Fix Q3_K_XS for MoE models (#5113)

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

.devops/main-intel.Dockerfile

@@ -0,0 +1,26 @@
+ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
+ARG UBUNTU_VERSION=22.04
+
+FROM intel/hpckit:$ONEAPI_VERSION as build
+
+RUN apt-get update && \
+    apt-get install -y git
+
+WORKDIR /app
+
+COPY . .
+
+# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance
+RUN mkdir build && \
+    cd build && \
+    cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \
+    cmake --build . --config Release --target main server
+
+FROM ubuntu:$UBUNTU_VERSION as runtime
+
+COPY --from=build /app/build/bin/main /main
+COPY --from=build /app/build/bin/server /server
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/main" ]

.devops/nix/nixpkgs-instances.nix

@@ -7,6 +7,18 @@
     { system, ... }:
     {
       _module.args = {
+        # Note: bringing up https://zimbatm.com/notes/1000-instances-of-nixpkgs
+        # again, the below creates several nixpkgs instances which the
+        # flake-centric CLI will be forced to evaluate e.g. on `nix flake show`.
+        #
+        # This is currently "slow" and "expensive", on a certain scale.
+        # This also isn't "right" in that this hinders dependency injection at
+        # the level of flake inputs. This might get removed in the foreseeable
+        # future.
+        #
+        # Note that you can use these expressions without Nix
+        # (`pkgs.callPackage ./devops/nix/scope.nix { }` is the entry point).
+
         pkgsCuda = import inputs.nixpkgs {
           inherit system;
           # Ensure dependencies use CUDA consistently (e.g. that openmpi, ucc,

.devops/nix/package.nix

@@ -73,6 +73,7 @@ let
     ps: [
       ps.numpy
       ps.sentencepiece
+      ps.tiktoken
       ps.torchWithoutCuda
       ps.transformers
     ]
@@ -114,14 +115,22 @@ effectiveStdenv.mkDerivation (
     pname = "llama-cpp${pnameSuffix}";
     version = llamaVersion;
 
+    # Note: none of the files discarded here are visible in the sandbox or
+    # affect the output hash. This also means they can be modified without
+    # triggering a rebuild.
     src = lib.cleanSourceWith {
       filter =
         name: type:
-        !(builtins.any (_: _) [
+        let
+          noneOf = builtins.all (x: !x);
+          baseName = baseNameOf name;
+        in
+        noneOf [
           (lib.hasSuffix ".nix" name) # Ignore *.nix files when computing outPaths
-          (name == "README.md") # Ignore *.md changes whe computing outPaths
-          (lib.hasPrefix "." name) # Skip hidden files and directories
-        ]);
+          (lib.hasSuffix ".md" name) # Ignore *.md changes whe computing outPaths
+          (lib.hasPrefix "." baseName) # Skip hidden files and directories
+          (baseName == "flake.lock")
+        ];
       src = lib.cleanSource ../../.;
     };
 
@@ -159,7 +168,7 @@ effectiveStdenv.mkDerivation (
 
     cmakeFlags =
       [
-        (cmakeBool "LLAMA_NATIVE" true)
+        (cmakeBool "LLAMA_NATIVE" false)
         (cmakeBool "LLAMA_BUILD_SERVER" true)
         (cmakeBool "BUILD_SHARED_LIBS" true)
         (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
@@ -216,6 +225,9 @@ effectiveStdenv.mkDerivation (
         description = "contains numpy and sentencepiece";
         buildInputs = [ llama-python ];
         inputsFrom = [ finalAttrs.finalPackage ];
+        shellHook = ''
+          addToSearchPath "LD_LIBRARY_PATH" "${lib.getLib effectiveStdenv.cc.cc}/lib"
+        '';
       };
 
       shell-extra = mkShell {

.devops/nix/scope.nix

@@ -4,6 +4,10 @@
   llamaVersion ? "0.0.0",
 }:
 
+# We're using `makeScope` instead of just writing out an attrset
+# because it allows users to apply overlays later using `overrideScope'`.
+# Cf. https://noogle.dev/f/lib/makeScope
+
 lib.makeScope newScope (
   self: {
     inherit llamaVersion;

.github/workflows/build.yml

@@ -295,7 +295,7 @@ jobs:
       OPENBLAS_VERSION: 0.3.23
       OPENCL_VERSION: 2023.04.17
       CLBLAST_VERSION: 1.6.0
-      SDE_VERSION: 9.21.1-2023-04-24
+      SDE_VERSION: 9.33.0-2024-01-07
 
     strategy:
       matrix:
@@ -400,7 +400,7 @@ jobs:
         id: cmake_test_sde
         if: ${{ matrix.build == 'avx512' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation
         run: |
-          curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/777395/sde-external-${env:SDE_VERSION}-win.tar.xz"
+          curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/813591/sde-external-${env:SDE_VERSION}-win.tar.xz"
           # for some weird reason windows tar doesn't like sde tar.xz
           7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar.xz
           7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar
@@ -515,6 +515,31 @@ jobs:
       - name: Build Xcode project
         run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build
 
+  android-build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Clone
+        uses: actions/checkout@v3
+
+      - name: Set up JDK
+        uses: actions/setup-java@v3
+        with:
+          java-version: 17
+          distribution: zulu
+
+      - name: Setup Android SDK
+        uses: android-actions/setup-android@v3
+        with:
+          log-accepted-android-sdk-licenses: false
+
+      - name: Build
+        run: |
+          cd examples/llama.android
+
+          # Skip armeabi-v7a for now (https://github.com/llvm/llvm-project/issues/65820).
+          ./gradlew build --no-daemon -Pskip-armeabi-v7a
+
 #  freeBSD-latest:
 #    runs-on: macos-12
 #    steps:

.github/workflows/docker.yml

@@ -35,6 +35,7 @@ jobs:
           - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" }
           - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
           - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" }
     steps:
       - name: Check out the repo
         uses: actions/checkout@v3

.github/workflows/nix-ci-aarch64.yml

@@ -2,13 +2,20 @@ name: Nix aarch64 builds
 
 on:
   workflow_dispatch: # allows manual triggering
+  schedule:
+    # Rebuild daily rather than on every push because QEMU is expensive (e.g.
+    # 1.5h instead of minutes with the cold cache).
+    #
+    # randint(0, 59), randint(0, 23)
+    - cron: '26 12 * * *'
+  # But also rebuild if we touched any of the Nix expressions:
   push:
     branches:
       - master
-    paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix']
+    paths: ['**/*.nix', 'flake.lock']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix']
+    paths: ['**/*.nix', 'flake.lock']
 
 jobs:
   nix-build-aarch64:

.github/workflows/nix-ci.yml

@@ -5,10 +5,8 @@ on:
   push:
     branches:
       - master
-    paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix']
 
 jobs:
   nix-eval:

.gitignore

@@ -105,3 +105,4 @@ poetry.toml
 /tests/test-tokenizer-1-bpe
 /tests/test-rope
 /tests/test-backend-ops
+/tests/test-autorelease

CMakeLists.txt

@@ -1,4 +1,4 @@
-cmake_minimum_required(VERSION 3.13)  # for add_link_options
+cmake_minimum_required(VERSION 3.14)  # for add_link_options and implicit target directories.
 project("llama.cpp" C CXX)
 
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
@@ -47,6 +47,7 @@ option(BUILD_SHARED_LIBS                "build shared libraries"
 option(LLAMA_STATIC                     "llama: static link libraries"                          OFF)
 option(LLAMA_NATIVE                     "llama: enable -march=native flag"                      ON)
 option(LLAMA_LTO                        "llama: enable link time optimization"                  OFF)
+option(LLAMA_CCACHE                     "llama: use ccache if available"                        ON)
 
 # debug
 option(LLAMA_ALL_WARNINGS               "llama: enable all compiler warnings"                   ON)
@@ -76,6 +77,10 @@ if (NOT MSVC)
     option(LLAMA_F16C                        "llama: enable F16C"                               ${INS_ENB})
 endif()
 
+if (WIN32)
+    option(LLAMA_WIN_VER                     "llama: Windows Version"                           0x602)
+endif()
+
 # 3rd party libs
 option(LLAMA_ACCELERATE                      "llama: enable Accelerate framework"               ON)
 option(LLAMA_BLAS                            "llama: use BLAS"                                  OFF)
@@ -103,6 +108,13 @@ option(LLAMA_BUILD_TESTS                     "llama: build tests"    ${LLAMA_STA
 option(LLAMA_BUILD_EXAMPLES                  "llama: build examples" ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER                    "llama: build server example"                      ON)
 
+
+# add perf arguments
+option(LLAMA_PERF                            "llama: enable perf"                               OFF)
+if (LLAMA_PERF)
+    add_definitions(-DGGML_PERF)
+endif()
+
 # Required for relocatable CMake package
 include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
 
@@ -466,6 +478,11 @@ function(get_flags CCID CCVER)
         if (CCVER VERSION_GREATER_EQUAL 8.1.0)
             set(CXX_FLAGS ${CXX_FLAGS} -Wextra-semi)
         endif()
+    elseif (CCID MATCHES "Intel")
+        # enable max optimization level when using Intel compiler
+        set(C_FLAGS   -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector)
+        set(CXX_FLAGS -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector)
+        add_link_options(-fuse-ld=lld -static-intel)
     endif()
 
     set(GF_C_FLAGS   ${C_FLAGS}   PARENT_SCOPE)
@@ -557,6 +574,17 @@ if (LLAMA_LTO)
     endif()
 endif()
 
+if (LLAMA_CCACHE)
+    find_program(LLAMA_CCACHE_FOUND ccache)
+    if (LLAMA_CCACHE_FOUND)
+        set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
+        set(ENV{CCACHE_SLOPPINESS} time_macros)
+        message(STATUS "Using ccache")
+    else()
+        message(STATUS "Warning: ccache not found - consider installing it or use LLAMA_CCACHE=OFF")
+    endif ()
+endif()
+
 # this version of Apple ld64 is buggy
 execute_process(
     COMMAND ${CMAKE_C_COMPILER} ${CMAKE_EXE_LINKER_FLAGS} -Wl,-v
@@ -590,6 +618,13 @@ if (NOT MSVC)
     endif()
 endif()
 
+function(add_compile_option_cpp ARG)
+    # Adds a compile option to C/C++ only, but not for Cuda.
+    # Use, e.g., for CPU-architecture flags.
+    add_compile_options($<$<COMPILE_LANGUAGE:CXX>:${ARG}>)
+    add_compile_options($<$<COMPILE_LANGUAGE:C>:${ARG}>)
+endfunction()
+
 if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64"))
     message(STATUS "ARM detected")
     if (MSVC)
@@ -624,8 +659,7 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE
             include(cmake/FindSIMD.cmake)
         endif ()
         if (LLAMA_AVX512)
-            add_compile_options($<$<COMPILE_LANGUAGE:C>:/arch:AVX512>)
-            add_compile_options($<$<COMPILE_LANGUAGE:CXX>:/arch:AVX512>)
+            add_compile_option_cpp(/arch:AVX512)
             # MSVC has no compile-time flags enabling specific
             # AVX512 extensions, neither it defines the
             # macros corresponding to the extensions.
@@ -639,37 +673,35 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE
                 add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VNNI__>)
             endif()
         elseif (LLAMA_AVX2)
-            add_compile_options($<$<COMPILE_LANGUAGE:C>:/arch:AVX2>)
-            add_compile_options($<$<COMPILE_LANGUAGE:CXX>:/arch:AVX2>)
+            add_compile_option_cpp(/arch:AVX2)
         elseif (LLAMA_AVX)
-            add_compile_options($<$<COMPILE_LANGUAGE:C>:/arch:AVX>)
-            add_compile_options($<$<COMPILE_LANGUAGE:CXX>:/arch:AVX>)
+            add_compile_option_cpp(/arch:AVX)
         endif()
     else()
         if (LLAMA_NATIVE)
-            add_compile_options(-march=native)
+            add_compile_option_cpp(-march=native)
         endif()
         if (LLAMA_F16C)
-            add_compile_options(-mf16c)
+            add_compile_option_cpp(-mf16c)
         endif()
         if (LLAMA_FMA)
-            add_compile_options(-mfma)
+            add_compile_option_cpp(-mfma)
         endif()
         if (LLAMA_AVX)
-            add_compile_options(-mavx)
+            add_compile_option_cpp(-mavx)
         endif()
         if (LLAMA_AVX2)
-            add_compile_options(-mavx2)
+            add_compile_option_cpp(-mavx2)
         endif()
         if (LLAMA_AVX512)
-            add_compile_options(-mavx512f)
-            add_compile_options(-mavx512bw)
+            add_compile_option_cpp(-mavx512f)
+            add_compile_option_cpp(-mavx512bw)
         endif()
         if (LLAMA_AVX512_VBMI)
-            add_compile_options(-mavx512vbmi)
+            add_compile_option_cpp(-mavx512vbmi)
         endif()
         if (LLAMA_AVX512_VNNI)
-            add_compile_options(-mavx512vnni)
+            add_compile_option_cpp(-mavx512vnni)
         endif()
     endif()
 elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64")
@@ -686,7 +718,7 @@ endif()
 
 if (MINGW)
     # Target Windows 8 for PrefetchVirtualMemory
-    add_compile_definitions(_WIN32_WINNT=0x602)
+    add_compile_definitions(_WIN32_WINNT=${LLAMA_WIN_VER})
 endif()
 
 #
@@ -838,7 +870,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake
               ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfigVersion.cmake
         DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama)
 
-set(GGML_PUBLIC_HEADERS "ggml.h"
+set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h"
         "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}"
         "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}")
 

Makefile

@@ -9,7 +9,7 @@ TEST_TARGETS = \
 	tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
 	tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama          \
 	tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope      \
-	tests/test-backend-ops
+	tests/test-backend-ops tests/test-autorelease
 
 # Code coverage output files
 COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
@@ -747,3 +747,6 @@ tests/test-c.o: tests/test-c.c llama.h
 
 tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
+tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

README.md

@@ -128,6 +128,7 @@ as the main playground for developing new features for the [ggml](https://github
 - React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
 - Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
 - Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
+- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
 
 **UI:**
 

awq-py/README.md

@@ -43,7 +43,7 @@ Example for llama model
 # For llama7b and llama2 models
 python convert.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/llama_7b_fp16.gguf
 # For mistral and mpt models
-python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf
+python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/mpt-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf
 ```
 
 ## Quantize

ci/run.sh

@@ -36,6 +36,10 @@ if [ ! -z ${GG_BUILD_METAL} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_METAL_SHADER_DEBUG=ON"
 fi
 
+if [ ! -z ${GG_BUILD_CUDA} ]; then
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_CUBLAS=1"
+fi
+
 ## helpers
 
 # download a file if it does not exist or if it is outdated
@@ -160,8 +164,8 @@ function gg_run_open_llama_3b_v2 {
 
     set -e
 
-    (time cmake -DCMAKE_BUILD_TYPE=Release -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                              ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                                             ) 2>&1 | tee -a $OUT/${ci}-make.log
 
     python3 ../convert.py ${path_models}
 
@@ -179,6 +183,8 @@ function gg_run_open_llama_3b_v2 {
 
     wiki_test_60="${path_wiki}/wiki.test-60.raw"
 
+    ./bin/test-autorelease ${model_f16}
+
     ./bin/quantize ${model_f16} ${model_q8_0} q8_0
     ./bin/quantize ${model_f16} ${model_q4_0} q4_0
     ./bin/quantize ${model_f16} ${model_q4_1} q4_1
@@ -214,6 +220,8 @@ function gg_run_open_llama_3b_v2 {
     (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
     (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
+    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+
     (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
@@ -241,6 +249,8 @@ function gg_run_open_llama_3b_v2 {
     check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
 
+    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
+
     # lora
     function compare_ppl {
         qnt="$1"
@@ -282,7 +292,6 @@ function gg_run_open_llama_3b_v2 {
     (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
     compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
 
-
     set +e
 }
 
@@ -292,6 +301,7 @@ function gg_sum_open_llama_3b_v2 {
     gg_printf 'OpenLLaMA 3B-v2:\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
     gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
     gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
     gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
@@ -337,8 +347,8 @@ function gg_run_open_llama_7b_v2 {
 
     set -e
 
-    (time cmake -DCMAKE_BUILD_TYPE=Release -DLLAMA_CUBLAS=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                              ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUBLAS=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                                             ) 2>&1 | tee -a $OUT/${ci}-make.log
 
     python3 ../convert.py ${path_models}
 
@@ -391,6 +401,8 @@ function gg_run_open_llama_7b_v2 {
     (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
     (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
+    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+
     (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
@@ -418,6 +430,8 @@ function gg_run_open_llama_7b_v2 {
     check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
 
+    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
+
     # lora
     function compare_ppl {
         qnt="$1"
@@ -469,6 +483,7 @@ function gg_sum_open_llama_7b_v2 {
     gg_printf 'OpenLLaMA 7B-v2:\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
     gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
     gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
     gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"

common/common.cpp

@@ -167,6 +167,24 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
             if (params.n_threads_batch <= 0) {
                 params.n_threads_batch = std::thread::hardware_concurrency();
             }
+        } else if (arg == "-td" || arg == "--threads-draft") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_threads_draft = std::stoi(argv[i]);
+            if (params.n_threads_draft <= 0) {
+                params.n_threads_draft = std::thread::hardware_concurrency();
+            }
+        } else if (arg == "-tbd" || arg == "--threads-batch-draft") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_threads_batch_draft = std::stoi(argv[i]);
+            if (params.n_threads_batch_draft <= 0) {
+                params.n_threads_batch_draft = std::thread::hardware_concurrency();
+            }
         } else if (arg == "-p" || arg == "--prompt") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -185,6 +203,23 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
             params.prompt_cache_all = true;
         } else if (arg == "--prompt-cache-ro") {
             params.prompt_cache_ro = true;
+        } else if (arg == "-bf" || arg == "--binary-file") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            std::ifstream file(argv[i], std::ios::binary);
+            if (!file) {
+                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+                invalid_param = true;
+                break;
+            }
+            // store the external file name in params
+            params.prompt_file = argv[i];
+            std::ostringstream ss;
+            ss << file.rdbuf();
+            params.prompt = ss.str();
+            fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
         } else if (arg == "-f" || arg == "--file") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -635,6 +670,12 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
             if (params.logdir.back() != DIRECTORY_SEPARATOR) {
                 params.logdir += DIRECTORY_SEPARATOR;
             }
+        } else if (arg == "--save-all-logits" || arg == "--kl-divergence-base") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.logits_file = argv[i];
         } else if (arg == "--perplexity" || arg == "--all-logits") {
             params.logits_all = true;
         } else if (arg == "--ppl-stride") {
@@ -663,6 +704,24 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.hellaswag_tasks = std::stoi(argv[i]);
+        } else if (arg == "--winogrande") {
+            params.winogrande = true;
+        } else if (arg == "--winogrande-tasks") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.winogrande_tasks = std::stoi(argv[i]);
+        } else if (arg == "--multiple-choice") {
+            params.multiple_choice = true;
+        } else if (arg == "--multiple-choice-tasks") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.multiple_choice_tasks = std::stoi(argv[i]);
+        } else if (arg == "--kl-divergence") {
+            params.kl_divergence = true;
         } else if (arg == "--ignore-eos") {
             params.ignore_eos = true;
         } else if (arg == "--no-penalize-nl") {
@@ -845,6 +904,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  -t N, --threads N     number of threads to use during generation (default: %d)\n", params.n_threads);
     printf("  -tb N, --threads-batch N\n");
     printf("                        number of threads to use during batch and prompt processing (default: same as --threads)\n");
+    printf("  -td N, --threads-draft N");
+    printf("                        number of threads to use during generation (default: same as --threads)");
+    printf("  -tbd N, --threads-batch-draft N\n");
+    printf("                        number of threads to use during batch and prompt processing (default: same as --threads-draft)\n");
     printf("  -p PROMPT, --prompt PROMPT\n");
     printf("                        prompt to start generation with (default: empty)\n");
     printf("  -e, --escape          process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
@@ -858,6 +921,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --in-suffix STRING    string to suffix after user inputs with (default: empty)\n");
     printf("  -f FNAME, --file FNAME\n");
     printf("                        prompt file to start generation.\n");
+    printf("  -bf FNAME, --binary-file FNAME\n");
+    printf("                        binary file containing multiple choice tasks.\n");
     printf("  -n N, --n-predict N   number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
     printf("  -c N, --ctx-size N    size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
     printf("  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
@@ -904,6 +969,11 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --logits-all          return logits for all tokens in the batch (default: disabled)\n");
     printf("  --hellaswag           compute HellaSwag score over random tasks from datafile supplied with -f\n");
     printf("  --hellaswag-tasks N   number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
+    printf("  --winogrande          compute Winogrande score over random tasks from datafile supplied with -f\n");
+    printf("  --winogrande-tasks N  number of tasks to use when computing the Winogrande score (default: %zu)\n", params.winogrande_tasks);
+    printf("  --multiple-choice     compute multiple choice score over random tasks from datafile supplied with -f\n");
+    printf("  --multiple-choice-tasks N number of tasks to use when computing the multiple choice score (default: %zu)\n", params.winogrande_tasks);
+    printf("  --kl-divergence       computes KL-divergence to logits provided via --kl-divergence-base");
     printf("  --keep N              number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
     printf("  --draft N             number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
     printf("  --chunks N            max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);

common/common.h

@@ -46,7 +46,9 @@ struct gpt_params {
     uint32_t seed                           = -1;    // RNG seed
 
     int32_t n_threads                       = get_num_physical_cores();
+    int32_t n_threads_draft                 = -1;
     int32_t n_threads_batch                 = -1;    // number of threads to use for batch processing (-1 = use n_threads)
+    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)
@@ -89,6 +91,7 @@ struct gpt_params {
     std::string input_suffix      = "";  // string to suffix user inputs with
     std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
     std::string logdir            = "";  // directory in which to save YAML log files
+    std::string logits_file       = "";  // file for saving *all* logits
 
     std::vector<llama_model_kv_override> kv_overrides;
 
@@ -103,6 +106,14 @@ struct gpt_params {
     bool   hellaswag       = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
     size_t hellaswag_tasks = 400;   // number of tasks to use when computing the HellaSwag score
 
+    bool   winogrande      = false; // compute Winogrande score over random tasks from datafile supplied in prompt
+    size_t winogrande_tasks= 0;     // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
+
+    bool   multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
+    size_t multiple_choice_tasks = 0;     // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
+
+    bool   kl_divergence   = false; // compute KL-divergence
+
     bool mul_mat_q         = true;  // if true, use mul_mat_q kernels instead of cuBLAS
     bool random_prompt     = false; // do not randomize prompt if none provided
     bool use_color         = false; // use color to distinguish generations and inputs

common/sampling.cpp

@@ -190,6 +190,11 @@ static llama_token llama_sampling_sample_impl(
         logits[it->first] += it->second;
     }
 
+    if (ctx_cfg) {
+        float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx);
+        llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
+    }
+
     cur.clear();
 
     for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
@@ -198,10 +203,6 @@ static llama_token llama_sampling_sample_impl(
 
     llama_token_data_array cur_p = { cur.data(), cur.size(), false };
 
-    if (ctx_cfg) {
-        llama_sample_classifier_free_guidance(ctx_main, &cur_p, ctx_cfg, params.cfg_scale);
-    }
-
     // apply penalties
     const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev;
     const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n);

common/sampling.h

@@ -17,7 +17,7 @@ typedef struct llama_sampling_params {
     float       min_p                 = 0.05f;    // 0.0 = disabled
     float       tfs_z                 = 1.00f;    // 1.0 = disabled
     float       typical_p             = 1.00f;    // 1.0 = disabled
-    float       temp                  = 0.80f;    // 1.0 = disabled
+    float       temp                  = 0.80f;    // <= 0.0 to sample greedily, 0.0 to not output probabilities
     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_freq          = 0.00f;    // 0.0 = disabled

convert-hf-to-gguf.py

@@ -10,7 +10,7 @@ import re
 import sys
 from enum import IntEnum
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast, Optional
+from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
 
 import numpy as np
 import torch
@@ -189,6 +189,8 @@ class Model:
             return StableLMModel
         if model_architecture == "QWenLMHeadModel":
             return QwenModel
+        if model_architecture == "Qwen2ForCausalLM":
+            return Model
         if model_architecture == "MixtralForCausalLM":
             return MixtralModel
         if model_architecture == "GPT2LMHeadModel":
@@ -197,6 +199,8 @@ class Model:
             return Phi2Model
         if model_architecture == "PlamoForCausalLM":
             return PlamoModel
+        if model_architecture == "CodeShellForCausalLM":
+            return CodeShellModel
         return Model
 
     def _is_model_safetensors(self) -> bool:
@@ -234,6 +238,8 @@ class Model:
             return gguf.MODEL_ARCH.STABLELM
         if arch == "QWenLMHeadModel":
             return gguf.MODEL_ARCH.QWEN
+        if arch == "Qwen2ForCausalLM":
+            return gguf.MODEL_ARCH.QWEN2
         if arch == "MixtralForCausalLM":
             return gguf.MODEL_ARCH.LLAMA
         if arch == "GPT2LMHeadModel":
@@ -242,6 +248,8 @@ class Model:
             return gguf.MODEL_ARCH.PHI2
         if arch == "PlamoForCausalLM":
             return gguf.MODEL_ARCH.PLAMO
+        if arch == "CodeShellForCausalLM":
+            return gguf.MODEL_ARCH.CODESHELL
 
         raise NotImplementedError(f'Architecture "{arch}" not supported!')
 
@@ -266,11 +274,10 @@ class Model:
                 toktypes.append(gguf.TokenType.USER_DEFINED)
             elif reverse_vocab[i] in added_vocab:
                 tokens.append(reverse_vocab[i])
-                if hasattr(tokenizer, "added_tokens_decoder"):
-                    if tokenizer.added_tokens_decoder[i].special:
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                if tokenizer.added_tokens_decoder[i].special:
+                    toktypes.append(gguf.TokenType.CONTROL)
+                else:
+                    toktypes.append(gguf.TokenType.USER_DEFINED)
             else:
                 tokens.append(reverse_vocab[i])
                 toktypes.append(gguf.TokenType.NORMAL)
@@ -282,6 +289,58 @@ class Model:
         special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
         special_vocab.add_to_gguf(self.gguf_writer)
 
+    def _set_vocab_qwen(self):
+        dir_model = self.dir_model
+        hparams = self.hparams
+        tokens: list[bytearray] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+        vocab_size = hparams["vocab_size"]
+        assert max(tokenizer.get_vocab().values()) < vocab_size
+
+        merges = []
+        vocab = {}
+        mergeable_ranks = tokenizer.mergeable_ranks
+        for token, rank in mergeable_ranks.items():
+            vocab[QwenModel.token_bytes_to_string(token)] = rank
+            if len(token) == 1:
+                continue
+            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+            assert len(merged) == 2
+            merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+
+        # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
+        added_vocab = tokenizer.special_tokens
+        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in (vocab | added_vocab).items()}
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                pad_token = f"[PAD{i}]".encode("utf-8")
+                tokens.append(bytearray(pad_token))
+                toktypes.append(gguf.TokenType.USER_DEFINED)
+            elif reverse_vocab[i] in added_vocab:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.CONTROL)
+            else:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.NORMAL)
+
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
+        special_vocab.merges = merges
+        # only add special tokens when they were not already loaded from config.json
+        if len(special_vocab.special_token_ids) == 0:
+            special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
+            special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
+        # this one is usually not in config.json anyway
+        special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
+        special_vocab.add_to_gguf(self.gguf_writer)
+
     def _set_vocab_sentencepiece(self):
         from sentencepiece import SentencePieceProcessor
 
@@ -480,7 +539,8 @@ class MPTModel(Model):
             # map tensor names
             if "scales" in name:
                 new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias", ".scales"))
-                new_name = new_name.replace("scales", "act.scales")
+                if new_name is not None:
+                    new_name = new_name.replace("scales", "act.scales")
             else:
                 new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
             if new_name is None:
@@ -869,6 +929,13 @@ class PersimmonModel(Model):
 
 
 class StableLMModel(Model):
+    def set_vocab(self):
+        if (self.dir_model / "tokenizer.json").is_file():
+            self._set_vocab_gpt2()
+        else:
+            # StableLM 2 1.6B uses a vocab in a similar format to Qwen's vocab
+            self._set_vocab_qwen()
+
     def set_gguf_parameters(self):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
@@ -897,7 +964,7 @@ class QwenModel(Model):
         return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
 
     @staticmethod
-    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: Optional[int] = None) -> list[bytes]:
+    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
         parts = [bytes([b]) for b in token]
         while True:
             min_idx = None
@@ -914,52 +981,7 @@ class QwenModel(Model):
         return parts
 
     def set_vocab(self):
-        dir_model = self.dir_model
-        hparams = self.hparams
-        tokens: list[bytearray] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
-        vocab_size = hparams["vocab_size"]
-        assert max(tokenizer.get_vocab().values()) < vocab_size
-
-        merges = []
-        vocab = {}
-        mergeable_ranks = tokenizer.mergeable_ranks
-        for token, rank in mergeable_ranks.items():
-            vocab[self.token_bytes_to_string(token)] = rank
-            if len(token) == 1:
-                continue
-            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
-            assert len(merged) == 2
-            merges.append(' '.join(map(self.token_bytes_to_string, merged)))
-
-        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in vocab.items()}
-        added_vocab = tokenizer.special_tokens
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                pad_token = f"[PAD{i}]".encode("utf-8")
-                tokens.append(bytearray(pad_token))
-                toktypes.append(gguf.TokenType.USER_DEFINED)
-            elif reverse_vocab[i] in added_vocab:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.CONTROL)
-            else:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.NORMAL)
-
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
-        special_vocab.merges = merges
-        special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
-        special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
-        special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
-        special_vocab.add_to_gguf(self.gguf_writer)
+        self._set_vocab_qwen()
 
     def set_gguf_parameters(self):
         self.gguf_writer.add_name("Qwen")
@@ -1177,6 +1199,70 @@ class PlamoModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
+class CodeShellModel(Model):
+    def set_gguf_parameters(self):
+        block_count = self.hparams["n_layer"]
+
+        self.gguf_writer.add_name("CodeShell")
+        self.gguf_writer.add_context_length(self.hparams["n_positions"])
+        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
+        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_head_count(self.hparams["n_head"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_rope_freq_base(10000.0)
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+        self.gguf_writer.add_rope_scaling_factor(1.0)
+
+    def write_tensors(self):
+        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        tensors = dict(self.get_tensors())
+        has_lm_head = "lm_head.weight" in tensors.keys() or "output.weight" in tensors.keys()
+        for name, data_torch in tensors.items():
+            # we don't need these
+            if name.endswith((".attn.rotary_emb.inv_freq")):
+                continue
+
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            data = data_torch.squeeze().numpy()
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+            if not has_lm_head and name == "transformer.wte.weight":
+                self.gguf_writer.add_tensor("output.weight", data)
+                print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
+
 ###### CONVERSION LOGIC ######
 
 
@@ -1214,7 +1300,7 @@ def main() -> None:
 
     if args.awq_path:
         sys.path.insert(1, str(Path(__file__).parent / 'awq-py'))
-        from awq.apply_awq import add_scale_weights
+        from awq.apply_awq import add_scale_weights  # type: ignore[import-not-found]
         tmp_model_path = args.model / "weighted_model"
         dir_model = tmp_model_path
         if tmp_model_path.is_dir():

convert-llama-ggml-to-gguf.py

@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import argparse
+import os
 import struct
 import sys
 from enum import IntEnum
@@ -9,7 +10,6 @@ from pathlib import Path
 
 import numpy as np
 
-import os
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
@@ -371,15 +371,11 @@ def handle_metadata(cfg, hp):
         params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path)
     else:
         raise ValueError('Unable to load metadata')
-    vocab = convert.load_vocab(
-        cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir,
-        cfg.vocabtype)
-    # FIXME: Respect cfg.vocab_dir?
-    svocab = gguf.SpecialVocab(cfg.model_metadata_dir,
-                               load_merges = cfg.vocabtype == 'bpe',
-                               n_vocab = vocab.vocab_size)
+    vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir)
+    vocab_factory = convert.VocabFactory(vocab_path)
+    vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype, cfg.model_metadata_dir)
     convert.check_vocab_size(params, vocab)
-    return (params, vocab, svocab)
+    return params, vocab, special_vocab
 
 
 def handle_args():

convert-lora-to-ggml.py

@@ -5,17 +5,16 @@ import json
 import os
 import struct
 import sys
+from pathlib import Path
 from typing import Any, BinaryIO, Sequence
 
 import numpy as np
 import torch
 
-from pathlib import Path
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
 import gguf
 
-
 NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
 
 
@@ -60,7 +59,14 @@ if __name__ == '__main__':
     input_model = os.path.join(sys.argv[1], "adapter_model.bin")
     output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
 
-    model = torch.load(input_model, map_location="cpu")
+    if os.path.exists(input_model):
+        model = torch.load(input_model, map_location="cpu")
+    else:
+        input_model = os.path.join(sys.argv[1], "adapter_model.safetensors")
+        # lazy import load_file only if lora is in safetensors format.
+        from safetensors.torch import load_file
+        model = load_file(input_model, device="cpu")
+
     arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
 
     if arch_name not in gguf.MODEL_ARCH_NAMES.values():

convert-persimmon-to-gguf.py

@@ -1,11 +1,13 @@
 #!/usr/bin/env python3
-import torch
-import os
-from pprint import pprint
-import sys
 import argparse
+import os
+import sys
 from pathlib import Path
+from pprint import pprint
+
+import torch
 from sentencepiece import SentencePieceProcessor
+
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
@@ -69,7 +71,7 @@ def main():
     persimmon_model = torch.load(args.ckpt_path)
     hparams = persimmon_model['args']
     pprint(hparams)
-    tensors = {}
+    tensors: dict[str, torch.Tensor] = {}
     _flatten_dict(persimmon_model['model'], tensors, None)
 
     arch = gguf.MODEL_ARCH.PERSIMMON

examples/benchmark/benchmark-matmult.cpp

@@ -194,7 +194,7 @@ int main(int argc, char ** argv)  {
     // Set up a the benchmark matrices
     // printf("Creating new tensor q11 & Running quantize\n");
     struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
-    ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements, hist_cur.data());
+    ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], hist_cur.data(), nullptr);
 
     // Set up a the compute graph
     // printf("Creating new tensor q31\n");
@@ -207,7 +207,7 @@ int main(int argc, char ** argv)  {
     // Set up a second graph computation to make sure we override the CPU cache lines
     // printf("Creating new tensor q12 & Running quantize\n");
     struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
-    ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements, hist_cur.data());
+    ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], hist_cur.data(), nullptr);
 
     // printf("Creating new tensor q32\n");
     struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2);

examples/finetune/finetune.cpp

@@ -1138,9 +1138,8 @@ static void save_as_llama_lora(const char * filename, struct my_llama_lora * lor
         return tn_buf.data();
     };
 
-    uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla'
     // write_magic
-    file.write_u32(LLAMA_FILE_MAGIC_LORA);   // magic
+    file.write_u32(LLAMA_FILE_MAGIC_GGLA);   // magic
     file.write_u32(1); // version
     // write_hparams
     file.write_u32(lora->hparams.lora_r);
@@ -1800,7 +1799,9 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> train_tokens;
     std::vector<size_t> train_samples_begin;
     std::vector<size_t> train_samples_size;
-    printf("%s: tokenize training data\n", __func__);
+    printf("%s: tokenize training data from %s\n", __func__, params.common.fn_train_data);
+    printf("%s: sample-start: %s\n", __func__, params.common.sample_start.c_str());
+    printf("%s: include-sample-start: %s\n", __func__, params.common.include_sample_start ? "true" : "false");
     tokenize_file(lctx,
             params.common.fn_train_data,
             params.common.sample_start,

examples/imatrix/README.md

@@ -0,0 +1,32 @@
+# llama.cpp/examples/imatrix
+
+Compute an importance matrix for a model and given text dataset. Can be used during quantization to enchance the quality of the quantum models.
+More information is available here: https://github.com/ggerganov/llama.cpp/pull/4861
+
+## Usage
+
+```
+./imatrix -m <some_fp_model> -f <some_training_data> [-o <output_file>] [--verbosity <verbosity_level>]
+        [-ofreq num_chunks] [-ow <0 or 1>] [other common params]
+```
+
+Here `-m` with a model name and `-f` with a file containing training data (such as e.g. `wiki.train.raw`) are mandatory.
+The parameters in square brackets are optional and have the following meaning:
+* `-o` (or `--output-file`) specifies the name of the file where the computed data will be stored. If missing `imatrix.dat` is used.
+* `--verbosity` specifies the verbosity level. If set to `0`, no output other than the perplexity of the processed chunks will be generated. If set to `1`, each time the results are saved a message is written to `stderr`. If `>=2`, a message is output each time data is collected for any tensor. Default verbosity level is `1`.
+* `-ofreq` (or `--output-frequency`) specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
+* `-ow` (or `--output-weight`) specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default.
+
+For faster computation, make sure to use GPU offloading via the `-ngl` argument
+
+## Example
+
+```bash
+LLAMA_CUBLAS=1 make -j
+
+# generate importance matrix (imatrix.dat)
+./imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99
+
+# use the imatrix to perform a Q4_K_M quantization
+./quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m
+```

examples/imatrix/imatrix.cpp

@@ -26,6 +26,7 @@ struct StatParams {
     std::string ofile = "imatrix.dat";
     int         n_output_frequency = 10;
     int         verbosity = 1;
+    int         keep_every = 0;
     bool        collect_output_weight = false;
 };
 
@@ -33,47 +34,144 @@ class IMatrixCollector {
 public:
     IMatrixCollector() = default;
     void set_parameters(StatParams&& params) { m_params = std::move(params); }
-    void collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1);
+    bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
     void save_imatrix() const;
 private:
     std::unordered_map<std::string, Stats> m_stats;
     StatParams                             m_params;
     std::mutex                             m_mutex;
     int                                    m_last_call = 0;
+    std::vector<float>                     m_src1_data;
+    std::vector<int>                       m_ids; // the expert ids from ggml_mul_mat_id
+                                                  //
+    void save_imatrix(const char * file_name) const;
+    void keep_imatrix(int ncall) const;
 };
 
-void IMatrixCollector::collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1) {
-    if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return;
-    if (!(strncmp(src0->name, "blk.", 4) == 0 || (m_params.collect_output_weight && strcmp(src0->name, "output.weight") == 0))) return;
+bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
+    GGML_UNUSED(user_data);
+
+    const struct ggml_tensor * src0 = t->src[0];
+    const struct ggml_tensor * src1 = t->src[1];
+
+    // 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;
+        return true;
+    }
+
     std::lock_guard<std::mutex> lock(m_mutex);
-    auto& e = m_stats[src0->name];
-    if (e.values.empty()) {
-        e.values.resize(src1->ne[0], 0);
+
+    // copy the data from the GPU memory if needed
+    const bool is_host = ggml_backend_buffer_is_host(src1->buffer);
+
+    if (!is_host) {
+        m_src1_data.resize(ggml_nelements(src1));
+        ggml_backend_tensor_get(src1, m_src1_data.data(), 0, ggml_nbytes(src1));
     }
-    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]);
-        exit(1); //GGML_ASSERT(false);
-    }
-    ++e.ncall;
-    if (m_params.verbosity > 1) {
-        printf("%s[%d]: %s, %d x %d, %d\n",__func__,m_last_call,src0->name,(int)src1->ne[0],(int)src1->ne[1],(int)src1->type);
-    }
-    for (int row = 0; row < (int)src1->ne[1]; ++row) {
-        const float * x = (const float *)src1->data + row * src1->ne[0];
-        for (int j = 0; j < (int)src1->ne[0]; ++j) {
-            e.values[j] += x[j]*x[j];
-        }
-    }
-    if (e.ncall > m_last_call) {
-        m_last_call = e.ncall;
-        if (m_last_call % m_params.n_output_frequency == 0) {
-            save_imatrix();
+
+    const float * data = is_host ? (const float *) src1->data : m_src1_data.data();
+
+    if (t->op == GGML_OP_MUL_MAT_ID) {
+        const int idx  = ((int32_t *) t->op_params)[0];
+        const int n_as = ((int32_t *) t->op_params)[1];
+
+        // the top-k selected expert ids are stored in the src0 tensor
+        // for simplicity, always copy src0 to host, because it is small
+        // take into account that src0 is not contiguous!
+        GGML_ASSERT(src0->ne[1] == src1->ne[1]);
+        GGML_ASSERT(n_as*ggml_nrows(src0)*sizeof(int) == GGML_PAD(ggml_nbytes(src0), n_as*sizeof(int)));
+        m_ids.resize(ggml_nbytes(src0)/sizeof(int));
+        ggml_backend_tensor_get(src0, m_ids.data(), 0, ggml_nbytes(src0));
+
+        // loop over all possible experts, regardless if they are used or not in the batch
+        // 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];
+            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]);
+                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
+            //       using the following line, we can correct for that if needed
+            //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);
+            }
+            for (int row = 0; row < (int)src1->ne[1]; ++row) {
+                const int excur = m_ids[row*n_as + idx];
+                GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check
+                if (excur != ex) continue;
+                const float * x = data + row * src1->ne[0];
+                for (int j = 0; j < (int)src1->ne[0]; ++j) {
+                    e.values[j] += x[j]*x[j];
+                }
+            }
+            if (e.ncall > m_last_call) {
+                m_last_call = e.ncall;
+                if (m_last_call % m_params.n_output_frequency == 0) {
+                    save_imatrix();
+                }
+                if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
+                    keep_imatrix(m_last_call);
+                }
+            }
+        }
+    } else {
+        auto& e = m_stats[src0->name];
+        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]);
+            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);
+        }
+        for (int row = 0; row < (int)src1->ne[1]; ++row) {
+            const float * x = data + row * src1->ne[0];
+            for (int j = 0; j < (int)src1->ne[0]; ++j) {
+                e.values[j] += x[j]*x[j];
+            }
+        }
+        if (e.ncall > m_last_call) {
+            m_last_call = e.ncall;
+            if (m_last_call % m_params.n_output_frequency == 0) {
+                save_imatrix();
+            }
+            if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
+                keep_imatrix(m_last_call);
+            }
         }
     }
+
+    return true;
 }
 
 void IMatrixCollector::save_imatrix() const {
-    const char * fname = m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str();
+    save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str());
+}
+
+void IMatrixCollector::keep_imatrix(int ncall) const {
+    auto file_name = m_params.ofile;
+    if (file_name.empty()) file_name = "imatrix.dat";
+    file_name += ".at_";
+    file_name += std::to_string(ncall);
+    save_imatrix(file_name.c_str());
+}
+
+void IMatrixCollector::save_imatrix(const char * fname) const {
     std::ofstream out(fname, std::ios::binary);
     int n_entries = m_stats.size();
     out.write((const char*)&n_entries, sizeof(n_entries));
@@ -93,8 +191,8 @@ void IMatrixCollector::save_imatrix() const {
 
 static IMatrixCollector g_collector;
 
-static void ik_collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1) {
-    g_collector.collect_imatrix(src0, src1);
+static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
+    return g_collector.collect_imatrix(t, ask, user_data);
 }
 
 
@@ -171,7 +269,7 @@ static void process_logits(
     }
 }
 
-static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
+static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl) {
 
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
     const int n_ctx = llama_n_ctx(ctx);
@@ -192,10 +290,12 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
     }
 
     std::vector<float> logit_history;
-    logit_history.resize(tokens.size());
-
     std::vector<float> prob_history;
-    prob_history.resize(tokens.size());
+
+    if (compute_ppl) {
+        logit_history.resize(tokens.size());
+        prob_history.resize(tokens.size());
+    }
 
     const int n_chunk_max = tokens.size() / n_ctx;
 
@@ -211,12 +311,17 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
 
     std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
 
+    const int num_batches = (n_ctx + n_batch - 1) / n_batch;
+
+    std::vector<float> logits;
+    if (compute_ppl && num_batches > 1) {
+        logits.reserve((size_t)n_ctx * n_vocab);
+    }
+
     for (int i = 0; i < n_chunk; ++i) {
         const int start =     i * n_ctx;
         const int end   = start + n_ctx;
 
-        const int num_batches = (n_ctx + n_batch - 1) / n_batch;
-
         std::vector<float> logits;
 
         const auto t_start = std::chrono::high_resolution_clock::now();
@@ -244,8 +349,10 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
             // restore the original token in case it was set to BOS
             tokens[batch_start] = token_org;
 
-            const auto * batch_logits = llama_get_logits(ctx);
-            logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
+            if (compute_ppl && num_batches > 1) {
+                const auto * batch_logits = llama_get_logits(ctx);
+                logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
+            }
         }
 
         const auto t_end = std::chrono::high_resolution_clock::now();
@@ -261,25 +368,32 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
             fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
         }
 
-        const int first = n_ctx/2;
-        process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
-                       workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
-        count += n_ctx - first - 1;
+        if (compute_ppl) {
+            const int first = n_ctx/2;
+            const auto all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
+            process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
+                    workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
+            count += n_ctx - first - 1;
 
-        printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
-        fflush(stdout);
+            printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+            fflush(stdout);
+
+            logits.clear();
+        }
     }
     printf("\n");
 
-    nll2 /= count;
-    nll /= count;
-    const double ppl = exp(nll);
-    nll2 -= nll * nll;
-    if (nll2 > 0) {
-        nll2 = sqrt(nll2/(count-1));
-        printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
-    } else {
-        printf("Unexpected negative standard deviation of log(prob)\n");
+    if (compute_ppl) {
+        nll2 /= count;
+        nll /= count;
+        const double ppl = exp(nll);
+        nll2 -= nll * nll;
+        if (nll2 > 0) {
+            nll2 = sqrt(nll2/(count-1));
+            printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
+        } else {
+            printf("Unexpected negative standard deviation of log(prob)\n");
+        }
     }
 
     return true;
@@ -288,6 +402,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
 int main(int argc, char ** argv) {
 
     StatParams sparams;
+    bool compute_ppl = true;
     std::vector<char*> args;
     args.push_back(argv[0]);
     int iarg = 1;
@@ -304,12 +419,21 @@ int main(int argc, char ** argv) {
         }
         else if (arg == "--verbosity") {
             sparams.verbosity = std::stoi(argv[++iarg]);
+        } else if (arg == "--no-ppl") {
+            compute_ppl = false;
+        } else if (arg == "--keep-imatrix") {
+            sparams.keep_every = std::stoi(argv[++iarg]);
         } else {
             args.push_back(argv[iarg]);
         }
     }
     if (iarg < argc) {
-        args.push_back(argv[iarg]);
+        std::string arg{argv[iarg]};
+        if (arg == "--no-ppl") {
+            compute_ppl = false;
+        } else {
+            args.push_back(argv[iarg]);
+        }
     }
 
     gpt_params params;
@@ -320,8 +444,6 @@ int main(int argc, char ** argv) {
 
     g_collector.set_parameters(std::move(sparams));
 
-    ggml_set_imatrix_collection(ik_collect_imatrix);
-
     params.logits_all = true;
     params.n_batch = std::min(params.n_batch, params.n_ctx);
 
@@ -340,16 +462,27 @@ int main(int argc, char ** argv) {
 
     llama_backend_init(params.numa);
 
-    llama_model * model;
-    llama_context * ctx;
+    llama_model_params mparams = llama_model_params_from_gpt_params(params);
 
-    // load the model and apply lora adapter, if any
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams);
     if (model == NULL) {
         fprintf(stderr, "%s: error: unable to load model\n", __func__);
         return 1;
     }
 
+    llama_context_params cparams = llama_context_params_from_gpt_params(params);
+
+    // pass the callback to the backend scheduler
+    // it will be executed for each node during the graph computation
+    cparams.cb_eval = ik_collect_imatrix;
+    cparams.cb_eval_user_data = NULL;
+
+    llama_context * ctx = llama_new_context_with_model(model, cparams);
+    if (ctx == NULL) {
+        fprintf(stderr, "%s: error: unable to create context\n", __func__);
+        return 1;
+    }
+
     const int n_ctx_train = llama_n_ctx_train(model);
     if (params.n_ctx > n_ctx_train) {
         fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
@@ -362,7 +495,7 @@ int main(int argc, char ** argv) {
         fprintf(stderr, "%s\n", get_system_info(params).c_str());
     }
 
-    bool OK = compute_imatrix(ctx, params);
+    bool OK = compute_imatrix(ctx, params, compute_ppl);
     if (!OK) {
         return 1;
     }

examples/llama.android/.gitignore

@@ -0,0 +1,33 @@
+# Gradle files
+.gradle/
+build/
+
+# Local configuration file (sdk path, etc)
+local.properties
+
+# Log/OS Files
+*.log
+
+# Android Studio generated files and folders
+captures/
+.externalNativeBuild/
+.cxx/
+*.apk
+output.json
+
+# IntelliJ
+*.iml
+.idea/
+misc.xml
+deploymentTargetDropDown.xml
+render.experimental.xml
+
+# Keystore files
+*.jks
+*.keystore
+
+# Google Services (e.g. APIs or Firebase)
+google-services.json
+
+# Android Profiling
+*.hprof

examples/llama.android/app/.gitignore

@@ -0,0 +1 @@
+/build

examples/llama.android/app/build.gradle.kts

@@ -0,0 +1,91 @@
+plugins {
+    id("com.android.application")
+    id("org.jetbrains.kotlin.android")
+}
+
+android {
+    namespace = "com.example.llama"
+    compileSdk = 34
+
+    ndkVersion = "26.1.10909125"
+
+    defaultConfig {
+        applicationId = "com.example.llama"
+        minSdk = 33
+        targetSdk = 34
+        versionCode = 1
+        versionName = "1.0"
+
+        testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
+        vectorDrawables {
+            useSupportLibrary = true
+        }
+        ndk {
+            // Workaround for https://github.com/llvm/llvm-project/issues/65820
+            // affecting armeabi-v7a. Skip armeabi-v7a when invoked with
+            // -Pskip-armeabi-v7a (e.g., ./gradlew build -Pskip-armeabi-v7a).
+            if (project.hasProperty("skip-armeabi-v7a")) {
+                abiFilters += listOf("arm64-v8a", "x86_64", "x86")
+            }
+        }
+        externalNativeBuild {
+            cmake {
+                cppFlags += listOf()
+                arguments += listOf()
+            }
+        }
+    }
+
+    buildTypes {
+        release {
+            isMinifyEnabled = false
+            proguardFiles(
+                getDefaultProguardFile("proguard-android-optimize.txt"),
+                "proguard-rules.pro"
+            )
+        }
+    }
+    compileOptions {
+        sourceCompatibility = JavaVersion.VERSION_1_8
+        targetCompatibility = JavaVersion.VERSION_1_8
+    }
+    kotlinOptions {
+        jvmTarget = "1.8"
+    }
+    buildFeatures {
+        compose = true
+    }
+    composeOptions {
+        kotlinCompilerExtensionVersion = "1.5.1"
+    }
+    packaging {
+        resources {
+            excludes += "/META-INF/{AL2.0,LGPL2.1}"
+        }
+    }
+    externalNativeBuild {
+        cmake {
+            path = file("src/main/cpp/CMakeLists.txt")
+            version = "3.22.1"
+        }
+    }
+}
+
+dependencies {
+
+    implementation("androidx.core:core-ktx:1.12.0")
+    implementation("androidx.lifecycle:lifecycle-runtime-ktx:2.6.2")
+    implementation("androidx.activity:activity-compose:1.8.2")
+    implementation(platform("androidx.compose:compose-bom:2023.08.00"))
+    implementation("androidx.compose.ui:ui")
+    implementation("androidx.compose.ui:ui-graphics")
+    implementation("androidx.compose.ui:ui-tooling-preview")
+    implementation("androidx.compose.material3:material3")
+    testImplementation("junit:junit:4.13.2")
+    androidTestImplementation("androidx.test.ext:junit:1.1.5")
+    androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
+    androidTestImplementation(platform("androidx.compose:compose-bom:2023.08.00"))
+    androidTestImplementation("androidx.compose.ui:ui-test-junit4")
+    debugImplementation("androidx.compose.ui:ui-tooling")
+    debugImplementation("androidx.compose.ui:ui-test-manifest")
+}

examples/llama.android/app/proguard-rules.pro

@@ -0,0 +1,21 @@
+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile

examples/llama.android/app/src/main/AndroidManifest.xml

@@ -0,0 +1,30 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:tools="http://schemas.android.com/tools">
+
+    <uses-permission android:name="android.permission.INTERNET" />
+
+    <application
+        android:allowBackup="true"
+        android:dataExtractionRules="@xml/data_extraction_rules"
+        android:fullBackupContent="@xml/backup_rules"
+        android:icon="@mipmap/ic_launcher"
+        android:label="@string/app_name"
+        android:roundIcon="@mipmap/ic_launcher_round"
+        android:supportsRtl="true"
+        android:theme="@style/Theme.LlamaAndroid"
+        >
+
+        <activity
+            android:name=".MainActivity"
+            android:exported="true"
+            android:theme="@style/Theme.LlamaAndroid">
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN" />
+
+                <category android:name="android.intent.category.LAUNCHER" />
+            </intent-filter>
+        </activity>
+    </application>
+
+</manifest>

examples/llama.android/app/src/main/cpp/CMakeLists.txt

@@ -0,0 +1,50 @@
+
+# For more information about using CMake with Android Studio, read the
+# documentation: https://d.android.com/studio/projects/add-native-code.html.
+# For more examples on how to use CMake, see https://github.com/android/ndk-samples.
+
+# Sets the minimum CMake version required for this project.
+cmake_minimum_required(VERSION 3.22.1)
+
+# Declares the project name. The project name can be accessed via ${ PROJECT_NAME},
+# Since this is the top level CMakeLists.txt, the project name is also accessible
+# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level
+# build script scope).
+project("llama-android")
+
+include(FetchContent)
+FetchContent_Declare(
+        llama
+        GIT_REPOSITORY https://github.com/ggerganov/llama.cpp
+        GIT_TAG        master
+)
+
+# Also provides "common"
+FetchContent_MakeAvailable(llama)
+
+# Creates and names a library, sets it as either STATIC
+# or SHARED, and provides the relative paths to its source code.
+# You can define multiple libraries, and CMake builds them for you.
+# Gradle automatically packages shared libraries with your APK.
+#
+# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define
+# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME}
+# is preferred for the same purpose.
+#
+# In order to load a library into your app from Java/Kotlin, you must call
+# System.loadLibrary() and pass the name of the library defined here;
+# for GameActivity/NativeActivity derived applications, the same library name must be
+# used in the AndroidManifest.xml file.
+add_library(${CMAKE_PROJECT_NAME} SHARED
+    # List C/C++ source files with relative paths to this CMakeLists.txt.
+    llama-android.cpp)
+
+# Specifies libraries CMake should link to your target library. You
+# can link libraries from various origins, such as libraries defined in this
+# build script, prebuilt third-party libraries, or Android system libraries.
+target_link_libraries(${CMAKE_PROJECT_NAME}
+    # List libraries link to the target library
+    llama
+    common
+    android
+    log)

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

@@ -0,0 +1,394 @@
+#include <android/log.h>
+#include <jni.h>
+#include <iomanip>
+#include <math.h>
+#include <string>
+#include <unistd.h>
+#include "llama.h"
+#include "common/common.h"
+
+// Write C++ code here.
+//
+// Do not forget to dynamically load the C++ library into your application.
+//
+// For instance,
+//
+// In MainActivity.java:
+//    static {
+//       System.loadLibrary("llama-android");
+//    }
+//
+// Or, in MainActivity.kt:
+//    companion object {
+//      init {
+//         System.loadLibrary("llama-android")
+//      }
+//    }
+
+#define TAG "llama-android.cpp"
+#define LOGi(...) __android_log_print(ANDROID_LOG_INFO, TAG, __VA_ARGS__)
+#define LOGe(...) __android_log_print(ANDROID_LOG_ERROR, TAG, __VA_ARGS__)
+
+jclass la_int_var;
+jmethodID la_int_var_value;
+jmethodID la_int_var_inc;
+
+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);
+    else if (level == GGML_LOG_LEVEL_WARN) __android_log_print(ANDROID_LOG_WARN, TAG, fmt, data);
+    else __android_log_print(ANDROID_LOG_DEFAULT, TAG, fmt, data);
+}
+
+extern "C"
+JNIEXPORT jlong JNICALL
+Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) {
+    llama_model_params model_params = llama_model_default_params();
+
+    auto path_to_model = env->GetStringUTFChars(filename, 0);
+    LOGi("Loading model from %s", path_to_model);
+
+    auto model = llama_load_model_from_file(path_to_model, model_params);
+    env->ReleaseStringUTFChars(filename, path_to_model);
+
+    if (!model) {
+        LOGe("load_model() failed");
+        env->ThrowNew(env->FindClass("java/lang/IllegalStateException"), "load_model() failed");
+        return 0;
+    }
+
+    return reinterpret_cast<jlong>(model);
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) {
+    llama_free_model(reinterpret_cast<llama_model *>(model));
+}
+
+extern "C"
+JNIEXPORT jlong JNICALL
+Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) {
+    auto model = reinterpret_cast<llama_model *>(jmodel);
+
+    if (!model) {
+        LOGe("new_context(): model cannot be null");
+        env->ThrowNew(env->FindClass("java/lang/IllegalArgumentException"), "Model cannot be null");
+        return 0;
+    }
+
+    int n_threads = std::max(1, std::min(8, (int) sysconf(_SC_NPROCESSORS_ONLN) - 2));
+    LOGi("Using %d threads", n_threads);
+
+    llama_context_params ctx_params = llama_context_default_params();
+    ctx_params.seed  = 1234;
+    ctx_params.n_ctx = 2048;
+    ctx_params.n_threads       = n_threads;
+    ctx_params.n_threads_batch = n_threads;
+
+    llama_context * context = llama_new_context_with_model(model, ctx_params);
+
+    if (!context) {
+        LOGe("llama_new_context_with_model() returned null)");
+        env->ThrowNew(env->FindClass("java/lang/IllegalStateException"),
+                      "llama_new_context_with_model() returned null)");
+        return 0;
+    }
+
+    return reinterpret_cast<jlong>(context);
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) {
+    llama_free(reinterpret_cast<llama_context *>(context));
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) {
+    llama_backend_free();
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) {
+    llama_log_set(log_callback, NULL);
+}
+
+extern "C"
+JNIEXPORT jstring JNICALL
+Java_com_example_llama_Llm_bench_1model(
+        JNIEnv *env,
+        jobject,
+        jlong context_pointer,
+        jlong model_pointer,
+        jlong batch_pointer,
+        jint pp,
+        jint tg,
+        jint pl,
+        jint nr
+        ) {
+    auto pp_avg = 0.0;
+    auto tg_avg = 0.0;
+    auto pp_std = 0.0;
+    auto tg_std = 0.0;
+
+    const auto context = reinterpret_cast<llama_context *>(context_pointer);
+    const auto model = reinterpret_cast<llama_model *>(model_pointer);
+    const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
+
+    const int n_ctx = llama_n_ctx(context);
+
+    LOGi("n_ctx = %d", n_ctx);
+
+    int i, j;
+    int nri;
+    for (nri = 0; nri < nr; nri++) {
+        LOGi("Benchmark prompt processing (pp)");
+
+        llama_batch_clear(*batch);
+
+        const int n_tokens = pp;
+        for (i = 0; i < n_tokens; i++) {
+            llama_batch_add(*batch, 0, i, { 0 }, false);
+        }
+
+        batch->logits[batch->n_tokens - 1] = true;
+        llama_kv_cache_clear(context);
+
+        const auto t_pp_start = ggml_time_us();
+        if (llama_decode(context, *batch) != 0) {
+            LOGi("llama_decode() failed during prompt processing");
+        }
+        const auto t_pp_end = ggml_time_us();
+
+        // bench text generation
+
+        LOGi("Benchmark text generation (tg)");
+
+        llama_kv_cache_clear(context);
+        const auto t_tg_start = ggml_time_us();
+        for (i = 0; i < tg; i++) {
+
+            llama_batch_clear(*batch);
+            for (j = 0; j < pl; j++) {
+                llama_batch_add(*batch, 0, i, { j }, true);
+            }
+
+            LOGi("llama_decode() text generation: %d", i);
+            if (llama_decode(context, *batch) != 0) {
+                LOGi("llama_decode() failed during text generation");
+            }
+        }
+
+        const auto t_tg_end = ggml_time_us();
+
+        llama_kv_cache_clear(context);
+
+        const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0;
+        const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0;
+
+        const auto speed_pp = double(pp) / t_pp;
+        const auto speed_tg = double(pl * tg) / t_tg;
+
+        pp_avg += speed_pp;
+        tg_avg += speed_tg;
+
+        pp_std += speed_pp * speed_pp;
+        tg_std += speed_tg * speed_tg;
+
+        LOGi("pp %f t/s, tg %f t/s", speed_pp, speed_tg);
+    }
+
+    pp_avg /= double(nr);
+    tg_avg /= double(nr);
+
+    if (nr > 1) {
+        pp_std = sqrt(pp_std / double(nr - 1) - pp_avg * pp_avg * double(nr) / double(nr - 1));
+        tg_std = sqrt(tg_std / double(nr - 1) - tg_avg * tg_avg * double(nr) / double(nr - 1));
+    } else {
+        pp_std = 0;
+        tg_std = 0;
+    }
+
+    char model_desc[128];
+    llama_model_desc(model, model_desc, sizeof(model_desc));
+
+    const auto model_size     = double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0;
+    const auto model_n_params = double(llama_model_n_params(model)) / 1e9;
+
+    const auto backend    = "(Android)"; // TODO: What should this be?
+
+    std::stringstream result;
+    result << std::setprecision(2);
+    result << "| model | size | params | backend | test | t/s |\n";
+    result << "| --- | --- | --- | --- | --- | --- |\n";
+    result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | pp " << pp << " | " << pp_avg << " ± " << pp_std << " |\n";
+    result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | tg " << tg << " | " << tg_avg << " ± " << tg_std << " |\n";
+
+    return env->NewStringUTF(result.str().c_str());
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
+    llama_batch_free(*reinterpret_cast<llama_batch *>(batch_pointer));
+}
+
+extern "C"
+JNIEXPORT jlong JNICALL
+Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
+
+    // Source: Copy of llama.cpp:llama_batch_init but heap-allocated.
+
+    llama_batch *batch = new llama_batch {
+        0,
+        nullptr,
+        nullptr,
+        nullptr,
+        nullptr,
+        nullptr,
+        nullptr,
+        0,
+        0,
+        0,
+    };
+
+    if (embd) {
+        batch->embd = (float *) malloc(sizeof(float) * n_tokens * embd);
+    } else {
+        batch->token = (llama_token *) malloc(sizeof(llama_token) * n_tokens);
+    }
+
+    batch->pos      = (llama_pos *)     malloc(sizeof(llama_pos)      * n_tokens);
+    batch->n_seq_id = (int32_t *)       malloc(sizeof(int32_t)        * n_tokens);
+    batch->seq_id   = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens);
+    for (int i = 0; i < n_tokens; ++i) {
+        batch->seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
+    }
+    batch->logits   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens);
+
+    return reinterpret_cast<jlong>(batch);
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject, jboolean numa) {
+    llama_backend_init(numa);
+}
+
+extern "C"
+JNIEXPORT jstring JNICALL
+Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) {
+    return env->NewStringUTF(llama_print_system_info());
+}
+
+extern "C"
+JNIEXPORT jint JNICALL
+Java_com_example_llama_Llm_completion_1init(
+        JNIEnv *env,
+        jobject,
+        jlong context_pointer,
+        jlong batch_pointer,
+        jstring jtext,
+        jint n_len
+    ) {
+
+    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);
+
+    const auto tokens_list = llama_tokenize(context, text, 1);
+
+    auto n_ctx = llama_n_ctx(context);
+    auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
+
+    LOGi("n_len = %d, n_ctx = %d, n_kv_req = %d", n_len, n_ctx, n_kv_req);
+
+    if (n_kv_req > n_ctx) {
+        LOGe("error: n_kv_req > n_ctx, the required KV cache size is not big enough");
+    }
+
+    for (auto id : tokens_list) {
+        LOGi("%s", llama_token_to_piece(context, id).c_str());
+    }
+
+    llama_batch_clear(*batch);
+
+    // evaluate the initial prompt
+    for (auto i = 0; i < tokens_list.size(); i++) {
+        llama_batch_add(*batch, tokens_list[i], i, { 0 }, false);
+    }
+
+    // llama_decode will output logits only for the last token of the prompt
+    batch->logits[batch->n_tokens - 1] = true;
+
+    if (llama_decode(context, *batch) != 0) {
+        LOGe("llama_decode() failed");
+    }
+
+    env->ReleaseStringUTFChars(jtext, text);
+
+    return batch->n_tokens;
+}
+
+extern "C"
+JNIEXPORT jstring JNICALL
+Java_com_example_llama_Llm_completion_1loop(
+        JNIEnv * env,
+        jobject,
+        jlong context_pointer,
+        jlong batch_pointer,
+        jint n_len,
+        jobject intvar_ncur
+) {
+    const auto context = reinterpret_cast<llama_context *>(context_pointer);
+    const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
+    const auto model = llama_get_model(context);
+
+    if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur);
+    if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I");
+    if (!la_int_var_inc) la_int_var_inc = env->GetMethodID(la_int_var, "inc", "()V");
+
+    auto n_vocab = llama_n_vocab(model);
+    auto logits = llama_get_logits_ith(context, batch->n_tokens - 1);
+
+    std::vector<llama_token_data> candidates;
+    candidates.reserve(n_vocab);
+
+    for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+        candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
+    }
+
+    llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+
+    // sample the most likely token
+    const auto new_token_id = llama_sample_token_greedy(context, &candidates_p);
+
+    const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
+    if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+        return env->NewStringUTF("");
+    }
+
+    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());
+
+    llama_batch_clear(*batch);
+    llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true);
+
+    env->CallVoidMethod(intvar_ncur, la_int_var_inc);
+
+    if (llama_decode(context, *batch) != 0) {
+        LOGe("llama_decode() returned null");
+    }
+
+    return new_token;
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
+    llama_kv_cache_clear(reinterpret_cast<llama_context *>(context));
+}

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

@@ -0,0 +1,119 @@
+package com.example.llama
+
+import android.app.DownloadManager
+import android.net.Uri
+import android.util.Log
+import androidx.compose.material3.Button
+import androidx.compose.material3.Text
+import androidx.compose.runtime.Composable
+import androidx.compose.runtime.getValue
+import androidx.compose.runtime.mutableDoubleStateOf
+import androidx.compose.runtime.mutableStateOf
+import androidx.compose.runtime.remember
+import androidx.compose.runtime.rememberCoroutineScope
+import androidx.compose.runtime.setValue
+import androidx.core.database.getLongOrNull
+import androidx.core.net.toUri
+import kotlinx.coroutines.delay
+import kotlinx.coroutines.launch
+import java.io.File
+
+data class Downloadable(val name: String, val source: Uri, val destination: File) {
+    companion object {
+        @JvmStatic
+        private val tag: String? = this::class.qualifiedName
+
+        sealed interface State
+        data object Ready: State
+        data class Downloading(val id: Long): State
+        data class Downloaded(val downloadable: Downloadable): State
+        data class Error(val message: String): State
+
+        @JvmStatic
+        @Composable
+        fun Button(viewModel: MainViewModel, dm: DownloadManager, item: Downloadable) {
+            var status: State by remember {
+                mutableStateOf(
+                    if (item.destination.exists()) Downloaded(item)
+                    else Ready
+                )
+            }
+            var progress by remember { mutableDoubleStateOf(0.0) }
+
+            val coroutineScope = rememberCoroutineScope()
+
+            suspend fun waitForDownload(result: Downloading, item: Downloadable): State {
+                while (true) {
+                    val cursor = dm.query(DownloadManager.Query().setFilterById(result.id))
+
+                    if (cursor == null) {
+                        Log.e(tag, "dm.query() returned null")
+                        return Error("dm.query() returned null")
+                    }
+
+                    if (!cursor.moveToFirst() || cursor.count < 1) {
+                        cursor.close()
+                        Log.i(tag, "cursor.moveToFirst() returned false or cursor.count < 1, download canceled?")
+                        return Ready
+                    }
+
+                    val pix = cursor.getColumnIndex(DownloadManager.COLUMN_BYTES_DOWNLOADED_SO_FAR)
+                    val tix = cursor.getColumnIndex(DownloadManager.COLUMN_TOTAL_SIZE_BYTES)
+                    val sofar = cursor.getLongOrNull(pix) ?: 0
+                    val total = cursor.getLongOrNull(tix) ?: 1
+                    cursor.close()
+
+                    if (sofar == total) {
+                        return Downloaded(item)
+                    }
+
+                    progress = (sofar * 1.0) / total
+
+                    delay(1000L)
+                }
+            }
+
+            fun onClick() {
+                when (val s = status) {
+                    is Downloaded -> {
+                        viewModel.load(item.destination.path)
+                    }
+
+                    is Downloading -> {
+                        coroutineScope.launch {
+                            status = waitForDownload(s, item)
+                        }
+                    }
+
+                    else -> {
+                        item.destination.delete()
+
+                        val request = DownloadManager.Request(item.source).apply {
+                            setTitle("Downloading model")
+                            setDescription("Downloading model: ${item.name}")
+                            setAllowedNetworkTypes(DownloadManager.Request.NETWORK_WIFI)
+                            setDestinationUri(item.destination.toUri())
+                        }
+
+                        viewModel.log("Saving ${item.name} to ${item.destination.path}")
+                        Log.i(tag, "Saving ${item.name} to ${item.destination.path}")
+
+                        val id = dm.enqueue(request)
+                        status = Downloading(id)
+                        onClick()
+                    }
+                }
+            }
+
+            Button(onClick = { onClick() }, enabled = status !is Downloading) {
+                when (status) {
+                    is Downloading -> Text(text = "Downloading ${(progress * 100).toInt()}%")
+                    is Downloaded -> Text("Load ${item.name}")
+                    is Ready -> Text("Download ${item.name}")
+                    is Error -> Text("Download ${item.name}")
+                }
+            }
+        }
+
+    }
+}

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

@@ -0,0 +1,172 @@
+package com.example.llama
+
+import android.util.Log
+import kotlinx.coroutines.CoroutineDispatcher
+import kotlinx.coroutines.asCoroutineDispatcher
+import kotlinx.coroutines.flow.Flow
+import kotlinx.coroutines.flow.flow
+import kotlinx.coroutines.flow.flowOn
+import kotlinx.coroutines.withContext
+import java.util.concurrent.Executors
+import kotlin.concurrent.thread
+
+class Llm {
+    private val tag: String? = this::class.simpleName
+
+    private val threadLocalState: ThreadLocal<State> = ThreadLocal.withInitial { State.Idle }
+
+    private val runLoop: CoroutineDispatcher = Executors.newSingleThreadExecutor {
+        thread(start = false, name = "Llm-RunLoop") {
+            Log.d(tag, "Dedicated thread for native code: ${Thread.currentThread().name}")
+
+            // No-op if called more than once.
+            System.loadLibrary("llama-android")
+
+            // Set llama log handler to Android
+            log_to_android()
+            backend_init(false)
+
+            Log.d(tag, system_info())
+
+            it.run()
+        }.apply {
+            uncaughtExceptionHandler = Thread.UncaughtExceptionHandler { _, exception: Throwable ->
+                Log.e(tag, "Unhandled exception", exception)
+            }
+        }
+    }.asCoroutineDispatcher()
+
+    private val nlen: Int = 64
+
+    private external fun log_to_android()
+    private external fun load_model(filename: String): Long
+    private external fun free_model(model: Long)
+    private external fun new_context(model: Long): Long
+    private external fun free_context(context: Long)
+    private external fun backend_init(numa: Boolean)
+    private external fun backend_free()
+    private external fun free_batch(batch: Long)
+    private external fun new_batch(nTokens: Int, embd: Int, nSeqMax: Int): Long
+    private external fun bench_model(
+        context: Long,
+        model: Long,
+        batch: Long,
+        pp: Int,
+        tg: Int,
+        pl: Int,
+        nr: Int
+    ): String
+
+    private external fun system_info(): String
+
+    private external fun completion_init(
+        context: Long,
+        batch: Long,
+        text: String,
+        nLen: Int
+    ): Int
+
+    private external fun completion_loop(
+        context: Long,
+        batch: Long,
+        nLen: Int,
+        ncur: IntVar
+    ): String
+
+    private external fun kv_cache_clear(context: Long)
+
+    suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1): String {
+        return withContext(runLoop) {
+            when (val state = threadLocalState.get()) {
+                is State.Loaded -> {
+                    Log.d(tag, "bench(): $state")
+                    bench_model(state.context, state.model, state.batch, pp, tg, pl, nr)
+                }
+
+                else -> throw IllegalStateException("No model loaded")
+            }
+        }
+    }
+
+    suspend fun load(pathToModel: String) {
+        withContext(runLoop) {
+            when (threadLocalState.get()) {
+                is State.Idle -> {
+                    val model = load_model(pathToModel)
+                    if (model == 0L)  throw IllegalStateException("load_model() failed")
+
+                    val context = new_context(model)
+                    if (context == 0L) throw IllegalStateException("new_context() failed")
+
+                    val batch = new_batch(512, 0, 1)
+                    if (batch == 0L) throw IllegalStateException("new_batch() failed")
+
+                    Log.i(tag, "Loaded model $pathToModel")
+                    threadLocalState.set(State.Loaded(model, context, batch))
+                }
+                else -> throw IllegalStateException("Model already loaded")
+            }
+        }
+    }
+
+    fun send(message: String): Flow<String> = flow {
+        when (val state = threadLocalState.get()) {
+            is State.Loaded -> {
+                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()) {
+                        break
+                    }
+                    emit(str)
+                }
+                kv_cache_clear(state.context)
+            }
+            else -> {}
+        }
+    }.flowOn(runLoop)
+
+    /**
+     * Unloads the model and frees resources.
+     *
+     * This is a no-op if there's no model loaded.
+     */
+    suspend fun unload() {
+        withContext(runLoop) {
+            when (val state = threadLocalState.get()) {
+                is State.Loaded -> {
+                    free_context(state.context)
+                    free_model(state.model)
+                    free_batch(state.batch)
+
+                    threadLocalState.set(State.Idle)
+                }
+                else -> {}
+            }
+        }
+    }
+
+    companion object {
+        private class IntVar(value: Int) {
+            @Volatile
+            var value: Int = value
+                private set
+
+            fun inc() {
+                synchronized(this) {
+                    value += 1
+                }
+            }
+        }
+
+        private sealed interface State {
+            data object Idle: State
+            data class Loaded(val model: Long, val context: Long, val batch: Long): State
+        }
+
+        // Enforce only one instance of Llm.
+        private val _instance: Llm = Llm()
+
+        fun instance(): Llm = _instance
+    }
+}

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

@@ -0,0 +1,154 @@
+package com.example.llama
+
+import android.app.ActivityManager
+import android.app.DownloadManager
+import android.content.ClipData
+import android.content.ClipboardManager
+import android.net.Uri
+import android.os.Bundle
+import android.os.StrictMode
+import android.os.StrictMode.VmPolicy
+import android.text.format.Formatter
+import androidx.activity.ComponentActivity
+import androidx.activity.compose.setContent
+import androidx.activity.viewModels
+import androidx.compose.foundation.layout.Box
+import androidx.compose.foundation.layout.Column
+import androidx.compose.foundation.layout.Row
+import androidx.compose.foundation.layout.fillMaxSize
+import androidx.compose.foundation.layout.padding
+import androidx.compose.foundation.lazy.LazyColumn
+import androidx.compose.foundation.lazy.items
+import androidx.compose.foundation.lazy.rememberLazyListState
+import androidx.compose.material3.Button
+import androidx.compose.material3.LocalContentColor
+import androidx.compose.material3.MaterialTheme
+import androidx.compose.material3.OutlinedTextField
+import androidx.compose.material3.Surface
+import androidx.compose.material3.Text
+import androidx.compose.runtime.Composable
+import androidx.compose.ui.Modifier
+import androidx.compose.ui.unit.dp
+import androidx.core.content.getSystemService
+import com.example.llama.ui.theme.LlamaAndroidTheme
+import java.io.File
+
+class MainActivity(
+    activityManager: ActivityManager? = null,
+    downloadManager: DownloadManager? = null,
+    clipboardManager: ClipboardManager? = null,
+): ComponentActivity() {
+    private val tag: String? = this::class.simpleName
+
+    private val activityManager by lazy { activityManager ?: getSystemService<ActivityManager>()!! }
+    private val downloadManager by lazy { downloadManager ?: getSystemService<DownloadManager>()!! }
+    private val clipboardManager by lazy { clipboardManager ?: getSystemService<ClipboardManager>()!! }
+
+    private val viewModel: MainViewModel by viewModels()
+
+    // Get a MemoryInfo object for the device's current memory status.
+    private fun availableMemory(): ActivityManager.MemoryInfo {
+        return ActivityManager.MemoryInfo().also { memoryInfo ->
+            activityManager.getMemoryInfo(memoryInfo)
+        }
+    }
+
+    override fun onCreate(savedInstanceState: Bundle?) {
+        super.onCreate(savedInstanceState)
+
+        StrictMode.setVmPolicy(
+            VmPolicy.Builder(StrictMode.getVmPolicy())
+                .detectLeakedClosableObjects()
+                .build()
+        )
+
+        val free = Formatter.formatFileSize(this, availableMemory().availMem)
+        val total = Formatter.formatFileSize(this, availableMemory().totalMem)
+
+        viewModel.log("Current memory: $free / $total")
+        viewModel.log("Downloads directory: ${getExternalFilesDir(null)}")
+
+        val extFilesDir = getExternalFilesDir(null)
+
+        val models = listOf(
+            Downloadable(
+                "Phi-2 7B (Q4_0, 1.6 GiB)",
+                Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true"),
+                File(extFilesDir, "phi-2-q4_0.gguf"),
+            ),
+            Downloadable(
+                "TinyLlama 1.1B (f16, 2.2 GiB)",
+                Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true"),
+                File(extFilesDir, "tinyllama-1.1-f16.gguf"),
+            ),
+            Downloadable(
+                "Phi 2 DPO (Q3_K_M, 1.48 GiB)",
+                Uri.parse("https://huggingface.co/TheBloke/phi-2-dpo-GGUF/resolve/main/phi-2-dpo.Q3_K_M.gguf?download=true"),
+                File(extFilesDir, "phi-2-dpo.Q3_K_M.gguf")
+            ),
+        )
+
+        setContent {
+            LlamaAndroidTheme {
+                // A surface container using the 'background' color from the theme
+                Surface(
+                    modifier = Modifier.fillMaxSize(),
+                    color = MaterialTheme.colorScheme.background
+                ) {
+                    MainCompose(
+                        viewModel,
+                        clipboardManager,
+                        downloadManager,
+                        models,
+                    )
+                }
+
+            }
+        }
+    }
+}
+
+@Composable
+fun MainCompose(
+    viewModel: MainViewModel,
+    clipboard: ClipboardManager,
+    dm: DownloadManager,
+    models: List<Downloadable>
+) {
+    Column {
+        val scrollState = rememberLazyListState()
+
+        Box(modifier = Modifier.weight(1f)) {
+            LazyColumn(state = scrollState) {
+                items(viewModel.messages) {
+                    Text(
+                        it,
+                        style = MaterialTheme.typography.bodyLarge.copy(color = LocalContentColor.current),
+                        modifier = Modifier.padding(16.dp)
+                    )
+                }
+            }
+        }
+        OutlinedTextField(
+            value = viewModel.message,
+            onValueChange = { viewModel.updateMessage(it) },
+            label = { Text("Message") },
+        )
+        Row {
+            Button({ viewModel.send() }) { Text("Send") }
+            Button({ viewModel.bench(8, 4, 1) }) { Text("Bench") }
+            Button({ viewModel.clear() }) { Text("Clear") }
+            Button({
+                viewModel.messages.joinToString("\n").let {
+                    clipboard.setPrimaryClip(ClipData.newPlainText("", it))
+                }
+            }) { Text("Copy") }
+        }
+
+        Column {
+            for (model in models) {
+                Downloadable.Button(viewModel, dm, model)
+            }
+        }
+    }
+}

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

@@ -0,0 +1,104 @@
+package com.example.llama
+
+import android.util.Log
+import androidx.compose.runtime.getValue
+import androidx.compose.runtime.mutableStateOf
+import androidx.compose.runtime.setValue
+import androidx.lifecycle.ViewModel
+import androidx.lifecycle.viewModelScope
+import kotlinx.coroutines.flow.catch
+import kotlinx.coroutines.launch
+
+class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
+    companion object {
+        @JvmStatic
+        private val NanosPerSecond = 1_000_000_000.0
+    }
+
+    private val tag: String? = this::class.simpleName
+
+    var messages by mutableStateOf(listOf("Initializing..."))
+        private set
+
+    var message by mutableStateOf("")
+        private set
+
+    override fun onCleared() {
+        super.onCleared()
+
+        viewModelScope.launch {
+            try {
+                llm.unload()
+            } catch (exc: IllegalStateException) {
+                messages += exc.message!!
+            }
+        }
+    }
+
+    fun send() {
+        val text = message
+        message = ""
+
+        // Add to messages console.
+        messages += text
+        messages += ""
+
+        viewModelScope.launch {
+            llm.send(text)
+                .catch {
+                    Log.e(tag, "send() failed", it)
+                    messages += it.message!!
+                }
+                .collect { messages = messages.dropLast(1) + (messages.last() + it) }
+        }
+    }
+
+    fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) {
+        viewModelScope.launch {
+            try {
+                val start = System.nanoTime()
+                val warmupResult = llm.bench(pp, tg, pl, nr)
+                val end = System.nanoTime()
+
+                messages += warmupResult
+
+                val warmup = (end - start).toDouble() / NanosPerSecond
+                messages += "Warm up time: $warmup seconds, please wait..."
+
+                if (warmup > 5.0) {
+                    messages += "Warm up took too long, aborting benchmark"
+                    return@launch
+                }
+
+                messages += llm.bench(512, 128, 1, 3)
+            } catch (exc: IllegalStateException) {
+                Log.e(tag, "bench() failed", exc)
+                messages += exc.message!!
+            }
+        }
+    }
+
+    fun load(pathToModel: String) {
+        viewModelScope.launch {
+            try {
+                llm.load(pathToModel)
+                messages += "Loaded $pathToModel"
+            } catch (exc: IllegalStateException) {
+                Log.e(tag, "load() failed", exc)
+                messages += exc.message!!
+            }
+        }
+    }
+
+    fun updateMessage(newMessage: String) {
+        message = newMessage
+    }
+
+    fun clear() {
+        messages = listOf()
+    }
+
+    fun log(message: String) {
+        messages += message
+    }
+}

examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt

@@ -0,0 +1,11 @@
+package com.example.llama.ui.theme
+
+import androidx.compose.ui.graphics.Color
+
+val Purple80 = Color(0xFFD0BCFF)
+val PurpleGrey80 = Color(0xFFCCC2DC)
+val Pink80 = Color(0xFFEFB8C8)
+
+val Purple40 = Color(0xFF6650a4)
+val PurpleGrey40 = Color(0xFF625b71)
+val Pink40 = Color(0xFF7D5260)

examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt

@@ -0,0 +1,70 @@
+package com.example.llama.ui.theme
+
+import android.app.Activity
+import android.os.Build
+import androidx.compose.foundation.isSystemInDarkTheme
+import androidx.compose.material3.MaterialTheme
+import androidx.compose.material3.darkColorScheme
+import androidx.compose.material3.dynamicDarkColorScheme
+import androidx.compose.material3.dynamicLightColorScheme
+import androidx.compose.material3.lightColorScheme
+import androidx.compose.runtime.Composable
+import androidx.compose.runtime.SideEffect
+import androidx.compose.ui.graphics.toArgb
+import androidx.compose.ui.platform.LocalContext
+import androidx.compose.ui.platform.LocalView
+import androidx.core.view.WindowCompat
+
+private val DarkColorScheme = darkColorScheme(
+    primary = Purple80,
+    secondary = PurpleGrey80,
+    tertiary = Pink80
+)
+
+private val LightColorScheme = lightColorScheme(
+    primary = Purple40,
+    secondary = PurpleGrey40,
+    tertiary = Pink40
+
+    /* Other default colors to override
+    background = Color(0xFFFFFBFE),
+    surface = Color(0xFFFFFBFE),
+    onPrimary = Color.White,
+    onSecondary = Color.White,
+    onTertiary = Color.White,
+    onBackground = Color(0xFF1C1B1F),
+    onSurface = Color(0xFF1C1B1F),
+    */
+)
+
+@Composable
+fun LlamaAndroidTheme(
+    darkTheme: Boolean = isSystemInDarkTheme(),
+    // Dynamic color is available on Android 12+
+    dynamicColor: Boolean = true,
+    content: @Composable () -> Unit
+) {
+    val colorScheme = when {
+        dynamicColor && Build.VERSION.SDK_INT >= Build.VERSION_CODES.S -> {
+            val context = LocalContext.current
+            if (darkTheme) dynamicDarkColorScheme(context) else dynamicLightColorScheme(context)
+        }
+
+        darkTheme -> DarkColorScheme
+        else -> LightColorScheme
+    }
+    val view = LocalView.current
+    if (!view.isInEditMode) {
+        SideEffect {
+            val window = (view.context as Activity).window
+            window.statusBarColor = colorScheme.primary.toArgb()
+            WindowCompat.getInsetsController(window, view).isAppearanceLightStatusBars = darkTheme
+        }
+    }
+
+    MaterialTheme(
+        colorScheme = colorScheme,
+        typography = Typography,
+        content = content
+    )
+}

examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt

@@ -0,0 +1,34 @@
+package com.example.llama.ui.theme
+
+import androidx.compose.material3.Typography
+import androidx.compose.ui.text.TextStyle
+import androidx.compose.ui.text.font.FontFamily
+import androidx.compose.ui.text.font.FontWeight
+import androidx.compose.ui.unit.sp
+
+// Set of Material typography styles to start with
+val Typography = Typography(
+    bodyLarge = TextStyle(
+        fontFamily = FontFamily.Default,
+        fontWeight = FontWeight.Normal,
+        fontSize = 16.sp,
+        lineHeight = 24.sp,
+        letterSpacing = 0.5.sp
+    )
+    /* Other default text styles to override
+    titleLarge = TextStyle(
+        fontFamily = FontFamily.Default,
+        fontWeight = FontWeight.Normal,
+        fontSize = 22.sp,
+        lineHeight = 28.sp,
+        letterSpacing = 0.sp
+    ),
+    labelSmall = TextStyle(
+        fontFamily = FontFamily.Default,
+        fontWeight = FontWeight.Medium,
+        fontSize = 11.sp,
+        lineHeight = 16.sp,
+        letterSpacing = 0.5.sp
+    )
+    */
+)

examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml

@@ -0,0 +1,170 @@
+<?xml version="1.0" encoding="utf-8"?>
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path
+        android:fillColor="#3DDC84"
+        android:pathData="M0,0h108v108h-108z" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M9,0L9,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,0L19,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,0L29,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,0L39,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M49,0L49,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M59,0L59,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M69,0L69,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M79,0L79,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M89,0L89,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M99,0L99,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,9L108,9"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,19L108,19"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,29L108,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,39L108,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,49L108,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,59L108,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,69L108,69"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,79L108,79"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,89L108,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,99L108,99"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,29L89,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,39L89,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,49L89,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,59L89,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,69L89,69"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,79L89,79"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,19L29,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,19L39,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M49,19L49,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M59,19L59,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M69,19L69,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M79,19L79,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+</vector>

examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml

@@ -0,0 +1,30 @@
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:aapt="http://schemas.android.com/aapt"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path android:pathData="M31,63.928c0,0 6.4,-11 12.1,-13.1c7.2,-2.6 26,-1.4 26,-1.4l38.1,38.1L107,108.928l-32,-1L31,63.928z">
+        <aapt:attr name="android:fillColor">
+            <gradient
+                android:endX="85.84757"
+                android:endY="92.4963"
+                android:startX="42.9492"
+                android:startY="49.59793"
+                android:type="linear">
+                <item
+                    android:color="#44000000"
+                    android:offset="0.0" />
+                <item
+                    android:color="#00000000"
+                    android:offset="1.0" />
+            </gradient>
+        </aapt:attr>
+    </path>
+    <path
+        android:fillColor="#FFFFFF"
+        android:fillType="nonZero"
+        android:pathData="M65.3,45.828l3.8,-6.6c0.2,-0.4 0.1,-0.9 -0.3,-1.1c-0.4,-0.2 -0.9,-0.1 -1.1,0.3l-3.9,6.7c-6.3,-2.8 -13.4,-2.8 -19.7,0l-3.9,-6.7c-0.2,-0.4 -0.7,-0.5 -1.1,-0.3C38.8,38.328 38.7,38.828 38.9,39.228l3.8,6.6C36.2,49.428 31.7,56.028 31,63.928h46C76.3,56.028 71.8,49.428 65.3,45.828zM43.4,57.328c-0.8,0 -1.5,-0.5 -1.8,-1.2c-0.3,-0.7 -0.1,-1.5 0.4,-2.1c0.5,-0.5 1.4,-0.7 2.1,-0.4c0.7,0.3 1.2,1 1.2,1.8C45.3,56.528 44.5,57.328 43.4,57.328L43.4,57.328zM64.6,57.328c-0.8,0 -1.5,-0.5 -1.8,-1.2s-0.1,-1.5 0.4,-2.1c0.5,-0.5 1.4,-0.7 2.1,-0.4c0.7,0.3 1.2,1 1.2,1.8C66.5,56.528 65.6,57.328 64.6,57.328L64.6,57.328z"
+        android:strokeWidth="1"
+        android:strokeColor="#00000000" />
+</vector>

examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+    <monochrome android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>

examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher_round.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+    <monochrome android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>

examples/llama.android/app/src/main/res/values/colors.xml

@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <color name="purple_200">#FFBB86FC</color>
+    <color name="purple_500">#FF6200EE</color>
+    <color name="purple_700">#FF3700B3</color>
+    <color name="teal_200">#FF03DAC5</color>
+    <color name="teal_700">#FF018786</color>
+    <color name="black">#FF000000</color>
+    <color name="white">#FFFFFFFF</color>
+</resources>

examples/llama.android/app/src/main/res/values/strings.xml

@@ -0,0 +1,3 @@
+<resources>
+    <string name="app_name">LlamaAndroid</string>
+</resources>

examples/llama.android/app/src/main/res/values/themes.xml

@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+
+    <style name="Theme.LlamaAndroid" parent="android:Theme.Material.Light.NoActionBar" />
+</resources>

examples/llama.android/app/src/main/res/xml/backup_rules.xml

@@ -0,0 +1,13 @@
+<?xml version="1.0" encoding="utf-8"?><!--
+   Sample backup rules file; uncomment and customize as necessary.
+   See https://developer.android.com/guide/topics/data/autobackup
+   for details.
+   Note: This file is ignored for devices older that API 31
+   See https://developer.android.com/about/versions/12/backup-restore
+-->
+<full-backup-content>
+    <!--
+   <include domain="sharedpref" path="."/>
+   <exclude domain="sharedpref" path="device.xml"/>
+-->
+</full-backup-content>

examples/llama.android/app/src/main/res/xml/data_extraction_rules.xml

@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="utf-8"?><!--
+   Sample data extraction rules file; uncomment and customize as necessary.
+   See https://developer.android.com/about/versions/12/backup-restore#xml-changes
+   for details.
+-->
+<data-extraction-rules>
+    <cloud-backup>
+        <!-- TODO: Use <include> and <exclude> to control what is backed up.
+        <include .../>
+        <exclude .../>
+        -->
+    </cloud-backup>
+    <!--
+    <device-transfer>
+        <include .../>
+        <exclude .../>
+    </device-transfer>
+    -->
+</data-extraction-rules>

examples/llama.android/build.gradle.kts

@@ -0,0 +1,5 @@
+// Top-level build file where you can add configuration options common to all sub-projects/modules.
+plugins {
+    id("com.android.application") version "8.2.0" apply false
+    id("org.jetbrains.kotlin.android") version "1.9.0" apply false
+}

examples/llama.android/gradle.properties

@@ -0,0 +1,23 @@
+# Project-wide Gradle settings.
+# IDE (e.g. Android Studio) users:
+# Gradle settings configured through the IDE *will override*
+# any settings specified in this file.
+# For more details on how to configure your build environment visit
+# http://www.gradle.org/docs/current/userguide/build_environment.html
+# Specifies the JVM arguments used for the daemon process.
+# The setting is particularly useful for tweaking memory settings.
+org.gradle.jvmargs=-Xmx2048m -Dfile.encoding=UTF-8
+# When configured, Gradle will run in incubating parallel mode.
+# This option should only be used with decoupled projects. More details, visit
+# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
+# org.gradle.parallel=true
+# AndroidX package structure to make it clearer which packages are bundled with the
+# Android operating system, and which are packaged with your app's APK
+# https://developer.android.com/topic/libraries/support-library/androidx-rn
+android.useAndroidX=true
+# Kotlin code style for this project: "official" or "obsolete":
+kotlin.code.style=official
+# Enables namespacing of each library's R class so that its R class includes only the
+# resources declared in the library itself and none from the library's dependencies,
+# thereby reducing the size of the R class for that library
+android.nonTransitiveRClass=true

examples/llama.android/gradle/wrapper/gradle-wrapper.properties

@@ -0,0 +1,6 @@
+#Thu Dec 21 14:31:09 AEDT 2023
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-8.2-bin.zip
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists

examples/llama.android/gradlew

@@ -0,0 +1,185 @@
+#!/usr/bin/env sh
+
+#
+# Copyright 2015 the original author or authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >/dev/null
+APP_HOME="`pwd -P`"
+cd "$SAVED" >/dev/null
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn () {
+    echo "$*"
+}
+
+die () {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+nonstop=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+  NONSTOP* )
+    nonstop=true
+    ;;
+esac
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin or MSYS, switch paths to Windows format before running java
+if [ "$cygwin" = "true" -o "$msys" = "true" ] ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+
+    JAVACMD=`cygpath --unix "$JAVACMD"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=`expr $i + 1`
+    done
+    case $i in
+        0) set -- ;;
+        1) set -- "$args0" ;;
+        2) set -- "$args0" "$args1" ;;
+        3) set -- "$args0" "$args1" "$args2" ;;
+        4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Escape application args
+save () {
+    for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
+    echo " "
+}
+APP_ARGS=`save "$@"`
+
+# Collect all arguments for the java command, following the shell quoting and substitution rules
+eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
+
+exec "$JAVACMD" "$@"

examples/llama.android/settings.gradle.kts

@@ -0,0 +1,17 @@
+pluginManagement {
+    repositories {
+        google()
+        mavenCentral()
+        gradlePluginPortal()
+    }
+}
+dependencyResolutionManagement {
+    repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
+    repositories {
+        google()
+        mavenCentral()
+    }
+}
+
+rootProject.name = "LlamaAndroid"
+include(":app")

examples/llama.vim

@@ -6,7 +6,7 @@
 " Similarly, you could add an insert mode keybind with
 " inoremap <C-B> <Cmd>call llama#doLlamaGen()<CR>
 "
-" g:llama_api_url and g:llama_overrides can be configured in your .vimrc
+" g:llama_api_url, g:llama_api_key and g:llama_overrides can be configured in your .vimrc
 " let g:llama_api_url = "192.168.1.10:8080"
 " llama_overrides can also be set through buffer/window scopes. For instance
 " autocmd filetype python let b:llama_overrides = {"temp": 0.2}
@@ -82,6 +82,9 @@ func llama#doLlamaGen()
    endif
    let l:querydata.prompt = join(l:buflines, "\n")
    let l:curlcommand = copy(s:curlcommand)
+   if exists("g:llama_api_key")
+       call extend(l:curlcommand, ['--header', 'Authorization: Bearer ' .. g:llama_api_key])
+   endif
    let l:curlcommand[2] = json_encode(l:querydata)
    let b:job = job_start(l:curlcommand, {"callback": function("s:callbackHandler", [l:cbuffer])})
 endfunction

examples/llava/MobileVLM-README.md

@@ -0,0 +1,131 @@
+# MobileVLM
+
+Currently this implementation supports [MobileVLM-v1.7](https://huggingface.co/mtgv/MobileVLM-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.
+
+## Usage
+Build with cmake or run `make llava-cli` to build it.
+
+After building, run: `./llava-cli` to see the usage. For example:
+
+```sh
+./llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \
+    --mmproj MobileVLM-1.7B/mmproj-model-f16.gguf \
+    --image path/to/an/image.jpg \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? Answer the question using a single word or phrase. ASSISTANT:"
+```
+
+## Model conversion
+
+- Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally:
+
+```sh
+git clone https://huggingface.co/mtgv/MobileVLM-1.7B
+
+git clone https://huggingface.co/openai/clip-vit-large-patch14-336
+```
+
+2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
+
+```sh
+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:
+
+```sh
+python ./examples/llava/convert-image-encoder-to-gguf \
+    -m path/to/clip-vit-large-patch14-336 \
+    --llava-projector path/to/MobileVLM-1.7B/llava.projector \
+    --output-dir path/to/MobileVLM-1.7B \
+    --projector-type ldp
+```
+
+4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
+
+```sh
+python ./convert.py path/to/MobileVLM-1.7B
+```
+
+5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k`
+```sh
+./quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s
+```
+
+Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directory.
+
+## Android compile and run
+### compile
+refer to `examples/llava/android/build_64.sh`
+```sh
+mkdir examples/llava/android/build_64
+cd examples/llava/android/build_64
+../build_64.sh
+```
+### run on Android
+refer to `android/adb_run.sh`, modify resources' `name` and `path`
+
+## some result on Android with `Snapdragon 888` chip
+### case 1
+**input**
+```sh
+/data/local/tmp/llava-cli \
+    -m /data/local/tmp/ggml-model-q4_k.gguf \
+    --mmproj /data/local/tmp/mmproj-model-f16.gguf \
+    -t 4 \
+    --image /data/local/tmp/demo.jpg \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:"
+```
+**output**
+```sh
+encode_image_with_clip: image encoded in 21148.71 ms by CLIP (  146.87 ms per image patch)
+ Susan Wise Bauer
+llama_print_timings:        load time =   23574.72 ms
+llama_print_timings:      sample time =       1.24 ms /     6 runs   (    0.21 ms per token,  4850.44 tokens per second)
+llama_print_timings: prompt eval time =   12460.15 ms /   246 tokens (   50.65 ms per token,    19.74 tokens per second)
+llama_print_timings:        eval time =     424.86 ms /     6 runs   (   70.81 ms per token,    14.12 tokens per second)
+llama_print_timings:       total time =   34731.93 ms
+```
+### case 2
+**input**
+```sh
+/data/local/tmp/llava-cli \
+    -m /data/local/tmp/ggml-model-q4_k.gguf \
+    --mmproj /data/local/tmp/mmproj-model-f16.gguf \
+    -t 4 \
+    --image /data/local/tmp/cat.jpeg \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
+```
+
+**output**
+```sh
+encode_image_with_clip: image encoded in 21149.51 ms by CLIP (  146.87 ms per image patch)
+ The image depicts a cat sitting in the grass near some tall green plants.
+llama_print_timings:        load time =   23257.32 ms
+llama_print_timings:      sample time =       5.25 ms /    18 runs   (    0.29 ms per token,  3430.53 tokens per second)
+llama_print_timings: prompt eval time =   11900.73 ms /   232 tokens (   51.30 ms per token,    19.49 tokens per second)
+llama_print_timings:        eval time =    1279.03 ms /    18 runs   (   71.06 ms per token,    14.07 tokens per second)
+llama_print_timings:       total time =   34570.79 ms
+```
+
+## Minor shortcomings
+The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quickly, we uniformly modified `clip_n_patches` function to a quarter. when counting the time consumption, the calculated time will be 4 times bigger than the real cost.
+
+## TODO
+
+- [ ] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid`
+- [ ] Optimize LDP projector performance
+
+      - Optimize the structure definition to avoid unnecessary memory rearrangements, to reduce the use of `ggml_permute_cpy`;
+      - Optimize operator implementation (ARM CPU/NVIDIA GPU): such as depthwise conv, hardswish, hardsigmoid, etc.
+- [ ] run MobileVLM on `Jetson Orin`
+- [ ] Support more model variants, such as `MobileVLM-3B`.
+
+
+## contributor
+```sh
+zhangjidong05, yangyang260, huyiming03, chenxiaotao03
+```

examples/llava/android/adb_run.sh

@@ -0,0 +1,53 @@
+#!/bin/bash
+
+model_dir="/Users/cxt/model/llm/mobileVLM/MobileVLM-1.7B_processed"
+projector_name="mmproj-model-f16.gguf"
+llama_name="ggml-model-q4_k.gguf"
+img_dir="/Users/cxt/model/llm"
+img_name="demo.jpg"
+prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:"
+# img_name="cat.jpeg"
+# prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
+
+program_dir="build_64/bin"
+binName="llava-cli"
+n_threads=4
+
+
+deviceDir="/data/local/tmp"
+saveDir="output"
+if [ ! -d ${saveDir} ]; then
+    mkdir ${saveDir}
+fi
+
+
+function android_run() {
+    # # copy resource into device
+    # adb push ${model_dir}/${projector_name} ${deviceDir}/${projector_name}
+    # adb push ${model_dir}/${llama_name} ${deviceDir}/${llama_name}
+    adb push ${img_dir}/${img_name} ${deviceDir}/${img_name}
+    # copy program into device
+    adb push ${program_dir}/${binName} ${deviceDir}/${binName}
+    adb shell "chmod 0777 ${deviceDir}/${binName}"
+
+    # run
+    adb shell "echo cd ${deviceDir} ${deviceDir}/${binName} \
+                                                 -m ${deviceDir}/${llama_name} \
+                                                 --mmproj ${deviceDir}/${projector_name} \
+                                                 -t ${n_threads} \
+                                                 --image ${deviceDir}/${img_name} \
+                                                 -p \"${prompt}\" \
+                                                 > ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt"
+    adb shell "cd ${deviceDir}; pwd; ${deviceDir}/${binName} \
+                                                 -m ${deviceDir}/${llama_name} \
+                                                 --mmproj ${deviceDir}/${projector_name} \
+                                                 -t ${n_threads} \
+                                                 --image ${deviceDir}/${img_name} \
+                                                 -p \"${prompt}\" \
+                                                 >> ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt 2>&1"
+    adb pull ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt ${saveDir}
+}
+
+android_run
+
+echo "android_run is Done!"

examples/llava/android/build_64.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+cmake ../../../../ \
+-DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
+-DCMAKE_BUILD_TYPE=Release \
+-DANDROID_ABI="arm64-v8a" \
+-DANDROID_PLATFORM=android-23 $1
+
+make -j4

examples/llava/clip.cpp

@@ -2,17 +2,6 @@
 // so there might be still unnecessary artifacts hanging around
 // I'll gradually clean and extend it
 
-#include <cassert>
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
-#include <fstream>
-#include <iostream>
-#include <map>
-#include <regex>
-#include <stdexcept>
-#include <vector>
-
 #include "clip.h"
 #include "ggml.h"
 #include "ggml-alloc.h"
@@ -29,6 +18,19 @@
 #define STB_IMAGE_IMPLEMENTATION
 #include "stb_image.h"
 
+#include <cassert>
+#include <cmath>
+#include <cstdlib>
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <map>
+#include <regex>
+#include <stdexcept>
+#include <vector>
+#include <sstream>
+#include <cinttypes>
+
 static std::string format(const char * fmt, ...) {
     va_list ap;
     va_list ap2;
@@ -67,6 +69,7 @@ static std::string format(const char * fmt, ...) {
 #define KEY_PATCH_SIZE "clip.vision.patch_size"
 #define KEY_IMAGE_MEAN "clip.vision.image_mean"
 #define KEY_IMAGE_STD "clip.vision.image_std"
+#define KEY_PROJ_TYPE "clip.projector_type"
 
 //
 // tensor name constants
@@ -89,6 +92,21 @@ static std::string format(const char * fmt, ...) {
 #define TN_TEXT_PROJ "text_projection.weight"
 #define TN_VIS_PROJ "visual_projection.weight"
 #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"
+
+
+enum projector_type {
+    PROJECTOR_TYPE_MLP,
+    PROJECTOR_TYPE_LDP,
+    PROJECTOR_TYPE_UNKNOWN,
+};
+
+static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
+    { PROJECTOR_TYPE_MLP,           "mlp"     },
+    { PROJECTOR_TYPE_LDP,          "ldp"    },
+};
+
 
 //
 // utilities to get data from a gguf file
@@ -129,6 +147,91 @@ static std::string get_ftype(int ftype) {
     return ggml_type_name(static_cast<ggml_type>(ftype));
 }
 
+static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
+    switch (type) {
+        case GGUF_TYPE_UINT8:   return std::to_string(((const uint8_t  *)data)[i]);
+        case GGUF_TYPE_INT8:    return std::to_string(((const int8_t   *)data)[i]);
+        case GGUF_TYPE_UINT16:  return std::to_string(((const uint16_t *)data)[i]);
+        case GGUF_TYPE_INT16:   return std::to_string(((const int16_t  *)data)[i]);
+        case GGUF_TYPE_UINT32:  return std::to_string(((const uint32_t *)data)[i]);
+        case GGUF_TYPE_INT32:   return std::to_string(((const int32_t  *)data)[i]);
+        case GGUF_TYPE_UINT64:  return std::to_string(((const uint64_t *)data)[i]);
+        case GGUF_TYPE_INT64:   return std::to_string(((const int64_t  *)data)[i]);
+        case GGUF_TYPE_FLOAT32: return std::to_string(((const float    *)data)[i]);
+        case GGUF_TYPE_FLOAT64: return std::to_string(((const double   *)data)[i]);
+        case GGUF_TYPE_BOOL:    return ((const bool *)data)[i] ? "true" : "false";
+        default:                return format("unknown type %d", type);
+    }
+}
+
+
+static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
+    std::string result;
+    for (size_t pos = 0; ; pos += search.length()) {
+        auto new_pos = s.find(search, pos);
+        if (new_pos == std::string::npos) {
+            result += s.substr(pos, s.size() - pos);
+            break;
+        }
+        result += s.substr(pos, new_pos - pos) + replace;
+        pos = new_pos;
+    }
+    s = std::move(result);
+}
+
+static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
+    const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
+
+    switch (type) {
+        case GGUF_TYPE_STRING:
+            return gguf_get_val_str(ctx_gguf, i);
+        case GGUF_TYPE_ARRAY:
+            {
+                const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
+                int arr_n = gguf_get_arr_n(ctx_gguf, i);
+                const void * data = gguf_get_arr_data(ctx_gguf, i);
+                std::stringstream ss;
+                ss << "[";
+                for (int j = 0; j < arr_n; j++) {
+                    if (arr_type == GGUF_TYPE_STRING) {
+                        std::string val = gguf_get_arr_str(ctx_gguf, i, j);
+                        // escape quotes
+                        replace_all(val, "\\", "\\\\");
+                        replace_all(val, "\"", "\\\"");
+                        ss << '"' << val << '"';
+                    } else if (arr_type == GGUF_TYPE_ARRAY) {
+                        ss << "???";
+                    } else {
+                        ss << gguf_data_to_str(arr_type, data, j);
+                    }
+                    if (j < arr_n - 1) {
+                        ss << ", ";
+                    }
+                }
+                ss << "]";
+                return ss.str();
+            }
+        default:
+            return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0);
+    }
+}
+
+static void print_tensor_info(const ggml_tensor* tensor, const char* prefix = "") {
+    size_t tensor_size = ggml_nbytes(tensor);
+    printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
+            prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
+            tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
+}
+
+static projector_type clip_projector_type_from_string(const std::string & name) {
+    for (const auto & kv : PROJECTOR_TYPE_NAMES) { // NOLINT
+        if (kv.second == name) {
+            return kv.first;
+        }
+    }
+    return PROJECTOR_TYPE_UNKNOWN;
+}
+
 //
 // image data
 //
@@ -205,6 +308,32 @@ struct clip_vision_model {
     struct ggml_tensor * mm_0_b;
     struct ggml_tensor * mm_2_w;
     struct ggml_tensor * mm_2_b;
+
+    // MobileVLM projection
+    struct ggml_tensor * mm_model_mlp_1_w;
+    struct ggml_tensor * mm_model_mlp_1_b;
+    struct ggml_tensor * mm_model_mlp_3_w;
+    struct ggml_tensor * mm_model_mlp_3_b;
+    struct ggml_tensor * mm_model_block_1_block_0_0_w;
+    struct ggml_tensor * mm_model_block_1_block_0_1_w;
+    struct ggml_tensor * mm_model_block_1_block_0_1_b;
+    struct ggml_tensor * mm_model_block_1_block_1_fc1_w;
+    struct ggml_tensor * mm_model_block_1_block_1_fc1_b;
+    struct ggml_tensor * mm_model_block_1_block_1_fc2_w;
+    struct ggml_tensor * mm_model_block_1_block_1_fc2_b;
+    struct ggml_tensor * mm_model_block_1_block_2_0_w;
+    struct ggml_tensor * mm_model_block_1_block_2_1_w;
+    struct ggml_tensor * mm_model_block_1_block_2_1_b;
+    struct ggml_tensor * mm_model_block_2_block_0_0_w;
+    struct ggml_tensor * mm_model_block_2_block_0_1_w;
+    struct ggml_tensor * mm_model_block_2_block_0_1_b;
+    struct ggml_tensor * mm_model_block_2_block_1_fc1_w;
+    struct ggml_tensor * mm_model_block_2_block_1_fc1_b;
+    struct ggml_tensor * mm_model_block_2_block_1_fc2_w;
+    struct ggml_tensor * mm_model_block_2_block_1_fc2_b;
+    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;
 };
 
 struct clip_ctx {
@@ -213,6 +342,7 @@ struct clip_ctx {
     bool has_llava_projector = false;
 
     struct clip_vision_model vision_model;
+    projector_type proj_type = PROJECTOR_TYPE_MLP;
 
     float image_mean[3];
     float image_std[3];
@@ -430,16 +560,135 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             free(patches_data);
         }
 
+        // shape [1, 576, 1024]
+        // ne is whcn, ne = [1024, 576, 1, 1]
         embeddings = ggml_get_rows(ctx0, embeddings, patches);
 
-        // mm projection 0
-        embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
-        embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
+        // print_tensor_info(embeddings, "embeddings");
 
-        embeddings = ggml_gelu(ctx0, embeddings);
+        // llava projector
+        if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
+            embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
+            embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
 
-        embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
-        embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
+            embeddings = ggml_gelu(ctx0, embeddings);
+
+            embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
+            embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
+        }
+        else if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
+            // MobileVLM projector
+            int n_patch = 24;
+            struct ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings);
+            mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b);
+            mlp_1 = ggml_gelu(ctx0, mlp_1);
+            struct ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1);
+            mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b);
+            // mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1]
+
+            // block 1
+            struct ggml_tensor * block_1 = nullptr;
+            {
+                // transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24]
+                mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3));
+                mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
+                // stride = 1, padding = 1, bias is nullptr
+                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
+
+                // layer norm
+                // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
+                // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
+
+                // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
+                // hardswish
+                struct ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
+
+                block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
+                // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
+                // pointwise conv
+                block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b);
+                block_1 = ggml_relu(ctx0, block_1);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b);
+                block_1 = ggml_hardsigmoid(ctx0, block_1);
+                // block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1]
+                block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
+                block_1 = ggml_mul(ctx0, block_1_hw, block_1);
+
+                int w = block_1->ne[0], h = block_1->ne[1];
+                block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
+
+                // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1);
+                block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
+
+                // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
+                // block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
+                // residual
+                block_1 = ggml_add(ctx0, mlp_3, block_1);
+            }
+
+            // block_2
+            {
+                // stride = 2
+                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
+
+                // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
+                // layer norm
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
+                // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
+                // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
+                // hardswish
+                struct ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
+
+                // not sure the parameters is right for globalAvgPooling
+                block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
+                // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
+                // pointwise conv
+                block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b);
+                block_1 = ggml_relu(ctx0, block_1);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b);
+                block_1 = ggml_hardsigmoid(ctx0, block_1);
+
+                // block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
+                block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
+                block_1 = ggml_mul(ctx0, block_1_hw, block_1);
+
+                int w = block_1->ne[0], h = block_1->ne[1];
+                block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
+                // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1);
+                block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
+
+
+                // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b);
+                block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]);
+                // block_1 shape = [1, 144, 2048], ne = [2048, 144, 1]
+            }
+            embeddings = block_1;
+        }
+        else {
+            GGML_ASSERT(false);
+        }
     }
 
     // build the graph
@@ -485,16 +734,47 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         printf("\n");
     }
     const int n_tensors = gguf_get_n_tensors(ctx);
+
     // kv
-    if (verbosity >= 3) {
-        const int n_kv = gguf_get_n_kv(ctx);
+    const int n_kv = gguf_get_n_kv(ctx);
+    printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
+        __func__, n_kv, n_tensors, fname);
+    {
+        std::map<enum ggml_type, uint32_t> n_type;
 
-        for (int i = 0; i < n_kv; ++i) {
-            const char * key = gguf_get_key(ctx, i);
+        for (int i = 0; i < n_tensors; i++) {
+            enum ggml_type type = gguf_get_tensor_type(ctx, i);
 
-            printf("%s: kv[%d]: key = %s\n", __func__, i, key);
+            n_type[type]++;
+        }
+
+        printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
+        for (int i = 0; i < n_kv; i++) {
+            const char * name           = gguf_get_key(ctx, i);
+            const enum gguf_type type   = gguf_get_kv_type(ctx, i);
+            const std::string type_name =
+                type == GGUF_TYPE_ARRAY
+                ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(ctx, i)), gguf_get_arr_n(ctx, i))
+                : gguf_type_name(type);
+
+            std::string value          = gguf_kv_to_str(ctx, i);
+            const size_t MAX_VALUE_LEN = 40;
+            if (value.size() > MAX_VALUE_LEN) {
+                value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
+            }
+            replace_all(value, "\n", "\\n");
+
+            printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
+        }
+
+        // print type counts
+        for (auto & kv : n_type) {
+            if (kv.second == 0) {
+                continue;
+            }
+
+            printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
         }
-        printf("\n");
     }
 
     // data
@@ -503,12 +783,13 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         for (int i = 0; i < n_tensors; ++i) {
             const char * name = gguf_get_tensor_name(ctx, i);
             const size_t offset = gguf_get_tensor_offset(ctx, i);
+            enum ggml_type type = gguf_get_tensor_type(ctx, i);
             struct ggml_tensor * cur = ggml_get_tensor(meta, name);
             size_t tensor_size = ggml_nbytes(cur);
             buffer_size += tensor_size;
             if (verbosity >= 3) {
-                printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu\n", __func__, i,
-                       ggml_n_dims(cur), cur->name, tensor_size, offset);
+                printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
+                       __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
             }
         }
     }
@@ -517,6 +798,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
 
     clip_ctx * new_clip = new clip_ctx;
 
+    // update projector type
+    {
+        int idx = gguf_find_key(ctx, KEY_PROJ_TYPE);
+        if (idx != -1) {
+            const std::string proj_type = gguf_get_val_str(ctx, idx);
+            new_clip->proj_type = clip_projector_type_from_string(proj_type);
+        }
+        else {
+            new_clip->proj_type = PROJECTOR_TYPE_MLP;
+        }
+    }
+
 #ifdef GGML_USE_CUBLAS
     new_clip->backend = ggml_backend_cuda_init(0);
     printf("%s: CLIP using CUDA backend\n", __func__);
@@ -661,10 +954,45 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
         vision_model.pre_ln_w            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
         vision_model.pre_ln_b            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
-        vision_model.mm_0_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
-        vision_model.mm_0_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
-        vision_model.mm_2_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
-        vision_model.mm_2_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
+
+        // LLaVA projection
+        if (new_clip->proj_type == PROJECTOR_TYPE_MLP) {
+            vision_model.mm_0_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
+            vision_model.mm_0_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
+            vision_model.mm_2_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
+            vision_model.mm_2_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
+        }
+        else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
+            // MobileVLM projection
+            vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight"));
+            vision_model.mm_model_mlp_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "bias"));
+            vision_model.mm_model_mlp_3_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "weight"));
+            vision_model.mm_model_mlp_3_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "bias"));
+            vision_model.mm_model_block_1_block_0_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "0.weight"));
+            vision_model.mm_model_block_1_block_0_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.weight"));
+            vision_model.mm_model_block_1_block_0_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.bias"));
+            vision_model.mm_model_block_1_block_1_fc1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.weight"));
+            vision_model.mm_model_block_1_block_1_fc1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.bias"));
+            vision_model.mm_model_block_1_block_1_fc2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.weight"));
+            vision_model.mm_model_block_1_block_1_fc2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.bias"));
+            vision_model.mm_model_block_1_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "0.weight"));
+            vision_model.mm_model_block_1_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.weight"));
+            vision_model.mm_model_block_1_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.bias"));
+            vision_model.mm_model_block_2_block_0_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "0.weight"));
+            vision_model.mm_model_block_2_block_0_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.weight"));
+            vision_model.mm_model_block_2_block_0_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.bias"));
+            vision_model.mm_model_block_2_block_1_fc1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.weight"));
+            vision_model.mm_model_block_2_block_1_fc1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.bias"));
+            vision_model.mm_model_block_2_block_1_fc2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.weight"));
+            vision_model.mm_model_block_2_block_1_fc2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.bias"));
+            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 {
+            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()));
+        }
 
         vision_model.layers.resize(hparams.n_layer);
         for (int il = 0; il < hparams.n_layer; ++il) {
@@ -1100,13 +1428,25 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
 }
 
 int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
-    return ctx->vision_model.mm_2_b->ne[0];
+    if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
+        return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0];
+    }
+    else if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
+        return ctx->vision_model.mm_2_b->ne[0];
+    }
+    else {
+        std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
+        throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
+    }
 }
 
 int clip_n_patches(const struct clip_ctx * ctx) {
     auto & params = ctx->vision_model.hparams;
-
-    return (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
+    int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
+    if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
+        n_patches /= 4;
+    }
+    return n_patches;
 }
 
 size_t clip_embd_nbytes(const struct clip_ctx * ctx) {

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

@@ -81,6 +81,7 @@ ap.add_argument("--vision-only", action="store_true", required=False,
 ap.add_argument("--clip_model_is_vision", action="store_true", required=False,
                 help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
 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("--image-mean", nargs=3, type=float, required=False, help="Override image mean values")
 ap.add_argument("--image-std", nargs=3, type=float, required=False, help="Override image std values")
 ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
@@ -174,6 +175,8 @@ elif args.vision_only and not has_llava_projector:
     fout.add_description("vision-only CLIP model")
 elif has_llava_projector:
     fout.add_description("image encoder for LLaVA")
+    # add projector type
+    fout.add_string("clip.projector_type", args.projector_type)
 else:
     fout.add_description("two-tower CLIP model")
 
@@ -218,7 +221,8 @@ if has_llava_projector:
     projector = torch.load(args.llava_projector)
     for name, data in projector.items():
         name = get_tensor_name(name)
-        if data.ndim == 2:
+        # pw and dw conv ndim==4
+        if data.ndim == 2 or data.ndim == 4:
             data = data.squeeze().numpy().astype(np.float16)
         else:
             data = data.squeeze().numpy().astype(np.float32)

examples/pydantic-models-to-grammar-examples.py

@@ -1,5 +1,5 @@
 # Function calling example using pydantic models.
-
+import datetime
 import json
 from enum import Enum
 from typing import Union, Optional
@@ -8,7 +8,8 @@ import requests
 from pydantic import BaseModel, Field
 
 import importlib
-from pydantic_models_to_grammar import generate_gbnf_grammar_and_documentation
+from pydantic_models_to_grammar import generate_gbnf_grammar_and_documentation, convert_dictionary_to_pydantic_model, add_run_method_to_dynamic_model, create_dynamic_model_from_function
+
 
 # Function to get completion on the llama.cpp server with grammar.
 def create_completion(prompt, grammar):
@@ -134,3 +135,121 @@ text = create_completion(prompt=prompt, grammar=gbnf_grammar)
 json_data = json.loads(text)
 
 print(Book(**json_data))
+# An example for parallel function calling with a Python function, a pydantic function model and an OpenAI like function definition.
+
+def get_current_datetime(output_format: Optional[str] = None):
+    """
+    Get the current date and time in the given format.
+    Args:
+         output_format: formatting string for the date and time, defaults to '%Y-%m-%d %H:%M:%S'
+    """
+    if output_format is None:
+        output_format = '%Y-%m-%d %H:%M:%S'
+    return datetime.datetime.now().strftime(output_format)
+
+
+# Enum for the calculator tool.
+class MathOperation(Enum):
+    ADD = "add"
+    SUBTRACT = "subtract"
+    MULTIPLY = "multiply"
+    DIVIDE = "divide"
+
+
+
+# Simple pydantic calculator tool for the agent that can add, subtract, multiply, and divide. Docstring and description of fields will be used in system prompt.
+class Calculator(BaseModel):
+    """
+    Perform a math operation on two numbers.
+    """
+    number_one: Union[int, float] = Field(..., description="First number.")
+    operation: MathOperation = Field(..., description="Math operation to perform.")
+    number_two: Union[int, float] = Field(..., description="Second number.")
+
+    def run(self):
+        if self.operation == MathOperation.ADD:
+            return self.number_one + self.number_two
+        elif self.operation == MathOperation.SUBTRACT:
+            return self.number_one - self.number_two
+        elif self.operation == MathOperation.MULTIPLY:
+            return self.number_one * self.number_two
+        elif self.operation == MathOperation.DIVIDE:
+            return self.number_one / self.number_two
+        else:
+            raise ValueError("Unknown operation.")
+
+
+# Example function to get the weather
+def get_current_weather(location, unit):
+    """Get the current weather in a given location"""
+    if "London" in location:
+        return json.dumps({"location": "London", "temperature": "42", "unit": unit.value})
+    elif "New York" in location:
+        return json.dumps({"location": "New York", "temperature": "24", "unit": unit.value})
+    elif "North Pole" in location:
+        return json.dumps({"location": "North Pole", "temperature": "-42", "unit": unit.value})
+    else:
+        return json.dumps({"location": location, "temperature": "unknown"})
+
+
+# Here is a function definition in OpenAI style
+current_weather_tool = {
+    "type": "function",
+    "function": {
+        "name": "get_current_weather",
+        "description": "Get the current weather in a given location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, CA",
+                },
+                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+            },
+            "required": ["location"],
+        },
+    },
+}
+
+# Convert OpenAI function definition into pydantic model
+current_weather_tool_model = convert_dictionary_to_pydantic_model(current_weather_tool)
+# Add the actual function to a pydantic model
+current_weather_tool_model = add_run_method_to_dynamic_model(current_weather_tool_model, get_current_weather)
+
+# Convert normal Python function to a pydantic model
+current_datetime_model = create_dynamic_model_from_function(get_current_datetime)
+
+tool_list = [SendMessageToUser, Calculator, current_datetime_model, current_weather_tool_model]
+
+
+gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
+    pydantic_model_list=tool_list, outer_object_name="function",
+    outer_object_content="params", model_prefix="Function", fields_prefix="Parameters", list_of_outputs=True)
+
+system_message = "You are an advanced AI assistant. You are interacting with the user and with your environment by calling functions. You call functions by writing JSON objects, which represent specific function calls.\nBelow is a list of your available function calls:\n\n" + documentation
+
+
+text = """Get the date and time, get the current weather in celsius in London and solve the following calculation: 42 * 42"""
+prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
+
+text = create_completion(prompt=prompt, grammar=gbnf_grammar)
+
+json_data = json.loads(text)
+
+print(json_data)
+# Should output something like this:
+# [{'function': 'get_current_datetime', 'params': {'output_format': '%Y-%m-%d %H:%M:%S'}}, {'function': 'get_current_weather', 'params': {'location': 'London', 'unit': 'celsius'}}, {'function': 'Calculator', 'params': {'number_one': 42, 'operation': 'multiply', 'number_two': 42}}]
+
+
+for call in json_data:
+    if call["function"] == "Calculator":
+        print(Calculator(**call["params"]).run())
+    elif call["function"] == "get_current_datetime":
+        print(current_datetime_model(**call["params"]).run())
+    elif call["function"] == "get_current_weather":
+        print(current_weather_tool_model(**call["params"]).run())
+# Should output something like this:
+# 2024-01-14 13:36:06
+# {"location": "London", "temperature": "42", "unit": "celsius"}
+# 1764

examples/quantize/quantize.cpp

@@ -5,6 +5,10 @@
 #include <cstring>
 #include <vector>
 #include <string>
+#include <unordered_map>
+#include <fstream>
+#include <cmath>
+#include <algorithm>
 
 struct quant_option {
     std::string name;
@@ -17,9 +21,12 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1,   " 3.90G, +0.1585 ppl @ LLaMA-v1-7B", },
     { "Q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0,   " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", },
     { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", },
+    { "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization",            },
+    { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization",            },
     { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
     { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
     { "Q3_K",   LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
+    { "Q3_K_XS",LLAMA_FTYPE_MOSTLY_Q3_K_XS,"3-bit extra small quantization"   , },
     { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
     { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", },
     { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
@@ -72,10 +79,14 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
 //
 [[noreturn]]
 static void usage(const char * executable) {
-    printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
+    printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
     printf("  --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
     printf("  --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
     printf("  --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
+    printf("  --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
+    printf("  --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
+    printf("  --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
+    printf("Note: --include-weights and --exclude-weights cannot be used together\n");
     printf("\nAllowed quantization types:\n");
     for (auto & it : QUANT_OPTIONS) {
         if (it.name != "COPY") {
@@ -83,11 +94,93 @@ static void usage(const char * executable) {
         } else {
             printf("          ");
         }
-        printf("%-6s : %s\n", it.name.c_str(), it.desc.c_str());
+        printf("%-7s : %s\n", it.name.c_str(), it.desc.c_str());
     }
     exit(1);
 }
 
+static void load_imatrix(const std::string& imatrix_file, std::unordered_map<std::string, std::vector<float>>& imatrix_data) {
+    std::ifstream in(imatrix_file.c_str(), std::ios::binary);
+    if (!in) {
+        printf("%s: failed to open %s\n",__func__,imatrix_file.c_str());
+        return;
+    }
+    int n_entries;
+    in.read((char*)&n_entries, sizeof(n_entries));
+    if (in.fail() || n_entries < 1) {
+        printf("%s: no data in file %s\n", __func__, imatrix_file.c_str());
+        return;
+    }
+    for (int i = 0; i < n_entries; ++i) {
+        int len; in.read((char *)&len, sizeof(len));
+        std::vector<char> name_as_vec(len+1);
+        in.read((char *)name_as_vec.data(), len);
+        if (in.fail()) {
+            printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file.c_str());
+            return;
+        }
+        name_as_vec[len] = 0;
+        std::string name{name_as_vec.data()};
+        auto& e = imatrix_data[std::move(name)];
+        int ncall;
+        in.read((char*)&ncall, sizeof(ncall));
+        int nval;
+        in.read((char *)&nval, sizeof(nval));
+        if (in.fail() || nval < 1) {
+            printf("%s: failed reading number of values for entry %d\n",__func__,i);
+            imatrix_data = {};
+            return;
+        }
+        e.resize(nval);
+        in.read((char*)e.data(), nval*sizeof(float));
+        if (in.fail()) {
+            printf("%s: failed reading data for entry %d\n",__func__,i);
+            imatrix_data = {};
+            return;
+        }
+        if (ncall > 0) {
+            for (auto& v : e) v /= ncall;
+        }
+    }
+    printf("%s: loaded %d importance matrix entries from %s\n",__func__,int(imatrix_data.size()),imatrix_file.c_str());
+}
+
+static void prepare_imatrix(const std::string& imatrix_file,
+        const std::vector<std::string>& included_weights,
+        const std::vector<std::string>& excluded_weights,
+        std::unordered_map<std::string, std::vector<float>>& imatrix_data) {
+    if (!imatrix_file.empty()) {
+        load_imatrix(imatrix_file, imatrix_data);
+    }
+    if (imatrix_data.empty()) {
+        return;
+    }
+    if (!excluded_weights.empty()) {
+        for (auto& name : excluded_weights) {
+            for (auto it = imatrix_data.begin(); it != imatrix_data.end(); ) {
+                auto pos = it->first.find(name);
+                if (pos != std::string::npos) it = imatrix_data.erase(it);
+                else ++it;
+            }
+        }
+    }
+    if (!included_weights.empty()) {
+        std::unordered_map<std::string, std::vector<float>> tmp;
+        for (auto& name : included_weights) {
+            for (auto& e : imatrix_data) {
+                auto pos = e.first.find(name);
+                if (pos != std::string::npos) {
+                    tmp.emplace(std::move(e));
+                }
+            }
+        }
+        imatrix_data = std::move(tmp);
+    }
+    if (!imatrix_data.empty()) {
+        printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size()));
+    }
+}
+
 int main(int argc, char ** argv) {
     if (argc < 3) {
         usage(argv[0]);
@@ -96,6 +189,8 @@ int main(int argc, char ** argv) {
     llama_model_quantize_params params = llama_model_quantize_default_params();
 
     int arg_idx = 1;
+    std::string imatrix_file;
+    std::vector<std::string> included_weights, excluded_weights;
 
     for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
         if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
@@ -104,14 +199,42 @@ int main(int argc, char ** argv) {
             params.allow_requantize = true;
         } else if (strcmp(argv[arg_idx], "--pure") == 0) {
             params.pure = true;
+        } else if (strcmp(argv[arg_idx], "--imatrix") == 0) {
+            if (arg_idx < argc-1) {
+                imatrix_file = argv[++arg_idx];
+            } else {
+                usage(argv[0]);
+            }
+        } else if (strcmp(argv[arg_idx], "--include-weights") == 0) {
+            if (arg_idx < argc-1) {
+                included_weights.push_back(argv[++arg_idx]);
+            } else {
+                usage(argv[0]);
+            }
+        } else if (strcmp(argv[arg_idx], "--exclude-weights") == 0) {
+            if (arg_idx < argc-1) {
+                excluded_weights.push_back(argv[++arg_idx]);
+            } else {
+                usage(argv[0]);
+            }
         } else {
             usage(argv[0]);
         }
     }
 
     if (argc - arg_idx < 2) {
+        printf("%s: bad arguments\n", argv[0]);
         usage(argv[0]);
     }
+    if (!included_weights.empty() && !excluded_weights.empty()) {
+        usage(argv[0]);
+    }
+
+    std::unordered_map<std::string, std::vector<float>> imatrix_data;
+    prepare_imatrix(imatrix_file, included_weights, excluded_weights, imatrix_data);
+    if (!imatrix_data.empty()) {
+        params.imatrix = &imatrix_data;
+    }
 
     llama_backend_init(false);
 
@@ -163,6 +286,13 @@ int main(int argc, char ** argv) {
         }
     }
 
+    if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) && imatrix_data.empty()) {
+        fprintf(stderr, "\n===============================================================================================\n");
+        fprintf(stderr, "Please do not use IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
+        fprintf(stderr, "===============================================================================================\n\n\n");
+        return 1;
+    }
+
     print_build_info();
 
     fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str());

examples/server/server.cpp

@@ -1558,6 +1558,7 @@ struct llama_server_context
     void process_tasks()
     {
         std::unique_lock<std::mutex> lock(mutex_tasks);
+        std::vector<task_server> deferred_tasks;
         while (!queue_tasks.empty())
         {
             task_server task = queue_tasks.front();
@@ -1568,9 +1569,8 @@ struct llama_server_context
                     llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1));
                     if (slot == nullptr)
                     {
-                        LOG_TEE("slot unavailable\n");
-                        // send error result
-                        send_error(task, "slot unavailable");
+                        // if no slot is available, we defer this task for processing later
+                        deferred_tasks.push_back(task);
                         break;
                     }
 
@@ -1616,6 +1616,12 @@ struct llama_server_context
             }
         }
 
+        // add all the deferred tasks back the the queue
+        for (task_server &task : deferred_tasks)
+        {
+            queue_tasks.push_back(task);
+        }
+
         // remove finished multitasks from the queue of multitasks, and add the corresponding result to the result queue
         std::vector<task_result> agg_results;
         auto queue_iterator = queue_multitasks.begin();

examples/speculative/speculative.cpp

@@ -65,6 +65,10 @@ int main(int argc, char ** argv) {
     // load the draft model
     params.model = params.model_draft;
     params.n_gpu_layers = params.n_gpu_layers_draft;
+    if (params.n_threads_draft > 0) {
+        params.n_threads = params.n_threads_draft;
+    }
+    params.n_threads_batch = params.n_threads_batch_draft;
     std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
 
     {

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

@@ -263,7 +263,6 @@ static void init_model(struct my_llama_model * model) {
     model->data.resize(size + tensor_alignment);
     alloc = ggml_allocr_new(model->data.data(), model->data.size(), tensor_alignment);
     alloc_model(alloc, model);
-    ggml_allocr_free(alloc);
 }
 
 static void randomize_model(struct my_llama_model * model, int seed, float mean, float std, float min, float max) {
@@ -1102,7 +1101,6 @@ int main(int argc, char ** argv) {
     alloc = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
     ggml_allocr_alloc(alloc, tokens_input);
     ggml_allocr_alloc(alloc, target_probs);
-    ggml_allocr_free(alloc);
 
     // context for compute tensors without their data
     const size_t estimated_compute_size_wo_data = (
@@ -1149,7 +1147,6 @@ int main(int argc, char ** argv) {
             best_compute_size = max_compute_size;
             best_order = gf->order;
         }
-        ggml_allocr_free(alloc);
         ggml_free(ctx_compute);
     }
     size_t max_compute_size = best_compute_size;
@@ -1177,7 +1174,6 @@ int main(int argc, char ** argv) {
         params.common.use_flash,
         params.common.use_checkpointing
     );
-    ggml_allocr_free(alloc);
 
     std::vector<llama_token> train_tokens;
     std::vector<size_t> train_samples_begin;

flake.lock

@@ -5,11 +5,11 @@
         "nixpkgs-lib": "nixpkgs-lib"
       },
       "locked": {
-        "lastModified": 1701473968,
-        "narHash": "sha256-YcVE5emp1qQ8ieHUnxt1wCZCC3ZfAS+SRRWZ2TMda7E=",
+        "lastModified": 1704982712,
+        "narHash": "sha256-2Ptt+9h8dczgle2Oo6z5ni5rt/uLMG47UFTR1ry/wgg=",
         "owner": "hercules-ci",
         "repo": "flake-parts",
-        "rev": "34fed993f1674c8d06d58b37ce1e0fe5eebcb9f5",
+        "rev": "07f6395285469419cf9d078f59b5b49993198c00",
         "type": "github"
       },
       "original": {
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1703637592,
-        "narHash": "sha256-8MXjxU0RfFfzl57Zy3OfXCITS0qWDNLzlBAdwxGZwfY=",
+        "lastModified": 1705677747,
+        "narHash": "sha256-eyM3okYtMgYDgmYukoUzrmuoY4xl4FUujnsv/P6I/zI=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "cfc3698c31b1fb9cdcf10f36c9643460264d0ca8",
+        "rev": "bbe7d8f876fbbe7c959c90ba2ae2852220573261",
         "type": "github"
       },
       "original": {
@@ -37,11 +37,11 @@
     "nixpkgs-lib": {
       "locked": {
         "dir": "lib",
-        "lastModified": 1701253981,
-        "narHash": "sha256-ztaDIyZ7HrTAfEEUt9AtTDNoCYxUdSd6NrRHaYOIxtk=",
+        "lastModified": 1703961334,
+        "narHash": "sha256-M1mV/Cq+pgjk0rt6VxoyyD+O8cOUiai8t9Q6Yyq4noY=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "e92039b55bcd58469325ded85d4f58dd5a4eaf58",
+        "rev": "b0d36bd0a420ecee3bc916c91886caca87c894e9",
         "type": "github"
       },
       "original": {

flake.nix

@@ -1,3 +1,17 @@
+# The flake interface to llama.cpp's Nix expressions. The flake is used as a
+# more discoverable entry-point, as well as a way to pin the dependencies and
+# expose default outputs, including the outputs built by the CI.
+
+# For more serious applications involving some kind of customization  you may
+# want to consider consuming the overlay, or instantiating `llamaPackages`
+# directly:
+#
+# ```nix
+# pkgs.callPackage ${llama-cpp-root}/.devops/nix/scope.nix { }`
+# ```
+
+# Cf. https://jade.fyi/blog/flakes-arent-real/ for a more detailed exposition
+# of the relation between Nix and the Nix Flakes.
 {
   description = "Port of Facebook's LLaMA model in C/C++";
 
@@ -6,28 +20,41 @@
     flake-parts.url = "github:hercules-ci/flake-parts";
   };
 
-  # Optional binary cache
-  nixConfig = {
-    extra-substituters = [
-      # Populated by the CI in ggerganov/llama.cpp
-      "https://llama-cpp.cachix.org"
-
-      # A development cache for nixpkgs imported with `config.cudaSupport = true`.
-      # Populated by https://hercules-ci.com/github/SomeoneSerge/nixpkgs-cuda-ci.
-      # This lets one skip building e.g. the CUDA-enabled openmpi.
-      # TODO: Replace once nix-community obtains an official one.
-      "https://cuda-maintainers.cachix.org"
-    ];
-
-    # Verify these are the same keys as published on
-    # - https://app.cachix.org/cache/llama-cpp
-    # - https://app.cachix.org/cache/cuda-maintainers
-    extra-trusted-public-keys = [
-      "llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc="
-      "cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E="
-    ];
-  };
-
+  # There's an optional binary cache available. The details are below, but they're commented out.
+  #
+  # Why? The terrible experience of being prompted to accept them on every single Nix command run.
+  # Plus, there are warnings shown about not being a trusted user on a default Nix install
+  # if you *do* say yes to the prompts.
+  #
+  # This experience makes having `nixConfig` in a flake a persistent UX problem.
+  #
+  # To make use of the binary cache, please add the relevant settings to your `nix.conf`.
+  # It's located at `/etc/nix/nix.conf` on non-NixOS systems. On NixOS, adjust the `nix.settings`
+  # option in your NixOS configuration to add `extra-substituters` and `extra-trusted-public-keys`,
+  # as shown below.
+  #
+  # ```
+  # nixConfig = {
+  #   extra-substituters = [
+  #     # Populated by the CI in ggerganov/llama.cpp
+  #     "https://llama-cpp.cachix.org"
+  #
+  #     # A development cache for nixpkgs imported with `config.cudaSupport = true`.
+  #     # Populated by https://hercules-ci.com/github/SomeoneSerge/nixpkgs-cuda-ci.
+  #     # This lets one skip building e.g. the CUDA-enabled openmpi.
+  #     # TODO: Replace once nix-community obtains an official one.
+  #     "https://cuda-maintainers.cachix.org"
+  #   ];
+  #
+  #   # Verify these are the same keys as published on
+  #   # - https://app.cachix.org/cache/llama-cpp
+  #   # - https://app.cachix.org/cache/cuda-maintainers
+  #   extra-trusted-public-keys = [
+  #     "llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc="
+  #     "cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E="
+  #   ];
+  # };
+  # ```
 
   # For inspection, use `nix flake show github:ggerganov/llama.cpp` or the nix repl:
   #

ggml-alloc.c

@@ -109,8 +109,8 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
         if (block->size >= size) {
             best_fit_block = alloc->n_free_blocks - 1;
         } else {
-            fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
-                    __func__, size, max_avail);
+            fprintf(stderr, "%s: not enough space in the buffer to allocate %s (needed %zu, largest block available %zu)\n",
+                    __func__, tensor->name, size, max_avail);
             GGML_ASSERT(!"not enough space in the buffer");
             return;
         }

ggml-backend-impl.h

@@ -16,14 +16,14 @@ extern "C" {
     typedef void * ggml_backend_buffer_type_context_t;
 
     struct ggml_backend_buffer_type_i {
-        const char *          (*get_name)        (ggml_backend_buffer_type_t buft);
-        ggml_backend_buffer_t (*alloc_buffer)    (ggml_backend_buffer_type_t buft, size_t size);
-        size_t                (*get_alignment)   (ggml_backend_buffer_type_t buft); // tensor alignment
-        size_t                (*get_alloc_size)  (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
-        bool                  (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
+        const char *          (*GGML_CALL get_name)        (ggml_backend_buffer_type_t buft);
+        ggml_backend_buffer_t (*GGML_CALL alloc_buffer)    (ggml_backend_buffer_type_t buft, size_t size);
+        size_t                (*GGML_CALL get_alignment)   (ggml_backend_buffer_type_t buft); // tensor alignment
+        size_t                (*GGML_CALL get_alloc_size)  (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
+        bool                  (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
         // check if tensor data is in host memory
         // should be equivalent to supports_backend(buft, ggml_backend_cpu_init())
-        bool                  (*is_host)         (ggml_backend_buffer_type_t buft);
+        bool                  (*GGML_CALL is_host)         (ggml_backend_buffer_type_t buft);
     };
 
     struct ggml_backend_buffer_type {
@@ -35,15 +35,15 @@ extern "C" {
     typedef void * ggml_backend_buffer_context_t;
 
     struct ggml_backend_buffer_i {
-        const char * (*get_name)   (ggml_backend_buffer_t buffer);
-        void         (*free_buffer)(ggml_backend_buffer_t buffer);
-        void *       (*get_base)   (ggml_backend_buffer_t buffer);
-        void         (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-        void         (*set_tensor) (ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-        void         (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
-        bool         (*cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
-        void         (*clear)      (ggml_backend_buffer_t buffer, uint8_t value);
-        void         (*reset)      (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
+        const char * (*GGML_CALL get_name)   (ggml_backend_buffer_t buffer);
+        void         (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer);
+        void *       (*GGML_CALL get_base)   (ggml_backend_buffer_t buffer);
+        void         (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+        void         (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+        void         (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        bool         (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
+        void         (*GGML_CALL clear)      (ggml_backend_buffer_t buffer, uint8_t value);
+        void         (*GGML_CALL reset)      (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
     };
 
     struct ggml_backend_buffer {
@@ -54,7 +54,7 @@ extern "C" {
         enum ggml_backend_buffer_usage usage;
     };
 
-    ggml_backend_buffer_t ggml_backend_buffer_init(
+    GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
                    ggml_backend_buffer_type_t      buft,
             struct ggml_backend_buffer_i           iface,
                    ggml_backend_buffer_context_t   context,
@@ -70,31 +70,31 @@ extern "C" {
     typedef void * ggml_backend_context_t;
 
     struct ggml_backend_i {
-        const char * (*get_name)(ggml_backend_t backend);
+        const char * (*GGML_CALL get_name)(ggml_backend_t backend);
 
-        void (*free)(ggml_backend_t backend);
+        void (*GGML_CALL free)(ggml_backend_t backend);
 
         // buffer allocation
-        ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend);
+        ggml_backend_buffer_type_t (*GGML_CALL get_default_buffer_type)(ggml_backend_t backend);
 
         // (optional) asynchronous tensor data access
-        void (*set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-        void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
-        bool (*cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst);
+        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);
 
         // (optional) complete all pending operations
-        void (*synchronize)(ggml_backend_t backend);
+        void (*GGML_CALL synchronize)(ggml_backend_t backend);
 
         // compute graph with a plan
-        ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
-        void                      (*graph_plan_free)   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-        void                      (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+        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);
+        void                      (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
         // compute graph without a plan (async)
-        bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+        bool (*GGML_CALL graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
 
         // check if the backend supports an operation
-        bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+        bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
     };
 
     struct ggml_backend {
@@ -107,9 +107,9 @@ extern "C" {
     // Backend registry
     //
 
-    typedef ggml_backend_t (*ggml_backend_init_fn)(const char * params, void * user_data);
+    typedef ggml_backend_t (*GGML_CALL ggml_backend_init_fn)(const char * params, void * user_data);
 
-    void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
+    GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
 
 #ifdef  __cplusplus
 }

ggml-backend.c

@@ -19,7 +19,7 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
     return buft->iface.get_name(buft);
 }
 
-ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     return buft->iface.alloc_buffer(buft, size);
 }
 
@@ -27,7 +27,7 @@ size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) {
     return buft->iface.get_alignment(buft);
 }
 
-size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) {
+GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) {
     // get_alloc_size is optional, defaults to ggml_nbytes
     if (buft->iface.get_alloc_size) {
         return buft->iface.get_alloc_size(buft, tensor);
@@ -48,7 +48,7 @@ bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
 
 // backend buffer
 
-ggml_backend_buffer_t ggml_backend_buffer_init(
+GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
                ggml_backend_buffer_type_t      buft,
         struct ggml_backend_buffer_i           iface,
                ggml_backend_buffer_context_t   context,
@@ -95,7 +95,7 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
     return base;
 }
 
-void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
+GGML_CALL void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
     // init_tensor is optional
     if (buffer->iface.init_tensor) {
         buffer->iface.init_tensor(buffer, tensor);
@@ -191,7 +191,7 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
     }
 }
 
-void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
@@ -201,7 +201,7 @@ void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, siz
     tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size);
 }
 
-void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
@@ -318,9 +318,9 @@ struct ggml_backend_reg {
 static struct ggml_backend_reg ggml_backend_registry[GGML_MAX_BACKENDS_REG];
 static size_t ggml_backend_registry_count = 0;
 
-static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
+GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
 
-static void ggml_backend_registry_init(void) {
+GGML_CALL static void ggml_backend_registry_init(void) {
     static bool initialized = false;
 
     if (initialized) {
@@ -333,18 +333,18 @@ static void ggml_backend_registry_init(void) {
 
     // add forward decls here to avoid including the backend headers
 #ifdef GGML_USE_CUBLAS
-    extern void ggml_backend_cuda_reg_devices(void);
+    extern GGML_CALL void ggml_backend_cuda_reg_devices(void);
     ggml_backend_cuda_reg_devices();
 #endif
 
 #ifdef GGML_USE_METAL
-    extern ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data);
-    extern ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
+    extern GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data);
+    extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
     ggml_backend_register("Metal", ggml_backend_reg_metal_init, ggml_backend_metal_buffer_type(), NULL);
 #endif
 }
 
-void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
+GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
     GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG);
 
     size_t id = ggml_backend_registry_count;
@@ -439,33 +439,33 @@ ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) {
 
 // backend CPU
 
-static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) {
+GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) {
     return "CPU";
 
     GGML_UNUSED(buffer);
 }
 
-static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
+GGML_CALL static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
     return (void *)buffer->context;
 }
 
-static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     free(buffer->context);
 }
 
-static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+GGML_CALL static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     memcpy((char *)tensor->data + offset, data, size);
 
     GGML_UNUSED(buffer);
 }
 
-static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+GGML_CALL static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     memcpy(data, (const char *)tensor->data + offset, size);
 
     GGML_UNUSED(buffer);
 }
 
-static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
+GGML_CALL static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
     if (ggml_backend_buffer_is_host(src->buffer)) {
         memcpy(dst->data, src->data, ggml_nbytes(src));
         return true;
@@ -475,7 +475,7 @@ static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, con
     GGML_UNUSED(buffer);
 }
 
-static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+GGML_CALL static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
     memset(buffer->context, value, buffer->size);
 }
 
@@ -506,13 +506,13 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
 
 static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512
 
-static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
     return "CPU";
 
     GGML_UNUSED(buft);
 }
 
-static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     size += TENSOR_ALIGNMENT;   // malloc may return an address that is not aligned
     void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC?
 
@@ -521,25 +521,25 @@ static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_back
     return ggml_backend_buffer_init(buft, cpu_backend_buffer_i, data, size);
 }
 
-static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
     return TENSOR_ALIGNMENT;
 
     GGML_UNUSED(buft);
 }
 
-static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+GGML_CALL static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
     return ggml_backend_is_cpu(backend);
 
     GGML_UNUSED(buft);
 }
 
-static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
     return true;
 
     GGML_UNUSED(buft);
 }
 
-ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
     static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = {
         /* .iface = */ {
             /* .get_name         = */ ggml_backend_cpu_buffer_type_get_name,
@@ -561,23 +561,23 @@ ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
 
 #include <hbwmalloc.h>
 
-static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
     return "CPU_HBM";
 
     GGML_UNUSED(buft);
 }
 
-static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) {
+GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) {
     return "CPU_HBM";
 
     GGML_UNUSED(buf);
 }
 
-static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+GGML_CALL static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     hbw_free(buffer->context);
 }
 
-static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     //void * ptr = hbw_malloc(size);
     void * ptr;
     int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size);
@@ -617,20 +617,20 @@ struct ggml_backend_cpu_context {
     size_t work_size;
 };
 
-static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
+GGML_CALL static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
     return "CPU";
 
     GGML_UNUSED(backend);
 }
 
-static void ggml_backend_cpu_free(ggml_backend_t backend) {
+GGML_CALL static void ggml_backend_cpu_free(ggml_backend_t backend) {
     struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
     free(cpu_ctx->work_data);
     free(cpu_ctx);
     free(backend);
 }
 
-static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) {
+GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) {
     return ggml_backend_cpu_buffer_type();
 
     GGML_UNUSED(backend);
@@ -641,7 +641,7 @@ struct ggml_backend_plan_cpu {
     struct ggml_cgraph cgraph;
 };
 
-static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) {
+GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) {
     struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
 
     struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu));
@@ -656,7 +656,7 @@ static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend
     return cpu_plan;
 }
 
-static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
     struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
 
     free(cpu_plan->cplan.work_data);
@@ -665,7 +665,7 @@ static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backen
     GGML_UNUSED(backend);
 }
 
-static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+GGML_CALL static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
     struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
 
     ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
@@ -673,7 +673,7 @@ static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_bac
     GGML_UNUSED(backend);
 }
 
-static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
 
     struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
@@ -690,8 +690,10 @@ static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c
     return true;
 }
 
-static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
     switch (op->op) {
+        case GGML_OP_CPY:
+            return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS; // missing type_traits.from_float
         case GGML_OP_MUL_MAT:
             return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
         default:
@@ -732,7 +734,7 @@ ggml_backend_t ggml_backend_cpu_init(void) {
     return cpu_backend;
 }
 
-bool ggml_backend_is_cpu(ggml_backend_t backend) {
+GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) {
     return backend && backend->iface.get_name == ggml_backend_cpu_name;
 }
 
@@ -743,11 +745,11 @@ void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
     ctx->n_threads = n_threads;
 }
 
-ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
+GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
     return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), cpu_backend_buffer_i_from_ptr, ptr, size);
 }
 
-static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) {
+GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) {
     return ggml_backend_cpu_init();
 
     GGML_UNUSED(params);
@@ -802,6 +804,9 @@ struct ggml_backend_sched {
     __attribute__((aligned(GGML_MEM_ALIGN)))
     #endif
     char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
+
+    ggml_backend_sched_eval_callback callback_eval;
+    void * callback_eval_user_data;
 };
 
 #define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node)
@@ -1186,6 +1191,24 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g
                 ggml_tallocr_t src_allocr = node_allocr(src);
                 GGML_ASSERT(src_allocr != NULL); // all inputs should be assigned by now
                 if (src_allocr != node_allocr) {
+                    // create a copy of the input in the split's backend
+                    size_t id = hash_id(src);
+                    if (sched->node_copies[id][cur_backend_id] == NULL) {
+                        ggml_backend_t backend = get_allocr_backend(sched, cur_allocr);
+                        struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
+                        ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name);
+
+                        sched->node_copies[id][cur_backend_id] = tensor_copy;
+                        node_allocr(tensor_copy) = cur_allocr;
+                        SET_CAUSE(tensor_copy, "4.cpy");
+
+                        int n_inputs = sched->splits[cur_split].n_inputs++;
+                        GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS);
+                        sched->splits[cur_split].inputs[n_inputs] = src;
+                    }
+                    node->src[j] = sched->node_copies[id][cur_backend_id];
+
+#if 0
                     // check if the input is already in the split
                     bool found = false;
                     for (int k = 0; k < sched->splits[cur_split].n_inputs; k++) {
@@ -1201,19 +1224,7 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g
                         GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS);
                         sched->splits[cur_split].inputs[n_inputs] = src;
                     }
-
-                    // create a copy of the input in the split's backend
-                    size_t id = hash_id(src);
-                    if (sched->node_copies[id][cur_backend_id] == NULL) {
-                        ggml_backend_t backend = get_allocr_backend(sched, cur_allocr);
-                        struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
-                        ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name);
-
-                        sched->node_copies[id][cur_backend_id] = tensor_copy;
-                        node_allocr(tensor_copy) = cur_allocr;
-                        SET_CAUSE(tensor_copy, "4.cpy");
-                    }
-                    node->src[j] = sched->node_copies[id][cur_backend_id];
+#endif
                 }
             }
         }
@@ -1324,9 +1335,38 @@ static void sched_compute_splits(ggml_backend_sched_t sched) {
         ggml_graph_dump_dot(split->graph, NULL, split_filename);
 #endif
 
+
         uint64_t compute_start_us = ggml_time_us();
-        ggml_backend_graph_compute(split_backend, &split->graph);
-        //ggml_backend_synchronize(split_backend); // necessary to measure compute time
+        if (!sched->callback_eval) {
+            ggml_backend_graph_compute(split_backend, &split->graph);
+            //ggml_backend_synchronize(split_backend); // necessary to measure compute time
+        } else {
+            // similar to ggml_backend_compare_graph_backend
+            for (int j0 = 0; j0 < split->graph.n_nodes; j0++) {
+                struct ggml_tensor * t = split->graph.nodes[j0];
+
+                // check if the user needs data from this node
+                bool need = sched->callback_eval(t, true, sched->callback_eval_user_data);
+
+                int j1 = j0;
+
+                // determine the range [j0, j1] of nodes that can be computed together
+                while (!need && j1 < split->graph.n_nodes - 1) {
+                    t = split->graph.nodes[++j1];
+                    need = sched->callback_eval(t, true, sched->callback_eval_user_data);
+                }
+
+                struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);
+
+                ggml_backend_graph_compute(split_backend, &gv);
+
+                if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
+                    break;
+                }
+
+                j0 = j1;
+            }
+        }
         uint64_t compute_end_us = ggml_time_us();
         compute_us[split_backend_id] += compute_end_us - compute_start_us;
     }
@@ -1431,6 +1471,12 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
     sched_reset(sched);
 }
 
+
+void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
+    sched->callback_eval = callback;
+    sched->callback_eval_user_data = user_data;
+}
+
 int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
     return sched->n_splits;
 }

ggml-backend.h

@@ -17,12 +17,12 @@ extern "C" {
     //
 
     // buffer type
-    GGML_API const char *          ggml_backend_buft_name            (ggml_backend_buffer_type_t buft);
-    GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer    (ggml_backend_buffer_type_t buft, size_t size);
-    GGML_API size_t                ggml_backend_buft_get_alignment   (ggml_backend_buffer_type_t buft);
-    GGML_API size_t                ggml_backend_buft_get_alloc_size  (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
-    GGML_API bool                  ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
-    GGML_API bool                  ggml_backend_buft_is_host         (ggml_backend_buffer_type_t buft);
+    GGML_API           const char *          ggml_backend_buft_name            (ggml_backend_buffer_type_t buft);
+    GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer    (ggml_backend_buffer_type_t buft, size_t size);
+    GGML_API           size_t                ggml_backend_buft_get_alignment   (ggml_backend_buffer_type_t buft);
+    GGML_API GGML_CALL size_t                ggml_backend_buft_get_alloc_size  (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
+    GGML_API           bool                  ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
+    GGML_API           bool                  ggml_backend_buft_is_host         (ggml_backend_buffer_type_t buft);
 
     // buffer
     enum ggml_backend_buffer_usage {
@@ -30,18 +30,18 @@ extern "C" {
         GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1,
     };
 
-    GGML_API const char *               ggml_backend_buffer_name          (ggml_backend_buffer_t buffer);
-    GGML_API void                       ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
-    GGML_API void *                     ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
-    GGML_API size_t                     ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
-    GGML_API void                       ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-    GGML_API size_t                     ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
-    GGML_API size_t                     ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-    GGML_API void                       ggml_backend_buffer_clear         (ggml_backend_buffer_t buffer, uint8_t value);
-    GGML_API bool                       ggml_backend_buffer_is_host       (ggml_backend_buffer_t buffer);
-    GGML_API void                       ggml_backend_buffer_set_usage     (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
-    GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type      (ggml_backend_buffer_t buffer);
-    GGML_API void                       ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);
+    GGML_API           const char *               ggml_backend_buffer_name          (ggml_backend_buffer_t buffer);
+    GGML_API           void                       ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
+    GGML_API           void *                     ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                     ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
+    GGML_API GGML_CALL void                       ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API           size_t                     ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                     ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API           void                       ggml_backend_buffer_clear         (ggml_backend_buffer_t buffer, uint8_t value);
+    GGML_API           bool                       ggml_backend_buffer_is_host       (ggml_backend_buffer_t buffer);
+    GGML_API           void                       ggml_backend_buffer_set_usage     (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
+    GGML_API           ggml_backend_buffer_type_t ggml_backend_buffer_get_type      (ggml_backend_buffer_t buffer);
+    GGML_API           void                       ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);
 
     //
     // Backend
@@ -58,8 +58,8 @@ extern "C" {
     GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
     GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
 
-    GGML_API void ggml_backend_tensor_set(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-    GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+    GGML_API GGML_CALL void ggml_backend_tensor_set(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
 
     GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
 
@@ -80,13 +80,13 @@ extern "C" {
 
     GGML_API ggml_backend_t ggml_backend_cpu_init(void);
 
-    GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend);
-    GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
+    GGML_API GGML_CALL bool ggml_backend_is_cpu           (ggml_backend_t backend);
+    GGML_API           void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
 
     // Create a backend buffer from an existing pointer
-    GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
+    GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
 
-    GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
+    GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
 
 #ifdef GGML_USE_CPU_HBM
     GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
@@ -148,6 +148,14 @@ extern "C" {
     struct ggml_backend_sched;
     typedef struct ggml_backend_sched * ggml_backend_sched_t;
 
+    // when ask == true, the scheduler wants to know if the user wants to observe this node
+    // this allows the scheduler to batch nodes together in order to evaluate them in a single call
+    //
+    // when ask == false, the scheduler is passing the node tensor to the user for observation
+    // if the user returns false, the scheduler will cancel the graph compute
+    //
+    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 void                  ggml_backend_sched_free(ggml_backend_sched_t sched);
@@ -168,6 +176,9 @@ extern "C" {
     // Reset all assignments and allocators - must be called before using the sched allocators to allocate inputs
     GGML_API void                  ggml_backend_sched_reset(ggml_backend_sched_t sched);
 
+    // Set a callback to be called for each resulting node during graph compute
+    GGML_API void                  ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
+
     //
     // Utils
     //
@@ -183,7 +194,7 @@ extern "C" {
     GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
     GGML_API void                           ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
 
-    typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
+    typedef bool (*GGML_CALL ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
 
     // Compare the output of two backends
     GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);

ggml-cuda.cu

@@ -12,9 +12,10 @@
 #include <vector>
 #include <map>
 #include <array>
-#include "ggml-cuda.h"
-#include "ggml.h"
-#include "ggml-backend-impl.h"
+
+// stringize macro for converting __CUDA_ARCH_LIST__ (list of integers) to string
+#define STRINGIZE_IMPL(...) #__VA_ARGS__
+#define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__)
 
 #if defined(GGML_USE_HIPBLAS)
 #include <hip/hip_runtime.h>
@@ -118,6 +119,11 @@
 
 #endif // defined(GGML_USE_HIPBLAS)
 
+// ggml-cuda need half type so keep ggml headers include at last
+#include "ggml-cuda.h"
+#include "ggml.h"
+#include "ggml-backend-impl.h"
+
 #define CUDART_HMAX     11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed)
 
 #define CC_PASCAL     600
@@ -582,13 +588,28 @@ static cuda_device_capabilities g_device_caps[GGML_CUDA_MAX_DEVICES] = { {0, 0,
 static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
 
 [[noreturn]]
-static __device__ void bad_arch() {
-    printf("ERROR: ggml-cuda was compiled without support for the current GPU architecture.\n");
+static __device__ void no_device_code(
+    const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n",
+           file_name, line, function_name, arch);
+    (void) arch_list;
+#else
+    printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n",
+           file_name, line, function_name, arch, arch_list);
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
     __trap();
 
-    (void) bad_arch; // suppress unused function warning
+    (void) no_device_code; // suppress unused function warning
 }
 
+#ifdef __CUDA_ARCH__
+#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__))
+#else
+#define NO_DEVICE_CODE GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.")
+#endif // __CUDA_ARCH__
+
 static __device__ __forceinline__ float warp_reduce_sum(float x) {
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
@@ -615,7 +636,7 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
     return a;
 #else
     (void) a;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
 }
 
@@ -636,7 +657,7 @@ static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
     return x;
 #else
     (void) x;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
 }
 
@@ -1105,6 +1126,61 @@ static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const in
 #endif // GGML_CUDA_F16
 }
 
+template<typename dst_t>
+static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) {
+
+    const int i = blockIdx.x;
+
+    // assume 32 threads
+    const int tid = threadIdx.x;
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_0 * x = (const block_q4_0 *)vx + ib;
+    const float d = __half2float(x->d);
+    const float dm = -8*d;
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l+ 0] = d * (q[l] & 0xF) + dm;
+        y[l+16] = d * (q[l] >>  4) + dm;
+    }
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) {
+
+    const int i = blockIdx.x;
+
+    // assume 32 threads
+    const int tid = threadIdx.x;
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_1 * x = (const block_q4_1 *)vx + ib;
+    const float2 d = __half22float2(x->dm);
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l+ 0] = d.x * (q[l] & 0xF) + d.y;
+        y[l+16] = d.x * (q[l] >>  4) + d.y;
+    }
+}
+
 //================================== k-quants
 
 template<typename dst_t>
@@ -2364,7 +2440,7 @@ static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, h
     }
 #else
     (void) vx; (void) y; (void) k;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= CC_PASCAL
 }
 
@@ -2395,7 +2471,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_imp
     // second part effectively subtracts 8 from each quant value
     return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2432,7 +2508,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_imp
     // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
     return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2467,7 +2543,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_imp
     // second part effectively subtracts 16 from each quant value
     return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2512,7 +2588,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp
     return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
 
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2533,7 +2609,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_q8_1_imp
 
     return d8_0*d8_1 * sumi;
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2563,7 +2639,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_imp
     // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
     return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2598,7 +2674,7 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
 
     return dm2f.x*sumf_d - dm2f.y*sumf_m;
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2635,7 +2711,7 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
 
     return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m);
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2675,7 +2751,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
 
     return d3 * sumf;
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2700,7 +2776,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
 
     return d3*d8 * sumi;
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2733,7 +2809,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
     return dm4f.x*sumf_d - dm4f.y*sumf_m;
 
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2766,7 +2842,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
     return dm4f.x*sumf_d - dm4f.y*sumf_m;
 
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2806,7 +2882,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
     return dm5f.x*sumf_d - dm5f.y*sumf_m;
 
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2839,7 +2915,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
     return dm4f.x*sumf_d - dm4f.y*sumf_m;
 
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2869,7 +2945,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
 
     return d*sumf;
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -2900,7 +2976,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
     return d6 * sumf_d;
 
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
@@ -3766,7 +3842,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
     return dall * sumf_d - dmin * sumf_m;
 
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 
 #endif
@@ -3949,7 +4025,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
     return d * sumf_d;
 
 #else
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 
 #endif
@@ -4207,7 +4283,7 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
         q8 += 8;
         aux32 >>= 7;
     }
-    const float d = (float)bq2->d * (0.5f + aux32) * (float)bq8_1[ib32].ds.x * 0.25f;
+    const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.25f;
     return d * sumi;
 #else
     // iqs is 0...15
@@ -4218,7 +4294,7 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
     const uint8_t  * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
     const uint8_t  * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
     const uint32_t aux32 = q2[2] | (q2[3] << 16);
-    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * (float)bq8_1[ib32].ds.x * 0.25f;
+    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * __low2float(bq8_1[ib32].ds) * 0.25f;
     const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
     const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
     const int8_t * q8 = bq8_1[ib32].qs + 16*il;
@@ -4263,7 +4339,7 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
         }
         q8 += 8;
     }
-    const float d = (float)bq2->d * (float)bq8_1[ib32].ds.x * 0.25f;
+    const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
     return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
 #else
     assert(false);
@@ -4444,7 +4520,7 @@ template <bool need_check> static __global__ void
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 #else
     (void) vec_dot_q4_0_q8_1_mul_mat;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 }
 
@@ -4513,7 +4589,7 @@ template <bool need_check> static __global__ void
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 #else
     (void) vec_dot_q4_1_q8_1_mul_mat;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 }
 
@@ -4580,7 +4656,7 @@ template <bool need_check> static __global__ void
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 #else
     (void) vec_dot_q5_0_q8_1_mul_mat;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 }
 
@@ -4647,7 +4723,7 @@ mul_mat_q5_1(
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 #else
     (void) vec_dot_q5_1_q8_1_mul_mat;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 }
 
@@ -4714,7 +4790,7 @@ template <bool need_check> static __global__ void
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 #else
     (void) vec_dot_q8_0_q8_1_mul_mat;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 }
 
@@ -4781,7 +4857,7 @@ mul_mat_q2_K(
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 #else
     (void) vec_dot_q2_K_q8_1_mul_mat;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 }
 
@@ -4850,7 +4926,7 @@ template <bool need_check> static __global__ void
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 #else
     (void) vec_dot_q3_K_q8_1_mul_mat;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 }
 
@@ -4919,7 +4995,7 @@ template <bool need_check> static __global__ void
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 #else
     (void) vec_dot_q4_K_q8_1_mul_mat;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 }
 
@@ -4986,7 +5062,7 @@ mul_mat_q5_K(
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 #else
     (void) vec_dot_q5_K_q8_1_mul_mat;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 }
 
@@ -5055,7 +5131,7 @@ template <bool need_check> static __global__ void
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 #else
     (void) vec_dot_q6_K_q8_1_mul_mat;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 }
 
@@ -5076,10 +5152,10 @@ static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void *
     const block_q_t  * x = (const block_q_t  *) vx;
     const block_q8_1 * y = (const block_q8_1 *) vy;
 
-    for (int i = 0; i < blocks_per_row; i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i + threadIdx.x / (qi/vdr); // x block index
+    for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row; i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
 
-        const int iby = (i + threadIdx.x / (qi/vdr)) * (qk/QK8_1); // y block index that aligns with ibx
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
 
         const int iqs  = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int
 
@@ -5778,7 +5854,7 @@ static __global__ void soft_max_f16(const float * x, const float * y, float * ds
     }
 #else
     (void) x; (void) y; (void) dst; (void) ncols_par; (void) nrows_y; (void) scale;
-    bad_arch();
+    NO_DEVICE_CODE;
 #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
 }
 
@@ -6253,6 +6329,20 @@ static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cu
 #endif
 }
 
+template<typename dst_t>
+static void dequantize_row_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    dequantize_block_q4_0<<<nb, 32, 0, stream>>>(vx, y, nb32);
+}
+
+template<typename dst_t>
+static void dequantize_row_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    dequantize_block_q4_1<<<nb, 32, 0, stream>>>(vx, y, nb32);
+}
+
 template<typename dst_t>
 static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
     const int nb = k / QK_K;
@@ -6301,9 +6391,9 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
     int id;
     switch (type) {
         case GGML_TYPE_Q4_0:
-            return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
+            return dequantize_row_q4_0_cuda;
         case GGML_TYPE_Q4_1:
-            return dequantize_block_cuda<QK4_1, QR4_1, dequantize_q4_1>;
+            return dequantize_row_q4_1_cuda;
         case GGML_TYPE_Q5_0:
             return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
         case GGML_TYPE_Q5_1:
@@ -6338,9 +6428,9 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
 static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
     switch (type) {
         case GGML_TYPE_Q4_0:
-            return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
+            return dequantize_row_q4_0_cuda;
         case GGML_TYPE_Q4_1:
-            return dequantize_block_cuda<QK4_1, QR4_1, dequantize_q4_1>;
+            return dequantize_row_q4_1_cuda;
         case GGML_TYPE_Q5_0:
             return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
         case GGML_TYPE_Q5_1:
@@ -7546,11 +7636,11 @@ struct cuda_pool_alloc {
 
 static bool g_cublas_loaded = false;
 
-bool ggml_cublas_loaded(void) {
+GGML_CALL bool ggml_cublas_loaded(void) {
     return g_cublas_loaded;
 }
 
-void ggml_init_cublas() {
+GGML_CALL void ggml_init_cublas() {
     static bool initialized = false;
 
     if (!initialized) {
@@ -7638,7 +7728,7 @@ void ggml_init_cublas() {
     }
 }
 
-void * ggml_cuda_host_malloc(size_t size) {
+GGML_CALL void * ggml_cuda_host_malloc(size_t size) {
     if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
         return nullptr;
     }
@@ -7656,7 +7746,7 @@ void * ggml_cuda_host_malloc(size_t size) {
     return ptr;
 }
 
-void ggml_cuda_host_free(void * ptr) {
+GGML_CALL void ggml_cuda_host_free(void * ptr) {
     CUDA_CHECK(cudaFreeHost(ptr));
 }
 
@@ -9173,7 +9263,7 @@ static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src
     ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm);
 }
 
-bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
     if (!g_cublas_loaded) return false;
 
     const int64_t ne10 = src1->ne[0];
@@ -9944,7 +10034,7 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl
     return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
 }
 
-static void ggml_cuda_set_main_device(const int main_device) {
+GGML_CALL static void ggml_cuda_set_main_device(const int main_device) {
     if (main_device >= g_device_count) {
         fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
                 main_device, g_device_count, g_main_device);
@@ -9959,7 +10049,7 @@ static void ggml_cuda_set_main_device(const int main_device) {
     }
 }
 
-bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
+GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
     if (!g_cublas_loaded) return false;
 
     ggml_cuda_func_t func;
@@ -10117,7 +10207,7 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
     return true;
 }
 
-int ggml_cuda_get_device_count() {
+GGML_CALL int ggml_cuda_get_device_count() {
     int device_count;
     if (cudaGetDeviceCount(&device_count) != cudaSuccess) {
         return 0;
@@ -10125,7 +10215,7 @@ int ggml_cuda_get_device_count() {
     return device_count;
 }
 
-void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
+GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
     cudaDeviceProp prop;
     CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
     snprintf(description, description_size, "%s", prop.name);
@@ -10175,27 +10265,27 @@ struct ggml_backend_cuda_buffer_context {
     }
 };
 
-static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) {
+GGML_CALL static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) {
     ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
     return ctx->name.c_str();
 }
 
-static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) {
+GGML_CALL static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) {
     return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name;
 }
 
-static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+GGML_CALL static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
     CUDA_CHECK(cudaFree(ctx->dev_ptr));
     delete ctx;
 }
 
-static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
+GGML_CALL static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
     ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
     return ctx->dev_ptr;
 }
 
-static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
     ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
 
     if (tensor->view_src != NULL && tensor->view_offs == 0) {
@@ -10227,7 +10317,7 @@ static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, g
     }
 }
 
-static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
 
     ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
@@ -10238,7 +10328,7 @@ static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, gg
     CUDA_CHECK(cudaDeviceSynchronize());
 }
 
-static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
 
     ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
@@ -10249,7 +10339,7 @@ static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, co
     CUDA_CHECK(cudaDeviceSynchronize());
 }
 
-static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
+GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
     if (ggml_backend_buffer_is_cuda(src->buffer)) {
         ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context;
         ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
@@ -10266,7 +10356,7 @@ static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, co
     return false;
 }
 
-static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
     ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
 
     ggml_cuda_set_device(ctx->device);
@@ -10288,19 +10378,18 @@ static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = {
 };
 
 // cuda buffer type
-
 struct ggml_backend_cuda_buffer_type_context {
     int device;
     std::string name;
 };
 
-static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) {
+GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) {
     ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
 
     return ctx->name.c_str();
 }
 
-static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
 
     ggml_cuda_set_device(buft_ctx->device);
@@ -10319,13 +10408,13 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac
     return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size);
 }
 
-static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
     return 128;
 
     UNUSED(buft);
 }
 
-static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
     int64_t row_low = 0;
     int64_t row_high = ggml_nrows(tensor);
     int64_t nrows_split = row_high - row_low;
@@ -10345,7 +10434,7 @@ static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_t
     UNUSED(buft);
 }
 
-static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
     if (!ggml_backend_is_cuda(backend)) {
         return false;
     }
@@ -10365,7 +10454,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = {
     /* .is_host          = */ NULL,
 };
 
-ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
     // FIXME: this is not thread safe
     if (device >= ggml_backend_cuda_get_device_count()) {
         return nullptr;
@@ -10410,7 +10499,7 @@ struct ggml_backend_cuda_split_buffer_context {
     std::vector<ggml_tensor_extra_gpu *> tensor_extras;
 };
 
-static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) {
+GGML_CALL static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) {
     return GGML_CUDA_NAME "_Split";
 
     UNUSED(buffer);
@@ -10421,19 +10510,19 @@ static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_
 //    return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name;
 //}
 
-static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+GGML_CALL static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
     delete ctx;
 }
 
-static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) {
+GGML_CALL static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) {
     // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced
     return (void *)0x1000;
 
     UNUSED(buffer);
 }
 
-static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
     GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported
 
     ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
@@ -10483,7 +10572,7 @@ static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buf
     tensor->extra = extra;
 }
 
-static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     // split tensors must always be set in their entirety at once
     GGML_ASSERT(offset == 0);
     GGML_ASSERT(size == ggml_nbytes(tensor));
@@ -10517,7 +10606,7 @@ static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buff
     }
 }
 
-static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     // split tensors must always be set in their entirety at once
     GGML_ASSERT(offset == 0);
     GGML_ASSERT(size == ggml_nbytes(tensor));
@@ -10551,7 +10640,7 @@ static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buff
     }
 }
 
-static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+GGML_CALL static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
     UNUSED(buffer);
     UNUSED(value);
 }
@@ -10570,13 +10659,13 @@ static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = {
 
 // cuda split buffer type
 
-static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
+GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
     return GGML_CUDA_NAME "_Split";
 
     UNUSED(buft);
 }
 
-static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
     // instead, we allocate them for each tensor separately in init_tensor
     // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated,
@@ -10586,13 +10675,13 @@ static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(gg
     return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size);
 }
 
-static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
     return 128;
 
     UNUSED(buft);
 }
 
-static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
     ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context;
 
     size_t total_size = 0;
@@ -10619,13 +10708,13 @@ static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_bu
     return total_size;
 }
 
-static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
     return ggml_backend_is_cuda(backend);
 
     UNUSED(buft);
 }
 
-static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
     return false;
 
     UNUSED(buft);
@@ -10640,7 +10729,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface
     /* .is_host          = */ ggml_backend_cuda_split_buffer_type_is_host,
 };
 
-ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) {
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) {
     // FIXME: this is not thread safe
     static std::map<std::array<float, GGML_CUDA_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
 
@@ -10676,23 +10765,23 @@ ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * ten
 
 // host buffer type
 
-static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
+GGML_CALL static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
     return GGML_CUDA_NAME "_Host";
 
     UNUSED(buft);
 }
 
-static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) {
+GGML_CALL static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) {
     return GGML_CUDA_NAME "_Host";
 
     UNUSED(buffer);
 }
 
-static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+GGML_CALL static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     ggml_cuda_host_free(buffer->context);
 }
 
-static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     void * ptr = ggml_cuda_host_malloc(size);
 
     if (ptr == nullptr) {
@@ -10708,7 +10797,7 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm
     return buffer;
 }
 
-ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
     static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = {
         /* .iface    = */ {
             /* .get_name         = */ ggml_backend_cuda_host_buffer_type_name,
@@ -10726,26 +10815,26 @@ ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
 
 // backend
 
-static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
+GGML_CALL static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     return cuda_ctx->name.c_str();
 }
 
-static void ggml_backend_cuda_free(ggml_backend_t backend) {
+GGML_CALL static void ggml_backend_cuda_free(ggml_backend_t backend) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     delete cuda_ctx;
     delete backend;
 }
 
-static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) {
+GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     return ggml_backend_cuda_buffer_type(cuda_ctx->device);
 }
 
-static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
@@ -10754,7 +10843,7 @@ static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tens
     CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0]));
 }
 
-static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
@@ -10763,7 +10852,7 @@ static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggm
     CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0]));
 }
 
-static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
+GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     if (dst->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && ggml_backend_buffer_is_cuda(src->buffer)) {
@@ -10774,7 +10863,7 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggm
     return false;
 }
 
-static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
+GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[cuda_ctx->device][0]));
@@ -10782,7 +10871,7 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
     UNUSED(backend);
 }
 
-static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     ggml_cuda_set_main_device(cuda_ctx->device);
@@ -10821,7 +10910,7 @@ static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph
     return true;
 }
 
-static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
+GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
     switch (op->op) {
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
@@ -10850,6 +10939,12 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
                 if (a->ne[3] != b->ne[3]) {
                     return false;
                 }
+                ggml_type a_type = a->type;
+                if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS) {
+                    if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
+                        return false;
+                    }
+                }
                 return true;
             } break;
         case GGML_OP_GET_ROWS:
@@ -10947,7 +11042,7 @@ static ggml_backend_i ggml_backend_cuda_interface = {
     /* .supports_op             = */ ggml_backend_cuda_supports_op,
 };
 
-ggml_backend_t ggml_backend_cuda_init(int device) {
+GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) {
     ggml_init_cublas(); // TODO: remove from ggml.c
 
     if (device < 0 || device >= ggml_cuda_get_device_count()) {
@@ -10971,35 +11066,35 @@ ggml_backend_t ggml_backend_cuda_init(int device) {
     return cuda_backend;
 }
 
-bool ggml_backend_is_cuda(ggml_backend_t backend) {
+GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend) {
     return backend && backend->iface.get_name == ggml_backend_cuda_name;
 }
 
-int ggml_backend_cuda_get_device_count() {
+GGML_CALL int ggml_backend_cuda_get_device_count() {
     return ggml_cuda_get_device_count();
 }
 
-void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) {
+GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) {
     ggml_cuda_get_device_description(device, description, description_size);
 }
 
-void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) {
+GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) {
     ggml_cuda_set_device(device);
 
     CUDA_CHECK(cudaMemGetInfo(free, total));
 }
 
 // backend registry
-static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) {
+GGML_CALL static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) {
     ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data);
     return cuda_backend;
 
     UNUSED(params);
 }
 
-extern "C" int ggml_backend_cuda_reg_devices();
+extern "C" GGML_CALL int ggml_backend_cuda_reg_devices();
 
-int ggml_backend_cuda_reg_devices() {
+GGML_CALL int ggml_backend_cuda_reg_devices() {
     int device_count = ggml_cuda_get_device_count();
     //int device_count = 1; // DEBUG: some tools require delaying CUDA initialization
     for (int i = 0; i < device_count; i++) {

ggml-cuda.h

@@ -18,34 +18,34 @@ extern "C" {
 #define GGML_CUDA_MAX_DEVICES       16
 
 // Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`.
-GGML_API void   ggml_init_cublas(void);
+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 bool   ggml_cublas_loaded(void);
+GGML_API GGML_CALL bool   ggml_cublas_loaded(void);
 
-GGML_API void * ggml_cuda_host_malloc(size_t size);
-GGML_API void   ggml_cuda_host_free(void * ptr);
+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 bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-GGML_API bool   ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
+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 int    ggml_cuda_get_device_count(void);
-GGML_API void   ggml_cuda_get_device_description(int device, char * description, size_t description_size);
+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_backend_t ggml_backend_cuda_init(int device);
+GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device);
 
-GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend);
+GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend);
 
-GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
+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_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
+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_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
 
-GGML_API int  ggml_backend_cuda_get_device_count(void);
-GGML_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
-GGML_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
+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);
 
 #ifdef  __cplusplus
 }

ggml-metal.h

@@ -27,7 +27,6 @@
 
 // max memory buffers that can be mapped to the device
 #define GGML_METAL_MAX_BUFFERS 64
-#define GGML_METAL_MAX_COMMAND_BUFFERS 32
 
 struct ggml_tensor;
 struct ggml_cgraph;
@@ -47,11 +46,11 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void);
 
 GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
 
-GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
+GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
 
 GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
 
-GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
 
 // helper to check if the device supports a specific family
 // ideally, the user code should be doing these checks

ggml-quants.h

@@ -196,8 +196,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
 void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);
 void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
 void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
-void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k);
-void quantize_row_iq2_xs_reference (const float * restrict x, block_iq2_xs  * restrict y, int k);
 
 void quantize_row_q4_0(const float * restrict x, void * restrict y, int k);
 void quantize_row_q4_1(const float * restrict x, void * restrict y, int k);
@@ -212,8 +210,6 @@ void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
 void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);
 void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
 void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_iq2_xxs(const float * restrict x, void * restrict y, int k);
-void quantize_row_iq2_xs (const float * restrict x, void * restrict y, int k);
 
 // Dequantization
 void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k);
@@ -246,3 +242,21 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx,
 void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+
+//
+// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
+//
+size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q2_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q3_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q4_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q5_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q6_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q4_0   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q4_1   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q5_0   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q5_1   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+
+void iq2xs_init_impl(int grid_size);
+void iq2xs_free_impl(int grid_size);

ggml.c

@@ -394,12 +394,6 @@ static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
 static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y);
 static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y);
 
-ggml_collect_imatrix_t g_imatrix_collect = NULL;
-
-void ggml_set_imatrix_collection(ggml_collect_imatrix_t imatrix_collect) {
-    g_imatrix_collect = imatrix_collect;
-}
-
 static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
     [GGML_TYPE_I8] = {
         .type_name                = "i8",
@@ -585,8 +579,8 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .type_size                = sizeof(block_iq2_xxs),
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq2_xxs,
-        .from_float               = quantize_row_iq2_xxs,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_iq2_xxs_reference,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
         .vec_dot                  = ggml_vec_dot_iq2_xxs_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
@@ -596,8 +590,8 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .type_size                = sizeof(block_iq2_xs),
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq2_xs,
-        .from_float               = quantize_row_iq2_xs,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_iq2_xs_reference,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
         .vec_dot                  = ggml_vec_dot_iq2_xs_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
@@ -1424,6 +1418,9 @@ inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) {
 inline static void ggml_vec_elu_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; }
 inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; }
 inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); }
+// TODO: optimize performance
+inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); }
+inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); }
 
 static const float GELU_COEF_A     = 0.044715f;
 static const float GELU_QUICK_COEF = -1.702f;
@@ -1782,9 +1779,11 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = {
     "GELU",
     "GELU_QUICK",
     "SILU",
+    "HARDSWISH",
+    "HARDSIGMOID",
 };
 
-static_assert(GGML_UNARY_OP_COUNT == 10, "GGML_UNARY_OP_COUNT != 10");
+static_assert(GGML_UNARY_OP_COUNT == 12, "GGML_UNARY_OP_COUNT != 12");
 
 
 static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
@@ -1990,19 +1989,19 @@ void ggml_print_objects(const struct ggml_context * ctx) {
     GGML_PRINT("%s: --- end ---\n", __func__);
 }
 
-int64_t ggml_nelements(const struct ggml_tensor * tensor) {
+GGML_CALL int64_t ggml_nelements(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
 }
 
-int64_t ggml_nrows(const struct ggml_tensor * tensor) {
+GGML_CALL int64_t ggml_nrows(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
 }
 
-size_t ggml_nbytes(const struct ggml_tensor * tensor) {
+GGML_CALL size_t ggml_nbytes(const struct ggml_tensor * tensor) {
     size_t nbytes;
     size_t blck_size = ggml_blck_size(tensor->type);
     if (blck_size == 1) {
@@ -2025,15 +2024,15 @@ size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) {
     return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN);
 }
 
-int ggml_blck_size(enum ggml_type type) {
+GGML_CALL int ggml_blck_size(enum ggml_type type) {
     return type_traits[type].blck_size;
 }
 
-size_t ggml_type_size(enum ggml_type type) {
+GGML_CALL size_t ggml_type_size(enum ggml_type type) {
     return type_traits[type].type_size;
 }
 
-size_t ggml_row_size(enum ggml_type type, int64_t ne) {
+GGML_CALL size_t ggml_row_size(enum ggml_type type, int64_t ne) {
     assert(ne % ggml_blck_size(type) == 0);
     return ggml_type_size(type)*ne/ggml_blck_size(type);
 }
@@ -2042,15 +2041,15 @@ double ggml_type_sizef(enum ggml_type type) {
     return ((double)(type_traits[type].type_size))/type_traits[type].blck_size;
 }
 
-const char * ggml_type_name(enum ggml_type type) {
+GGML_CALL const char * ggml_type_name(enum ggml_type type) {
     return type_traits[type].type_name;
 }
 
-bool ggml_is_quantized(enum ggml_type type) {
+GGML_CALL bool ggml_is_quantized(enum ggml_type type) {
     return type_traits[type].is_quantized;
 }
 
-const char * ggml_op_name(enum ggml_op op) {
+GGML_CALL const char * ggml_op_name(enum ggml_op op) {
     return GGML_OP_NAME[op];
 }
 
@@ -2062,7 +2061,7 @@ const char * ggml_unary_op_name(enum ggml_unary_op op) {
     return GGML_UNARY_OP_NAME[op];
 }
 
-const char * ggml_op_desc(const struct ggml_tensor * t) {
+GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t) {
     if (t->op == GGML_OP_UNARY) {
         enum ggml_unary_op uop = ggml_get_unary_op(t);
         return ggml_unary_op_name(uop);
@@ -2072,7 +2071,7 @@ const char * ggml_op_desc(const struct ggml_tensor * t) {
     }
 }
 
-size_t ggml_element_size(const struct ggml_tensor * tensor) {
+GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor) {
     return ggml_type_size(tensor->type);
 }
 
@@ -2154,11 +2153,11 @@ size_t ggml_tensor_overhead(void) {
     return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE;
 }
 
-bool ggml_is_transposed(const struct ggml_tensor * tensor) {
+GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor) {
     return tensor->nb[0] > tensor->nb[1];
 }
 
-bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
+GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
@@ -2177,7 +2176,7 @@ static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * te
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
 
-bool ggml_is_permuted(const struct ggml_tensor * tensor) {
+GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3];
@@ -3079,7 +3078,7 @@ float * ggml_get_data_f32(const struct ggml_tensor * tensor) {
     return (float *)(tensor->data);
 }
 
-enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
+GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
     GGML_ASSERT(tensor->op == GGML_OP_UNARY);
     return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0);
 }
@@ -3951,6 +3950,20 @@ struct ggml_tensor * ggml_silu_back(
     return result;
 }
 
+// ggml hardswish
+struct ggml_tensor * ggml_hardswish(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_HARDSWISH);
+}
+
+// ggml hardsigmoid
+struct ggml_tensor * ggml_hardsigmoid(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_HARDSIGMOID);
+}
+
 // ggml_norm
 
 static struct ggml_tensor * ggml_norm_impl(
@@ -5350,6 +5363,31 @@ GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
     return result;
 }
 
+// ggml_conv_depthwise
+struct ggml_tensor * ggml_conv_depthwise_2d(
+    struct ggml_context * ctx,
+    struct ggml_tensor * a,
+    struct ggml_tensor * b,
+    int                  s0,
+    int                  s1,
+    int                  p0,
+    int                  p1,
+    int                  d0,
+    int                  d1) {
+    struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]);
+    struct ggml_tensor * im2col = ggml_im2col(ctx, new_a,
+                                        ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]),
+                                        s0, s1, p0, p1, d0, d1, true); // [N * IC, OH, OW, KH * KW]
+
+    struct ggml_tensor * result =
+        ggml_mul_mat(ctx,
+                ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2],  new_a->ne[3], 1),                       // [OC，1, KH, KW] => [1, OC, 1, KH * KW]
+                ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3])); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW]
+
+    result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW]
+
+    return result;
+}
 // ggml_conv_2d
 
 // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
@@ -7770,6 +7808,9 @@ static void ggml_compute_forward_acc_f32(
     bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
 
     if (!inplace && (params->type == GGML_TASK_INIT)) {
+        if (params->ith != 0) {
+            return;
+        }
         // memcpy needs to be synchronized across threads to avoid race conditions.
         // => do it in INIT phase
         memcpy(
@@ -9339,6 +9380,87 @@ static void ggml_compute_forward_silu_back(
     }
 }
 
+
+static void ggml_compute_forward_hardswish_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_hardswish_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+static void ggml_compute_forward_hardswish(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_hardswish_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+static void ggml_compute_forward_hardsigmoid_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_hardsigmoid_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_hardsigmoid(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_hardsigmoid_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+
 // ggml_compute_forward_norm
 
 static void ggml_compute_forward_norm_f32(
@@ -9790,10 +9912,6 @@ static void ggml_compute_forward_mul_mat(
     const int ith = params->ith;
     const int nth = params->nth;
 
-    if (ith == 1 && g_imatrix_collect) {
-        g_imatrix_collect(src0, src1);
-    }
-
     const enum ggml_type type = src0->type;
 
     const bool src1_cont = ggml_is_contiguous(src1);
@@ -9835,11 +9953,30 @@ static void ggml_compute_forward_mul_mat(
 
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
     if (ggml_compute_forward_mul_mat_use_blas(dst)) {
-        if (params->ith != 0) {
-            return;
-        }
+        const int64_t ne_plane      = ne01*ne00;
+        const int64_t desired_wsize = ne13*ne12*ne_plane*sizeof(float);
+        UNUSED(desired_wsize);
 
         if (params->type == GGML_TASK_INIT) {
+            if (type != GGML_TYPE_F32) {
+                assert(params->wsize >= desired_wsize);
+                // parallelize by src0 rows
+                for (int64_t i13 = 0; i13 < ne13; i13++) {
+                    for (int64_t i12 = 0; i12 < ne12; i12++) {
+                        // broadcast src0 into src1 across 2nd,3rd dimension
+                        const int64_t i03 = i13/r3;
+                        const int64_t i02 = i12/r2;
+
+                        const void           *       x        = (char *)  src0->data    + i02*nb02          + i03*nb03;
+                              float          * const wdata    = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane;
+                              ggml_to_float_t  const to_float = type_traits[type].to_float;
+
+                        for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
+                            to_float((const char *) x + i01*nb01, wdata + i01*ne00, ne00);
+                        }
+                    }
+                }
+            }
             return;
         }
 
@@ -9847,9 +9984,14 @@ static void ggml_compute_forward_mul_mat(
             return;
         }
 
+        // perform sgemm, parallelization controlled by blas lib
+        if (ith != 0) {
+            return;
+        }
+
+        //const int64_t tgemm0 = ggml_perf_time_us();
         for (int64_t i13 = 0; i13 < ne13; i13++) {
             for (int64_t i12 = 0; i12 < ne12; i12++) {
-                // broadcast src0 into src1 across 2nd,3rd dimension
                 const int64_t i03 = i13/r3;
                 const int64_t i02 = i12/r2;
 
@@ -9858,17 +10000,7 @@ static void ggml_compute_forward_mul_mat(
                       float * d = (float *) ((char *)  dst->data + i12*nb2  + i13*nb3);
 
                 if (type != GGML_TYPE_F32) {
-                            float * const wdata    = params->wdata;
-                    ggml_to_float_t const to_float = type_traits[type].to_float;
-
-                    size_t id = 0;
-                    for (int64_t i01 = 0; i01 < ne01; ++i01) {
-                        to_float((const char *) x + i01*nb01, wdata + id, ne00);
-                        id += ne00;
-                    }
-
-                    assert(id*sizeof(float) <= params->wsize);
-                    x = wdata;
+                    x = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane;
                 }
 
                 cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
@@ -9878,6 +10010,7 @@ static void ggml_compute_forward_mul_mat(
                          0.0f,    d, ne01);
             }
         }
+        //printf("cblas_sgemm = %.3f ms, %lld flops\n", (ggml_perf_time_us() - tgemm0)/1000.0, ne13*ne12*ne1*ne01*ne10*2);
 
         //printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
 
@@ -9886,6 +10019,9 @@ static void ggml_compute_forward_mul_mat(
 #endif
 
     if (params->type == GGML_TASK_INIT) {
+        if (ith != 0) {
+            return;
+        }
         if (src1->type != vec_dot_type) {
             char * wdata = params->wdata;
             const size_t row_size = ggml_row_size(vec_dot_type, ne10);
@@ -10050,6 +10186,9 @@ static void ggml_compute_forward_mul_mat_id(
     #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)]
 
    if (params->type == GGML_TASK_INIT) {
+        if (ith != 0) {
+            return;
+        }
         char * wdata = params->wdata;
         if (src1->type != vec_dot_type) {
             const size_t row_size = ggml_row_size(vec_dot_type, ne10);
@@ -10097,10 +10236,6 @@ static void ggml_compute_forward_mul_mat_id(
 
         const struct ggml_tensor * src0_cur = dst->src[cur_a + 2];
 
-        if (ith == 1 && g_imatrix_collect) {
-            g_imatrix_collect(src0_cur, src1);
-        }
-
         const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
         const size_t row_size = ggml_row_size(vec_dot_type, ne10);
 
@@ -10239,6 +10374,9 @@ static void ggml_compute_forward_out_prod_f32(
             return;
         }
 #endif
+        if (ith != 0) {
+            return;
+        }
         ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
         return;
     }
@@ -10422,6 +10560,9 @@ static void ggml_compute_forward_out_prod_q_f32(
     // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
 
     if (params->type == GGML_TASK_INIT) {
+        if (ith != 0) {
+            return;
+        }
         ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
         return;
     }
@@ -10606,6 +10747,9 @@ static void ggml_compute_forward_set_f32(
     bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
 
     if (!inplace && (params->type == GGML_TASK_INIT)) {
+        if (params->ith != 0) {
+            return;
+        }
         // memcpy needs to be synchronized across threads to avoid race conditions.
         // => do it in INIT phase
         memcpy(
@@ -10930,6 +11074,9 @@ static void ggml_compute_forward_get_rows_back_f32_f16(
     // ggml_compute_forward_dup_same_cont(params, opt0, dst);
 
     if (params->type == GGML_TASK_INIT) {
+        if (params->ith != 0) {
+            return;
+        }
         memset(dst->data, 0, ggml_nbytes(dst));
     }
 
@@ -10964,6 +11111,9 @@ static void ggml_compute_forward_get_rows_back_f32(
     // ggml_compute_forward_dup_same_cont(params, opt0, dst);
 
     if (params->type == GGML_TASK_INIT) {
+        if (params->ith != 0) {
+            return;
+        }
         memset(dst->data, 0, ggml_nbytes(dst));
     }
 
@@ -11101,6 +11251,9 @@ static void ggml_compute_forward_diag_mask_f32(
     GGML_ASSERT(n_past >= 0);
 
     if (!inplace && (params->type == GGML_TASK_INIT)) {
+        if (ith != 0) {
+            return;
+        }
         // memcpy needs to be synchronized across threads to avoid race conditions.
         // => do it in INIT phase
         GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
@@ -11653,7 +11806,7 @@ static void ggml_rope_cache_init(
     }
 }
 
-void ggml_rope_yarn_corr_dims(
+GGML_CALL void ggml_rope_yarn_corr_dims(
     int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
 ) {
     // start and end correction dims
@@ -12071,6 +12224,9 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
     GGML_ASSERT(nb10 == sizeof(float));
 
     if (params->type == GGML_TASK_INIT) {
+        if (ith != 0) {
+            return;
+        }
         memset(params->wdata, 0, params->wsize);
 
         // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
@@ -12165,6 +12321,9 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
     GGML_ASSERT(nb10 == sizeof(float));
 
     if (params->type == GGML_TASK_INIT) {
+        if (ith != 0) {
+            return;
+        }
         memset(params->wdata, 0, params->wsize);
 
         // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
@@ -12363,6 +12522,7 @@ static void ggml_compute_forward_im2col(
     }
 }
 
+
 // ggml_compute_forward_conv_transpose_2d
 
 static void ggml_compute_forward_conv_transpose_2d(
@@ -12388,6 +12548,9 @@ static void ggml_compute_forward_conv_transpose_2d(
     GGML_ASSERT(nb10 == sizeof(float));
 
     if (params->type == GGML_TASK_INIT) {
+        if (ith != 0) {
+            return;
+        }
         memset(params->wdata, 0, params->wsize);
 
         // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout)
@@ -13931,6 +14094,14 @@ static void ggml_compute_forward_unary(
             {
                 ggml_compute_forward_silu(params, src0, dst);
             } break;
+        case GGML_UNARY_OP_HARDSWISH:
+            {
+                ggml_compute_forward_hardswish(params, src0, dst);
+            } break;
+        case GGML_UNARY_OP_HARDSIGMOID:
+            {
+                ggml_compute_forward_hardsigmoid(params, src0, dst);
+            } break;
         default:
             {
                 GGML_ASSERT(false);
@@ -13994,6 +14165,9 @@ static void ggml_compute_forward_add_rel_pos_f32(
 
     const bool inplace = (bool) ((int32_t *) dst->op_params)[0];
     if (!inplace && params->type == GGML_TASK_INIT) {
+        if (params->ith != 0) {
+            return;
+        }
         memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst));
         return;
     }
@@ -16287,8 +16461,9 @@ struct ggml_compute_state_shared {
     const int n_threads;
 
     // synchronization primitives
-    atomic_int n_active; // num active threads
-    atomic_int node_n;   // active graph node
+    atomic_int n_active;  // num active threads
+    atomic_int node_n;    // active graph node
+    atomic_int node_task; // active graph node task phase
 
     bool (*abort_callback)(void * data); // abort ggml_graph_compute when true
     void * abort_callback_data;
@@ -16344,6 +16519,8 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
                 case GGML_UNARY_OP_TANH:
                 case GGML_UNARY_OP_ELU:
                 case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_HARDSWISH: // to opt for multiple threads
+                case GGML_UNARY_OP_HARDSIGMOID: // to opt for multiple threads
                     {
                         n_tasks = 1;
                     } break;
@@ -16534,6 +16711,34 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
     return n_tasks;
 }
 
+static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_compute_state * state, const bool do_yield) {
+    // wait for other threads to finish
+    const int last_node_n = * node_n;
+
+    while (true) {
+        if (do_yield) {
+            sched_yield();
+        }
+
+        * node_n = atomic_load(&state->shared->node_n);
+        if (* node_n != last_node_n) break;
+    }
+}
+
+static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_compute_state * state, const bool do_yield) {
+    // wait for other threads to finish
+    const int last_task_phase = * task_phase;
+
+    while (true) {
+        if (do_yield) {
+            sched_yield();
+        }
+
+        * task_phase = atomic_load(&state->shared->node_task);
+        if (* task_phase != last_task_phase) break;
+    }
+}
+
 static thread_ret_t ggml_graph_compute_thread(void * data) {
     struct ggml_compute_state * state = (struct ggml_compute_state *) data;
 
@@ -16544,7 +16749,8 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
 
     set_numa_thread_affinity(state->ith, n_threads);
 
-    int node_n = -1;
+    int node_n     = -1;
+    int task_phase = GGML_TASK_FINALIZE;
 
     while (true) {
         if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
@@ -16576,7 +16782,6 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
             // distribute new work or execute it direct if 1T
             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);
 
@@ -16585,13 +16790,13 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
 
                 params.nth = n_tasks;
 
-                /* INIT */
-                if (GGML_OP_HAS_INIT[node->op]) {
-                    params.type = GGML_TASK_INIT;
-                    ggml_compute_forward(&params, node);
-                }
-
                 if (n_tasks == 1) {
+                    /* INIT */
+                    if (GGML_OP_HAS_INIT[node->op]) {
+                        params.type = GGML_TASK_INIT;
+                        ggml_compute_forward(&params, node);
+                    }
+
                     // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1,
                     // they do something more efficient than spinning (?)
                     params.type = GGML_TASK_COMPUTE;
@@ -16612,38 +16817,24 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
                 }
             }
 
-            atomic_store(&state->shared->n_active, n_threads);
-            atomic_store(&state->shared->node_n,   node_n);
+            task_phase = GGML_TASK_INIT;
+            atomic_store(&state->shared->n_active,  n_threads);
+            atomic_store(&state->shared->node_n,    node_n);
+            atomic_store(&state->shared->node_task, task_phase);
         } else {
-            // wait for other threads to finish
-            const int last = node_n;
-
-            const bool do_yield = last < 0 || cgraph->nodes[last]->op == GGML_OP_MUL_MAT;
-
-            while (true) {
-                // TODO: this sched_yield can have significant impact on the performance - either positive or negative
-                //       depending on the workload and the operating system.
-                //       since it is not clear what is the best approach, it should potentially become user-configurable
-                //       ref: https://github.com/ggerganov/ggml/issues/291
-                // UPD:  adding the do_yield flag seems to resolve the issue universally
-                if (do_yield) {
-                    sched_yield();
-                }
-
-                node_n = atomic_load(&state->shared->node_n);
-                if (node_n != last) break;
-            };
+            ggml_graph_compute_thread_sync_node(&node_n,     state, false);
+            ggml_graph_compute_thread_sync_task(&task_phase, state, false);
         }
 
         // check if we should stop
         if (node_n >= cgraph->n_nodes) break;
 
-        /* COMPUTE */
+        /* INIT & COMPUTE */
         struct ggml_tensor * node = cgraph->nodes[node_n];
         const int n_tasks = ggml_get_n_tasks(node, n_threads);
 
         struct ggml_compute_params params = {
-            /*.type  =*/ GGML_TASK_COMPUTE,
+            /*.type  =*/ GGML_TASK_INIT,
             /*.ith   =*/ state->ith,
             /*.nth   =*/ n_tasks,
             /*.wsize =*/ cplan->work_size,
@@ -16651,8 +16842,39 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
         };
 
         if (state->ith < n_tasks) {
+            if (GGML_OP_HAS_INIT[node->op]) {
+                ggml_compute_forward(&params, node);
+            }
+        }
+
+        if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
+            task_phase = GGML_TASK_COMPUTE;
+            atomic_store(&state->shared->n_active,  n_threads);
+            atomic_store(&state->shared->node_task, task_phase);
+        }
+        else {
+            // TODO: this sched_yield can have significant impact on the performance - either positive or negative
+            //       depending on the workload and the operating system.
+            //       since it is not clear what is the best approach, it should potentially become user-configurable
+            //       ref: https://github.com/ggerganov/ggml/issues/291
+            // UPD:  adding the do_yield flag seems to resolve the issue universally
+            const bool do_yield = node_n < 0 || cgraph->nodes[node_n]->op == GGML_OP_MUL_MAT;
+            ggml_graph_compute_thread_sync_task(&task_phase, state, do_yield);
+        }
+
+        if (state->ith < n_tasks) {
+            params.type = GGML_TASK_COMPUTE;
             ggml_compute_forward(&params, node);
         }
+
+        if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
+            task_phase = GGML_TASK_FINALIZE;
+            atomic_store(&state->shared->n_active,  n_threads);
+            atomic_store(&state->shared->node_task, task_phase);
+        }
+        else {
+            ggml_graph_compute_thread_sync_task(&task_phase, state, false);
+        }
     }
 
     return GGML_EXIT_SUCCESS;
@@ -16709,8 +16931,11 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
                     if (ggml_compute_forward_mul_mat_use_blas(node)) {
                         if (node->src[0]->type != GGML_TYPE_F32) {
-                            // here we need memory just for single 2D matrix from src0
-                            cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]);
+                            // here we need memory for fully dequantized matrix from src0
+                            // take into account that src0 can be broadcasted into src1[2,3]
+                            cur = ggml_type_size(GGML_TYPE_F32)
+                                * node->src[0]->ne[0]*node->src[0]->ne[1]
+                                * node->src[1]->ne[2]*node->src[1]->ne[3];
                         }
                     } else
 #endif
@@ -16864,6 +17089,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
         /*.n_threads               =*/ n_threads,
         /*.n_active                =*/ n_threads,
         /*.node_n                  =*/ -1,
+        /*.node_task               =*/ GGML_TASK_FINALIZE,
         /*.abort_callback          =*/ NULL,
         /*.abort_callback_data     =*/ NULL,
     };
@@ -18538,6 +18764,28 @@ enum ggml_opt_result ggml_opt_resume_g(
 
 ////////////////////////////////////////////////////////////////////////////////
 
+void ggml_quantize_init(enum ggml_type type) {
+    ggml_critical_section_start();
+
+    switch (type) {
+        case GGML_TYPE_IQ2_XXS: iq2xs_init_impl(256); break;
+        case GGML_TYPE_IQ2_XS:  iq2xs_init_impl(512); break;
+        default: // nothing
+            break;
+    }
+
+    ggml_critical_section_end();
+}
+
+void ggml_quantize_free(void) {
+    ggml_critical_section_start();
+
+    iq2xs_free_impl(256);
+    iq2xs_free_impl(512);
+
+    ggml_critical_section_end();
+}
+
 size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) {
     assert(k % QK4_0 == 0);
     const int nb = k / QK4_0;
@@ -18665,32 +18913,53 @@ size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t *
     return (n/QK8_0*sizeof(block_q8_0));
 }
 
-size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) {
+bool ggml_quantize_requires_imatrix(enum ggml_type type) {
+    return
+        type == GGML_TYPE_IQ2_XXS ||
+        type == GGML_TYPE_IQ2_XS;
+}
+
+size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start,
+        int nrows, int n_per_row, int64_t * hist, const float * imatrix) {
+    ggml_quantize_init(type); // this is noop if already initialized
     size_t result = 0;
+    int n = nrows * n_per_row;
     switch (type) {
         case GGML_TYPE_Q4_0:
             {
                 GGML_ASSERT(start % QK4_0 == 0);
-                block_q4_0 * block = (block_q4_0*)dst + start / QK4_0;
-                result = ggml_quantize_q4_0(src + start, block, n, n, hist);
+                GGML_ASSERT(start % n_per_row == 0);
+                size_t start_row = start / n_per_row;
+                size_t row_size = ggml_row_size(type, n_per_row);
+                result = quantize_q4_0(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+                GGML_ASSERT(result == row_size * nrows);
             } break;
         case GGML_TYPE_Q4_1:
             {
                 GGML_ASSERT(start % QK4_1 == 0);
-                block_q4_1 * block = (block_q4_1*)dst + start / QK4_1;
-                result = ggml_quantize_q4_1(src + start, block, n, n, hist);
+                GGML_ASSERT(start % n_per_row == 0);
+                size_t start_row = start / n_per_row;
+                size_t row_size = ggml_row_size(type, n_per_row);
+                result = quantize_q4_1(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+                GGML_ASSERT(result == row_size * nrows);
             } break;
         case GGML_TYPE_Q5_0:
             {
                 GGML_ASSERT(start % QK5_0 == 0);
-                block_q5_0 * block = (block_q5_0*)dst + start / QK5_0;
-                result = ggml_quantize_q5_0(src + start, block, n, n, hist);
+                GGML_ASSERT(start % n_per_row == 0);
+                size_t start_row = start / n_per_row;
+                size_t row_size = ggml_row_size(type, n_per_row);
+                result = quantize_q5_0(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+                GGML_ASSERT(result == row_size * nrows);
             } break;
         case GGML_TYPE_Q5_1:
             {
                 GGML_ASSERT(start % QK5_1 == 0);
-                block_q5_1 * block = (block_q5_1*)dst + start / QK5_1;
-                result = ggml_quantize_q5_1(src + start, block, n, n, hist);
+                GGML_ASSERT(start % n_per_row == 0);
+                size_t start_row = start / n_per_row;
+                size_t row_size = ggml_row_size(type, n_per_row);
+                result = quantize_q5_1(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+                GGML_ASSERT(result == row_size * nrows);
             } break;
         case GGML_TYPE_Q8_0:
             {
@@ -18701,54 +18970,77 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
         case GGML_TYPE_Q2_K:
             {
                 GGML_ASSERT(start % QK_K == 0);
-                block_q2_K * block = (block_q2_K*)dst + start / QK_K;
-                result = ggml_quantize_q2_K(src + start, block, n, n, hist);
+                GGML_ASSERT(start % n_per_row == 0);
+                size_t start_row = start / n_per_row;
+                size_t row_size = ggml_row_size(type, n_per_row);
+                result = quantize_q2_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+                GGML_ASSERT(result == row_size * nrows);
             } break;
         case GGML_TYPE_Q3_K:
             {
                 GGML_ASSERT(start % QK_K == 0);
-                block_q3_K * block = (block_q3_K*)dst + start / QK_K;
-                result = ggml_quantize_q3_K(src + start, block, n, n, hist);
+                GGML_ASSERT(start % n_per_row == 0);
+                size_t start_row = start / n_per_row;
+                size_t row_size = ggml_row_size(type, n_per_row);
+                result = quantize_q3_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+                GGML_ASSERT(result == row_size * nrows);
             } break;
         case GGML_TYPE_Q4_K:
             {
                 GGML_ASSERT(start % QK_K == 0);
-                block_q4_K * block = (block_q4_K*)dst + start / QK_K;
-                result = ggml_quantize_q4_K(src + start, block, n, n, hist);
+                GGML_ASSERT(start % n_per_row == 0);
+                size_t start_row = start / n_per_row;
+                size_t row_size = ggml_row_size(type, n_per_row);
+                result = quantize_q4_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+                GGML_ASSERT(result == row_size * nrows);
             } break;
         case GGML_TYPE_Q5_K:
             {
                 GGML_ASSERT(start % QK_K == 0);
-                block_q5_K * block = (block_q5_K*)dst + start / QK_K;
-                result = ggml_quantize_q5_K(src + start, block, n, n, hist);
+                GGML_ASSERT(start % n_per_row == 0);
+                size_t start_row = start / n_per_row;
+                size_t row_size = ggml_row_size(type, n_per_row);
+                result = quantize_q5_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+                GGML_ASSERT(result == row_size * nrows);
             } break;
         case GGML_TYPE_Q6_K:
             {
                 GGML_ASSERT(start % QK_K == 0);
-                block_q6_K * block = (block_q6_K*)dst + start / QK_K;
-                result = ggml_quantize_q6_K(src + start, block, n, n, hist);
+                GGML_ASSERT(start % n_per_row == 0);
+                size_t start_row = start / n_per_row;
+                size_t row_size = ggml_row_size(type, n_per_row);
+                result = quantize_q6_K(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+                GGML_ASSERT(result == row_size * nrows);
             } break;
         case GGML_TYPE_IQ2_XXS:
             {
                 GGML_ASSERT(start % QK_K == 0);
-                block_iq2_xxs * block = (block_iq2_xxs*)dst + start / QK_K;
-                result = ggml_quantize_iq2_xxs(src + start, block, n, n, hist);
+                GGML_ASSERT(start % n_per_row == 0);
+                GGML_ASSERT(imatrix);
+                size_t start_row = start / n_per_row;
+                size_t row_size = ggml_row_size(type, n_per_row);
+                result = quantize_iq2_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+                GGML_ASSERT(result == row_size * nrows);
             } break;
         case GGML_TYPE_IQ2_XS:
             {
                 GGML_ASSERT(start % QK_K == 0);
-                block_iq2_xs * block = (block_iq2_xs*)dst + start / QK_K;
-                result = ggml_quantize_iq2_xs(src + start, block, n, n, hist);
+                GGML_ASSERT(start % n_per_row == 0);
+                GGML_ASSERT(imatrix);
+                size_t start_row = start / n_per_row;
+                size_t row_size = ggml_row_size(type, n_per_row);
+                result = quantize_iq2_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+                GGML_ASSERT(result == row_size * nrows);
             } break;
         case GGML_TYPE_F16:
             {
-                int elemsize = sizeof(ggml_fp16_t);
+                size_t elemsize = sizeof(ggml_fp16_t);
                 ggml_fp32_to_fp16_row(src + start, (ggml_fp16_t *)dst + start, n);
                 result = n * elemsize;
             } break;
         case GGML_TYPE_F32:
             {
-                int elemsize = sizeof(float);
+                size_t elemsize = sizeof(float);
                 result = n * elemsize;
                 memcpy((uint8_t *)dst + start * elemsize, src + start, result);
             } break;

ggml.h

@@ -187,6 +187,16 @@
 #    define GGML_API
 #endif
 
+#ifdef GGML_MULTIPLATFORM
+#    if defined(_WIN32)
+#        define GGML_CALL
+#    else
+#        define GGML_CALL __attribute__((__ms_abi__))
+#    endif
+#else
+#    define GGML_CALL
+#endif
+
 // TODO: support for clang
 #ifdef __GNUC__
 #    define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
@@ -479,6 +489,8 @@ extern "C" {
         GGML_UNARY_OP_GELU,
         GGML_UNARY_OP_GELU_QUICK,
         GGML_UNARY_OP_SILU,
+        GGML_UNARY_OP_HARDSWISH,
+        GGML_UNARY_OP_HARDSIGMOID,
 
         GGML_UNARY_OP_COUNT,
     };
@@ -649,41 +661,41 @@ extern "C" {
     GGML_API void    ggml_print_object (const struct ggml_object * obj);
     GGML_API void    ggml_print_objects(const struct ggml_context * ctx);
 
-    GGML_API int64_t ggml_nelements   (const struct ggml_tensor * tensor);
-    GGML_API int64_t ggml_nrows       (const struct ggml_tensor * tensor);
-    GGML_API size_t  ggml_nbytes      (const struct ggml_tensor * tensor);
-    GGML_API size_t  ggml_nbytes_pad  (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
+    GGML_API GGML_CALL int64_t ggml_nelements   (const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL int64_t ggml_nrows       (const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL size_t  ggml_nbytes      (const struct ggml_tensor * tensor);
+    GGML_API           size_t  ggml_nbytes_pad  (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
 
-    GGML_API int    ggml_blck_size(enum ggml_type type);
-    GGML_API size_t ggml_type_size(enum ggml_type type);             // size in bytes for all elements in a block
-    GGML_API size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
+    GGML_API GGML_CALL int    ggml_blck_size(enum ggml_type type);
+    GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type);             // size in bytes for all elements in a block
+    GGML_API GGML_CALL size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
 
     GGML_DEPRECATED(
     GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
     "use ggml_row_size() instead");
 
-    GGML_API const char * ggml_type_name(enum ggml_type type);
-    GGML_API const char * ggml_op_name  (enum ggml_op   op);
-    GGML_API const char * ggml_op_symbol(enum ggml_op   op);
+    GGML_API GGML_CALL const char * ggml_type_name(enum ggml_type type);
+    GGML_API GGML_CALL const char * ggml_op_name  (enum ggml_op   op);
+    GGML_API           const char * ggml_op_symbol(enum ggml_op   op);
 
-    GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op);
-    GGML_API const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
+    GGML_API           const char * ggml_unary_op_name(enum ggml_unary_op op);
+    GGML_API GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
 
-    GGML_API size_t  ggml_element_size(const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL size_t  ggml_element_size(const struct ggml_tensor * tensor);
 
-    GGML_API bool    ggml_is_quantized(enum ggml_type type);
+    GGML_API GGML_CALL bool    ggml_is_quantized(enum ggml_type type);
 
     // TODO: temporary until model loading of ggml examples is refactored
     GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
 
-    GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_permuted  (const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_scalar    (const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_vector    (const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_matrix    (const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_3d        (const struct ggml_tensor * tensor);
-    GGML_API int  ggml_n_dims       (const struct ggml_tensor * tensor); // returns 1 for scalars
+    GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL bool ggml_is_permuted  (const struct ggml_tensor * tensor);
+    GGML_API           bool ggml_is_scalar    (const struct ggml_tensor * tensor);
+    GGML_API           bool ggml_is_vector    (const struct ggml_tensor * tensor);
+    GGML_API           bool ggml_is_matrix    (const struct ggml_tensor * tensor);
+    GGML_API           bool ggml_is_3d        (const struct ggml_tensor * tensor);
+    GGML_API           int  ggml_n_dims       (const struct ggml_tensor * tensor); // returns 1 for scalars
 
     GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
 
@@ -770,7 +782,7 @@ extern "C" {
     GGML_API void *  ggml_get_data    (const struct ggml_tensor * tensor);
     GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
 
-    GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
 
     GGML_API const char *         ggml_get_name   (const struct ggml_tensor * tensor);
     GGML_API struct ggml_tensor * ggml_set_name   (      struct ggml_tensor * tensor, const char * name);
@@ -1022,6 +1034,16 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
+    // hardswish(x) = x * relu6(x + 3) / 6
+    GGML_API struct ggml_tensor * ggml_hardswish(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // hardsigmoid(x) = relu6(x + 3) / 6
+    GGML_API struct ggml_tensor * ggml_hardsigmoid(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
     // normalize along rows
     GGML_API struct ggml_tensor * ggml_norm(
             struct ggml_context * ctx,
@@ -1413,7 +1435,7 @@ extern "C" {
             float                 beta_slow);
 
     // compute correction dims for YaRN RoPE scaling
-    void ggml_rope_yarn_corr_dims(
+    GGML_CALL void ggml_rope_yarn_corr_dims(
         int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]);
 
     // xPos RoPE, in-place, returns view(a)
@@ -1473,6 +1495,17 @@ extern "C" {
             int                  d1,
             bool                 is_2D);
 
+    GGML_API struct ggml_tensor * ggml_conv_depthwise_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                  s0,
+            int                  s1,
+            int                  p0,
+            int                  p1,
+            int                  d0,
+            int                  d1);
+
     GGML_API struct ggml_tensor * ggml_conv_1d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -2055,6 +2088,18 @@ extern "C" {
     // quantization
     //
 
+    // - ggml_quantize_init can be called multiple times with the same type
+    //   it will only initialize the quantization tables for the first call or after ggml_quantize_free
+    //   automatically called by ggml_quantize_chunk for convenience
+    //
+    // - ggml_quantize_free will free any memory allocated by ggml_quantize_init
+    //   call this at the end of the program to avoid memory leaks
+    //
+    // note: these are thread-safe
+    //
+    GGML_API void ggml_quantize_init(enum ggml_type type);
+    GGML_API void ggml_quantize_free(void);
+
     // TODO: these would probably get removed in favor of the more general ggml_quantize_chunk
     GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
     GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
@@ -2067,16 +2112,13 @@ extern "C" {
     GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
     GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
     GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_iq2_xxs(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_iq2_xs (const float * src, void * dst, int n, int k, int64_t * hist);
 
-    GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
+    // some quantization type cannot be used without an importance matrix
+    GGML_API bool ggml_quantize_requires_imatrix(enum ggml_type type);
 
-    //
-    // Importance matrix
-    //
-    typedef void(*ggml_collect_imatrix_t)(const struct ggml_tensor * src0, const struct ggml_tensor * src1);
-    GGML_API void ggml_set_imatrix_collection(ggml_collect_imatrix_t imatrix_collect);
+    // calls ggml_quantize_init internally (i.e. can allocate memory)
+    GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst,
+            int start, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
 
     //
     // gguf

gguf-py/gguf/constants.py

@@ -97,8 +97,10 @@ class MODEL_ARCH(IntEnum):
     BLOOM     = auto()
     STABLELM  = auto()
     QWEN      = auto()
+    QWEN2     = auto()
     PHI2      = auto()
     PLAMO     = auto()
+    CODESHELL = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -145,8 +147,10 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.BLOOM:          "bloom",
     MODEL_ARCH.STABLELM:       "stablelm",
     MODEL_ARCH.QWEN:           "qwen",
+    MODEL_ARCH.QWEN2:          "qwen2",
     MODEL_ARCH.PHI2:           "phi2",
     MODEL_ARCH.PLAMO:          "plamo",
+    MODEL_ARCH.CODESHELL:      "codeshell",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -356,6 +360,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.QWEN2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.PLAMO: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -396,6 +414,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_NORM,
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
+    ],
+    MODEL_ARCH.CODESHELL: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.POS_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
     ]
     # TODO
 }
@@ -417,6 +448,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
     ],
+    MODEL_ARCH.CODESHELL: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
 }
 
 #

gguf-py/gguf/tensor_mapping.py

@@ -154,6 +154,7 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.rotary_emb.inv_freq",        # llama-hf
             "layers.{bid}.attention.inner_attention.rope.freqs",       # llama-pth
             "model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo
+            "transformer.h.{bid}.attn.rotary_emb.inv_freq",            # codeshell
         ),
 
         # Feed-forward norm

llama.h

@@ -2,6 +2,7 @@
 #define LLAMA_H
 
 #include "ggml.h"
+#include "ggml-backend.h"
 #ifdef GGML_USE_CUBLAS
 #include "ggml-cuda.h"
 #define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES
@@ -106,6 +107,7 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_IQ2_XXS       = 19, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ2_XS        = 20, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q2_K_S        = 21, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q3_K_XS       = 22, // except 1d tensors
 
         LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };
@@ -231,6 +233,9 @@ extern "C" {
         float    yarn_beta_slow;   // YaRN high correction dim
         uint32_t yarn_orig_ctx;    // YaRN original context size
 
+        ggml_backend_sched_eval_callback cb_eval;
+        void * cb_eval_user_data;
+
         enum ggml_type type_k; // data type for K cache
         enum ggml_type type_v; // data type for V cache
 
@@ -249,6 +254,7 @@ extern "C" {
         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
+        void * imatrix;              // pointer to importance matrix data
     } llama_model_quantize_params;
 
     // grammar types
@@ -713,14 +719,21 @@ extern "C" {
                            float   penalty_present);
 
     /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
-    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
-    /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
-    /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
-    LLAMA_API void llama_sample_classifier_free_guidance(
+    /// @param logits Logits extracted from the original generation context.
+    /// @param logits_guidance Logits extracted from a separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
+    /// @param scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
+    LLAMA_API void llama_sample_apply_guidance(
+              struct llama_context * ctx,
+                             float * logits,
+                             float * logits_guidance,
+                             float   scale);
+
+    LLAMA_API DEPRECATED(void llama_sample_classifier_free_guidance(
               struct llama_context * ctx,
             llama_token_data_array * candidates,
               struct llama_context * guidance_ctx,
-                             float   scale);
+                             float   scale),
+              "use llama_sample_apply_guidance() instead");
 
     /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
     LLAMA_API void llama_sample_softmax(

mypy.ini

@@ -4,3 +4,4 @@ allow_untyped_calls = true
 allow_untyped_defs = true
 allow_incomplete_defs = true
 disable_error_code = import-untyped
+warn_return_any = false

scripts/get-hellaswag.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+
+wget https://raw.githubusercontent.com/klosax/hellaswag_text_data/main/hellaswag_val_full.txt
+
+echo "Usage:"
+echo ""
+echo "  ./perplexity -m model.gguf -f hellaswag_val_full.txt --hellaswag [--hellaswag-tasks N] [other params]"
+echo ""
+
+exit 0