 0x1FAC2}, {0x1FAD0, 0x1FAD6}, {0x1FB00, 0x1FB92}, {0x1FB94, 0x1FBCA},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> control_ranges = {
-{0x0, 0x8}, {0xE, 0x1B}, {0x7F, 0x84}, {0x86, 0x9F}, {0xAD, 0xAD}, {0x378, 0x379}, {0x380, 0x383}, {0x38B, 0x38B}, {0x38D, 0x38D}, {0x3A2, 0x3A2}, {0x530, 0x530}, {0x557, 0x558}, {0x58B, 0x58C},
-{0x590, 0x590}, {0x5C8, 0x5CF}, {0x5EB, 0x5EE}, {0x5F5, 0x605}, {0x61C, 0x61D}, {0x6DD, 0x6DD}, {0x70E, 0x70F}, {0x74B, 0x74C}, {0x7B2, 0x7BF}, {0x7FB, 0x7FC}, {0x82E, 0x82F}, {0x83F, 0x83F},
-{0x85C, 0x85D}, {0x85F, 0x85F}, {0x86B, 0x89F}, {0x8B5, 0x8B5}, {0x8C8, 0x8D2}, {0x8E2, 0x8E2}, {0x984, 0x984}, {0x98D, 0x98E}, {0x991, 0x992}, {0x9A9, 0x9A9}, {0x9B1, 0x9B1}, {0x9B3, 0x9B5},
-{0x9BA, 0x9BB}, {0x9C5, 0x9C6}, {0x9C9, 0x9CA}, {0x9CF, 0x9D6}, {0x9D8, 0x9DB}, {0x9DE, 0x9DE}, {0x9E4, 0x9E5}, {0x9FF, 0xA00}, {0xA04, 0xA04}, {0xA0B, 0xA0E}, {0xA11, 0xA12}, {0xA29, 0xA29},
-{0xA31, 0xA31}, {0xA34, 0xA34}, {0xA37, 0xA37}, {0xA3A, 0xA3B}, {0xA3D, 0xA3D}, {0xA43, 0xA46}, {0xA49, 0xA4A}, {0xA4E, 0xA50}, {0xA52, 0xA58}, {0xA5D, 0xA5D}, {0xA5F, 0xA65}, {0xA77, 0xA80},
-{0xA84, 0xA84}, {0xA8E, 0xA8E}, {0xA92, 0xA92}, {0xAA9, 0xAA9}, {0xAB1, 0xAB1}, {0xAB4, 0xAB4}, {0xABA, 0xABB}, {0xAC6, 0xAC6}, {0xACA, 0xACA}, {0xACE, 0xACF}, {0xAD1, 0xADF}, {0xAE4, 0xAE5},
-{0xAF2, 0xAF8}, {0xB00, 0xB00}, {0xB04, 0xB04}, {0xB0D, 0xB0E}, {0xB11, 0xB12}, {0xB29, 0xB29}, {0xB31, 0xB31}, {0xB34, 0xB34}, {0xB3A, 0xB3B}, {0xB45, 0xB46}, {0xB49, 0xB4A}, {0xB4E, 0xB54},
-{0xB58, 0xB5B}, {0xB5E, 0xB5E}, {0xB64, 0xB65}, {0xB78, 0xB81}, {0xB84, 0xB84}, {0xB8B, 0xB8D}, {0xB91, 0xB91}, {0xB96, 0xB98}, {0xB9B, 0xB9B}, {0xB9D, 0xB9D}, {0xBA0, 0xBA2}, {0xBA5, 0xBA7},
-{0xBAB, 0xBAD}, {0xBBA, 0xBBD}, {0xBC3, 0xBC5}, {0xBC9, 0xBC9}, {0xBCE, 0xBCF}, {0xBD1, 0xBD6}, {0xBD8, 0xBE5}, {0xBFB, 0xBFF}, {0xC0D, 0xC0D}, {0xC11, 0xC11}, {0xC29, 0xC29}, {0xC3A, 0xC3C},
-{0xC45, 0xC45}, {0xC49, 0xC49}, {0xC4E, 0xC54}, {0xC57, 0xC57}, {0xC5B, 0xC5F}, {0xC64, 0xC65}, {0xC70, 0xC76}, {0xC8D, 0xC8D}, {0xC91, 0xC91}, {0xCA9, 0xCA9}, {0xCB4, 0xCB4}, {0xCBA, 0xCBB},
-{0xCC5, 0xCC5}, {0xCC9, 0xCC9}, {0xCCE, 0xCD4}, {0xCD7, 0xCDD}, {0xCDF, 0xCDF}, {0xCE4, 0xCE5}, {0xCF0, 0xCF0}, {0xCF3, 0xCFF}, {0xD0D, 0xD0D}, {0xD11, 0xD11}, {0xD45, 0xD45}, {0xD49, 0xD49},
-{0xD50, 0xD53}, {0xD64, 0xD65}, {0xD80, 0xD80}, {0xD84, 0xD84}, {0xD97, 0xD99}, {0xDB2, 0xDB2}, {0xDBC, 0xDBC}, {0xDBE, 0xDBF}, {0xDC7, 0xDC9}, {0xDCB, 0xDCE}, {0xDD5, 0xDD5}, {0xDD7, 0xDD7},
-{0xDE0, 0xDE5}, {0xDF0, 0xDF1}, {0xDF5, 0xE00}, {0xE3B, 0xE3E}, {0xE5C, 0xE80}, {0xE83, 0xE83}, {0xE85, 0xE85}, {0xE8B, 0xE8B}, {0xEA4, 0xEA4}, {0xEA6, 0xEA6}, {0xEBE, 0xEBF}, {0xEC5, 0xEC5},
-{0xEC7, 0xEC7}, {0xECE, 0xECF}, {0xEDA, 0xEDB}, {0xEE0, 0xEFF}, {0xF48, 0xF48}, {0xF6D, 0xF70}, {0xF98, 0xF98}, {0xFBD, 0xFBD}, {0xFCD, 0xFCD}, {0xFDB, 0xFFF}, {0x10C6, 0x10C6}, {0x10C8, 0x10CC},
-{0x10CE, 0x10CF}, {0x1249, 0x1249}, {0x124E, 0x124F}, {0x1257, 0x1257}, {0x1259, 0x1259}, {0x125E, 0x125F}, {0x1289, 0x1289}, {0x128E, 0x128F}, {0x12B1, 0x12B1}, {0x12B6, 0x12B7}, {0x12BF, 0x12BF},
-{0x12C1, 0x12C1}, {0x12C6, 0x12C7}, {0x12D7, 0x12D7}, {0x1311, 0x1311}, {0x1316, 0x1317}, {0x135B, 0x135C}, {0x137D, 0x137F}, {0x139A, 0x139F}, {0x13F6, 0x13F7}, {0x13FE, 0x13FF}, {0x169D, 0x169F},
-{0x16F9, 0x16FF}, {0x170D, 0x170D}, {0x1715, 0x171F}, {0x1737, 0x173F}, {0x1754, 0x175F}, {0x176D, 0x176D}, {0x1771, 0x1771}, {0x1774, 0x177F}, {0x17DE, 0x17DF}, {0x17EA, 0x17EF}, {0x17FA, 0x17FF},
-{0x180E, 0x180F}, {0x181A, 0x181F}, {0x1879, 0x187F}, {0x18AB, 0x18AF}, {0x18F6, 0x18FF}, {0x191F, 0x191F}, {0x192C, 0x192F}, {0x193C, 0x193F}, {0x1941, 0x1943}, {0x196E, 0x196F}, {0x1975, 0x197F},
-{0x19AC, 0x19AF}, {0x19CA, 0x19CF}, {0x19DB, 0x19DD}, {0x1A1C, 0x1A1D}, {0x1A5F, 0x1A5F}, {0x1A7D, 0x1A7E}, {0x1A8A, 0x1A8F}, {0x1A9A, 0x1A9F}, {0x1AAE, 0x1AAF}, {0x1AC1, 0x1AFF}, {0x1B4C, 0x1B4F},
-{0x1B7D, 0x1B7F}, {0x1BF4, 0x1BFB}, {0x1C38, 0x1C3A}, {0x1C4A, 0x1C4C}, {0x1C89, 0x1C8F}, {0x1CBB, 0x1CBC}, {0x1CC8, 0x1CCF}, {0x1CFB, 0x1CFF}, {0x1DFA, 0x1DFA}, {0x1F16, 0x1F17}, {0x1F1E, 0x1F1F},
-{0x1F46, 0x1F47}, {0x1F4E, 0x1F4F}, {0x1F58, 0x1F58}, {0x1F5A, 0x1F5A}, {0x1F5C, 0x1F5C}, {0x1F5E, 0x1F5E}, {0x1F7E, 0x1F7F}, {0x1FB5, 0x1FB5}, {0x1FC5, 0x1FC5}, {0x1FD4, 0x1FD5}, {0x1FDC, 0x1FDC},
-{0x1FF0, 0x1FF1}, {0x1FF5, 0x1FF5}, {0x1FFF, 0x1FFF}, {0x200B, 0x200F}, {0x202A, 0x202E}, {0x2060, 0x206F}, {0x2072, 0x2073}, {0x208F, 0x208F}, {0x209D, 0x209F}, {0x20C0, 0x20CF}, {0x20F1, 0x20FF},
-{0x218C, 0x218F}, {0x2427, 0x243F}, {0x244B, 0x245F}, {0x2B74, 0x2B75}, {0x2B96, 0x2B96}, {0x2C2F, 0x2C2F}, {0x2C5F, 0x2C5F}, {0x2CF4, 0x2CF8}, {0x2D26, 0x2D26}, {0x2D28, 0x2D2C}, {0x2D2E, 0x2D2F},
-{0x2D68, 0x2D6E}, {0x2D71, 0x2D7E}, {0x2D97, 0x2D9F}, {0x2DA7, 0x2DA7}, {0x2DAF, 0x2DAF}, {0x2DB7, 0x2DB7}, {0x2DBF, 0x2DBF}, {0x2DC7, 0x2DC7}, {0x2DCF, 0x2DCF}, {0x2DD7, 0x2DD7}, {0x2DDF, 0x2DDF},
-{0x2E53, 0x2E7F}, {0x2E9A, 0x2E9A}, {0x2EF4, 0x2EFF}, {0x2FD6, 0x2FEF}, {0x2FFC, 0x2FFF}, {0x3040, 0x3040}, {0x3097, 0x3098}, {0x3100, 0x3104}, {0x3130, 0x3130}, {0x318F, 0x318F}, {0x31E4, 0x31EF},
-{0x321F, 0x321F}, {0x9FFD, 0x9FFF}, {0xA48D, 0xA48F}, {0xA4C7, 0xA4CF}, {0xA62C, 0xA63F}, {0xA6F8, 0xA6FF}, {0xA7C0, 0xA7C1}, {0xA7CB, 0xA7F4}, {0xA82D, 0xA82F}, {0xA83A, 0xA83F}, {0xA878, 0xA87F},
-{0xA8C6, 0xA8CD}, {0xA8DA, 0xA8DF}, {0xA954, 0xA95E}, {0xA97D, 0xA97F}, {0xA9CE, 0xA9CE}, {0xA9DA, 0xA9DD}, {0xA9FF, 0xA9FF}, {0xAA37, 0xAA3F}, {0xAA4E, 0xAA4F}, {0xAA5A, 0xAA5B}, {0xAAC3, 0xAADA},
-{0xAAF7, 0xAB00}, {0xAB07, 0xAB08}, {0xAB0F, 0xAB10}, {0xAB17, 0xAB1F}, {0xAB27, 0xAB27}, {0xAB2F, 0xAB2F}, {0xAB6C, 0xAB6F}, {0xABEE, 0xABEF}, {0xABFA, 0xABFF}, {0xD7A4, 0xD7AF}, {0xD7C7, 0xD7CA},
-{0xD7FC, 0xF8FF}, {0xFA6E, 0xFA6F}, {0xFADA, 0xFAFF}, {0xFB07, 0xFB12}, {0xFB18, 0xFB1C}, {0xFB37, 0xFB37}, {0xFB3D, 0xFB3D}, {0xFB3F, 0xFB3F}, {0xFB42, 0xFB42}, {0xFB45, 0xFB45}, {0xFBC2, 0xFBD2},
-{0xFD40, 0xFD4F}, {0xFD90, 0xFD91}, {0xFDC8, 0xFDEF}, {0xFDFE, 0xFDFF}, {0xFE1A, 0xFE1F}, {0xFE53, 0xFE53}, {0xFE67, 0xFE67}, {0xFE6C, 0xFE6F}, {0xFE75, 0xFE75}, {0xFEFD, 0xFF00}, {0xFFBF, 0xFFC1},
-{0xFFC8, 0xFFC9}, {0xFFD0, 0xFFD1}, {0xFFD8, 0xFFD9}, {0xFFDD, 0xFFDF}, {0xFFE7, 0xFFE7}, {0xFFEF, 0xFFFB}, {0xFFFE, 0xFFFF}, {0x1000C, 0x1000C}, {0x10027, 0x10027}, {0x1003B, 0x1003B},
-{0x1003E, 0x1003E}, {0x1004E, 0x1004F}, {0x1005E, 0x1007F}, {0x100FB, 0x100FF}, {0x10103, 0x10106}, {0x10134, 0x10136}, {0x1018F, 0x1018F}, {0x1019D, 0x1019F}, {0x101A1, 0x101CF}, {0x101FE, 0x1027F},
-{0x1029D, 0x1029F}, {0x102D1, 0x102DF}, {0x102FC, 0x102FF}, {0x10324, 0x1032C}, {0x1034B, 0x1034F}, {0x1037B, 0x1037F}, {0x1039E, 0x1039E}, {0x103C4, 0x103C7}, {0x103D6, 0x103FF}, {0x1049E, 0x1049F},
-{0x104AA, 0x104AF}, {0x104D4, 0x104D7}, {0x104FC, 0x104FF}, {0x10528, 0x1052F}, {0x10564, 0x1056E}, {0x10570, 0x105FF}, {0x10737, 0x1073F}, {0x10756, 0x1075F}, {0x10768, 0x107FF}, {0x10806, 0x10807},
-{0x10809, 0x10809}, {0x10836, 0x10836}, {0x10839, 0x1083B}, {0x1083D, 0x1083E}, {0x10856, 0x10856}, {0x1089F, 0x108A6}, {0x108B0, 0x108DF}, {0x108F3, 0x108F3}, {0x108F6, 0x108FA}, {0x1091C, 0x1091E},
-{0x1093A, 0x1093E}, {0x10940, 0x1097F}, {0x109B8, 0x109BB}, {0x109D0, 0x109D1}, {0x10A04, 0x10A04}, {0x10A07, 0x10A0B}, {0x10A14, 0x10A14}, {0x10A18, 0x10A18}, {0x10A36, 0x10A37}, {0x10A3B, 0x10A3E},
-{0x10A49, 0x10A4F}, {0x10A59, 0x10A5F}, {0x10AA0, 0x10ABF}, {0x10AE7, 0x10AEA}, {0x10AF7, 0x10AFF}, {0x10B36, 0x10B38}, {0x10B56, 0x10B57}, {0x10B73, 0x10B77}, {0x10B92, 0x10B98}, {0x10B9D, 0x10BA8},
-{0x10BB0, 0x10BFF}, {0x10C49, 0x10C7F}, {0x10CB3, 0x10CBF}, {0x10CF3, 0x10CF9}, {0x10D28, 0x10D2F}, {0x10D3A, 0x10E5F}, {0x10E7F, 0x10E7F}, {0x10EAA, 0x10EAA}, {0x10EAE, 0x10EAF}, {0x10EB2, 0x10EFF},
-{0x10F28, 0x10F2F}, {0x10F5A, 0x10FAF}, {0x10FCC, 0x10FDF}, {0x10FF7, 0x10FFF}, {0x1104E, 0x11051}, {0x11070, 0x1107E}, {0x110BD, 0x110BD}, {0x110C2, 0x110CF}, {0x110E9, 0x110EF}, {0x110FA, 0x110FF},
-{0x11135, 0x11135}, {0x11148, 0x1114F}, {0x11177, 0x1117F}, {0x111E0, 0x111E0}, {0x111F5, 0x111FF}, {0x11212, 0x11212}, {0x1123F, 0x1127F}, {0x11287, 0x11287}, {0x11289, 0x11289}, {0x1128E, 0x1128E},
-{0x1129E, 0x1129E}, {0x112AA, 0x112AF}, {0x112EB, 0x112EF}, {0x112FA, 0x112FF}, {0x11304, 0x11304}, {0x1130D, 0x1130E}, {0x11311, 0x11312}, {0x11329, 0x11329}, {0x11331, 0x11331}, {0x11334, 0x11334},
-{0x1133A, 0x1133A}, {0x11345, 0x11346}, {0x11349, 0x1134A}, {0x1134E, 0x1134F}, {0x11351, 0x11356}, {0x11358, 0x1135C}, {0x11364, 0x11365}, {0x1136D, 0x1136F}, {0x11375, 0x113FF}, {0x1145C, 0x1145C},
-{0x11462, 0x1147F}, {0x114C8, 0x114CF}, {0x114DA, 0x1157F}, {0x115B6, 0x115B7}, {0x115DE, 0x115FF}, {0x11645, 0x1164F}, {0x1165A, 0x1165F}, {0x1166D, 0x1167F}, {0x116B9, 0x116BF}, {0x116CA, 0x116FF},
-{0x1171B, 0x1171C}, {0x1172C, 0x1172F}, {0x11740, 0x117FF}, {0x1183C, 0x1189F}, {0x118F3, 0x118FE}, {0x11907, 0x11908}, {0x1190A, 0x1190B}, {0x11914, 0x11914}, {0x11917, 0x11917}, {0x11936, 0x11936},
-{0x11939, 0x1193A}, {0x11947, 0x1194F}, {0x1195A, 0x1199F}, {0x119A8, 0x119A9}, {0x119D8, 0x119D9}, {0x119E5, 0x119FF}, {0x11A48, 0x11A4F}, {0x11AA3, 0x11ABF}, {0x11AF9, 0x11BFF}, {0x11C09, 0x11C09},
-{0x11C37, 0x11C37}, {0x11C46, 0x11C4F}, {0x11C6D, 0x11C6F}, {0x11C90, 0x11C91}, {0x11CA8, 0x11CA8}, {0x11CB7, 0x11CFF}, {0x11D07, 0x11D07}, {0x11D0A, 0x11D0A}, {0x11D37, 0x11D39}, {0x11D3B, 0x11D3B},
-{0x11D3E, 0x11D3E}, {0x11D48, 0x11D4F}, {0x11D5A, 0x11D5F}, {0x11D66, 0x11D66}, {0x11D69, 0x11D69}, {0x11D8F, 0x11D8F}, {0x11D92, 0x11D92}, {0x11D99, 0x11D9F}, {0x11DAA, 0x11EDF}, {0x11EF9, 0x11FAF},
-{0x11FB1, 0x11FBF}, {0x11FF2, 0x11FFE}, {0x1239A, 0x123FF}, {0x1246F, 0x1246F}, {0x12475, 0x1247F}, {0x12544, 0x12FFF}, {0x1342F, 0x143FF}, {0x14647, 0x167FF}, {0x16A39, 0x16A3F}, {0x16A5F, 0x16A5F},
-{0x16A6A, 0x16A6D}, {0x16A70, 0x16ACF}, {0x16AEE, 0x16AEF}, {0x16AF6, 0x16AFF}, {0x16B46, 0x16B4F}, {0x16B5A, 0x16B5A}, {0x16B62, 0x16B62}, {0x16B78, 0x16B7C}, {0x16B90, 0x16E3F}, {0x16E9B, 0x16EFF},
-{0x16F4B, 0x16F4E}, {0x16F88, 0x16F8E}, {0x16FA0, 0x16FDF}, {0x16FE5, 0x16FEF}, {0x16FF2, 0x16FFF}, {0x187F8, 0x187FF}, {0x18CD6, 0x18CFF}, {0x18D09, 0x1AFFF}, {0x1B11F, 0x1B14F}, {0x1B153, 0x1B163},
-{0x1B168, 0x1B16F}, {0x1B2FC, 0x1BBFF}, {0x1BC6B, 0x1BC6F}, {0x1BC7D, 0x1BC7F}, {0x1BC89, 0x1BC8F}, {0x1BC9A, 0x1BC9B}, {0x1BCA0, 0x1CFFF}, {0x1D0F6, 0x1D0FF}, {0x1D127, 0x1D128}, {0x1D173, 0x1D17A},
-{0x1D1E9, 0x1D1FF}, {0x1D246, 0x1D2DF}, {0x1D2F4, 0x1D2FF}, {0x1D357, 0x1D35F}, {0x1D379, 0x1D3FF}, {0x1D455, 0x1D455}, {0x1D49D, 0x1D49D}, {0x1D4A0, 0x1D4A1}, {0x1D4A3, 0x1D4A4}, {0x1D4A7, 0x1D4A8},
-{0x1D4AD, 0x1D4AD}, {0x1D4BA, 0x1D4BA}, {0x1D4BC, 0x1D4BC}, {0x1D4C4, 0x1D4C4}, {0x1D506, 0x1D506}, {0x1D50B, 0x1D50C}, {0x1D515, 0x1D515}, {0x1D51D, 0x1D51D}, {0x1D53A, 0x1D53A}, {0x1D53F, 0x1D53F},
-{0x1D545, 0x1D545}, {0x1D547, 0x1D549}, {0x1D551, 0x1D551}, {0x1D6A6, 0x1D6A7}, {0x1D7CC, 0x1D7CD}, {0x1DA8C, 0x1DA9A}, {0x1DAA0, 0x1DAA0}, {0x1DAB0, 0x1DFFF}, {0x1E007, 0x1E007}, {0x1E019, 0x1E01A},
-{0x1E022, 0x1E022}, {0x1E025, 0x1E025}, {0x1E02B, 0x1E0FF}, {0x1E12D, 0x1E12F}, {0x1E13E, 0x1E13F}, {0x1E14A, 0x1E14D}, {0x1E150, 0x1E2BF}, {0x1E2FA, 0x1E2FE}, {0x1E300, 0x1E7FF}, {0x1E8C5, 0x1E8C6},
-{0x1E8D7, 0x1E8FF}, {0x1E94C, 0x1E94F}, {0x1E95A, 0x1E95D}, {0x1E960, 0x1EC70}, {0x1ECB5, 0x1ED00}, {0x1ED3E, 0x1EDFF}, {0x1EE04, 0x1EE04}, {0x1EE20, 0x1EE20}, {0x1EE23, 0x1EE23}, {0x1EE25, 0x1EE26},
-{0x1EE28, 0x1EE28}, {0x1EE33, 0x1EE33}, {0x1EE38, 0x1EE38}, {0x1EE3A, 0x1EE3A}, {0x1EE3C, 0x1EE41}, {0x1EE43, 0x1EE46}, {0x1EE48, 0x1EE48}, {0x1EE4A, 0x1EE4A}, {0x1EE4C, 0x1EE4C}, {0x1EE50, 0x1EE50},
-{0x1EE53, 0x1EE53}, {0x1EE55, 0x1EE56}, {0x1EE58, 0x1EE58}, {0x1EE5A, 0x1EE5A}, {0x1EE5C, 0x1EE5C}, {0x1EE5E, 0x1EE5E}, {0x1EE60, 0x1EE60}, {0x1EE63, 0x1EE63}, {0x1EE65, 0x1EE66}, {0x1EE6B, 0x1EE6B},
-{0x1EE73, 0x1EE73}, {0x1EE78, 0x1EE78}, {0x1EE7D, 0x1EE7D}, {0x1EE7F, 0x1EE7F}, {0x1EE8A, 0x1EE8A}, {0x1EE9C, 0x1EEA0}, {0x1EEA4, 0x1EEA4}, {0x1EEAA, 0x1EEAA}, {0x1EEBC, 0x1EEEF}, {0x1EEF2, 0x1EFFF},
-{0x1F02C, 0x1F02F}, {0x1F094, 0x1F09F}, {0x1F0AF, 0x1F0B0}, {0x1F0C0, 0x1F0C0}, {0x1F0D0, 0x1F0D0}, {0x1F0F6, 0x1F0FF}, {0x1F1AE, 0x1F1E5}, {0x1F203, 0x1F20F}, {0x1F23C, 0x1F23F}, {0x1F249, 0x1F24F},
-{0x1F252, 0x1F25F}, {0x1F266, 0x1F2FF}, {0x1F6D8, 0x1F6DF}, {0x1F6ED, 0x1F6EF}, {0x1F6FD, 0x1F6FF}, {0x1F774, 0x1F77F}, {0x1F7D9, 0x1F7DF}, {0x1F7EC, 0x1F7FF}, {0x1F80C, 0x1F80F}, {0x1F848, 0x1F84F},
-{0x1F85A, 0x1F85F}, {0x1F888, 0x1F88F}, {0x1F8AE, 0x1F8AF}, {0x1F8B2, 0x1F8FF}, {0x1F979, 0x1F979}, {0x1F9CC, 0x1F9CC}, {0x1FA54, 0x1FA5F}, {0x1FA6E, 0x1FA6F}, {0x1FA75, 0x1FA77}, {0x1FA7B, 0x1FA7F},
-{0x1FA87, 0x1FA8F}, {0x1FAA9, 0x1FAAF}, {0x1FAB7, 0x1FABF}, {0x1FAC3, 0x1FACF}, {0x1FAD7, 0x1FAFF}, {0x1FB93, 0x1FB93}, {0x1FBCB, 0x1FBEF}, {0x1FBFA, 0x1FFFF}, {0x2A6DE, 0x2A6FF}, {0x2B735, 0x2B73F},
-{0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF},
-};
-
-static const std::multimap<uint32_t, uint32_t> nfd_map = {
-{0xC0, 0x41}, {0xC0, 0x300}, {0xC1, 0x41}, {0xC1, 0x301}, {0xC2, 0x41}, {0xC2, 0x302}, {0xC3, 0x41}, {0xC3, 0x303}, {0xC4, 0x41}, {0xC4, 0x308}, {0xC5, 0x41}, {0xC5, 0x30A}, {0xC7, 0x43},
-{0xC7, 0x327}, {0xC8, 0x45}, {0xC8, 0x300}, {0xC9, 0x45}, {0xC9, 0x301}, {0xCA, 0x45}, {0xCA, 0x302}, {0xCB, 0x45}, {0xCB, 0x308}, {0xCC, 0x49}, {0xCC, 0x300}, {0xCD, 0x49}, {0xCD, 0x301},
-{0xCE, 0x49}, {0xCE, 0x302}, {0xCF, 0x49}, {0xCF, 0x308}, {0xD1, 0x4E}, {0xD1, 0x303}, {0xD2, 0x4F}, {0xD2, 0x300}, {0xD3, 0x4F}, {0xD3, 0x301}, {0xD4, 0x4F}, {0xD4, 0x302}, {0xD5, 0x4F},
-{0xD5, 0x303}, {0xD6, 0x4F}, {0xD6, 0x308}, {0xD9, 0x55}, {0xD9, 0x300}, {0xDA, 0x55}, {0xDA, 0x301}, {0xDB, 0x55}, {0xDB, 0x302}, {0xDC, 0x55}, {0xDC, 0x308}, {0xDD, 0x59}, {0xDD, 0x301},
-{0xE0, 0x61}, {0xE0, 0x300}, {0xE1, 0x61}, {0xE1, 0x301}, {0xE2, 0x61}, {0xE2, 0x302}, {0xE3, 0x61}, {0xE3, 0x303}, {0xE4, 0x61}, {0xE4, 0x308}, {0xE5, 0x61}, {0xE5, 0x30A}, {0xE7, 0x63},
-{0xE7, 0x327}, {0xE8, 0x65}, {0xE8, 0x300}, {0xE9, 0x65}, {0xE9, 0x301}, {0xEA, 0x65}, {0xEA, 0x302}, {0xEB, 0x65}, {0xEB, 0x308}, {0xEC, 0x69}, {0xEC, 0x300}, {0xED, 0x69}, {0xED, 0x301},
-{0xEE, 0x69}, {0xEE, 0x302}, {0xEF, 0x69}, {0xEF, 0x308}, {0xF1, 0x6E}, {0xF1, 0x303}, {0xF2, 0x6F}, {0xF2, 0x300}, {0xF3, 0x6F}, {0xF3, 0x301}, {0xF4, 0x6F}, {0xF4, 0x302}, {0xF5, 0x6F},
-{0xF5, 0x303}, {0xF6, 0x6F}, {0xF6, 0x308}, {0xF9, 0x75}, {0xF9, 0x300}, {0xFA, 0x75}, {0xFA, 0x301}, {0xFB, 0x75}, {0xFB, 0x302}, {0xFC, 0x75}, {0xFC, 0x308}, {0xFD, 0x79}, {0xFD, 0x301},
-{0xFF, 0x79}, {0xFF, 0x308}, {0x100, 0x41}, {0x100, 0x304}, {0x101, 0x61}, {0x101, 0x304}, {0x102, 0x41}, {0x102, 0x306}, {0x103, 0x61}, {0x103, 0x306}, {0x104, 0x41}, {0x104, 0x328}, {0x105, 0x61},
-{0x105, 0x328}, {0x106, 0x43}, {0x106, 0x301}, {0x107, 0x63}, {0x107, 0x301}, {0x108, 0x43}, {0x108, 0x302}, {0x109, 0x63}, {0x109, 0x302}, {0x10A, 0x43}, {0x10A, 0x307}, {0x10B, 0x63},
-{0x10B, 0x307}, {0x10C, 0x43}, {0x10C, 0x30C}, {0x10D, 0x63}, {0x10D, 0x30C}, {0x10E, 0x44}, {0x10E, 0x30C}, {0x10F, 0x64}, {0x10F, 0x30C}, {0x112, 0x45}, {0x112, 0x304}, {0x113, 0x65},
-{0x113, 0x304}, {0x114, 0x45}, {0x114, 0x306}, {0x115, 0x65}, {0x115, 0x306}, {0x116, 0x45}, {0x116, 0x307}, {0x117, 0x65}, {0x117, 0x307}, {0x118, 0x45}, {0x118, 0x328}, {0x119, 0x65},
-{0x119, 0x328}, {0x11A, 0x45}, {0x11A, 0x30C}, {0x11B, 0x65}, {0x11B, 0x30C}, {0x11C, 0x47}, {0x11C, 0x302}, {0x11D, 0x67}, {0x11D, 0x302}, {0x11E, 0x47}, {0x11E, 0x306}, {0x11F, 0x67},
-{0x11F, 0x306}, {0x120, 0x47}, {0x120, 0x307}, {0x121, 0x67}, {0x121, 0x307}, {0x122, 0x47}, {0x122, 0x327}, {0x123, 0x67}, {0x123, 0x327}, {0x124, 0x48}, {0x124, 0x302}, {0x125, 0x68},
-{0x125, 0x302}, {0x128, 0x49}, {0x128, 0x303}, {0x129, 0x69}, {0x129, 0x303}, {0x12A, 0x49}, {0x12A, 0x304}, {0x12B, 0x69}, {0x12B, 0x304}, {0x12C, 0x49}, {0x12C, 0x306}, {0x12D, 0x69},
-{0x12D, 0x306}, {0x12E, 0x49}, {0x12E, 0x328}, {0x12F, 0x69}, {0x12F, 0x328}, {0x130, 0x49}, {0x130, 0x307}, {0x134, 0x4A}, {0x134, 0x302}, {0x135, 0x6A}, {0x135, 0x302}, {0x136, 0x4B},
-{0x136, 0x327}, {0x137, 0x6B}, {0x137, 0x327}, {0x139, 0x4C}, {0x139, 0x301}, {0x13A, 0x6C}, {0x13A, 0x301}, {0x13B, 0x4C}, {0x13B, 0x327}, {0x13C, 0x6C}, {0x13C, 0x327}, {0x13D, 0x4C},
-{0x13D, 0x30C}, {0x13E, 0x6C}, {0x13E, 0x30C}, {0x143, 0x4E}, {0x143, 0x301}, {0x144, 0x6E}, {0x144, 0x301}, {0x145, 0x4E}, {0x145, 0x327}, {0x146, 0x6E}, {0x146, 0x327}, {0x147, 0x4E},
-{0x147, 0x30C}, {0x148, 0x6E}, {0x148, 0x30C}, {0x14C, 0x4F}, {0x14C, 0x304}, {0x14D, 0x6F}, {0x14D, 0x304}, {0x14E, 0x4F}, {0x14E, 0x306}, {0x14F, 0x6F}, {0x14F, 0x306}, {0x150, 0x4F},
-{0x150, 0x30B}, {0x151, 0x6F}, {0x151, 0x30B}, {0x154, 0x52}, {0x154, 0x301}, {0x155, 0x72}, {0x155, 0x301}, {0x156, 0x52}, {0x156, 0x327}, {0x157, 0x72}, {0x157, 0x327}, {0x158, 0x52},
-{0x158, 0x30C}, {0x159, 0x72}, {0x159, 0x30C}, {0x15A, 0x53}, {0x15A, 0x301}, {0x15B, 0x73}, {0x15B, 0x301}, {0x15C, 0x53}, {0x15C, 0x302}, {0x15D, 0x73}, {0x15D, 0x302}, {0x15E, 0x53},
-{0x15E, 0x327}, {0x15F, 0x73}, {0x15F, 0x327}, {0x160, 0x53}, {0x160, 0x30C}, {0x161, 0x73}, {0x161, 0x30C}, {0x162, 0x54}, {0x162, 0x327}, {0x163, 0x74}, {0x163, 0x327}, {0x164, 0x54},
-{0x164, 0x30C}, {0x165, 0x74}, {0x165, 0x30C}, {0x168, 0x55}, {0x168, 0x303}, {0x169, 0x75}, {0x169, 0x303}, {0x16A, 0x55}, {0x16A, 0x304}, {0x16B, 0x75}, {0x16B, 0x304}, {0x16C, 0x55},
-{0x16C, 0x306}, {0x16D, 0x75}, {0x16D, 0x306}, {0x16E, 0x55}, {0x16E, 0x30A}, {0x16F, 0x75}, {0x16F, 0x30A}, {0x170, 0x55}, {0x170, 0x30B}, {0x171, 0x75}, {0x171, 0x30B}, {0x172, 0x55},
-{0x172, 0x328}, {0x173, 0x75}, {0x173, 0x328}, {0x174, 0x57}, {0x174, 0x302}, {0x175, 0x77}, {0x175, 0x302}, {0x176, 0x59}, {0x176, 0x302}, {0x177, 0x79}, {0x177, 0x302}, {0x178, 0x59},
-{0x178, 0x308}, {0x179, 0x5A}, {0x179, 0x301}, {0x17A, 0x7A}, {0x17A, 0x301}, {0x17B, 0x5A}, {0x17B, 0x307}, {0x17C, 0x7A}, {0x17C, 0x307}, {0x17D, 0x5A}, {0x17D, 0x30C}, {0x17E, 0x7A},
-{0x17E, 0x30C}, {0x1A0, 0x4F}, {0x1A0, 0x31B}, {0x1A1, 0x6F}, {0x1A1, 0x31B}, {0x1AF, 0x55}, {0x1AF, 0x31B}, {0x1B0, 0x75}, {0x1B0, 0x31B}, {0x1CD, 0x41}, {0x1CD, 0x30C}, {0x1CE, 0x61},
-{0x1CE, 0x30C}, {0x1CF, 0x49}, {0x1CF, 0x30C}, {0x1D0, 0x69}, {0x1D0, 0x30C}, {0x1D1, 0x4F}, {0x1D1, 0x30C}, {0x1D2, 0x6F}, {0x1D2, 0x30C}, {0x1D3, 0x55}, {0x1D3, 0x30C}, {0x1D4, 0x75},
-{0x1D4, 0x30C}, {0x1D5, 0x55}, {0x1D5, 0x308}, {0x1D5, 0x304}, {0x1D6, 0x75}, {0x1D6, 0x308}, {0x1D6, 0x304}, {0x1D7, 0x55}, {0x1D7, 0x308}, {0x1D7, 0x301}, {0x1D8, 0x75}, {0x1D8, 0x308},
-{0x1D8, 0x301}, {0x1D9, 0x55}, {0x1D9, 0x308}, {0x1D9, 0x30C}, {0x1DA, 0x75}, {0x1DA, 0x308}, {0x1DA, 0x30C}, {0x1DB, 0x55}, {0x1DB, 0x308}, {0x1DB, 0x300}, {0x1DC, 0x75}, {0x1DC, 0x308},
-{0x1DC, 0x300}, {0x1DE, 0x41}, {0x1DE, 0x308}, {0x1DE, 0x304}, {0x1DF, 0x61}, {0x1DF, 0x308}, {0x1DF, 0x304}, {0x1E0, 0x41}, {0x1E0, 0x307}, {0x1E0, 0x304}, {0x1E1, 0x61}, {0x1E1, 0x307},
-{0x1E1, 0x304}, {0x1E2, 0xC6}, {0x1E2, 0x304}, {0x1E3, 0xE6}, {0x1E3, 0x304}, {0x1E6, 0x47}, {0x1E6, 0x30C}, {0x1E7, 0x67}, {0x1E7, 0x30C}, {0x1E8, 0x4B}, {0x1E8, 0x30C}, {0x1E9, 0x6B},
-{0x1E9, 0x30C}, {0x1EA, 0x4F}, {0x1EA, 0x328}, {0x1EB, 0x6F}, {0x1EB, 0x328}, {0x1EC, 0x4F}, {0x1EC, 0x328}, {0x1EC, 0x304}, {0x1ED, 0x6F}, {0x1ED, 0x328}, {0x1ED, 0x304}, {0x1EE, 0x1B7},
-{0x1EE, 0x30C}, {0x1EF, 0x292}, {0x1EF, 0x30C}, {0x1F0, 0x6A}, {0x1F0, 0x30C}, {0x1F4, 0x47}, {0x1F4, 0x301}, {0x1F5, 0x67}, {0x1F5, 0x301}, {0x1F8, 0x4E}, {0x1F8, 0x300}, {0x1F9, 0x6E},
-{0x1F9, 0x300}, {0x1FA, 0x41}, {0x1FA, 0x30A}, {0x1FA, 0x301}, {0x1FB, 0x61}, {0x1FB, 0x30A}, {0x1FB, 0x301}, {0x1FC, 0xC6}, {0x1FC, 0x301}, {0x1FD, 0xE6}, {0x1FD, 0x301}, {0x1FE, 0xD8},
-{0x1FE, 0x301}, {0x1FF, 0xF8}, {0x1FF, 0x301}, {0x200, 0x41}, {0x200, 0x30F}, {0x201, 0x61}, {0x201, 0x30F}, {0x202, 0x41}, {0x202, 0x311}, {0x203, 0x61}, {0x203, 0x311}, {0x204, 0x45},
-{0x204, 0x30F}, {0x205, 0x65}, {0x205, 0x30F}, {0x206, 0x45}, {0x206, 0x311}, {0x207, 0x65}, {0x207, 0x311}, {0x208, 0x49}, {0x208, 0x30F}, {0x209, 0x69}, {0x209, 0x30F}, {0x20A, 0x49},
-{0x20A, 0x311}, {0x20B, 0x69}, {0x20B, 0x311}, {0x20C, 0x4F}, {0x20C, 0x30F}, {0x20D, 0x6F}, {0x20D, 0x30F}, {0x20E, 0x4F}, {0x20E, 0x311}, {0x20F, 0x6F}, {0x20F, 0x311}, {0x210, 0x52},
-{0x210, 0x30F}, {0x211, 0x72}, {0x211, 0x30F}, {0x212, 0x52}, {0x212, 0x311}, {0x213, 0x72}, {0x213, 0x311}, {0x214, 0x55}, {0x214, 0x30F}, {0x215, 0x75}, {0x215, 0x30F}, {0x216, 0x55},
-{0x216, 0x311}, {0x217, 0x75}, {0x217, 0x311}, {0x218, 0x53}, {0x218, 0x326}, {0x219, 0x73}, {0x219, 0x326}, {0x21A, 0x54}, {0x21A, 0x326}, {0x21B, 0x74}, {0x21B, 0x326}, {0x21E, 0x48},
-{0x21E, 0x30C}, {0x21F, 0x68}, {0x21F, 0x30C}, {0x226, 0x41}, {0x226, 0x307}, {0x227, 0x61}, {0x227, 0x307}, {0x228, 0x45}, {0x228, 0x327}, {0x229, 0x65}, {0x229, 0x327}, {0x22A, 0x4F},
-{0x22A, 0x308}, {0x22A, 0x304}, {0x22B, 0x6F}, {0x22B, 0x308}, {0x22B, 0x304}, {0x22C, 0x4F}, {0x22C, 0x303}, {0x22C, 0x304}, {0x22D, 0x6F}, {0x22D, 0x303}, {0x22D, 0x304}, {0x22E, 0x4F},
-{0x22E, 0x307}, {0x22F, 0x6F}, {0x22F, 0x307}, {0x230, 0x4F}, {0x230, 0x307}, {0x230, 0x304}, {0x231, 0x6F}, {0x231, 0x307}, {0x231, 0x304}, {0x232, 0x59}, {0x232, 0x304}, {0x233, 0x79},
-{0x233, 0x304}, {0x340, 0x300}, {0x341, 0x301}, {0x343, 0x313}, {0x344, 0x308}, {0x344, 0x301}, {0x374, 0x2B9}, {0x37E, 0x3B}, {0x385, 0xA8}, {0x385, 0x301}, {0x386, 0x391}, {0x386, 0x301},
-{0x387, 0xB7}, {0x388, 0x395}, {0x388, 0x301}, {0x389, 0x397}, {0x389, 0x301}, {0x38A, 0x399}, {0x38A, 0x301}, {0x38C, 0x39F}, {0x38C, 0x301}, {0x38E, 0x3A5}, {0x38E, 0x301}, {0x38F, 0x3A9},
-{0x38F, 0x301}, {0x390, 0x3B9}, {0x390, 0x308}, {0x390, 0x301}, {0x3AA, 0x399}, {0x3AA, 0x308}, {0x3AB, 0x3A5}, {0x3AB, 0x308}, {0x3AC, 0x3B1}, {0x3AC, 0x301}, {0x3AD, 0x3B5}, {0x3AD, 0x301},
-{0x3AE, 0x3B7}, {0x3AE, 0x301}, {0x3AF, 0x3B9}, {0x3AF, 0x301}, {0x3B0, 0x3C5}, {0x3B0, 0x308}, {0x3B0, 0x301}, {0x3CA, 0x3B9}, {0x3CA, 0x308}, {0x3CB, 0x3C5}, {0x3CB, 0x308}, {0x3CC, 0x3BF},
-{0x3CC, 0x301}, {0x3CD, 0x3C5}, {0x3CD, 0x301}, {0x3CE, 0x3C9}, {0x3CE, 0x301}, {0x3D3, 0x3D2}, {0x3D3, 0x301}, {0x3D4, 0x3D2}, {0x3D4, 0x308}, {0x400, 0x415}, {0x400, 0x300}, {0x401, 0x415},
-{0x401, 0x308}, {0x403, 0x413}, {0x403, 0x301}, {0x407, 0x406}, {0x407, 0x308}, {0x40C, 0x41A}, {0x40C, 0x301}, {0x40D, 0x418}, {0x40D, 0x300}, {0x40E, 0x423}, {0x40E, 0x306}, {0x419, 0x418},
-{0x419, 0x306}, {0x439, 0x438}, {0x439, 0x306}, {0x450, 0x435}, {0x450, 0x300}, {0x451, 0x435}, {0x451, 0x308}, {0x453, 0x433}, {0x453, 0x301}, {0x457, 0x456}, {0x457, 0x308}, {0x45C, 0x43A},
-{0x45C, 0x301}, {0x45D, 0x438}, {0x45D, 0x300}, {0x45E, 0x443}, {0x45E, 0x306}, {0x476, 0x474}, {0x476, 0x30F}, {0x477, 0x475}, {0x477, 0x30F}, {0x4C1, 0x416}, {0x4C1, 0x306}, {0x4C2, 0x436},
-{0x4C2, 0x306}, {0x4D0, 0x410}, {0x4D0, 0x306}, {0x4D1, 0x430}, {0x4D1, 0x306}, {0x4D2, 0x410}, {0x4D2, 0x308}, {0x4D3, 0x430}, {0x4D3, 0x308}, {0x4D6, 0x415}, {0x4D6, 0x306}, {0x4D7, 0x435},
-{0x4D7, 0x306}, {0x4DA, 0x4D8}, {0x4DA, 0x308}, {0x4DB, 0x4D9}, {0x4DB, 0x308}, {0x4DC, 0x416}, {0x4DC, 0x308}, {0x4DD, 0x436}, {0x4DD, 0x308}, {0x4DE, 0x417}, {0x4DE, 0x308}, {0x4DF, 0x437},
-{0x4DF, 0x308}, {0x4E2, 0x418}, {0x4E2, 0x304}, {0x4E3, 0x438}, {0x4E3, 0x304}, {0x4E4, 0x418}, {0x4E4, 0x308}, {0x4E5, 0x438}, {0x4E5, 0x308}, {0x4E6, 0x41E}, {0x4E6, 0x308}, {0x4E7, 0x43E},
-{0x4E7, 0x308}, {0x4EA, 0x4E8}, {0x4EA, 0x308}, {0x4EB, 0x4E9}, {0x4EB, 0x308}, {0x4EC, 0x42D}, {0x4EC, 0x308}, {0x4ED, 0x44D}, {0x4ED, 0x308}, {0x4EE, 0x423}, {0x4EE, 0x304}, {0x4EF, 0x443},
-{0x4EF, 0x304}, {0x4F0, 0x423}, {0x4F0, 0x308}, {0x4F1, 0x443}, {0x4F1, 0x308}, {0x4F2, 0x423}, {0x4F2, 0x30B}, {0x4F3, 0x443}, {0x4F3, 0x30B}, {0x4F4, 0x427}, {0x4F4, 0x308}, {0x4F5, 0x447},
-{0x4F5, 0x308}, {0x4F8, 0x42B}, {0x4F8, 0x308}, {0x4F9, 0x44B}, {0x4F9, 0x308}, {0x622, 0x627}, {0x622, 0x653}, {0x623, 0x627}, {0x623, 0x654}, {0x624, 0x648}, {0x624, 0x654}, {0x625, 0x627},
-{0x625, 0x655}, {0x626, 0x64A}, {0x626, 0x654}, {0x6C0, 0x6D5}, {0x6C0, 0x654}, {0x6C2, 0x6C1}, {0x6C2, 0x654}, {0x6D3, 0x6D2}, {0x6D3, 0x654}, {0x929, 0x928}, {0x929, 0x93C}, {0x931, 0x930},
-{0x931, 0x93C}, {0x934, 0x933}, {0x934, 0x93C}, {0x958, 0x915}, {0x958, 0x93C}, {0x959, 0x916}, {0x959, 0x93C}, {0x95A, 0x917}, {0x95A, 0x93C}, {0x95B, 0x91C}, {0x95B, 0x93C}, {0x95C, 0x921},
-{0x95C, 0x93C}, {0x95D, 0x922}, {0x95D, 0x93C}, {0x95E, 0x92B}, {0x95E, 0x93C}, {0x95F, 0x92F}, {0x95F, 0x93C}, {0x9CB, 0x9C7}, {0x9CB, 0x9BE}, {0x9CC, 0x9C7}, {0x9CC, 0x9D7}, {0x9DC, 0x9A1},
-{0x9DC, 0x9BC}, {0x9DD, 0x9A2}, {0x9DD, 0x9BC}, {0x9DF, 0x9AF}, {0x9DF, 0x9BC}, {0xA33, 0xA32}, {0xA33, 0xA3C}, {0xA36, 0xA38}, {0xA36, 0xA3C}, {0xA59, 0xA16}, {0xA59, 0xA3C}, {0xA5A, 0xA17},
-{0xA5A, 0xA3C}, {0xA5B, 0xA1C}, {0xA5B, 0xA3C}, {0xA5E, 0xA2B}, {0xA5E, 0xA3C}, {0xB48, 0xB47}, {0xB48, 0xB56}, {0xB4B, 0xB47}, {0xB4B, 0xB3E}, {0xB4C, 0xB47}, {0xB4C, 0xB57}, {0xB5C, 0xB21},
-{0xB5C, 0xB3C}, {0xB5D, 0xB22}, {0xB5D, 0xB3C}, {0xB94, 0xB92}, {0xB94, 0xBD7}, {0xBCA, 0xBC6}, {0xBCA, 0xBBE}, {0xBCB, 0xBC7}, {0xBCB, 0xBBE}, {0xBCC, 0xBC6}, {0xBCC, 0xBD7}, {0xC48, 0xC46},
-{0xC48, 0xC56}, {0xCC0, 0xCBF}, {0xCC0, 0xCD5}, {0xCC7, 0xCC6}, {0xCC7, 0xCD5}, {0xCC8, 0xCC6}, {0xCC8, 0xCD6}, {0xCCA, 0xCC6}, {0xCCA, 0xCC2}, {0xCCB, 0xCC6}, {0xCCB, 0xCC2}, {0xCCB, 0xCD5},
-{0xD4A, 0xD46}, {0xD4A, 0xD3E}, {0xD4B, 0xD47}, {0xD4B, 0xD3E}, {0xD4C, 0xD46}, {0xD4C, 0xD57}, {0xDDA, 0xDD9}, {0xDDA, 0xDCA}, {0xDDC, 0xDD9}, {0xDDC, 0xDCF}, {0xDDD, 0xDD9}, {0xDDD, 0xDCF},
-{0xDDD, 0xDCA}, {0xDDE, 0xDD9}, {0xDDE, 0xDDF}, {0xF43, 0xF42}, {0xF43, 0xFB7}, {0xF4D, 0xF4C}, {0xF4D, 0xFB7}, {0xF52, 0xF51}, {0xF52, 0xFB7}, {0xF57, 0xF56}, {0xF57, 0xFB7}, {0xF5C, 0xF5B},
-{0xF5C, 0xFB7}, {0xF69, 0xF40}, {0xF69, 0xFB5}, {0xF73, 0xF71}, {0xF73, 0xF72}, {0xF75, 0xF71}, {0xF75, 0xF74}, {0xF76, 0xFB2}, {0xF76, 0xF80}, {0xF78, 0xFB3}, {0xF78, 0xF80}, {0xF81, 0xF71},
-{0xF81, 0xF80}, {0xF93, 0xF92}, {0xF93, 0xFB7}, {0xF9D, 0xF9C}, {0xF9D, 0xFB7}, {0xFA2, 0xFA1}, {0xFA2, 0xFB7}, {0xFA7, 0xFA6}, {0xFA7, 0xFB7}, {0xFAC, 0xFAB}, {0xFAC, 0xFB7}, {0xFB9, 0xF90},
-{0xFB9, 0xFB5}, {0x1026, 0x1025}, {0x1026, 0x102E}, {0x1B06, 0x1B05}, {0x1B06, 0x1B35}, {0x1B08, 0x1B07}, {0x1B08, 0x1B35}, {0x1B0A, 0x1B09}, {0x1B0A, 0x1B35}, {0x1B0C, 0x1B0B}, {0x1B0C, 0x1B35},
-{0x1B0E, 0x1B0D}, {0x1B0E, 0x1B35}, {0x1B12, 0x1B11}, {0x1B12, 0x1B35}, {0x1B3B, 0x1B3A}, {0x1B3B, 0x1B35}, {0x1B3D, 0x1B3C}, {0x1B3D, 0x1B35}, {0x1B40, 0x1B3E}, {0x1B40, 0x1B35}, {0x1B41, 0x1B3F},
-{0x1B41, 0x1B35}, {0x1B43, 0x1B42}, {0x1B43, 0x1B35}, {0x1E00, 0x41}, {0x1E00, 0x325}, {0x1E01, 0x61}, {0x1E01, 0x325}, {0x1E02, 0x42}, {0x1E02, 0x307}, {0x1E03, 0x62}, {0x1E03, 0x307},
-{0x1E04, 0x42}, {0x1E04, 0x323}, {0x1E05, 0x62}, {0x1E05, 0x323}, {0x1E06, 0x42}, {0x1E06, 0x331}, {0x1E07, 0x62}, {0x1E07, 0x331}, {0x1E08, 0x43}, {0x1E08, 0x327}, {0x1E08, 0x301}, {0x1E09, 0x63},
-{0x1E09, 0x327}, {0x1E09, 0x301}, {0x1E0A, 0x44}, {0x1E0A, 0x307}, {0x1E0B, 0x64}, {0x1E0B, 0x307}, {0x1E0C, 0x44}, {0x1E0C, 0x323}, {0x1E0D, 0x64}, {0x1E0D, 0x323}, {0x1E0E, 0x44}, {0x1E0E, 0x331},
-{0x1E0F, 0x64}, {0x1E0F, 0x331}, {0x1E10, 0x44}, {0x1E10, 0x327}, {0x1E11, 0x64}, {0x1E11, 0x327}, {0x1E12, 0x44}, {0x1E12, 0x32D}, {0x1E13, 0x64}, {0x1E13, 0x32D}, {0x1E14, 0x45}, {0x1E14, 0x304},
-{0x1E14, 0x300}, {0x1E15, 0x65}, {0x1E15, 0x304}, {0x1E15, 0x300}, {0x1E16, 0x45}, {0x1E16, 0x304}, {0x1E16, 0x301}, {0x1E17, 0x65}, {0x1E17, 0x304}, {0x1E17, 0x301}, {0x1E18, 0x45}, {0x1E18, 0x32D},
-{0x1E19, 0x65}, {0x1E19, 0x32D}, {0x1E1A, 0x45}, {0x1E1A, 0x330}, {0x1E1B, 0x65}, {0x1E1B, 0x330}, {0x1E1C, 0x45}, {0x1E1C, 0x327}, {0x1E1C, 0x306}, {0x1E1D, 0x65}, {0x1E1D, 0x327}, {0x1E1D, 0x306},
-{0x1E1E, 0x46}, {0x1E1E, 0x307}, {0x1E1F, 0x66}, {0x1E1F, 0x307}, {0x1E20, 0x47}, {0x1E20, 0x304}, {0x1E21, 0x67}, {0x1E21, 0x304}, {0x1E22, 0x48}, {0x1E22, 0x307}, {0x1E23, 0x68}, {0x1E23, 0x307},
-{0x1E24, 0x48}, {0x1E24, 0x323}, {0x1E25, 0x68}, {0x1E25, 0x323}, {0x1E26, 0x48}, {0x1E26, 0x308}, {0x1E27, 0x68}, {0x1E27, 0x308}, {0x1E28, 0x48}, {0x1E28, 0x327}, {0x1E29, 0x68}, {0x1E29, 0x327},
-{0x1E2A, 0x48}, {0x1E2A, 0x32E}, {0x1E2B, 0x68}, {0x1E2B, 0x32E}, {0x1E2C, 0x49}, {0x1E2C, 0x330}, {0x1E2D, 0x69}, {0x1E2D, 0x330}, {0x1E2E, 0x49}, {0x1E2E, 0x308}, {0x1E2E, 0x301}, {0x1E2F, 0x69},
-{0x1E2F, 0x308}, {0x1E2F, 0x301}, {0x1E30, 0x4B}, {0x1E30, 0x301}, {0x1E31, 0x6B}, {0x1E31, 0x301}, {0x1E32, 0x4B}, {0x1E32, 0x323}, {0x1E33, 0x6B}, {0x1E33, 0x323}, {0x1E34, 0x4B}, {0x1E34, 0x331},
-{0x1E35, 0x6B}, {0x1E35, 0x331}, {0x1E36, 0x4C}, {0x1E36, 0x323}, {0x1E37, 0x6C}, {0x1E37, 0x323}, {0x1E38, 0x4C}, {0x1E38, 0x323}, {0x1E38, 0x304}, {0x1E39, 0x6C}, {0x1E39, 0x323}, {0x1E39, 0x304},
-{0x1E3A, 0x4C}, {0x1E3A, 0x331}, {0x1E3B, 0x6C}, {0x1E3B, 0x331}, {0x1E3C, 0x4C}, {0x1E3C, 0x32D}, {0x1E3D, 0x6C}, {0x1E3D, 0x32D}, {0x1E3E, 0x4D}, {0x1E3E, 0x301}, {0x1E3F, 0x6D}, {0x1E3F, 0x301},
-{0x1E40, 0x4D}, {0x1E40, 0x307}, {0x1E41, 0x6D}, {0x1E41, 0x307}, {0x1E42, 0x4D}, {0x1E42, 0x323}, {0x1E43, 0x6D}, {0x1E43, 0x323}, {0x1E44, 0x4E}, {0x1E44, 0x307}, {0x1E45, 0x6E}, {0x1E45, 0x307},
-{0x1E46, 0x4E}, {0x1E46, 0x323}, {0x1E47, 0x6E}, {0x1E47, 0x323}, {0x1E48, 0x4E}, {0x1E48, 0x331}, {0x1E49, 0x6E}, {0x1E49, 0x331}, {0x1E4A, 0x4E}, {0x1E4A, 0x32D}, {0x1E4B, 0x6E}, {0x1E4B, 0x32D},
-{0x1E4C, 0x4F}, {0x1E4C, 0x303}, {0x1E4C, 0x301}, {0x1E4D, 0x6F}, {0x1E4D, 0x303}, {0x1E4D, 0x301}, {0x1E4E, 0x4F}, {0x1E4E, 0x303}, {0x1E4E, 0x308}, {0x1E4F, 0x6F}, {0x1E4F, 0x303}, {0x1E4F, 0x308},
-{0x1E50, 0x4F}, {0x1E50, 0x304}, {0x1E50, 0x300}, {0x1E51, 0x6F}, {0x1E51, 0x304}, {0x1E51, 0x300}, {0x1E52, 0x4F}, {0x1E52, 0x304}, {0x1E52, 0x301}, {0x1E53, 0x6F}, {0x1E53, 0x304}, {0x1E53, 0x301},
-{0x1E54, 0x50}, {0x1E54, 0x301}, {0x1E55, 0x70}, {0x1E55, 0x301}, {0x1E56, 0x50}, {0x1E56, 0x307}, {0x1E57, 0x70}, {0x1E57, 0x307}, {0x1E58, 0x52}, {0x1E58, 0x307}, {0x1E59, 0x72}, {0x1E59, 0x307},
-{0x1E5A, 0x52}, {0x1E5A, 0x323}, {0x1E5B, 0x72}, {0x1E5B, 0x323}, {0x1E5C, 0x52}, {0x1E5C, 0x323}, {0x1E5C, 0x304}, {0x1E5D, 0x72}, {0x1E5D, 0x323}, {0x1E5D, 0x304}, {0x1E5E, 0x52}, {0x1E5E, 0x331},
-{0x1E5F, 0x72}, {0x1E5F, 0x331}, {0x1E60, 0x53}, {0x1E60, 0x307}, {0x1E61, 0x73}, {0x1E61, 0x307}, {0x1E62, 0x53}, {0x1E62, 0x323}, {0x1E63, 0x73}, {0x1E63, 0x323}, {0x1E64, 0x53}, {0x1E64, 0x301},
-{0x1E64, 0x307}, {0x1E65, 0x73}, {0x1E65, 0x301}, {0x1E65, 0x307}, {0x1E66, 0x53}, {0x1E66, 0x30C}, {0x1E66, 0x307}, {0x1E67, 0x73}, {0x1E67, 0x30C}, {0x1E67, 0x307}, {0x1E68, 0x53}, {0x1E68, 0x323},
-{0x1E68, 0x307}, {0x1E69, 0x73}, {0x1E69, 0x323}, {0x1E69, 0x307}, {0x1E6A, 0x54}, {0x1E6A, 0x307}, {0x1E6B, 0x74}, {0x1E6B, 0x307}, {0x1E6C, 0x54}, {0x1E6C, 0x323}, {0x1E6D, 0x74}, {0x1E6D, 0x323},
-{0x1E6E, 0x54}, {0x1E6E, 0x331}, {0x1E6F, 0x74}, {0x1E6F, 0x331}, {0x1E70, 0x54}, {0x1E70, 0x32D}, {0x1E71, 0x74}, {0x1E71, 0x32D}, {0x1E72, 0x55}, {0x1E72, 0x324}, {0x1E73, 0x75}, {0x1E73, 0x324},
-{0x1E74, 0x55}, {0x1E74, 0x330}, {0x1E75, 0x75}, {0x1E75, 0x330}, {0x1E76, 0x55}, {0x1E76, 0x32D}, {0x1E77, 0x75}, {0x1E77, 0x32D}, {0x1E78, 0x55}, {0x1E78, 0x303}, {0x1E78, 0x301}, {0x1E79, 0x75},
-{0x1E79, 0x303}, {0x1E79, 0x301}, {0x1E7A, 0x55}, {0x1E7A, 0x304}, {0x1E7A, 0x308}, {0x1E7B, 0x75}, {0x1E7B, 0x304}, {0x1E7B, 0x308}, {0x1E7C, 0x56}, {0x1E7C, 0x303}, {0x1E7D, 0x76}, {0x1E7D, 0x303},
-{0x1E7E, 0x56}, {0x1E7E, 0x323}, {0x1E7F, 0x76}, {0x1E7F, 0x323}, {0x1E80, 0x57}, {0x1E80, 0x300}, {0x1E81, 0x77}, {0x1E81, 0x300}, {0x1E82, 0x57}, {0x1E82, 0x301}, {0x1E83, 0x77}, {0x1E83, 0x301},
-{0x1E84, 0x57}, {0x1E84, 0x308}, {0x1E85, 0x77}, {0x1E85, 0x308}, {0x1E86, 0x57}, {0x1E86, 0x307}, {0x1E87, 0x77}, {0x1E87, 0x307}, {0x1E88, 0x57}, {0x1E88, 0x323}, {0x1E89, 0x77}, {0x1E89, 0x323},
-{0x1E8A, 0x58}, {0x1E8A, 0x307}, {0x1E8B, 0x78}, {0x1E8B, 0x307}, {0x1E8C, 0x58}, {0x1E8C, 0x308}, {0x1E8D, 0x78}, {0x1E8D, 0x308}, {0x1E8E, 0x59}, {0x1E8E, 0x307}, {0x1E8F, 0x79}, {0x1E8F, 0x307},
-{0x1E90, 0x5A}, {0x1E90, 0x302}, {0x1E91, 0x7A}, {0x1E91, 0x302}, {0x1E92, 0x5A}, {0x1E92, 0x323}, {0x1E93, 0x7A}, {0x1E93, 0x323}, {0x1E94, 0x5A}, {0x1E94, 0x331}, {0x1E95, 0x7A}, {0x1E95, 0x331},
-{0x1E96, 0x68}, {0x1E96, 0x331}, {0x1E97, 0x74}, {0x1E97, 0x308}, {0x1E98, 0x77}, {0x1E98, 0x30A}, {0x1E99, 0x79}, {0x1E99, 0x30A}, {0x1E9B, 0x17F}, {0x1E9B, 0x307}, {0x1EA0, 0x41}, {0x1EA0, 0x323},
-{0x1EA1, 0x61}, {0x1EA1, 0x323}, {0x1EA2, 0x41}, {0x1EA2, 0x309}, {0x1EA3, 0x61}, {0x1EA3, 0x309}, {0x1EA4, 0x41}, {0x1EA4, 0x302}, {0x1EA4, 0x301}, {0x1EA5, 0x61}, {0x1EA5, 0x302}, {0x1EA5, 0x301},
-{0x1EA6, 0x41}, {0x1EA6, 0x302}, {0x1EA6, 0x300}, {0x1EA7, 0x61}, {0x1EA7, 0x302}, {0x1EA7, 0x300}, {0x1EA8, 0x41}, {0x1EA8, 0x302}, {0x1EA8, 0x309}, {0x1EA9, 0x61}, {0x1EA9, 0x302}, {0x1EA9, 0x309},
-{0x1EAA, 0x41}, {0x1EAA, 0x302}, {0x1EAA, 0x303}, {0x1EAB, 0x61}, {0x1EAB, 0x302}, {0x1EAB, 0x303}, {0x1EAC, 0x41}, {0x1EAC, 0x323}, {0x1EAC, 0x302}, {0x1EAD, 0x61}, {0x1EAD, 0x323}, {0x1EAD, 0x302},
-{0x1EAE, 0x41}, {0x1EAE, 0x306}, {0x1EAE, 0x301}, {0x1EAF, 0x61}, {0x1EAF, 0x306}, {0x1EAF, 0x301}, {0x1EB0, 0x41}, {0x1EB0, 0x306}, {0x1EB0, 0x300}, {0x1EB1, 0x61}, {0x1EB1, 0x306}, {0x1EB1, 0x300},
-{0x1EB2, 0x41}, {0x1EB2, 0x306}, {0x1EB2, 0x309}, {0x1EB3, 0x61}, {0x1EB3, 0x306}, {0x1EB3, 0x309}, {0x1EB4, 0x41}, {0x1EB4, 0x306}, {0x1EB4, 0x303}, {0x1EB5, 0x61}, {0x1EB5, 0x306}, {0x1EB5, 0x303},
-{0x1EB6, 0x41}, {0x1EB6, 0x323}, {0x1EB6, 0x306}, {0x1EB7, 0x61}, {0x1EB7, 0x323}, {0x1EB7, 0x306}, {0x1EB8, 0x45}, {0x1EB8, 0x323}, {0x1EB9, 0x65}, {0x1EB9, 0x323}, {0x1EBA, 0x45}, {0x1EBA, 0x309},
-{0x1EBB, 0x65}, {0x1EBB, 0x309}, {0x1EBC, 0x45}, {0x1EBC, 0x303}, {0x1EBD, 0x65}, {0x1EBD, 0x303}, {0x1EBE, 0x45}, {0x1EBE, 0x302}, {0x1EBE, 0x301}, {0x1EBF, 0x65}, {0x1EBF, 0x302}, {0x1EBF, 0x301},
-{0x1EC0, 0x45}, {0x1EC0, 0x302}, {0x1EC0, 0x300}, {0x1EC1, 0x65}, {0x1EC1, 0x302}, {0x1EC1, 0x300}, {0x1EC2, 0x45}, {0x1EC2, 0x302}, {0x1EC2, 0x309}, {0x1EC3, 0x65}, {0x1EC3, 0x302}, {0x1EC3, 0x309},
-{0x1EC4, 0x45}, {0x1EC4, 0x302}, {0x1EC4, 0x303}, {0x1EC5, 0x65}, {0x1EC5, 0x302}, {0x1EC5, 0x303}, {0x1EC6, 0x45}, {0x1EC6, 0x323}, {0x1EC6, 0x302}, {0x1EC7, 0x65}, {0x1EC7, 0x323}, {0x1EC7, 0x302},
-{0x1EC8, 0x49}, {0x1EC8, 0x309}, {0x1EC9, 0x69}, {0x1EC9, 0x309}, {0x1ECA, 0x49}, {0x1ECA, 0x323}, {0x1ECB, 0x69}, {0x1ECB, 0x323}, {0x1ECC, 0x4F}, {0x1ECC, 0x323}, {0x1ECD, 0x6F}, {0x1ECD, 0x323},
-{0x1ECE, 0x4F}, {0x1ECE, 0x309}, {0x1ECF, 0x6F}, {0x1ECF, 0x309}, {0x1ED0, 0x4F}, {0x1ED0, 0x302}, {0x1ED0, 0x301}, {0x1ED1, 0x6F}, {0x1ED1, 0x302}, {0x1ED1, 0x301}, {0x1ED2, 0x4F}, {0x1ED2, 0x302},
-{0x1ED2, 0x300}, {0x1ED3, 0x6F}, {0x1ED3, 0x302}, {0x1ED3, 0x300}, {0x1ED4, 0x4F}, {0x1ED4, 0x302}, {0x1ED4, 0x309}, {0x1ED5, 0x6F}, {0x1ED5, 0x302}, {0x1ED5, 0x309}, {0x1ED6, 0x4F}, {0x1ED6, 0x302},
-{0x1ED6, 0x303}, {0x1ED7, 0x6F}, {0x1ED7, 0x302}, {0x1ED7, 0x303}, {0x1ED8, 0x4F}, {0x1ED8, 0x323}, {0x1ED8, 0x302}, {0x1ED9, 0x6F}, {0x1ED9, 0x323}, {0x1ED9, 0x302}, {0x1EDA, 0x4F}, {0x1EDA, 0x31B},
-{0x1EDA, 0x301}, {0x1EDB, 0x6F}, {0x1EDB, 0x31B}, {0x1EDB, 0x301}, {0x1EDC, 0x4F}, {0x1EDC, 0x31B}, {0x1EDC, 0x300}, {0x1EDD, 0x6F}, {0x1EDD, 0x31B}, {0x1EDD, 0x300}, {0x1EDE, 0x4F}, {0x1EDE, 0x31B},
-{0x1EDE, 0x309}, {0x1EDF, 0x6F}, {0x1EDF, 0x31B}, {0x1EDF, 0x309}, {0x1EE0, 0x4F}, {0x1EE0, 0x31B}, {0x1EE0, 0x303}, {0x1EE1, 0x6F}, {0x1EE1, 0x31B}, {0x1EE1, 0x303}, {0x1EE2, 0x4F}, {0x1EE2, 0x31B},
-{0x1EE2, 0x323}, {0x1EE3, 0x6F}, {0x1EE3, 0x31B}, {0x1EE3, 0x323}, {0x1EE4, 0x55}, {0x1EE4, 0x323}, {0x1EE5, 0x75}, {0x1EE5, 0x323}, {0x1EE6, 0x55}, {0x1EE6, 0x309}, {0x1EE7, 0x75}, {0x1EE7, 0x309},
-{0x1EE8, 0x55}, {0x1EE8, 0x31B}, {0x1EE8, 0x301}, {0x1EE9, 0x75}, {0x1EE9, 0x31B}, {0x1EE9, 0x301}, {0x1EEA, 0x55}, {0x1EEA, 0x31B}, {0x1EEA, 0x300}, {0x1EEB, 0x75}, {0x1EEB, 0x31B}, {0x1EEB, 0x300},
-{0x1EEC, 0x55}, {0x1EEC, 0x31B}, {0x1EEC, 0x309}, {0x1EED, 0x75}, {0x1EED, 0x31B}, {0x1EED, 0x309}, {0x1EEE, 0x55}, {0x1EEE, 0x31B}, {0x1EEE, 0x303}, {0x1EEF, 0x75}, {0x1EEF, 0x31B}, {0x1EEF, 0x303},
-{0x1EF0, 0x55}, {0x1EF0, 0x31B}, {0x1EF0, 0x323}, {0x1EF1, 0x75}, {0x1EF1, 0x31B}, {0x1EF1, 0x323}, {0x1EF2, 0x59}, {0x1EF2, 0x300}, {0x1EF3, 0x79}, {0x1EF3, 0x300}, {0x1EF4, 0x59}, {0x1EF4, 0x323},
-{0x1EF5, 0x79}, {0x1EF5, 0x323}, {0x1EF6, 0x59}, {0x1EF6, 0x309}, {0x1EF7, 0x79}, {0x1EF7, 0x309}, {0x1EF8, 0x59}, {0x1EF8, 0x303}, {0x1EF9, 0x79}, {0x1EF9, 0x303}, {0x1F00, 0x3B1}, {0x1F00, 0x313},
-{0x1F01, 0x3B1}, {0x1F01, 0x314}, {0x1F02, 0x3B1}, {0x1F02, 0x313}, {0x1F02, 0x300}, {0x1F03, 0x3B1}, {0x1F03, 0x314}, {0x1F03, 0x300}, {0x1F04, 0x3B1}, {0x1F04, 0x313}, {0x1F04, 0x301},
-{0x1F05, 0x3B1}, {0x1F05, 0x314}, {0x1F05, 0x301}, {0x1F06, 0x3B1}, {0x1F06, 0x313}, {0x1F06, 0x342}, {0x1F07, 0x3B1}, {0x1F07, 0x314}, {0x1F07, 0x342}, {0x1F08, 0x391}, {0x1F08, 0x313},
-{0x1F09, 0x391}, {0x1F09, 0x314}, {0x1F0A, 0x391}, {0x1F0A, 0x313}, {0x1F0A, 0x300}, {0x1F0B, 0x391}, {0x1F0B, 0x314}, {0x1F0B, 0x300}, {0x1F0C, 0x391}, {0x1F0C, 0x313}, {0x1F0C, 0x301},
-{0x1F0D, 0x391}, {0x1F0D, 0x314}, {0x1F0D, 0x301}, {0x1F0E, 0x391}, {0x1F0E, 0x313}, {0x1F0E, 0x342}, {0x1F0F, 0x391}, {0x1F0F, 0x314}, {0x1F0F, 0x342}, {0x1F10, 0x3B5}, {0x1F10, 0x313},
-{0x1F11, 0x3B5}, {0x1F11, 0x314}, {0x1F12, 0x3B5}, {0x1F12, 0x313}, {0x1F12, 0x300}, {0x1F13, 0x3B5}, {0x1F13, 0x314}, {0x1F13, 0x300}, {0x1F14, 0x3B5}, {0x1F14, 0x313}, {0x1F14, 0x301},
-{0x1F15, 0x3B5}, {0x1F15, 0x314}, {0x1F15, 0x301}, {0x1F18, 0x395}, {0x1F18, 0x313}, {0x1F19, 0x395}, {0x1F19, 0x314}, {0x1F1A, 0x395}, {0x1F1A, 0x313}, {0x1F1A, 0x300}, {0x1F1B, 0x395},
-{0x1F1B, 0x314}, {0x1F1B, 0x300}, {0x1F1C, 0x395}, {0x1F1C, 0x313}, {0x1F1C, 0x301}, {0x1F1D, 0x395}, {0x1F1D, 0x314}, {0x1F1D, 0x301}, {0x1F20, 0x3B7}, {0x1F20, 0x313}, {0x1F21, 0x3B7},
-{0x1F21, 0x314}, {0x1F22, 0x3B7}, {0x1F22, 0x313}, {0x1F22, 0x300}, {0x1F23, 0x3B7}, {0x1F23, 0x314}, {0x1F23, 0x300}, {0x1F24, 0x3B7}, {0x1F24, 0x313}, {0x1F24, 0x301}, {0x1F25, 0x3B7},
-{0x1F25, 0x314}, {0x1F25, 0x301}, {0x1F26, 0x3B7}, {0x1F26, 0x313}, {0x1F26, 0x342}, {0x1F27, 0x3B7}, {0x1F27, 0x314}, {0x1F27, 0x342}, {0x1F28, 0x397}, {0x1F28, 0x313}, {0x1F29, 0x397},
-{0x1F29, 0x314}, {0x1F2A, 0x397}, {0x1F2A, 0x313}, {0x1F2A, 0x300}, {0x1F2B, 0x397}, {0x1F2B, 0x314}, {0x1F2B, 0x300}, {0x1F2C, 0x397}, {0x1F2C, 0x313}, {0x1F2C, 0x301}, {0x1F2D, 0x397},
-{0x1F2D, 0x314}, {0x1F2D, 0x301}, {0x1F2E, 0x397}, {0x1F2E, 0x313}, {0x1F2E, 0x342}, {0x1F2F, 0x397}, {0x1F2F, 0x314}, {0x1F2F, 0x342}, {0x1F30, 0x3B9}, {0x1F30, 0x313}, {0x1F31, 0x3B9},
-{0x1F31, 0x314}, {0x1F32, 0x3B9}, {0x1F32, 0x313}, {0x1F32, 0x300}, {0x1F33, 0x3B9}, {0x1F33, 0x314}, {0x1F33, 0x300}, {0x1F34, 0x3B9}, {0x1F34, 0x313}, {0x1F34, 0x301}, {0x1F35, 0x3B9},
-{0x1F35, 0x314}, {0x1F35, 0x301}, {0x1F36, 0x3B9}, {0x1F36, 0x313}, {0x1F36, 0x342}, {0x1F37, 0x3B9}, {0x1F37, 0x314}, {0x1F37, 0x342}, {0x1F38, 0x399}, {0x1F38, 0x313}, {0x1F39, 0x399},
-{0x1F39, 0x314}, {0x1F3A, 0x399}, {0x1F3A, 0x313}, {0x1F3A, 0x300}, {0x1F3B, 0x399}, {0x1F3B, 0x314}, {0x1F3B, 0x300}, {0x1F3C, 0x399}, {0x1F3C, 0x313}, {0x1F3C, 0x301}, {0x1F3D, 0x399},
-{0x1F3D, 0x314}, {0x1F3D, 0x301}, {0x1F3E, 0x399}, {0x1F3E, 0x313}, {0x1F3E, 0x342}, {0x1F3F, 0x399}, {0x1F3F, 0x314}, {0x1F3F, 0x342}, {0x1F40, 0x3BF}, {0x1F40, 0x313}, {0x1F41, 0x3BF},
-{0x1F41, 0x314}, {0x1F42, 0x3BF}, {0x1F42, 0x313}, {0x1F42, 0x300}, {0x1F43, 0x3BF}, {0x1F43, 0x314}, {0x1F43, 0x300}, {0x1F44, 0x3BF}, {0x1F44, 0x313}, {0x1F44, 0x301}, {0x1F45, 0x3BF},
-{0x1F45, 0x314}, {0x1F45, 0x301}, {0x1F48, 0x39F}, {0x1F48, 0x313}, {0x1F49, 0x39F}, {0x1F49, 0x314}, {0x1F4A, 0x39F}, {0x1F4A, 0x313}, {0x1F4A, 0x300}, {0x1F4B, 0x39F}, {0x1F4B, 0x314},
-{0x1F4B, 0x300}, {0x1F4C, 0x39F}, {0x1F4C, 0x313}, {0x1F4C, 0x301}, {0x1F4D, 0x39F}, {0x1F4D, 0x314}, {0x1F4D, 0x301}, {0x1F50, 0x3C5}, {0x1F50, 0x313}, {0x1F51, 0x3C5}, {0x1F51, 0x314},
-{0x1F52, 0x3C5}, {0x1F52, 0x313}, {0x1F52, 0x300}, {0x1F53, 0x3C5}, {0x1F53, 0x314}, {0x1F53, 0x300}, {0x1F54, 0x3C5}, {0x1F54, 0x313}, {0x1F54, 0x301}, {0x1F55, 0x3C5}, {0x1F55, 0x314},
-{0x1F55, 0x301}, {0x1F56, 0x3C5}, {0x1F56, 0x313}, {0x1F56, 0x342}, {0x1F57, 0x3C5}, {0x1F57, 0x314}, {0x1F57, 0x342}, {0x1F59, 0x3A5}, {0x1F59, 0x314}, {0x1F5B, 0x3A5}, {0x1F5B, 0x314},
-{0x1F5B, 0x300}, {0x1F5D, 0x3A5}, {0x1F5D, 0x314}, {0x1F5D, 0x301}, {0x1F5F, 0x3A5}, {0x1F5F, 0x314}, {0x1F5F, 0x342}, {0x1F60, 0x3C9}, {0x1F60, 0x313}, {0x1F61, 0x3C9}, {0x1F61, 0x314},
-{0x1F62, 0x3C9}, {0x1F62, 0x313}, {0x1F62, 0x300}, {0x1F63, 0x3C9}, {0x1F63, 0x314}, {0x1F63, 0x300}, {0x1F64, 0x3C9}, {0x1F64, 0x313}, {0x1F64, 0x301}, {0x1F65, 0x3C9}, {0x1F65, 0x314},
-{0x1F65, 0x301}, {0x1F66, 0x3C9}, {0x1F66, 0x313}, {0x1F66, 0x342}, {0x1F67, 0x3C9}, {0x1F67, 0x314}, {0x1F67, 0x342}, {0x1F68, 0x3A9}, {0x1F68, 0x313}, {0x1F69, 0x3A9}, {0x1F69, 0x314},
-{0x1F6A, 0x3A9}, {0x1F6A, 0x313}, {0x1F6A, 0x300}, {0x1F6B, 0x3A9}, {0x1F6B, 0x314}, {0x1F6B, 0x300}, {0x1F6C, 0x3A9}, {0x1F6C, 0x313}, {0x1F6C, 0x301}, {0x1F6D, 0x3A9}, {0x1F6D, 0x314},
-{0x1F6D, 0x301}, {0x1F6E, 0x3A9}, {0x1F6E, 0x313}, {0x1F6E, 0x342}, {0x1F6F, 0x3A9}, {0x1F6F, 0x314}, {0x1F6F, 0x342}, {0x1F70, 0x3B1}, {0x1F70, 0x300}, {0x1F71, 0x3B1}, {0x1F71, 0x301},
-{0x1F72, 0x3B5}, {0x1F72, 0x300}, {0x1F73, 0x3B5}, {0x1F73, 0x301}, {0x1F74, 0x3B7}, {0x1F74, 0x300}, {0x1F75, 0x3B7}, {0x1F75, 0x301}, {0x1F76, 0x3B9}, {0x1F76, 0x300}, {0x1F77, 0x3B9},
-{0x1F77, 0x301}, {0x1F78, 0x3BF}, {0x1F78, 0x300}, {0x1F79, 0x3BF}, {0x1F79, 0x301}, {0x1F7A, 0x3C5}, {0x1F7A, 0x300}, {0x1F7B, 0x3C5}, {0x1F7B, 0x301}, {0x1F7C, 0x3C9}, {0x1F7C, 0x300},
-{0x1F7D, 0x3C9}, {0x1F7D, 0x301}, {0x1F80, 0x3B1}, {0x1F80, 0x313}, {0x1F80, 0x345}, {0x1F81, 0x3B1}, {0x1F81, 0x314}, {0x1F81, 0x345}, {0x1F82, 0x3B1}, {0x1F82, 0x313}, {0x1F82, 0x300},
-{0x1F82, 0x345}, {0x1F83, 0x3B1}, {0x1F83, 0x314}, {0x1F83, 0x300}, {0x1F83, 0x345}, {0x1F84, 0x3B1}, {0x1F84, 0x313}, {0x1F84, 0x301}, {0x1F84, 0x345}, {0x1F85, 0x3B1}, {0x1F85, 0x314},
-{0x1F85, 0x301}, {0x1F85, 0x345}, {0x1F86, 0x3B1}, {0x1F86, 0x313}, {0x1F86, 0x342}, {0x1F86, 0x345}, {0x1F87, 0x3B1}, {0x1F87, 0x314}, {0x1F87, 0x342}, {0x1F87, 0x345}, {0x1F88, 0x391},
-{0x1F88, 0x313}, {0x1F88, 0x345}, {0x1F89, 0x391}, {0x1F89, 0x314}, {0x1F89, 0x345}, {0x1F8A, 0x391}, {0x1F8A, 0x313}, {0x1F8A, 0x300}, {0x1F8A, 0x345}, {0x1F8B, 0x391}, {0x1F8B, 0x314},
-{0x1F8B, 0x300}, {0x1F8B, 0x345}, {0x1F8C, 0x391}, {0x1F8C, 0x313}, {0x1F8C, 0x301}, {0x1F8C, 0x345}, {0x1F8D, 0x391}, {0x1F8D, 0x314}, {0x1F8D, 0x301}, {0x1F8D, 0x345}, {0x1F8E, 0x391},
-{0x1F8E, 0x313}, {0x1F8E, 0x342}, {0x1F8E, 0x345}, {0x1F8F, 0x391}, {0x1F8F, 0x314}, {0x1F8F, 0x342}, {0x1F8F, 0x345}, {0x1F90, 0x3B7}, {0x1F90, 0x313}, {0x1F90, 0x345}, {0x1F91, 0x3B7},
-{0x1F91, 0x314}, {0x1F91, 0x345}, {0x1F92, 0x3B7}, {0x1F92, 0x313}, {0x1F92, 0x300}, {0x1F92, 0x345}, {0x1F93, 0x3B7}, {0x1F93, 0x314}, {0x1F93, 0x300}, {0x1F93, 0x345}, {0x1F94, 0x3B7},
-{0x1F94, 0x313}, {0x1F94, 0x301}, {0x1F94, 0x345}, {0x1F95, 0x3B7}, {0x1F95, 0x314}, {0x1F95, 0x301}, {0x1F95, 0x345}, {0x1F96, 0x3B7}, {0x1F96, 0x313}, {0x1F96, 0x342}, {0x1F96, 0x345},
-{0x1F97, 0x3B7}, {0x1F97, 0x314}, {0x1F97, 0x342}, {0x1F97, 0x345}, {0x1F98, 0x397}, {0x1F98, 0x313}, {0x1F98, 0x345}, {0x1F99, 0x397}, {0x1F99, 0x314}, {0x1F99, 0x345}, {0x1F9A, 0x397},
-{0x1F9A, 0x313}, {0x1F9A, 0x300}, {0x1F9A, 0x345}, {0x1F9B, 0x397}, {0x1F9B, 0x314}, {0x1F9B, 0x300}, {0x1F9B, 0x345}, {0x1F9C, 0x397}, {0x1F9C, 0x313}, {0x1F9C, 0x301}, {0x1F9C, 0x345},
-{0x1F9D, 0x397}, {0x1F9D, 0x314}, {0x1F9D, 0x301}, {0x1F9D, 0x345}, {0x1F9E, 0x397}, {0x1F9E, 0x313}, {0x1F9E, 0x342}, {0x1F9E, 0x345}, {0x1F9F, 0x397}, {0x1F9F, 0x314}, {0x1F9F, 0x342},
-{0x1F9F, 0x345}, {0x1FA0, 0x3C9}, {0x1FA0, 0x313}, {0x1FA0, 0x345}, {0x1FA1, 0x3C9}, {0x1FA1, 0x314}, {0x1FA1, 0x345}, {0x1FA2, 0x3C9}, {0x1FA2, 0x313}, {0x1FA2, 0x300}, {0x1FA2, 0x345},
-{0x1FA3, 0x3C9}, {0x1FA3, 0x314}, {0x1FA3, 0x300}, {0x1FA3, 0x345}, {0x1FA4, 0x3C9}, {0x1FA4, 0x313}, {0x1FA4, 0x301}, {0x1FA4, 0x345}, {0x1FA5, 0x3C9}, {0x1FA5, 0x314}, {0x1FA5, 0x301},
-{0x1FA5, 0x345}, {0x1FA6, 0x3C9}, {0x1FA6, 0x313}, {0x1FA6, 0x342}, {0x1FA6, 0x345}, {0x1FA7, 0x3C9}, {0x1FA7, 0x314}, {0x1FA7, 0x342}, {0x1FA7, 0x345}, {0x1FA8, 0x3A9}, {0x1FA8, 0x313},
-{0x1FA8, 0x345}, {0x1FA9, 0x3A9}, {0x1FA9, 0x314}, {0x1FA9, 0x345}, {0x1FAA, 0x3A9}, {0x1FAA, 0x313}, {0x1FAA, 0x300}, {0x1FAA, 0x345}, {0x1FAB, 0x3A9}, {0x1FAB, 0x314}, {0x1FAB, 0x300},
-{0x1FAB, 0x345}, {0x1FAC, 0x3A9}, {0x1FAC, 0x313}, {0x1FAC, 0x301}, {0x1FAC, 0x345}, {0x1FAD, 0x3A9}, {0x1FAD, 0x314}, {0x1FAD, 0x301}, {0x1FAD, 0x345}, {0x1FAE, 0x3A9}, {0x1FAE, 0x313},
-{0x1FAE, 0x342}, {0x1FAE, 0x345}, {0x1FAF, 0x3A9}, {0x1FAF, 0x314}, {0x1FAF, 0x342}, {0x1FAF, 0x345}, {0x1FB0, 0x3B1}, {0x1FB0, 0x306}, {0x1FB1, 0x3B1}, {0x1FB1, 0x304}, {0x1FB2, 0x3B1},
-{0x1FB2, 0x300}, {0x1FB2, 0x345}, {0x1FB3, 0x3B1}, {0x1FB3, 0x345}, {0x1FB4, 0x3B1}, {0x1FB4, 0x301}, {0x1FB4, 0x345}, {0x1FB6, 0x3B1}, {0x1FB6, 0x342}, {0x1FB7, 0x3B1}, {0x1FB7, 0x342},
-{0x1FB7, 0x345}, {0x1FB8, 0x391}, {0x1FB8, 0x306}, {0x1FB9, 0x391}, {0x1FB9, 0x304}, {0x1FBA, 0x391}, {0x1FBA, 0x300}, {0x1FBB, 0x391}, {0x1FBB, 0x301}, {0x1FBC, 0x391}, {0x1FBC, 0x345},
-{0x1FBE, 0x3B9}, {0x1FC1, 0xA8}, {0x1FC1, 0x342}, {0x1FC2, 0x3B7}, {0x1FC2, 0x300}, {0x1FC2, 0x345}, {0x1FC3, 0x3B7}, {0x1FC3, 0x345}, {0x1FC4, 0x3B7}, {0x1FC4, 0x301}, {0x1FC4, 0x345},
-{0x1FC6, 0x3B7}, {0x1FC6, 0x342}, {0x1FC7, 0x3B7}, {0x1FC7, 0x342}, {0x1FC7, 0x345}, {0x1FC8, 0x395}, {0x1FC8, 0x300}, {0x1FC9, 0x395}, {0x1FC9, 0x301}, {0x1FCA, 0x397}, {0x1FCA, 0x300},
-{0x1FCB, 0x397}, {0x1FCB, 0x301}, {0x1FCC, 0x397}, {0x1FCC, 0x345}, {0x1FCD, 0x1FBF}, {0x1FCD, 0x300}, {0x1FCE, 0x1FBF}, {0x1FCE, 0x301}, {0x1FCF, 0x1FBF}, {0x1FCF, 0x342}, {0x1FD0, 0x3B9},
-{0x1FD0, 0x306}, {0x1FD1, 0x3B9}, {0x1FD1, 0x304}, {0x1FD2, 0x3B9}, {0x1FD2, 0x308}, {0x1FD2, 0x300}, {0x1FD3, 0x3B9}, {0x1FD3, 0x308}, {0x1FD3, 0x301}, {0x1FD6, 0x3B9}, {0x1FD6, 0x342},
-{0x1FD7, 0x3B9}, {0x1FD7, 0x308}, {0x1FD7, 0x342}, {0x1FD8, 0x399}, {0x1FD8, 0x306}, {0x1FD9, 0x399}, {0x1FD9, 0x304}, {0x1FDA, 0x399}, {0x1FDA, 0x300}, {0x1FDB, 0x399}, {0x1FDB, 0x301},
-{0x1FDD, 0x1FFE}, {0x1FDD, 0x300}, {0x1FDE, 0x1FFE}, {0x1FDE, 0x301}, {0x1FDF, 0x1FFE}, {0x1FDF, 0x342}, {0x1FE0, 0x3C5}, {0x1FE0, 0x306}, {0x1FE1, 0x3C5}, {0x1FE1, 0x304}, {0x1FE2, 0x3C5},
-{0x1FE2, 0x308}, {0x1FE2, 0x300}, {0x1FE3, 0x3C5}, {0x1FE3, 0x308}, {0x1FE3, 0x301}, {0x1FE4, 0x3C1}, {0x1FE4, 0x313}, {0x1FE5, 0x3C1}, {0x1FE5, 0x314}, {0x1FE6, 0x3C5}, {0x1FE6, 0x342},
-{0x1FE7, 0x3C5}, {0x1FE7, 0x308}, {0x1FE7, 0x342}, {0x1FE8, 0x3A5}, {0x1FE8, 0x306}, {0x1FE9, 0x3A5}, {0x1FE9, 0x304}, {0x1FEA, 0x3A5}, {0x1FEA, 0x300}, {0x1FEB, 0x3A5}, {0x1FEB, 0x301},
-{0x1FEC, 0x3A1}, {0x1FEC, 0x314}, {0x1FED, 0xA8}, {0x1FED, 0x300}, {0x1FEE, 0xA8}, {0x1FEE, 0x301}, {0x1FEF, 0x60}, {0x1FF2, 0x3C9}, {0x1FF2, 0x300}, {0x1FF2, 0x345}, {0x1FF3, 0x3C9}, {0x1FF3, 0x345},
-{0x1FF4, 0x3C9}, {0x1FF4, 0x301}, {0x1FF4, 0x345}, {0x1FF6, 0x3C9}, {0x1FF6, 0x342}, {0x1FF7, 0x3C9}, {0x1FF7, 0x342}, {0x1FF7, 0x345}, {0x1FF8, 0x39F}, {0x1FF8, 0x300}, {0x1FF9, 0x39F},
-{0x1FF9, 0x301}, {0x1FFA, 0x3A9}, {0x1FFA, 0x300}, {0x1FFB, 0x3A9}, {0x1FFB, 0x301}, {0x1FFC, 0x3A9}, {0x1FFC, 0x345}, {0x1FFD, 0xB4}, {0x2000, 0x2002}, {0x2001, 0x2003}, {0x2126, 0x3A9},
-{0x212A, 0x4B}, {0x212B, 0x41}, {0x212B, 0x30A}, {0x219A, 0x2190}, {0x219A, 0x338}, {0x219B, 0x2192}, {0x219B, 0x338}, {0x21AE, 0x2194}, {0x21AE, 0x338}, {0x21CD, 0x21D0}, {0x21CD, 0x338},
-{0x21CE, 0x21D4}, {0x21CE, 0x338}, {0x21CF, 0x21D2}, {0x21CF, 0x338}, {0x2204, 0x2203}, {0x2204, 0x338}, {0x2209, 0x2208}, {0x2209, 0x338}, {0x220C, 0x220B}, {0x220C, 0x338}, {0x2224, 0x2223},
-{0x2224, 0x338}, {0x2226, 0x2225}, {0x2226, 0x338}, {0x2241, 0x223C}, {0x2241, 0x338}, {0x2244, 0x2243}, {0x2244, 0x338}, {0x2247, 0x2245}, {0x2247, 0x338}, {0x2249, 0x2248}, {0x2249, 0x338},
-{0x2260, 0x3D}, {0x2260, 0x338}, {0x2262, 0x2261}, {0x2262, 0x338}, {0x226D, 0x224D}, {0x226D, 0x338}, {0x226E, 0x3C}, {0x226E, 0x338}, {0x226F, 0x3E}, {0x226F, 0x338}, {0x2270, 0x2264},
-{0x2270, 0x338}, {0x2271, 0x2265}, {0x2271, 0x338}, {0x2274, 0x2272}, {0x2274, 0x338}, {0x2275, 0x2273}, {0x2275, 0x338}, {0x2278, 0x2276}, {0x2278, 0x338}, {0x2279, 0x2277}, {0x2279, 0x338},
-{0x2280, 0x227A}, {0x2280, 0x338}, {0x2281, 0x227B}, {0x2281, 0x338}, {0x2284, 0x2282}, {0x2284, 0x338}, {0x2285, 0x2283}, {0x2285, 0x338}, {0x2288, 0x2286}, {0x2288, 0x338}, {0x2289, 0x2287},
-{0x2289, 0x338}, {0x22AC, 0x22A2}, {0x22AC, 0x338}, {0x22AD, 0x22A8}, {0x22AD, 0x338}, {0x22AE, 0x22A9}, {0x22AE, 0x338}, {0x22AF, 0x22AB}, {0x22AF, 0x338}, {0x22E0, 0x227C}, {0x22E0, 0x338},
-{0x22E1, 0x227D}, {0x22E1, 0x338}, {0x22E2, 0x2291}, {0x22E2, 0x338}, {0x22E3, 0x2292}, {0x22E3, 0x338}, {0x22EA, 0x22B2}, {0x22EA, 0x338}, {0x22EB, 0x22B3}, {0x22EB, 0x338}, {0x22EC, 0x22B4},
-{0x22EC, 0x338}, {0x22ED, 0x22B5}, {0x22ED, 0x338}, {0x2329, 0x3008}, {0x232A, 0x3009}, {0x2ADC, 0x2ADD}, {0x2ADC, 0x338}, {0x304C, 0x304B}, {0x304C, 0x3099}, {0x304E, 0x304D}, {0x304E, 0x3099},
-{0x3050, 0x304F}, {0x3050, 0x3099}, {0x3052, 0x3051}, {0x3052, 0x3099}, {0x3054, 0x3053}, {0x3054, 0x3099}, {0x3056, 0x3055}, {0x3056, 0x3099}, {0x3058, 0x3057}, {0x3058, 0x3099}, {0x305A, 0x3059},
-{0x305A, 0x3099}, {0x305C, 0x305B}, {0x305C, 0x3099}, {0x305E, 0x305D}, {0x305E, 0x3099}, {0x3060, 0x305F}, {0x3060, 0x3099}, {0x3062, 0x3061}, {0x3062, 0x3099}, {0x3065, 0x3064}, {0x3065, 0x3099},
-{0x3067, 0x3066}, {0x3067, 0x3099}, {0x3069, 0x3068}, {0x3069, 0x3099}, {0x3070, 0x306F}, {0x3070, 0x3099}, {0x3071, 0x306F}, {0x3071, 0x309A}, {0x3073, 0x3072}, {0x3073, 0x3099}, {0x3074, 0x3072},
-{0x3074, 0x309A}, {0x3076, 0x3075}, {0x3076, 0x3099}, {0x3077, 0x3075}, {0x3077, 0x309A}, {0x3079, 0x3078}, {0x3079, 0x3099}, {0x307A, 0x3078}, {0x307A, 0x309A}, {0x307C, 0x307B}, {0x307C, 0x3099},
-{0x307D, 0x307B}, {0x307D, 0x309A}, {0x3094, 0x3046}, {0x3094, 0x3099}, {0x309E, 0x309D}, {0x309E, 0x3099}, {0x30AC, 0x30AB}, {0x30AC, 0x3099}, {0x30AE, 0x30AD}, {0x30AE, 0x3099}, {0x30B0, 0x30AF},
-{0x30B0, 0x3099}, {0x30B2, 0x30B1}, {0x30B2, 0x3099}, {0x30B4, 0x30B3}, {0x30B4, 0x3099}, {0x30B6, 0x30B5}, {0x30B6, 0x3099}, {0x30B8, 0x30B7}, {0x30B8, 0x3099}, {0x30BA, 0x30B9}, {0x30BA, 0x3099},
-{0x30BC, 0x30BB}, {0x30BC, 0x3099}, {0x30BE, 0x30BD}, {0x30BE, 0x3099}, {0x30C0, 0x30BF}, {0x30C0, 0x3099}, {0x30C2, 0x30C1}, {0x30C2, 0x3099}, {0x30C5, 0x30C4}, {0x30C5, 0x3099}, {0x30C7, 0x30C6},
-{0x30C7, 0x3099}, {0x30C9, 0x30C8}, {0x30C9, 0x3099}, {0x30D0, 0x30CF}, {0x30D0, 0x3099}, {0x30D1, 0x30CF}, {0x30D1, 0x309A}, {0x30D3, 0x30D2}, {0x30D3, 0x3099}, {0x30D4, 0x30D2}, {0x30D4, 0x309A},
-{0x30D6, 0x30D5}, {0x30D6, 0x3099}, {0x30D7, 0x30D5}, {0x30D7, 0x309A}, {0x30D9, 0x30D8}, {0x30D9, 0x3099}, {0x30DA, 0x30D8}, {0x30DA, 0x309A}, {0x30DC, 0x30DB}, {0x30DC, 0x3099}, {0x30DD, 0x30DB},
-{0x30DD, 0x309A}, {0x30F4, 0x30A6}, {0x30F4, 0x3099}, {0x30F7, 0x30EF}, {0x30F7, 0x3099}, {0x30F8, 0x30F0}, {0x30F8, 0x3099}, {0x30F9, 0x30F1}, {0x30F9, 0x3099}, {0x30FA, 0x30F2}, {0x30FA, 0x3099},
-{0x30FE, 0x30FD}, {0x30FE, 0x3099}, {0xF900, 0x8C48}, {0xF901, 0x66F4}, {0xF902, 0x8ECA}, {0xF903, 0x8CC8}, {0xF904, 0x6ED1}, {0xF905, 0x4E32}, {0xF906, 0x53E5}, {0xF907, 0x9F9C}, {0xF908, 0x9F9C},
-{0xF909, 0x5951}, {0xF90A, 0x91D1}, {0xF90B, 0x5587}, {0xF90C, 0x5948}, {0xF90D, 0x61F6}, {0xF90E, 0x7669}, {0xF90F, 0x7F85}, {0xF910, 0x863F}, {0xF911, 0x87BA}, {0xF912, 0x88F8}, {0xF913, 0x908F},
-{0xF914, 0x6A02}, {0xF915, 0x6D1B}, {0xF916, 0x70D9}, {0xF917, 0x73DE}, {0xF918, 0x843D}, {0xF919, 0x916A}, {0xF91A, 0x99F1}, {0xF91B, 0x4E82}, {0xF91C, 0x5375}, {0xF91D, 0x6B04}, {0xF91E, 0x721B},
-{0xF91F, 0x862D}, {0xF920, 0x9E1E}, {0xF921, 0x5D50}, {0xF922, 0x6FEB}, {0xF923, 0x85CD}, {0xF924, 0x8964}, {0xF925, 0x62C9}, {0xF926, 0x81D8}, {0xF927, 0x881F}, {0xF928, 0x5ECA}, {0xF929, 0x6717},
-{0xF92A, 0x6D6A}, {0xF92B, 0x72FC}, {0xF92C, 0x90CE}, {0xF92D, 0x4F86}, {0xF92E, 0x51B7}, {0xF92F, 0x52DE}, {0xF930, 0x64C4}, {0xF931, 0x6AD3}, {0xF932, 0x7210}, {0xF933, 0x76E7}, {0xF934, 0x8001},
-{0xF935, 0x8606}, {0xF936, 0x865C}, {0xF937, 0x8DEF}, {0xF938, 0x9732}, {0xF939, 0x9B6F}, {0xF93A, 0x9DFA}, {0xF93B, 0x788C}, {0xF93C, 0x797F}, {0xF93D, 0x7DA0}, {0xF93E, 0x83C9}, {0xF93F, 0x9304},
-{0xF940, 0x9E7F}, {0xF941, 0x8AD6}, {0xF942, 0x58DF}, {0xF943, 0x5F04}, {0xF944, 0x7C60}, {0xF945, 0x807E}, {0xF946, 0x7262}, {0xF947, 0x78CA}, {0xF948, 0x8CC2}, {0xF949, 0x96F7}, {0xF94A, 0x58D8},
-{0xF94B, 0x5C62}, {0xF94C, 0x6A13}, {0xF94D, 0x6DDA}, {0xF94E, 0x6F0F}, {0xF94F, 0x7D2F}, {0xF950, 0x7E37}, {0xF951, 0x964B}, {0xF952, 0x52D2}, {0xF953, 0x808B}, {0xF954, 0x51DC}, {0xF955, 0x51CC},
-{0xF956, 0x7A1C}, {0xF957, 0x7DBE}, {0xF958, 0x83F1}, {0xF959, 0x9675}, {0xF95A, 0x8B80}, {0xF95B, 0x62CF}, {0xF95C, 0x6A02}, {0xF95D, 0x8AFE}, {0xF95E, 0x4E39}, {0xF95F, 0x5BE7}, {0xF960, 0x6012},
-{0xF961, 0x7387}, {0xF962, 0x7570}, {0xF963, 0x5317}, {0xF964, 0x78FB}, {0xF965, 0x4FBF}, {0xF966, 0x5FA9}, {0xF967, 0x4E0D}, {0xF968, 0x6CCC}, {0xF969, 0x6578}, {0xF96A, 0x7D22}, {0xF96B, 0x53C3},
-{0xF96C, 0x585E}, {0xF96D, 0x7701}, {0xF96E, 0x8449}, {0xF96F, 0x8AAA}, {0xF970, 0x6BBA}, {0xF971, 0x8FB0}, {0xF972, 0x6C88}, {0xF973, 0x62FE}, {0xF974, 0x82E5}, {0xF975, 0x63A0}, {0xF976, 0x7565},
-{0xF977, 0x4EAE}, {0xF978, 0x5169}, {0xF979, 0x51C9}, {0xF97A, 0x6881}, {0xF97B, 0x7CE7}, {0xF97C, 0x826F}, {0xF97D, 0x8AD2}, {0xF97E, 0x91CF}, {0xF97F, 0x52F5}, {0xF980, 0x5442}, {0xF981, 0x5973},
-{0xF982, 0x5EEC}, {0xF983, 0x65C5}, {0xF984, 0x6FFE}, {0xF985, 0x792A}, {0xF986, 0x95AD}, {0xF987, 0x9A6A}, {0xF988, 0x9E97}, {0xF989, 0x9ECE}, {0xF98A, 0x529B}, {0xF98B, 0x66C6}, {0xF98C, 0x6B77},
-{0xF98D, 0x8F62}, {0xF98E, 0x5E74}, {0xF98F, 0x6190}, {0xF990, 0x6200}, {0xF991, 0x649A}, {0xF992, 0x6F23}, {0xF993, 0x7149}, {0xF994, 0x7489}, {0xF995, 0x79CA}, {0xF996, 0x7DF4}, {0xF997, 0x806F},
-{0xF998, 0x8F26}, {0xF999, 0x84EE}, {0xF99A, 0x9023}, {0xF99B, 0x934A}, {0xF99C, 0x5217}, {0xF99D, 0x52A3}, {0xF99E, 0x54BD}, {0xF99F, 0x70C8}, {0xF9A0, 0x88C2}, {0xF9A1, 0x8AAA}, {0xF9A2, 0x5EC9},
-{0xF9A3, 0x5FF5}, {0xF9A4, 0x637B}, {0xF9A5, 0x6BAE}, {0xF9A6, 0x7C3E}, {0xF9A7, 0x7375}, {0xF9A8, 0x4EE4}, {0xF9A9, 0x56F9}, {0xF9AA, 0x5BE7}, {0xF9AB, 0x5DBA}, {0xF9AC, 0x601C}, {0xF9AD, 0x73B2},
-{0xF9AE, 0x7469}, {0xF9AF, 0x7F9A}, {0xF9B0, 0x8046}, {0xF9B1, 0x9234}, {0xF9B2, 0x96F6}, {0xF9B3, 0x9748}, {0xF9B4, 0x9818}, {0xF9B5, 0x4F8B}, {0xF9B6, 0x79AE}, {0xF9B7, 0x91B4}, {0xF9B8, 0x96B8},
-{0xF9B9, 0x60E1}, {0xF9BA, 0x4E86}, {0xF9BB, 0x50DA}, {0xF9BC, 0x5BEE}, {0xF9BD, 0x5C3F}, {0xF9BE, 0x6599}, {0xF9BF, 0x6A02}, {0xF9C0, 0x71CE}, {0xF9C1, 0x7642}, {0xF9C2, 0x84FC}, {0xF9C3, 0x907C},
-{0xF9C4, 0x9F8D}, {0xF9C5, 0x6688}, {0xF9C6, 0x962E}, {0xF9C7, 0x5289}, {0xF9C8, 0x677B}, {0xF9C9, 0x67F3}, {0xF9CA, 0x6D41}, {0xF9CB, 0x6E9C}, {0xF9CC, 0x7409}, {0xF9CD, 0x7559}, {0xF9CE, 0x786B},
-{0xF9CF, 0x7D10}, {0xF9D0, 0x985E}, {0xF9D1, 0x516D}, {0xF9D2, 0x622E}, {0xF9D3, 0x9678}, {0xF9D4, 0x502B}, {0xF9D5, 0x5D19}, {0xF9D6, 0x6DEA}, {0xF9D7, 0x8F2A}, {0xF9D8, 0x5F8B}, {0xF9D9, 0x6144},
-{0xF9DA, 0x6817}, {0xF9DB, 0x7387}, {0xF9DC, 0x9686}, {0xF9DD, 0x5229}, {0xF9DE, 0x540F}, {0xF9DF, 0x5C65}, {0xF9E0, 0x6613}, {0xF9E1, 0x674E}, {0xF9E2, 0x68A8}, {0xF9E3, 0x6CE5}, {0xF9E4, 0x7406},
-{0xF9E5, 0x75E2}, {0xF9E6, 0x7F79}, {0xF9E7, 0x88CF}, {0xF9E8, 0x88E1}, {0xF9E9, 0x91CC}, {0xF9EA, 0x96E2}, {0xF9EB, 0x533F}, {0xF9EC, 0x6EBA}, {0xF9ED, 0x541D}, {0xF9EE, 0x71D0}, {0xF9EF, 0x7498},
-{0xF9F0, 0x85FA}, {0xF9F1, 0x96A3}, {0xF9F2, 0x9C57}, {0xF9F3, 0x9E9F}, {0xF9F4, 0x6797}, {0xF9F5, 0x6DCB}, {0xF9F6, 0x81E8}, {0xF9F7, 0x7ACB}, {0xF9F8, 0x7B20}, {0xF9F9, 0x7C92}, {0xF9FA, 0x72C0},
-{0xF9FB, 0x7099}, {0xF9FC, 0x8B58}, {0xF9FD, 0x4EC0}, {0xF9FE, 0x8336}, {0xF9FF, 0x523A}, {0xFA00, 0x5207}, {0xFA01, 0x5EA6}, {0xFA02, 0x62D3}, {0xFA03, 0x7CD6}, {0xFA04, 0x5B85}, {0xFA05, 0x6D1E},
-{0xFA06, 0x66B4}, {0xFA07, 0x8F3B}, {0xFA08, 0x884C}, {0xFA09, 0x964D}, {0xFA0A, 0x898B}, {0xFA0B, 0x5ED3}, {0xFA0C, 0x5140}, {0xFA0D, 0x55C0}, {0xFA10, 0x585A}, {0xFA12, 0x6674}, {0xFA15, 0x51DE},
-{0xFA16, 0x732A}, {0xFA17, 0x76CA}, {0xFA18, 0x793C}, {0xFA19, 0x795E}, {0xFA1A, 0x7965}, {0xFA1B, 0x798F}, {0xFA1C, 0x9756}, {0xFA1D, 0x7CBE}, {0xFA1E, 0x7FBD}, {0xFA20, 0x8612}, {0xFA22, 0x8AF8},
-{0xFA25, 0x9038}, {0xFA26, 0x90FD}, {0xFA2A, 0x98EF}, {0xFA2B, 0x98FC}, {0xFA2C, 0x9928}, {0xFA2D, 0x9DB4}, {0xFA2E, 0x90DE}, {0xFA2F, 0x96B7}, {0xFA30, 0x4FAE}, {0xFA31, 0x50E7}, {0xFA32, 0x514D},
-{0xFA33, 0x52C9}, {0xFA34, 0x52E4}, {0xFA35, 0x5351}, {0xFA36, 0x559D}, {0xFA37, 0x5606}, {0xFA38, 0x5668}, {0xFA39, 0x5840}, {0xFA3A, 0x58A8}, {0xFA3B, 0x5C64}, {0xFA3C, 0x5C6E}, {0xFA3D, 0x6094},
-{0xFA3E, 0x6168}, {0xFA3F, 0x618E}, {0xFA40, 0x61F2}, {0xFA41, 0x654F}, {0xFA42, 0x65E2}, {0xFA43, 0x6691}, {0xFA44, 0x6885}, {0xFA45, 0x6D77}, {0xFA46, 0x6E1A}, {0xFA47, 0x6F22}, {0xFA48, 0x716E},
-{0xFA49, 0x722B}, {0xFA4A, 0x7422}, {0xFA4B, 0x7891}, {0xFA4C, 0x793E}, {0xFA4D, 0x7949}, {0xFA4E, 0x7948}, {0xFA4F, 0x7950}, {0xFA50, 0x7956}, {0xFA51, 0x795D}, {0xFA52, 0x798D}, {0xFA53, 0x798E},
-{0xFA54, 0x7A40}, {0xFA55, 0x7A81}, {0xFA56, 0x7BC0}, {0xFA57, 0x7DF4}, {0xFA58, 0x7E09}, {0xFA59, 0x7E41}, {0xFA5A, 0x7F72}, {0xFA5B, 0x8005}, {0xFA5C, 0x81ED}, {0xFA5D, 0x8279}, {0xFA5E, 0x8279},
-{0xFA5F, 0x8457}, {0xFA60, 0x8910}, {0xFA61, 0x8996}, {0xFA62, 0x8B01}, {0xFA63, 0x8B39}, {0xFA64, 0x8CD3}, {0xFA65, 0x8D08}, {0xFA66, 0x8FB6}, {0xFA67, 0x9038}, {0xFA68, 0x96E3}, {0xFA69, 0x97FF},
-{0xFA6A, 0x983B}, {0xFA6B, 0x6075}, {0xFA6C, 0x242EE}, {0xFA6D, 0x8218}, {0xFA70, 0x4E26}, {0xFA71, 0x51B5}, {0xFA72, 0x5168}, {0xFA73, 0x4F80}, {0xFA74, 0x5145}, {0xFA75, 0x5180}, {0xFA76, 0x52C7},
-{0xFA77, 0x52FA}, {0xFA78, 0x559D}, {0xFA79, 0x5555}, {0xFA7A, 0x5599}, {0xFA7B, 0x55E2}, {0xFA7C, 0x585A}, {0xFA7D, 0x58B3}, {0xFA7E, 0x5944}, {0xFA7F, 0x5954}, {0xFA80, 0x5A62}, {0xFA81, 0x5B28},
-{0xFA82, 0x5ED2}, {0xFA83, 0x5ED9}, {0xFA84, 0x5F69}, {0xFA85, 0x5FAD}, {0xFA86, 0x60D8}, {0xFA87, 0x614E}, {0xFA88, 0x6108}, {0xFA89, 0x618E}, {0xFA8A, 0x6160}, {0xFA8B, 0x61F2}, {0xFA8C, 0x6234},
-{0xFA8D, 0x63C4}, {0xFA8E, 0x641C}, {0xFA8F, 0x6452}, {0xFA90, 0x6556}, {0xFA91, 0x6674}, {0xFA92, 0x6717}, {0xFA93, 0x671B}, {0xFA94, 0x6756}, {0xFA95, 0x6B79}, {0xFA96, 0x6BBA}, {0xFA97, 0x6D41},
-{0xFA98, 0x6EDB}, {0xFA99, 0x6ECB}, {0xFA9A, 0x6F22}, {0xFA9B, 0x701E}, {0xFA9C, 0x716E}, {0xFA9D, 0x77A7}, {0xFA9E, 0x7235}, {0xFA9F, 0x72AF}, {0xFAA0, 0x732A}, {0xFAA1, 0x7471}, {0xFAA2, 0x7506},
-{0xFAA3, 0x753B}, {0xFAA4, 0x761D}, {0xFAA5, 0x761F}, {0xFAA6, 0x76CA}, {0xFAA7, 0x76DB}, {0xFAA8, 0x76F4}, {0xFAA9, 0x774A}, {0xFAAA, 0x7740}, {0xFAAB, 0x78CC}, {0xFAAC, 0x7AB1}, {0xFAAD, 0x7BC0},
-{0xFAAE, 0x7C7B}, {0xFAAF, 0x7D5B}, {0xFAB0, 0x7DF4}, {0xFAB1, 0x7F3E}, {0xFAB2, 0x8005}, {0xFAB3, 0x8352}, {0xFAB4, 0x83EF}, {0xFAB5, 0x8779}, {0xFAB6, 0x8941}, {0xFAB7, 0x8986}, {0xFAB8, 0x8996},
-{0xFAB9, 0x8ABF}, {0xFABA, 0x8AF8}, {0xFABB, 0x8ACB}, {0xFABC, 0x8B01}, {0xFABD, 0x8AFE}, {0xFABE, 0x8AED}, {0xFABF, 0x8B39}, {0xFAC0, 0x8B8A}, {0xFAC1, 0x8D08}, {0xFAC2, 0x8F38}, {0xFAC3, 0x9072},
-{0xFAC4, 0x9199}, {0xFAC5, 0x9276}, {0xFAC6, 0x967C}, {0xFAC7, 0x96E3}, {0xFAC8, 0x9756}, {0xFAC9, 0x97DB}, {0xFACA, 0x97FF}, {0xFACB, 0x980B}, {0xFACC, 0x983B}, {0xFACD, 0x9B12}, {0xFACE, 0x9F9C},
-{0xFACF, 0x2284A}, {0xFAD0, 0x22844}, {0xFAD1, 0x233D5}, {0xFAD2, 0x3B9D}, {0xFAD3, 0x4018}, {0xFAD4, 0x4039}, {0xFAD5, 0x25249}, {0xFAD6, 0x25CD0}, {0xFAD7, 0x27ED3}, {0xFAD8, 0x9F43},
-{0xFAD9, 0x9F8E}, {0xFB1D, 0x5D9}, {0xFB1D, 0x5B4}, {0xFB1F, 0x5F2}, {0xFB1F, 0x5B7}, {0xFB2A, 0x5E9}, {0xFB2A, 0x5C1}, {0xFB2B, 0x5E9}, {0xFB2B, 0x5C2}, {0xFB2C, 0x5E9}, {0xFB2C, 0x5BC},
-{0xFB2C, 0x5C1}, {0xFB2D, 0x5E9}, {0xFB2D, 0x5BC}, {0xFB2D, 0x5C2}, {0xFB2E, 0x5D0}, {0xFB2E, 0x5B7}, {0xFB2F, 0x5D0}, {0xFB2F, 0x5B8}, {0xFB30, 0x5D0}, {0xFB30, 0x5BC}, {0xFB31, 0x5D1},
-{0xFB31, 0x5BC}, {0xFB32, 0x5D2}, {0xFB32, 0x5BC}, {0xFB33, 0x5D3}, {0xFB33, 0x5BC}, {0xFB34, 0x5D4}, {0xFB34, 0x5BC}, {0xFB35, 0x5D5}, {0xFB35, 0x5BC}, {0xFB36, 0x5D6}, {0xFB36, 0x5BC},
-{0xFB38, 0x5D8}, {0xFB38, 0x5BC}, {0xFB39, 0x5D9}, {0xFB39, 0x5BC}, {0xFB3A, 0x5DA}, {0xFB3A, 0x5BC}, {0xFB3B, 0x5DB}, {0xFB3B, 0x5BC}, {0xFB3C, 0x5DC}, {0xFB3C, 0x5BC}, {0xFB3E, 0x5DE},
-{0xFB3E, 0x5BC}, {0xFB40, 0x5E0}, {0xFB40, 0x5BC}, {0xFB41, 0x5E1}, {0xFB41, 0x5BC}, {0xFB43, 0x5E3}, {0xFB43, 0x5BC}, {0xFB44, 0x5E4}, {0xFB44, 0x5BC}, {0xFB46, 0x5E6}, {0xFB46, 0x5BC},
-{0xFB47, 0x5E7}, {0xFB47, 0x5BC}, {0xFB48, 0x5E8}, {0xFB48, 0x5BC}, {0xFB49, 0x5E9}, {0xFB49, 0x5BC}, {0xFB4A, 0x5EA}, {0xFB4A, 0x5BC}, {0xFB4B, 0x5D5}, {0xFB4B, 0x5B9}, {0xFB4C, 0x5D1},
-{0xFB4C, 0x5BF}, {0xFB4D, 0x5DB}, {0xFB4D, 0x5BF}, {0xFB4E, 0x5E4}, {0xFB4E, 0x5BF}, {0x1109A, 0x11099}, {0x1109A, 0x110BA}, {0x1109C, 0x1109B}, {0x1109C, 0x110BA}, {0x110AB, 0x110A5},
-{0x110AB, 0x110BA}, {0x1112E, 0x11131}, {0x1112E, 0x11127}, {0x1112F, 0x11132}, {0x1112F, 0x11127}, {0x1134B, 0x11347}, {0x1134B, 0x1133E}, {0x1134C, 0x11347}, {0x1134C, 0x11357}, {0x114BB, 0x114B9},
-{0x114BB, 0x114BA}, {0x114BC, 0x114B9}, {0x114BC, 0x114B0}, {0x114BE, 0x114B9}, {0x114BE, 0x114BD}, {0x115BA, 0x115B8}, {0x115BA, 0x115AF}, {0x115BB, 0x115B9}, {0x115BB, 0x115AF}, {0x1D15E, 0x1D157},
-{0x1D15E, 0x1D165}, {0x1D15F, 0x1D158}, {0x1D15F, 0x1D165}, {0x1D160, 0x1D158}, {0x1D160, 0x1D165}, {0x1D160, 0x1D16E}, {0x1D161, 0x1D158}, {0x1D161, 0x1D165}, {0x1D161, 0x1D16F}, {0x1D162, 0x1D158},
-{0x1D162, 0x1D165}, {0x1D162, 0x1D170}, {0x1D163, 0x1D158}, {0x1D163, 0x1D165}, {0x1D163, 0x1D171}, {0x1D164, 0x1D158}, {0x1D164, 0x1D165}, {0x1D164, 0x1D172}, {0x1D1BB, 0x1D1B9}, {0x1D1BB, 0x1D165},
-{0x1D1BC, 0x1D1BA}, {0x1D1BC, 0x1D165}, {0x1D1BD, 0x1D1B9}, {0x1D1BD, 0x1D165}, {0x1D1BD, 0x1D16E}, {0x1D1BE, 0x1D1BA}, {0x1D1BE, 0x1D165}, {0x1D1BE, 0x1D16E}, {0x1D1BF, 0x1D1B9}, {0x1D1BF, 0x1D165},
-{0x1D1BF, 0x1D16F}, {0x1D1C0, 0x1D1BA}, {0x1D1C0, 0x1D165}, {0x1D1C0, 0x1D16F}, {0x2F800, 0x4E3D}, {0x2F801, 0x4E38}, {0x2F802, 0x4E41}, {0x2F803, 0x20122}, {0x2F804, 0x4F60}, {0x2F805, 0x4FAE},
-{0x2F806, 0x4FBB}, {0x2F807, 0x5002}, {0x2F808, 0x507A}, {0x2F809, 0x5099}, {0x2F80A, 0x50E7}, {0x2F80B, 0x50CF}, {0x2F80C, 0x349E}, {0x2F80D, 0x2063A}, {0x2F80E, 0x514D}, {0x2F80F, 0x5154},
-{0x2F810, 0x5164}, {0x2F811, 0x5177}, {0x2F812, 0x2051C}, {0x2F813, 0x34B9}, {0x2F814, 0x5167}, {0x2F815, 0x518D}, {0x2F816, 0x2054B}, {0x2F817, 0x5197}, {0x2F818, 0x51A4}, {0x2F819, 0x4ECC},
-{0x2F81A, 0x51AC}, {0x2F81B, 0x51B5}, {0x2F81C, 0x291DF}, {0x2F81D, 0x51F5}, {0x2F81E, 0x5203}, {0x2F81F, 0x34DF}, {0x2F820, 0x523B}, {0x2F821, 0x5246}, {0x2F822, 0x5272}, {0x2F823, 0x5277},
-{0x2F824, 0x3515}, {0x2F825, 0x52C7}, {0x2F826, 0x52C9}, {0x2F827, 0x52E4}, {0x2F828, 0x52FA}, {0x2F829, 0x5305}, {0x2F82A, 0x5306}, {0x2F82B, 0x5317}, {0x2F82C, 0x5349}, {0x2F82D, 0x5351},
-{0x2F82E, 0x535A}, {0x2F82F, 0x5373}, {0x2F830, 0x537D}, {0x2F831, 0x537F}, {0x2F832, 0x537F}, {0x2F833, 0x537F}, {0x2F834, 0x20A2C}, {0x2F835, 0x7070}, {0x2F836, 0x53CA}, {0x2F837, 0x53DF},
-{0x2F838, 0x20B63}, {0x2F839, 0x53EB}, {0x2F83A, 0x53F1}, {0x2F83B, 0x5406}, {0x2F83C, 0x549E}, {0x2F83D, 0x5438}, {0x2F83E, 0x5448}, {0x2F83F, 0x5468}, {0x2F840, 0x54A2}, {0x2F841, 0x54F6},
-{0x2F842, 0x5510}, {0x2F843, 0x5553}, {0x2F844, 0x5563}, {0x2F845, 0x5584}, {0x2F846, 0x5584}, {0x2F847, 0x5599}, {0x2F848, 0x55AB}, {0x2F849, 0x55B3}, {0x2F84A, 0x55C2}, {0x2F84B, 0x5716},
-{0x2F84C, 0x5606}, {0x2F84D, 0x5717}, {0x2F84E, 0x5651}, {0x2F84F, 0x5674}, {0x2F850, 0x5207}, {0x2F851, 0x58EE}, {0x2F852, 0x57CE}, {0x2F853, 0x57F4}, {0x2F854, 0x580D}, {0x2F855, 0x578B},
-{0x2F856, 0x5832}, {0x2F857, 0x5831}, {0x2F858, 0x58AC}, {0x2F859, 0x214E4}, {0x2F85A, 0x58F2}, {0x2F85B, 0x58F7}, {0x2F85C, 0x5906}, {0x2F85D, 0x591A}, {0x2F85E, 0x5922}, {0x2F85F, 0x5962},
-{0x2F860, 0x216A8}, {0x2F861, 0x216EA}, {0x2F862, 0x59EC}, {0x2F863, 0x5A1B}, {0x2F864, 0x5A27}, {0x2F865, 0x59D8}, {0x2F866, 0x5A66}, {0x2F867, 0x36EE}, {0x2F868, 0x36FC}, {0x2F869, 0x5B08},
-{0x2F86A, 0x5B3E}, {0x2F86B, 0x5B3E}, {0x2F86C, 0x219C8}, {0x2F86D, 0x5BC3}, {0x2F86E, 0x5BD8}, {0x2F86F, 0x5BE7}, {0x2F870, 0x5BF3}, {0x2F871, 0x21B18}, {0x2F872, 0x5BFF}, {0x2F873, 0x5C06},
-{0x2F874, 0x5F53}, {0x2F875, 0x5C22}, {0x2F876, 0x3781}, {0x2F877, 0x5C60}, {0x2F878, 0x5C6E}, {0x2F879, 0x5CC0}, {0x2F87A, 0x5C8D}, {0x2F87B, 0x21DE4}, {0x2F87C, 0x5D43}, {0x2F87D, 0x21DE6},
-{0x2F87E, 0x5D6E}, {0x2F87F, 0x5D6B}, {0x2F880, 0x5D7C}, {0x2F881, 0x5DE1}, {0x2F882, 0x5DE2}, {0x2F883, 0x382F}, {0x2F884, 0x5DFD}, {0x2F885, 0x5E28}, {0x2F886, 0x5E3D}, {0x2F887, 0x5E69},
-{0x2F888, 0x3862}, {0x2F889, 0x22183}, {0x2F88A, 0x387C}, {0x2F88B, 0x5EB0}, {0x2F88C, 0x5EB3}, {0x2F88D, 0x5EB6}, {0x2F88E, 0x5ECA}, {0x2F88F, 0x2A392}, {0x2F890, 0x5EFE}, {0x2F891, 0x22331},
-{0x2F892, 0x22331}, {0x2F893, 0x8201}, {0x2F894, 0x5F22}, {0x2F895, 0x5F22}, {0x2F896, 0x38C7}, {0x2F897, 0x232B8}, {0x2F898, 0x261DA}, {0x2F899, 0x5F62}, {0x2F89A, 0x5F6B}, {0x2F89B, 0x38E3},
-{0x2F89C, 0x5F9A}, {0x2F89D, 0x5FCD}, {0x2F89E, 0x5FD7}, {0x2F89F, 0x5FF9}, {0x2F8A0, 0x6081}, {0x2F8A1, 0x393A}, {0x2F8A2, 0x391C}, {0x2F8A3, 0x6094}, {0x2F8A4, 0x226D4}, {0x2F8A5, 0x60C7},
-{0x2F8A6, 0x6148}, {0x2F8A7, 0x614C}, {0x2F8A8, 0x614E}, {0x2F8A9, 0x614C}, {0x2F8AA, 0x617A}, {0x2F8AB, 0x618E}, {0x2F8AC, 0x61B2}, {0x2F8AD, 0x61A4}, {0x2F8AE, 0x61AF}, {0x2F8AF, 0x61DE},
-{0x2F8B0, 0x61F2}, {0x2F8B1, 0x61F6}, {0x2F8B2, 0x6210}, {0x2F8B3, 0x621B}, {0x2F8B4, 0x625D}, {0x2F8B5, 0x62B1}, {0x2F8B6, 0x62D4}, {0x2F8B7, 0x6350}, {0x2F8B8, 0x22B0C}, {0x2F8B9, 0x633D},
-{0x2F8BA, 0x62FC}, {0x2F8BB, 0x6368}, {0x2F8BC, 0x6383}, {0x2F8BD, 0x63E4}, {0x2F8BE, 0x22BF1}, {0x2F8BF, 0x6422}, {0x2F8C0, 0x63C5}, {0x2F8C1, 0x63A9}, {0x2F8C2, 0x3A2E}, {0x2F8C3, 0x6469},
-{0x2F8C4, 0x647E}, {0x2F8C5, 0x649D}, {0x2F8C6, 0x6477}, {0x2F8C7, 0x3A6C}, {0x2F8C8, 0x654F}, {0x2F8C9, 0x656C}, {0x2F8CA, 0x2300A}, {0x2F8CB, 0x65E3}, {0x2F8CC, 0x66F8}, {0x2F8CD, 0x6649},
-{0x2F8CE, 0x3B19}, {0x2F8CF, 0x6691}, {0x2F8D0, 0x3B08}, {0x2F8D1, 0x3AE4}, {0x2F8D2, 0x5192}, {0x2F8D3, 0x5195}, {0x2F8D4, 0x6700}, {0x2F8D5, 0x669C}, {0x2F8D6, 0x80AD}, {0x2F8D7, 0x43D9},
-{0x2F8D8, 0x6717}, {0x2F8D9, 0x671B}, {0x2F8DA, 0x6721}, {0x2F8DB, 0x675E}, {0x2F8DC, 0x6753}, {0x2F8DD, 0x233C3}, {0x2F8DE, 0x3B49}, {0x2F8DF, 0x67FA}, {0x2F8E0, 0x6785}, {0x2F8E1, 0x6852},
-{0x2F8E2, 0x6885}, {0x2F8E3, 0x2346D}, {0x2F8E4, 0x688E}, {0x2F8E5, 0x681F}, {0x2F8E6, 0x6914}, {0x2F8E7, 0x3B9D}, {0x2F8E8, 0x6942}, {0x2F8E9, 0x69A3}, {0x2F8EA, 0x69EA}, {0x2F8EB, 0x6AA8},
-{0x2F8EC, 0x236A3}, {0x2F8ED, 0x6ADB}, {0x2F8EE, 0x3C18}, {0x2F8EF, 0x6B21}, {0x2F8F0, 0x238A7}, {0x2F8F1, 0x6B54}, {0x2F8F2, 0x3C4E}, {0x2F8F3, 0x6B72}, {0x2F8F4, 0x6B9F}, {0x2F8F5, 0x6BBA},
-{0x2F8F6, 0x6BBB}, {0x2F8F7, 0x23A8D}, {0x2F8F8, 0x21D0B}, {0x2F8F9, 0x23AFA}, {0x2F8FA, 0x6C4E}, {0x2F8FB, 0x23CBC}, {0x2F8FC, 0x6CBF}, {0x2F8FD, 0x6CCD}, {0x2F8FE, 0x6C67}, {0x2F8FF, 0x6D16},
-{0x2F900, 0x6D3E}, {0x2F901, 0x6D77}, {0x2F902, 0x6D41}, {0x2F903, 0x6D69}, {0x2F904, 0x6D78}, {0x2F905, 0x6D85}, {0x2F906, 0x23D1E}, {0x2F907, 0x6D34}, {0x2F908, 0x6E2F}, {0x2F909, 0x6E6E},
-{0x2F90A, 0x3D33}, {0x2F90B, 0x6ECB}, {0x2F90C, 0x6EC7}, {0x2F90D, 0x23ED1}, {0x2F90E, 0x6DF9}, {0x2F90F, 0x6F6E}, {0x2F910, 0x23F5E}, {0x2F911, 0x23F8E}, {0x2F912, 0x6FC6}, {0x2F913, 0x7039},
-{0x2F914, 0x701E}, {0x2F915, 0x701B}, {0x2F916, 0x3D96}, {0x2F917, 0x704A}, {0x2F918, 0x707D}, {0x2F919, 0x7077}, {0x2F91A, 0x70AD}, {0x2F91B, 0x20525}, {0x2F91C, 0x7145}, {0x2F91D, 0x24263},
-{0x2F91E, 0x719C}, {0x2F91F, 0x243AB}, {0x2F920, 0x7228}, {0x2F921, 0x7235}, {0x2F922, 0x7250}, {0x2F923, 0x24608}, {0x2F924, 0x7280}, {0x2F925, 0x7295}, {0x2F926, 0x24735}, {0x2F927, 0x24814},
-{0x2F928, 0x737A}, {0x2F929, 0x738B}, {0x2F92A, 0x3EAC}, {0x2F92B, 0x73A5}, {0x2F92C, 0x3EB8}, {0x2F92D, 0x3EB8}, {0x2F92E, 0x7447}, {0x2F92F, 0x745C}, {0x2F930, 0x7471}, {0x2F931, 0x7485},
-{0x2F932, 0x74CA}, {0x2F933, 0x3F1B}, {0x2F934, 0x7524}, {0x2F935, 0x24C36}, {0x2F936, 0x753E}, {0x2F937, 0x24C92}, {0x2F938, 0x7570}, {0x2F939, 0x2219F}, {0x2F93A, 0x7610}, {0x2F93B, 0x24FA1},
-{0x2F93C, 0x24FB8}, {0x2F93D, 0x25044}, {0x2F93E, 0x3FFC}, {0x2F93F, 0x4008}, {0x2F940, 0x76F4}, {0x2F941, 0x250F3}, {0x2F942, 0x250F2}, {0x2F943, 0x25119}, {0x2F944, 0x25133}, {0x2F945, 0x771E},
-{0x2F946, 0x771F}, {0x2F947, 0x771F}, {0x2F948, 0x774A}, {0x2F949, 0x4039}, {0x2F94A, 0x778B}, {0x2F94B, 0x4046}, {0x2F94C, 0x4096}, {0x2F94D, 0x2541D}, {0x2F94E, 0x784E}, {0x2F94F, 0x788C},
-{0x2F950, 0x78CC}, {0x2F951, 0x40E3}, {0x2F952, 0x25626}, {0x2F953, 0x7956}, {0x2F954, 0x2569A}, {0x2F955, 0x256C5}, {0x2F956, 0x798F}, {0x2F957, 0x79EB}, {0x2F958, 0x412F}, {0x2F959, 0x7A40},
-{0x2F95A, 0x7A4A}, {0x2F95B, 0x7A4F}, {0x2F95C, 0x2597C}, {0x2F95D, 0x25AA7}, {0x2F95E, 0x25AA7}, {0x2F95F, 0x7AEE}, {0x2F960, 0x4202}, {0x2F961, 0x25BAB}, {0x2F962, 0x7BC6}, {0x2F963, 0x7BC9},
-{0x2F964, 0x4227}, {0x2F965, 0x25C80}, {0x2F966, 0x7CD2}, {0x2F967, 0x42A0}, {0x2F968, 0x7CE8}, {0x2F969, 0x7CE3}, {0x2F96A, 0x7D00}, {0x2F96B, 0x25F86}, {0x2F96C, 0x7D63}, {0x2F96D, 0x4301},
-{0x2F96E, 0x7DC7}, {0x2F96F, 0x7E02}, {0x2F970, 0x7E45}, {0x2F971, 0x4334}, {0x2F972, 0x26228}, {0x2F973, 0x26247}, {0x2F974, 0x4359}, {0x2F975, 0x262D9}, {0x2F976, 0x7F7A}, {0x2F977, 0x2633E},
-{0x2F978, 0x7F95}, {0x2F979, 0x7FFA}, {0x2F97A, 0x8005}, {0x2F97B, 0x264DA}, {0x2F97C, 0x26523}, {0x2F97D, 0x8060}, {0x2F97E, 0x265A8}, {0x2F97F, 0x8070}, {0x2F980, 0x2335F}, {0x2F981, 0x43D5},
-{0x2F982, 0x80B2}, {0x2F983, 0x8103}, {0x2F984, 0x440B}, {0x2F985, 0x813E}, {0x2F986, 0x5AB5}, {0x2F987, 0x267A7}, {0x2F988, 0x267B5}, {0x2F989, 0x23393}, {0x2F98A, 0x2339C}, {0x2F98B, 0x8201},
-{0x2F98C, 0x8204}, {0x2F98D, 0x8F9E}, {0x2F98E, 0x446B}, {0x2F98F, 0x8291}, {0x2F990, 0x828B}, {0x2F991, 0x829D}, {0x2F992, 0x52B3}, {0x2F993, 0x82B1}, {0x2F994, 0x82B3}, {0x2F995, 0x82BD},
-{0x2F996, 0x82E6}, {0x2F997, 0x26B3C}, {0x2F998, 0x82E5}, {0x2F999, 0x831D}, {0x2F99A, 0x8363}, {0x2F99B, 0x83AD}, {0x2F99C, 0x8323}, {0x2F99D, 0x83BD}, {0x2F99E, 0x83E7}, {0x2F99F, 0x8457},
-{0x2F9A0, 0x8353}, {0x2F9A1, 0x83CA}, {0x2F9A2, 0x83CC}, {0x2F9A3, 0x83DC}, {0x2F9A4, 0x26C36}, {0x2F9A5, 0x26D6B}, {0x2F9A6, 0x26CD5}, {0x2F9A7, 0x452B}, {0x2F9A8, 0x84F1}, {0x2F9A9, 0x84F3},
-{0x2F9AA, 0x8516}, {0x2F9AB, 0x273CA}, {0x2F9AC, 0x8564}, {0x2F9AD, 0x26F2C}, {0x2F9AE, 0x455D}, {0x2F9AF, 0x4561}, {0x2F9B0, 0x26FB1}, {0x2F9B1, 0x270D2}, {0x2F9B2, 0x456B}, {0x2F9B3, 0x8650},
-{0x2F9B4, 0x865C}, {0x2F9B5, 0x8667}, {0x2F9B6, 0x8669}, {0x2F9B7, 0x86A9}, {0x2F9B8, 0x8688}, {0x2F9B9, 0x870E}, {0x2F9BA, 0x86E2}, {0x2F9BB, 0x8779}, {0x2F9BC, 0x8728}, {0x2F9BD, 0x876B},
-{0x2F9BE, 0x8786}, {0x2F9BF, 0x45D7}, {0x2F9C0, 0x87E1}, {0x2F9C1, 0x8801}, {0x2F9C2, 0x45F9}, {0x2F9C3, 0x8860}, {0x2F9C4, 0x8863}, {0x2F9C5, 0x27667}, {0x2F9C6, 0x88D7}, {0x2F9C7, 0x88DE},
-{0x2F9C8, 0x4635}, {0x2F9C9, 0x88FA}, {0x2F9CA, 0x34BB}, {0x2F9CB, 0x278AE}, {0x2F9CC, 0x27966}, {0x2F9CD, 0x46BE}, {0x2F9CE, 0x46C7}, {0x2F9CF, 0x8AA0}, {0x2F9D0, 0x8AED}, {0x2F9D1, 0x8B8A},
-{0x2F9D2, 0x8C55}, {0x2F9D3, 0x27CA8}, {0x2F9D4, 0x8CAB}, {0x2F9D5, 0x8CC1}, {0x2F9D6, 0x8D1B}, {0x2F9D7, 0x8D77}, {0x2F9D8, 0x27F2F}, {0x2F9D9, 0x20804}, {0x2F9DA, 0x8DCB}, {0x2F9DB, 0x8DBC},
-{0x2F9DC, 0x8DF0}, {0x2F9DD, 0x208DE}, {0x2F9DE, 0x8ED4}, {0x2F9DF, 0x8F38}, {0x2F9E0, 0x285D2}, {0x2F9E1, 0x285ED}, {0x2F9E2, 0x9094}, {0x2F9E3, 0x90F1}, {0x2F9E4, 0x9111}, {0x2F9E5, 0x2872E},
-{0x2F9E6, 0x911B}, {0x2F9E7, 0x9238}, {0x2F9E8, 0x92D7}, {0x2F9E9, 0x92D8}, {0x2F9EA, 0x927C}, {0x2F9EB, 0x93F9}, {0x2F9EC, 0x9415}, {0x2F9ED, 0x28BFA}, {0x2F9EE, 0x958B}, {0x2F9EF, 0x4995},
-{0x2F9F0, 0x95B7}, {0x2F9F1, 0x28D77}, {0x2F9F2, 0x49E6}, {0x2F9F3, 0x96C3}, {0x2F9F4, 0x5DB2}, {0x2F9F5, 0x9723}, {0x2F9F6, 0x29145}, {0x2F9F7, 0x2921A}, {0x2F9F8, 0x4A6E}, {0x2F9F9, 0x4A76},
-{0x2F9FA, 0x97E0}, {0x2F9FB, 0x2940A}, {0x2F9FC, 0x4AB2}, {0x2F9FD, 0x29496}, {0x2F9FE, 0x980B}, {0x2F9FF, 0x980B}, {0x2FA00, 0x9829}, {0x2FA01, 0x295B6}, {0x2FA02, 0x98E2}, {0x2FA03, 0x4B33},
-{0x2FA04, 0x9929}, {0x2FA05, 0x99A7}, {0x2FA06, 0x99C2}, {0x2FA07, 0x99FE}, {0x2FA08, 0x4BCE}, {0x2FA09, 0x29B30}, {0x2FA0A, 0x9B12}, {0x2FA0B, 0x9C40}, {0x2FA0C, 0x9CFD}, {0x2FA0D, 0x4CCE},
-{0x2FA0E, 0x4CED}, {0x2FA0F, 0x9D67}, {0x2FA10, 0x2A0CE}, {0x2FA11, 0x4CF8}, {0x2FA12, 0x2A105}, {0x2FA13, 0x2A20E}, {0x2FA14, 0x2A291}, {0x2FA15, 0x9EBB}, {0x2FA16, 0x4D56}, {0x2FA17, 0x9EF9},
-{0x2FA18, 0x9EFE}, {0x2FA19, 0x9F05}, {0x2FA1A, 0x9F0F}, {0x2FA1B, 0x9F16}, {0x2FA1D, 0x2A600},
-};
-
-static std::string codepoint_to_utf8(uint32_t cp) {
-    std::string result;
-    if (/* 0x00 <= cp && */ cp <= 0x7f) {
-        result.push_back(cp);
-    }
-    else if (0x80 <= cp && cp <= 0x7ff) {
-        result.push_back(0xc0 | ((cp >> 6) & 0x1f));
-        result.push_back(0x80 | (cp & 0x3f));
-    }
-    else if (0x800 <= cp && cp <= 0xffff) {
-        result.push_back(0xe0 | ((cp >> 12) & 0x0f));
-        result.push_back(0x80 | ((cp >> 6) & 0x3f));
-        result.push_back(0x80 | (cp & 0x3f));
-    }
-    else if (0x10000 <= cp && cp <= 0x10ffff) {
-        result.push_back(0xf0 | ((cp >> 18) & 0x07));
-        result.push_back(0x80 | ((cp >> 12) & 0x3f));
-        result.push_back(0x80 | ((cp >> 6) & 0x3f));
-        result.push_back(0x80 | (cp & 0x3f));
-    }
-    else {
-        throw std::invalid_argument("invalid codepoint");
-    }
-    return result;
-}
-
-static std::string codepoints_to_utf8(const std::vector<uint32_t> & cps) {
-    std::string result;
-    for (size_t i = 0; i < cps.size(); ++i) {
-        result.append(codepoint_to_utf8(cps[i]));
-    }
-    return result;
-}
-
-static uint32_t codepoint_from_utf8(const std::string & utf8, size_t & offset) {
-    assert(offset < utf8.size());
-    if (!(utf8[offset + 0] & 0x80)) {
-        auto result = utf8[offset + 0];
-        offset += 1;
-        return result;
-    }
-    if (!(utf8[offset + 0] & 0x40)) {
-        throw std::invalid_argument("invalid character");
-    }
-    if (!(utf8[offset + 0] & 0x20)) {
-        if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80)) {
-            throw std::invalid_argument("invalid character");
-        }
-        auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f);
-        offset += 2;
-        return result;
-    }
-    if (!(utf8[offset + 0] & 0x10)) {
-        if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80)) {
-            throw std::invalid_argument("invalid character");
-        }
-        auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f);
-        offset += 3;
-        return result;
-    }
-    if (!(utf8[offset + 0] & 0x08)) {
-        if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80)) {
-            throw std::invalid_argument("invalid character");
-        }
-        auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f);
-        offset += 4;
-        return result;
-    }
-    throw std::invalid_argument("invalid string");
-}
-
-static std::vector<uint32_t> codepoints_from_utf8(const std::string & utf8) {
-    std::vector<uint32_t> result;
-    size_t offset = 0;
-    while (offset < utf8.size()) {
-        result.push_back(codepoint_from_utf8(utf8, offset));
-    }
-    return result;
-}
-
-static std::vector<uint16_t> codepoint_to_utf16(uint32_t cp) {
-    std::vector<uint16_t> result;
-    if (/* 0x0000 <= cp && */ cp <= 0xffff) {
-        result.emplace_back(cp);
-    }
-    else if (0x10000 <= cp && cp <= 0x10ffff) {
-        result.emplace_back(0xd800 | ((cp - 0x10000) >> 10));
-        result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff));
-    }
-    else {
-        throw std::invalid_argument("invalid codepoint");
-    }
-    return result;
-}
-
-static std::vector<uint16_t> codepoints_to_utf16(const std::vector<uint32_t> & cps) {
-    std::vector<uint16_t> result;
-    for (size_t i = 0; i < cps.size(); ++i) {
-        auto temp = codepoint_to_utf16(cps[i]);
-        result.insert(result.end(), temp.begin(), temp.end());
-    }
-    return result;
-}
-
-static uint32_t codepoint_from_utf16(const std::vector<uint16_t> & utf16, size_t & offset) {
-    assert(offset < utf16.size());
-    if (((utf16[0] >> 10) << 10) != 0xd800) {
-        auto result = utf16[offset + 0];
-        offset += 1;
-        return result;
-    }
-
-    if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) {
-        throw std::invalid_argument("invalid character");
-    }
-
-    auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff));
-    offset += 2;
-    return result;
-}
-
-static std::vector<uint32_t> codepoints_from_utf16(const std::vector<uint16_t> & utf16) {
-    std::vector<uint32_t> result;
-    size_t offset = 0;
-    while (offset < utf16.size()) {
-        result.push_back(codepoint_from_utf16(utf16, offset));
-    }
-    return result;
-}
-
 #define CODEPOINT_TYPE_UNIDENTIFIED 0
-#define CODEPOINT_TYPE_DIGIT 1
-#define CODEPOINT_TYPE_LETTER 2
-#define CODEPOINT_TYPE_WHITESPACE 3
-#define CODEPOINT_TYPE_ACCENT_MARK 4
-#define CODEPOINT_TYPE_PUNCTUATION 5
-#define CODEPOINT_TYPE_SYMBOL 6
-#define CODEPOINT_TYPE_CONTROL 7
+#define CODEPOINT_TYPE_DIGIT        1
+#define CODEPOINT_TYPE_LETTER       2
+#define CODEPOINT_TYPE_WHITESPACE   3
+#define CODEPOINT_TYPE_ACCENT_MARK  4
+#define CODEPOINT_TYPE_PUNCTUATION  5
+#define CODEPOINT_TYPE_SYMBOL       6
+#define CODEPOINT_TYPE_CONTROL      7
 
-static std::unordered_map<uint32_t, int> codepoint_type_map() {
-    std::unordered_map<uint32_t, int> codepoint_types;
-    for (auto p : digit_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_DIGIT;
-        }
-    }
-    for (auto p : letter_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_LETTER;
-        }
-    }
-    for (auto p : whitespace_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_WHITESPACE;
-        }
-    }
-    for (auto p : accent_mark_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_ACCENT_MARK;
-        }
-    }
-    for (auto p : punctuation_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_PUNCTUATION;
-        }
-    }
-    for  (auto p : symbol_ranges) {
-        for (auto i = p.first; i <= p.second; ++i) {
-            codepoint_types[i] = CODEPOINT_TYPE_SYMBOL;
-        }
-    }
-    for (auto p : control_ranges) {
-        for (auto i = p.first; i <= p.second; ++ i) {
-            codepoint_types[i] = CODEPOINT_TYPE_CONTROL;
-        }
-    }
-    return codepoint_types;
-}
+std::string unicode_cpt_to_utf8(uint32_t cp);
+std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8);
 
-static int codepoint_type(uint32_t cp) {
-    static std::unordered_map<uint32_t, int> codepoint_types = codepoint_type_map();
-    const auto it = codepoint_types.find(cp);
-    return it == codepoint_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second;
-}
+std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts);
 
-static int codepoint_type(const std::string & utf8) {
-    if (utf8.length() == 0) {
-        return CODEPOINT_TYPE_UNIDENTIFIED;
-    }
-    size_t offset = 0;
-    return codepoint_type(codepoint_from_utf8(utf8, offset));
-}
+int unicode_cpt_type(uint32_t cp);
+int unicode_cpt_type(const std::string & utf8);
 
-static std::unordered_map<uint8_t, std::string> bytes_to_unicode_map_bpe() {
-    std::unordered_map<uint8_t, std::string> map;
-    for (int ch = u'!'; ch <= u'~'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[ch] = codepoint_to_utf8(ch);
-    }
-    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[ch] = codepoint_to_utf8(ch);
-    }
-    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[ch] = codepoint_to_utf8(ch);
-    }
-    auto n = 0;
-    for (int ch = 0; ch < 256; ++ch) {
-        if (map.find(ch) == map.end()) {
-            map[ch] = codepoint_to_utf8(256 + n);
-            ++n;
-        }
-    }
-    return map;
-}
-
-static std::string bytes_to_unicode_bpe(uint8_t byte) {
-    static std::unordered_map<uint8_t, std::string> map = bytes_to_unicode_map_bpe();
-    return map.at(byte);
-}
-
-static std::unordered_map<std::string, uint8_t> unicode_to_bytes_map_bpe() {
-    std::unordered_map<std::string, uint8_t> map;
-    for (int ch = u'!'; ch <= u'~'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[codepoint_to_utf8(ch)] = ch;
-    }
-    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[codepoint_to_utf8(ch)] = ch;
-    }
-    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[codepoint_to_utf8(ch)] = ch;
-    }
-    auto n = 0;
-    for (int ch = 0; ch < 256; ++ch) {
-        if (map.find(codepoint_to_utf8(ch)) == map.end()) {
-            map[codepoint_to_utf8(256 + n)] = ch;
-            ++n;
-        }
-    }
-    return map;
-}
-
-static uint8_t unicode_to_bytes_bpe(const std::string & utf8) {
-    static std::unordered_map<std::string, uint8_t> map = unicode_to_bytes_map_bpe();
-    return map.at(utf8);
-}
+std::string unicode_byte_to_utf8(uint8_t byte);
+uint8_t unicode_utf8_to_byte(const std::string & utf8);