chat : fix LFM2 tool call arg name prefix ambiguity (#26960)

Assisted-by: Claude Opus 5

.github/workflows/build-cache.yml

@@ -119,27 +119,27 @@ jobs:
           version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
           version_full: ${{ env.OPENVINO_VERSION_FULL }}
 
-  windows-2022-rocm-cache:
-    runs-on: windows-2022
+  # windows-2022-rocm-cache:
+  #   runs-on: windows-2022
 
-    env:
-      # Make sure this is in sync with release.yml and build-cuda-windows.yml
-      ROCM_VERSION: "7.14.0"
+  #   env:
+  #     # Make sure this is in sync with release.yml and build-cuda-windows.yml
+  #     ROCM_VERSION: "7.14.0"
 
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
+  #   steps:
+  #     - name: Clone
+  #       id: checkout
+  #       uses: actions/checkout@v6
 
-      - name: Setup Cache
-        uses: actions/cache@v5
-        id: cache-rocm
-        with:
-          path: C:\TheRock\build
-          key: rocm-wheels-${{ env.ROCM_VERSION }}-multi-arch-${{ runner.os }}
+  #     - name: Setup Cache
+  #       uses: actions/cache@v5
+  #       id: cache-rocm
+  #       with:
+  #         path: C:\TheRock\build
+  #         key: rocm-wheels-${{ env.ROCM_VERSION }}-multi-arch-${{ runner.os }}
 
-      - name: Setup ROCm
-        if: steps.cache-rocm.outputs.cache-hit != 'true'
-        uses: ./.github/actions/windows-setup-rocm
-        with:
-          version: ${{ env.ROCM_VERSION }}
+  #     - name: Setup ROCm
+  #       if: steps.cache-rocm.outputs.cache-hit != 'true'
+  #       uses: ./.github/actions/windows-setup-rocm
+  #       with:
+  #         version: ${{ env.ROCM_VERSION }}

.github/workflows/build-cmake-pkg.yml

@@ -5,7 +5,7 @@ on:
 
 jobs:
   linux:
-    runs-on: [self-hosted, Linux, CPU]
+    runs-on: [self-hosted, Linux]
     steps:
       - uses: actions/checkout@v6
         with:
@@ -21,15 +21,21 @@ jobs:
                 -DLLAMA_BUILD_TOOLS=OFF \
                 -DLLAMA_BUILD_EXAMPLES=OFF \
                 -DLLAMA_BUILD_APP=OFF \
+                -DLLAMA_BUILD_IS_DEV=OFF \
                 -DCMAKE_BUILD_TYPE=Release
-          cmake --build build --config Release
+          cmake --build build --config Release -j $(nproc)
           cmake --install build --prefix "$PREFIX" --config Release
 
           export LLAMA_CONFIG="$PREFIX"/lib/cmake/llama/llama-config.cmake
           tclsh <<'EOF'
           set build(commit)  [string trim [exec git rev-parse --short HEAD]]
           set build(number)  [string trim [exec git rev-list  --count HEAD]]
-          set build(version) "0.0.$build(number)"
+
+          set cmakelists [read [open "CMakeLists.txt" r]]
+          regexp {set\(LLAMA_VERSION_MAJOR\s+(\d+)\)} $cmakelists -> major
+          regexp {set\(LLAMA_VERSION_MINOR\s+(\d+)\)} $cmakelists -> minor
+          regexp {set\(LLAMA_VERSION_PATCH\s+(\d+)\)} $cmakelists -> patch
+          set build(version) "$major.$minor.$patch"
 
           set llamaconfig [read [open "$env(LLAMA_CONFIG)" r]]
           set checks [list "set\\(LLAMA_VERSION     \\s+$build(version)\\)" \
@@ -48,4 +54,4 @@ jobs:
 
           cd examples/simple-cmake-pkg
           cmake -S . -B build -DCMAKE_PREFIX_PATH="$PREFIX"/lib/cmake
-          cmake --build build
+          cmake --build build -j $(nproc)

.github/workflows/build-cpu.yml

@@ -94,8 +94,10 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
+            -DGGML_NATIVE=OFF \
             -DLLAMA_FATAL_WARNINGS=ON \
-            -DGGML_RPC=ON
+            -DGGML_RPC=ON \
+            -DGGML_NATIVE=OFF
           time cmake --build build --config Release -j $(nproc)
 
       - name: Test

.github/workflows/build-cuda-windows.yml

@@ -97,15 +97,15 @@ jobs:
         id: checkout
         uses: actions/checkout@v6
 
-      - name: Cache ROCm Installation
-        uses: actions/cache@v5
-        id: cache-rocm
-        with:
-          path: C:\TheRock\build
-          key: rocm-wheels-${{ env.ROCM_VERSION }}-multi-arch-${{ runner.os }}
+      # - name: Cache ROCm Installation
+      #   uses: actions/cache@v5
+      #   id: cache-rocm
+      #   with:
+      #     path: C:\TheRock\build
+      #     key: rocm-wheels-${{ env.ROCM_VERSION }}-multi-arch-${{ runner.os }}
 
       - name: Setup ROCm
-        if: steps.cache-rocm.outputs.cache-hit != 'true'
+        # if: steps.cache-rocm.outputs.cache-hit != 'true'
         uses: ./.github/actions/windows-setup-rocm
         with:
           version: ${{ env.ROCM_VERSION }}

.github/workflows/build-sanitize.yml

@@ -39,9 +39,9 @@ jobs:
     strategy:
       matrix:
         include:
+        # thread and address doesn't run properly on some self hosted machines, so run it on Github instead
         - sanitizer: ADDRESS
-          machine: [self-hosted, X64, Linux]
-        # thread doesn't run properly on some self hosted machines, so run it on Github instead
+          machine: ubuntu-24.04
         - sanitizer: THREAD
           machine: ubuntu-24.04
         - sanitizer: UNDEFINED
@@ -54,14 +54,14 @@ jobs:
         id: checkout
         uses: actions/checkout@v6
 
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        if: ${{ matrix.sanitizer == 'THREAD' }}
-        with:
-          key: ctest-thread-ubuntu-24.04
-          variant: ccache
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+      # - name: ccache
+      #   uses: ggml-org/ccache-action@v1.2.21
+      #   if: ${{ matrix.sanitizer != 'UNDEFINED' }}
+      #   with:
+      #     key: ctest-${{ matrix.sanitizer }}-ubuntu-24.04
+      #     variant: ccache
+      #     evict-old-files: 1d
+      #     save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       # with UNDEFINED sanitizer, we have to build in Debug to avoid GCC 13 false-positive warnings
       - name: Build (undefined)

.github/workflows/make-release.yml

@@ -0,0 +1,46 @@
+name: Make Release
+
+on:
+  workflow_dispatch:
+    inputs:
+      dry_run:
+        description: 'Dry run - validate without creating the tag'
+        required: true
+        type: boolean
+        default: true
+
+env:
+  GH_TOKEN: ${{ github.token }}
+
+permissions:
+  contents: write
+
+jobs:
+  make-release:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+
+      - name: Run release checks
+        id: checks
+        run: bash scripts/make-release-checks.sh ${{ github.event.inputs.dry_run == 'true' && '--dry-run' || '' }}
+        env:
+          GITHUB_REPOSITORY: ${{ github.repository }}
+
+      - name: Create release tag
+        if: ${{ github.event.inputs.dry_run == 'false' }}
+        run: |
+          VERSION="${{ steps.checks.outputs.version }}"
+          git config user.name "github-actions[bot]"
+          git config user.email "github-actions[bot]@users.noreply.github.com"
+          git tag -a "${VERSION}" -m "Release ${VERSION}"
+          git push origin "${VERSION}"
+          echo "Created and pushed tag ${VERSION}"
+
+      - name: Dry run summary
+        if: ${{ github.event.inputs.dry_run == 'true' }}
+        run: |
+          echo "Dry run complete - all checks passed."
+          echo "Would have created tag: ${{ steps.checks.outputs.version }}"

.github/workflows/release.yml

@@ -774,15 +774,15 @@ jobs:
           key: windows-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
           evict-old-files: 1d
 
-      - name: Cache ROCm Installation
-        id: cache-rocm
-        uses: actions/cache@v5
-        with:
-          path: C:\TheRock\build
-          key: rocm-wheels-${{ matrix.ROCM_VERSION }}-multi-arch-${{ runner.os }}
+      # - name: Cache ROCm Installation
+      #   id: cache-rocm
+      #   uses: actions/cache@v5
+      #   with:
+      #     path: C:\TheRock\build
+      #     key: rocm-wheels-${{ matrix.ROCM_VERSION }}-multi-arch-${{ runner.os }}
 
       - name: Setup ROCm
-        if: steps.cache-rocm.outputs.cache-hit != 'true'
+        # if: steps.cache-rocm.outputs.cache-hit != 'true'
         uses: ./.github/actions/windows-setup-rocm
         with:
           version: ${{ matrix.ROCM_VERSION }}
@@ -1285,123 +1285,123 @@ jobs:
           path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz
           name: llama-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz
 
-  ubuntu-22-rocm:
-    needs: [check-release, get-version]
-    if: ${{ needs.check-release.outputs.should_release == 'true' }}
+#   ubuntu-22-rocm:
+#     needs: [check-release, get-version]
+#     if: ${{ needs.check-release.outputs.should_release == 'true' }}
 
-    runs-on: ubuntu-22.04
+#     runs-on: ubuntu-22.04
 
-    permissions:
-      actions: write
+#     permissions:
+#       actions: write
 
-    strategy:
-      matrix:
-        include:
-          - ROCM_VERSION: "7.14.0"
-            gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
-            build: 'x64'
+#     strategy:
+#       matrix:
+#         include:
+#           - ROCM_VERSION: "7.14.0"
+#             gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
+#             build: 'x64'
 
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
+#     steps:
+#       - name: Clone
+#         id: checkout
+#         uses: actions/checkout@v6
+#         with:
+#           fetch-depth: 0
 
-      - name: Setup Node.js
-        uses: actions/setup-node@v6
-        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+#       - name: Setup Node.js
+#         uses: actions/setup-node@v6
+#         with:
+#           node-version: "24"
+#           cache: "npm"
+#           cache-dependency-path: "tools/ui/package-lock.json"
 
-      - name: Free up disk space
-        uses: ggml-org/free-disk-space@v1.3.1
-        with:
-          tool-cache: true
+#       - name: Free up disk space
+#         uses: ggml-org/free-disk-space@v1.3.1
+#         with:
+#           tool-cache: true
 
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: release-ubuntu-22.04-rocm-${{ matrix.ROCM_VERSION }}
+#       # - name: ccache
+#       #   uses: ggml-org/ccache-action@v1.2.21
+#       #   with:
+#       #     key: release-ubuntu-22.04-rocm-${{ matrix.ROCM_VERSION }}
 
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt install -y build-essential git cmake wget
+#       - name: Dependencies
+#         id: depends
+#         run: |
+#           sudo apt install -y build-essential git cmake wget
 
-      - name: Setup TheRock with Wheels
-        id: therock_env
-        run: |
-          # Create Python virtual environment
-          python3 -m venv .venv
-          source .venv/bin/activate
+#       - name: Setup TheRock with Wheels
+#         id: therock_env
+#         run: |
+#           # Create Python virtual environment
+#           python3 -m venv .venv
+#           source .venv/bin/activate
 
-          # Install ROCm wheels for build
-          # libraries = HIP runtime and CMake configs needed for linking
-          # devel = compilers, headers, static libs
-          python -m pip install --upgrade pip
-          python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{ matrix.ROCM_VERSION }}"
+#           # Install ROCm wheels for build
+#           # libraries = HIP runtime and CMake configs needed for linking
+#           # devel = compilers, headers, static libs
+#           python -m pip install --upgrade pip
+#           python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{ matrix.ROCM_VERSION }}"
 
-          # Get ROCm installation paths using the rocm-sdk CLI tool
-          ROCM_PATH=$(rocm-sdk path --root)
-          CMAKE_PATH=$(rocm-sdk path --cmake)
-          BIN_PATH=$(rocm-sdk path --bin)
-          echo "ROCM_PATH=$ROCM_PATH"
-          echo "CMAKE_PATH=$CMAKE_PATH"
-          echo "BIN_PATH=$BIN_PATH"
+#           # Get ROCm installation paths using the rocm-sdk CLI tool
+#           ROCM_PATH=$(rocm-sdk path --root)
+#           CMAKE_PATH=$(rocm-sdk path --cmake)
+#           BIN_PATH=$(rocm-sdk path --bin)
+#           echo "ROCM_PATH=$ROCM_PATH"
+#           echo "CMAKE_PATH=$CMAKE_PATH"
+#           echo "BIN_PATH=$BIN_PATH"
 
-          # Set environment variables
-          echo "ROCM_PATH=$ROCM_PATH" >> $GITHUB_ENV
-          echo "CMAKE_PREFIX_PATH=$CMAKE_PATH" >> $GITHUB_ENV
-          echo "HIP_PATH=$ROCM_PATH" >> $GITHUB_ENV
-          echo "PATH=$BIN_PATH:${PATH}" >> $GITHUB_ENV
-          echo "LD_LIBRARY_PATH=$ROCM_PATH/lib:${LD_LIBRARY_PATH:-}" >> $GITHUB_ENV
+#           # Set environment variables
+#           echo "ROCM_PATH=$ROCM_PATH" >> $GITHUB_ENV
+#           echo "CMAKE_PREFIX_PATH=$CMAKE_PATH" >> $GITHUB_ENV
+#           echo "HIP_PATH=$ROCM_PATH" >> $GITHUB_ENV
+#           echo "PATH=$BIN_PATH:${PATH}" >> $GITHUB_ENV
+#           echo "LD_LIBRARY_PATH=$ROCM_PATH/lib:${LD_LIBRARY_PATH:-}" >> $GITHUB_ENV
 
-          # Keep venv activated for subsequent steps
-          echo "$(pwd)/.venv/bin" >> $GITHUB_PATH
+#           # Keep venv activated for subsequent steps
+#           echo "$(pwd)/.venv/bin" >> $GITHUB_PATH
 
-      - name: Build with native CMake HIP support
-        id: cmake_build
-        run: |
-          cmake -B build -S . \
-            -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
-            -DCMAKE_BUILD_TYPE=Release \
-            -DGGML_BACKEND_DL=ON \
-            -DGGML_NATIVE=OFF \
-            -DCMAKE_INSTALL_RPATH='$ORIGIN' \
-            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
-            -DGGML_CPU_ALL_VARIANTS=ON \
-            -DGPU_TARGETS="${{ matrix.gpu_targets }}" \
-            -DGGML_HIP=ON \
-            -DHIP_PLATFORM=amd \
-            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
-            ${{ env.CMAKE_ARGS }}
-          cmake --build build --config Release -j $(nproc)
+#       - name: Build with native CMake HIP support
+#         id: cmake_build
+#         run: |
+#           cmake -B build -S . \
+#             -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
+#             -DCMAKE_BUILD_TYPE=Release \
+#             -DGGML_BACKEND_DL=ON \
+#             -DGGML_NATIVE=OFF \
+#             -DCMAKE_INSTALL_RPATH='$ORIGIN' \
+#             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
+#             -DGGML_CPU_ALL_VARIANTS=ON \
+#             -DGPU_TARGETS="${{ matrix.gpu_targets }}" \
+#             -DGGML_HIP=ON \
+#             -DHIP_PLATFORM=amd \
+#             -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
+#             ${{ env.CMAKE_ARGS }}
+#           cmake --build build --config Release -j $(nproc)
 
-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-ubuntu-22.04-rocm-${{ matrix.ROCM_VERSION }}
+#       # - name: ccache-clear
+#       #   uses: ./.github/actions/ccache-clear
+#       #   with:
+#       #     key: release-ubuntu-22.04-rocm-${{ matrix.ROCM_VERSION }}
 
-      - name: Determine tag name
-        id: tag
-        uses: ./.github/actions/get-tag-name
+#       - name: Determine tag name
+#         id: tag
+#         uses: ./.github/actions/get-tag-name
 
-      - name: Get ROCm short version
-        run: echo "ROCM_VERSION_SHORT=$(echo '${{ matrix.ROCM_VERSION }}' | cut -d '.' -f 1,2)" >> $GITHUB_ENV
+#       - name: Get ROCm short version
+#         run: echo "ROCM_VERSION_SHORT=$(echo '${{ matrix.ROCM_VERSION }}' | cut -d '.' -f 1,2)" >> $GITHUB_ENV
 
-      - name: Pack artifacts
-        id: pack_artifacts
-        run: |
-          cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
+#       - name: Pack artifacts
+#         id: pack_artifacts
+#         run: |
+#           cp LICENSE ./build/bin/
+#           tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v6
-        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
-          name: llama-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
+#       - name: Upload artifacts
+#         uses: actions/upload-artifact@v6
+#         with:
+#           path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
+#           name: llama-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
 
   ios-xcode:
     needs: [check-release, get-version]
@@ -1578,7 +1578,7 @@ jobs:
       #- windows-sycl
       - windows-rocm
       - windows-openvino
-      - ubuntu-22-rocm
+      #- ubuntu-22-rocm
       - ubuntu-cpu
       - ubuntu-vulkan
       - ubuntu-24-openvino
@@ -1688,7 +1688,7 @@ jobs:
             - [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-s390x.tar.gz)
             - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz)
             - [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-arm64.tar.gz)
-            - [Ubuntu x64 (ROCm 7.14)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-7.14-x64.tar.gz)
+            - Ubuntu x64 (ROCm 7.14)[DISABLED](https://github.com/ggml-org/llama.cpp/pull/26969)
             - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ needs.ubuntu-24-openvino.outputs.openvino_version }}-x64.tar.gz)
             - [Ubuntu x64 (SYCL FP32)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp32-x64.tar.gz)
             - [Ubuntu x64 (SYCL FP16)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp16-x64.tar.gz)

.github/workflows/winget.yml

@@ -19,6 +19,8 @@ jobs:
         run: |
           cargo binstall komac@2.16.0 -y
 
+      # TODO: This should later be updated to publish releases instead of
+      # development release builds.
       - name: Find latest release
         id: find_latest_release
         uses: actions/github-script@v8

CMakeLists.txt

@@ -2,6 +2,26 @@ cmake_minimum_required(VERSION 3.14...3.28) # for add_link_options and implicit
 project("llama.cpp" C CXX)
 include(CheckIncludeFileCXX)
 
+### llama.cpp version
+set(LLAMA_VERSION_MAJOR 0)
+set(LLAMA_VERSION_MINOR 1)
+set(LLAMA_VERSION_PATCH 0)
+set(LLAMA_VERSION_BASE "${LLAMA_VERSION_MAJOR}.${LLAMA_VERSION_MINOR}.${LLAMA_VERSION_PATCH}")
+
+# whether this is a development/nightly build
+# set this to OFF when making a release from a release tag (vX.Y.Z)
+# ref: https://github.com/ggml-org/ggml/discussions/1579
+option(LLAMA_BUILD_IS_DEV "llama: dev build" ON)
+
+if (LLAMA_BUILD_IS_DEV)
+    set(LLAMA_VERSION "${LLAMA_VERSION_BASE}-dev")
+else()
+    # TODO: check that the current commit is tagged correctly according to the version specified above
+    set(LLAMA_VERSION "${LLAMA_VERSION_BASE}")
+endif()
+
+message(STATUS "llama.cpp version: ${LLAMA_VERSION}")
+
 #set(CMAKE_WARN_DEPRECATED YES)
 set(CMAKE_WARN_UNUSED_CLI YES)
 
@@ -24,9 +44,6 @@ if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
     set(LLAMA_STANDALONE ON)
 
     include(git-vars)
-
-    # configure project version
-    # TODO
 else()
     set(LLAMA_STANDALONE OFF)
 endif()
@@ -139,7 +156,6 @@ endif()
 if (NOT DEFINED LLAMA_BUILD_COMMIT)
     set(LLAMA_BUILD_COMMIT        ${BUILD_COMMIT})
 endif()
-set(LLAMA_INSTALL_VERSION 0.0.${LLAMA_BUILD_NUMBER})
 
 # override ggml options
 set(GGML_ALL_WARNINGS   ${LLAMA_ALL_WARNINGS})
@@ -275,12 +291,12 @@ configure_package_config_file(
               LLAMA_BIN_INSTALL_DIR )
 
 write_basic_package_version_file(
-        ${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake
-    VERSION ${LLAMA_INSTALL_VERSION}
+        ${CMAKE_CURRENT_BINARY_DIR}/llama-config-version.cmake
+        VERSION ${LLAMA_VERSION}
     COMPATIBILITY SameMajorVersion)
 
 install(FILES ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake
-              ${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake
+              ${CMAKE_CURRENT_BINARY_DIR}/llama-config-version.cmake
         DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama)
 
 configure_file(cmake/llama.pc.in

README.md

@@ -106,6 +106,7 @@ The `llama.cpp` project is build on top of the [ggml](https://github.com/ggml-or
 - [XCFramework](docs/xcframework.md)
 - [Completions](docs/completions.md)
 - [Models](docs/models.md)
+- [Release process](docs/release.md)
 
 ## Contributing
 

app/llama.cpp

@@ -1,5 +1,7 @@
 #include "build-info.h"
 
+#include "llama.h"
+
 #include <cstdio>
 #include <cstdlib>
 #include <string>
@@ -77,12 +79,12 @@ static const command cmds[] = {
 
 #undef UPDATE_HIDDEN
 
-static int version(int argc, char ** argv) {
-    printf("%s\n", llama_build_info());
+static int version(int /*argc*/, char ** /*argv*/) {
+    llama_print_build_info(llama_version());
     return 0;
 }
 
-static int licenses(int argc, char ** argv) {
+static int licenses(int /*argc*/, char ** /*argv*/) {
     for (int i = 0; LICENSES[i]; ++i) {
         printf("%s\n", LICENSES[i]);
     }

cmake/llama-config.cmake.in

@@ -1,4 +1,4 @@
-set(LLAMA_VERSION      @LLAMA_INSTALL_VERSION@)
+set(LLAMA_VERSION      @LLAMA_VERSION@)
 set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
 set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
 set(LLAMA_SHARED_LIB   @BUILD_SHARED_LIBS@)

cmake/llama.pc.in

@@ -5,6 +5,6 @@ includedir=@CMAKE_INSTALL_FULL_INCLUDEDIR@
 
 Name: llama
 Description: Port of Facebook's LLaMA model in C/C++
-Version: @LLAMA_INSTALL_VERSION@
+Version: @LLAMA_VERSION@
 Libs: -L${libdir} -lggml -lggml-base -lllama
 Cflags: -I${includedir}

common/CMakeLists.txt

@@ -121,8 +121,8 @@ add_library(${TARGET}
     )
 
 set_target_properties(${TARGET} PROPERTIES
-    VERSION ${LLAMA_INSTALL_VERSION}
-    SOVERSION 0
+    VERSION ${LLAMA_VERSION_BASE}
+    SOVERSION ${LLAMA_VERSION_MAJOR}
     MACHO_CURRENT_VERSION 0 # keep macOS linker from seeing oversized version number
 )
 

common/arg.cpp

@@ -35,6 +35,7 @@
 #include <regex>
 #include <set>
 #include <string>
+#include <system_error>
 #include <thread> // for hardware_concurrency
 #include <vector>
 
@@ -560,6 +561,15 @@ void common_models_handler_apply(common_models_handler & handler, common_params
         }
     }
 
+    // infer the speculative type from the draft GGUF metadata when none is requested
+    // note: reads only the first split - sharded drafts need an explicit --spec-type
+    if (spec_types_is_default(params) && !params.speculative.draft.mparams.path.empty()) {
+        const auto types_gguf = common_speculative_types_from_gguf(params.speculative.draft.mparams.path);
+        if (!types_gguf.empty()) {
+            params.speculative.types = types_gguf;
+        }
+    }
+
     // when a sidecar type is requested, the draft repo resolves to its sidecar instead of a full model
     const bool spec_sidecar_found = !plan_spec.mtp.local_path.empty() ||
                                     !plan_spec.dflash.local_path.empty() ||
@@ -704,12 +714,61 @@ void common_models_handler_apply(common_models_handler & handler, common_params
 // CLI argument parsing functions
 //
 
+// apply config files (if present), a later file overrides an earlier one:
+// 1. system-wide: /etc/llama.cpp/config.ini (%PROGRAMDATA%\llama.cpp\config.ini on windows)
+// 2. user-level: ${XDG_CONFIG_HOME:-~/.config}/llama.cpp/config.ini (%APPDATA%\llama.cpp\config.ini on windows)
+static void common_params_apply_system_config(common_params & params, llama_example ex) {
+    std::vector<std::string> paths;
+
+#if defined(_WIN32)
+    const std::string program_data = common_get_env("PROGRAMDATA");
+    if (!program_data.empty()) {
+        paths.push_back(program_data + "\\llama.cpp\\config.ini");
+    }
+#else
+    paths.push_back("/etc/llama.cpp/config.ini");
+#endif
+
+    try {
+        paths.push_back(fs_get_config_directory() + "config.ini");
+    } catch (const std::exception & e) {
+        LOG_DBG("cannot read user-level config file, skipping: %s\n", e.what());
+    }
+
+    std::vector<std::string> found;
+    for (const auto & path : paths) {
+        std::error_code ec;
+        if (std::filesystem::exists(path, ec)) {
+            found.push_back(path);
+        }
+    }
+    if (found.empty()) {
+        return;
+    }
+
+    common_preset_context ctx(ex);
+    ctx.ignore_unknown_keys = true; // the same config file is shared by all programs
+    for (const auto & path : found) {
+        LOG_INF("using config file: %s\n", path.c_str());
+        common_preset global;
+        common_presets presets = ctx.load_from_ini(path, global);
+        global.apply_to_params(params);
+        auto it = presets.find(COMMON_PRESET_DEFAULT_NAME);
+        if (it != presets.end()) {
+            it->second.apply_to_params(params);
+        }
+    }
+}
+
 static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
     common_params & params = ctx_arg.params;
 
     // setup log directly from params.verbosity: see tools/cli/cli.cpp
     common_log_set_verbosity_thold(params.verbosity);
 
+    // config file applies first, so env variables and CLI arguments override it
+    common_params_apply_system_config(params, ctx_arg.ex);
+
     std::unordered_map<std::string, std::pair<common_arg *, bool>> arg_to_options;
     for (auto & opt : ctx_arg.options) {
         for (const auto & arg : opt.args) {
@@ -1390,8 +1449,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--version"},
         "show version and build info",
         [](common_params &) {
-            fprintf(stderr, "version: %d (%s)\n", llama_build_number(), llama_commit());
-            fprintf(stderr, "built with %s for %s\n", llama_compiler(), llama_build_target());
+            llama_print_build_info(llama_version());
             exit(0);
         }
     ));

common/build-info.cpp.in

@@ -29,7 +29,7 @@ const char * llama_build_info(void) {
     return s.c_str();
 }
 
-void llama_print_build_info(void) {
-    fprintf(stderr, "%s: build = %d (%s)\n",      __func__, llama_build_number(), llama_commit());
-    fprintf(stderr, "%s: built with %s for %s\n", __func__, llama_compiler(), llama_build_target());
+void llama_print_build_info(const char * llama_version) {
+    fprintf(stderr, "version: %s (build %d, commit %s)\n", llama_version, llama_build_number(), llama_commit());
+    fprintf(stderr, "built with %s for %s\n", llama_compiler(), llama_build_target());
 }

common/build-info.h

@@ -8,4 +8,4 @@ const char * llama_compiler(void);
 const char * llama_build_target(void);
 const char * llama_build_info(void);
 
-void llama_print_build_info(void);
+void llama_print_build_info(const char *);

common/chat-peg-parser.cpp

@@ -594,9 +594,7 @@ common_peg_parser common_chat_peg_builder::python_style_tool_calls(
 
                 // Full argument: name="value" or name=value
                 auto arg_rule = tool_arg(
-                    tool_arg_open(eps()) +
-                    tool_arg_name(arg_name_parser) +
-                    literal("=") +
+                    tool_arg_open(tool_arg_name(arg_name_parser) + literal("=")) +
                     arg_value_parser +
                     tool_arg_close(eps())
                 );

common/common.cpp

@@ -1019,20 +1019,21 @@ std::string fs_get_cache_directory() {
     std::string cache_directory = "";
     auto ensure_trailing_slash = [](std::string p) {
         // Make sure to add trailing slash
-        if (p.back() != DIRECTORY_SEPARATOR) {
+        if (p.empty() || p.back() != DIRECTORY_SEPARATOR) {
             p += DIRECTORY_SEPARATOR;
         }
         return p;
     };
-    if (getenv("LLAMA_CACHE")) {
-        cache_directory = std::getenv("LLAMA_CACHE");
-    } else {
+    cache_directory = common_get_env("LLAMA_CACHE");
+    if (cache_directory.empty()) {
 #if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || \
         defined(__OpenBSD__) || defined(__NetBSD__)
-        if (std::getenv("XDG_CACHE_HOME")) {
-            cache_directory = std::getenv("XDG_CACHE_HOME");
-        } else if (std::getenv("HOME")) {
-            cache_directory = std::getenv("HOME") + std::string("/.cache/");
+        const std::string xdg_cache_home = common_get_env("XDG_CACHE_HOME");
+        const std::string home           = common_get_env("HOME");
+        if (!xdg_cache_home.empty()) {
+            cache_directory = xdg_cache_home;
+        } else if (!home.empty()) {
+            cache_directory = home + "/.cache/";
         } else {
 #if defined(__linux__)
             /* no $HOME is defined, fallback to getpwuid */
@@ -1047,9 +1048,16 @@ std::string fs_get_cache_directory() {
 #endif /* defined(__linux__) */
         }
 #elif defined(__APPLE__)
-        cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
+        cache_directory = common_get_env("HOME");
+        if (cache_directory.empty()) {
+            throw std::runtime_error("Failed to find $HOME directory");
+        }
+        cache_directory += "/Library/Caches/";
 #elif defined(_WIN32)
-        cache_directory = std::getenv("LOCALAPPDATA");
+        cache_directory = common_get_env("LOCALAPPDATA");
+        if (cache_directory.empty()) {
+            throw std::runtime_error("Failed to find %LOCALAPPDATA% directory");
+        }
 #elif defined(__EMSCRIPTEN__)
         GGML_ABORT("not implemented on this platform");
 #else
@@ -1061,6 +1069,51 @@ std::string fs_get_cache_directory() {
     return ensure_trailing_slash(cache_directory);
 }
 
+std::string fs_get_config_directory() {
+    std::string config_directory = "";
+    auto ensure_trailing_slash = [](std::string p) {
+        if (p.empty() || p.back() != DIRECTORY_SEPARATOR) {
+            p += DIRECTORY_SEPARATOR;
+        }
+        return p;
+    };
+#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || \
+        defined(__OpenBSD__) || defined(__NetBSD__) || defined(__APPLE__)
+    const std::string xdg_config_home = common_get_env("XDG_CONFIG_HOME");
+    const std::string home            = common_get_env("HOME");
+    if (!xdg_config_home.empty()) {
+        config_directory = xdg_config_home;
+    } else if (!home.empty()) {
+        config_directory = home + "/.config/";
+    } else {
+#if defined(__linux__)
+        /* no $HOME is defined, fallback to getpwuid */
+        struct passwd *pw = getpwuid(getuid());
+        if ((!pw) || (!pw->pw_dir)) {
+            throw std::runtime_error("Failed to find $HOME directory");
+        }
+
+        config_directory = std::string(pw->pw_dir) + std::string("/.config/");
+#else
+        throw std::runtime_error("Failed to find $HOME directory");
+#endif
+    }
+#elif defined(_WIN32)
+    config_directory = common_get_env("APPDATA");
+    if (config_directory.empty()) {
+        throw std::runtime_error("Failed to find %APPDATA% directory");
+    }
+#elif defined(__EMSCRIPTEN__)
+    // caller decides what to do when there is no config directory
+    throw std::runtime_error("not implemented on this platform");
+#else
+#  error Unknown architecture
+#endif
+    config_directory = ensure_trailing_slash(config_directory);
+    config_directory += "llama.cpp";
+    return ensure_trailing_slash(config_directory);
+}
+
 std::string fs_get_cache_file(const std::string & filename) {
     GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos);
     std::string cache_directory = fs_get_cache_directory();

common/common.h

@@ -881,6 +881,7 @@ bool fs_is_directory(const std::string & path);
 
 std::string fs_get_cache_directory();
 std::string fs_get_cache_file(const std::string & filename);
+std::string fs_get_config_directory();
 
 struct common_file_info {
     std::string path;

common/preset.cpp

@@ -322,6 +322,8 @@ common_presets common_preset_context::load_from_ini(const std::string & path, co
                     preset.options[opt] = value;
                 }
                 LOG_DBG("accepted option: %s = %s\n", key.c_str(), preset.options[opt].c_str());
+            } else if (ignore_unknown_keys) {
+                LOG_WRN("ignoring option '%s' from %s: not supported by this program\n", key.c_str(), path.c_str());
             } else {
                 throw std::runtime_error(string_format(
                     "option '%s' not recognized in preset '%s'",

common/preset.h

@@ -59,6 +59,10 @@ struct common_preset_context {
     bool filter_allowed_keys = false;
     std::set<std::string> allowed_keys;
 
+    // if true, options unknown to the current example are skipped instead of being an error
+    // used for config files shared by all binaries, where each binary only knows a subset of options
+    bool ignore_unknown_keys = false;
+
     // if only_remote_allowed is true, only accept whitelisted keys
     common_preset_context(llama_example ex);
 

common/speculative.cpp

@@ -2,6 +2,7 @@
 
 #include "common.h"
 #include "ggml.h"
+#include "ggml-cpp.h"
 #include "llama.h"
 #include "log.h"
 #include "ngram-cache.h"
@@ -912,6 +913,9 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
 
     std::vector<common_sampler_ptr> smpls;
 
+    // backend sampler chain per seq, attached to ctx_dft
+    std::vector<llama_sampler *> backend_chains;
+
     int32_t n_embd_dec = 0;  // draft hidden size
     int32_t n_embd_enc = 0;  // target_layer_ids_n * target_hidden_size
     int32_t n_embd_tgt = 0;  // target model hidden size
@@ -985,6 +989,22 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
             s.reset(common_sampler_init(model_dft, sparams));
         }
 
+        // offload draft sampling to the backend
+        backend_chains.assign(n_seq, nullptr);
+        if (this->params.backend_sampling) {
+            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                llama_sampler * chain = llama_sampler_chain_init(llama_sampler_chain_default_params());
+                llama_sampler_chain_add(chain, llama_sampler_init_top_k(10));
+
+                if (!llama_set_sampler(ctx_dft, seq_id, chain)) {
+                    SPC_WRN("backend offload failed for seq_id=%d; using CPU sampler\n", (int) seq_id);
+                    llama_sampler_free(chain);
+                    chain = nullptr;
+                }
+                backend_chains[seq_id] = chain;
+            }
+        }
+
         // turn on extraction of the target layers' input embeddings
         for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
             llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true);
@@ -995,6 +1015,18 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
     }
 
     ~common_speculative_impl_draft_dflash() override {
+        auto * ctx_dft = this->params.ctx_dft;
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) backend_chains.size(); ++seq_id) {
+            if (backend_chains[seq_id] == nullptr) {
+                continue;
+            }
+            if (ctx_dft) {
+                llama_set_sampler(ctx_dft, seq_id, nullptr);
+            }
+            llama_sampler_free(backend_chains[seq_id]);
+        }
+        backend_chains.clear();
+
         llama_batch_free(batch);
         llama_batch_free(batch_inject);
     }
@@ -2196,6 +2228,43 @@ common_speculative_type common_speculative_type_from_name(const std::string & na
     return it->second;
 }
 
+std::vector<common_speculative_type> common_speculative_types_from_gguf(const std::string & path) {
+    struct gguf_init_params gguf_params = {
+        /* .no_alloc = */ true,
+        /* .ctx      = */ nullptr,
+    };
+
+    gguf_context_ptr gguf_ctx(gguf_init_from_file(path.c_str(), gguf_params));
+    if (!gguf_ctx) {
+        return {};
+    }
+
+    const int64_t arch_id = gguf_find_key(gguf_ctx.get(), "general.architecture");
+    if (arch_id < 0 || gguf_get_kv_type(gguf_ctx.get(), arch_id) != GGUF_TYPE_STRING) {
+        return {};
+    }
+
+    const std::string arch = gguf_get_val_str(gguf_ctx.get(), arch_id);
+    if (arch != "dflash") {
+        const uint32_t block_count = gguf_get_val_u32(gguf_ctx.get(), gguf_find_key(gguf_ctx.get(), (arch + ".block_count").c_str()));
+
+        if (gguf_find_tensor(gguf_ctx.get(), ("blk." + std::to_string(block_count - 1) + ".nextn.eh_proj.weight").c_str()) >= 0) {
+            return { COMMON_SPECULATIVE_TYPE_DRAFT_MTP };
+        }
+
+        return {};
+    }
+
+    // the Markov head distinguishes draft-dspark from draft-dflash
+    const auto type = gguf_find_tensor(gguf_ctx.get(), "markov_w1.weight") >= 0
+                    ? COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK
+                    : COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH;
+
+    SPC_INF("auto-detected speculative type '%s' from the draft model metadata\n", common_speculative_type_to_str(type).c_str());
+
+    return { type };
+}
+
 static uint32_t common_get_enabled_speculative_configs(const std::vector<common_speculative_type> & configs) {
     uint32_t result = 0;
     for (size_t i = 0; i < configs.size(); i++) {
@@ -2263,6 +2332,23 @@ common_params common_base_params_to_speculative(const common_params & params) {
     result.n_outputs_max = params.n_parallel;
     result.n_outputs_max_per_seq = 1;
 
+    // dflash/dspark decode the whole noise block in a single pass and sample every block position on the backend
+    // TODO: refactor such properties to be announced by the speculative types
+    //       something like `struct common_speculative_type_props common_speculative_type_get_props(...);`
+    const bool has_block_draft = std::any_of(
+        params.speculative.types.begin(), params.speculative.types.end(),
+        [](common_speculative_type t) {
+            return t == COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH || t == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK;
+        });
+    if (has_block_draft) {
+        // per-seq output positions: DFlash decodes anchor + n_max masks (n_max + 1); DSpark n_max -> +1 covers both
+        const int32_t per_seq = std::max(1, params_spec.n_max + 1);
+        result.n_outputs_max = params.n_parallel * per_seq;
+        if (params_spec.backend_sampling) {
+            result.n_outputs_max_per_seq = per_seq;
+        }
+    }
+
     return result;
 }
 

common/speculative.h

@@ -14,6 +14,9 @@ const char * common_speculative_all_types_str();
 // parse user provided types
 std::vector<enum common_speculative_type> common_speculative_types_from_names(const std::vector<std::string> & names);
 
+// infer the spec types from the GGUF metadata of a draft model; empty if unknown
+std::vector<enum common_speculative_type> common_speculative_types_from_gguf(const std::string & path);
+
 // convert string to type
 enum common_speculative_type common_speculative_type_from_name(const std::string & name);
 

conversion/gemma.py

@@ -665,7 +665,18 @@ class Gemma4Model(Gemma3Model):
         swa_layers = [t == "sliding_attention" for t in self.hparams["layer_types"]]
         self.gguf_writer.add_sliding_window_pattern(swa_layers)
 
-        head_dim_full = self.hparams["global_head_dim"]
+        per_layer_config = self.hparams.get("per_layer_config")
+        layer_types = self.hparams.get("layer_types", [])
+        if (head_dim_full := self.hparams.get("global_head_dim")) is None and per_layer_config is not None:
+            for layer_idx, layer_config in per_layer_config.items():
+                layer_idx = int(layer_idx)
+                if layer_idx < len(layer_types):
+                    if layer_types[layer_idx] == "full_attention" and "head_dim" in layer_config:
+                        head_dim_full = layer_config["head_dim"]
+                        break
+
+        assert head_dim_full is not None
+
         head_dim_swa = self.hparams["head_dim"]
         # correct the head dim for global/swa layers
         self.gguf_writer.add_key_length(head_dim_full)
@@ -685,8 +696,14 @@ class Gemma4Model(Gemma3Model):
             n_ff_arr = [n_ff if il < first_kv_shared_layer_idx else n_ff * 2 for il in range(self.block_count)]
             self.gguf_writer.add_feed_forward_length(n_ff_arr)
 
-        # handle num_global_key_value_heads
-        num_key_value_heads_full = self.hparams.get("num_global_key_value_heads")
+        if (num_key_value_heads_full := self.hparams.get("num_global_key_value_heads")) is None and per_layer_config is not None:
+            for layer_idx, layer_config in per_layer_config.items():
+                layer_idx = int(layer_idx)
+                if layer_idx < len(layer_types):
+                    if layer_types[layer_idx] == "full_attention" and "num_key_value_heads" in layer_config:
+                        num_key_value_heads_full = layer_config["num_key_value_heads"]
+                        break
+
         num_key_value_heads_swa = self.hparams.get("num_key_value_heads")
         if num_key_value_heads_full is not None and num_key_value_heads_swa is not None:
             value_arr = [num_key_value_heads_swa if is_swa else num_key_value_heads_full for is_swa in swa_layers]
@@ -708,7 +725,19 @@ class Gemma4Model(Gemma3Model):
         # IMPORTANT: this ROPE_FREQS tensor is ONLY used by the full_attention layers
         rope_params_full = self.hparams["rope_parameters"]["full_attention"]
         assert rope_params_full["rope_type"] == "proportional"
-        head_dim_full = (self.hparams["global_head_dim"])
+
+        per_layer_config = self.hparams.get("per_layer_config")
+        if (head_dim_full := self.hparams.get("global_head_dim")) is None and per_layer_config is not None:
+            layer_types = self.hparams.get("layer_types", [])
+            for layer_idx, layer_config in per_layer_config.items():
+                layer_idx = int(layer_idx)
+                if layer_idx < len(layer_types):
+                    if layer_types[layer_idx] == "full_attention" and "head_dim" in layer_config:
+                        head_dim_full = layer_config["head_dim"]
+                        break
+
+        assert head_dim_full is not None
+
         partial_rotary_factor_full = rope_params_full["partial_rotary_factor"]
         n_rot_full = int(head_dim_full * partial_rotary_factor_full / 2)
         n_unrot_full = int(head_dim_full / 2) - n_rot_full

docs/backend/SYCL.md

@@ -804,6 +804,7 @@ User can use the device management in [docs/multi-gpu.md](https://github.com/ggm
 | GGML_SYCL_MKL_FA_DEBUG | 0 (default) or 1 | Enable per-call diagnostic logging for MKL flash attention: GEMM/softmax timings, interleaved-head detection, and buffer memory usage. |
 | GGML_SYCL_MKL_FA_DIAG | 0 (default) or 1 | Enable output fingerprinting for MKL flash attention. Dumps the first 64 float output values for the first 6 FA calls with n_kv ≥ 1024, labeled with kernel type (MKL/TILE/VEC) for cross-kernel comparison. |
 | GGML_SYCL_ENABLE_FUSION | 0 or 1 (default) | Enable fused-kernel dispatch in graph compute (currently top-k MoE gating). |
+| GGML_SYCL_ENABLE_ESIMD | 0 or 1 (default)| Enable ESIMD kernels when available. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
 | UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Allow SYCL/Unified Runtime Level Zero device allocations larger than 4 GiB. llama.cpp's direct Level Zero allocation path requests the relaxed maximum-size limit itself when GGML_SYCL_ENABLE_LEVEL_ZERO=1. |
 | GGML_SYCL_USM_SYSTEM | 0 (default) or 1 | Enable experimental support for [USM system allocations](https://github.khronos.org/SYCL_Reference/iface/usm_basic_concept.html#system-allocations) for large GPU buffers. This requires enough host memory for model weights and caches, an Intel Xe2+ GPU such as BMG or newer and supported on Linux only, with CONFIG_DRM_XE_GPUSVM enabled. |

docs/preset.md

@@ -4,7 +4,7 @@
 
 The INI preset feature, introduced in [PR#17859](https://github.com/ggml-org/llama.cpp/pull/17859), allows users to create reusable and shareable parameter configurations for llama.cpp.
 
-### Using Presets with the Server
+## Using Presets with the Server
 
 When running multiple models on the server (router mode), INI preset files can be used to configure model-specific parameters. Please refer to the [server documentation](../tools/server/README.md) for more details.
 
@@ -93,3 +93,18 @@ llama-server -hf user/repo:gpt-oss-120b-hf
 ```
 
 Please make sure to provide the correct `hf-repo` for each child preset. Otherwise, you may get error: `The specified tag is not a valid quantization scheme.`
+
+## System-level config
+
+The system-level config, added in PR [#26118](https://github.com/ggml-org/llama.cpp/pull/26118), allows sharing the same set of options among multiple tools and examples. Unlike the sections above, it is not limited to the server.
+
+These files are loaded on startup if present. A later file overrides an earlier one:
+1. System-wide: `/etc/llama.cpp/config.ini` (or `%PROGRAMDATA%\llama.cpp\config.ini` on Windows)
+2. User-level: `$XDG_CONFIG_HOME/llama.cpp/config.ini`, `~/.config/llama.cpp/config.ini` by default (or `%APPDATA%\llama.cpp\config.ini` on Windows)
+
+The config file is applied first, then its options are overridden by ENV variables, CLI arguments and model presets (in router mode).
+
+Note:
+- Only the `[*]` and default sections are used; options written before any section header belong to "default. Named sections are ignored
+- Tool-specific options can be specified, but will be ignored (with a warning) if the example doesn't support it<br/>Example: if you specify `port = 1234`, only `llama-server` will use it, other examples will ignore it
+- `model` or `hf-repo` are not recommended to be configured system-level, because it may introduce conflicts<br/>Example: a `hf-repo` in the config file still takes effect when you pass `-m` on the command line, so you may load a different model than expected

docs/release.md

@@ -0,0 +1,49 @@
+# Release process
+
+llama.cpp uses [semantic versioning](https://semver.org) (`MAJOR.MINOR.PATCH`).
+
+## Version bump guidelines
+
+| Change type | Version component |
+|---|---|
+| Breaking change to the public C API (`include/llama.h`)         | `MAJOR` |
+| Backward-compatible features, model support, or API addition    | `MINOR` |
+| Bug fix with no API change                                      | `PATCH` |
+
+The version is set in the three variables at the top of the root `CMakeLists.txt`:
+
+```cmake
+set(LLAMA_VERSION_MAJOR 0)
+set(LLAMA_VERSION_MINOR 1)
+set(LLAMA_VERSION_PATCH 0)
+```
+
+_A version bump should be included in the PR that introduces the change, or in a
+dedicated bump commit merged before the release is cut._
+
+_TODO: add PR labels (`semver: patch`, `semver: minor`, `semver: major`) to help
+identify which PRs require a version bump before cutting a release._
+
+## Making a release
+
+Releases are created by running the [make-release](.github/workflows/make-release.yml)
+which is a manual workflow.
+
+The workflow creates an annotated git tag (e.g. `v0.1.0`) and pushes it to the
+remote. No GitHub Release object is created, the tag is the release artifact.
+
+## Building a release
+
+By default, `LLAMA_BUILD_IS_DEV=ON` which appends a `-dev` suffix to `LLAMA_VERSION`,
+marking the build as a nightly/development build. Distributors building from a
+release tag must pass `-DLLAMA_BUILD_IS_DEV=OFF` to produce a clean version string
+(e.g. `0.1.0` instead of `0.1.0-dev`).
+
+## How releases reach users
+Currently releases are not published to github releases, only nightly/development
+builds are available there. The way users can access releases are using the following
+channels:
+
+- **llama-install.sh**  — downloads pre-built binaries built from the release tag.
+- **Package managers**  — consume the git tag directly.
+- **Build from source** — users clone the repo and check out the tag.

examples/test-cmake/.gitignore

@@ -0,0 +1,3 @@
+llama-build-install
+install
+build

examples/test-cmake/CMakeLists.txt

@@ -0,0 +1,13 @@
+cmake_minimum_required(VERSION 3.14)
+project(llama-simple)
+
+set(CMAKE_CXX_STANDARD 17)
+
+find_package(llama 0.1.0 REQUIRED)
+
+add_executable(test-cmake test-cmake.cpp)
+target_link_libraries(test-cmake PRIVATE llama)
+target_compile_definitions(test-cmake PRIVATE
+    LLAMA_BUILD_NUMBER=${LLAMA_BUILD_NUMBER}
+    LLAMA_BUILD_COMMIT="${LLAMA_BUILD_COMMIT}"
+)

examples/test-cmake/README.md

@@ -0,0 +1,36 @@
+## cmake-test
+
+This is just for manually testing/developing of a llama.cpp installation to
+enable troubleshooting issues and exploration. The idea is that this can be used
+after making changes to llama.cpp installation cmake configuration and then
+verify it locally.
+
+### Usage
+The following will configure, build, and install llama.cpp
+
+Configuring/build/install:
+```console
+./build-install.sh
+```
+The above command will create a directory named `install` in the current directory
+which will have the follwing files in its lib directory:
+```console
+(venv) $ ls install/lib/
+cmake                   libggml.so          libllama-common.so.0      libllama.so.0.1.0  llama.cpp
+libggml-base.so         libggml.so.0        libllama-common.so.0.1.0  libmtmd.so         pkgconfig
+libggml-base.so.0       libggml.so.0.19.0   libllama.so               libmtmd.so.0
+libggml-base.so.0.19.0  libllama-common.so  libllama.so.0             libmtmd.so.0.1.0
+```
+
+Build/run this project using the installation created above:
+```console
+(venv) $ ./build.sh
+-- Configuring done (0.0s)
+-- Generating done (0.0s)
+-- Build files have been written to: /home/danbev/work/ai/llama.cpp/examples/test-cmake/build
+[100%] Built target test-cmake
+[test-cmake] Using llama.cpp version 0.1.0-dev-b10335
+[test-cmake] Initializing backend...
+load_backend: loaded CPU backend from /home/danbev/work/ai/llama.cpp/examples/test-cmake/install/lib/llama.cpp/libggml-cpu-alderlake.so
+[test-cmake] Backend initialized.
+```

examples/test-cmake/build-install.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+set -e
+
+rm -rf llama-build-install install
+
+cmake --fresh -S ../../. -B llama-build-install -DCMAKE_BUILD_TYPE=Release \
+  -DBUILD_SHARED_LIBS=ON \
+  -DGGML_BACKEND_DL=ON \
+  -DGGML_CPU_ALL_VARIANTS=ON \
+  -DLLAMA_TESTS_INSTALL=OFF \
+  -DCMAKE_INSTALL_PREFIX="${PWD}/install" \
+  -DGGML_BACKEND_DIR="${PWD}/install/lib/llama.cpp" \
+  -DGGML_LIB_INSTALL_DIR="${PWD}/install/lib/llama.cpp" \
+  -DLLAMA_LIB_INSTALL_DIR="${PWD}/install/lib/llama.cpp" \
+  -DLLAMA_TOOLS_INSTALL=OFF
+
+cmake --build llama-build-install --parallel 12
+cmake --install llama-build-install

examples/test-cmake/build.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+set -e
+
+cmake -S . -B build -DCMAKE_PREFIX_PATH="${PWD}/install"
+cmake --build build
+LD_LIBRARY_PATH="${PWD}/install/lib/llama.cpp:${PWD}/install/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" ./build/test-cmake

examples/test-cmake/test-cmake.cpp

@@ -0,0 +1,12 @@
+#include "llama.h"
+#include <cstdio>
+
+int main(void) {
+    printf("[test-cmake] version: %s, build: %d (%s)\n",
+           llama_version(), LLAMA_BUILD_NUMBER, LLAMA_BUILD_COMMIT);
+    printf("[test-cmake] Initializing backend...\n");
+    llama_backend_init();
+    printf("[test-cmake] Backend initialized.\n");
+    llama_backend_free();
+    return 0;
+}

ggml/CMakeLists.txt

@@ -402,7 +402,7 @@ configure_package_config_file(
               GGML_BIN_INSTALL_DIR)
 
 write_basic_package_version_file(
-        ${CMAKE_CURRENT_BINARY_DIR}/ggml-version.cmake
+        ${CMAKE_CURRENT_BINARY_DIR}/ggml-config-version.cmake
     VERSION ${GGML_INSTALL_VERSION}
     COMPATIBILITY SameMajorVersion)
 
@@ -414,7 +414,7 @@ message(STATUS "ggml version: ${GGML_INSTALL_VERSION}")
 message(STATUS "ggml commit:  ${GGML_BUILD_COMMIT}")
 
 install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ggml-config.cmake
-              ${CMAKE_CURRENT_BINARY_DIR}/ggml-version.cmake
+              ${CMAKE_CURRENT_BINARY_DIR}/ggml-config-version.cmake
         DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/ggml)
 
 if (MSVC)

ggml/cmake/ggml-config.cmake.in

@@ -113,6 +113,7 @@ set_and_check(GGML_LIB_DIR "@PACKAGE_GGML_LIB_INSTALL_DIR@")
 if(NOT TARGET ggml::ggml)
     find_package(Threads REQUIRED)
 
+    unset(GGML_LIBRARY CACHE)
     find_library(GGML_LIBRARY ggml
         REQUIRED
         HINTS ${GGML_LIB_DIR}
@@ -121,8 +122,10 @@ if(NOT TARGET ggml::ggml)
     add_library(ggml::ggml UNKNOWN IMPORTED)
     set_target_properties(ggml::ggml
         PROPERTIES
-            IMPORTED_LOCATION "${GGML_LIBRARY}")
+            IMPORTED_LOCATION "${GGML_LIBRARY}"
+            INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}")
 
+    unset(GGML_BASE_LIBRARY CACHE)
     find_library(GGML_BASE_LIBRARY ggml-base
         REQUIRED
         HINTS ${GGML_LIB_DIR}
@@ -132,6 +135,7 @@ if(NOT TARGET ggml::ggml)
     set_target_properties(ggml::ggml-base
         PROPERTIES
             IMPORTED_LOCATION "${GGML_BASE_LIBRARY}"
+            INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}"
             INTERFACE_LINK_LIBRARIES "${GGML_BASE_INTERFACE_LINK_LIBRARIES}")
 
     set(_ggml_all_targets "")
@@ -140,6 +144,7 @@ if(NOT TARGET ggml::ggml)
             string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}")
             string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx)
 
+            unset(${_ggml_backend_pfx}_LIBRARY CACHE)
             find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend}
                 REQUIRED
                 HINTS ${GGML_LIB_DIR}

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

@@ -1,90 +1,12 @@
 #include "ggml-backend-impl.h"
+#include "ggml-feats.h"
 
-#if defined(__aarch64__)
-
-#if defined(__linux__)
-#include <sys/auxv.h>
-#elif defined(__APPLE__)
-#include <sys/sysctl.h>
-#endif
-
-#if !defined(HWCAP_FPHP)
-#define HWCAP_FPHP (1 << 9)
-#endif
-
-#if !defined(HWCAP_ASIMDHP)
-#define HWCAP_ASIMDHP (1 << 10)
-#endif
-
-#if !defined(HWCAP_ASIMDDP)
-#define HWCAP_ASIMDDP (1 << 20)
-#endif
-
-#if !defined(HWCAP_SVE)
-#define HWCAP_SVE (1 << 22)
-#endif
-
-#if !defined(HWCAP2_SVE2)
-#define HWCAP2_SVE2 (1 << 1)
-#endif
-
-#if !defined(HWCAP2_I8MM)
-#define HWCAP2_I8MM (1 << 13)
-#endif
-
-#if !defined(HWCAP2_SME)
-#define HWCAP2_SME (1 << 23)
-#endif
-
-struct aarch64_features {
-    // has_neon not needed, aarch64 has NEON guaranteed
-    bool has_dotprod     = false;
-    bool has_fp16        = false;
-    bool has_sve         = false;
-    bool has_sve2        = false;
-    bool has_i8mm        = false;
-    bool has_sme         = false;
-    bool has_sme2        = false;
-
-    aarch64_features() {
-#if defined(__linux__)
-        uint32_t hwcap = getauxval(AT_HWCAP);
-        uint32_t hwcap2 = getauxval(AT_HWCAP2);
-
-        has_dotprod = !!(hwcap & HWCAP_ASIMDDP);
-        has_fp16    = !!(hwcap & HWCAP_FPHP) && !!(hwcap & HWCAP_ASIMDHP);
-        has_sve     = !!(hwcap & HWCAP_SVE);
-        has_sve2    = !!(hwcap2 & HWCAP2_SVE2);
-        has_i8mm    = !!(hwcap2 & HWCAP2_I8MM);
-        has_sme     = !!(hwcap2 & HWCAP2_SME);
-#elif defined(__APPLE__)
-        int oldp = 0;
-        size_t size = sizeof(oldp);
-
-        if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, NULL, 0) == 0) {
-            has_dotprod = static_cast<bool>(oldp);
-        }
-
-        if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) == 0) {
-            has_i8mm = static_cast<bool>(oldp);
-        }
-
-        if (sysctlbyname("hw.optional.arm.FEAT_SME", &oldp, &size, NULL, 0) == 0) {
-            has_sme = static_cast<bool>(oldp);
-        }
-
-        if (sysctlbyname("hw.optional.arm.FEAT_SME2", &oldp, &size, NULL, 0) == 0) {
-            has_sme2 = static_cast<bool>(oldp);
-        }
-
-        // Apple apparently does not implement SVE yet
-#endif
-    }
-};
+#if defined(__aarch64__) || defined(_M_ARM64)
 
 static int ggml_backend_cpu_aarch64_score() {
     int score = 1;
-    aarch64_features af;
+    const ggml_feats_arch64_runtime_t af = ggml_feats_get_arch64_runtime();
+    GGML_UNUSED(af);
 
 #ifdef GGML_USE_DOTPROD
     if (!af.has_dotprod) { return 0; }
@@ -116,4 +38,4 @@ static int ggml_backend_cpu_aarch64_score() {
 
 GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_aarch64_score)
 
-# endif // defined(__aarch64__)
+# endif // defined(__aarch64__) || defined(_M_ARM64)

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

@@ -2,10 +2,12 @@
 // SPDX-License-Identifier: MIT
 //
 #include <arm_neon.h>
-#include <assert.h>
-#include <stdio.h>
+#include <cassert>
+#include <cstdio>
+#include <cstdlib>
 #include <atomic>
 #include <cfloat>
+#include <cctype>
 #include <algorithm>
 #include <cmath>
 #include <stdexcept>
@@ -17,25 +19,21 @@
 #include <cstddef>
 #include <cstdint>
 #include <fstream>
-#include <set>
+#include <map>
 #include <iostream>
 #include <climits>
+#include <charconv>
+#include <system_error>
 #if defined(__linux__)
 #include <asm/hwcap.h>
+#include <dirent.h>
 #include <sys/auxv.h>
 #include <sys/types.h>
 #include <sys/stat.h>
 #include <unistd.h>
-#ifndef HWCAP2_SME2
-#define HWCAP2_SME2 (1UL << 37)
-#endif
 #elif defined(__APPLE__)
-#include <string_view>
 #include <sys/sysctl.h>
 #include <sys/types.h>
-#elif defined(_WIN32)
-#include <windows.h>
-#include <excpt.h>
 #endif
 
 #include "kleidiai.h"
@@ -43,6 +41,7 @@
 #include "ggml-cpu.h"
 #include "ggml-cpu-impl.h"
 #include "ggml-impl.h"
+#include "ggml-feats.h"
 #include "ggml-backend-impl.h"
 #include "ggml-threading.h"
 #include "traits.h"
@@ -64,8 +63,8 @@ struct ggml_kleidiai_context {
     ggml_kleidiai_kernels * kernels_q4;
     ggml_kleidiai_kernels * kernels_q8;
     ggml_kleidiai_kernels * kernels_f32;
-    int sme_thread_cap; // <= 0 means “SME disabled/unknown”;
-    int thread_hint;    // <= 0 means “no hint”
+    int sme_thread_cap; // <= 0 means "SME disabled/unknown"
+    int thread_hint;    // <= 0 means "no hint"
     int chunk_multiplier;
 } static ctx = { CPU_FEATURE_NONE, nullptr, nullptr, nullptr, 0, -1, 4 };
 
@@ -93,24 +92,117 @@ static const char* cpu_feature_to_string(cpu_feature f) {
     }
 }
 
+#if defined(__linux__) && defined(__aarch64__)
+static bool parse_cpu_dir_name(const char* name, size_t* cpu) {
+    if (strncmp(name, "cpu", 3) != 0 ||
+        name[3] < '0' || name[3] > '9') {
+        return false;
+    }
+
+    const char* first = name + 3;
+    const char* last = name + strlen(name);
+
+    size_t value = 0;
+    const auto [end, ec] = std::from_chars(first, last, value, 10);
+
+    if (ec != std::errc{} || end != last) {
+        return false;
+    }
+
+    *cpu = value;
+    return true;
+}
+
+static std::vector<size_t> detect_cpu_ids() {
+    std::vector<size_t> cpus;
+
+    DIR * dir = opendir("/sys/devices/system/cpu");
+    if (dir == nullptr) {
+        return cpus;
+    }
+
+    while (dirent * entry = readdir(dir)) {
+        size_t cpu = 0;
+        if (parse_cpu_dir_name(entry->d_name, &cpu)) {
+            cpus.push_back(cpu);
+        }
+    }
+    closedir(dir);
+
+    std::sort(cpus.begin(), cpus.end());
+    cpus.erase(std::unique(cpus.begin(), cpus.end()), cpus.end());
+    return cpus;
+}
+#endif
+
+#if defined(__APPLE__) && defined(__aarch64__)
+static bool apple_sme_counted_perf_level(std::string name) {
+    for (std::string::size_type i = 0; i < name.size(); ++i) {
+        name[i] = (char) std::tolower((unsigned char) name[i]);
+    }
+
+    // Conservative ceiling: only count perf-level names observed to provide full SME throughput.
+    // Future names should be calibrated here before they raise the automatic SME thread cap.
+    return name.find("super") != std::string::npos ||
+           name.find("performance") != std::string::npos;
+}
+#endif
+
+static void add_smcus_from_smidr(uint64_t smidr, size_t & num_private, std::map<uint32_t, size_t> & shared_counts) {
+    // Arm ARM: SMIDR_EL1. SH==0 is implementation-defined; keep the existing
+    // conservative policy and only treat zero affinity as private.
+    const uint32_t sh = (uint32_t)((smidr >> 13) & 0x3);
+    const uint32_t nsmc = (uint32_t)((smidr >> 56) & 0xF);
+    const size_t shared_count = nsmc == 0xF ? 1 : (size_t)nsmc + 1;
+    const uint32_t affinity = (uint32_t)(smidr & 0xFFFu);
+    const uint32_t affinity2 = (uint32_t)((smidr >> 32) & 0xFFFFFu);
+    const uint32_t id = (affinity2 << 12) | affinity;
+
+    if (nsmc == 0xF) {
+        GGML_LOG_WARN("kleidiai: NSMC detected as 0xF indicating reseved value, setting min safe shared SMCU count to 1");
+    }
+
+    switch (sh) {
+        case 2: // private SMCU
+            ++num_private;
+            break;
+        case 3: // shared SMCU
+            if (shared_counts[id] < shared_count) {
+                shared_counts[id] = shared_count;
+            }
+            break;
+        case 0:
+            if (id == 0) {
+                ++num_private;
+            } else if (shared_counts[id] < shared_count) {
+                shared_counts[id] = shared_count;
+            }
+            break;
+        default:
+            break;
+    }
+}
+
 static size_t detect_num_smcus() {
-    if (!ggml_cpu_has_sme()) {
+    const auto runtime_feat = ggml_feats_get_arch64_runtime();
+    if (!runtime_feat.has_sme) {
         return 0;
     }
 
 #if defined(__linux__) && defined(__aarch64__)
     // Linux/aarch64: Best-effort count of Streaming Mode Compute Units (SMCUs) via SMIDR_EL1 sysfs.
     size_t num_private = 0;
-    std::set<uint32_t> shared_ids;
+    std::map<uint32_t, size_t> shared_counts;
 
-    for (size_t cpu = 0;; ++cpu) {
+    const std::vector<size_t> cpus = detect_cpu_ids();
+    for (const size_t cpu : cpus) {
         const std::string path =
             "/sys/devices/system/cpu/cpu" + std::to_string(cpu) +
             "/regs/identification/smidr_el1";
 
         std::ifstream file(path);
         if (!file.is_open()) {
-            break;
+            continue;
         }
 
         uint64_t smidr = 0;
@@ -118,54 +210,69 @@ static size_t detect_num_smcus() {
             continue;
         }
 
-        // Arm ARM: SMIDR_EL1
-        const uint32_t sh = (uint32_t)((smidr >> 13) & 0x3);
-        // Build an "affinity-like" identifier for shared SMCUs.
-        // Keep the original packing logic, but isolate it here.
-        const uint32_t id = (uint32_t)((smidr & 0xFFFu) | ((smidr >> 20) & 0xFFFFF000u));
-
-        switch (sh) {
-            case 0b10: // private SMCU
-                ++num_private;
-                break;
-            case 0b11: // shared SMCU
-                shared_ids.emplace(id);
-                break;
-            case 0b00:
-                // Ambiguous / implementation-defined. Be conservative:
-                // treat id==0 as private, otherwise as shared.
-                if (id == 0) ++num_private;
-                else shared_ids.emplace(id);
-                break;
-            default:
-                break;
-        }
+        add_smcus_from_smidr(smidr, num_private, shared_counts);
     }
 
-    return num_private + shared_ids.size();
+    size_t total = num_private;
+    for (const auto & entry : shared_counts) {
+        total += entry.second;
+    }
+    return total;
 
 #elif defined(__APPLE__) && defined(__aarch64__)
-    // table for known M4 variants. Users can override via GGML_KLEIDIAI_SME=<n>.
-    char chip_name[256] = {};
-    size_t size = sizeof(chip_name);
+    int perf_levels = 0;
+    size_t size = sizeof(perf_levels);
+    if (sysctlbyname("hw.nperflevels", &perf_levels, &size, nullptr, 0) != 0 ||
+        size != sizeof(perf_levels) || perf_levels <= 0) {
+        return 0;
+    }
 
-    if (sysctlbyname("machdep.cpu.brand_string", chip_name, &size, nullptr, 0) == 0) {
-        const std::string brand(chip_name);
+    size_t units = 0;
+    for (int i = 0; i < perf_levels; ++i) {
+        char key[64] = {};
+        int physical_cpus = 0;
+        int cpus_per_l2 = 0;
 
-        struct ModelSMCU { const char *match; size_t smcus; };
-        static const ModelSMCU table[] = {
-            { "M4 Ultra", 2 },
-            { "M4 Max",   2 },
-            { "M4 Pro",   2 },
-            { "M4",       1 },
-        };
+        snprintf(key, sizeof(key), "hw.perflevel%d.physicalcpu", i);
+        size = sizeof(physical_cpus);
+        if (sysctlbyname(key, &physical_cpus, &size, nullptr, 0) != 0 ||
+            size != sizeof(physical_cpus) || physical_cpus <= 0) {
+            continue;
+        }
 
-        for (const auto &e : table) {
-            if (brand.find(e.match) != std::string::npos) {
-                return e.smcus;
-            }
+        snprintf(key, sizeof(key), "hw.perflevel%d.cpusperl2", i);
+        size = sizeof(cpus_per_l2);
+        if (sysctlbyname(key, &cpus_per_l2, &size, nullptr, 0) != 0 ||
+            size != sizeof(cpus_per_l2) || cpus_per_l2 <= 0) {
+            continue;
+        }
+
+        snprintf(key, sizeof(key), "hw.perflevel%d.name", i);
+        size = 0;
+        if (sysctlbyname(key, nullptr, &size, nullptr, 0) != 0 || size == 0) {
+            continue;
+        }
+
+        std::string name(size, '\0');
+        if (sysctlbyname(key, &name[0], &size, nullptr, 0) != 0) {
+            continue;
+        }
+        name.resize(size);
+        while (!name.empty() && name.back() == '\0') {
+            name.pop_back();
+        }
+
+        if (apple_sme_counted_perf_level(name)) {
+            units += (size_t) ((physical_cpus + cpus_per_l2 - 1) / cpus_per_l2);
         }
     }
+
+    return units;
+
+#elif defined(_WIN32) && (defined(_M_ARM64) || defined(__aarch64__))
+    // No verified Windows arm64 SMCU detection path yet. Return unknown and use
+    // GGML_KLEIDIAI_SME=N as a diagnostics/debug override for SME thread cap
+    // calibration until a detection mechanism is verified on real hardware.
     return 0;
 
 #else
@@ -198,15 +305,18 @@ static void init_kleidiai_context(void) {
     if (!initialized) {
         initialized = true;
 
+        // Optional diagnostics/debug overrides; production defaults come from runtime detection.
         const char *env_sme         = getenv("GGML_KLEIDIAI_SME");
         const char *env_threads     = getenv("GGML_TOTAL_THREADS");
         const char *env_chunk_mult  = getenv("GGML_KLEIDIAI_CHUNK_MULTIPLIER");
 
+        const auto runtime_feat = ggml_feats_get_arch64_runtime();
+
         size_t detected_smcus = 0;
 
-        ctx.features  = (ggml_cpu_has_dotprod()     ? CPU_FEATURE_DOTPROD : CPU_FEATURE_NONE) |
-                        (ggml_cpu_has_matmul_int8() ? CPU_FEATURE_I8MM    : CPU_FEATURE_NONE) |
-                        ((ggml_cpu_has_sve() && ggml_cpu_get_sve_cnt() == QK8_0) ? CPU_FEATURE_SVE : CPU_FEATURE_NONE);
+        ctx.features  = (runtime_feat.has_dotprod  ? CPU_FEATURE_DOTPROD : CPU_FEATURE_NONE) |
+                        (runtime_feat.has_i8mm     ? CPU_FEATURE_I8MM    : CPU_FEATURE_NONE) |
+                        (runtime_feat.sve_cnt == QK8_0 ? CPU_FEATURE_SVE : CPU_FEATURE_NONE);
 
         if (env_threads) {
             bool ok = false;
@@ -224,54 +334,54 @@ static void init_kleidiai_context(void) {
             }
         }
 
-        // SME policy:
-        // - env unset => auto-detect SMCUs; enable SME only if detected > 0.
-        // - env=0     => force off.
-        // - env>0     => force N cores, if the binary was built with SME.
         int sme_cores = 0;
         bool sme_env_ok = false;
         bool sme_env_set = (env_sme != nullptr);
 
+        const bool has_supported_sme_family = runtime_feat.has_sme;
+        bool sme_cap_detected = false;
+
+        if (has_supported_sme_family) {
+            detected_smcus = detect_num_smcus();
+            sme_cap_detected = detected_smcus > 0;
+            // Some platforms expose SME without exposing a calibrated SMCU count.
+            // Use one SME thread as the conservative default; add platform SMCU detection to raise it.
+            sme_cores = sme_cap_detected ? (int)detected_smcus : 1;
+
+            if (!sme_env_set && !sme_cap_detected) {
+                GGML_LOG_INFO("kleidiai: SME detected; SMCU count unavailable, using conservative SME thread cap=1\n");
+            }
+        }
+
+        // Runtime-detect SME support and available SMCUs first. The detected SMCU
+        // count is used as the SME thread cap, and GGML_KLEIDIAI_SME can debug-override that:
+        //   - unset: use runtime detection.
+        //   - 0:     disable SME-family kernels.
+        //   - N > 0: use N as the SME thread cap, if an SME-family kernel is selectable.
         if (sme_env_set) {
             bool ok = false;
             int v = parse_uint_env(env_sme, "GGML_KLEIDIAI_SME", &ok);
             sme_env_ok = ok;
 
-            if (!ok) {
-                GGML_LOG_WARN("kleidiai: GGML_KLEIDIAI_SME set but parsing failed; falling back to runtime SME-core detection\n");
-                detected_smcus = detect_num_smcus();
-                sme_cores = detected_smcus > 0 ? (int)detected_smcus : 0;
-            } else if (v == 0) {
-                sme_cores = 0;
-            } else if (!ggml_cpu_has_sme()) {
-                GGML_LOG_WARN("kleidiai: GGML_KLEIDIAI_SME=%d but the binary was not built with SME; disabling SME\n", v);
-                sme_cores = 0;
+            if (ok) {
+                if (has_supported_sme_family) {
+                    sme_cores = v;
+                } else {
+                    if (v > 0) {
+                        GGML_LOG_WARN("kleidiai: GGML_KLEIDIAI_SME=%d but SME is not supported on this CPU; disabling SME-family kernels\n", v);
+                    }
+                    sme_cores = 0;
+                }
             } else {
-                sme_cores = v;
+                GGML_LOG_WARN("kleidiai: GGML_KLEIDIAI_SME set but parsing failed; using automatic SME thread cap\n");
             }
-        } else {
-            detected_smcus = detect_num_smcus();
-            sme_cores = detected_smcus > 0 ? (int)detected_smcus : 0;
         }
 
-        if (!sme_env_set && ggml_cpu_has_sme() && sme_cores == 0) {
-            GGML_LOG_WARN("kleidiai: runtime SME-core detection returned 0; falling back to NEON\n");
-        }
-
-        if (sme_cores > 0) {
+        if (sme_cores > 0 && has_supported_sme_family) {
             ctx.features |= CPU_FEATURE_SME;
-#if defined(__aarch64__) && defined(__linux__)
-            // ARM guarantees SME2 implies SME, so only check SME2 when SME is enabled.
-            if (getauxval(AT_HWCAP2) & HWCAP2_SME2) {
+            if (runtime_feat.has_sme2) {
                 ctx.features |= CPU_FEATURE_SME2;
             }
-#elif defined(__aarch64__) && defined(__APPLE__)
-            int feat_sme2 = 0;
-            size_t size = sizeof(feat_sme2);
-            if (sysctlbyname("hw.optional.arm.FEAT_SME2", &feat_sme2, &size, NULL, 0) == 0 && feat_sme2) {
-                ctx.features |= CPU_FEATURE_SME2;
-            }
-#endif
         }
 
         // Kernel selection
@@ -297,16 +407,19 @@ static void init_kleidiai_context(void) {
             GGML_LOG_INFO("kleidiai: primary f32 kernel feature %s\n", cpu_feature_to_string(ctx.kernels_f32->required_cpu));
         }
 
-        ctx.sme_thread_cap = (ctx.features & CPU_FEATURE_SME) ? sme_cores : 0;
+        const bool has_selected_sme_family_kernel =
+            (ctx.kernels_q4  && is_sme_family(ctx.kernels_q4->required_cpu)) ||
+            (ctx.kernels_q8  && is_sme_family(ctx.kernels_q8->required_cpu)) ||
+            (ctx.kernels_f32 && is_sme_family(ctx.kernels_f32->required_cpu));
+        ctx.sme_thread_cap = has_selected_sme_family_kernel ? sme_cores : 0;
 
-        if (ctx.features & CPU_FEATURE_SME) {
-            const bool has_sme2 = (ctx.features & CPU_FEATURE_SME2) != CPU_FEATURE_NONE;
+        if (has_selected_sme_family_kernel) {
             if (sme_env_set && sme_env_ok && sme_cores > 0) {
-                GGML_LOG_INFO("kleidiai: SME%s enabled (GGML_KLEIDIAI_SME=%d override)\n",
-                    has_sme2 ? "2" : "", sme_cores);
+                GGML_LOG_INFO("kleidiai: SME enabled (GGML_KLEIDIAI_SME=%d debug override)\n", sme_cores);
+            } else if (sme_cap_detected) {
+                GGML_LOG_INFO("kleidiai: SME enabled (runtime-detected SME thread cap=%d)\n", sme_cores);
             } else {
-                GGML_LOG_INFO("kleidiai: SME%s enabled (runtime-detected SME cores=%d)\n",
-                    has_sme2 ? "2" : "", sme_cores);
+                GGML_LOG_INFO("kleidiai: SME enabled (runtime SME detected, conservative thread cap=%d)\n", sme_cores);
             }
         } else {
             GGML_LOG_INFO("kleidiai: SME disabled\n");
@@ -467,7 +580,7 @@ static int kleidiai_collect_kernel_chain_common(
     }
 
     if (is_sme_family(primary->required_cpu)) {
-        const cpu_feature fallback_mask = static_cast<cpu_feature>(features & ~CPU_FEATURE_SME & ~CPU_FEATURE_SME2);
+        const cpu_feature fallback_mask = static_cast<cpu_feature>(features & ~(CPU_FEATURE_SME | CPU_FEATURE_SME2));
         if (fallback_mask != CPU_FEATURE_NONE) {
             ggml_kleidiai_kernels * fallback = select_fallback(fallback_mask);
             if (fallback && fallback != primary &&
@@ -1077,13 +1190,14 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         const int ith_total = params->ith;
 
         int sme_slot = -1;
+        int non_sme_slot = -1;
         for (int i = 0; i < runtime_count; ++i) {
             if (is_sme_family(runtime[i].kernels->required_cpu)) {
                 sme_slot = i;
                 break;
             }
         }
-        int non_sme_slot = -1;
+
         for (int i = 0; i < runtime_count; ++i) {
             if (!is_sme_family(runtime[i].kernels->required_cpu)) {
                 non_sme_slot = i;

ggml/src/ggml-cpu/ops.cpp

@@ -8941,7 +8941,7 @@ static void ggml_compute_forward_flash_attn_ext_tiled(
             for (int tk = 0; tk < kv_tile; tk++) {
                 const char * v_data = (const char *)v->data + (ic + tk)*nbv1 + iv2*nbv2 + iv3*nbv3;
                 if (kv_type == GGML_TYPE_F16) {
-                    ggml_fp16_to_fp32_row((const ggml_fp16_t *)v_data, V32 + tk * DV, DV);
+                    ggml_cpu_fp16_to_fp32((const ggml_fp16_t *)v_data, V32 + tk * DV, DV);
                 } else {
                     memcpy(V32 + tk * DV, v_data, DV * sizeof(float));
                 }

ggml/src/ggml-feats.h

@@ -0,0 +1,166 @@
+#pragma once
+
+#if defined(__aarch64__) || defined(_M_ARM64)
+
+#if defined(__linux__)
+#include <sys/auxv.h>
+#include <sys/prctl.h>
+
+#if !defined(HWCAP2_SVE2)
+#define HWCAP2_SVE2 (1ULL << 1)
+#endif
+
+#if !defined(HWCAP_FPHP)
+#define HWCAP_FPHP (1 << 9)
+#endif
+
+#if !defined(HWCAP_ASIMDHP)
+#define HWCAP_ASIMDHP (1 << 10)
+#endif
+
+#if !defined(HWCAP2_I8MM)
+#define HWCAP2_I8MM (1ULL << 13)
+#endif
+
+#if !defined(HWCAP_ASIMDDP)
+#define HWCAP_ASIMDDP (1 << 20)
+#endif
+
+#if !defined(HWCAP_SVE)
+#define HWCAP_SVE (1 << 22)
+#endif
+
+#if !defined(HWCAP2_SME)
+#define HWCAP2_SME (1ULL << 23)
+#endif
+
+#if !defined(HWCAP2_SME2)
+#define HWCAP2_SME2 (1ULL << 37)
+#endif
+
+#if !defined(PR_SVE_GET_VL)
+#define PR_SVE_GET_VL 51
+#endif
+
+#if !defined(PR_SVE_VL_LEN_MASK)
+#define PR_SVE_VL_LEN_MASK 0xffff
+#endif
+
+#elif defined(__APPLE__)
+#include <sys/sysctl.h>
+#elif defined(_WIN32)
+#include <windows.h>
+
+#if !defined(PF_ARM_V82_DP_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_V82_DP_INSTRUCTIONS_AVAILABLE 43
+#endif
+
+#if !defined(PF_ARM_SVE_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_SVE_INSTRUCTIONS_AVAILABLE 46
+#endif
+
+#if !defined(PF_ARM_SVE2_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_SVE2_INSTRUCTIONS_AVAILABLE 47
+#endif
+
+#if !defined(PF_ARM_V82_I8MM_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_V82_I8MM_INSTRUCTIONS_AVAILABLE 66
+#endif
+
+#if !defined(PF_ARM_V82_FP16_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_V82_FP16_INSTRUCTIONS_AVAILABLE 67
+#endif
+
+#if !defined(PF_ARM_SME_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_SME_INSTRUCTIONS_AVAILABLE 70
+#endif
+
+#if !defined(PF_ARM_SME2_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_SME2_INSTRUCTIONS_AVAILABLE 71
+#endif
+
+#endif
+
+typedef struct ggml_feats_arch64_runtime {
+    bool has_dotprod;
+    bool has_fp16;
+    bool has_sve;
+    bool has_sve2;
+    bool has_i8mm;
+    bool has_sme;
+    bool has_sme2;
+    int sve_cnt;
+} ggml_feats_arch64_runtime_t;
+
+static inline ggml_feats_arch64_runtime_t ggml_feats_get_arch64_runtime(void) {
+    ggml_feats_arch64_runtime_t runtime_feat = {};
+
+#if defined(__linux__)
+    const unsigned long hwcap  = getauxval(AT_HWCAP);
+    const unsigned long hwcap2 = getauxval(AT_HWCAP2);
+
+    runtime_feat.has_dotprod = !!(hwcap & HWCAP_ASIMDDP);
+    runtime_feat.has_fp16    = !!(hwcap & HWCAP_FPHP) && !!(hwcap & HWCAP_ASIMDHP);;
+    runtime_feat.has_sve     = !!(hwcap & HWCAP_SVE);
+    runtime_feat.has_sve2    = !!(hwcap2 & HWCAP2_SVE2);
+    runtime_feat.has_i8mm    = !!(hwcap2 & HWCAP2_I8MM);
+    runtime_feat.has_sme     = !!(hwcap2 & HWCAP2_SME);
+    runtime_feat.has_sme2    = !!(hwcap2 & HWCAP2_SME2);
+
+    if (runtime_feat.has_sve) {
+        const int vl = prctl(PR_SVE_GET_VL);
+        if (vl >= 0) {
+            runtime_feat.sve_cnt = vl & PR_SVE_VL_LEN_MASK;
+        }
+    }
+#elif defined(__APPLE__)
+    int oldp = 0;
+    size_t size = sizeof(oldp);
+
+    if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_dotprod = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_FP16", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_fp16 = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_SVE", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_sve = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_SVE2", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_sve2 = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_i8mm = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_SME", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_sme = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_SME2", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_sme2 = static_cast<bool>(oldp);
+    }
+
+    // Apple does not support userspace non-streaming SVE; keep SVE vector length unknown.
+    runtime_feat.sve_cnt = 0;
+#elif defined (_WIN32)
+    runtime_feat.has_dotprod = IsProcessorFeaturePresent(PF_ARM_V82_DP_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_fp16    = IsProcessorFeaturePresent(PF_ARM_V82_FP16_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_sve     = IsProcessorFeaturePresent(PF_ARM_SVE_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_sve2    = IsProcessorFeaturePresent(PF_ARM_SVE2_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_i8mm    = IsProcessorFeaturePresent(PF_ARM_V82_I8MM_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_sme     = IsProcessorFeaturePresent(PF_ARM_SME_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_sme2    = IsProcessorFeaturePresent(PF_ARM_SME2_INSTRUCTIONS_AVAILABLE) != 0;
+
+    // Windows exposes SVE feature presence, but not the runtime SVE vector length here.
+    runtime_feat.sve_cnt = 0;
+#endif
+
+    return runtime_feat;
+}
+
+#endif // defined(__aarch64__) || defined(_M_ARM64)

ggml/src/ggml-hip/CMakeLists.txt

@@ -126,9 +126,6 @@ if (GGML_HIP_EXPORT_METRICS)
     set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -Rpass-analysis=kernel-resource-usage --save-temps")
 endif()
 
-# Fast math for HIP, like CUDA's -use_fast_math. Not -ffast-math: that implies -ffinite-math-only, which breaks ggml's INFINITY masking and produces NaNs.
-set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -funsafe-math-optimizations")
-
 if (NOT GGML_CUDA_FA)
     add_compile_definitions(GGML_CUDA_NO_FA)
 endif()

ggml/src/ggml-metal/ggml-metal-device.cpp

@@ -953,6 +953,11 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv(ggml_meta
                 nr0 = N_R0_IQ4_XS;
                 smem = 32*sizeof(float);
             } break;
+        case GGML_TYPE_TQ2_0:
+            {
+                nsg = N_SG_TQ2_0;
+                nr0 = N_R0_TQ2_0;
+            } break;
         default:
             {
                 GGML_LOG_ERROR("Asserting on type %d\n", (int) tsrc0);
@@ -1182,6 +1187,11 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_id(ggml_m
                 nr0 = N_R0_IQ4_XS;
                 smem = 32*sizeof(float);
             } break;
+        case GGML_TYPE_TQ2_0:
+            {
+                nsg = N_SG_TQ2_0;
+                nr0 = N_R0_TQ2_0;
+            } break;
         default:
             {
                 GGML_LOG_ERROR("Asserting on type %d\n", (int)op->src[2]->type);

ggml/src/ggml-metal/ggml-metal-device.m

@@ -1407,6 +1407,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                            case GGML_TYPE_Q5_0:
                            case GGML_TYPE_Q5_1:
                            case GGML_TYPE_IQ4_NL:
+                           case GGML_TYPE_TQ2_0:
                            case GGML_TYPE_I32:
                                 return true;
                            default:
@@ -1435,6 +1436,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                     case GGML_TYPE_Q5_0:
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_TQ2_0:
                         switch (op->type) {
                             case GGML_TYPE_F32:
                             case GGML_TYPE_F16:
@@ -1470,6 +1472,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                     case GGML_TYPE_Q5_0:
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_IQ4_NL:
+                    case GGML_TYPE_TQ2_0:
                         return true;
                     default:
                         return false;

ggml/src/ggml-metal/ggml-metal-impl.h

@@ -87,6 +87,9 @@
 #define N_R0_IQ4_XS 2
 #define N_SG_IQ4_XS 2
 
+#define N_R0_TQ2_0 4
+#define N_SG_TQ2_0 2
+
 // function constants offsets
 #define FC_FLASH_ATTN_EXT_PAD          100
 #define FC_FLASH_ATTN_EXT_BLK          200

ggml/src/ggml-metal/ggml-metal.metal

@@ -468,6 +468,34 @@ void quantize_iq4_nl(device const float * src, device block_iq4_nl & dst) {
     dst.d = sumq2 > 0 ? sumqx/sumq2 : d;
 }
 
+void quantize_tq2_0(device const float * src, device block_tq2_0 & dst) {
+#pragma METAL fp math_mode(safe)
+    float amax = 0.0f; // absolute max
+
+    for (int j = 0; j < QK_K; j++) {
+        const float v = src[j];
+        amax = MAX(amax, fabs(v));
+    }
+
+    const float d = amax;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dst.d = (half) d;
+
+    for (int j = 0; j < QK_K/4; j += 32) {
+        for (int m = 0; m < 32; ++m) {
+            uint8_t q = 0;
+            for (int n = 0; n < 4; ++n) {
+                // -1, 0, 1 -> 0, 1, 2
+                int xi = (int)round(src[m + n*32] * id) + 1;
+                q += (uint8_t)((xi & 3) << (2*n));
+            }
+            dst.qs[j + m] = q;
+        }
+        src += 4*32;
+    }
+}
+
 template <typename type4x4>
 void dequantize_q4_1(device const block_q4_1 * xb, short il, thread type4x4 & reg) {
     device const uint16_t * qs = ((device const uint16_t *)xb + 2);
@@ -1021,6 +1049,25 @@ void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4
     }
 }
 
+template <typename type4x4>
+void dequantize_tq2_0(device const block_tq2_0 * xb, short il, thread type4x4 & reg) {
+    device const uint8_t * qs = xb->qs;
+    const float d = xb->d;
+
+    float4x4 reg_f;
+
+    // 2 bits per element, 4 elements per byte, 128 elements per 32-byte group
+    const short base = il * 16;
+    for (int k = 0; k < 16; k++) {
+        const int i = base + k;
+        const int byte = ((i >> 7) & 1) * 32 + (i & 31);
+        const int l = (i >> 5) & 3;
+        reg_f[k/4][k%4] = d * (float)(((qs[byte] >> (2*l)) & 3) - 1);
+    }
+
+    reg = (type4x4) reg_f;
+}
+
 enum ggml_sort_order {
     GGML_SORT_ORDER_ASC,
     GGML_SORT_ORDER_DESC,
@@ -8001,6 +8048,7 @@ template [[host_name("kernel_cpy_f32_q4_1")]]   kernel cpy_f_q_t kernel_cpy_f32_
 template [[host_name("kernel_cpy_f32_q5_0")]]   kernel cpy_f_q_t kernel_cpy_f32_q<QK5_0,  block_q5_0,   quantize_q5_0>;
 template [[host_name("kernel_cpy_f32_q5_1")]]   kernel cpy_f_q_t kernel_cpy_f32_q<QK5_1,  block_q5_1,   quantize_q5_1>;
 template [[host_name("kernel_cpy_f32_iq4_nl")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK4_NL, block_iq4_nl, quantize_iq4_nl>;
+template [[host_name("kernel_cpy_f32_tq2_0")]]  kernel cpy_f_q_t kernel_cpy_f32_q<QK_K,   block_tq2_0,  quantize_tq2_0>;
 
 template<typename T4x4, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread T4x4 &)>
 kernel void kernel_cpy_q_f32(
@@ -8048,6 +8096,8 @@ template [[host_name("kernel_cpy_q5_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<
 template [[host_name("kernel_cpy_q5_1_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q5_1, 2, dequantize_q5_1>;
 template [[host_name("kernel_cpy_q8_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q8_0, 2, dequantize_q8_0>;
 
+template [[host_name("kernel_cpy_tq2_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_tq2_0, QK_NL, dequantize_tq2_0>;
+
 template [[host_name("kernel_cpy_q1_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q1_0, 8, dequantize_q1_0>;
 template [[host_name("kernel_cpy_q2_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q2_0, 4, dequantize_q2_0>;
 template [[host_name("kernel_cpy_q4_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q4_0, 2, dequantize_q4_0>;
@@ -8056,6 +8106,8 @@ template [[host_name("kernel_cpy_q5_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<
 template [[host_name("kernel_cpy_q5_1_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q5_1, 2, dequantize_q5_1>;
 template [[host_name("kernel_cpy_q8_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q8_0, 2, dequantize_q8_0>;
 
+template [[host_name("kernel_cpy_tq2_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_tq2_0, QK_NL, dequantize_tq2_0>;
+
 template<typename T>
 kernel void kernel_concat(
         constant ggml_metal_kargs_concat & args,
@@ -9822,6 +9874,121 @@ kernel void kernel_mul_mv_mxfp4_f32(
     kernel_mul_mv_mxfp4_f32_impl<N_R0_MXFP4, constant ggml_metal_kargs_mul_mv &>(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg);
 }
 
+template<int nr0, typename args_t>
+void kernel_mul_mv_tq2_0_f32_impl(
+        args_t args,
+        device const char * src0,
+        device const char * src1,
+        device       char * dst,
+        threadgroup  char * shmem,
+        uint3  tgpig,
+        ushort tiisg,
+        ushort sgitg) {
+    const short NSG = FC_mul_mv_nsg;
+
+    const int nb = args.ne00/QK_K;
+
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * NSG + sgitg) * nr0;
+
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
+
+    const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
+
+    device const float * y = (device const float *) (src1 + offset1);
+
+    device const block_tq2_0 * ax[nr0];
+    for (int row = 0; row < nr0; ++row) {
+        const uint64_t offset0 = (first_row + row)*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
+        ax[row] = (device const block_tq2_0 *) ((device char *) src0 + offset0);
+    }
+
+    float sumf[nr0] = {0.f};
+
+    // 8 threads per block, NBLOCK blocks per pass, 2 halves per block per pass
+    constexpr short NBLOCK = 4;
+
+    constexpr short NB = N_SIMDWIDTH/NBLOCK; // threads per block
+
+    const short blk = tiisg / NB;    // 0..NBLOCK-1, block handled by this thread
+    const short htg = tiisg % NB;    // 0..NB-1, thread within block (0..7)
+
+    // byte and y base offsets within the block (32 elements per thread, 4 per byte)
+    device const float4 * yb4 = (device const float4 *)(y + 4*htg + blk*QK_K);
+
+    // hoisted per-byte coefficients (from y) and total y-sum, shared across rows
+    // ref: https://github.com/ggml-org/llama.cpp/pull/26980
+    float4 coef[4];
+
+    for (int ib = blk; ib < nb; ib += NBLOCK) {
+        FOR_UNROLL (short h0 = 0; h0 < 2; ++h0) {
+            const float4 y0 = yb4[ 0 + 32*h0];
+            const float4 y1 = yb4[ 8 + 32*h0];
+            const float4 y2 = yb4[16 + 32*h0];
+            const float4 y3 = yb4[24 + 32*h0];
+
+            float sumy = 0.f;
+            FOR_UNROLL (short j = 0; j < 4; ++j) {
+                coef[j] = float4(
+                        y0[j],
+                        y1[j] - 4.0f*y0[j],
+                        y2[j] - 4.0f*y1[j],
+                        y3[j] - 4.0f*y2[j]);
+
+                sumy += (y0[j] + y1[j]) + (y2[j] + y3[j]);
+            }
+
+            FOR_UNROLL (short row = 0; row < nr0; ++row) {
+                device const block_tq2_0 & xb = ax[row][ib];
+                device const uchar * qs = xb.qs + 4*htg + 32*h0;
+
+                float sum = -sumy;
+                FOR_UNROLL (short j = 0; j < 4; ++j) {
+                    // express the 2-bit field shifts (v>>2, v>>4, v>>6) as float floor ops
+                    const float v = (float)qs[j];
+
+                    const float f0 = v;
+                    const float f1 = floor(v*0.25f);    // v>>2
+                    const float f2 = floor(v*0.0625);   // v>>4
+                    const float f3 = floor(v*0.015625); // v>>6
+
+                    sum += coef[j][0]*f0 + coef[j][1]*f1 + coef[j][2]*f2 + coef[j][3]*f3;
+                }
+
+                sumf[row] += xb.d * sum;
+            }
+        }
+
+        yb4 += QK_K * NBLOCK / 4;
+    }
+
+    device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
+
+    for (int row = 0; row < nr0; ++row) {
+        const float tot = simd_sum(sumf[row]);
+        if (tiisg == 0 && first_row + row < args.ne01) {
+            dst_f32[first_row + row] = tot;
+        }
+    }
+}
+
+[[host_name("kernel_mul_mv_tq2_0_f32")]]
+kernel void kernel_mul_mv_tq2_0_f32(
+        constant ggml_metal_kargs_mul_mv & args,
+        device const char * src0,
+        device const char * src1,
+        device       char * dst,
+        uint3  tgpig[[threadgroup_position_in_grid]],
+        ushort tiisg[[thread_index_in_simdgroup]],
+        ushort sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_tq2_0_f32_impl<N_R0_TQ2_0, constant ggml_metal_kargs_mul_mv &>(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg);
+}
+
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
 kernel void kernel_get_rows_q(
         constant ggml_metal_kargs_get_rows & args,
@@ -9915,6 +10082,38 @@ template [[host_name("kernel_get_rows_iq1_s")]]   kernel get_rows_q_t kernel_get
 template [[host_name("kernel_get_rows_iq1_m")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq1_m,   QK_NL, dequantize_iq1_m>;
 template [[host_name("kernel_get_rows_iq4_nl")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_nl,  2,     dequantize_iq4_nl>;
 template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
+template [[host_name("kernel_get_rows_tq2_0")]]   kernel get_rows_q_t kernel_get_rows_q<block_tq2_0,   QK_NL, dequantize_tq2_0>;
+
+template<typename TS, typename TI, short QK, typename block_q, void (*quantize_func)(device const float *, device block_q &)>
+kernel void kernel_set_rows_q(
+        constant ggml_metal_kargs_set_rows & args,
+        device const  void * src0,
+        device const  void * src1,
+        device       float * dst,
+        uint3                tgpig[[threadgroup_position_in_grid]],
+        uint                 tiitg[[thread_index_in_threadgroup]],
+        uint3                tptg [[threads_per_threadgroup]]) {
+    const int32_t i03 = tgpig.z;
+    const int32_t i02 = tgpig.y;
+
+    const int32_t i12 = i03%args.ne12;
+    const int32_t i11 = i02%args.ne11;
+
+    const int32_t i01 = tgpig.x*tptg.y + tiitg/tptg.x;
+    if (i01 >= args.ne01) {
+        return;
+    }
+
+    const int32_t i10 = i01;
+    const TI      i1  = ((const device TI *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
+
+          device block_q * dst_row = (      device block_q *) ((      device char *) dst  +  i1*args.nb1  + i02*args.nb2  + i03*args.nb3);
+    const device TS      * src_row = (const device TS      *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
+
+    for (int ind = tiitg%tptg.x; ind < args.nk0; ind += tptg.x) {
+        quantize_func(src_row + QK*ind, dst_row[ind]);
+    }
+}
 
 template<typename TS, typename TI, typename block_q, void (*quantize_func)(device const float *, device block_q &)>
 kernel void kernel_set_rows_q32(
@@ -10011,6 +10210,11 @@ template [[host_name("kernel_set_rows_f32_i32_q5_1")]]   kernel set_rows_q32_t k
 template [[host_name("kernel_set_rows_f32_i64_iq4_nl")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_iq4_nl, quantize_iq4_nl>;
 template [[host_name("kernel_set_rows_f32_i32_iq4_nl")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_iq4_nl, quantize_iq4_nl>;
 
+typedef decltype(kernel_set_rows_q<float, int64_t, QK_K, block_tq2_0, quantize_tq2_0>) set_rows_qK_t;
+
+template [[host_name("kernel_set_rows_f32_i64_tq2_0")]]  kernel set_rows_qK_t kernel_set_rows_q<float, int64_t, QK_K, block_tq2_0, quantize_tq2_0>;
+template [[host_name("kernel_set_rows_f32_i32_tq2_0")]]  kernel set_rows_qK_t kernel_set_rows_q<float, int32_t, QK_K, block_tq2_0, quantize_tq2_0>;
+
 kernel void kernel_diag_f32(
         constant ggml_metal_kargs_diag & args,
         device   const char * src0,
@@ -10786,6 +10990,7 @@ template [[host_name("kernel_mul_mm_iq1_s_f32")]]   kernel mul_mm_t kernel_mul_m
 template [[host_name("kernel_mul_mm_iq1_m_f32")]]   kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq1_m,   QK_NL, dequantize_iq1_m,   float,  float4x4,  float, float2x4>;
 template [[host_name("kernel_mul_mm_iq4_nl_f32")]]  kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_nl,  2,     dequantize_iq4_nl,  float,  float4x4,  float, float2x4>;
 template [[host_name("kernel_mul_mm_iq4_xs_f32")]]  kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_xs,  QK_NL, dequantize_iq4_xs,  float,  float4x4,  float, float2x4>;
+template [[host_name("kernel_mul_mm_tq2_0_f32")]]   kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_tq2_0,   QK_NL, dequantize_tq2_0,   float,  float4x4,  float, float2x4>;
 
 template [[host_name("kernel_mul_mm_f32_f16")]]     kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   float4x4,      1,     dequantize_f32,     float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_f16_f16")]]     kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   half4x4,       1,     dequantize_f16,     half,   half4x4,   half, half2x4>;
@@ -10811,6 +11016,7 @@ template [[host_name("kernel_mul_mm_iq1_s_f16")]]   kernel mul_mm_t kernel_mul_m
 template [[host_name("kernel_mul_mm_iq1_m_f16")]]   kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq1_m,   QK_NL, dequantize_iq1_m,   float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_iq4_nl_f16")]]  kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_nl,  2,     dequantize_iq4_nl,  float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_iq4_xs_f16")]]  kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_xs,  QK_NL, dequantize_iq4_xs,  float,  float4x4,  half, half2x4>;
+template [[host_name("kernel_mul_mm_tq2_0_f16")]]   kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_tq2_0,   QK_NL, dequantize_tq2_0,   float,  float4x4,  half, half2x4>;
 
 //
 // indirect matrix-matrix multiplication
@@ -10845,6 +11051,7 @@ template [[host_name("kernel_mul_mm_id_iq1_s_f32")]]   kernel mul_mm_id kernel_m
 template [[host_name("kernel_mul_mm_id_iq1_m_f32")]]   kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq1_m,   QK_NL, dequantize_iq1_m,   float,  float4x4,  float, float2x4>;
 template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]]  kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_nl,  2,     dequantize_iq4_nl,  float,  float4x4,  float, float2x4>;
 template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]]  kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_xs,  QK_NL, dequantize_iq4_xs,  float,  float4x4,  float, float2x4>;
+template [[host_name("kernel_mul_mm_id_tq2_0_f32")]]   kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_tq2_0,   QK_NL, dequantize_tq2_0,   float,  float4x4,  float, float2x4>;
 
 template [[host_name("kernel_mul_mm_id_f32_f16")]]     kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   float4x4,      1,     dequantize_f32,     float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_id_f16_f16")]]     kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   half4x4,       1,     dequantize_f16,     half,   half4x4,   half, half2x4>;
@@ -10870,6 +11077,7 @@ template [[host_name("kernel_mul_mm_id_iq1_s_f16")]]   kernel mul_mm_id kernel_m
 template [[host_name("kernel_mul_mm_id_iq1_m_f16")]]   kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq1_m,   QK_NL, dequantize_iq1_m,   float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_id_iq4_nl_f16")]]  kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_nl,  2,     dequantize_iq4_nl,  float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_id_iq4_xs_f16")]]  kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_xs,  QK_NL, dequantize_iq4_xs,  float,  float4x4,  half, half2x4>;
+template [[host_name("kernel_mul_mm_id_tq2_0_f16")]]   kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_tq2_0,   QK_NL, dequantize_tq2_0,   float,  float4x4,  half, half2x4>;
 
 //
 // matrix-vector multiplication
@@ -11027,6 +11235,7 @@ template [[host_name("kernel_mul_mv_id_iq3_s_f32")]]   kernel kernel_mul_mv_id_t
 template [[host_name("kernel_mul_mv_id_iq2_s_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_s_f32_impl  <N_R0_IQ2_S>>>;
 template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_nl_f32_impl <N_R0_IQ4_NL>>>;
 template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_xs_f32_impl <N_R0_IQ4_XS>>>;
+template [[host_name("kernel_mul_mv_id_tq2_0_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_tq2_0_f32_impl  <N_R0_TQ2_0>>>;
 
 kernel void kernel_pool_2d_max_f32(
         constant    ggml_metal_kargs_pool_2d & args,

ggml/src/ggml-opencl/ggml-opencl.cpp

@@ -4929,8 +4929,13 @@ static bool ggml_opencl_ensure_fa_variant(ggml_backend_opencl_context * backend_
         const int x = (e && e[0]) ? atoi(e) : 0;
         return (x == 8 || x == 16 || x == 32) ? x : 0;   // 0 = per-gen default
     }();
+    // X2E needs 16 to keep per-lane o_acc at 128B (the compiler spills the
+    // kernel-default width); X1E does not spill, but C=16 is still a measured
+    // +28-30% DK128-GQA4 decode win there (X1-85, kv 4096/8192), neutral on
+    // DK64 / GQA1 / quant-KV.
     const int fa_cl_c_gqa4 = fa_cl_c_env ? fa_cl_c_env
-        : (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X2E ? 16 : 0);
+        : (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X2E ||
+           backend_ctx->adreno_gen == ADRENO_GPU_GEN::X1E ? 16 : 0);
     const std::string opts_cl_c_gqa4 = fa_cl_c_gqa4
         ? " -D FA_CL_C=" + std::to_string(fa_cl_c_gqa4) : std::string();
     const std::string fa_cl_c_g8_val = std::to_string(fa_cl_c_gqa4 ? fa_cl_c_gqa4 * 2 : 16);
@@ -7076,6 +7081,19 @@ inline bool enable_adreno_trans_weight(const ggml_backend_opencl_context *backen
     return ((elem_num < 128 * 1024 * 1024) && adreno_kernel && shape_ok);  // max element num: 2**27
 }
 
+inline bool enable_adreno_trans_weight_q5_K(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) {
+    if (!use_adreno_kernels(backend_ctx, tensor)) {
+        return false;
+    }
+
+    const size_t elem_num = ggml_nelements(tensor);
+    const size_t q_img_width = elem_num / 8;
+    const size_t qh_img_width = elem_num / 16;
+
+    return q_img_width <= backend_ctx->image_max_buffer_size &&
+           qh_img_width <= backend_ctx->image_max_buffer_size;
+}
+
 static inline bool use_flat_gemv_for_large_m_q4_K(const ggml_tensor *tensor) {
     // gemv_noshuffle variant perf drops for large M, use flat variant for large M.
     // threshold is well above typical hidden/FFN dims, but below typical vocab sizes.
@@ -9255,7 +9273,7 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
 
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
         cl_kernel kernel = backend_ctx->kernel_convert_block_q5_K;
-        if (use_adreno_kernels(backend_ctx, tensor)) {
+        if (enable_adreno_trans_weight_q5_K(backend_ctx, tensor)) {
             kernel = backend_ctx->kernel_convert_block_q5_K_noshuffle;
         }
 #else
@@ -9290,7 +9308,7 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
 
         tensor->extra = extra;
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
-        if (use_adreno_kernels(backend_ctx, tensor)) {
+        if (enable_adreno_trans_weight_q5_K(backend_ctx, tensor)) {
 
             int M = tensor->ne[1];
             int K = tensor->ne[0];
@@ -10388,7 +10406,7 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
             CL_CHECK(clReleaseMemObject(data_device));
             return;
         }
-        if (use_adreno_kernels(backend_ctx, tensor)) {
+        if (enable_adreno_trans_weight_q5_K(backend_ctx, tensor)) {
             int M = tensor->ne[1];
             int K = tensor->ne[0];
 
@@ -18928,7 +18946,8 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
         }
 
         // q5_K x fp32
-        if (src0t == GGML_TYPE_Q5_K && src1t == GGML_TYPE_F32) {
+        if (src0t == GGML_TYPE_Q5_K && src1t == GGML_TYPE_F32 &&
+            enable_adreno_trans_weight_q5_K(backend_ctx, src0)) {
             ggml_cl_mul_mat_q5_K_f32_adreno(backend, src0, src1, dst);
             return;
         }

ggml/src/ggml-sycl/common.hpp

@@ -61,6 +61,7 @@ void ggml_sycl_host_free(void* ptr);
 extern int g_ggml_sycl_debug;
 extern int g_ggml_sycl_enable_optimize;
 extern int g_ggml_sycl_enable_fusion;
+extern int g_ggml_sycl_enable_esimd;
 extern int g_ggml_sycl_prioritize_dmmv;
 extern int g_ggml_sycl_enable_flash_attention;
 extern int g_ggml_sycl_dev2dev_memcpy;

ggml/src/ggml-sycl/concat.cpp

@@ -184,8 +184,8 @@ void concat_impl_sycl(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
             const size_t size0 = ggml_nbytes(src0);
             const size_t size1 = ggml_nbytes(src1);
 
-            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0).wait()));
-            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d + size0 / type_size, src1_d, size1).wait()));
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0)));
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d + size0 / type_size, src1_d, size1)));
         }
     } else {
         concat_T_sycl_non_cont<T>(stream, (const char *) src0->data, (const char *) src1->data, (char *) dst->data,
@@ -196,6 +196,270 @@ void concat_impl_sycl(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
     }
 }
 
+static void concat_impl_q4_0_sycl(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    const ggml_tensor * src0   = dst->src[0];
+    const ggml_tensor * src1   = dst->src[1];
+    queue_ptr           stream = ctx.stream();
+
+    const int32_t dim = ((int32_t *) dst->op_params)[0];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_Q4_0);
+    GGML_ASSERT(src1->type == GGML_TYPE_Q4_0);
+    GGML_ASSERT(dst->type == GGML_TYPE_Q4_0);
+    GGML_ASSERT(src0->ne[0] % QK4_0 == 0);
+    GGML_ASSERT(src1->ne[0] % QK4_0 == 0);
+    GGML_ASSERT(dst->ne[0] % QK4_0 == 0);
+
+    const int ne00_blk = src0->ne[0] / QK4_0;
+    const int ne0_blk  = dst->ne[0] / QK4_0;
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
+        const block_q4_0 * src0_d = (const block_q4_0 *) src0->data;
+        const block_q4_0 * src1_d = (const block_q4_0 *) src1->data;
+        block_q4_0 * dst_d        = (block_q4_0 *) dst->data;
+        const size_t type_size    = sizeof(block_q4_0);
+
+        if (dim != 3) {
+            for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+                concat_T_sycl<block_q4_0>(
+                    src0_d + i3 * (src0->nb[3] / type_size),
+                    src1_d + i3 * (src1->nb[3] / type_size),
+                    dst_d + i3 * (dst->nb[3] / type_size),
+                    ne00_blk, src0->ne[1], src0->ne[2], ne0_blk,
+                    dst->ne[1], dst->ne[2], dim, stream);
+            }
+        } else {
+            const size_t size0 = ggml_nbytes(src0);
+            const size_t size1 = ggml_nbytes(src1);
+
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0)));
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy((char *) dst_d + size0, src1_d, size1)));
+        }
+    } else {
+        concat_T_sycl_non_cont<block_q4_0>(
+            stream, (const char *) src0->data, (const char *) src1->data,
+            (char *) dst->data,
+            ne00_blk, src0->ne[1], src0->ne[2], src0->ne[3],
+            src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+            src1->ne[0] / QK4_0, src1->ne[1], src1->ne[2], src1->ne[3],
+            src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3],
+            ne0_blk, dst->ne[1], dst->ne[2], dst->ne[3],
+            dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
+    }
+}
+
+static void concat_impl_q4_1_sycl(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    const ggml_tensor * src0   = dst->src[0];
+    const ggml_tensor * src1   = dst->src[1];
+    queue_ptr           stream = ctx.stream();
+
+    const int32_t dim = ((int32_t *) dst->op_params)[0];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_Q4_1);
+    GGML_ASSERT(src1->type == GGML_TYPE_Q4_1);
+    GGML_ASSERT(dst->type == GGML_TYPE_Q4_1);
+    GGML_ASSERT(src0->ne[0] % QK4_1 == 0);
+    GGML_ASSERT(src1->ne[0] % QK4_1 == 0);
+    GGML_ASSERT(dst->ne[0] % QK4_1 == 0);
+
+    const int ne00_blk = src0->ne[0] / QK4_1;
+    const int ne0_blk  = dst->ne[0] / QK4_1;
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
+        const block_q4_1 * src0_d = (const block_q4_1 *) src0->data;
+        const block_q4_1 * src1_d = (const block_q4_1 *) src1->data;
+        block_q4_1 * dst_d        = (block_q4_1 *) dst->data;
+        const size_t type_size    = sizeof(block_q4_1);
+
+        if (dim != 3) {
+            for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+                concat_T_sycl<block_q4_1>(
+                    src0_d + i3 * (src0->nb[3] / type_size),
+                    src1_d + i3 * (src1->nb[3] / type_size),
+                    dst_d + i3 * (dst->nb[3] / type_size),
+                    ne00_blk, src0->ne[1], src0->ne[2], ne0_blk,
+                    dst->ne[1], dst->ne[2], dim, stream);
+            }
+        } else {
+            const size_t size0 = ggml_nbytes(src0);
+            const size_t size1 = ggml_nbytes(src1);
+
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0)));
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy((char *) dst_d + size0, src1_d, size1)));
+        }
+    } else {
+        concat_T_sycl_non_cont<block_q4_1>(
+            stream, (const char *) src0->data, (const char *) src1->data,
+            (char *) dst->data,
+            ne00_blk, src0->ne[1], src0->ne[2], src0->ne[3],
+            src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+            src1->ne[0] / QK4_1, src1->ne[1], src1->ne[2], src1->ne[3],
+            src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3],
+            ne0_blk, dst->ne[1], dst->ne[2], dst->ne[3],
+            dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
+    }
+}
+
+static void concat_impl_q5_0_sycl(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    const ggml_tensor * src0   = dst->src[0];
+    const ggml_tensor * src1   = dst->src[1];
+    queue_ptr           stream = ctx.stream();
+
+    const int32_t dim = ((int32_t *) dst->op_params)[0];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_Q5_0);
+    GGML_ASSERT(src1->type == GGML_TYPE_Q5_0);
+    GGML_ASSERT(dst->type == GGML_TYPE_Q5_0);
+    GGML_ASSERT(src0->ne[0] % QK5_0 == 0);
+    GGML_ASSERT(src1->ne[0] % QK5_0 == 0);
+    GGML_ASSERT(dst->ne[0] % QK5_0 == 0);
+
+    const int ne00_blk = src0->ne[0] / QK5_0;
+    const int ne0_blk  = dst->ne[0] / QK5_0;
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
+        const block_q5_0 * src0_d = (const block_q5_0 *) src0->data;
+        const block_q5_0 * src1_d = (const block_q5_0 *) src1->data;
+        block_q5_0 * dst_d        = (block_q5_0 *) dst->data;
+        const size_t type_size    = sizeof(block_q5_0);
+
+        if (dim != 3) {
+            for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+                concat_T_sycl<block_q5_0>(
+                    src0_d + i3 * (src0->nb[3] / type_size),
+                    src1_d + i3 * (src1->nb[3] / type_size),
+                    dst_d + i3 * (dst->nb[3] / type_size),
+                    ne00_blk, src0->ne[1], src0->ne[2], ne0_blk,
+                    dst->ne[1], dst->ne[2], dim, stream);
+            }
+        } else {
+            const size_t size0 = ggml_nbytes(src0);
+            const size_t size1 = ggml_nbytes(src1);
+
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0)));
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy((char *) dst_d + size0, src1_d, size1)));
+        }
+    } else {
+        concat_T_sycl_non_cont<block_q5_0>(
+            stream, (const char *) src0->data, (const char *) src1->data,
+            (char *) dst->data,
+            ne00_blk, src0->ne[1], src0->ne[2], src0->ne[3],
+            src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+            src1->ne[0] / QK5_0, src1->ne[1], src1->ne[2], src1->ne[3],
+            src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3],
+            ne0_blk, dst->ne[1], dst->ne[2], dst->ne[3],
+            dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
+    }
+}
+
+static void concat_impl_q5_1_sycl(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    const ggml_tensor * src0   = dst->src[0];
+    const ggml_tensor * src1   = dst->src[1];
+    queue_ptr           stream = ctx.stream();
+
+    const int32_t dim = ((int32_t *) dst->op_params)[0];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_Q5_1);
+    GGML_ASSERT(src1->type == GGML_TYPE_Q5_1);
+    GGML_ASSERT(dst->type == GGML_TYPE_Q5_1);
+    GGML_ASSERT(src0->ne[0] % QK5_1 == 0);
+    GGML_ASSERT(src1->ne[0] % QK5_1 == 0);
+    GGML_ASSERT(dst->ne[0] % QK5_1 == 0);
+
+    const int ne00_blk = src0->ne[0] / QK5_1;
+    const int ne0_blk  = dst->ne[0] / QK5_1;
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
+        const block_q5_1 * src0_d = (const block_q5_1 *) src0->data;
+        const block_q5_1 * src1_d = (const block_q5_1 *) src1->data;
+        block_q5_1 * dst_d        = (block_q5_1 *) dst->data;
+        const size_t type_size    = sizeof(block_q5_1);
+
+        if (dim != 3) {
+            for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+                concat_T_sycl<block_q5_1>(
+                    src0_d + i3 * (src0->nb[3] / type_size),
+                    src1_d + i3 * (src1->nb[3] / type_size),
+                    dst_d + i3 * (dst->nb[3] / type_size),
+                    ne00_blk, src0->ne[1], src0->ne[2], ne0_blk,
+                    dst->ne[1], dst->ne[2], dim, stream);
+            }
+        } else {
+            const size_t size0 = ggml_nbytes(src0);
+            const size_t size1 = ggml_nbytes(src1);
+
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0)));
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy((char *) dst_d + size0, src1_d, size1)));
+        }
+    } else {
+        concat_T_sycl_non_cont<block_q5_1>(
+            stream, (const char *) src0->data, (const char *) src1->data,
+            (char *) dst->data,
+            ne00_blk, src0->ne[1], src0->ne[2], src0->ne[3],
+            src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+            src1->ne[0] / QK5_1, src1->ne[1], src1->ne[2], src1->ne[3],
+            src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3],
+            ne0_blk, dst->ne[1], dst->ne[2], dst->ne[3],
+            dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
+    }
+}
+
+static void concat_impl_q8_0_sycl(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    const ggml_tensor * src0   = dst->src[0];
+    const ggml_tensor * src1   = dst->src[1];
+    queue_ptr           stream = ctx.stream();
+
+    const int32_t dim = ((int32_t *) dst->op_params)[0];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_Q8_0);
+    GGML_ASSERT(src1->type == GGML_TYPE_Q8_0);
+    GGML_ASSERT(dst->type == GGML_TYPE_Q8_0);
+    GGML_ASSERT(src0->ne[0] % QK8_0 == 0);
+    GGML_ASSERT(src1->ne[0] % QK8_0 == 0);
+    GGML_ASSERT(dst->ne[0] % QK8_0 == 0);
+
+    const int ne00_blk = src0->ne[0] / QK8_0;
+    const int ne0_blk  = dst->ne[0] / QK8_0;
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
+        const block_q8_0 * src0_d = (const block_q8_0 *) src0->data;
+        const block_q8_0 * src1_d = (const block_q8_0 *) src1->data;
+        block_q8_0 * dst_d        = (block_q8_0 *) dst->data;
+        const size_t type_size    = sizeof(block_q8_0);
+
+        if (dim != 3) {
+            for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+                concat_T_sycl<block_q8_0>(
+                    src0_d + i3 * (src0->nb[3] / type_size),
+                    src1_d + i3 * (src1->nb[3] / type_size),
+                    dst_d + i3 * (dst->nb[3] / type_size),
+                    ne00_blk, src0->ne[1], src0->ne[2], ne0_blk,
+                    dst->ne[1], dst->ne[2], dim, stream);
+            }
+        } else {
+            const size_t size0 = ggml_nbytes(src0);
+            const size_t size1 = ggml_nbytes(src1);
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0)));
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy((char *) dst_d + size0, src1_d, size1)));
+        }
+    } else {
+        concat_T_sycl_non_cont<block_q8_0>(
+            stream, (const char *) src0->data, (const char *) src1->data,
+            (char *) dst->data,
+            ne00_blk, src0->ne[1], src0->ne[2], src0->ne[3],
+            src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+            src1->ne[0] / QK8_0, src1->ne[1], src1->ne[2], src1->ne[3],
+            src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3],
+            ne0_blk, dst->ne[1], dst->ne[2], dst->ne[3],
+            dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
+    }
+}
+
 void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
 
     switch (dst->type) {
@@ -222,6 +486,21 @@ void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
     case GGML_TYPE_I8:
         concat_impl_sycl<int8_t>(ctx, dst);
         break;
+    case GGML_TYPE_Q4_0:
+        concat_impl_q4_0_sycl(ctx, dst);
+        break;
+    case GGML_TYPE_Q4_1:
+        concat_impl_q4_1_sycl(ctx, dst);
+        break;
+    case GGML_TYPE_Q5_0:
+        concat_impl_q5_0_sycl(ctx, dst);
+        break;
+    case GGML_TYPE_Q5_1:
+        concat_impl_q5_1_sycl(ctx, dst);
+        break;
+    case GGML_TYPE_Q8_0:
+        concat_impl_q8_0_sycl(ctx, dst);
+        break;
     default:
         fprintf(stderr, "%s: unsupported types: dst: %s\n", __func__, ggml_type_name(dst->type));
         GGML_ASSERT(false);

ggml/src/ggml-sycl/dmmv.cpp

@@ -8,6 +8,9 @@
         #include <sycl/ext/oneapi/bfloat16.hpp>
         #define GGML_SYCL_DMMV_HAS_BF16
     #endif
+    #include <sycl/ext/intel/esimd.hpp>
+    #include "esimd.hpp"
+    #define GGML_SYCL_DMMV_HAS_ESIMD
 #endif
 
 static void convert_f16(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
@@ -1864,6 +1867,113 @@ static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y,
         });
 }
 
+#ifdef GGML_SYCL_DMMV_HAS_ESIMD
+using ggml_sycl_esimd::GGML_SYCL_DMMV_ESIMD_WG_SIZE;
+
+// generic reordered dequantize-matvec: each work-group owns a pair of
+// consecutive output rows and updates one 32-wide accumulator per row
+template <ggml_type T>
+ESIMD_INLINE void dequantize_mul_mat_vec_reorder_esimd(
+        const void * vx, const float * y, float * dst,
+        const int ncols, const int nrows,
+        sycl::local_accessor<float, 1> lmem,
+        const sycl::nd_item<1> & it) {
+    using namespace sycl::ext::intel::esimd;
+    using traits = ggml_sycl_esimd::esimd_reorder_q_traits<T>;
+
+    const int    num_blocks_per_row = ncols / QK_K;
+    const size_t nb = (size_t) nrows * num_blocks_per_row;
+    const auto   ps = traits::make_ptrs(vx, nb);
+
+    const int  tid      = it.get_local_id(0);
+    const int  row_pair = it.get_group(0);
+    const int  row0     = row_pair * 2; // two consecutive output rows
+    const bool has_row1 = row0 + 1 < nrows;
+
+    // one 32-wide accumulator per output row (small footprint, no spill)
+    simd<float, 32> acc0 = 0.0f;
+    simd<float, 32> acc1 = 0.0f;
+
+    for (int ib = tid; ib < num_blocks_per_row; ib += GGML_SYCL_DMMV_ESIMD_WG_SIZE) {
+        simd<float, 256> y_vec = block_load<float, 256>(y + (size_t) ib * QK_K);
+
+        const size_t bi0 = (size_t) (row0 + 0) * num_blocks_per_row + ib;
+        const size_t bi1 = (size_t) (row0 + 1) * num_blocks_per_row + ib;
+
+        traits::mac_pair(ps, bi0, ps, bi1, has_row1, y_vec, acc0, acc1);
+    }
+
+    lmem[tid * 2 + 0] = reduce<float>(acc0, std::plus<>{});
+    lmem[tid * 2 + 1] = reduce<float>(acc1, std::plus<>{});
+    it.barrier(sycl::access::fence_space::local_space);
+
+    if (tid == 0) {
+        float sum0 = 0.0f;
+        float sum1 = 0.0f;
+        for (int p = 0; p < GGML_SYCL_DMMV_ESIMD_WG_SIZE; ++p) {
+            sum0 += lmem[p * 2 + 0];
+            sum1 += lmem[p * 2 + 1];
+        }
+        dst[row0 + 0] = sum0;
+        if (has_row1) {
+            dst[row0 + 1] = sum1;
+        }
+    }
+}
+
+static void dequantize_mul_mat_vec_q3_K_sycl_reorder_esimd(const void *vx, const float *y,
+                                                           float *dst, const int ncols,
+                                                           const int nrows,
+                                                           dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int workgroups = (nrows + 1) / 2;
+    stream->submit([&](sycl::handler &h) {
+        sycl::local_accessor<float, 1> lmem(sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE * 2), h);
+        h.parallel_for(
+            sycl::nd_range<1>(sycl::range<1>((size_t)workgroups * GGML_SYCL_DMMV_ESIMD_WG_SIZE), sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE)),
+            [=](sycl::nd_item<1> it) [[intel::sycl_explicit_simd]] {
+                dequantize_mul_mat_vec_reorder_esimd<GGML_TYPE_Q3_K>(
+                    vx, y, dst, ncols, nrows, lmem, it);
+            });
+    });
+}
+
+static void dequantize_mul_mat_vec_q4_K_sycl_reorder_esimd(const void *vx, const float *y,
+                                                           float *dst, const int ncols,
+                                                           const int nrows,
+                                                           dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int workgroups = (nrows + 1) / 2;
+    stream->submit([&](sycl::handler &h) {
+        sycl::local_accessor<float, 1> lmem(sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE * 2), h);
+        h.parallel_for(
+            sycl::nd_range<1>(sycl::range<1>((size_t)workgroups * GGML_SYCL_DMMV_ESIMD_WG_SIZE), sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE)),
+            [=](sycl::nd_item<1> it) [[intel::sycl_explicit_simd]] {
+                dequantize_mul_mat_vec_reorder_esimd<GGML_TYPE_Q4_K>(
+                    vx, y, dst, ncols, nrows, lmem, it);
+            });
+    });
+}
+
+static void dequantize_mul_mat_vec_q6_K_sycl_reorder_esimd(const void *vx, const float *y,
+                                                           float *dst, const int ncols,
+                                                           const int nrows,
+                                                           dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int workgroups = (nrows + 1) / 2;
+    stream->submit([&](sycl::handler &h) {
+        sycl::local_accessor<float, 1> lmem(sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE * 2), h);
+        h.parallel_for(
+            sycl::nd_range<1>(sycl::range<1>((size_t)workgroups * GGML_SYCL_DMMV_ESIMD_WG_SIZE), sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE)),
+            [=](sycl::nd_item<1> it) [[intel::sycl_explicit_simd]] {
+                dequantize_mul_mat_vec_reorder_esimd<GGML_TYPE_Q6_K>(
+                    vx, y, dst, ncols, nrows, lmem, it);
+            });
+    });
+}
+
+#endif // GGML_SYCL_DMMV_HAS_ESIMD
+
 static void dequantize_mul_mat_vec_q4_K_sycl_reorder(const void *vx, const float *y,
                                                      float *dst, const int ncols,
                                                      const int nrows,
@@ -1992,7 +2102,15 @@ void ggml_sycl_op_dequantize_mul_mat_vec(
         case GGML_TYPE_Q3_K:
             if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
                 ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
-                dequantize_mul_mat_vec_q3_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+#ifdef GGML_SYCL_DMMV_HAS_ESIMD
+                if (g_ggml_sycl_enable_esimd) {
+                    dequantize_mul_mat_vec_q3_K_sycl_reorder_esimd(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+                }
+                else
+#endif
+                {
+                    dequantize_mul_mat_vec_q3_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+                }
             } else {
                 dequantize_mul_mat_vec_q3_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
             }
@@ -2000,7 +2118,15 @@ void ggml_sycl_op_dequantize_mul_mat_vec(
         case GGML_TYPE_Q4_K:
             if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
                 ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
-                dequantize_mul_mat_vec_q4_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+#ifdef GGML_SYCL_DMMV_HAS_ESIMD
+                if (g_ggml_sycl_enable_esimd) {
+                    dequantize_mul_mat_vec_q4_K_sycl_reorder_esimd(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+                }
+                else
+#endif
+                {
+                    dequantize_mul_mat_vec_q4_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+                }
             } else {
                 dequantize_mul_mat_vec_q4_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
             }
@@ -2016,7 +2142,15 @@ void ggml_sycl_op_dequantize_mul_mat_vec(
         case GGML_TYPE_Q6_K:
             if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
                 ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
-                dequantize_mul_mat_vec_q6_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+#ifdef GGML_SYCL_DMMV_HAS_ESIMD
+                if (g_ggml_sycl_enable_esimd) {
+                    dequantize_mul_mat_vec_q6_K_sycl_reorder_esimd(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+                }
+                else
+#endif
+                {
+                    dequantize_mul_mat_vec_q6_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+                }
             } else {
                 dequantize_mul_mat_vec_q6_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
             }

ggml/src/ggml-sycl/element_wise.cpp

@@ -448,6 +448,47 @@ static void unary_gated_op_generic_kernel(
     }
 }
 
+// Fused UNARY + MUL. Unlike the gated ops above, `x` and `g` are separate tensors of the
+// same shape; `o0`/`o1` are their row strides in elements, so a half-view needs no repack.
+// `dst` is contiguous and indexed flat. Math is done in f32, as the CPU and CUDA references do.
+template<typename T, typename F>
+static void unary_mul_flat_kernel(const T * x, const T * g, T * dst, const int64_t k, const sycl::nd_item<1> &item_ct1, F op) {
+    SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
+        dst[i] = (T) (op((float) x[i]) * (float) g[i]);
+    }
+}
+
+template<typename T, typename F>
+static void unary_mul_strided_kernel(const T * x, const T * g, T * dst, const int64_t k, const sycl::uint3 n_fd, const int64_t o0, const int64_t o1, const sycl::nd_item<1> &item_ct1, F op) {
+    SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
+        const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd);
+        const int64_t j0 = rc.x() * o0 + rc.y();
+        const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y();
+        dst[i] = (T) (op((float) x[j0]) * (float) g[j1]);
+    }
+}
+
+template<typename T, typename F>
+static void unary_mul_sycl(const T * x, const T * g, T * dst, const int64_t k, const int64_t n, const int64_t o0, const int64_t o1, queue_ptr main_stream, F op) {
+    const size_t            num_blocks = ceil_div((size_t) k, (size_t) SYCL_GLU_BLOCK_SIZE);
+    const sycl::nd_range<1> range(num_blocks * sycl::range<1>(SYCL_GLU_BLOCK_SIZE), sycl::range<1>(SYCL_GLU_BLOCK_SIZE));
+
+    // o0 == o1 == n makes (i/n)*o0 + (i%n) == i, so the strided kernel degenerates to the flat one
+    if (o0 == n && o1 == n) {
+        main_stream->parallel_for(range, [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+            unary_mul_flat_kernel(x, g, dst, k, item_ct1, op);
+        });
+        return;
+    }
+
+    // 32-bit fastdiv, exact only below 2^31; ggml_sycl_can_fuse() already declined past that
+    GGML_ASSERT(k < ((int64_t) 1 << 31));
+    const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
+    main_stream->parallel_for(range, [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+        unary_mul_strided_kernel(x, g, dst, k, n_fd, o0, o1, item_ct1, op);
+    });
+}
+
 namespace ggml_sycl_detail {
 static void acc_f32_sycl(const char *x, const char *y, float *dst,
                          const int64_t n_elements,
@@ -991,6 +1032,52 @@ static inline void ggml_sycl_op_swiglu(ggml_backend_sycl_context & ctx, ggml_ten
     });
 }
 
+// dst = op(unary_node->src[0]) * other, written straight to the MUL output, saving the
+// standalone unary launch. Preconditions come from ggml_sycl_can_fuse(); re-asserted here.
+void ggml_sycl_op_unary_mul_fused(ggml_backend_sycl_context & ctx, ggml_tensor * unary_node, ggml_tensor * mul_node) {
+    scope_op_debug_print scope_dbg_print(__func__, mul_node, /*num_src=*/2);
+
+    const ggml_tensor * x = unary_node->src[0];
+    const ggml_tensor * g = (mul_node->src[0] == unary_node) ? mul_node->src[1] : mul_node->src[0];
+
+    // g is picked by elimination; ggml_can_fuse()'s single-use rule rules out MUL(unary, unary)
+    GGML_ASSERT(g != unary_node);
+    GGML_ASSERT(x->type == g->type && x->type == mul_node->type);
+    GGML_ASSERT(ggml_are_same_shape(x, g) && ggml_are_same_shape(x, mul_node));
+    GGML_ASSERT(ggml_is_contiguous_1(x) && ggml_is_contiguous_1(g));
+    // dst is indexed flat
+    GGML_ASSERT(ggml_is_contiguous(mul_node));
+
+    queue_ptr main_stream = ctx.stream();
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+
+    const int64_t k = ggml_nelements(mul_node);
+    const int64_t n = mul_node->ne[0];
+
+    const auto dispatch_type = [&](auto op) {
+        switch (mul_node->type) {
+            case GGML_TYPE_F32:
+                unary_mul_sycl((const float *) x->data, (const float *) g->data, (float *) mul_node->data,
+                               k, n, x->nb[1] / sizeof(float), g->nb[1] / sizeof(float), main_stream, op);
+                break;
+            case GGML_TYPE_F16:
+                unary_mul_sycl((const sycl::half *) x->data, (const sycl::half *) g->data, (sycl::half *) mul_node->data,
+                               k, n, x->nb[1] / sizeof(sycl::half), g->nb[1] / sizeof(sycl::half), main_stream, op);
+                break;
+            default:
+                GGML_ABORT("fused unary+mul: unsupported type %s", ggml_type_name(mul_node->type));
+        }
+    };
+
+    switch (ggml_get_unary_op(unary_node)) {
+        case GGML_UNARY_OP_SILU:     dispatch_type([](float v) { return op_silu(v); });     break;
+        case GGML_UNARY_OP_SIGMOID:  dispatch_type([](float v) { return op_sigmoid(v); });  break;
+        case GGML_UNARY_OP_SOFTPLUS: dispatch_type([](float v) { return op_softplus(v); }); break;
+        default:
+            GGML_ABORT("fused unary+mul: unsupported unary op %s", ggml_unary_op_name(ggml_get_unary_op(unary_node)));
+    }
+}
+
 __dpct_inline__ float ggml_sycl_op_swiglu_oai_single(float x, float g, float alpha = 1.702f, float limit = 7.0f) {
     x = sycl::fmin(x, limit);
     g = sycl::fmax(sycl::fmin(g, limit), -limit);

ggml/src/ggml-sycl/element_wise.hpp

@@ -95,4 +95,7 @@ void ggml_sycl_trunc(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
 void ggml_sycl_arange(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
+// fused UNARY(silu|sigmoid|softplus) + MUL; see ggml_sycl_can_fuse() for the accepted shapes
+void ggml_sycl_op_unary_mul_fused(ggml_backend_sycl_context & ctx, ggml_tensor * unary_node, ggml_tensor * mul_node);
+
 #endif // GGML_SYCL_ELEMENTWISE_HPP

ggml/src/ggml-sycl/esimd.hpp

@@ -0,0 +1,392 @@
+//
+// MIT license
+// Copyright (C) 2026 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_ESIMD_HPP
+#define GGML_SYCL_ESIMD_HPP
+
+#include <sycl/ext/intel/esimd.hpp>
+
+#include "common.hpp"
+
+namespace ggml_sycl_esimd {
+
+constexpr int GGML_SYCL_DMMV_ESIMD_WG_SIZE = 4;
+
+//
+// Shared ESIMD building blocks for the reordered K-quant dequantize-matvec
+// kernels.
+//
+// The reordered K-quant ESIMD matvec kernels share one skeleton: per super-block,
+// load a 256-float activation slice, load one weight block, dequantize it into 8
+// chunks of 32 and MAC each chunk against the matching activation slice, then
+// reduce and run a lane-0 epilogue.
+//
+// Each K-quant kernel emits exactly 8 chunks of 32 mapping to activation slices
+// 0..7, so the per-block work is captured by esimd_reorder_q_traits<T>::mac_pair,
+// which dequantizes two weight blocks and MACs both against a shared activation
+// vector with the two FMA chains interleaved (co-scheduled to hide FMA latency).
+// The "pair" is the (row0,row1) row pair owned by one work-group, so the
+// layout+dequant is written once per quant type here.
+//
+
+template <ggml_type T> struct esimd_reorder_q_traits;
+
+// build a 32-lane vector whose low 16 lanes are `lo` and high 16 are `hi`
+// (a super-chunk splits into two 16-wide halves with distinct scale/min codes).
+static ESIMD_INLINE sycl::ext::intel::esimd::simd<float, 32> splat_lo_hi(float lo, float hi) {
+    using namespace sycl::ext::intel::esimd;
+    simd<float, 32> v;
+    v.select<16, 1>(0)  = lo;
+    v.select<16, 1>(16) = hi;
+    return v;
+}
+
+// unpack one block of Q4_K/Q5_K scale/min codes (get_scale_min_k4 layout) into 8
+// float scales (dall * sc) and 8 float mins (-dmin * m); the min carries the
+// negation so the dequant epilogue adds.
+static ESIMD_INLINE void unpack_scale_min_k4(
+        sycl::ext::intel::esimd::simd<uint8_t, 12> scales, float dall, float dmin,
+        sycl::ext::intel::esimd::simd<float, 8> & scale_f,
+        sycl::ext::intel::esimd::simd<float, 8> & min_f) {
+    using namespace sycl::ext::intel::esimd;
+    simd<uint8_t, 8> sc = 0;
+    simd<uint8_t, 8> m  = 0;
+    simd<uint8_t, 4> scale_lo = scales.select<4, 1>(0);
+    simd<uint8_t, 4> min_lo   = scales.select<4, 1>(4);
+    simd<uint8_t, 4> hi_bits  = scales.select<4, 1>(8);
+    sc.select<4, 1>(0) = scale_lo & simd<uint8_t, 4>(0x3F);
+    sc.select<4, 1>(4) = (hi_bits & simd<uint8_t, 4>(0x0F)) |
+                         ((scale_lo >> simd<uint8_t, 4>(6)) << simd<uint8_t, 4>(4));
+    m.select<4, 1>(0)  = min_lo & simd<uint8_t, 4>(0x3F);
+    m.select<4, 1>(4)  = (hi_bits >> simd<uint8_t, 4>(4)) |
+                         ((min_lo >> simd<uint8_t, 4>(6)) << simd<uint8_t, 4>(4));
+    scale_f = convert<float>(sc) * dall;
+    min_f   = convert<float>(m) * (-dmin);
+}
+
+// ---------------------------------------------------------------------------
+// Q3_K, SOA reorder layout produced by reorder_qw_q3_k:
+//   [qs: nb*(QK_K/4)] [hmask: nb*(QK_K/8)] [scales: nb*12] [d: nb*sizeof(half)]
+// with nb = nrows*num_blocks_per_row. Single super-block scale d, no dmin.
+//
+// 3 bits per weight: 2 low bits in qs, 1 high bit in hmask. The 8 output chunks
+// of 32 (matching dequantize_row_q3_K) map to super-chunk s (0..7): byte base
+// 32*(s/4) into the 64-byte qs array, bit shift 2*(s%4); the low 16 lanes use
+// scale code 2s, the high 16 use 2s+1. hmask is a 32-byte array (like Q5_K's
+// qh) where chunk s uses bit s of the same 32 bytes, but INVERTED: the value is
+// (q & 3) - (hmask_bit_set ? 0 : 4), i.e. (q & 3) + 4*bit - 4.
+//
+// The 16 6-bit scale codes are packed into 12 bytes (get_scale_min layout for
+// Q3_K): low nibbles from bytes 0..7, high 2 bits from bytes 8..11 shifted by
+// 0/2/4/6; the dequant scale is d * (code - 32).
+// ---------------------------------------------------------------------------
+template <> struct esimd_reorder_q_traits<GGML_TYPE_Q3_K> {
+    struct ptrs {
+        const uint8_t *    qs;
+        const uint8_t *    hmask;
+        const uint8_t *    scales;
+        const sycl::half * d;
+    };
+
+    static ESIMD_INLINE ptrs make_ptrs(const void * vx, size_t nb) {
+        const uint8_t * qs     = (const uint8_t *) vx;
+        const uint8_t * hmask  = qs + nb * (QK_K / 4);
+        const uint8_t * scales = hmask + nb * (QK_K / 8);
+        const sycl::half * d   = (const sycl::half *) (scales + nb * 12);
+        return { qs, hmask, scales, d };
+    }
+
+    // unpack the 12 packed bytes into 16 6-bit scale codes (dequantize_row_q3_K
+    // aux layout), returned as float scale = d * (code - 32).
+    // done with wide (8/16-lane) ops rather than four 4-lane groups.
+    static ESIMD_INLINE sycl::ext::intel::esimd::simd<float, 16> unpack_scales(
+            sycl::ext::intel::esimd::simd<uint8_t, 12> in, float d) {
+        using namespace sycl::ext::intel::esimd;
+
+        // low 6-bit part: codes 0..7 = low nibble of bytes 0..7,
+        //                 codes 8..15 = high nibble of bytes 0..7
+        simd<uint8_t, 8>  lo8 = in.select<8, 1>(0);
+        simd<uint8_t, 16> code;
+        code.select<8, 1>(0) = lo8 & simd<uint8_t, 8>(0x0F);
+        code.select<8, 1>(8) = lo8 >> simd<uint8_t, 8>(4);
+
+        // high 2-bit part: bytes 8..11 replicated 4x, group g (0..3) shifted 2*g
+        simd<uint8_t, 16> hib;
+        hib.select<4, 1>(0)  = in.select<4, 1>(8);
+        hib.select<4, 1>(4)  = in.select<4, 1>(8);
+        hib.select<4, 1>(8)  = in.select<4, 1>(8);
+        hib.select<4, 1>(12) = in.select<4, 1>(8);
+        simd<uint8_t, 16> hshift;
+        hshift.select<4, 1>(0)  = 0;
+        hshift.select<4, 1>(4)  = 2;
+        hshift.select<4, 1>(8)  = 4;
+        hshift.select<4, 1>(12) = 6;
+        hib = (hib >> hshift) & simd<uint8_t, 16>(0x03);
+
+        code = code | (hib << simd<uint8_t, 16>(4));
+        return (convert<float>(code) - 32.0f) * d;
+    }
+
+    static ESIMD_INLINE void mac_pair(
+            const ptrs & pa, size_t bia,
+            const ptrs & pb, size_t bib, bool has_b,
+            sycl::ext::intel::esimd::simd<float, 256> & y_vec,
+            sycl::ext::intel::esimd::simd<float, 32> & acc_a,
+            sycl::ext::intel::esimd::simd<float, 32> & acc_b) {
+        using namespace sycl::ext::intel::esimd;
+
+        simd<uint8_t, 64> qs_a     = block_load<uint8_t, 64>(pa.qs + bia * (QK_K / 4));
+        simd<uint8_t, 64> qs_b     = 0;
+        simd<uint8_t, 32> hmask_a  = block_load<uint8_t, 32>(pa.hmask + bia * (QK_K / 8));
+        simd<uint8_t, 32> hmask_b  = 0;
+        simd<uint8_t, 12> scales_a = block_load<uint8_t, 12>(pa.scales + bia * 12);
+        simd<uint8_t, 12> scales_b = 0;
+
+        const float d_a = (float) pa.d[bia];
+        float d_b = 0.0f;
+        if (has_b) {
+            qs_b     = block_load<uint8_t, 64>(pb.qs + bib * (QK_K / 4));
+            hmask_b  = block_load<uint8_t, 32>(pb.hmask + bib * (QK_K / 8));
+            scales_b = block_load<uint8_t, 12>(pb.scales + bib * 12);
+            d_b = (float) pb.d[bib];
+        }
+
+        simd<float, 16> scale_f_a = unpack_scales(scales_a, d_a);
+        simd<float, 16> scale_f_b = unpack_scales(scales_b, d_b);
+
+#pragma unroll
+        for (int s = 0; s < 8; ++s) {
+            const int     byte_base = 32 * (s / 4);
+            const uint8_t shift     = (uint8_t) (2 * (s % 4));
+            simd<float, 32> y_s = y_vec.select<32, 1>(s * 32);
+
+            // 2 low bits from qs, high bit from hmask (bit s of the same 32 bytes);
+            // value = (q & 3) + 4*bit - 4  (inverted hmask: subtract 4 when bit clear).
+            // merge in the integer domain: q3 = (q & 3) | (bit << 2) in {0..7},
+            // then a single convert + subtract yields q3 - 4 (one convert, not two)
+            simd<uint16_t, 32> q3_a = convert<uint16_t>(
+                    (qs_a.select<32, 1>(byte_base) >> shift) & simd<uint8_t, 32>(3));
+            q3_a |= convert<uint16_t>(
+                    ((hmask_a >> simd<uint8_t, 32>((uint8_t) s)) & simd<uint8_t, 32>(1)) << simd<uint8_t, 32>(2));
+            simd<uint16_t, 32> q3_b = convert<uint16_t>(
+                    (qs_b.select<32, 1>(byte_base) >> shift) & simd<uint8_t, 32>(3));
+            q3_b |= convert<uint16_t>(
+                    ((hmask_b >> simd<uint8_t, 32>((uint8_t) s)) & simd<uint8_t, 32>(1)) << simd<uint8_t, 32>(2));
+
+            simd<float, 32> qf_a = convert<float>(q3_a) - 4.0f;
+            simd<float, 32> qf_b = convert<float>(q3_b) - 4.0f;
+
+            const float scale_a_lo = scale_f_a[2 * s + 0];
+            const float scale_a_hi = scale_f_a[2 * s + 1];
+            const float scale_b_lo = scale_f_b[2 * s + 0];
+            const float scale_b_hi = scale_f_b[2 * s + 1];
+
+            simd<float, 32> scale_vec_a = splat_lo_hi(scale_a_lo, scale_a_hi);
+            simd<float, 32> scale_vec_b = splat_lo_hi(scale_b_lo, scale_b_hi);
+
+            simd<float, 32> deq_a = qf_a * scale_vec_a;
+            simd<float, 32> deq_b = qf_b * scale_vec_b;
+
+            acc_a += y_s * deq_a;
+            acc_b += y_s * deq_b;
+        }
+    }
+};
+
+// ---------------------------------------------------------------------------
+// Q4_K, SOA reorder layout produced by reorder_qw_q4_k:
+//   [qs: nb*(QK_K/2)] [scales: nb*K_SCALE_SIZE] [dm: nb*sizeof(half2)]
+// with nb = nrows*num_blocks_per_row.
+// ---------------------------------------------------------------------------
+template <> struct esimd_reorder_q_traits<GGML_TYPE_Q4_K> {
+    struct ptrs {
+        const uint8_t *    qs;
+        const uint8_t *    scales;
+        const sycl::half * dm;
+    };
+
+    static ESIMD_INLINE ptrs make_ptrs(const void * vx, size_t nb) {
+        const uint8_t * qs     = (const uint8_t *) vx;
+        const uint8_t * scales = qs + nb * (QK_K / 2);
+        const sycl::half * dm  = (const sycl::half *) (scales + nb * K_SCALE_SIZE);
+        return { qs, scales, dm };
+    }
+
+    static ESIMD_INLINE void mac_pair(
+            const ptrs & pa, size_t bia,
+            const ptrs & pb, size_t bib, bool has_b,
+            sycl::ext::intel::esimd::simd<float, 256> & y_vec,
+            sycl::ext::intel::esimd::simd<float, 32> & acc_a,
+            sycl::ext::intel::esimd::simd<float, 32> & acc_b) {
+        using namespace sycl::ext::intel::esimd;
+
+        simd<uint8_t, 128> qs_a     = block_load<uint8_t, 128>(pa.qs + bia * (QK_K / 2));
+        simd<uint8_t, 128> qs_b     = 0;
+        simd<uint8_t, 12>  scales_a = block_load<uint8_t, 12>(pa.scales + bia * K_SCALE_SIZE);
+        simd<uint8_t, 12>  scales_b = 0;
+
+        const float dall_a = (float) pa.dm[bia * 2 + 0];
+        const float dmin_a = (float) pa.dm[bia * 2 + 1];
+        float dall_b = 0.0f;
+        float dmin_b = 0.0f;
+        if (has_b) {
+            qs_b     = block_load<uint8_t, 128>(pb.qs + bib * (QK_K / 2));
+            scales_b = block_load<uint8_t, 12>(pb.scales + bib * K_SCALE_SIZE);
+            dall_b = (float) pb.dm[bib * 2 + 0];
+            dmin_b = (float) pb.dm[bib * 2 + 1];
+        }
+
+        simd<float, 8> scale_f_a, min_f_a, scale_f_b, min_f_b;
+        unpack_scale_min_k4(scales_a, dall_a, dmin_a, scale_f_a, min_f_a);
+        unpack_scale_min_k4(scales_b, dall_b, dmin_b, scale_f_b, min_f_b);
+
+        simd<uint8_t, 128> qs_lo_a = qs_a & simd<uint8_t, 128>(0x0F);
+        simd<uint8_t, 128> qs_hi_a = qs_a >> simd<uint8_t, 128>(4);
+        simd<uint8_t, 128> qs_lo_b = qs_b & simd<uint8_t, 128>(0x0F);
+        simd<uint8_t, 128> qs_hi_b = qs_b >> simd<uint8_t, 128>(4);
+
+#pragma unroll
+        for (int sb = 0; sb < 8; sb += 2) {
+            const int q_offset = sb * 16;
+            simd<float, 32> y_lo = y_vec.select<32, 1>(sb * 32);
+            simd<float, 32> y_hi = y_vec.select<32, 1>((sb + 1) * 32);
+
+            const float scale_a_lo = scale_f_a[sb];
+            const float scale_a_hi = scale_f_a[sb + 1];
+            const float min_a_lo   = min_f_a[sb];
+            const float min_a_hi   = min_f_a[sb + 1];
+            const float scale_b_lo = scale_f_b[sb];
+            const float scale_b_hi = scale_f_b[sb + 1];
+            const float min_b_lo   = min_f_b[sb];
+            const float min_b_hi   = min_f_b[sb + 1];
+
+            simd<uint8_t, 32> qa_lo = qs_lo_a.select<32, 1>(q_offset);
+            simd<uint8_t, 32> qa_hi = qs_hi_a.select<32, 1>(q_offset);
+            simd<uint8_t, 32> qb_lo = qs_lo_b.select<32, 1>(q_offset);
+            simd<uint8_t, 32> qb_hi = qs_hi_b.select<32, 1>(q_offset);
+
+            simd<float, 32> deq_a_lo = convert<float>(qa_lo) * scale_a_lo + min_a_lo;
+            simd<float, 32> deq_a_hi = convert<float>(qa_hi) * scale_a_hi + min_a_hi;
+            simd<float, 32> deq_b_lo = convert<float>(qb_lo) * scale_b_lo + min_b_lo;
+            simd<float, 32> deq_b_hi = convert<float>(qb_hi) * scale_b_hi + min_b_hi;
+
+            acc_a += y_lo * deq_a_lo;
+            acc_b += y_lo * deq_b_lo;
+            acc_a += y_hi * deq_a_hi;
+            acc_b += y_hi * deq_b_hi;
+        }
+    }
+};
+
+// ---------------------------------------------------------------------------
+// Q6_K, SOA reorder layout:
+//   [ql: nb*(QK_K/2)] [qh: nb*(QK_K/4)] [scales(int8): nb*(QK_K/16)] [d: nb*half]
+// ---------------------------------------------------------------------------
+template <> struct esimd_reorder_q_traits<GGML_TYPE_Q6_K> {
+    struct ptrs {
+        const uint8_t *    ql;
+        const uint8_t *    qh;
+        const int8_t *     scales;
+        const sycl::half * d;
+    };
+
+    static ESIMD_INLINE ptrs make_ptrs(const void * vx, size_t nb) {
+        const uint8_t *    ql     = (const uint8_t *) vx;
+        const uint8_t *    qh     = ql + nb * (QK_K / 2);
+        const int8_t *     scales = (const int8_t *) (qh + nb * (QK_K / 4));
+        const sycl::half * d      = (const sycl::half *) (scales + nb * (QK_K / 16));
+        return { ql, qh, scales, d };
+    }
+
+    static ESIMD_INLINE void mac_pair(
+            const ptrs & pa, size_t bia,
+            const ptrs & pb, size_t bib, bool has_b,
+            sycl::ext::intel::esimd::simd<float, 256> & y_vec,
+            sycl::ext::intel::esimd::simd<float, 32> & acc_a,
+            sycl::ext::intel::esimd::simd<float, 32> & acc_b) {
+        using namespace sycl::ext::intel::esimd;
+
+        simd<uint8_t, 128> ql_a     = block_load<uint8_t, 128>(pa.ql + bia * (QK_K / 2));
+        simd<uint8_t, 128> ql_b     = 0;
+        simd<uint8_t, 64>  qh_a     = block_load<uint8_t, 64>(pa.qh + bia * (QK_K / 4));
+        simd<uint8_t, 64>  qh_b     = 0;
+        simd<int8_t, 16>   scales_a = block_load<int8_t, 16>(pa.scales + bia * (QK_K / 16));
+        simd<int8_t, 16>   scales_b = 0;
+
+        const float d_a = (float) pa.d[bia];
+        float d_b = 0.0f;
+        if (has_b) {
+            ql_b     = block_load<uint8_t, 128>(pb.ql + bib * (QK_K / 2));
+            qh_b     = block_load<uint8_t, 64>(pb.qh + bib * (QK_K / 4));
+            scales_b = block_load<int8_t, 16>(pb.scales + bib * (QK_K / 16));
+            d_b = (float) pb.d[bib];
+        }
+
+        simd<float, 16> sc_a = convert<float>(scales_a);
+        simd<float, 16> sc_b = convert<float>(scales_b);
+
+#pragma unroll
+        for (int im = 0; im < 2; ++im) {
+            simd<uint8_t, 32> ql_lo_a   = ql_a.select<32, 1>(64 * im);
+            simd<uint8_t, 32> ql_hi_a   = ql_a.select<32, 1>(64 * im + 32);
+            simd<uint8_t, 32> qh_bits_a = qh_a.select<32, 1>(32 * im);
+            simd<uint8_t, 32> ql_lo_b   = ql_b.select<32, 1>(64 * im);
+            simd<uint8_t, 32> ql_hi_b   = ql_b.select<32, 1>(64 * im + 32);
+            simd<uint8_t, 32> qh_bits_b = qh_b.select<32, 1>(32 * im);
+
+            // reconstruct each 32-wide 6-bit group (matches dequantize_row_q6_K)
+#pragma unroll
+            for (int g = 0; g < 4; ++g) {
+                simd<float, 32> y_g = y_vec.select<32, 1>(32 * (4 * im + g));
+
+                const float scale_a_lo = sc_a[8 * im + 2 * g + 0] * d_a;
+                const float scale_a_hi = sc_a[8 * im + 2 * g + 1] * d_a;
+                const float scale_b_lo = sc_b[8 * im + 2 * g + 0] * d_b;
+                const float scale_b_hi = sc_b[8 * im + 2 * g + 1] * d_b;
+
+                simd<float, 32> scale_vec_a = splat_lo_hi(scale_a_lo, scale_a_hi);
+                simd<float, 32> scale_vec_b = splat_lo_hi(scale_b_lo, scale_b_hi);
+
+                simd<uint8_t, 32> qa;
+                simd<uint8_t, 32> qb;
+                switch (g) {
+                    case 0:
+                        qa = (ql_lo_a & simd<uint8_t, 32>(0x0F)) | ((qh_bits_a & simd<uint8_t, 32>(0x03)) << simd<uint8_t, 32>(4));
+                        qb = (ql_lo_b & simd<uint8_t, 32>(0x0F)) | ((qh_bits_b & simd<uint8_t, 32>(0x03)) << simd<uint8_t, 32>(4));
+                        break;
+                    case 1:
+                        qa = (ql_hi_a & simd<uint8_t, 32>(0x0F)) | ((qh_bits_a & simd<uint8_t, 32>(0x0C)) << simd<uint8_t, 32>(2));
+                        qb = (ql_hi_b & simd<uint8_t, 32>(0x0F)) | ((qh_bits_b & simd<uint8_t, 32>(0x0C)) << simd<uint8_t, 32>(2));
+                        break;
+                    case 2:
+                        qa = (ql_lo_a >> simd<uint8_t, 32>(4)) | (qh_bits_a & simd<uint8_t, 32>(0x30));
+                        qb = (ql_lo_b >> simd<uint8_t, 32>(4)) | (qh_bits_b & simd<uint8_t, 32>(0x30));
+                        break;
+                    default:
+                        qa = (ql_hi_a >> simd<uint8_t, 32>(4)) | ((qh_bits_a & simd<uint8_t, 32>(0xC0)) >> simd<uint8_t, 32>(2));
+                        qb = (ql_hi_b >> simd<uint8_t, 32>(4)) | ((qh_bits_b & simd<uint8_t, 32>(0xC0)) >> simd<uint8_t, 32>(2));
+                        break;
+                }
+
+                simd<float, 32> deq_a = (convert<float>(qa) - 32.0f) * scale_vec_a;
+                simd<float, 32> deq_b = (convert<float>(qb) - 32.0f) * scale_vec_b;
+
+                acc_a += y_g * deq_a;
+                acc_b += y_g * deq_b;
+            }
+        }
+    }
+};
+
+} // namespace ggml_sycl_esimd
+
+#endif // GGML_SYCL_ESIMD_HPP

ggml/src/ggml-sycl/fusion.cpp

@@ -1,6 +1,14 @@
 #include "fusion.hpp"
 
-bool ggml_sycl_can_fuse(const ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops) {
+#include <algorithm>
+
+bool ggml_sycl_can_fuse(const ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops,
+                        std::initializer_list<enum ggml_unary_op> unary_ops) {
+#ifndef NDEBUG
+    const size_t num_unary = std::count(ops.begin(), ops.end(), GGML_OP_UNARY);
+    GGML_ASSERT(unary_ops.size() == num_unary);
+#endif
+
     if (!g_ggml_sycl_enable_fusion) {
         return false;
     }
@@ -40,5 +48,45 @@ bool ggml_sycl_can_fuse(const ggml_cgraph * cgraph, int node_idx, std::initializ
         return true;
     }
 
+    if (ops.size() == 2 && ops.begin()[0] == GGML_OP_UNARY && ops.begin()[1] == GGML_OP_MUL &&
+        unary_ops.size() == 1) {
+        const ggml_tensor * unary = cgraph->nodes[node_idx];
+        const ggml_tensor * mul   = cgraph->nodes[node_idx + 1];
+
+        const ggml_unary_op unary_op = ggml_get_unary_op(unary);
+        if (unary_op != unary_ops.begin()[0]) {
+            return false;
+        }
+
+        // the ops ggml_sycl_op_unary_mul_fused() has a kernel for
+        if (unary_op != GGML_UNARY_OP_SILU && unary_op != GGML_UNARY_OP_SIGMOID &&
+            unary_op != GGML_UNARY_OP_SOFTPLUS) {
+            return false;
+        }
+
+        if (unary->type != GGML_TYPE_F32 && unary->type != GGML_TYPE_F16) {
+            return false;
+        }
+
+        const ggml_tensor * other = (mul->src[0] == unary) ? mul->src[1] : mul->src[0];
+        if (other->type != unary->type) {
+            return false;
+        }
+
+        // one row stride per source comes from nb[1], so rows must be contiguous and equally
+        // shaped; the destination is written flat, so it must be fully contiguous
+        if (!ggml_is_contiguous_1(unary->src[0]) || !ggml_is_contiguous_1(other) ||
+            !ggml_are_same_shape(other, unary) || !ggml_is_contiguous(mul)) {
+            return false;
+        }
+
+        // the 32-bit fastdiv is inexact past 2^31; decline, the unfused path handles it
+        if (ggml_nelements(mul) >= ((int64_t) 1 << 31)) {
+            return false;
+        }
+
+        return true;
+    }
+
     return false;
 }

ggml/src/ggml-sycl/fusion.hpp

@@ -6,10 +6,12 @@
 #include "common.hpp"
 
 // Backend-side fusability test. `ops` names a candidate op sequence starting at cgraph node
-// `node_idx`; the result is true only if ggml considers that subgraph fusable *and* the SYCL
+// `node_idx`, and `unary_ops` the GGML_UNARY_OP each GGML_OP_UNARY in `ops` must carry, in
+// order; the result is true only if ggml considers that subgraph fusable *and* the SYCL
 // kernel which would service it accepts the tensors involved (types, shapes, contiguity).
 //
 // Lives in its own translation unit because it grows a branch per supported op sequence.
-bool ggml_sycl_can_fuse(const ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops);
+bool ggml_sycl_can_fuse(const ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops,
+                        std::initializer_list<enum ggml_unary_op> unary_ops);
 
 #endif  // GGML_SYCL_FUSION_HPP

ggml/src/ggml-sycl/ggml-sycl.cpp

@@ -43,6 +43,9 @@
 #    include <sycl/ext/oneapi/virtual_mem/virtual_mem.hpp>
 #    define GGML_SYCL_SUPPORT_VMM
 #endif
+#if defined(__INTEL_LLVM_COMPILER)
+    #define GGML_SYCL_DMMV_HAS_ESIMD
+#endif
 #include <sycl/half_type.hpp>
 
 #include "ggml.h"
@@ -90,6 +93,7 @@ int g_ggml_sycl_fa_onednn = 1;
 int g_ggml_sycl_fa_onednn_max_kv = 0;
 int g_ggml_sycl_enable_vmm = 1;
 int g_ggml_sycl_enable_fusion = 1;
+int g_ggml_sycl_enable_esimd = 1;
 int g_ggml_sycl_prioritize_dmmv = 0;
 int g_ggml_sycl_use_async_mem_op = 0;
 int g_ggml_sycl_use_async_mem_op_requested = 1;
@@ -298,6 +302,7 @@ static void ggml_check_sycl() try {
         g_ggml_sycl_fa_onednn_max_kv = ggml_sycl_get_env("GGML_SYCL_FA_ONEDNN_MAX_KV", 0);
         g_ggml_sycl_enable_vmm = ggml_sycl_get_env("GGML_SYCL_ENABLE_VMM", 1);
         g_ggml_sycl_enable_fusion = ggml_sycl_get_env("GGML_SYCL_ENABLE_FUSION", 1);
+        g_ggml_sycl_enable_esimd = ggml_sycl_get_env("GGML_SYCL_ENABLE_ESIMD", 1);
         g_ggml_sycl_prioritize_dmmv = ggml_sycl_get_env("GGML_SYCL_PRIORITIZE_DMMV", 0);
 
         g_ggml_sycl_dev2dev_memcpy = ggml_sycl_get_env("GGML_SYCL_DEV2DEV_MEMCPY", DEV2DEV_MEMCPY_SYCL);
@@ -392,6 +397,12 @@ static void ggml_check_sycl() try {
 
         GGML_LOG_INFO("  GGML_SYCL_ENABLE_FUSION: %d\n", g_ggml_sycl_enable_fusion);
 
+#if defined(__INTEL_LLVM_COMPILER)
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_ESIMD: %d\n", g_ggml_sycl_enable_esimd);
+#else
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_ESIMD: %d disabled by compile flag\n", g_ggml_sycl_enable_esimd);
+#endif
+
         GGML_LOG_INFO("  GGML_SYCL_PRIORITIZE_DMMV: %d\n", g_ggml_sycl_prioritize_dmmv);
 
         g_ggml_sycl_use_async_mem_op_requested = ggml_sycl_get_env("GGML_SYCL_USE_ASYNC_MEM_OP", 1);
@@ -2676,21 +2687,15 @@ inline void ggml_sycl_op_mul_mat_sycl(
         else
 #endif
         {
-            ggml_sycl_pool_alloc<sycl::half> dst_f16(ctx.pool(), row_diff * src1_ncols);
-
-            const sycl::half alpha_f16 = 1.0f;
-            const sycl::half beta_f16  = 0.0f;
+            const float alpha = 1.0f;
+            const float beta  = 0.0f;
             SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
                 *stream, oneapi::mkl::transpose::trans,
                 oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
-                &alpha_f16, src0_ptr, dpct::library_data_t::real_half, ne00,
-                src1_ptr, dpct::library_data_t::real_half, ne10, &beta_f16,
-                dst_f16.get(), dpct::library_data_t::real_half, ldc,
-                dpct::library_data_t::real_half)));
-            scope_op_debug_print scope_dbg_print(__func__, "/to_fp32_sycl", dst, /*num_src=*/2,
-                                                 " : converting dst to fp32");
-            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16, dst);
-            to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
+                &alpha, src0_ptr, dpct::library_data_t::real_half, ne00,
+                src1_ptr, dpct::library_data_t::real_half, ne10, &beta,
+                dst_dd_i, dpct::library_data_t::real_float, ldc,
+                dpct::library_data_t::real_float)));
         }
     } else {
         ggml_sycl_pool_alloc<float> src0_ddq_as_f32(ctx.pool());
@@ -3740,6 +3745,22 @@ inline bool ggml_sycl_supports_reorder_mmvq(enum ggml_type type) {
     }
 }
 
+static bool ggml_sycl_supports_reorder_esimd(enum ggml_type type) {
+#ifdef GGML_SYCL_DMMV_HAS_ESIMD
+    switch (type) {
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q6_K:
+            return true;
+        default:
+            return false;
+    }
+#else
+    GGML_UNUSED(type);
+    return false;
+#endif
+}
+
 static bool ggml_sycl_supports_dmmv(enum ggml_type type) {
     switch (type) {
         case GGML_TYPE_Q1_0:
@@ -4443,19 +4464,22 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
     use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
 #endif // SYCL_USE_XMX
 
-    // Dispatch becomes obscure with the reorder, MMVQ when the reorder optimization
-    // is enabled takes precedence over DMMV, the current if-else implementation
-    // requires disabling DMMV if both conditions are met
+    // When reorder is enabled, both ESIMD, MMVQ and DMMV kernels may be used. For
+    // best performance use ESIMD when supported, followed by MMVQ, and finally DMMV.
+    // But the reordered ESIMD path cannot be used without reordered MMVQ. A later
+    // multi-token call (ne[1] in 2..8) will take the MMVQ path and it would read the
+    // reordered bytes as if they were still the unreordered layout.
 
     if (!g_ggml_sycl_prioritize_dmmv && ((should_reorder_tensor(ctx, dst) &&
                                           ggml_sycl_supports_reorder_mmvq(src0->type)))) {
-      // Arc770 get benefit with Q4_0 by skipping it.
-      if (!(ggml_sycl_info().devices[ctx.device].hw_info.arch ==
-                gpu_arch::intel_gpu_acm_g10 &&
-            src0->type == GGML_TYPE_Q4_0)) {
-        use_dequantize_mul_mat_vec =
-            use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
-      }
+        bool use = g_ggml_sycl_enable_esimd && ggml_sycl_supports_reorder_esimd(src0->type);
+        // Arc770 get benefit with Q4_0 by skipping MMVQ path
+        if (!(ggml_sycl_info().devices[ctx.device].hw_info.arch ==
+                    gpu_arch::intel_gpu_acm_g10 &&
+                src0->type == GGML_TYPE_Q4_0)) {
+            use = use || !use_mul_mat_vec_q;
+        }
+        use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && use;
     }
 
     if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
@@ -5422,11 +5446,17 @@ static void ggml_backend_sycl_graph_compute_impl(ggml_backend_sycl_context * syc
         }
 #endif
         if (node->op == GGML_OP_RMS_NORM &&
-            ggml_sycl_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+            ggml_sycl_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL }, {})) {
             ggml_sycl_op_rms_norm_fused(*sycl_ctx, node, cgraph->nodes[i + 1]);
             i++;
             continue;
         }
+        if (node->op == GGML_OP_UNARY &&
+            ggml_sycl_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { ggml_get_unary_op(node) })) {
+            ggml_sycl_op_unary_mul_fused(*sycl_ctx, node, cgraph->nodes[i + 1]);
+            i++;
+            continue;
+        }
 
         bool ok = ggml_sycl_compute_forward(*sycl_ctx, node);
         if (!ok) {

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -4627,6 +4627,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
         CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_1], matmul_q5_1_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q8_0], matmul_q8_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q2_K], matmul_q2_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_TQ2_0], matmul_tq2_0_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q3_K], matmul_q3_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_K], matmul_q4_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_K], matmul_q5_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
@@ -4667,6 +4668,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
         CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_TQ2_0], matmul_id_subgroup_tq2_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
@@ -4739,6 +4741,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
         CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0], matmul_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
 
         CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K], matmul_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM2(GGML_TYPE_TQ2_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_TQ2_0], matmul_tq2_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K], matmul_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K], matmul_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K], matmul_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
@@ -4783,6 +4786,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
         CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
         CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
         CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
+        CREATE_MM2(GGML_TYPE_TQ2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_TQ2_0], matmul_id_subgroup_tq2_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
         CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
         CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
         CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
@@ -4873,6 +4877,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
         CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1], matmul_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
         CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0], matmul_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
         CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K], matmul_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_TQ2_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_TQ2_0], matmul_tq2_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
         CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K], matmul_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
         CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K], matmul_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
         CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K], matmul_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
@@ -4921,6 +4926,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
             CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
             CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
             CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_TQ2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_TQ2_0], matmul_id_subgroup_tq2_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
             CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
             CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
             CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
@@ -4968,6 +4974,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
             CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
             CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
             CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
+            CREATE_MM2(GGML_TYPE_TQ2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_TQ2_0], matmul_id_tq2_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
             CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
             CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
             CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
@@ -5047,6 +5054,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
         CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
 
         CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_TQ2_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_TQ2_0].f32acc, matmul_tq2_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
         CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
         CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
         CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
@@ -5094,6 +5102,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
             CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f32acc, matmul_id_subgroup_q5_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
             CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f32acc, matmul_id_subgroup_q8_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
             CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f32acc, matmul_id_subgroup_q2_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_TQ2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_TQ2_0].f32acc, matmul_id_subgroup_tq2_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
             CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f32acc, matmul_id_subgroup_q3_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
             CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_subgroup_q4_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
             CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_subgroup_q5_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
@@ -5123,6 +5132,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
             CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f32acc, matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
             CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f32acc, matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
             CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f32acc, matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
+            CREATE_MM(GGML_TYPE_TQ2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_TQ2_0].f32acc, matmul_id_tq2_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
             CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f32acc, matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
             CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
             CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
@@ -5226,6 +5236,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32", arr_dmmv_q5_1_f32_f32_len[reduc], arr_dmmv_q5_1_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32", arr_dmmv_q8_0_f32_f32_len[reduc], arr_dmmv_q8_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32", arr_dmmv_q2_k_f32_f32_len[reduc16], arr_dmmv_q2_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_TQ2_0][i], "mul_mat_vec_tq2_0_f32_f32", arr_dmmv_tq2_0_f32_f32_len[reduc16], arr_dmmv_tq2_0_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32", arr_dmmv_q3_k_f32_f32_len[reduc16], arr_dmmv_q3_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32", arr_dmmv_q4_k_f32_f32_len[reduc16], arr_dmmv_q4_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32", arr_dmmv_q5_k_f32_f32_len[reduc16], arr_dmmv_q5_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
@@ -5253,6 +5264,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32", arr_dmmv_q5_1_f16_f32_len[reduc], arr_dmmv_q5_1_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32", arr_dmmv_q8_0_f16_f32_len[reduc], arr_dmmv_q8_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32", arr_dmmv_q2_k_f16_f32_len[reduc16], arr_dmmv_q2_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_TQ2_0][i], "mul_mat_vec_tq2_0_f16_f32", arr_dmmv_tq2_0_f16_f32_len[reduc16], arr_dmmv_tq2_0_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32", arr_dmmv_q3_k_f16_f32_len[reduc16], arr_dmmv_q3_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32", arr_dmmv_q4_k_f16_f32_len[reduc16], arr_dmmv_q4_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32", arr_dmmv_q5_k_f16_f32_len[reduc16], arr_dmmv_q5_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
@@ -5307,6 +5319,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32",       arr_dmmv_id_q5_1_f32_f32_len[reduc],    arr_dmmv_id_q5_1_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32",       arr_dmmv_id_q8_0_f32_f32_len[reduc],    arr_dmmv_id_q8_0_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32",       arr_dmmv_id_q2_k_f32_f32_len[reduc16],    arr_dmmv_id_q2_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_TQ2_0], "mul_mat_vec_id_tq2_0_f32",     arr_dmmv_id_tq2_0_f32_f32_len[reduc16],   arr_dmmv_id_tq2_0_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32",       arr_dmmv_id_q3_k_f32_f32_len[reduc16],    arr_dmmv_id_q3_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32",       arr_dmmv_id_q4_k_f32_f32_len[reduc16],    arr_dmmv_id_q4_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32",       arr_dmmv_id_q5_k_f32_f32_len[reduc16],    arr_dmmv_id_q5_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
@@ -5368,6 +5381,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_k", dequant_q2_k_len, dequant_q2_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_TQ2_0], "dequant_tq2_0", dequant_tq2_0_len, dequant_tq2_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_k", dequant_q3_k_len, dequant_q3_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_k", dequant_q4_k_len, dequant_q4_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_k", dequant_q5_k_len, dequant_q5_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
@@ -5396,6 +5410,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q2_K], "get_rows_q2_k", get_rows_q2_k_len, get_rows_q2_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_TQ2_0], "get_rows_tq2_0", get_rows_tq2_0_len, get_rows_tq2_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q3_K], "get_rows_q3_k", get_rows_q3_k_len, get_rows_q3_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_K], "get_rows_q4_k", get_rows_q4_k_len, get_rows_q4_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_K], "get_rows_q5_k", get_rows_q5_k_len, get_rows_q5_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
@@ -5424,6 +5439,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q2_K], "get_rows_q2_k_f32", get_rows_q2_k_f32_len, get_rows_q2_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_TQ2_0], "get_rows_tq2_0_f32", get_rows_tq2_0_f32_len, get_rows_tq2_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q3_K], "get_rows_q3_k_f32", get_rows_q3_k_f32_len, get_rows_q3_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_K], "get_rows_q4_k_f32", get_rows_q4_k_f32_len, get_rows_q4_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_K], "get_rows_q5_k_f32", get_rows_q5_k_f32_len, get_rows_q5_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
@@ -7638,6 +7654,7 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type
         case GGML_TYPE_IQ4_NL:
         case GGML_TYPE_MXFP4:
         case GGML_TYPE_NVFP4:
+        case GGML_TYPE_TQ2_0:
             break;
         default:
             return nullptr;
@@ -7712,6 +7729,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
         case GGML_TYPE_IQ4_NL:
         case GGML_TYPE_MXFP4:
         case GGML_TYPE_NVFP4:
+        case GGML_TYPE_TQ2_0:
             break;
         default:
             return nullptr;
@@ -7781,6 +7799,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
         case GGML_TYPE_IQ4_NL:
         case GGML_TYPE_MXFP4:
         case GGML_TYPE_NVFP4:
+        case GGML_TYPE_TQ2_0:
             break;
         default:
             return nullptr;
@@ -7874,6 +7893,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
         case GGML_TYPE_IQ4_NL:
         case GGML_TYPE_MXFP4:
         case GGML_TYPE_NVFP4:
+        case GGML_TYPE_TQ2_0:
             break;
         default:
             return nullptr;
@@ -7946,6 +7966,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context
         case GGML_TYPE_IQ4_NL:
         case GGML_TYPE_MXFP4:
         case GGML_TYPE_NVFP4:
+        case GGML_TYPE_TQ2_0:
             break;
         default:
             return nullptr;
@@ -18014,6 +18035,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_IQ4_NL:
                     case GGML_TYPE_MXFP4:
                     case GGML_TYPE_NVFP4:
+                    case GGML_TYPE_TQ2_0:
                         break;
                     default:
                         return false;
@@ -18119,6 +18141,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_IQ4_NL:
                     case GGML_TYPE_MXFP4:
                     case GGML_TYPE_NVFP4:
+                    case GGML_TYPE_TQ2_0:
                     case GGML_TYPE_I32:
                         return true;
                     default:

ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl

@@ -608,6 +608,20 @@ vec2 get_dm(uint ib, uint a_offset) {
 }
 #endif
 
+#if defined(DATA_A_TQ2_0)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    // elem e -> byte qs[(e/128)*32 + e%32], bits 2*((e%128)/32); w = q - 1 (d applied via get_dm)
+    const uint qsi   = (iqs / 128) * 32 + (iqs % 32);  // iqs even -> qsi, qsi+1 in same group/level
+    const uint shift = 2 * ((iqs % 128) / 32);
+
+    const uvec2 qs = uvec2(data_a[a_offset + ib].qs[qsi], data_a[a_offset + ib].qs[qsi + 1]);
+    return vec2((qs >> shift) & 3) - 1.0;
+}
+vec2 get_dm(uint ib, uint a_offset) {
+    return vec2(float(data_a[a_offset + ib].d), 0);
+}
+#endif
+
 #if defined(DATA_A_Q3_K)
 vec2 dequantize(uint ib, uint iqs, uint a_offset) {
     iqs /= 2;

ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl

@@ -247,6 +247,44 @@ f16vec4 dequantFuncQ8_0_v(const in decodeBufQ8_0 bl, const in uint blockCoords[2
     return f16vec4(vec4(qi) * vec4(float(d)));
 }
 
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufTQ2_0 {
+   block_tq2_0 block;
+};
+
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufTQ2_0_packed16 {
+   block_tq2_0_packed16 block;
+};
+
+float16_t dequantFuncTQ2_0(const in decodeBufTQ2_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2])
+{
+    decodeBufTQ2_0_packed16 bl16 = decodeBufTQ2_0_packed16(bl);
+    const uint idx = coordInBlock[1];
+
+    const uint qsshift = (idx & 0x60) >> 4;             // 0,2,4,6
+
+    uint qs = uint32_t(bl16.block.qs[((idx & 0x80) >> 3) + ((idx & 0x1E) >> 1)]);
+    qs = (qs >> qsshift) & 0x0303;
+    qs = unpack8(qs)[idx & 1];
+
+    return bl.block.d * (float16_t(int(qs)) - float16_t(1.0));
+}
+
+f16vec4 dequantFuncTQ2_0_v(const in decodeBufTQ2_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2])
+{
+    const uint idx = coordInBlock[1];
+
+    const uint qsshift = (idx & 0x60) >> 4;             // 0,2,4,6
+    const uint qsi = ((idx & 0x80) >> 2) + (idx & 0x1C); // byte index of 4-aligned group
+
+    const uint qsw = (uint(bl.block.qs[qsi]))
+                   | (uint(bl.block.qs[qsi + 1]) << 8)
+                   | (uint(bl.block.qs[qsi + 2]) << 16)
+                   | (uint(bl.block.qs[qsi + 3]) << 24);
+    const u8vec4 q = unpack8((qsw >> qsshift) & 0x03030303);
+
+    return bl.block.d * (f16vec4(q) - f16vec4(1.0));
+}
+
 layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufQ2_K {
    block_q2_K block;
 };
@@ -1368,6 +1406,9 @@ f16vec4 dequantFuncNVFP4_v(const in decodeBufNVFP4 bl, const in uint blockCoords
 #elif defined(DATA_A_Q8_0)
 #define dequantFuncA dequantFuncQ8_0
 #define dequantFuncA_v dequantFuncQ8_0_v
+#elif defined(DATA_A_TQ2_0)
+#define dequantFuncA dequantFuncTQ2_0
+#define dequantFuncA_v dequantFuncTQ2_0_v
 #elif defined(DATA_A_Q2_K)
 #define dequantFuncA dequantFuncQ2_K
 #define dequantFuncA_v dequantFuncQ2_K_v

ggml/src/ggml-vulkan/vulkan-shaders/dequant_tq2_0.comp

@@ -0,0 +1,31 @@
+#version 450
+
+#include "dequant_head.glsl"
+
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.nel / QUANT_K) {
+            return;
+        }
+
+        const uint tid = gl_LocalInvocationID.x;
+        const uint ip = tid / 32;         // group 0,1 (128 elems each)
+        const uint il = tid - 32 * ip;    // byte in group 0..31
+
+        const uint y_idx = i * QUANT_K + 128 * ip + il;
+
+        const uint8_t qs = data_a[i].qs[32 * ip + il];
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d);
+        data_b[y_idx +  0] = D_TYPE(d * FLOAT_TYPE(int((qs >> 0) & 3) - 1));
+        data_b[y_idx + 32] = D_TYPE(d * FLOAT_TYPE(int((qs >> 2) & 3) - 1));
+        data_b[y_idx + 64] = D_TYPE(d * FLOAT_TYPE(int((qs >> 4) & 3) - 1));
+        data_b[y_idx + 96] = D_TYPE(d * FLOAT_TYPE(int((qs >> 6) & 3) - 1));
+    }
+}

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_tq2_0.comp

@@ -0,0 +1,102 @@
+#version 450
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
+
+#include "mul_mat_vec_base.glsl"
+
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
+
+// ternary TQ2_0: w = (q - 1) * d. Same qs group/level layout as q2_K, but a
+// single f16 scale per 256-block and no mins:
+//   sum_e b_e * (q_e - 1) * d  =  d * (sum_e b_e * q_e  -  sum_e b_e)
+void calc_superblock(const uint a_offset, const uint b_offset, const uint v_im, const uint q_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
+    const uint y_idx = i * QUANT_K + y_offset;
+
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row;
+        if (i >= num_blocks_per_row) {
+            continue;
+        }
+
+        const uint32_t qs_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16);
+        const vec4 qs_u32_0 = vec4(unpack8(qs_u32 & 0x03030303));
+        const vec4 qs_u32_2 = vec4(unpack8((qs_u32 >> 2) & 0x03030303));
+        const vec4 qs_u32_4 = vec4(unpack8((qs_u32 >> 4) & 0x03030303));
+        const vec4 qs_u32_6 = vec4(unpack8((qs_u32 >> 6) & 0x03030303));
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
+
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            vec2 b0 =   vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  0]);
+            vec2 b16 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  8]);
+            vec2 b32 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]);
+            vec2 b48 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]);
+            vec2 b64 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]);
+            vec2 b80 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]);
+            vec2 b96 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]);
+            vec2 b112 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]);
+
+            FLOAT_TYPE sumq = FLOAT_TYPE(0.0);
+            FLOAT_TYPE sumb = FLOAT_TYPE(0.0);
+            [[unroll]] for (int l = 0; l < 2; ++l) {
+                sumq = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(qs_u32_0[l  ]),
+                       fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(qs_u32_0[l+2]),
+                       fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(qs_u32_2[l  ]),
+                       fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(qs_u32_2[l+2]),
+                       fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(qs_u32_4[l  ]),
+                       fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(qs_u32_4[l+2]),
+                       fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(qs_u32_6[l  ]),
+                       fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(qs_u32_6[l+2]), sumq))))))));
+                sumb += FLOAT_TYPE(b0[l]) + FLOAT_TYPE(b16[l]) + FLOAT_TYPE(b32[l]) + FLOAT_TYPE(b48[l])
+                      + FLOAT_TYPE(b64[l]) + FLOAT_TYPE(b80[l]) + FLOAT_TYPE(b96[l]) + FLOAT_TYPE(b112[l]);
+            }
+            temp[j][n] = fma(d, sumq - sumb, temp[j][n]);
+        }
+    }
+}
+
+void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+
+    // 16 threads are used to process each block
+    const uint it_size = gl_WorkGroupSize.x/16;
+    const uint tid = gl_LocalInvocationID.x;
+    const uint itid = tid%16;  // 0...15
+    const uint ix = tid/16;
+
+    const uint v_im = itid/8;                                // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = itid - 8*v_im;                         // 0...7
+
+    const uint l0 = 2*v_in;                                  // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 128*v_im + l0;
+
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+            temp[j][i] = FLOAT_TYPE(0);
+        }
+    }
+
+    for (uint i0 = 0; i0 < num_blocks_per_row; i0 += it_size)
+        calc_superblock(a_offset, b_offset, v_im, q_offset, y_offset, i0 + ix, num_blocks_per_row, first_row, num_rows);
+
+    reduce_result(temp, d_offset, first_row, num_rows, tid);
+}
+
+void main() {
+    const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
+
+    // do NUM_ROWS at a time, unless there aren't enough remaining rows
+    if (first_row + NUM_ROWS <= p.stride_d) {
+        compute_outputs(first_row, NUM_ROWS);
+    } else {
+        if (first_row >= p.stride_d) {
+            return;
+        }
+        compute_outputs(first_row, p.stride_d - first_row);
+    }
+}

ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl

@@ -182,6 +182,22 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
 
             buf_a[buf_idx    ] = FLOAT_TYPEV2(v.xy);
             buf_a[buf_idx + 1] = FLOAT_TYPEV2(v.zw);
+#elif defined(DATA_A_TQ2_0)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
+
+            const uint ib = idx / 128;                         // 2 values per idx
+            const uint iqs = (idx % 128) * 2;                  // elem 0,2,4..254
+
+            const uint qsi   = (iqs / 128) * 32 + (iqs % 32);  // byte pair start
+            const uint shift = 2 * ((iqs % 128) / 32);         // 0,2,4,6
+
+            const uvec2 qs = uvec2(data_a[ib].qs[qsi], data_a[ib].qs[qsi + 1]);
+            const float d = float(data_a[ib].d);
+
+            const vec2 v = d * (vec2((qs >> shift) & 3) - 1.0);
+
+            buf_a[buf_idx] = FLOAT_TYPEV2(v.xy);
 #elif defined(DATA_A_Q3_K)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
             const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;

ggml/src/ggml-vulkan/vulkan-shaders/types.glsl

@@ -303,6 +303,30 @@ struct block_q2_K_packed32
 #define DATA_A_QUANT_K
 #endif
 
+#define QUANT_K_TQ2_0 256
+
+// ternary (BitNet): 2-bit codes, w = (q - 1) * d; qs layout matches q2_K's
+// two 32-byte groups with four bit-levels per byte
+struct block_tq2_0
+{
+    uint8_t qs[QUANT_K_TQ2_0/4];
+    float16_t d;
+};
+
+struct block_tq2_0_packed16
+{
+    uint16_t qs[QUANT_K_TQ2_0/4/2];
+    float16_t d;
+};
+
+#if defined(DATA_A_TQ2_0)
+#define QUANT_K QUANT_K_TQ2_0
+#define QUANT_R 1
+#define A_TYPE block_tq2_0
+#define A_TYPE_PACKED16 block_tq2_0_packed16
+#define DATA_A_QUANT_K
+#endif
+
 #define QUANT_K_Q3_K 256
 
 struct block_q3_K

ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp

@@ -72,6 +72,7 @@ const std::vector<std::string> type_names = {
     "iq4_nl",
     "mxfp4",
     "nvfp4",
+    "tq2_0",
     "bf16",
 };
 
@@ -733,7 +734,7 @@ void process_shaders() {
     for (const auto& tname : type_names) {
         // mul mat vec
         std::string data_a_key = "DATA_A_" + to_uppercase(tname);
-        std::string shader = (string_ends_with(tname, "_k") || string_starts_with(tname, "iq1_") || string_starts_with(tname, "iq2_") || string_starts_with(tname, "iq3_")) ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp";
+        std::string shader = (string_ends_with(tname, "_k") || string_starts_with(tname, "iq1_") || string_starts_with(tname, "iq2_") || string_starts_with(tname, "iq3_") || tname == "tq2_0") ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp";
 
         string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPEV2", "vec2"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}}));
         string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPEV2", "f16vec2"}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}}));

ggml/src/gguf.cpp

@@ -611,6 +611,13 @@ static struct gguf_context * gguf_init_from_reader(const struct gguf_reader & gr
         GGML_ASSERT(int64_t(ctx->kv.size()) == n_kv);
 
         const int alignment_idx = gguf_find_key(ctx, GGUF_KEY_GENERAL_ALIGNMENT);
+        if (alignment_idx != -1 && gguf_get_kv_type(ctx, alignment_idx) != GGUF_TYPE_UINT32) {
+            GGML_LOG_ERROR("%s: key '%s' must be of type %s but is %s\n",
+                __func__, GGUF_KEY_GENERAL_ALIGNMENT, gguf_type_name(GGUF_TYPE_UINT32),
+                gguf_type_name(gguf_get_kv_type(ctx, alignment_idx)));
+            gguf_free(ctx);
+            return nullptr;
+        }
         ctx->alignment = alignment_idx == -1 ? GGUF_DEFAULT_ALIGNMENT : gguf_get_val_u32(ctx, alignment_idx);
 
         if (ctx->alignment == 0 || (ctx->alignment & (ctx->alignment - 1)) != 0) {
@@ -682,9 +689,11 @@ static struct gguf_context * gguf_init_from_reader(const struct gguf_reader & gr
             }
 
             // check that the total number of elements is representable
-            if (ok && ((INT64_MAX/info.t.ne[1] <= info.t.ne[0]) ||
-                       (INT64_MAX/info.t.ne[2] <= info.t.ne[0]*info.t.ne[1]) ||
-                       (INT64_MAX/info.t.ne[3] <= info.t.ne[0]*info.t.ne[1]*info.t.ne[2]))) {
+            // (a zero-element tensor is trivially representable; the guard also avoids a division by zero below)
+            if (ok && ggml_nelements(&info.t) > 0 &&
+                ((INT64_MAX/info.t.ne[1] <= info.t.ne[0]) ||
+                 (INT64_MAX/info.t.ne[2] <= info.t.ne[0]*info.t.ne[1]) ||
+                 (INT64_MAX/info.t.ne[3] <= info.t.ne[0]*info.t.ne[1]*info.t.ne[2]))) {
 
                 GGML_LOG_ERROR("%s: total number of elements in tensor '%s' with shape "
                     "(%" PRIi64 ", %" PRIi64 ", %" PRIi64 ", %" PRIi64 ") is >= %" PRIi64 "\n",

include/llama.h

@@ -457,6 +457,8 @@ extern "C" {
     // lora adapter
     struct llama_adapter_lora;
 
+    LLAMA_API const char * llama_version(void);
+
     // Helpers for getting default parameters
     // TODO: update API to start accepting pointers to params structs (https://github.com/ggml-org/llama.cpp/discussions/9172)
     LLAMA_API struct llama_model_params          llama_model_default_params(void);
@@ -883,6 +885,7 @@ extern "C" {
                const llama_token * tokens,
                           size_t   n_token_count);
 
+    // If tokens_out is NULL, only the token count is reported through n_token_count_out and no state is loaded
     LLAMA_API size_t llama_state_seq_load_file(
             struct llama_context * ctx,
                       const char * filepath,

scripts/make-release-checks.sh

@@ -0,0 +1,83 @@
+#!/bin/bash
+# Run all pre-release checks and determine the release version.
+#
+# Usage: make-release-checks.sh [--dry-run]
+#   --dry-run: warn on failures instead of aborting
+#
+# Env (when running in GitHub Actions): GH_TOKEN, GITHUB_REPOSITORY, GITHUB_OUTPUT
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
+
+DRY_RUN=false
+for arg in "$@"; do
+    case "$arg" in
+        --dry-run) DRY_RUN=true ;;
+        *) echo "Unknown argument: $arg"; exit 1 ;;
+    esac
+done
+
+MAJOR=$(grep "set(LLAMA_VERSION_MAJOR" "$REPO_ROOT/CMakeLists.txt" | grep -oP '\d+')
+MINOR=$(grep "set(LLAMA_VERSION_MINOR" "$REPO_ROOT/CMakeLists.txt" | grep -oP '\d+')
+PATCH=$(grep "set(LLAMA_VERSION_PATCH" "$REPO_ROOT/CMakeLists.txt" | grep -oP '\d+')
+VERSION="v${MAJOR}.${MINOR}.${PATCH}"
+echo "Determined version: ${VERSION}"
+if [[ -n "${GITHUB_OUTPUT:-}" ]]; then
+    echo "version=${VERSION}" >> "$GITHUB_OUTPUT"
+fi
+
+echo "Checking that tag ${VERSION} does not already exist..."
+if git ls-remote --tags origin "${VERSION}" | grep -q "${VERSION}"; then
+    echo "Error: tag ${VERSION} already exists on remote"
+    exit 1
+fi
+echo "Tag ${VERSION} does not exist on remote - OK"
+
+SHA=$(git rev-parse HEAD)
+echo "Checking release.yml status for commit ${SHA}..."
+if [[ -z "${GITHUB_REPOSITORY:-}" ]]; then
+    echo "Warning: GITHUB_REPOSITORY not set - skipping CI check (local run)"
+else
+    RUNS=$(gh api "repos/${GITHUB_REPOSITORY}/actions/workflows/release.yml/runs" \
+        --jq "[.workflow_runs[] | select(.head_sha == \"${SHA}\" and .conclusion == \"success\")] | length")
+    if [[ "$RUNS" -eq 0 ]]; then
+        if [[ "$DRY_RUN" == "true" ]]; then
+            echo "Warning: no successful release.yml run found for HEAD (${SHA}) (dry run, continuing)."
+        else
+            echo "Error: no successful release.yml run found for HEAD (${SHA})"
+            echo "The nightly build must complete successfully before making a release."
+            exit 1
+        fi
+    else
+        echo "Found successful release.yml run for HEAD."
+    fi
+fi
+
+MAJOR=$(grep "set(GGML_VERSION_MAJOR" "$REPO_ROOT/ggml/CMakeLists.txt" | grep -oP '\d+')
+MINOR=$(grep "set(GGML_VERSION_MINOR" "$REPO_ROOT/ggml/CMakeLists.txt" | grep -oP '\d+')
+PATCH=$(grep "set(GGML_VERSION_PATCH" "$REPO_ROOT/ggml/CMakeLists.txt" | grep -oP '\d+')
+GGML_VERSION="v${MAJOR}.${MINOR}.${PATCH}"
+echo "Local ggml version: ${GGML_VERSION}"
+
+if ! git clone --depth 1 --branch "${GGML_VERSION}" https://github.com/ggml-org/ggml.git upstream-ggml 2>/dev/null; then
+    echo "Warning: tag ${GGML_VERSION} not found in upstream ggml - skipping comparison"
+else
+    echo "Comparing local ggml/ src and include with upstream ${GGML_VERSION}..."
+    DIFF=$(diff -rq "$REPO_ROOT/ggml/src"          upstream-ggml/src          2>&1 || true)
+    DIFF+=$(diff -rq "$REPO_ROOT/ggml/include"     upstream-ggml/include      2>&1 || true)
+    DIFF+=$(diff     "$REPO_ROOT/ggml/CMakeLists.txt" upstream-ggml/CMakeLists.txt 2>&1 || true)
+    rm -rf upstream-ggml
+    if [[ -n "$DIFF" ]]; then
+        echo "local ggml/ differs from upstream ${GGML_VERSION}:"
+        echo "$DIFF"
+        if [[ "$DRY_RUN" == "true" ]]; then
+            echo "Warning: would abort release due to ggml mismatch (dry run, continuing)."
+        else
+            echo "Error: ggml must match upstream before making a release."
+            exit 1
+        fi
+    else
+        echo "local ggml/ matches upstream ${GGML_VERSION}"
+    fi
+fi

scripts/sync-ggml.last

@@ -1 +1 @@
-30bf8685ed4eb0a47f2b06229543327749904150
+8846b79e66747bb9f68597420e95114c177315ce

src/CMakeLists.txt

@@ -45,11 +45,16 @@ add_library(llama
             )
 
 set_target_properties(llama PROPERTIES
-    VERSION ${LLAMA_INSTALL_VERSION}
-    SOVERSION 0
+    VERSION ${LLAMA_VERSION_BASE}
+    SOVERSION ${LLAMA_VERSION_MAJOR}
     MACHO_CURRENT_VERSION 0 # keep macOS linker from seeing oversized version number
 )
 
+target_compile_definitions(llama PRIVATE
+    LLAMA_VERSION="${LLAMA_VERSION}"
+    LLAMA_COMMIT="${LLAMA_BUILD_COMMIT}"
+)
+
 target_include_directories(llama PRIVATE .)
 target_include_directories(llama PUBLIC ../include)
 target_compile_features   (llama PRIVATE cxx_std_17) # don't bump

src/llama-context.cpp

@@ -3110,6 +3110,17 @@ size_t llama_context::state_seq_load_file(llama_seq_id seq_id, const char * file
     {
         const uint32_t n_token_count = file.read_u32();
 
+        if (tokens_out == nullptr) {
+            const size_t n_token_max = (file.size() - file.tell()) / sizeof(llama_token);
+            if (n_token_count > n_token_max) {
+                LLAMA_LOG_ERROR("%s: token count in sequence state file exceeds the file size! %u > %zu\n", __func__, n_token_count, n_token_max);
+                return 0;
+            }
+
+            *n_token_count_out = n_token_count;
+            return file.tell();
+        }
+
         if (n_token_count > n_token_capacity) {
             LLAMA_LOG_ERROR("%s: token count in sequence state file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
             return 0;

src/llama-model.cpp

@@ -1122,6 +1122,9 @@ void llama_model_base::load_hparams(llama_model_loader & ml) {
 
         ml.get_key(LLM_KV_CONVNEXT_EMBEDDING_LENGTH, hparams.convnext.n_embd);
         ml.get_key(LLM_KV_CONVNEXT_BLOCK_COUNT,      hparams.convnext.n_layer);
+
+        GGML_ASSERT(hparams.posnet.n_layer   <= hparams.n_layer_all);
+        GGML_ASSERT(hparams.convnext.n_layer <= hparams.n_layer_all);
     }
 
     GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);

src/llama.cpp

@@ -114,6 +114,10 @@ bool llama_supports_rpc(void) {
     return ggml_backend_reg_by_name("RPC") != nullptr;
 }
 
+const char * llama_version(void) {
+    return LLAMA_VERSION;
+}
+
 void llama_backend_init(void) {
     ggml_time_init();
 

src/models/dflash.cpp

@@ -66,7 +66,7 @@ void llama_model_dflash::load_arch_hparams(llama_model_loader & ml) {
     // DFlash has a single rope, so the SWA rope == main rope.
     if (ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false) && hparams.n_swa > 0) {
         hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
+        ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);
         hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
         hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
     }

tests/test-backend-ops.cpp

@@ -3695,6 +3695,117 @@ struct test_relu_sqr : public test_case {
     }
 };
 
+// GGML_OP_UNARY(SILU|SIGMOID|SOFTPLUS) + GGML_OP_MUL (fused operation).
+// `layout` and `tail` are used for fallback cases where fusion must be skipped
+struct test_unary_mul : public test_case {
+    const ggml_unary_op op;
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+    const bool swap;          // unary result is the second MUL operand
+    const std::string layout; // operand layout, see build_graph()
+    const std::string tail;   // extra consumer past the MUL, see build_graph()
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return std::string(ggml_unary_op_name(op)) + "_MUL";
+    }
+
+    bool run_whole_graph() override { return true; }
+
+    double max_nmse_err() override {
+        // the fused kernel elides the rounding of the unary result that the CPU chain
+        // performs; relax the tolerance to match that drift
+        switch (type) {
+            case GGML_TYPE_F16: return 5e-5;
+            default:            return 1e-7;
+        }
+    }
+
+    std::string vars() override {
+        return VARS_TO_STR5(type, ne, swap, layout, tail);
+    }
+
+    test_unary_mul(ggml_unary_op op,
+            ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {128, 2, 2, 2},
+            bool swap = false,
+            std::string layout = "packed",
+            std::string tail = "")
+        : op(op), type(type), ne(ne), swap(swap), layout(std::move(layout)), tail(std::move(tail)) {}
+
+    // `ne` viewed out of a wider tensor: rows stay contiguous, but the stride exceeds the width
+    ggml_tensor * padded(ggml_context * ctx, const char * name, int64_t mul0, int64_t off0) {
+        std::array<int64_t, 4> ne_w = ne;
+        ne_w[0] *= mul0;
+        ggml_tensor * base = ggml_new_tensor(ctx, type, 4, ne_w.data());
+        ggml_set_name(base, name);
+        return ggml_view_4d(ctx, base, ne[0], ne[1], ne[2], ne[3],
+                            base->nb[1], base->nb[2], base->nb[3], off0 * base->nb[0]);
+    }
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = nullptr; // unary source
+        ggml_tensor * b = nullptr; // other MUL operand
+
+        if (layout == "packed") {
+            a = ggml_new_tensor(ctx, type, 4, ne.data());
+            b = ggml_new_tensor(ctx, type, 4, ne.data());
+        } else if (layout == "pad_unary") {
+            a = padded(ctx, "a", 3, 0);
+            b = ggml_new_tensor(ctx, type, 4, ne.data());
+        } else if (layout == "pad_other") {
+            a = ggml_new_tensor(ctx, type, 4, ne.data());
+            b = padded(ctx, "b", 3, 0);
+        } else if (layout == "halves") {
+            // the shape the Conformer audio encoders build: one tensor split in two
+            std::array<int64_t, 4> ne_w = ne;
+            ne_w[0] *= 2;
+            ggml_tensor * base = ggml_new_tensor(ctx, type, 4, ne_w.data());
+            ggml_set_name(base, "base");
+            b = ggml_view_4d(ctx, base, ne[0], ne[1], ne[2], ne[3], base->nb[1], base->nb[2], base->nb[3], 0);
+            a = ggml_view_4d(ctx, base, ne[0], ne[1], ne[2], ne[3], base->nb[1], base->nb[2], base->nb[3],
+                             ne[0] * base->nb[0]);
+        } else if (layout == "strided_dim1") {
+            // contiguous rows but a strided dim 1: not ggml_is_contiguous_1, must not fuse
+            std::array<int64_t, 4> ne_w = ne;
+            ne_w[1] *= 3;
+            ggml_tensor * base = ggml_new_tensor(ctx, type, 4, ne_w.data());
+            ggml_set_name(base, "a");
+            a = ggml_view_4d(ctx, base, ne[0], ne[1], ne[2], ne[3], base->nb[1], base->nb[2], base->nb[3], 0);
+            b = ggml_new_tensor(ctx, type, 4, ne.data());
+        } else if (layout == "bcast") {
+            a = ggml_new_tensor(ctx, type, 4, ne.data());
+            b = ggml_new_tensor_4d(ctx, type, ne[0], 1, 1, 1);
+        } else {
+            GGML_ABORT("unknown layout %s", layout.c_str());
+        }
+        ggml_set_name(a, "a");
+        ggml_set_name(b, "b");
+
+        ggml_tensor * u = ggml_unary(ctx, a, op);
+        ggml_set_name(u, "unary");
+
+        // a broadcasting operand can only be the second one
+        const bool second = swap && layout != "bcast";
+        ggml_tensor * out = second ? ggml_mul(ctx, b, u) : ggml_mul(ctx, u, b);
+
+        if (tail == "reuse") {
+            // a second read of the unary result must block the fusion
+            ggml_set_name(out, "mul");
+            out = ggml_add(ctx, out, u);
+        } else if (tail == "consumer") {
+            // fusion still applies; catches a dispatcher that skips one node too many
+            ggml_set_name(out, "mul");
+            out = ggml_add(ctx, out, b);
+        } else if (!tail.empty()) {
+            GGML_ABORT("unknown tail %s", tail.c_str());
+        }
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+};
+
 // SNAKE activation fusion: y = x + sin(a*x)^2 * inv_b
 // CUDA backend matches the naive 5-op chain (mul, sin, sqr, mul, add)
 // and dispatches a single fused kernel.
@@ -8000,7 +8111,8 @@ static const ggml_type all_types[] = {
     GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
     GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
     GGML_TYPE_Q6_K,
-    // GGML_TYPE_TQ1_0, GGML_TYPE_TQ2_0, // TODO: implement for all backends
+    GGML_TYPE_TQ2_0,
+    // GGML_TYPE_TQ1_0, // TODO: implement for all backends
     GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
     GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
     GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
@@ -8027,7 +8139,8 @@ static const ggml_type other_types[] = {
     GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
     GGML_TYPE_Q5_K,
     GGML_TYPE_Q6_K,
-    // GGML_TYPE_TQ1_0, GGML_TYPE_TQ2_0, // TODO: implement for all backends
+    GGML_TYPE_TQ2_0,
+    // GGML_TYPE_TQ1_0, // TODO: implement for all backends
     GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
     GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
     GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
@@ -8063,6 +8176,25 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_relu_sqr(type, { 5, 7, 11, 13 }));
     }
 
+    // fused unary + mul (gated activations that are not expressed as GGML_OP_GLU)
+    for (ggml_unary_op op : { GGML_UNARY_OP_SILU, GGML_UNARY_OP_SIGMOID, GGML_UNARY_OP_SOFTPLUS }) {
+        for (ggml_type type : { GGML_TYPE_F16, GGML_TYPE_F32 }) {
+            for (bool swap : { false, true }) {
+                test_cases.emplace_back(new test_unary_mul(op, type, { 128, 2, 2, 2 }, swap));
+            }
+            test_cases.emplace_back(new test_unary_mul(op, type, { 5, 7, 11, 13 }));
+            test_cases.emplace_back(new test_unary_mul(op, type, { 128, 2, 2, 2 }, false, "pad_unary"));
+            // a view only stays out from between the two ops when the unary result is second
+            test_cases.emplace_back(new test_unary_mul(op, type, { 128, 2, 2, 2 }, true, "pad_other"));
+            test_cases.emplace_back(new test_unary_mul(op, type, { 128, 2, 2, 2 }, true, "halves"));
+            test_cases.emplace_back(new test_unary_mul(op, type, { 128, 2, 2, 2 }, false, "packed", "consumer"));
+            // must not fuse
+            test_cases.emplace_back(new test_unary_mul(op, type, { 128, 2, 2, 2 }, false, "strided_dim1"));
+            test_cases.emplace_back(new test_unary_mul(op, type, { 128, 2, 2, 2 }, false, "bcast"));
+            test_cases.emplace_back(new test_unary_mul(op, type, { 128, 2, 2, 2 }, false, "packed", "reuse"));
+        }
+    }
+
     // SNAKE activation fusion: x + sin(a*x)^2 * inv_b
     for (ggml_type type : { GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16 }) {
         test_cases.emplace_back(new test_snake_fuse(type, {   5,   7, 1, 1}));   // primes sub-block
@@ -8863,7 +8995,13 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
 
     for (ggml_type type_a : all_types) {
         for (int i = 1; i < 10; ++i) {
-            test_cases.emplace_back(new test_mul_mat(type_a,    GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));
+            test_cases.emplace_back(new test_mul_mat(type_a,    GGML_TYPE_F32, 16,  i, 1*256, { 1,  1}, {1, 1}));
+            //test_cases.emplace_back(new test_mul_mat(type_a,    GGML_TYPE_F32, 12,  i, 2*256, { 2,  1}, {1, 1}));
+            //test_cases.emplace_back(new test_mul_mat(type_a,    GGML_TYPE_F32, 11,  i, 3*256, { 1,  3}, {5, 1}));
+            //test_cases.emplace_back(new test_mul_mat(type_a,    GGML_TYPE_F32, 13,  i, 4*256, { 2,  3}, {1, 1}));
+            //test_cases.emplace_back(new test_mul_mat(type_a,    GGML_TYPE_F32, 17,  i, 31*256, { 4,  1}, {1, 1}));
+            //test_cases.emplace_back(new test_mul_mat(type_a,    GGML_TYPE_F32, 18,  i, 32*256, { 1,  1}, {8, 1}));
+            //test_cases.emplace_back(new test_mul_mat(type_a,    GGML_TYPE_F32, 19,  i, 33*256, { 1,  1}, {1, 1}));
         }
     }
 

tests/test-gguf.cpp

@@ -31,11 +31,13 @@ enum handcrafted_file_type {
     // HANDCRAFTED_KV_BAD_VALUE_SIZE          =  30 + offset_has_kv, // removed because it can result in allocations > 1 TB (default sanitizer limit)
     HANDCRAFTED_KV_DUPLICATE_KEY           =  40 + offset_has_kv,
     HANDCRAFTED_KV_BAD_ALIGN               =  50 + offset_has_kv,
+    HANDCRAFTED_KV_WRONG_TYPE_ALIGN        =  55 + offset_has_kv,
     HANDCRAFTED_KV_SUCCESS                 = 800 + offset_has_kv,
 
     HANDCRAFTED_TENSORS_BAD_NAME_SIZE      =  10 + offset_has_tensors,
     HANDCRAFTED_TENSORS_BAD_N_DIMS         =  20 + offset_has_tensors,
     HANDCRAFTED_TENSORS_BAD_SHAPE          =  30 + offset_has_tensors,
+    HANDCRAFTED_TENSORS_ZERO_DIM           =  35 + offset_has_tensors,
     HANDCRAFTED_TENSORS_NE_TOO_BIG         =  40 + offset_has_tensors,
     HANDCRAFTED_TENSORS_NBYTES_TOO_BIG     =  45 + offset_has_tensors,
     HANDCRAFTED_TENSORS_BAD_TYPE           =  50 + offset_has_tensors,
@@ -69,11 +71,13 @@ static std::string handcrafted_file_type_name(const enum handcrafted_file_type h
         case HANDCRAFTED_KV_BAD_TYPE:                return "KV_BAD_TYPE";
         case HANDCRAFTED_KV_DUPLICATE_KEY:           return "KV_DUPLICATE_KEY";
         case HANDCRAFTED_KV_BAD_ALIGN:               return "KV_BAD_ALIGN";
+        case HANDCRAFTED_KV_WRONG_TYPE_ALIGN:        return "KV_WRONG_TYPE_ALIGN";
         case HANDCRAFTED_KV_SUCCESS:                 return "KV_RANDOM_KV";
 
         case HANDCRAFTED_TENSORS_BAD_NAME_SIZE:      return "TENSORS_BAD_NAME_SIZE";
         case HANDCRAFTED_TENSORS_BAD_N_DIMS:         return "TENSORS_BAD_N_DIMS";
         case HANDCRAFTED_TENSORS_BAD_SHAPE:          return "TENSORS_BAD_SHAPE";
+        case HANDCRAFTED_TENSORS_ZERO_DIM:           return "TENSORS_ZERO_DIM";
         case HANDCRAFTED_TENSORS_NE_TOO_BIG:         return "TENSORS_NE_TOO_BIG";
         case HANDCRAFTED_TENSORS_NBYTES_TOO_BIG:     return "TENSORS_NBYTES_TOO_BIG";
         case HANDCRAFTED_TENSORS_BAD_TYPE:           return "TENSORS_BAD_TYPE";
@@ -95,6 +99,9 @@ static std::string handcrafted_file_type_name(const enum handcrafted_file_type h
 }
 
 static bool expect_context_not_null(const enum handcrafted_file_type hft) {
+    if (hft == HANDCRAFTED_TENSORS_ZERO_DIM) {
+        return true;
+    }
     if (hft < offset_has_kv) {
         return hft >= HANDCRAFTED_HEADER_EMPTY;
     }
@@ -257,9 +264,9 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
     }
     {
         uint64_t n_kv = kv_types.size();
-        if (hft == HANDCRAFTED_KV_BAD_ALIGN      ||
-            hft == HANDCRAFTED_TENSORS_BAD_ALIGN || hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN ||
-            hft == HANDCRAFTED_DATA_BAD_ALIGN    || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+        if (hft == HANDCRAFTED_KV_BAD_ALIGN        || hft == HANDCRAFTED_KV_WRONG_TYPE_ALIGN ||
+            hft == HANDCRAFTED_TENSORS_BAD_ALIGN   || hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN ||
+            hft == HANDCRAFTED_DATA_BAD_ALIGN      || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
 
             n_kv += 1;
         } else if (hft == HANDCRAFTED_HEADER_BAD_N_KV) {
@@ -344,15 +351,17 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
         helper_write(file, data, hft == HANDCRAFTED_KV_BAD_TYPE ? 1 : gguf_type_size(type));
     }
 
-    if (hft == HANDCRAFTED_KV_BAD_ALIGN      ||
-        hft == HANDCRAFTED_TENSORS_BAD_ALIGN || hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN ||
-        hft == HANDCRAFTED_DATA_BAD_ALIGN    || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
+    if (hft == HANDCRAFTED_KV_BAD_ALIGN        || hft == HANDCRAFTED_KV_WRONG_TYPE_ALIGN ||
+        hft == HANDCRAFTED_TENSORS_BAD_ALIGN   || hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN ||
+        hft == HANDCRAFTED_DATA_BAD_ALIGN      || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) {
 
         const uint64_t n = strlen(GGUF_KEY_GENERAL_ALIGNMENT);
         helper_write(file, n);
         helper_write(file, GGUF_KEY_GENERAL_ALIGNMENT, n);
 
-        const int32_t type = gguf_type(GGUF_TYPE_UINT32);
+        // HANDCRAFTED_KV_WRONG_TYPE_ALIGN declares general.alignment with a non-UINT32 type,
+        // which the loader must reject cleanly instead of aborting on an assertion
+        const int32_t type = hft == HANDCRAFTED_KV_WRONG_TYPE_ALIGN ? int32_t(GGUF_TYPE_INT32) : int32_t(GGUF_TYPE_UINT32);
         helper_write(file, type);
 
         alignment = expect_context_not_null(hft) ? 1 : 13;
@@ -403,6 +412,9 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
                 break;
             }
         }
+        if (hft == HANDCRAFTED_TENSORS_ZERO_DIM) {
+            n_dims = 2;
+        }
         if (hft == HANDCRAFTED_TENSORS_BAD_N_DIMS) {
             const uint32_t n_dims_bad = GGML_MAX_DIMS + 1;
             helper_write(file, n_dims_bad);
@@ -415,6 +427,9 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
             for (uint32_t j = 0; j < n_dims; ++j) {
                 helper_write(file, bad_dim);
             }
+        } else if (hft == HANDCRAFTED_TENSORS_ZERO_DIM) {
+            const int64_t zero_shape[2] = { shape[0], 0 };
+            helper_write(file, zero_shape, 2*sizeof(int64_t));
         } else if (hft == HANDCRAFTED_TENSORS_NE_TOO_BIG){
             const int64_t big_dim = 4*int64_t(INT32_MAX);
             for (uint32_t j = 0; j < n_dims; ++j) {
@@ -446,6 +461,9 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft
         for (uint32_t i = 1; i < n_dims; ++i) {
             ne *= shape[i];
         }
+        if (hft == HANDCRAFTED_TENSORS_ZERO_DIM) {
+            ne = 0;
+        }
 
         offset += GGML_PAD(ggml_row_size(type, ne), (uint64_t) alignment);
     }
@@ -747,11 +765,13 @@ static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
         HANDCRAFTED_KV_BAD_TYPE,
         HANDCRAFTED_KV_DUPLICATE_KEY,
         HANDCRAFTED_KV_BAD_ALIGN,
+        HANDCRAFTED_KV_WRONG_TYPE_ALIGN,
         HANDCRAFTED_KV_SUCCESS,
 
         HANDCRAFTED_TENSORS_BAD_NAME_SIZE,
         HANDCRAFTED_TENSORS_BAD_N_DIMS,
         HANDCRAFTED_TENSORS_BAD_SHAPE,
+        HANDCRAFTED_TENSORS_ZERO_DIM,
         HANDCRAFTED_TENSORS_NE_TOO_BIG,
         HANDCRAFTED_TENSORS_NBYTES_TOO_BIG,
         HANDCRAFTED_TENSORS_BAD_TYPE,
@@ -840,7 +860,9 @@ static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
             ntest++;
         }
 
-        if (expect_context_not_null(hft) && hft >= offset_has_tensors) {
+        // HANDCRAFTED_TENSORS_ZERO_DIM deliberately mangles the tensor shapes to 0 elements,
+        // so only assert that it loads without crashing; skip the exact-geometry comparison.
+        if (expect_context_not_null(hft) && hft >= offset_has_tensors && hft != HANDCRAFTED_TENSORS_ZERO_DIM) {
             printf("%s:   - check_tensors: ", __func__);
             if (handcrafted_check_tensors(gguf_ctx, seed)) {
                 printf("\033[1;32mOK\033[0m\n");

tests/test-quantize-stats.cpp

@@ -301,7 +301,7 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    llama_print_build_info();
+    llama_print_build_info(llama_version());
 
     // load the model
     fprintf(stderr, "Loading model\n");

tools/cvector-generator/cvector-generator.cpp

@@ -421,7 +421,7 @@ int main(int argc, char ** argv) {
     params.cb_eval_user_data = &cb_data;
     params.warmup = false;
 
-    llama_print_build_info();
+    llama_print_build_info(llama_version());
     llama_backend_init();
     llama_numa_init(params.numa);
 

tools/gguf-split/gguf-split.cpp

@@ -106,7 +106,7 @@ static void split_params_parse_ex(int argc, const char ** argv, split_params & p
             split_print_usage(argv[0]);
             exit(0);
         } else if (arg == "--version") {
-            fprintf(stderr, "version: %d (%s)\n", llama_build_number(), llama_commit());
+            fprintf(stderr, "version: %s (build %d, commit %s)\n", llama_version(), llama_build_number(), llama_commit());
             fprintf(stderr, "built with %s for %s\n", llama_compiler(), llama_build_target());
             exit(0);
         } else if (arg == "--dry-run") {

tools/mtmd/CMakeLists.txt

@@ -72,8 +72,8 @@ add_library(mtmd
             )
 
 set_target_properties(mtmd PROPERTIES
-    VERSION ${LLAMA_INSTALL_VERSION}
-    SOVERSION 0
+    VERSION ${LLAMA_VERSION_BASE}
+    SOVERSION ${LLAMA_VERSION_MAJOR}
     MACHO_CURRENT_VERSION 0 # keep macOS linker from seeing oversized version number
 )
 

tools/quantize/quantize.cpp

@@ -611,7 +611,7 @@ int llama_quantize(int argc, char ** argv) {
         }
     }
 
-    llama_print_build_info();
+    llama_print_build_info(llama_version());
 
     if (params.dry_run) {
         fprintf(stderr, "%s: calculating quantization size for '%s' as %s", __func__, fname_inp.c_str(), ftype_str.c_str());

tools/server/server-common.cpp

@@ -13,6 +13,8 @@
 #include <sstream>
 #include <fstream>
 #include <limits>
+#include <cstring>
+#include <type_traits>
 
 json format_error_response(const std::string & message, const enum error_type type) {
     std::string type_str;
@@ -58,6 +60,33 @@ json format_error_response(const std::string & message, const enum error_type ty
     };
 }
 
+//
+// server_slot_stats
+//
+
+json server_slot_stats::to_json() const {
+    json base = {
+        {"cache_n",                n_prompt_cached},
+
+        {"prompt_n",               n_prompt_processed},
+        {"prompt_ms",              t_prompt_ms()},
+        {"prompt_per_token_ms",    t_prompt_per_token_ms()},
+        {"prompt_per_second",      n_prompt_tps()},
+
+        {"predicted_n",            n_gen},
+        {"predicted_ms",           t_gen_ms()},
+        {"predicted_per_token_ms", t_gen_per_token_ms()},
+        {"predicted_per_second",   n_gen_tps()},
+    };
+
+    if (n_draft_tokens > 0) {
+        base["draft_n"]          = n_draft_tokens;
+        base["draft_n_accepted"] = n_draft_accepted;
+    }
+
+    return base;
+}
+
 //
 // random string / id
 //
@@ -235,6 +264,102 @@ static inline raw_buffer base64_decode(const std::string & encoded_string) {
 // server_tokens implementation
 //
 
+namespace {
+
+constexpr uint32_t SERVER_TOKENS_STATE_VERSION = 1;
+
+uint32_t server_tokens_state_u32(size_t value) {
+    if (value > std::numeric_limits<uint32_t>::max()) {
+        throw std::runtime_error("Server tokens state is too large");
+    }
+    return value;
+}
+
+class server_tokens_state_writer {
+public:
+    template <typename T>
+    void write(T value) {
+        static_assert(std::is_trivially_copyable<T>::value, "T must be trivially copyable");
+        const auto * ptr = reinterpret_cast<const char *>(&value);
+        data.insert(data.end(), ptr, ptr + sizeof(value));
+    }
+
+    template <typename T>
+    void write(const std::vector<T> & values) {
+        static_assert(std::is_trivially_copyable<T>::value, "T must be trivially copyable");
+        write(server_tokens_state_u32(values.size()));
+        if (values.empty()) {
+            return;
+        }
+        const auto * ptr = reinterpret_cast<const char *>(values.data());
+        data.insert(data.end(), ptr, ptr + values.size() * sizeof(T));
+    }
+
+    void write_media_chunk(const mtmd_input_chunk * chunk) {
+        size_t chunk_size = 0;
+        if (mtmd_input_chunk_save(chunk, nullptr, 0, &chunk_size) != 0 || chunk_size == 0) {
+            throw std::runtime_error("Cannot serialize media chunk in server tokens");
+        }
+        std::vector<char> chunk_data(server_tokens_state_u32(chunk_size));
+        if (mtmd_input_chunk_save(chunk, chunk_data.data(), chunk_data.size(), nullptr) != 0) {
+            throw std::runtime_error("Cannot serialize media chunk in server tokens");
+        }
+        write(chunk_data);
+    }
+
+    std::vector<char> take() {
+        data.resize((data.size() + sizeof(llama_token) - 1) / sizeof(llama_token) * sizeof(llama_token), 0);
+        return std::move(data);
+    }
+
+private:
+    std::vector<char> data;
+};
+
+class server_tokens_state_reader {
+public:
+    server_tokens_state_reader(const char * data, size_t size) : data(data), size(size) {}
+
+    template <typename T>
+    T read() {
+        static_assert(std::is_trivially_copyable<T>::value, "T must be trivially copyable");
+        if (size - pos < sizeof(T)) {
+            throw std::runtime_error("Unexpected end of server tokens state");
+        }
+        T value;
+        std::memcpy(&value, data + pos, sizeof(value));
+        pos += sizeof(value);
+        return value;
+    }
+
+    template <typename T>
+    std::vector<T> read_vector() {
+        static_assert(std::is_trivially_copyable<T>::value, "T must be trivially copyable");
+        const uint32_t n_values = read<uint32_t>();
+        // reject before resizing, so that a small corrupted payload cannot request a huge allocation
+        if (n_values > remaining() / sizeof(T)) {
+            throw std::runtime_error("Unexpected end of server tokens state");
+        }
+        std::vector<T> values(n_values);
+        if (n_values > 0) {
+            std::memcpy(values.data(), data + pos, values.size() * sizeof(T));
+            pos += values.size() * sizeof(T);
+        }
+        return values;
+    }
+
+    size_t remaining() const {
+        return size - pos;
+    }
+
+private:
+    const char * data;
+    size_t size;
+    size_t pos = 0;
+};
+
+} // namespace
+
 server_tokens::server_tokens(mtmd::input_chunks & mtmd_chunks, bool has_mtmd) : has_mtmd(has_mtmd) {
     for (size_t i = 0; i < mtmd_chunks.size(); ++i) {
         push_back(mtmd_chunks[i]);
@@ -408,6 +533,73 @@ const llama_tokens & server_tokens::get_tokens() const {
     return tokens;
 }
 
+std::vector<char> server_tokens::serialize() const {
+    static_assert(sizeof(llama_token) == sizeof(uint32_t), "unexpected llama_token size");
+
+    server_tokens_state_writer writer;
+    writer.write((llama_token) LLAMA_TOKEN_NULL);
+    writer.write(SERVER_TOKENS_STATE_VERSION);
+    writer.write(tokens);
+
+    std::vector<uint32_t> media_keys;
+    media_keys.reserve(map_idx_to_media.size());
+    for (const auto & item : map_idx_to_media) {
+        media_keys.push_back(server_tokens_state_u32(item.first));
+    }
+    writer.write(media_keys);
+
+    for (const auto & item : map_idx_to_media) {
+        writer.write_media_chunk(item.second.get());
+    }
+
+    return writer.take();
+}
+
+server_tokens server_tokens::deserialize(const llama_tokens & packed, bool has_mtmd) {
+    static_assert(sizeof(llama_token) == sizeof(uint32_t), "unexpected llama_token size");
+
+    if (packed.empty() || packed[0] != LLAMA_TOKEN_NULL) {
+        // plain token list, as written by older versions
+        return server_tokens(packed, has_mtmd);
+    }
+
+    server_tokens_state_reader reader(reinterpret_cast<const char *>(packed.data()), packed.size() * sizeof(llama_token));
+    reader.read<llama_token>(); // format marker
+    if (reader.read<uint32_t>() != SERVER_TOKENS_STATE_VERSION) {
+        throw std::runtime_error("Unsupported server tokens state version");
+    }
+
+    const llama_tokens tokens = reader.read_vector<llama_token>();
+
+    // the media start indices, followed by the media chunks in the same order
+    const std::vector<uint32_t> media_keys = reader.read_vector<uint32_t>();
+    if (!media_keys.empty() && !has_mtmd) {
+        throw std::runtime_error("Cannot restore media tokens without an mmproj");
+    }
+
+    server_tokens result(tokens, has_mtmd);
+
+    for (const uint32_t key : media_keys) {
+        const size_t start_idx = key;
+        const std::vector<char> chunk_data = reader.read_vector<char>();
+        if (chunk_data.empty()) {
+            throw std::runtime_error("Cannot load media chunk from server tokens state");
+        }
+
+        mtmd::input_chunk_ptr chunk(mtmd_input_chunk_load(chunk_data.data(), chunk_data.size()));
+        if (!chunk) {
+            throw std::runtime_error("Cannot load media chunk from server tokens state");
+        }
+        result.map_idx_to_media[start_idx] = std::move(chunk);
+    }
+
+    if (reader.remaining() >= sizeof(llama_token)) {
+        throw std::runtime_error("Trailing data in server tokens state");
+    }
+
+    return result;
+}
+
 llama_tokens server_tokens::get_text_tokens() const {
     llama_tokens res;
     res.reserve(tokens.size());
@@ -530,14 +722,28 @@ bool server_tokens::validate(const struct llama_context * ctx) const {
     const llama_model * model = llama_get_model(ctx);
     const llama_vocab * vocab = llama_model_get_vocab(model);
     const int32_t n_vocab = llama_vocab_n_tokens(vocab);
+    size_t n_media = 0;
 
     for (size_t i = 0; i < tokens.size(); ++i) {
         const auto & t = tokens[i];
         if (t == LLAMA_TOKEN_NULL) {
             try {
                 const auto & chunk = find_chunk(i);
-                size_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk.get());
-                i += n_tokens - 1; // will be +1 by the for loop
+                if (mtmd_input_chunk_get_type(chunk.get()) == MTMD_INPUT_CHUNK_TYPE_TEXT) {
+                    return false;
+                }
+                const size_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk.get());
+                const llama_pos n_pos = mtmd_input_chunk_get_n_pos(chunk.get());
+                if (n_tokens == 0 || n_pos <= 0 || n_tokens > tokens.size() - i) {
+                    return false;
+                }
+                for (size_t j = i; j < i + n_tokens; ++j) {
+                    if (tokens[j] != LLAMA_TOKEN_NULL) {
+                        return false;
+                    }
+                }
+                ++n_media;
+                i += n_tokens - 1;
             } catch (const std::exception & e) {
                 return false;
             }
@@ -545,7 +751,7 @@ bool server_tokens::validate(const struct llama_context * ctx) const {
             return false;
         }
     }
-    return true;
+    return n_media == map_idx_to_media.size();
 }
 
 server_tokens server_tokens::clone() const {

tools/server/server-common.h

@@ -201,11 +201,14 @@ public:
     // for compatibility with context shift and prompt truncation
     void insert(const llama_tokens & inp_tokens);
 
-    // for compatibility with speculative decoding, ctx shift, slot save/load
+    // for compatibility with speculative decoding, ctx shift
     const llama_tokens & get_tokens() const;
 
     llama_tokens get_text_tokens() const;
 
+    std::vector<char> serialize() const;
+    static server_tokens deserialize(const llama_tokens & packed, bool has_mtmd);
+
     // for compatibility with speculative decoding
     void set_token(llama_pos pos, llama_token id);
 
@@ -213,9 +216,6 @@ public:
 
     bool empty() const { return tokens.empty(); }
 
-    // true if the sequence actually contains image/audio chunks.
-    bool has_media() const { return !map_idx_to_media.empty(); }
-
     void clear() {
         map_idx_to_media.clear();
         tokens.clear();
@@ -230,7 +230,7 @@ public:
     // split the tokens into message spans, skipping over media chunks
     common_chat_msg_spans find_message_spans(const common_chat_msg_delimiters & delims) const;
 
-    // make sure all text tokens are within the vocab range
+    // check text token IDs and the mapping between media chunks and token ranges
     bool validate(const struct llama_context * ctx) const;
 
     server_tokens clone() const;
@@ -334,6 +334,160 @@ json format_response_rerank(
         std::vector<std::string> & texts,
         int top_n);
 
+//
+// stats and metrics
+//
+
+// shared between server_slot and server_task_result_*
+struct server_slot_stats {
+    uint64_t n_prompt_cached    = 0;
+    uint64_t n_prompt_processed = 0;
+    uint64_t n_gen              = 0;
+
+    // speculative decoding stats
+    // note: the per-position breakdown lives in server_slot, it is not needed in a task result
+    uint64_t n_draft_tokens      = 0;
+    uint64_t n_draft_accepted    = 0;
+    uint64_t n_draft_verif_steps = 0;
+
+    // these are absolute timestamps (in us)
+    // note: must be signed - they are subtracted before the later ones are set
+    int64_t t_start       = 0;
+    int64_t t_prompt_last = 0;
+    int64_t t_gen_last    = 0;
+
+    // can only move one direction: start -> prompt -> gen
+    void update_prompt_start() {
+        GGML_ASSERT(t_start == 0);
+        t_start = ggml_time_us();
+    }
+    void set_prompt_last(int64_t t_us) {
+        GGML_ASSERT(t_start > 0);
+        t_prompt_last = t_us;
+    }
+    void update_prompt_last() {
+        set_prompt_last(ggml_time_us());
+    }
+    void update_gen_last() {
+        GGML_ASSERT(t_prompt_last > 0);
+        t_gen_last = ggml_time_us();
+    }
+
+    // these are time durations
+    int64_t t_elapsed_us() const {
+        return ggml_time_us() - t_start;
+    }
+    double t_prompt_ms() const {
+        if (t_prompt_last == 0) {
+            return 0.0; // the prompt is not processed yet
+        }
+        return (t_prompt_last - t_start) / 1000.0;
+    }
+    int64_t t_gen_us() const {
+        if (t_gen_last == 0) {
+            return 0; // the generation is not started yet
+        }
+        // clamp to 1 us, the first token can land in the same us as t_prompt_last
+        return std::max<int64_t>(1, t_gen_last - t_prompt_last);
+    }
+    double t_gen_ms() const {
+        return t_gen_us() / 1000.0;
+    }
+
+    // number of decode steps spent on generation
+    // the first token is free, it comes from the logits of the last prompt batch
+    uint64_t n_gen_steps() const {
+        return n_gen > 0 ? n_gen - 1 : 0;
+    }
+
+    // other derived metrics
+    // note: all of them return 0.0 if the divisor is not known yet
+    double t_prompt_per_token_ms() const {
+        return n_prompt_processed > 0 ? t_prompt_ms() / n_prompt_processed : 0.0;
+    }
+    double t_gen_per_token_ms() const {
+        return n_gen_steps() > 0 ? t_gen_ms() / n_gen_steps() : 0.0;
+    }
+    double n_prompt_tps() const {
+        const double t_ms = t_prompt_ms();
+        return t_ms > 0.0 ? 1e3 / t_ms * n_prompt_processed : 0.0;
+    }
+    double n_gen_tps() const {
+        const double t_ms = t_gen_ms();
+        return t_ms > 0.0 ? 1e3 / t_ms * n_gen_steps() : 0.0;
+    }
+
+    // false if the slot never started, i.e. the task result carries no stats
+    bool is_set() const {
+        return t_start > 0;
+    }
+
+    json to_json() const;
+};
+
+// shared between server_context_impl and server_task_result_*
+// unlike server_slot_stats, server_metrics is server-global and cumulative, not tied to a slot
+struct server_metrics {
+    int64_t t_start = 0;
+
+    struct bucket {
+        uint64_t count = 0; // number of tokens
+        uint64_t steps = 0; // number of decode steps,
+                            // this excludes first generated token (logits from prompt batch)
+        uint64_t time  = 0; // in microseconds
+
+        // the rate uses the decode steps, so that "free" tokens do not inflate it
+        double n_per_second() const {
+            return time > 0 ? (double) steps / (double) time * 1e6 : 0.0;
+        }
+
+        void add(uint64_t n, uint64_t n_steps, uint64_t t_us) {
+            count += n;
+            steps += n_steps;
+            time  += t_us;
+        }
+    };
+
+    // these are reset by reset_bucket(), only the rate is read from them
+    bucket prompt_bucket;
+    bucket predict_bucket;
+
+    // metrics below are cumulative since the server started
+    bucket prompt; // only processed tokens, cached ones are counted separately below
+    bucket predict;
+
+    // tokens reused from the cache need no decode, so they only have a count
+    uint64_t n_prompt_cached = 0;
+
+    uint64_t n_tokens_max = 0;
+
+    uint64_t n_decode     = 0;
+    uint64_t n_busy_slots = 0;
+
+    uint64_t n_draft_tokens      = 0; // Total draft tokens generated
+    uint64_t n_draft_accepted    = 0; // Draft tokens actually accepted
+    uint64_t n_draft_verif_steps = 0; // Total draft token verification steps by the target model
+    std::vector<uint64_t> n_accepted_per_pos; // Accepted tokens per draft position
+
+    void init() {
+        t_start = ggml_time_us();
+    }
+
+    void reset_bucket() {
+        prompt_bucket  = {};
+        predict_bucket = {};
+    }
+
+    void add_prompt(uint64_t n_tokens, uint64_t t_us) {
+        prompt       .add(n_tokens, n_tokens, t_us);
+        prompt_bucket.add(n_tokens, n_tokens, t_us);
+    }
+
+    void add_prompt_cached(uint64_t n_tokens) {
+        n_prompt_cached += n_tokens;
+    }
+};
+
 //
 // other utils
 //

tools/server/server-http.cpp

@@ -355,8 +355,15 @@ bool server_http_context::init(const common_params & params) {
                 return true;
             };
 
-            auto serve_asset_cached = [](const std::string & name, bool isolation) {
-                return [name, isolation](const httplib::Request & req, httplib::Response & res) {
+            // Hashed assets never change under a given name, so they can be cached forever.
+            // `index.html` is the exception: its name is stable while its contents change on
+            // every build, and it is what names the hashed asset versions the UI loads.
+            static constexpr auto cache_immutable  = "public, max-age=31536000, immutable";
+            static constexpr auto cache_revalidate = "no-cache";
+
+            // Serves an asset with ETag/304 handling, under the given caching policy.
+            auto serve_asset_cached = [](const std::string & name, bool isolation, const char * cache_control) {
+                return [name, isolation, cache_control](const httplib::Request & req, httplib::Response & res) {
                     if (!handle_gzip_header(req, res)) {
                         return true; // returns error message
                     }
@@ -372,7 +379,7 @@ bool server_http_context::init(const common_params & params) {
                         res.set_header("Cross-Origin-Embedder-Policy", "require-corp");
                         res.set_header("Cross-Origin-Opener-Policy",   "same-origin");
                     }
-                    res.set_header("Cache-Control", "public, max-age=31536000, immutable");
+                    res.set_header("Cache-Control", cache_control);
                     res.set_content(reinterpret_cast<const char*>(a->data), a->size, a->type.c_str());
                     return false;
                 };
@@ -394,9 +401,9 @@ bool server_http_context::init(const common_params & params) {
                 };
             };
 
-            // main index file
-            srv->Get(params.api_prefix + "/",           serve_asset_cached("index.html", true));
-            srv->Get(params.api_prefix + "/index.html", serve_asset_cached("index.html", true));
+            // main index file -- revalidated, so a new build is picked up on the next load
+            srv->Get(params.api_prefix + "/",           serve_asset_cached("index.html", true, cache_revalidate));
+            srv->Get(params.api_prefix + "/index.html", serve_asset_cached("index.html", true, cache_revalidate));
 
             // All remaining assets registered directly from the embedded asset table.
             // PWA revalidation files (sw.js, manifest, version.json) use no-cache;
@@ -414,7 +421,7 @@ bool server_http_context::init(const common_params & params) {
                     SRV_DBG("serve nocache for %s\n", a.name.c_str());
                     srv->Get(params.api_prefix + "/" + a.name, serve_asset_nocache(a.name));
                 } else {
-                    srv->Get(params.api_prefix + "/" + a.name, serve_asset_cached(a.name, false));
+                    srv->Get(params.api_prefix + "/" + a.name, serve_asset_cached(a.name, false, cache_immutable));
                 }
             }
 

tools/server/server-task.cpp

@@ -10,6 +10,8 @@
 #include "speculative.h"
 #include "server-common.h"
 
+#include <sstream>
+
 using json = nlohmann::ordered_json;
 
 //
@@ -236,34 +238,6 @@ common_chat_msg task_result_state::update_chat_msg(
     return chat_msg;
 }
 
-//
-
-// result_timings
-//
-
-json result_timings::to_json() const {
-    json base = {
-        {"cache_n",                cache_n},
-
-        {"prompt_n",               prompt_n},
-        {"prompt_ms",              prompt_ms},
-        {"prompt_per_token_ms",    prompt_per_token_ms},
-        {"prompt_per_second",      prompt_per_second},
-
-        {"predicted_n",            predicted_n},
-        {"predicted_ms",           predicted_ms},
-        {"predicted_per_token_ms", predicted_per_token_ms},
-        {"predicted_per_second",   predicted_per_second},
-    };
-
-    if (draft_n > 0) {
-        base["draft_n"] = draft_n;
-        base["draft_n_accepted"] = draft_n_accepted;
-    }
-
-    return base;
-}
-
 //
 // result_prompt_progress
 //
@@ -382,7 +356,7 @@ json server_task_result_cmpl_final::to_json_non_oaicompat() {
         {"stop_type",           stop_type_to_str(stop)},
         {"stopping_word",       stopping_word},
         {"tokens_cached",       n_tokens_cached},
-        {"timings",             timings.to_json()},
+        {"timings",             stats.to_json()},
     };
     if (!stream && !probs_output.empty()) {
         res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs);
@@ -432,8 +406,8 @@ json server_task_result_cmpl_final::to_json_oaicompat() {
     if (verbose) {
         res["__verbose"] = to_json_non_oaicompat();
     }
-    if (timings.prompt_n >= 0) {
-        res.push_back({"timings", timings.to_json()});
+    if (stats.is_set()) {
+        res.push_back({"timings", stats.to_json()});
     }
 
     return res;
@@ -480,8 +454,8 @@ json server_task_result_cmpl_final::to_json_oaicompat_chat() {
     if (verbose) {
         res["__verbose"] = to_json_non_oaicompat();
     }
-    if (timings.prompt_n >= 0) {
-        res.push_back({"timings", timings.to_json()});
+    if (stats.is_set()) {
+        res.push_back({"timings", stats.to_json()});
     }
 
     return res;
@@ -541,8 +515,8 @@ json server_task_result_cmpl_final::to_json_oaicompat_chat_stream() {
         });
     }
 
-    if (timings.prompt_n >= 0) {
-        deltas.back().push_back({"timings", timings.to_json()});
+    if (stats.is_set()) {
+        deltas.back().push_back({"timings", stats.to_json()});
     }
 
     // extra fields for debugging purposes
@@ -734,8 +708,8 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() {
         }}
     });
 
-    if (timings.prompt_n >= 0) {
-        server_sent_events.back().at("data").push_back({"timings", timings.to_json()});
+    if (stats.is_set()) {
+        server_sent_events.back().at("data").push_back({"timings", stats.to_json()});
     }
 
     return server_sent_events;
@@ -1086,8 +1060,8 @@ json server_task_result_cmpl_partial::to_json_non_oaicompat() {
         {"tokens_evaluated", n_prompt_tokens},
     };
     // populate the timings object when needed (usually for the last response or with timings_per_token enabled)
-    if (timings.prompt_n > 0) {
-        res.push_back({"timings", timings.to_json()});
+    if (stats.is_set()) {
+        res.push_back({"timings", stats.to_json()});
     }
     if (is_progress) {
         res.push_back({"prompt_progress", progress.to_json()});
@@ -1126,8 +1100,8 @@ json server_task_result_cmpl_partial::to_json_oaicompat() {
     if (verbose) {
         res["__verbose"] = to_json_non_oaicompat();
     }
-    if (timings.prompt_n >= 0) {
-        res.push_back({"timings", timings.to_json()});
+    if (stats.is_set()) {
+        res.push_back({"timings", stats.to_json()});
     }
     if (is_progress) {
         res.push_back({"prompt_progress", progress.to_json()});
@@ -1180,8 +1154,8 @@ json server_task_result_cmpl_partial::to_json_oaicompat_chat() {
             };
         }
 
-        if (timings.prompt_n >= 0) {
-            last_json.push_back({"timings", timings.to_json()});
+        if (stats.is_set()) {
+            last_json.push_back({"timings", stats.to_json()});
         }
         if (is_progress) {
             last_json.push_back({"prompt_progress", progress.to_json()});
@@ -1330,8 +1304,8 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() {
 
     if (!events.empty()) {
         json & data = events.back().at("data");
-        if (timings.prompt_n >= 0) {
-            data.push_back({"timings", timings.to_json()});
+        if (stats.is_set()) {
+            data.push_back({"timings", stats.to_json()});
         }
         if (is_progress) {
             data.push_back({"prompt_progress", progress.to_json()});
@@ -1539,34 +1513,104 @@ json server_task_result_error::to_json() {
 // server_task_result_metrics
 //
 json server_task_result_metrics::to_json() {
-    return json {
-        { "idle",                            n_idle_slots },
-        { "processing",                      n_processing_slots },
-        { "deferred",                        n_tasks_deferred },
-        { "t_start",                         t_start },
+    return slots_data;
+}
 
-        { "n_prompt_tokens_processed_total", n_prompt_tokens_processed_total },
-        { "t_tokens_generation_total",       t_tokens_generation_total },
-        { "n_tokens_predicted_total",        n_tokens_predicted_total },
-        { "t_prompt_processing_total",       t_prompt_processing_total },
-
-        { "n_tokens_max",                    n_tokens_max },
-
-        { "n_prompt_tokens_processed",       n_prompt_tokens_processed },
-        { "t_prompt_processing",             t_prompt_processing },
-        { "n_tokens_predicted",              n_tokens_predicted },
-        { "t_tokens_generation",             t_tokens_generation },
-
-        { "n_decode_total",                  n_decode_total },
-        { "n_busy_slots_total",              n_busy_slots_total },
-
-        { "n_draft_tokens_total",            n_draft_tokens_total },
-        { "n_draft_accepted_total",          n_draft_accepted_total },
-        { "n_draft_verif_steps_total",       n_draft_verif_steps_total },
-        { "n_accepted_per_pos_total",        n_accepted_per_pos_total },
-
-        { "slots",                           slots_data },
+// metrics definition: https://prometheus.io/docs/practices/naming/#metric-names
+std::string server_task_result_metrics::to_metrics() {
+    const std::vector<metric_item> counters = {
+        {
+            "prompt_tokens_total",
+            "Number of prompt tokens processed, excluding cached tokens",
+            (double) metrics.prompt.count
+        }, {
+            "prompt_tokens_cached_total",
+            "Number of prompt tokens reused from the cache",
+            (double) metrics.n_prompt_cached
+        }, {
+            "prompt_seconds_total",
+            "Total time spent processing prompts",
+            metrics.prompt.time / 1.e6
+        }, {
+            "tokens_predicted_total",
+            "Number of generation tokens processed",
+            (double) metrics.predict.count
+        }, {
+            "tokens_predicted_seconds_total",
+            "Total time spent generating tokens",
+            metrics.predict.time / 1.e6
+        }, {
+            "n_decode_total",
+            "Total number of llama_decode() calls, excluding speculative decoding and multimodal decoding",
+            (double) metrics.n_decode
+        }, {
+            "n_tokens_max",
+            "Largest observed sequence length (prompt + generation)",
+            (double) metrics.n_tokens_max
+        }, {
+            "spec_decode_num_draft_tokens_total",
+            "Speculative: Total draft tokens generated",
+            (double) metrics.n_draft_tokens
+        }, {
+            "spec_decode_num_accepted_tokens_total",
+            "Speculative: Total draft tokens accepted by the target model",
+            (double) metrics.n_draft_accepted
+        }, {
+            "spec_decode_num_drafts_total",
+            "Speculative: Total speculative decoding verification steps",
+            (double) metrics.n_draft_verif_steps
+        },
     };
+
+    const std::vector<metric_item> gauges = {
+        {
+            "prompt_tokens_seconds",
+            "Average prompt throughput in tokens/s",
+            metrics.prompt_bucket.n_per_second()
+        }, {
+            "predicted_tokens_seconds",
+            "Average generation throughput in tokens/s",
+            metrics.predict_bucket.n_per_second()
+        }, {
+            "requests_processing",
+            "Number of requests processing",
+            (double) n_processing_slots
+        }, {
+            "requests_deferred",
+            "Number of requests deferred",
+            (double) n_tasks_deferred
+        }, {
+            "n_busy_slots_per_decode",
+            "Average number of busy slots per llama_decode() call",
+            (double) metrics.n_busy_slots / std::max((double) metrics.n_decode, 1.0)
+        },
+    };
+
+    std::stringstream prometheus;
+
+    auto add_items = [&prometheus](const char * type, const std::vector<metric_item> & items) {
+        for (const auto & item : items) {
+            prometheus << "# HELP llamacpp:" << item.name << " " << item.description << "\n"
+                       << "# TYPE llamacpp:" << item.name << " " << type             << "\n"
+                       << "llamacpp:"        << item.name << " " << item.value       << "\n";
+        }
+    };
+
+    add_items("counter", counters);
+    add_items("gauge",   gauges);
+
+    // labeled counter: one time series per draft position
+    if (!metrics.n_accepted_per_pos.empty()) {
+        prometheus << "# HELP llamacpp:spec_decode_num_accepted_tokens_per_pos_total"
+                      " Accepted tokens per draft position\n"
+                   << "# TYPE llamacpp:spec_decode_num_accepted_tokens_per_pos_total counter\n";
+        for (size_t i = 0; i < metrics.n_accepted_per_pos.size(); i++) {
+            prometheus << "llamacpp:spec_decode_num_accepted_tokens_per_pos_total{position=\""
+                       << i << "\"} " << metrics.n_accepted_per_pos[i] << "\n";
+        }
+    }
+
+    return prometheus.str();
 }
 
 //

tools/server/server-task.h

@@ -259,26 +259,6 @@ struct server_task {
     }
 };
 
-struct result_timings {
-    int32_t cache_n = -1;
-
-    int32_t prompt_n = -1;
-    double prompt_ms = 0.0;
-    double prompt_per_token_ms = 0.0;
-    double prompt_per_second = 0.0;
-
-    int32_t predicted_n = -1;
-    double predicted_ms = 0.0;
-    double predicted_per_token_ms = 0.0;
-    double predicted_per_second = 0.0;
-
-    // Optional speculative metrics - only included when > 0
-    int32_t draft_n = 0;
-    int32_t draft_n_accepted = 0;
-
-    json to_json() const;
-};
-
 struct result_prompt_progress {
     int32_t total = 0;
     int32_t cache = 0;
@@ -343,7 +323,7 @@ struct server_task_result_cmpl_final : server_task_result {
 
     bool stream;
     bool include_usage;
-    result_timings timings;
+    server_slot_stats stats;
     std::string prompt;
 
     bool truncated;
@@ -425,7 +405,7 @@ struct server_task_result_cmpl_partial : server_task_result {
     bool is_begin = false; // whether to send 200 status to HTTP client (begin of SSE stream)
                            // ref: https://github.com/ggml-org/llama.cpp/pull/23884
     completion_token_output prob_output;
-    result_timings timings;
+    server_slot_stats stats;
     result_prompt_progress progress;
 
     // response formatting
@@ -510,38 +490,27 @@ struct server_task_result_error : server_task_result {
 };
 
 struct server_task_result_metrics : server_task_result {
+    // these are immediate stats, not accumulated (server_metrics is cumulative)
     int n_idle_slots;
     int n_processing_slots;
     int n_tasks_deferred;
-    int64_t t_start;
 
-    // TODO: somehow reuse server_metrics in the future, instead of duplicating the fields
-    uint64_t n_prompt_tokens_processed_total = 0;
-    uint64_t t_prompt_processing_total       = 0;
-    uint64_t n_tokens_predicted_total        = 0;
-    uint64_t t_tokens_generation_total       = 0;
-
-    uint64_t n_tokens_max = 0;
-
-    uint64_t n_prompt_tokens_processed = 0;
-    uint64_t t_prompt_processing       = 0;
-
-    uint64_t n_tokens_predicted  = 0;
-    uint64_t t_tokens_generation = 0;
-
-    uint64_t n_decode_total     = 0;
-    uint64_t n_busy_slots_total = 0;
-
-    uint64_t n_draft_tokens_total      = 0;
-    uint64_t n_draft_accepted_total    = 0;
-    uint64_t n_draft_verif_steps_total = 0;
-    std::vector<uint64_t> n_accepted_per_pos_total;
+    server_metrics metrics;
 
     // while we can also use std::vector<server_slot> this requires copying the slot object which can be quite messy
     // therefore, we use json to temporarily store the slot.to_json() result
     json slots_data = json::array();
 
+    // used by /slots API
     virtual json to_json() override;
+
+    // used by /metrics API
+    struct metric_item {
+        std::string name;
+        std::string description;
+        double value; // prometheus values are always float64
+    };
+    std::string to_metrics();
 };
 
 struct server_task_result_slot_save_load : server_task_result {

tools/server/server-tools.cpp

@@ -1,6 +1,7 @@
 #include "server-tools.h"
 
 #include "subproc.h"
+#include "base64.hpp"
 
 #include <filesystem>
 #include <fstream>
@@ -864,6 +865,7 @@ static bool path_glob_match(const std::string & pattern, const std::string & rel
 //
 
 static constexpr size_t SERVER_TOOL_READ_FILE_MAX_SIZE = 16 * 1024; // 16 KB
+static constexpr size_t SERVER_TOOL_READ_FILE_MAX_SIZE_BASE64 = 32 * 1024 * 1024; // 32 MB
 
 struct server_tool_read_file : server_tool {
     server_tool_read_file() {
@@ -899,6 +901,8 @@ struct server_tool_read_file : server_tool {
         int  start_line   = json_value(params, "start_line", 1);
         int  end_line     = json_value(params, "end_line",  -1); // -1 = no limit
         bool append_loc   = json_value(params, "append_loc", false);
+        // comes from the x-resp-type header, the model cannot ask for it
+        bool as_base64    = json_value(params, "resp_type", std::string()) == "base64";
 
         auto io = make_tools_io(params);
 
@@ -906,6 +910,23 @@ struct server_tool_read_file : server_tool {
         if (!io->file_size(path, file_size)) {
             return {{"error", "cannot stat file: " + path}};
         }
+
+        if (as_base64) {
+            if (file_size > SERVER_TOOL_READ_FILE_MAX_SIZE_BASE64) {
+                return {{"error", string_format(
+                    "file too large (%zu bytes, max %zu)",
+                    (size_t)file_size, SERVER_TOOL_READ_FILE_MAX_SIZE_BASE64)}};
+            }
+            std::string content;
+            if (!io->read_file(path, content)) {
+                return {{"error", "failed to open file: " + path}};
+            }
+            return {
+                {"base64",     base64::encode(content.data(), content.size())},
+                {"size_bytes", (size_t) content.size()},
+            };
+        }
+
         if (file_size > SERVER_TOOL_READ_FILE_MAX_SIZE && end_line == -1) {
             return {{"error", string_format(
                 "file too large (%zu bytes, max %zu). Use start_line/end_line to read a portion.",
@@ -2135,6 +2156,15 @@ void server_tools::setup(const std::vector<std::string> & enabled_tools,
                 params["runtime"] = runtime->spec();
             }
 
+            // x-resp-type header is only used by read_file for now
+            if (params.contains("resp_type")) {
+                params.erase("resp_type");
+            }
+            auto resp_type = get_header(req.headers, "x-resp-type");
+            if (!resp_type.empty()) {
+                params["resp_type"] = resp_type;
+            }
+
             server_tool & tool = find_tool(tools, tool_name, stream);
 
             if (stream) {

tools/server/tests/unit/test_metrics.py

@@ -0,0 +1,227 @@
+import pytest
+from utils import *
+
+server = ServerPreset.tinyllama2()
+
+
+@pytest.fixture(autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+    server.server_metrics = True
+
+
+def fetch_metrics(server: ServerProcess) -> str:
+    """get /metrics as raw prometheus text"""
+    res = server.make_request("GET", "/metrics")
+    assert res.status_code == 200
+    assert "Process-Start-Time-Unix" in res.headers
+    assert isinstance(res.body, str)
+    return res.body
+
+
+def parse_metrics(text: str) -> dict:
+    """parse the prometheus text format into {name: (type, value)}"""
+    out = {}
+    types = {}
+    for line in text.splitlines():
+        if line.startswith("# TYPE "):
+            _, _, name, kind = line.split(" ", 3)
+            types[name] = kind
+        elif line.startswith("llamacpp:") and "{" not in line:
+            name, value = line.split(" ", 1)
+            assert name in types, f"{name} has no # TYPE line"
+            out[name] = (types[name], float(value))
+    return out
+
+
+def test_metrics_disabled():
+    global server
+    server.server_metrics = False
+    server.start()
+    res = server.make_request("GET", "/metrics")
+    assert res.status_code == 501  # ERROR_TYPE_NOT_SUPPORTED
+
+
+def test_metrics_prometheus_format():
+    global server
+    server.start()
+    server.make_request("POST", "/completion", data={"prompt": "I believe", "n_predict": 8})
+
+    text = fetch_metrics(server)
+    metrics = parse_metrics(text)
+
+    expected_counters = [
+        "llamacpp:prompt_tokens_total",
+        "llamacpp:prompt_tokens_cached_total",
+        "llamacpp:prompt_seconds_total",
+        "llamacpp:tokens_predicted_total",
+        "llamacpp:tokens_predicted_seconds_total",
+        "llamacpp:n_decode_total",
+        "llamacpp:n_tokens_max",
+        "llamacpp:spec_decode_num_draft_tokens_total",
+        "llamacpp:spec_decode_num_accepted_tokens_total",
+        "llamacpp:spec_decode_num_drafts_total",
+    ]
+    expected_gauges = [
+        "llamacpp:prompt_tokens_seconds",
+        "llamacpp:predicted_tokens_seconds",
+        "llamacpp:requests_processing",
+        "llamacpp:requests_deferred",
+        "llamacpp:n_busy_slots_per_decode",
+    ]
+
+    for name in expected_counters:
+        assert metrics[name][0] == "counter"
+    for name in expected_gauges:
+        assert metrics[name][0] == "gauge"
+
+    # every metric must carry a help line
+    for name in expected_counters + expected_gauges:
+        assert f"# HELP {name} " in text
+
+    assert metrics["llamacpp:n_decode_total"][1] > 0
+    assert metrics["llamacpp:requests_processing"][1] == 0
+
+
+def test_metrics_prompt_processed_and_cached():
+    global server
+    server.n_slots = 1  # keep the prompt cache on a single slot
+    server.start()
+
+    prompt = "the quick brown fox jumps over the lazy dog"
+
+    n_processed = 0
+    n_cached = 0
+    for _ in range(2):
+        res = server.make_request("POST", "/completion", data={"prompt": prompt, "n_predict": 4})
+        assert res.status_code == 200
+        n_processed += res.body["timings"]["prompt_n"]
+        n_cached += res.body["timings"]["cache_n"]
+
+    # the second request must reuse the prompt of the first one
+    assert n_cached > 0
+
+    metrics = parse_metrics(fetch_metrics(server))
+
+    # cached tokens are counted apart, they cost no decode
+    assert metrics["llamacpp:prompt_tokens_total"][1] == n_processed
+    assert metrics["llamacpp:prompt_tokens_cached_total"][1] == n_cached
+
+
+def test_metrics_predicted_total_matches_requests():
+    global server
+    server.start()
+
+    n_predicted = 0
+    for n_predict in [1, 4, 16]:
+        res = server.make_request("POST", "/completion", data={"prompt": "I believe", "n_predict": n_predict})
+        assert res.status_code == 200
+        n_predicted += res.body["timings"]["predicted_n"]
+
+    metrics = parse_metrics(fetch_metrics(server))
+    assert metrics["llamacpp:tokens_predicted_total"][1] == n_predicted
+
+
+def test_metrics_generation_rate_excludes_first_token():
+    global server
+    server.start()
+
+    # the first token comes from the logits of the last prompt batch, so it costs no decode step
+    res = server.make_request("POST", "/completion", data={"prompt": "I believe", "n_predict": 1})
+    timings = res.body["timings"]
+    assert timings["predicted_n"] == 1
+    assert timings["predicted_per_second"] == 0.0
+    assert timings["predicted_per_token_ms"] == 0.0
+
+    res = server.make_request("POST", "/completion", data={"prompt": "I believe", "n_predict": 16})
+    timings = res.body["timings"]
+    assert timings["predicted_n"] == 16
+    # the rate is over 15 decode steps, not 16 tokens
+    expected = 1e3 / timings["predicted_ms"] * 15
+    assert abs(timings["predicted_per_second"] - expected) < 1e-6
+
+
+@pytest.mark.parametrize("n_predict", [1, 8])
+def test_metrics_timings_are_finite(n_predict: int):
+    global server
+    server.start()
+    res = server.make_request("POST", "/completion", data={"prompt": "I believe", "n_predict": n_predict})
+    timings = res.body["timings"]
+
+    # a null here means the server produced inf or nan
+    for key, value in timings.items():
+        assert value is not None, f"{key} is null"
+        assert value >= 0, f"{key} is negative"
+
+    assert timings["prompt_ms"] > 0
+    assert timings["prompt_per_token_ms"] > 0
+
+
+def test_metrics_timings_on_prompt_progress():
+    global server
+    server.start()
+
+    # a long prompt so that it is split over several batches (n_batch = 32)
+    prompt = "the quick brown fox jumps over the lazy dog " * 8
+    chunks = list(server.make_stream_request("POST", "/completion", data={
+        "prompt": prompt,
+        "n_predict": 4,
+        "stream": True,
+        "timings_per_token": True,
+        "return_progress": True,
+    }))
+
+    progress = [c for c in chunks if "prompt_progress" in c]
+    assert len(progress) > 1  # the prompt did not fit in a single batch
+
+    # the very first update is sent before any prompt token is decoded
+    first = progress[0]["timings"]
+    assert first["prompt_n"] == 0
+    assert first["prompt_ms"] == 0.0
+    assert first["predicted_n"] == 0
+    assert first["predicted_ms"] == 0.0
+
+    # timings must never go backwards, nor report bogus values
+    prompt_ms = 0.0
+    for chunk in progress:
+        timings = chunk["timings"]
+        for key, value in timings.items():
+            assert value is not None, f"{key} is null"
+            assert value >= 0, f"{key} is negative"
+        assert timings["prompt_ms"] >= prompt_ms
+        prompt_ms = timings["prompt_ms"]
+
+    assert prompt_ms > 0
+
+
+def test_metrics_slots_idle_after_completion():
+    global server
+    server.server_slots = True
+    server.start()
+    server.make_request("POST", "/completion", data={"prompt": "I believe", "n_predict": 8})
+
+    res = server.make_request("GET", "/slots")
+    assert res.status_code == 200
+    for slot in res.body:
+        assert slot["is_processing"] is False
+        if "next_token" in slot:
+            # the budget of the finished task must not leak into the idle slot
+            assert slot["next_token"][0]["n_remain"] == -1
+            assert slot["next_token"][0]["n_decoded"] == 0
+
+
+def test_metrics_embedding_prompt_is_counted():
+    global server
+    server = ServerPreset.bert_bge_small()
+    server.server_metrics = True
+    server.start()
+
+    res = server.make_request("POST", "/v1/embeddings", data={"input": ["hello world", "goodbye world"]})
+    assert res.status_code == 200
+
+    # embedding tasks never sample a token, but their prompt still costs a decode
+    metrics = parse_metrics(fetch_metrics(server))
+    assert metrics["llamacpp:prompt_tokens_total"][1] > 0
+    assert metrics["llamacpp:n_decode_total"][1] > 0
+    assert metrics["llamacpp:tokens_predicted_total"][1] == 0

tools/server/tests/unit/test_slot_save.py

@@ -2,6 +2,10 @@ import pytest
 from utils import *
 import base64
 import requests
+import struct
+
+# sequence state file: magic(4) version(4) payload_size(4), then payload_size llama_token words
+STATE_FILE_HEADER_SIZE = 12
 
 server = ServerPreset.tinyllama2()
 
@@ -72,6 +76,60 @@ def test_slot_save_restore():
     assert res.body["timings"]["prompt_n"] == 1
 
 
+def test_slot_restore_legacy_token_list():
+    global server
+    server.start()
+
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "What is the capital of France?",
+        "id_slot": 1,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+
+    res = server.make_request("POST", "/slots/1?action=save", data={
+        "filename": "slot_legacy.bin",
+    })
+    assert res.status_code == 200
+    assert res.body["n_saved"] == 84
+
+    # rewrite the token payload into a plain token list, as written by servers that predate the packed server_tokens format
+    path = os.path.join("tmp", "slot_legacy.bin")
+    with open(path, "rb") as f:
+        data = bytearray(f.read())
+
+    # the payload written by this server starts with a packed header: LLAMA_TOKEN_NULL(4) version(4) n_tokens(4)
+    packed_header_size = 12
+
+    payload_size = struct.unpack_from("=I", data, STATE_FILE_HEADER_SIZE - 4)[0]
+    payload_end = STATE_FILE_HEADER_SIZE + payload_size * 4
+    n_tokens = struct.unpack_from("=I", data, STATE_FILE_HEADER_SIZE + 8)[0]
+    assert n_tokens == 84
+
+    tokens_start = STATE_FILE_HEADER_SIZE + packed_header_size
+    data = data[:STATE_FILE_HEADER_SIZE] + data[tokens_start:tokens_start + n_tokens * 4] + data[payload_end:]
+    struct.pack_into("=I", data, STATE_FILE_HEADER_SIZE - 4, n_tokens)
+
+    with open(path, "wb") as f:
+        f.write(data)
+
+    # the plain token list must restore, and the restored KV must be reusable
+    res = server.make_request("POST", "/slots/0?action=restore", data={
+        "filename": "slot_legacy.bin",
+    })
+    assert res.status_code == 200
+    assert res.body["n_restored"] == 84
+
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "What is the capital of Germany?",
+        "id_slot": 0,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    assert res.body["timings"]["prompt_n"] == 6  # only the different part is processed
+
+
+
 def test_slot_erase():
     global server
     server.start()
@@ -103,14 +161,12 @@ def test_slot_erase():
 #
 # Multimodal server (mmproj loaded) slot save/restore.
 #
-# Regression coverage for issue #21133: slot save/restore/erase must be gated on
-# the slot's CONTENT (does it actually hold image/audio tokens) rather than the
-# model's CAPABILITY (is an mmproj loaded). A pure-text slot on a multimodal
-# server must save/restore/erase normally; a slot that actually holds an image
-# must be rejected with ERROR_TYPE_NOT_SUPPORTED (HTTP 501).
+# A pure-text slot on a multimodal server and a slot containing images must both support save/restore.
+# Erase remains gated on the slot's content.
 #
 
 IMG_URL_CAT = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/91_cat.png"
+IMG_URL_TRUCK = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/11_truck.png"
 
 
 def _get_img_base64(url: str) -> str:
@@ -121,8 +177,7 @@ def _get_img_base64(url: str) -> str:
 
 @pytest.fixture
 def mmproj_server():
-    # tinygemma3 is a small multimodal model: the mmproj is provided by the HF
-    # registry API and auto-downloaded on first run.
+    # tinygemma3 is a small multimodal model: the mmproj is provided by the HF registry API and auto-downloaded on first run.
     os.environ['LLAMA_MEDIA_MARKER'] = '<__media__>'
     mm_server = ServerPreset.tinygemma3()
     mm_server.slot_save_path = "./tmp"
@@ -159,10 +214,7 @@ def test_slot_save_restore_text_only_on_multimodal(mmproj_server):
     assert res.status_code == 200
     assert res.body["n_restored"] == n_saved
 
-    # The restored slot is usable for a follow-up completion. We do NOT assert
-    # prefix reuse here: tinygemma3 is a SWA model, which forces full prompt
-    # re-processing after a restore (a model property, not the save/restore gate
-    # under test).
+    # Prefix reuse is not checked with the default SWA cache.
     res = server.make_request("POST", "/completion", data={
         "prompt": "The quick brown fox jumps over the lazy dog.",
         "id_slot": 0,
@@ -171,54 +223,307 @@ def test_slot_save_restore_text_only_on_multimodal(mmproj_server):
     assert res.status_code == 200
 
 
-def test_slot_save_rejected_when_slot_holds_image(mmproj_server):
+def test_slot_save_restore_with_image(mmproj_server):
     server = mmproj_server
+    # Use the full SWA cache so the restored image prefix can be reused.
+    server.swa_full = True
     server.start()
 
-    # Process a prompt that actually contains an image on slot 1.
+    prompt_cat = {
+        "prompt_string": "What is this: <__media__>\n",
+        "multimodal_data": [_get_img_base64(IMG_URL_CAT)],
+    }
     res = server.make_request("POST", "/completions", data={
         "temperature": 0.0,
         "top_k": 1,
         "id_slot": 1,
         "cache_prompt": True,
+        "prompt": prompt_cat,
+    })
+    assert res.status_code == 200
+    content_cat = res.body["content"]
+    prompt_n_full = res.body["timings"]["prompt_n"]
+    assert res.body["timings"]["cache_n"] == 0
+    assert prompt_n_full > 32  # text plus image tokens are all processed
+
+    res = server.make_request("POST", "/slots/1?action=save", data={
+        "filename": "mm_slot_image.bin",
+    })
+    assert res.status_code == 200
+    n_saved = res.body["n_saved"]
+    n_written = res.body["n_written"]
+    assert n_saved > 0
+    assert n_written > 0
+
+    res = server.make_request("POST", "/slots/1?action=erase")
+    assert res.status_code == 200
+
+    res = server.make_request("POST", "/slots/0?action=restore", data={
+        "filename": "mm_slot_image.bin",
+    })
+    assert res.status_code == 200
+    assert res.body["n_restored"] == n_saved
+    assert res.body["n_read"] == n_written
+
+    # a different image must not reuse the restored image tokens; only the text prefix before the image is common
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 0,
+        "cache_prompt": True,
         "prompt": {
             "prompt_string": "What is this: <__media__>\n",
-            "multimodal_data": [ _get_img_base64(IMG_URL_CAT) ],
+            "multimodal_data": [_get_img_base64(IMG_URL_TRUCK)],
+        },
+    })
+    assert res.status_code == 200
+    cache_n = res.body["timings"]["cache_n"]
+    assert cache_n < 16
+    assert res.body["timings"]["prompt_n"] == prompt_n_full - cache_n
+
+    # restore again and resend the same image: the image tokens must be reused and greedy sampling must reproduce the original content
+    res = server.make_request("POST", "/slots/0?action=restore", data={
+        "filename": "mm_slot_image.bin",
+    })
+    assert res.status_code == 200
+    assert res.body["n_restored"] == n_saved
+
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 0,
+        "cache_prompt": True,
+        "prompt": prompt_cat,
+    })
+    assert res.status_code == 200
+    assert res.body["timings"]["cache_n"] == prompt_n_full - 1
+    assert res.body["timings"]["prompt_n"] == 1
+    assert res.body["content"] == content_cat
+
+
+def test_slot_save_restore_with_two_images(mmproj_server):
+    server = mmproj_server
+    server.swa_full = True
+    server.n_ctx = 2048  # two images need more than the default 512 per slot
+    server.start()
+
+    prompt = {
+        "prompt_string": "A: <__media__> B: <__media__>\n",
+        "multimodal_data": [_get_img_base64(IMG_URL_CAT), _get_img_base64(IMG_URL_TRUCK)],
+    }
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 1,
+        "cache_prompt": True,
+        "prompt": prompt,
+    })
+    assert res.status_code == 200
+    content = res.body["content"]
+    prompt_n_full = res.body["timings"]["prompt_n"]
+    assert prompt_n_full > 64
+
+    res = server.make_request("POST", "/slots/1?action=save", data={
+        "filename": "mm_slot_two_images.bin",
+    })
+    assert res.status_code == 200
+    n_saved = res.body["n_saved"]
+
+    res = server.make_request("POST", "/slots/0?action=restore", data={
+        "filename": "mm_slot_two_images.bin",
+    })
+    assert res.status_code == 200
+    assert res.body["n_restored"] == n_saved
+
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 0,
+        "cache_prompt": True,
+        "prompt": prompt,
+    })
+    assert res.status_code == 200
+    assert res.body["timings"]["cache_n"] == prompt_n_full - 1
+    assert res.body["timings"]["prompt_n"] == 1
+    assert res.body["content"] == content
+
+
+def test_slot_save_restore_with_image_across_restart(mmproj_server):
+    server = mmproj_server
+    server.swa_full = True
+    server.start()
+
+    prompt_cat = {
+        "prompt_string": "What is this: <__media__>\n",
+        "multimodal_data": [_get_img_base64(IMG_URL_CAT)],
+    }
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 0,
+        "cache_prompt": True,
+        "prompt": prompt_cat,
+    })
+    assert res.status_code == 200
+    content = res.body["content"]
+    prompt_n_full = res.body["timings"]["prompt_n"]
+
+    res = server.make_request("POST", "/slots/0?action=save", data={
+        "filename": "mm_slot_restart.bin",
+    })
+    assert res.status_code == 200
+    n_saved = res.body["n_saved"]
+
+    # restart the server with the same model and mmproj: the saved file must restore in the new process and the image KV must be reused
+    server.stop()
+    server.start()
+
+    res = server.make_request("POST", "/slots/0?action=restore", data={
+        "filename": "mm_slot_restart.bin",
+    })
+    assert res.status_code == 200
+    assert res.body["n_restored"] == n_saved
+
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 0,
+        "cache_prompt": True,
+        "prompt": prompt_cat,
+    })
+    assert res.status_code == 200
+    assert res.body["timings"]["cache_n"] == prompt_n_full - 1
+    assert res.body["timings"]["prompt_n"] == 1
+    assert res.body["content"] == content
+
+
+def test_slot_save_restore_image_payload_larger_than_context(mmproj_server):
+    server = mmproj_server
+    server.swa_full = True
+    server.start()
+
+    # the slot context, as the server computed it (n_ctx split across the slots)
+    res = server.make_request("GET", "/props")
+    assert res.status_code == 200
+    n_ctx_slot = res.body["default_generation_settings"]["n_ctx"]
+
+    # a filler token, used to grow the prompt up to the slot context
+    res = server.make_request("POST", "/tokenize", data={"content": " hello" * 8})
+    assert res.status_code == 200
+    assert len(res.body["tokens"]) == 8
+
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 0,
+        "cache_prompt": True,
+        "prompt": {
+            "prompt_string": "What is this: <__media__>\n",
+            "multimodal_data": [_get_img_base64(IMG_URL_CAT)],
         },
     })
     assert res.status_code == 200
 
-    # Saving a slot that holds image tokens must be rejected (HTTP 501,
-    # not_supported_error).
-    res = server.make_request("POST", "/slots/1?action=save", data={
-        "filename": "mm_slot_image.bin",
+    prompt_cat = {
+        "prompt_string": "What is this: <__media__>\n" + " hello" * (n_ctx_slot - res.body["timings"]["prompt_n"] - 8),
+        "multimodal_data": [_get_img_base64(IMG_URL_CAT)],
+    }
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 0,
+        "cache_prompt": True,
+        "prompt": prompt_cat,
     })
-    assert res.status_code != 200
-    assert res.body["error"]["type"] == "not_supported_error"
+    assert res.status_code == 200
+    prompt_n_full = res.body["timings"]["cache_n"] + res.body["timings"]["prompt_n"]
+
+    res = server.make_request("POST", "/slots/0?action=save", data={
+        "filename": "mm_slot_large_payload.bin",
+    })
+    assert res.status_code == 200
+
+    path = os.path.join("tmp", "mm_slot_large_payload.bin")
+    with open(path, "rb") as f:
+        data = bytearray(f.read())
+    payload_size = struct.unpack_from("=I", data, STATE_FILE_HEADER_SIZE - 4)[0]
+    assert payload_size > n_ctx_slot  # the scenario under test: the payload does not fit in n_ctx
+
+    # drop the image from the slot, then restore it from the file
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "The quick brown fox",
+        "id_slot": 0,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+
+    res = server.make_request("POST", "/slots/0?action=restore", data={
+        "filename": "mm_slot_large_payload.bin",
+    })
+    assert res.status_code == 200
+
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 0,
+        "cache_prompt": True,
+        "prompt": prompt_cat,
+    })
+    assert res.status_code == 200
+    assert res.body["timings"]["cache_n"] == prompt_n_full - 1
+    assert res.body["timings"]["prompt_n"] == 1
 
 
-def test_slot_erase_text_only_on_multimodal(mmproj_server):
+def test_slot_restore_media_file_without_mmproj(mmproj_server):
     server = mmproj_server
     server.start()
 
-    res = server.make_request("POST", "/completion", data={
-        "prompt": "The quick brown fox jumps over the lazy dog.",
-        "id_slot": 1,
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 0,
         "cache_prompt": True,
+        "prompt": {
+            "prompt_string": "What is this: <__media__>\n",
+            "multimodal_data": [_get_img_base64(IMG_URL_CAT)],
+        },
     })
     assert res.status_code == 200
-    prompt_n = res.body["timings"]["prompt_n"]
-    assert prompt_n > 0  # all tokens are processed
 
-    # Erasing a pure-text slot must succeed even though an mmproj is loaded.
-    res = server.make_request("POST", "/slots/1?action=erase")
-    assert res.status_code == 200
-
-    # Re-running the same prompt should process all tokens again.
-    res = server.make_request("POST", "/completion", data={
-        "prompt": "The quick brown fox jumps over the lazy dog.",
-        "id_slot": 1,
-        "cache_prompt": True,
+    res = server.make_request("POST", "/slots/0?action=save", data={
+        "filename": "mm_slot_no_mmproj.bin",
     })
     assert res.status_code == 200
-    assert res.body["timings"]["prompt_n"] == prompt_n  # all tokens are processed again
+
+    # restart the same model without the mmproj: restoring the media file must fail gracefully and leave the slot usable
+    server.stop()
+    server.no_mmproj = True
+    server.start()
+
+    res = server.make_request("POST", "/slots/0?action=restore", data={
+        "filename": "mm_slot_no_mmproj.bin",
+    })
+    assert res.status_code == 400
+    assert "Cannot restore media tokens without an mmproj" in res.body["error"]["message"]
+
+    # A failed restore must leave the slot empty and usable.
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 1,
+        "cache_prompt": True,
+        "prompt": "The quick brown fox",
+    })
+    assert res.status_code == 200
+    content = res.body["content"]
+
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "id_slot": 0,
+        "cache_prompt": True,
+        "prompt": "The quick brown fox",
+    })
+    assert res.status_code == 200
+    assert res.body["timings"]["cache_n"] == 0
+    assert res.body["content"] == content

tools/server/tests/unit/test_speculative.py

@@ -27,8 +27,8 @@ def test_with_and_without_draft():
     global server
     request = {
         "prompt": "I believe the meaning of life is",
-        "temperature": 0.8,
-        "top_k": 40,
+        "temperature": 0.2,
+        "top_k": 5,
         "seed": 4242,
         "n_predict": 16,
         "return_tokens": True,
@@ -36,7 +36,6 @@ def test_with_and_without_draft():
 
     server.model_draft = None  # disable draft model
     server.spec_type = None
-    server.backend_sampling = True
     server.start()
     res = server.make_request("POST", "/completion", data=request)
     assert res.status_code == 200
@@ -45,7 +44,6 @@ def test_with_and_without_draft():
 
     # create new server with draft model
     create_server()
-    server.backend_sampling = True
     server.start()
     res = server.make_request("POST", "/completion", data=request)
     assert res.status_code == 200

tools/server/tests/utils.py

@@ -86,6 +86,7 @@ class ServerProcess:
     server_reranking: bool | None = False
     server_metrics: bool | None = False
     kv_unified: bool | None = False
+    swa_full: bool | None = False
     server_slots: bool | None = False
     pooling: str | None = None
     api_key: str | None = None
@@ -106,6 +107,7 @@ class ServerProcess:
     chat_template_file: str | None = None
     server_path: str | None = None
     mmproj_url: str | None = None
+    no_mmproj: bool | None = None
     media_path: str | None = None
     sleep_idle_seconds: int | None = None
     cache_ram: int | None = None
@@ -198,6 +200,8 @@ class ServerProcess:
             server_args.append("--metrics")
         if self.kv_unified:
             server_args.append("--kv-unified")
+        if self.swa_full:
+            server_args.append("--swa-full")
         if self.server_slots:
             server_args.append("--slots")
         else:
@@ -259,6 +263,8 @@ class ServerProcess:
             server_args.extend(["--chat-template-file", self.chat_template_file])
         if self.mmproj_url:
             server_args.extend(["--mmproj-url", self.mmproj_url])
+        if self.no_mmproj:
+            server_args.append("--no-mmproj")
         if self.media_path:
             server_args.extend(["--media-path", self.media_path])
         if self.sleep_idle_seconds is not None:

tools/ui/pwa-assets-dark.config.ts

@@ -1,5 +1,5 @@
 import { writeThemeFavicons } from './scripts/favicon-colorize';
-import { FAVICON_COLORS, PWA_ASSET_GENERATOR } from './src/lib/constants/pwa';
+import { FAVICON_COLORS, PWA_ASSET_GENERATOR } from './src/lib/constants/pwa.constants';
 import { defineConfig } from '@vite-pwa/assets-generator/config';
 
 writeThemeFavicons(FAVICON_COLORS.LIGHT, FAVICON_COLORS.DARK, {

tools/ui/pwa-assets.config.ts

@@ -4,7 +4,7 @@ import {
 	PWA_ASSET_GENERATOR,
 	PWA_GENERATOR_DEVICES,
 	THEME_COLORS
-} from './src/lib/constants/pwa';
+} from './src/lib/constants/pwa.constants';
 import { SplashOrientation } from './src/lib/enums/splash.enums';
 import {
 	combinePresetAndAppleSplashScreens,

tools/ui/scripts/vite-plugin-build-info.ts

@@ -1,4 +1,4 @@
-import { BUILD_CONFIG } from '../src/lib/constants/pwa';
+import { BUILD_CONFIG } from '../src/lib/constants/pwa.constants';
 import { existsSync, writeFileSync } from 'node:fs';
 import { resolve } from 'path';
 import type { Plugin } from 'vite';

tools/ui/scripts/vite-plugin-relativize-base.ts

@@ -1,4 +1,4 @@
-import { BUILD_CONFIG } from '../src/lib/constants/pwa';
+import { BUILD_CONFIG } from '../src/lib/constants/pwa.constants';
 import { existsSync, readFileSync, writeFileSync } from 'node:fs';
 import { resolve } from 'path';
 import type { Plugin } from 'vite';

tools/ui/scripts/vite-plugin-splash-screen.ts

@@ -1,5 +1,10 @@
-import { NEWLINE, TAB } from '../src/lib/constants/code';
-import { APPLE_DEVICES, BUILD_CONFIG, REGEX_PATTERNS, SPLASH_LINK } from '../src/lib/constants/pwa';
+import {
+	APPLE_DEVICES,
+	BUILD_CONFIG,
+	REGEX_PATTERNS,
+	SPLASH_LINK
+} from '../src/lib/constants/pwa.constants';
+import { NEWLINE, TAB } from '../src/lib/constants/special-characters.constants';
 import { SplashOrientation } from '../src/lib/enums/splash.enums';
 import type { SplashDimensions } from '../src/lib/types';
 import { existsSync, readdirSync, readFileSync, writeFileSync } from 'node:fs';

tools/ui/src/app.d.ts

@@ -32,8 +32,8 @@ import type {
 	ApiRouterModelsStatusResponse,
 	ApiRouterModelsUnloadRequest,
 	ApiRouterModelsUnloadResponse,
-	// Chat types
 	ChatAttachmentDisplayItem,
+	// Chat types
 	ChatMessagePromptProgress,
 	ChatMessageSiblingInfo,
 	ChatMessageTimings,

tools/ui/src/app.html

@@ -2,6 +2,7 @@
 <html lang="en">
 	<head>
 		<meta charset="utf-8" />
+
 		<link rel="icon" href="favicon.ico" sizes="48x48" />
 		<link rel="icon" href="favicon.svg" sizes="any" type="image/svg+xml" />
 

tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte

@@ -1,7 +1,7 @@
 <script lang="ts">
 	import ActionIcon from './ActionIcon.svelte';
 	import { Copy } from '@lucide/svelte';
-	import { ICON_CLASS_DEFAULT } from '$lib/constants/css-classes';
+	import { ICON_CLASS_DEFAULT } from '$lib/constants';
 	import { copyToClipboard } from '$lib/utils';
 
 	export let ariaLabel: string = 'Copy to clipboard';

tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpResource.svelte

@@ -3,7 +3,7 @@
 	import { X } from '@lucide/svelte';
 	import { ActionIcon } from '$lib/components/app';
 	import * as Tooltip from '$lib/components/ui/tooltip';
-	import { mcpStore } from '$lib/stores/mcp.svelte';
+	import { mcpStore } from '$lib/stores';
 	import type { MCPResourceAttachment } from '$lib/types';
 	import { getResourceDisplayName, getResourceIcon } from '$lib/utils';
 

tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemThumbnailFile.svelte

@@ -1,7 +1,7 @@
 <script lang="ts">
 	import { Music, Video, X } from '@lucide/svelte';
 	import { ActionIcon } from '$lib/components/app';
-	import { ICON_CLASS_DEFAULT } from '$lib/constants/css-classes';
+	import { ICON_CLASS_DEFAULT } from '$lib/constants';
 	import { AttachmentType } from '$lib/enums';
 	import {
 		formatFileSize,