HIP: add cmake option to enable compiler output of kernel resource usage metrics (#15103)

Any available libraries are found and loaded dynamically at runtime.

.clang-format

@@ -22,8 +22,8 @@ AllowShortIfStatementsOnASingleLine: Never
 AllowShortLambdasOnASingleLine: Inline
 AllowShortLoopsOnASingleLine: false
 AlwaysBreakBeforeMultilineStrings: true
-BinPackArguments: true
-BinPackParameters: true # OnePerLine
+BinPackArguments: false
+BinPackParameters: false # OnePerLine
 BitFieldColonSpacing: Both
 BreakBeforeBraces: Custom # Attach
 BraceWrapping:
@@ -70,15 +70,18 @@ ExperimentalAutoDetectBinPacking: false
 FixNamespaceComments: true
 IncludeBlocks:   Regroup
 IncludeCategories:
-  - Regex:           '^<.*\.h>'
+  - Regex:           '".*"'
     Priority:        1
     SortPriority:    0
-  - Regex:           '^<.*'
+  - Regex:           '^<.*\.h>'
     Priority:        2
     SortPriority:    0
-  - Regex:           '.*'
+  - Regex:           '^<.*'
     Priority:        3
     SortPriority:    0
+  - Regex:           '.*'
+    Priority:        4
+    SortPriority:    0
 IncludeIsMainRegex: '([-_](test|unittest))?$'
 IncludeIsMainSourceRegex: ''
 IndentAccessModifiers: false

.devops/cann.Dockerfile

@@ -0,0 +1,130 @@
+# ==============================================================================
+# ARGUMENTS
+# ==============================================================================
+
+# Define the CANN base image for easier version updates later
+ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.1.rc1-910b-openeuler22.03-py3.10
+
+# ==============================================================================
+# BUILD STAGE
+# Compile all binary files and libraries
+# ==============================================================================
+FROM ${CANN_BASE_IMAGE} AS build
+
+# Define the Ascend chip model for compilation. Default is Ascend910B3
+ARG ASCEND_SOC_TYPE=Ascend910B3
+
+# -- Install build dependencies --
+RUN yum install -y gcc g++ cmake make git libcurl-devel python3 python3-pip && \
+    yum clean all && \
+    rm -rf /var/cache/yum
+
+# -- Set the working directory --
+WORKDIR /app
+
+# -- Copy project files --
+COPY . .
+
+# -- Set CANN environment variables (required for compilation) --
+# Using ENV instead of `source` allows environment variables to persist across the entire image layer
+ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
+ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${LD_LIBRARY_PATH}
+ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${PATH}
+ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp
+ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH
+# ... You can add other environment variables from the original file as needed ...
+# For brevity, only core variables are listed here. You can paste the original ENV list here.
+
+# -- Build llama.cpp --
+# Use the passed ASCEND_SOC_TYPE argument and add general build options
+RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh --force \
+    && \
+    cmake -B build \
+        -DGGML_CANN=ON \
+        -DCMAKE_BUILD_TYPE=Release \
+        -DSOC_TYPE=${ASCEND_SOC_TYPE} \
+        . && \
+    cmake --build build --config Release -j$(nproc)
+
+# -- Organize build artifacts for copying in later stages --
+# Create a lib directory to store all .so files
+RUN mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib \;
+
+# Create a full directory to store all executables and Python scripts
+RUN mkdir -p /app/full && \
+    cp build/bin/* /app/full/ && \
+    cp *.py /app/full/ && \
+    cp -r gguf-py /app/full/ && \
+    cp -r requirements /app/full/ && \
+    cp requirements.txt /app/full/
+    # If you have a tools.sh script, make sure it is copied here
+    # cp .devops/tools.sh /app/full/tools.sh
+
+# ==============================================================================
+# BASE STAGE
+# Create a minimal base image with CANN runtime and common libraries
+# ==============================================================================
+FROM ${CANN_BASE_IMAGE} AS base
+
+# -- Install runtime dependencies --
+RUN yum install -y libgomp curl && \
+    yum clean all && \
+    rm -rf /var/cache/yum
+
+# -- Set CANN environment variables (required for runtime) --
+ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
+ENV LD_LIBRARY_PATH=/app:${ASCEND_TOOLKIT_HOME}/lib64:${LD_LIBRARY_PATH}
+ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${PATH}
+ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp
+# ... You can add other environment variables from the original file as needed ...
+
+WORKDIR /app
+
+# Copy compiled .so files from the build stage
+COPY --from=build /app/lib/ /app
+
+# ==============================================================================
+# FINAL STAGES (TARGETS)
+# ==============================================================================
+
+### Target: full
+# Complete image with all tools, Python bindings, and dependencies
+# ==============================================================================
+FROM base AS full
+
+COPY --from=build /app/full /app
+
+# Install Python dependencies
+RUN yum install -y git python3 python3-pip && \
+    pip3 install --no-cache-dir --upgrade pip setuptools wheel && \
+    pip3 install --no-cache-dir -r requirements.txt && \
+    yum clean all && \
+    rm -rf /var/cache/yum
+
+# You need to provide a tools.sh script as the entrypoint
+ENTRYPOINT ["/app/tools.sh"]
+# If there is no tools.sh, you can set the default to start the server
+# ENTRYPOINT ["/app/llama-server"]
+
+### Target: light
+# Lightweight image containing only llama-cli
+# ==============================================================================
+FROM base AS light
+
+COPY --from=build /app/full/llama-cli /app
+
+ENTRYPOINT [ "/app/llama-cli" ]
+
+### Target: server
+# Dedicated server image containing only llama-server
+# ==============================================================================
+FROM base AS server
+
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+COPY --from=build /app/full/llama-server /app
+
+HEALTHCHECK --interval=5m CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]

.devops/musa.Dockerfile

@@ -1,10 +1,10 @@
 ARG UBUNTU_VERSION=22.04
 # This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc4.0.1
+ARG MUSA_VERSION=rc4.2.0
 # Target the MUSA build image
-ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-mudnn-devel-ubuntu${UBUNTU_VERSION}
+ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64
 
-ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-mudnn-runtime-ubuntu${UBUNTU_VERSION}
+ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64
 
 FROM ${BASE_MUSA_DEV_CONTAINER} AS build
 

.devops/nix/package.nix

@@ -47,6 +47,7 @@ let
   inherit (lib)
     cmakeBool
     cmakeFeature
+    optionalAttrs
     optionals
     strings
     ;
@@ -197,7 +198,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {
     ];
 
   # Environment variables needed for ROCm
-  env = optionals useRocm {
+  env = optionalAttrs useRocm {
     ROCM_PATH = "${rocmPackages.clr}";
     HIP_DEVICE_LIB_PATH = "${rocmPackages.rocm-device-libs}/amdgcn/bitcode";
   };

.devops/rocm.Dockerfile

@@ -1,8 +1,8 @@
 ARG UBUNTU_VERSION=24.04
 
 # This needs to generally match the container host's environment.
-ARG ROCM_VERSION=6.3
-ARG AMDGPU_VERSION=6.3
+ARG ROCM_VERSION=6.4
+ARG AMDGPU_VERSION=6.4
 
 # Target the CUDA build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete

.github/workflows/build-linux-cross.yml

@@ -48,98 +48,98 @@ jobs:
 
           cmake --build build --config Release -j $(nproc)
 
-  ubuntu-24-riscv64-vulkan-cross:
-    runs-on: ubuntu-24.04
+  # ubuntu-24-riscv64-vulkan-cross:
+  #   runs-on: ubuntu-24.04
 
-    steps:
-      - uses: actions/checkout@v4
-      - name: Setup Riscv
-        run: |
-          sudo dpkg --add-architecture riscv64
+  #   steps:
+  #     - uses: actions/checkout@v4
+  #     - name: Setup Riscv
+  #       run: |
+  #         sudo dpkg --add-architecture riscv64
 
-          # Add arch-specific repositories for non-amd64 architectures
-          cat << EOF | sudo tee /etc/apt/sources.list.d/riscv64-ports.list
-          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
-          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
-          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
-          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
-          EOF
+  #         # Add arch-specific repositories for non-amd64 architectures
+  #         cat << EOF | sudo tee /etc/apt/sources.list.d/riscv64-ports.list
+  #         deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
+  #         deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
+  #         deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
+  #         deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
+  #         EOF
 
-          sudo apt-get update || true    ;# Prevent failure due to missing URLs.
+  #         sudo apt-get update || true    ;# Prevent failure due to missing URLs.
 
-          sudo apt-get install -y --no-install-recommends \
-                  build-essential \
-                  glslc \
-                  gcc-14-riscv64-linux-gnu \
-                  g++-14-riscv64-linux-gnu \
-                  libvulkan-dev:riscv64
+  #         sudo apt-get install -y --no-install-recommends \
+  #                 build-essential \
+  #                 glslc \
+  #                 gcc-14-riscv64-linux-gnu \
+  #                 g++-14-riscv64-linux-gnu \
+  #                 libvulkan-dev:riscv64
 
-      - name: Build
-        run: |
-          cmake -B build -DLLAMA_CURL=OFF \
-                         -DCMAKE_BUILD_TYPE=Release \
-                         -DGGML_VULKAN=ON \
-                         -DGGML_OPENMP=OFF \
-                         -DLLAMA_BUILD_EXAMPLES=ON \
-                         -DLLAMA_BUILD_TOOLS=ON \
-                         -DLLAMA_BUILD_TESTS=OFF \
-                         -DCMAKE_SYSTEM_NAME=Linux \
-                         -DCMAKE_SYSTEM_PROCESSOR=riscv64 \
-                         -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
-                         -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \
-                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
-                         -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+  #     - name: Build
+  #       run: |
+  #         cmake -B build -DLLAMA_CURL=OFF \
+  #                        -DCMAKE_BUILD_TYPE=Release \
+  #                        -DGGML_VULKAN=ON \
+  #                        -DGGML_OPENMP=OFF \
+  #                        -DLLAMA_BUILD_EXAMPLES=ON \
+  #                        -DLLAMA_BUILD_TOOLS=ON \
+  #                        -DLLAMA_BUILD_TESTS=OFF \
+  #                        -DCMAKE_SYSTEM_NAME=Linux \
+  #                        -DCMAKE_SYSTEM_PROCESSOR=riscv64 \
+  #                        -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
+  #                        -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \
+  #                        -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+  #                        -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
 
-          cmake --build build --config Release -j $(nproc)
+  #         cmake --build build --config Release -j $(nproc)
 
-  ubuntu-24-arm64-vulkan-cross:
-    runs-on: ubuntu-24.04
+  # ubuntu-24-arm64-vulkan-cross:
+  #   runs-on: ubuntu-24.04
 
-    steps:
-      - uses: actions/checkout@v4
-      - name: Setup Arm64
-        run: |
-          sudo dpkg --add-architecture arm64
+  #   steps:
+  #     - uses: actions/checkout@v4
+  #     - name: Setup Arm64
+  #       run: |
+  #         sudo dpkg --add-architecture arm64
 
-          # Add arch-specific repositories for non-amd64 architectures
-          cat << EOF | sudo tee /etc/apt/sources.list.d/arm64-ports.list
-          deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
-          deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
-          deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
-          deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
-          EOF
+  #         # Add arch-specific repositories for non-amd64 architectures
+  #         cat << EOF | sudo tee /etc/apt/sources.list.d/arm64-ports.list
+  #         deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
+  #         deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
+  #         deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
+  #         deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
+  #         EOF
 
-          sudo apt-get update || true    ;# Prevent failure due to missing URLs.
+  #         sudo apt-get update || true    ;# Prevent failure due to missing URLs.
 
-          sudo apt-get install -y --no-install-recommends \
-                  build-essential \
-                  glslc \
-                  crossbuild-essential-arm64 \
-                  libvulkan-dev:arm64
+  #         sudo apt-get install -y --no-install-recommends \
+  #                 build-essential \
+  #                 glslc \
+  #                 crossbuild-essential-arm64 \
+  #                 libvulkan-dev:arm64
 
-      - name: Build
-        run: |
-          cmake -B build -DLLAMA_CURL=OFF \
-                         -DCMAKE_BUILD_TYPE=Release \
-                         -DGGML_VULKAN=ON \
-                         -DGGML_OPENMP=OFF \
-                         -DLLAMA_BUILD_EXAMPLES=ON \
-                         -DLLAMA_BUILD_TOOLS=ON \
-                         -DLLAMA_BUILD_TESTS=OFF \
-                         -DCMAKE_SYSTEM_NAME=Linux \
-                         -DCMAKE_SYSTEM_PROCESSOR=aarch64 \
-                         -DCMAKE_C_COMPILER=aarch64-linux-gnu-gcc \
-                         -DCMAKE_CXX_COMPILER=aarch64-linux-gnu-g++ \
-                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
-                         -DCMAKE_FIND_ROOT_PATH=/usr/lib/aarch64-linux-gnu \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+  #     - name: Build
+  #       run: |
+  #         cmake -B build -DLLAMA_CURL=OFF \
+  #                        -DCMAKE_BUILD_TYPE=Release \
+  #                        -DGGML_VULKAN=ON \
+  #                        -DGGML_OPENMP=OFF \
+  #                        -DLLAMA_BUILD_EXAMPLES=ON \
+  #                        -DLLAMA_BUILD_TOOLS=ON \
+  #                        -DLLAMA_BUILD_TESTS=OFF \
+  #                        -DCMAKE_SYSTEM_NAME=Linux \
+  #                        -DCMAKE_SYSTEM_PROCESSOR=aarch64 \
+  #                        -DCMAKE_C_COMPILER=aarch64-linux-gnu-gcc \
+  #                        -DCMAKE_CXX_COMPILER=aarch64-linux-gnu-g++ \
+  #                        -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+  #                        -DCMAKE_FIND_ROOT_PATH=/usr/lib/aarch64-linux-gnu \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
 
-          cmake --build build --config Release -j $(nproc)
+  #         cmake --build build --config Release -j $(nproc)
 
   ubuntu-24-ppc64el-cpu-cross:
     runs-on: ubuntu-24.04
@@ -185,52 +185,52 @@ jobs:
 
           cmake --build build --config Release -j $(nproc)
 
-  ubuntu-24-ppc64el-vulkan-cross:
-    runs-on: ubuntu-24.04
+  # ubuntu-24-ppc64el-vulkan-cross:
+  #   runs-on: ubuntu-24.04
 
-    steps:
-      - uses: actions/checkout@v4
-      - name: Setup PowerPC64le
-        run: |
-          sudo dpkg --add-architecture ppc64el
+  #   steps:
+  #     - uses: actions/checkout@v4
+  #     - name: Setup PowerPC64le
+  #       run: |
+  #         sudo dpkg --add-architecture ppc64el
 
-          # Add arch-specific repositories for non-amd64 architectures
-          cat << EOF | sudo tee /etc/apt/sources.list.d/ppc64el-ports.list
-          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
-          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
-          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
-          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
-          EOF
+  #         # Add arch-specific repositories for non-amd64 architectures
+  #         cat << EOF | sudo tee /etc/apt/sources.list.d/ppc64el-ports.list
+  #         deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
+  #         deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
+  #         deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
+  #         deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
+  #         EOF
 
-          sudo apt-get update || true    ;# Prevent failure due to missing URLs.
+  #         sudo apt-get update || true    ;# Prevent failure due to missing URLs.
 
-          sudo apt-get install -y --no-install-recommends \
-                  build-essential \
-                  glslc \
-                  gcc-14-powerpc64le-linux-gnu \
-                  g++-14-powerpc64le-linux-gnu \
-                  libvulkan-dev:ppc64el
+  #         sudo apt-get install -y --no-install-recommends \
+  #                 build-essential \
+  #                 glslc \
+  #                 gcc-14-powerpc64le-linux-gnu \
+  #                 g++-14-powerpc64le-linux-gnu \
+  #                 libvulkan-dev:ppc64el
 
-      - name: Build
-        run: |
-          cmake -B build -DLLAMA_CURL=OFF \
-                         -DCMAKE_BUILD_TYPE=Release \
-                         -DGGML_VULKAN=ON \
-                         -DGGML_OPENMP=OFF \
-                         -DLLAMA_BUILD_EXAMPLES=ON \
-                         -DLLAMA_BUILD_TOOLS=ON \
-                         -DLLAMA_BUILD_TESTS=OFF \
-                         -DCMAKE_SYSTEM_NAME=Linux \
-                         -DCMAKE_SYSTEM_PROCESSOR=ppc64 \
-                         -DCMAKE_C_COMPILER=powerpc64le-linux-gnu-gcc-14 \
-                         -DCMAKE_CXX_COMPILER=powerpc64le-linux-gnu-g++-14 \
-                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
-                         -DCMAKE_FIND_ROOT_PATH=/usr/lib/powerpc64le-linux-gnu \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+  #     - name: Build
+  #       run: |
+  #         cmake -B build -DLLAMA_CURL=OFF \
+  #                        -DCMAKE_BUILD_TYPE=Release \
+  #                        -DGGML_VULKAN=ON \
+  #                        -DGGML_OPENMP=OFF \
+  #                        -DLLAMA_BUILD_EXAMPLES=ON \
+  #                        -DLLAMA_BUILD_TOOLS=ON \
+  #                        -DLLAMA_BUILD_TESTS=OFF \
+  #                        -DCMAKE_SYSTEM_NAME=Linux \
+  #                        -DCMAKE_SYSTEM_PROCESSOR=ppc64 \
+  #                        -DCMAKE_C_COMPILER=powerpc64le-linux-gnu-gcc-14 \
+  #                        -DCMAKE_CXX_COMPILER=powerpc64le-linux-gnu-g++-14 \
+  #                        -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+  #                        -DCMAKE_FIND_ROOT_PATH=/usr/lib/powerpc64le-linux-gnu \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
 
-          cmake --build build --config Release -j $(nproc)
+  #         cmake --build build --config Release -j $(nproc)
 
   debian-13-loongarch64-cpu-cross:
     runs-on: ubuntu-24.04

.github/workflows/build.yml

@@ -135,6 +135,53 @@ jobs:
           cd build
           ctest -L main --verbose --timeout 900
 
+  macOS-latest-cmake-arm64-webgpu:
+    runs-on: macos-14
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: macOS-latest-cmake-arm64-webgpu
+          evict-old-files: 1d
+
+      - name: Dependencies
+        id: depends
+        continue-on-error: true
+        run: |
+          brew update
+          brew install curl
+
+      - name: Dawn Dependency
+        id: dawn-depends
+        run: |
+          DAWN_VERSION="v1.0.0"
+          DAWN_OWNER="reeselevine"
+          DAWN_REPO="dawn"
+          DAWN_ASSET_NAME="Dawn-a1a6b45cced25a3b7f4fb491e0ae70796cc7f22b-macos-latest-Release.tar.gz"
+          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+          curl -L -o artifact.tar.gz \
+            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+          mkdir dawn
+          tar -xvf artifact.tar.gz -C dawn --strip-components=1
+
+      - name: Build
+        id: cmake_build
+        run: |
+          export CMAKE_PREFIX_PATH=dawn
+          cmake -B build -DGGML_WEBGPU=ON -DGGML_METAL=OFF -DGGML_BLAS=OFF
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
+
+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest -L main --verbose --timeout 900
+
   ubuntu-cpu-cmake:
     strategy:
       matrix:
@@ -344,6 +391,56 @@ jobs:
           # This is using llvmpipe and runs slower than other backends
           ctest -L main --verbose --timeout 4200
 
+  ubuntu-22-cmake-webgpu:
+    runs-on: ubuntu-22.04
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ubuntu-22-cmake-webgpu
+          evict-old-files: 1d
+
+      - name: Vulkan SDK Dependencies
+        id: vulkan-depends
+        run: |
+          wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
+          sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
+          sudo apt-get update -y
+          sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libcurl4-openssl-dev
+
+      - name: Dawn Dependency
+        id: dawn-depends
+        run: |
+          sudo apt-get install -y libxrandr-dev libxinerama-dev libxcursor-dev mesa-common-dev libx11-xcb-dev libxi-dev
+          DAWN_VERSION="v1.0.0"
+          DAWN_OWNER="reeselevine"
+          DAWN_REPO="dawn"
+          DAWN_ASSET_NAME="Dawn-a1a6b45cced25a3b7f4fb491e0ae70796cc7f22b-ubuntu-latest-Release.tar.gz"
+          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+          curl -L -o artifact.tar.gz \
+            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+          mkdir dawn
+          tar -xvf artifact.tar.gz -C dawn --strip-components=1
+
+      - name: Build
+        id: cmake_build
+        run: |
+          export Dawn_DIR=dawn/lib64/cmake/Dawn
+          cmake -B build -DGGML_WEBGPU=ON
+          cmake --build build --config Release -j $(nproc)
+
+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          # This is using llvmpipe and runs slower than other backends
+          ctest -L main --verbose --timeout 3600
+
   ubuntu-22-cmake-hip:
     runs-on: ubuntu-22.04
     container: rocm/dev-ubuntu-22.04:6.0.2
@@ -386,7 +483,7 @@ jobs:
 
   ubuntu-22-cmake-musa:
     runs-on: ubuntu-22.04
-    container: mthreads/musa:rc4.0.1-mudnn-devel-ubuntu22.04
+    container: mthreads/musa:rc4.2.0-devel-ubuntu22.04-amd64
 
     steps:
       - name: Clone

.github/workflows/close-issue.yml

@@ -17,7 +17,7 @@ jobs:
     steps:
       - uses: actions/stale@v5
         with:
-          exempt-issue-labels: "refactor,help wanted,good first issue,research,bug,roadmap"
+          exempt-issue-labels: "refactoring,help wanted,good first issue,research,bug,roadmap"
           days-before-issue-stale: 30
           days-before-issue-close: 14
           stale-issue-label: "stale"

.github/workflows/pre-tokenizer-hashes.yml

@@ -0,0 +1,45 @@
+name: Check Pre-Tokenizer Hashes
+
+on:
+    push:
+        paths:
+            - 'convert_hf_to_gguf.py'
+            - 'convert_hf_to_gguf_update.py'
+    pull_request:
+        paths:
+            - 'convert_hf_to_gguf.py'
+            - 'convert_hf_to_gguf_update.py'
+
+jobs:
+    pre-tokenizer-hashes:
+        runs-on: ubuntu-latest
+
+        steps:
+        - name: Checkout repository
+          uses: actions/checkout@v4
+
+        - name: Set up Python
+          uses: actions/setup-python@v5
+          with:
+              python-version: '3.11'
+
+        - name: Install Python dependencies
+          run: |
+              python3 -m venv .venv
+              .venv/bin/pip install -r requirements/requirements-convert_hf_to_gguf_update.txt
+
+        - name: Update pre-tokenizer hashes
+          run: |
+              cp convert_hf_to_gguf.py /tmp
+              .venv/bin/python convert_hf_to_gguf_update.py --check-missing
+
+        - name: Check if committed pre-tokenizer hashes matches generated version
+          run: |
+              if ! diff -q convert_hf_to_gguf.py /tmp/convert_hf_to_gguf.py; then
+                  echo "Model pre-tokenizer hashes (in convert_hf_to_gguf.py) do not match generated hashes (from convert_hf_to_gguf_update.py)."
+                  echo "To fix: run ./convert_hf_to_gguf_update.py and commit the updated convert_hf_to_gguf.py along with your changes"
+                  echo "Differences found:"
+                  diff convert_hf_to_gguf.py /tmp/convert_hf_to_gguf.py || true
+                  exit 1
+              fi
+              echo "Model pre-tokenizer hashes are up to date."

.gitignore

@@ -82,6 +82,7 @@ models/*
 models-mnt
 !models/.editorconfig
 !models/ggml-vocab-*.gguf*
+!models/templates
 
 # Zig
 zig-out/

CODEOWNERS

@@ -9,3 +9,4 @@
 /ggml/src/ggml-cuda/mmvq.* @JohannesGaessler
 /ggml/src/ggml-opt.cpp @JohannesGaessler
 /ggml/src/gguf.cpp @JohannesGaessler
+/ggml/src/ggml-vulkan/ @0cc4m

README.md

@@ -17,6 +17,7 @@ LLM inference in C/C++
 
 ## Hot topics
 
+- Support for the `gpt-oss` model with native MXFP4 format has been added | [PR](https://github.com/ggml-org/llama.cpp/pull/15091) | [Collaboration with NVIDIA](https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss) | [Comment](https://github.com/ggml-org/llama.cpp/discussions/15095)
 - Hot PRs: [All](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+) | [Open](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+is%3Aopen)
 - Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md)
 - VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
@@ -269,6 +270,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [Vulkan](docs/build.md#vulkan) | GPU |
 | [CANN](docs/build.md#cann) | Ascend NPU |
 | [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
+| [WebGPU [In Progress]](docs/build.md#webgpu) | All |
 | [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
 
 ## Obtaining and quantizing models
@@ -434,7 +436,7 @@ To learn more about model quantization, [read this documentation](tools/quantize
 
 ## [`llama-perplexity`](tools/perplexity)
 
-#### A tool for measuring the perplexity [^1][^2] (and other quality metrics) of a model over a given text.
+#### A tool for measuring the [perplexity](tools/perplexity/README.md) [^1] (and other quality metrics) of a model over a given text.
 
 - <details open>
     <summary>Measure the perplexity over a text file</summary>
@@ -457,8 +459,7 @@ To learn more about model quantization, [read this documentation](tools/quantize
 
     </details>
 
-[^1]: [tools/perplexity/README.md](./tools/perplexity/README.md)
-[^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
+[^1]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
 
 ## [`llama-bench`](tools/llama-bench)
 

ci/README.md

@@ -54,7 +54,7 @@ docker run --privileged -it \
     -v $HOME/llama.cpp/ci-cache:/ci-cache \
     -v $HOME/llama.cpp/ci-results:/ci-results \
     -v $PWD:/ws -w /ws \
-    mthreads/musa:rc4.0.1-mudnn-devel-ubuntu22.04
+    mthreads/musa:rc4.2.0-devel-ubuntu22.04-amd64
 ```
 
 Inside the container, execute the following commands:

ci/run.sh

@@ -16,6 +16,9 @@
 # # with VULKAN support
 # GG_BUILD_VULKAN=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 #
+# # with WebGPU support
+# GG_BUILD_WEBGPU=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
+#
 # # with MUSA support
 # GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 #
@@ -81,6 +84,10 @@ if [ ! -z ${GG_BUILD_VULKAN} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_VULKAN=1"
 fi
 
+if [ ! -z ${GG_BUILD_WEBGPU} ]; then
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_WEBGPU=1"
+fi
+
 if [ ! -z ${GG_BUILD_MUSA} ]; then
     # Use qy1 by default (MTT S80)
     MUSA_ARCH=${MUSA_ARCH:-21}

common/arg.cpp

@@ -24,6 +24,7 @@
 #include <cstdarg>
 #include <filesystem>
 #include <fstream>
+#include <list>
 #include <regex>
 #include <set>
 #include <string>
@@ -977,6 +978,10 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         for (auto & seq_breaker : params.sampling.dry_sequence_breakers) {
             string_process_escapes(seq_breaker);
         }
+        for (auto & pair : params.speculative.replacements) {
+            string_process_escapes(pair.first);
+            string_process_escapes(pair.second);
+        }
     }
 
     if (!params.kv_overrides.empty()) {
@@ -1612,7 +1617,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.antiprompt.emplace_back(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-sp", "--special"},
         string_format("special tokens output enabled (default: %s)", params.special ? "true" : "false"),
@@ -2091,6 +2096,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.no_kv_offload = true;
         }
     ).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
+    add_opt(common_arg(
+        {"-nr", "--no-repack"},
+        "disable weight repacking",
+        [](common_params & params) {
+            params.no_extra_bufts = true;
+        }
+    ).set_env("LLAMA_ARG_NO_REPACK"));
     add_opt(common_arg(
         {"-ctk", "--cache-type-k"}, "TYPE",
         string_format(
@@ -2364,11 +2376,35 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                     }
                     throw std::invalid_argument("unknown buffer type");
                 }
-                // FIXME: this leaks memory
-                params.tensor_buft_overrides.push_back({strdup(tensor_name.c_str()), buft_list.at(buffer_type)});
+                // keep strings alive and avoid leaking memory by storing them in a static vector
+                static std::list<std::string> buft_overrides;
+                buft_overrides.push_back(tensor_name);
+                params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), buft_list.at(buffer_type)});
             }
         }
     ));
+    add_opt(common_arg(
+        {"--cpu-moe", "-cmoe"},
+        "keep all Mixture of Experts (MoE) weights in the CPU",
+        [](common_params & params) {
+            params.tensor_buft_overrides.push_back({"\\.ffn_(up|down|gate)_exps", ggml_backend_cpu_buffer_type()});
+        }
+    ).set_env("LLAMA_ARG_CPU_MOE"));
+    add_opt(common_arg(
+        {"--n-cpu-moe", "-ncmoe"}, "N",
+        "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU",
+        [](common_params & params, int value) {
+            if (value < 0) {
+                throw std::invalid_argument("invalid value");
+            }
+            for (int i = 0; i < value; ++i) {
+                // keep strings alive and avoid leaking memory by storing them in a static vector
+                static std::list<std::string> buft_overrides;
+                buft_overrides.push_back(string_format("blk\\.%d\\.ffn_(up|down|gate)_exps", i));
+                params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), ggml_backend_cpu_buffer_type()});
+            }
+        }
+    ).set_env("LLAMA_ARG_N_CPU_MOE"));
     add_opt(common_arg(
         {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
         "number of layers to store in VRAM",
@@ -2627,6 +2663,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.n_out_freq = value;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+    add_opt(common_arg(
+        {"--output-format"}, "{gguf,dat}",
+        string_format("output format for imatrix file (default: %s)", params.imat_dat > 0 ? "dat" : "gguf"),
+        [](common_params & params, const std::string & value) {
+            /**/ if (value == "gguf") { params.imat_dat = -1; }
+            else if (value == "dat")  { params.imat_dat = 1;  }
+            else { throw std::invalid_argument("invalid output format"); }
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
         {"--save-frequency"}, "N",
         string_format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq),
@@ -2655,6 +2700,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.i_chunk = value;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+    add_opt(common_arg(
+        {"--show-statistics"},
+        string_format("show imatrix statistics and then exit (default: %s)", params.show_statistics ? "true" : "false"),
+        [](common_params & params) {
+            params.show_statistics = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
         {"--parse-special"},
         string_format("prase special tokens (chat, tool, etc) (default: %s)", params.parse_special ? "true" : "false"),
@@ -2895,11 +2947,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
         "- none: leaves thoughts unparsed in `message.content`\n"
         "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n"
-        "(default: deepseek)",
+        "(default: auto)",
         [](common_params & params, const std::string & value) {
             /**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; }
             else if (value == "deepseek-legacy") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY; }
             else if (value == "none") {     params.reasoning_format = COMMON_REASONING_FORMAT_NONE; }
+            else if (value == "auto") {     params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; }
             else { throw std::invalid_argument("invalid value"); }
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK"));
@@ -3242,6 +3295,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.speculative.model.path = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT"));
+    add_opt(common_arg(
+        {"--spec-replace"}, "TARGET", "DRAFT",
+        "translate the string in TARGET into DRAFT if the draft model and main model are not compatible",
+        [](common_params & params, const std::string & tgt, const std::string & dft) {
+            params.speculative.replacements.push_back({ tgt, dft });
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-ctkd", "--cache-type-k-draft"}, "TYPE",
         string_format(
@@ -3431,28 +3491,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}));
 
-    // diffusion parameters
     add_opt(common_arg(
         { "--diffusion-steps" }, "N",
         string_format("number of diffusion steps (default: %d)", params.diffusion.steps),
         [](common_params & params, int value) { params.diffusion.steps = value; }
     ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
-    add_opt(common_arg(
-        { "--diffusion-eps" }, "F",
-        string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps),
-        [](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); }
-    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
-    add_opt(common_arg(
-        { "--diffusion-algorithm" }, "N",
-        string_format("diffusion algorithm: 0=ORIGIN, 1=MASKGIT_PLUS, 2=TOPK_MARGIN, 3=ENTROPY (default: %d)",
-                      params.diffusion.algorithm),
-        [](common_params & params, int value) { params.diffusion.algorithm = value; }
-    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
-    add_opt(common_arg(
-        { "--diffusion-alg-temp" }, "F",
-        string_format("algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp),
-        [](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); }
-    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
     add_opt(common_arg(
         { "--diffusion-visual" },
         string_format("enable visual diffusion mode (show progressive generation) (default: %s)",
@@ -3460,5 +3503,39 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) { params.diffusion.visual_mode = true; }
     ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
 
+    add_opt(common_arg(
+        { "--diffusion-eps" }, "F",
+        string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps),
+        [](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); }
+    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
+    add_opt(common_arg(
+        { "--diffusion-algorithm" }, "N",
+        string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)",
+                      params.diffusion.algorithm),
+        [](common_params & params, int value) { params.diffusion.algorithm = value; }
+    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
+    add_opt(common_arg(
+        { "--diffusion-alg-temp" }, "F",
+        string_format("dream algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp),
+        [](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); }
+    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
+
+    add_opt(common_arg(
+        { "--diffusion-block-length" }, "N",
+        string_format("llada block length for generation (default: %d)", params.diffusion.block_length),
+        [](common_params & params, int value) { params.diffusion.block_length = value; }
+    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
+    add_opt(common_arg(
+        { "--diffusion-cfg-scale" }, "F",
+        string_format("llada classifier-free guidance scale (default: %.3f)", (double) params.diffusion.cfg_scale),
+        [](common_params & params, const std::string & value) { params.diffusion.cfg_scale = std::stof(value); }
+    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
+    add_opt(common_arg(
+        { "--diffusion-add-gumbel-noise" }, "F",
+        string_format("add gumbel noise to the logits if temp > 0.0 (default: %s)", params.diffusion.add_gumbel_noise ? "true" : "false"),
+        [](common_params & params, const std::string & value) { params.diffusion.add_gumbel_noise = std::stof(value); }
+    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
+
+
     return ctx_arg;
 }

common/chat-parser.cpp

@@ -55,7 +55,15 @@ bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::
 bool common_chat_msg_parser::add_tool_call(const json & tool_call) {
     std::string name = tool_call.contains("name") ? tool_call.at("name") : "";
     std::string id = tool_call.contains("id") ? tool_call.at("id") : "";
-    std::string arguments = tool_call.contains("arguments") ? tool_call.at("arguments") : "";
+    std::string arguments = "";
+    if (tool_call.contains("arguments")) {
+        if (tool_call.at("arguments").is_object()) {
+            arguments = tool_call.at("arguments").dump();
+        } else {
+            arguments = tool_call.at("arguments");
+        }
+    }
+
     return add_tool_call(name, id, arguments);
 }
 

common/chat.cpp

@@ -126,6 +126,8 @@ std::vector<common_chat_msg_diff> common_chat_msg_diff::compute_diffs(const comm
 typedef minja::chat_template common_chat_template;
 
 struct common_chat_templates {
+    bool add_bos;
+    bool add_eos;
     bool has_explicit_template; // Model had builtin template or template overridde was specified.
     std::unique_ptr<common_chat_template> template_default; // always set (defaults to chatml)
     std::unique_ptr<common_chat_template> template_tool_use;
@@ -143,6 +145,8 @@ struct templates_params {
     bool enable_thinking = true;
     std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
     json extra_context;
+    bool add_bos;
+    bool add_eos;
 };
 
 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice) {
@@ -445,6 +449,8 @@ std::string common_chat_format_single(
 
     common_chat_templates_inputs inputs;
     inputs.use_jinja = use_jinja;
+    inputs.add_bos = tmpls->add_bos;
+    inputs.add_eos = tmpls->add_eos;
 
     std::string fmt_past_msg;
     if (!past_msg.empty()) {
@@ -469,6 +475,8 @@ std::string common_chat_format_single(
 std::string common_chat_format_example(const struct common_chat_templates * tmpls, bool use_jinja) {
     common_chat_templates_inputs inputs;
     inputs.use_jinja = use_jinja;
+    inputs.add_bos = tmpls->add_bos;
+    inputs.add_eos = tmpls->add_eos;
     auto add_simple_msg = [&](auto role, auto content) {
         common_chat_msg msg;
         msg.role = role;
@@ -546,6 +554,8 @@ common_chat_templates_ptr common_chat_templates_init(
     }
     std::string token_bos = bos_token_override;
     std::string token_eos = eos_token_override;
+    bool add_bos = false;
+    bool add_eos = false;
     if (model) {
         const auto * vocab = llama_model_get_vocab(model);
         const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) {
@@ -560,9 +570,13 @@ common_chat_templates_ptr common_chat_templates_init(
         };
         token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
         token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
+        add_bos = llama_vocab_get_add_bos(vocab);
+        add_eos = llama_vocab_get_add_eos(vocab);
     }
     common_chat_templates_ptr tmpls(new common_chat_templates());
     tmpls->has_explicit_template = has_explicit_template;
+    tmpls->add_bos = add_bos;
+    tmpls->add_eos = add_eos;
     try {
         tmpls->template_default = std::make_unique<minja::chat_template>(default_template_src, token_bos, token_eos);
     } catch (const std::exception & e) {
@@ -592,6 +606,8 @@ const char * common_chat_format_name(common_chat_format format) {
         case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
         case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
         case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
+        case COMMON_CHAT_FORMAT_GRANITE: return "Granite";
+        case COMMON_CHAT_FORMAT_GPT_OSS: return "GPT-OSS";
         default:
             throw std::runtime_error("Unknown chat format");
     }
@@ -600,8 +616,10 @@ const char * common_chat_format_name(common_chat_format format) {
 const char * common_reasoning_format_name(common_reasoning_format format) {
     switch (format) {
         case COMMON_REASONING_FORMAT_NONE:     return "none";
+        case COMMON_REASONING_FORMAT_AUTO:     return "auto";
         case COMMON_REASONING_FORMAT_DEEPSEEK: return "deepseek";
         case COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY: return "deepseek-legacy";
+        case COMMON_REASONING_FORMAT_GRANITE: return "granite";
         default:
             throw std::runtime_error("Unknown reasoning format");
     }
@@ -748,10 +766,10 @@ static std::string apply(
     // instead of using `chat_template_options.use_bos_token = false`, since these tokens
     // may be needed inside the template / between messages too.
     auto result = tmpl.apply(tmpl_inputs, tmpl_opts);
-    if (string_starts_with(result, tmpl.bos_token())) {
+    if (inputs.add_bos && string_starts_with(result, tmpl.bos_token())) {
         result = result.substr(tmpl.bos_token().size());
     }
-    if (string_ends_with(result, tmpl.eos_token())) {
+    if (inputs.add_eos && string_ends_with(result, tmpl.eos_token())) {
         result = result.substr(0, result.size() - tmpl.eos_token().size());
     }
     return result;
@@ -1289,6 +1307,26 @@ static void common_chat_parse_deepseek_r1(common_chat_msg_parser & builder) {
         tool_calls_end);
 }
 
+static common_chat_params common_chat_params_init_gpt_oss(const common_chat_template & tmpl, const struct templates_params & inputs) {
+    common_chat_params data;
+    auto prompt = apply(tmpl, inputs);
+
+    data.prompt = prompt;
+    data.format = COMMON_CHAT_FORMAT_GPT_OSS;
+
+    // TODO: support tool calls in GPT-OSS?
+
+    return data;
+}
+static void common_chat_parse_gpt_oss(common_chat_msg_parser & builder) {
+    // TODO @ngxson : this won't work with --special enabled, we should fix that
+    builder.try_parse_reasoning("<|channel|>analysis<|message|>", "<|start|>assistant<|channel|>final<|message|>");
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+}
+
 static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct templates_params & inputs) {
     LOG_DBG("%s\n", __func__);
     common_chat_params data;
@@ -1646,7 +1684,7 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
         "|<function name=\"([^\"]+)\">"  // match 5 (function name again)
     );
 
-    if (auto res = builder.try_find_regex(open_regex)) {
+    while (auto res = builder.try_find_regex(open_regex)) {
         const auto & block_start = res->groups[1];
         std::string block_end = block_start.empty() ? "" : "```";
 
@@ -1668,7 +1706,6 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
                     builder.consume_literal(block_end);
                     builder.consume_spaces();
                 }
-                builder.add_content(builder.consume_rest());
             } else {
                 throw common_chat_msg_partial_exception("failed to parse tool call");
             }
@@ -1693,7 +1730,124 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
                     builder.consume_spaces();
                 }
             }
-            builder.add_content(builder.consume_rest());
+        }
+    }
+
+    builder.add_content(builder.consume_rest());
+}
+
+static common_chat_params common_chat_params_init_granite(const common_chat_template & tmpl, const struct templates_params & inputs) {
+    common_chat_params data;
+
+    // Pass thinking context for Granite template
+    json additional_context = {
+        {"thinking", inputs.enable_thinking},
+    };
+
+    data.prompt = apply(tmpl, inputs, /* messages_override= */ std::nullopt, /* tools_override= */ std::nullopt, additional_context);
+    data.format = COMMON_CHAT_FORMAT_GRANITE;
+
+    if (string_ends_with(data.prompt, "<think>\n") || string_ends_with(data.prompt, "<think>")) {
+        if (!inputs.enable_thinking) {
+            data.prompt += "</think>";
+        } else {
+            data.thinking_forced_open = true;
+        }
+    }
+
+    if (!inputs.tools.is_null()) {
+        // Granite uses <|tool_call|> followed by JSON list
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            std::vector<std::string> tool_rules;
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                auto parameters = function.at("parameters");
+                builder.resolve_refs(parameters);
+                tool_rules.push_back(builder.add_rule(name + "-call", builder.add_schema(name +
+"-args", {
+                    {"type", "object"},
+                    {"properties", {
+                        {"name", {{"const", name}}},
+                        {"arguments", parameters},
+                    }},
+                    {"required", json::array({"name", "arguments"})},
+                })));
+            });
+
+            auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | "));
+            auto tool_list = builder.add_rule("tool_list", "\"[\" space " + tool_call + " (\",\" space " + tool_call + ")* space \"]\"");
+
+            if (data.thinking_forced_open) {
+                builder.add_rule("root", "\"</think>\" space \"<response>\" space [^<]* \"</response>\" space \"<|tool_call|>\" space " + tool_list);
+            } else {
+                builder.add_rule("root", "\"<|tool_call|>\" space " + tool_list);
+            }
+
+            data.grammar_triggers.push_back({
+                COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
+                "<|tool_call|>"
+            });
+
+            data.preserved_tokens = {
+                "<think>",
+                "</think>",
+                "<response>",
+                "</response>",
+                "<|tool_call|>",
+            };
+        });
+    } else {
+        // Handle thinking tags for non-tool responses
+        if (data.thinking_forced_open && inputs.enable_thinking) {
+            data.grammar_lazy = false;
+            data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+                builder.add_rule("root", "\"</think>\" space \"<response>\" space .* \"</response>\" space");
+            });
+            data.preserved_tokens = {
+                "<think>",
+                "</think>",
+                "<response>",
+                "</response>",
+            };
+        }
+    }
+
+    return data;
+}
+
+static void common_chat_parse_granite(common_chat_msg_parser & builder) {
+    // Parse thinking tags
+    builder.try_parse_reasoning("<think>", "</think>");
+
+    // Parse response tags using regex
+    static const common_regex response_regex("<response>([\\s\\S]*?)</response>");
+    if (auto res = builder.try_find_regex(response_regex)) {
+        // Extract the content between the tags (capture group 1)
+        auto content = builder.str(res->groups[1]);
+        builder.add_content(content);
+        builder.move_to(res->groups[0].end);
+    }
+
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    // Look for tool calls
+    static const common_regex tool_call_regex(regex_escape("<|tool_call|>"));
+    if (auto res = builder.try_find_regex(tool_call_regex)) {
+        builder.move_to(res->groups[0].end);
+
+        // Expect JSON array of tool calls
+        auto tool_calls_data = builder.consume_json();
+        if (tool_calls_data.json.is_array()) {
+            if (!builder.add_tool_calls(tool_calls_data.json)) {
+                builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
+            }
+        } else {
+            builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
         }
     } else {
         builder.add_content(builder.consume_rest());
@@ -1733,6 +1887,8 @@ static common_chat_params common_chat_templates_apply_jinja(
     params.enable_thinking = inputs.enable_thinking;
     params.grammar = inputs.grammar;
     params.now = inputs.now;
+    params.add_bos = inputs.add_bos;
+    params.add_eos = inputs.add_eos;
 
     params.extra_context = json::object();
     for (auto el : inputs.chat_template_kwargs) {
@@ -1769,11 +1925,21 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_command_r7b(tmpl, params);
     }
 
+    // Granite (IBM) - detects thinking / tools support
+    if (src.find("elif thinking") != std::string::npos && src.find("<|tool_call|>") != std::string::npos) {
+        return common_chat_params_init_granite(tmpl, params);
+    }
+
     // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools)
     if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null()) {
         return common_chat_params_init_hermes_2_pro(tmpl, params);
     }
 
+    // GPT-OSS
+    if (src.find("<|channel|>") != std::string::npos && params.json_schema.is_null()) {
+        return common_chat_params_init_gpt_oss(tmpl, params);
+    }
+
     // Use generic handler when mixing tools + JSON schema.
     // TODO: support that mix in handlers below.
     if ((params.tools.is_array() && params.json_schema.is_object())) {
@@ -1824,6 +1990,7 @@ static common_chat_params common_chat_templates_apply_legacy(
     int alloc_size = 0;
     std::vector<llama_chat_message> chat;
     std::vector<std::string> contents;
+
     for (const auto & msg : inputs.messages) {
         auto content = msg.content;
         for (const auto & part : msg.content_parts) {
@@ -1925,6 +2092,12 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
         case COMMON_CHAT_FORMAT_COMMAND_R7B:
             common_chat_parse_command_r7b(builder);
             break;
+        case COMMON_CHAT_FORMAT_GRANITE:
+            common_chat_parse_granite(builder);
+            break;
+        case COMMON_CHAT_FORMAT_GPT_OSS:
+            common_chat_parse_gpt_oss(builder);
+            break;
         default:
             throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
     }
@@ -1944,6 +2117,8 @@ common_chat_msg common_chat_parse(const std::string & input, bool is_partial, co
         }
     }
     auto msg = builder.result();
-    LOG_DBG("Parsed message: %s\n", common_chat_msgs_to_json_oaicompat<json>({msg}).at(0).dump().c_str());
+    if (!is_partial) {
+        LOG_DBG("Parsed message: %s\n", common_chat_msgs_to_json_oaicompat<json>({msg}).at(0).dump().c_str());
+    }
     return msg;
 }

common/chat.h

@@ -109,6 +109,8 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
     COMMON_CHAT_FORMAT_HERMES_2_PRO,
     COMMON_CHAT_FORMAT_COMMAND_R7B,
+    COMMON_CHAT_FORMAT_GRANITE,
+    COMMON_CHAT_FORMAT_GPT_OSS,
 
     COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
 };
@@ -127,6 +129,8 @@ struct common_chat_templates_inputs {
     bool enable_thinking = true;
     std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
     std::map<std::string, std::string> chat_template_kwargs;
+    bool add_bos = false;
+    bool add_eos = false;
 };
 
 struct common_chat_params {

common/common.cpp

@@ -448,6 +448,15 @@ void string_replace_all(std::string & s, const std::string & search, const std::
 bool string_ends_with(const std::string_view & str, const std::string_view & suffix) {
     return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
 }
+
+bool string_remove_suffix(std::string & str, const std::string_view & suffix) {
+    bool has_suffix = string_ends_with(str, suffix);
+    if (has_suffix) {
+        str = str.substr(0, str.size() - suffix.size());
+    }
+    return has_suffix;
+}
+
 size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop) {
     if (!str.empty() && !stop.empty()) {
         const char text_last_char = str.back();
@@ -1113,6 +1122,7 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
     mparams.use_mmap        = params.use_mmap;
     mparams.use_mlock       = params.use_mlock;
     mparams.check_tensors   = params.check_tensors;
+    mparams.use_extra_bufts = !params.no_extra_bufts;
 
     if (params.kv_overrides.empty()) {
         mparams.kv_overrides = NULL;

common/common.h

@@ -201,6 +201,7 @@ struct common_params_speculative {
     int32_t n_gpu_layers =    -1; // number of layers to store in VRAM for the draft model (-1 - use default)
     float   p_split      =  0.1f; // speculative decoding split probability
     float   p_min        = 0.75f; // minimum speculative decoding probability (greedy)
+    std::vector<std::pair<std::string, std::string>> replacements; // main to speculative model replacements
 
     ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
     ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
@@ -220,17 +221,25 @@ struct common_params_vocoder {
 };
 
 struct common_params_diffusion {
-    int32_t steps       = 64;     // number of diffusion steps
-    float   eps         = 1e-3f;  // epsilon for timesteps
-    int32_t algorithm   = 0;      // diffusion algorithm (0=ORIGIN, 1=MASKGIT_PLUS, 2=TOPK_MARGIN, 3=ENTROPY)
-    float   alg_temp    = 0.0f;   // algorithm temperature
-    bool    visual_mode = false;  // show progressive diffusion on screen
+    int32_t steps         = 128;
+    bool    visual_mode   = false;
+
+    float   eps           = 0;        // epsilon for timesteps
+    int32_t block_length  = 0;        // block length for generation
+
+    int32_t algorithm     = 4;        // default algorithm: low-confidence
+    float   alg_temp      = 0.0f;     // algorithm temperature
+
+    float   cfg_scale     = 0;        // classifier-free guidance scale
+    bool    add_gumbel_noise = false; // add gumbel noise to the logits if temp > 0.0
 };
 
 enum common_reasoning_format {
     COMMON_REASONING_FORMAT_NONE,
+    COMMON_REASONING_FORMAT_AUTO,
     COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode
     COMMON_REASONING_FORMAT_DEEPSEEK,        // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
+    COMMON_REASONING_FORMAT_GRANITE,         // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
 };
 
 struct common_params {
@@ -352,6 +361,7 @@ struct common_params {
     bool warmup            = true;  // warmup run
     bool check_tensors     = false; // validate tensor data
     bool no_op_offload     = false; // globally disable offload host tensor operations to device
+    bool no_extra_bufts    = false; // disable extra buffer types (used for weight repacking)
 
     bool single_turn       = false; // single turn chat conversation
 
@@ -386,7 +396,7 @@ struct common_params {
     std::string chat_template = "";                                                                         // NOLINT
     bool use_jinja = false;                                                                                 // NOLINT
     bool enable_chat_template = true;
-    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
+    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_AUTO;
     int reasoning_budget = -1;
     bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response
 
@@ -431,10 +441,12 @@ struct common_params {
     int32_t n_out_freq  = 10; // output the imatrix every n_out_freq iterations
     int32_t n_save_freq =  0; // save the imatrix every n_save_freq iterations
     int32_t i_chunk     =  0; // start processing from this chunk
+    int8_t  imat_dat    =  0; // whether the legacy imatrix.dat format should be output (gguf <= 0 < dat)
 
-    bool process_output = false; // collect data for the output tensor
-    bool compute_ppl    = true;  // whether to compute perplexity
-    bool parse_special  = false; // whether to parse special tokens during imatrix tokenization
+    bool process_output  = false; // collect data for the output tensor
+    bool compute_ppl     = true;  // whether to compute perplexity
+    bool show_statistics = false; // show imatrix statistics per tensor
+    bool parse_special   = false; // whether to parse special tokens during imatrix tokenization
 
     // cvector-generator params
     int n_pca_batch = 100;
@@ -534,6 +546,7 @@ static bool string_starts_with(const std::string & str,
 
 // While we wait for C++20's std::string::ends_with...
 bool string_ends_with(const std::string_view & str, const std::string_view & suffix);
+bool string_remove_suffix(std::string & str, const std::string_view & suffix);
 size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop);
 
 bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);

common/speculative.cpp

@@ -1,30 +1,39 @@
 #include "speculative.h"
 
+#include "ggml.h"
+#include "llama.h"
 #include "log.h"
 #include "common.h"
 #include "sampling.h"
 
 #include <cstring>
 #include <algorithm>
+#include <map>
 
 #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE  128
 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
 
 struct common_speculative {
-    struct llama_context * ctx;
+    struct llama_context * ctx_tgt; // only used for retokenizing from ctx_dft
+    struct llama_context * ctx_dft;
     struct common_sampler * smpl;
 
     llama_batch batch;
-    llama_tokens prompt;
+    llama_tokens prompt_dft;
+    bool vocab_dft_compatible = true; // whether retokenization is needed
+    std::map<std::string, std::string> tgt_dft_replacements = {};
 };
 
 struct common_speculative * common_speculative_init(
+        struct llama_context * ctx_tgt,
         struct llama_context * ctx_dft) {
     auto * result = new common_speculative {
-        /* .ctx    = */ ctx_dft,
-        /* .smpl   = */ nullptr,
-        /* .batch  = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1),
-        /* .prompt = */ {},
+        /* .ctx_tgt    = */ ctx_tgt,
+        /* .ctx_dft    = */ ctx_dft,
+        /* .smpl       = */ nullptr,
+        /* .batch      = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1),
+        /* .prompt_dft = */ {},
+        /* .vocab_dft_compatible = */ false,
     };
 
     // TODO: optimize or pass from outside?
@@ -59,6 +68,9 @@ struct common_speculative * common_speculative_init(
     }
 #endif
 
+    result->vocab_dft_compatible = common_speculative_are_compatible(ctx_tgt, ctx_dft);
+    LOG_DBG("vocab_dft_compatible = %d\n", result->vocab_dft_compatible);
+
     return result;
 }
 
@@ -75,8 +87,8 @@ void common_speculative_free(struct common_speculative * spec) {
 }
 
 bool common_speculative_are_compatible(
-        const struct llama_context * ctx_tgt,
-        const struct llama_context * ctx_dft) {
+    const struct llama_context * ctx_tgt,
+    const struct llama_context * ctx_dft) {
     const struct llama_model * model_tgt = llama_get_model(ctx_tgt);
     const struct llama_model * model_dft = llama_get_model(ctx_dft);
 
@@ -90,31 +102,32 @@ bool common_speculative_are_compatible(
     LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
 
     if (vocab_type_tgt != vocab_type_dft) {
-        LOG_ERR("%s: draft model vocab type must match target model to use speculation but "
-                     "vocab_type_dft = %d while vocab_type_tgt = %d\n", __func__, vocab_type_dft, vocab_type_tgt);
+        LOG_DBG("%s: draft model vocab type must match target model to use speculation but ", __func__);
+        LOG_DBG("vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
         return false;
     }
 
-    if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
+    if (
+        llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
         llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
         llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
-        llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)) {
-        LOG_ERR("%s: draft vocab special tokens must match target vocab to use speculation\n", __func__);
-        LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_tgt), llama_vocab_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_tgt));
-        LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_dft), llama_vocab_get_add_bos(vocab_dft), llama_vocab_eos(vocab_dft), llama_vocab_get_add_eos(vocab_dft));
+        llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)
+    ) {
+        LOG_DBG("%s: draft model special tokens must match target model to use speculation\n", __func__);
         return false;
     }
 
     {
         const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt);
         const int n_vocab_dft = llama_vocab_n_tokens(vocab_dft);
-
-        const int vocab_diff = std::abs(n_vocab_tgt - n_vocab_dft);
+        const int vocab_diff  = n_vocab_tgt > n_vocab_dft
+            ? n_vocab_tgt - n_vocab_dft
+            : n_vocab_dft - n_vocab_tgt;
 
         if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
-            LOG_ERR("%s: draft model vocab must closely match target model to use speculation but "
-                         "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
-                    __func__, n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
+            LOG_DBG("%s: draft model vocab must closely match target model to use speculation but ", __func__);
+            LOG_DBG("target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
+                    n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
             return false;
         }
 
@@ -122,8 +135,8 @@ bool common_speculative_are_compatible(
             const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i);
             const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
             if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
-                LOG_ERR("%s: draft vocab vocab must match target vocab to use speculation but "
-                             "token %d content differs - target '%s', draft '%s'\n", __func__, i,
+                LOG_DBG("%s: draft model vocab must match target model to use speculation but ", __func__);
+                LOG_DBG("token %d content differs - target '%s', draft '%s'\n", i,
                         common_token_to_piece(ctx_tgt, i).c_str(),
                         common_token_to_piece(ctx_dft, i).c_str());
                 return false;
@@ -134,32 +147,93 @@ bool common_speculative_are_compatible(
     return true;
 }
 
+void common_speculative_add_replacement_tgt_dft(
+        struct common_speculative * spec,
+        const char *source, const char *dest) {
+    spec->tgt_dft_replacements[source] = dest;
+}
+
+static std::string replace_to_dft(
+        struct common_speculative * spec,
+        const std::string& input) {
+    std::string result = input;
+    for (const auto & pair : spec->tgt_dft_replacements) {
+        size_t pos = result.find(pair.first);
+        while (pos != std::string::npos) {
+            result.replace(pos, pair.first.length(), pair.second);
+            pos = result.find(pair.first, pos + pair.second.length());
+        }
+    }
+    return result;
+}
+
+static std::string replace_to_tgt(
+        struct common_speculative * spec,
+        const std::string& input) {
+    std::string result = input;
+    for (const auto& pair : spec->tgt_dft_replacements) {
+        size_t pos = result.find(pair.second);
+        while (pos != std::string::npos) {
+            result.replace(pos, pair.second.length(), pair.first);
+            pos = result.find(pair.second, pos + pair.first.length());
+        }
+    }
+    return result;
+}
+
+
 llama_tokens common_speculative_gen_draft(
         struct common_speculative * spec,
         struct common_speculative_params params,
-        const llama_tokens & prompt_tgt,
+        const llama_tokens & prompt_tgt_main_model, // specified in target model vocab
         llama_token id_last) {
     auto & batch  = spec->batch;
-    auto & ctx    = spec->ctx;
+    auto & ctx_tgt = spec->ctx_tgt;
+    auto & ctx_dft = spec->ctx_dft;
     auto & smpl   = spec->smpl;
-    auto & prompt = spec->prompt;
+    auto & prompt_dft = spec->prompt_dft;
 
-    auto * mem = llama_get_memory(ctx);
+    auto * mem_dft = llama_get_memory(ctx_dft);
 
     int reuse_i = 0;
     int reuse_n = 0;
 
-    const int n_ctx = llama_n_ctx(ctx) - params.n_draft;
+    const int n_ctx = llama_n_ctx(ctx_dft) - params.n_draft;
+
+    llama_tokens prompt_tgt_draft_model;
+    if (!spec->vocab_dft_compatible) {
+        std::string text;
+        text = common_detokenize(ctx_tgt, prompt_tgt_main_model, true);
+        text = replace_to_dft(spec, text);
+        LOG_DBG("%s: main->draft detokenized string: '%s'\n", __func__, text.c_str());
+        prompt_tgt_draft_model = common_tokenize(ctx_dft, text, false, true);
+
+        // convert id_last to draft vocab. llama_detokenize is called directly to avoid an allocation
+        const auto * model_tgt = llama_get_model(ctx_tgt);
+        const auto * vocab_tgt = llama_model_get_vocab(model_tgt);
+
+        int32_t n_chars = llama_detokenize(vocab_tgt, &id_last, 1, nullptr, 0, false, false);
+        GGML_ASSERT(n_chars < 0 && "failed to detokenize id_last");
+        text.resize(-n_chars);
+        llama_detokenize(vocab_tgt, &id_last, 1, text.data(), text.size(), false, false);
+        text = replace_to_dft(spec, text);
+
+        LOG_DBG("main->draft detokenized id_last(%d): '%s'\n", id_last, text.c_str());
+        id_last = common_tokenize(ctx_dft, text, false, true)[0];
+    }
+    // prompt_tgt's tokens will always be compatible with ctx_dft
+    const llama_tokens &prompt_tgt =
+        spec->vocab_dft_compatible ? prompt_tgt_main_model : prompt_tgt_draft_model;
 
     const int i_start = std::max<int>(0, (int) prompt_tgt.size() - n_ctx);
 
     // reuse as much as possible from the old draft context
     // ideally, the draft context should be as big as the target context and we will always reuse the entire prompt
-    for (int i = 0; i < (int) prompt.size(); ++i) {
+    for (int i = 0; i < (int) prompt_dft.size(); ++i) {
         int cur = 0;
         while (i_start + cur < (int) prompt_tgt.size() &&
-               i       + cur < (int) prompt.size() &&
-               prompt_tgt[i_start + cur] == prompt[i + cur]) {
+               i       + cur < (int) prompt_dft.size() &&
+               prompt_tgt[i_start + cur] == prompt_dft[i + cur]) {
             cur++;
         }
 
@@ -169,21 +243,20 @@ llama_tokens common_speculative_gen_draft(
         }
     }
 
-    LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt.size());
+    LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt_dft.size());
 
     llama_tokens result;
     result.reserve(params.n_draft);
 
     if (reuse_n == 0) {
-        llama_memory_clear(mem, false);
-
-        prompt.clear();
+        llama_memory_clear(mem_dft, false);
+        prompt_dft.clear();
     } else {
         // this happens when a previous draft has been discarded (for example, due to being too small), but the
         // target model agreed with it. in this case, we simply pass back the previous results to save compute
-        if (reuse_i + reuse_n < (int) prompt.size() && prompt[reuse_i + reuse_n] == id_last) {
-            for (int i = reuse_i + reuse_n + 1; i < (int) prompt.size(); ++i) {
-                result.push_back(prompt[i]);
+        if (reuse_i + reuse_n < (int) prompt_dft.size() && prompt_dft[reuse_i + reuse_n] == id_last) {
+            for (int i = reuse_i + reuse_n + 1; i < (int) prompt_dft.size(); ++i) {
+                result.push_back(prompt_dft[i]);
 
                 if (params.n_draft <= (int) result.size()) {
                     break;
@@ -194,16 +267,15 @@ llama_tokens common_speculative_gen_draft(
         }
 
         if (reuse_i > 0) {
-            llama_memory_seq_rm (mem, 0, 0, reuse_i);
-            llama_memory_seq_add(mem, 0, reuse_i, -1, -reuse_i);
+            llama_memory_seq_rm (mem_dft, 0, 0, reuse_i);
+            llama_memory_seq_add(mem_dft, 0, reuse_i, -1, -reuse_i);
 
-            prompt.erase(prompt.begin(), prompt.begin() + reuse_i);
+            prompt_dft.erase(prompt_dft.begin(), prompt_dft.begin() + reuse_i);
         }
 
-        if (reuse_n < (int) prompt.size()) {
-            llama_memory_seq_rm (mem, 0, reuse_n, -1);
-
-            prompt.erase(prompt.begin() + reuse_n, prompt.end());
+        if (reuse_n < (int) prompt_dft.size()) {
+            llama_memory_seq_rm (mem_dft, 0, reuse_n, -1);
+            prompt_dft.erase(prompt_dft.begin() + reuse_n, prompt_dft.end());
         }
     }
 
@@ -214,28 +286,28 @@ llama_tokens common_speculative_gen_draft(
         //LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_tgt[i]);
         common_batch_add(batch, prompt_tgt[i], i - i_start, { 0 }, false);
 
-        prompt.push_back(prompt_tgt[i]);
+        prompt_dft.push_back(prompt_tgt[i]);
     }
 
     // we should rarely end-up here during normal decoding
     if (batch.n_tokens > 0) {
         //LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str());
 
-        llama_decode(ctx, batch);
+        llama_decode(ctx_dft, batch);
     }
 
-    const llama_pos n_past = prompt.size();
+    const llama_pos n_past = prompt_dft.size();
 
     LOG_DBG("%s: n_past = %d\n", __func__, n_past);
 
     common_batch_clear(batch);
     common_batch_add  (batch, id_last, n_past, { 0 }, true);
 
-    prompt.push_back(id_last);
+    prompt_dft.push_back(id_last);
 
-    //LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx, prompt).c_str());
+    LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx_dft, prompt_dft).c_str());
 
-    llama_decode(ctx, batch);
+    llama_decode(ctx_dft, batch);
 
     common_sampler_reset(smpl);
 
@@ -243,13 +315,13 @@ llama_tokens common_speculative_gen_draft(
     for (int i = 0; i < params.n_draft; ++i) {
         common_batch_clear(batch);
 
-        common_sampler_sample(smpl, ctx, 0, true);
+        common_sampler_sample(smpl, ctx_dft, 0, true);
 
         const auto * cur_p = common_sampler_get_candidates(smpl);
 
         for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
             LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
-                    k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx, cur_p->data[k].id).c_str());
+                    k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
         }
 
         // add drafted token for each sequence
@@ -271,10 +343,19 @@ llama_tokens common_speculative_gen_draft(
         common_batch_add(batch, id, n_past + i + 1, { 0 }, true);
 
         // evaluate the drafted tokens on the draft model
-        llama_decode(ctx, batch);
+        llama_decode(ctx_dft, batch);
 
-        prompt.push_back(id);
+        prompt_dft.push_back(id);
     }
 
+    if (!spec->vocab_dft_compatible) {
+        std::string detokenized = common_detokenize(ctx_dft, result, true);
+        detokenized = replace_to_tgt(spec, detokenized);
+        LOG_DBG("draft->main detokenized string: '%s'\n", detokenized.c_str());
+        result = common_tokenize(ctx_tgt, detokenized, false, true);
+        if (result.size() > (size_t)params.n_draft) {
+            result.resize(params.n_draft);
+        }
+    }
     return result;
 }

common/speculative.h

@@ -12,7 +12,10 @@ struct common_speculative_params {
     float p_min = 0.75f; // min probability required to accept a token in the draft
 };
 
-struct common_speculative * common_speculative_init(struct llama_context * ctx_dft);
+struct common_speculative * common_speculative_init(
+        struct llama_context * ctx_tgt,
+        struct llama_context * ctx_dft
+);
 
 void common_speculative_free(struct common_speculative * spec);
 
@@ -20,6 +23,10 @@ bool common_speculative_are_compatible(
         const struct llama_context * ctx_tgt,
         const struct llama_context * ctx_dft);
 
+void common_speculative_add_replacement_tgt_dft(
+        struct common_speculative * spec,
+        const char *source, const char *dest);
+
 // sample up to n_draft tokens and add them to the batch using the draft model
 llama_tokens common_speculative_gen_draft(
                struct common_speculative * spec,

convert_hf_to_gguf.py

@@ -678,12 +678,18 @@ class TextModel(ModelBase):
         if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
             # ref: https://huggingface.co/THUDM/glm-4-9b-hf
             res = "glm4"
+        if chkhsh == "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902":
+            # ref: https://huggingface.co/zai-org/GLM-4.5-Air
+            res = "glm4"
         if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
             # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
             res = "minerva-7b"
         if chkhsh == "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664":
             # ref: https://huggingface.co/tencent/Hunyuan-A13B-Instruct
             res = "hunyuan"
+        if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
+            # ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
+            res = "hunyuan-dense"
         if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
             # ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
             res = "falcon-h1"
@@ -699,6 +705,9 @@ class TextModel(ModelBase):
         if chkhsh == "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890":
             # ref: https://huggingface.co/moonshotai/Kimi-K2-Base
             res = "kimi-k2"
+        if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c":
+            # ref: https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
+            res = "qwen2"
         if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
             # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
             res = "llama-bpe"
@@ -843,6 +852,12 @@ class TextModel(ModelBase):
         if chkhsh == "169bf0296a13c4d9b7672313f749eb36501d931022de052aad6e36f2bf34dd51":
             # ref: https://huggingface.co/LiquidAI/LFM2-Tokenizer
             res = "lfm2"
+        if chkhsh == "2085e1638f6c377a0aa4ead21b27bb4cb941bf800df86ed391011769c1758dfb":
+            # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B
+            res = "exaone4"
+        if chkhsh == "a1e163ecab2e718a4c829d1148b6e86824ec36163bb71941c3dca9cd5ac25756":
+            # ref: https://huggingface.co/JetBrains/Mellum-4b-base
+            res = "mellum"
 
         if res is None:
             logger.warning("\n")
@@ -1897,6 +1912,7 @@ class StableLMModel(TextModel):
     "MixtralForCausalLM",
     "VLlama3ForCausalLM",
     "LlavaForConditionalGeneration",
+    "VoxtralForConditionalGeneration",
     "LlamaModel")
 class LlamaModel(TextModel):
     model_arch = gguf.MODEL_ARCH.LLAMA
@@ -1909,6 +1925,11 @@ class LlamaModel(TextModel):
             self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32)
 
     def set_vocab(self):
+        path_tekken_json = self.dir_model / "tekken.json"
+        path_tokenizer_json = self.dir_model / "tokenizer.json"
+        if path_tekken_json.is_file() and not path_tokenizer_json.is_file():
+            return self.set_vocab_tekken()
+
         try:
             self._set_vocab_sentencepiece()
         except FileNotFoundError:
@@ -1941,6 +1962,52 @@ class LlamaModel(TextModel):
         if self.hparams.get("vocab_size", 32000) == 49152:
             self.gguf_writer.add_add_bos_token(False)
 
+    def set_vocab_tekken(self):
+        vocab = gguf.vocab.MistralVocab(self.dir_model)
+        self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model)
+
+        tokens = []
+        scores = []
+        toktypes = []
+
+        for text, score, toktype in vocab.all_tokens():
+            tokens.append(text)
+            scores.append(score)
+            toktypes.append(toktype)
+
+        assert len(tokens) == vocab.vocab_size, (
+            f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})"
+        )
+
+        if vocab.tokenizer_type == gguf.vocab.MistralTokenizerType.tekken:
+            self.gguf_writer.add_tokenizer_pre("tekken")
+            self.gguf_writer.add_token_merges(
+                vocab.extract_vocab_merges_from_model()
+            )
+
+        logger.info(
+            f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}."
+        )
+
+        self.gguf_writer.add_bos_token_id(vocab.bos_id)
+        self.gguf_writer.add_eos_token_id(vocab.eos_id)
+        self.gguf_writer.add_unk_token_id(vocab.unk_id)
+        self.gguf_writer.add_pad_token_id(vocab.pad_id)
+
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_vocab_size(vocab.vocab_size)
+
+        self.gguf_writer.add_add_bos_token(True)
+        self.gguf_writer.add_add_eos_token(False)
+
+        script_dir = Path(__file__).parent
+        template_path = script_dir / "models/templates/unsloth-mistral-Devstral-Small-2507.jinja"
+        with open(template_path, "r", encoding="utf-8") as f:
+            template = f.read()
+            self.gguf_writer.add_chat_template(template)
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         hparams = self.hparams
@@ -1968,12 +2035,13 @@ class LlamaModel(TextModel):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         n_head = self.hparams["num_attention_heads"]
         n_kv_head = self.hparams.get("num_key_value_heads")
-        is_vision_tensor = "vision_tower" in name \
+        is_multimodal_tensor = "vision_tower" in name \
             or "vision_model" in name \
+            or "audio_tower" in name \
             or "model.connector" in name \
             or "multi_modal_projector" in name
 
-        if is_vision_tensor:
+        if is_multimodal_tensor:
             return [] # skip vision tensors
         elif self.hf_arch == "LlamaModel":
             name = "model." + name
@@ -2848,6 +2916,107 @@ class DreamModel(TextModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("LLaDAModelLM")
+class LLaDAModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.LLADA
+    undo_permute = True
+
+    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+
+        vocab_dict = tokenizer.get_vocab()
+        vocab_size = self.hparams.get("vocab_size", len(vocab_dict))
+        assert max(vocab_dict.values()) < vocab_size
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()}
+        added_vocab = tokenizer.get_added_vocab()
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            elif reverse_vocab[i] in added_vocab:
+                tokens.append(reverse_vocab[i])
+                # Check if it's a special token - treat special tokens as CONTROL tokens
+                if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder:
+                    if tokenizer.added_tokens_decoder[i].special:
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                else:
+                    # Fallback: treat all added vocab as control tokens for special tokens like <|im_start|>
+                    toktypes.append(gguf.TokenType.CONTROL)
+            else:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.NORMAL)
+
+        return tokens, toktypes, tokpre
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+        # LLaDA specific parameters
+        self.gguf_writer.add_add_bos_token(True)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self._try_set_pooling_type()
+
+        # Add parameters similar to LlamaModel
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        if (rope_dim := hparams.get("head_dim")) is None:
+            n_heads = hparams.get("num_attention_heads", hparams.get("n_heads"))
+            rope_dim = hparams.get("hidden_size", hparams.get("d_model")) // n_heads
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+
+        # Set context length for LLaDA
+        context_length = self.hparams.get("max_sequence_length", 4096)
+        self.gguf_writer.add_context_length(context_length)
+
+        # Set embedding length (dimension size)
+        embedding_length = self.hparams.get("d_model", 4096)
+        self.gguf_writer.add_embedding_length(embedding_length)
+
+        # Set feed forward length (MLP hidden size)
+        feed_forward_length = self.hparams.get("mlp_hidden_size", 12288)
+        self.gguf_writer.add_feed_forward_length(feed_forward_length)
+
+        # LLaDA models use non-causal attention for diffusion, similar to Dream
+        self.gguf_writer.add_causal_attention(False)
+
+        # LLaDA models don't shift their logits
+        self.gguf_writer.add_diffusion_shift_logits(False)
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams.get("num_attention_heads", self.hparams.get("n_heads"))
+        n_kv_head = self.hparams.get("num_key_value_heads", self.hparams.get("n_kv_heads"))
+
+        if self.undo_permute:
+            if name.endswith(("q_proj.weight", "q_proj.bias")):
+                data_torch = LLaDAModel.permute(data_torch, n_head, n_head)
+            if name.endswith(("k_proj.weight", "k_proj.bias")):
+                data_torch = LLaDAModel.permute(data_torch, n_head, n_kv_head)
+
+        # LLaDA model tensors should be mapped directly since it's the base model
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("Ernie4_5_ForCausalLM")
 class Ernie4_5Model(TextModel):
     model_arch = gguf.MODEL_ARCH.ERNIE4_5
@@ -2861,7 +3030,8 @@ class Ernie4_5Model(TextModel):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         num_heads = self.hparams["num_attention_heads"]
         num_kv_heads = self.hparams["num_key_value_heads"]
-        head_dim = self.hparams["head_dim"]
+        if (head_dim := self.hparams.get("head_dim")) is None:
+            head_dim = self.hparams["hidden_size"] // num_heads
 
         if "ernie." in name:
             name = name.replace("ernie.", "model.")
@@ -2894,6 +3064,93 @@ class Ernie4_5Model(TextModel):
         return [(self.map_tensor_name(name), data_torch)]
 
 
+@ModelBase.register("Ernie4_5_MoeForCausalLM")
+class Ernie4_5MoeModel(Ernie4_5Model):
+    model_arch = gguf.MODEL_ARCH.ERNIE4_5_MOE
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._experts = [{} for _ in range(self.block_count)]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
+        self.gguf_writer.add_expert_used_count(self.hparams["moe_k"])
+        self.gguf_writer.add_interleave_moe_layer_step(self.hparams["moe_layer_interval"])
+        self.gguf_writer.add_leading_dense_block_count(self.hparams["moe_layer_start_index"])
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        if (shared_expert_count := self.hparams.get('moe_num_shared_experts')) is not None:
+            self.gguf_writer.add_expert_shared_count(shared_expert_count)
+            if shared_expert_count > 0 and (shared_expert_intermediate_size := self.hparams.get('intermediate_size')) is not None and (num_key_value_heads := self.hparams.get('num_key_value_heads')) is not None:
+                self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size // num_key_value_heads)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Modify correction bias name as in DeepseekV2
+        if name.endswith("e_score_correction_bias"):
+            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+
+        # skip Multi-Token Prediction (MTP) layers (again, same as DeepseekV2)
+        match = re.match(r"model.mtp_block.(\d+)", name)
+        if match:
+            return []
+
+        # skip all other MTP tensors for now
+        match = re.match(r"model.mtp_emb_norm.(\d+)", name)
+        if match:
+            return []
+
+        match = re.match(r"model.mtp_hidden_norm.(\d+)", name)
+        if match:
+            return []
+
+        match = re.match(r"model.mtp_linear_proj.(\d+)", name)
+        if match:
+            return []
+
+        # process the experts separately
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["moe_num_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["gate_proj", "up_proj", "down_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename_to_retrieve])
+                        del self._experts[bid][ename_to_retrieve]
+
+                    data_torch = torch.stack(datas, dim=0)
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+                    new_name = self.map_tensor_name(merged_name)
+                    tensors.append((new_name, data_torch))
+
+                return tensors
+            else:
+                return []
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
 @ModelBase.register(
     "Qwen2VLModel",
     "Qwen2VLForConditionalGeneration",
@@ -3700,7 +3957,7 @@ class Plamo2Model(TextModel):
         self.gguf_writer.add_block_count(block_count)
         self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 32))
         self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06))
-        self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 1000000.0))
+        self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 10000))
 
         # Mamba parameters
         self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64))
@@ -3711,7 +3968,7 @@ class Plamo2Model(TextModel):
         self.gguf_writer.add_ssm_group_count(0)
 
         # MLP feed forward parameters (for attention layers)
-        self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 16384))
+        self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 13312))
         self.gguf_writer.add_file_type(self.ftype)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
@@ -5808,6 +6065,7 @@ class DeepseekModel(TextModel):
 
 @ModelBase.register("DeepseekV2ForCausalLM")
 @ModelBase.register("DeepseekV3ForCausalLM")
+@ModelBase.register("KimiVLForConditionalGeneration")
 class DeepseekV2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2
 
@@ -5910,6 +6168,13 @@ class DeepseekV2Model(TextModel):
     _experts: list[dict[str, Tensor]] | None = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # skip vision tensors and remove "language_model." for Kimi-VL
+        if "vision_tower" in name or "multi_modal_projector" in name:
+            return []
+
+        if name.startswith("language_model."):
+            name = name.replace("language_model.", "")
+
         # rename e_score_correction_bias tensors
         if name.endswith("e_score_correction_bias"):
             name = name.replace("e_score_correction_bias", "e_score_correction.bias")
@@ -6395,7 +6660,7 @@ class JaisModel(TextModel):
         self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
 
 
-@ModelBase.register("Glm4ForCausalLM")
+@ModelBase.register("Glm4ForCausalLM", "Glm4vForConditionalGeneration")
 class Glm4Model(TextModel):
     model_arch = gguf.MODEL_ARCH.GLM4
 
@@ -6417,7 +6682,8 @@ class Glm4Model(TextModel):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-        rope_dim = self.hparams["head_dim"]
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
         rope_scaling = self.hparams.get("rope_scaling") or {}
         if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
@@ -6425,6 +6691,146 @@ class Glm4Model(TextModel):
             self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
             self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
 
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("model.visual."): # ignore visual part of Glm4v
+            return []
+        elif name.startswith("model.language_model."):
+            name = name.replace("language_model.", "") # for Glm4v
+        return super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Glm4MoeForCausalLM")
+class Glm4MoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GLM4_MOE
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # GLM4_MOE has num_hidden_layers + 1 actual layers (including NextN layer)
+        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_vocab(self):
+        from transformers import AutoTokenizer
+
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        # Special tokens
+        # Note: Using <|endoftext|> (151329) for eot causes endless generation
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"])  # 151331
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # 151336
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) # 151329
+        special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"])  # 151338
+
+        # Patch broken chat template
+        if isinstance(special_vocab.chat_template, str) and "visible_text(m.content).endswith" in special_vocab.chat_template:
+            special_vocab.chat_template = special_vocab.chat_template.replace(
+                """{{ visible_text(m.content) }}\n{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not visible_text(m.content).endswith("/nothink")) else '' -}}""",
+                """{% set content = visible_text(m.content) %}{{ content }}\n{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not content.endswith("/nothink")) else '' -}}""")
+
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = (
+                self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+            )
+        self.gguf_writer.add_rope_dimension_count(
+            int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))
+        )
+
+        # MoE parameters - Use only routed expert count (shared experts handled separately)
+        if (n_routed_experts := self.hparams.get("n_routed_experts")) is not None:
+            self.gguf_writer.add_expert_count(n_routed_experts)
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        if (n_shared_experts := self.hparams.get("n_shared_experts")) is not None:
+            self.gguf_writer.add_expert_shared_count(n_shared_experts)
+        if (first_k_dense_replace := self.hparams.get("first_k_dense_replace")) is not None:
+            self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
+
+        # Expert gating function (sigmoid for GLM4_MOE)
+        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+
+        # Routed scaling factor
+        if (routed_scaling_factor := self.hparams.get("routed_scaling_factor")) is not None:
+            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
+
+        # Normalise topk probabilities
+        if (norm_topk_prob := self.hparams.get("norm_topk_prob")) is not None:
+            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
+
+        # NextN/MTP prediction layers
+        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(
+        self, data_torch: Tensor, name: str, bid: int | None
+    ) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("model.visual."):  # ignore visual part
+            return []
+        elif name.startswith("model.language_model."):
+            name = name.replace("language_model.", "")  # for multimodal variants
+
+        # Handle main token embedding (but not layer-specific NextN embeddings)
+        if name == "model.embed_tokens.weight" and ".layers." not in name:
+            return [(self.map_tensor_name("token_embd.weight"), data_torch)]
+
+        # Handle routed experts
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["n_routed_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        if name.endswith("e_score_correction_bias"):
+            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+
+        new_name = self.map_tensor_name(name)
+
+        return [(new_name, data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
 
 @ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
 class ChatGLMModel(TextModel):
@@ -6692,6 +7098,75 @@ class ExaoneModel(TextModel):
                 yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
 
 
+@ModelBase.register("Exaone4ForCausalLM")
+class Exaone4Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.EXAONE4
+
+    def set_vocab(self):
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        if hparams.get("sliding_window") is not None:
+            self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+            if "layer_types" in hparams:
+                self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]])
+            elif "sliding_window_pattern" in hparams:
+                sliding_window_pattern = []
+                if isinstance(hparams["sliding_window_pattern"], str):  # e.g. LLLG
+                    for i in range(hparams["num_hidden_layers"]):
+                        sliding_window_pattern.append(hparams["sliding_window_pattern"][i % len(hparams["sliding_window_pattern"])] == "L")
+                if isinstance(hparams["sliding_window_pattern"], int):  # e.g. 4
+                    for i in range(hparams["num_hidden_layers"]):
+                        sliding_window_pattern.append((i + 1) % hparams["sliding_window_pattern"] != 0)
+                if len(sliding_window_pattern) == hparams["num_hidden_layers"]:
+                    self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
+
+        rope_scaling = self.hparams.get("rope_scaling") or {}
+        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling:
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
+            if rope_scaling.get("rope_type", '').lower() == "llama3":
+                base = self.hparams.get("rope_theta", 10_000.0)
+                if (dim := self.hparams.get("head_dim")) is None:
+                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+                factor = rope_scaling.get("factor", 16.0)
+                low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
+                high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
+                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+
+                low_freq_wavelen = old_context_len / low_freq_factor
+                high_freq_wavelen = old_context_len / high_freq_factor
+
+                rope_factors = []
+                for freq in freqs:
+                    wavelen = 2 * math.pi / freq
+                    if wavelen < high_freq_wavelen:
+                        rope_factors.append(1)
+                    elif wavelen > low_freq_wavelen:
+                        rope_factors.append(factor)
+                    else:
+                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))
+
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
+
+
 @ModelBase.register("GraniteForCausalLM")
 class GraniteModel(LlamaModel):
     """Conversion for IBM's GraniteForCausalLM"""
@@ -7063,9 +7538,10 @@ class WhisperEncoderModel(MmprojModel):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.hparams["hidden_size"] = self.hparams["d_model"]
-        self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
-        self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
+        if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams:
+            self.hparams["hidden_size"] = self.hparams["d_model"]
+            self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
+            self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
@@ -7104,9 +7580,21 @@ class UltravoxWhisperEncoderModel(WhisperEncoderModel):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.ULTRAVOX)
         self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])
 
 
+@ModelBase.register("VoxtralForConditionalGeneration")
+class VoxtralWhisperEncoderModel(WhisperEncoderModel):
+    has_vision_encoder = False # no vision encoder
+    has_audio_encoder = True
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.VOXTRAL)
+        self.gguf_writer.add_audio_stack_factor(4) # == intermediate_size // hidden_size
+
+
 @ModelBase.register("FalconH1ForCausalLM")
 class FalconH1Model(Mamba2Model):
     model_arch = gguf.MODEL_ARCH.FALCON_H1
@@ -7218,11 +7706,6 @@ class FalconH1Model(Mamba2Model):
 class HunYuanMoEModel(TextModel):
     model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # For handling tied embeddings
-        self._tok_embd = None
-
     def set_vocab(self):
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
@@ -7316,9 +7799,6 @@ class HunYuanMoEModel(TextModel):
     _experts: list[dict[str, Tensor]] | None = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name == "model.embed_tokens.weight":
-            self._tok_embd = data_torch.clone()
-
         if name == "lm_head.weight":
             if self.hparams.get("tie_word_embeddings", False):
                 logger.info("Skipping tied output layer 'lm_head.weight'")
@@ -7363,6 +7843,98 @@ class HunYuanMoEModel(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("HunYuanDenseV1ForCausalLM")
+class HunYuanModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE
+
+    def set_vocab(self):
+        if (self.dir_model / "tokenizer.json").is_file():
+            self._set_vocab_gpt2()
+        else:
+            from transformers import AutoTokenizer
+            tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+
+            # 1. Get the pre-tokenizer identifier hash
+            tokpre = self.get_vocab_base_pre(tokenizer)
+
+            # 2. Reverse-engineer the merges list from mergeable_ranks
+            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)
+                if len(merged) == 2:
+                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+
+            # 3. Generate the tokens and toktypes lists
+            vocab_size = self.hparams["vocab_size"]
+            assert tokenizer.vocab_size == vocab_size
+            special_tokens = tokenizer.special_tokens
+            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
+            tokens: list[str] = []
+            toktypes: list[int] = []
+            for i in range(vocab_size):
+                if i not in reverse_vocab:
+                    tokens.append(f"[PAD{i}]")
+                    toktypes.append(gguf.TokenType.UNUSED)
+                else:
+                    token = reverse_vocab[i]
+                    tokens.append(token)
+                    if i in special_tokens.values():
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.NORMAL)
+
+            # 4. Write all vocab-related fields to the GGUF writer
+            self.gguf_writer.add_tokenizer_model("gpt2")
+            self.gguf_writer.add_tokenizer_pre(tokpre)
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_types(toktypes)
+            self.gguf_writer.add_token_merges(merges)
+
+            # 5. Add special tokens and chat templates
+            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+            special_vocab.add_to_gguf(self.gguf_writer)
+            # FIX for BOS token: Overwrite incorrect id read from config.json
+            if self.hparams['hidden_size'] == 4096:
+                self.gguf_writer.add_bos_token_id(127958) # only for 7b dense, fix <|bos|> token
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        # Rope
+        rope_scaling = hparams.get("rope_scaling", {})
+        if rope_scaling.get("type") == "dynamic":
+            # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+            # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
+            alpha = rope_scaling.get("alpha", 50)
+            base = hparams.get("rope_theta", 10000.0)
+            dim = hparams["head_dim"]
+            scaled_base = base * (alpha ** (dim / (dim - 2)))
+            self.gguf_writer.add_rope_freq_base(scaled_base)
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+            self.gguf_writer.add_rope_scaling_factor(1)
+            # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length
+            self.gguf_writer.add_context_length(256 * 1024) # 256k context length
+
+            # if any of our assumptions about the values are wrong, something has changed and this may need to be updated
+            assert base == 10000.0 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \
+                "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually"
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "lm_head.weight":
+            if self.hparams.get("tie_word_embeddings", False):
+                logger.info("Skipping tied output layer 'lm_head.weight'")
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
 @ModelBase.register("SmolLM3ForCausalLM")
 class SmolLM3Model(LlamaModel):
     model_arch = gguf.MODEL_ARCH.SMOLLM3
@@ -7378,6 +7950,119 @@ class SmolLM3Model(LlamaModel):
             self.gguf_writer.add_chat_template(chat_template)
 
 
+@ModelBase.register("GptOssForCausalLM")
+class GptOssModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GPT_OSS
+
+    def transform_nibble_layout(self, tensor):
+        assert tensor.dtype == torch.uint8
+        assert tensor.shape[-1] == 16
+        # swap nibbles
+        t_lo = tensor & 0x0F
+        t_hi = tensor & 0xF0
+        t_swapped = (t_lo << 4) | (t_hi >> 4)
+        tensor = t_swapped
+        # transform aaaa...bbbb... to abababab...
+        blk_a, blk_b = tensor.chunk(2, dim=-1)
+        # get a_
+        blk_a0 = (blk_a & 0xF0).view(-1, 1)
+        blk_a1 = (blk_a << 4).view(-1, 1)
+        blk_a = torch.stack((blk_a0, blk_a1), dim=2).view(tensor.shape)
+        # get _b
+        blk_b0 = (blk_b >> 4).view(-1, 1)
+        blk_b1 = (blk_b & 0x0F).view(-1, 1)
+        blk_b = torch.stack((blk_b0, blk_b1), dim=2).view(tensor.shape)
+        # swap once more
+        out = blk_a | blk_b
+        out_h = out & 0xF0
+        out_l = out & 0x0F
+        out = (out_h >> 4) | (out_l << 4)
+        return out
+
+    def repack_mxfp4(self, new_name: str, blocks: Tensor, scales: Tensor):
+        assert blocks.dtype == torch.uint8
+        assert scales.dtype == torch.uint8
+        scales = scales.unsqueeze(-1)
+        assert len(blocks.shape) == 4
+        assert len(scales.shape) == 4
+        blocks = self.transform_nibble_layout(blocks)
+        new_data = torch.concat((scales, blocks), dim=-1)
+        new_shape = [new_data.shape[0], new_data.shape[1], new_data.shape[2] * 32]
+        logger.info(f"Repacked {new_name} with shape {new_shape} and quantization MXFP4")
+        # flatten last dim
+        new_data = new_data.view(new_data.shape[0], new_data.shape[1], new_data.shape[2] * new_data.shape[3])
+        new_data = new_data.numpy()
+        self.gguf_writer.add_tensor(new_name, new_data, raw_dtype=gguf.GGMLQuantizationType.MXFP4)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        blocks0: Tensor = torch.zeros(1)
+        blocks1: Tensor = torch.zeros(1)
+        found_mxfp4_tensors = False
+        # we assume that tensors are loaded in the correct order
+        for name, data_torch in self.get_tensors():
+            if "mlp.experts.down_proj_blocks" in name:
+                blocks0 = data_torch
+            elif "mlp.experts.down_proj_scales" in name:
+                new_name = self.map_tensor_name(name.replace("_scales", ".weight"))
+                self.repack_mxfp4(new_name, blocks0, data_torch)
+                found_mxfp4_tensors = True
+            elif "mlp.experts.gate_up_proj_blocks" in name:
+                blocks0, blocks1 = data_torch[:, ::2, :, :], data_torch[:, 1::2, :, :]
+            elif "mlp.experts.gate_up_proj_scales" in name:
+                scales0, scales1 = data_torch[:, ::2, :], data_torch[:, 1::2, :]
+                new_name_gate = self.map_tensor_name(name.replace("gate_up_proj_scales", "gate_proj.weight"))
+                new_name_up = self.map_tensor_name(name.replace("gate_up_proj_scales", "up_proj.weight"))
+                self.repack_mxfp4(new_name_gate, blocks0, scales0)
+                self.repack_mxfp4(new_name_up, blocks1, scales1)
+                found_mxfp4_tensors = True
+        if not found_mxfp4_tensors:
+            raise ValueError("No MXFP4 tensors found in the model. Please make sure you are using MXFP4 model.")
+        return []
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        if "sinks" in name:
+            name += ".weight"
+
+        # correct naming for down_proj
+        if "down_proj" in name:
+            if name.endswith("_bias"):
+                name = name.replace("down_proj_bias", "down_proj.bias")
+            else:
+                return []
+
+        # split the gate_up into gate and up
+        if "gate_up_proj" in name:
+            if name.endswith("_bias"):
+                name_up = name.replace("gate_up_proj_bias", "up_proj.bias")
+                name_gate = name.replace("gate_up_proj_bias", "gate_proj.bias")
+                gate_proj_bias, up_proj_bias = data_torch[..., ::2], data_torch[..., 1::2]
+                return [
+                    (self.map_tensor_name(name_gate), gate_proj_bias),
+                    (self.map_tensor_name(name_up), up_proj_bias)
+                ]
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size"])
+
+        rope_scaling = self.hparams.get("rope_scaling") or {}
+        rope_type = rope_scaling.get("rope_type", rope_scaling.get("type"))
+        assert rope_type == "yarn", f"GPT-OSS only supports yarn rope scaling, got {rope_type}"
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+        self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+        self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling.get("original_max_position_embeddings", 4096))
+
+
 @ModelBase.register("Lfm2ForCausalLM")
 @ModelBase.register("LFM2ForCausalLM")
 class LFM2Model(TextModel):
@@ -7421,6 +8106,88 @@ class LFM2Model(TextModel):
         return [(self.map_tensor_name(name), data_torch)]
 
 
+@ModelBase.register("SmallThinkerForCausalLM")
+class SmallThinkerModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.SMALLTHINKER
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (n_experts := self.hparams.get("num_experts", self.hparams.get("moe_num_primary_experts"))) is not None:
+            self.gguf_writer.add_expert_count(n_experts)
+        if (n_experts_used := self.hparams.get("num_experts_per_tok", self.hparams.get("moe_num_active_primary_experts"))) is not None:
+            self.gguf_writer.add_expert_used_count(n_experts_used)
+        if (moe_intermediate_size := self.hparams.get("moe_ffn_hidden_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+            self.gguf_writer.add_feed_forward_length(moe_intermediate_size)
+            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
+        if (self.hparams.get('moe_primary_router_apply_softmax')):
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+        else:
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+        # YaRN is not enabled by default
+        # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts
+        rope_scaling = self.hparams.get("rope_scaling") or {}
+        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
+
+        sliding_window_layout = self.hparams.get("sliding_window_layout")
+        if sliding_window_layout:
+            for i in sliding_window_layout:
+                if i != 0:
+                    sliding_window = self.hparams.get("sliding_window_size")
+                    if sliding_window:
+                        self.gguf_writer.add_sliding_window(sliding_window)
+                    break
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("experts") != -1:
+            n_experts = self.hparams.get("num_experts", self.hparams.get("moe_num_primary_experts"))
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down", "gate", "up"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
 ###### CONVERSION LOGIC ######
 
 
@@ -7435,6 +8202,7 @@ class LazyTorchTensor(gguf.LazyBase):
     _dtype_map: dict[torch.dtype, type] = {
         torch.float16: np.float16,
         torch.float32: np.float32,
+        torch.uint8: np.uint8,
     }
 
     # used for safetensors slices

convert_hf_to_gguf_update.py

@@ -7,7 +7,6 @@ import pathlib
 import re
 
 import requests
-import sys
 import json
 import shutil
 import argparse
@@ -60,6 +59,10 @@ parser.add_argument(
     "--full", action="store_true",
     help="download full list of models - make sure you have access to all of them",
 )
+parser.add_argument(
+    "--check-missing", action="store_true",
+    help="only check for missing pre-tokenizer hashes",
+)
 parser.add_argument(
     "hf_token",
     help="optional HF token",
@@ -69,8 +72,11 @@ args = parser.parse_args()
 hf_token = args.hf_token if args.hf_token is not None else hf_token
 
 if hf_token is None:
-    logger.error("HF token is required. Please provide it as an argument or set it in ~/.cache/huggingface/token")
-    sys.exit(1)
+    logger.warning("HF token not found. You can provide it as an argument or set it in ~/.cache/huggingface/token")
+
+if args.check_missing and args.full:
+    logger.warning("Downloading full list of models requested, ignoring --check-missing!")
+    args.check_missing = False
 
 # TODO: this string has to exercise as much pre-tokenizer functionality as possible
 #       will be updated with time - contributions welcome
@@ -131,6 +137,8 @@ models = [
     {"name": "a.x-4.0",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/skt/A.X-4.0", },
     {"name": "midm-2.0",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct", },
     {"name": "lfm2",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
+    {"name": "exaone4",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
+    {"name": "mellum",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -139,19 +147,22 @@ pre_computed_hashes = [
     {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b"},
     {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"},
     {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"},
+    {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/zai-org/GLM-4.5-Air", "chkhsh": "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902"},
     {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
     {"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
+    {"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},
     # falcon-h1 series uses 4 different tokenizers across model sizes (0.5b - 34b), hence we need to define 4 different hashes
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base", "chkhsh": "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6"},
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-1B-Base", "chkhsh": "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86"},
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-7B-Base", "chkhsh": "3eda48b4c4dc7de733d1a8b3e3b4a85243dbbf704da2ee9d42c6beced8897896"},
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-34B-Base", "chkhsh": "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b"},
     {"name": "kimi-k2",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base",   "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
+    {"name": "qwen2",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
 ]
 
 
 def download_file_with_auth(url, token, save_path):
-    headers = {"Authorization": f"Bearer {token}"}
+    headers = {"Authorization": f"Bearer {token}"} if token else None
     response = sess.get(url, headers=headers)
     response.raise_for_status()
     os.makedirs(os.path.dirname(save_path), exist_ok=True)
@@ -221,12 +232,13 @@ if not args.full:
     all_models = models.copy()
     models = [model for model in all_models if model["name"] not in existing_models]
 
-logging.info(f"Downloading {len(models)} models...")
-for model in models:
-    try:
-        download_model(model)
-    except Exception as e:
-        logger.error(f"Failed to download model {model['name']}. Error: {e}")
+if not args.check_missing:
+    logging.info(f"Downloading {len(models)} models...")
+    for model in models:
+        try:
+            download_model(model)
+        except Exception as e:
+            logger.error(f"Failed to download model {model['name']}. Error: {e}")
 
 
 # generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function:
@@ -250,10 +262,9 @@ for model in [*pre_computed_hashes, *all_models]:
     else:
         # otherwise, compute the hash of the tokenizer
 
-        # Skip if the tokenizer folder does not exist or there are other download issues previously
-        if not os.path.exists(f"models/tokenizers/{name}"):
-            logger.warning(f"Directory for tokenizer {name} not found. Skipping...")
-            continue
+        # Fail if the tokenizer folder with config does not exist or there are other download issues previously
+        if not os.path.isfile(f"models/tokenizers/{name}/tokenizer_config.json"):
+            raise OSError(f"Config for tokenizer {name} not found. The model may not exist or is not accessible with the provided token.")
 
         try:
             logger.info(f"Loading tokenizer from {f'models/tokenizers/{name}'}...")
@@ -261,9 +272,8 @@ for model in [*pre_computed_hashes, *all_models]:
                 tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
             else:
                 tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
-        except OSError as e:
-            logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
-            continue  # Skip to the next model if the tokenizer can't be loaded
+        except Exception as e:
+            raise OSError(f"Error loading tokenizer for model {name}.") from e
 
         chktok = tokenizer.encode(CHK_TXT)
         chkhsh = sha256(str(chktok).encode()).hexdigest()

docs/backend/CANN.md

@@ -310,5 +310,7 @@ Specifies the memory pool management strategy:
 
 Controls automatic cleanup of the memory pool. This option is only effective when using the prio or leg memory pool strategies.
 
-## TODO
-- Support more models and data types.
+### GGML_CANN_WEIGHT_NZ
+
+Converting the matmul weight format from ND to NZ can significantly improve performance on the 310I DUO NPU.
+

docs/build-s390x.md

@@ -42,14 +42,14 @@ cmake --build build --config Release -j $(nproc)
     cmake --build build --config Release -j $(nproc)
     ```
 
--   By default, NNPA is enabled when available. To disable it (not recommended):
+-   By default, NNPA is disabled by default. To enable it:
 
     ```bash
     cmake -S . -B build             \
         -DCMAKE_BUILD_TYPE=Release  \
         -DGGML_BLAS=ON              \
         -DGGML_BLAS_VENDOR=OpenBLAS \
-        -DGGML_NNPA=OFF
+        -DGGML_NNPA=ON
 
     cmake --build build --config Release -j $(nproc)
     ```
@@ -84,9 +84,9 @@ All models need to be converted to Big-Endian. You can achieve this in three cas
 
     ![File Type - gguf](https://img.shields.io/badge/File_Type-gguf-fff)
 
-    You can find popular models pre-converted and verified at [s390x Ready Models](https://huggingface.co/collections/taronaeo/s390x-ready-models-672765393af438d0ccb72a08).
+    You can find popular models pre-converted and verified at [s390x Verified Models](https://huggingface.co/collections/taronaeo/s390x-verified-models-672765393af438d0ccb72a08) or [s390x Runnable Models](https://huggingface.co/collections/taronaeo/s390x-runnable-models-686e951824198df12416017e).
 
-    These models have already been converted from `safetensors` to `GGUF Big-Endian` and their respective tokenizers verified to run correctly on IBM z15 and later system.
+    These models have already been converted from `safetensors` to `GGUF` Big-Endian and their respective tokenizers verified to run correctly on IBM z15 and later system.
 
 2. **Convert safetensors model to GGUF Big-Endian directly (recommended)**
 
@@ -94,6 +94,14 @@ All models need to be converted to Big-Endian. You can achieve this in three cas
 
     The model you are trying to convert must be in `safetensors` file format (for example [IBM Granite 3.3 2B](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct)). Make sure you have downloaded the model repository for this case.
 
+    Ensure that you have installed the required packages in advance
+
+    ```bash
+    pip3 install -r requirements.txt
+    ```
+
+    Convert the `safetensors` model to `GGUF`
+
     ```bash
     python3 convert_hf_to_gguf.py \
         --outfile model-name-be.f16.gguf \
@@ -116,7 +124,7 @@ All models need to be converted to Big-Endian. You can achieve this in three cas
 
     ![File Type - gguf](https://img.shields.io/badge/File_Type-gguf-fff)
 
-    The model you are trying to convert must be in `gguf` file format (for example [IBM Granite 3.3 2B](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct-GGUF)). Make sure you have downloaded the model file for this case.
+    The model you are trying to convert must be in `gguf` file format (for example [IBM Granite 3.3 2B GGUF](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct-GGUF)). Make sure you have downloaded the model file for this case.
 
     ```bash
     python3 gguf-py/gguf/scripts/gguf_convert_endian.py model-name.f16.gguf BIG
@@ -141,15 +149,15 @@ Only available in IBM z15 or later system with the `-DGGML_VXE=ON` (turned on by
 
 ### 2. NNPA Vector Intrinsics Acceleration
 
-Only available in IBM z16 or later system with the `-DGGML_NNPA=ON` (turned on when available) compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z15/arch13. In such systems, the APIs can still run but will use a scalar implementation.
+Only available in IBM z16 or later system with the `-DGGML_NNPA=ON` (turned off by default) compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z15/arch13. In such systems, the APIs can still run but will use a scalar implementation.
 
 ### 3. zDNN Accelerator
 
-_Only available in IBM z16 or later system. No direction at the moment._
+_Only available in IBM z16 / LinuxONE 4 or later system. No support currently available._
 
 ### 4. Spyre Accelerator
 
-_No direction at the moment._
+_Only available with IBM z17 / LinuxONE 5 or later system. No support currently available._
 
 ## Performance Tuning
 
@@ -189,6 +197,26 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 
     Answer: Please ensure that your GCC compiler is of minimum GCC 15.1.0 version, and have `binutils` updated to the latest version. If this does not fix the problem, kindly open an issue.
 
+4. Failing to install the `sentencepiece` package using GCC 15+
+
+    Answer: The `sentencepiece` team are aware of this as seen in [this issue](https://github.com/google/sentencepiece/issues/1108).
+
+    As a temporary workaround, please run the installation command with the following environment variables.
+
+    ```bash
+    export CXXFLAGS="-include cstdint"
+    ```
+
+    For example,
+
+    ```bash
+    CXXFLAGS="-include cstdint" pip3 install -r requirements.txt
+    ```
+
+5. `-DGGML_NNPA=ON` generates gibberish output
+
+    Answer: We are aware of this as detailed in [this issue](https://github.com/ggml-org/llama.cpp/issues/14877). Please either try reducing the number of threads, or disable the compile option using `-DGGML_NNPA=OFF`.
+
 ## Getting Help on IBM Z & LinuxONE
 
 1. **Bugs, Feature Requests**
@@ -244,3 +272,5 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 -   ✅ - acceleration available
 -   🚫 - acceleration unavailable, will still run using scalar implementation
 -   ❓ - acceleration unknown, please contribute if you can test it yourself
+
+Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on July 25, 2025.

docs/build.md

@@ -68,6 +68,9 @@ cmake --build build --config Release
       cmake --build build-x64-windows-llvm-release
       ```
 - Curl usage is enabled by default and can be turned off with `-DLLAMA_CURL=OFF`. Otherwise you need to install development libraries for libcurl.
+  - **Debian / Ubuntu:** `sudo apt-get install libcurl4-openssl-dev`  # (or `libcurl4-gnutls-dev` if you prefer GnuTLS)
+  - **Fedora / RHEL / Rocky / Alma:** `sudo dnf install libcurl-devel`
+  - **Arch / Manjaro:** `sudo pacman -S curl`  # includes libcurl headers
 
 ## BLAS Build
 
@@ -305,9 +308,8 @@ On Linux it is possible to use unified memory architecture (UMA) to share main m
 
 ## Vulkan
 
-**Windows**
-
-### w64devkit
+### For Windows Users:
+**w64devkit**
 
 Download and extract [`w64devkit`](https://github.com/skeeto/w64devkit/releases).
 
@@ -334,7 +336,7 @@ cmake -B build -DGGML_VULKAN=ON
 cmake --build build --config Release
 ```
 
-### Git Bash MINGW64
+**Git Bash MINGW64**
 
 Download and install [`Git-SCM`](https://git-scm.com/downloads/win) with the default settings
 
@@ -357,7 +359,8 @@ Now you can load the model in conversation mode using `Vulkan`
 build/bin/Release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv
 ```
 
-### MSYS2
+**MSYS2**
+
 Install [MSYS2](https://www.msys2.org/) and then run the following commands in a UCRT terminal to install dependencies.
 ```sh
 pacman -S git \
@@ -373,9 +376,9 @@ cmake -B build -DGGML_VULKAN=ON
 cmake --build build --config Release
 ```
 
-**With docker**:
+### For Docker users:
 
-You don't need to install Vulkan SDK. It will be installed inside the container.
+You don't need to install the Vulkan SDK. It will be installed inside the container.
 
 ```sh
 # Build the image
@@ -385,32 +388,29 @@ docker build -t llama-cpp-vulkan --target light -f .devops/vulkan.Dockerfile .
 docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
 ```
 
-**Without docker**:
+### For Linux users:
 
-Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html)
+First, follow the official LunarG instructions for the installation and setup of the Vulkan SDK in the [Getting Started with the Linux Tarball Vulkan SDK](https://vulkan.lunarg.com/doc/sdk/latest/linux/getting_started.html) guide.
 
-For example, on Ubuntu 22.04 (jammy), use the command below:
+> [!IMPORTANT]
+> After completing the first step, ensure that you have used the `source` command on the `setup_env.sh` file inside of the Vulkan SDK in your current terminal session. Otherwise, the build won't work. Additionally, if you close out of your terminal, you must perform this step again if you intend to perform a build. However, there are ways to make this persistent. Refer to the Vulkan SDK guide linked in the first step for more information about any of this.
 
+Second, after verifying that you have followed all of the SDK installation/setup steps, use this command to make sure before proceeding:
 ```bash
-wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add -
-wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
-apt update -y
-apt-get install -y vulkan-sdk
-# To verify the installation, use the command below:
 vulkaninfo
 ```
 
-Alternatively your package manager might be able to provide the appropriate libraries.
-For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
-For Fedora 40, you can install `vulkan-devel`, `glslc` and `glslang` packages.
-
-Then, build llama.cpp using the cmake command below:
-
+Then, assuming you have `cd` into your llama.cpp folder and there are no errors with running `vulkaninfo`, you can proceed to build llama.cpp using the CMake commands below:
 ```bash
 cmake -B build -DGGML_VULKAN=1
 cmake --build build --config Release
-# Test the output binary (with "-ngl 33" to offload all layers to GPU)
-./bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4
+```
+
+Finally, after finishing your build, you should be able to do something like this:
+```bash
+# Test the output binary
+# "-ngl 99" should offload all of the layers to GPU for most (if not all) models.
+./build/bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -ngl 99
 
 # You should see in the output, ggml_vulkan detected your GPU. For example:
 # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
@@ -557,6 +557,23 @@ ninja
 
 To read documentation for how to build on Android, [click here](./android.md)
 
+## WebGPU [In Progress]
+
+The WebGPU backend relies on [Dawn](https://dawn.googlesource.com/dawn). Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/docs/quickstart-cmake.md) to install Dawn locally so that llama.cpp can find it using CMake. The currrent implementation is up-to-date with Dawn commit `bed1a61`.
+
+In the llama.cpp directory, build with CMake:
+
+```
+cmake -B build -DGGML_WEBGPU=ON
+cmake --build build --config Release
+```
+
+### Browser Support
+
+WebGPU allows cross-platform access to the GPU from supported browsers. We utilize [Emscripten](https://emscripten.org/) to compile ggml's WebGPU backend to WebAssembly. Emscripten does not officially support WebGPU bindings yet, but Dawn currently maintains its own WebGPU bindings called emdawnwebgpu.
+
+Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/src/emdawnwebgpu/) to download or build the emdawnwebgpu package (Note that it might be safer to build the emdawbwebgpu package locally, so that it stays in sync with the version of Dawn you have installed above). When building using CMake, the path to the emdawnwebgpu port file needs to be set with the flag `EMDAWNWEBGPU_DIR`.
+
 ## IBM Z & LinuxONE
 
 To read documentation for how to build on IBM Z & LinuxONE, [click here](./build-s390x.md)

docs/development/HOWTO-add-model.md

@@ -23,11 +23,19 @@ The convert script reads the model configuration, tokenizer, tensor names+data a
 
 The required steps to implement for an HF model are:
 
-1. Define the model `Model.register` annotation in a new `Model` subclass, example:
+1. Define the model `ModelBase.register` annotation in a new `TextModel` or `MmprojModel` subclass, example:
 
 ```python
-@Model.register("MyModelForCausalLM")
-class MyModel(Model):
+@ModelBase.register("MyModelForCausalLM")
+class MyModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.MYMODEL
+```
+
+or
+
+```python
+@ModelBase.register("MyModelForConditionalGeneration")
+class MyModel(MmprojModel):
     model_arch = gguf.MODEL_ARCH.MYMODEL
 ```
 
@@ -75,9 +83,10 @@ block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
 `transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF.
 
 Depending on the model configuration, tokenizer, code and tensors layout, you will have to override:
-- `Model#set_gguf_parameters`
-- `Model#set_vocab`
-- `Model#write_tensors`
+- `TextModel#set_gguf_parameters`
+- `MmprojModel#set_gguf_parameters`
+- `ModelBase#set_vocab`
+- `ModelBase#modify_tensors`
 
 NOTE: Tensor names must end with `.weight` or `.bias` suffixes, that is the convention and several tools like `quantize` expect this to proceed the weights.
 

docs/docker.md

@@ -110,7 +110,7 @@ You may want to pass in some different `ARGS`, depending on the MUSA environment
 
 The defaults are:
 
-- `MUSA_VERSION` set to `rc4.0.1`
+- `MUSA_VERSION` set to `rc4.2.0`
 
 The resulting images, are essentially the same as the non-MUSA images:
 

docs/multimodal.md

@@ -97,6 +97,9 @@ NOTE: some models may require large context window, for example: `-c 8192`
 # Qwen2-Audio and SeaLLM-Audio
 # note: no pre-quantized GGUF this model, as they have very poor result
 # ref: https://github.com/ggml-org/llama.cpp/pull/13760
+
+# Mistral's Voxtral
+(tool_name) -hf ggml-org/Voxtral-Mini-3B-2507-GGUF
 ```
 
 **Mixed modalities**:

docs/multimodal/minicpmo2.6.md

@@ -29,8 +29,8 @@ cmake --build build --config Release
 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us)
 
 ```bash
-python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-o-2_6
-python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4
+python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-o-2_6
+python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --minicpmv_version 4
 python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model
 
 # quantize int4 version

docs/multimodal/minicpmo4.0.md

@@ -0,0 +1,47 @@
+## MiniCPM-o 4
+
+### Prepare models and code
+
+Download [MiniCPM-o-4](https://huggingface.co/openbmb/MiniCPM-o-4) PyTorch model from huggingface to "MiniCPM-o-4" folder.
+
+
+### Build llama.cpp
+Readme modification time: 20250206
+
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+
+Clone llama.cpp:
+```bash
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+```
+
+Build llama.cpp using `CMake`:
+```bash
+cmake -B build
+cmake --build build --config Release
+```
+
+
+### Usage of MiniCPM-o 4
+
+Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-4-gguf) by us)
+
+```bash
+python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-o-4
+python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-4 --minicpmv-projector ../MiniCPM-o-4/minicpmv.projector --output-dir ../MiniCPM-o-4/ --minicpmv_version 6
+python ./convert_hf_to_gguf.py ../MiniCPM-o-4/model
+
+# quantize int4 version
+./build/bin/llama-quantize ../MiniCPM-o-4/model/ggml-model-f16.gguf ../MiniCPM-o-4/model/ggml-model-Q4_K_M.gguf Q4_K_M
+```
+
+
+Inference on Linux or Mac
+```bash
+# run in single-turn mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-o-4/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-4/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+
+# run in conversation mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-o-4/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-4/mmproj-model-f16.gguf
+```

docs/multimodal/minicpmv2.5.md

@@ -28,8 +28,8 @@ cmake --build build --config Release
 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us)
 
 ```bash
-python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
-python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2
+python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
+python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --minicpmv_version 2
 python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model
 
 # quantize int4 version

docs/multimodal/minicpmv2.6.md

@@ -28,8 +28,8 @@ cmake --build build --config Release
 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) by us)
 
 ```bash
-python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-V-2_6
-python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 3
+python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-V-2_6
+python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --minicpmv_version 3
 python ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model
 
 # quantize int4 version

docs/multimodal/minicpmv4.0.md

@@ -0,0 +1,47 @@
+## MiniCPM-V 4
+
+### Prepare models and code
+
+Download [MiniCPM-V-4](https://huggingface.co/openbmb/MiniCPM-V-4) PyTorch model from huggingface to "MiniCPM-V-4" folder.
+
+
+### Build llama.cpp
+Readme modification time: 20250206
+
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+
+Clone llama.cpp:
+```bash
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+```
+
+Build llama.cpp using `CMake`:
+```bash
+cmake -B build
+cmake --build build --config Release
+```
+
+
+### Usage of MiniCPM-V 4
+
+Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-4-gguf) by us)
+
+```bash
+python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-V-4
+python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-4 --minicpmv-projector ../MiniCPM-V-4/minicpmv.projector --output-dir ../MiniCPM-V-4/ --minicpmv_version 5
+python ./convert_hf_to_gguf.py ../MiniCPM-V-4/model
+
+# quantize int4 version
+./build/bin/llama-quantize ../MiniCPM-V-4/model/ggml-model-f16.gguf ../MiniCPM-V-4/model/ggml-model-Q4_K_M.gguf Q4_K_M
+```
+
+
+Inference on Linux or Mac
+```bash
+# run in single-turn mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4/model/ggml-model-f16.gguf --mmproj ../MiniCPM-V-4/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+
+# run in conversation mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-4/mmproj-model-f16.gguf
+```

docs/ops.md

@@ -2,94 +2,101 @@
 
 List of GGML operations and backend support status.
 
+## How to add a backend to this table:
+
+1. Run `test-backend-ops support --output csv` with your backend name and redirect output to a csv file in `docs/ops/` (e.g., `docs/ops/CUDA.csv`)
+2. Regenerate `/docs/ops.md` via `./scripts/create_ops_docs.py`
+
 Legend:
 - ✅ Fully supported by this backend
 - 🟡 Partially supported by this backend
 - ❌ Not supported by this backend
 
-| Operation | BLAS | CPU | CUDA | Metal |
-|-----------|------|------|------|------|
-|                              ABS | ❌ | ✅ | 🟡 | ❌ |
-|                              ACC | ❌ | ✅ | ✅ | ✅ |
-|                              ADD | ❌ | ✅ | ✅ | 🟡 |
-|                             ADD1 | ❌ | ✅ | ✅ | ❌ |
-|                           ARANGE | ❌ | ✅ | ✅ | ✅ |
-|                           ARGMAX | ❌ | ✅ | ✅ | ✅ |
-|                          ARGSORT | ❌ | ✅ | ✅ | ✅ |
-|                            CLAMP | ❌ | ✅ | ✅ | 🟡 |
-|                           CONCAT | ❌ | ✅ | 🟡 | ✅ |
-|                             CONT | ❌ | ✅ | 🟡 | ✅ |
-|                       CONV_2D_DW | ❌ | ✅ | ✅ | ❌ |
-|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ |
-|                CONV_TRANSPOSE_2D | ❌ | ✅ | ✅ | ❌ |
-|                              COS | ❌ | ✅ | ✅ | 🟡 |
-|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ❌ |
-|                              CPY | ❌ | 🟡 | 🟡 | 🟡 |
-|               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ❌ |
-|          CROSS_ENTROPY_LOSS_BACK | ❌ | ✅ | ✅ | ❌ |
-|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | 🟡 |
-|                              DIV | ❌ | ✅ | ✅ | 🟡 |
-|                              DUP | ❌ | ✅ | 🟡 | 🟡 |
-|                              ELU | ❌ | ✅ | ❌ | 🟡 |
-|                              EXP | ❌ | ✅ | 🟡 | ❌ |
-|                   FLASH_ATTN_EXT | ❌ | ✅ | 🟡 | 🟡 |
-|                GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ❌ |
-|                            GEGLU | ❌ | ✅ | ✅ | 🟡 |
-|                        GEGLU_ERF | ❌ | ✅ | ✅ | 🟡 |
-|                      GEGLU_QUICK | ❌ | ✅ | ✅ | 🟡 |
-|                             GELU | ❌ | ✅ | 🟡 | 🟡 |
-|                         GELU_ERF | ❌ | ✅ | 🟡 | 🟡 |
-|                       GELU_QUICK | ❌ | ✅ | 🟡 | 🟡 |
-|                         GET_ROWS | ❌ | ✅ | 🟡 | ✅ |
-|                    GET_ROWS_BACK | ❌ | 🟡 | 🟡 | ❌ |
-|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ |
-|                      HARDSIGMOID | ❌ | ✅ | 🟡 | ❌ |
-|                        HARDSWISH | ❌ | ✅ | 🟡 | ❌ |
-|                           IM2COL | ❌ | ✅ | ✅ | 🟡 |
-|                          L2_NORM | ❌ | ✅ | ✅ | ✅ |
-|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ |
-|                              LOG | ❌ | ✅ | ✅ | ❌ |
-|                             MEAN | ❌ | ✅ | ✅ | ✅ |
-|                              MUL | ❌ | ✅ | ✅ | 🟡 |
-|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 |
-|                       MUL_MAT_ID | ❌ | ✅ | ✅ | ✅ |
-|                              NEG | ❌ | ✅ | 🟡 | 🟡 |
-|                             NORM | ❌ | ✅ | ✅ | 🟡 |
-|                   OPT_STEP_ADAMW | ❌ | ✅ | ✅ | ❌ |
-|                         OUT_PROD | 🟡 | 🟡 | 🟡 | ❌ |
-|                              PAD | ❌ | ✅ | ✅ | ✅ |
-|                   PAD_REFLECT_1D | ❌ | ✅ | ❌ | ✅ |
-|                          POOL_2D | ❌ | ✅ | ✅ | ✅ |
-|                            REGLU | ❌ | ✅ | ✅ | 🟡 |
-|                             RELU | ❌ | ✅ | 🟡 | 🟡 |
-|                           REPEAT | ❌ | ✅ | 🟡 | ✅ |
-|                      REPEAT_BACK | ❌ | ✅ | ✅ | ❌ |
-|                         RMS_NORM | ❌ | ✅ | ✅ | 🟡 |
-|                    RMS_NORM_BACK | ❌ | ✅ | ✅ | ❌ |
-|                     RMS_NORM_MUL | ❌ | ✅ | ✅ | ✅ |
-|                             ROPE | ❌ | ✅ | ✅ | ✅ |
-|                        ROPE_BACK | ❌ | ✅ | ✅ | ❌ |
-|                        RWKV_WKV6 | ❌ | ✅ | ✅ | ✅ |
-|                        RWKV_WKV7 | ❌ | ✅ | ✅ | ✅ |
-|                            SCALE | ❌ | ✅ | ✅ | ✅ |
-|                              SET | ❌ | ✅ | ❌ | ✅ |
-|                         SET_ROWS | ❌ | 🟡 | ❌ | 🟡 |
-|                              SGN | ❌ | ✅ | 🟡 | ❌ |
-|                          SIGMOID | ❌ | ✅ | 🟡 | 🟡 |
-|                             SILU | ❌ | ✅ | 🟡 | 🟡 |
-|                        SILU_BACK | ❌ | ✅ | ✅ | ❌ |
-|                              SIN | ❌ | ✅ | ✅ | 🟡 |
-|                         SOFT_MAX | ❌ | ✅ | ✅ | ✅ |
-|                    SOFT_MAX_BACK | ❌ | 🟡 | 🟡 | ❌ |
-|                              SQR | ❌ | ✅ | ✅ | 🟡 |
-|                             SQRT | ❌ | ✅ | ✅ | 🟡 |
-|                         SSM_CONV | ❌ | ✅ | ✅ | ✅ |
-|                         SSM_SCAN | ❌ | ✅ | ✅ | ✅ |
-|                             STEP | ❌ | ✅ | 🟡 | ❌ |
-|                              SUB | ❌ | ✅ | ✅ | 🟡 |
-|                              SUM | ❌ | ✅ | ✅ | ❌ |
-|                         SUM_ROWS | ❌ | ✅ | ✅ | ✅ |
-|                           SWIGLU | ❌ | ✅ | ✅ | 🟡 |
-|                             TANH | ❌ | ✅ | 🟡 | 🟡 |
-|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ |
-|                          UPSCALE | ❌ | ✅ | ✅ | 🟡 |
+| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan |
+|-----------|------|------|------|------|------|------|------|------|
+|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
+|                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
+|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
+|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
+|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
+|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 |
+|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ |
+|                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 |
+|                          CONV_2D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ |
+|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
+|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
+|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
+|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
+|                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
+|                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
+|                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 |
+|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
+|                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
+|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 |
+|                GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
+|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
+|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
+|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
+|                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 |
+|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ |
+|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
+|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
+|                           IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ |
+|                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
+|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
+|                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
+|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
+|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ |
+|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
+|                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
+|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
+|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ |
+|                              PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
+|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
+|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 |
+|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
+|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ |
+|                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
+|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                             ROLL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ |
+|                             ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
+|                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
+|                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
+|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                              SET | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                         SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
+|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
+|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
+|                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ |
+|                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ |
+|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
+|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
+|                              SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ |
+|                         SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
+|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 |
+|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ |

examples/diffusion/README.md

@@ -0,0 +1,13 @@
+# Diffusion Text Generation
+
+This directory contains implementations for Diffusion LLMs (DLLMs)
+
+More Info:
+- https://github.com/ggml-org/llama.cpp/pull/14644
+- https://github.com/ggml-org/llama.cpp/pull/14771
+
+
+Example of using Dream architechture: `llama-diffusion-cli -m dream7b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-eps 0.001 --diffusion-algorithm 3 --diffusion-steps 256 --diffusion-visual`
+
+Example of using LLaDA architechture: `llama-diffusion-cli -m llada-8b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-block-length 32 --diffusion-steps 256 --diffusion-visual`
+

examples/diffusion/diffusion-cli.cpp

@@ -5,344 +5,128 @@
 #include "log.h"
 
 #include <limits.h>
-#include <string>
-#include <vector>
+
 #include <algorithm>
 #include <cmath>
+#include <cstring>
 #include <limits>
 #include <random>
+#include <string>
+#include <vector>
 
-typedef bool (*diffusion_step_callback_t)(int32_t step,
-                                          int32_t total_steps,
-                                          const llama_token * tokens,
-                                          int32_t n_tokens,
-                                          void * user_data);
+enum diffusion_algorithm { ORIGIN = 0, ENTROPY_BASED = 1, MARGIN_BASED = 2, RANDOM = 3, CONFIDENCE_BASED = 4 };
 
-enum diffusion_alg {
-    DIFFUSION_ALG_ORIGIN       = 0,
-    DIFFUSION_ALG_MASKGIT_PLUS = 1,
-    DIFFUSION_ALG_TOPK_MARGIN  = 2,
-    DIFFUSION_ALG_ENTROPY      = 3,
+// Unified transfer scheduling methods
+enum transfer_schedule {
+    TIMESTEP_BASED = 0,  // Dream-style: (1.0 - s/t) * remaining
+    BLOCK_BASED    = 1,  // LLaDA-style: process in blocks with get_num_transfer_tokens
 };
 
+typedef bool (*diffusion_step_callback_t)(int32_t             step,
+                                          int32_t             total_steps,
+                                          const llama_token * tokens,
+                                          int32_t             n_tokens,
+                                          void *              user_data);
+
 struct diffusion_params {
-    int32_t                   steps;
-    float                     eps;
-    float                     temperature;
-    float                     top_p;
-    int32_t                   top_k;
-    llama_token               mask_token_id;
-    enum diffusion_alg        algorithm;
-    float                     alg_temp;
-    diffusion_step_callback_t step_callback;
-    void *                    step_callback_user_data;
-    int32_t                   seed;
+    int32_t                   steps                   = 0;
+    float                     temperature             = 0;
+    llama_token               mask_token_id           = LLAMA_TOKEN_NULL;
+    diffusion_step_callback_t step_callback           = nullptr;
+    void *                    step_callback_user_data = nullptr;
+    int32_t                   seed                    = 0;
+    bool                      visual_mode             = false;
+    bool                      shift_logits            = false;  // Shift logits by -1 after decode
+
+    float   top_p = 0.;
+    int32_t top_k = 0.;
+
+    diffusion_algorithm algorithm = CONFIDENCE_BASED;
+    transfer_schedule   schedule  = TIMESTEP_BASED;
+
+    float   cfg_scale        = 0.;     // Config scale for classifier-free guidance
+    float   eps              = 0.;     // Timestep scheduling
+    int32_t block_length     = 0;      // Block size (for block scheduling)
+    float   alg_temp         = 0;      // algorithm temperature (0.0 = deterministic)
+    bool    add_gumbel_noise = false;  // Add gumbel noise to the logits if temp > 0.0
+
+    int32_t max_length = 0;            // Maximum sequence length
 };
 
-
-static diffusion_params diffusion_default_params() {
-    diffusion_params params        = {};
-    params.steps                   = 64;
-    params.eps                     = 1e-3f;
-    params.temperature             = 0.2f;
-    params.top_p                   = 0.95f;
-    params.top_k                   = 0;
-    params.mask_token_id           = LLAMA_TOKEN_NULL;
-    params.algorithm               = DIFFUSION_ALG_ORIGIN;
-    params.alg_temp                = 0.0f;
-    params.step_callback           = nullptr;
-    params.step_callback_user_data = nullptr;
-    params.seed                    = 0;
-    return params;
-}
-
-static void diffusion_generate(llama_context * ctx,
-                        const llama_token * input_tokens,
-                        llama_token * output_tokens,
-                        int32_t n_input,
-                        int32_t max_length,
-                        struct diffusion_params params,
-                        int32_t & n_generated) {
-
-    n_generated = 0;
-    if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || max_length <= n_input) {
-        return;
-    }
-
-    const llama_model * model = llama_get_model(ctx);
-
-    // Initialize with input and pad with mask tokens
-    std::copy(input_tokens, input_tokens + n_input, output_tokens);
-    std::fill(output_tokens + n_input, output_tokens + max_length, params.mask_token_id);
-
-    std::mt19937 rng(params.seed);
-
-    std::vector<float> timesteps(params.steps + 1);
-    for (int32_t i = 0; i <= params.steps; i++) {
-        timesteps[i] = 1.0f - (float) i / params.steps * (1.0f - params.eps);
-    }
-
-    llama_set_causal_attn(ctx, false);
-
-    int32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
-
-    std::vector<llama_token_data> candidates(n_vocab);
-
-    std::vector<llama_token_data> conf_candidates;
-    conf_candidates.reserve(max_length);
-
-    std::vector<int32_t> mask_positions;
-    mask_positions.reserve(max_length);
-
-    struct llama_sampler * sampler = llama_sampler_chain_init(llama_sampler_chain_default_params());
-    if (params.top_k > 0) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_top_k(params.top_k));
-    }
-    if (params.top_p < 1.0f) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_top_p(params.top_p, 1));
-    }
-    if (params.temperature > 0.0f) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_temp(params.temperature));
-    }
-    llama_sampler_chain_add(sampler, llama_sampler_init_dist(params.seed));
-
-    struct llama_sampler * dist_sampler = llama_sampler_init_dist(params.seed);
-
-    llama_batch batch = llama_batch_init(max_length, 0, 1);
-    batch.n_tokens    = max_length;
-
-    int64_t total_sampling_time = 0;
-    int64_t total_time = 0;
-
-    int64_t time_start = ggml_time_us();
-    for (int32_t step = 0; step < params.steps; step++) {
-        if (params.step_callback) {
-            if (!params.step_callback(step, params.steps, output_tokens, max_length, params.step_callback_user_data)) {
-                break;
-            }
-        }
-
-        for (int32_t i = 0; i < max_length; i++) {
-            batch.token[i]     = output_tokens[i];
-            batch.pos[i]       = i;
-            batch.n_seq_id[i]  = 1;
-            batch.seq_id[i][0] = 0;
-            batch.logits[i]    = 1;
-        }
-
-        int ret = llama_decode(ctx, batch);
-        if (ret != 0) {
-            LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, step, ret);
-            break;
-        }
-
-        float * raw_logits = llama_get_logits(ctx);
-        if (!raw_logits) {
-            LOG_ERR("%s: failed to get logits at step %d\n", __func__, step);
-            break;
-        }
-
-        auto get_logits_for_pos = [&](int32_t pos) -> const float * {
-            return pos == 0 ? raw_logits : raw_logits + (pos - 1) * n_vocab;
-        };
-
-        int64_t time_start_sampling = ggml_time_us();
-
-        mask_positions.clear();
-        for (int32_t i = 0; i < max_length; i++) {
-            if (output_tokens[i] == params.mask_token_id) {
-                mask_positions.push_back(i);
-            }
-        }
-
-        if (mask_positions.empty()) {
-            break;
-        }
-
-        float t = timesteps[step];
-        float s = timesteps[step + 1];
-
-        if (params.algorithm == DIFFUSION_ALG_ORIGIN) {
-            float p_transfer = (step < params.steps - 1) ? (1.0f - s / t) : 1.0f;
-
-            for (int32_t pos : mask_positions) {
-                if (std::uniform_real_distribution<float>(0.0f, 1.0f)(rng) < p_transfer) {
-                    const float * pos_logits = get_logits_for_pos(pos);
-                    for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
-                        candidates[token_id].id    = token_id;
-                        candidates[token_id].logit = pos_logits[token_id];
-                        candidates[token_id].p     = 0.0f;
-                    }
-
-                    llama_token_data_array cur_p = {
-                        /* .data       = */ candidates.data(),
-                        /* .size       = */ (size_t) n_vocab,  // Reset size to full vocab
-                        /* .selected   = */ -1,
-                        /* .sorted     = */ false,
-                    };
-
-                    llama_sampler_apply(sampler, &cur_p);
-                    output_tokens[pos] = cur_p.data[cur_p.selected].id;
-                }
-            }
-        } else {
-            std::vector<std::pair<float, int32_t>> confidences;
-            std::vector<llama_token>               sampled_tokens(mask_positions.size());
-
-            for (size_t i = 0; i < mask_positions.size(); i++) {
-                int32_t       pos        = mask_positions[i];
-                const float * pos_logits = get_logits_for_pos(pos);
-
-                for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
-                    candidates[token_id].logit = pos_logits[token_id];
-                    candidates[token_id].p     = 0.0f;
-                    candidates[token_id].id    = token_id;
-                }
-
-                llama_token_data_array cur_p = {
-                    /* .data       = */ candidates.data(),
-                    /* .size       = */ candidates.size(),
-                    /* .selected   = */ -1,
-                    /* .sorted     = */ false,
-                };
-
-                llama_sampler_apply(sampler, &cur_p);
-
-                llama_token sampled_token = cur_p.data[cur_p.selected].id;
-
-                float confidence = 0.0f;
-                if (params.algorithm == DIFFUSION_ALG_ENTROPY) {
-                    const float epsilon = 1e-10f;
-                    for (size_t j = 0; j < cur_p.size; j++) {
-                        float prob = cur_p.data[j].p;
-                        confidence += prob * logf(prob + epsilon);
-                    }
-                } else if (params.algorithm == DIFFUSION_ALG_TOPK_MARGIN) {
-                    confidence = cur_p.data[0].p - cur_p.data[1].p;
-                } else {
-                    confidence = cur_p.data[cur_p.selected].p;
-                }
-
-                sampled_tokens[i] = sampled_token;
-                confidences.emplace_back(confidence, i);
-            }
-
-            int32_t num_transfer =
-                (step < params.steps - 1) ? (int32_t) (mask_positions.size() * (1.0f - s / t)) : mask_positions.size();
-
-            if (num_transfer > 0) {
-                if (params.alg_temp == 0.0f) {
-                    std::partial_sort(confidences.begin(), confidences.begin() + num_transfer, confidences.end(),
-                                      [](const std::pair<float, int32_t> & a, const std::pair<float, int32_t> & b) {
-                                          if (a.first != b.first) {
-                                              return a.first > b.first;
-                                          }
-                                          return a.second < b.second;
-                                      });
-                } else {
-                    conf_candidates.clear();
-
-                    for (int32_t pos = 0; pos < max_length; pos++) {
-                        float conf_logit = -std::numeric_limits<float>::infinity();
-
-                        auto it = std::find(mask_positions.begin(), mask_positions.end(), pos);
-                        if (it != mask_positions.end()) {
-                            size_t mask_idx = std::distance(mask_positions.begin(), it);
-                            conf_logit = confidences[mask_idx].first / params.alg_temp;  // Apply temperature scaling
-                        }
-
-                        conf_candidates.emplace_back(llama_token_data{ pos, conf_logit, 0.0f });
-                    }
-
-                    llama_token_data_array conf_array = {
-                        /* .data       = */ conf_candidates.data(),
-                        /* .size       = */ conf_candidates.size(),
-                        /* .selected   = */ -1,
-                        /* .sorted     = */ false,
-                    };
-
-                    for (int32_t i = 0; i < num_transfer; i++) {
-                        // Apply distribution sampler to get selected index
-                        llama_sampler_apply(dist_sampler, &conf_array);
-                        int selected_idx      = conf_array.selected;
-                        confidences[i].second = conf_candidates[selected_idx].id;
-
-                        conf_candidates[selected_idx].p = 0.0f;
-                        conf_array.selected             = -1;
-                    }
-                }
-
-                if (params.alg_temp == 0.0f) {
-                    // Deterministic - use confidence order
-                    for (int32_t i = 0; i < num_transfer; i++) {
-                        int32_t     mask_idx = confidences[i].second;
-                        int32_t     pos      = mask_positions[mask_idx];
-                        llama_token token    = sampled_tokens[mask_idx];
-                        output_tokens[pos]   = token;
-                    }
-                } else {
-                    for (int32_t i = 0; i < num_transfer; i++) {
-                        int32_t pos = confidences[i].second;
-                        auto    it  = std::find(mask_positions.begin(), mask_positions.end(), pos);
-                        if (it != mask_positions.end()) {
-                            int32_t mask_idx   = std::distance(mask_positions.begin(), it);
-                            output_tokens[pos] = sampled_tokens[mask_idx];
-                        }
-                    }
-                }
-            }
-        }
-        int64_t time_end_sampling = ggml_time_us();
-        total_sampling_time += time_end_sampling - time_start_sampling;
-    }
-    int64_t time_end = ggml_time_us();
-    total_time += time_end - time_start;
-
-    LOG_INF("\ntotal time: %0.2fms, time per step: %0.2fms, sampling time per step: %0.2fms\n",
-            total_time / 1000.0, total_time / 1000.0 / params.steps, total_sampling_time / 1000.0 / params.steps);
-
-
-    llama_batch_free(batch);
-    llama_sampler_free(sampler);
-    llama_sampler_free(dist_sampler);
-
-    n_generated = max_length;
-}
-
-
-
-
-static std::string format_input_text(const std::string & prompt, bool use_chat_template, llama_model * model) {
-    if (!use_chat_template) {
-        return prompt;
-    }
-
-    auto chat_templates = common_chat_templates_init(model, "");
-
-    common_chat_templates_inputs inputs;
-    common_chat_msg              user_msg;
-    user_msg.role                = "user";
-    user_msg.content             = prompt;
-    inputs.add_generation_prompt = true;
-    inputs.messages.push_back(user_msg);
-
-    auto result = common_chat_templates_apply(chat_templates.get(), inputs);
-
-    return result.prompt;
-}
-
 struct callback_data {
-    const common_params_diffusion * diff_params;
-    const llama_vocab *             vocab;
-    int32_t                         n_input;
+    diffusion_params *  diff_params;
+    const llama_vocab * vocab;
+    int32_t             n_input;
 };
 
-static bool diffusion_step_callback(int32_t step,
-                                    int32_t total_steps,
+static float calculate_confidence(const llama_token_data_array & cur_p,
+                                  diffusion_algorithm            algorithm,
+                                  std::mt19937 &                 rng) {
+    switch (algorithm) {
+        case CONFIDENCE_BASED:
+            return cur_p.data[cur_p.selected].p;  // Selected token probability
+
+        case ENTROPY_BASED:
+            {
+                float       entropy = 0.0f;
+                const float epsilon = 1e-10f;
+                for (size_t i = 0; i < cur_p.size; i++) {
+                    float prob = cur_p.data[i].p;
+                    entropy += prob * logf(prob + epsilon);
+                }
+                return -entropy;  // Higher entropy = lower confidence
+            }
+
+        case MARGIN_BASED:
+            return (cur_p.size > 1) ? cur_p.data[0].p - cur_p.data[1].p : cur_p.data[0].p;
+
+        case RANDOM:
+            {
+                std::uniform_real_distribution<float> uniform(0.0f, 1.0f);
+                return uniform(rng);  // Random confidence
+            }
+
+        case ORIGIN:
+            return cur_p.data[cur_p.selected].p;
+
+        default:
+            return 0.0f;
+    }
+}
+
+// Unified transfer count calculation function
+static int32_t calculate_transfer_count(int32_t                      step,
+                                        int32_t                      total_steps,
+                                        int32_t                      remaining_masked,
+                                        transfer_schedule            schedule,
+                                        float                        eps,
+                                        const std::vector<int32_t> & num_transfer_tokens = {}) {
+    switch (schedule) {
+        case TIMESTEP_BASED:
+            {
+                float t          = 1.0f - (float) step / total_steps * (1.0f - eps);
+                float s          = 1.0f - (float) (step + 1) / total_steps * (1.0f - eps);
+                float p_transfer = (step < total_steps - 1) ? (1.0f - s / t) : 1.0f;
+                return (int32_t) (remaining_masked * p_transfer);
+            }
+
+        case BLOCK_BASED:
+            if (!num_transfer_tokens.empty() && step < (int32_t) num_transfer_tokens.size()) {
+                return num_transfer_tokens[step];
+            }
+            return remaining_masked / (total_steps - step);  // Fallback
+
+        default:
+            return remaining_masked / (total_steps - step);
+    }
+}
+
+static bool diffusion_step_callback(int32_t             step,
+                                    int32_t             total_steps,
                                     const llama_token * tokens,
-                                    int32_t n_tokens,
-                                    void * user_data) {
-    (void)user_data;
+                                    int32_t             n_tokens,
+                                    void *              user_data) {
+    (void) user_data;
 
     callback_data * data = static_cast<callback_data *>(user_data);
 
@@ -350,11 +134,11 @@ static bool diffusion_step_callback(int32_t step,
         int progress_percent = (step * 100) / total_steps;
         int progress_bars    = (step * 50) / total_steps;
         LOG_INF("\rdiffusion step: %d/%d [%s%s] %d%%",
-            step,
-            total_steps,
-            std::string(progress_bars, '=').c_str(),
-            std::string(50 - progress_bars, ' ').c_str(),
-            progress_percent);
+                step,
+                total_steps,
+                std::string(progress_bars, '=').c_str(),
+                std::string(50 - progress_bars, ' ').c_str(),
+                progress_percent);
     };
 
     if (data->diff_params->visual_mode) {
@@ -391,6 +175,360 @@ static bool diffusion_step_callback(int32_t step,
     return true;
 }
 
+static void add_gumbel_noise(float * logits, int32_t n_vocab, float temperature, std::mt19937 & rng) {
+    if (temperature == 0.0f) {
+        return;
+    }
+
+    std::uniform_real_distribution<double> uniform(0.0, 1.0);
+    for (int32_t i = 0; i < n_vocab; i++) {
+        double noise        = uniform(rng);
+        // Prevent log(0)
+        noise               = std::max(noise, 1e-20);
+        double gumbel_noise = std::pow(-std::log(noise), temperature);
+        logits[i]           = std::exp(logits[i]) / gumbel_noise;
+    }
+}
+
+static std::vector<int32_t> get_num_transfer_tokens(int32_t mask_count, int32_t steps) {
+    std::vector<int32_t> num_transfer_tokens(steps);
+
+    int32_t base      = mask_count / steps;
+    int32_t remainder = mask_count % steps;
+
+    for (int32_t i = 0; i < steps; i++) {
+        num_transfer_tokens[i] = base + (i < remainder ? 1 : 0);
+    }
+
+    return num_transfer_tokens;
+}
+
+static void diffusion_generate(llama_context *          ctx,
+                               const llama_token *      input_tokens,
+                               llama_token *            output_tokens,
+                               int32_t                  n_input,
+                               const diffusion_params & params,
+                               int32_t &                n_generated) {
+    n_generated = 0;
+    if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || params.max_length <= n_input) {
+        return;
+    }
+
+    const llama_model * model = llama_get_model(ctx);
+
+    // Initialize with input and pad with mask tokens
+    std::copy(input_tokens, input_tokens + n_input, output_tokens);
+    std::fill(output_tokens + n_input, output_tokens + params.max_length, params.mask_token_id);
+
+    std::mt19937 rng(params.seed);
+
+    llama_set_causal_attn(ctx, false);
+
+    int32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
+
+    std::vector<llama_token_data> candidates(n_vocab);
+    std::vector<llama_token_data> conf_candidates;
+    conf_candidates.reserve(params.max_length);
+    std::vector<int32_t> mask_positions;
+    mask_positions.reserve(params.max_length);
+
+    // Setup sampler chain
+    struct llama_sampler * sampler = llama_sampler_chain_init(llama_sampler_chain_default_params());
+    if (params.top_k > 0) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_top_k(params.top_k));
+    }
+    if (params.top_p < 1.0f) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_top_p(params.top_p, 1));
+    }
+    if (params.temperature > 0.0f) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_temp(params.temperature));
+    }
+    llama_sampler_chain_add(sampler, llama_sampler_init_dist(params.seed));
+
+    struct llama_sampler * dist_sampler = llama_sampler_init_dist(params.seed);
+
+    llama_batch batch = llama_batch_init(params.max_length, 0, 1);
+    batch.n_tokens    = params.max_length;
+
+    // Pre-allocate buffers for CFG if needed
+    int32_t                  logits_size = n_vocab * params.max_length;
+    std::vector<float>       cond_logits_buffer;
+    std::vector<llama_token> un_x_buffer;
+    if (params.cfg_scale > 0.0f) {
+        cond_logits_buffer.resize(logits_size);
+        un_x_buffer.resize(params.max_length);
+    }
+
+    // For block-based processing
+    std::vector<int32_t> num_transfer_tokens;
+    int32_t              num_blocks      = 1;
+    int32_t              steps_per_block = params.steps;
+
+    if (params.schedule == BLOCK_BASED) {
+        GGML_ASSERT(params.max_length % params.block_length == 0);
+        num_blocks = params.max_length / params.block_length;
+        GGML_ASSERT(params.steps % num_blocks == 0);
+        steps_per_block = params.steps / num_blocks;
+    }
+
+    std::vector<float> confidence(params.max_length);
+
+    int64_t total_sampling_time = 0;
+    int64_t total_time          = 0;
+    int64_t time_start          = ggml_time_us();
+
+    for (int block_num = 0; block_num < num_blocks; block_num++) {
+        int32_t block_start = (params.schedule == BLOCK_BASED) ? n_input + block_num * params.block_length : 0;
+        int32_t block_end   = (params.schedule == BLOCK_BASED) ?
+                                  std::min(n_input + (block_num + 1) * params.block_length, params.max_length) :
+                                  params.max_length;
+
+        // Count masked tokens in current block for block-based processing
+        if (params.schedule == BLOCK_BASED) {
+            int32_t block_mask_count = 0;
+            for (int i = block_start; i < block_end; i++) {
+                if (output_tokens[i] == params.mask_token_id) {
+                    block_mask_count++;
+                }
+            }
+            num_transfer_tokens = get_num_transfer_tokens(block_mask_count, steps_per_block);
+        }
+
+        for (int32_t step = 0; step < steps_per_block; step++) {
+            int32_t global_step = block_num * steps_per_block + step;
+
+            if (params.step_callback) {
+                if (!params.step_callback(
+                        global_step, params.steps, output_tokens, params.max_length, params.step_callback_user_data)) {
+                    break;
+                }
+            }
+
+            // Setup batch
+            for (int32_t i = 0; i < params.max_length; i++) {
+                batch.token[i]     = output_tokens[i];
+                batch.pos[i]       = i;
+                batch.n_seq_id[i]  = 1;
+                batch.seq_id[i][0] = 0;
+                batch.logits[i]    = 1;
+            }
+
+            float * logits = nullptr;
+
+            if (params.cfg_scale > 0.0f) {
+                int ret = llama_decode(ctx, batch);
+                if (ret != 0) {
+                    LOG_ERR("Failed to generate conditional");
+                    break;
+                }
+                float * cond_logits_ptr = llama_get_logits(ctx);
+                std::memcpy(cond_logits_buffer.data(), cond_logits_ptr, logits_size * sizeof(float));
+
+                // Unconditional generation (mask input)
+                std::copy(output_tokens, output_tokens + params.max_length, un_x_buffer.begin());
+                for (int32_t i = 0; i < n_input; i++) {
+                    un_x_buffer[i] = params.mask_token_id;
+                }
+
+                for (int32_t i = 0; i < params.max_length; i++) {
+                    batch.token[i] = un_x_buffer[i];
+                }
+                ret = llama_decode(ctx, batch);
+                if (ret != 0) {
+                    LOG_ERR("Failed to generate unconditional");
+                    break;
+                }
+                float * uncond_logits = llama_get_logits(ctx);
+
+                // Apply CFG
+                for (int32_t i = 0; i < logits_size; i++) {
+                    cond_logits_buffer[i] =
+                        uncond_logits[i] + (params.cfg_scale + 1.0f) * (cond_logits_buffer[i] - uncond_logits[i]);
+                }
+                logits = cond_logits_buffer.data();
+            } else {
+                int ret = llama_decode(ctx, batch);
+                if (ret != 0) {
+                    LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, global_step, ret);
+                    break;
+                }
+                logits = llama_get_logits(ctx);
+            }
+
+            if (!logits) {
+                LOG_ERR("%s: failed to get logits at step %d\n", __func__, global_step);
+                break;
+            }
+
+            auto get_logits_for_pos = [&](int32_t pos) -> const float * {
+                if (params.shift_logits) {
+                    return pos == 0 ? logits : logits + (pos - 1) * n_vocab;
+                }
+                return logits + (pos) *n_vocab;
+            };
+
+            int64_t time_start_sampling = ggml_time_us();
+
+            mask_positions.clear();
+            for (int32_t i = 0; i < params.max_length; i++) {
+                if (output_tokens[i] == params.mask_token_id) {
+                    // For block-based, only consider current block
+                    if (params.schedule != BLOCK_BASED || (i >= block_start && i < block_end)) {
+                        mask_positions.push_back(i);
+                    }
+                }
+            }
+
+            if (mask_positions.empty()) {
+                break;
+            }
+
+            if (params.add_gumbel_noise && params.temperature > 0.0f) {
+                add_gumbel_noise(logits, n_vocab, params.temperature, rng);
+            }
+
+            if (params.algorithm == ORIGIN) {
+                int32_t transfer_count = calculate_transfer_count(
+                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
+                float p_transfer = (float) transfer_count / mask_positions.size();
+
+                for (int32_t pos : mask_positions) {
+                    if (std::uniform_real_distribution<float>(0.0f, 1.0f)(rng) < p_transfer) {
+                        const float * pos_logits = get_logits_for_pos(pos);
+                        for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
+                            candidates[token_id].id    = token_id;
+                            candidates[token_id].logit = pos_logits[token_id];
+                            candidates[token_id].p     = 0.0f;
+                        }
+
+                        llama_token_data_array cur_p = {
+                            candidates.data(),
+                            (size_t) n_vocab,
+                            -1,
+                            false,
+                        };
+
+                        llama_sampler_apply(sampler, &cur_p);
+                        output_tokens[pos] = cur_p.data[cur_p.selected].id;
+                    }
+                }
+            } else {
+                std::vector<std::pair<float, int32_t>> confidences;
+                std::vector<llama_token>               sampled_tokens(mask_positions.size());
+
+                for (size_t i = 0; i < mask_positions.size(); i++) {
+                    int32_t       pos        = mask_positions[i];
+                    const float * pos_logits = get_logits_for_pos(pos);
+
+                    for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
+                        candidates[token_id].logit = pos_logits[token_id];
+                        candidates[token_id].p     = 0.0f;
+                        candidates[token_id].id    = token_id;
+                    }
+
+                    llama_token_data_array cur_p = {
+                        candidates.data(),
+                        candidates.size(),
+                        -1,
+                        false,
+                    };
+
+                    llama_sampler_apply(sampler, &cur_p);
+                    llama_token sampled_token = cur_p.data[cur_p.selected].id;
+
+                    float conf = calculate_confidence(cur_p, params.algorithm, rng);
+
+                    sampled_tokens[i] = sampled_token;
+                    confidences.emplace_back(conf, i);
+                }
+
+                int32_t transfer_count = calculate_transfer_count(
+                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
+
+                if (transfer_count > 0) {
+                    if (params.alg_temp == 0.0f) {
+                        std::partial_sort(confidences.begin(),
+                                          confidences.begin() + std::min(transfer_count, (int32_t) confidences.size()),
+                                          confidences.end(),
+                                          [](const std::pair<float, int32_t> & a, const std::pair<float, int32_t> & b) {
+                                              if (a.first != b.first) {
+                                                  return a.first > b.first;
+                                              }
+                                              return a.second < b.second;
+                                          });
+
+                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
+                            int32_t mask_idx   = confidences[i].second;
+                            int32_t pos        = mask_positions[mask_idx];
+                            output_tokens[pos] = sampled_tokens[mask_idx];
+                        }
+                    } else {
+                        conf_candidates.clear();
+                        for (size_t i = 0; i < confidences.size(); i++) {
+                            float conf_logit = confidences[i].first / params.alg_temp;
+                            conf_candidates.emplace_back(llama_token_data{ (int32_t) i, conf_logit, 0.0f });
+                        }
+
+                        llama_token_data_array conf_array = {
+                            conf_candidates.data(),
+                            conf_candidates.size(),
+                            -1,
+                            false,
+                        };
+
+                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
+                            llama_sampler_apply(dist_sampler, &conf_array);
+                            int32_t selected_idx = conf_array.selected;
+                            int32_t mask_idx     = selected_idx;
+                            int32_t pos          = mask_positions[mask_idx];
+                            output_tokens[pos]   = sampled_tokens[mask_idx];
+
+                            conf_candidates[selected_idx].p = 0.0f;
+                            conf_array.selected             = -1;
+                        }
+                    }
+                }
+            }
+
+            int64_t time_end_sampling = ggml_time_us();
+            total_sampling_time += time_end_sampling - time_start_sampling;
+        }
+    }
+
+    int64_t time_end = ggml_time_us();
+    total_time += time_end - time_start;
+
+    LOG_INF("\ntotal time: %0.2fms, time per step: %0.2fms, sampling time per step: %0.2fms\n",
+            total_time / 1000.0,
+            total_time / 1000.0 / params.steps,
+            total_sampling_time / 1000.0 / params.steps);
+
+    llama_batch_free(batch);
+    llama_sampler_free(sampler);
+    llama_sampler_free(dist_sampler);
+
+    n_generated = params.max_length;
+}
+
+static std::string format_input_text(const std::string & prompt, bool use_chat_template, llama_model * model) {
+    if (!use_chat_template) {
+        return prompt;
+    }
+
+    auto chat_templates = common_chat_templates_init(model, "");
+
+    common_chat_templates_inputs inputs;
+    common_chat_msg              user_msg;
+    user_msg.role                = "user";
+    user_msg.content             = prompt;
+    inputs.add_generation_prompt = true;
+    inputs.messages.push_back(user_msg);
+
+    auto result = common_chat_templates_apply(chat_templates.get(), inputs);
+
+    return result.prompt;
+}
+
 int main(int argc, char ** argv) {
     ggml_time_init();
 
@@ -400,11 +538,6 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    const char * alg_names[] = { "ORIGIN", "MASKGIT_PLUS", "TOPK_MARGIN", "ENTROPY" };
-    const char * alg_name    = (params.diffusion.algorithm >= 0 && params.diffusion.algorithm <= 3) ?
-                                   alg_names[params.diffusion.algorithm] :
-                                   "UNKNOWN";
-
     common_init();
     llama_backend_init();
 
@@ -421,6 +554,12 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    if (!llama_model_is_diffusion(model)) {
+        LOG_ERR("error: unsupported model for diffusion");
+        llama_model_free(model);
+        return 1;
+    }
+
     llama_context_params ctx_params = llama_context_default_params();
     ctx_params.n_ctx                = params.n_ctx;
     ctx_params.n_batch              = params.n_batch;
@@ -442,10 +581,12 @@ int main(int argc, char ** argv) {
     const llama_vocab * vocab            = llama_model_get_vocab(model);
     std::string         formatted_prompt = format_input_text(params.prompt, params.enable_chat_template, model);
 
-    std::vector<llama_token> input_tokens = common_tokenize(vocab, formatted_prompt,
+    std::vector<llama_token> input_tokens = common_tokenize(vocab,
+                                                            formatted_prompt,
                                                             /*add special tokens*/ true,
                                                             /*parse special*/ true);
-    int                      n_input      = input_tokens.size();
+
+    int n_input = input_tokens.size();
 
     if (n_input >= params.n_ctx) {
         LOG_ERR("error: input too long (%d tokens), max context is %d\n", n_input, params.n_ctx);
@@ -454,44 +595,79 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    struct diffusion_params ldiff_params = diffusion_default_params();
-    ldiff_params.steps                   = params.diffusion.steps;
-    ldiff_params.eps                     = params.diffusion.eps;
-    ldiff_params.temperature             = params.sampling.temp;
-    ldiff_params.top_p                   = params.sampling.top_p;
-    ldiff_params.top_k                   = params.sampling.top_k;
-    ldiff_params.algorithm               = static_cast<enum diffusion_alg>(params.diffusion.algorithm);
-    ldiff_params.alg_temp                = params.diffusion.alg_temp;
-    ldiff_params.seed                    = params.sampling.seed;
-
     llama_token mask_token_id = llama_vocab_mask(vocab);
     GGML_ASSERT(mask_token_id != LLAMA_TOKEN_NULL);
 
-    LOG_INF("diffusion_params: - %-25s llama_token      = %d\n", "mask_token_id", mask_token_id);
-    LOG_INF("diffusion_params: - %-25s u32              = %d\n", "steps", params.diffusion.steps);
-    LOG_INF("diffusion_params: - %-25s f32              = %.6f\n", "eps", params.diffusion.eps);
-    LOG_INF("diffusion_params: - %-25s u32              = %d (%s)\n", "algorithm", params.diffusion.algorithm,
-            alg_name);
-    LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "alg_temp", params.diffusion.alg_temp);
-
-    ldiff_params.mask_token_id = mask_token_id;
-
-    callback_data cb_data = { &params.diffusion, vocab, n_input };
-
-    ldiff_params.step_callback           = diffusion_step_callback;
-    ldiff_params.step_callback_user_data = &cb_data;
-
-    int32_t n_generated = 0;
+    bool visual_mode = params.diffusion.visual_mode;
 
+    int32_t                  n_generated = 0;
     std::vector<llama_token> output_tokens(params.n_ubatch);
-    diffusion_generate(ctx, input_tokens.data(), output_tokens.data(), n_input, params.n_ubatch,
-                       ldiff_params, n_generated);
+
+    struct diffusion_params diff_params;
+
+    char shift_logits_str[8];
+    if (llama_model_meta_val_str(model, "diffusion.shift_logits", shift_logits_str, sizeof(shift_logits_str)) >= 0) {
+        diff_params.shift_logits = (strcmp(shift_logits_str, "true") == 0);
+    } else {
+        diff_params.shift_logits = true;
+    }
+
+    //Use either eps or block length, but not both
+    GGML_ASSERT((params.diffusion.eps == 0) ^ (params.diffusion.block_length == 0));
+
+    if (params.diffusion.eps) {
+        diff_params.schedule = TIMESTEP_BASED;
+        diff_params.eps      = params.diffusion.eps;
+    } else if (params.diffusion.block_length) {
+        diff_params.schedule     = BLOCK_BASED;
+        diff_params.block_length = params.diffusion.block_length;
+    }
+
+    diff_params.mask_token_id    = mask_token_id;
+    diff_params.seed             = params.sampling.seed;
+    diff_params.temperature      = params.sampling.temp;
+    diff_params.steps            = params.diffusion.steps;
+    diff_params.algorithm        = static_cast<diffusion_algorithm>(params.diffusion.algorithm);
+    diff_params.max_length       = params.n_ubatch;
+    diff_params.top_p            = params.sampling.top_p;
+    diff_params.top_k            = params.sampling.top_k;
+    diff_params.visual_mode      = params.diffusion.visual_mode;
+    diff_params.add_gumbel_noise = params.diffusion.add_gumbel_noise;
+
+    diff_params.step_callback           = diffusion_step_callback;
+    callback_data cb_data               = { &diff_params, vocab, n_input };
+    diff_params.step_callback_user_data = &cb_data;
+
+    const char * alg_names[]   = { "ORIGIN", "ENTROPY_BASED", "MARGIN_BASED", "RANDOM", "CONFIDENCE_BASED" };
+    const char * sched_names[] = { "TIMESTEP_BASED", "BLOCK_BASED" };
+    const char * alg_name =
+        (diff_params.algorithm >= 0 && diff_params.algorithm <= 4) ? alg_names[diff_params.algorithm] : "UNKNOWN";
+    const char * sched_name =
+        (diff_params.schedule >= 0 && diff_params.schedule <= 1) ? sched_names[diff_params.schedule] : "UNKNOWN";
+
+    LOG_INF("diffusion_params: - %-25s llama_token      = %d\n", "mask_token_id", mask_token_id);
+    LOG_INF("diffusion_params: - %-25s u32              = %d\n", "steps", diff_params.steps);
+    LOG_INF("diffusion_params: - %-25s u32              = %d\n", "max_length", diff_params.max_length);
+    LOG_INF("diffusion_params: - %-25s enum             = %d (%s)\n", "algorithm", diff_params.algorithm, alg_name);
+    LOG_INF("diffusion_params: - %-25s enum             = %d (%s)\n", "schedule", diff_params.schedule, sched_name);
+    LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "temperature", diff_params.temperature);
+    if (diff_params.schedule == TIMESTEP_BASED) {
+        LOG_INF("diffusion_params: - %-25s f32              = %.6f\n", "eps", diff_params.eps);
+        LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "alg_temp", diff_params.alg_temp);
+    }
+    if (diff_params.schedule == BLOCK_BASED) {
+        LOG_INF("diffusion_params: - %-25s u32              = %d\n", "block_length", diff_params.block_length);
+        LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "cfg_scale", diff_params.cfg_scale);
+    }
+
+    diffusion_generate(ctx, input_tokens.data(), output_tokens.data(), n_input, diff_params, n_generated);
 
     if (n_generated > 0) {
-        if (params.diffusion.visual_mode) {
+        if (visual_mode) {
             //clear screen and move cursor to top-left
             LOG_INF("\033[2J\033[H");
         }
+
         output_tokens.erase(output_tokens.begin(), output_tokens.begin() + n_input);
         std::string output_data = common_detokenize(vocab, output_tokens, false);
         LOG_INF("\n%s\n", output_data.c_str());

examples/embedding/embedding.cpp

@@ -81,6 +81,14 @@ int main(int argc, char ** argv) {
 
     params.embedding = true;
 
+    // if the number of prompts that would be encoded is known in advance, it's more efficient to specify the
+    //   --parallel argument accordingly. for convenience, if not specified, we fallback to unified KV cache
+    //   in order to support any number of prompts
+    if (params.n_parallel == 1) {
+        LOG_INF("%s: n_parallel == 1 -> unified KV cache is enabled\n", __func__);
+        params.kv_unified = true;
+    }
+
     // utilize the full context
     if (params.n_batch < params.n_ctx) {
         LOG_WRN("%s: setting batch size to %d\n", __func__, params.n_ctx);

examples/parallel/parallel.cpp

@@ -184,6 +184,9 @@ int main(int argc, char ** argv) {
     // extra text to insert in each client's prompt in order to make it larger
     const int32_t n_junk = std::max(1, params.n_junk);
 
+    // signed seed, use negative values to indicate different seeds for the different clients
+    const int32_t & sseed = params.sampling.seed;
+
     // init llama.cpp
     llama_backend_init();
     llama_numa_init(params.numa);
@@ -219,12 +222,21 @@ int main(int argc, char ** argv) {
 
     const int n_ctx = llama_n_ctx(ctx);
 
+    if (sseed >= 0) {
+        LOG_INF("%s: initializing all samplers with the same RNG seed: %d (use a negative seed to have different seeds)\n", __func__, sseed);
+    } else {
+        LOG_INF("%s: initializing samplers with different RNG seeds, starting from %d\n", __func__, sseed);
+    }
+
     std::vector<client> clients(n_clients);
     for (size_t i = 0; i < clients.size(); ++i) {
         auto & client = clients[i];
         client.id = i;
         client.smpl = common_sampler_init(model, params.sampling);
-        //params.sampling.seed++;
+
+        if (sseed < 0) {
+            params.sampling.seed--;
+        }
     }
 
     std::vector<llama_token> tokens_system;

examples/save-load-state/save-load-state.cpp

@@ -15,6 +15,12 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    if (params.n_parallel == 1) {
+        // the example uses 2 sequences, so when n_parallel == 1, we need to enable unified kv cache
+        printf("%s: n_parallel == 1, enabling unified kv cache\n", __func__);
+        params.kv_unified = true;
+    }
+
     common_init();
 
     if (params.n_predict < 0) {

examples/speculative-simple/speculative-simple.cpp

@@ -65,7 +65,7 @@ int main(int argc, char ** argv) {
     ctx_dft   = llama_init_dft.context.get();
 
     if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) {
-        return 1;
+        LOG_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params.speculative.model.path.c_str(), params.model.path.c_str());
     }
 
     // Tokenize the prompt
@@ -130,7 +130,10 @@ int main(int argc, char ** argv) {
     params_spec.n_reuse = llama_n_ctx(ctx_dft) - n_draft;
     params_spec.p_min   = p_min;
 
-    struct common_speculative * spec = common_speculative_init(ctx_dft);
+    struct common_speculative * spec = common_speculative_init(ctx_tgt, ctx_dft);
+    for (auto &pair : params.speculative.replacements) {
+        common_speculative_add_replacement_tgt_dft(spec, pair.first.c_str(), pair.second.c_str());
+    }
 
     llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, 1);
 

ggml/CMakeLists.txt

@@ -39,8 +39,9 @@ if (WIN32)
     set(CMAKE_SHARED_MODULE_PREFIX  "")
 endif()
 
-option(BUILD_SHARED_LIBS "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
-option(GGML_BACKEND_DL   "ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS)" OFF)
+option(BUILD_SHARED_LIBS           "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
+option(GGML_BACKEND_DL             "ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS)" OFF)
+set(GGML_BACKEND_DIR "" CACHE PATH "ggml: directory to load dynamic backends from (requires GGML_BACKEND_DL")
 
 #
 # option list
@@ -131,7 +132,7 @@ option(GGML_RVV              "ggml: enable rvv"              ON)
 option(GGML_RV_ZFH           "ggml: enable riscv zfh"        OFF)
 option(GGML_XTHEADVECTOR     "ggml: enable xtheadvector"     OFF)
 option(GGML_VXE              "ggml: enable vxe"              ON)
-option(GGML_NNPA             "ggml: enable nnpa"             ON)
+option(GGML_NNPA             "ggml: enable nnpa"             OFF)  # temp disabled by default, see: https://github.com/ggml-org/llama.cpp/issues/14877
 
 option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
 set(GGML_CPU_ARM_ARCH        "" CACHE STRING "ggml: CPU architecture for ARM")
@@ -174,6 +175,10 @@ option(GGML_HIP_GRAPHS                      "ggml: use HIP graph, experimental,
 option(GGML_HIP_NO_VMM                      "ggml: do not try to use HIP VMM"                 ON)
 option(GGML_HIP_ROCWMMA_FATTN               "ggml: enable rocWMMA for FlashAttention"         OFF)
 option(GGML_HIP_FORCE_ROCWMMA_FATTN_GFX12   "ggml: enable rocWMMA FlashAttention on GFX12"    OFF)
+option(GGML_HIP_MMQ_MFMA                    "ggml: enable MFMA MMA for CDNA in MMQ"           ON)
+option(GGML_HIP_EXPORT_METRICS              "ggml: enable kernel perf metrics output"         OFF)
+option(GGML_MUSA_GRAPHS                     "ggml: use MUSA graph, experimental, unstable"    OFF)
+option(GGML_MUSA_MUDNN_COPY                 "ggml: enable muDNN for accelerated copy"         OFF)
 option(GGML_VULKAN                          "ggml: use Vulkan"                                OFF)
 option(GGML_VULKAN_CHECK_RESULTS            "ggml: run Vulkan op checks"                      OFF)
 option(GGML_VULKAN_DEBUG                    "ggml: enable Vulkan debug output"                OFF)
@@ -181,6 +186,8 @@ option(GGML_VULKAN_MEMORY_DEBUG             "ggml: enable Vulkan memory debug ou
 option(GGML_VULKAN_SHADER_DEBUG_INFO        "ggml: enable Vulkan shader debug info"           OFF)
 option(GGML_VULKAN_VALIDATE                 "ggml: enable Vulkan validation"                  OFF)
 option(GGML_VULKAN_RUN_TESTS                "ggml: run Vulkan tests"                          OFF)
+option(GGML_WEBGPU                          "ggml: use WebGPU"                                OFF)
+option(GGML_WEBGPU_DEBUG                    "ggml: enable WebGPU debug output"                OFF)
 option(GGML_METAL                           "ggml: use Metal"                                 ${GGML_METAL_DEFAULT})
 option(GGML_METAL_USE_BF16                  "ggml: use bfloat if available"                   OFF)
 option(GGML_METAL_NDEBUG                    "ggml: disable Metal debugging"                   OFF)
@@ -270,6 +277,7 @@ set(GGML_PUBLIC_HEADERS
     include/ggml-rpc.h
     include/ggml-sycl.h
     include/ggml-vulkan.h
+    include/ggml-webgpu.h
     include/gguf.h)
 
 set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")

ggml/cmake/ggml-config.cmake.in

@@ -1,152 +1,191 @@
+@PACKAGE_INIT@
 
 @GGML_VARIABLES_EXPANDED@
 
-@PACKAGE_INIT@
-
-set_and_check(GGML_INCLUDE_DIR "@PACKAGE_GGML_INCLUDE_INSTALL_DIR@")
-set_and_check(GGML_LIB_DIR "@PACKAGE_GGML_LIB_INSTALL_DIR@")
-#set_and_check(GGML_BIN_DIR "@PACKAGE_GGML_BIN_INSTALL_DIR@")
-
-find_package(Threads REQUIRED)
-
-find_library(GGML_LIBRARY ggml
-    REQUIRED
-    HINTS ${GGML_LIB_DIR}
-    NO_CMAKE_FIND_ROOT_PATH)
-
-add_library(ggml::ggml UNKNOWN IMPORTED)
-set_target_properties(ggml::ggml
-    PROPERTIES
-        IMPORTED_LOCATION "${GGML_LIBRARY}")
-
-find_library(GGML_BASE_LIBRARY ggml-base
-    REQUIRED
-    HINTS ${GGML_LIB_DIR}
-    NO_CMAKE_FIND_ROOT_PATH)
-
-add_library(ggml::ggml-base UNKNOWN IMPORTED)
-set_target_properties(ggml::ggml-base
-    PROPERTIES
-        IMPORTED_LOCATION "${GGML_BASE_LIBRARY}")
-
+# Find all dependencies before creating any target.
+include(CMakeFindDependencyMacro)
+find_dependency(Threads)
 if (NOT GGML_SHARED_LIB)
+    set(GGML_CPU_INTERFACE_LINK_LIBRARIES "")
+    set(GGML_CPU_INTERFACE_LINK_OPTIONS   "")
+
     if (APPLE AND GGML_ACCELERATE)
-        find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
+        find_library(ACCELERATE_FRAMEWORK Accelerate)
+        if(NOT ACCELERATE_FRAMEWORK)
+            set(${CMAKE_FIND_PACKAGE_NAME}_FOUND 0)
+            return()
+        endif()
         list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES ${ACCELERATE_FRAMEWORK})
     endif()
 
-    if (GGML_OPENMP)
-        find_package(OpenMP REQUIRED)
+    if (GGML_OPENMP_ENABLED)
+        find_dependency(OpenMP)
         list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
     endif()
 
     if (GGML_CPU_HBM)
-        find_library(memkind memkind REQUIRED)
+        find_library(memkind memkind)
+        if(NOT memkind)
+            set(${CMAKE_FIND_PACKAGE_NAME}_FOUND 0)
+            return()
+        endif()
         list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES memkind)
     endif()
 
     if (GGML_BLAS)
-        find_package(BLAS REQUIRED)
-        list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES ${BLAS_LIBRARIES})
-        list(APPEND GGML_CPU_INTERFACE_LINK_OPTIONS   ${BLAS_LINKER_FLAGS})
+        find_dependency(BLAS)
+        list(APPEND GGML_BLAS_INTERFACE_LINK_LIBRARIES ${BLAS_LIBRARIES})
+        list(APPEND GGML_BLAS_INTERFACE_LINK_OPTIONS   ${BLAS_LINKER_FLAGS})
     endif()
 
     if (GGML_CUDA)
-        find_package(CUDAToolkit REQUIRED)
+        set(GGML_CUDA_INTERFACE_LINK_LIBRARIES "")
+        find_dependency(CUDAToolkit)
+        if (GGML_STATIC)
+            list(APPEND GGML_CUDA_INTERFACE_LINK_LIBRARIES $<LINK_ONLY:CUDA::cudart_static>)
+            if (WIN32)
+                list(APPEND GGML_CUDA_INTERFACE_LINK_LIBRARIES $<LINK_ONLY:CUDA::cublas> $<LINK_ONLY:CUDA::cublasLt>)
+            else()
+                list(APPEND GGML_CUDA_INTERFACE_LINK_LIBRARIES $<LINK_ONLY:CUDA::cublas_static> $<LINK_ONLY:CUDA::cublasLt_static>)
+            endif()
+        endif()
+        if (NOT GGML_CUDA_NO_VMM)
+            list(APPEND GGML_CUDA_INTERFACE_LINK_LIBRARIES $<LINK_ONLY:CUDA::cuda_driver>)
+        endif()
     endif()
 
     if (GGML_METAL)
-        find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
-        find_library(METAL_FRAMEWORK    Metal REQUIRED)
-        find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
+        find_library(FOUNDATION_LIBRARY Foundation)
+        find_library(METAL_FRAMEWORK    Metal)
+        find_library(METALKIT_FRAMEWORK MetalKit)
+        if(NOT FOUNDATION_LIBRARY OR NOT METAL_FRAMEWORK OR NOT METALKIT_FRAMEWORK)
+            set(${CMAKE_FIND_PACKAGE_NAME}_FOUND 0)
+            return()
+        endif()
+        set(GGML_METAL_INTERFACE_LINK_LIBRARIES
+            ${FOUNDATION_LIBRARY} ${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK})
+    endif()
 
-        list(APPEND GGML_METAL_INTERFACE_LINK_LIBRARIES
-                    ${FOUNDATION_LIBRARY} ${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK})
+    if (GGML_OPENCL)
+        find_dependency(OpenCL)
+        set(GGML_OPENCL_INTERFACE_LINK_LIBRARIES $<LINK_ONLY:OpenCL::OpenCL>)
     endif()
 
     if (GGML_VULKAN)
-        find_package(Vulkan REQUIRED)
-        list(APPEND GGML_VULKAN_INTERFACE_LINK_LIBRARIES Vulkan::Vulkan)
+        find_dependency(Vulkan)
+        set(GGML_VULKAN_INTERFACE_LINK_LIBRARIES $<LINK_ONLY:Vulkan::Vulkan>)
     endif()
 
     if (GGML_HIP)
-        find_package(hip     REQUIRED)
-        find_package(hipblas REQUIRED)
-        find_package(rocblas REQUIRED)
-        list(APPEND GGML_HIP_INTERFACE_LINK_LIBRARIES hip::host roc::rocblas roc::hipblas)
+        find_dependency(hip)
+        find_dependency(hipblas)
+        find_dependency(rocblas)
+        set(GGML_HIP_INTERFACE_LINK_LIBRARIES hip::host roc::rocblas roc::hipblas)
     endif()
 
     if (GGML_SYCL)
+        set(GGML_SYCL_INTERFACE_LINK_LIBRARIES "")
         find_package(DNNL)
         if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
             list(APPEND GGML_SYCL_INTERFACE_LINK_LIBRARIES DNNL::dnnl)
         endif()
         if (WIN32)
-            find_package(IntelSYCL REQUIRED)
-            find_package(MKL       REQUIRED)
+            find_dependency(IntelSYCL)
+            find_dependency(MKL)
             list(APPEND GGML_SYCL_INTERFACE_LINK_LIBRARIES IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
         endif()
     endif()
 endif()
 
-set(_ggml_all_targets "")
-foreach(_ggml_backend ${GGML_AVAILABLE_BACKENDS})
-    string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}")
-    string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx)
+set_and_check(GGML_INCLUDE_DIR "@PACKAGE_GGML_INCLUDE_INSTALL_DIR@")
+set_and_check(GGML_LIB_DIR "@PACKAGE_GGML_LIB_INSTALL_DIR@")
+#set_and_check(GGML_BIN_DIR "@PACKAGE_GGML_BIN_INSTALL_DIR@")
 
-    find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend}
+if(NOT TARGET ggml::ggml)
+    find_package(Threads REQUIRED)
+
+    find_library(GGML_LIBRARY ggml
         REQUIRED
         HINTS ${GGML_LIB_DIR}
         NO_CMAKE_FIND_ROOT_PATH)
 
-    message(STATUS "Found ${${_ggml_backend_pfx}_LIBRARY}")
-
-    add_library(ggml::${_ggml_backend} UNKNOWN IMPORTED)
-    set_target_properties(ggml::${_ggml_backend}
+    add_library(ggml::ggml UNKNOWN IMPORTED)
+    set_target_properties(ggml::ggml
         PROPERTIES
-            INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}"
-            IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
-            IMPORTED_LOCATION "${${_ggml_backend_pfx}_LIBRARY}"
-            INTERFACE_COMPILE_FEATURES c_std_90
-            POSITION_INDEPENDENT_CODE ON)
+            IMPORTED_LOCATION "${GGML_LIBRARY}")
 
-    string(REGEX MATCH "^ggml-cpu" is_cpu_variant "${_ggml_backend}")
-    if(is_cpu_variant)
-        list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
-        set_target_properties(ggml::${_ggml_backend}
-           PROPERTIES
-               INTERFACE_LINK_LIBRARIES "${GGML_CPU_INTERFACE_LINK_LIBRARIES}")
+    find_library(GGML_BASE_LIBRARY ggml-base
+        REQUIRED
+        HINTS ${GGML_LIB_DIR}
+        NO_CMAKE_FIND_ROOT_PATH)
 
-        if(GGML_CPU_INTERFACE_LINK_OPTIONS)
+    add_library(ggml::ggml-base UNKNOWN IMPORTED)
+    set_target_properties(ggml::ggml-base
+        PROPERTIES
+            IMPORTED_LOCATION "${GGML_BASE_LIBRARY}")
+
+    set(_ggml_all_targets "")
+    if (NOT GGML_BACKEND_DL)
+        foreach(_ggml_backend ${GGML_AVAILABLE_BACKENDS})
+            string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}")
+            string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx)
+
+            find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend}
+                REQUIRED
+                HINTS ${GGML_LIB_DIR}
+                NO_CMAKE_FIND_ROOT_PATH)
+
+            message(STATUS "Found ${${_ggml_backend_pfx}_LIBRARY}")
+
+            add_library(ggml::${_ggml_backend} UNKNOWN IMPORTED)
             set_target_properties(ggml::${_ggml_backend}
                 PROPERTIES
-                    INTERFACE_LINK_OPTIONS "${GGML_CPU_INTERFACE_LINK_OPTIONS}")
-        endif()
+                    INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}"
+                    IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
+                    IMPORTED_LOCATION "${${_ggml_backend_pfx}_LIBRARY}"
+                    INTERFACE_COMPILE_FEATURES c_std_90
+                    POSITION_INDEPENDENT_CODE ON)
 
-    else()
-        list(APPEND ${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
-        set_target_properties(ggml::${_ggml_backend}
-            PROPERTIES
-                INTERFACE_LINK_LIBRARIES "${${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES}")
-
-        if(${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS)
-            set_target_properties(ggml::${_ggml_backend}
+            string(REGEX MATCH "^ggml-cpu" is_cpu_variant "${_ggml_backend}")
+            if(is_cpu_variant)
+                list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
+                set_target_properties(ggml::${_ggml_backend}
                 PROPERTIES
-                    INTERFACE_LINK_OPTIONS "${${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS}")
-        endif()
+                    INTERFACE_LINK_LIBRARIES "${GGML_CPU_INTERFACE_LINK_LIBRARIES}")
+
+                if(GGML_CPU_INTERFACE_LINK_OPTIONS)
+                    set_target_properties(ggml::${_ggml_backend}
+                        PROPERTIES
+                            INTERFACE_LINK_OPTIONS "${GGML_CPU_INTERFACE_LINK_OPTIONS}")
+                endif()
+
+            else()
+                list(APPEND ${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
+                set_target_properties(ggml::${_ggml_backend}
+                    PROPERTIES
+                        INTERFACE_LINK_LIBRARIES "${${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES}")
+
+                if(${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS)
+                    set_target_properties(ggml::${_ggml_backend}
+                        PROPERTIES
+                            INTERFACE_LINK_OPTIONS "${${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS}")
+                endif()
+            endif()
+
+            list(APPEND _ggml_all_targets ggml::${_ggml_backend})
+        endforeach()
     endif()
 
-    list(APPEND _ggml_all_targets ggml::${_ggml_backend})
-endforeach()
+    list(APPEND GGML_INTERFACE_LINK_LIBRARIES ggml::ggml-base "${_ggml_all_targets}")
+    set_target_properties(ggml::ggml
+        PROPERTIES
+            INTERFACE_LINK_LIBRARIES "${GGML_INTERFACE_LINK_LIBRARIES}")
 
-list(APPEND GGML_INTERFACE_LINK_LIBRARIES ggml::ggml-base "${_ggml_all_targets}")
-set_target_properties(ggml::ggml
-    PROPERTIES
-        INTERFACE_LINK_LIBRARIES "${GGML_INTERFACE_LINK_LIBRARIES}")
+    add_library(ggml::all INTERFACE IMPORTED)
+    set_target_properties(ggml::all
+        PROPERTIES
+            INTERFACE_LINK_LIBRARIES "${_ggml_all_targets}")
 
-add_library(ggml::all INTERFACE IMPORTED)
-set_target_properties(ggml::all
-    PROPERTIES
-        INTERFACE_LINK_LIBRARIES "${_ggml_all_targets}")
+endif()
 
 check_required_components(ggml)

ggml/include/ggml-webgpu.h

@@ -0,0 +1,19 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#define GGML_WEBGPU_NAME "WebGPU"
+
+// Needed for examples in ggml
+GGML_BACKEND_API ggml_backend_t ggml_backend_webgpu_init(void);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_webgpu_reg(void);
+
+#ifdef  __cplusplus
+}
+#endif

ggml/include/ggml.h

@@ -304,6 +304,16 @@
     GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
     GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
 
+#define GGML_TENSOR_TERNARY_OP_LOCALS \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne2, src2, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb2, src2, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
 #define GGML_TENSOR_BINARY_OP_LOCALS01 \
     GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
     GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
@@ -395,7 +405,8 @@ extern "C" {
         // GGML_TYPE_IQ4_NL_4_4 = 36,
         // GGML_TYPE_IQ4_NL_4_8 = 37,
         // GGML_TYPE_IQ4_NL_8_8 = 38,
-        GGML_TYPE_COUNT   = 39,
+        GGML_TYPE_MXFP4   = 39, // MXFP4 (1 block)
+        GGML_TYPE_COUNT   = 40,
     };
 
     // precision
@@ -430,6 +441,7 @@ extern "C" {
         GGML_FTYPE_MOSTLY_IQ4_XS  = 22, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
         GGML_FTYPE_MOSTLY_BF16    = 24, // except 1d tensors
+        GGML_FTYPE_MOSTLY_MXFP4   = 25, // except 1d tensors
     };
 
     // available tensor operations:
@@ -438,6 +450,7 @@ extern "C" {
 
         GGML_OP_DUP,
         GGML_OP_ADD,
+        GGML_OP_ADD_ID,
         GGML_OP_ADD1,
         GGML_OP_ACC,
         GGML_OP_SUB,
@@ -557,6 +570,7 @@ extern "C" {
         GGML_GLU_OP_REGLU,
         GGML_GLU_OP_GEGLU,
         GGML_GLU_OP_SWIGLU,
+        GGML_GLU_OP_SWIGLU_OAI,
         GGML_GLU_OP_GEGLU_ERF,
         GGML_GLU_OP_GEGLU_QUICK,
 
@@ -831,6 +845,13 @@ extern "C" {
             struct ggml_tensor  * b,
             enum   ggml_type      type);
 
+    // dst[i0, i1, i2] = a[i0, i1, i2] + b[i0, ids[i1, i2]]
+    GGML_API struct ggml_tensor * ggml_add_id(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * ids);
+
     GGML_API struct ggml_tensor * ggml_add1(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -1198,6 +1219,13 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
+    GGML_API struct ggml_tensor * ggml_swiglu_oai(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            float                 alpha,
+            float                 limit);
+
     // normalize along rows
     GGML_API struct ggml_tensor * ggml_norm(
             struct ggml_context * ctx,
@@ -1570,6 +1598,10 @@ extern "C" {
             float                 scale,
             float                 max_bias);
 
+    GGML_API void ggml_soft_max_add_sinks(
+            struct ggml_tensor * a,
+            struct ggml_tensor * sinks);
+
     GGML_API struct ggml_tensor * ggml_soft_max_ext_back(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -2052,6 +2084,10 @@ extern "C" {
     GGML_API enum ggml_prec ggml_flash_attn_ext_get_prec(
             const struct ggml_tensor * a);
 
+    GGML_API void ggml_flash_attn_ext_add_sinks(
+            struct ggml_tensor * a,
+            struct ggml_tensor * sinks);
+
     // TODO: needs to be adapted to ggml_flash_attn_ext
     GGML_API struct ggml_tensor * ggml_flash_attn_back(
            struct ggml_context * ctx,

ggml/src/CMakeLists.txt

@@ -214,6 +214,13 @@ add_library(ggml
             ggml-backend-reg.cpp)
 add_library(ggml::ggml ALIAS ggml)
 
+if (GGML_BACKEND_DIR)
+    if (NOT GGML_BACKEND_DL)
+        message(FATAL_ERROR "GGML_BACKEND_DIR requires GGML_BACKEND_DL")
+    endif()
+    target_compile_definitions(ggml PUBLIC GGML_BACKEND_DIR="${GGML_BACKEND_DIR}")
+endif()
+
 target_link_libraries(ggml PUBLIC ggml-base)
 
 if (CMAKE_SYSTEM_NAME MATCHES "Linux")
@@ -227,7 +234,11 @@ function(ggml_add_backend_library backend)
         set_target_properties(${backend} PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
         target_compile_definitions(${backend} PRIVATE GGML_BACKEND_DL)
         add_dependencies(ggml ${backend})
-        install(TARGETS ${backend} LIBRARY DESTINATION ${CMAKE_INSTALL_BINDIR})
+        if (GGML_BACKEND_DIR)
+            install(TARGETS ${backend} LIBRARY DESTINATION ${GGML_BACKEND_DIR})
+        else()
+            install(TARGETS ${backend} LIBRARY DESTINATION ${CMAKE_INSTALL_BINDIR})
+        endif()
     else()
         add_library(${backend} ${ARGN})
         target_link_libraries(ggml PUBLIC ${backend})
@@ -370,6 +381,7 @@ ggml_add_backend(MUSA)
 ggml_add_backend(RPC)
 ggml_add_backend(SYCL)
 ggml_add_backend(Vulkan)
+ggml_add_backend(WebGPU)
 ggml_add_backend(OpenCL)
 
 foreach (target ggml-base ggml)

ggml/src/ggml-alloc.c

@@ -22,21 +22,6 @@ static bool ggml_is_view(const struct ggml_tensor * t) {
     return t->view_src != NULL;
 }
 
-static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
-    if (a->type != b->type) {
-        return false;
-    }
-    for (int i = 0; i < GGML_MAX_DIMS; i++) {
-        if (a->ne[i] != b->ne[i]) {
-            return false;
-        }
-        if (a->nb[i] != b->nb[i]) {
-            return false;
-        }
-    }
-    return true;
-}
-
 // ops that return true for this function must not use restrict pointers for their backend implementations
 static bool ggml_op_can_inplace(enum ggml_op op) {
     switch (op) {
@@ -44,6 +29,7 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
         case GGML_OP_DIAG_MASK_ZERO:
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_ADD:
+        case GGML_OP_ADD_ID:
         case GGML_OP_ADD1:
         case GGML_OP_SUB:
         case GGML_OP_MUL:

ggml/src/ggml-backend-reg.cpp

@@ -45,6 +45,10 @@
 #include "ggml-vulkan.h"
 #endif
 
+#ifdef GGML_USE_WEBGPU
+#include "ggml-webgpu.h"
+#endif
+
 #ifdef GGML_USE_OPENCL
 #include "ggml-opencl.h"
 #endif
@@ -173,6 +177,9 @@ struct ggml_backend_registry {
 #ifdef GGML_USE_VULKAN
         register_backend(ggml_backend_vk_reg());
 #endif
+#ifdef GGML_USE_WEBGPU
+        register_backend(ggml_backend_webgpu_reg());
+#endif
 #ifdef GGML_USE_OPENCL
         register_backend(ggml_backend_opencl_reg());
 #endif
@@ -491,6 +498,9 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
 
     std::vector<fs::path> search_paths;
     if (user_search_path == nullptr) {
+#ifdef GGML_BACKEND_DIR
+        search_paths.push_back(fs::u8path(GGML_BACKEND_DIR));
+#endif
         // default search paths: executable directory, current directory
         search_paths.push_back(get_executable_path());
         search_paths.push_back(fs::current_path());

ggml/src/ggml-backend.cpp

@@ -352,21 +352,6 @@ ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend) {
 
 // backend copy
 
-static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
-    if (a->type != b->type) {
-        return false;
-    }
-    for (int i = 0; i < GGML_MAX_DIMS; i++) {
-        if (a->ne[i] != b->ne[i]) {
-            return false;
-        }
-        if (a->nb[i] != b->nb[i]) {
-            return false;
-        }
-    }
-    return true;
-}
-
 void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) {
     GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
 
@@ -662,6 +647,7 @@ struct ggml_backend_sched {
     // pipeline parallelism support
     int n_copies;
     int cur_copy;
+    int next_copy;
     ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
     struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
     int n_graph_inputs;
@@ -1085,6 +1071,11 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                 }
             }
         }
+        // if the node is still unassigned, assign it to the first backend that supports it
+        for (int b = 0; b < sched->n_backends && *cur_backend_id == -1; b++) {
+            ggml_backend_sched_set_if_supported(sched, node, b, cur_backend_id);
+        }
+        GGML_ASSERT(*cur_backend_id != -1);
     }
 
     // pass 5: split graph, find tensors that need to be copied
@@ -1112,7 +1103,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
             const int node_backend_id = tensor_backend_id(node);
 
-            assert(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
+            GGML_ASSERT(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
 
             // check if we should start a new split based on the sources of the current node
             bool need_new_split = false;
@@ -1170,7 +1161,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
                 size_t src_id = hash_id(src);
                 const int src_backend_id = sched->hv_tensor_backend_ids[src_id];
-                assert(src_backend_id != -1); // all inputs should be assigned by now
+                GGML_ASSERT(src_backend_id != -1); // all inputs should be assigned by now
 
                 if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) {
                     if (tensor_id_copy(src_id, src_backend_id, 0) == NULL) {
@@ -1448,8 +1439,6 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
         }
     }
 
-    sched->cur_copy = (sched->cur_copy + 1) % sched->n_copies;
-
     return GGML_STATUS_SUCCESS;
 }
 
@@ -1550,10 +1539,10 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
 bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
     GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
 
-    ggml_backend_sched_split_graph(sched, measure_graph);
-
     ggml_backend_sched_synchronize(sched);
 
+    ggml_backend_sched_split_graph(sched, measure_graph);
+
     if (!ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) {
         return false;
     }
@@ -1565,6 +1554,10 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph *
 
 bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
     GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + graph->n_leafs);
+    GGML_ASSERT(!sched->is_alloc);
+
+    sched->cur_copy = sched->next_copy;
+    sched->next_copy = (sched->next_copy + 1) % sched->n_copies;
 
     ggml_backend_sched_split_graph(sched, graph);
 
@@ -1605,7 +1598,7 @@ void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
         // if the graph is not already allocated, always use copy 0 after a synchronization
         // this ensures that during generation the same copy is used every time,
         // which avoids changes in the graph that could cause CUDA or other graphs to be disabled
-        sched->cur_copy = 0;
+        sched->next_copy = 0;
     }
 }
 

ggml/src/ggml-cann/CMakeLists.txt

@@ -31,6 +31,13 @@ string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}")
 set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}")
 string(TOUPPER ${SOC_TYPE_COMPILE_OPTION} SOC_TYPE_COMPILE_OPTION)
 message(STATUS "CANN: SOC_VERSION =  ${SOC_VERSION}")
+option(USE_ACL_GRAPH "Enable CANN graph execution (ACL graph mode)" OFF)
+
+if(USE_ACL_GRAPH AND (SOC_TYPE_MAJOR_SN STREQUAL "310P" OR SOC_TYPE_COMPILE_OPTION STREQUAL "ASCEND_310P"))
+    message(FATAL_ERROR
+        "CANN Graph (ACL graph mode) is not supported on 310P devices. "
+        "Please build with -DUSE_ACL_GRAPH=OFF or use a supported SOC.")
+endif()
 
 if (CANN_INSTALL_DIR)
     # Only Support Linux.
@@ -68,6 +75,13 @@ if (CANN_INSTALL_DIR)
 
     target_compile_definitions(ggml-cann PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}")
 
+    if (USE_ACL_GRAPH)
+        target_compile_definitions(ggml-cann PRIVATE USE_ACL_GRAPH)
+        message(STATUS "CANN: USE_ACL_GRAPH is enabled.")
+    else()
+        message(STATUS "CANN: USE_ACL_GRAPH is disabled.")
+    endif()
+
     message(STATUS "CANN: CANN_INCLUDE_DIRS =  ${CANN_INCLUDE_DIRS}")
     message(STATUS "CANN: CANN_LIBRARIES =  ${CANN_LIBRARIES}")
 else()

ggml/src/ggml-cann/acl_tensor.cpp

@@ -77,6 +77,8 @@ aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne,
     for (int i = 0; i < final_dims; i++) {
         acl_storage_len += (acl_ne[i] - 1) * acl_stride[i];
     }
+    size_t elem_offset = offset / ggml_element_size(tensor);
+    acl_storage_len += elem_offset;
 
     // Reverse ne and stride.
     std::reverse(acl_ne, acl_ne + final_dims);
@@ -84,7 +86,7 @@ aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne,
 
     aclTensor* acl_tensor = aclCreateTensor(
         acl_ne, final_dims, ggml_cann_type_mapping(tensor->type), acl_stride,
-        offset / ggml_element_size(tensor), format, &acl_storage_len, 1,
+        elem_offset, format, &acl_storage_len, 1,
         tensor->data);
 
     return acl_tensor;

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -68,6 +68,8 @@
 #include <aclnnop/aclnn_grouped_matmul_v3.h>
 #include <aclnnop/aclnn_fused_infer_attention_score_v2.h>
 #include <aclnnop/aclnn_zero.h>
+#include <aclnnop/aclnn_index_copy.h>
+#include <aclnnop/aclnn_index_select.h>
 #include <float.h>
 
 #include <cmath>
@@ -99,7 +101,7 @@ void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, aclT
     }
 }
 
-void ggml_cann_unary_op(
+void ggml_cann_op_unary(
     std::function<void(ggml_backend_cann_context&, aclTensor*, aclTensor*)> unary_op,
     ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     ggml_tensor* src = dst->src[0];
@@ -111,6 +113,42 @@ void ggml_cann_unary_op(
     ggml_cann_release_resources(ctx, acl_src, acl_dst);
 }
 
+void ggml_cann_op_unary_gated(
+    std::function<void(ggml_backend_cann_context&, aclTensor*, aclTensor*)> unary_op,
+    ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];
+
+    GGML_ASSERT(ggml_is_contiguous_1(src0));
+    GGML_ASSERT(ggml_is_contiguous_1(dst));
+    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
+
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+    aclTensor *acl_src0 = nullptr, *acl_src1 = nullptr;
+    if(src1) {
+        GGML_ASSERT(ggml_is_contiguous_1(src1));
+        GGML_ASSERT(src0->type == src1->type);
+
+        acl_src0 = ggml_cann_create_tensor(src0);
+        acl_src1 = ggml_cann_create_tensor(src1);
+    } else {
+        int64_t ne[] = {src0->ne[0] / 2, src0->ne[1], src0->ne[2], src0->ne[3]};
+        size_t nb[] = {src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]};
+        acl_src0 = ggml_cann_create_tensor(src0, ne, nb, GGML_MAX_DIMS, ACL_FORMAT_ND, 0);
+        acl_src1 = ggml_cann_create_tensor(src0, ne, nb, GGML_MAX_DIMS, ACL_FORMAT_ND, ne[0] * ggml_element_size(src0));
+        if (swapped) {
+            std::swap(acl_src0, acl_src1);
+        }
+    }
+
+    unary_op(ctx, acl_src0, acl_dst);
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, acl_dst, acl_src1);
+
+    ggml_cann_release_resources(ctx, acl_src0, acl_dst);
+    if(src1)
+        ggml_cann_release_resources(ctx, acl_src1);
+}
+
 /**
  * @brief Repeats elements of a tensor along each dimension according to the
  * specified repeat array.
@@ -1578,50 +1616,97 @@ void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 }
 
 /**
- * @brief Performs embedding operation on a 4D tensor using the CANN backend.
+ * @brief Performs index select operation on a 4D tensor using the CANN backend.
  *
- * This function extracts slices from the source tensor (`src_buffer`),
- * index tensor (`index`), and destination tensor (`dst`), and performs an
- * embedding operation on them. The embedding operation is applied by iterating
- * over the last two dimensions of the source tensor, creating the necessary
- * tensors for the source, index, and output, and executing the embedding operation.
+ * This function applies the `IndexSelect` operation along a specific dimension
+ * of the source tensor (`src_buffer`) using the indices from the index tensor (`index`).
+ * It iterates over the last two dimensions of the source tensor, creates the corresponding
+ * CANN tensors for the source, index, and output slices, and executes the `IndexSelect`
+ * operation for each slice.
  *
  * @param ctx The context for CANN backend operations.
- * @param src_buffer The source buffer holding the data for the source tensor.
+ * @param src_buffer The source buffer containing the 4D input tensor data.
  * @param src_ne The dimensions of the source tensor.
  * @param src_nb The strides (byte offsets) of the source tensor.
- * @param index The index tensor used in the embedding operation.
- * @param dst The destination tensor where the result will be stored.
+ * @param dst_buffer The destination buffer where the output tensor data will be written.
+ * @param dst_ne The dimensions of the destination tensor.
+ * @param dst_nb The strides (byte offsets) of the destination tensor.
+ * @param index The index tensor specifying the indices to select from the source tensor.
+ * @param type The data type of the source and destination tensors.
  */
-static void aclnn_embedding_4d(ggml_backend_cann_context& ctx, void* src_buffer,
-                            int64_t* src_ne, size_t* src_nb, ggml_tensor* index,
-                            ggml_tensor* dst) {
+static void aclnn_index_select_4d(ggml_backend_cann_context& ctx,
+                                void* src_buffer,int64_t* src_ne, size_t* src_nb,
+                                void* dst_buffer, int64_t* dst_ne, size_t* dst_nb,
+                                ggml_tensor* index, ggml_type type) {
     for (int64_t i = 0; i < src_ne[3]; i++) {
         for (int64_t j = 0; j < src_ne[2]; j++) {
             // src
-            int64_t acl_src_ne[2] = {src_ne[0], src_ne[1]};
-            size_t acl_src_nb[2] = {src_nb[0], src_nb[1]};
             aclTensor* acl_src_tensor = ggml_cann_create_tensor(
                 (char*)src_buffer + i * src_nb[3] + j * src_nb[2],
-                ggml_cann_type_mapping(dst->type), ggml_element_size(dst),
-                acl_src_ne, acl_src_nb, 2);
+                ggml_cann_type_mapping(type), ggml_type_size(type),
+                src_ne, src_nb, 2);
 
             // index
-            int64_t acl_index_ne[1] = {index->ne[0]};
-            size_t acl_index_nb[1] = {index->nb[0]};
             aclTensor* acl_index = ggml_cann_create_tensor(
-                (char*)index->data + i * index->nb[2] + j * index->nb[1],
+                (char*)index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1],
                 ggml_cann_type_mapping(index->type), ggml_element_size(index),
-                acl_index_ne, acl_index_nb, 1);
+                index->ne, index->nb, 1);
 
             // out
-            int64_t acl_out_ne[2] = {dst->ne[0], dst->ne[1]};
-            size_t acl_out_nb[2] = {dst->nb[0], dst->nb[1]};
             aclTensor* acl_out = ggml_cann_create_tensor(
-                (char*)dst->data + i * dst->nb[3] + j * dst->nb[2],
-                ggml_cann_type_mapping(dst->type), ggml_element_size(dst),
-                acl_out_ne, acl_out_nb, 2);
-            GGML_CANN_CALL_ACLNN_OP(ctx, Embedding, acl_src_tensor, acl_index, acl_out);
+                (char*)dst_buffer + i * dst_nb[3] + j * dst_nb[2],
+                ggml_cann_type_mapping(type), ggml_type_size(type),
+                dst_ne, dst_nb, 2);
+            GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, acl_src_tensor, 0, acl_index, acl_out);
+            ggml_cann_release_resources(ctx, acl_src_tensor, acl_index, acl_out);
+        }
+    }
+}
+
+/**
+ * @brief Performs inplace index copy operation on a 4D tensor using the CANN backend.
+ *
+ * This function applies the `IndexCopy` operation along a specific dimension of the
+ * destination tensor (`dst_buffer`) by copying elements from the source tensor (`src_buffer`)
+ * to positions specified by the index tensor (`index`).
+ * It iterates over the last two dimensions of the tensors, creates the corresponding
+ * CANN tensors for source, index, and destination slices, and performs the index copy
+ * operation for each slice.
+ *
+ * @param ctx The context for CANN backend operations.
+ * @param src_buffer The source buffer containing the 4D input tensor data to be copied.
+ * @param src_ne The dimensions of the source tensor.
+ * @param src_nb The strides (byte offsets) of the source tensor.
+ * @param dst_buffer The destination buffer where values will be copied to.
+ * @param dst_ne The dimensions of the destination tensor.
+ * @param dst_nb The strides (byte offsets) of the destination tensor.
+ * @param index The index tensor specifying target positions in the destination tensor.
+ * @param type The data type of the source and destination tensors.
+ */
+static void aclnn_index_copy_4d(ggml_backend_cann_context& ctx,
+                                void* src_buffer,int64_t* src_ne, size_t* src_nb,
+                                void* dst_buffer, int64_t* dst_ne, size_t* dst_nb,
+                                ggml_tensor* index, ggml_type type) {
+    for (int64_t i = 0; i < src_ne[3]; i++) {
+        for (int64_t j = 0; j < src_ne[2]; j++) {
+            // src
+            aclTensor* acl_src_tensor = ggml_cann_create_tensor(
+                (char*)src_buffer + i * src_nb[3] + j * src_nb[2],
+                ggml_cann_type_mapping(type), ggml_type_size(type),
+                src_ne, src_nb, 2);
+
+            // index
+            aclTensor* acl_index = ggml_cann_create_tensor(
+                (char*)index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1],
+                ggml_cann_type_mapping(index->type), ggml_element_size(index),
+                index->ne, index->nb, 1);
+
+            // out
+            aclTensor* acl_out = ggml_cann_create_tensor(
+                (char*)dst_buffer + i * dst_nb[3] + j * dst_nb[2],
+                ggml_cann_type_mapping(type), ggml_type_size(type),
+                dst_ne, dst_nb, 2);
+            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexCopy, acl_out, 0, acl_index, acl_src_tensor);
             ggml_cann_release_resources(ctx, acl_src_tensor, acl_index, acl_out);
         }
     }
@@ -1633,8 +1718,9 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
     switch (src0->type) {
         case GGML_TYPE_F32: {
-            aclnn_embedding_4d(ctx, src0->data, src0->ne, src0->nb, src1,
-                                   dst);
+            aclnn_index_select_4d(ctx, src0->data, src0->ne, src0->nb,
+                                dst->data, dst->ne, dst->nb,
+                                src1, dst->type);
             break;
         }
         case GGML_TYPE_F16: {
@@ -1651,8 +1737,9 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                 src_trans_buffer, ACL_FLOAT, ggml_type_size(dst->type),
                 src0->ne, src_trans_nb, GGML_MAX_DIMS);
             aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type));
-            aclnn_embedding_4d(ctx, src_trans_buffer, src0->ne,
-                                   src_trans_nb, src1, dst);
+            aclnn_index_select_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb,
+                                dst->data, dst->ne, dst->nb,
+                                src1, dst->type);
             ggml_cann_release_resources(ctx, acl_src0, src_trans_tensor);
             break;
         }
@@ -1712,8 +1799,10 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                 dequant_nb[i] = dequant_nb[i - 1] * src0->ne[i - 1];
             }
 
-            aclnn_embedding_4d(ctx, dequant_buffer_allocator.get(),
-                                   dequant_ne, dequant_nb, src1, dst);
+            aclnn_index_select_4d(ctx, dequant_buffer_allocator.get(),
+                                   dequant_ne, dequant_nb,
+                                   dst->data, dst->ne, dst->nb,
+                                   src1, dst->type);
 
             ggml_cann_release_resources(ctx, dequant_tensor);
             break;
@@ -1724,6 +1813,43 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     }
 }
 
+void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];  // src
+    ggml_tensor* src1 = dst->src[1];  // index
+
+    switch (dst->type) {
+        case GGML_TYPE_F32: {
+            aclnn_index_copy_4d(ctx, src0->data, src0->ne, src0->nb,
+                                dst->data, dst->ne, dst->nb,
+                                src1, dst->type);
+            break;
+        }
+        case GGML_TYPE_F16: {
+            aclTensor* acl_src0 = ggml_cann_create_tensor(src0);
+            ggml_cann_pool_alloc src_buffer_allocator(
+                ctx.pool(), ggml_nelements(src0) * sizeof(uint16_t));
+            void* src_trans_buffer = src_buffer_allocator.get();
+            size_t src_trans_nb[GGML_MAX_DIMS];
+            src_trans_nb[0] = sizeof(uint16_t);
+            for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
+            }
+            aclTensor* src_trans_tensor = ggml_cann_create_tensor(
+                src_trans_buffer, ACL_FLOAT16, ggml_type_size(dst->type),
+                src0->ne, src_trans_nb, GGML_MAX_DIMS);
+            aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type));
+            aclnn_index_copy_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb,
+                                dst->data, dst->ne, dst->nb,
+                                src1, dst->type);
+            ggml_cann_release_resources(ctx, acl_src0, src_trans_tensor);
+            break;
+        }
+        default:
+            GGML_ABORT("Unsupported tensor type for GGML_OP_SET_ROWS");
+            break;
+    }
+}
+
 /**
  * @brief Repeats elements of a tensor along a specified dimension.
  *
@@ -1785,8 +1911,25 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx,
     size_t transpose_nb[] = {bcast_weight_nb[1], bcast_weight_nb[0],
                              bcast_weight_nb[2], bcast_weight_nb[3],
                              bcast_weight_nb[4], bcast_weight_nb[5]};
-    aclTensor* acl_weight_tensor =
-        ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims);
+    aclTensor* acl_weight_tensor;
+
+    // Only check env once.
+    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
+    if (weight_to_nz && is_matmul_weight(weight)) {
+        int64_t acl_stride[2] = {1, transpose_ne[1]};
+
+        // Reverse ne.
+        std::reverse(transpose_ne, transpose_ne + n_dims);
+
+        std::vector<int64_t> storageDims = {transpose_ne[0], transpose_ne[1]};
+
+        acl_weight_tensor = aclCreateTensor(
+            transpose_ne, n_dims, ggml_cann_type_mapping(weight->type), acl_stride,
+            0, ACL_FORMAT_FRACTAL_NZ, storageDims.data(), 2, weight->data);
+    } else {
+        acl_weight_tensor =
+            ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_ND);
+    }
     aclTensor* acl_dst =
         ggml_cann_create_tensor(dst, bcast_dst_ne, bcast_dst_nb, n_dims);
 

ggml/src/ggml-cann/aclnn_ops.h

@@ -23,6 +23,7 @@
 #ifndef CANN_ACLNN_OPS
 #define CANN_ACLNN_OPS
 
+#include <unordered_set>
 #include <functional>
 #include <aclnnop/aclnn_abs.h>
 #include <aclnnop/aclnn_neg.h>
@@ -423,15 +424,25 @@ void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst);
  *
  * @details This function retrieves rows from a source tensor src0 according to
  *          the indices provided in another tensor src1 and stores the result in
- *          a destination tensor (\p dst). It supports different data types
- *          including F32, F16, Q4_0, and Q8_0.
+ *          a destination tensor (\p dst).
  *
  * @param ctx The backend CANN context for executing operations.
  * @param dst The destination tensor where the extracted rows will be stored.
- *            dst->op is `GGML_OP_GET_ROWS`.
  */
 void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst);
 
+/**
+ * @brief   Writes specific rows into a tensor at positions specified by indices.
+ *
+ * @details This function copies rows from a source tensor into a destination
+ *          tensor (\p dst) at the positions indicated by the indices in another
+ *          tensor.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor where the specified rows will be updated.
+ */
+void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
 /**
  * @brief   Executes matrix multiplication for the given tensor.
  *
@@ -1020,6 +1031,37 @@ inline void ggml_cann_async_memset(ggml_backend_cann_context & ctx, void * buffe
  */
 void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst);
 
+/**
+ * @brief   Check whether a tensor is a weight tensor for matrix multiplication.
+ *
+ * @details Checks whether the given tensor serves as weight parameters in matrix multiplication operations,
+ *          typically within neural network layers. The function maintains a static set of canonical weight
+ *          naming suffixes from Transformer-based architectures. Uses substring matching to identify weight
+ *          tensors even with hierarchical naming patterns.
+ *
+ * @param tensor Pointer to the target ggml_tensor object (const-qualified).
+ */
+static bool is_matmul_weight(const ggml_tensor* tensor) {
+    std::string name = ggml_get_name(tensor);
+    static const std::unordered_set<std::string> weight_suffixes{
+        "output.weight",
+        "attn_q.weight",
+        "attn_k.weight",
+        "attn_v.weight",
+        "attn_output.weight",
+        "ffn_gate.weight",
+        "ffn_up.weight",
+        "ffn_down.weight"
+    };
+
+    for (const auto& suffix : weight_suffixes) {
+        if (name.find(suffix) != std::string::npos) {
+            return true;
+        }
+    }
+    return false;
+}
+
 /**
  * @brief Applies a element-wise operation to two input tensors using the CANN
  * backend.
@@ -1066,7 +1108,7 @@ void ggml_cann_binary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
  * @param dst The destination tensor. Its src[0] is treated as the input tensor.
  */
 template <void unary_op(ggml_backend_cann_context&, aclTensor*, aclTensor*)>
-    void ggml_cann_unary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    void ggml_cann_op_unary(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     ggml_tensor* src = dst->src[0];
 
     aclTensor* acl_src = ggml_cann_create_tensor(src);
@@ -1077,49 +1119,125 @@ template <void unary_op(ggml_backend_cann_context&, aclTensor*, aclTensor*)>
 }
 
 /**
- * @brief   Applies a unary operation to a ggml tensor using the CANN backend.
+ * @brief Applies a unary operation to a ggml tensor using the CANN backend.
  *
- * @details This function performs a unary operation on the input tensor using
- * a user-provided lambda or callable object `unary_op`, which accepts the CANN
- * context and two ACL tensors (source and destination). Internally, this function
- * creates ACL representations of the ggml tensors and invokes the unary operation.
- * The result is stored in the destination tensor `dst`. This utility abstracts the
- * common boilerplate of tensor conversion and cleanup when implementing unary ops.
+ * @details This function applies a unary operation to the input tensor using
+ * a user-provided lambda or callable `unary_op`. The lambda receives the
+ * CANN backend context and two ACL tensors: the source and the destination.
  *
- * @param unary_op A callable that performs the unary operation using CANN APIs.
- * @param ctx The CANN context used for operations.
- * @param dst The destination tensor where the result will be stored.
- *            The source tensor is retrieved from `dst->src[0]`.
+ * Internally, this function handles the conversion from GGML tensors to ACL tensors,
+ * calls the provided unary op, and manages resource cleanup. The input is assumed
+ * to be `dst->src[0]`, and the result is written to `dst`.
+ *
+ * This utility simplifies writing unary op wrappers by abstracting tensor preparation.
+ *
+ * @param unary_op A callable that performs the unary operation using CANN ACL APIs.
+ * @param ctx The CANN context for operation execution.
+ * @param dst The destination ggml_tensor where the result will be stored.
+ *            The input tensor is assumed to be `dst->src[0]`.
+ *
+ * @see GGML_CANN_CALL_OP_UNARY
  */
-void ggml_cann_unary_op(
+void ggml_cann_op_unary(
     std::function<void(ggml_backend_cann_context&, aclTensor*, aclTensor*)> unary_op,
     ggml_backend_cann_context& ctx, ggml_tensor* dst);
 
 /**
- * @brief Helper macro to invoke a unary ACL operation using ggml_cann_unary_op.
+ * @brief Applies a gated (GLU-style) unary operation using the CANN backend.
  *
- * This macro defines an inline lambda wrapping a specific ACL operation name,
- * and passes it to the templated ggml_cann_unary_op function. It simplifies
- * calling unary ops by hiding the lambda boilerplate.
+ * @details This function performs a gated activation such as GEGLU or ReGLU.
+ * It supports two input modes:
+ *
+ * 1. **Dual input mode**: `dst->src[0]` and `dst->src[1]` are both valid tensors.
+ *    These are used directly as the value and gate tensors.
+ *
+ * 2. **Packed input mode**: Only `dst->src[0]` is valid, and it is assumed to
+ *    contain a concatenation of value and gate along the first dimension. This tensor
+ *    will be split into two equal halves to form the value and gate inputs.
+ *
+ * The function applies a user-provided unary operation (e.g., GELU) to the value tensor,
+ * then multiplies the result in-place with the gate tensor:
  *
- * Internally, the lambda will call:
  * @code
- * GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst);
+ * dst = unary_op(value) * gate;
  * @endcode
  *
+ * The `swapped` parameter (from `dst->op_params[1]`) allows flipping the
+ * order of value/gate in the packed input case.
+ *
+ * @param unary_op A callable that performs the unary operation using CANN ACL APIs.
+ *                 It receives (ctx, acl_value_tensor, acl_output_tensor).
+ * @param ctx      The CANN context used for execution.
+ * @param dst      The destination ggml_tensor. Source tensors are in `dst->src[0]` and optionally `src[1]`.
+ *
+ * @see GGML_CANN_CALL_OP_UNARY_GATED
+ */
+void ggml_cann_op_unary_gated(
+    std::function<void(ggml_backend_cann_context&, aclTensor*, aclTensor*)> unary_op,
+    ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief Helper macro to call a unary ACL operator via ggml_cann_op_unary.
+ *
+ * This macro wraps the specified ACLNN unary operator name into a lambda expression,
+ * and passes it to `ggml_cann_op_unary`, which handles the common logic for executing
+ * unary ops in the CANN backend.
+ *
+ * Internally, this macro expands to a lambda like:
+ * @code
+ * [](ggml_backend_cann_context& ctx, aclTensor* acl_src, aclTensor* acl_dst) {
+ *     GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst);
+ * };
+ * @endcode
+ *
+ * This lambda is then passed to `ggml_cann_op_unary`, which applies the operation.
+ *
  * @param OP_NAME The name of the ACL unary operator to invoke via GGML_CANN_CALL_ACLNN_OP.
  *
- * @see ggml_cann_unary_op
+ * @see ggml_cann_op_unary
  * @see GGML_CANN_CALL_ACLNN_OP
  */
-#define GGML_CANN_CALL_UNARY_OP(OP_NAME)                              \
+#define GGML_CANN_CALL_OP_UNARY(OP_NAME)                              \
     do {                                                              \
         auto lambda = [](ggml_backend_cann_context& ctx,              \
             aclTensor* acl_src,                                       \
             aclTensor* acl_dst) {                                     \
             GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst);  \
         };                                                            \
-        ggml_cann_unary_op(lambda, ctx, dst);                         \
+        ggml_cann_op_unary(lambda, ctx, dst);                         \
     }                                                                 \
     while (0)
+
+/**
+ * @brief Helper macro to call a gated unary ACL operator via ggml_cann_op_unary_gated.
+ *
+ * This macro wraps the specified ACLNN unary operator name into a lambda expression,
+ * and passes it to `ggml_cann_op_unary_gated`, which handles the common logic for
+ * executing gated unary ops in the CANN backend.
+ *
+ * Internally, this macro expands to a lambda like:
+ * @code
+ * [](ggml_backend_cann_context& ctx, aclTensor* acl_src, aclTensor* acl_dst) {
+ *     GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst);
+ * };
+ * @endcode
+ *
+ * This lambda is then passed to `ggml_cann_op_unary_gated`, which applies the operation.
+ *
+ * @param OP_NAME The name of the ACL unary operator to invoke via GGML_CANN_CALL_ACLNN_OP.
+ *
+ * @see ggml_cann_op_unary_gated
+ * @see GGML_CANN_CALL_ACLNN_OP
+ */
+#define GGML_CANN_CALL_OP_UNARY_GATED(OP_NAME)                        \
+    do {                                                              \
+        auto lambda = [](ggml_backend_cann_context& ctx,              \
+            aclTensor* acl_src,                                       \
+            aclTensor* acl_dst) {                                     \
+            GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst);  \
+        };                                                            \
+        ggml_cann_op_unary_gated(lambda, ctx, dst);                   \
+    }                                                                 \
+    while (0)
+
 #endif  // CANN_ACLNN_OPS

ggml/src/ggml-cann/common.h

@@ -337,6 +337,29 @@ private:
     int32_t device_;
 };
 
+#ifdef USE_ACL_GRAPH
+struct ggml_graph_node_properties {
+    void * node_address;
+    ggml_op node_op;
+    int64_t ne[GGML_MAX_DIMS];
+    size_t nb[GGML_MAX_DIMS];
+    void * src_address[GGML_MAX_SRC];
+    int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
+};
+
+struct ggml_cann_graph {
+    ~ggml_cann_graph() {
+        if (graph != nullptr) {
+            aclmdlRIDestroy(graph);
+        }
+    }
+
+    aclmdlRI graph = nullptr;
+
+    std::vector<ggml_graph_node_properties> ggml_graph_properties;
+};
+#endif  // USE_ACL_GRAPH
+
 /**
  * @brief Context for managing CANN backend operations.
  */
@@ -345,8 +368,13 @@ struct ggml_backend_cann_context {
     std::string name;                /**< Name of the device. */
     std::string description;         /**< Description of the device. */
     aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
+#ifdef USE_ACL_GRAPH
+    /// Cached CANN ACL graph used for executing the current ggml computation graph.
+    std::unique_ptr<ggml_cann_graph> cann_graph;
+#endif
     cann_task_queue task_queue;
     bool async_mode;
+    bool support_set_rows;
 
     aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */
 
@@ -362,6 +390,14 @@ struct ggml_backend_cann_context {
         async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or(""));
         GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__,
             device, async_mode ? "ON" : "OFF");
+
+        support_set_rows = parse_bool(get_env("LLAMA_SET_ROWS").value_or(""));
+        GGML_LOG_INFO("%s: LLAMA_SET_ROWS is %s\n", __func__, support_set_rows ? "ON" : "OFF");
+
+        if (!support_set_rows) {
+            GGML_LOG_INFO("%s: CANN Graph currently only supports execution when LLAMA_SET_ROWS is ON. "
+                    "Falling back to eager mode.\n", __func__);
+        }
     }
 
     /**

ggml/src/ggml-cann/ggml-cann.cpp

@@ -24,6 +24,7 @@
 
 #include <acl/acl.h>
 #include <stdarg.h>
+#include <aclnnop/aclnn_trans_matmul_weight.h>
 
 #include <cmath>
 #include <cstdio>
@@ -1115,6 +1116,61 @@ static enum ggml_status ggml_backend_cann_buffer_init_tensor(
     return GGML_STATUS_SUCCESS;
 }
 
+// ND to NZ Workspace Cache Management. Thread-safety: Not guaranteed
+namespace {
+    void* g_nz_workspace = nullptr;
+    size_t g_nz_workspace_allocated = 0;
+
+    void release_nz_workspace() {
+        if (g_nz_workspace) {
+            aclrtFree(g_nz_workspace);
+            g_nz_workspace = nullptr;
+            g_nz_workspace_allocated = 0;
+        }
+    }
+
+    void relloc_nz_workspace(size_t new_size) {
+        if (new_size > g_nz_workspace_allocated) {
+        if (g_nz_workspace) {
+            aclrtFree(g_nz_workspace);
+            g_nz_workspace = nullptr;
+        }
+        ACL_CHECK(aclrtMalloc(&g_nz_workspace, new_size, ACL_MEM_MALLOC_HUGE_FIRST));
+        g_nz_workspace_allocated = new_size;
+    }
+    }
+}
+
+/**
+ * @brief Convert tensor weights to NZ format using Ascend CANN API.
+ *
+ * This function creates a transposed tensor descriptor and performs the
+ * TransMatmulWeight operation. Converting tensor formats can significantly
+ * improve performance on certain hardware.
+ *
+ * @param tensor Pointer to the input ggml_tensor containing the weights.
+ * @param data Pointer to the raw data buffer for the tensor weights.
+ * @param offset Byte offset within the tensor data buffer where weights start.
+ *
+ * @note The workspace buffer used in this function is managed globally and reused
+ *       across calls. This reduces overhead from repeated memory allocation and deallocation.
+ */
+static void weight_format_to_nz(ggml_tensor *tensor, const void *data, size_t offset) {
+    aclTensor* weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne,
+                                    tensor->nb, 2, ACL_FORMAT_ND, offset);
+    uint64_t workspaceSize = 0;
+    aclOpExecutor *executor;
+
+    // TransMatmulWeight
+    ACL_CHECK(aclnnTransMatmulWeightGetWorkspaceSize(weightTransposed,
+                                                    &workspaceSize, &executor));
+    // Avoid frequent malloc/free of the workspace.
+    relloc_nz_workspace(workspaceSize);
+
+    ACL_CHECK(aclnnTransMatmulWeight(g_nz_workspace, workspaceSize, executor, nullptr));
+    ACL_CHECK(aclDestroyTensor(weightTransposed));
+}
+
 // TODO: need handle tensor which has paddings.
 /**
  * @brief Set tensor data in a CANN buffer.
@@ -1139,9 +1195,16 @@ static void ggml_backend_cann_buffer_set_tensor(
     // For acl, synchronous functions use this default stream.
     // Why aclrtSynchronizeDevice?
 
+    // Only check env once.
+    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
     if (!need_transform(tensor->type)) {
         ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, data, size,
                               ACL_MEMCPY_HOST_TO_DEVICE));
+        if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) {
+            GGML_ASSERT(tensor->ne[2] == 1);
+            GGML_ASSERT(tensor->ne[3] == 1);
+            weight_format_to_nz(tensor, data, offset);
+        }
     } else {
         void *transform_buffer = malloc(size);
         ggml_backend_cann_transform(tensor, data, transform_buffer);
@@ -1375,20 +1438,32 @@ static size_t ggml_backend_cann_buffer_type_get_alloc_size(
     size_t size = ggml_nbytes(tensor);
     int64_t ne0 = tensor->ne[0];
 
+    // Only check env once.
+    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
+
     // last line must bigger than 32, because every single op deal at
     // least 32 bytes.
     // TODO: quantized type?
     // int64_t line_size = ne0 * ggml_element_size(tensor);
     // int64_t line_size_align_32 = (line_size + 31) & ~31;
     // size += (line_size_align_32 - line_size);
-
-    // TODO: not support quantized yet.
-    // TODO: consider un-continue tensor.
     if (ggml_is_quantized(tensor->type)) {
         if (ne0 % MATRIX_ROW_PADDING != 0) {
             size += ggml_row_size(
                 tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
         }
+    } else if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) {
+        // NZ format weight are not support quantized yet.
+        // If ND tensor transform to NZ, size may changed.
+        int64_t shape[] = {tensor->ne[1], tensor->ne[0]};
+        GGML_ASSERT(tensor->ne[2] == 1);
+        GGML_ASSERT(tensor->ne[3] == 1);
+        const aclIntArray *acl_shape = aclCreateIntArray(shape, 2);
+        size_t new_size;
+        ACL_CHECK(aclnnCalculateMatmulWeightSizeV2(acl_shape,
+                    ggml_cann_type_mapping(tensor->type), &new_size));
+        ACL_CHECK(aclDestroyIntArray(acl_shape));
+        size = std::max(size, new_size);
     }
 
     return size;
@@ -1594,6 +1669,9 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
         case GGML_OP_GET_ROWS:
             ggml_cann_get_rows(ctx, dst);
             break;
+        case GGML_OP_SET_ROWS:
+            ggml_cann_set_rows(ctx, dst);
+            break;
         case GGML_OP_DUP:
             ggml_cann_dup(ctx, dst);
             break;
@@ -1616,16 +1694,18 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(dst)) {
                 case GGML_UNARY_OP_ABS:
-                    GGML_CANN_CALL_UNARY_OP(Abs);
+                    GGML_CANN_CALL_OP_UNARY(Abs);
                     break;
                 case GGML_UNARY_OP_NEG:
-                    GGML_CANN_CALL_UNARY_OP(Neg);
+                    GGML_CANN_CALL_OP_UNARY(Neg);
                     break;
                 case GGML_UNARY_OP_GELU:
-                    GGML_CANN_CALL_UNARY_OP(Gelu);
+                case GGML_UNARY_OP_GELU_ERF:
+                    // aclnnGelu internally uses the erf-based approximation.
+                    GGML_CANN_CALL_OP_UNARY(Gelu);
                     break;
                 case GGML_UNARY_OP_SILU:
-                    GGML_CANN_CALL_UNARY_OP(Silu);
+                    GGML_CANN_CALL_OP_UNARY(Silu);
                     break;
                 case GGML_UNARY_OP_GELU_QUICK: {
                     auto lambda = [](ggml_backend_cann_context& ctx,
@@ -1633,31 +1713,31 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
                         aclTensor* acl_dst) {
                         GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst);
                     };
-                    ggml_cann_unary_op(lambda, ctx, dst);
+                    ggml_cann_op_unary(lambda, ctx, dst);
                 } break;
                 case GGML_UNARY_OP_TANH:
-                    GGML_CANN_CALL_UNARY_OP(Tanh);
+                    GGML_CANN_CALL_OP_UNARY(Tanh);
                     break;
                 case GGML_UNARY_OP_RELU:
-                    GGML_CANN_CALL_UNARY_OP(Relu);
+                    GGML_CANN_CALL_OP_UNARY(Relu);
                     break;
                 case GGML_UNARY_OP_SIGMOID:
-                    GGML_CANN_CALL_UNARY_OP(Sigmoid);
+                    GGML_CANN_CALL_OP_UNARY(Sigmoid);
                     break;
                 case GGML_UNARY_OP_HARDSIGMOID:
-                    GGML_CANN_CALL_UNARY_OP(Hardsigmoid);
+                    GGML_CANN_CALL_OP_UNARY(Hardsigmoid);
                     break;
                 case GGML_UNARY_OP_HARDSWISH:
-                    GGML_CANN_CALL_UNARY_OP(Hardswish);
+                    GGML_CANN_CALL_OP_UNARY(Hardswish);
                     break;
                 case GGML_UNARY_OP_EXP:
-                    GGML_CANN_CALL_UNARY_OP(Exp);
+                    GGML_CANN_CALL_OP_UNARY(Exp);
                     break;
                 case GGML_UNARY_OP_ELU:
                     ggml_cann_elu(ctx, dst);
                     break;
                 case GGML_UNARY_OP_SGN:
-                    GGML_CANN_CALL_UNARY_OP(Sign);
+                    GGML_CANN_CALL_OP_UNARY(Sign);
                     break;
                 case GGML_UNARY_OP_STEP:
                     ggml_cann_step(ctx, dst);
@@ -1666,6 +1746,31 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
                     return false;
             }
             break;
+        case GGML_OP_GLU:
+            switch (ggml_get_glu_op(dst)) {
+                case GGML_GLU_OP_REGLU:
+                    GGML_CANN_CALL_OP_UNARY_GATED(Relu);
+                    break;
+                case GGML_GLU_OP_GEGLU:
+                case GGML_GLU_OP_GEGLU_ERF:
+                    // aclnnGelu internally uses the erf-based approximation.
+                    GGML_CANN_CALL_OP_UNARY_GATED(Gelu);
+                    break;
+                case GGML_GLU_OP_SWIGLU:
+                    GGML_CANN_CALL_OP_UNARY_GATED(Silu);
+                    break;
+                case GGML_GLU_OP_GEGLU_QUICK: {
+                    auto lambda = [](ggml_backend_cann_context& ctx,
+                        aclTensor* acl_src,
+                        aclTensor* acl_dst) {
+                        GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst);
+                    };
+                    ggml_cann_op_unary_gated(lambda, ctx, dst);
+                } break;
+                default:
+                    return false;
+            }
+            break;
         case GGML_OP_NORM:
             ggml_cann_norm(ctx, dst);
             break;
@@ -1708,7 +1813,7 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
             ggml_cann_binary_op<aclnn_mul>(ctx, dst);
             break;
         case GGML_OP_SQRT:
-            GGML_CANN_CALL_UNARY_OP(Sqrt);
+            GGML_CANN_CALL_OP_UNARY(Sqrt);
             break;
         case GGML_OP_CLAMP:
             ggml_cann_clamp(ctx, dst);
@@ -1753,16 +1858,16 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
             ggml_cann_argmax(ctx, dst);
             break;
         case GGML_OP_COS:
-            ggml_cann_unary_op<aclnn_cos>(ctx, dst);
+            ggml_cann_op_unary<aclnn_cos>(ctx, dst);
             break;
         case GGML_OP_SIN:
-            ggml_cann_unary_op<aclnn_sin>(ctx, dst);
+            ggml_cann_op_unary<aclnn_sin>(ctx, dst);
             break;
         case GGML_OP_CONV_TRANSPOSE_1D:
             ggml_cann_conv_transpose_1d(ctx, dst);
             break;
         case GGML_OP_LOG:
-            GGML_CANN_CALL_UNARY_OP(Log);
+            GGML_CANN_CALL_OP_UNARY(Log);
             break;
         case GGML_OP_MEAN:
             ggml_cann_mean(ctx, dst);
@@ -1911,6 +2016,9 @@ static bool ggml_backend_cann_cpy_tensor_async(
         (ggml_backend_cann_context*)backend_dst->context;
 
     size_t copy_size = ggml_nbytes(dst);
+    if (copy_size == 0) {
+        return true;
+    }
     if (backend_src != backend_dst) {
         ggml_backend_cann_buffer_context* buf_ctx_src =
             (ggml_backend_cann_buffer_context*)buf_src->context;
@@ -1967,6 +2075,160 @@ static void ggml_backend_cann_synchronize(ggml_backend_t backend) {
     ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream()));
 }
 
+#ifdef USE_ACL_GRAPH
+/**
+ * @brief Populate the internal CANN graph node properties from the ggml computation graph.
+ *
+ * This function copies all node attributes (operation type, dimensions, strides, input sources,
+ * and operation parameters) into the cached CANN graph structure for later reuse or comparison.
+ *
+ * @param cann_ctx  The CANN backend context.
+ * @param cgraph    The ggml computational graph.
+ */
+static void set_ggml_graph_node_properties(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
+    for (int node_idx = 0; node_idx < cgraph->n_nodes; node_idx++) {
+        ggml_tensor * node = cgraph->nodes[node_idx];
+        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_address = node->data;
+        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_op = node->op;
+
+        for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].ne[dim] = node->ne[dim];
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].nb[dim] = node->nb[dim];
+        }
+        for (int src = 0; src < GGML_MAX_SRC; src++) {
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].src_address[src] =
+                node->src[src] ? node->src[src]->data : nullptr;
+        }
+        memcpy(cann_ctx->cann_graph->ggml_graph_properties[node_idx].op_params, node->op_params, GGML_MAX_OP_PARAMS);
+    }
+}
+
+/**
+ * @brief Check if a ggml tensor node matches a previously captured CANN graph node.
+ *
+ * This function compares all relevant fields (address, op type, shape, source inputs, op params)
+ * to determine whether the current node matches a previously recorded version.
+ *
+ * @param node                  The current ggml tensor node.
+ * @param graph_node_properties The stored properties of a CANN graph node.
+ * @return true if all fields match (excluding GGML_OP_VIEW); false otherwise.
+ */
+static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
+    if (node->data != graph_node_properties->node_address &&
+           node->op != GGML_OP_VIEW) {
+        return false;
+    }
+    if (node->op != graph_node_properties->node_op) {
+        return false;
+    }
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (node->ne[i] != graph_node_properties->ne[i]) {
+            return false;
+        }
+        if (node->nb[i] != graph_node_properties->nb[i]) {
+            return false;
+        }
+    }
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (node->src[i] &&
+            node->src[i]->data != graph_node_properties->src_address[i] &&
+            node->op != GGML_OP_VIEW
+        ) {
+            return false;
+        }
+    }
+    if (node->op == GGML_OP_SCALE &&
+        memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
+        return false;
+    }
+    return true;
+}
+
+/**
+ * @brief Determine if the CANN graph needs to be rebuilt due to graph changes.
+ *
+ * This checks whether the number or properties of ggml graph nodes have changed
+ * compared to the last captured CANN graph. If so, the CANN graph must be re-captured.
+ *
+ * @param cann_ctx  The CANN backend context.
+ * @param cgraph    The current ggml computation graph.
+ * @return true if an update is required; false otherwise.
+ */
+static bool is_cann_graph_update_required(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
+    // The number of nodes is different, so the graph needs to be reconstructed.
+    if (cann_ctx->cann_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
+        cann_ctx->cann_graph->ggml_graph_properties.resize(cgraph->n_nodes);
+        return true;
+    }
+
+    // The number of nodes is the same; iterate over each node to check whether they match.
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        bool has_matching_properties = ggml_graph_node_has_matching_properties(
+            cgraph->nodes[i], &cann_ctx->cann_graph->ggml_graph_properties[i]);
+        if(!has_matching_properties) {
+            return true;
+        }
+    }
+    return false;
+}
+#endif  // USE_ACL_GRAPH
+
+/**
+ * @brief Evaluate the computation graph and optionally capture or execute it using CANN graph API.
+ *
+ * If CANN graph execution is enabled and graph capture is required, this function begins
+ * graph capture, runs the graph, ends capture, and stores the captured graph.
+ *
+ * Otherwise, it falls back to op-by-op execution using the CANN compute kernel dispatcher.
+ *
+ * @param cann_ctx                 The CANN backend context.
+ * @param cgraph                   The ggml computation graph.
+ * @param use_cann_graph           Whether to use CANN graph execution.
+ * @param cann_graph_update_required Whether graph capture is needed due to graph changes.
+ */
+static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph,
+    bool & use_cann_graph, bool & cann_graph_update_required) {
+#ifdef USE_ACL_GRAPH
+    if (use_cann_graph && cann_graph_update_required) {
+        if (cann_ctx->cann_graph->graph != nullptr) {
+            ACL_CHECK(aclmdlRIDestroy(cann_ctx->cann_graph->graph));
+            cann_ctx->cann_graph->graph = nullptr;
+        }
+        ACL_CHECK(aclmdlRICaptureBegin(cann_ctx->stream(), ACL_MODEL_RI_CAPTURE_MODE_GLOBAL));
+    }
+#endif // USE_ACL_GRAPH
+
+    // Only perform the graph execution if CANN graphs are not enabled, or we are capturing the graph.
+    // With the use of CANN graphs, the execution will be performed by the graph launch.
+    if (!use_cann_graph || cann_graph_update_required) {
+        for (int i = 0; i < cgraph->n_nodes; i++) {
+            ggml_tensor * node = cgraph->nodes[i];
+
+            if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
+                continue;
+            }
+
+            bool ok = ggml_cann_compute_forward(*cann_ctx, node);
+            if (!ok) {
+                GGML_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
+            }
+            GGML_ASSERT(ok);
+        }
+    }
+
+#ifdef USE_ACL_GRAPH
+    if (use_cann_graph && cann_graph_update_required) { // End CANN graph capture
+        ACL_CHECK(aclmdlRICaptureEnd(cann_ctx->stream(), &cann_ctx->cann_graph->graph));
+    }
+
+    if (use_cann_graph) {
+        // Execute graph
+        ACL_CHECK(aclmdlRIExecuteAsync(cann_ctx->cann_graph->graph, cann_ctx->stream()));
+    }
+#endif // USE_ACL_GRAPH
+}
+
+
 /**
  * @brief Computes a computational graph using a CANN backend.
  *
@@ -1983,25 +2245,38 @@ static enum ggml_status ggml_backend_cann_graph_compute(
     ggml_backend_t backend, ggml_cgraph* cgraph) {
     ggml_backend_cann_context* cann_ctx =
         (ggml_backend_cann_context*)backend->context;
-
     ggml_cann_set_device(cann_ctx->device);
+    release_nz_workspace();
+#ifdef USE_ACL_GRAPH
+    bool use_cann_graph = true;
+    bool cann_graph_update_required = false;
 
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_tensor* node = cgraph->nodes[i];
-
-        if (ggml_is_empty(node) || node->op == GGML_OP_NONE) {
-            continue;
-        }
-
-        bool ok = ggml_cann_compute_forward(*cann_ctx, node);
-
-        if (!ok) {
-            GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__,
-                    node->name, ggml_op_name(node->op));
-        }
-        GGML_ASSERT(ok);
+    // check environment LLAMA_SET_ROWS
+    if (!cann_ctx->support_set_rows) {
+        use_cann_graph = false;
     }
 
+    if (use_cann_graph) {
+        if (cann_ctx->cann_graph == nullptr) {
+            cann_ctx->cann_graph.reset(new ggml_cann_graph());
+            cann_graph_update_required = true;
+        }
+
+        cann_graph_update_required = is_cann_graph_update_required(cann_ctx, cgraph);
+        set_ggml_graph_node_properties(cann_ctx, cgraph);
+    }
+#else
+    bool use_cann_graph = false;
+    bool cann_graph_update_required = false;
+#endif  // USE_ACL_GRAPH
+
+    evaluate_and_capture_cann_graph(
+        cann_ctx,
+        cgraph,
+        use_cann_graph,
+        cann_graph_update_required
+    );
+
     return GGML_STATUS_SUCCESS;
 }
 
@@ -2036,10 +2311,23 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 case GGML_UNARY_OP_ELU:
                 case GGML_UNARY_OP_SGN:
                 case GGML_UNARY_OP_STEP:
+                case GGML_UNARY_OP_GELU_ERF:
                     return true;
                 default:
                     return false;
             }
+        case GGML_OP_GLU:
+            switch (ggml_get_glu_op(op)) {
+                case GGML_GLU_OP_REGLU:
+                case GGML_GLU_OP_GEGLU:
+                case GGML_GLU_OP_SWIGLU:
+                case GGML_GLU_OP_GEGLU_ERF:
+                case GGML_GLU_OP_GEGLU_QUICK:
+                    return true;
+                default:
+                    return false;
+            }
+            break;
         case GGML_OP_MUL_MAT: {
             switch (op->src[0]->type) {
                 case GGML_TYPE_F16:
@@ -2086,13 +2374,15 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                     return false;
             }
         } break;
-        case GGML_OP_SET_ROWS:
-            {
-                // TODO: add support
-                // ref: https://github.com/ggml-org/llama.cpp/pull/14274
-#pragma message("TODO: implement F32, F16, BF16, Q4_0, Q4_1, Q5_0, Q5_1, Q8_0, IQ4_NL support (https://github.com/ggml-org/llama.cpp/pull/14661)")
-                return false;
-            } break;
+        case GGML_OP_SET_ROWS: {
+            switch (op->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                    return true;
+                default:
+                    return false;
+            }
+        } break;
         case GGML_OP_CPY: {
             ggml_tensor *src = op->src[0];
             if ((op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16) ||
@@ -2101,12 +2391,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 // only support F32 and F16.
                 return false;
             }
-
-            if (!ggml_are_same_shape(op, src) && !ggml_is_contiguous(op)) {
-                // unsupport dst is not contiguous.
-                return false;
-            }
-
             return true;
         } break;
         case GGML_OP_CONT: {
@@ -2215,6 +2499,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             memcpy(&bias, (float*)op->op_params + 1, sizeof(float));
             return bias == 0.0f; // TODO: support bias != 0.0f
         case GGML_OP_SOFT_MAX:
+            // TODO: support attention sinks [TAG_ATTN_SINKS]
+            if (op->src[2]) {
+                return false;
+            }
             // TODO: support broadcast
             // ref: https://github.com/ggml-org/llama.cpp/pull/14435
             return !op->src[1] || (op->src[1]->ne[2] == 1 && op->src[1]->ne[3] == 1);
@@ -2229,6 +2517,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             if(op->type != GGML_TYPE_F16 && op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_BF16){
                 return false;
             }
+            // TODO: support attention sinks [TAG_ATTN_SINKS]
+            if (op->src[4]) {
+                return false;
+            }
             if (op->src[1]->ne[0] != op->src[2]->ne[0]) {
                 // different head sizes of K and V are not supported yet
                 return false;

ggml/src/ggml-common.h

@@ -99,6 +99,9 @@ typedef sycl::half2 ggml_half2;
 #define QI4_1 (QK4_1 / (4 * QR4_1))
 #define QR4_1 2
 
+#define QI_MXFP4 (QK_MXFP4 / (4 * QR_MXFP4))
+#define QR_MXFP4 2
+
 #define QI5_0 (QK5_0 / (4 * QR5_0))
 #define QR5_0 2
 
@@ -184,6 +187,13 @@ typedef struct {
 } block_q4_1;
 static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_half) + QK4_1 / 2, "wrong q4_1 block size/padding");
 
+#define QK_MXFP4 32
+typedef struct {
+    uint8_t e; // E8M0
+    uint8_t qs[QK_MXFP4/2];
+} block_mxfp4;
+static_assert(sizeof(block_mxfp4) == sizeof(uint8_t) + QK_MXFP4/2, "wrong mxfp4 block size/padding");
+
 #define QK5_0 32
 typedef struct {
     ggml_half d;           // delta
@@ -1074,10 +1084,17 @@ GGML_TABLE_BEGIN(uint32_t, iq3s_grid, 512)
     0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101,
 GGML_TABLE_END()
 
+// TODO: fix name to kvalues_iq4_nl
 GGML_TABLE_BEGIN(int8_t, kvalues_iq4nl, 16)
     -127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113,
 GGML_TABLE_END()
 
+// e2m1 values (doubled)
+// ref: https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf
+GGML_TABLE_BEGIN(int8_t, kvalues_mxfp4, 16)
+    0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12,
+GGML_TABLE_END()
+
 #define NGRID_IQ1S 2048
 #define IQ1S_DELTA 0.125f
 #define IQ1M_DELTA 0.125f

ggml/src/ggml-cpu/CMakeLists.txt

@@ -70,10 +70,12 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
     if (GGML_OPENMP)
         find_package(OpenMP)
         if (OpenMP_FOUND)
+            set(GGML_OPENMP_ENABLED "ON" CACHE INTERNAL "")
             target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_OPENMP)
 
             target_link_libraries(${GGML_CPU_NAME} PRIVATE OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
         else()
+            set(GGML_OPENMP_ENABLED "OFF" CACHE INTERNAL "")
             message(WARNING "OpenMP not found")
         endif()
     endif()
@@ -456,6 +458,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             list(APPEND ARCH_FLAGS -march=z16)
         elseif (${S390X_M} MATCHES "9175|9176")
             # NOTE: Only available from GCC 15.1.0 onwards. Any z17 machine with compile issues must first verify their GCC version.
+            #       binutils must also be updated to the latest for the -march=z17 flag to work. Otherwise, use -march=arch15.
             message(STATUS "z17 target")
             list(APPEND ARCH_FLAGS -march=z17)
         else()
@@ -494,9 +497,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         # Fetch KleidiAI sources:
         include(FetchContent)
-        set(KLEIDIAI_COMMIT_TAG "v1.9.0")
+        set(KLEIDIAI_COMMIT_TAG "v1.11.0")
         set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz")
-        set(KLEIDIAI_ARCHIVE_MD5  "2a8e1bb55d201557553545536489a017")
+        set(KLEIDIAI_ARCHIVE_MD5  "3fe9e5ab964c375c53839296eb71eaa2")
 
         if (POLICY CMP0135)
             cmake_policy(SET CMP0135 NEW)

ggml/src/ggml-cpu/arch-fallback.h

@@ -13,6 +13,7 @@
 #define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
 #define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
 #define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
@@ -37,17 +38,21 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64)
 // repack.cpp
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64)
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
@@ -64,6 +69,7 @@
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -72,11 +78,13 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #elif defined(__loongarch64)
 // quants.c
@@ -84,6 +92,7 @@
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -92,11 +101,13 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #elif defined(__riscv)
 // quants.c
@@ -112,6 +123,7 @@
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
 #define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
 #define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -119,10 +131,12 @@
 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #elif defined(__s390x__)
 // quants.c
@@ -139,6 +153,7 @@
 #define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
 #define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -147,11 +162,13 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #elif defined(__wasm__)
 // quants.c
@@ -167,6 +184,7 @@
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
 #define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
 #define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -175,10 +193,12 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #endif

ggml/src/ggml-cpu/arch/arm/quants.c

@@ -589,6 +589,67 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 }
 
+void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    assert(n % QK_MXFP4 == 0);
+    static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same");
+
+    const block_mxfp4 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    const int nb = n / QK_MXFP4;
+
+    int ib = 0;
+    float sumf = 0;
+
+#if defined __ARM_NEON
+    const int8x16_t values = vld1q_s8(kvalues_mxfp4);
+    const uint8x16_t m4b = vdupq_n_u8(0x0f);
+    uint8x16x2_t q4bits;
+    int8x16x4_t q4b;
+    int8x16x4_t q8b;
+    int32x4_t prod_1;
+    int32x4_t prod_2;
+
+    for (; ib + 1 < nb; ib += 2) {
+        q4bits.val[0] = vld1q_u8(x[ib + 0].qs);
+        q4bits.val[1] = vld1q_u8(x[ib + 1].qs);
+        q8b.val[0]    = vld1q_s8(y[ib + 0].qs);
+        q8b.val[1]    = vld1q_s8(y[ib + 0].qs + 16);
+        q8b.val[2]    = vld1q_s8(y[ib + 1].qs);
+        q8b.val[3]    = vld1q_s8(y[ib + 1].qs + 16);
+
+        q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[0], m4b));
+        q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
+        q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[1], m4b));
+        q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4));
+
+        prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]);
+        prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
+
+        sumf +=
+            GGML_E8M0_TO_FP32_HALF(x[ib + 0].e) * GGML_CPU_FP16_TO_FP32(y[ib + 0].d) * vaddvq_s32(prod_1) +
+            GGML_E8M0_TO_FP32_HALF(x[ib + 1].e) * GGML_CPU_FP16_TO_FP32(y[ib + 1].d) * vaddvq_s32(prod_2);
+    }
+
+#endif
+    for (; ib < nb; ++ib) {
+        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_E8M0_TO_FP32_HALF(x[ib].e);
+        int sumi1 = 0;
+        int sumi2 = 0;
+        for (int j = 0; j < QK_MXFP4/2; ++j) {
+            sumi1 += y[ib].qs[j +          0] * kvalues_mxfp4[x[ib].qs[j] & 0xf];
+            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_mxfp4[x[ib].qs[j] >>  4];
+        }
+        sumf += d * (sumi1 + sumi2);
+    }
+    *s = sumf;
+}
+
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;
@@ -1236,44 +1297,10 @@ void ggml_vec_dot_tq1_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = sumf;
 
 #else
-    const uint8_t pow3[6] = {1, 3, 9, 27, 81, 243};
-
-    float sumf = 0.0f;
-
-    for (int i = 0; i < nb; ++i) {
-        int sum = 0;
-
-        for (size_t j = 0; j < sizeof(x->qs) - sizeof(x->qs) % 32; j += 32) {
-            for (size_t l = 0; l < 5; ++l) {
-                for (size_t m = 0; m < 32; ++m) {
-                    uint8_t q = x[i].qs[j + m] * pow3[l];
-                    uint16_t xi = ((uint16_t) q * 3) >> 8;
-                    sum += (xi - 1) * y[i].qs[j*5 + l*32 + m];
-                }
-            }
-        }
-        for (size_t j = sizeof(x->qs) - sizeof(x->qs) % 32; j < sizeof(x->qs); j += 16) {
-            for (size_t l = 0; l < 5; ++l) {
-                for (size_t m = 0; m < 16; ++m) {
-                    uint8_t q = x[i].qs[j + m] * pow3[l];
-                    uint16_t xi = ((uint16_t) q * 3) >> 8;
-                    sum += (xi - 1) * y[i].qs[j*5 + l*16 + m];
-                }
-            }
-        }
-
-        for (size_t l = 0; l < 4; ++l) {
-            for (size_t j = 0; j < sizeof(x->qh); ++j) {
-                uint8_t q = x[i].qh[j] * pow3[l];
-                uint16_t xi = ((uint16_t) q * 3) >> 8;
-                sum += (xi - 1) * y[i].qs[sizeof(x->qs)*5 + l*sizeof(x->qh) + j];
-            }
-        }
-
-        sumf += (float) sum * (GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d);
-    }
-
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_tq1_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1381,25 +1408,10 @@ void ggml_vec_dot_tq2_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = sumf;
 
 #else
-    float sumf = 0.0f;
-
-    for (int i = 0; i < nb; ++i) {
-        int32_t sumi = 0;
-
-        for (size_t j = 0; j < sizeof(x->qs); j += 32) {
-            for (size_t l = 0; l < 4; ++l) {
-                for (size_t k = 0; k < 32; ++k) {
-                    sumi += y[i].qs[j*4 + l*32 + k] * (((x[i].qs[j + k] >> (l*2)) & 3) - 1);
-                }
-            }
-        }
-
-        const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
-
-        sumf += (float) sumi * d;
-    }
-
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_tq2_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1729,45 +1741,10 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sum;
 
 #else
-
-    float sumf = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const uint8_t * q2 = x[i].qs;
-        const  int8_t * q8 = y[i].qs;
-        const uint8_t * sc = x[i].scales;
-
-        int summs = 0;
-        for (int j = 0; j < 16; ++j) {
-            summs += y[i].bsums[j] * (sc[j] >> 4);
-        }
-
-        const float dall = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
-        const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
-
-        int isum = 0;
-        int is = 0;
-        int d;
-        for (int k = 0; k < QK_K/128; ++k) {
-            int shift = 0;
-            for (int j = 0; j < 4; ++j) {
-                d = sc[is++] & 0xF;
-                int isuml = 0;
-                for (int l =  0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
-                isum += d * isuml;
-                d = sc[is++] & 0xF;
-                isuml = 0;
-                for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
-                isum += d * isuml;
-                shift += 2;
-                q8 += 32;
-            }
-            q2 += 32;
-        }
-        sumf += dall * isum - dmin * summs;
-    }
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q2_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -2057,68 +2034,12 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sum;
 
 #else
-    // scalar version
-    // This function is written like this so the compiler can manage to vectorize most of it
-    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
-    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
-    // The ideal situation would be if we could just write the code once, and the compiler would
-    // automatically produce the best possible set of machine instructions, instead of us having to manually
-    // write vectorized versions for AVX, ARM_NEON, etc.
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    uint32_t auxs[4];
-    const int8_t * scales = (const int8_t*)auxs;
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            q3 += 32;
-        }
-        a = aux8;
-
-        memcpy(auxs, x[i].scales, 12);
-        uint32_t tmp = auxs[2];
-        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
-        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
-        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
-        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
-        for (int j = 0; j < QK_K/16; ++j) {
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
-
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 
 }
@@ -2431,61 +2352,14 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            a += 32;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            a += 32; q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(utmp);
+    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -2578,66 +2452,14 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(utmp);
+    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -3093,47 +2915,10 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     }
     *s = sum;
 #else
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
-        const uint8_t * GGML_RESTRICT qh = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) {
-                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
-                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
-                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
-                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
-            }
-            a  += 128;
-            q4 += 64;
-            qh += 32;
-        }
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            int scale = x[i].scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -3229,34 +3014,10 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.25f * sumf;
 
 #else
-
-    uint32_t aux32[2];
-    const uint8_t * aux8 = (const uint8_t *)aux32;
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
-        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            memcpy(aux32, q2, 2*sizeof(uint32_t));
-            q2 += 4;
-            const uint32_t ls = 2*(aux32[1] >> 28) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
-                const uint8_t  signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127];
-                for (int j = 0; j < 8; ++j) {
-                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += sumi * ls;
-        }
-        sumf += d * bsum;
-    }
-    *s = 0.125f * sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq2_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -3327,42 +3088,10 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = 0.125f * sumf;
 
 #else
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
-        const uint8_t  * GGML_RESTRICT sc = x[i].scales;
-        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1;
-            const uint16_t ls2 = 2*(sc[ib32] >>  4) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 2; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
-                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
-                for (int j = 0; j < 8; ++j) {
-                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += sumi * ls1;
-            sumi = 0;
-            for (int l = 2; l < 4; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
-                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
-                for (int j = 0; j < 8; ++j) {
-                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += sumi * ls2;
-            q2 += 4;
-        }
-        sumf += d * bsum;
-    }
-    *s = 0.125f * sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq2_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -3455,45 +3184,10 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = 0.125f * sumf;
 
 #else
-
-    float sumf = 0;
-    for (int i = 0; i < nb; i++) {
-
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const int8_t  * q8 = y[i].qs;
-        const uint8_t * qs = x[i].qs;
-        const uint8_t * qh = x[i].qh;
-        const uint8_t * signs = qs + QK_K/8;
-
-        int bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf);
-            int ls2 = 1 + 2*(x[i].scales[ib32] >>  4);
-            int sumi1 = 0, sumi2 = 0;
-            for (int l = 0; l < 2; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
-                for (int j = 0; j < 8; ++j) {
-                    sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            for (int l = 2; l < 4; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
-                for (int j = 0; j < 8; ++j) {
-                    sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += ls1 * sumi1 + ls2 * sumi2;
-            qs += 4;
-            signs += 4;
-        }
-
-        sumf += d * bsum;
-    }
-
-    *s = 0.125f * sumf;
-
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq2_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 
 }
@@ -3553,36 +3247,10 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.5f * sumf;
 
 #else
-
-    uint32_t aux32;
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
-        const uint8_t * GGML_RESTRICT gas = x[i].qs + QK_K/4;
-        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t);
-            const uint32_t ls = 2*(aux32 >> 28) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]);
-                const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]);
-                const uint8_t  signs = ksigns_iq2xs[(aux32 >> 7*l) & 127];
-                for (int j = 0; j < 4; ++j) {
-                    sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1);
-                    sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            q3 += 8;
-            bsum += sumi * ls;
-        }
-        sumf += d * bsum;
-    }
-    *s = 0.25f * sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq3_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -3689,48 +3357,10 @@ void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = sumf;
 
 #else
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint8_t * GGML_RESTRICT qs = x[i].qs;
-        const uint8_t * GGML_RESTRICT qh = x[i].qh;
-        const uint8_t * GGML_RESTRICT signs = x[i].signs;
-        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
-            const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1;
-            const uint32_t ls2 = 2*(x[i].scales[ib32/2] >>  4) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256)));
-                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256)));
-                for (int j = 0; j < 4; ++j) {
-                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
-                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            qs += 8;
-            signs += 4;
-            bsum += sumi * ls1;
-            sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256)));
-                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256)));
-                for (int j = 0; j < 4; ++j) {
-                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
-                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            qs += 8;
-            signs += 4;
-            bsum += sumi * ls2;
-        }
-        sumf += d * bsum;
-    }
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq3_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -3793,36 +3423,10 @@ void ggml_vec_dot_iq1_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = sumf;
 
 #else
-
-    float sumf = 0;
-    for (int i = 0; i < nb; i++) {
-
-        const int8_t   * q8 = y[i].qs;
-        const uint8_t  * qs = x[i].qs;
-        const uint16_t * qh = x[i].qh;
-
-        int sumi = 0, sumi1 = 0;
-        for (int ib = 0; ib < QK_K/32; ++ib) {
-            const int ls = 2*((qh[ib] >> 12) & 7) + 1;
-            const int delta = qh[ib] & 0x8000 ? -1 : 1;
-            int lsum = 0;
-            for (int l = 0; l < 4; ++l) {
-                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8)));
-                for (int j = 0; j < 8; ++j) {
-                    lsum += q8[j] * grid[j];
-                }
-                q8 += 8;
-            }
-            sumi  += ls * lsum;
-            sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]);
-            qs += 4;
-        }
-
-        sumf += GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1);
-    }
-
-    *s = sumf;
-
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq1_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -3912,52 +3516,11 @@ void ggml_vec_dot_iq1_m_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = sumf;
 
 #else
-
-    int sum1[2], sum2[2], delta[4];
-
-    float sumf = 0;
-    for (int i = 0; i < nb; i++) {
-
-        const int8_t   * q8 = y[i].qs;
-        const uint8_t  * qs = x[i].qs;
-        const uint8_t  * qh = x[i].qh;
-        const uint16_t * sc = (const uint16_t *)x[i].scales;
-
-        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-
-        int sumi1 = 0, sumi2 = 0;
-        for (int ib = 0; ib < QK_K/32; ++ib) {
-            delta[0] = qh[0] & 0x08 ? -1 : 1;
-            delta[1] = qh[0] & 0x80 ? -1 : 1;
-            delta[2] = qh[1] & 0x08 ? -1 : 1;
-            delta[3] = qh[1] & 0x80 ? -1 : 1;
-            sum1[0] = sum1[1] = sum2[0] = sum2[1] = 0;
-            for (int l = 0; l < 4; ++l) {
-                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((uint16_t)qh[l/2] << (8 - 4*(l%2))) & 0x700)));
-                int lsum1 = 0, lsum2 = 0;
-                for (int j = 0; j < 8; ++j) {
-                    lsum1 += q8[j] * grid[j];
-                    lsum2 += q8[j];
-                }
-                q8 += 8;
-                sum1[l/2] += lsum1;
-                sum2[l/2] += lsum2*delta[l];
-            }
-
-            const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1;
-            const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1;
-
-            sumi1 += sum1[0] * ls1 + sum1[1] * ls2;
-            sumi2 += sum2[0] * ls1 + sum2[1] * ls2;
-            qs += 4;
-            qh += 2;
-        }
-
-        sumf += GGML_CPU_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
-    }
-
-    *s = sumf;
-
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(scale);
+    ggml_vec_dot_iq1_m_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -4078,37 +3641,10 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = sumf;
 
 #else
-    float sumf = 0;
-    for (int ibl = 0; ibl < nb; ++ibl) {
-        const float d4d8 = GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
-        uint16_t h = x[ibl].scales_h;
-        const uint8_t * qs = x[ibl].qs;
-        const int8_t  * q8 = y[ibl].qs;
-        for (int ib = 0; ib < QK_K/32; ib += 2) {
-            const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30);
-            const uint8_t ls2 = (x[ibl].scales_l[ib/2] >>  4) | ((h << 2) & 0x30);
-            h >>= 4;
-            const float d1 = d4d8*(ls1 - 32);
-            const float d2 = d4d8*(ls2 - 32);
-            int sumi1 = 0, sumi2 = 0;
-            for (int j = 0; j < 16; ++j) {
-                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
-                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
-            }
-            sumf += d1 * (sumi1 + sumi2);
-            qs += 16;
-            q8 += 32;
-            sumi1 = sumi2 = 0;
-            for (int j = 0; j < 16; ++j) {
-                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
-                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
-            }
-            sumf += d2 * (sumi1 + sumi2);
-            qs += 16;
-            q8 += 32;
-        }
-    }
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 

ggml/src/ggml-cpu/arch/arm/repack.cpp

@@ -86,35 +86,9 @@ void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTR
         }
     }
 #else
-    // scalar
-    const int blck_size_interleave = 4;
-    float srcv[4][QK8_0];
-    float id[4];
-
-    for (int i = 0; i < nb; i++) {
-        for (int row_iter = 0; row_iter < 4; row_iter++) {
-            float amax = 0.0f; // absolute max
-
-            for (int j = 0; j < QK8_0; j++) {
-                srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j];
-                amax = MAX(amax, fabsf(srcv[row_iter][j]));
-            }
-
-            const float d = amax / ((1 << 7) - 1);
-            id[row_iter] = d ? 1.0f / d : 0.0f;
-
-            y[i].d[row_iter] = GGML_CPU_FP32_TO_FP16(d);
-        }
-
-        for (int j = 0; j < QK8_0 * 4; j++) {
-            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
-            int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
-            src_offset += (j % blck_size_interleave);
-
-            float x0 = srcv[src_id][src_offset] * id[src_id];
-            y[i].qs[j] = roundf(x0);
-        }
-    }
+    UNUSED(nb);
+    UNUSED(y);
+    ggml_quantize_mat_q8_0_4x4_generic(x, vy, k);
 #endif
 }
 
@@ -205,35 +179,9 @@ void ggml_quantize_mat_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTR
     }
 
 #else
-    // scalar
-    const int blck_size_interleave = 8;
-    float srcv[4][QK8_0];
-    float id[4];
-
-    for (int i = 0; i < nb; i++) {
-        for (int row_iter = 0; row_iter < 4; row_iter++) {
-            float amax = 0.0f; // absolute max
-
-            for (int j = 0; j < QK8_0; j++) {
-                srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j];
-                amax = MAX(amax, fabsf(srcv[row_iter][j]));
-            }
-
-            const float d = amax / ((1 << 7) - 1);
-            id[row_iter] = d ? 1.0f / d : 0.0f;
-
-            y[i].d[row_iter] = GGML_CPU_FP32_TO_FP16(d);
-        }
-
-        for (int j = 0; j < QK8_0 * 4; j++) {
-            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
-            int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
-            src_offset += (j % blck_size_interleave);
-
-            float x0 = srcv[src_id][src_offset] * id[src_id];
-            y[i].qs[j] = roundf(x0);
-        }
-    }
+    UNUSED(nb);
+    UNUSED(y);
+    ggml_quantize_mat_q8_0_4x8_generic(x, vy, k);
 #endif
 }
 
@@ -295,29 +243,7 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     }
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
-    float sumf[4];
-    int sumi;
-
-    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
-    for (int x = 0; x < nc / ncols_interleaved; x++) {
-        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
-
-        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
-        for (int l = 0; l < nb; l++) {
-            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                for (int j = 0; j < ncols_interleaved; j++) {
-                    sumi = 0;
-                    for (int i = 0; i < blocklen; ++i) {
-                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
-                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
-                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
-                    }
-                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
-                }
-            }
-        }
-        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
-    }
+    ggml_gemv_q4_0_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
 void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -383,29 +309,7 @@ void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     }
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
-    float sumf[4];
-    int sumi;
-
-    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
-    for (int x = 0; x < nc / ncols_interleaved; x++) {
-        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
-
-        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
-        for (int l = 0; l < nb; l++) {
-            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                for (int j = 0; j < ncols_interleaved; j++) {
-                    sumi = 0;
-                    for (int i = 0; i < blocklen; ++i) {
-                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
-                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
-                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
-                    }
-                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
-                }
-            }
-        }
-        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
-    }
+    ggml_gemv_q4_0_4x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
 void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -497,31 +401,7 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
 #endif // #if defined(__ARM_FEATURE_SVE)
 
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
-    {
-        float sumf[8];
-        int sumi;
-
-        const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
-        for (int x = 0; x < nc / ncols_interleaved; x++) {
-            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
-
-            for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
-            for (int l = 0; l < nb; l++) {
-                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                    for (int j = 0; j < ncols_interleaved; j++) {
-                        sumi = 0;
-                        for (int i = 0; i < blocklen; ++i) {
-                            const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
-                            const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
-                            sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
-                        }
-                        sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
-                    }
-                }
-            }
-            for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
-        }
-    }
+    ggml_gemv_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
 void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -591,31 +471,7 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
     }
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
-    {
-        float sumf[4];
-        int sumi;
-
-        const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
-        for (int x = 0; x < nc / ncols_interleaved; x++) {
-            const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb);
-
-            for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
-            for (int l = 0; l < nb; l++) {
-                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                    for (int j = 0; j < ncols_interleaved; j++) {
-                        sumi = 0;
-                        for (int i = 0; i < blocklen; ++i) {
-                            const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
-                            const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
-                            sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2]));
-                        }
-                        sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
-                    }
-                }
-            }
-            for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
-        }
-    }
+    ggml_gemv_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
 void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -1096,40 +952,7 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     );
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
-    {
-        float sumf[4][4];
-        int sumi;
-
-        for (int y = 0; y < nr / 4; y++) {
-            const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
-            for (int x = 0; x < nc / ncols_interleaved; x++) {
-                const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
-                for (int m = 0; m < 4; m++) {
-                    for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
-                }
-                for (int l = 0; l < nb; l++) {
-                    for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                        for (int m = 0; m < 4; m++) {
-                            for (int j = 0; j < ncols_interleaved; j++) {
-                                sumi = 0;
-                                for (int i = 0; i < blocklen; ++i) {
-                                    const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
-                                    const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
-                                    sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
-                                            (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
-                                }
-                                sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
-                            }
-                        }
-                    }
-                }
-                for (int m = 0; m < 4; m++) {
-                    for (int j = 0; j < ncols_interleaved; j++)
-                        s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
-                }
-            }
-        }
-    }
+    ggml_gemm_q4_0_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
 void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -1550,38 +1373,7 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     );
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
-    float sumf[4][4];
-    int sumi;
-
-    for (int y = 0; y < nr / 4; y++) {
-        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
-        for (int x = 0; x < nc / ncols_interleaved; x++) {
-            const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
-            for (int m = 0; m < 4; m++) {
-                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
-            }
-            for (int l = 0; l < nb; l++) {
-                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                    for (int m = 0; m < 4; m++) {
-                        for (int j = 0; j < ncols_interleaved; j++) {
-                            sumi = 0;
-                            for (int i = 0; i < blocklen; ++i) {
-                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
-                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
-                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
-                                        (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
-                            }
-                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
-                        }
-                    }
-                }
-            }
-            for (int m = 0; m < 4; m++) {
-                for (int j = 0; j < ncols_interleaved; j++)
-                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
-            }
-        }
-    }
+    ggml_gemm_q4_0_4x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -2019,38 +1811,7 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
 #endif // #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
 
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
-    float sumf[4][8];
-    int sumi;
-
-    for (int y = 0; y < nr / 4; y++) {
-        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
-        for (int x = 0; x < nc / ncols_interleaved; x++) {
-            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
-            for (int m = 0; m < 4; m++) {
-                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
-            }
-            for (int l = 0; l < nb; l++) {
-                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                    for (int m = 0; m < 4; m++) {
-                        for (int j = 0; j < ncols_interleaved; j++) {
-                            sumi = 0;
-                            for (int i = 0; i < blocklen; ++i) {
-                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
-                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
-                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
-                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
-                            }
-                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
-                        }
-                    }
-                }
-            }
-            for (int m = 0; m < 4; m++) {
-                for (int j = 0; j < ncols_interleaved; j++)
-                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
-            }
-        }
-    }
+    ggml_gemm_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
 void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -2126,38 +1887,5 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
     }
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
-    {
-        float sumf[4][4];
-        int sumi;
-
-        for (int y = 0; y < nr / 4; y++) {
-            const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
-            for (int x = 0; x < nc / ncols_interleaved; x++) {
-                const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb);
-                for (int m = 0; m < 4; m++) {
-                    for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
-                }
-                for (int l = 0; l < nb; l++) {
-                    for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                        for (int m = 0; m < 4; m++) {
-                            for (int j = 0; j < ncols_interleaved; j++) {
-                                sumi = 0;
-                                for (int i = 0; i < blocklen; ++i) {
-                                    const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
-                                    const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
-                                    sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
-                                            (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4]));
-                                }
-                                sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
-                            }
-                        }
-                    }
-                }
-                for (int m = 0; m < 4; m++) {
-                    for (int j = 0; j < ncols_interleaved; j++)
-                        s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
-                }
-            }
-        }
-    }
+    ggml_gemm_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }

ggml/src/ggml-cpu/arch/loongarch/quants.c

@@ -544,7 +544,7 @@ void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
         __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs, 0) );
         max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) );
         __m128 tmp = max4;
-        max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vextrins_w((__m128i)tmp, (__m128i)max4, 0x10 ));
+        max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vextrins_w((__m128i)tmp, (__m128i)max4, 0x1 ));
         const float max_scalar = ((v4f32)max4)[0];
 
         // Quantize these floats
@@ -821,24 +821,15 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = hsum_float_8(acc) + summs;
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[ib].qs[j] & 0x0F);
-            const int v1 = (x[ib].qs[j] >>   4);
-
-            sumi0 += (v0 * y[ib].qs[j]);
-            sumi1 += (v1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_q4_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -883,30 +874,15 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = hsum_float_8(acc);
 
-#endif
-    for (; ib < nb; ++ib) {
-        uint32_t qh;
-        memcpy(&qh, x[ib].qh, sizeof(qh));
-
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
-            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
-
-            const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16);
-            const int32_t x1 = (int8_t)(((x[ib].qs[j] >>   4) | xh_1) - 16);
-
-            sumi0 += (x0 * y[ib].qs[j]);
-            sumi1 += (x1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d)) * sumi;
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(ib);
+    UNUSED(sumf);
+    UNUSED(x);
+    UNUSED(y);
+    ggml_vec_dot_q5_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -954,30 +930,15 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = hsum_float_8(acc) + summs;
 
-#endif
-    for (; ib < nb; ++ib) {
-        uint32_t qh;
-        memcpy(&qh, x[ib].qh, sizeof(qh));
-
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
-            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
-
-            const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0;
-            const int32_t x1 = (x[ib].qs[j] >>  4) | xh_1;
-
-            sumi0 += (x0 * y[ib].qs[j]);
-            sumi1 += (x1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(ib);
+    UNUSED(sumf);
+    UNUSED(x);
+    UNUSED(y);
+    ggml_vec_dot_q5_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -1016,18 +977,15 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = hsum_float_8(acc);
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi = 0;
-
-        for (int j = 0; j < qk; j++) {
-            sumi += x[ib].qs[j]*y[ib].qs[j];
-        }
-
-        sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(ib);
+    UNUSED(sumf);
+    UNUSED(x);
+    UNUSED(y);
+    ggml_vec_dot_q8_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -1103,45 +1061,10 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = hsum_float_8(acc);
 
 #else
-
-    float sumf = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const uint8_t * q2 = x[i].qs;
-        const  int8_t * q8 = y[i].qs;
-        const uint8_t * sc = x[i].scales;
-
-        int summs = 0;
-        for (int j = 0; j < 16; ++j) {
-            summs += y[i].bsums[j] * (sc[j] >> 4);
-        }
-
-        const float dall = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
-        const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
-
-        int isum = 0;
-        int is = 0;
-        int d;
-        for (int k = 0; k < QK_K/128; ++k) {
-            int shift = 0;
-            for (int j = 0; j < 4; ++j) {
-                d = sc[is++] & 0xF;
-                int isuml = 0;
-                for (int l =  0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
-                isum += d * isuml;
-                d = sc[is++] & 0xF;
-                isuml = 0;
-                for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
-                isum += d * isuml;
-                shift += 2;
-                q8 += 32;
-            }
-            q2 += 32;
-        }
-        sumf += dall * isum - dmin * summs;
-    }
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q2_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1239,70 +1162,13 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = hsum_float_8(acc);
 
 #else
-    // scalar version
-    // This function is written like this so the compiler can manage to vectorize most of it
-    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
-    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
-    // The ideal situation would be if we could just write the code once, and the compiler would
-    // automatically produce the best possible set of machine instructions, instead of us having to manually
-    // write vectorized versions for AVX, ARM_NEON, etc.
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    uint32_t auxs[4];
-    const int8_t * scales = (const int8_t*)auxs;
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            q3 += 32;
-        }
-        a = aux8;
-
-        memcpy(auxs, x[i].scales, 12);
-        uint32_t tmp = auxs[2];
-        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
-        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
-        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
-        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
-        for (int j = 0; j < QK_K/16; ++j) {
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
-
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
-
 }
 
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -1391,61 +1257,14 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = hsum_float_8(acc) + ((v4f32)acc_m)[0];
 
 #else
-
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            a += 32;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            a += 32; q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(utmp);
+    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1541,66 +1360,14 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = hsum_float_8(acc) + ((v4f32)acc_m)[0];
 
 #else
-
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(utmp);
+    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1678,47 +1445,10 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = hsum_float_8(acc);
 
 #else
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
-        const uint8_t * GGML_RESTRICT qh = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) {
-                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
-                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
-                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
-                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
-            }
-            a  += 128;
-            q4 += 64;
-            qh += 32;
-        }
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            int scale = x[i].scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1815,34 +1545,10 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.125f * hsum_float_8(accumf);
 
 #else
-
-    uint32_t aux32[2];
-    const uint8_t * aux8 = (const uint8_t *)aux32;
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
-        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            memcpy(aux32, q2, 2*sizeof(uint32_t));
-            q2 += 4;
-            const uint32_t ls = 2*(aux32[1] >> 28) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
-                const uint8_t  signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127];
-                for (int j = 0; j < 8; ++j) {
-                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += sumi * ls;
-        }
-        sumf += d * bsum;
-    }
-    *s = 0.125f * sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq2_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1978,42 +1684,10 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = 0.125f * hsum_float_8(accumf);
 
 #else
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
-        const uint8_t  * GGML_RESTRICT sc = x[i].scales;
-        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1;
-            const uint16_t ls2 = 2*(sc[ib32] >>  4) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 2; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
-                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
-                for (int j = 0; j < 8; ++j) {
-                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += sumi * ls1;
-            sumi = 0;
-            for (int l = 2; l < 4; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
-                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
-                for (int j = 0; j < 8; ++j) {
-                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += sumi * ls2;
-            q2 += 4;
-        }
-        sumf += d * bsum;
-    }
-    *s = 0.125f * sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq2_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -2105,47 +1779,11 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = 0.125f * hsum_float_8(accumf);
 
 #else
-
-    float sumf = 0;
-    for (int i = 0; i < nb; i++) {
-
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const int8_t  * q8 = y[i].qs;
-        const uint8_t * qs = x[i].qs;
-        const uint8_t * qh = x[i].qh;
-        const uint8_t * signs = qs + QK_K/8;
-
-        int bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf);
-            int ls2 = 1 + 2*(x[i].scales[ib32] >>  4);
-            int sumi1 = 0, sumi2 = 0;
-            for (int l = 0; l < 2; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
-                for (int j = 0; j < 8; ++j) {
-                    sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            for (int l = 2; l < 4; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
-                for (int j = 0; j < 8; ++j) {
-                    sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += ls1 * sumi1 + ls2 * sumi2;
-            qs += 4;
-            signs += 4;
-        }
-
-        sumf += d * bsum;
-    }
-
-    *s = 0.125f * sumf;
-
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq2_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
-
 }
 
 void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -2209,36 +1847,10 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.25f * hsum_float_8(accumf);
 
 #else
-
-    uint32_t aux32;
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
-        const uint8_t * GGML_RESTRICT gas = x[i].qs + QK_K/4;
-        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t);
-            const uint32_t ls = 2*(aux32 >> 28) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]);
-                const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]);
-                const uint8_t  signs = ksigns_iq2xs[(aux32 >> 7*l) & 127];
-                for (int j = 0; j < 4; ++j) {
-                    sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1);
-                    sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            q3 += 8;
-            bsum += sumi * ls;
-        }
-        sumf += d * bsum;
-    }
-    *s = 0.25f * sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq3_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -2338,48 +1950,10 @@ void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = hsum_float_8(accumf);
 
 #else
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint8_t * GGML_RESTRICT qs = x[i].qs;
-        const uint8_t * GGML_RESTRICT qh = x[i].qh;
-        const uint8_t * GGML_RESTRICT signs = x[i].signs;
-        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
-            const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1;
-            const uint32_t ls2 = 2*(x[i].scales[ib32/2] >>  4) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256)));
-                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256)));
-                for (int j = 0; j < 4; ++j) {
-                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
-                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            qs += 8;
-            signs += 4;
-            bsum += sumi * ls1;
-            sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256)));
-                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256)));
-                for (int j = 0; j < 4; ++j) {
-                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
-                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            qs += 8;
-            signs += 4;
-            bsum += sumi * ls2;
-        }
-        sumf += d * bsum;
-    }
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq3_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -2460,36 +2034,10 @@ void ggml_vec_dot_iq1_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = hsum_float_8(accum) + IQ1S_DELTA * accum1;
 
 #else
-
-    float sumf = 0;
-    for (int i = 0; i < nb; i++) {
-
-        const int8_t   * q8 = y[i].qs;
-        const uint8_t  * qs = x[i].qs;
-        const uint16_t * qh = x[i].qh;
-
-        int sumi = 0, sumi1 = 0;
-        for (int ib = 0; ib < QK_K/32; ++ib) {
-            const int ls = 2*((qh[ib] >> 12) & 7) + 1;
-            const int delta = qh[ib] & 0x8000 ? -1 : 1;
-            int lsum = 0;
-            for (int l = 0; l < 4; ++l) {
-                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8)));
-                for (int j = 0; j < 8; ++j) {
-                    lsum += q8[j] * grid[j];
-                }
-                q8 += 8;
-            }
-            sumi  += ls * lsum;
-            sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]);
-            qs += 4;
-        }
-
-        sumf += GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1);
-    }
-
-    *s = sumf;
-
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq1_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -2603,37 +2151,10 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = hsum_float_8(accum);
 
 #else
-    float sumf = 0;
-    for (int ibl = 0; ibl < nb; ++ibl) {
-        const float d4d8 = GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
-        uint16_t h = x[ibl].scales_h;
-        const uint8_t * qs = x[ibl].qs;
-        const int8_t  * q8 = y[ibl].qs;
-        for (int ib = 0; ib < QK_K/32; ib += 2) {
-            const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30);
-            const uint8_t ls2 = (x[ibl].scales_l[ib/2] >>  4) | ((h << 2) & 0x30);
-            h >>= 4;
-            const float d1 = d4d8*(ls1 - 32);
-            const float d2 = d4d8*(ls2 - 32);
-            int sumi1 = 0, sumi2 = 0;
-            for (int j = 0; j < 16; ++j) {
-                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
-                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
-            }
-            sumf += d1 * (sumi1 + sumi2);
-            qs += 16;
-            q8 += 32;
-            sumi1 = sumi2 = 0;
-            for (int j = 0; j < 16; ++j) {
-                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
-                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
-            }
-            sumf += d2 * (sumi1 + sumi2);
-            qs += 16;
-            q8 += 32;
-        }
-    }
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 

ggml/src/ggml-cpu/arch/powerpc/quants.c

@@ -201,24 +201,14 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = vec_extract(vsumf0, 0);
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[ib].qs[j] & 0x0F) - 8;
-            const int v1 = (x[ib].qs[j] >>   4) - 8;
-
-            sumi0 += (v0 * y[ib].qs[j]);
-            sumi1 += (v1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += sumi*GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d);
-    }
-
     *s = sumf;
+#else
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_q4_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -278,24 +268,14 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = vec_extract(vsumf0, 0);
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[ib].qs[j] & 0x0F);
-            const int v1 = (x[ib].qs[j] >>   4);
-
-            sumi0 += (v0 * y[ib].qs[j]);
-            sumi1 += (v1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
-    }
-
     *s = sumf;
+#else
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_q4_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -360,30 +340,14 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = vec_extract(vsumf0, 0);
 
-#endif
-    for (; ib < nb; ++ib) {
-        uint32_t qh;
-        memcpy(&qh, x[ib].qh, sizeof(qh));
-
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
-            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
-
-            const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16);
-            const int32_t x1 = (int8_t)(((x[ib].qs[j] >>   4) | xh_1) - 16);
-
-            sumi0 += (x0 * y[ib].qs[j]);
-            sumi1 += (x1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d)) * sumi;
-    }
-
     *s = sumf;
+#else
+    UNUSED(ib);
+    UNUSED(sumf);
+    UNUSED(x);
+    UNUSED(y);
+    ggml_vec_dot_q5_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -451,30 +415,15 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = vec_extract(vsumf0, 0);
 
-#endif
-    for (; ib < nb; ++ib) {
-        uint32_t qh;
-        memcpy(&qh, x[ib].qh, sizeof(qh));
-
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
-            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
-
-            const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0;
-            const int32_t x1 = (x[ib].qs[j] >>  4) | xh_1;
-
-            sumi0 += (x0 * y[ib].qs[j]);
-            sumi1 += (x1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(ib);
+    UNUSED(sumf);
+    UNUSED(x);
+    UNUSED(y);
+    ggml_vec_dot_q5_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -535,18 +484,15 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = vec_extract(vsumf0, 0);
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi = 0;
-
-        for (int j = 0; j < qk; j++) {
-            sumi += x[ib].qs[j]*y[ib].qs[j];
-        }
-
-        sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_q8_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -695,45 +641,10 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = vec_extract(vsumf0, 0);
 
 #else
-
-    float sumf = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const uint8_t * q2 = x[i].qs;
-        const  int8_t * q8 = y[i].qs;
-        const uint8_t * sc = x[i].scales;
-
-        int summs = 0;
-        for (int j = 0; j < 16; ++j) {
-            summs += y[i].bsums[j] * (sc[j] >> 4);
-        }
-
-        const float dall = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
-        const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
-
-        int isum = 0;
-        int is = 0;
-        int d;
-        for (int k = 0; k < QK_K/128; ++k) {
-            int shift = 0;
-            for (int j = 0; j < 4; ++j) {
-                d = sc[is++] & 0xF;
-                int isuml = 0;
-                for (int l =  0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
-                isum += d * isuml;
-                d = sc[is++] & 0xF;
-                isuml = 0;
-                for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
-                isum += d * isuml;
-                shift += 2;
-                q8 += 32;
-            }
-            q2 += 32;
-        }
-        sumf += dall * isum - dmin * summs;
-    }
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q2_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -907,70 +818,13 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = vec_extract(vsumf0, 0);
 
 #else
-    // scalar version
-    // This function is written like this so the compiler can manage to vectorize most of it
-    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
-    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
-    // The ideal situation would be if we could just write the code once, and the compiler would
-    // automatically produce the best possible set of machine instructions, instead of us having to manually
-    // write vectorized versions for AVX, ARM_NEON, etc.
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    uint32_t auxs[4];
-    const int8_t * scales = (const int8_t*)auxs;
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            q3 += 32;
-        }
-        a = aux8;
-
-        memcpy(auxs, x[i].scales, 12);
-        uint32_t tmp = auxs[2];
-        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
-        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
-        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
-        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
-        for (int j = 0; j < QK_K/16; ++j) {
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
-
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
-
 }
 
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -1130,61 +984,14 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = vec_extract(vsumf0, 0);
 
 #else
-
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            a += 32;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            a += 32; q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(utmp);
+    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1342,66 +1149,14 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = vec_extract(vsumf0, 0);
 
 #else
-
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(utmp);
+    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1556,47 +1311,10 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = vec_extract(vsumf0, 0);
 
 #else
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
-        const uint8_t * GGML_RESTRICT qh = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) {
-                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
-                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
-                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
-                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
-            }
-            a  += 128;
-            q4 += 64;
-            qh += 32;
-        }
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            int scale = x[i].scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1737,34 +1455,10 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.125f * vec_extract(vsumf0, 0);
 
 #else
-
-    uint32_t aux32[2];
-    const uint8_t * aux8 = (const uint8_t *)aux32;
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
-        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            memcpy(aux32, q2, 2*sizeof(uint32_t));
-            q2 += 4;
-            const uint32_t ls = 2*(aux32[1] >> 28) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
-                const uint8_t  signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127];
-                for (int j = 0; j < 8; ++j) {
-                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += sumi * ls;
-        }
-        sumf += d * bsum;
-    }
-    *s = 0.125f * sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq2_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1869,42 +1563,10 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = 0.125f * vec_extract(vsumf0, 0);
 
 #else
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
-        const uint8_t  * GGML_RESTRICT sc = x[i].scales;
-        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1;
-            const uint16_t ls2 = 2*(sc[ib32] >>  4) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 2; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
-                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
-                for (int j = 0; j < 8; ++j) {
-                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += sumi * ls1;
-            sumi = 0;
-            for (int l = 2; l < 4; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
-                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
-                for (int j = 0; j < 8; ++j) {
-                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += sumi * ls2;
-            q2 += 4;
-        }
-        sumf += d * bsum;
-    }
-    *s = 0.125f * sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq2_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -2030,47 +1692,11 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = 0.125f * vec_extract(vsumf0, 0);
 
 #else
-
-    float sumf = 0;
-    for (int i = 0; i < nb; i++) {
-
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const int8_t  * q8 = y[i].qs;
-        const uint8_t * qs = x[i].qs;
-        const uint8_t * qh = x[i].qh;
-        const uint8_t * signs = qs + QK_K/8;
-
-        int bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf);
-            int ls2 = 1 + 2*(x[i].scales[ib32] >>  4);
-            int sumi1 = 0, sumi2 = 0;
-            for (int l = 0; l < 2; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
-                for (int j = 0; j < 8; ++j) {
-                    sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            for (int l = 2; l < 4; ++l) {
-                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
-                for (int j = 0; j < 8; ++j) {
-                    sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            bsum += ls1 * sumi1 + ls2 * sumi2;
-            qs += 4;
-            signs += 4;
-        }
-
-        sumf += d * bsum;
-    }
-
-    *s = 0.125f * sumf;
-
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq2_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
-
 }
 
 void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -2172,36 +1798,10 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.25f * vec_extract(vsumf0, 0);
 
 #else
-
-    uint32_t aux32;
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
-        const uint8_t * GGML_RESTRICT gas = x[i].qs + QK_K/4;
-        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
-            memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t);
-            const uint32_t ls = 2*(aux32 >> 28) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]);
-                const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]);
-                const uint8_t  signs = ksigns_iq2xs[(aux32 >> 7*l) & 127];
-                for (int j = 0; j < 4; ++j) {
-                    sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1);
-                    sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            q3 += 8;
-            bsum += sumi * ls;
-        }
-        sumf += d * bsum;
-    }
-    *s = 0.25f * sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq3_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -2327,48 +1927,10 @@ void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = vec_extract(vsumf0, 0);
 
 #else
-
-    float sumf = 0.f;
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        const uint8_t * GGML_RESTRICT qs = x[i].qs;
-        const uint8_t * GGML_RESTRICT qh = x[i].qh;
-        const uint8_t * GGML_RESTRICT signs = x[i].signs;
-        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
-        int32_t bsum = 0;
-        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
-            const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1;
-            const uint32_t ls2 = 2*(x[i].scales[ib32/2] >>  4) + 1;
-            int32_t sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256)));
-                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256)));
-                for (int j = 0; j < 4; ++j) {
-                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
-                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            qs += 8;
-            signs += 4;
-            bsum += sumi * ls1;
-            sumi = 0;
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256)));
-                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256)));
-                for (int j = 0; j < 4; ++j) {
-                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
-                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
-                }
-                q8 += 8;
-            }
-            qs += 8;
-            signs += 4;
-            bsum += sumi * ls2;
-        }
-        sumf += d * bsum;
-    }
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq3_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -2481,36 +2043,10 @@ void ggml_vec_dot_iq1_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = vec_extract(vsumf0, 0);
 
 #else
-
-    float sumf = 0;
-    for (int i = 0; i < nb; i++) {
-
-        const int8_t   * q8 = y[i].qs;
-        const uint8_t  * qs = x[i].qs;
-        const uint16_t * qh = x[i].qh;
-
-        int sumi = 0, sumi1 = 0;
-        for (int ib = 0; ib < QK_K/32; ++ib) {
-            const int ls = 2*((qh[ib] >> 12) & 7) + 1;
-            const int delta = qh[ib] & 0x8000 ? -1 : 1;
-            int lsum = 0;
-            for (int l = 0; l < 4; ++l) {
-                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8)));
-                for (int j = 0; j < 8; ++j) {
-                    lsum += q8[j] * grid[j];
-                }
-                q8 += 8;
-            }
-            sumi  += ls * lsum;
-            sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]);
-            qs += 4;
-        }
-
-        sumf += GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1);
-    }
-
-    *s = sumf;
-
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq1_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -2581,17 +2117,15 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const v
 
     sumf = vec_extract(vsumf0, 0);
 
-#endif
-    for (; ib < nb; ++ib) {
-        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_CPU_FP16_TO_FP32(x[ib].d);
-        int sumi1 = 0, sumi2 = 0;
-        for (int j = 0; j < QK4_NL/2; ++j) {
-            sumi1 += y[ib].qs[j+       0] * kvalues_iq4nl[x[ib].qs[j] & 0xf];
-            sumi2 += y[ib].qs[j+QK4_NL/2] * kvalues_iq4nl[x[ib].qs[j] >>  4];
-        }
-        sumf += d * (sumi1 + sumi2);
-    }
     *s = sumf;
+#else
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_iq4_nl_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -2696,37 +2230,10 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = vec_extract(vsumf0, 0);
 
 #else
-    float sumf = 0;
-    for (int ibl = 0; ibl < nb; ++ibl) {
-        const float d4d8 = GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
-        uint16_t h = x[ibl].scales_h;
-        const uint8_t * qs = x[ibl].qs;
-        const int8_t  * q8 = y[ibl].qs;
-        for (int ib = 0; ib < QK_K/32; ib += 2) {
-            const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30);
-            const uint8_t ls2 = (x[ibl].scales_l[ib/2] >>  4) | ((h << 2) & 0x30);
-            h >>= 4;
-            const float d1 = d4d8*(ls1 - 32);
-            const float d2 = d4d8*(ls2 - 32);
-            int sumi1 = 0, sumi2 = 0;
-            for (int j = 0; j < 16; ++j) {
-                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
-                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
-            }
-            sumf += d1 * (sumi1 + sumi2);
-            qs += 16;
-            q8 += 32;
-            sumi1 = sumi2 = 0;
-            for (int j = 0; j < 16; ++j) {
-                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
-                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
-            }
-            sumf += d2 * (sumi1 + sumi2);
-            qs += 16;
-            q8 += 32;
-        }
-    }
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 

ggml/src/ggml-cpu/arch/riscv/quants.c

@@ -116,6 +116,7 @@ void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
 //===================================== Dot products =================================
 
 void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+#if defined(__riscv_v)
     const int qk = QK8_0;
     const int nb = n / qk;
 
@@ -132,7 +133,6 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
     int ib = 0;
     float sumf = 0;
 
-#if defined(__riscv_v)
     size_t vl = qk / 2;
 
     for (; ib < nb; ++ib) {
@@ -164,27 +164,14 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
         sumf += sumi*GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d);
     }
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[ib].qs[j] & 0x0F) - 8;
-            const int v1 = (x[ib].qs[j] >>   4) - 8;
-
-            sumi0 += (v0 * y[ib].qs[j]);
-            sumi1 += (v1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += sumi*GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d);
-    }
-
     *s = sumf;
+#else
+    ggml_vec_dot_q4_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+#if defined(__riscv_v)
     const int qk = QK8_1;
     const int nb = n / qk;
 
@@ -201,7 +188,6 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
     int ib = 0;
     float sumf = 0;
 
-#if defined(__riscv_v)
     size_t vl = qk / 2;
 
     for (; ib < nb; ++ib) {
@@ -229,27 +215,14 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
         sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
     }
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[ib].qs[j] & 0x0F);
-            const int v1 = (x[ib].qs[j] >>   4);
-
-            sumi0 += (v0 * y[ib].qs[j]);
-            sumi1 += (v1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
-    }
-
     *s = sumf;
+#else
+    ggml_vec_dot_q4_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+#if defined(__riscv_v)
     const int qk = QK8_0;
     const int nb = n / qk;
 
@@ -267,7 +240,6 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
     const block_q5_0 * GGML_RESTRICT x = vx;
     const block_q8_0 * GGML_RESTRICT y = vy;
 
-#if defined(__riscv_v)
     size_t vl;
     size_t vlenb = __riscv_vlenb();
 
@@ -297,33 +269,14 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
         sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d) * GGML_CPU_FP16_TO_FP32(y[ib].d)) * sumi;
     }
 
-#endif
-    for (; ib < nb; ++ib) {
-        uint32_t qh;
-        memcpy(&qh, x[ib].qh, sizeof(qh));
-
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
-            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
-
-            const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16);
-            const int32_t x1 = (int8_t)(((x[ib].qs[j] >>   4) | xh_1) - 16);
-
-            sumi0 += (x0 * y[ib].qs[j]);
-            sumi1 += (x1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d)) * sumi;
-    }
-
     *s = sumf;
+#else
+    ggml_vec_dot_q5_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+#if defined(__riscv_v)
     const int qk = QK8_1;
     const int nb = n / qk;
 
@@ -341,7 +294,6 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
     const block_q5_1 * GGML_RESTRICT x = vx;
     const block_q8_1 * GGML_RESTRICT y = vy;
 
-#if defined(__riscv_v)
     size_t vl;
     size_t vlenb = __riscv_vlenb();
 
@@ -370,30 +322,10 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
         sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
     }
 
-#endif
-    for (; ib < nb; ++ib) {
-        uint32_t qh;
-        memcpy(&qh, x[ib].qh, sizeof(qh));
-
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
-            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
-
-            const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0;
-            const int32_t x1 = (x[ib].qs[j] >>  4) | xh_1;
-
-            sumi0 += (x0 * y[ib].qs[j]);
-            sumi1 += (x1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
-    }
-
     *s = sumf;
+#else
+    ggml_vec_dot_q5_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -431,18 +363,17 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
         sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
     }
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi = 0;
-
-        for (int j = 0; j < qk; j++) {
-            sumi += x[ib].qs[j]*y[ib].qs[j];
-        }
-
-        sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
-    }
-
     *s = sumf;
+#else
+
+    UNUSED(nb);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+
+    ggml_vec_dot_q8_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -738,44 +669,11 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
 #else
 
-    float sumf = 0;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
 
-    for (int i = 0; i < nb; ++i) {
-
-        const uint8_t * q2 = x[i].qs;
-        const  int8_t * q8 = y[i].qs;
-        const uint8_t * sc = x[i].scales;
-
-        int summs = 0;
-        for (int j = 0; j < 16; ++j) {
-            summs += y[i].bsums[j] * (sc[j] >> 4);
-        }
-
-        const float dall = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
-        const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
-
-        int isum = 0;
-        int is = 0;
-        int d;
-        for (int k = 0; k < QK_K/128; ++k) {
-            int shift = 0;
-            for (int j = 0; j < 4; ++j) {
-                d = sc[is++] & 0xF;
-                int isuml = 0;
-                for (int l =  0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
-                isum += d * isuml;
-                d = sc[is++] & 0xF;
-                isuml = 0;
-                for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
-                isum += d * isuml;
-                shift += 2;
-                q8 += 32;
-            }
-            q2 += 32;
-        }
-        sumf += dall * isum - dmin * summs;
-    }
-    *s = sumf;
+    ggml_vec_dot_q2_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1147,68 +1045,14 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-    // scalar version
-    // This function is written like this so the compiler can manage to vectorize most of it
-    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
-    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
-    // The ideal situation would be if we could just write the code once, and the compiler would
-    // automatically produce the best possible set of machine instructions, instead of us having to manually
-    // write vectorized versions for AVX, ARM_NEON, etc.
 
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    uint32_t auxs[4];
-    const int8_t * scales = (const int8_t*)auxs;
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            q3 += 32;
-        }
-        a = aux8;
-
-        memcpy(auxs, x[i].scales, 12);
-        uint32_t tmp = auxs[2];
-        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
-        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
-        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
-        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
-        for (int j = 0; j < QK_K/16; ++j) {
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
 
+    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 
 }
@@ -1534,60 +1378,15 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
 #else
 
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(nb);
+    UNUSED(utmp);
 
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            a += 32;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            a += 32; q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1698,65 +1497,15 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
 #else
 
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(nb);
+    UNUSED(utmp);
 
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -2024,46 +1773,11 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
 #else
 
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
 
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
-        const uint8_t * GGML_RESTRICT qh = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) {
-                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
-                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
-                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
-                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
-            }
-            a  += 128;
-            q4 += 64;
-            qh += 32;
-        }
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            int scale = x[i].scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 

ggml/src/ggml-cpu/arch/riscv/repack.cpp

@@ -112,31 +112,7 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     }
 
 #endif
-    {
-        float sumf[8];
-        int sumi;
-
-        const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
-        for (int x = 0; x < nc / ncols_interleaved; x++) {
-            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
-
-            for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
-            for (int l = 0; l < nb; l++) {
-                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                    for (int j = 0; j < ncols_interleaved; j++) {
-                        sumi = 0;
-                        for (int i = 0; i < blocklen; ++i) {
-                            const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
-                            const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
-                            sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
-                        }
-                        sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
-                    }
-                }
-            }
-            for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
-        }
-    }
+    ggml_gemv_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -361,37 +337,6 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
         return;
     }
 
-#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
-    float sumf[4][8];
-    int sumi;
-
-    for (int y = 0; y < nr / 4; y++) {
-        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
-        for (int x = 0; x < nc / ncols_interleaved; x++) {
-            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
-            for (int m = 0; m < 4; m++) {
-                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
-            }
-            for (int l = 0; l < nb; l++) {
-                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                    for (int m = 0; m < 4; m++) {
-                        for (int j = 0; j < ncols_interleaved; j++) {
-                            sumi = 0;
-                            for (int i = 0; i < blocklen; ++i) {
-                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
-                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
-                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
-                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
-                            }
-                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
-                        }
-                    }
-                }
-            }
-            for (int m = 0; m < 4; m++) {
-                for (int j = 0; j < ncols_interleaved; j++)
-                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
-            }
-        }
-    }
+#endif
+    ggml_gemm_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }

ggml/src/ggml-cpu/arch/s390/quants.c

@@ -172,24 +172,15 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = acc[0] + acc[1] + acc[2] + acc[3];
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[ib].qs[j] & 0x0F) - 8;
-            const int v1 = (x[ib].qs[j] >>   4) - 8;
-
-            sumi0 += (v0 * y[ib].qs[j]);
-            sumi1 += (v1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += sumi*GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d);
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_q4_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -239,24 +230,15 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = acc[0] + acc[1] + acc[2] + acc[3] + summs;
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[ib].qs[j] & 0x0F);
-            const int v1 = (x[ib].qs[j] >>   4);
-
-            sumi0 += (v0 * y[ib].qs[j]);
-            sumi1 += (v1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_q4_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -298,18 +280,15 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = acc[0] + acc[1] + acc[2] + acc[3];
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi = 0;
-
-        for (int j = 0; j < qk; j++) {
-            sumi += x[ib].qs[j]*y[ib].qs[j];
-        }
-
-        sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_q8_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -442,70 +421,13 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sum;
 
 #else
-    // scalar version
-    // This function is written like this so the compiler can manage to vectorize most of it
-    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
-    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
-    // The ideal situation would be if we could just write the code once, and the compiler would
-    // automatically produce the best possible set of machine instructions, instead of us having to manually
-    // write vectorized versions for AVX, ARM_NEON, etc.
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    uint32_t auxs[4];
-    const int8_t * scales = (const int8_t*)auxs;
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            q3 += 32;
-        }
-        a = aux8;
-
-        memcpy(auxs, x[i].scales, 12);
-        uint32_t tmp = auxs[2];
-        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
-        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
-        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
-        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
-        for (int j = 0; j < QK_K/16; ++j) {
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
-
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
-
 }
 
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -600,61 +522,14 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            a += 32;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            a += 32; q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(utmp);
+    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -767,66 +642,14 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(utmp);
+    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -969,47 +792,10 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sum;
 
 #else
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
-        const uint8_t * GGML_RESTRICT qh = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) {
-                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
-                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
-                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
-                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
-            }
-            a  += 128;
-            q4 += 64;
-            qh += 32;
-        }
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            int scale = x[i].scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1186,17 +972,15 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const v
         sumf += GGML_CPU_FP16_TO_FP32(x0->d) * GGML_CPU_FP16_TO_FP32(y0->d) * (v_xy[0] + v_xy[1] + v_xy[2] + v_xy[3]);
     }
 
-#endif
-    for (; ib < nb; ++ib) {
-        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_CPU_FP16_TO_FP32(x[ib].d);
-        int sumi1 = 0, sumi2 = 0;
-        for (int j = 0; j < QK4_NL/2; ++j) {
-            sumi1 += y[ib].qs[j+       0] * kvalues_iq4nl[x[ib].qs[j] & 0xf];
-            sumi2 += y[ib].qs[j+QK4_NL/2] * kvalues_iq4nl[x[ib].qs[j] >>  4];
-        }
-        sumf += d * (sumi1 + sumi2);
-    }
     *s = sumf;
+#else
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_iq4_nl_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -1264,37 +1048,10 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = sumf;
 
 #else
-    float sumf = 0;
-    for (int ibl = 0; ibl < nb; ++ibl) {
-        const float d4d8 = GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
-        uint16_t h = x[ibl].scales_h;
-        const uint8_t * qs = x[ibl].qs;
-        const int8_t  * q8 = y[ibl].qs;
-        for (int ib = 0; ib < QK_K/32; ib += 2) {
-            const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30);
-            const uint8_t ls2 = (x[ibl].scales_l[ib/2] >>  4) | ((h << 2) & 0x30);
-            h >>= 4;
-            const float d1 = d4d8*(ls1 - 32);
-            const float d2 = d4d8*(ls2 - 32);
-            int sumi1 = 0, sumi2 = 0;
-            for (int j = 0; j < 16; ++j) {
-                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
-                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
-            }
-            sumf += d1 * (sumi1 + sumi2);
-            qs += 16;
-            q8 += 32;
-            sumi1 = sumi2 = 0;
-            for (int j = 0; j < 16; ++j) {
-                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
-                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
-            }
-            sumf += d2 * (sumi1 + sumi2);
-            qs += 16;
-            q8 += 32;
-        }
-    }
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 

ggml/src/ggml-cpu/arch/wasm/quants.c

@@ -435,30 +435,15 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
     sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
            wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
 
-#endif
-    for (; ib < nb; ++ib) {
-        uint32_t qh;
-        memcpy(&qh, x[ib].qh, sizeof(qh));
-
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
-            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
-
-            const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16);
-            const int32_t x1 = (int8_t)(((x[ib].qs[j] >>   4) | xh_1) - 16);
-
-            sumi0 += (x0 * y[ib].qs[j]);
-            sumi1 += (x1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d)) * sumi;
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(ib);
+    UNUSED(sumf);
+    UNUSED(x);
+    UNUSED(y);
+    ggml_vec_dot_q5_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -545,30 +530,15 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
     sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
            wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs;
 
-#endif
-    for (; ib < nb; ++ib) {
-        uint32_t qh;
-        memcpy(&qh, x[ib].qh, sizeof(qh));
-
-        int sumi0 = 0;
-        int sumi1 = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
-            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
-
-            const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0;
-            const int32_t x1 = (x[ib].qs[j] >>  4) | xh_1;
-
-            sumi0 += (x0 * y[ib].qs[j]);
-            sumi1 += (x1 * y[ib].qs[j + qk/2]);
-        }
-
-        int sumi = sumi0 + sumi1;
-        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(ib);
+    UNUSED(sumf);
+    UNUSED(x);
+    UNUSED(y);
+    ggml_vec_dot_q5_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -628,18 +598,15 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
     sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
            wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
 
-#endif
-    for (; ib < nb; ++ib) {
-        int sumi = 0;
-
-        for (int j = 0; j < qk; j++) {
-            sumi += x[ib].qs[j]*y[ib].qs[j];
-        }
-
-        sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
-    }
-
     *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_q8_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
 }
 
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -755,45 +722,10 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-
-    float sumf = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const uint8_t * q2 = x[i].qs;
-        const  int8_t * q8 = y[i].qs;
-        const uint8_t * sc = x[i].scales;
-
-        int summs = 0;
-        for (int j = 0; j < 16; ++j) {
-            summs += y[i].bsums[j] * (sc[j] >> 4);
-        }
-
-        const float dall = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
-        const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
-
-        int isum = 0;
-        int is = 0;
-        int d;
-        for (int k = 0; k < QK_K/128; ++k) {
-            int shift = 0;
-            for (int j = 0; j < 4; ++j) {
-                d = sc[is++] & 0xF;
-                int isuml = 0;
-                for (int l =  0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
-                isum += d * isuml;
-                d = sc[is++] & 0xF;
-                isuml = 0;
-                for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
-                isum += d * isuml;
-                shift += 2;
-                q8 += 32;
-            }
-            q2 += 32;
-        }
-        sumf += dall * isum - dmin * summs;
-    }
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q2_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -902,68 +834,12 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-    // scalar version
-    // This function is written like this so the compiler can manage to vectorize most of it
-    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
-    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
-    // The ideal situation would be if we could just write the code once, and the compiler would
-    // automatically produce the best possible set of machine instructions, instead of us having to manually
-    // write vectorized versions for AVX, ARM_NEON, etc.
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    uint32_t auxs[4];
-    const int8_t * scales = (const int8_t*)auxs;
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            q3 += 32;
-        }
-        a = aux8;
-
-        memcpy(auxs, x[i].scales, 12);
-        uint32_t tmp = auxs[2];
-        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
-        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
-        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
-        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
-        for (int j = 0; j < QK_K/16; ++j) {
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
-
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 
 }
@@ -1089,61 +965,14 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            a += 32;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            a += 32; q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(utmp);
+    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1279,66 +1108,14 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-
-    const uint8_t * scales = (const uint8_t*)&utmp[0];
-    const uint8_t * mins   = (const uint8_t*)&utmp[2];
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
-        const uint8_t * GGML_RESTRICT hm = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K/64; ++j) {
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
-            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
-            a += 32; m <<= 1;
-            q4 += 32;
-        }
-        memcpy(utmp, x[i].scales, 12);
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
-
-        int sumi = 0;
-        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            int32_t scale = scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    UNUSED(kmask1);
+    UNUSED(kmask2);
+    UNUSED(kmask3);
+    UNUSED(utmp);
+    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 
@@ -1435,47 +1212,10 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
-        const uint8_t * GGML_RESTRICT qh = x[i].qh;
-        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * GGML_RESTRICT a = aux8;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) {
-                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
-                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
-                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
-                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
-            }
-            a  += 128;
-            q4 += 64;
-            qh += 32;
-        }
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            int scale = x[i].scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(nb);
+    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
 

ggml/src/ggml-cpu/ggml-cpu.c

@@ -253,6 +253,12 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_1,
         .nrows                    = 1,
     },
+    [GGML_TYPE_MXFP4] = {
+        .from_float               = quantize_row_mxfp4,
+        .vec_dot                  = ggml_vec_dot_mxfp4_q8_0,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
+    },
     [GGML_TYPE_Q2_K] = {
         .from_float               = quantize_row_q2_K,
         .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
@@ -1670,6 +1676,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_add(params, tensor);
             } break;
+        case GGML_OP_ADD_ID:
+            {
+                ggml_compute_forward_add_id(params, tensor);
+            } break;
         case GGML_OP_ADD1:
             {
                 ggml_compute_forward_add1(params, tensor);
@@ -1924,7 +1934,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             } break;
         case GGML_OP_FLASH_ATTN_EXT:
             {
-                ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor);
+                ggml_compute_forward_flash_attn_ext(params, tensor);
             } break;
         case GGML_OP_FLASH_ATTN_BACK:
             {
@@ -2111,6 +2121,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_DUP:
         case GGML_OP_CONT:
         case GGML_OP_ADD:
+        case GGML_OP_ADD_ID:
         case GGML_OP_ADD1:
         case GGML_OP_ACC:
             {
@@ -2172,6 +2183,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
                 case GGML_GLU_OP_REGLU:
                 case GGML_GLU_OP_GEGLU:
                 case GGML_GLU_OP_SWIGLU:
+                case GGML_GLU_OP_SWIGLU_OAI:
                 case GGML_GLU_OP_GEGLU_ERF:
                 case GGML_GLU_OP_GEGLU_QUICK:
                     {
@@ -2673,6 +2685,7 @@ struct ggml_cplan ggml_graph_plan(
                         }
                     } break;
                 case GGML_OP_ADD:
+                case GGML_OP_ADD_ID:
                 case GGML_OP_ADD1:
                     {
                         if (ggml_is_quantized(node->src[0]->type)) {

ggml/src/ggml-cpu/ggml-cpu.cpp

@@ -35,7 +35,7 @@
 
 // ggml-backend interface
 
-std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type() {
+std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types() {
     static std::vector<ggml_backend_buffer_type_t> bufts = []() {
         std::vector<ggml_backend_buffer_type_t> bufts;
 
@@ -57,8 +57,6 @@ std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type
         }
 #endif
 
-        bufts.push_back(NULL);
-
         return bufts;
     }();
 
@@ -66,14 +64,20 @@ std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type
 }
 
 static ggml_backend_buffer_type_t * ggml_backend_cpu_device_get_extra_buffers_type(ggml_backend_dev_t device) {
-    return ggml_backend_cpu_get_extra_buffers_type().data();
+    static std::vector<ggml_backend_buffer_type_t> extra_bufts = [] {
+        std::vector<ggml_backend_buffer_type_t> bufts = ggml_backend_cpu_get_extra_buffer_types();
+        bufts.push_back(nullptr);
+        return bufts;
+    }();
+
+    return extra_bufts.data();
 
     GGML_UNUSED(device);
 }
 
 static bool ggml_backend_cpu_is_extra_buffer_type(ggml_backend_buffer_type_t buft) {
-    for (auto * extra : ggml_backend_cpu_get_extra_buffers_type()) {
-        if (extra && extra == buft) {
+    for (auto * extra : ggml_backend_cpu_get_extra_buffer_types()) {
+        if (extra == buft) {
             return true;
         }
     }
@@ -397,20 +401,13 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
         return true;
     }
 
-    // extra_buffer_op?
-    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
-        if (extra) {
-            auto buf_extra = (ggml::cpu::extra_buffer_type*) extra->context;
-            if (buf_extra && buf_extra->supports_op(dev, op)) {
-                return true;
-            }
-        }
-    }
-
-    // the other case need host buffer.
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        if (op->src[i] && op->src[i]->buffer && !ggml_backend_buft_is_host(op->src[i]->buffer->buft)) {
-            return false;
+    // check extra buffer types
+    // note: only the first sources are checked for extra buffer types to reduce overhead, increase if necessary
+    for (int i = 0; i < 4; i++) {
+        if (op->src[i] && op->src[i]->buffer &&
+            ggml_backend_cpu_is_extra_buffer_type(op->src[i]->buffer->buft)) {
+            auto * buf_extra = (ggml::cpu::extra_buffer_type *) op->src[i]->buffer->buft->context;
+            return buf_extra->supports_op(dev, op);
         }
     }
 

ggml/src/ggml-cpu/kleidiai/kernels.cpp

@@ -22,9 +22,94 @@
 
 #include "kai_common.h"
 
+#include "simd-mappings.h"
+
 #include "kernels.h"
 
 #define NELEMS(x) sizeof(x) / sizeof(*x)
+
+static const size_t INT4_PER_BYTE = 2;
+static const size_t INT4_BITS     = 4;
+static const int Q4_0_ZERO_POINT  = 8;
+const size_t INT4_PER_UINT16      = 4;
+
+static void dequantize_row_qsi4c32pscalef16(
+    const void *packed_data,
+    int32_t row_idx,
+    int64_t nc,
+    float *out,
+    size_t nr_pack,
+    size_t packed_row_stride,
+    size_t kr,
+    size_t bl,
+    size_t num_bytes_multiplier
+) {
+    size_t group_idx = row_idx / nr_pack;
+    size_t row_in_group = row_idx % nr_pack;
+    const uint8_t *packed_group = (const uint8_t *)packed_data + group_idx * packed_row_stride;
+    size_t num_blocks = nc / bl;
+    const uint8_t *block_ptr = packed_group;
+
+    for (size_t b = 0; b < num_blocks; ++b) {
+        uint16_t scale_f16 = *((const uint16_t *)(block_ptr + row_in_group * num_bytes_multiplier));
+        float scale = GGML_CPU_FP16_TO_FP32(scale_f16);
+
+        const uint8_t *segment_ptr = block_ptr + nr_pack * num_bytes_multiplier;
+        size_t num_segments = bl / kr;
+        size_t num_bytes_per_segment = kr / INT4_PER_BYTE;
+
+        for (size_t s = 0; s < num_segments; ++s) {
+            const uint8_t *seg_base = segment_ptr + s * nr_pack * num_bytes_per_segment;
+            const uint8_t *qbytes = seg_base + row_in_group * num_bytes_per_segment;
+            for (size_t k = 0; k < num_bytes_per_segment; ++k) {
+                uint8_t byte = qbytes[k] ^ 0x88;
+                int x0 = (byte & 0x0F) - Q4_0_ZERO_POINT;
+                int x1 = (byte >> INT4_BITS) - Q4_0_ZERO_POINT;
+                out[b * bl + s * num_bytes_per_segment + k] = x0 * scale;
+                out[b * bl + s * num_bytes_per_segment + k + bl/2] = x1 * scale;
+            }
+        }
+        block_ptr += nr_pack * num_bytes_multiplier + num_segments * nr_pack * num_bytes_per_segment;
+    }
+}
+
+static void dequantize_row_qsi4c32ps1s0scalef16(
+    const void *packed_data,
+    int32_t row_idx,
+    int64_t k,
+    float *out,
+    size_t nr,
+    size_t packed_row_stride,
+    size_t kr,
+    size_t bl,
+    size_t num_bytes_multiplier
+) {
+    const size_t num_blocks = k / bl;
+    const size_t bl4 = bl / INT4_PER_UINT16;
+
+    size_t group_idx = row_idx / nr;
+    size_t row_in_group = row_idx % nr;
+
+    const uint8_t *packed_group = (const uint8_t *)packed_data + group_idx * packed_row_stride;
+    const uint16_t *qdata = (const uint16_t *)packed_group;
+    const uint16_t *scales = (const uint16_t *)(packed_group + packed_row_stride - (nr * num_blocks * num_bytes_multiplier));
+
+    for (size_t block_idx = 0; block_idx < num_blocks; ++block_idx) {
+        uint16_t scale_f16 = scales[row_in_group + block_idx * nr];
+        float scale = GGML_CPU_FP16_TO_FP32(scale_f16);
+
+        for (size_t bl4_idx = 0; bl4_idx < bl4; ++bl4_idx) {
+            uint16_t q = qdata[(block_idx * bl4 + bl4_idx) * nr + row_in_group];
+
+            for (size_t qidx = 0; qidx < INT4_PER_UINT16; ++qidx) {
+                int v = ((q >> (qidx * 4)) & 0xF) - Q4_0_ZERO_POINT;
+                out[block_idx * bl + bl4_idx * INT4_BITS + qidx] = v * scale;
+            }
+        }
+    }
+    GGML_UNUSED(kr);
+}
+
 static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
 #if defined(__ARM_FEATURE_SME)
     {
@@ -63,8 +148,10 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32_neon,
         },
         /* .rhs_info = */ {
-            /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon,
-            /* .pack_func   = */ kai_run_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon,
+            /* .packed_size   = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon,
+            /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon,
+            /* .pack_func     = */ kai_run_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon,
+            /* .to_float      = */ dequantize_row_qsi4c32ps1s0scalef16,
         },
         /* .required_cpu       = */ CPU_FEATURE_SME,
         /* .lhs_type           = */ GGML_TYPE_F32,
@@ -107,8 +194,10 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .pack_func             = */ kai_run_lhs_pack_bf16p2vlx2_f32_sme,
         },
         /* .rhs_info = */ {
-            /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme,
-            /* .pack_func   = */ kai_run_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme,
+            /* .packed_size   = */ kai_get_rhs_packed_size_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme,
+            /* .packed_stride = */ NULL,
+            /* .pack_func     = */ kai_run_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme,
+            /* .to_float      = */ NULL,
         },
         /* .required_cpu       = */ CPU_FEATURE_SME,
         /* .lhs_type           = */ GGML_TYPE_F32,
@@ -154,8 +243,10 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
         },
         /* .rhs_info = */ {
-            /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
-            /* .pack_func   = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .packed_size   = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .pack_func     = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .to_float      = */ dequantize_row_qsi4c32pscalef16,
         },
         /* .required_cpu       = */ CPU_FEATURE_DOTPROD,
         /* .lhs_type           = */ GGML_TYPE_F32,
@@ -200,8 +291,10 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
         },
         /* .rhs_info = */ {
-            /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
-            /* .pack_func   = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .packed_size   = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .pack_func     = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .to_float      = */ dequantize_row_qsi4c32pscalef16,
         },
         /* .required_cpu       = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM,
         /* .lhs_type           = */ GGML_TYPE_F32,
@@ -247,8 +340,10 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
         },
         /* .rhs_info = */ {
-            /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
-            /* .pack_func   = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .packed_size   = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .pack_func     = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .to_float      = */ dequantize_row_qsi4c32pscalef16,
         },
         /* .required_cpu       = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM,
         /* .lhs_type           = */ GGML_TYPE_F32,
@@ -293,8 +388,10 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
         },
         /* .rhs_info = */ {
-            /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
-            /* .pack_func   = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .packed_size   = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .pack_func     = */ kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
+            /* .to_float      = */ dequantize_row_qsi4c32pscalef16,
         },
         /* .required_cpu       = */ CPU_FEATURE_DOTPROD,
         /* .lhs_type           = */ GGML_TYPE_F32,

ggml/src/ggml-cpu/kleidiai/kernels.h

@@ -71,12 +71,15 @@ struct rhs_packing_info {
         std::function<size_t(size_t n, size_t k, size_t nr, size_t kr, size_t bl)>,
         std::function<size_t(size_t n, size_t k)>
     > packed_size;
+    size_t (*packed_stride)(size_t k, size_t nr, size_t kr, size_t bl);
     std::variant<
         std::function<void(size_t num_groups, size_t n, size_t k, size_t nr, size_t kr, size_t sr, size_t bl, const uint8_t* rhs,
             const float* bias, void* rhs_packed, size_t extra_bytes, const struct kai_rhs_pack_qs4cxs1s0_param* params)>,
         std::function<void(size_t num_groups, size_t n, size_t k, size_t nr, size_t kr, size_t sr, size_t rhs_stride, const void* rhs,
             const void* bias, const void* scale, void* rhs_packed, size_t extra_bytes, const void* params)>
     > pack_func;
+    void (*to_float)(const void *packed_data, int32_t row_idx, int64_t nc, float *out, size_t nr_pack, size_t packed_row_stride,
+          size_t kr, size_t bl, size_t num_bytes_multiplier);
 };
 
 struct ggml_kleidiai_kernels {

ggml/src/ggml-cpu/kleidiai/kleidiai.cpp

@@ -40,6 +40,17 @@ struct ggml_kleidiai_context {
     ggml_kleidiai_kernels * kernels;
 } static ctx = { CPU_FEATURE_NONE, NULL };
 
+static const char* cpu_feature_to_string(cpu_feature f) {
+    switch (f) {
+        case CPU_FEATURE_NONE:    return "NONE";
+        case CPU_FEATURE_DOTPROD: return "DOTPROD";
+        case CPU_FEATURE_I8MM:    return "I8MM";
+        case CPU_FEATURE_SVE:     return "SVE";
+        case CPU_FEATURE_SME:     return "SME";
+        default:                  return "UNKNOWN";
+    }
+}
+
 static void init_kleidiai_context(void) {
 
     ggml_critical_section_start();
@@ -62,6 +73,11 @@ static void init_kleidiai_context(void) {
             ctx.features |= ggml_cpu_has_sme() ? CPU_FEATURE_SME : CPU_FEATURE_NONE;
         }
         ctx.kernels = ggml_kleidiai_select_kernels_q4_0(ctx.features);
+#ifndef NDEBUG
+        if (ctx.kernels) {
+            GGML_LOG_DEBUG("kleidiai: using kernel with CPU feature %s\n", cpu_feature_to_string(ctx.kernels->required_cpu));
+        }
+#endif
     }
     ggml_critical_section_end();
 }
@@ -102,6 +118,9 @@ static void transpose_f32kxn_f16nxk(size_t n, size_t k, float * dst, const uint1
 
 class tensor_traits : public ggml::cpu::tensor_traits {
     bool work_size(int /* n_threads */, const struct ggml_tensor * op, size_t & size) override {
+        if (op->op != GGML_OP_MUL_MAT) {
+            return false;
+        }
         ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, op);
         GGML_ASSERT(kernels);
         kernel_info * kernel = op->src[1]->ne[1] == 1 ? &kernels->gemv : &kernels->gemm;
@@ -135,6 +154,10 @@ class tensor_traits : public ggml::cpu::tensor_traits {
             } else if (dst->src[0]->type == GGML_TYPE_F16) {
                 return compute_forward_kv_cache(params, dst);
             }
+        } else if (dst->op == GGML_OP_GET_ROWS) {
+            if (dst->src[0]->type == GGML_TYPE_Q4_0) {
+                return compute_forward_get_rows(params, dst);
+            }
         }
         return false;
     }
@@ -270,6 +293,8 @@ class tensor_traits : public ggml::cpu::tensor_traits {
     }
 
     bool compute_forward_q4_0(struct ggml_compute_params * params, struct ggml_tensor * dst) {
+        GGML_ASSERT(dst->src[0]->type == GGML_TYPE_Q4_0);
+
         const ggml_tensor * src0 = dst->src[0];
         const ggml_tensor * src1 = dst->src[1];
 
@@ -342,8 +367,49 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         return true;
     }
 
+    bool compute_forward_get_rows(struct ggml_compute_params * params, struct ggml_tensor * dst) {
+        GGML_ASSERT(dst->src[0]->type == GGML_TYPE_Q4_0);
+        GGML_ASSERT(ctx.kernels);
+
+        const ggml_tensor * src0 = dst->src[0];
+        const ggml_tensor * src1 = dst->src[1];
+
+        GGML_TENSOR_BINARY_OP_LOCALS
+
+        rhs_packing_info * rhs_info = &ctx.kernels->rhs_info;
+        kernel_info * kernel        = &ctx.kernels->gemm;
+
+        const int64_t nc     = ne00;
+        const int64_t nr     = ggml_nelements(src1);
+
+        const size_t block_rows = kernel->get_nr();
+        const size_t kr         = kernel->get_kr();
+
+        const size_t num_bytes_multiplier = sizeof(uint16_t);
+        const size_t packed_stride = rhs_info->packed_stride(nc, block_rows, kr, QK4_0);
+
+        const int ith = params->ith;
+        const int nth = params->nth;
+
+        const int dr = (nr + nth - 1) / nth;
+        const int ir0 = dr * ith;
+        const int ir1 = MIN(ir0 + dr, nr);
+
+        for (int64_t i = ir0; i < ir1; ++i) {
+            GGML_ASSERT(src1->type == GGML_TYPE_I32);
+            int64_t row_idx = ((const int32_t *)src1->data)[i];
+            GGML_ASSERT(row_idx >= 0 && row_idx < src0->ne[1]);
+
+            float *out = (float *)((char *)dst->data + i * nb1);
+            rhs_info->to_float(src0->data, row_idx, nc, out, block_rows, packed_stride, kr, QK4_0, num_bytes_multiplier);
+        }
+
+        return true;
+    }
+
 public:
     int repack(struct ggml_tensor * tensor, const void * data, size_t data_size) {
+        GGML_ASSERT(tensor->type == GGML_TYPE_Q4_0);
         GGML_ASSERT(ctx.kernels);
         const size_t n = tensor->ne[1];
         const size_t k = tensor->ne[0];
@@ -351,17 +417,12 @@ public:
         size_t kr      = ctx.kernels->gemm.get_kr();
         size_t sr      = ctx.kernels->gemm.get_sr();
 
-#ifndef NDEBUG
-        const size_t repacked_size = variant_call<size_t>(ctx.kernels->rhs_info.packed_size, n, k, nr, kr, QK4_0);
-        GGML_ASSERT(repacked_size <= data_size && "repacked size larger than the packed size!");
-#endif
         struct kai_rhs_pack_qs4cxs1s0_param params;
         params.lhs_zero_point = 1;
         params.rhs_zero_point = 8;
         variant_call<void>(ctx.kernels->rhs_info.pack_func, 1, n, k, nr, kr, sr, QK4_0, (const uint8_t*)data, nullptr, tensor->data, 0, &params);
 
         return 0;
-
         GGML_UNUSED(data_size);
     }
 };
@@ -375,8 +436,8 @@ static ggml::cpu::tensor_traits * get_tensor_traits(ggml_backend_buffer_t, struc
 static enum ggml_status ggml_backend_cpu_kleidiai_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
     tensor->extra = (void *) ggml::cpu::kleidiai::get_tensor_traits(buffer, tensor);
 
-    GGML_UNUSED(buffer);
     return GGML_STATUS_SUCCESS;
+    GGML_UNUSED(buffer);
 }
 
 static void ggml_backend_cpu_kleidiai_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor,
@@ -418,18 +479,35 @@ static size_t ggml_backend_cpu_kleidiai_buffer_type_get_alignment(ggml_backend_b
     GGML_UNUSED(buft);
 }
 
+static size_t ggml_backend_cpu_kleidiai_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor) {
+    GGML_ASSERT(tensor->type == GGML_TYPE_Q4_0);
+    GGML_ASSERT(ctx.kernels);
+
+    const size_t n  = tensor->ne[1];
+    const size_t k  = tensor->ne[0];
+    const size_t nr = ctx.kernels->gemm.get_nr();
+    const size_t kr = ctx.kernels->gemm.get_kr();
+
+    return variant_call<size_t>(ctx.kernels->rhs_info.packed_size, n, k, nr, kr, QK4_0);
+
+    GGML_UNUSED(buft);
+}
+
 namespace ggml::cpu::kleidiai {
 class extra_buffer_type : ggml::cpu::extra_buffer_type {
     bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override {
-        if (op->op == GGML_OP_MUL_MAT &&
+        if ((op->op == GGML_OP_MUL_MAT || op->op == GGML_OP_GET_ROWS) &&
             op->src[0]->type == GGML_TYPE_Q4_0 &&
             op->src[0]->buffer &&
             (ggml_n_dims(op->src[0]) == 2) &&
             op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type() && ctx.kernels) {
+            if (op->op == GGML_OP_GET_ROWS && op->src[1]->ne[0] != 8) {
+                return false;
+            }
             if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
                 return false;
             }
-            if (op->src[1]->type == GGML_TYPE_F32 &&
+            if ((op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_I32) &&
                 ggml_ne(op->src[1], 2) == 1 && ggml_ne(op->src[1], 3) == 1) {
                 return true;
             }
@@ -438,7 +516,7 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type {
     }
 
     ggml::cpu::tensor_traits * get_tensor_traits(const struct ggml_tensor * op) override {
-        if (op->op == GGML_OP_MUL_MAT) {
+        if (op->op == GGML_OP_MUL_MAT || op->op == GGML_OP_GET_ROWS) {
             if (op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type()) {
                 return (ggml::cpu::tensor_traits *) op->src[0]->extra;
             }
@@ -469,7 +547,7 @@ ggml_backend_buffer_type_t ggml_backend_cpu_kleidiai_buffer_type(void) {
                            /* .alloc_buffer     = */ ggml_backend_cpu_kleidiai_buffer_type_alloc_buffer,
                            /* .get_alignment    = */ ggml_backend_cpu_kleidiai_buffer_type_get_alignment,
                            /* .get_max_size     = */ nullptr,  // defaults to SIZE_MAX
-                           /* .get_alloc_size   = */ nullptr,  // defaults to ggml_nbytes
+                           /* .get_alloc_size   = */ ggml_backend_cpu_kleidiai_buffer_type_get_alloc_size,
                            /* .is_host          = */ nullptr,
                            },
         /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0),

ggml/src/ggml-cpu/ops.cpp

@@ -8,6 +8,7 @@
 #include "vec.h"
 
 #include <float.h>
+#include <algorithm>
 
 // ggml_compute_forward_dup
 
@@ -1283,6 +1284,7 @@ void ggml_compute_forward_add(
         case GGML_TYPE_Q5_0:
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
+        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -1309,6 +1311,77 @@ void ggml_compute_forward_add(
     }
 }
 
+// ggml_compute_forward_add_id
+
+static void ggml_compute_forward_add_id_f32(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const ggml_tensor * src2 = dst->src[2];
+
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(src2->type == GGML_TYPE_I32);
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+    GGML_ASSERT(src1->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_TERNARY_OP_LOCALS
+
+    GGML_ASSERT( nb0 == sizeof(float));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // src0 indices
+        const int i3 = ir/(ne2*ne1);
+        const int i2 = (ir - i3*ne2*ne1)/ne1;
+        const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+        // src1 indices
+        const int i11 = *(int32_t *) ((char *) src2->data + i1*nb20 + i2*nb21);
+
+        GGML_ASSERT(i11 >= 0 && i11 < ne11);
+
+        ggml_vec_add_f32(ne0,
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
+                (float *) ((char *) src1->data + i11*nb11));
+    }
+}
+
+void ggml_compute_forward_add_id(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_add_id_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("unsupported type for ggml_compute_forward_add_id: %s", ggml_type_name(src0->type));
+            }
+    }
+}
+
 // ggml_compute_forward_add1
 
 static void ggml_compute_forward_add1_f32(
@@ -1660,6 +1733,7 @@ void ggml_compute_forward_add1(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
+        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -1787,6 +1861,7 @@ void ggml_compute_forward_acc(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
+        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -3614,6 +3689,93 @@ static void ggml_compute_forward_swiglu(
     }
 }
 
+// ggml_compute_forward_swiglu_oai
+
+static void ggml_compute_forward_swiglu_oai_f32(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    char * src0_d = (char *) src0->data;
+    char * src1_d = (char *) (src1 ? src1->data : src0->data);
+    const size_t src0_o = src0->nb[1];
+    const size_t src1_o = src1 ? src1->nb[1] : src0->nb[1];
+
+    GGML_ASSERT(ggml_is_contiguous_1(src0));
+    GGML_ASSERT(ggml_is_contiguous_1(dst));
+
+    if (src1) {
+        GGML_ASSERT(ggml_is_contiguous_1(src1));
+        GGML_ASSERT(src0->type == src1->type);
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc = src1 ? src0->ne[0] : src0->ne[0] / 2;
+    const int nr = ggml_nrows(src0);
+
+    GGML_ASSERT(dst->ne[0] == nc);
+    GGML_ASSERT(ggml_nrows(dst) == nr);
+
+    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
+    const float alpha = ggml_get_op_params_f32(dst, 2);
+    const float limit = ggml_get_op_params_f32(dst, 3);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        float * src0_p = (float *) (src0_d + i1*src0_o);
+        float * src1_p = (float *) (src1_d + i1*src1_o);
+        float * dst_p  = (float *) ((char *) dst->data + i1*(dst->nb[1]));
+
+        if (!src1) {
+            src0_p += swapped ? nc : 0;
+            src1_p += swapped ? 0 : nc;
+        }
+
+        for (int k = 0; k < nc; k++) {
+            const float x = std::min(src0_p[k], limit);
+            const float y = std::clamp(src1_p[k], -limit, limit);
+            const float out_glu = x / (1.f + expf(alpha * (-x)));
+            dst_p[k] = out_glu * (y + 1.f);
+        }
+
+#ifndef NDEBUG
+        for (int k = 0; k < nc; k++) {
+            const float x = dst_p[k];
+            GGML_UNUSED(x);
+            assert(!isnan(x));
+            assert(!isinf(x));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_swiglu_oai(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_swiglu_oai_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
 // ggml_compute_forward_geglu_erf
 
 static void ggml_compute_forward_geglu_erf_f32(
@@ -4599,6 +4761,7 @@ void ggml_compute_forward_out_prod(
         case GGML_TYPE_Q5_0:
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
+        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -4873,6 +5036,7 @@ void ggml_compute_forward_set(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
+        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -5134,6 +5298,7 @@ void ggml_compute_forward_get_rows(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
+        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -5523,6 +5688,7 @@ static void ggml_compute_forward_soft_max_f32(
 
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
+    const ggml_tensor * src2 = dst->src[2];
 
     assert(ggml_is_contiguous(dst));
     assert(ggml_are_same_shape(src0, dst));
@@ -5557,6 +5723,9 @@ static void ggml_compute_forward_soft_max_f32(
 
     const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
 
+    // sinks
+    const float * sk = src2 ? (float *)((char *) src2->data) : nullptr;
+
     for (int64_t i03 = 0; i03 < ne03; i03++) {
         for (int64_t i02 = 0; i02 < ne02; i02++) {
             for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
@@ -5599,9 +5768,18 @@ static void ggml_compute_forward_soft_max_f32(
                 float max = -INFINITY;
                 ggml_vec_max_f32(ne00, &max, wp);
 
+                // if we have sinks, make a correction as if they were included in the softmax
+                if (sk) {
+                    max = MAX(max, sk[i02]);
+                }
+
                 ggml_float sum = ggml_vec_soft_max_f32(ne00, dp, wp, max);
                 assert(sum > 0.0);
 
+                if (sk) {
+                    sum += (ggml_float) expf(sk[i02] - max);
+                }
+
                 sum = 1.0/sum;
                 ggml_vec_scale_f32(ne00, dp, sum);
 
@@ -5836,6 +6014,7 @@ void ggml_compute_forward_clamp(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
+        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -7989,12 +8168,14 @@ void ggml_compute_forward_argsort(
 
 static void ggml_compute_forward_flash_attn_ext_f16(
         const ggml_compute_params * params,
-        const ggml_tensor * q,
-        const ggml_tensor * k,
-        const ggml_tensor * v,
-        const ggml_tensor * mask,
         ggml_tensor * dst) {
 
+    const ggml_tensor * q     = dst->src[0];
+    const ggml_tensor * k     = dst->src[1];
+    const ggml_tensor * v     = dst->src[2];
+    const ggml_tensor * mask  = dst->src[3];
+    const ggml_tensor * sinks = dst->src[4];
+
     GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
     GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
     GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
@@ -8189,6 +8370,23 @@ static void ggml_compute_forward_flash_attn_ext_f16(
             }
         }
 
+        // sinks
+        if (sinks) {
+            const float s = ((float *)((char *) sinks->data))[h];
+
+            float ms = 1.0f;
+            float vs = 1.0f;
+
+            if (s > M) {
+                ms = expf(M - s);
+                ggml_vec_scale_f32(DV, VKQ32, ms);
+            } else {
+                vs = expf(s - M);
+            }
+
+            S = S*ms + vs;
+        }
+
         // V /= S
         const float S_inv = 1.0f/S;
         ggml_vec_scale_f32(DV, VKQ32, S_inv);
@@ -8208,17 +8406,13 @@ static void ggml_compute_forward_flash_attn_ext_f16(
 
 void ggml_compute_forward_flash_attn_ext(
         const ggml_compute_params * params,
-        const ggml_tensor * q,
-        const ggml_tensor * k,
-        const ggml_tensor * v,
-        const ggml_tensor * mask,
         ggml_tensor * dst) {
     switch (dst->op_params[3]) {
         case GGML_PREC_DEFAULT:
         case GGML_PREC_F32:
             {
                 // uses F32 accumulators
-                ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst);
+                ggml_compute_forward_flash_attn_ext_f16(params, dst);
             } break;
         default:
             {
@@ -9080,6 +9274,10 @@ void ggml_compute_forward_glu(
             {
                 ggml_compute_forward_swiglu(params, dst);
             } break;
+        case GGML_GLU_OP_SWIGLU_OAI:
+            {
+                ggml_compute_forward_swiglu_oai(params, dst);
+            } break;
         case GGML_GLU_OP_GEGLU_ERF:
             {
                 ggml_compute_forward_geglu_erf(params, dst);

ggml/src/ggml-cpu/ops.h

@@ -29,6 +29,7 @@ extern "C" {
 
 void ggml_compute_forward_dup(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_add(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_add_id(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_add1(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_acc(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_sum(const struct ggml_compute_params * params, struct ggml_tensor * dst);
@@ -82,13 +83,7 @@ void ggml_compute_forward_arange(const struct ggml_compute_params * params, stru
 void ggml_compute_forward_timestep_embedding(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_argsort(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_leaky_relu(const struct ggml_compute_params * params, struct ggml_tensor * dst);
-void ggml_compute_forward_flash_attn_ext(
-    const struct ggml_compute_params * params,
-    const struct ggml_tensor * q,
-    const struct ggml_tensor * k,
-    const struct ggml_tensor * v,
-    const struct ggml_tensor * mask,
-    struct ggml_tensor * dst);
+void ggml_compute_forward_flash_attn_ext(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_flash_attn_back(
         const struct ggml_compute_params * params,
         const bool masked,

ggml/src/ggml-cpu/quants.c

@@ -46,6 +46,10 @@ void quantize_row_q8_1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRI
     quantize_row_q8_1_ref(x, y, k);
 }
 
+void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
+    quantize_row_mxfp4_ref(x, y, k);
+}
+
 //
 // 2-6 bit quantization in super-blocks
 //
@@ -181,6 +185,37 @@ void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
     *s = sumf;
 }
 
+void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    assert(n % QK_MXFP4 == 0);
+    static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same");
+
+    const block_mxfp4 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    const int nb = n / QK_MXFP4;
+
+    int ib = 0;
+    float sumf = 0;
+
+    for (; ib < nb; ++ib) {
+        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_E8M0_TO_FP32_HALF(x[ib].e);
+
+        int sumi1 = 0;
+        int sumi2 = 0;
+        for (int j = 0; j < QK_MXFP4/2; ++j) {
+            sumi1 += y[ib].qs[j +          0] * kvalues_mxfp4[x[ib].qs[j] & 0xf];
+            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_mxfp4[x[ib].qs[j] >>  4];
+        }
+        sumf += d * (sumi1 + sumi2);
+    }
+    *s = sumf;
+}
+
 void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;

ggml/src/ggml-cpu/quants.h

@@ -19,6 +19,8 @@ void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
 void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 
+void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+
 void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
@@ -39,6 +41,8 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
+void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@@ -67,8 +71,12 @@ void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
 void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
+void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
 void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
 void ggml_vec_dot_q2_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q3_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q4_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);

ggml/src/ggml-cpu/repack.cpp

@@ -14,7 +14,6 @@
 #include <cmath>
 #include <cstring>
 #include <cassert>
-#include <cstdlib> // for qsort
 #include <cstdio>  // for GGML_ASSERT
 
 #include "repack.h"
@@ -413,6 +412,82 @@ void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
     }
 }
 
+void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK_K;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 8;
+
+    assert (n % qk == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+    float sumf[8];
+    float sum_minf[8];
+    int sumi1,sumi2,sumi3,sumi4;
+    int sumi;
+
+    const block_q8_K * a_ptr = (const block_q8_K *)vy;
+    for(int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q2_Kx8 * b_ptr = (const block_q2_Kx8 *) vx + (x * nb);
+        for (int j = 0; j < ncols_interleaved; j++) {
+            sumf[j] = 0.0;
+            sum_minf[j] = 0.0;
+        }
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (4 * blocklen)); k++) {
+                const uint8_t *scales_0 = b_ptr[l].scales + (k / 4) * 64 ;
+                const uint8_t *scales_1 = b_ptr[l].scales + (k / 4) * 64 + 16;
+                const uint8_t *scales_2 = b_ptr[l].scales + (k / 4) * 64 + 32;
+                const uint8_t *scales_3 = b_ptr[l].scales + (k / 4) * 64 + 48;
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi1 = 0;
+                    sumi2 = 0;
+                    sumi3 = 0;
+                    sumi4 = 0;
+                    sumi = 0;
+                    int offset = ((k / 2) % 2) + j * 2;
+                    for (int i = 0; i < blocklen; ++i){
+                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 3);
+                        const int v1 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 2 ) & 3);
+                        const int v2 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4 ) & 3);
+                        const int v3 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 6 ) & 3);
+                        sumi1 = (v0 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i]);
+                        sumi2 = (v1 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i + 32]);
+                        sumi3 = (v2 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i + 64]);
+                        sumi4 = (v3 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i + 96]);
+
+                        sumi1 = sumi1 * (scales_0[offset] & 0xF);
+                        sumi2 = sumi2 * (scales_1[offset] & 0xF);
+                        sumi3 = sumi3 * (scales_2[offset] & 0xF);
+                        sumi4 = sumi4 * (scales_3[offset] & 0xF);
+                        sumi += sumi1 + sumi2 + sumi3 + sumi4;
+                    }
+                    sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d;
+                }
+            }
+            for(int sb = 0; sb < 8; sb++) {
+                const uint8_t *mins = b_ptr[l].scales + sb * 16;
+                for(int j = 0; j < ncols_interleaved; j++){
+                    sum_minf[j] += ((mins[j * 2] >> 4) * a_ptr[l].bsums[sb * 2] + (mins[(j * 2)+ 1] >> 4) * a_ptr[l].bsums[sb * 2 + 1]) * GGML_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d;
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) {
+            s[x * ncols_interleaved + j] = sumf[j] - sum_minf[j];
+        }
+    }
+}
+
 void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
     const int qk = QK8_0;
     const int nb = n / qk;
@@ -712,6 +787,97 @@ void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
     }
 }
 
+void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK_K;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 8;
+
+    assert (n % qk == 0);
+    assert (nr % 4 == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+    float sumf[4][8];
+    float sum_minf[4][8];
+    int sumi1, sumi2, sumi3, sumi4;
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q2_Kx8 * b_ptr = (const block_q2_Kx8 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumf[m][j] = 0.0;
+                    sum_minf[m][j] = 0.0;
+                }
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (4 * blocklen)); k++) {
+
+                    const uint8_t *scales_0 = b_ptr[l].scales + (k / 4) * 64 ;
+                    const uint8_t *scales_1 = b_ptr[l].scales + (k / 4) * 64 + 16;
+                    const uint8_t *scales_2 = b_ptr[l].scales + (k / 4) * 64 + 32;
+                    const uint8_t *scales_3 = b_ptr[l].scales + (k / 4) * 64 + 48;
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi1 = 0;
+                            sumi2 = 0;
+                            sumi3 = 0;
+                            sumi4 = 0;
+                            sumi = 0;
+                            int offset = ((k / 2) % 2) + j * 2;
+                            for (int i = 0; i < blocklen; ++i){
+                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 3);
+                                const int v1 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 2 ) & 3);
+                                const int v2 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4 ) & 3);
+                                const int v3 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 6 ) & 3);
+                                sumi1 = (v0 * a_ptr[l].qs[(k >> 2) * 512 + (k % 4) * 4 * blocklen + m * blocklen + i]);
+                                sumi2 = (v1 * a_ptr[l].qs[(k >> 2) * 512  + (k % 4) * 4 * blocklen + m * blocklen + i + 128]);
+                                sumi3 = (v2 * a_ptr[l].qs[(k >> 2) * 512  + (k % 4) * 4 * blocklen + m * blocklen + i + 256]);
+                                sumi4 = (v3 * a_ptr[l].qs[(k >> 2) * 512  + (k % 4) * 4 * blocklen + m * blocklen + i + 384]);
+                                sumi1 = sumi1 * (scales_0[offset] & 0xF);
+                                sumi2 = sumi2 * (scales_1[offset] & 0xF);
+                                sumi3 = sumi3 * (scales_2[offset] & 0xF);
+                                sumi4 = sumi4 * (scales_3[offset] & 0xF);
+                                sumi += sumi1 + sumi2 + sumi3 + sumi4;
+                            }
+                            sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d[m];
+                        }
+                    }
+                }
+                for(int sb = 0; sb < 8; sb++) {
+                    const uint8_t *mins = b_ptr[l].scales + sb * 16;
+                    for(int m = 0; m < 4; m++) {
+                        const int16_t *bsums = a_ptr[l].bsums + (sb * 8) + (m * 4) - ((sb % 2) *  6);
+                        for(int j = 0; j < ncols_interleaved; j++) {
+                            int mins_prod = ((mins[j * 2] >> 4) * bsums[0] + (mins[(j * 2)+ 1] >> 4) * bsums[1]);
+                            sum_minf[m][j] += (mins_prod) * GGML_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d[m];
+                        }
+                    }
+                }
+            }
+
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j] - sum_minf[m][j];
+                }
+            }
+        }
+    }
+}
+
+
 void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
     const int qk = QK8_0;
     const int nb = n / qk;
@@ -915,6 +1081,50 @@ static block_q4_Kx8 make_block_q4_Kx8(block_q4_K * in, unsigned int blck_size_in
     return out;
 }
 
+static block_q2_Kx8 make_block_q2_Kx8(block_q2_K * in, unsigned int blck_size_interleave) {
+    block_q2_Kx8 out;
+
+    // Delta(scale) and dmin values of the eight Q2_K structures are copied onto the output interleaved structure
+    for (int i = 0; i < 8; i++) {
+        out.d[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d;
+    }
+
+    for (int i = 0; i < 8; i++) {
+        out.dmin[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.dmin;
+    }
+
+    const int end = QK_K * 2 / blck_size_interleave;
+
+    // Interleave Q2_K quants by taking 8 bytes at a time
+    for (int i = 0; i < end; ++i) {
+        int src_id = i % 8;
+        int src_offset = (i / 8) * blck_size_interleave;
+        int dst_offset = i * blck_size_interleave;
+
+        uint64_t elems;
+        memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t));
+        memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t));
+    }
+
+    // The below logic is designed so as to unpack and rearrange scales and mins values in Q2_K
+    // Currently the Q2_K structure has 16 scales and 16 mins packed in 16 bytes ( 4 bits for each value)
+    // The output Q2_Kx8 structure has 128 bytes for storing scales and mins
+    // Every 16 byte is packed such that it contains scales and mins for corresponding sub blocks from Q2_K structure
+    // For eg - First 16 bytes contains 16 scales and 16 mins - each of first and second sub blocks from different Q2_K structures
+
+    for(int i = 0; i < 128; i++){
+
+        // Index for selecting which q2k super block
+        int src1 = (i % 16) / 2;
+        // Index for selecting scale
+        int src2 = ((i / 16) * 2) + (i % 2);
+
+        out.scales[i] = in[src1].scales[src2];
+    }
+    return out;
+
+}
+
 static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
     GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
     GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
@@ -976,6 +1186,37 @@ static int repack_q4_K_to_q4_K_8_bl(struct ggml_tensor * t, int interleave_block
     GGML_UNUSED(data_size);
 }
 
+static int repack_q2_K_to_q2_K_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q2_K);
+    GGML_ASSERT(interleave_block == 8);
+    constexpr int nrows_interleaved = 8;
+
+    block_q2_Kx8 * dst = (block_q2_Kx8*)t->data;
+    const block_q2_K * src = (const block_q2_K*) data;
+    block_q2_K dst_tmp[8];
+    int nrow = ggml_nrows(t);
+    int nblocks = t->ne[0] / QK_K;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q2_K));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i  = 0; i < nrows_interleaved; i++ ) {
+                dst_tmp[i] = src[x + i * nblocks];
+            }
+            *dst++ = make_block_q2_Kx8(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
 static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
     GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
     GGML_ASSERT(interleave_block == 8);
@@ -1096,6 +1337,10 @@ template <> int repack<block_q4_K, 8, 8>(struct ggml_tensor * t, const void * da
     return repack_q4_K_to_q4_K_8_bl(t, 8, data, data_size);
 }
 
+template <> int repack<block_q2_K, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q2_K_to_q2_K_8_bl(t, 8, data, data_size);
+}
+
 template <> int repack<block_iq4_nl, 4, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
     return repack_iq4_nl_to_iq4_nl_4_bl(t, 4, data, data_size);
 }
@@ -1125,6 +1370,10 @@ template <> void gemv<block_q4_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t
     ggml_gemv_q4_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
 
+template <> void gemv<block_q2_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemv_q2_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
+}
+
 template <> void gemv<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemv_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
@@ -1149,6 +1398,10 @@ template <> void gemm<block_q4_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t
     ggml_gemm_q4_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
 
+template <> void gemm<block_q2_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemm_q2_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
+}
+
 template <> void gemm<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemm_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
@@ -1422,6 +1675,9 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
     static const ggml::cpu::repack::tensor_traits<block_q4_0, 8, 8, GGML_TYPE_Q8_0> q4_0_8x8_q8_0;
     static const ggml::cpu::repack::tensor_traits<block_q4_K, 8, 8, GGML_TYPE_Q8_K> q4_K_8x8_q8_K;
 
+    // instance for Q2
+    static const ggml::cpu::repack::tensor_traits<block_q2_K, 8, 8, GGML_TYPE_Q8_K> q2_K_8x8_q8_K;
+
     // instance for IQ4
     static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0> iq4_nl_4x4_q8_0;
 
@@ -1447,6 +1703,12 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
                 return &q4_K_8x8_q8_K;
             }
         }
+    } else if (cur->type == GGML_TYPE_Q2_K) {
+        if (ggml_cpu_has_avx512()) {
+            if (cur->ne[1] % 8 == 0) {
+                return &q2_K_8x8_q8_K;
+            }
+        }
     } else if (cur->type == GGML_TYPE_IQ4_NL) {
         if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
             if (cur->ne[1] % 4 == 0) {

ggml/src/ggml-cpu/repack.h

@@ -44,7 +44,14 @@ struct block_q4_Kx8 {
 };
 
 static_assert(sizeof(block_q4_Kx8) == sizeof(ggml_half) * 16 + K_SCALE_SIZE * 8 + QK_K * 4, "wrong q4_K block size/padding");
+struct block_q2_Kx8 {
+    ggml_half d[8];      // super-block scale for quantized scales
+    ggml_half dmin[8];   // super-block scale for quantized mins
+    uint8_t scales[128];  // scales and mins, quantized with 4 bits
+    uint8_t qs[512];    // 2--bit quants
+};
 
+static_assert(sizeof(block_q2_Kx8) == sizeof(ggml_half) * 16 + QK_K/2 + QK_K * 2, "wrong q2_K block size/padding");
 struct block_q8_Kx4 {
     float d[4];              // delta
     int8_t qs[QK_K * 4];     // quants
@@ -71,11 +78,13 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
 void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 
 // Native implementations
@@ -86,11 +95,13 @@ void ggml_gemv_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
 void ggml_gemv_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 
 #if defined(__cplusplus)

ggml/src/ggml-cpu/traits.cpp

@@ -10,7 +10,7 @@ extra_buffer_type::~extra_buffer_type() {}
 }  // namespace ggml::cpu
 
 bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) {
-    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
+    for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
         if (extra && extra->context) {
             auto buf_extra     = (ggml::cpu::extra_buffer_type *) extra->context;
             auto tensor_traits = buf_extra->get_tensor_traits(op);
@@ -23,7 +23,7 @@ bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct
 }
 
 bool ggml_cpu_extra_work_size(int n_threads, const struct ggml_tensor * op, size_t * size) {
-    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
+    for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
         if (extra && extra->context) {
             auto buf_extra     = (ggml::cpu::extra_buffer_type *) extra->context;
             auto tensor_traits = buf_extra->get_tensor_traits(op);

ggml/src/ggml-cpu/traits.h

@@ -33,6 +33,6 @@ class extra_buffer_type {
 }  // namespace ggml::cpu
 
 // implemented in ggml-cpu.cpp.
-std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffers_type();
+std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types();
 
 #endif

ggml/src/ggml-cpu/vec.h

@@ -55,7 +55,22 @@ inline static void ggml_vec_cpy_i32(const int n, int32_t * y, const int32_t * x)
 
 inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const ggml_fp16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
 inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
-inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] + y[i]; }
+
+inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) {
+    int i = 0;
+#if defined(__AVX2__)
+    for (; i + 7 < n; i += 8) {
+        __m256 vx = _mm256_loadu_ps(x + i);
+        __m256 vy = _mm256_loadu_ps(y + i);
+        __m256 vz = _mm256_add_ps(vx, vy);
+        _mm256_storeu_ps(z + i, vz);
+    }
+#endif
+    for (; i < n; ++i) {
+        z[i] = x[i] + y[i];
+    }
+}
+
 inline static void ggml_vec_add_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) {
     for (int i = 0; i < n; ++i) {
         z[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(x[i]) + GGML_CPU_FP16_TO_FP32(y[i]));
@@ -992,9 +1007,9 @@ void ggml_vec_swiglu_f32(const int n, float * y, const float * x, const float *
 
 inline static void ggml_vec_swiglu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x, const ggml_fp16_t * g) {
     for (int i = 0; i < n; ++i) {
-        float v = GGML_CPU_FP16_TO_FP32(x[i]);
-        float w = GGML_CPU_FP16_TO_FP32(g[i]);
-        y[i] = GGML_CPU_FP32_TO_FP16((v/(1.0f + expf(-v))) * w);
+        float xi = GGML_CPU_FP16_TO_FP32(x[i]);
+        float gi = GGML_CPU_FP16_TO_FP32(g[i]);
+        y[i] = GGML_CPU_FP32_TO_FP16((xi/(1.0f + expf(-xi))) * gi);
     }
 }
 

ggml/src/ggml-cuda/CMakeLists.txt

@@ -102,12 +102,12 @@ if (CUDAToolkit_FOUND)
     if (GGML_STATIC)
         if (WIN32)
             # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library
-            target_link_libraries(ggml-cuda PRIVATE CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
+            target_link_libraries(ggml-cuda PRIVATE CUDA::cudart_static CUDA::cublas)
         else ()
-            target_link_libraries(ggml-cuda PRIVATE  CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
+            target_link_libraries(ggml-cuda PRIVATE  CUDA::cudart_static CUDA::cublas_static)
         endif()
     else()
-        target_link_libraries(ggml-cuda PRIVATE CUDA::cudart CUDA::cublas CUDA::cublasLt)
+        target_link_libraries(ggml-cuda PRIVATE CUDA::cudart CUDA::cublas)
     endif()
 
     if (GGML_CUDA_NO_VMM)

ggml/src/ggml-cuda/add-id.cu

@@ -0,0 +1,58 @@
+#include "add-id.cuh"
+
+static __global__ void add_id_kernel(
+        const float * src0, const float * src1, const int32_t * src2, float * dst,
+        int64_t ne0, int64_t ne1,
+        size_t nb01, size_t nb02,
+        size_t nb11,
+        size_t nb21
+    ) {
+
+    const int64_t i1 = blockIdx.x;
+    const int64_t i2 = blockIdx.y;
+
+    const int i11 = *(int32_t *) ((char *) src2 + i1*sizeof(int32_t) + i2*nb21);
+
+    const size_t nb1 = ne0 * sizeof(float);
+    const size_t nb2 = ne1 * nb1;
+
+    float * dst_row = (float *)((char *)dst + i1*nb1 + i2*nb2);
+    const float * src0_row = (const float *)((char *)src0 +  i1*nb01 + i2*nb02);
+    const float * src1_row = (const float *)((char *)src1 + i11*nb11);
+
+    for (int64_t i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
+        dst_row[i0] = src0_row[i0] + src1_row[i0];
+    }
+}
+
+void ggml_cuda_op_add_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const ggml_tensor * src2 = dst->src[2];
+
+    GGML_TENSOR_TERNARY_OP_LOCALS
+
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(src2->type == GGML_TYPE_I32);
+
+    GGML_ASSERT(nb00 == sizeof(float));
+    GGML_ASSERT(nb10 == sizeof(float));
+    GGML_ASSERT(nb20 == sizeof(int32_t));
+
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+    const int32_t * src2_d = (const int32_t *)src2->data;
+    float * dst_d = (float *)dst->data;
+
+    int threads = std::min((int)ne00, 768); // cols
+    dim3 blocks(ne01, ne02); // n_experts_used, n_tokens
+    add_id_kernel<<<blocks, threads, 0, ctx.stream()>>>(
+        src0_d, src1_d, src2_d, dst_d,
+        ne0, ne1,
+        nb01, nb02,
+        nb11,
+        nb21
+    );
+}

ggml/src/ggml-cuda/add-id.cuh

@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_add_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-cuda/common.cuh

@@ -1,6 +1,7 @@
 #pragma once
 
 #include "ggml.h"
+#include "ggml-impl.h"
 #include "ggml-cuda.h"
 
 #include <cstdint>
@@ -56,7 +57,7 @@
 #define GGML_CUDA_CC_GCN4       (GGML_CUDA_CC_OFFSET_AMD + 0x803)  // Tonga, Fiji, Polaris, minimum for fast fp16
 #define GGML_CUDA_CC_VEGA       (GGML_CUDA_CC_OFFSET_AMD + 0x900)  // Vega56/64, minimum for fp16 dual issue
 #define GGML_CUDA_CC_VEGA20     (GGML_CUDA_CC_OFFSET_AMD + 0x906)  // MI50/Radeon VII, minimum for dp4a
-#define GGML_CUDA_CC_CDNA       (GGML_CUDA_CC_OFFSET_AMD + 0x908)  // MI100, minimum for MFMA, acc registers
+#define GGML_CUDA_CC_CDNA1      (GGML_CUDA_CC_OFFSET_AMD + 0x908)  // MI100, minimum for MFMA, acc registers
 #define GGML_CUDA_CC_CDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x910)  // MI210, minimum acc register renameing
 #define GGML_CUDA_CC_CDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x942)  // MI300
 
@@ -72,8 +73,9 @@
 #define GGML_CUDA_CC_IS_RDNA2(cc) (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3)
 #define GGML_CUDA_CC_IS_RDNA3(cc) (cc >= GGML_CUDA_CC_RDNA3 && cc < GGML_CUDA_CC_RDNA4)
 #define GGML_CUDA_CC_IS_RDNA4(cc) (cc >= GGML_CUDA_CC_RDNA4)
-#define GGML_CUDA_CC_IS_GCN(cc)   (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA)
-#define GGML_CUDA_CC_IS_CDNA(cc)  (cc >= GGML_CUDA_CC_CDNA && cc < GGML_CUDA_CC_RDNA1)
+#define GGML_CUDA_CC_IS_GCN(cc)   (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA1)
+#define GGML_CUDA_CC_IS_CDNA(cc)  (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_RDNA1)
+#define GGML_CUDA_CC_IS_CDNA3(cc) (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_RDNA1)
 
 // Moore Threads
 #define GGML_CUDA_CC_QY1 (GGML_CUDA_CC_OFFSET_MTHREADS + 0x210) // MTT S80, MTT S3000
@@ -175,7 +177,7 @@ static const char * cu_get_error_str(CUresult err) {
 #define CU_CHECK(err) CUDA_CHECK_GEN(err, CUDA_SUCCESS, cu_get_error_str)
 #endif
 
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
 #    define CUDA_SET_SHARED_MEMORY_LIMIT(kernel, nbytes)                                                       \
         do {                                                                                                   \
             static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = { false };                         \
@@ -190,7 +192,7 @@ static const char * cu_get_error_str(CUresult err) {
         do {                                             \
             GGML_UNUSED(nbytes);                         \
         } while (0)
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
+#endif // !(defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
 
 #if CUDART_VERSION >= 11010 || defined(GGML_USE_MUSA)
 #define GGML_CUDA_ASSUME(x) __builtin_assume(x)
@@ -210,9 +212,9 @@ typedef float2 dfloat2;
 #define GGML_USE_VMM
 #endif // (!defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)) || (defined(GGML_USE_HIP) && !defined(GGML_HIP_NO_VMM))
 
-#if (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
+#if defined(GGML_USE_HIP) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
 #define FP16_AVAILABLE
-#endif // (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
+#endif // defined(GGML_USE_HIP) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
 
 #if defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610
 #define FAST_FP16_AVAILABLE
@@ -226,13 +228,21 @@ typedef float2 dfloat2;
 #define FP16_MMA_AVAILABLE
 #endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || (defined(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4)))
 
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
-#define NEW_MMA_AVAILABLE
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
+#if defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
+#define AMD_MFMA_AVAILABLE
+#endif // defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
 
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
+#define TURING_MMA_AVAILABLE
+#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
+
+#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#define AMPERE_MMA_AVAILABLE
+#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+
+#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #define CP_ASYNC_AVAILABLE
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 
 #if !defined(GGML_CUDA_NO_FA) && !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ < 220)
 #define FLASH_ATTN_AVAILABLE
@@ -254,7 +264,7 @@ static bool fast_fp16_hardware_available(const int cc) {
 
 // Any FP16 tensor core instructions are available for ggml code.
 static bool fp16_mma_available(const int cc) {
-#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(GGML_HIP_ROCWMMA_FATTN)
+#if defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN)
     return false;
 #else
     if ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA) ||
@@ -270,7 +280,7 @@ static bool fp16_mma_available(const int cc) {
     } else {
         return false;
     }
-#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(GGML_HIP_ROCWMMA_FATTN)
+#endif // defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN)
 }
 
 // To be used for feature selection of external libraries, e.g. cuBLAS.
@@ -288,35 +298,47 @@ static bool fp32_mma_hardware_available(const int cc) {
     return GGML_CUDA_CC_IS_CDNA(cc);
 }
 
+static bool amd_mfma_available(const int cc) {
+#if !defined(GGML_HIP_NO_MMQ_MFMA)
+    return GGML_CUDA_CC_IS_CDNA(cc);
+#else
+    return false;
+#endif //!defined(GGML_HIP_NO_MMQ_MFMA)
+}
+
 // Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
-static bool new_mma_available(const int cc) {
+static bool turing_mma_available(const int cc) {
     return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING;
 }
 
+static bool ampere_mma_available(const int cc) {
+    return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
+}
+
 static bool cp_async_available(const int cc) {
     return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
 }
 
 static constexpr __device__ int ggml_cuda_get_physical_warp_size() {
-#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(__GFX9__) || defined(__GFX8__))
+#if defined(GGML_USE_HIP) && (defined(__GFX9__) || defined(__GFX8__))
     return 64;
 #else
     return 32;
-#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(__GFX9__) || defined(__GFX8__))
+#endif // defined(GGML_USE_HIP) && (defined(__GFX9__) || defined(__GFX8__))
 }
 
 [[noreturn]]
 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_HIP) && defined(__HIP_PLATFORM_AMD__)
+#if defined(GGML_USE_HIP)
     printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n",
            file_name, line, function_name, arch);
     GGML_UNUSED(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_HIP) && defined(__HIP_PLATFORM_AMD__)
+#endif // defined(GGML_USE_HIP)
     __trap();
 
     GGML_UNUSED(no_device_code); // suppress unused function warning
@@ -353,7 +375,7 @@ struct ggml_cuda_unroll<1> {
 
 template<int width = WARP_SIZE>
 static __device__ __forceinline__ int warp_reduce_sum(int x) {
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
     return __reduce_add_sync(0xffffffff, x);
 #else
 #pragma unroll
@@ -361,7 +383,7 @@ static __device__ __forceinline__ int warp_reduce_sum(int x) {
         x += __shfl_xor_sync(0xffffffff, x, offset, width);
     }
     return x;
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 }
 
 template<int width = WARP_SIZE>
@@ -418,6 +440,20 @@ static __global__ void reduce_rows_f32(const float * x, float * dst, const int n
     dst[row] = norm ? sum / ncols : sum;
 }
 
+template<int width = WARP_SIZE>
+static __device__ __forceinline__ int warp_reduce_all(int x) {
+#ifdef GGML_USE_HIP
+#pragma unroll
+    for (int offset = width/2; offset > 0; offset >>= 1) {
+        x = x && __shfl_xor_sync(0xffffffff, x, offset, width);
+    }
+    return x;
+#else
+    static_assert(width == WARP_SIZE, "width != WARP_SIZE not implemented");
+    return __all_sync(0xffffffff, x);
+#endif // GGML_USE_HIP
+}
+
 template<int width = WARP_SIZE>
 static __device__ __forceinline__ float warp_reduce_max(float x) {
 #pragma unroll
@@ -430,11 +466,11 @@ static __device__ __forceinline__ float warp_reduce_max(float x) {
 static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) {
 #ifdef FP16_AVAILABLE
 
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
+#if !defined(GGML_USE_HIP) && CUDART_VERSION < CUDART_HMAX
     return __float2half(fmaxf(__half2float(a), __half2float(b)));
 #else
     return __hmax(a, b);
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
+#endif // !defined(GGML_USE_HIP) && CUDART_VERSION < CUDART_HMAX
 
 #else
    NO_DEVICE_CODE;
@@ -462,7 +498,7 @@ static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const hal
 
 template<int width = WARP_SIZE>
 static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
+#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
 #pragma unroll
    for (int offset = width/2; offset > 0; offset >>= 1) {
        x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, width));
@@ -471,7 +507,7 @@ static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
 #else
    GGML_UNUSED(x);
    NO_DEVICE_CODE;
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
+#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
 }
 
 #if CUDART_VERSION < CUDART_HMASK
@@ -483,7 +519,7 @@ static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half
 #endif // CUDART_VERSION < CUDART_HMASK
 
 static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) {
-#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
+#if defined(GGML_USE_HIP)
 #if defined(CDNA) || defined(RDNA2) || defined(__gfx906__)
     c = __builtin_amdgcn_sdot4(a, b, c, false);
 #elif defined(RDNA3) || defined(RDNA4)
@@ -509,7 +545,7 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i
 #endif
     return c;
 
-#else // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
+#else // defined(GGML_USE_HIP)
 
 #if __CUDA_ARCH__ >= GGML_CUDA_CC_DP4A || defined(GGML_USE_MUSA)
     return __dp4a(a, b, c);
@@ -519,7 +555,25 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i
     return c + a8[0]*b8[0] + a8[1]*b8[1] + a8[2]*b8[2] + a8[3]*b8[3];
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_DP4A || defined(GGML_USE_MUSA)
 
-#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
+#endif // defined(GGML_USE_HIP)
+}
+
+static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
+#if CUDART_VERSION >= 12080
+    const nv_bfloat16 e = __nv_cvt_e8m0_to_bf16raw(x);
+    return (float) e;
+#else
+    uint32_t bits;
+    if (x == 0) {
+        bits = 0x00400000;
+    } else {
+        bits = (uint32_t) x << 23;
+    }
+
+    float result;
+    memcpy(&result, &bits, sizeof(float));
+    return result;
+#endif // CUDART_VERSION >= 12050
 }
 
 typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v);
@@ -580,6 +634,13 @@ struct ggml_cuda_type_traits<GGML_TYPE_Q8_0> {
     static constexpr int qi = QI8_0;
 };
 
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_MXFP4> {
+    static constexpr int qk = QK_MXFP4;
+    static constexpr int qr = QR_MXFP4;
+    static constexpr int qi = QI_MXFP4;
+};
+
 template<>
 struct ggml_cuda_type_traits<GGML_TYPE_Q2_K> {
     static constexpr int qk = QK_K;
@@ -765,7 +826,7 @@ struct ggml_tensor_extra_gpu {
 };
 
 
-#if (defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS))
+#if (defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS)) || defined(GGML_MUSA_GRAPHS)
 #define USE_CUDA_GRAPH
 #endif
 

ggml/src/ggml-cuda/convert.cu

@@ -6,24 +6,33 @@
 #define CUDA_Q8_0_NE_ALIGN 2048
 
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
-static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k) {
-    const int64_t i = (int64_t)2*(blockDim.x*blockIdx.x + threadIdx.x);
+static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y,
+        const int64_t ne00, const int64_t ne01, const int64_t ne02,
+        const int64_t s01, const int64_t s02, const int64_t s03) {
+    const int64_t i00 = 2 * (int64_t(blockDim.x)*blockIdx.x + threadIdx.x);
 
-    if (i >= k) {
+    if (i00 >= ne00) {
         return;
     }
 
-    const int64_t ib = i/qk; // block index
-    const int64_t iqs = (i%qk)/qr; // quant index
-    const int64_t iybs = i - i%qk; // y block start index
+    const int64_t i01 = blockIdx.y;
+    const int64_t i02 = blockIdx.z % ne02;
+    const int64_t i03 = blockIdx.z / ne02;
+
+    const int64_t ibx0 = i03*s03 + i02*s02 + i01*s01;
+
+    const int64_t ib = ibx0 + i00/qk; // block index
+    const int64_t iqs = (i00%qk)/qr; // quant index
+    const int64_t iybs = i00 - i00%qk; // y block start index
     const int64_t y_offset = qr == 1 ? 1 : qk/2;
 
     // dequantize
     dfloat2 v;
     dequantize_kernel(vx, ib, iqs, v);
 
-    y[iybs + iqs + 0]        = v.x;
-    y[iybs + iqs + y_offset] = v.y;
+    const int64_t iy0 = ((i03*ne02 + i02)*ne01 + i01)*ne00 + iybs + iqs;
+    y[iy0 + 0]        = float(v.x);
+    y[iy0 + y_offset] = float(v.y);
 }
 
 template <bool need_check>
@@ -456,10 +465,36 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst
     }
 }
 
+template<typename dst_t>
+static __global__ void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4);
+
+    const int64_t tid = threadIdx.x;
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[ib].qs + 4*il;
+    const float d = ggml_cuda_e8m0_to_fp32(x[ib].e);
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_mxfp4[q4[j] & 0xf]*0.5f;
+        y[j+16] = d * kvalues_mxfp4[q4[j] >>  4]*0.5f;
+    }
+}
+
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
-static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) {
-    const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE);
-    dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+static void dequantize_block_cuda(const void * vx, dst_t * y,
+        const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
+        const int64_t s01, const int64_t s02, const int64_t s03, cudaStream_t stream) {
+    const dim3 num_blocks((ne00 + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE), ne01, ne02*ne03);
+    dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>
+        (vx, y, ne00, ne01, ne02, s01, s02, s03);
+}
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static void dequantize_block_cont_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) {
+    dequantize_block_cuda<qk, qr, dequantize_kernel, dst_t>(vx, y, k, 1, 1, 1, k/qk, k/qk, k/qk, stream);
 }
 
 static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * __restrict__ y, const int64_t k, cudaStream_t stream) {
@@ -571,6 +606,12 @@ static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t
     dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
 }
 
+template<typename dst_t>
+static void dequantize_row_mxfp4_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+    dequantize_block_mxfp4<<<nb, 32, 0, stream>>>(vx, y);
+}
+
 template <typename src_t, typename dst_t>
 static __global__ void convert_unary(
         const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01, const int64_t ne02,
@@ -624,14 +665,14 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
         case GGML_TYPE_Q4_1:
             return dequantize_row_q4_1_cuda;
         case GGML_TYPE_Q5_0:
-            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
+            return dequantize_block_cont_cuda<QK5_0, QR5_0, dequantize_q5_0>;
         case GGML_TYPE_Q5_1:
-            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
+            return dequantize_block_cont_cuda<QK5_1, QR5_1, dequantize_q5_1>;
         case GGML_TYPE_Q8_0:
             if (fp16_available(ggml_cuda_info().devices[ggml_cuda_get_device()].cc)) {
                 return dequantize_block_q8_0_f16_cuda;
             }
-            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
+            return dequantize_block_cont_cuda<QK8_0, QR8_0, dequantize_q8_0>;
         case GGML_TYPE_Q2_K:
             return dequantize_row_q2_K_cuda;
         case GGML_TYPE_Q3_K:
@@ -660,6 +701,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
             return dequantize_row_iq4_xs_cuda;
         case GGML_TYPE_IQ3_S:
             return dequantize_row_iq3_s_cuda;
+        case GGML_TYPE_MXFP4:
+            return dequantize_row_mxfp4_cuda;
         case GGML_TYPE_F32:
             return convert_unary_cont_cuda<float>;
         case GGML_TYPE_BF16:
@@ -676,11 +719,11 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
         case GGML_TYPE_Q4_1:
             return dequantize_row_q4_1_cuda;
         case GGML_TYPE_Q5_0:
-            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
+            return dequantize_block_cont_cuda<QK5_0, QR5_0, dequantize_q5_0>;
         case GGML_TYPE_Q5_1:
-            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
+            return dequantize_block_cont_cuda<QK5_1, QR5_1, dequantize_q5_1>;
         case GGML_TYPE_Q8_0:
-            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
+            return dequantize_block_cont_cuda<QK8_0, QR8_0, dequantize_q8_0>;
         case GGML_TYPE_Q2_K:
             return dequantize_row_q2_K_cuda;
         case GGML_TYPE_Q3_K:
@@ -709,6 +752,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
             return dequantize_row_iq4_xs_cuda;
         case GGML_TYPE_IQ3_S:
             return dequantize_row_iq3_s_cuda;
+        case GGML_TYPE_MXFP4:
+            return dequantize_row_mxfp4_cuda;
         case GGML_TYPE_F16:
             return convert_unary_cont_cuda<half>;
         case GGML_TYPE_BF16:
@@ -722,6 +767,16 @@ to_fp16_nc_cuda_t ggml_get_to_fp16_nc_cuda(ggml_type type) {
     switch (type) {
         case GGML_TYPE_F32:
             return convert_unary_cuda<float>;
+        case GGML_TYPE_Q4_0:
+            return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
+        case GGML_TYPE_Q4_1:
+            return dequantize_block_cuda<QK4_1, QR4_1, dequantize_q4_1>;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
         case GGML_TYPE_BF16:
             return convert_unary_cuda<nv_bfloat16>;
         default:
@@ -733,6 +788,16 @@ to_bf16_nc_cuda_t ggml_get_to_bf16_nc_cuda(ggml_type type) {
     switch (type) {
         case GGML_TYPE_F32:
             return convert_unary_cuda<float, nv_bfloat16>;
+        case GGML_TYPE_Q4_0:
+            return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
+        case GGML_TYPE_Q4_1:
+            return dequantize_block_cuda<QK4_1, QR4_1, dequantize_q4_1>;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
         case GGML_TYPE_F16:
             return convert_unary_cuda<half, nv_bfloat16>;
         default:
@@ -744,6 +809,16 @@ to_fp32_nc_cuda_t ggml_get_to_fp32_nc_cuda(ggml_type type) {
     switch (type) {
         case GGML_TYPE_F16:
             return convert_unary_cuda<half, float>;
+        case GGML_TYPE_Q4_0:
+            return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
+        case GGML_TYPE_Q4_1:
+            return dequantize_block_cuda<QK4_1, QR4_1, dequantize_q4_1>;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
         case GGML_TYPE_BF16:
             return convert_unary_cuda<nv_bfloat16, float>;
         default:

ggml/src/ggml-cuda/cpy-utils.cuh

@@ -0,0 +1,225 @@
+#pragma once
+
+#include "ggml-common.h"
+
+template<typename src_t, typename dst_t>
+static __device__ __forceinline__ void convert_flt(const src_t * src, dst_t * dst) {
+    if constexpr (std::is_same_v<src_t, dst_t>) {
+        *dst = *src;
+    } else {
+        *dst = float(*src);
+    }
+}
+
+static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) {
+    if (x <= val[0]) return 0;
+    if (x >= val[n-1]) return n-1;
+    int ml = 0, mu = n-1;
+    while (mu-ml > 1) {
+        int mav = (ml+mu)/2;
+        if (x < val[mav]) mu = mav; else ml = mav;
+    }
+    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
+}
+
+static __device__ void quantize_f32_q4_0_block(const float * __restrict__ x, block_q4_0 * __restrict__ y) {
+    float amax = 0.0f;
+    float vmax = 0.0f;
+
+    for (int j = 0; j < QK4_0; ++j) {
+        const float v = x[j];
+        if (amax < fabsf(v)) {
+            amax = fabsf(v);
+            vmax = v;
+        }
+    }
+
+    const float d  = vmax / -8;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    y->d = d;
+
+    for (int j = 0; j < QK4_0/2; ++j) {
+        const float x0 = x[0       + j]*id;
+        const float x1 = x[QK4_0/2 + j]*id;
+
+        const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f));
+        const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f));
+
+        y->qs[j]  = xi0;
+        y->qs[j] |= xi1 << 4;
+    }
+}
+
+static __device__ void quantize_f32_q4_1_block(const float * __restrict__ x, block_q4_1 * __restrict__ y) {
+    float vmin = FLT_MAX;
+    float vmax = -FLT_MAX;
+
+    for (int j = 0; j < QK4_1; ++j) {
+        const float v = x[j];
+        if (v < vmin) vmin = v;
+        if (v > vmax) vmax = v;
+    }
+
+    const float d  = (vmax - vmin) / ((1 << 4) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
+
+    y->dm.x = d;
+    y->dm.y = vmin;
+
+    for (int j = 0; j < QK4_1/2; ++j) {
+        const float x0 = (x[0       + j] - vmin)*id;
+        const float x1 = (x[QK4_1/2 + j] - vmin)*id;
+
+        const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f));
+        const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f));
+
+        y->qs[j]  = xi0;
+        y->qs[j] |= xi1 << 4;
+    }
+}
+
+static __device__ void quantize_f32_q5_0_block(const float * __restrict__ x, block_q5_0 * __restrict__ y) {
+    float amax = 0.0f;
+    float vmax = 0.0f;
+
+    for (int j = 0; j < QK5_0; ++j) {
+        const float v = x[j];
+        if (amax < fabsf(v)) {
+            amax = fabsf(v);
+            vmax = v;
+        }
+    }
+
+    const float d  = vmax / -16;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    y->d = d;
+
+    uint32_t qh = 0;
+    for (int j = 0; j < QK5_0/2; ++j) {
+        const float x0 = x[0       + j]*id;
+        const float x1 = x[QK5_0/2 + j]*id;
+
+        const uint8_t xi0 = min(31, (int8_t)(x0 + 16.5f));
+        const uint8_t xi1 = min(31, (int8_t)(x1 + 16.5f));
+
+        y->qs[j]  = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
+        qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+        qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
+    }
+    memcpy(y->qh, &qh, sizeof(qh));
+}
+
+static __device__ void quantize_f32_q5_1_block(const float * __restrict__ x, block_q5_1 * __restrict__ y) {
+    float min = x[0];
+    float max = x[0];
+
+    for (int j = 1; j < QK5_1; ++j) {
+        const float v = x[j];
+        min = v < min ? v : min;
+        max = v > max ? v : max;
+    }
+
+    const float d  = (max - min) / 31;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    y->dm.x = d;
+    y->dm.y = min;
+
+    uint32_t qh = 0;
+    for (int j = 0; j < QK5_1/2; ++j) {
+        const float x0 = (x[0       + j] - min)*id;
+        const float x1 = (x[QK5_1/2 + j] - min)*id;
+
+        const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
+        const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
+
+        y->qs[j]  = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
+        qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+        qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2);
+    }
+    memcpy(y->qh, &qh, sizeof(qh));
+}
+
+static __device__ void quantize_f32_q8_0_block(const float * __restrict__ x, block_q8_0 * __restrict__ y) {
+    float amax = 0.0f; // absolute max
+
+    for (int j = 0; j < QK8_0; j++) {
+        const float v = x[j];
+        amax = fmaxf(amax, fabsf(v));
+    }
+
+    const float d = amax / ((1 << 7) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
+
+    y->d = d;
+
+    for (int j = 0; j < QK8_0; ++j) {
+        const float x0 = x[j]*id;
+        y->qs[j] = roundf(x0);
+    }
+}
+
+static __device__ void quantize_f32_iq4_nl_block(const float * __restrict__ x, block_iq4_nl * __restrict__ y) {
+    float amax = 0.0f;
+    float vmax = 0.0f;
+
+    for (int j = 0; j < QK4_NL; ++j) {
+        const float v = x[j];
+        if (amax < fabsf(v)) {
+            amax = fabsf(v);
+            vmax = v;
+        }
+    }
+
+    float d = vmax / kvalues_iq4nl[0];
+    const float id = d ? 1.0f/d : 0.0f;
+
+    float sumqx = 0, sumq2 = 0;
+    for (int j = 0; j < QK4_NL/2; ++j) {
+        const float x0 = x[0        + j]*id;
+        const float x1 = x[QK4_NL/2 + j]*id;
+        const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl, x0);
+        const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl, x1);
+        y->qs[j] = xi0 | (xi1 << 4);
+        const float v0 = kvalues_iq4nl[xi0];
+        const float v1 = kvalues_iq4nl[xi1];
+        const float w0 = x[0        + j]*x[0        + j];
+        const float w1 = x[QK4_NL/2 + j]*x[QK4_NL/2 + j];
+        sumqx += w0*v0*x[j] + w1*v1*x[QK4_NL/2 + j];
+        sumq2 += w0*v0*v0 + w1*v1*v1;
+    }
+
+    y->d = sumq2 > 0 ? sumqx/sumq2 : d;
+}
+
+// Wrapper functions for cpy.cu compatibility
+static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
+    quantize_f32_q4_0_block((const float *)cxi, (block_q4_0 *)cdsti);
+}
+
+static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
+    quantize_f32_q4_1_block((const float *)cxi, (block_q4_1 *)cdsti);
+}
+
+static __device__ void cpy_blck_f32_q5_0(const char * cxi, char * cdsti) {
+    quantize_f32_q5_0_block((const float *)cxi, (block_q5_0 *)cdsti);
+}
+
+static __device__ void cpy_blck_f32_q5_1(const char * cxi, char * cdsti) {
+    quantize_f32_q5_1_block((const float *)cxi, (block_q5_1 *)cdsti);
+}
+
+static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
+    quantize_f32_q8_0_block((const float *)cxi, (block_q8_0 *)cdsti);
+}
+
+static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) {
+    quantize_f32_iq4_nl_block((const float *)cxi, (block_iq4_nl *)cdsti);
+}
+
+template<typename src_t, typename dst_t>
+static __device__ void cpy_1_flt(const char * cxi, char * cdsti) {
+    convert_flt((const src_t *)cxi, (dst_t *)cdsti);
+}

ggml/src/ggml-cuda/cpy.cu

@@ -1,51 +1,17 @@
 #include "cpy.cuh"
 #include "dequantize.cuh"
-#ifdef GGML_USE_MUSA
+#include "cpy-utils.cuh"
+#if defined(GGML_USE_MUSA) && defined(GGML_MUSA_MUDNN_COPY)
 #include "ggml-musa/mudnn.cuh"
-#endif // GGML_USE_MUSA
+#endif // GGML_USE_MUSA && GGML_MUSA_MUDNN_COPY
 
 typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
 
-static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    float * dsti = (float *) cdsti;
-
-    *dsti = *xi;
-}
-
-static __device__ void cpy_1_f32_bf16(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    nv_bfloat16 * dsti = (nv_bfloat16 *) cdsti;
-
-    *dsti = *xi;
-}
-
-static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    half * dsti = (half *) cdsti;
-
-    *dsti = __float2half(*xi);
-}
-
-static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) {
-    const half * xi = (const half *) cxi;
-    half * dsti = (half *) cdsti;
-
-    *dsti = *xi;
-}
-
-static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) {
-    const half * xi = (const half *) cxi;
-    float * dsti = (float *) cdsti;
-
-    *dsti = *xi;
-}
-
 template <cpy_kernel_t cpy_1>
-static __global__ void cpy_f32_f16(const char * cx, char * cdst_direct, const int ne,
-                                   const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-                                   const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
-                                   const int nb12, const int nb13, char ** cdst_indirect, int graph_cpynode_index) {
+static __global__ void cpy_flt(const char * cx, char * cdst_direct, const int ne,
+                               const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+                               const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                               const int nb12, const int nb13, char ** cdst_indirect, int graph_cpynode_index) {
     const int64_t i = blockDim.x*blockIdx.x + threadIdx.x;
 
     if (i >= ne) {
@@ -71,29 +37,6 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst_direct, const in
     cpy_1(cx + x_offset, cdst + dst_offset);
 }
 
-static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q8_0 * dsti = (block_q8_0 *) cdsti;
-
-    float amax = 0.0f; // absolute max
-
-    for (int j = 0; j < QK8_0; j++) {
-        const float v = xi[j];
-        amax = fmaxf(amax, fabsf(v));
-    }
-
-    const float d = amax / ((1 << 7) - 1);
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->d = d;
-
-    for (int j = 0; j < QK8_0; ++j) {
-        const float x0 = xi[j]*id;
-
-        dsti->qs[j] = roundf(x0);
-    }
-}
-
 static __device__ void cpy_blck_q8_0_f32(const char * cxi, char * cdsti) {
     float * cdstf = (float *)(cdsti);
 
@@ -106,139 +49,6 @@ static __device__ void cpy_blck_q8_0_f32(const char * cxi, char * cdsti) {
     }
 }
 
-static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q4_0 * dsti = (block_q4_0 *) cdsti;
-
-    float amax = 0.0f;
-    float vmax = 0.0f;
-
-    for (int j = 0; j < QK4_0; ++j) {
-        const float v = xi[j];
-        if (amax < fabsf(v)) {
-            amax = fabsf(v);
-            vmax = v;
-        }
-    }
-
-    const float d  = vmax / -8;
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->d = d;
-
-    for (int j = 0; j < QK4_0/2; ++j) {
-        const float x0 = xi[0       + j]*id;
-        const float x1 = xi[QK4_0/2 + j]*id;
-
-        const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f));
-        const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f));
-
-        dsti->qs[j]  = xi0;
-        dsti->qs[j] |= xi1 << 4;
-    }
-}
-
-static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q4_1 * dsti = (block_q4_1 *) cdsti;
-
-    float vmin = FLT_MAX;
-    float vmax = -FLT_MAX;
-
-    for (int j = 0; j < QK4_1; ++j) {
-        const float v = xi[j];
-
-        if (v < vmin) vmin = v;
-        if (v > vmax) vmax = v;
-    }
-
-    const float d  = (vmax - vmin) / ((1 << 4) - 1);
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->dm.x = d;
-    dsti->dm.y = vmin;
-
-    for (int j = 0; j < QK4_1/2; ++j) {
-        const float x0 = (xi[0       + j] - vmin)*id;
-        const float x1 = (xi[QK4_1/2 + j] - vmin)*id;
-
-        const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f));
-        const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f));
-
-        dsti->qs[j]  = xi0;
-        dsti->qs[j] |= xi1 << 4;
-    }
-}
-
-static __device__ void cpy_blck_f32_q5_0(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q5_0 * dsti = (block_q5_0 *) cdsti;
-
-    float amax = 0.0f;
-    float vmax = 0.0f;
-
-    for (int j = 0; j < QK5_0; ++j) {
-        const float v = xi[j];
-        if (amax < fabsf(v)) {
-            amax = fabsf(v);
-            vmax = v;
-        }
-    }
-
-    const float d  = vmax / -16;
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->d = d;
-
-    uint32_t qh = 0;
-    for (int j = 0; j < QK5_0/2; ++j) {
-        const float x0 = xi[0       + j]*id;
-        const float x1 = xi[QK5_0/2 + j]*id;
-
-        const uint8_t xi0 = min(31, (int8_t)(x0 + 16.5f));
-        const uint8_t xi1 = min(31, (int8_t)(x1 + 16.5f));
-
-        dsti->qs[j]  = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
-        qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
-        qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
-    }
-    memcpy(dsti->qh, &qh, sizeof(qh));
-}
-
-static __device__ void cpy_blck_f32_q5_1(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q5_1 * dsti = (block_q5_1 *) cdsti;
-
-    float min = xi[0];
-    float max = xi[0];
-
-    for (int j = 1; j < QK5_1; ++j) {
-        const float v = xi[j];
-        min = v < min ? v : min;
-        max = v > max ? v : max;
-    }
-
-    const float d  = (max - min) / 31;
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->dm.x = d;
-    dsti->dm.y = min;
-
-    uint32_t qh = 0;
-    for (int j = 0; j < QK5_1/2; ++j) {
-        const float x0 = (xi[0       + j] - min)*id;
-        const float x1 = (xi[QK5_1/2 + j] - min)*id;
-
-        const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
-        const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
-
-        dsti->qs[j]  = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
-        qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
-        qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2);
-    }
-    memcpy(dsti->qh, &qh, sizeof(qh));
-}
-
 template<dequantize_kernel_t dequant, int qk>
 static __device__ void cpy_blck_q_f32(const char * cxi, char * cdsti) {
     float * cdstf = (float *)(cdsti);
@@ -252,53 +62,6 @@ static __device__ void cpy_blck_q_f32(const char * cxi, char * cdsti) {
     }
 }
 
-static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) {
-    if (x <= val[0]) return 0;
-    if (x >= val[n-1]) return n-1;
-    int ml = 0, mu = n-1;
-    while (mu-ml > 1) {
-        int mav = (ml+mu)/2;
-        if (x < val[mav]) mu = mav; else ml = mav;
-    }
-    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
-}
-
-static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_iq4_nl * dsti = (block_iq4_nl *) cdsti;
-
-    float amax = 0.0f;
-    float vmax = 0.0f;
-
-    for (int j = 0; j < QK4_NL; ++j) {
-        const float v = xi[j];
-        if (amax < fabsf(v)) {
-            amax = fabsf(v);
-            vmax = v;
-        }
-    }
-
-    float d = vmax / kvalues_iq4nl[0];
-    const float id = d ? 1.0f/d : 0.0f;
-
-    float sumqx = 0, sumq2 = 0;
-    for (int j = 0; j < QK4_NL/2; ++j) {
-        const float x0 = xi[0        + j]*id;
-        const float x1 = xi[QK4_NL/2 + j]*id;
-        const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl, x0);
-        const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl, x1);
-        dsti->qs[j] = xi0 | (xi1 << 4);
-        const float v0 = kvalues_iq4nl[xi0];
-        const float v1 = kvalues_iq4nl[xi1];
-        const float w0 = xi[0        + j]*xi[0        + j];
-        const float w1 = xi[QK4_NL/2 + j]*xi[QK4_NL/2 + j];
-        sumqx += w0*v0*xi[j] + w1*v1*xi[QK4_NL/2 + j];
-        sumq2 += w0*v0*v0 + w1*v1*v1;
-    }
-
-    dsti->d = sumq2 > 0 ? sumqx/sumq2 : d;
-}
-
 template <cpy_kernel_t cpy_blck, int qk>
 static __global__ void cpy_f32_q(const char * cx, char * cdst_direct, const int ne,
                                  const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
@@ -358,7 +121,7 @@ static __global__ void cpy_q_f32(const char * cx, char * cdst_direct, const int
 // Copy destination pointers to GPU to be available when pointer indirection is in use
 
 void ggml_cuda_cpy_dest_ptrs_copy(ggml_cuda_graph * cuda_graph, char ** host_dest_ptrs, const int host_dest_ptrs_size, cudaStream_t stream) {
-#if defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS)
+#if defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS) || defined(GGML_MUSA_GRAPHS)
     if (cuda_graph->dest_ptrs_size < host_dest_ptrs_size) { // (re-)allocate GPU memory for destination pointers
         CUDA_CHECK(cudaStreamSynchronize(stream));
         if (cuda_graph->dest_ptrs_d != nullptr) {
@@ -376,43 +139,14 @@ void ggml_cuda_cpy_dest_ptrs_copy(ggml_cuda_graph * cuda_graph, char ** host_des
 #endif
 }
 
-static void ggml_cpy_f16_f32_cuda(
+template<typename src_t, typename dst_t>
+static void ggml_cpy_flt_cuda(
     const char * cx, char * cdst, const int ne,
     const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
     const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) {
 
     const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f16_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++);
-}
-
-static void ggml_cpy_f32_f32_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) {
-
-    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f32_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++);
-}
-
-static void ggml_cpy_f32_bf16_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) {
-
-    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f32_bf16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++);
-}
-
-static void ggml_cpy_f32_f16_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) {
-
-    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f32_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
+    cpy_flt<cpy_1_flt<src_t, dst_t>><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++);
 }
 
@@ -544,16 +278,6 @@ static void ggml_cpy_f32_iq4_nl_cuda(
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++);
 }
 
-static void ggml_cpy_f16_f16_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) {
-
-    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f16_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++);
-}
-
 void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1, bool disable_indirection_for_this_node) {
     const int64_t ne = ggml_nelements(src0);
     GGML_ASSERT(ne == ggml_nelements(src1));
@@ -590,7 +314,7 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
 
     char ** dest_ptrs_d = nullptr;
     int graph_cpynode_index = -1;
-#if defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS)
+#if defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS) || defined(GGML_MUSA_GRAPHS)
     if(ctx.cuda_graph->use_cpy_indirection && !disable_indirection_for_this_node) {
         dest_ptrs_d = ctx.cuda_graph->dest_ptrs_d;
         graph_cpynode_index = ctx.cuda_graph->graph_cpynode_index;
@@ -600,20 +324,20 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
 #endif
     if (src0->type == src1->type && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
         GGML_ASSERT(ggml_nbytes(src0) == ggml_nbytes(src1));
-#ifdef GGML_USE_MUSA
+#if defined(GGML_USE_MUSA) && defined(GGML_MUSA_MUDNN_COPY)
         if (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) {
             CUDA_CHECK(mudnnMemcpyAsync(ctx, src1, src0));
         } else
-#endif // GGML_USE_MUSA
+#endif // GGML_USE_MUSA && GGML_MUSA_MUDNN_COPY
         {
             CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream));
         }
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
-        ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
+        ggml_cpy_flt_cuda<float, float> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_BF16) {
-        ggml_cpy_f32_bf16_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
+        ggml_cpy_flt_cuda<float, nv_bfloat16> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
-        ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
+        ggml_cpy_flt_cuda<float, half> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
         ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
     } else if (src0->type == GGML_TYPE_Q8_0 && src1->type == GGML_TYPE_F32) {
@@ -640,14 +364,22 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
     } else if (src0->type == GGML_TYPE_Q5_1 && src1->type == GGML_TYPE_F32) {
         ggml_cpy_q5_1_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
     } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
-        ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
+        ggml_cpy_flt_cuda<half, half> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_BF16) {
+        ggml_cpy_flt_cuda<half, nv_bfloat16> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
     } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
-        ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
+        ggml_cpy_flt_cuda<half, float> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
+    } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_BF16) {
+        ggml_cpy_flt_cuda<nv_bfloat16, nv_bfloat16> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
+    } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F16) {
+        ggml_cpy_flt_cuda<nv_bfloat16, half> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
+    } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_flt_cuda<nv_bfloat16, float> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
     } else {
         GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__,
                 ggml_type_name(src0->type), ggml_type_name(src1->type));
     }
-#if defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS)
+#if defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS) || defined(GGML_MUSA_GRAPHS)
     if(ctx.cuda_graph->use_cpy_indirection && !disable_indirection_for_this_node) {
         ctx.cuda_graph->graph_cpynode_index = graph_cpynode_index;
     }
@@ -667,11 +399,11 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
     if (src0->type == src1->type && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
         return nullptr;
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
-        return (void*) cpy_f32_f16<cpy_1_f32_f32>;
+        return (void*) cpy_flt<cpy_1_flt<float, float>>;
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_BF16) {
-        return (void*) cpy_f32_f16<cpy_1_f32_bf16>;
+        return (void*) cpy_flt<cpy_1_flt<float, nv_bfloat16>>;
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
-        return (void*) cpy_f32_f16<cpy_1_f32_f16>;
+        return (void*) cpy_flt<cpy_1_flt<float, half>>;
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
         return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>;
     } else if (src0->type == GGML_TYPE_Q8_0 && src1->type == GGML_TYPE_F32) {
@@ -695,9 +427,17 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
     } else if (src0->type == GGML_TYPE_Q5_1 && src1->type == GGML_TYPE_F32) {
         return (void*) cpy_q_f32<cpy_blck_q_f32<dequantize_q5_1, QK5_1>, QK5_1>;
     } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
-        return (void*) cpy_f32_f16<cpy_1_f32_f16>;
+        return (void*) cpy_flt<cpy_1_flt<half, half>>;
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_BF16) {
+        return (void*) cpy_flt<cpy_1_flt<half, nv_bfloat16>>;
     } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
-        return (void*) cpy_f32_f16<cpy_1_f16_f32>;
+        return (void*) cpy_flt<cpy_1_flt<half, float>>;
+    } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F16) {
+        return (void*) cpy_flt<cpy_1_flt<nv_bfloat16, half>>;
+    } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_BF16) {
+        return (void*) cpy_flt<cpy_1_flt<nv_bfloat16, nv_bfloat16>>;
+    } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F32) {
+        return (void*) cpy_flt<cpy_1_flt<nv_bfloat16, float>>;
     } else {
         GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__,
                 ggml_type_name(src0->type), ggml_type_name(src1->type));

ggml/src/ggml-cuda/fattn-common.cuh

@@ -15,6 +15,8 @@ typedef void (* fattn_kernel_t)(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
+        const char * __restrict__ sinks,
+        const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
         const float scale,
@@ -23,33 +25,13 @@ typedef void (* fattn_kernel_t)(
         const float m1,
         const uint32_t n_head_log2,
         const float logit_softcap,
-        const int ne00,
-        const int ne01,
-        const int ne02,
-        const int ne03,
-        const int ne10,
-        const int ne11,
-        const int ne12,
-        const int ne13,
-        const int ne31,
-        const int ne32,
-        const int ne33,
-        const int nb31,
-        const int nb32,
-        const int nb33,
-        const int nb01,
-        const int nb02,
-        const int nb03,
-        const int nb11,
-        const int nb12,
-        const int nb13,
-        const int nb21,
-        const int nb22,
-        const int nb23,
-        const int ne0,
-        const int ne1,
-        const int ne2,
-        const int ne3);
+        const int32_t ne00, const int32_t ne01, const int32_t ne02, const int32_t ne03,
+                            const int32_t nb01, const int32_t nb02, const int32_t nb03,
+        const int32_t ne10, const int32_t ne11, const int32_t ne12, const int32_t ne13,
+                            const int32_t nb11, const int32_t nb12, const int64_t nb13,
+                            const int32_t nb21, const int32_t nb22, const int64_t nb23,
+                            const int32_t ne31, const int32_t ne32, const int32_t ne33,
+                            const int32_t nb31, const int32_t nb32, const int64_t nb33);
 
 typedef half (*vec_dot_KQ_f16_t)(
     const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds);
@@ -520,6 +502,55 @@ constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) {
         nullptr;
 }
 
+template <int ncols1>
+__launch_bounds__(FATTN_KQ_STRIDE/2, 1)
+static __global__ void flash_attn_mask_to_KV_max(
+        const half2 * __restrict__ mask, int * __restrict__ KV_max, const int ne30, const int s31, const int s33) {
+    const int ne31     = gridDim.x;
+    const int tid      = threadIdx.x;
+    const int sequence = blockIdx.y;
+    const int jt       = blockIdx.x;
+
+    mask += sequence*s33 + jt*ncols1*s31;
+
+    __shared__ int buf_iw[WARP_SIZE];
+    if (tid < WARP_SIZE) {
+        buf_iw[tid] = 1;
+    }
+    __syncthreads();
+
+    int KV_max_sj = (ne30 - 1) * FATTN_KQ_STRIDE;
+    for (; KV_max_sj >= 0; KV_max_sj -= FATTN_KQ_STRIDE) {
+        int all_inf = 1;
+
+#pragma unroll
+        for (int j = 0; j < ncols1; ++j) {
+            const float2 tmp = __half22float2(mask[j*s31 + KV_max_sj/2 + tid]);
+            all_inf = all_inf && int(isinf(tmp.x)) && int(isinf(tmp.y));
+        }
+
+        all_inf = warp_reduce_all(all_inf);
+        if (tid % WARP_SIZE == 0) {
+            buf_iw[tid / WARP_SIZE] = all_inf;
+        }
+        __syncthreads();
+        all_inf = buf_iw[tid % WARP_SIZE];
+        __syncthreads();
+        all_inf = warp_reduce_all(all_inf);
+
+        if (!all_inf) {
+            KV_max_sj += FATTN_KQ_STRIDE;
+            break;
+        }
+    }
+
+    if (threadIdx.x != 0) {
+        return;
+    }
+
+    KV_max[sequence*ne31 + jt] = KV_max_sj;
+}
+
 template<int D, int ncols1, int ncols2> // D == head size
 __launch_bounds__(D, 1)
 static __global__ void flash_attn_stream_k_fixup(
@@ -612,9 +643,9 @@ static __global__ void flash_attn_stream_k_fixup(
 }
 
 template<int D> // D == head size
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#if !defined(GGML_USE_HIP)
 __launch_bounds__(D, 1)
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#endif // !(defined(GGML_USE_HIP)
 static __global__ void flash_attn_combine_results(
         const float  * __restrict__ VKQ_parts,
         const float2 * __restrict__ VKQ_meta,
@@ -706,7 +737,8 @@ void launch_fattn(
 
     GGML_ASSERT(V || is_mla);
 
-    const ggml_tensor * mask = dst->src[3];
+    const ggml_tensor * mask  = dst->src[3];
+    const ggml_tensor * sinks = dst->src[4];
 
     ggml_tensor * KQV = dst;
 
@@ -731,6 +763,7 @@ void launch_fattn(
 
     ggml_cuda_pool_alloc<half>   K_f16(pool);
     ggml_cuda_pool_alloc<half>   V_f16(pool);
+    ggml_cuda_pool_alloc<int>    KV_max(pool);
     ggml_cuda_pool_alloc<float>  dst_tmp(pool);
     ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
 
@@ -745,40 +778,84 @@ void launch_fattn(
     size_t nb23 = V ? V->nb[3] : nb13;
 
     if (need_f16_K && K->type != GGML_TYPE_F16) {
-        GGML_ASSERT(ggml_is_contiguously_allocated(K));
-        K_f16.alloc(ggml_nelements(K));
-        to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(K->type);
-        to_fp16(K_data, K_f16.ptr, ggml_nelements(K), main_stream);
-        K_data = (char *) K_f16.ptr;
-
         const size_t bs = ggml_blck_size(K->type);
         const size_t ts = ggml_type_size(K->type);
 
-        nb11 = nb11*bs*sizeof(half)/ts;
-        nb12 = nb12*bs*sizeof(half)/ts;
-        nb13 = nb13*bs*sizeof(half)/ts;
+        K_f16.alloc(ggml_nelements(K));
+        if (ggml_is_contiguously_allocated(K)) {
+            to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(K->type);
+            to_fp16(K_data, K_f16.ptr, ggml_nelements(K), main_stream);
+
+            nb11 = nb11*bs*sizeof(half)/ts;
+            nb12 = nb12*bs*sizeof(half)/ts;
+            nb13 = nb13*bs*sizeof(half)/ts;
+        } else {
+            GGML_ASSERT(K->nb[0] == ts);
+            to_fp16_nc_cuda_t to_fp16 = ggml_get_to_fp16_nc_cuda(K->type);
+            const int64_t s01 = nb11 / ts;
+            const int64_t s02 = nb12 / ts;
+            const int64_t s03 = nb13 / ts;
+            to_fp16(K_data, K_f16.ptr, K->ne[0], K->ne[1], K->ne[2], K->ne[3], s01, s02, s03, main_stream);
+
+            nb11 = K->ne[0] * sizeof(half);
+            nb12 = K->ne[1] * nb11;
+            nb13 = K->ne[2] * nb12;
+        }
+        K_data = (char *) K_f16.ptr;
     }
 
     if (V && need_f16_V && V->type != GGML_TYPE_F16) {
-        GGML_ASSERT(ggml_is_contiguously_allocated(V));
-        V_f16.alloc(ggml_nelements(V));
-        to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(V->type);
-        to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream);
-        V_data = (char *) V_f16.ptr;
-
         const size_t bs = ggml_blck_size(V->type);
         const size_t ts = ggml_type_size(V->type);
 
-        nb21 = nb21*bs*sizeof(half)/ts;
-        nb22 = nb22*bs*sizeof(half)/ts;
-        nb23 = nb23*bs*sizeof(half)/ts;
-    }
+        V_f16.alloc(ggml_nelements(V));
+        if (ggml_is_contiguously_allocated(V)) {
+            to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(V->type);
+            to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream);
+            V_data = (char *) V_f16.ptr;
 
-    int parallel_blocks = 1;
+            nb21 = nb21*bs*sizeof(half)/ts;
+            nb22 = nb22*bs*sizeof(half)/ts;
+            nb23 = nb23*bs*sizeof(half)/ts;
+        } else {
+            GGML_ASSERT(V->nb[0] == ts);
+            to_fp16_nc_cuda_t to_fp16 = ggml_get_to_fp16_nc_cuda(V->type);
+            const int64_t s01 = nb21 / ts;
+            const int64_t s02 = nb22 / ts;
+            const int64_t s03 = nb23 / ts;
+            to_fp16(V_data, V_f16.ptr, V->ne[0], V->ne[1], V->ne[2], V->ne[3], s01, s02, s03, main_stream);
+
+            nb21 = V->ne[0] * sizeof(half);
+            nb22 = V->ne[1] * nb21;
+            nb23 = V->ne[2] * nb22;
+        }
+        V_data = (char *) V_f16.ptr;
+    }
 
     const int ntiles_x = ((Q->ne[1] + ncols1 - 1) / ncols1);
     const int ntiles_total = ntiles_x * (Q->ne[2] / ncols2) * Q->ne[3];
 
+    // Optional optimization where the mask is scanned to determine whether part of the calculation can be skipped.
+    // Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or
+    //     multiple sequences of possibly different lengths.
+    if (mask && (Q->ne[1] >= 1024 || Q->ne[3] > 1)) {
+        const int s31 = mask->nb[1] / sizeof(half2);
+        const int s33 = mask->nb[3] / sizeof(half2);
+
+        const dim3 blocks_num_KV_max(ntiles_x, Q->ne[3], 1);
+        const dim3 block_dim_KV_max(FATTN_KQ_STRIDE/2, 1, 1);
+
+        const int ne_KV_max = blocks_num_KV_max.x*blocks_num_KV_max.y;
+        const int iter_k = K->ne[1] / FATTN_KQ_STRIDE;
+
+        KV_max.alloc(ne_KV_max);
+        flash_attn_mask_to_KV_max<ncols1><<<blocks_num_KV_max, block_dim_KV_max, 0, main_stream>>>
+            ((const half2 *) mask->data, KV_max.ptr, iter_k, s31, s33);
+        CUDA_CHECK(cudaGetLastError());
+    }
+
+    int parallel_blocks = 1;
+
     const dim3 block_dim(warp_size, nwarps, 1);
     int max_blocks_per_sm = 1; // Max. number of active blocks limited by occupancy.
     CUDA_CHECK(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&max_blocks_per_sm, fattn_kernel, block_dim.x * block_dim.y * block_dim.z, nbytes_shared));
@@ -865,16 +942,15 @@ void launch_fattn(
         K_data,
         V_data,
         mask ? ((const char *) mask->data) : nullptr,
+        sinks ? ((const char *) sinks->data) : nullptr,
+        KV_max.ptr,
         !stream_k && parallel_blocks > 1 ? dst_tmp.ptr : (float *) KQV->data, dst_tmp_meta.ptr,
         scale, max_bias, m0, m1, n_head_log2, logit_softcap,
-        Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
-        K->ne[0], K->ne[1], K->ne[2], K->ne[3],
-        mask ? mask->ne[1] : 0, mask ? mask->ne[2] : 0, mask ? mask->ne[3] : 0,
-        mask ? mask->nb[1] : 0, mask ? mask->nb[2] : 0, mask ? mask->nb[3] : 0,
-        Q->nb[1], Q->nb[2], Q->nb[3],
-        nb11, nb12, nb13,
+        Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], Q->nb[1], Q->nb[2], Q->nb[3],
+        K->ne[0], K->ne[1], K->ne[2], K->ne[3], nb11, nb12, nb13,
         nb21, nb22, nb23,
-        KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
+        mask ? mask->ne[1] : 0, mask ? mask->ne[2] : 0, mask ? mask->ne[3] : 0,
+        mask ? mask->nb[1] : 0, mask ? mask->nb[2] : 0, mask ? mask->nb[3] : 0
     );
     CUDA_CHECK(cudaGetLastError());
 

ggml/src/ggml-cuda/fattn-mma-f16.cuh

@@ -392,7 +392,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask(
     }
 }
 
-template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles, bool use_logit_softcap, bool mla, bool needs_fixup, bool is_fixup, bool last_iter>
+template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles,
+    bool use_logit_softcap, bool mla, bool needs_fixup, bool is_fixup, bool last_iter>
 static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         const float2 * const __restrict__ Q_f2,
         const half2  * const __restrict__ K_h2,
@@ -408,7 +409,6 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         const int stride_K,
         const int stride_V,
         const int stride_mask,
-        const int jt,
         half2        * const __restrict__ tile_Q,
         half2        * const __restrict__ tile_K,
         half2        * const __restrict__ tile_V,
@@ -418,7 +418,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         float        * const __restrict__ KQ_max,
         float        * const __restrict__ KQ_rowsum,
         const int kb0) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
     typedef fattn_mma_f16_config<DKQ, DV> c;
 
 #ifdef CP_ASYNC_AVAILABLE
@@ -455,7 +455,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         cp_async_wait_all();
         __syncthreads();
         flash_attn_ext_f16_load_tile<stride_tile_V, nwarps, c::nbatch_fa, use_cp_async>
-            (V_h2 + k_VKQ_0*stride_V, tile_V, nbatch_V2, stride_V);
+            (V_h2 + int64_t(k_VKQ_0)*stride_V, tile_V, nbatch_V2, stride_V);
     } else {
         constexpr bool use_cp_async = nstages == 1;
         if (ncols2 > 1 || mask_h2) {
@@ -471,7 +471,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         if (nstages <= 1) {
             constexpr bool use_cp_async = nstages == 1;
             flash_attn_ext_f16_load_tile<stride_tile_K, nwarps, c::nbatch_fa, use_cp_async>
-                (K_h2 + k_VKQ_0*stride_K + k0_start, tile_K, k0_diff, stride_K);
+                (K_h2 + int64_t(k_VKQ_0)*stride_K + k0_start, tile_K, k0_diff, stride_K);
             if (use_cp_async) {
                 cp_async_wait_all();
             }
@@ -715,7 +715,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                     (mask_h2 + (k_VKQ_0 + c::nbatch_fa)/2, tile_mask, stride_mask);
             }
             flash_attn_ext_f16_load_tile<stride_tile_K, nwarps, c::nbatch_fa, use_cp_async>
-                (K_h2 + (k_VKQ_0 + c::nbatch_fa)*stride_K, tile_K, nbatch_K2, stride_K);
+                (K_h2 + int64_t(k_VKQ_0 + c::nbatch_fa)*stride_K, tile_K, nbatch_K2, stride_K);
         }
     }
 
@@ -732,7 +732,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         if (nstages <= 1 && i0_start < reusable_cutoff) {
             constexpr bool use_cp_async = nstages == 1;
             flash_attn_ext_f16_load_tile<stride_tile_V, nwarps, c::nbatch_fa, use_cp_async>
-                (V_h2 + k_VKQ_0*stride_V + i0_start/2, tile_V, i0_diff/2, stride_V);
+                (V_h2 + int64_t(k_VKQ_0)*stride_V + i0_start/2, tile_V, i0_diff/2, stride_V);
             if (use_cp_async) {
                 cp_async_wait_all();
             }
@@ -771,13 +771,12 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
     GGML_UNUSED(mask_h2); GGML_UNUSED(dstk); GGML_UNUSED(dstk_fixup);
     GGML_UNUSED(scale); GGML_UNUSED(slope); GGML_UNUSED(logit_softcap);
     GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(stride_K); GGML_UNUSED(stride_V);
-    GGML_UNUSED(stride_mask); GGML_UNUSED(jt); GGML_UNUSED(tile_K);
-    GGML_UNUSED(stride_mask); GGML_UNUSED(jt); GGML_UNUSED(tile_K);
+    GGML_UNUSED(stride_mask); GGML_UNUSED(tile_K);
     GGML_UNUSED(tile_V); GGML_UNUSED(tile_mask); GGML_UNUSED(Q_B);
     GGML_UNUSED(VKQ_C); GGML_UNUSED(KQ_max); GGML_UNUSED(KQ_rowsum);
     GGML_UNUSED(kb0); GGML_UNUSED(tile_Q);
     NO_DEVICE_CODE;
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
 }
 
 template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles, bool use_logit_softcap, bool mla, bool needs_fixup, bool is_fixup>
@@ -801,7 +800,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         const int jt,
         const int kb0_start,
         const int kb0_stop) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     typedef fattn_mma_f16_config<DKQ, DV> c;
@@ -920,21 +919,22 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
                 (mask_h2 + kb0_start*c::nbatch_fa/2, tile_mask, stride_mask);
         }
         flash_attn_ext_f16_load_tile<stride_tile_K, nwarps, c::nbatch_fa, use_cp_async>
-            (K_h2 + kb0_start*c::nbatch_fa*stride_K, tile_K, nbatch_K2, stride_K);
+            (K_h2 + int64_t(kb0_start)*c::nbatch_fa*stride_K, tile_K, nbatch_K2, stride_K);
     }
 
     // Iterate over ne11 == previous tokens:
-    for (int kb0 = kb0_start; kb0 < kb0_stop-1; ++kb0) {
+    int kb0 = kb0_start;
+    for (; kb0 < kb0_stop-1; ++kb0) {
         constexpr bool last_iter = false;
         flash_attn_ext_f16_iter<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla, needs_fixup, is_fixup, last_iter>
             (Q_f2, K_h2, V_h2, mask_h2, dstk, dstk_fixup, scale, slope, logit_softcap,
-             ne01, ne02, stride_K, stride_V, stride_mask, jt, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0);
+             ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0);
     }
     { // kb0_start is always < kb0_stop so the last iter can be executed unconditionally.
         constexpr bool last_iter = true;
         flash_attn_ext_f16_iter<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla, needs_fixup, is_fixup, last_iter>
             (Q_f2, K_h2, V_h2, mask_h2, dstk, dstk_fixup, scale, slope, logit_softcap,
-             ne01, ne02, stride_K, stride_V, stride_mask, jt, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0_stop-1);
+             ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0);
     }
 
     // With multi-stage loading there is no __syncthreads at the end of the iter,
@@ -1196,7 +1196,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
     GGML_UNUSED(stride_Q2); GGML_UNUSED(stride_K); GGML_UNUSED(stride_V); GGML_UNUSED(stride_mask);
     GGML_UNUSED(jt); GGML_UNUSED(kb0_start); GGML_UNUSED(kb0_stop);
     NO_DEVICE_CODE;
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
 }
 
 template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles, bool use_logit_softcap, bool mla>
@@ -1206,6 +1206,8 @@ static __global__ void flash_attn_ext_f16(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
+        const char * __restrict__ sinks,
+        const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
         const float scale,
@@ -1214,34 +1216,14 @@ static __global__ void flash_attn_ext_f16(
         const float m1,
         const uint32_t n_head_log2,
         const float logit_softcap,
-        const int ne00,
-        const int ne01,
-        const int ne02,
-        const int ne03,
-        const int ne10,
-        const int ne11,
-        const int ne12,
-        const int ne13,
-        const int ne31,
-        const int ne32,
-        const int ne33,
-        const int nb31,
-        const int nb32,
-        const int nb33,
-        const int nb01,
-        const int nb02,
-        const int nb03,
-        const int nb11,
-        const int nb12,
-        const int nb13,
-        const int nb21,
-        const int nb22,
-        const int nb23,
-        const int ne0,
-        const int ne1,
-        const int ne2,
-        const int ne3) {
-#if defined(FLASH_ATTN_AVAILABLE) && defined(NEW_MMA_AVAILABLE)
+        const int32_t ne00, const int32_t ne01, const int32_t ne02, const int32_t ne03,
+                            const int32_t nb01, const int32_t nb02, const int32_t nb03,
+        const int32_t ne10, const int32_t ne11, const int32_t ne12, const int32_t ne13,
+                            const int32_t nb11, const int32_t nb12, const int64_t nb13,
+                            const int32_t nb21, const int32_t nb22, const int64_t nb23,
+                            const int32_t ne31, const int32_t ne32, const int32_t ne33,
+                            const int32_t nb31, const int32_t nb32, const int64_t nb33) {
+#if defined(FLASH_ATTN_AVAILABLE) && defined(TURING_MMA_AVAILABLE)
 
     // Skip unused kernel variants for faster compilation:
     if (use_logit_softcap && !(DKQ == 128 || DKQ == 256)) {
@@ -1286,6 +1268,7 @@ static __global__ void flash_attn_ext_f16(
     // kb0 == k start index when in the output tile.
     int kb0_start = kbc % iter_k;
     int kb0_stop  = min(iter_k, kb0_start + kbc_stop - kbc);
+
     while (kbc < kbc_stop && kb0_stop == iter_k) {
         const int sequence = kbc / (iter_k*iter_j*(ne02/ncols2));
         const int head = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j);
@@ -1302,7 +1285,11 @@ static __global__ void flash_attn_ext_f16(
         const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head, n_head_log2, m0, m1) : 1.0f;
 
         const int kb0_start_kernel = kb0_start * kb_niter;
-        const int kb0_stop_kernel  = kb0_stop  * kb_niter;
+        int       kb0_stop_kernel  = kb0_stop  * kb_niter;
+
+        if (KV_max) {
+            kb0_stop_kernel = min(kb0_stop_kernel, KV_max[sequence*iter_j + jt] / c::nbatch_fa);
+        }
 
         constexpr bool is_fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer.
         if (kb0_start == 0) {
@@ -1343,7 +1330,11 @@ static __global__ void flash_attn_ext_f16(
     const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head, n_head_log2, m0, m1) : 1.0f;
 
     const int kb0_start_kernel = kb0_start * kb_niter;
-    const int kb0_stop_kernel  = kb0_stop  * kb_niter;
+    int       kb0_stop_kernel  = kb0_stop  * kb_niter;
+
+    if (KV_max) {
+        kb0_stop_kernel = min(kb0_stop_kernel, KV_max[sequence*iter_j + jt] / c::nbatch_fa);
+    }
 
     constexpr bool is_fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks.
     constexpr bool needs_fixup = false;
@@ -1351,18 +1342,19 @@ static __global__ void flash_attn_ext_f16(
         (Q_f2, K_h2, V_h2, mask_h2, dstk, dst_meta, scale, slope, logit_softcap,
          ne01, ne02, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start_kernel, kb0_stop_kernel);
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
-    GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale);
-    GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
-    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap); GGML_UNUSED(ne00);
-    GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03); GGML_UNUSED(ne10);
-    GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13); GGML_UNUSED(ne31); GGML_UNUSED(ne32);
-    GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb01); GGML_UNUSED(nb02); GGML_UNUSED(nb03);
-    GGML_UNUSED(nb11); GGML_UNUSED(nb12); GGML_UNUSED(nb13); GGML_UNUSED(nb21);
-    GGML_UNUSED(nb22); GGML_UNUSED(nb23); GGML_UNUSED(ne0); GGML_UNUSED(ne1);
-    GGML_UNUSED(ne2); GGML_UNUSED(ne3);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
+    GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
+    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
+    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
+    GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
+    GGML_UNUSED(nb01); GGML_UNUSED(nb02); GGML_UNUSED(nb03);
+    GGML_UNUSED(ne10); GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13);
+    GGML_UNUSED(nb11); GGML_UNUSED(nb12); GGML_UNUSED(nb13);
+    GGML_UNUSED(nb21); GGML_UNUSED(nb22); GGML_UNUSED(nb23);
+    GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
+    GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33);
     NO_DEVICE_CODE;
-#endif // defined(FLASH_ATTN_AVAILABLE) && defined(NEW_MMA_AVAILABLE)
+#endif // defined(FLASH_ATTN_AVAILABLE) && defined(TURING_MMA_AVAILABLE)
 }
 
 template <int DKQ, int DV, int ncols1, int ncols2>
@@ -1412,24 +1404,24 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml
         constexpr bool use_logit_softcap = false;
         fattn_kernel = flash_attn_ext_f16<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla>;
 
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
         static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
         if (!shared_memory_limit_raised[id]) {
             CUDA_CHECK(cudaFuncSetAttribute(fattn_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
             shared_memory_limit_raised[id] = true;
         }
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
     } else {
         constexpr bool use_logit_softcap = true;
         fattn_kernel = flash_attn_ext_f16<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla>;
 
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
         static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
         if (!shared_memory_limit_raised[id]) {
             CUDA_CHECK(cudaFuncSetAttribute(fattn_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
             shared_memory_limit_raised[id] = true;
         }
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
     }
 
     launch_fattn<DV, ncols1, ncols2>