jinja : add count/d/e filter aliases (#24606)

* Add arch support for cohere2-MoE

* Removed redundant gating_func checks

* Changed ffn lookup to prefer prefix_dense_intermediate_size

* Renamed arch to cohere2moe

* Removed redundant lmhead check and chat template changes

* Removed lm_head.weight check from modify tensors, load output tensor not required, fallback to token_embd.weight

* Changed to (routed+shared)*0.5 for shared expert combined avg

* fixed sliding_window_pattern issue and pattern

* Fixed transformers crash 'first_k_dense_replace' error

* Remove comment

* Removed cohere2-moe as a tokenizer type and kept as tiny_aya.  Renamed North-Mini-Code-1.0.

* Fixed MTP fail, changed to use iSWA

* Fixed remaining todos: cohere2moe renamed, changed swa parsing to use get_key_or_arr, removed extra get_arr use

* Force metadata usage

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Remove Cohere2 checkpoint comment

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Remove MTP comment

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Regenerate cohere2moe tokenizer hash

* Add cohere2moe to Llama Model Saver supported list

* Check for zerobios tensors and add support for Command to use LayerNorm

* Map expert_selection_fn to sigmoid in base.py instead of command.py

* use bools for foundnorm/foundnormrms

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

.devops/cuda.Dockerfile

@@ -1,6 +1,7 @@
 ARG UBUNTU_VERSION=24.04
 # This needs to generally match the container host's environment.
 ARG CUDA_VERSION=12.8.1
+ARG GCC_VERSION=14
 # Target the CUDA build image
 ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
 
@@ -12,13 +13,14 @@ ARG APP_REVISION=N/A
 
 FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 
+ARG GCC_VERSION
 # CUDA architecture to build for (defaults to all supported archs)
 ARG CUDA_DOCKER_ARCH=default
 
 RUN apt-get update && \
-    apt-get install -y gcc-14 g++-14 build-essential cmake python3 python3-pip git libssl-dev libgomp1
+    apt-get install -y gcc-${GCC_VERSION} g++-${GCC_VERSION} build-essential cmake python3 python3-pip git libssl-dev libgomp1
 
-ENV CC=gcc-14 CXX=g++-14 CUDAHOSTCXX=g++-14
+ENV CC=gcc-${GCC_VERSION} CXX=g++-${GCC_VERSION} CUDAHOSTCXX=g++-${GCC_VERSION}
 
 WORKDIR /app
 

.github/labeler.yml

@@ -12,7 +12,7 @@ SYCL:
             - ggml/src/ggml-sycl/**
             - docs/backend/SYCL.md
             - examples/sycl/**
-Nvidia GPU:
+CUDA:
     - changed-files:
         - any-glob-to-any-file:
             - ggml/include/ggml-cuda.h

.github/workflows/build-sycl.yml

@@ -34,129 +34,108 @@ env:
   LLAMA_ARG_LOG_TIMESTAMPS: 1
 
 jobs:
+  ubuntu-24-sycl:
+    strategy:
+      matrix:
+        build: [fp32, fp16]
+        include:
+          - build: fp32
+            fp16: OFF
+          - build: fp16
+            fp16: ON
 
-# TODO: this build is disabled to save Github Actions resources (https://github.com/ggml-org/llama.cpp/pull/23705)
-#       in order to enable it again, we have to provision dedicated runners  to run it
-#  ubuntu-24-sycl:
-#    strategy:
-#      matrix:
-#        build: [fp32]
-#        include:
-#          - build: fp32
-#            fp16: OFF
-#
-#    runs-on: ubuntu-24.04
-#
-#    env:
-#      ONEAPI_ROOT: /opt/intel/oneapi/
-#      ONEAPI_INSTALLER_VERSION: "2025.3.3"
-#      LEVEL_ZERO_VERSION: "1.28.2"
-#      LEVEL_ZERO_UBUNTU_VERSION: "u24.04"
-#
-#    continue-on-error: true
-#
-#    steps:
-#      - uses: actions/checkout@v6
-#
-#      - name: Use oneAPI Installation Cache
-#        uses: actions/cache@v5
-#        id: cache-sycl
-#        with:
-#          path: ${{ env.ONEAPI_ROOT }}
-#          key: cache-gha-oneAPI-${{ env.ONEAPI_INSTALLER_VERSION }}-${{ runner.os }}
-#
-#      - name: Download & Install oneAPI
-#        shell: bash
-#        if: steps.cache-sycl.outputs.cache-hit != 'true'
-#        run: |
-#          cd /tmp
-#          wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/56f7923a-adb8-43f3-8b02-2b60fcac8cab/intel-deep-learning-essentials-2025.3.3.16_offline.sh -O intel-deep-learning-essentials_offline.sh
-#          sudo bash intel-deep-learning-essentials_offline.sh -s -a --silent --eula accept
-#
-#      - name: Install Level Zero SDK
-#        shell: bash
-#        run: |
-#          cd /tmp
-#          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero.deb
-#          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero-devel_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero-devel.deb
-#          sudo apt-get install -y ./level-zero.deb ./level-zero-devel.deb
-#
-#      - name: Clone
-#        id: checkout
-#        uses: actions/checkout@v6
-#
-#      - name: ccache
-#        uses: ggml-org/ccache-action@v1.2.21
-#        with:
-#          key: sycl-ubuntu-24-${{ matrix.build }}
-#          evict-old-files: 1d
-#          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-#
-#      - name: Build
-#        id: cmake_build
-#        run: |
-#          source /opt/intel/oneapi/setvars.sh
-#          cmake -B build \
-#            -G "Ninja" \
-#            -DCMAKE_BUILD_TYPE=Release \
-#            -DGGML_SYCL=ON \
-#            -DCMAKE_C_COMPILER=icx \
-#            -DCMAKE_CXX_COMPILER=icpx \
-#            -DLLAMA_OPENSSL=OFF \
-#            -DGGML_NATIVE=OFF \
-#            -DGGML_SYCL_F16=${{ matrix.fp16 }}
-#          time cmake --build build --config Release -j $(nproc)
+    runs-on: ubuntu-24.04
 
-# TODO: this build is disabled to save Github Actions resources (https://github.com/ggml-org/llama.cpp/pull/23705)
-#       in order to enable it again, we have to provision dedicated runners  to run it
-#  windows-latest-sycl:
-#    runs-on: windows-2022
-#
-#    defaults:
-#      run:
-#        shell: bash
-#
-#    env:
-#      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b60765d1-2b85-4e85-86b6-cb0e9563a699/intel-deep-learning-essentials-2025.3.3.18_offline.exe
-#      WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
-#      LEVEL_ZERO_SDK_URL: https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero-win-sdk-1.28.2.zip
-#      ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
-#      ONEAPI_INSTALLER_VERSION: "2025.3.3"
-#    steps:
-#      - name: Clone
-#        id: checkout
-#        uses: actions/checkout@v6
-#
-#      - name: Use oneAPI Installation Cache
-#        uses: actions/cache@v5
-#        id: cache-sycl
-#        with:
-#          path: ${{ env.ONEAPI_ROOT }}
-#          key: cache-gha-oneAPI-${{ env.ONEAPI_INSTALLER_VERSION }}-${{ runner.os }}
-#
-#      - name: Download & Install oneAPI
-#        shell: bash
-#        if: steps.cache-sycl.outputs.cache-hit != 'true'
-#        run: |
-#          scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
-#
-#      - name: Install Level Zero SDK
-#        shell: pwsh
-#        run: |
-#          Invoke-WebRequest -Uri "${{ env.LEVEL_ZERO_SDK_URL }}" -OutFile "level-zero-win-sdk.zip"
-#          Expand-Archive -Path "level-zero-win-sdk.zip" -DestinationPath "C:/level-zero-sdk" -Force
-#          "LEVEL_ZERO_V1_SDK_PATH=C:/level-zero-sdk" | Out-File -FilePath $env:GITHUB_ENV -Append
-#
-#      - name: ccache
-#        uses: ggml-org/ccache-action@v1.2.21
-#        with:
-#          key: sycl-windows-latest
-#          variant: ccache
-#          evict-old-files: 1d
-#          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-#
-#      # TODO: add ssl support ; we will also need to modify win-build-sycl.bat to accept user-specified args
-#
-#      - name: Build
-#        id: cmake_build
-#        run:  examples/sycl/win-build-sycl.bat
+    env:
+      ONEAPI_ROOT: /opt/intel/oneapi/
+      ONEAPI_INSTALLER_VERSION: "2025.3.3"
+      LEVEL_ZERO_VERSION: "1.28.2"
+      LEVEL_ZERO_UBUNTU_VERSION: "u24.04"
+
+    continue-on-error: true
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Download & Install oneAPI
+        shell: bash
+        run: |
+          cd /tmp
+          wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/56f7923a-adb8-43f3-8b02-2b60fcac8cab/intel-deep-learning-essentials-2025.3.3.16_offline.sh -O intel-deep-learning-essentials_offline.sh
+          sudo bash intel-deep-learning-essentials_offline.sh -s -a --silent --eula accept
+
+      - name: Install Level Zero SDK
+        shell: bash
+        run: |
+          cd /tmp
+          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero.deb
+          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero-devel_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero-devel.deb
+          sudo apt-get install -y ./level-zero.deb ./level-zero-devel.deb
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: sycl-ubuntu-24-${{ matrix.build }}
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+
+      - name: Build
+        id: cmake_build
+        run: |
+          source /opt/intel/oneapi/setvars.sh
+          cmake -B build \
+            -G "Ninja" \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DGGML_SYCL=ON \
+            -DCMAKE_C_COMPILER=icx \
+            -DCMAKE_CXX_COMPILER=icpx \
+            -DLLAMA_OPENSSL=OFF \
+            -DGGML_NATIVE=OFF \
+            -DGGML_SYCL_F16=${{ matrix.fp16 }}
+          time cmake --build build --config Release -j $(nproc)
+
+  windows-latest-sycl:
+    runs-on: windows-2022
+
+    defaults:
+      run:
+        shell: bash
+
+    env:
+      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b60765d1-2b85-4e85-86b6-cb0e9563a699/intel-deep-learning-essentials-2025.3.3.18_offline.exe
+      WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
+      LEVEL_ZERO_SDK_URL: https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero-win-sdk-1.28.2.zip
+      ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
+      ONEAPI_INSTALLER_VERSION: "2025.3.3"
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Download & Install oneAPI
+        shell: bash
+        run: |
+          scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
+
+      - name: Install Level Zero SDK
+        shell: pwsh
+        run: |
+          Invoke-WebRequest -Uri "${{ env.LEVEL_ZERO_SDK_URL }}" -OutFile "level-zero-win-sdk.zip"
+          Expand-Archive -Path "level-zero-win-sdk.zip" -DestinationPath "C:/level-zero-sdk" -Force
+          "LEVEL_ZERO_V1_SDK_PATH=C:/level-zero-sdk" | Out-File -FilePath $env:GITHUB_ENV -Append
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: sycl-windows-latest
+          variant: ccache
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+
+      # TODO: add ssl support ; we will also need to modify win-build-sycl.bat to accept user-specified args
+
+      - name: Build
+        id: cmake_build
+        run:  examples/sycl/win-build-sycl.bat

.github/workflows/release.yml

@@ -59,8 +59,31 @@ jobs:
             echo "should_release=false" >> $GITHUB_OUTPUT
           fi
 
+  get-version:
+    runs-on: ubuntu-slim
+    outputs:
+      ui_version: ${{ steps.version.outputs.ui_version }}
+    steps:
+      - uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+      - id: version
+        run: |
+          # Resolve UI version: BUILD_NUMBER from cmake/build-info.cmake > git hash + epoch > fallback
+          version=""
+          if grep -q "BUILD_NUMBER" cmake/build-info.cmake; then
+            build_number=$(grep "set(BUILD_NUMBER" cmake/build-info.cmake | grep -oP '\d+')
+            if [ -n "$build_number" ] && [ "$build_number" -gt 0 ]; then
+              version="b${build_number}"
+            fi
+          fi
+          if [ -z "$version" ]; then
+            version=$(git rev-parse --short HEAD)-$(date +%s)
+          fi
+          echo "ui_version=${version}" >> $GITHUB_OUTPUT
+
   macos-cpu:
-    needs: [check-release]
+    needs: [check-release, get-version]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
     strategy:
       matrix:
@@ -116,6 +139,7 @@ jobs:
             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_BUILD_BORINGSSL=ON \
+            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
             ${{ env.CMAKE_ARGS }}
           cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
 
@@ -141,7 +165,7 @@ jobs:
           name: llama-bin-macos-${{ matrix.build }}.tar.gz
 
   ubuntu-cpu:
-    needs: [check-release]
+    needs: [check-release, get-version]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
     strategy:
       matrix:
@@ -201,6 +225,7 @@ jobs:
             -DGGML_NATIVE=OFF \
             -DGGML_CPU_ALL_VARIANTS=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
+            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
             ${{ env.CMAKE_ARGS }}
           cmake --build build --config Release -j $(nproc)
 
@@ -227,7 +252,7 @@ jobs:
           name: llama-bin-ubuntu-${{ matrix.build }}.tar.gz
 
   ubuntu-vulkan:
-    needs: [check-release]
+    needs: [check-release, get-version]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
 
     strategy:
@@ -287,6 +312,7 @@ jobs:
             -DGGML_NATIVE=OFF \
             -DGGML_CPU_ALL_VARIANTS=ON \
             -DGGML_VULKAN=ON \
+            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
             ${{ env.CMAKE_ARGS }}
           cmake --build build --config Release -j $(nproc)
 
@@ -312,7 +338,7 @@ jobs:
           name: llama-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz
 
   android-arm64:
-    needs: [check-release]
+    needs: [check-release, get-version]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
 
     runs-on: ubuntu-latest
@@ -379,6 +405,7 @@ jobs:
             -DLLAMA_FATAL_WARNINGS=ON \
             -DGGML_OPENMP=OFF \
             -DLLAMA_BUILD_BORINGSSL=ON \
+            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
             ${{ env.CMAKE_ARGS }}
           cmake --build build --config Release -j $(nproc)
 
@@ -404,7 +431,7 @@ jobs:
           name: llama-bin-android-arm64.tar.gz
 
   ubuntu-24-openvino:
-    needs: [check-release]
+    needs: [check-release, get-version]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
 
     runs-on: ubuntu-24.04
@@ -476,7 +503,8 @@ jobs:
           source ./openvino_toolkit/setupvars.sh
           cmake -B build/ReleaseOV -G Ninja \
             -DCMAKE_BUILD_TYPE=Release \
-            -DGGML_OPENVINO=ON
+            -DGGML_OPENVINO=ON \
+            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }}
           cmake --build build/ReleaseOV --config Release -j $(nproc)
 
       - name: ccache-clear
@@ -504,7 +532,7 @@ jobs:
     needs: [check-release]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
 
-    runs-on: windows-2025
+    runs-on: windows-2025-vs2026
 
     permissions:
       actions: write
@@ -535,12 +563,12 @@ jobs:
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
         with:
-          key: release-windows-2025-${{ matrix.arch }}-cpu
+          key: release-windows-2025-vs2026-${{ matrix.arch }}-cpu
 
       - name: Build
         shell: cmd
         run: |
-          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" ${{ matrix.arch == 'x64' && 'x64' || 'amd64_arm64' }}
+          call "C:\Program Files\Microsoft Visual Studio\18\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" ${{ matrix.arch == 'x64' && 'x64' || 'amd64_arm64' }}
           cmake -S . -B build -G "Ninja Multi-Config" ^
             -D CMAKE_TOOLCHAIN_FILE=cmake/${{ matrix.arch }}-windows-llvm.cmake ^
             -DLLAMA_BUILD_BORINGSSL=ON ^
@@ -554,12 +582,12 @@ jobs:
       - name: ccache-clear
         uses: ./.github/actions/ccache-clear
         with:
-          key: release-windows-2025-${{ matrix.arch }}-cpu
+          key: release-windows-2025-vs2026-${{ matrix.arch }}-cpu
 
       - name: Pack artifacts
         id: pack_artifacts
         run: |
-          Copy-Item "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Redist\MSVC\14.44.35112\debug_nonredist\${{ matrix.arch }}\Microsoft.VC143.OpenMP.LLVM\libomp140.${{ matrix.arch == 'x64' && 'x86_64' || 'aarch64' }}.dll" .\build\bin\Release\
+          Copy-Item "C:\Program Files\Microsoft Visual Studio\18\Enterprise\VC\Redist\MSVC\14.51.36231\debug_nonredist\${{ matrix.arch }}\Microsoft.VC145.OpenMP.LLVM\libomp140.${{ matrix.arch == 'x64' && 'x86_64' || 'aarch64' }}.dll" .\build\bin\Release\
           7z a -snl llama-bin-win-cpu-${{ matrix.arch }}.zip .\build\bin\Release\*
 
       - name: Upload artifacts
@@ -754,213 +782,209 @@ jobs:
           path: cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip
           name: cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip
 
-# TODO: this build is disabled to save Github Actions resources (https://github.com/ggml-org/llama.cpp/pull/23705)
-#       in order to enable it again, we have to provision dedicated runners  to run it
-#  windows-sycl:
-#
-#    runs-on: windows-2022
-#
-#    defaults:
-#      run:
-#        shell: bash
-#
-#    env:
-#      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b60765d1-2b85-4e85-86b6-cb0e9563a699/intel-deep-learning-essentials-2025.3.3.18_offline.exe
-#      WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
-#      LEVEL_ZERO_SDK_URL: https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero-win-sdk-1.28.2.zip
-#      ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
-#      ONEAPI_INSTALLER_VERSION: "2025.3.3"
-#
-#    steps:
-#      - name: Clone
-#        id: checkout
-#        uses: actions/checkout@v6
-#
-#      - name: Use oneAPI Installation Cache
-#        uses: actions/cache@v5
-#        id: cache-sycl
-#        with:
-#          path: ${{ env.ONEAPI_ROOT }}
-#          key: cache-gha-oneAPI-${{ env.ONEAPI_INSTALLER_VERSION }}-${{ runner.os }}
-#
-#      - name: Download & Install oneAPI
-#        shell: bash
-#        if: steps.cache-sycl.outputs.cache-hit != 'true'
-#        run: |
-#          scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
-#
-#      - name: Install Level Zero SDK
-#        shell: pwsh
-#        run: |
-#          Invoke-WebRequest -Uri "${{ env.LEVEL_ZERO_SDK_URL }}" -OutFile "level-zero-win-sdk.zip"
-#          Expand-Archive -Path "level-zero-win-sdk.zip" -DestinationPath "C:/level-zero-sdk" -Force
-#          "LEVEL_ZERO_V1_SDK_PATH=C:/level-zero-sdk" | Out-File -FilePath $env:GITHUB_ENV -Append
-#
-#      - name: Setup Node.js
-#        uses: actions/setup-node@v6
-#        with:
-#          node-version: "24"
-#          cache: "npm"
-#          cache-dependency-path: "tools/ui/package-lock.json"
-#
-#      - name: ccache
-#        uses: ggml-org/ccache-action@v1.2.21
-#        with:
-#          key: release-windows-2022-x64-sycl
-#
-#      - name: Build
-#        id: cmake_build
-#        shell: cmd
-#        run: |
-#          call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
-#          cmake -G "Ninja" -B build ^
-#            -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx ^
-#            -DCMAKE_BUILD_TYPE=Release ^
-#            -DGGML_BACKEND_DL=ON -DBUILD_SHARED_LIBS=ON ^
-#            -DGGML_CPU=OFF -DGGML_SYCL=ON ^
-#            -DLLAMA_BUILD_BORINGSSL=ON
-#          cmake --build build --target ggml-sycl -j
-#
-#      - name: Build the release package
-#        id: pack_artifacts
-#        run: |
-#          echo "cp oneAPI running time dll files in ${{ env.ONEAPI_ROOT }} to ./build/bin"
-#
-#          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.5.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_core.2.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_tbb_thread.2.dll" ./build/bin
-#
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero_v2.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_opencl.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_loader.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_win_proxy_loader.dll" ./build/bin
-#          ZE_LOADER_DLL=$(find "${{ env.ONEAPI_ROOT }}" "$LEVEL_ZERO_V1_SDK_PATH" -iname ze_loader.dll -print -quit 2>/dev/null || true)
-#          if [ -n "$ZE_LOADER_DLL" ]; then
-#            echo "Using Level Zero loader: $ZE_LOADER_DLL"
-#            cp "$ZE_LOADER_DLL" ./build/bin
-#          else
-#            echo "Level Zero loader DLL not found in oneAPI or SDK; relying on system driver/runtime"
-#          fi
-#
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl8.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libiomp5md.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl-ls.exe" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libsycl-fallback-bfloat16.spv" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libsycl-native-bfloat16.spv" ./build/bin
-#
-#          cp "${{ env.ONEAPI_ROOT }}/dnnl/latest/bin/dnnl.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/tbb/latest/bin/tbb12.dll" ./build/bin
-#
-#          cp "${{ env.ONEAPI_ROOT }}/tcm/latest/bin/tcm.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/tcm/latest/bin/libhwloc-15.dll" ./build/bin
-#          cp "${{ env.ONEAPI_ROOT }}/umf/latest/bin/umf.dll" ./build/bin
-#
-#          echo "cp oneAPI running time dll files to ./build/bin done"
-#          7z a -snl llama-bin-win-sycl-x64.zip ./build/bin/*
-#
-#      - name: Upload the release package
-#        uses: actions/upload-artifact@v6
-#        with:
-#          path: llama-bin-win-sycl-x64.zip
-#          name: llama-bin-win-sycl-x64.zip
+  windows-sycl:
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}
 
-# TODO: this build is disabled to save Github Actions resources (https://github.com/ggml-org/llama.cpp/pull/23705)
-#       in order to enable it again, we have to provision dedicated runners  to run it
-#  ubuntu-24-sycl:
-#
-#    strategy:
-#      matrix:
-#        build: [fp32]
-#        include:
-#          - build: fp32
-#            fp16: OFF
-#
-#    runs-on: ubuntu-24.04
-#
-#    env:
-#      ONEAPI_ROOT: /opt/intel/oneapi/
-#      ONEAPI_INSTALLER_VERSION: "2025.3.3"
-#      LEVEL_ZERO_VERSION: "1.28.2"
-#      LEVEL_ZERO_UBUNTU_VERSION: "u24.04"
-#
-#    steps:
-#      - name: Clone
-#        id: checkout
-#        uses: actions/checkout@v6
-#        with:
-#          fetch-depth: 0
-#
-#      - name: Use oneAPI Installation Cache
-#        uses: actions/cache@v5
-#        id: cache-sycl
-#        with:
-#          path: ${{ env.ONEAPI_ROOT }}
-#          key: cache-gha-oneAPI-${{ env.ONEAPI_INSTALLER_VERSION }}-${{ runner.os }}
-#
-#      - name: Download & Install oneAPI
-#        shell: bash
-#        if: steps.cache-sycl.outputs.cache-hit != 'true'
-#        run: |
-#          cd /tmp
-#          wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/56f7923a-adb8-43f3-8b02-2b60fcac8cab/intel-deep-learning-essentials-2025.3.3.16_offline.sh -O intel-deep-learning-essentials_offline.sh
-#          sudo bash intel-deep-learning-essentials_offline.sh -s -a --silent --eula accept
-#
-#      - name: Install Level Zero SDK
-#        shell: bash
-#        run: |
-#          cd /tmp
-#          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero.deb
-#          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero-devel_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero-devel.deb
-#          sudo apt-get install -y ./level-zero.deb ./level-zero-devel.deb
-#
-#      - name: Setup Node.js
-#        uses: actions/setup-node@v6
-#        with:
-#          node-version: "24"
-#          cache: "npm"
-#          cache-dependency-path: "tools/ui/package-lock.json"
-#
-#      - name: ccache
-#        uses: ggml-org/ccache-action@v1.2.21
-#        with:
-#          key: release-ubuntu-24.04-sycl
-#
-#      - name: Build
-#        id: cmake_build
-#        run: |
-#          source /opt/intel/oneapi/setvars.sh
-#          cmake -B build \
-#            -G "Ninja" \
-#            -DCMAKE_BUILD_TYPE=Release \
-#            -DGGML_SYCL=ON \
-#            -DCMAKE_C_COMPILER=icx \
-#            -DCMAKE_CXX_COMPILER=icpx \
-#            -DLLAMA_OPENSSL=OFF \
-#            -DGGML_NATIVE=OFF \
-#            -DGGML_SYCL_F16=${{ matrix.fp16 }}
-#          time cmake --build build --config Release -j $(nproc)
-#
-#      - name: Determine tag name
-#        id: tag
-#        uses: ./.github/actions/get-tag-name
-#
-#      - name: Pack artifacts
-#        id: pack_artifacts
-#        run: |
-#          cp LICENSE ./build/bin/
-#          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
-#
-#      - name: Upload artifacts
-#        uses: actions/upload-artifact@v6
-#        with:
-#          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz
-#          name: llama-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz
+    runs-on: windows-2022
+
+    defaults:
+      run:
+        shell: bash
+
+    env:
+      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b60765d1-2b85-4e85-86b6-cb0e9563a699/intel-deep-learning-essentials-2025.3.3.18_offline.exe
+      WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
+      LEVEL_ZERO_SDK_URL: https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero-win-sdk-1.28.2.zip
+      ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
+      ONEAPI_INSTALLER_VERSION: "2025.3.3"
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Download & Install oneAPI
+        shell: bash
+        run: |
+          scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
+
+      - name: Install Level Zero SDK
+        shell: pwsh
+        run: |
+          Invoke-WebRequest -Uri "${{ env.LEVEL_ZERO_SDK_URL }}" -OutFile "level-zero-win-sdk.zip"
+          Expand-Archive -Path "level-zero-win-sdk.zip" -DestinationPath "C:/level-zero-sdk" -Force
+          "LEVEL_ZERO_V1_SDK_PATH=C:/level-zero-sdk" | Out-File -FilePath $env:GITHUB_ENV -Append
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: release-windows-2022-x64-sycl
+
+      - name: Build
+        id: cmake_build
+        shell: cmd
+        run: |
+          call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
+          cmake -G "Ninja" -B build ^
+            -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx ^
+            -DCMAKE_BUILD_TYPE=Release ^
+            -DGGML_BACKEND_DL=ON -DBUILD_SHARED_LIBS=ON ^
+            -DGGML_CPU=OFF -DGGML_SYCL=ON ^
+            -DLLAMA_BUILD_BORINGSSL=ON
+          cmake --build build --target ggml-sycl -j %NUMBER_OF_PROCESSORS%
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-windows-2022-x64-sycl
+
+      - name: Build the release package
+        id: pack_artifacts
+        run: |
+          echo "cp oneAPI running time dll files in ${{ env.ONEAPI_ROOT }} to ./build/bin"
+
+          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.5.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_core.2.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_tbb_thread.2.dll" ./build/bin
+
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero_v2.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_opencl.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_loader.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_win_proxy_loader.dll" ./build/bin
+          ZE_LOADER_DLL=$(find "${{ env.ONEAPI_ROOT }}" "$LEVEL_ZERO_V1_SDK_PATH" -iname ze_loader.dll -print -quit 2>/dev/null || true)
+          if [ -n "$ZE_LOADER_DLL" ]; then
+            echo "Using Level Zero loader: $ZE_LOADER_DLL"
+            cp "$ZE_LOADER_DLL" ./build/bin
+          else
+            echo "Level Zero loader DLL not found in oneAPI or SDK; relying on system driver/runtime"
+          fi
+
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl8.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libiomp5md.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl-ls.exe" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libsycl-fallback-bfloat16.spv" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libsycl-native-bfloat16.spv" ./build/bin
+
+          cp "${{ env.ONEAPI_ROOT }}/dnnl/latest/bin/dnnl.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/tbb/latest/bin/tbb12.dll" ./build/bin
+
+          cp "${{ env.ONEAPI_ROOT }}/tcm/latest/bin/tcm.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/tcm/latest/bin/libhwloc-15.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/umf/latest/bin/umf.dll" ./build/bin
+
+          echo "cp oneAPI running time dll files to ./build/bin done"
+          7z a -snl llama-bin-win-sycl-x64.zip ./build/bin/*
+
+      - name: Upload the release package
+        uses: actions/upload-artifact@v6
+        with:
+          path: llama-bin-win-sycl-x64.zip
+          name: llama-bin-win-sycl-x64.zip
+
+  ubuntu-24-sycl:
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}
+
+    strategy:
+      matrix:
+        build: [fp32, fp16]
+        include:
+          - build: fp32
+            fp16: OFF
+          - build: fp16
+            fp16: ON
+
+    runs-on: ubuntu-24.04
+
+    env:
+      ONEAPI_ROOT: /opt/intel/oneapi/
+      ONEAPI_INSTALLER_VERSION: "2025.3.3"
+      LEVEL_ZERO_VERSION: "1.28.2"
+      LEVEL_ZERO_UBUNTU_VERSION: "u24.04"
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      - name: Download & Install oneAPI
+        shell: bash
+        run: |
+          cd /tmp
+          wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/56f7923a-adb8-43f3-8b02-2b60fcac8cab/intel-deep-learning-essentials-2025.3.3.16_offline.sh -O intel-deep-learning-essentials_offline.sh
+          sudo bash intel-deep-learning-essentials_offline.sh -s -a --silent --eula accept
+
+      - name: Install Level Zero SDK
+        shell: bash
+        run: |
+          cd /tmp
+          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero.deb
+          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero-devel_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero-devel.deb
+          sudo apt-get install -y ./level-zero.deb ./level-zero-devel.deb
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: release-ubuntu-24.04-sycl-${{ matrix.build }}
+
+      - name: Build
+        id: cmake_build
+        run: |
+          source /opt/intel/oneapi/setvars.sh
+          cmake -B build \
+            -G "Ninja" \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DGGML_SYCL=ON \
+            -DCMAKE_C_COMPILER=icx \
+            -DCMAKE_CXX_COMPILER=icpx \
+            -DLLAMA_OPENSSL=OFF \
+            -DGGML_NATIVE=OFF \
+            -DGGML_SYCL_F16=${{ matrix.fp16 }}
+          time cmake --build build --config Release -j $(nproc)
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-ubuntu-24.04-sycl-${{ matrix.build }}
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        run: |
+          cp LICENSE ./build/bin/
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-${{ matrix.build }}-x64.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-sycl-${{ matrix.build }}-x64.tar.gz
+          name: llama-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz
 
   ubuntu-22-rocm:
-    needs: [check-release]
+    needs: [check-release, get-version]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
 
     runs-on: ubuntu-22.04
@@ -1052,6 +1076,7 @@ jobs:
             -DGGML_HIP=ON \
             -DHIP_PLATFORM=amd \
             -DGGML_HIP_ROCWMMA_FATTN=ON \
+            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
             ${{ env.CMAKE_ARGS }}
           cmake --build build --config Release -j $(nproc)
 
@@ -1080,7 +1105,7 @@ jobs:
           name: llama-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
 
   windows-hip:
-    needs: [check-release]
+    needs: [check-release, get-version]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
 
     runs-on: windows-2022
@@ -1176,6 +1201,7 @@ jobs:
             -DGPU_TARGETS="${{ matrix.gpu_targets }}" `
             -DGGML_HIP_ROCWMMA_FATTN=ON `
             -DGGML_HIP=ON `
+            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} `
             -DLLAMA_BUILD_BORINGSSL=ON
           cmake --build build --target ggml-hip -j ${env:NUMBER_OF_PROCESSORS}
           md "build\bin\rocblas\library\"
@@ -1203,7 +1229,7 @@ jobs:
           name: llama-bin-win-hip-${{ matrix.name }}-x64.zip
 
   ios-xcode:
-    needs: [check-release]
+    needs: [check-release, get-version]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
     runs-on: macos-26
 
@@ -1232,7 +1258,8 @@ jobs:
             -DLLAMA_BUILD_SERVER=OFF \
             -DCMAKE_SYSTEM_NAME=iOS \
             -DCMAKE_OSX_DEPLOYMENT_TARGET=16.0 \
-            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
+            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml \
+            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }}
           cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
 
       - name: xcodebuild for swift package
@@ -1352,10 +1379,12 @@ jobs:
 #          path: llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz
 #          name: llama-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz
 
-  ui:
-    needs: [check-release]
+  ui-build:
+    needs: [check-release, get-version]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
     uses: ./.github/workflows/ui-build.yml
+    with:
+      hf_ui_version: ${{ needs.get-version.outputs.ui_version }}
 
   release:
     if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
@@ -1368,6 +1397,7 @@ jobs:
     runs-on: ubuntu-slim
 
     needs:
+      - get-version
       - windows
       - windows-cpu
       - windows-cuda
@@ -1382,7 +1412,7 @@ jobs:
       - macos-cpu
       - ios-xcode
       #- openEuler-cann
-      - ui
+      - ui-build
 
     outputs:
       tag_name: ${{ steps.tag.outputs.name }}
@@ -1482,7 +1512,8 @@ jobs:
             - [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-arm64.tar.gz)
             - [Ubuntu x64 (ROCm 7.2)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-7.2-x64.tar.gz)
             - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ 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) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23705)
+            - [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)
 
             **Android:**
             - [Android arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-android-arm64.tar.gz)
@@ -1493,7 +1524,7 @@ jobs:
             - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-12.4-x64.zip)
             - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.3-x64.zip) - [CUDA 13.3 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.3-x64.zip)
             - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)
-            - Windows x64 (SYCL) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23705)
+            - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip)
             - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-hip-radeon-x64.zip)
 
             **openEuler:**

.github/workflows/ui-build-self-hosted.yml

@@ -28,13 +28,6 @@ jobs:
         run: npm run build
         working-directory: tools/ui
 
-      - name: Generate checksums
-        run: |
-          cd tools/ui/dist
-          for f in *; do
-            sha256sum "$f" | awk '{print $1, $2}' >> checksums.txt
-          done
-
       - name: Upload built UI
         uses: actions/upload-artifact@v6
         with:

.github/workflows/ui-build.yml

@@ -2,6 +2,11 @@ name: UI Build
 
 on:
   workflow_call:
+    inputs:
+      hf_ui_version:
+        description: 'Version string for version.json (e.g. 12345)'
+        required: false
+        type: string
 
 jobs:
   build:
@@ -25,15 +30,15 @@ jobs:
         working-directory: tools/ui
 
       - name: Build application
+        env:
+          HF_UI_VERSION: ${{ inputs.hf_ui_version || '' }}
+          LLAMA_BUILD_NUMBER: ${{ inputs.hf_ui_version || 'b0000' }}
         run: npm run build
         working-directory: tools/ui
 
-      - name: Generate checksums
-        run: |
-          cd tools/ui/dist
-          for f in *; do
-            sha256sum "$f" | awk '{print $1, $2}' >> checksums.txt
-          done
+      - name: Run PWA unit tests (versioned build output)
+        run: npx vitest --project=unit --run tests/unit/pwa.spec.ts
+        working-directory: tools/ui
 
       - name: Upload built UI
         uses: actions/upload-artifact@v6

.github/workflows/ui-publish.yml

@@ -40,6 +40,12 @@ jobs:
           name: ui-build
           path: tools/ui/dist/
 
+      - name: Create distribution archive
+        run: |
+          tar -czf dist.tar.gz -C tools/ui/dist .
+          sha256sum dist.tar.gz > dist.tar.gz.sha256
+          mv dist.tar.gz dist.tar.gz.sha256 tools/ui/dist/
+
       - name: Install Hugging Face Hub CLI
         run: pip install -U huggingface_hub
 

.github/workflows/ui-self-hosted.yml

@@ -1,8 +1,8 @@
 name: UI (self-hosted)
 
 # these are the same as ui.yml, but with self-hosted runners
-# the runners come with pre-installed Playwright browsers version: 1.56.1
-# the jobs are much lighter because they don't need to install node and playwright browsers
+# the jobs are lighter because they don't need to install Node.js or Playwright browsers
+# the runner has pre-installed Playwright browsers for @playwright/test (1.56.1) at /ms-playwright/
 
 on:
   workflow_dispatch:
@@ -61,6 +61,12 @@ jobs:
         run: npm ci
         working-directory: tools/ui
 
+      - name: Download built UI artifacts
+        uses: actions/download-artifact@v6
+        with:
+          name: ui-build
+          path: tools/ui/dist/
+
       - name: Run type checking
         if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npm run check
@@ -72,12 +78,12 @@ jobs:
         working-directory: tools/ui
 
       - name: Run Client tests
-        if: ${{ always() }}
+        if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npm run test:client
         working-directory: tools/ui
 
       - name: Run Unit tests
-        if: ${{ always() }}
+        if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npm run test:unit
         working-directory: tools/ui
 
@@ -97,22 +103,23 @@ jobs:
         run: npm ci
         working-directory: tools/ui
 
-      - name: Build application
-        if: ${{ always() && steps.setup.conclusion == 'success' }}
-        run: npm run build
-        working-directory: tools/ui
+      - name: Download built UI artifacts
+        uses: actions/download-artifact@v6
+        with:
+          name: ui-build
+          path: tools/ui/dist/
 
       - name: Build Storybook
-        if: ${{ always() }}
+        if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npm run build-storybook
         working-directory: tools/ui
 
       - name: Run UI tests
-        if: ${{ always() }}
+        if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npm run test:ui -- --testTimeout=60000
         working-directory: tools/ui
 
       - name: Run E2E tests
-        if: ${{ always() }}
+        if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npm run test:e2e
         working-directory: tools/ui

.github/workflows/ui.yml

@@ -43,7 +43,7 @@ jobs:
   ui-checks:
     name: Checks
     needs: ui-build
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04
     continue-on-error: true
     steps:
       - name: Checkout code
@@ -60,6 +60,12 @@ jobs:
           cache: "npm"
           cache-dependency-path: "tools/ui/package-lock.json"
 
+      - name: Download built UI artifacts
+        uses: actions/download-artifact@v6
+        with:
+          name: ui-build
+          path: tools/ui/dist/
+
       - name: Install dependencies
         id: setup
         if: ${{ steps.node.conclusion == 'success' }}
@@ -87,7 +93,7 @@ jobs:
         run: npm run test:client
         working-directory: tools/ui
 
-      - name: Run Unit tests
+      - name: Run Unit tests (uses pre-built dist/ from ui-build)
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:unit
         working-directory: tools/ui
@@ -95,7 +101,7 @@ jobs:
   e2e-tests:
     name: E2E Tests
     needs: ui-build
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04
     steps:
       - name: Checkout code
         uses: actions/checkout@v6
@@ -117,10 +123,11 @@ jobs:
         run: npm ci
         working-directory: tools/ui
 
-      - name: Build application
-        if: ${{ always() && steps.setup.conclusion == 'success' }}
-        run: npm run build
-        working-directory: tools/ui
+      - name: Download built UI artifacts (reuses ui-build)
+        uses: actions/download-artifact@v6
+        with:
+          name: ui-build
+          path: tools/ui/dist/
 
       - name: Install Playwright browsers
         id: playwright
@@ -138,7 +145,7 @@ jobs:
         run: npm run test:ui -- --testTimeout=60000
         working-directory: tools/ui
 
-      - name: Run E2E tests
+      - name: Run E2E tests (uses pre-built dist/ from ui-build)
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:e2e
         working-directory: tools/ui

.github/workflows/winget.yml

@@ -17,7 +17,7 @@ jobs:
 
       - name: Install komac
         run: |
-          cargo binstall komac@2.15.0 -y
+          cargo binstall komac@2.16.0 -y
 
       - name: Find latest release
         id: find_latest_release

.gitignore

@@ -92,13 +92,6 @@
 !/examples/sycl/*.bat
 !/examples/sycl/*.sh
 
-# Server Web UI temporary files (+ legacy directory)
-
-/tools/server/webui/node_modules
-/tools/server/webui/dist
-/tools/ui/node_modules
-/tools/ui/dist
-
 # Python
 
 /.venv

README.md

@@ -1,6 +1,6 @@
 # llama.cpp
 
-![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
+![llama](https://raw.githubusercontent.com/ggml-org/llama.brand/refs/heads/master/cover/llama-cpp/cover-llama-cpp-dark.svg)
 
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
 [![Release](https://img.shields.io/github/v/release/ggml-org/llama.cpp)](https://github.com/ggml-org/llama.cpp/releases)

common/arg.cpp

@@ -2243,6 +2243,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.image_max_tokens = value;
         }
     ).set_examples(mmproj_examples).set_env("LLAMA_ARG_IMAGE_MAX_TOKENS"));
+    add_opt(common_arg(
+        {"--mtmd-batch-max-tokens"}, "N",
+        string_format("maximum number of image tokens per batch when encoding images (default: %d)", params.mtmd_batch_max_tokens),
+        [](common_params & params, int value) {
+            params.mtmd_batch_max_tokens = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MTMD_BATCH_MAX_TOKENS"));
     if (llama_supports_rpc()) {
         add_opt(common_arg(
             {"--rpc"}, "SERVERS",

common/chat.cpp

@@ -1647,11 +1647,12 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
     data.thinking_start_tag = THINK_START;
     data.thinking_end_tag   = THINK_END;
 
-    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
     // Gate by reasoning format and whether the template supports <think>
     auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE &&
                              tmpl.source().find(THINK_START) != std::string::npos;
-    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+    auto include_grammar   = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
 
     if (inputs.has_continuation()) {
         const auto & msg = inputs.continue_msg;
@@ -1674,6 +1675,10 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
         }
 
         if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            if (has_response_format) {
+                auto response_format = p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema));
+                return generation_prompt + reasoning + response_format + end;
+            }
             return generation_prompt + reasoning + p.content(p.rest()) + end;
         }
         auto tool_calls = p.rule("tool-calls",
@@ -1692,13 +1697,17 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
     data.parser = parser.save();
 
     if (include_grammar) {
-        data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+        data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
         data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
             foreach_function(inputs.tools, [&](const json & tool) {
                 const auto & function = tool.at("function");
                 auto         schema   = function.at("parameters");
                 builder.resolve_refs(schema);
             });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
             parser.build_grammar(builder, data.grammar_lazy);
         });
 

common/common.h

@@ -575,6 +575,7 @@ struct common_params {
     std::vector<std::string> image; // path to image file(s) ; TODO: change the name to "media"
     int image_min_tokens = -1;
     int image_max_tokens = -1;
+    int mtmd_batch_max_tokens = 1024;
 
     // finetune
     struct lr_opt lr;

common/fit.cpp

@@ -26,7 +26,7 @@ class common_params_fit_exception : public std::runtime_error {
     using std::runtime_error::runtime_error;
 };
 
-std::vector<llama_device_memory_data> common_get_device_memory_data(
+static std::vector<llama_device_memory_data> common_get_device_memory_data_impl(
         const char * path_model,
         const llama_model_params * mparams,
         const llama_context_params * cparams,
@@ -150,6 +150,29 @@ std::vector<llama_device_memory_data> common_get_device_memory_data(
     return ret;
 }
 
+common_device_memory_data_vec common_get_device_memory_data(
+        const char * path_model,
+        const llama_model_params * mparams,
+        const llama_context_params * cparams,
+        std::vector<ggml_backend_dev_t> & devs,
+        uint32_t & hp_ngl,
+        uint32_t & hp_n_ctx_train,
+        uint32_t & hp_n_expert,
+        ggml_log_level log_level) {
+    std::vector<llama_device_memory_data> impl = common_get_device_memory_data_impl(
+            path_model, mparams, cparams, devs, hp_ngl, hp_n_ctx_train, hp_n_expert, log_level);
+
+    common_device_memory_data_vec ret(impl.size());
+    for (size_t i = 0; i < impl.size(); i++) {
+        ret[i].total   = impl[i].total;
+        ret[i].free    = impl[i].free;
+        ret[i].model   = impl[i].mb.model;
+        ret[i].context = impl[i].mb.context;
+        ret[i].compute = impl[i].mb.compute;
+    }
+    return ret;
+}
+
 static void common_params_fit_impl(
         const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
         float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
@@ -169,7 +192,7 @@ static void common_params_fit_impl(
     // step 1: get data for default parameters and check whether any changes are necessary in the first place
 
     LOG_TRC("%s: getting device memory data for initial parameters:\n", __func__);
-    const dmds_t dmds_full = common_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+    const dmds_t dmds_full = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
     const size_t nd = devs.size(); // number of devices
 
     std::vector<int64_t> margins; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits
@@ -304,7 +327,7 @@ static void common_params_fit_impl(
 
                     int64_t sum_projected_used_min_ctx = 0;
                     cparams->n_ctx = n_ctx_min;
-                    const dmds_t dmds_min_ctx = common_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+                    const dmds_t dmds_min_ctx = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
                     if (nd == 0) {
                         sum_projected_used_min_ctx = dmds_min_ctx.back().mb.total();
                     } else {
@@ -482,7 +505,7 @@ static void common_params_fit_impl(
         llama_model_params mparams_copy = *mparams;
         set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, mparams_copy);
 
-        const dmds_t dmd_nl = common_get_device_memory_data(
+        const dmds_t dmd_nl = common_get_device_memory_data_impl(
             path_model, &mparams_copy, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
 
         LOG_TRC("%s: memory for test allocation by device:\n", func_name);
@@ -510,7 +533,7 @@ static void common_params_fit_impl(
         mparams->tensor_buft_overrides = tensor_buft_overrides;
 
         LOG_TRC("%s: getting device memory data with all MoE tensors moved to system memory:\n", __func__);
-        const dmds_t dmds_cpu_moe = common_get_device_memory_data(
+        const dmds_t dmds_cpu_moe = common_get_device_memory_data_impl(
             path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
 
         for (size_t id = 0; id < nd; id++) {
@@ -940,7 +963,7 @@ void common_fit_print(
     uint32_t hp_nct = 0; // hparams.n_ctx_train
     uint32_t hp_nex = 0; // hparams.n_expert
 
-    auto dmd = common_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, GGML_LOG_LEVEL_ERROR);
+    auto dmd = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, GGML_LOG_LEVEL_ERROR);
     GGML_ASSERT(dmd.size() == devs.size() + 1);
 
     for (size_t id = 0; id < devs.size(); id++) {

common/fit.h

@@ -1,9 +1,7 @@
 #pragma once
 
 #include "ggml.h"
-#include "ggml-backend.h"
 #include "llama.h"
-#include "../src/llama-ext.h"
 
 #include <vector>
 
@@ -18,31 +16,41 @@ enum common_params_fit_status {
 //   - this function is NOT thread safe because it modifies the global llama logger state
 //   - only parameters that have the same value as in llama_default_model_params are modified
 //     with the exception of the context size which is modified if and only if equal to 0
-enum common_params_fit_status common_fit_params(
-                               const char   * path_model,
-                struct llama_model_params   * mparams,
-                struct llama_context_params * cparams,
-                                      float * tensor_split,          // writable buffer for tensor split, needs at least llama_max_devices elements
-    struct llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements
-                                     size_t * margins,               // margins of memory to leave per device in bytes
-                                   uint32_t   n_ctx_min,             // minimum context size to set when trying to reduce memory use
-                        enum ggml_log_level   log_level);            // minimum log level to print during fitting, lower levels go to debug log
+common_params_fit_status common_fit_params(
+                         const char * path_model,
+                 llama_model_params * mparams,
+               llama_context_params * cparams,
+                              float * tensor_split,          // writable buffer for tensor split, needs at least llama_max_devices elements
+   llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements
+                             size_t * margins,               // margins of memory to leave per device in bytes
+                           uint32_t   n_ctx_min,             // minimum context size to set when trying to reduce memory use
+                     ggml_log_level   log_level);            // minimum log level to print during fitting, lower levels go to debug log
 
 // print estimated memory to stdout
 void common_fit_print(
-                               const char   * path_model,
-                struct llama_model_params   * mparams,
-                struct llama_context_params * cparams);
+                         const char * path_model,
+                 llama_model_params * mparams,
+               llama_context_params * cparams);
 
-void common_memory_breakdown_print(const struct llama_context * ctx);
+void common_memory_breakdown_print(const llama_context * ctx);
+
+struct common_device_memory_data {
+    int64_t total;
+    int64_t free;
+    size_t  model;
+    size_t  context;
+    size_t  compute;
+};
+
+using common_device_memory_data_vec = std::vector<common_device_memory_data>;
 
 // Load a model + context with no_alloc and return the per-device memory breakdown.
-std::vector<llama_device_memory_data> common_get_device_memory_data(
-                                  const char   * path_model,
-        const struct llama_model_params         * mparams,
-        const struct llama_context_params       * cparams,
-        std::vector<ggml_backend_dev_t>         & devs,
-                                      uint32_t  & hp_ngl,
-                                      uint32_t  & hp_n_ctx_train,
-                                      uint32_t  & hp_n_expert,
-                           enum ggml_log_level    log_level);
+common_device_memory_data_vec common_get_device_memory_data(
+                         const char * path_model,
+           const llama_model_params * mparams,
+         const llama_context_params * cparams,
+    std::vector<ggml_backend_dev_t> & devs,
+                           uint32_t & hp_ngl,
+                           uint32_t & hp_n_ctx_train,
+                           uint32_t & hp_n_expert,
+                     ggml_log_level   log_level);

common/jinja/runtime.cpp

@@ -316,12 +316,22 @@ value filter_expression::execute_impl(context & ctx) {
 
     JJ_DEBUG("Applying filter to %s", input->type().c_str());
 
+    auto set_filter_alias = [](auto & filter_id) {
+        if (filter_id == "count") {
+            filter_id = "length";
+        } else if (filter_id == "d") {
+            filter_id = "default";
+        } else if (filter_id == "e") {
+            filter_id = "escape";
+        } else if (filter_id == "trim") {
+            filter_id = "strip";
+        }
+    };
+
     if (is_stmt<identifier>(filter)) {
         auto filter_id = cast_stmt<identifier>(filter)->val;
 
-        if (filter_id == "trim") {
-            filter_id = "strip"; // alias
-        }
+        set_filter_alias(filter_id);
         JJ_DEBUG("Applying filter '%s' to %s", filter_id.c_str(), input->type().c_str());
         // TODO: Refactor filters so this coercion can be done automatically
         if (!input->is_undefined() && !is_val<value_string>(input) && (
@@ -345,9 +355,7 @@ value filter_expression::execute_impl(context & ctx) {
         }
         auto filter_id = cast_stmt<identifier>(call->callee)->val;
 
-        if (filter_id == "trim") {
-            filter_id = "strip"; // alias
-        }
+        set_filter_alias(filter_id);
         JJ_DEBUG("Applying filter '%s' with arguments to %s", filter_id.c_str(), input->type().c_str());
         func_args args(ctx);
         for (const auto & arg_expr : call->args) {
@@ -761,9 +769,9 @@ value member_expression::execute_impl(context & ctx) {
 
         if (is_stmt<slice_expression>(this->property)) {
             auto s = cast_stmt<slice_expression>(this->property);
-            value start_val = s->start_expr ? s->start_expr->execute(ctx) : mk_val<value_int>(0);
-            value stop_val  = s->stop_expr  ? s->stop_expr->execute(ctx)  : mk_val<value_int>(arr_size);
             value step_val  = s->step_expr  ? s->step_expr->execute(ctx)  : mk_val<value_int>(1);
+            value start_val = s->start_expr ? s->start_expr->execute(ctx) : (step_val->as_int() < 0 ? mk_val<value_int>(arr_size - 1) : mk_val<value_int>(0));
+            value stop_val  = s->stop_expr  ? s->stop_expr->execute(ctx)  : (step_val->as_int() < 0 ? mk_val<value_int>(-1) : mk_val<value_int>(arr_size));
 
             // translate to function call: obj.slice(start, stop, step)
             JJ_DEBUG("Member expression is a slice: start %s, stop %s, step %s",

common/jinja/value.cpp

@@ -90,14 +90,14 @@ static T slice(const T & array, int64_t start, int64_t stop, int64_t step = 1) {
             stop_val = std::min(stop_val, len);
         }
     } else {
-        start_val = len - 1;
+        start_val = start;
         if (start_val < 0) {
-            start_val = std::max(len + start_val, (int64_t)-1);
+            start_val = std::max(len + start_val, (int64_t)0);
         } else {
             start_val = std::min(start_val, len - 1);
         }
 
-        stop_val = -1;
+        stop_val = stop;
         if (stop_val < -1) {
             stop_val = std::max(len + stop_val, (int64_t)-1);
         } else {
@@ -673,6 +673,9 @@ const func_builtins & value_string_t::get_builtins() const {
             std::string str = val_input->as_string().str();
             // FIXME: Support non-specified delimiter (split on consecutive (no leading or trailing) whitespace)
             std::string delim = (args.count() > 1) ? args.get_pos(1)->as_string().str() : " ";
+            if (delim.empty()) {
+                throw raised_exception("empty separator");
+            }
             int64_t maxsplit = (args.count() > 2) ? args.get_pos(2)->as_int() : -1;
             auto result = mk_val<value_array>();
             size_t pos = 0;
@@ -697,6 +700,9 @@ const func_builtins & value_string_t::get_builtins() const {
             std::string str = val_input->as_string().str();
             // FIXME: Support non-specified delimiter (split on consecutive (no leading or trailing) whitespace)
             std::string delim = (args.count() > 1) ? args.get_pos(1)->as_string().str() : " ";
+            if (delim.empty()) {
+                throw raised_exception("empty separator");
+            }
             int64_t maxsplit = (args.count() > 2) ? args.get_pos(2)->as_int() : -1;
             auto result = mk_val<value_array>();
             size_t pos = 0;
@@ -722,10 +728,23 @@ const func_builtins & value_string_t::get_builtins() const {
             if (count > 0) {
                 throw not_implemented_exception("String replace with count argument not implemented");
             }
-            size_t pos = 0;
-            while ((pos = str.find(old_str, pos)) != std::string::npos) {
-                str.replace(pos, old_str.length(), new_str);
-                pos += new_str.length();
+            if (old_str != new_str) {
+                size_t pos = 0;
+                if (old_str.empty()) {
+                    std::string new_res;
+                    new_res.reserve(str.length() + new_str.length() * (str.length() + 1));
+                    new_res += new_str;
+                    for (const char c : str) {
+                        new_res.push_back(c);
+                        new_res += new_str;
+                    }
+                    str = new_res;
+                } else {
+                    while ((pos = str.find(old_str, pos)) != std::string::npos) {
+                        str.replace(pos, old_str.length(), new_str);
+                        pos += new_str.length();
+                    }
+                }
             }
             auto res = mk_val<value_string>(str);
             res->val_str.mark_input_based_on(args.get_pos(0)->val_str);

common/speculative.cpp

@@ -375,31 +375,437 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
     }
 };
 
+
+// EAGLE3 speculative decoding state
+//
+// Input of draft decoder: (This is different compared to MTP)
+//   At "pos P", the decoder takes input pair (t_{P+1}, g_P), with RoPE at P.
+//     - t_{P+1} = token at sequence pos P+1 (the *next* token after P)
+//     - g_P     = encoder output = projection of target's extracted hidden states at P
+//
+// Deferred boundary (MTP doesn't have this issue):
+//   Within a single process() call with n_tokens, we can only write decoder KV for
+//   training pos 0..n_tokens-2. The last training pos (n_tokens-1) needs t_{n_tokens}
+//   which lies *outside* this batch — it is the token target will sample next or the first token from next ubatch.
+//   So the last training pos of each process() call is *deferred* to whichever next call has
+//   the missing token in hand:
+//     - multi-ubatch prefill: the next process()'s first token completes the pair
+//                              (handled by the per-seq "cross-ubatch bridge")
+//     - single-ubatch prefill / after verify: draft()'s seed step uses "dp.id_last"
+//                              (target's freshest sample) to complete the pair
+//
+// Per-seq carry-over state:
+//   pending_g_last    [n_embd_dec]  ┐  the deferred boundary's (g, pos). Set by
+//   pending_pos_last  llama_pos     ┘  process() at end of ubatch (= last row);
+//                                       rebased by accept() to first-non-accepted pos.
+//   verify_g          [N × n_embd_dec] snapshot of process()'s encoder output;
+//   verify_pos_first  llama_pos         consumed by accept() to recover the right
+//   verify_g_rows     int32_t           pending_g_last row for any n_accepted value.
+//
+// Performance is overall good but there is waste in verify cycle:
+//   process() runs encoder + decoder on the *full* verify batch including rows for
+//   rejected drafts. The KV at those positions is then dropped.
+//
+// TODO: Not sure if we need optimization for this waste?
+// If so we may need hybrid stash:
+//      in verify mode, have process() only stash features and let draft() seed run
+//      encoder+decoder on n_accepted+1 rows).
 struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
-    //common_params_speculative_eagle3 params;
+    common_params_speculative_draft params;
+    llama_batch batch;
+
+    std::vector<common_sampler_ptr> smpls;
+
+    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
+
+    const int32_t * target_layer_ids   = nullptr; // model_dft's extract layer indices
+    uint32_t        target_layer_ids_n = 0;
+
+    // [per-seq] deferred boundary state
+    std::vector<std::vector<float>> pending_g_last;
+    std::vector<llama_pos>          pending_pos_last;
+
+    // [per-seq] snapshot of the most recent process()'s encoder output
+    std::vector<std::vector<float>> verify_g;         // [n_seq][n_rows * n_embd_dec]
+    std::vector<llama_pos>          verify_pos_first; // [n_seq] — pos of verify_g[seq][0]
+    std::vector<int32_t>            verify_g_rows;    // [n_seq] — number of rows
+
+    // scratch buffer for concatenated target features [n_tokens, n_embd_enc]
+    std::vector<float> features_buf;
+    std::vector<float> g_embd_buf;
 
     common_speculative_impl_draft_eagle3(const common_params_speculative & params, uint32_t n_seq)
         : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq)
+        , params(params.draft)
     {
         LOG_INF("%s: adding speculative implementation 'draft-eagle3'\n", __func__);
         LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%f\n", __func__, params.draft.n_max, params.draft.n_min, params.draft.p_min);
+
+        auto * ctx_tgt = this->params.ctx_tgt;
+        auto * ctx_dft = this->params.ctx_dft;
+        GGML_ASSERT(ctx_tgt && ctx_dft && "EAGLE3 requires ctx_tgt and ctx_dft to be set");
+
+        const llama_model * model_dft = llama_get_model(ctx_dft);
+        const llama_model * model_tgt = llama_get_model(ctx_tgt);
+
+        target_layer_ids   = llama_model_target_layer_ids  (model_dft);
+        target_layer_ids_n = llama_model_target_layer_ids_n(model_dft);
+        if (target_layer_ids_n != 3) {
+            throw std::runtime_error("draft model is not eagle3 (expected 3 extract layers, got " +
+                                     std::to_string(target_layer_ids_n) + ")");
+        }
+
+        n_embd_tgt = llama_model_n_embd(model_tgt);
+        n_embd_dec = llama_model_n_embd(model_dft);
+        n_embd_enc = (int32_t) target_layer_ids_n * n_embd_tgt;
+
+        const int32_t n_b = (int32_t) llama_n_batch(ctx_dft);
+        batch = llama_batch_init(/*n_tokens=*/ n_b, /*embd=*/ n_embd_dec, /*n_seq_max=*/ 1);
+        // llama_batch_init allocates only one of token/embd; eagle3 decoder needs both.
+        // TODO: fix, how to call without malloc
+        batch.token = (llama_token *) malloc(sizeof(llama_token) * n_b);
+
+        smpls.resize(n_seq);
+        for (auto & s : smpls) {
+            common_params_sampling sparams;
+            sparams.no_perf  = false;
+            sparams.top_k    = 10;
+            sparams.samplers = { COMMON_SAMPLER_TYPE_TOP_K };
+            s.reset(common_sampler_init(llama_get_model(ctx_dft), sparams));
+        }
+
+        // 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);
+        }
+
+        // turn on extraction of the draft model's pre-norm hidden state
+        // (used both for the encoder output g_embd and the decoder pre-norm output).
+        llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);
+
+        pending_g_last.assign(n_seq, std::vector<float>(n_embd_dec, 0.0f));
+        pending_pos_last.assign(n_seq, -1);
+
+        verify_g.assign(n_seq, std::vector<float>());
+        verify_pos_first.assign(n_seq, -1);
+        verify_g_rows.assign(n_seq, 0);
     }
 
-    void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override {
-        // noop
+    ~common_speculative_impl_draft_eagle3() override {
+        if (batch.token != nullptr) {
+            free(batch.token);
+            batch.token = nullptr;
+        }
+        llama_batch_free(batch);
     }
 
-    bool process(const llama_batch & /*batch*/) override {
-        // TODO: implement
+    void begin(llama_seq_id seq_id, const llama_tokens & prompt) override {
+        const int32_t N = (int32_t) prompt.size();
+        if (N <= 0) {
+            return;
+        }
+        // expected state after prefill: ctx_dft has pos 0..N-2 (last position is deferred to
+        // draft()'s seed step). Warn only if more than one position is missing.
+        auto * ctx_dft = this->params.ctx_dft;
+        const llama_pos pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
+        if (pos_max < N - 2) {
+            LOG_WRN("%s: ctx_dft pos_max=%d < N-2=%d — process() did not run on every prefill ubatch. "
+                    "Drafts may degrade.\n",
+                    __func__, (int) pos_max, N - 2);
+        }
+    }
+
+    bool process(const llama_batch & batch_in) override {
+        if (batch_in.n_tokens <= 0) {
+            return true;
+        }
+
+        if (batch_in.token == nullptr || batch_in.embd != nullptr) {
+            return true;
+        }
+
+        const int32_t n_tokens = batch_in.n_tokens;
+
+        // i_batch_beg[seq] / i_batch_end[seq]: inclusive batch indices of this seq's
+        // first/last token in batch_in. Assumes per-seq tokens are contiguous within
+        // the ubatch (server's default ordering).
+        std::vector<int32_t> i_batch_beg(n_seq, -1);
+        std::vector<int32_t> i_batch_end(n_seq, -1);
+        for (int k = 0; k < n_tokens; ++k) {
+            GGML_ASSERT(batch_in.n_seq_id[k] == 1);
+            const llama_seq_id seq_id = batch_in.seq_id[k][0];
+            if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+                continue;
+            }
+            i_batch_end[seq_id] = k;
+            if (i_batch_beg[seq_id] < 0) {
+                i_batch_beg[seq_id] = k;
+            }
+        }
+
+        auto * ctx_tgt = this->params.ctx_tgt;
+        auto * ctx_dft = this->params.ctx_dft;
+
+        // Interleave each extract_layer's hidden state into a contiguous buffer of
+        // shape [n_tokens, target_layer_ids_n * n_embd_tgt]. Then run EAGLE3 encoder
+        // to get one g_embd row per token.
+        features_buf.resize((size_t) n_tokens * n_embd_enc, 0.0f);
+
+        for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
+            const float * layer = llama_get_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k]);
+            if (!layer) {
+                GGML_ABORT("EAGLE3: target layer %d input not extracted.", target_layer_ids[k]);
+            }
+            for (int32_t i = 0; i < n_tokens; ++i) {
+                float * dst = features_buf.data() + (size_t) i * n_embd_enc + k * (size_t) n_embd_tgt;
+                const float * src = layer + (size_t) i * n_embd_tgt;
+                std::memcpy(dst, src, (size_t) n_embd_tgt * sizeof(float));
+            }
+        }
+
+        g_embd_buf.resize((size_t) n_tokens * n_embd_dec);
+
+        // llama_encode() requires the full encoder batch to fit in n_ubatch.
+        // Allow batch > ubatch: eagle3's per-token encoder can be chunked safely.
+        const int32_t n_ubatch_dft = (int32_t) llama_n_ubatch(ctx_dft);
+        for (int32_t i = 0; i < n_tokens; i += n_ubatch_dft) {
+            const int32_t n_chunk = std::min(n_ubatch_dft, n_tokens - i);
+
+            llama_batch enc_batch = {
+                /*.n_tokens =*/ n_chunk,
+                /*.token    =*/ nullptr,
+                /*.embd     =*/ features_buf.data() + (size_t) i * n_embd_enc,
+                /*.pos      =*/ nullptr,
+                /*.n_seq_id =*/ nullptr,
+                /*.seq_id   =*/ nullptr,
+                /*.logits   =*/ nullptr,
+            };
+            const int32_t rc = llama_encode(ctx_dft, enc_batch);
+            if (rc != 0) {
+                LOG_ERR("%s: llama_encode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
+                        __func__, rc, (int) n_chunk, (int) i);
+                return false;
+            }
+
+            // g_embd has shape [n_chunk, n_embd_dec] in ctx_dft's pre-norm embeddings buffer.
+            const float * g_embd_chunk = llama_get_embeddings_nextn(ctx_dft);
+            GGML_ASSERT(g_embd_chunk && "EAGLE3 encoder produced no output.");
+            std::memcpy(g_embd_buf.data() + (size_t) i * n_embd_dec,
+                        g_embd_chunk,
+                        (size_t) n_chunk * n_embd_dec * sizeof(float));
+        }
+
+        const float * g_embd = g_embd_buf.data();
+
+        const size_t row_bytes = (size_t) n_embd_dec * sizeof(float);
+
+        // EAGLE3 decoder input convention: at memory pos P the input pair is
+        // (token[P+1], g_embd[P]). This shifts the token index "left by one" relative to g_embd.
+        //
+        // Per seq, in order:
+        //   (a) cross-ubatch bridge — when applicable, write the previously-deferred
+        //       pos using this ubatch's first token + pending_g_last.
+        //   (b) main write loop — for k in [beg, end-1], write (token[k+1], g_embd[k])
+        //       at pos[k]. The last training pos (k=end) is left unwritten = new
+        //       deferred boundary, completed by the next process() or draft() call.
+        //   (c) refresh deferred state — stash this ubatch's full g_embd into verify_g,
+        //       update pending_g_last / pending_pos_last to the last row.
+        common_batch_clear(batch);
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            const int32_t beg = i_batch_beg[seq_id];
+            const int32_t end = i_batch_end[seq_id];
+            if (beg < 0 || end < 0) {
+                continue;
+            }
+
+            // cross-ubatch bridge — complete the prior ubatch's deferred boundary.
+            // Fires iff all three preconditions hold:
+            //   1) pending_pos_last >= 0
+            //   2) pending_pos_last + 1 == pos[beg]
+            //   3) pending_pos_last > dft_pos_max // TODO: is this check needed?
+            const llama_pos pending_pos = pending_pos_last[seq_id];
+            if (pending_pos >= 0 && pending_pos + 1 == batch_in.pos[beg]) {
+                const llama_pos dft_pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
+                if (pending_pos > dft_pos_max) {
+                    common_batch_add(batch, batch_in.token[beg], pending_pos, { seq_id }, /*logits=*/ false);
+                    std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec,
+                                pending_g_last[seq_id].data(), row_bytes);
+                }
+            }
+
+            for (int32_t k = beg; k < end; ++k) {
+                common_batch_add(batch, batch_in.token[k + 1], batch_in.pos[k], { seq_id }, /*logits=*/ false);
+                std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec,
+                            g_embd + (size_t) k * n_embd_dec, row_bytes);
+            }
+
+            // refresh deferred state
+            const int32_t n_rows = end - beg + 1;
+            verify_pos_first[seq_id] = batch_in.pos[beg];
+            pending_pos_last[seq_id] = batch_in.pos[end];
+            verify_g_rows[seq_id]    = n_rows;
+            verify_g[seq_id].resize((size_t) n_rows * n_embd_dec, 0.0f);
+            std::memcpy(verify_g[seq_id].data(),       g_embd + (size_t) beg * n_embd_dec, row_bytes * n_rows);
+            std::memcpy(pending_g_last[seq_id].data(), g_embd + (size_t) end * n_embd_dec, row_bytes);
+        }
+
+        if (batch.n_tokens > 0) {
+            const int32_t rc = llama_decode(ctx_dft, batch);
+            if (rc != 0) {
+                LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (n_tokens=%d, ubatch_pos[0]=%d)\n",
+                        __func__, rc, (int) batch.n_tokens, (int) batch_in.pos[0]);
+                return false;
+            }
+        }
+
         return true;
     }
 
-    void draft(common_speculative_draft_params_vec & /*dparams*/) override {
-        // TODO: implement
+    void draft(common_speculative_draft_params_vec & dparams) override {
+        auto & ctx_dft = params.ctx_dft;
+
+        common_batch_clear(batch);
+
+        // keep track of which sequences are still drafting
+        int n_drafting = 0;
+        std::vector<bool> drafting(n_seq);
+
+        const size_t row_bytes = (size_t) n_embd_dec * sizeof(float);
+
+        // Complete the deferred boundary pair (dp.id_last, pending_g_last) at memory
+        // pos pending_pos_last. dp.id_last is target's freshest sample (= corrected
+        // token after verify, or first generated token after prefill), matching the
+        // EAGLE3 input convention (token[P+1], g_embd[P]) at pos P.
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+
+            if (!dp.drafting) {
+                continue;
+            }
+            if (pending_pos_last[seq_id] < 0) {
+                continue;
+            }
+
+            n_drafting++;
+            drafting[seq_id] = true;
+            common_sampler_reset(smpls[seq_id].get());
+
+            llama_memory_seq_rm(llama_get_memory(ctx_dft), seq_id, pending_pos_last[seq_id], -1);
+
+            common_batch_add(batch, dp.id_last, pending_pos_last[seq_id], { seq_id }, true);
+            std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec,
+                        pending_g_last[seq_id].data(),
+                        row_bytes);
+        }
+
+        if (batch.n_tokens == 0) {
+            return;
+        }
+
+        int ret = llama_decode(ctx_dft, batch);
+        if (ret != 0) {
+            LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
+            return;
+        }
+
+        int i = 0;
+
+        while (n_drafting > 0) {
+            int i_batch = 0;
+
+            common_batch_clear(batch);
+
+            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                if (!drafting[seq_id]) {
+                    continue;
+                }
+
+                auto * smpl = smpls[seq_id].get();
+
+                common_sampler_sample(smpl, ctx_dft, i_batch, true);
+                // pre-norm hidden state of this position becomes g_embd for the next step
+                const float * prenorm = llama_get_embeddings_nextn_ith(ctx_dft, i_batch);
+                ++i_batch;
+
+                const auto * cur_p = common_sampler_get_candidates(smpl, true);
+
+                for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
+                    LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                            seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
+                            common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
+                }
+
+                const llama_token id = cur_p->data[0].id;
+
+                // only collect very high-confidence draft tokens
+                // (configurable via --spec-draft-p-min, set to 0.0 to disable early-stop)
+                if (cur_p->data[0].p < params.p_min) {
+                    drafting[seq_id] = false;
+                    n_drafting--;
+
+                    continue;
+                }
+
+                common_sampler_accept(smpl, id, true);
+
+                auto & dp = dparams.at(seq_id);
+                auto & result = *dp.result;
+
+                result.push_back(id);
+
+                if (params.n_max <= (int) result.size()) {
+                    drafting[seq_id] = false;
+                    n_drafting--;
+                    continue;
+                }
+
+                common_batch_add(batch, id, pending_pos_last[seq_id] + (i + 1), { seq_id }, true);
+                std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec, prenorm, row_bytes);
+            }
+
+            if (batch.n_tokens == 0) {
+                break;
+            }
+
+            ret = llama_decode(ctx_dft, batch);
+            if (ret != 0) {
+                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+                break;
+            }
+
+            ++i;
+        }
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+            if (!dp.drafting) {
+                continue;
+            }
+
+            if (dp.result->size() < (size_t) params.n_min) {
+                dp.result->clear();
+            }
+        }
     }
 
-    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
-        // noop
+    void accept(llama_seq_id seq_id, uint16_t n_accepted, bool /*is_other*/) override {
+        if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+            return;
+        }
+
+        const int32_t n_rows = verify_g_rows[seq_id];
+        if (n_rows <= 0) {
+            return;
+        }
+
+        const int32_t i_g = std::min<int32_t>(n_accepted, n_rows - 1);
+        pending_pos_last[seq_id] = verify_pos_first[seq_id] + i_g;
+        std::memcpy(pending_g_last[seq_id].data(),
+                    verify_g[seq_id].data() + (size_t) i_g * n_embd_dec,
+                    (size_t) n_embd_dec * sizeof(float));
     }
 
     bool need_embd() const override {
@@ -843,7 +1249,8 @@ struct common_speculative_impl_ngram_map_k : public common_speculative_impl {
     common_speculative_impl_ngram_map_k(
             const common_ngram_map & config,
             uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, n_seq)
+        : common_speculative_impl(config.key_only ? COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K
+            : COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, n_seq)
     {
         for (uint32_t i = 0; i < n_seq; i++) {
             this->config.push_back(config);
@@ -1369,9 +1776,11 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         uint32_t enabled_configs = common_get_enabled_speculative_configs(params.types);
 
         bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
-        bool has_draft_eagle3 = false; // TODO PR-18039: if params.speculative.eagle3
+        bool has_draft_eagle3 = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3)) && params.draft.ctx_dft != nullptr;
         bool has_mtp = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP)) && params.draft.ctx_dft != nullptr;
 
+
+
         bool has_ngram_cache   = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_CACHE));
         bool has_ngram_simple  = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE));
         bool has_ngram_map_k   = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K));

conversion/__init__.py

@@ -40,6 +40,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "ChatGLMModel": "chatglm",
     "CodeShellForCausalLM": "codeshell",
     "CogVLMForCausalLM": "cogvlm",
+    "Cohere2MoeForCausalLM": "command_r",
     "Cohere2ForCausalLM": "command_r",
     "CohereForCausalLM": "command_r",
     "DbrxForCausalLM": "dbrx",
@@ -130,6 +131,9 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "LlamaBidirectionalModel": "llama",
     "LlamaForCausalLM": "llama",
     "LlamaModel": "llama",
+    "Eagle3DraftModel": "llama",
+    "Eagle3Speculator": "llama",
+    "LlamaForCausalLMEagle3": "llama",
     "LlavaForConditionalGeneration": "llama",
     "LlavaStableLMEpochForCausalLM": "stablelm",
     "MPTForCausalLM": "mpt",

conversion/base.py

@@ -94,6 +94,7 @@ class ModelBase:
     metadata: gguf.Metadata
     dir_model_card: Path
     remote_hf_model_id: str | None
+    target_model_dir: Path | None
 
     # subclasses should define this!
     model_arch: gguf.MODEL_ARCH
@@ -119,6 +120,7 @@ class ModelBase:
                  small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None,
                  disable_mistral_community_chat_template: bool = False,
                  sentence_transformers_dense_modules: bool = False,
+                 target_model_dir: Path | None = None,
                  fuse_gate_up_exps: bool = False,
                  fp8_as_q8: bool = False):
         if type(self) is ModelBase or \
@@ -139,6 +141,7 @@ class ModelBase:
         self.dry_run = dry_run
         self.remote_hf_model_id = remote_hf_model_id
         self.sentence_transformers_dense_modules = sentence_transformers_dense_modules
+        self.target_model_dir = target_model_dir
         self.fuse_gate_up_exps = fuse_gate_up_exps
         self._gate_exp_buffer: dict[int, Tensor] = {}
         self._up_exp_buffer: dict[int, Tensor] = {}
@@ -1192,7 +1195,7 @@ class TextModel(ModelBase):
             self.gguf_writer.add_embedding_length(n_embd)
             logger.info(f"gguf: embedding length = {n_embd}")
 
-        if (n_ff := self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
+        if (n_ff := self.find_hparam(["prefix_dense_intermediate_size", "intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
             self.gguf_writer.add_feed_forward_length(n_ff)
             logger.info(f"gguf: feed forward length = {n_ff}")
 
@@ -1277,7 +1280,7 @@ class TextModel(ModelBase):
             self.gguf_writer.add_expert_group_used_count(n_group_used)
             logger.info(f"gguf: expert groups used count = {n_group_used}")
 
-        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func"], optional=True)) is not None:
+        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func", "expert_selection_fn"], optional=True)) is not None:
             if score_func == "sigmoid":
                 self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
             elif score_func == "softmax":
@@ -1492,6 +1495,9 @@ class TextModel(ModelBase):
         if chkhsh == "d772b220ace2baec124bed8cfafce0ead7d6c38a4b65ef11261cf9d5d62246d1":
             # ref: https://huggingface.co/CohereLabs/tiny-aya-base
             res = "tiny_aya"
+        if chkhsh == "52df12b4c8d4176e7481aab4b6e8454d1fd0a210a04a574f6d4e067d10e23c3e":
+            # ref: https://huggingface.co/CohereLabs/North-Mini-Code-1.0
+            res = "cohere2moe"
         if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea":
             # ref: https://huggingface.co/Qwen/Qwen1.5-7B
             res = "qwen2"
@@ -2481,6 +2487,7 @@ class LazyTorchTensor(gguf.LazyBase):
         torch.float16: np.float16,
         torch.float32: np.float32,
         torch.uint8: np.uint8,
+        torch.int64: np.int64,
     }
 
     # only used when byteswapping data. Only correct size is needed

conversion/command_r.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import re
 from typing import Iterable, TYPE_CHECKING
 
 import torch
@@ -55,3 +56,122 @@ class Cohere2Model(TextModel):
             return
 
         yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Cohere2MoeForCausalLM")
+class Cohere2MoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.COHERE2MOE
+    _n_main_layers: int | None = None
+    _expert_tensor_re = re.compile(
+        r"model\.layers\.(\d+)\.mlp\.experts\.(\d+)\.(down_proj|gate_proj|up_proj)\.weight"
+    )
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if (n_nextn := int(self.hparams.get("num_nextn_predict_layers", 0) or 0)) > 0 and not self.no_mtp:
+            self.block_count += n_nextn
+            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+        self._experts: list[dict[str, Tensor]] = [{} for _ in range(self.block_count)]
+
+    def _set_vocab_gpt2(self) -> None:
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+        expert_intermediate_size = hparams["intermediate_size"]
+        mlp_layer_types = hparams.get("mlp_layer_types")
+        n_dense_lead = hparams.get("first_k_dense_replace", 0)
+        if mlp_layer_types is not None:
+            n_dense_lead = next((i for i, t in enumerate(mlp_layer_types) if t != "dense"), len(mlp_layer_types))
+
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_logit_scale(hparams["logit_scale"])
+        self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+        self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]])
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)
+        self.gguf_writer.add_leading_dense_block_count(n_dense_lead)
+        self.gguf_writer.add_expert_weights_norm(hparams.get("norm_topk_prob", False))
+        if (num_shared_experts := hparams.get("num_shared_experts", 0)) > 0:
+            if hparams.get("shared_expert_combination_strategy", "average") != "average":
+                raise ValueError("Cohere2 MoE only supports average shared expert combination")
+            self.gguf_writer.add_expert_shared_count(num_shared_experts)
+            self.gguf_writer.add_expert_shared_feed_forward_length(expert_intermediate_size * num_shared_experts)
+        if (n_nextn := hparams.get("num_nextn_predict_layers", 0)) > 0 and not self.no_mtp:
+            self.gguf_writer.add_nextn_predict_layers(n_nextn)
+        self.gguf_writer.add_rope_dimension_count(hparams["head_dim"])
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+    def index_tensors(self, remote_hf_model_id: str | None = None):
+        hparams = {**self.hparams, **self.hparams.get("text_config", {})}
+        self._n_main_layers = hparams.get("num_hidden_layers")
+        type(self)._n_main_layers = self._n_main_layers
+        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
+
+    @classmethod
+    def filter_tensors(cls, item):
+        if (titem := super().filter_tensors(item)) is None:
+            return None
+        name, gen = titem
+
+        if cls._n_main_layers is not None:
+            is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers
+            if is_mtp and cls.no_mtp:
+                return None
+            if cls.mtp_only and not is_mtp and name not in (
+                "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+            ):
+                return None
+
+        return name, gen
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.endswith(".bias"):
+            if torch.any(data_torch != 0):
+                raise ValueError(f"Bias tensor {name!r} is not zero.")
+            logger.debug(f"Skipping bias tensor {name!r}.")
+            return
+
+        if (m := self._expert_tensor_re.fullmatch(name)) is not None:
+            n_experts = self.hparams["num_experts"]
+            layer_idx = int(m.group(1))
+            assert bid is None or bid == layer_idx
+
+            self._experts[layer_idx][name] = data_torch
+
+            expected = {
+                f"model.layers.{layer_idx}.mlp.experts.{xid}.{w_name}.weight"
+                for xid in range(n_experts)
+                for w_name in ("down_proj", "gate_proj", "up_proj")
+            }
+            if expected.issubset(self._experts[layer_idx]):
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{layer_idx}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[layer_idx][ename])
+                        del self._experts[layer_idx][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+                    merged_name = f"model.layers.{layer_idx}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, layer_idx)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        experts = [k for d in self._experts for k in d.keys()]
+        if len(experts) > 0:
+            raise ValueError(f"Unprocessed experts: {experts}")

conversion/llama.py

@@ -5,12 +5,13 @@ import math
 
 from typing import Callable, Iterable, TYPE_CHECKING
 
+import numpy as np
 import torch
 
 if TYPE_CHECKING:
     from torch import Tensor
 
-from .base import ModelBase, TextModel, gguf
+from .base import ModelBase, TextModel, gguf, logger
 
 
 @ModelBase.register(
@@ -21,6 +22,9 @@ from .base import ModelBase, TextModel, gguf
     "VLlama3ForCausalLM",
     "LlavaForConditionalGeneration",
     "VoxtralForConditionalGeneration",
+    "LlamaForCausalLMEagle3",
+    "Eagle3Speculator",
+    "Eagle3DraftModel",
     "IQuestCoderForCausalLM",
     "LlamaModel")
 class LlamaModel(TextModel):
@@ -39,7 +43,61 @@ class LlamaModel(TextModel):
             hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
             self.origin_hf_arch = hparams.get('architectures', [None])[0]
 
+        # Detect eagle3 draft checkpoint by hparams (some models don't use a distinct HF arch name)
+        if "draft_vocab_size" in self.hparams and self.hparams["num_hidden_layers"] == 1:
+            self.is_eagle3 = True
+            self.model_arch = gguf.MODEL_ARCH.EAGLE3
+            logger.info("Detected EAGLE-3 draft model, switching to EAGLE3 architecture")
+            # Re-initialize tensor_map with eagle3 architecture
+            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+            # Update gguf_writer architecture
+            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
+            self.gguf_writer.add_architecture()
+            if self.target_model_dir is None:
+                raise ValueError(
+                    "EAGLE-3 model requires --target-model-dir to be specified. "
+                    "Please provide the path to the target model directory to read config.json"
+                )
+            # Read both eagle3 raw config and target model config
+            with open(self.dir_model / "config.json", 'r', encoding='utf-8') as f:
+                eagle3_raw_config = json.load(f)
+            with open(self.target_model_dir / "config.json", 'r', encoding='utf-8') as f:
+                target_config = json.load(f)
+
+            if "text_config" in target_config:
+                target_config = {**target_config, **target_config["text_config"]}
+            self.target_vocab_size = target_config["vocab_size"]
+
+            # target_layers: derived from target model layer count (low/mid/high)
+            target_num_layers = target_config["num_hidden_layers"]
+            target_layers = [2, target_num_layers // 2, target_num_layers - 3]
+            logger.info(f"EAGLE-3: target_layers = {target_layers} (target model has {target_num_layers} layers)")
+            self.gguf_writer.add_array(f"{self.gguf_writer.arch}.target_layers", target_layers)
+
+            # target_hidden_size: prefer eagle3 config, fallback to target config
+            if eagle3_raw_config.get("target_hidden_size") is not None:
+                target_hidden_size = eagle3_raw_config["target_hidden_size"]
+                src = "EAGLE-3 config"
+            else:
+                target_hidden_size = target_config["hidden_size"]
+                src = "target model config"
+            logger.info(f"EAGLE-3: target_hidden_size = {target_hidden_size} (from {src})")
+            self.gguf_writer.add_uint32(f"{self.gguf_writer.arch}.target_hidden_size", target_hidden_size)
+
+            # norm_before_residual (RedHat-style eagle3 specific)
+            norm_before_residual = eagle3_raw_config.get("norm_before_residual", False)
+            logger.info(f"EAGLE-3: norm_before_residual = {norm_before_residual}")
+            self.gguf_writer.add_bool(f"{self.gguf_writer.arch}.norm_before_residual", norm_before_residual)
+
     def set_vocab(self):
+        # eagle3: use tokenizer from target model if provided
+        original_dir_model = None
+        if getattr(self, 'is_eagle3', False):
+            assert self.target_model_dir is not None
+            logger.info(f"EAGLE-3: Using tokenizer from target model: {self.target_model_dir}")
+            original_dir_model = self.dir_model
+            self.dir_model = self.target_model_dir
+
         if self.origin_hf_arch == "GlmasrModel":
             return self._set_vocab_glmedge()
 
@@ -85,6 +143,10 @@ class LlamaModel(TextModel):
         if self.hparams.get("vocab_size", 32000) == 49152:
             self.gguf_writer.add_add_bos_token(False)
 
+        # eagle3: Restore original dir_model
+        if original_dir_model is not None:
+            self.dir_model = original_dir_model
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         hparams = self.hparams
@@ -129,7 +191,49 @@ class LlamaModel(TextModel):
 
         return super().filter_tensors((name, gen))
 
+    def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
+        tensors = super().index_tensors(remote_hf_model_id)
+
+        # Handle Eagle3Speculator nested config
+        if "transformer_layer_config" in self.hparams:
+            self.hparams = {**self.hparams, **self.hparams["transformer_layer_config"]}
+
+        # eagle3 detection
+        if "draft_vocab_size" in self.hparams and self.hparams["num_hidden_layers"] == 1:
+            logger.info("EAGLE-3: renaming midlayer.* / layers.0.* to model.layers.0.*")
+            new_tensors = {}
+            for name, gen in tensors.items():
+                if name.startswith("midlayer."):
+                    new_name = "model.layers.0." + name[len("midlayer."):]
+                    new_tensors[new_name] = gen
+                elif name.startswith("layers.0."):  # Eagle3Speculator format
+                    new_name = "model." + name
+                    new_tensors[new_name] = gen
+                else:
+                    new_tensors[name] = gen
+            return new_tensors
+
+        return tensors
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # eagle3: special tensors that bypass standard llama mapping
+        if getattr(self, 'is_eagle3', False):
+            if name == "fc.weight":
+                yield (name, data_torch)
+                return
+            if name == "d2t":
+                # store for manual int64 handling in prepare_tensors (avoid F32 conversion)
+                if not hasattr(self, '_eagle3_int_tensors'):
+                    self._eagle3_int_tensors = {}
+                self._eagle3_int_tensors[name] = data_torch
+                return
+            if name == "t2d":
+                # not used at runtime, skip
+                return
+            if name.endswith(".hidden_norm.weight"):
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_NORM_2, bid), data_torch)
+                return
+
         n_head = self.find_hparam(["n_heads", "num_attention_heads"])
         n_kv_head = self.find_hparam(["n_kv_heads", "num_key_value_heads"])
 
@@ -205,8 +309,33 @@ class LlamaModel(TextModel):
                 yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
 
     def prepare_tensors(self):
+        # eagle3: collect d2t original dtype before parent converts tensors to F32
+        eagle3_original_dtypes = {}
+        if getattr(self, 'is_eagle3', False):
+            for name, data_torch in self.get_tensors():
+                if name == "d2t":
+                    eagle3_original_dtypes[name] = data_torch.dtype
+
         super().prepare_tensors()
 
+        # eagle3: write d2t as absolute target token ids
+        if getattr(self, 'is_eagle3', False) and hasattr(self, '_eagle3_int_tensors'):
+            for name, data_torch in self._eagle3_int_tensors.items():
+                old_dtype = eagle3_original_dtypes.get(name, data_torch.dtype)
+                data = data_torch.to(torch.int64).cpu().numpy()
+                if name == "d2t":
+                    data = data.reshape(-1)
+                    data = data + np.arange(data.size, dtype=np.int64)
+                    if np.any((data < 0) | (data >= self.target_vocab_size)):
+                        raise ValueError(f"EAGLE-3 d2t target ids out of range for target vocab size {self.target_vocab_size}")
+                    if np.unique(data).size != data.size:
+                        raise ValueError("EAGLE-3 d2t contains duplicate target ids")
+                data_qtype = gguf.GGMLQuantizationType.I64
+
+                shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}"
+                logger.info(f"{name + ',':<30} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
+                self.gguf_writer.add_tensor(name, data, raw_dtype=data_qtype)
+
         if self._experts is not None:
             # flatten `list[dict[str, Tensor]]` into `list[str]`
             experts = [k for d in self._experts for k in d.keys()]

convert_hf_to_gguf.py

@@ -153,6 +153,15 @@ def parse_args() -> argparse.Namespace:
         help="Store tensors dequantized from FP8 as Q8_0 instead of BF16/F16.",
     )
 
+    parser.add_argument(
+        "--target-model-dir", type=str, default=None,
+        help=(
+            "path to the target model directory; required when converting a standalone draft model "
+            "(e.g. EAGLE3 / DFlash) that needs target-model metadata such as tokenizer, hidden size, and "
+            "layer count to populate its GGUF."
+        ),
+    )
+
     args = parser.parse_args()
     if not args.print_supported_models and args.model is None:
         parser.error("the following arguments are required: model")
@@ -269,6 +278,7 @@ def main() -> None:
                                      small_first_shard=args.no_tensor_first_split,
                                      remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template,
                                      sentence_transformers_dense_modules=args.sentence_transformers_dense_modules,
+                                     target_model_dir=Path(args.target_model_dir) if args.target_model_dir else None,
                                      fuse_gate_up_exps=args.fuse_gate_up_exps,
                                      fp8_as_q8=args.fp8_as_q8,
                                      )

convert_hf_to_gguf_update.py

@@ -100,6 +100,7 @@ models = [
     {"name": "refact",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
     {"name": "command-r",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
     {"name": "tiny_aya",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereLabs/tiny-aya-base", },
+    {"name": "cohere2moe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0", },
     {"name": "qwen2",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
     {"name": "olmo",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
     {"name": "dbrx",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },

docs/ops.md

@@ -14,15 +14,15 @@ Legend:
 
 | Operation | BLAS | CANN | CPU | CUDA | MTL | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
 |-----------|------|------|------|------|------|------|------|------|------|------|------|
-|                              ABS | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              ABS | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
@@ -41,25 +41,25 @@ Legend:
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              ELU | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                              EXP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                              ELU | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              EXP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
 |                             FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
+|                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             GELU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             GELU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          L2_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -68,9 +68,9 @@ Legend:
 |                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ |
-|                              NEG | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              NEG | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
@@ -79,27 +79,27 @@ Legend:
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                          POOL_1D | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             RELU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             RELU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             ROLL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             ROPE | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            ROUND | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                            ROUND | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SET | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                              SGN | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             SILU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              SGN | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             SILU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                              SIN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
@@ -107,16 +107,16 @@ Legend:
 |                             SQRT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                             STEP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             STEP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SUM | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             TANH | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             TANH | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            XIELU | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |

ggml/CMakeLists.txt

@@ -4,8 +4,8 @@ project("ggml" C CXX ASM)
 
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
-set(GGML_VERSION_MINOR 14)
-set(GGML_VERSION_PATCH 0)
+set(GGML_VERSION_MINOR 15)
+set(GGML_VERSION_PATCH 1)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")

ggml/include/ggml.h

@@ -2553,10 +2553,16 @@ extern "C" {
     // TODO: add ggml_gated_delta_net_set_bcast() to be able to configure Q, K broadcast type: tiled vs interleaved [TAG_GGML_GDN_BCAST]
     // ref: https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306
     //
-    // state is a 3D tensor of shape (S_v*S_v*H, K, n_seqs):
-    //   K == 1: output carries the final state only.
-    //   K  > 1: output carries K snapshot slots; the kernel writes the last min(n_tokens, K)
-    //   per-token snapshots into the trailing slots
+    // tensor shapes (S_k == S_v, H_v % H_k == 0):
+    //   q, k  : [S_k, H_k, n_tokens, n_seqs]
+    //   v     : [S_v, H_v, n_tokens, n_seqs]
+    //   g     : [1, H_v, n_tokens, n_seqs] (scalar gate) or [S_v, H_v, n_tokens, n_seqs] (KDA)
+    //   beta  : [1, H_v, n_tokens, n_seqs]
+    //   state : [S_v, S_v, H_v, n_seqs] -- initial recurrent state s0
+    //
+    // the output packs the attention scores [S_v, H_v, n_tokens, n_seqs] followed by K state
+    // snapshots, most-recent first (slot 0 = final state, slot s = state s tokens back). K == 1
+    // keeps only the final state; when n_tokens < K only slots 0..n_tokens-1 are written.
     GGML_API struct ggml_tensor * ggml_gated_delta_net(
             struct ggml_context * ctx,
             struct ggml_tensor  * q,
@@ -2564,7 +2570,8 @@ extern "C" {
             struct ggml_tensor  * v,
             struct ggml_tensor  * g,
             struct ggml_tensor  * beta,
-            struct ggml_tensor  * state);
+            struct ggml_tensor  * state,
+            int64_t               K);
 
     // custom operators
 

ggml/src/ggml-backend-meta.cpp

@@ -776,8 +776,8 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
         GGML_ASSERT(src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_1);
         GGML_ASSERT(src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_1);
         GGML_ASSERT(src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_1);
-        // state shape is (S_v*S_v*H, K, n_seqs); the heads dim is nested inside axis 0,
-        // so a head-aligned split on the input cache reshapes to axis 0 here (not axis 2).
+        // state shape is [S_v, S_v, H_v, n_seqs] (s0 only); the heads dim is its own axis 2,
+        // so a head-aligned split on the input cache lands on axis 2 here.
         GGML_ASSERT(src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_2 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_1 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_0);
         return {GGML_BACKEND_SPLIT_AXIS_0, {0}, {1}, 1};
     };

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

@@ -2948,7 +2948,7 @@ struct ggml_cplan ggml_graph_plan(
                 case GGML_OP_GATED_DELTA_NET:
                     {
                         const int64_t S_v = node->src[2]->ne[0];
-                        const int64_t K   = node->src[5]->ne[1];  // state is (D, K, n_seqs)
+                        const int64_t K   = ggml_get_op_params_i32(node, 0);
                         const int64_t per_thread = S_v + (K > 1 ? S_v * S_v : 0);
                         cur = per_thread * sizeof(float) * n_tasks;
                     } break;

ggml/src/ggml-cpu/ops.cpp

@@ -10624,11 +10624,11 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
 
     const bool kda = (neg0 == S_v);
 
-    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
-    const int64_t K = src_state->ne[1];
+    // K (snapshot slot count) is an op param; state holds s0 only [S_v, S_v, H, n_seqs].
+    const int64_t K = ggml_get_op_params_i32(dst, 0);
     GGML_ASSERT(K >= 1);
-    // per-seq stride in floats (slot 0 of seq s lives at state + s * seq_stride)
-    const int64_t state_seq_stride = src_state->nb[2] / sizeof(float);
+    // per-seq stride in floats (seq s starts at state + s * seq_stride)
+    const int64_t state_seq_stride = src_state->nb[3] / sizeof(float);
 
     const int64_t per_thread = S_v + (K > 1 ? S_v * S_v : 0);
     const int ith = params->ith;
@@ -10644,9 +10644,8 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
     float * attn_out_base  = (float *)dst->data;
     float * state_out_base = (float *)dst->data + attn_score_elems;
 
-    // snapshot slot mapping: target_slot = t - shift. When n_tokens < K only the last
-    // n_tokens slots are written; earlier slots are left untouched (caller-owned).
-    const int64_t shift = n_tokens - K;
+    // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+    // When n_tokens < K only slots 0..n_tokens-1 are written; older slots are caller-owned.
 
     const float * state_in_base = (const float *)src_state->data;
 
@@ -10674,7 +10673,7 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
             : state_out_base + (iv3 * H + iv1) * S_v * S_v;
 
         // copy input state into the working buffer and operate in-place
-        // state layout (D, K, n_seqs): slot 0 of seq iv3 starts at iv3 * state_seq_stride.
+        // state layout [S_v, S_v, H, n_seqs]: seq iv3 starts at iv3 * state_seq_stride.
         const float * s_in = state_in_base + iv3 * state_seq_stride + iv1 * S_v * S_v;
         memcpy(s_out, s_in, S_v * S_v * sizeof(float));
 
@@ -10727,7 +10726,7 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
             attn_data += S_v * H; // advance to next token
 
             if (K > 1) {
-                const int64_t target_slot = t - shift;
+                const int64_t target_slot = n_tokens - 1 - t;
                 if (target_slot >= 0 && target_slot < K) {
                     float * curr_state_o = state_out_base + target_slot * state_size_per_snap +
                                      (iv3 * H + iv1) * S_v * S_v;

ggml/src/ggml-cuda/concat.cu

@@ -1,16 +1,18 @@
 #include "concat.cuh"
 
+#include <stdint.h>
+
 // contiguous kernels
-template <int dim>
-static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE) concat_f32_cont(const float * x,
-                                                                                 const float * y,
-                                                                                 float *       dst,
-                                                                                 int64_t       ne00,
-                                                                                 int64_t       ne01,
-                                                                                 int64_t       ne02,
-                                                                                 int64_t       ne0,
-                                                                                 int64_t       ne1,
-                                                                                 int64_t       ne2) {
+template <typename T, int dim>
+static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE) concat_cont(const T * x,
+                                                                             const T * y,
+                                                                             T *       dst,
+                                                                             int64_t   ne00,
+                                                                             int64_t   ne01,
+                                                                             int64_t   ne02,
+                                                                             int64_t   ne0,
+                                                                             int64_t   ne1,
+                                                                             int64_t   ne2) {
     static_assert(dim >= 0 && dim <= 2, "dim must be in [0, 2]");
 
     const int64_t n = ne0 * ne1 * ne2;
@@ -50,37 +52,37 @@ static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE) concat_f32_cont
     }
 }
 
-static void concat_f32_cuda(const float * x,
-                            const float * y,
-                            float *       dst,
-                            int64_t       ne00,
-                            int64_t       ne01,
-                            int64_t       ne02,
-                            int64_t       ne0,
-                            int64_t       ne1,
-                            int64_t       ne2,
-                            int           dim,
-                            cudaStream_t  stream) {
+template <typename T>
+static void concat_cont_cuda(const T * x,
+                             const T * y,
+                             T *       dst,
+                             int64_t   ne00,
+                             int64_t   ne01,
+                             int64_t   ne02,
+                             int64_t   ne0,
+                             int64_t   ne1,
+                             int64_t   ne2,
+                             int       dim,
+                             cudaStream_t stream) {
     const int64_t n          = ne0 * ne1 * ne2;
     const int     num_blocks = (n + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
 
     if (dim == 0) {
         const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream);
-        ggml_cuda_kernel_launch(concat_f32_cont<0>, launch_params,x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
+        ggml_cuda_kernel_launch(concat_cont<T, 0>, launch_params, x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
         return;
     }
     if (dim == 1) {
-        concat_f32_cont<1>
-            <<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
+        concat_cont<T, 1><<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
         return;
     }
-    concat_f32_cont<2><<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
+    concat_cont<T, 2><<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
 }
 
 // non-contiguous kernel (slow)
-template <int dim>
+template <typename T, int dim>
 static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE)
-    concat_f32_non_cont(
+    concat_non_cont(
         const char * src0,
         const char * src1,
               char * dst,
@@ -107,61 +109,49 @@ static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE)
           uint64_t   nb0,
           uint64_t   nb1,
           uint64_t   nb2,
-          uint64_t   nb3){
+          uint64_t   nb3) {
     static_assert(dim >= 0 && dim <= 3, "dim must be in [0, 3]");
 
     const int64_t i3 = blockIdx.z;
     const int64_t i2 = blockIdx.y;
     const int64_t i1 = blockIdx.x;
 
-    const float * x;
+    const T * x;
 
     for (int64_t i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
         if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
-            x = (const float *)(src0 + (i3       )*nb03 + (i2       )*nb02 + (i1       )*nb01 + (i0       )*nb00);
+            x = (const T *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
         } else {
             if constexpr (dim == 0) {
-                x = (const float *) (src1 + i3 * nb13 + i2 * nb12 + i1 * nb11 + (i0 - ne00) * nb10);
+                x = (const T *)(src1 + i3*nb13 + i2*nb12 + i1*nb11 + (i0 - ne00)*nb10);
             } else if constexpr (dim == 1) {
-                x = (const float *) (src1 + i3 * nb13 + i2 * nb12 + (i1 - ne01) * nb11 + i0 * nb10);
+                x = (const T *)(src1 + i3*nb13 + i2*nb12 + (i1 - ne01)*nb11 + i0*nb10);
             } else if constexpr (dim == 2) {
-                x = (const float *) (src1 + i3 * nb13 + (i2 - ne02) * nb12 + i1 * nb11 + i0 * nb10);
+                x = (const T *)(src1 + i3*nb13 + (i2 - ne02)*nb12 + i1*nb11 + i0*nb10);
             } else if constexpr (dim == 3) {
-                x = (const float *) (src1 + (i3 - ne03) * nb13 + i2 * nb12 + i1 * nb11 + i0 * nb10);
+                x = (const T *)(src1 + (i3 - ne03)*nb13 + i2*nb12 + i1*nb11 + i0*nb10);
             }
         }
 
-        float * y = (float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+        T * y = (T *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
 
         *y = *x;
     }
 }
 
-
-void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-
-    cudaStream_t stream = ctx.stream();
-
-    const int32_t dim = ((int32_t *) dst->op_params)[0];
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
-
+template <typename T>
+static void concat_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, int dim, cudaStream_t stream) {
     if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
-        const float * src0_d = (const float *)src0->data;
-        const float * src1_d = (const float *)src1->data;
-
-        float * dst_d = (float *)dst->data;
+        const T * src0_d = (const T *) src0->data;
+        const T * src1_d = (const T *) src1->data;
+        T *       dst_d  = (T *) dst->data;
 
         if (dim != 3) {
-            for (int i3 = 0; i3 < dst->ne[3]; i3++) {
-                concat_f32_cuda(
-                        src0_d + i3 * (src0->nb[3] / 4),
-                        src1_d + i3 * (src1->nb[3] / 4),
-                        dst_d + i3 * ( dst->nb[3] / 4),
+            for (int64_t i3 = 0; i3 < dst->ne[3]; i3++) {
+                concat_cont_cuda(
+                        src0_d + i3*(src0->nb[3] / sizeof(T)),
+                        src1_d + i3*(src1->nb[3] / sizeof(T)),
+                        dst_d  + i3*( dst->nb[3] / sizeof(T)),
                         src0->ne[0], src0->ne[1], src0->ne[2],
                         dst->ne[0],  dst->ne[1],  dst->ne[2], dim, stream);
             }
@@ -169,13 +159,13 @@ void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
             const size_t size0 = ggml_nbytes(src0);
             const size_t size1 = ggml_nbytes(src1);
 
-            CUDA_CHECK(cudaMemcpyAsync(dst_d,           src0_d, size0, cudaMemcpyDeviceToDevice, stream));
-            CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream));
+            CUDA_CHECK(cudaMemcpyAsync((char *) dst->data,         src0->data, size0, cudaMemcpyDeviceToDevice, stream));
+            CUDA_CHECK(cudaMemcpyAsync((char *) dst->data + size0, src1->data, size1, cudaMemcpyDeviceToDevice, stream));
         }
     } else {
         dim3 grid_dim(dst->ne[1], dst->ne[2], dst->ne[3]);
         auto launch_kernel = [&](auto dim) {
-            concat_f32_non_cont<dim><<<grid_dim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(
+            concat_non_cont<T, dim><<<grid_dim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(
                 (const char *) src0->data, (const char *) src1->data, (char *) dst->data,
                 src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
                 src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
@@ -203,3 +193,35 @@ void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
         }
     }
 }
+
+void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    cudaStream_t stream = ctx.stream();
+
+    const int32_t dim = ((int32_t *) dst->op_params)[0];
+
+    GGML_ASSERT(src0->type == src1->type);
+    GGML_ASSERT(dst->type  == src0->type);
+    GGML_ASSERT(!ggml_is_quantized(src0->type));
+    GGML_ASSERT(ggml_blck_size(src0->type) == 1);
+
+    switch (ggml_type_size(src0->type)) {
+        case 1:
+            concat_cuda<uint8_t>(src0, src1, dst, dim, stream);
+            break;
+        case 2:
+            concat_cuda<uint16_t>(src0, src1, dst, dim, stream);
+            break;
+        case 4:
+            concat_cuda<uint32_t>(src0, src1, dst, dim, stream);
+            break;
+        case 8:
+            concat_cuda<uint64_t>(src0, src1, dst, dim, stream);
+            break;
+        default:
+            GGML_ABORT("Unsupported type size: %zu", ggml_type_size(src0->type));
+            break;
+    }
+}

ggml/src/ggml-cuda/gated_delta_net.cu

@@ -39,9 +39,9 @@ gated_delta_net_cuda(const float * q,
     float *       attn_data        = dst;
     float *       state            = dst + attn_score_elems;
 
-    // input state layout (D, K, n_seqs) — seq stride is K * D = K * H * S_v * S_v.
+    // input state holds s0 only: [S_v, S_v, H, n_seqs] — seq stride is D = H * S_v * S_v.
     // output state layout (per-slot D * n_seqs) — same per-(seq,head) offset as before.
-    const int64_t state_in_offset      = sequence * K * H * S_v * S_v + h_idx * S_v * S_v;
+    const int64_t state_in_offset      = sequence * H * S_v * S_v + h_idx * S_v * S_v;
     const int64_t state_out_offset     = (sequence * H + h_idx) * S_v * S_v;
     state += state_out_offset;
     curr_state += state_in_offset + col * S_v;
@@ -143,12 +143,10 @@ gated_delta_net_cuda(const float * q,
         attn_data += S_v * H;
 
         if constexpr (keep_rs_t) {
-            // slot mapping: target_slot = t - shift. When n_tokens < K only the last n_tokens slots
-            // are written; earlier slots are left untouched (caller-owned).
-            const int shift = (int) n_tokens - K;
-
+            // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+            // When n_tokens < K only slots 0..n_tokens-1 are written; older slots are caller-owned.
             const int64_t state_size_per_token = S_v * S_v * H * n_seqs; // per-slot stride in output
-            const int target_slot = t - shift;
+            const int target_slot = (int) n_tokens - 1 - t;
             if (target_slot >= 0 && target_slot < K) {
                 float * curr_state = (dst + attn_score_elems) + target_slot * state_size_per_token + state_out_offset;
 #pragma unroll
@@ -286,8 +284,8 @@ void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor *
 
     cudaStream_t stream = ctx.stream();
 
-    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
-    const int K = (int) src_state->ne[1];
+    // K (snapshot slot count) is an op param; state holds s0 only [S_v, S_v, H, n_seqs].
+    const int K = ggml_get_op_params_i32(dst, 0);
     const bool keep_rs = K > 1;
 
     if (kda) {

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -5345,7 +5345,15 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_CONCAT:
             {
                 ggml_type src0_type = op->src[0]->type;
-                return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
+                ggml_type src1_type = op->src[1]->type;
+                return src0_type == src1_type &&
+                       src0_type == op->type &&
+                       !ggml_is_quantized(src0_type) &&
+                       ggml_blck_size(src0_type) == 1 &&
+                       (ggml_type_size(src0_type) == 1 ||
+                        ggml_type_size(src0_type) == 2 ||
+                        ggml_type_size(src0_type) == 4 ||
+                        ggml_type_size(src0_type) == 8);
             } break;
         case GGML_OP_CONV_TRANSPOSE_1D:
             {

ggml/src/ggml-cuda/ssm-scan.cu

@@ -67,6 +67,7 @@ __global__ void __launch_bounds__(splitD, 1)
     __shared__ CubTempStorage cub_temp_storage;
 
     BlockLoad(cub_temp_storage.load_temp).Load(A_block, regA);
+    __syncthreads();
     BlockLoad(cub_temp_storage.load_temp).Load(s0_block, regs0);
 #else
     const int stride_s0 = src0_nb2 / sizeof(float);
@@ -105,6 +106,7 @@ __global__ void __launch_bounds__(splitD, 1)
             regs0[n] = state;
         }
         y_block[i * stride_y + threadIdx.x] = sumf;
+        __syncthreads();
     }
 
 #ifdef USE_CUB
@@ -249,9 +251,8 @@ static void ssm_scan_f32_cuda(const float * src0, const float * src1, const floa
         GGML_ASSERT(head_dim == 1);
         GGML_ASSERT(n_group == 1);
         const dim3 blocks(n_seq, (n_head + threads - 1) / threads, 1);
-        const int  smem_size = (threads * (d_state + 1) * 2) * sizeof(float);
         if (d_state == 16) {
-            const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(blocks, threads, smem_size, stream);
+            const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(blocks, threads, 0, stream);
             switch (n_tok)
             {
             case 1:

ggml/src/ggml-hexagon/ggml-hexagon.cpp

@@ -2538,7 +2538,7 @@ static bool ggml_hexagon_supported_gated_delta_net(const struct ggml_hexagon_ses
     const int64_t H        = v->ne[1];
     const int64_t n_tokens = v->ne[2];
     const int64_t n_seqs   = v->ne[3];
-    const int64_t K        = state->ne[1];
+    const int64_t K        = ggml_get_op_params_i32(op, 0);
 
     if (S_v <= 0 || S_v > 128 || H <= 0 || n_tokens <= 0 || n_seqs <= 0) {
         return false;
@@ -2551,7 +2551,8 @@ static bool ggml_hexagon_supported_gated_delta_net(const struct ggml_hexagon_ses
     if ((g->ne[0] != 1 && g->ne[0] != S_v) || beta->ne[0] != 1) {
         return false;
     }
-    if (ggml_nelements(state) != S_v * S_v * H * n_seqs * K) {
+    // state holds s0 only [S_v, S_v, H, n_seqs]; K is op param 0.
+    if (ggml_nelements(state) != S_v * S_v * H * n_seqs) {
         return false;
     }
     if (dst->ne[0] != S_v * H || dst->ne[1] != n_tokens * n_seqs + S_v * n_seqs * K) {

ggml/src/ggml-hexagon/htp/gated-delta-net-ops.c

@@ -584,7 +584,7 @@ static void gated_delta_net_f32_pp_thread(unsigned int nth, unsigned int ith, vo
     const uint32_t H        = v->ne[1];
     const uint32_t n_tokens = v->ne[2];
     const uint32_t n_seqs   = v->ne[3];
-    const uint32_t K        = state->ne[1];
+    const uint32_t K        = octx->op_params[0];
 
     const uint32_t total_rows = H * n_seqs;
     if (ith >= total_rows) {
@@ -618,9 +618,8 @@ static void gated_delta_net_f32_pp_thread(unsigned int nth, unsigned int ith, vo
     struct fastdiv_values fd_rq3 = init_fastdiv_values(rq3);
     struct fastdiv_values fd_rk3 = init_fastdiv_values(rk3);
 
-    const uint64_t state_seq_stride = state->nb[2] / sizeof(float);
+    const uint64_t state_seq_stride = state->nb[3] / sizeof(float);
     const uint64_t state_size_per_snap = (uint64_t) S_v * S_v * H * n_seqs;
-    const int64_t shift = (int64_t) n_tokens - (int64_t) K;
 
     uint32_t ir_prefetch = ith;
     int spad_idx = 0;
@@ -630,7 +629,8 @@ static void gated_delta_net_f32_pp_thread(unsigned int nth, unsigned int ith, vo
         const uint32_t piv1 = fastmodulo(ir_prefetch, H, &fd_H);
         const uint32_t piv3 = fastdiv(ir_prefetch, &fd_H);
         const float * ps_in = state_in_base + (uint64_t) piv3 * state_seq_stride + (uint64_t) piv1 * S_v * S_v;
-        float * ps_out = state_out_base + (uint64_t) (K - 1) * state_size_per_snap + ((uint64_t) piv3 * H + piv1) * S_v * S_v;
+        // final state lands in snapshot slot 0 (most-recent-first ordering)
+        float * ps_out = state_out_base + ((uint64_t) piv3 * H + piv1) * S_v * S_v;
 
         // Push dummy write-back
         dma_queue_push(dma, dma_make_ptr(ps_out, s_work[spad_idx]),
@@ -661,7 +661,8 @@ static void gated_delta_net_f32_pp_thread(unsigned int nth, unsigned int ith, vo
         const uint32_t iq3 = fastdiv(iv3, &fd_rq3);
         const uint32_t ik3 = fastdiv(iv3, &fd_rk3);
 
-        float * s_out = state_out_base + (uint64_t) (K - 1) * state_size_per_snap + ((uint64_t) iv3 * H + iv1) * S_v * S_v;
+        // final state lands in snapshot slot 0 (most-recent-first ordering)
+        float * s_out = state_out_base + ((uint64_t) iv3 * H + iv1) * S_v * S_v;
 
         float * attn_data = dst_base + ((uint64_t) iv3 * n_tokens * H + iv1) * S_v;
 
@@ -792,7 +793,8 @@ static void gated_delta_net_f32_pp_thread(unsigned int nth, unsigned int ith, vo
             }
 
             if (K > 1) {
-                const int64_t target_slot = (int64_t) t - shift;
+                // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+                const int64_t target_slot = (int64_t) n_tokens - 1 - (int64_t) t;
                 if (target_slot >= 0 && target_slot < (int64_t) K) {
                     float * curr_state_o = state_out_base + (uint64_t) target_slot * state_size_per_snap + ((uint64_t) iv3 * H + iv1) * S_v * S_v;
                     if (curr_state_o != s_out) {
@@ -844,7 +846,6 @@ static void gated_delta_net_f32_tg_thread(unsigned int nth, unsigned int ith, vo
     const uint32_t S_v      = v->ne[0];
     const uint32_t H        = v->ne[1];
     const uint32_t n_seqs   = v->ne[3];
-    const uint32_t K        = state->ne[1];
 
     const uint32_t total_rows = H * n_seqs;
     if (ith >= total_rows) {
@@ -878,8 +879,7 @@ static void gated_delta_net_f32_tg_thread(unsigned int nth, unsigned int ith, vo
     struct fastdiv_values fd_rq3 = init_fastdiv_values(rq3);
     struct fastdiv_values fd_rk3 = init_fastdiv_values(rk3);
 
-    const uint64_t state_seq_stride = state->nb[2] / sizeof(float);
-    const uint64_t state_size_per_snap = (uint64_t) S_v * S_v * H * n_seqs;
+    const uint64_t state_seq_stride = state->nb[3] / sizeof(float);
 
     uint32_t ir_prefetch = ith;
     int spad_idx = 0;
@@ -889,7 +889,8 @@ static void gated_delta_net_f32_tg_thread(unsigned int nth, unsigned int ith, vo
         const uint32_t piv1 = fastmodulo(ir_prefetch, H, &fd_H);
         const uint32_t piv3 = fastdiv(ir_prefetch, &fd_H);
         const float * ps_in = state_in_base + (uint64_t) piv3 * state_seq_stride + (uint64_t) piv1 * S_v * S_v;
-        float * ps_out = state_out_base + (uint64_t) (K - 1) * state_size_per_snap + ((uint64_t) piv3 * H + piv1) * S_v * S_v;
+        // final state lands in snapshot slot 0 (most-recent-first ordering)
+        float * ps_out = state_out_base + ((uint64_t) piv3 * H + piv1) * S_v * S_v;
 
         // Push dummy write-back
         dma_queue_push(dma, dma_make_ptr(ps_out, s_work[spad_idx]),
@@ -920,7 +921,8 @@ static void gated_delta_net_f32_tg_thread(unsigned int nth, unsigned int ith, vo
         const uint32_t iq3 = fastdiv(iv3, &fd_rq3);
         const uint32_t ik3 = fastdiv(iv3, &fd_rk3);
 
-        float * s_out = state_out_base + (uint64_t) (K - 1) * state_size_per_snap + ((uint64_t) iv3 * H + iv1) * S_v * S_v;
+        // final state lands in snapshot slot 0 (most-recent-first ordering)
+        float * s_out = state_out_base + ((uint64_t) iv3 * H + iv1) * S_v * S_v;
 
         float * attn_data = dst_base + ((uint64_t) iv3 * H + iv1) * S_v;
 
@@ -1097,7 +1099,7 @@ int op_gated_delta_net(struct htp_ops_context * octx) {
     const uint32_t H        = v->ne[1];
     const uint32_t n_tokens = v->ne[2];
     const uint32_t n_seqs   = v->ne[3];
-    const uint32_t K        = state->ne[1];
+    const uint32_t K        = octx->op_params[0];
 
     if (S_v == 0 || S_v > HTP_GDN_MAX_SV || H == 0 || n_tokens == 0 || n_seqs == 0) {
         return HTP_STATUS_NO_SUPPORT;
@@ -1110,7 +1112,8 @@ int op_gated_delta_net(struct htp_ops_context * octx) {
         (n_seqs % q->ne[3]) != 0 || (n_seqs % k->ne[3]) != 0) {
         return HTP_STATUS_NO_SUPPORT;
     }
-    if (state->ne[0] * state->ne[2] * state->ne[3] != S_v * S_v * H * n_seqs) {
+    // state holds s0 only: [S_v, S_v, H, n_seqs]
+    if (state->ne[0] != S_v || state->ne[1] != S_v || state->ne[2] != H || state->ne[3] != n_seqs) {
         return HTP_STATUS_NO_SUPPORT;
     }
     if (dst->ne[0] != S_v * H || dst->ne[1] != n_tokens * n_seqs + S_v * n_seqs * K) {

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

@@ -590,8 +590,8 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_gated_delta_net(
     const int ne20 = op->src[2]->ne[0]; // S_v
     const int ne21 = op->src[2]->ne[1]; // H
     const int ne30 = op->src[3]->ne[0]; // G
-    // state is src[5], 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
-    const int K = op->src[5]->ne[1];
+    // state is src[5], 4D [S_v, S_v, H_v, n_seqs] (s0 only); K is op param 0.
+    const int K = ggml_get_op_params_i32(op, 0);
 
     const int nsg = op->src[2]->ne[0]/32;
 

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

@@ -1120,8 +1120,17 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_VIEW:
         case GGML_OP_TRANSPOSE:
         case GGML_OP_PERMUTE:
-        case GGML_OP_CONCAT:
             return true;
+        case GGML_OP_CONCAT:
+            {
+                // kernel_concat copies one float-sized value per element.
+                // Other scalar types need a type-generic copy kernel first.
+                const enum ggml_type src0_type = op->src[0]->type;
+                const enum ggml_type src1_type = op->src[1]->type;
+                return src0_type == src1_type &&
+                       src0_type == op->type &&
+                       (src0_type == GGML_TYPE_F32 || src0_type == GGML_TYPE_I32);
+            }
         case GGML_OP_ADD:
         case GGML_OP_SUB:
         case GGML_OP_MUL:

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

@@ -2599,9 +2599,9 @@ kernel void kernel_gated_delta_net_impl(
 
     const float scale = 1.0f / sqrt((float)S_v);
 
-    // input state layout (D, K, n_seqs): per-seq stride is K*H*D; we read slot 0.
+    // input state layout [S_v, S_v, H, n_seqs] (s0 only): per-seq stride is H*D.
     // state is stored transposed: M[i20][is] = S[is][i20], so row i20 is contiguous
-    const uint state_in_base = (i23*K*args.ne21 + i21)*S_v*S_v + i20*S_v;
+    const uint state_in_base = (i23*args.ne21 + i21)*S_v*S_v + i20*S_v;
     device const float * s_ptr = (device const float *) (s) + state_in_base;
 
     float ls[NSG];
@@ -2620,9 +2620,8 @@ kernel void kernel_gated_delta_net_impl(
     device const float * b_ptr = (device const float *) (b) + (i23*args.ne22*args.ne21 + i21);
     device const float * g_ptr = (device const float *) (g) + (i23*args.ne22*args.ne21 + i21)*G;
 
-    // snapshot slot mapping: target_slot = t - shift. When n_tokens < K, only the last
-    // n_tokens slots are written; earlier slots are left untouched (caller-owned).
-    const int shift = (int)args.ne22 - (int)K;
+    // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+    // When n_tokens < K, only slots 0..n_tokens-1 are written; older slots are caller-owned.
 
     // output state base offset: after attention scores
     const uint attn_size = args.ne22 * args.ne21 * S_v * args.ne23;
@@ -2680,7 +2679,7 @@ kernel void kernel_gated_delta_net_impl(
         g_ptr += args.ne21*G;
 
         if (K > 1) {
-            const int target_slot = (int)t - shift;
+            const int target_slot = (int)args.ne22 - 1 - (int)t;
             if (target_slot >= 0 && target_slot < (int)K) {
                 device float * dst_state = (device float *) (dst) + attn_size + (uint)target_slot * state_size_per_snap + state_out_base;
                 FOR_UNROLL (short j = 0; j < NSG; j++) {

ggml/src/ggml-opencl/CMakeLists.txt

@@ -142,6 +142,10 @@ set(GGML_OPENCL_KERNELS
     gemm_noshuffle_q4_0_f32
     gemv_noshuffle_q4_1_f32
     gemm_noshuffle_q4_1_f32
+    gemv_noshuffle_q5_0_f32
+    gemm_noshuffle_q5_0_f32
+    gemv_noshuffle_q5_1_f32
+    gemm_noshuffle_q5_1_f32
     gemv_noshuffle_iq4_nl_f32
     gemm_noshuffle_iq4_nl_f32
     gemv_noshuffle_q8_0_f32

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

@@ -593,6 +593,10 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_restore_block_q4_0_noshuffle;
     cl_kernel kernel_convert_block_q4_1_noshuffle;
     cl_kernel kernel_restore_block_q4_1_noshuffle;
+    cl_kernel kernel_convert_block_q5_0_noshuffle;
+    cl_kernel kernel_restore_block_q5_0_noshuffle;
+    cl_kernel kernel_convert_block_q5_1_noshuffle;
+    cl_kernel kernel_restore_block_q5_1_noshuffle;
     cl_kernel kernel_convert_block_q4_K_noshuffle;
     cl_kernel kernel_restore_block_q4_K_noshuffle;
     cl_kernel kernel_convert_block_q4_K, kernel_restore_block_q4_K;
@@ -829,6 +833,10 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_gemm_noshuffle_q6_K_f32;
     cl_kernel kernel_gemv_noshuffle_q5_k_f32;
     cl_kernel kernel_gemm_noshuffle_q5_k_f32;
+    cl_kernel kernel_gemv_noshuffle_q5_0_f32;
+    cl_kernel kernel_gemm_noshuffle_q5_0_f32;
+    cl_kernel kernel_gemv_noshuffle_q5_1_f32;
+    cl_kernel kernel_gemm_noshuffle_q5_1_f32;
     cl_kernel kernel_gemv_noshuffle_iq4_nl_f32;
     cl_kernel kernel_gemm_noshuffle_iq4_nl_f32;
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
@@ -1152,6 +1160,10 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         CL_CHECK((backend_ctx->kernel_restore_block_q4_1_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_1_trans4_ns", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q5_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q5_0", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q5_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q5_0", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_q5_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q5_0_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_q5_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q5_0_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_q5_1_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q5_1_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_q5_1_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q5_1_noshuffle", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q5_0_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q5_0_trans4_ns", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q5_0_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q5_0_trans4_ns", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q5_1  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q5_1", &err), err));
@@ -3065,6 +3077,80 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         GGML_LOG_CONT(".");
     }
 
+    // gemm_noshuffle_q5_0_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_noshuffle_q5_0_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemm_noshuffle_q5_0_f32.cl");
+#endif
+        cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+        CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemv_noshuffle_q5_0_f32
+    {
+        std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                                       " -cl-mad-enable ";
+        if (backend_ctx->has_vector_subgroup_broadcast) {
+            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
+        }
+
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemv_noshuffle_q5_0_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemv_noshuffle_q5_0_f32.cl");
+#endif
+        cl_program prog = build_program_from_source(
+            backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts);
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q5_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q5_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemm_noshuffle_q5_1_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_noshuffle_q5_1_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemm_noshuffle_q5_1_f32.cl");
+#endif
+        cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+        CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_1_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_1_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemv_noshuffle_q5_1_f32
+    {
+        std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                                       " -cl-mad-enable ";
+        if (backend_ctx->has_vector_subgroup_broadcast) {
+            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
+        }
+
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemv_noshuffle_q5_1_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemv_noshuffle_q5_1_f32.cl");
+#endif
+        cl_program prog = build_program_from_source(
+            backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts);
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q5_1_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q5_1_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // gemm_noshuffle_iq4_nl_f32
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -6107,15 +6193,16 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
             return;
         }
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
-            cl_kernel kernel = backend_ctx->kernel_convert_block_q5_0;
-            cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+            cl_kernel kernel = backend_ctx->kernel_convert_block_q5_0_noshuffle;
             CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
             CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->qs));
             CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->qh));
             CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->d));
-            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &n_blk));
 
-            size_t global_work_size[] = {(size_t)CEIL_DIV(n_blk, 64) * 64, 1, 1};
+            size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
             size_t local_work_size[] = {64, 1, 1};
 
             cl_event evt;
@@ -6124,7 +6211,39 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
             CL_CHECK(clReleaseMemObject(data_device));
 
             tensor->extra = extra;
+
+            int M = tensor->ne[1];
+            int K = tensor->ne[0];
+            GGML_ASSERT(K % 32 == 0);
+
+            // Transpose qs as ushort
+            transpose_2d_as_16b(backend_ctx, extra->qs, extra->qs, size_qs, K/4, M);
+            // Transpose qh as uchar
+            transpose_2d_as_8b(backend_ctx, extra->qh, extra->qh, size_qh, K/8, M);
+            // Transpose d as ushort
+            transpose_2d_as_16b(backend_ctx, extra->d, extra->d, size_d, K/32, M);
+
             return;
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+        cl_kernel kernel = backend_ctx->kernel_convert_block_q5_0;
+        cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->qs));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->qh));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->d));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &n_blk));
+
+        size_t global_work_size[] = {(size_t)CEIL_DIV(n_blk, 64) * 64, 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clReleaseMemObject(data_device));
+
+        tensor->extra = extra;
+        return;
     }
     if (tensor->type == GGML_TYPE_Q5_1) {
         ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
@@ -6225,6 +6344,42 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
             return;
         }
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+            cl_kernel kernel = backend_ctx->kernel_convert_block_q5_1_noshuffle;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->qs));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->qh));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->d));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra->m));
+
+            size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+            size_t local_work_size[] = {64, 1, 1};
+
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+            CL_CHECK(clWaitForEvents(1, &evt));
+            CL_CHECK(clReleaseMemObject(data_device));
+
+            tensor->extra = extra;
+
+            int M = tensor->ne[1];
+            int K = tensor->ne[0];
+            GGML_ASSERT(K % 32 == 0);
+
+            // Transpose qs as ushort
+            transpose_2d_as_16b(backend_ctx, extra->qs, extra->qs, size_qs, K/4, M);
+            // Transpose qh as uchar
+            transpose_2d_as_8b(backend_ctx, extra->qh, extra->qh, size_qh, K/8, M);
+            // Transpose d as ushort
+            transpose_2d_as_16b(backend_ctx, extra->d, extra->d, size_d, K/32, M);
+            // Transpose m as ushort
+            transpose_2d_as_16b(backend_ctx, extra->m, extra->m, size_m, K/32, M);
+
+            return;
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
         cl_kernel kernel = backend_ctx->kernel_convert_block_q5_1;
         cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
         CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
@@ -7299,6 +7454,48 @@ 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)) {
+            ggml_cl_buffer buf_trans_qs;
+            ggml_cl_buffer buf_trans_qh;
+            ggml_cl_buffer buf_trans_d;
+            ggml_cl_buffer buf_unpacked;
+
+            cl_int M = tensor->ne[1];
+            cl_int K = tensor->ne[0];
+
+            GGML_ASSERT(K % 32 == 0);
+
+            size_t size_qs = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*ggml_blck_size(tensor->type)/2;
+            size_t size_qh = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(int32_t);
+            size_t size_d = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(ggml_fp16_t);
+
+            buf_trans_qs.allocate(backend_ctx->context, size_qs);
+            buf_trans_qh.allocate(backend_ctx->context, size_qh);
+            buf_trans_d.allocate(backend_ctx->context, size_d);
+            buf_unpacked.allocate(backend_ctx->context, ggml_nbytes(tensor));
+
+            transpose_2d_as_16b(backend_ctx, extra->qs, buf_trans_qs.buffer, size_qs, M, K/4);
+            transpose_2d_as_8b(backend_ctx, extra->qh, buf_trans_qh.buffer, size_qh, M, K/8);
+            transpose_2d_as_16b(backend_ctx, extra->d,  buf_trans_d.buffer,  size_d,  M, K/32);
+
+            cl_uchar mask_0F = 0x0F;
+            cl_uchar mask_F0 = 0xF0;
+
+            size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+            size_t local_work_size[] = {1, 1, 1};
+
+            cl_kernel kernel = backend_ctx->kernel_restore_block_q5_0_noshuffle;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &buf_trans_qs.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &buf_trans_qh.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &buf_trans_d.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &buf_unpacked.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_uchar), &mask_0F));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask_F0));
+
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+            CL_CHECK(clEnqueueReadBuffer(queue, buf_unpacked.buffer, CL_TRUE, offset, size, data, 0, NULL, NULL));
+            return;
+        }
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
 
         cl_int err;
@@ -7362,6 +7559,54 @@ 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)) {
+            ggml_cl_buffer buf_trans_qs;
+            ggml_cl_buffer buf_trans_qh;
+            ggml_cl_buffer buf_trans_d;
+            ggml_cl_buffer buf_trans_m;
+            ggml_cl_buffer buf_unpacked;
+
+            cl_int M = tensor->ne[1];
+            cl_int K = tensor->ne[0];
+            GGML_ASSERT(K % 32 == 0);
+
+            size_t size_qs = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*ggml_blck_size(tensor->type)/2;
+            size_t size_qh = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(int32_t);
+            size_t size_d  = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(ggml_fp16_t);
+            size_t size_m  = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(ggml_fp16_t);
+
+            buf_trans_qs.allocate(backend_ctx->context, size_qs);
+            buf_trans_qh.allocate(backend_ctx->context, size_qh);
+            buf_trans_d.allocate(backend_ctx->context, size_d);
+            buf_trans_m.allocate(backend_ctx->context, size_m);
+            buf_unpacked.allocate(backend_ctx->context, ggml_nbytes(tensor));
+
+            // Transpose back: from col-major to row-major
+            transpose_2d_as_16b(backend_ctx, extra->qs, buf_trans_qs.buffer, size_qs, M, K/4);
+            transpose_2d_as_8b(backend_ctx, extra->qh, buf_trans_qh.buffer, size_qh, M, K/8);
+            transpose_2d_as_16b(backend_ctx, extra->d,  buf_trans_d.buffer,  size_d,  M, K/32);
+            transpose_2d_as_16b(backend_ctx, extra->m,  buf_trans_m.buffer,  size_m,  M, K/32);
+
+            cl_uchar mask_0F = 0x0F;
+            cl_uchar mask_F0 = 0xF0;
+
+            size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+            size_t local_work_size[] = {1, 1, 1};
+
+            cl_kernel kernel = backend_ctx->kernel_restore_block_q5_1_noshuffle;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &buf_trans_qs.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &buf_trans_qh.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &buf_trans_d.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &buf_trans_m.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &buf_unpacked.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask_0F));
+            CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_uchar), &mask_F0));
+
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+            CL_CHECK(clEnqueueReadBuffer(queue, buf_unpacked.buffer, CL_TRUE, offset, size, data, 0, NULL, NULL));
+            return;
+        }
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
         cl_int err;
         cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
@@ -12205,6 +12450,368 @@ static void ggml_cl_mul_mat_q4_1_f32_adreno(ggml_backend_t backend, const ggml_t
 #endif
 }
 
+static void ggml_cl_mul_mat_q5_0_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+    ggml_tensor_extra_cl_q5_0 * extra0_q5_0 = (ggml_tensor_extra_cl_q5_0 *)src0->extra;
+
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+
+    const int ne1 = dst->ne[1];
+
+    GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);
+
+    cl_context context = backend_ctx->context;
+    cl_kernel kernel;
+
+    cl_int              err;
+    cl_image_format     img_fmt;
+    cl_image_desc       img_desc;
+    cl_buffer_region    region;
+
+    int M = ne01;
+    int N = ne1;
+    int K = ne00;
+
+    if (ne1 == 1) {
+        cl_mem qs_img = nullptr;
+        cl_mem b_sub_buf = nullptr;
+        cl_mem b_img = nullptr;
+
+        // image for qs
+        img_fmt = { CL_R, CL_UNSIGNED_INT32 };
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = M * K / 2 / 4;
+        img_desc.buffer = extra0_q5_0->qs;
+        CL_CHECK((qs_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // subbuffer for activations
+        region.origin = offset1;
+        region.size = K * N * sizeof(float);
+        CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for activations
+        img_fmt = {CL_RGBA, CL_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * N / 4;
+        img_desc.buffer = b_sub_buf;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        kernel = backend_ctx->kernel_gemv_noshuffle_q5_0_f32;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &qs_img));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_q5_0->qh));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra0_q5_0->d));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &b_img));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int),   &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &ne01));
+
+        size_t local_work_size[3] = {64, 4, 1};
+        size_t global_work_size[3] = {(size_t)CEIL_DIV(ne01/2, 64)*64, 4, 1};
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(qs_img));
+        CL_CHECK(clReleaseMemObject(b_sub_buf));
+        CL_CHECK(clReleaseMemObject(b_img));
+    } else {
+        cl_mem b_sub_buf = nullptr;
+        cl_mem b_sub_buf_trans = nullptr;
+        cl_mem b_img = nullptr;
+        cl_mem b_img_trans = nullptr;
+        cl_mem d_sub_buf = nullptr;
+
+        // subbuffer for activations
+        region.origin = offset1;
+        region.size = K * N * sizeof(float);
+        CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for activations
+        img_fmt = {CL_RGBA, CL_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * N / 4;
+        img_desc.buffer = b_sub_buf;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // pad N to multiple of 8
+        int extra_elements = N % 8;
+        int padding = 0;
+        if (extra_elements > 0){
+            padding = 8 - extra_elements;
+        }
+
+        // subbuffer for transposed activations
+        region.origin = 0;
+        region.size = K * (N + padding) * sizeof(float)/2;
+        backend_ctx->prealloc_act_trans.allocate(context, region.size);
+        CL_CHECK((b_sub_buf_trans = clCreateSubBuffer(backend_ctx->prealloc_act_trans.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for transposed activations
+        img_fmt = {CL_RGBA, CL_HALF_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * (N + padding) / 4;
+        img_desc.buffer = b_sub_buf_trans;
+        CL_CHECK((b_img_trans = clCreateImage(context, 0, &img_fmt, &img_desc, NULL, &err), err));
+
+        // subbuffer for output
+        region.origin = extrad->offset;
+        region.size = M * N * sizeof(float);
+        CL_CHECK((d_sub_buf = clCreateSubBuffer(extrad->data_device, CL_MEM_WRITE_ONLY, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // transpose activations
+        int height_B = N/4;
+        if (height_B == 0) {
+            height_B = 1;
+        }
+        int width_B = K/4;
+        int padded_height_B = (N + padding)/4;
+
+        kernel = backend_ctx->kernel_transpose_32_16;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &b_img));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_B));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_B));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &padded_height_B));
+
+        size_t local_work_size_t[2] = { 1, 16 };
+        size_t global_work_size_t[2] = { (size_t)width_B, (size_t)padded_height_B };
+        backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size_t, local_work_size_t, dst);
+
+        // gemm
+        kernel = backend_ctx->kernel_gemm_noshuffle_q5_0_f32;
+        int padded_N = N + padding;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0_q5_0->qs));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_q5_0->qh));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra0_q5_0->d));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &d_sub_buf));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_int),   &ne01));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int),   &padded_N));
+        CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int),   &ne1));
+
+        size_t global_work_size[3] = {(size_t)CEIL_DIV(ne1, 8), (size_t)CEIL_DIV(ne01, 4), 1};
+        size_t local_work_size[3] = {1, 128, 1};
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(b_sub_buf));
+        CL_CHECK(clReleaseMemObject(b_sub_buf_trans));
+        CL_CHECK(clReleaseMemObject(b_img));
+        CL_CHECK(clReleaseMemObject(b_img_trans));
+        CL_CHECK(clReleaseMemObject(d_sub_buf));
+    }
+#else
+    GGML_UNUSED(backend);
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+#endif
+}
+
+static void ggml_cl_mul_mat_q5_1_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+    ggml_tensor_extra_cl_q5_1 * extra0_q5_1 = (ggml_tensor_extra_cl_q5_1 *)src0->extra;
+
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+
+    const int ne1 = dst->ne[1];
+
+    GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);
+
+    cl_context context = backend_ctx->context;
+    cl_kernel kernel;
+
+    cl_int              err;
+    cl_image_format     img_fmt;
+    cl_image_desc       img_desc;
+    cl_buffer_region    region;
+
+    int M = ne01;
+    int N = ne1;
+    int K = ne00;
+
+    if (ne1 == 1) {
+        cl_mem qs_img = nullptr;
+        cl_mem b_sub_buf = nullptr;
+        cl_mem b_img = nullptr;
+
+        // image for qs
+        img_fmt = { CL_R, CL_UNSIGNED_INT32 };
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = M * K / 2 / 4;
+        img_desc.buffer = extra0_q5_1->qs;
+        CL_CHECK((qs_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // subbuffer for activations
+        region.origin = offset1;
+        region.size = K * N * sizeof(float);
+        CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for activations
+        img_fmt = {CL_RGBA, CL_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * N / 4;
+        img_desc.buffer = b_sub_buf;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        kernel = backend_ctx->kernel_gemv_noshuffle_q5_1_f32;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &qs_img));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_q5_1->qh));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra0_q5_1->d));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &extra0_q5_1->m));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &b_img));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int),   &ne01));
+
+        size_t local_work_size[3] = {64, 4, 1};
+        size_t global_work_size[3] = {(size_t)CEIL_DIV(ne01/2, 64)*64, 4, 1};
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(qs_img));
+        CL_CHECK(clReleaseMemObject(b_sub_buf));
+        CL_CHECK(clReleaseMemObject(b_img));
+    } else {
+        cl_mem b_sub_buf = nullptr;
+        cl_mem b_sub_buf_trans = nullptr;
+        cl_mem b_img = nullptr;
+        cl_mem b_img_trans = nullptr;
+        cl_mem d_sub_buf = nullptr;
+
+        // subbuffer for activations
+        region.origin = offset1;
+        region.size = K * N * sizeof(float);
+        CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for activations
+        img_fmt = {CL_RGBA, CL_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * N / 4;
+        img_desc.buffer = b_sub_buf;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // pad N to multiple of 8
+        int extra_elements = N % 8;
+        int padding = 0;
+        if (extra_elements > 0){
+            padding = 8 - extra_elements;
+        }
+
+        // subbuffer for transposed activations
+        region.origin = 0;
+        region.size = K * (N + padding) * sizeof(float)/2;
+        backend_ctx->prealloc_act_trans.allocate(context, region.size);
+        CL_CHECK((b_sub_buf_trans = clCreateSubBuffer(backend_ctx->prealloc_act_trans.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for transposed activations
+        img_fmt = {CL_RGBA, CL_HALF_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * (N + padding) / 4;
+        img_desc.buffer = b_sub_buf_trans;
+        CL_CHECK((b_img_trans = clCreateImage(context, 0, &img_fmt, &img_desc, NULL, &err), err));
+
+        // subbuffer for output
+        region.origin = extrad->offset;
+        region.size = M * N * sizeof(float);
+        CL_CHECK((d_sub_buf = clCreateSubBuffer(extrad->data_device, CL_MEM_WRITE_ONLY, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // transpose activations
+        int height_B = N/4;
+        if (height_B == 0) {
+            height_B = 1;
+        }
+        int width_B = K/4;
+        int padded_height_B = (N + padding)/4;
+
+        kernel = backend_ctx->kernel_transpose_32_16;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &b_img));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_B));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_B));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &padded_height_B));
+
+        size_t local_work_size_t[2] = { 1, 16 };
+        size_t global_work_size_t[2] = { (size_t)width_B, (size_t)padded_height_B };
+        backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size_t, local_work_size_t, dst);
+
+        // gemm
+        kernel = backend_ctx->kernel_gemm_noshuffle_q5_1_f32;
+        int padded_N = N + padding;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0_q5_1->qs));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_q5_1->qh));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra0_q5_1->d));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &extra0_q5_1->m));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_mem),   &d_sub_buf));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int),   &ne01));
+        CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &padded_N));
+        CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int),   &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_int),   &ne1));
+
+        size_t global_work_size[3] = {(size_t)CEIL_DIV(ne1, 8), (size_t)CEIL_DIV(ne01, 4), 1};
+        size_t local_work_size[3] = {1, 128, 1};
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(b_sub_buf));
+        CL_CHECK(clReleaseMemObject(b_sub_buf_trans));
+        CL_CHECK(clReleaseMemObject(b_img));
+        CL_CHECK(clReleaseMemObject(b_img_trans));
+        CL_CHECK(clReleaseMemObject(d_sub_buf));
+    }
+#else
+    GGML_UNUSED(backend);
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+#endif
+}
+
 static void ggml_cl_mul_mat_iq4_nl_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
     GGML_ASSERT(src0);
@@ -13243,6 +13850,18 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
             return;
         }
 
+        // q5_0 x fp32
+        if (src0t == GGML_TYPE_Q5_0 && src1t == GGML_TYPE_F32) {
+            ggml_cl_mul_mat_q5_0_f32_adreno(backend, src0, src1, dst);
+            return;
+        }
+
+        // q5_1 x fp32
+        if (src0t == GGML_TYPE_Q5_1 && src1t == GGML_TYPE_F32) {
+            ggml_cl_mul_mat_q5_1_f32_adreno(backend, src0, src1, dst);
+            return;
+        }
+
         // iq4_nl x fp32
         if (src0t == GGML_TYPE_IQ4_NL && src1t == GGML_TYPE_F32) {
             ggml_cl_mul_mat_iq4_nl_f32_adreno(backend, src0, src1, dst);
@@ -17750,7 +18369,7 @@ static void ggml_cl_gated_delta_net(ggml_backend_t backend, ggml_tensor * dst) {
     const cl_uint H_v      = (cl_uint) src_v->ne[1];
     const cl_uint n_tokens = (cl_uint) src_v->ne[2];
     const cl_uint n_seqs   = (cl_uint) src_v->ne[3];
-    const cl_uint K        = (cl_uint) src_state->ne[1];
+    const cl_uint K        = (cl_uint) ggml_get_op_params_i32(dst, 0);
 
     int si;
     switch (S_v) {

ggml/src/ggml-opencl/kernels/cvt.cl

@@ -584,6 +584,60 @@ kernel void kernel_restore_block_q5_0(
     }
 }
 
+kernel void kernel_convert_block_q5_0_noshuffle(
+    global struct block_q5_0 * src0,
+    global uchar * dst_q,
+    global uint  * dst_qh,
+    global half  * dst_d
+) {
+    global struct block_q5_0 * b = (global struct block_q5_0 *) src0 + get_global_id(0);
+    global uchar * q  = (global uchar *) dst_q + QK5_0/2*get_global_id(0);
+    global uint  * qh = (global uint  *) dst_qh + get_global_id(0);
+    global half  * d  = (global half  *) dst_d  + get_global_id(0);
+
+    *d = b->d;
+    *qh = *((global uint *)(b->qh));
+
+    for (int i = 0; i < QK5_0/4; ++i) {
+        uchar x0 = b->qs[2*i + 0];
+        uchar x1 = b->qs[2*i + 1];
+
+        q[i + 0      ] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        q[i + QK5_0/4] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+
+#ifdef ADRENO_GPU
+        if (get_global_id(0) == 65536*4096) {
+            printf("%04x - %02x\n", *(global ushort*)d, ((x0 & 0xF0) >> 4) | (x1 & 0xF0));
+        }
+#endif
+    }
+}
+
+kernel void kernel_restore_block_q5_0_noshuffle(
+    global uchar * src_q,
+    global uint  * src_qh,
+    global half  * src_d,
+    global struct block_q5_0 * dst,
+    uchar mask_0F,
+    uchar mask_F0
+) {
+    global struct block_q5_0 * b = (global struct block_q5_0 *) dst + get_global_id(0);
+    global uchar * q  = (global uchar *) src_q + QK5_0/2*get_global_id(0);
+    global uint  * qh = (global uint  *) src_qh + get_global_id(0);
+    global half  * d  = (global half  *) src_d  + get_global_id(0);
+
+    b->d = *d;
+    *((global uint *)(b->qh)) = *qh;
+
+    for (int i = 0; i < QK5_0/4; ++i) {
+        uchar x0 = q[i + 0      ];
+        uchar x1 = q[i + QK5_0/4];
+
+        b->qs[2*i + 0] = convert_uchar((x0 & mask_0F) | ((x1 & mask_0F) << 4));
+        b->qs[2*i + 1] = convert_uchar(((x0 & mask_F0) >> 4) | (x1 & mask_F0));
+    }
+}
+
 kernel void kernel_convert_block_q5_0_trans4_ns(
     __global struct block_q5_0 * src0,
     __global uint * dst_qs,
@@ -736,6 +790,66 @@ kernel void kernel_restore_block_q5_1(
     }
 }
 
+kernel void kernel_convert_block_q5_1_noshuffle(
+    global struct block_q5_1 * src0,
+    global uchar * dst_q,
+    global uint  * dst_qh,
+    global half  * dst_d,
+    global half  * dst_m
+) {
+    global struct block_q5_1 * b = (global struct block_q5_1 *) src0 + get_global_id(0);
+    global uchar * q  = (global uchar *) dst_q + QK5_1/2*get_global_id(0);
+    global uint  * qh = (global uint  *) dst_qh + get_global_id(0);
+    global half  * d  = (global half  *) dst_d  + get_global_id(0);
+    global half  * m  = (global half  *) dst_m  + get_global_id(0);
+
+    *d = b->d;
+    *m = b->m;
+    *qh = *((global uint *)(b->qh));
+
+    for (int i = 0; i < QK5_1/4; ++i) {
+        uchar x0 = b->qs[2*i + 0];
+        uchar x1 = b->qs[2*i + 1];
+
+        q[i + 0      ] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        q[i + QK5_1/4] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+
+#ifdef ADRENO_GPU
+        if (get_global_id(0) == 65536*4096) {
+            printf("%04x - %02x\n", *(global ushort*)d, ((x0 & 0xF0) >> 4) | (x1 & 0xF0));
+        }
+#endif
+    }
+}
+
+kernel void kernel_restore_block_q5_1_noshuffle(
+    global uchar * src_q,
+    global uint  * src_qh,
+    global half  * src_d,
+    global half  * src_m,
+    global struct block_q5_1 * dst,
+    uchar mask_0F,
+    uchar mask_F0
+) {
+    global struct block_q5_1 * b = (global struct block_q5_1 *) dst + get_global_id(0);
+    global uchar * q  = (global uchar *) src_q + QK5_1/2*get_global_id(0);
+    global uint  * qh = (global uint  *) src_qh + get_global_id(0);
+    global half  * d  = (global half  *) src_d  + get_global_id(0);
+    global half  * m  = (global half  *) src_m  + get_global_id(0);
+
+    b->d = *d;
+    b->m = *m;
+    *((global uint *)(b->qh)) = *qh;
+
+    for (int i = 0; i < QK5_1/4; ++i) {
+        uchar x0 = q[i + 0      ];
+        uchar x1 = q[i + QK5_1/4];
+
+        b->qs[2*i + 0] = convert_uchar((x0 & mask_0F) | ((x1 & mask_0F) << 4));
+        b->qs[2*i + 1] = convert_uchar(((x0 & mask_F0) >> 4) | (x1 & mask_F0));
+    }
+}
+
 kernel void kernel_convert_block_q5_1_trans4_ns(
     __global struct block_q5_1 * src0,
     __global uint * dst_qs,

ggml/src/ggml-opencl/kernels/gated_delta_net.cl

@@ -123,7 +123,8 @@ kernel void kernel_gated_delta_net(
     const uint iq3 = seq_id / rq3; // seq index for Q and K
 
     const uint state_size = S_V * S_V;
-    const uint state_base = (seq_id * K * H_v + head_id) * state_size;
+    // input state holds s0 only [S_v, S_v, H, n_seqs]: per-seq stride is H*D.
+    const uint state_base = (seq_id * H_v + head_id) * state_size;
     const uint q_off_base  = iq3 * sq3 + iq1 * sq1;
     const uint v_off_base  = seq_id * sv3 + head_id * sv1;
     const uint gb_off_base = seq_id * sb3 + head_id * sb1;
@@ -143,7 +144,8 @@ kernel void kernel_gated_delta_net(
         }
     }
 
-    const int shift = (int)n_tokens - (int)K;
+    // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+    // When n_tokens < K only slots 0..n_tokens-1 are written; older slots are caller-owned.
     uint attn_off = (seq_id * n_tokens * H_v + head_id) * S_V;
 
     for (uint t = 0; t < n_tokens; t++) {
@@ -219,7 +221,7 @@ kernel void kernel_gated_delta_net(
         attn_off += S_V * H_v;
 
         if (K > 1u) {
-            const int target_slot = (int)t - shift;
+            const int target_slot = (int)n_tokens - 1 - (int)t;
             if (target_slot >= 0 && target_slot < (int)K) {
                 #pragma unroll
                 for (uint cg = 0; cg < COLS_PER_LANE_GROUP; cg++) {

ggml/src/ggml-opencl/kernels/gemm_noshuffle_q5_0_f32.cl

@@ -0,0 +1,131 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+#ifdef cl_qcom_reqd_sub_group_size
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_128
+#endif
+
+kernel void kernel_gemm_noshuffle_q5_0_f32(
+        global const ushort * src0_qs,      // quantized A
+        global const uchar  * src0_qh,      // 5th bits
+        global const half   * src0_d,       // A scales
+        __read_only image1d_buffer_t src1,  // B (1d image)
+        global float * dst,                 // C
+        int m,                              // M
+        int n,                              // N with padding
+        int k,                              // K
+        int n_no_padding                    // N without padding
+) {
+
+    int n_4 = n >> 2;
+
+    int gy = get_global_id(0);
+    int gx = get_global_id(1);
+    int gx_2 = gx << 2;
+
+    half8 c0 = 0, c1 = 0, c2 = 0, c3 = 0;
+    half8 B;
+    half4 dequantized_weights;
+
+    global const ushort * weight_ptr = src0_qs + gx_2;
+    global const uchar  * qh_ptr    = src0_qh + gx_2;
+    global const half   * scale_ptr = src0_d  + gx_2;
+
+    for (int i = 0; i < k; i += 4) {
+
+        B.s0123 = read_imageh(src1, gy*2 + i*n_4);
+        B.s4567 = read_imageh(src1, gy*2 + i*n_4 + 1);
+
+        ushort4 bits4 = vload4(0, weight_ptr + (i >> 2)*m);
+        uchar4  bits1 = vload4(0, qh_ptr + (i >> 3)*m);
+        uchar4  qh = bits1 >> (uchar4)(i & 4);
+
+        half4 scale = vload4(0, scale_ptr + (i >> 5)*m);
+
+        // j=0
+        dequantized_weights.s0 = (convert_half((bits4.s0 & 0x000F) | ((qh.s0 & 0x01) << 4)) - 16.0h) * scale.s0;
+        dequantized_weights.s1 = (convert_half((bits4.s1 & 0x000F) | ((qh.s1 & 0x01) << 4)) - 16.0h) * scale.s1;
+        dequantized_weights.s2 = (convert_half((bits4.s2 & 0x000F) | ((qh.s2 & 0x01) << 4)) - 16.0h) * scale.s2;
+        dequantized_weights.s3 = (convert_half((bits4.s3 & 0x000F) | ((qh.s3 & 0x01) << 4)) - 16.0h) * scale.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=1
+        B.s0123 = read_imageh(src1, gy*2 + (i+1)*n_4);
+        B.s4567 = read_imageh(src1, gy*2 + (i+1)*n_4 + 1);
+        dequantized_weights.s0 = (convert_half(((bits4.s0 & 0x00F0) >> 4) | ((qh.s0 & 0x02) << 3)) - 16.0h) * scale.s0;
+        dequantized_weights.s1 = (convert_half(((bits4.s1 & 0x00F0) >> 4) | ((qh.s1 & 0x02) << 3)) - 16.0h) * scale.s1;
+        dequantized_weights.s2 = (convert_half(((bits4.s2 & 0x00F0) >> 4) | ((qh.s2 & 0x02) << 3)) - 16.0h) * scale.s2;
+        dequantized_weights.s3 = (convert_half(((bits4.s3 & 0x00F0) >> 4) | ((qh.s3 & 0x02) << 3)) - 16.0h) * scale.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=2
+        B.s0123 = read_imageh(src1, gy*2 + (i+2)*n_4);
+        B.s4567 = read_imageh(src1, gy*2 + (i+2)*n_4 + 1);
+        dequantized_weights.s0 = (convert_half(((bits4.s0 & 0x0F00) >> 8) | ((qh.s0 & 0x04) << 2)) - 16.0h) * scale.s0;
+        dequantized_weights.s1 = (convert_half(((bits4.s1 & 0x0F00) >> 8) | ((qh.s1 & 0x04) << 2)) - 16.0h) * scale.s1;
+        dequantized_weights.s2 = (convert_half(((bits4.s2 & 0x0F00) >> 8) | ((qh.s2 & 0x04) << 2)) - 16.0h) * scale.s2;
+        dequantized_weights.s3 = (convert_half(((bits4.s3 & 0x0F00) >> 8) | ((qh.s3 & 0x04) << 2)) - 16.0h) * scale.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=3
+        B.s0123 = read_imageh(src1, gy*2 + (i+3)*n_4);
+        B.s4567 = read_imageh(src1, gy*2 + (i+3)*n_4 + 1);
+        dequantized_weights.s0 = (convert_half(((bits4.s0 & 0xF000) >> 12) | ((qh.s0 & 0x08) << 1)) - 16.0h) * scale.s0;
+        dequantized_weights.s1 = (convert_half(((bits4.s1 & 0xF000) >> 12) | ((qh.s1 & 0x08) << 1)) - 16.0h) * scale.s1;
+        dequantized_weights.s2 = (convert_half(((bits4.s2 & 0xF000) >> 12) | ((qh.s2 & 0x08) << 1)) - 16.0h) * scale.s2;
+        dequantized_weights.s3 = (convert_half(((bits4.s3 & 0xF000) >> 12) | ((qh.s3 & 0x08) << 1)) - 16.0h) * scale.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+    }
+
+    int idx = (gy<<3)*m + (gx<<2);
+
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s0, c1.s0, c2.s0, c3.s0), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s1, c1.s1, c2.s1, c3.s1), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s2, c1.s2, c2.s2, c3.s2), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s3, c1.s3, c2.s3, c3.s3), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s4, c1.s4, c2.s4, c3.s4), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s5, c1.s5, c2.s5, c3.s5), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s6, c1.s6, c2.s6, c3.s6), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s7, c1.s7, c2.s7, c3.s7), 0, dst + idx);
+    }
+}

ggml/src/ggml-opencl/kernels/gemm_noshuffle_q5_1_f32.cl

@@ -0,0 +1,134 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+#ifdef cl_qcom_reqd_sub_group_size
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_128
+#endif
+
+kernel void kernel_gemm_noshuffle_q5_1_f32(
+        global const ushort * src0_qs,      // quantized A
+        global const uchar  * src0_qh,      // 5th bits
+        global const half   * src0_d,       // A scales
+        global const half   * src0_m,       // A mins
+        __read_only image1d_buffer_t src1,  // B (1d image)
+        global float * dst,                 // C
+        int m,                              // M
+        int n,                              // N with padding
+        int k,                              // K
+        int n_no_padding                    // N without padding
+) {
+
+    int n_4 = n >> 2;
+
+    int gy = get_global_id(0);
+    int gx = get_global_id(1);
+    int gx_2 = gx << 2;
+
+    half8 c0 = 0, c1 = 0, c2 = 0, c3 = 0;
+    half8 B;
+    half4 dequantized_weights;
+
+    global const ushort * weight_ptr = src0_qs + gx_2;
+    global const uchar  * qh_ptr    = src0_qh + gx_2;
+    global const half   * scale_ptr = src0_d  + gx_2;
+    global const half   * min_ptr   = src0_m  + gx_2;
+
+    for (int i = 0; i < k; i += 4) {
+
+        B.s0123 = read_imageh(src1, gy*2 + i*n_4);
+        B.s4567 = read_imageh(src1, gy*2 + i*n_4 + 1);
+
+        ushort4 bits4 = vload4(0, weight_ptr + (i >> 2)*m);
+        uchar4  bits1 = vload4(0, qh_ptr + (i >> 3)*m);
+        uchar4  qh = bits1 >> (uchar4)(i & 4);
+
+        half4 scale = vload4(0, scale_ptr + (i >> 5)*m);
+        half4 minv  = vload4(0, min_ptr   + (i >> 5)*m);
+
+        // j=0
+        dequantized_weights.s0 = convert_half((bits4.s0 & 0x000F) | ((qh.s0 & 0x01) << 4)) * scale.s0 + minv.s0;
+        dequantized_weights.s1 = convert_half((bits4.s1 & 0x000F) | ((qh.s1 & 0x01) << 4)) * scale.s1 + minv.s1;
+        dequantized_weights.s2 = convert_half((bits4.s2 & 0x000F) | ((qh.s2 & 0x01) << 4)) * scale.s2 + minv.s2;
+        dequantized_weights.s3 = convert_half((bits4.s3 & 0x000F) | ((qh.s3 & 0x01) << 4)) * scale.s3 + minv.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=1
+        B.s0123 = read_imageh(src1, gy*2 + (i+1)*n_4);
+        B.s4567 = read_imageh(src1, gy*2 + (i+1)*n_4 + 1);
+        dequantized_weights.s0 = convert_half(((bits4.s0 & 0x00F0) >> 4) | ((qh.s0 & 0x02) << 3)) * scale.s0 + minv.s0;
+        dequantized_weights.s1 = convert_half(((bits4.s1 & 0x00F0) >> 4) | ((qh.s1 & 0x02) << 3)) * scale.s1 + minv.s1;
+        dequantized_weights.s2 = convert_half(((bits4.s2 & 0x00F0) >> 4) | ((qh.s2 & 0x02) << 3)) * scale.s2 + minv.s2;
+        dequantized_weights.s3 = convert_half(((bits4.s3 & 0x00F0) >> 4) | ((qh.s3 & 0x02) << 3)) * scale.s3 + minv.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=2
+        B.s0123 = read_imageh(src1, gy*2 + (i+2)*n_4);
+        B.s4567 = read_imageh(src1, gy*2 + (i+2)*n_4 + 1);
+        dequantized_weights.s0 = convert_half(((bits4.s0 & 0x0F00) >> 8) | ((qh.s0 & 0x04) << 2)) * scale.s0 + minv.s0;
+        dequantized_weights.s1 = convert_half(((bits4.s1 & 0x0F00) >> 8) | ((qh.s1 & 0x04) << 2)) * scale.s1 + minv.s1;
+        dequantized_weights.s2 = convert_half(((bits4.s2 & 0x0F00) >> 8) | ((qh.s2 & 0x04) << 2)) * scale.s2 + minv.s2;
+        dequantized_weights.s3 = convert_half(((bits4.s3 & 0x0F00) >> 8) | ((qh.s3 & 0x04) << 2)) * scale.s3 + minv.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=3
+        B.s0123 = read_imageh(src1, gy*2 + (i+3)*n_4);
+        B.s4567 = read_imageh(src1, gy*2 + (i+3)*n_4 + 1);
+        dequantized_weights.s0 = convert_half(((bits4.s0 & 0xF000) >> 12) | ((qh.s0 & 0x08) << 1)) * scale.s0 + minv.s0;
+        dequantized_weights.s1 = convert_half(((bits4.s1 & 0xF000) >> 12) | ((qh.s1 & 0x08) << 1)) * scale.s1 + minv.s1;
+        dequantized_weights.s2 = convert_half(((bits4.s2 & 0xF000) >> 12) | ((qh.s2 & 0x08) << 1)) * scale.s2 + minv.s2;
+        dequantized_weights.s3 = convert_half(((bits4.s3 & 0xF000) >> 12) | ((qh.s3 & 0x08) << 1)) * scale.s3 + minv.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+    }
+
+    int idx = (gy<<3)*m + (gx<<2);
+
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s0, c1.s0, c2.s0, c3.s0), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s1, c1.s1, c2.s1, c3.s1), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s2, c1.s2, c2.s2, c3.s2), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s3, c1.s3, c2.s3, c3.s3), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s4, c1.s4, c2.s4, c3.s4), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s5, c1.s5, c2.s5, c3.s5), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s6, c1.s6, c2.s6, c3.s6), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s7, c1.s7, c2.s7, c3.s7), 0, dst + idx);
+    }
+}

ggml/src/ggml-opencl/kernels/gemv_noshuffle_q5_0_f32.cl

@@ -0,0 +1,291 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+
+#ifdef cl_qcom_reqd_sub_group_size
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
+#endif
+
+#define QK5_0 32
+#define NSUBGROUPS 4
+#define SUBGROUP_SIZE 64
+
+#define dequantizeBlockAccum_ns_q5_0_sgbroadcast_1_hi(total_sums, bits4, bits1, scale, y) \
+    float shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 0); \
+    total_sums.s0 += (((bits4.s0 & 0x000F) | (((bits1.s0      ) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x000F) | (((bits1.s4      ) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 0); \
+    total_sums.s0 += ((((bits4.s0 & 0x00F0) >> 4) | (((bits1.s0 >> 1) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0x00F0) >> 4) | (((bits1.s4 >> 1) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 0); \
+    total_sums.s0 += ((((bits4.s0 & 0x0F00) >> 8) | (((bits1.s0 >> 2) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0x0F00) >> 8) | (((bits1.s4 >> 2) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 0); \
+    total_sums.s0 += ((((bits4.s0 & 0xF000) >> 12) | (((bits1.s0 >> 3) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0xF000) >> 12) | (((bits1.s4 >> 3) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 0); \
+    total_sums.s0 += (((bits4.s2 & 0x000F) | (((bits1.s0 >> 4) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x000F) | (((bits1.s4 >> 4) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 0); \
+    total_sums.s0 += ((((bits4.s2 & 0x00F0) >> 4) | (((bits1.s0 >> 5) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0x00F0) >> 4) | (((bits1.s4 >> 5) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 0); \
+    total_sums.s0 += ((((bits4.s2 & 0x0F00) >> 8) | (((bits1.s0 >> 6) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0x0F00) >> 8) | (((bits1.s4 >> 6) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 0); \
+    total_sums.s0 += ((((bits4.s2 & 0xF000) >> 12) | (((bits1.s0 >> 7) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0xF000) >> 12) | (((bits1.s4 >> 7) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 1); \
+    total_sums.s0 += (((bits4.s4 & 0x000F) | (((bits1.s1      ) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x000F) | (((bits1.s5      ) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 1); \
+    total_sums.s0 += ((((bits4.s4 & 0x00F0) >> 4) | (((bits1.s1 >> 1) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0x00F0) >> 4) | (((bits1.s5 >> 1) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 1); \
+    total_sums.s0 += ((((bits4.s4 & 0x0F00) >> 8) | (((bits1.s1 >> 2) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0x0F00) >> 8) | (((bits1.s5 >> 2) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 1); \
+    total_sums.s0 += ((((bits4.s4 & 0xF000) >> 12) | (((bits1.s1 >> 3) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0xF000) >> 12) | (((bits1.s5 >> 3) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 1); \
+    total_sums.s0 += (((bits4.s6 & 0x000F) | (((bits1.s1 >> 4) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x000F) | (((bits1.s5 >> 4) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 1); \
+    total_sums.s0 += ((((bits4.s6 & 0x00F0) >> 4) | (((bits1.s1 >> 5) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0x00F0) >> 4) | (((bits1.s5 >> 5) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 1); \
+    total_sums.s0 += ((((bits4.s6 & 0x0F00) >> 8) | (((bits1.s1 >> 6) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0x0F00) >> 8) | (((bits1.s5 >> 6) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 1); \
+    total_sums.s0 += ((((bits4.s6 & 0xF000) >> 12) | (((bits1.s1 >> 7) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0xF000) >> 12) | (((bits1.s5 >> 7) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_q5_0_sgbroadcast_1_lo(total_sums, bits4, bits1, scale, y) \
+    shared_y = sub_group_broadcast(y.s0, 2); \
+    total_sums.s0 += (((bits4.s0 & 0x000F) | (((bits1.s2      ) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x000F) | (((bits1.s6      ) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 2); \
+    total_sums.s0 += ((((bits4.s0 & 0x00F0) >> 4) | (((bits1.s2 >> 1) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0x00F0) >> 4) | (((bits1.s6 >> 1) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 2); \
+    total_sums.s0 += ((((bits4.s0 & 0x0F00) >> 8) | (((bits1.s2 >> 2) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0x0F00) >> 8) | (((bits1.s6 >> 2) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 2); \
+    total_sums.s0 += ((((bits4.s0 & 0xF000) >> 12) | (((bits1.s2 >> 3) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0xF000) >> 12) | (((bits1.s6 >> 3) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 2); \
+    total_sums.s0 += (((bits4.s2 & 0x000F) | (((bits1.s2 >> 4) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x000F) | (((bits1.s6 >> 4) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 2); \
+    total_sums.s0 += ((((bits4.s2 & 0x00F0) >> 4) | (((bits1.s2 >> 5) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0x00F0) >> 4) | (((bits1.s6 >> 5) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 2); \
+    total_sums.s0 += ((((bits4.s2 & 0x0F00) >> 8) | (((bits1.s2 >> 6) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0x0F00) >> 8) | (((bits1.s6 >> 6) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 2); \
+    total_sums.s0 += ((((bits4.s2 & 0xF000) >> 12) | (((bits1.s2 >> 7) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0xF000) >> 12) | (((bits1.s6 >> 7) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 3); \
+    total_sums.s0 += (((bits4.s4 & 0x000F) | (((bits1.s3      ) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x000F) | (((bits1.s7      ) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 3); \
+    total_sums.s0 += ((((bits4.s4 & 0x00F0) >> 4) | (((bits1.s3 >> 1) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0x00F0) >> 4) | (((bits1.s7 >> 1) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 3); \
+    total_sums.s0 += ((((bits4.s4 & 0x0F00) >> 8) | (((bits1.s3 >> 2) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0x0F00) >> 8) | (((bits1.s7 >> 2) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 3); \
+    total_sums.s0 += ((((bits4.s4 & 0xF000) >> 12) | (((bits1.s3 >> 3) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0xF000) >> 12) | (((bits1.s7 >> 3) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 3); \
+    total_sums.s0 += (((bits4.s6 & 0x000F) | (((bits1.s3 >> 4) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x000F) | (((bits1.s7 >> 4) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 3); \
+    total_sums.s0 += ((((bits4.s6 & 0x00F0) >> 4) | (((bits1.s3 >> 5) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0x00F0) >> 4) | (((bits1.s7 >> 5) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 3); \
+    total_sums.s0 += ((((bits4.s6 & 0x0F00) >> 8) | (((bits1.s3 >> 6) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0x0F00) >> 8) | (((bits1.s7 >> 6) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 3); \
+    total_sums.s0 += ((((bits4.s6 & 0xF000) >> 12) | (((bits1.s3 >> 7) & 0x01) << 4)) - 16) * scale.s0 * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0xF000) >> 12) | (((bits1.s7 >> 7) & 0x01) << 4)) - 16) * scale.s1 * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_q5_0_sgbroadcast_8_hi(total_sums, bits4, bits1, scale, y) \
+    float8 shared_y; \
+    shared_y = sub_group_broadcast(y, 0); \
+    total_sums.s0 += (((bits4.s0 & 0x000F)         | (((bits1.s0     ) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += ((((bits4.s0 & 0x00F0) >> 4)  | (((bits1.s0 >> 1) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += ((((bits4.s0 & 0x0F00) >> 8)  | (((bits1.s0 >> 2) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += ((((bits4.s0 & 0xF000) >> 12) | (((bits1.s0 >> 3) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += (((bits4.s2 & 0x000F)         | (((bits1.s0 >> 4) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += ((((bits4.s2 & 0x00F0) >> 4)  | (((bits1.s0 >> 5) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += ((((bits4.s2 & 0x0F00) >> 8)  | (((bits1.s0 >> 6) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += ((((bits4.s2 & 0xF000) >> 12) | (((bits1.s0 >> 7) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += (((bits4.s1 & 0x000F)         | (((bits1.s4     ) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += ((((bits4.s1 & 0x00F0) >> 4)  | (((bits1.s4 >> 1) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += ((((bits4.s1 & 0x0F00) >> 8)  | (((bits1.s4 >> 2) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += ((((bits4.s1 & 0xF000) >> 12) | (((bits1.s4 >> 3) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += (((bits4.s3 & 0x000F)         | (((bits1.s4 >> 4) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += ((((bits4.s3 & 0x00F0) >> 4)  | (((bits1.s4 >> 5) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += ((((bits4.s3 & 0x0F00) >> 8)  | (((bits1.s4 >> 6) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += ((((bits4.s3 & 0xF000) >> 12) | (((bits1.s4 >> 7) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 1); \
+    total_sums.s0 += (((bits4.s4 & 0x000F)         | (((bits1.s1     ) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += ((((bits4.s4 & 0x00F0) >> 4)  | (((bits1.s1 >> 1) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += ((((bits4.s4 & 0x0F00) >> 8)  | (((bits1.s1 >> 2) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += ((((bits4.s4 & 0xF000) >> 12) | (((bits1.s1 >> 3) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += (((bits4.s6 & 0x000F)         | (((bits1.s1 >> 4) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += ((((bits4.s6 & 0x00F0) >> 4)  | (((bits1.s1 >> 5) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += ((((bits4.s6 & 0x0F00) >> 8)  | (((bits1.s1 >> 6) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += ((((bits4.s6 & 0xF000) >> 12) | (((bits1.s1 >> 7) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += (((bits4.s5 & 0x000F)         | (((bits1.s5     ) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += ((((bits4.s5 & 0x00F0) >> 4)  | (((bits1.s5 >> 1) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += ((((bits4.s5 & 0x0F00) >> 8)  | (((bits1.s5 >> 2) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += ((((bits4.s5 & 0xF000) >> 12) | (((bits1.s5 >> 3) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += (((bits4.s7 & 0x000F)         | (((bits1.s5 >> 4) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += ((((bits4.s7 & 0x00F0) >> 4)  | (((bits1.s5 >> 5) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += ((((bits4.s7 & 0x0F00) >> 8)  | (((bits1.s5 >> 6) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += ((((bits4.s7 & 0xF000) >> 12) | (((bits1.s5 >> 7) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s7; \
+
+
+#define dequantizeBlockAccum_ns_q5_0_sgbroadcast_8_lo(total_sums, bits4, bits1, scale, y) \
+    shared_y = sub_group_broadcast(y, 2); \
+    total_sums.s0 += (((bits4.s0 & 0x000F)         | (((bits1.s2     ) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += ((((bits4.s0 & 0x00F0) >> 4)  | (((bits1.s2 >> 1) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += ((((bits4.s0 & 0x0F00) >> 8)  | (((bits1.s2 >> 2) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += ((((bits4.s0 & 0xF000) >> 12) | (((bits1.s2 >> 3) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += (((bits4.s2 & 0x000F)         | (((bits1.s2 >> 4) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += ((((bits4.s2 & 0x00F0) >> 4)  | (((bits1.s2 >> 5) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += ((((bits4.s2 & 0x0F00) >> 8)  | (((bits1.s2 >> 6) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += ((((bits4.s2 & 0xF000) >> 12) | (((bits1.s2 >> 7) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += (((bits4.s1 & 0x000F)         | (((bits1.s6     ) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += ((((bits4.s1 & 0x00F0) >> 4)  | (((bits1.s6 >> 1) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += ((((bits4.s1 & 0x0F00) >> 8)  | (((bits1.s6 >> 2) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += ((((bits4.s1 & 0xF000) >> 12) | (((bits1.s6 >> 3) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += (((bits4.s3 & 0x000F)         | (((bits1.s6 >> 4) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += ((((bits4.s3 & 0x00F0) >> 4)  | (((bits1.s6 >> 5) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += ((((bits4.s3 & 0x0F00) >> 8)  | (((bits1.s6 >> 6) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += ((((bits4.s3 & 0xF000) >> 12) | (((bits1.s6 >> 7) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 3); \
+    total_sums.s0 += (((bits4.s4 & 0x000F)         | (((bits1.s3     ) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s0; \
+    total_sums.s0 += ((((bits4.s4 & 0x00F0) >> 4)  | (((bits1.s3 >> 1) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s1; \
+    total_sums.s0 += ((((bits4.s4 & 0x0F00) >> 8)  | (((bits1.s3 >> 2) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s2; \
+    total_sums.s0 += ((((bits4.s4 & 0xF000) >> 12) | (((bits1.s3 >> 3) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += (((bits4.s6 & 0x000F)         | (((bits1.s3 >> 4) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s4; \
+    total_sums.s0 += ((((bits4.s6 & 0x00F0) >> 4)  | (((bits1.s3 >> 5) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s5; \
+    total_sums.s0 += ((((bits4.s6 & 0x0F00) >> 8)  | (((bits1.s3 >> 6) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s6; \
+    total_sums.s0 += ((((bits4.s6 & 0xF000) >> 12) | (((bits1.s3 >> 7) & 0x01) << 4)) - 16) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += (((bits4.s5 & 0x000F)         | (((bits1.s7     ) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s0; \
+    total_sums.s1 += ((((bits4.s5 & 0x00F0) >> 4)  | (((bits1.s7 >> 1) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s1; \
+    total_sums.s1 += ((((bits4.s5 & 0x0F00) >> 8)  | (((bits1.s7 >> 2) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s2; \
+    total_sums.s1 += ((((bits4.s5 & 0xF000) >> 12) | (((bits1.s7 >> 3) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += (((bits4.s7 & 0x000F)         | (((bits1.s7 >> 4) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s4; \
+    total_sums.s1 += ((((bits4.s7 & 0x00F0) >> 4)  | (((bits1.s7 >> 5) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s5; \
+    total_sums.s1 += ((((bits4.s7 & 0x0F00) >> 8)  | (((bits1.s7 >> 6) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s6; \
+    total_sums.s1 += ((((bits4.s7 & 0xF000) >> 12) | (((bits1.s7 >> 7) & 0x01) << 4)) - 16) * scale.s1 * shared_y.s7; \
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+__kernel void kernel_gemv_noshuffle_q5_0_f32(
+        __read_only  image1d_buffer_t src0_qs,  // quantized A
+        global ushort * src0_qh,                 // 5th bits
+        global half2  * src0_d,                  // A scales
+        __read_only  image1d_buffer_t src1,      // B activations
+        global float * dst,
+        ulong offsetd,
+        int ne00,               // K
+        int ne01)               // M
+{
+    uint groupId = get_local_id(1);
+    uint gid     = get_global_id(0);
+    ushort slid  = get_sub_group_local_id();
+
+    uint K = ne00;
+    uint M = ne01;
+
+    uint LINE_STRIDE_A    = M / 2;
+    uint BLOCK_STRIDE_A  = NSUBGROUPS * M;
+
+    private uint4     regA;
+    private half2     regS;
+    private float8    regB;
+
+    private float2 totalSum = (float2)(0.0f);
+
+    for (uint k = groupId; k < (K / QK5_0); k += NSUBGROUPS) {
+        regS = src0_d[gid + k * LINE_STRIDE_A];
+
+        ushort4 qh_raw;
+        qh_raw.s0 = src0_qh[gid + (4*k + 0) * LINE_STRIDE_A];
+        qh_raw.s1 = src0_qh[gid + (4*k + 1) * LINE_STRIDE_A];
+        qh_raw.s2 = src0_qh[gid + (4*k + 2) * LINE_STRIDE_A];
+        qh_raw.s3 = src0_qh[gid + (4*k + 3) * LINE_STRIDE_A];
+
+        uchar8 raw = as_uchar8(qh_raw);
+        uchar8 qh_bytes = (uchar8)(raw.s0, raw.s2, raw.s4, raw.s6,
+                                    raw.s1, raw.s3, raw.s5, raw.s7);
+
+        // Load activations
+        if (slid < 4) {
+            regB.s0123 = read_imagef(src1, (slid * 2 + k * 8));
+            regB.s4567 = read_imagef(src1, (1 + slid * 2 + k * 8));
+        }
+
+        regA.s0 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 0)).x;
+        regA.s1 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x;
+        regA.s2 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x;
+        regA.s3 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x;
+
+#ifdef VECTOR_SUB_GROUP_BROADCAST
+        dequantizeBlockAccum_ns_q5_0_sgbroadcast_8_hi(totalSum, as_ushort8(regA), qh_bytes, regS, regB);
+#else
+        dequantizeBlockAccum_ns_q5_0_sgbroadcast_1_hi(totalSum, as_ushort8(regA), qh_bytes, regS, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAST
+
+        regA.s0 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x;
+        regA.s1 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x;
+        regA.s2 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
+        regA.s3 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
+#ifdef VECTOR_SUB_GROUP_BROADCAST
+        dequantizeBlockAccum_ns_q5_0_sgbroadcast_8_lo(totalSum, as_ushort8(regA), qh_bytes, regS, regB);
+#else
+        dequantizeBlockAccum_ns_q5_0_sgbroadcast_1_lo(totalSum, as_ushort8(regA), qh_bytes, regS, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAST
+    }
+
+    // reduction in local memory, assumes #wave=4
+    local float2 reduceLM[SUBGROUP_SIZE * 3];
+    if (groupId == 1) {
+        reduceLM[SUBGROUP_SIZE * 0 + slid] = totalSum;
+    }
+    if (groupId == 2) {
+        reduceLM[SUBGROUP_SIZE * 1 + slid] = totalSum;
+    }
+    if (groupId == 3) {
+        reduceLM[SUBGROUP_SIZE * 2 + slid] = totalSum;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (groupId == 0) {
+        totalSum += reduceLM[SUBGROUP_SIZE * 0 + slid];
+    }
+    if (groupId == 0) {
+        totalSum += reduceLM[SUBGROUP_SIZE * 1 + slid];
+    }
+    if (groupId == 0) {
+        totalSum += reduceLM[SUBGROUP_SIZE * 2 + slid];
+    }
+
+    // 2 outputs per fiber in wave 0
+    if (groupId == 0) {
+        dst = (global float*)((global char*)dst + offsetd);
+        vstore2(totalSum, 0, &(dst[gid * 2]));
+    }
+
+}

ggml/src/ggml-opencl/kernels/gemv_noshuffle_q5_1_f32.cl

@@ -0,0 +1,294 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+
+#ifdef cl_qcom_reqd_sub_group_size
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
+#endif
+
+#define QK5_1 32
+#define NSUBGROUPS 4
+#define SUBGROUP_SIZE 64
+
+#define dequantizeBlockAccum_ns_q5_1_sgbroadcast_1_hi(total_sums, bits4, bits1, scale, minv, y) \
+    float shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 0); \
+    total_sums.s0 += (((bits4.s0 & 0x000F) | (((bits1.s0      ) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x000F) | (((bits1.s4      ) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 0); \
+    total_sums.s0 += ((((bits4.s0 & 0x00F0) >> 4) | (((bits1.s0 >> 1) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0x00F0) >> 4) | (((bits1.s4 >> 1) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 0); \
+    total_sums.s0 += ((((bits4.s0 & 0x0F00) >> 8) | (((bits1.s0 >> 2) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0x0F00) >> 8) | (((bits1.s4 >> 2) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 0); \
+    total_sums.s0 += ((((bits4.s0 & 0xF000) >> 12) | (((bits1.s0 >> 3) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0xF000) >> 12) | (((bits1.s4 >> 3) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 0); \
+    total_sums.s0 += (((bits4.s2 & 0x000F) | (((bits1.s0 >> 4) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x000F) | (((bits1.s4 >> 4) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 0); \
+    total_sums.s0 += ((((bits4.s2 & 0x00F0) >> 4) | (((bits1.s0 >> 5) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0x00F0) >> 4) | (((bits1.s4 >> 5) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 0); \
+    total_sums.s0 += ((((bits4.s2 & 0x0F00) >> 8) | (((bits1.s0 >> 6) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0x0F00) >> 8) | (((bits1.s4 >> 6) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 0); \
+    total_sums.s0 += ((((bits4.s2 & 0xF000) >> 12) | (((bits1.s0 >> 7) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0xF000) >> 12) | (((bits1.s4 >> 7) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 1); \
+    total_sums.s0 += (((bits4.s4 & 0x000F) | (((bits1.s1      ) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x000F) | (((bits1.s5      ) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 1); \
+    total_sums.s0 += ((((bits4.s4 & 0x00F0) >> 4) | (((bits1.s1 >> 1) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0x00F0) >> 4) | (((bits1.s5 >> 1) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 1); \
+    total_sums.s0 += ((((bits4.s4 & 0x0F00) >> 8) | (((bits1.s1 >> 2) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0x0F00) >> 8) | (((bits1.s5 >> 2) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 1); \
+    total_sums.s0 += ((((bits4.s4 & 0xF000) >> 12) | (((bits1.s1 >> 3) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0xF000) >> 12) | (((bits1.s5 >> 3) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 1); \
+    total_sums.s0 += (((bits4.s6 & 0x000F) | (((bits1.s1 >> 4) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x000F) | (((bits1.s5 >> 4) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 1); \
+    total_sums.s0 += ((((bits4.s6 & 0x00F0) >> 4) | (((bits1.s1 >> 5) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0x00F0) >> 4) | (((bits1.s5 >> 5) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 1); \
+    total_sums.s0 += ((((bits4.s6 & 0x0F00) >> 8) | (((bits1.s1 >> 6) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0x0F00) >> 8) | (((bits1.s5 >> 6) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 1); \
+    total_sums.s0 += ((((bits4.s6 & 0xF000) >> 12) | (((bits1.s1 >> 7) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0xF000) >> 12) | (((bits1.s5 >> 7) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_q5_1_sgbroadcast_1_lo(total_sums, bits4, bits1, scale, minv, y) \
+    shared_y = sub_group_broadcast(y.s0, 2); \
+    total_sums.s0 += (((bits4.s0 & 0x000F) | (((bits1.s2      ) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x000F) | (((bits1.s6      ) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 2); \
+    total_sums.s0 += ((((bits4.s0 & 0x00F0) >> 4) | (((bits1.s2 >> 1) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0x00F0) >> 4) | (((bits1.s6 >> 1) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 2); \
+    total_sums.s0 += ((((bits4.s0 & 0x0F00) >> 8) | (((bits1.s2 >> 2) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0x0F00) >> 8) | (((bits1.s6 >> 2) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 2); \
+    total_sums.s0 += ((((bits4.s0 & 0xF000) >> 12) | (((bits1.s2 >> 3) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s1 & 0xF000) >> 12) | (((bits1.s6 >> 3) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 2); \
+    total_sums.s0 += (((bits4.s2 & 0x000F) | (((bits1.s2 >> 4) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x000F) | (((bits1.s6 >> 4) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 2); \
+    total_sums.s0 += ((((bits4.s2 & 0x00F0) >> 4) | (((bits1.s2 >> 5) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0x00F0) >> 4) | (((bits1.s6 >> 5) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 2); \
+    total_sums.s0 += ((((bits4.s2 & 0x0F00) >> 8) | (((bits1.s2 >> 6) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0x0F00) >> 8) | (((bits1.s6 >> 6) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 2); \
+    total_sums.s0 += ((((bits4.s2 & 0xF000) >> 12) | (((bits1.s2 >> 7) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s3 & 0xF000) >> 12) | (((bits1.s6 >> 7) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 3); \
+    total_sums.s0 += (((bits4.s4 & 0x000F) | (((bits1.s3      ) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x000F) | (((bits1.s7      ) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 3); \
+    total_sums.s0 += ((((bits4.s4 & 0x00F0) >> 4) | (((bits1.s3 >> 1) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0x00F0) >> 4) | (((bits1.s7 >> 1) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 3); \
+    total_sums.s0 += ((((bits4.s4 & 0x0F00) >> 8) | (((bits1.s3 >> 2) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0x0F00) >> 8) | (((bits1.s7 >> 2) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 3); \
+    total_sums.s0 += ((((bits4.s4 & 0xF000) >> 12) | (((bits1.s3 >> 3) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s5 & 0xF000) >> 12) | (((bits1.s7 >> 3) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 3); \
+    total_sums.s0 += (((bits4.s6 & 0x000F) | (((bits1.s3 >> 4) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x000F) | (((bits1.s7 >> 4) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 3); \
+    total_sums.s0 += ((((bits4.s6 & 0x00F0) >> 4) | (((bits1.s3 >> 5) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0x00F0) >> 4) | (((bits1.s7 >> 5) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 3); \
+    total_sums.s0 += ((((bits4.s6 & 0x0F00) >> 8) | (((bits1.s3 >> 6) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0x0F00) >> 8) | (((bits1.s7 >> 6) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 3); \
+    total_sums.s0 += ((((bits4.s6 & 0xF000) >> 12) | (((bits1.s3 >> 7) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y; \
+    total_sums.s1 += ((((bits4.s7 & 0xF000) >> 12) | (((bits1.s7 >> 7) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_q5_1_sgbroadcast_8_hi(total_sums, bits4, bits1, scale, minv, y) \
+    float8 shared_y; \
+    shared_y = sub_group_broadcast(y, 0); \
+    total_sums.s0 += (((bits4.s0 & 0x000F)         | (((bits1.s0     ) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s0; \
+    total_sums.s0 += ((((bits4.s0 & 0x00F0) >> 4)  | (((bits1.s0 >> 1) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s1; \
+    total_sums.s0 += ((((bits4.s0 & 0x0F00) >> 8)  | (((bits1.s0 >> 2) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s2; \
+    total_sums.s0 += ((((bits4.s0 & 0xF000) >> 12) | (((bits1.s0 >> 3) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s3; \
+    total_sums.s0 += (((bits4.s2 & 0x000F)         | (((bits1.s0 >> 4) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s4; \
+    total_sums.s0 += ((((bits4.s2 & 0x00F0) >> 4)  | (((bits1.s0 >> 5) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s5; \
+    total_sums.s0 += ((((bits4.s2 & 0x0F00) >> 8)  | (((bits1.s0 >> 6) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s6; \
+    total_sums.s0 += ((((bits4.s2 & 0xF000) >> 12) | (((bits1.s0 >> 7) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s7; \
+    total_sums.s1 += (((bits4.s1 & 0x000F)         | (((bits1.s4     ) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s0; \
+    total_sums.s1 += ((((bits4.s1 & 0x00F0) >> 4)  | (((bits1.s4 >> 1) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s1; \
+    total_sums.s1 += ((((bits4.s1 & 0x0F00) >> 8)  | (((bits1.s4 >> 2) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s2; \
+    total_sums.s1 += ((((bits4.s1 & 0xF000) >> 12) | (((bits1.s4 >> 3) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s3; \
+    total_sums.s1 += (((bits4.s3 & 0x000F)         | (((bits1.s4 >> 4) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s4; \
+    total_sums.s1 += ((((bits4.s3 & 0x00F0) >> 4)  | (((bits1.s4 >> 5) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s5; \
+    total_sums.s1 += ((((bits4.s3 & 0x0F00) >> 8)  | (((bits1.s4 >> 6) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s6; \
+    total_sums.s1 += ((((bits4.s3 & 0xF000) >> 12) | (((bits1.s4 >> 7) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 1); \
+    total_sums.s0 += (((bits4.s4 & 0x000F)         | (((bits1.s1     ) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s0; \
+    total_sums.s0 += ((((bits4.s4 & 0x00F0) >> 4)  | (((bits1.s1 >> 1) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s1; \
+    total_sums.s0 += ((((bits4.s4 & 0x0F00) >> 8)  | (((bits1.s1 >> 2) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s2; \
+    total_sums.s0 += ((((bits4.s4 & 0xF000) >> 12) | (((bits1.s1 >> 3) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s3; \
+    total_sums.s0 += (((bits4.s6 & 0x000F)         | (((bits1.s1 >> 4) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s4; \
+    total_sums.s0 += ((((bits4.s6 & 0x00F0) >> 4)  | (((bits1.s1 >> 5) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s5; \
+    total_sums.s0 += ((((bits4.s6 & 0x0F00) >> 8)  | (((bits1.s1 >> 6) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s6; \
+    total_sums.s0 += ((((bits4.s6 & 0xF000) >> 12) | (((bits1.s1 >> 7) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s7; \
+    total_sums.s1 += (((bits4.s5 & 0x000F)         | (((bits1.s5     ) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s0; \
+    total_sums.s1 += ((((bits4.s5 & 0x00F0) >> 4)  | (((bits1.s5 >> 1) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s1; \
+    total_sums.s1 += ((((bits4.s5 & 0x0F00) >> 8)  | (((bits1.s5 >> 2) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s2; \
+    total_sums.s1 += ((((bits4.s5 & 0xF000) >> 12) | (((bits1.s5 >> 3) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s3; \
+    total_sums.s1 += (((bits4.s7 & 0x000F)         | (((bits1.s5 >> 4) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s4; \
+    total_sums.s1 += ((((bits4.s7 & 0x00F0) >> 4)  | (((bits1.s5 >> 5) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s5; \
+    total_sums.s1 += ((((bits4.s7 & 0x0F00) >> 8)  | (((bits1.s5 >> 6) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s6; \
+    total_sums.s1 += ((((bits4.s7 & 0xF000) >> 12) | (((bits1.s5 >> 7) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s7; \
+
+
+#define dequantizeBlockAccum_ns_q5_1_sgbroadcast_8_lo(total_sums, bits4, bits1, scale, minv, y) \
+    shared_y = sub_group_broadcast(y, 2); \
+    total_sums.s0 += (((bits4.s0 & 0x000F)         | (((bits1.s2     ) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s0; \
+    total_sums.s0 += ((((bits4.s0 & 0x00F0) >> 4)  | (((bits1.s2 >> 1) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s1; \
+    total_sums.s0 += ((((bits4.s0 & 0x0F00) >> 8)  | (((bits1.s2 >> 2) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s2; \
+    total_sums.s0 += ((((bits4.s0 & 0xF000) >> 12) | (((bits1.s2 >> 3) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s3; \
+    total_sums.s0 += (((bits4.s2 & 0x000F)         | (((bits1.s2 >> 4) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s4; \
+    total_sums.s0 += ((((bits4.s2 & 0x00F0) >> 4)  | (((bits1.s2 >> 5) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s5; \
+    total_sums.s0 += ((((bits4.s2 & 0x0F00) >> 8)  | (((bits1.s2 >> 6) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s6; \
+    total_sums.s0 += ((((bits4.s2 & 0xF000) >> 12) | (((bits1.s2 >> 7) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s7; \
+    total_sums.s1 += (((bits4.s1 & 0x000F)         | (((bits1.s6     ) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s0; \
+    total_sums.s1 += ((((bits4.s1 & 0x00F0) >> 4)  | (((bits1.s6 >> 1) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s1; \
+    total_sums.s1 += ((((bits4.s1 & 0x0F00) >> 8)  | (((bits1.s6 >> 2) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s2; \
+    total_sums.s1 += ((((bits4.s1 & 0xF000) >> 12) | (((bits1.s6 >> 3) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s3; \
+    total_sums.s1 += (((bits4.s3 & 0x000F)         | (((bits1.s6 >> 4) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s4; \
+    total_sums.s1 += ((((bits4.s3 & 0x00F0) >> 4)  | (((bits1.s6 >> 5) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s5; \
+    total_sums.s1 += ((((bits4.s3 & 0x0F00) >> 8)  | (((bits1.s6 >> 6) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s6; \
+    total_sums.s1 += ((((bits4.s3 & 0xF000) >> 12) | (((bits1.s6 >> 7) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 3); \
+    total_sums.s0 += (((bits4.s4 & 0x000F)         | (((bits1.s3     ) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s0; \
+    total_sums.s0 += ((((bits4.s4 & 0x00F0) >> 4)  | (((bits1.s3 >> 1) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s1; \
+    total_sums.s0 += ((((bits4.s4 & 0x0F00) >> 8)  | (((bits1.s3 >> 2) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s2; \
+    total_sums.s0 += ((((bits4.s4 & 0xF000) >> 12) | (((bits1.s3 >> 3) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s3; \
+    total_sums.s0 += (((bits4.s6 & 0x000F)         | (((bits1.s3 >> 4) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s4; \
+    total_sums.s0 += ((((bits4.s6 & 0x00F0) >> 4)  | (((bits1.s3 >> 5) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s5; \
+    total_sums.s0 += ((((bits4.s6 & 0x0F00) >> 8)  | (((bits1.s3 >> 6) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s6; \
+    total_sums.s0 += ((((bits4.s6 & 0xF000) >> 12) | (((bits1.s3 >> 7) & 0x01) << 4)) * scale.s0 + minv.s0) * shared_y.s7; \
+    total_sums.s1 += (((bits4.s5 & 0x000F)         | (((bits1.s7     ) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s0; \
+    total_sums.s1 += ((((bits4.s5 & 0x00F0) >> 4)  | (((bits1.s7 >> 1) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s1; \
+    total_sums.s1 += ((((bits4.s5 & 0x0F00) >> 8)  | (((bits1.s7 >> 2) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s2; \
+    total_sums.s1 += ((((bits4.s5 & 0xF000) >> 12) | (((bits1.s7 >> 3) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s3; \
+    total_sums.s1 += (((bits4.s7 & 0x000F)         | (((bits1.s7 >> 4) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s4; \
+    total_sums.s1 += ((((bits4.s7 & 0x00F0) >> 4)  | (((bits1.s7 >> 5) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s5; \
+    total_sums.s1 += ((((bits4.s7 & 0x0F00) >> 8)  | (((bits1.s7 >> 6) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s6; \
+    total_sums.s1 += ((((bits4.s7 & 0xF000) >> 12) | (((bits1.s7 >> 7) & 0x01) << 4)) * scale.s1 + minv.s1) * shared_y.s7; \
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+__kernel void kernel_gemv_noshuffle_q5_1_f32(
+        __read_only  image1d_buffer_t src0_qs,  // quantized A
+        global ushort * src0_qh,                 // 5th bits
+        global half2  * src0_d,                  // A scales
+        global half2  * src0_m,                  // A mins
+        __read_only  image1d_buffer_t src1,      // B activations
+        global float * dst,
+        ulong offsetd,
+        int ne00,               // K
+        int ne01)               // M
+{
+    uint groupId = get_local_id(1);
+    uint gid     = get_global_id(0);
+    ushort slid  = get_sub_group_local_id();
+
+    uint K = ne00;
+    uint M = ne01;
+
+    uint LINE_STRIDE_A    = M / 2;
+   uint BLOCK_STRIDE_A  = NSUBGROUPS * M;
+
+    __private uint4     regA;
+    __private half2     regS;
+    __private half2     regM;
+    __private float8    regB;
+
+    __private float2 totalSum = (float2)(0.0f);
+
+    for (uint k = groupId; k < (K / QK5_1); k += NSUBGROUPS) {
+        regS = src0_d[gid + k * LINE_STRIDE_A];
+        regM = src0_m[gid + k * LINE_STRIDE_A];
+
+        ushort4 qh_raw;
+        qh_raw.s0 = src0_qh[gid + (4*k + 0) * LINE_STRIDE_A];
+        qh_raw.s1 = src0_qh[gid + (4*k + 1) * LINE_STRIDE_A];
+        qh_raw.s2 = src0_qh[gid + (4*k + 2) * LINE_STRIDE_A];
+        qh_raw.s3 = src0_qh[gid + (4*k + 3) * LINE_STRIDE_A];
+
+        uchar8 raw = as_uchar8(qh_raw);
+        uchar8 qh_bytes = (uchar8)(raw.s0, raw.s2, raw.s4, raw.s6,
+                                    raw.s1, raw.s3, raw.s5, raw.s7);
+
+        // Load activations
+        if (slid < 4) {
+            regB.s0123 = read_imagef(src1, (slid * 2 + k * 8));
+            regB.s4567 = read_imagef(src1, (1 + slid * 2 + k * 8));
+        }
+
+        regA.s0 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 0)).x;
+        regA.s1 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x;
+        regA.s2 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x;
+        regA.s3 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x;
+
+#ifdef VECTOR_SUB_GROUP_BROADCAST
+        dequantizeBlockAccum_ns_q5_1_sgbroadcast_8_hi(totalSum, as_ushort8(regA), qh_bytes, regS, regM, regB);
+#else
+        dequantizeBlockAccum_ns_q5_1_sgbroadcast_1_hi(totalSum, as_ushort8(regA), qh_bytes, regS, regM, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAST
+
+        regA.s0 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x;
+        regA.s1 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x;
+        regA.s2 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
+        regA.s3 = read_imageui(src0_qs, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
+#ifdef VECTOR_SUB_GROUP_BROADCAST
+        dequantizeBlockAccum_ns_q5_1_sgbroadcast_8_lo(totalSum, as_ushort8(regA), qh_bytes, regS, regM, regB);
+#else
+        dequantizeBlockAccum_ns_q5_1_sgbroadcast_1_lo(totalSum, as_ushort8(regA), qh_bytes, regS, regM, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAST
+    }
+
+    // reduction in local memory, assumes #wave=4
+    local float2 reduceLM[SUBGROUP_SIZE * 3];
+    if (groupId == 1) {
+        reduceLM[SUBGROUP_SIZE * 0 + slid] = totalSum;
+    }
+    if (groupId == 2) {
+        reduceLM[SUBGROUP_SIZE * 1 + slid] = totalSum;
+    }
+    if (groupId == 3) {
+        reduceLM[SUBGROUP_SIZE * 2 + slid] = totalSum;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (groupId == 0) {
+        totalSum += reduceLM[SUBGROUP_SIZE * 0 + slid];
+    }
+    if (groupId == 0) {
+        totalSum += reduceLM[SUBGROUP_SIZE * 1 + slid];
+    }
+    if (groupId == 0) {
+        totalSum += reduceLM[SUBGROUP_SIZE * 2 + slid];
+    }
+
+    // 2 outputs per fiber in wave 0
+    if (groupId == 0) {
+        dst = (global float*)((global char*)dst + offsetd);
+        vstore2(totalSum, 0, &(dst[gid * 2]));
+    }
+
+}

ggml/src/ggml-sycl/gated_delta_net.cpp

@@ -44,9 +44,9 @@ void gated_delta_net_sycl(const float *     q,
     float *       attn_data        = dst;
     float *       state            = dst + attn_score_elems;
 
-    // input state layout (D, K, n_seqs) — seq stride is K * D = K * H * S_v * S_v.
+    // input state holds s0 only [S_v, S_v, H, n_seqs] — seq stride is D = H * S_v * S_v.
     // output state layout (per-slot D * n_seqs) — same per-(seq,head) offset as before.
-    const int64_t state_in_offset      = sequence * K * H * S_v * S_v + h_idx * S_v * S_v;
+    const int64_t state_in_offset      = sequence * H * S_v * S_v + h_idx * S_v * S_v;
     const int64_t state_out_offset     = (sequence * H + h_idx) * S_v * S_v;
     const int64_t state_size_per_token = S_v * S_v * H * n_seqs; // per-slot stride in output
     state += state_out_offset;
@@ -63,9 +63,8 @@ void gated_delta_net_sycl(const float *     q,
         s_shard[r]  = curr_state[i];
     }
 
-    // slot mapping: target_slot = t - shift. When n_tokens < K only the last n_tokens slots
-    // are written; earlier slots are left untouched (caller-owned).
-    const int shift = (int) n_tokens - K;
+    // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+    // When n_tokens < K only slots 0..n_tokens-1 are written; older slots are caller-owned.
 
     for (int t = 0; t < n_tokens; t++) {
         const float * q_t = q + iq3 * sq3 + t * sq2 + iq1 * sq1;
@@ -144,7 +143,7 @@ void gated_delta_net_sycl(const float *     q,
 
     // Write state back to global memory
         if constexpr (keep_rs_t) {
-            const int target_slot = t - shift;
+            const int target_slot = (int) n_tokens - 1 - t;
             if (target_slot >= 0 && target_slot < K) {
                 float * curr_state = (dst + attn_score_elems) + target_slot * state_size_per_token + state_out_offset;
 #pragma unroll
@@ -315,8 +314,8 @@ void ggml_sycl_op_gated_delta_net(ggml_backend_sycl_context & ctx, ggml_tensor *
 
     dpct::queue_ptr stream = ctx.stream();
 
-    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
-    const int K = (int) src_state->ne[1];
+    // K (snapshot slot count) is an op param; state holds s0 only [S_v, S_v, H, n_seqs].
+    const int K = ggml_get_op_params_i32(dst, 0);
     const bool keep_rs = K > 1;
 
     if (kda) {

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

@@ -833,6 +833,7 @@ struct vk_device_struct {
 
     // [src/dst 0=fp32,1=fp16]
     vk_pipeline pipeline_exp[2];
+    vk_pipeline pipeline_expm1[2];
     vk_pipeline pipeline_elu[2];
     vk_pipeline pipeline_gelu[2];
     vk_pipeline pipeline_gelu_erf[2];
@@ -1202,30 +1203,35 @@ struct vk_op_glu_push_constants {
     uint32_t mode;  // 0: default, 1: swapped, 2: split
     float alpha; // for swiglu_oai
     float limit;
+    uint32_t nb00;
     uint32_t nb01;
     uint32_t nb02;
     uint32_t nb03;
-    uint32_t ne01;
-    uint32_t ne02;
+    uint32_t nb10;
     uint32_t nb11;
     uint32_t nb12;
     uint32_t nb13;
-    uint32_t ne11;
-    uint32_t ne12;
+    uint32_t nb20;
+    uint32_t nb21;
+    uint32_t nb22;
+    uint32_t nb23;
+    uint32_t ne21;
+    uint32_t ne22;
+    uint32_t misalign_offsets;
+    uint32_t ne2_012mp; uint32_t ne2_012L;
+    uint32_t ne2_01mp;  uint32_t ne2_01L;
+    uint32_t ne2_0mp;   uint32_t ne2_0L;
 };
+static_assert(sizeof(vk_op_glu_push_constants) <= 128, "sizeof(vk_op_glu_push_constants) must be <= 128");
 
 struct vk_op_unary_push_constants {
     uint32_t ne;
     uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
     uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
     uint32_t misalign_offsets;
-    float param1; float param2;
-    uint32_t ne0_012mp; uint32_t ne0_012L;
-    uint32_t ne0_01mp;  uint32_t ne0_01L;
-    uint32_t ne0_0mp;   uint32_t ne0_0L;
-    uint32_t ne1_012mp; uint32_t ne1_012L;
-    uint32_t ne1_01mp;  uint32_t ne1_01L;
-    uint32_t ne1_0mp;   uint32_t ne1_0L;
+    float param1; float param2; float param3; float param4;
+    uint32_t ne0_012mp; uint32_t ne0_01mp; uint32_t ne0_0mp; uint32_t ne0_Ls;
+    uint32_t ne1_012mp; uint32_t ne1_01mp; uint32_t ne1_0mp; uint32_t ne1_Ls;
 };
 static_assert(sizeof(vk_op_unary_push_constants) <= 128, "sizeof(vk_op_unary_push_constants) must be <= 128");
 
@@ -1330,6 +1336,10 @@ static void init_fastdiv_values(uint32_t d, uint32_t &mp, uint32_t &L)
     mp = (uint32_t)((uint64_t{1} << 32) * ((uint64_t{1} << L) - d) / d + 1);
 }
 
+static uint32_t pack_fastdiv_L(uint32_t L0, uint32_t L1, uint32_t L2) {
+    return L0 | (L1 << 8) | (L2 << 16);
+}
+
 template <typename T> void init_pushconst_fastdiv(T &p) {
     GGML_UNUSED(p);
     static_assert(!std::is_const<T>::value, "unexpected type");
@@ -1337,12 +1347,29 @@ template <typename T> void init_pushconst_fastdiv(T &p) {
 
 template <> void init_pushconst_fastdiv(vk_op_unary_push_constants &p) {
     // Compute magic values to divide by these six numbers.
-    init_fastdiv_values(p.ne02*p.ne01*p.ne00,  p.ne0_012mp,    p.ne0_012L);
-    init_fastdiv_values(p.ne01*p.ne00,         p.ne0_01mp,     p.ne0_01L);
-    init_fastdiv_values(p.ne00,                p.ne0_0mp,      p.ne0_0L);
-    init_fastdiv_values(p.ne12*p.ne11*p.ne10,  p.ne1_012mp,    p.ne1_012L);
-    init_fastdiv_values(p.ne11*p.ne10,         p.ne1_01mp,     p.ne1_01L);
-    init_fastdiv_values(p.ne10,                p.ne1_0mp,      p.ne1_0L);
+    uint32_t ne0_012L;
+    uint32_t ne0_01L;
+    uint32_t ne0_0L;
+    uint32_t ne1_012L;
+    uint32_t ne1_01L;
+    uint32_t ne1_0L;
+
+    init_fastdiv_values(p.ne02*p.ne01*p.ne00,  p.ne0_012mp,    ne0_012L);
+    init_fastdiv_values(p.ne01*p.ne00,         p.ne0_01mp,     ne0_01L);
+    init_fastdiv_values(p.ne00,                p.ne0_0mp,      ne0_0L);
+    init_fastdiv_values(p.ne12*p.ne11*p.ne10,  p.ne1_012mp,    ne1_012L);
+    init_fastdiv_values(p.ne11*p.ne10,         p.ne1_01mp,     ne1_01L);
+    init_fastdiv_values(p.ne10,                p.ne1_0mp,      ne1_0L);
+
+    p.ne0_Ls = pack_fastdiv_L(ne0_012L, ne0_01L, ne0_0L);
+    p.ne1_Ls = pack_fastdiv_L(ne1_012L, ne1_01L, ne1_0L);
+}
+
+template <> void init_pushconst_fastdiv(vk_op_glu_push_constants &p) {
+    // GLU linearizes over dst, then uses dst coordinates for src0/src1.
+    init_fastdiv_values(p.ne22*p.ne21*p.ne20,  p.ne2_012mp,    p.ne2_012L);
+    init_fastdiv_values(p.ne21*p.ne20,         p.ne2_01mp,     p.ne2_01L);
+    init_fastdiv_values(p.ne20,                p.ne2_0mp,      p.ne2_0L);
 }
 
 struct vk_op_binary_push_constants {
@@ -3394,7 +3421,9 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
     switch (src0_type) {
     case GGML_TYPE_IQ1_S:
     case GGML_TYPE_IQ1_M:
-        lut_size = 2*2048 + 4*2048;
+        // Regular matmul uses the compact uint16_t IQ1 grid; the expanded
+        // uint32_t grid is only enabled for the q8_1/int-dot vector path.
+        lut_size = 2*2048;
         break;
     case GGML_TYPE_IQ2_XXS:
         lut_size = 8*256;
@@ -5004,8 +5033,8 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     ggml_vk_create_pipeline(device, device->pipeline_repeat_i16, "repeat_i16", repeat_i16_len, repeat_i16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
 #define CREATE_UNARY(name)  \
-    ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);  \
-    ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);  \
+    ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
     CREATE_UNARY(elu)
     CREATE_UNARY(gelu)
@@ -5028,6 +5057,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     CREATE_UNARY(trunc)
     CREATE_UNARY(sgn)
     CREATE_UNARY(exp)
+    CREATE_UNARY(expm1)
 #undef CREATE_UNARY
 
     ggml_vk_create_pipeline(device, device->pipeline_add1_f16_f16, "add1_f16_f16", add1_f16_f16_len, add1_f16_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
@@ -6200,6 +6230,19 @@ static vk_device ggml_vk_get_device(size_t idx) {
                 break;
             }
 
+#if VK_HEADER_VERSION >= 287
+            // Honeykrisp driver for Asahi Linux doesn't report VK_VENDOR_ID_APPLE.
+            // Check for Honeykrisp driver and force same configuration as the VK_VENDOR_ID_APPLE case.
+            if (device->driver_id == vk::DriverId::eMesaHoneykrisp) {
+                device->mul_mat_l[i] = false;
+                device->mul_mat_m[i] = true;
+                device->mul_mat_s[i] = false;
+                device->mul_mat_id_l[i] = false;
+                device->mul_mat_id_m[i] = true;
+                device->mul_mat_id_s[i] = false;
+            }
+#endif
+
             device->mul_mat_l_int[i]    = device->mul_mat_l[i];
             device->mul_mat_m_int[i]    = device->mul_mat_m[i];
             device->mul_mat_s_int[i]    = device->mul_mat_s[i];
@@ -7602,8 +7645,12 @@ static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void *
     if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
         GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
 
-        for (size_t i = 0; i < height; i++) {
-            memcpy((uint8_t *)dst->ptr + offset + i * dpitch, (const uint8_t *) src + i * spitch, width);
+        if (width == spitch && width == dpitch) {
+            memcpy((uint8_t *)dst->ptr + offset, src, width * height);
+        } else {
+            for (size_t i = 0; i < height; i++) {
+                memcpy((uint8_t *)dst->ptr + offset + i * dpitch, (const uint8_t *) src + i * spitch, width);
+            }
         }
     } else {
         std::lock_guard<std::recursive_mutex> guard(dst->device->mutex);
@@ -7722,8 +7769,29 @@ static void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, si
     if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible && src->device->uma) {
         GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
 
-        for (size_t i = 0; i < height; i++) {
-            memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) src->ptr + offset + i * spitch, width);
+        std::lock_guard<std::recursive_mutex> guard(src->device->mutex);
+        vk_context subctx = ggml_vk_create_temporary_context(src->device->compute_queue.cmd_pool);
+        ggml_vk_ctx_begin(src->device, subctx);
+        subctx->s->buffer->buf.pipelineBarrier(
+            vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer,
+            vk::PipelineStageFlagBits::eHost,
+            {},
+            { { vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferWrite,
+                vk::AccessFlagBits::eHostRead } },
+            {}, {});
+        ggml_vk_ctx_end(subctx);
+        ggml_vk_submit(subctx, src->device->fence);
+        VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX),
+                 "vk_buffer_read_2d uma waitForFences");
+        src->device->device.resetFences({ src->device->fence });
+        ggml_vk_queue_command_pools_cleanup(src->device);
+
+        if (width == spitch && width == dpitch) {
+            memcpy(dst, (const uint8_t *) src->ptr + offset, width * height);
+        } else {
+            for (size_t i = 0; i < height; i++) {
+                memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) src->ptr + offset + i * spitch, width);
+            }
         }
     } else {
         std::lock_guard<std::recursive_mutex> guard(src->device->mutex);
@@ -8152,7 +8220,6 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const
 static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline pipeline, const ggml_tensor * tensor, const vk_subbuffer & in, const vk_subbuffer & out) {
     VK_LOG_DEBUG("ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), ";
     std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")");
-    const int tensor_type_size = ggml_type_size(tensor->type);
 
     const uint32_t ne = ggml_nelements(tensor);
     std::array<uint32_t, 3> elements;
@@ -8165,14 +8232,11 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context&
         elements = { ne, 1, 1 };
     }
 
-    vk_op_unary_push_constants pc = {
-        (uint32_t)ne,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3],                       1                   , (uint32_t)tensor->ne[0]                   , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]),
-        0,
-        0.0f, 0.0f,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    };
+    vk_op_unary_push_constants pc = vk_op_unary_push_constants_init(tensor, tensor, ne);
+    pc.nb10 = 1;
+    pc.nb11 = (uint32_t)tensor->ne[0];
+    pc.nb12 = (uint32_t)(tensor->ne[0] * tensor->ne[1]);
+    pc.nb13 = (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]);
     init_pushconst_fastdiv(pc);
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, pc, elements);
     ggml_vk_sync_buffers(ctx, subctx);
@@ -8186,7 +8250,6 @@ static void ggml_vk_cpy_to_strided(
         uint32_t nb10, uint32_t nb11, uint32_t nb12, uint32_t nb13) {
     VK_LOG_DEBUG("ggml_vk_cpy_to_strided((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), ";
     std::cerr << "dst_nb=(" << nb10 << ", " << nb11 << ", " << nb12 << ", " << nb13 << "), buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")");
-    const int tensor_type_size = ggml_type_size(tensor->type);
 
     const uint32_t ne = ggml_nelements(tensor);
     std::array<uint32_t, 3> elements;
@@ -8199,14 +8262,11 @@ static void ggml_vk_cpy_to_strided(
         elements = { ne, 1, 1 };
     }
 
-    vk_op_unary_push_constants pc = {
-        (uint32_t)ne,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], nb10, nb11, nb12, nb13,
-        0,
-        0.0f, 0.0f,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    };
+    vk_op_unary_push_constants pc = vk_op_unary_push_constants_init(tensor, tensor, ne);
+    pc.nb10 = nb10;
+    pc.nb11 = nb11;
+    pc.nb12 = nb12;
+    pc.nb13 = nb13;
     init_pushconst_fastdiv(pc);
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, pc, elements);
     ggml_vk_sync_buffers(ctx, subctx);
@@ -10411,6 +10471,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         switch (ggml_get_unary_op(dst)) {
             case GGML_UNARY_OP_EXP:
                 return ctx->device->pipeline_exp[dst->type == GGML_TYPE_F16];
+            case GGML_UNARY_OP_EXPM1:
+                return ctx->device->pipeline_expm1[dst->type == GGML_TYPE_F16];
             case GGML_UNARY_OP_ELU:
                 return ctx->device->pipeline_elu[dst->type == GGML_TYPE_F16];
             case GGML_UNARY_OP_SILU:
@@ -10809,6 +10871,21 @@ template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk
     GGML_UNUSED(src3);
 }
 
+template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_glu_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
+    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
+    const uint32_t b_offset = src1 ? get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type) : a_offset;
+    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
+
+    GGML_ASSERT(a_offset < (1u << 8));
+    GGML_ASSERT(b_offset < (1u << 8));
+    GGML_ASSERT(d_offset < (1u << 8));
+
+    p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset;
+
+    GGML_UNUSED(src2);
+    GGML_UNUSED(src3);
+}
+
 template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_sum_rows_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
     const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
     const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
@@ -11526,7 +11603,6 @@ static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& s
     const ggml_tensor * src_q     = dst->src[0];
     const ggml_tensor * src_v     = dst->src[2];
     const ggml_tensor * src_beta  = dst->src[4];
-    const ggml_tensor * src_state = dst->src[5];
 
     GGML_ASSERT(dst->buffer != nullptr);
 
@@ -11535,8 +11611,8 @@ static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& s
     const uint32_t n_tokens = (uint32_t)src_v->ne[2];
     const uint32_t n_seqs   = (uint32_t)src_v->ne[3];
 
-    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
-    const uint32_t K = (uint32_t)src_state->ne[1];
+    // K (snapshot slot count) is an op param; state holds s0 only [S_v, S_v, H, n_seqs].
+    const uint32_t K = (uint32_t)ggml_get_op_params_i32(dst, 0);
 
     const uint32_t s_off = S_v * H * n_tokens * n_seqs;
 
@@ -12159,17 +12235,17 @@ static void ggml_vk_l2_norm(ggml_backend_vk_context * ctx, vk_context& subctx, c
 }
 
 static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f, 0.0f, 0.0f });
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY, vk_op_unary_push_constants_init(src0, dst));
 }
 
 static void ggml_vk_xielu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     float * op_params = (float *)dst->op_params;
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY,
-        {
-            (uint32_t)ggml_nelements(src0), 0,
-            op_params[1], op_params[2], op_params[3], op_params[4]
-        }
-    );
+    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
+    p.param1 = op_params[1];
+    p.param2 = op_params[2];
+    p.param3 = op_params[3];
+    p.param4 = op_params[4];
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY, std::move(p));
 }
 
 static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -12189,6 +12265,9 @@ static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const
     }
 
     const uint32_t mode = split ? 2 : (swapped ? 1 : 0);
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = split ? ggml_type_size(src1->type) : src0_type_size;
+    const uint32_t dst_type_size  = ggml_type_size(dst->type);
 
     ggml_vk_op_f32<vk_op_glu_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_GLU,
         {
@@ -12198,16 +12277,22 @@ static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const
             mode,
             alpha,
             limit,
-            (uint32_t)(src0->nb[1] / src0->nb[0]),
-            (uint32_t)(src0->nb[2] / src0->nb[0]),
-            (uint32_t)(src0->nb[3] / src0->nb[0]),
-            (uint32_t)src0->ne[1],
-            (uint32_t)src0->ne[2],
-            (uint32_t)(dst->nb[1] / dst->nb[0]),
-            (uint32_t)(dst->nb[2] / dst->nb[0]),
-            (uint32_t)(dst->nb[3] / dst->nb[0]),
+            (uint32_t)(src0->nb[0] / src0_type_size),
+            (uint32_t)(src0->nb[1] / src0_type_size),
+            (uint32_t)(src0->nb[2] / src0_type_size),
+            (uint32_t)(src0->nb[3] / src0_type_size),
+            (uint32_t)((split ? src1->nb[0] : src0->nb[0]) / src1_type_size),
+            (uint32_t)((split ? src1->nb[1] : src0->nb[1]) / src1_type_size),
+            (uint32_t)((split ? src1->nb[2] : src0->nb[2]) / src1_type_size),
+            (uint32_t)((split ? src1->nb[3] : src0->nb[3]) / src1_type_size),
+            (uint32_t)(dst->nb[0] / dst_type_size),
+            (uint32_t)(dst->nb[1] / dst_type_size),
+            (uint32_t)(dst->nb[2] / dst_type_size),
+            (uint32_t)(dst->nb[3] / dst_type_size),
             (uint32_t)dst->ne[1],
-            (uint32_t)dst->ne[2]
+            (uint32_t)dst->ne[2],
+            0,
+            0, 0, 0, 0, 0, 0,
         });
 }
 
@@ -14210,6 +14295,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         switch (ggml_get_unary_op(node)) {
         case GGML_UNARY_OP_ELU:
         case GGML_UNARY_OP_EXP:
+        case GGML_UNARY_OP_EXPM1:
         case GGML_UNARY_OP_SILU:
         case GGML_UNARY_OP_GELU:
         case GGML_UNARY_OP_GELU_ERF:
@@ -16599,6 +16685,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_EXP:
+                case GGML_UNARY_OP_EXPM1:
                 case GGML_UNARY_OP_ELU:
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_GELU_ERF:
@@ -16619,8 +16706,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 case GGML_UNARY_OP_FLOOR:
                 case GGML_UNARY_OP_TRUNC:
                 case GGML_UNARY_OP_SGN:
-                    return ggml_is_contiguous(op->src[0]) &&
-                           (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
+                    return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                            (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
                            (op->src[0]->type == op->type);
                 default:
@@ -16636,7 +16722,8 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 case GGML_GLU_OP_GEGLU_QUICK:
                     return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                            (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
-                           (op->src[0]->type == op->type);
+                           (op->src[0]->type == op->type) &&
+                           (!op->src[1] || op->src[1]->type == op->src[0]->type);
                 default:
                     return false;
             }
@@ -17766,6 +17853,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
             case GGML_UNARY_OP_EXP:
                 tensor_clone = ggml_exp(ggml_ctx, src_clone[0]);
                 break;
+            case GGML_UNARY_OP_EXPM1:
+                tensor_clone = ggml_expm1(ggml_ctx, src_clone[0]);
+                break;
             case GGML_UNARY_OP_ELU:
                 tensor_clone = ggml_elu(ggml_ctx, src_clone[0]);
                 break;
@@ -17952,7 +18042,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
             src_clone[4], src_clone[5], src_clone[6]);
         } else if (tensor->op == GGML_OP_GATED_DELTA_NET) {
             tensor_clone = ggml_gated_delta_net(ggml_ctx, src_clone[0], src_clone[1],
-            src_clone[2], src_clone[3], src_clone[4], src_clone[5]);
+            src_clone[2], src_clone[3], src_clone[4], src_clone[5],
+            ggml_get_op_params_i32(tensor, 0));
         } else if (tensor->op == GGML_OP_OPT_STEP_ADAMW) {
             src_clone[0]->flags = tensor->src[0]->flags;
             tensor_clone = ggml_opt_step_adamw(ggml_ctx, src_clone[0], src_clone[1],

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

@@ -1,21 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    data_d[i] = D_TYPE(abs(float(data_a[i])));
-}

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(ceil(x));
-}

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

@@ -12,11 +12,11 @@ void main() {
         return;
     }
 
-    const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0));
     const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
-    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1));
     const uint i12_offset = i12*p.ne11*p.ne10;
-    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2));
     const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
 
     if (i10 == i11) {

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

@@ -1,27 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    float x = float(data_a[i]);
-
-    if (x < 0.0f) {
-        x = exp(x) - 1;
-    }
-
-    data_d[i] = D_TYPE(x);
-}

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

@@ -1,20 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-    data_d[i] = D_TYPE(exp(float(data_a[i])));
-}

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(floor(x));
-}

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

@@ -102,8 +102,8 @@ void main() {
     const uint iq3 = seq_id / rq3;
 
     const uint state_size = S_V * S_V;
-    // input state layout (D, K, n_seqs): per-seq stride is K*H*D; we read slot 0.
-    const uint state_in_base       = (seq_id * K * H + head_id) * state_size;
+    // input state holds s0 only [S_v, S_v, H, n_seqs]: per-seq stride is H*D.
+    const uint state_in_base       = (seq_id * H + head_id) * state_size;
     // output state layout per slot: same per-(seq,head) offset as the single-slot case.
     const uint state_out_base      = (seq_id * H + head_id) * state_size;
     const uint state_size_per_snap = state_size * H * n_seqs;
@@ -113,9 +113,8 @@ void main() {
         s_shard[r] = FLOAT_TYPE(data_state[state_in_base + col * S_V + r * LANES_PER_COLUMN + lane]);
     }
 
-    // snapshot slot mapping: target_slot = t - shift. When n_tokens < K, only the last
-    // n_tokens slots are written; earlier slots are left untouched (caller-owned).
-    const int shift = int(n_tokens) - int(K);
+    // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+    // When n_tokens < K, only slots 0..n_tokens-1 are written; older slots are caller-owned.
 
     uint attn_off = (seq_id * n_tokens * H + head_id) * S_V;
 
@@ -172,7 +171,7 @@ void main() {
         attn_off += S_V * H;
 
         if (K > 1u) {
-            const int target_slot = int(t) - shift;
+            const int target_slot = int(n_tokens) - 1 - int(t);
             if (target_slot >= 0 && target_slot < int(K)) {
                 const uint slot_base = s_off + uint(target_slot) * state_size_per_snap + state_out_base;
                 [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) {

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

@@ -1,25 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const float GELU_COEF_A    = 0.044715f;
-    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float xi = float(data_a[i]);
-    const float val = SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi);
-    data_d[i] = D_TYPE(0.5f*xi*(2.0f - 2.0f / (exp(2 * val) + 1)));
-}

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

@@ -1,39 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    // based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation
-    // ref: https://www.johndcook.com/blog/python_erf/
-    const float p_erf  = 0.3275911f;
-    const float a1_erf = 0.254829592f;
-    const float a2_erf = -0.284496736f;
-    const float a3_erf = 1.421413741f;
-    const float a4_erf = -1.453152027f;
-    const float a5_erf = 1.061405429f;
-
-    const float SQRT_2_INV = 0.70710678118654752440084436210484f;
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float a = float(data_a[i]);
-    const float a_div_sqr2 = a * SQRT_2_INV;
-    const float sign_x = sign(a_div_sqr2);
-    const float x = abs(a_div_sqr2);
-    const float t = 1.0f / (1.0f + p_erf * x);
-    const float y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x);
-    const float erf_approx = sign_x * y;
-
-    data_d[i] = D_TYPE(0.5f * a * (1.0f + erf_approx));
-}

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

@@ -1,23 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const float GELU_QUICK_COEF = -1.702f;
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(x * (1.0f / (1.0f + exp(GELU_QUICK_COEF * x))));
-}

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

@@ -7,14 +7,12 @@ layout (push_constant) uniform parameter
     uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
     uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
     uint misalign_offsets;
-    float param1; float param2;
+    float param1; float param2; float param3; float param4;
 
-    uint ne0_012mp; uint ne0_012L;
-    uint ne0_01mp;  uint ne0_01L;
-    uint ne0_0mp;   uint ne0_0L;
-    uint ne1_012mp; uint ne1_012L;
-    uint ne1_01mp;  uint ne1_01L;
-    uint ne1_0mp;   uint ne1_0L;
+    // The three L values are packed as bytes to keep this layout under the 128B
+    // push constant limit while still leaving room for four float parameters.
+    uint ne0_012mp; uint ne0_01mp;  uint ne0_0mp;  uint ne0_Ls;
+    uint ne1_012mp; uint ne1_01mp;  uint ne1_0mp;  uint ne1_Ls;
 } p;
 
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
@@ -42,42 +40,46 @@ uint fastdiv(uint n, uint mp, uint L) {
     return (msbs + n) >> L;
 }
 
+uint fastdiv_L(uint packed, uint slot) {
+    return (packed >> (slot * 8)) & 0x3Fu;
+}
+
 uint src0_idx(uint idx) {
-    const uint i03 = fastdiv(idx, p.ne0_012mp, p.ne0_012L);
+    const uint i03 = fastdiv(idx, p.ne0_012mp, fastdiv_L(p.ne0_Ls, 0));
     const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
-    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, p.ne0_01L);
+    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, fastdiv_L(p.ne0_Ls, 1));
     const uint i02_offset = i02*p.ne01*p.ne00;
-    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, p.ne0_0L);
+    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, fastdiv_L(p.ne0_Ls, 2));
     const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
     return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
 }
 
 uint dst_idx(uint idx) {
-    const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0));
     const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
-    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1));
     const uint i12_offset = i12*p.ne11*p.ne10;
-    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2));
     const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
     return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10;
 }
 
 uint src0_idx_quant(uint idx, uint qk) {
-    const uint i03 = fastdiv(idx, p.ne0_012mp, p.ne0_012L);
+    const uint i03 = fastdiv(idx, p.ne0_012mp, fastdiv_L(p.ne0_Ls, 0));
     const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
-    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, p.ne0_01L);
+    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, fastdiv_L(p.ne0_Ls, 1));
     const uint i02_offset = i02*p.ne01*p.ne00;
-    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, p.ne0_0L);
+    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, fastdiv_L(p.ne0_Ls, 2));
     const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
     return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + (i00/qk)*p.nb00;
 }
 
 uint dst_idx_quant(uint idx, uint qk) {
-    const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0));
     const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
-    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1));
     const uint i12_offset = i12*p.ne11*p.ne10;
-    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2));
     const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
     return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + (i10/qk)*p.nb10;
 }

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

@@ -15,14 +15,33 @@ layout (push_constant) uniform parameter
     uint mode;
     float alpha;
     float limit;
+    uint nb00;
     uint nb01;
     uint nb02;
     uint nb03;
-    uint ne01;
-    uint ne02;
+    uint nb10;
     uint nb11;
     uint nb12;
     uint nb13;
-    uint ne11;
-    uint ne12;
+    uint nb20;
+    uint nb21;
+    uint nb22;
+    uint nb23;
+    uint ne21;
+    uint ne22;
+    uint misalign_offsets;
+    uint ne2_012mp; uint ne2_012L;
+    uint ne2_01mp;  uint ne2_01L;
+    uint ne2_0mp;   uint ne2_0L;
 } p;
+
+uint get_aoffset() { return p.misalign_offsets >> 16; }
+uint get_boffset() { return (p.misalign_offsets >> 8) & 0xFF; }
+uint get_doffset() { return p.misalign_offsets & 0xFF; }
+
+// see init_fastdiv_values in ggml-vulkan.cpp
+uint fastdiv(uint n, uint mp, uint L) {
+    uint msbs, lsbs;
+    umulExtended(n, mp, msbs, lsbs);
+    return (msbs + n) >> L;
+}

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

@@ -5,35 +5,31 @@ void main() {
         return;
     }
 
-    const uint row = i / p.ne20;
-    const uint col = i - row * p.ne20;
+    const uint i23 = fastdiv(i, p.ne2_012mp, p.ne2_012L);
+    const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20;
+    const uint i22 = fastdiv(i - i23_offset, p.ne2_01mp, p.ne2_01L);
+    const uint i22_offset = i22*p.ne21*p.ne20;
+    const uint i21 = fastdiv(i - i23_offset - i22_offset, p.ne2_0mp, p.ne2_0L);
+    const uint i20 = i - i23_offset - i22_offset - i21*p.ne20;
 
-    const uint i3 = row / (p.ne01 * p.ne02);
-    const uint i2 = (row % (p.ne01 * p.ne02)) / p.ne01;
-    const uint i1 = row % p.ne01;
-    const uint src_idx = i3 * p.nb03 + i2 * p.nb02 + i1 * p.nb01 + col;
-
-    const uint dst_i3 = row / (p.ne11 * p.ne12);
-    const uint dst_i2 = (row % (p.ne11 * p.ne12)) / p.ne11;
-    const uint dst_i1 = row % p.ne11;
-    const uint dst_idx = dst_i3 * p.nb13 + dst_i2 * p.nb12 + dst_i1 * p.nb11 + col;
+    const uint src_idx_a = get_aoffset() + i23 * p.nb03 + i22 * p.nb02 + i21 * p.nb01 + i20 * p.nb00;
+    const uint src_idx_b = get_boffset() + i23 * p.nb13 + i22 * p.nb12 + i21 * p.nb11 + i20 * p.nb10;
+    const uint dst_idx = get_doffset() + i23 * p.nb23 + i22 * p.nb22 + i21 * p.nb21 + i20 * p.nb20;
 
     if (p.mode == 0) {
         // Default
-        const uint offset = p.ne00 / 2;
-        const uint idx = src_idx;
+        const uint offset = (p.ne00 / 2) * p.nb00;
+        const uint idx = src_idx_a;
 
         data_d[dst_idx] = D_TYPE(op(float(data_a[idx]), float(data_a[idx + offset])));
     } else if (p.mode == 1) {
         // Swapped
-        const uint offset = p.ne00 / 2;
-        const uint idx = src_idx;
+        const uint offset = (p.ne00 / 2) * p.nb00;
+        const uint idx = src_idx_a;
 
         data_d[dst_idx] = D_TYPE(op(float(data_a[idx + offset]), float(data_a[idx])));
     } else {
         // Split
-        const uint idx = src_idx;
-
-        data_d[dst_idx] = D_TYPE(op(float(data_a[idx]), float(data_b[idx])));
+        data_d[dst_idx] = D_TYPE(op(float(data_a[src_idx_a]), float(data_b[src_idx_b])));
     }
 }

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(min(1.0f, max(0.0f, (x + 3.0f) / 6.0f)));
-}

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(x * min(1.0f, max(0.0f, (x + 3.0f) / 6.0f)));
-}

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

@@ -4,6 +4,7 @@
 #extension GL_EXT_integer_dot_product : require
 
 #define MMQ
+#define NEEDS_IQ1S_GRID_GPU
 #define B_TYPE block_q8_1_x4
 
 #include "mul_mat_vec_base.glsl"

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

@@ -1,20 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-    data_d[i] = D_TYPE(-float(data_a[i]));
-}

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

@@ -1,21 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    data_d[i] = D_TYPE(max(float(data_a[i]), 0));
-}

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

@@ -13,11 +13,11 @@ void main() {
     }
 
     // Destination multi-index (inlined dst_idx)
-    const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0));
     const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
-    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1));
     const uint i12_offset = i12*p.ne11*p.ne10;
-    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2));
     const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
     const uint d_idx = i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10;
 

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

@@ -20,11 +20,11 @@ void main() {
         return;
     }
 
-    const uint i3 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i3 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0));
     const uint i3_offset = i3 * p.ne12*p.ne11*p.ne10;
-    const uint i2 = fastdiv(idx - i3_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i2 = fastdiv(idx - i3_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1));
     const uint i2_offset = i2*p.ne11*p.ne10;
-    const uint i1 = fastdiv(idx - i3_offset - i2_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i1 = fastdiv(idx - i3_offset - i2_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2));
     const uint i0 = idx - i3_offset - i2_offset - i1*p.ne10;
 
     const uint p1 = floatBitsToUint(p.param1);

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

@@ -1,29 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    float result;
-    // Round halfway cases away from zero as roundf does.
-    if (x >= 0.0) {
-        result = floor(x + 0.5);
-    } else {
-        result = ceil(x - 0.5);
-    }
-    data_d[i] = D_TYPE(result);
-}

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

@@ -1,21 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    data_d[i] = D_TYPE(sign(float(data_a[i])));
-}

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

@@ -1,20 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-    data_d[i] = D_TYPE(1. / (1 + exp(-1. * float(data_a[i]))));
-}

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float xi = float(data_a[i]);
-    data_d[i] = D_TYPE(xi / (1.0f + exp(-xi)));
-}

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

@@ -1,23 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    const float result = (x > 20.0f) ? x : log(1.0f + exp(x));
-    data_d[i] = D_TYPE(result);
-}

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(x >= 0.0f ? 1.0f : 0.0f);
-}

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

@@ -1,20 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-    data_d[i] = D_TYPE(1. - 2. / (exp(2.*float(data_a[i])) + 1.));
-}

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

@@ -17,11 +17,11 @@ void main() {
         return;
     }
 
-    const uint i03 = fastdiv(idx, p.ne0_012mp, p.ne0_012L);
+    const uint i03 = fastdiv(idx, p.ne0_012mp, fastdiv_L(p.ne0_Ls, 0));
     const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
-    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, p.ne0_01L);
+    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, fastdiv_L(p.ne0_Ls, 1));
     const uint i02_offset = i02*p.ne01*p.ne00;
-    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, p.ne0_0L);
+    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, fastdiv_L(p.ne0_Ls, 2));
     const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
 
     int param = floatBitsToInt(p.param1);

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

@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float x = float(data_a[i]);
-    data_d[i] = D_TYPE(trunc(x));
-}

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

@@ -598,9 +598,10 @@ const uint[1024] iq1s_grid_const = {
     0x55dd55df, 0x55d555d7, 0x5503550c, 0x557f5501, 0x5577557d, 0x55405575, 0x555d555f, 0x55555557
 };
 
+#if defined(NEEDS_IQ1S_GRID_GPU)
 // Same content as iq1s_grid_const except each 2-bit value is expanded to 4-bit
 // and has 1 added to it (allows packed values to be extracted with & 0x0F0F0F0F
-// and 0xF0F0F0F0).
+// and 0xF0F0F0F0). This is only used by the q8_1/int-dot vector path.
 const uint32_t[2048] iq1s_grid_gpu_const = {
     0x00000000, 0x00000002, 0x00000101, 0x00000200, 0x00000202, 0x00010001, 0x00010101, 0x00020000,
     0x00020002, 0x00020200, 0x00020202, 0x01000101, 0x01010001, 0x01010100, 0x01010102, 0x01020101,
@@ -859,9 +860,12 @@ const uint32_t[2048] iq1s_grid_gpu_const = {
     0x20222020, 0x20222022, 0x20222220, 0x20222222, 0x21212021, 0x21212120, 0x21212122, 0x22202020,
     0x22202022, 0x22202220, 0x22202222, 0x22212121, 0x22222020, 0x22222022, 0x22222220, 0x22222222,
 };
+#endif
 
 shared uint16_t iq1s_grid[2048];
+#if defined(NEEDS_IQ1S_GRID_GPU)
 shared uint32_t iq1s_grid_gpu[2048];
+#endif
 
 #define NEEDS_INIT_IQ_SHMEM
 void init_iq_shmem(uvec3 wgsize)
@@ -875,12 +879,14 @@ void init_iq_shmem(uvec3 wgsize)
             iq1s_grid[2*idx+1] = g.y;
         }
     }
+#if defined(NEEDS_IQ1S_GRID_GPU)
     [[unroll]] for (uint i = 0; i < iq1s_grid_gpu_const.length(); i += wgsize.x) {
         uint idx = i + gl_LocalInvocationIndex.x;
         if (iq1s_grid_gpu_const.length() % wgsize.x == 0 || idx < iq1s_grid_gpu_const.length()) {
             iq1s_grid_gpu[idx] = iq1s_grid_gpu_const[idx];
         }
     }
+#endif
     barrier();
 }
 #endif

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

@@ -0,0 +1,144 @@
+#version 450
+
+#include "types.glsl"
+#include "generic_unary_head.glsl"
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+float op_abs(float x) {
+    return abs(x);
+}
+
+float op_sgn(float x) {
+    return sign(x);
+}
+
+float op_neg(float x) {
+    return -x;
+}
+
+float op_step(float x) {
+    return x >= 0.0f ? 1.0f : 0.0f;
+}
+
+float op_tanh(float x) {
+    return 1.0f - 2.0f / (exp(2.0f*x) + 1.0f);
+}
+
+float op_elu(float x) {
+    return x < 0.0f ? exp(x) - 1.0f : x;
+}
+
+float op_relu(float x) {
+    return max(x, 0.0f);
+}
+
+float op_sigmoid(float x) {
+    return 1.0f / (1.0f + exp(-x));
+}
+
+float op_gelu(float x) {
+    const float GELU_COEF_A    = 0.044715f;
+    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+    const float val = SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x);
+    return 0.5f*x*(2.0f - 2.0f / (exp(2.0f * val) + 1.0f));
+}
+
+float op_gelu_quick(float x) {
+    const float GELU_QUICK_COEF = -1.702f;
+    return x * (1.0f / (1.0f + exp(GELU_QUICK_COEF * x)));
+}
+
+float op_silu(float x) {
+    return x / (1.0f + exp(-x));
+}
+
+float op_hardswish(float x) {
+    return x * min(1.0f, max(0.0f, (x + 3.0f) / 6.0f));
+}
+
+float op_hardsigmoid(float x) {
+    return min(1.0f, max(0.0f, (x + 3.0f) / 6.0f));
+}
+
+float op_exp(float x) {
+    return exp(x);
+}
+
+float op_expm1(float x) {
+    // exp(x) - 1 loses many ulps to cancellation near zero.  Use a degree-6
+    // Taylor expansion for |x| <= 1/4: the omitted x^7/5040 term is < 1.3e-8,
+    // about 0.5 ulp at expm1(0.25), and a host-side f32 model stays within
+    // 2 ulps over the interval.  The first native exp(x)-1 values outside the
+    // cutoff are about 1 ulp for +0.25 and 2 ulps for -0.25.
+    if (abs(x) <= 0.25f) {
+        return x * (1.0f + x * (0.5f + x * ((1.0f/6.0f) + x * ((1.0f/24.0f) + x * ((1.0f/120.0f) + x * (1.0f/720.0f))))));
+    }
+    return exp(x) - 1.0f;
+}
+
+float op_softplus(float x) {
+    return (x > 20.0f) ? x : log(1.0f + exp(x));
+}
+
+float op_gelu_erf(float a) {
+    // based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation
+    const float p_erf  = 0.3275911f;
+    const float a1_erf = 0.254829592f;
+    const float a2_erf = -0.284496736f;
+    const float a3_erf = 1.421413741f;
+    const float a4_erf = -1.453152027f;
+    const float a5_erf = 1.061405429f;
+
+    const float SQRT_2_INV = 0.70710678118654752440084436210484f;
+    const float a_div_sqr2 = a * SQRT_2_INV;
+    const float sign_x = sign(a_div_sqr2);
+    const float x = abs(a_div_sqr2);
+    const float t = 1.0f / (1.0f + p_erf * x);
+    const float y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x);
+    return 0.5f * a * (1.0f + sign_x * y);
+}
+
+float op_xielu(float x) {
+    const float alpha_n = p.param1;
+    const float alpha_p = p.param2;
+    const float beta = p.param3;
+    const float eps = p.param4;
+
+    if (x > 0.0f) {
+        return alpha_p * x * x + beta * x;
+    }
+
+    const float min_x_eps = min(x, eps);
+    return (op_expm1(min_x_eps) - x) * alpha_n + beta * x;
+}
+
+float op_floor(float x) {
+    return floor(x);
+}
+
+float op_ceil(float x) {
+    return ceil(x);
+}
+
+float op_round(float x) {
+    // Round halfway cases away from zero as roundf does.
+    return x >= 0.0f ? floor(x + 0.5f) : ceil(x - 0.5f);
+}
+
+float op_trunc(float x) {
+    return trunc(x);
+}
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint a_idx = get_aoffset() + src0_idx(idx);
+    const uint d_idx = get_doffset() + dst_idx(idx);
+
+    data_d[d_idx] = D_TYPE(OP(float(data_a[a_idx])));
+}

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

@@ -868,47 +868,49 @@ void process_shaders() {
 
     string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
 
-    string_to_spv("exp_f16",        "exp.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("exp_f32",        "exp.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("exp_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_exp"}});
+    string_to_spv("exp_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_exp"}});
+    string_to_spv("expm1_f16",      "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_expm1"}});
+    string_to_spv("expm1_f32",      "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_expm1"}});
 
     string_to_spv("log_f16",        "log.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("log_f32",        "log.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("gelu_f16",       "gelu.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("gelu_f32",       "gelu.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("gelu_erf_f16",   "gelu_erf.comp",    {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("gelu_erf_f32",   "gelu_erf.comp",    {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("gelu_quick_f16", "gelu_quick.comp",  {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("gelu_quick_f32", "gelu_quick.comp",  {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("silu_f16",       "silu.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("silu_f32",       "silu.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("relu_f16",       "relu.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("relu_f32",       "relu.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("neg_f16",        "neg.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("neg_f32",        "neg.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("tanh_f16",       "tanh.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("tanh_f32",       "tanh.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("sigmoid_f16",    "sigmoid.comp",     {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("sigmoid_f32",    "sigmoid.comp",     {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("hardsigmoid_f16","hardsigmoid.comp", {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("hardsigmoid_f32","hardsigmoid.comp", {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("hardswish_f16",  "hardswish.comp",   {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("hardswish_f32",  "hardswish.comp",   {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("abs_f16",        "abs.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("abs_f32",        "abs.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("elu_f16",        "elu.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("elu_f32",        "elu.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("xielu_f16",      "xielu.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("xielu_f32",      "xielu.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("sgn_f16",        "sgn.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("sgn_f32",        "sgn.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("gelu_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_gelu"}});
+    string_to_spv("gelu_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_gelu"}});
+    string_to_spv("gelu_erf_f16",   "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_gelu_erf"}});
+    string_to_spv("gelu_erf_f32",   "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_gelu_erf"}});
+    string_to_spv("gelu_quick_f16", "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_gelu_quick"}});
+    string_to_spv("gelu_quick_f32", "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_gelu_quick"}});
+    string_to_spv("silu_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_silu"}});
+    string_to_spv("silu_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_silu"}});
+    string_to_spv("relu_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_relu"}});
+    string_to_spv("relu_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_relu"}});
+    string_to_spv("neg_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_neg"}});
+    string_to_spv("neg_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_neg"}});
+    string_to_spv("tanh_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_tanh"}});
+    string_to_spv("tanh_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_tanh"}});
+    string_to_spv("sigmoid_f16",    "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_sigmoid"}});
+    string_to_spv("sigmoid_f32",    "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_sigmoid"}});
+    string_to_spv("hardsigmoid_f16","unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_hardsigmoid"}});
+    string_to_spv("hardsigmoid_f32","unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_hardsigmoid"}});
+    string_to_spv("hardswish_f16",  "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_hardswish"}});
+    string_to_spv("hardswish_f32",  "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_hardswish"}});
+    string_to_spv("abs_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_abs"}});
+    string_to_spv("abs_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_abs"}});
+    string_to_spv("elu_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_elu"}});
+    string_to_spv("elu_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_elu"}});
+    string_to_spv("xielu_f16",      "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_xielu"}});
+    string_to_spv("xielu_f32",      "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_xielu"}});
+    string_to_spv("sgn_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_sgn"}});
+    string_to_spv("sgn_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_sgn"}});
 
     string_to_spv("tri_f16",        "tri.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("tri_f32",        "tri.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
     string_to_spv("diag_f16",       "diag.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("diag_f32",       "diag.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
 
-    string_to_spv("softplus_f16",   "softplus.comp",    {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("softplus_f32",   "softplus.comp",    {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("softplus_f16",   "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_softplus"}});
+    string_to_spv("softplus_f32",   "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_softplus"}});
 
     string_to_spv("add1_f16_f16",   "add1.comp",        {{"A_TYPE", "float16_t"},   {"B_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}});
     string_to_spv("add1_f16_f32",   "add1.comp",        {{"A_TYPE", "float16_t"},   {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}});
@@ -916,16 +918,16 @@ void process_shaders() {
     string_to_spv("arange_f32",     "arange.comp",      {{"A_TYPE", "float"},       {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
     string_to_spv("fill_f32",       "fill.comp",        {{"D_TYPE", "float"},       {"FLOAT_TYPE", "float"}});
     string_to_spv("fill_f16",       "fill.comp",        {{"D_TYPE", "float16_t"},   {"FLOAT_TYPE", "float"}});
-    string_to_spv("step_f16",       "step.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("step_f32",       "step.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("round_f16",      "round.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("round_f32",      "round.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("ceil_f16",       "ceil.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("ceil_f32",       "ceil.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("floor_f16",      "floor.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("floor_f32",      "floor.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
-    string_to_spv("trunc_f16",      "trunc.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
-    string_to_spv("trunc_f32",      "trunc.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("step_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_step"}});
+    string_to_spv("step_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_step"}});
+    string_to_spv("round_f16",      "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_round"}});
+    string_to_spv("round_f32",      "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_round"}});
+    string_to_spv("ceil_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_ceil"}});
+    string_to_spv("ceil_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_ceil"}});
+    string_to_spv("floor_f16",      "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_floor"}});
+    string_to_spv("floor_f32",      "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_floor"}});
+    string_to_spv("trunc_f16",      "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_trunc"}});
+    string_to_spv("trunc_f32",      "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_trunc"}});
 
     string_to_spv("geglu_f16",      "geglu.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("geglu_f32",      "geglu.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"}});

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

@@ -1,35 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    float x = float(data_a[i]);
-
-    float alpha_n = p.param1;
-    float alpha_p = p.param2;
-    float beta = p.param3;
-    float eps = p.param4;
-
-    if (x > 0.0f) {
-        x = alpha_p * x * x + beta * x;
-    } else {
-        const float min_x_eps = min(x, eps);
-        x = (exp(min_x_eps) - 1 - x) * alpha_n + beta * x;
-    }
-
-    data_d[i] = D_TYPE(x);
-}

ggml/src/ggml-webgpu/ggml-webgpu.cpp

@@ -1245,7 +1245,7 @@ static webgpu_encoded_op ggml_webgpu_gated_delta_net(webgpu_context & ctx,
     const uint32_t h        = (uint32_t) src2->ne[1];
     const uint32_t n_tokens = (uint32_t) src2->ne[2];
     const uint32_t n_seqs   = (uint32_t) src2->ne[3];
-    const uint32_t K        = (uint32_t) src5->ne[1];
+    const uint32_t K        = (uint32_t) ggml_get_op_params_i32(dst, 0);
     const float    scale    = 1.0f / sqrtf((float) s_v);
     uint32_t       scale_u32;
     memcpy(&scale_u32, &scale, sizeof(scale_u32));

ggml/src/ggml-webgpu/wgsl-shaders/gated_delta_net.wgsl

@@ -63,10 +63,10 @@ fn main(
     let iq3 = seq_id / params.rq3;
 
     let state_size = S_V * S_V;
-    let state_in_base = (seq_id * params.K * params.h + head_id) * state_size;
+    // input state holds s0 only [S_v, S_v, H, n_seqs]: per-seq stride is H*D.
+    let state_in_base = (seq_id * params.h + head_id) * state_size;
     let state_out_base = (seq_id * params.h + head_id) * state_size;
     let state_size_per_snap = state_size * params.h * params.n_seqs;
-    let shift = i32(params.n_tokens) - i32(params.K);
 
     var state: array<f32, S_V>;
     for (var i = 0u; i < S_V; i++) {
@@ -128,7 +128,8 @@ fn main(
         attn_off += S_V * params.h;
 
         if (params.K > 1u) {
-            let target_slot = i32(t) - shift;
+            // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+            let target_slot = i32(params.n_tokens) - 1 - i32(t);
             if (target_slot >= 0 && target_slot < i32(params.K)) {
                 let slot_base = params.s_off + u32(target_slot) * state_size_per_snap + state_out_base;
                 for (var i = 0u; i < S_V; i++) {

ggml/src/ggml.c

@@ -6223,7 +6223,8 @@ struct ggml_tensor * ggml_gated_delta_net(
         struct ggml_tensor  * v,
         struct ggml_tensor  * g,
         struct ggml_tensor  * beta,
-        struct ggml_tensor  * state) {
+        struct ggml_tensor  * state,
+        int64_t               K) {
     GGML_ASSERT(ggml_is_contiguous_rows(q));
     GGML_ASSERT(ggml_is_contiguous_rows(k));
     GGML_ASSERT(ggml_is_contiguous_rows(v));
@@ -6247,15 +6248,18 @@ struct ggml_tensor * ggml_gated_delta_net(
     GGML_ASSERT(g->ne[0] == 1 || g->ne[0] == S_v);
     GGML_ASSERT(beta->ne[0] == 1);
 
-    // state is a 3D tensor (S_v*S_v*H, K, n_seqs). K is the snapshot slot count.
-    GGML_ASSERT(state->ne[0] == S_v * S_v * H);
-    GGML_ASSERT(state->ne[2] == n_seqs);
-    GGML_ASSERT(state->ne[3] == 1);
-    const int64_t K = state->ne[1];
+    // state holds the initial state s0 only: [S_v, S_v, H, n_seqs]. K (snapshot slot count) is an op param.
+    GGML_ASSERT(state->ne[0] == S_v);
+    GGML_ASSERT(state->ne[1] == S_v);
+    GGML_ASSERT(state->ne[2] == H);
+    GGML_ASSERT(state->ne[3] == n_seqs);
+    GGML_ASSERT(K >= 1);
     const int64_t state_rows = K * S_v * n_seqs;
     const int64_t ne[4] = { S_v * H, n_tokens * n_seqs + state_rows, 1, 1 };
     struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
 
+    ggml_set_op_params_i32(result, 0, (int32_t) K);
+
     result->op     = GGML_OP_GATED_DELTA_NET;
     result->src[0] = q;
     result->src[1] = k;

gguf-py/gguf/constants.py

@@ -154,6 +154,9 @@ class Keys:
         HIDDEN_ACT                        = "{arch}.hidden_activation"
         DENSE_FEAT_IN_SIZE                = "{arch}.{dense}_feat_in"
         DENSE_FEAT_OUT_SIZE               = "{arch}.{dense}_feat_out"
+        TARGET_LAYERS                     = "{arch}.target_layers"
+        TARGET_HIDDEN_SIZE                = "{arch}.target_hidden_size"
+        NORM_BEFORE_RESIDUAL              = "{arch}.norm_before_residual"
 
     class Attention:
         HEAD_COUNT                   = "{arch}.attention.head_count"
@@ -272,7 +275,8 @@ class Keys:
         CHAT_TEMPLATE_N      = "tokenizer.chat_template.{name}"
         CHAT_TEMPLATES       = "tokenizer.chat_templates"
         # Normalizer constants
-        NORMALIZER_LOWERCASE = "tokenizer.ggml.normalizer.lowercase"
+        NORMALIZER_LOWERCASE     = "tokenizer.ggml.normalizer.lowercase"
+        NORMALIZER_STRIP_ACCENTS = "tokenizer.ggml.normalizer.strip_accents"
         # FIM/Infill special tokens constants
         FIM_PRE_ID           = "tokenizer.ggml.fim_pre_token_id"
         FIM_SUF_ID           = "tokenizer.ggml.fim_suf_token_id"
@@ -453,6 +457,7 @@ class MODEL_ARCH(IntEnum):
     XVERSE           = auto()
     COMMAND_R        = auto()
     COHERE2          = auto()
+    COHERE2MOE       = auto()
     DBRX             = auto()
     OLMO             = auto()
     OLMO2            = auto()
@@ -510,6 +515,7 @@ class MODEL_ARCH(IntEnum):
     RND1             = auto()
     PANGU_EMBED      = auto()
     MISTRAL3         = auto()
+    EAGLE3           = auto()
     MISTRAL4         = auto()
     PADDLEOCR        = auto()
     MIMO2            = auto()
@@ -900,14 +906,17 @@ class MODEL_TENSOR(IntEnum):
     A_PER_DIM_K_SCALE     = auto() # gemma4
     A_PER_DIM_SCALE       = auto() # gemma4
     # nextn/mtp
-    NEXTN_PROJ_PRE       = auto()
-    NEXTN_PROJ_POST      = auto()
-    NEXTN_EH_PROJ        = auto()
-    NEXTN_EMBED_TOKENS   = auto()
-    NEXTN_ENORM          = auto()
-    NEXTN_HNORM          = auto()
+    NEXTN_PROJ_PRE         = auto()
+    NEXTN_PROJ_POST        = auto()
+    NEXTN_EH_PROJ          = auto()
+    NEXTN_EMBED_TOKENS     = auto()
+    NEXTN_ENORM            = auto()
+    NEXTN_HNORM            = auto()
     NEXTN_SHARED_HEAD_HEAD = auto()
     NEXTN_SHARED_HEAD_NORM = auto()
+    # eagle3
+    FC                     = auto()  # feature fusion layer
+    D2T                    = auto()  # draft to target vocabulary mapping
     # lfm2 audio
     A_ENC_NORM_CONV        = auto()
     A_ENC_LINEAR_POS       = auto()
@@ -1004,6 +1013,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.XVERSE:           "xverse",
     MODEL_ARCH.COMMAND_R:        "command-r",
     MODEL_ARCH.COHERE2:          "cohere2",
+    MODEL_ARCH.COHERE2MOE:       "cohere2moe",
     MODEL_ARCH.DBRX:             "dbrx",
     MODEL_ARCH.OLMO:             "olmo",
     MODEL_ARCH.OLMO2:            "olmo2",
@@ -1062,6 +1072,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.RND1:             "rnd1",
     MODEL_ARCH.PANGU_EMBED:      "pangu-embedded",
     MODEL_ARCH.MISTRAL3:         "mistral3",
+    MODEL_ARCH.EAGLE3:           "eagle3",
     MODEL_ARCH.MISTRAL4:         "mistral4",
     MODEL_ARCH.PADDLEOCR:        "paddleocr",
     MODEL_ARCH.MIMO2:            "mimo2",
@@ -1094,8 +1105,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.POS_EMBD:                  "position_embd",
     MODEL_TENSOR.OUTPUT_NORM:               "output_norm",
     MODEL_TENSOR.OUTPUT:                    "output",
-    MODEL_TENSOR.DENSE_2_OUT:                "dense_2", # embeddinggemma 2_Dense
-    MODEL_TENSOR.DENSE_3_OUT:                "dense_3", # embeddinggemma 2_Dense
+    MODEL_TENSOR.DENSE_2_OUT:               "dense_2", # embeddinggemma 2_Dense
+    MODEL_TENSOR.DENSE_3_OUT:               "dense_3", # embeddinggemma 2_Dense
     MODEL_TENSOR.ROPE_FREQS:                "rope_freqs",
     MODEL_TENSOR.ROPE_FACTORS_LONG:         "rope_factors_long",
     MODEL_TENSOR.ROPE_FACTORS_SHORT:        "rope_factors_short",
@@ -1487,6 +1498,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.NEXTN_HNORM:               "blk.{bid}.nextn.hnorm",
     MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD:    "blk.{bid}.nextn.shared_head_head",
     MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM:    "blk.{bid}.nextn.shared_head_norm",
+    MODEL_TENSOR.FC:                        "fc",
+    MODEL_TENSOR.D2T:                       "d2t",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -2861,6 +2874,33 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.COHERE2MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_GATE_UP_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
+    ],
     MODEL_ARCH.DBRX: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -4027,6 +4067,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
+    MODEL_ARCH.EAGLE3: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_NORM_2,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FC,
+        MODEL_TENSOR.D2T,
+    ],
     MODEL_ARCH.MISTRAL4: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,

gguf-py/gguf/gguf_writer.py

@@ -1124,6 +1124,9 @@ class GGUFWriter:
     def add_normalizer_lowercase(self, value: bool) -> None:
         self.add_bool(Keys.Tokenizer.NORMALIZER_LOWERCASE, value)
 
+    def add_normalizer_strip_accents(self, value: bool) -> None:
+        self.add_bool(Keys.Tokenizer.NORMALIZER_STRIP_ACCENTS, value)
+
     def add_eot_token_id(self, id: int) -> None:
         self.add_uint32(Keys.Tokenizer.EOT_ID, id)
 

gguf-py/gguf/vocab.py

@@ -53,6 +53,7 @@ class SpecialVocab:
     special_token_ids: dict[str, int]
     chat_template: str | Sequence[Mapping[str, str]] | None
     normalizer_lowercase: bool | None
+    normalizer_strip_accents: bool | None
 
     def __init__(
         self, path: str | os.PathLike[str], load_merges: bool = False,
@@ -66,6 +67,7 @@ class SpecialVocab:
         self.merges = []
         self.chat_template = None
         self.normalizer_lowercase = None
+        self.normalizer_strip_accents = None
         if special_token_types is not None:
             self.special_token_types = special_token_types
         else:
@@ -108,6 +110,10 @@ class SpecialVocab:
             if not quiet:
                 logger.info(f'Setting normalizer_lowercase to {self.normalizer_lowercase}')
             gw.add_normalizer_lowercase(self.normalizer_lowercase)
+        if self.normalizer_strip_accents is not None:
+            if not quiet:
+                logger.info(f'Setting normalizer_strip_accents to {self.normalizer_strip_accents}')
+            gw.add_normalizer_strip_accents(self.normalizer_strip_accents)
 
     def _load(self, path: Path) -> None:
         self._try_load_from_tokenizer_json(path)
@@ -155,17 +161,21 @@ class SpecialVocab:
     def _parse_normalizer(self, normalizer: dict) -> None:
         # ref: https://huggingface.co/docs/tokenizers/api/normalizers
         #
-        # Detects lowercase normalization in three possible formats:
-        # 1. Standalone: {"type": "Lowercase"}
-        # 2. BertNormalizer attribute: {"type": "BertNormalizer", "lowercase": true, ...}
-        # 3. Nested in Sequence: {"type": "Sequence", "normalizers": [...]}
+        # Extracts normalizer flags from three possible formats:
+        # 1. Standalone:           {"type": "Lowercase"}
+        # 2. BertNormalizer attrs: {"type": "BertNormalizer", ...}
+        # 3. Nested in Sequence:   {"type": "Sequence", "normalizers": [...]}
 
         normalizer_type = normalizer.get('type')
         if normalizer_type == 'Lowercase':
             self.normalizer_lowercase = True
+        elif normalizer_type == 'StripAccents':
+            self.normalizer_strip_accents = True
         elif normalizer_type == 'BertNormalizer':
             if 'lowercase' in normalizer:
                 self.normalizer_lowercase = normalizer['lowercase']
+            if 'strip_accents' in normalizer:
+                self.normalizer_strip_accents = normalizer['strip_accents']
         elif normalizer_type == 'Sequence':
             for norm in normalizer.get('normalizers', []):
                 self._parse_normalizer(norm)
@@ -246,6 +256,11 @@ class SpecialVocab:
                             if special_first := tmpl_single[0].get('SpecialToken', {}).get('id'):
                                 if not tokenizer_config:
                                     special_bos = special_first
+                                elif special_first not in (special_bos, special_cls):
+                                    if not special_bos:
+                                        tokenizer_config['bos_token'] = special_bos = special_first
+                                    if not special_cls:
+                                        tokenizer_config['cls_token'] = special_cls = special_first
                                 self.add_special_token['bos'] = True if special_first in (special_bos, special_cls) else False
                                 if special_first not in (special_bos, special_cls):
                                     logger.warning(f'Unknown leading special token {special_first!r} in TemplateProcessing<single>')

scripts/sync-ggml.last

@@ -1 +1 @@
-7142aa6bf9fcaeec0fef8d80fcd90afe4268adf1
+3af5f5760e19a96427f5f7a93b79cbdf3d4b265b

scripts/sync_vendor.py

@@ -5,7 +5,7 @@ import os
 import sys
 import subprocess
 
-HTTPLIB_VERSION = "refs/tags/v0.46.1"
+HTTPLIB_VERSION = "refs/tags/v0.47.0"
 
 vendor = {
     "https://github.com/nlohmann/json/releases/latest/download/json.hpp":     "vendor/nlohmann/json.hpp",

scripts/ui-assets.cmake

@@ -4,8 +4,9 @@
 #   1. Pre-built assets in SRC_DIST_DIR (manually built by user)
 #   2. If BUILD_UI=ON: npm build
 #   3. If above did not produce assets and HF_ENABLED=ON: HF Bucket download
+#      of dist.tar.gz (verified against dist.tar.gz.sha256)
 
-cmake_minimum_required(VERSION 3.16)
+cmake_minimum_required(VERSION 3.18)
 
 set(UI_SOURCE_DIR     "" CACHE STRING "UI source directory (to run npm build)")
 set(UI_BINARY_DIR     "" CACHE STRING "UI binary directory (to store generated files)")
@@ -15,13 +16,7 @@ set(HF_VERSION        "" CACHE STRING "Version to download (empty = resolve from
 set(HF_ENABLED        "" CACHE STRING "Whether to allow HF Bucket download (ON/OFF)")
 set(BUILD_UI          "" CACHE STRING "Build UI via npm (ON/OFF)")
 set(LLAMA_UI_EMBED    "" CACHE STRING "Path to llama-ui-embed helper")
-
-set(ASSETS
-    bundle.css
-    bundle.js
-    index.html
-    loading.html
-)
+set(LLAMA_UI_GZIP     "" CACHE STRING "Apply gzip compress to assets to save bandwidth")
 
 set(DIST_DIR     "${UI_BINARY_DIR}/dist")
 set(SRC_DIST_DIR "${UI_SOURCE_DIR}/dist")
@@ -29,42 +24,10 @@ set(STAMP_FILE   "${UI_BINARY_DIR}/.ui-stamp")
 set(UI_CPP       "${UI_BINARY_DIR}/ui.cpp")
 set(UI_H         "${UI_BINARY_DIR}/ui.h")
 
-function(assets_present out_var)
-    set(present TRUE)
-    foreach(asset ${ASSETS})
-        if(NOT EXISTS "${DIST_DIR}/${asset}")
-            set(present FALSE)
-            break()
-        endif()
-    endforeach()
-    set(${out_var} ${present} PARENT_SCOPE)
-endfunction()
-
-function(copy_src_dist out_var)
-    set(${out_var} FALSE PARENT_SCOPE)
-
-    foreach(asset ${ASSETS})
-        if(NOT EXISTS "${SRC_DIST_DIR}/${asset}")
-            return()
-        endif()
-    endforeach()
-
-    file(MAKE_DIRECTORY "${DIST_DIR}")
-    message(STATUS "UI: using pre-built assets from ${SRC_DIST_DIR}")
-    foreach(asset ${ASSETS})
-        execute_process(
-            COMMAND ${CMAKE_COMMAND} -E copy_if_different
-                "${SRC_DIST_DIR}/${asset}" "${DIST_DIR}/${asset}"
-        )
-    endforeach()
-    set(${out_var} TRUE PARENT_SCOPE)
-endfunction()
-
 function(npm_build_should_skip out_var)
     set(${out_var} FALSE PARENT_SCOPE)
 
-    assets_present(present)
-    if(NOT present)
+    if(NOT EXISTS "${DIST_DIR}/index.html")
         return()
     endif()
 
@@ -159,7 +122,7 @@ function(npm_build out_var)
 
     message(STATUS "UI: running npm run build, output -> ${DIST_DIR}")
     execute_process(
-        COMMAND ${CMAKE_COMMAND} -E env "LLAMA_UI_OUT_DIR=${DIST_DIR}"
+        COMMAND ${CMAKE_COMMAND} -E env "LLAMA_UI_OUT_DIR=${DIST_DIR}" "LLAMA_UI_VERSION=${HF_VERSION}" "LLAMA_BUILD_NUMBER=${LLAMA_BUILD_NUMBER}"
                 ${NPM_EXECUTABLE} run build
         WORKING_DIRECTORY "${UI_SOURCE_DIR}"
         RESULT_VARIABLE rc
@@ -171,8 +134,7 @@ function(npm_build out_var)
         return()
     endif()
 
-    assets_present(present)
-    if(NOT present)
+    if(NOT EXISTS "${DIST_DIR}/index.html")
         message(STATUS "UI: npm build finished but assets missing in ${DIST_DIR}")
         return()
     endif()
@@ -203,7 +165,7 @@ function(hf_download version out_var out_resolved)
     set(${out_var}      FALSE PARENT_SCOPE)
     set(${out_resolved} ""    PARENT_SCOPE)
 
-    file(MAKE_DIRECTORY "${DIST_DIR}")
+    set(archive "${UI_BINARY_DIR}/dist.tar.gz")
 
     set(candidates "")
     if(NOT "${version}" STREQUAL "")
@@ -212,68 +174,88 @@ function(hf_download version out_var out_resolved)
     list(APPEND candidates "latest")
 
     foreach(resolved ${candidates})
-        set(base "https://huggingface.co/buckets/ggml-org/${HF_BUCKET}/resolve/${resolved}")
+        set(base "https://huggingface.co/buckets/${HF_BUCKET}/resolve/${resolved}")
 
-        message(STATUS "UI: downloading from ${resolved}: ${base}")
+        message(STATUS "UI: downloading from ${resolved}: ${base}/dist.tar.gz")
 
-        set(ok TRUE)
-        foreach(asset ${ASSETS})
-            file(DOWNLOAD "${base}/${asset}?download=true" "${DIST_DIR}/${asset}"
-                STATUS status TIMEOUT 60
-            )
-            list(GET status 0 rc)
-            if(NOT rc EQUAL 0)
-                list(GET status 1 errmsg)
-                message(STATUS "UI: download ${asset} from ${resolved} failed: ${errmsg}")
-                set(ok FALSE)
-                break()
-            endif()
-            message(STATUS "UI: downloaded ${asset}")
-        endforeach()
-
-        if(NOT ok)
+        file(DOWNLOAD "${base}/dist.tar.gz?download=true" "${archive}"
+            STATUS status TIMEOUT 300
+        )
+        list(GET status 0 rc)
+        if(NOT rc EQUAL 0)
+            list(GET status 1 errmsg)
+            message(STATUS "UI: download dist.tar.gz from ${resolved} failed: ${errmsg}")
             continue()
         endif()
 
-        # Best-effort checksum verification
-        file(DOWNLOAD "${base}/checksums.txt?download=true" "${DIST_DIR}/checksums.txt"
-            STATUS cs_status TIMEOUT 30
+        file(DOWNLOAD "${base}/dist.tar.gz.sha256?download=true" "${archive}.sha256"
+            STATUS status TIMEOUT 30
         )
-        list(GET cs_status 0 cs_rc)
-        if(cs_rc EQUAL 0)
-            message(STATUS "UI: verifying checksums")
-            file(STRINGS "${DIST_DIR}/checksums.txt" cs_lines)
-            foreach(asset ${ASSETS})
-                file(SHA256 "${DIST_DIR}/${asset}" h)
-                string(TOLOWER "${h}" h)
-                string(REGEX MATCH "${h}[ \t]+${asset}" m "${cs_lines}")
-                if(NOT m)
-                    message(WARNING "UI: checksum verification failed for ${asset}")
-                    set(ok FALSE)
-                    break()
-                endif()
-            endforeach()
-            if(ok)
-                message(STATUS "UI: all checksums verified")
-            endif()
+        list(GET status 0 rc)
+        if(NOT rc EQUAL 0)
+            list(GET status 1 errmsg)
+            message(STATUS "UI: download dist.tar.gz.sha256 from ${resolved} failed: ${errmsg}")
+            continue()
         endif()
 
-        if(ok)
-            set(${out_var}      TRUE         PARENT_SCOPE)
-            set(${out_resolved} "${resolved}" PARENT_SCOPE)
-            return()
+        # Validate sha256 checkums
+        file(READ "${archive}.sha256" expected)
+        string(REGEX MATCH "^[0-9a-fA-F]+" expected "${expected}")
+        string(TOLOWER "${expected}" expected)
+        file(SHA256 "${archive}" actual)
+        if("${expected}" STREQUAL "" OR NOT "${actual}" STREQUAL "${expected}")
+            message(STATUS "UI: checksum mismatch for dist.tar.gz from ${resolved}")
+            continue()
         endif()
+
+        # Clear DIST_DIR to remove stale files first
+        file(REMOVE_RECURSE "${DIST_DIR}")
+
+        file(ARCHIVE_EXTRACT INPUT "${archive}" DESTINATION "${DIST_DIR}")
+
+        if(NOT EXISTS "${DIST_DIR}/index.html")
+            message(STATUS "UI: archive from ${resolved} is missing required assets")
+            continue()
+        endif()
+
+        message(STATUS "UI: archive verified and extracted")
+        set(${out_var}      TRUE          PARENT_SCOPE)
+        set(${out_resolved} "${resolved}" PARENT_SCOPE)
+        return()
     endforeach()
 endfunction()
 
-function(emit_files)
-    assets_present(present)
+function(emit_files dist_dir)
+    # If gzip is requested, compress every asset into a parallel _gzip/ tree
+    # the structure stays the same; for ex: /abc/def --> /_gzip/abc/def
+    # embed.cpp will check for _gzip and will pick it up
+    if(LLAMA_UI_GZIP AND EXISTS "${dist_dir}/index.html")
+        find_program(GZIP_EXECUTABLE gzip)
+        if(NOT GZIP_EXECUTABLE)
+            message(WARNING "UI: LLAMA_UI_GZIP requested but gzip not found, embedding uncompressed")
+        else()
+            set(gzip_dir "${dist_dir}/_gzip")
+            file(REMOVE_RECURSE "${gzip_dir}")
+            file(GLOB_RECURSE all_files RELATIVE "${dist_dir}" "${dist_dir}/*")
+            foreach(f ${all_files})
+                get_filename_component(dst_dir "${gzip_dir}/${f}" DIRECTORY)
+                file(MAKE_DIRECTORY "${dst_dir}")
+                execute_process(
+                    COMMAND "${GZIP_EXECUTABLE}" -c "${dist_dir}/${f}"
+                    OUTPUT_FILE "${gzip_dir}/${f}"
+                    RESULT_VARIABLE gz_rc
+                )
+                if(NOT gz_rc EQUAL 0)
+                    message(FATAL_ERROR "UI: gzip failed for ${f}")
+                endif()
+            endforeach()
+            message(STATUS "UI: gzip compression applied (${gzip_dir})")
+        endif()
+    endif()
 
     set(args "${UI_CPP}" "${UI_H}")
-    if(present)
-        foreach(asset ${ASSETS})
-            list(APPEND args "${asset}" "${DIST_DIR}/${asset}")
-        endforeach()
+    if(EXISTS "${dist_dir}/index.html")
+        list(APPEND args "${dist_dir}")
     endif()
 
     execute_process(
@@ -288,9 +270,9 @@ endfunction()
 # ---------------------------------------------------------------------------
 # 1. Priority 1: pre-built assets supplied in tools/ui/dist
 # ---------------------------------------------------------------------------
-copy_src_dist(SRC_OK)
-if(SRC_OK)
-    emit_files()
+if(EXISTS "${SRC_DIST_DIR}/index.html")
+    message(STATUS "UI: using pre-built assets from ${SRC_DIST_DIR}")
+    emit_files("${SRC_DIST_DIR}")
     return()
 endif()
 
@@ -300,6 +282,8 @@ endif()
 set(provisioned FALSE)
 
 if(BUILD_UI)
+    # Resolve version from git build-info if not explicitly set
+    resolve_version(HF_VERSION)
     npm_build(NPM_OK)
     if(NPM_OK)
         set(provisioned TRUE)
@@ -321,7 +305,10 @@ if(NOT provisioned AND HF_ENABLED)
         endif()
     endif()
 
-    assets_present(have_assets)
+    set(have_assets FALSE)
+    if(EXISTS "${DIST_DIR}/index.html")
+        set(have_assets TRUE)
+    endif()
     if(stamp_ok AND have_assets)
         message(STATUS "UI: HF stamp '${stamped}' matches version, skipping HF fetch")
         set(provisioned TRUE)
@@ -341,8 +328,7 @@ endif()
 # 4. Fallback: warn about stale or missing assets, then emit whatever we have
 # ---------------------------------------------------------------------------
 if(NOT provisioned)
-    assets_present(have_assets)
-    if(have_assets)
+    if(EXISTS "${DIST_DIR}/index.html")
         message(WARNING "UI: provisioning failed; embedding stale assets from ${DIST_DIR}")
     else()
         message(WARNING "UI: no assets available - building without an embedded UI. "
@@ -353,4 +339,4 @@ if(NOT provisioned)
     endif()
 endif()
 
-emit_files()
+emit_files("${DIST_DIR}")

src/llama-arch.cpp

@@ -3,7 +3,6 @@
 #include "llama-impl.h"
 
 #include <map>
-#include <set>
 #include <vector>
 
 static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
@@ -67,6 +66,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_XVERSE,           "xverse"           },
     { LLM_ARCH_COMMAND_R,        "command-r"        },
     { LLM_ARCH_COHERE2,          "cohere2"          },
+    { LLM_ARCH_COHERE2MOE,       "cohere2moe"       },
     { LLM_ARCH_DBRX,             "dbrx"             },
     { LLM_ARCH_OLMO,             "olmo"             },
     { LLM_ARCH_OLMO2,            "olmo2"            },
@@ -128,6 +128,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_RND1,             "rnd1"             },
     { LLM_ARCH_PANGU_EMBED,      "pangu-embedded"   },
     { LLM_ARCH_MISTRAL3,         "mistral3"         },
+    { LLM_ARCH_EAGLE3,           "eagle3"           },
     { LLM_ARCH_MISTRAL4,         "mistral4"         },
     { LLM_ARCH_PADDLEOCR,        "paddleocr"        },
     { LLM_ARCH_MIMO2,            "mimo2"            },
@@ -292,46 +293,51 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
 
     { LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
 
+    { LLM_KV_TARGET_LAYERS,         "%s.target_layers"        },
+    { LLM_KV_TARGET_HIDDEN_SIZE,    "%s.target_hidden_size"   },
+    { LLM_KV_NORM_BEFORE_RESIDUAL,  "%s.norm_before_residual" },
+
     { LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" },
     // sentence-transformers dense modules feature dims
     { LLM_KV_DENSE_2_FEAT_IN,        "%s.dense_2_feat_in"  },
-    { LLM_KV_DENSE_2_FEAT_OUT,       "%s.dense_2_feat_out"  },
-    { LLM_KV_DENSE_3_FEAT_IN,        "%s.dense_3_feat_in"   },
-    { LLM_KV_DENSE_3_FEAT_OUT,       "%s.dense_3_feat_out"  },
+    { LLM_KV_DENSE_2_FEAT_OUT,       "%s.dense_2_feat_out" },
+    { LLM_KV_DENSE_3_FEAT_IN,        "%s.dense_3_feat_in"  },
+    { LLM_KV_DENSE_3_FEAT_OUT,       "%s.dense_3_feat_out" },
 
-    { LLM_KV_TOKENIZER_MODEL,                "tokenizer.ggml.model"                    },
-    { LLM_KV_TOKENIZER_PRE,                  "tokenizer.ggml.pre"                      },
-    { LLM_KV_TOKENIZER_LIST,                 "tokenizer.ggml.tokens"                   },
-    { LLM_KV_TOKENIZER_TOKEN_TYPE,           "tokenizer.ggml.token_type"               },
-    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,     "tokenizer.ggml.token_type_count"         },
-    { LLM_KV_TOKENIZER_SCORES,               "tokenizer.ggml.scores"                   },
-    { LLM_KV_TOKENIZER_MERGES,               "tokenizer.ggml.merges"                   },
-    { LLM_KV_TOKENIZER_BOS_ID,               "tokenizer.ggml.bos_token_id"             },
-    { LLM_KV_TOKENIZER_EOS_ID,               "tokenizer.ggml.eos_token_id"             },
-    { LLM_KV_TOKENIZER_EOT_ID,               "tokenizer.ggml.eot_token_id"             },
-    { LLM_KV_TOKENIZER_EOM_ID,               "tokenizer.ggml.eom_token_id"             },
-    { LLM_KV_TOKENIZER_UNK_ID,               "tokenizer.ggml.unknown_token_id"         },
-    { LLM_KV_TOKENIZER_SEP_ID,               "tokenizer.ggml.seperator_token_id"       },
-    { LLM_KV_TOKENIZER_PAD_ID,               "tokenizer.ggml.padding_token_id"         },
-    { LLM_KV_TOKENIZER_CLS_ID,               "tokenizer.ggml.cls_token_id"             },
-    { LLM_KV_TOKENIZER_MASK_ID,              "tokenizer.ggml.mask_token_id"            },
-    { LLM_KV_TOKENIZER_ADD_BOS,              "tokenizer.ggml.add_bos_token"            },
-    { LLM_KV_TOKENIZER_ADD_EOS,              "tokenizer.ggml.add_eos_token"            },
-    { LLM_KV_TOKENIZER_ADD_SEP,              "tokenizer.ggml.add_sep_token"            },
-    { LLM_KV_TOKENIZER_ADD_PREFIX,           "tokenizer.ggml.add_space_prefix"         },
-    { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,      "tokenizer.ggml.remove_extra_whitespaces" },
-    { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap"     },
-    { LLM_KV_TOKENIZER_HF_JSON,              "tokenizer.huggingface.json"              },
-    { LLM_KV_TOKENIZER_RWKV,                 "tokenizer.rwkv.world"                    },
-    { LLM_KV_TOKENIZER_CHAT_TEMPLATE,        "tokenizer.chat_template"                 },
-    { LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE, "tokenizer.ggml.normalizer.lowercase"     },
-    { LLM_KV_TOKENIZER_FIM_PRE_ID,           "tokenizer.ggml.fim_pre_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_SUF_ID,           "tokenizer.ggml.fim_suf_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_MID_ID,           "tokenizer.ggml.fim_mid_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_PAD_ID,           "tokenizer.ggml.fim_pad_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_REP_ID,           "tokenizer.ggml.fim_rep_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_SEP_ID,           "tokenizer.ggml.fim_sep_token_id"         },
-    { LLM_KV_TOKENIZER_SUPPRESS_TOKENS,      "tokenizer.ggml.suppress_tokens"          },
+    { LLM_KV_TOKENIZER_MODEL,                    "tokenizer.ggml.model"                    },
+    { LLM_KV_TOKENIZER_PRE,                      "tokenizer.ggml.pre"                      },
+    { LLM_KV_TOKENIZER_LIST,                     "tokenizer.ggml.tokens"                   },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE,               "tokenizer.ggml.token_type"               },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,         "tokenizer.ggml.token_type_count"         },
+    { LLM_KV_TOKENIZER_SCORES,                   "tokenizer.ggml.scores"                   },
+    { LLM_KV_TOKENIZER_MERGES,                   "tokenizer.ggml.merges"                   },
+    { LLM_KV_TOKENIZER_BOS_ID,                   "tokenizer.ggml.bos_token_id"             },
+    { LLM_KV_TOKENIZER_EOS_ID,                   "tokenizer.ggml.eos_token_id"             },
+    { LLM_KV_TOKENIZER_EOT_ID,                   "tokenizer.ggml.eot_token_id"             },
+    { LLM_KV_TOKENIZER_EOM_ID,                   "tokenizer.ggml.eom_token_id"             },
+    { LLM_KV_TOKENIZER_UNK_ID,                   "tokenizer.ggml.unknown_token_id"         },
+    { LLM_KV_TOKENIZER_SEP_ID,                   "tokenizer.ggml.seperator_token_id"       },
+    { LLM_KV_TOKENIZER_PAD_ID,                   "tokenizer.ggml.padding_token_id"         },
+    { LLM_KV_TOKENIZER_CLS_ID,                   "tokenizer.ggml.cls_token_id"             },
+    { LLM_KV_TOKENIZER_MASK_ID,                  "tokenizer.ggml.mask_token_id"            },
+    { LLM_KV_TOKENIZER_ADD_BOS,                  "tokenizer.ggml.add_bos_token"            },
+    { LLM_KV_TOKENIZER_ADD_EOS,                  "tokenizer.ggml.add_eos_token"            },
+    { LLM_KV_TOKENIZER_ADD_SEP,                  "tokenizer.ggml.add_sep_token"            },
+    { LLM_KV_TOKENIZER_ADD_PREFIX,               "tokenizer.ggml.add_space_prefix"         },
+    { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,          "tokenizer.ggml.remove_extra_whitespaces" },
+    { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,     "tokenizer.ggml.precompiled_charsmap"     },
+    { LLM_KV_TOKENIZER_HF_JSON,                  "tokenizer.huggingface.json"              },
+    { LLM_KV_TOKENIZER_RWKV,                     "tokenizer.rwkv.world"                    },
+    { LLM_KV_TOKENIZER_CHAT_TEMPLATE,            "tokenizer.chat_template"                 },
+    { LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE,     "tokenizer.ggml.normalizer.lowercase"     },
+    { LLM_KV_TOKENIZER_NORMALIZER_STRIP_ACCENTS, "tokenizer.ggml.normalizer.strip_accents" },
+    { LLM_KV_TOKENIZER_FIM_PRE_ID,               "tokenizer.ggml.fim_pre_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_SUF_ID,               "tokenizer.ggml.fim_suf_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_MID_ID,               "tokenizer.ggml.fim_mid_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_PAD_ID,               "tokenizer.ggml.fim_pad_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_REP_ID,               "tokenizer.ggml.fim_rep_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_SEP_ID,               "tokenizer.ggml.fim_sep_token_id"         },
+    { LLM_KV_TOKENIZER_SUPPRESS_TOKENS,          "tokenizer.ggml.suppress_tokens"          },
 
     { LLM_KV_ADAPTER_TYPE,                    "adapter.type"               },
     { LLM_KV_ADAPTER_LORA_ALPHA,              "adapter.lora.alpha"         },
@@ -561,6 +567,8 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
     { LLM_TENSOR_INDEXER_ATTN_Q_B,                       "blk.%d.indexer.attn_q_b" },
     { LLM_TENSOR_MASKED_EMBD_CENTROIDS,                  "masked_embd_centroids" },
     { LLM_TENSOR_MASKED_EMBD_ORDERING,                   "masked_embd_ordering" },
+    { LLM_TENSOR_FC,                                     "fc" },
+    { LLM_TENSOR_D2T,                                    "d2t" },
 };
 
 // declare information about the model weight tensors:
@@ -787,6 +795,9 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_FFN_LATENT_UP,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_MASKED_EMBD_CENTROIDS,      {LLM_TENSOR_LAYER_INPUT,     GGML_OP_NONE}},
     {LLM_TENSOR_MASKED_EMBD_ORDERING,       {LLM_TENSOR_LAYER_INPUT,     GGML_OP_NONE}},
+    // eagle3
+    {LLM_TENSOR_FC,                         {LLM_TENSOR_LAYER_OUTPUT,    GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_D2T,                        {LLM_TENSOR_LAYER_OUTPUT,    GGML_OP_GET_ROWS}},
 };
 
 LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}

src/llama-arch.h

@@ -71,6 +71,7 @@ enum llm_arch {
     LLM_ARCH_XVERSE,
     LLM_ARCH_COMMAND_R,
     LLM_ARCH_COHERE2,
+    LLM_ARCH_COHERE2MOE,
     LLM_ARCH_DBRX,
     LLM_ARCH_OLMO,
     LLM_ARCH_OLMO2,
@@ -141,6 +142,7 @@ enum llm_arch {
     LLM_ARCH_KIMI_LINEAR,
     LLM_ARCH_TALKIE,
     LLM_ARCH_MELLUM,
+    LLM_ARCH_EAGLE3,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -314,6 +316,7 @@ enum llm_kv {
     LLM_KV_TOKENIZER_RWKV,
     LLM_KV_TOKENIZER_CHAT_TEMPLATE,
     LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE,
+    LLM_KV_TOKENIZER_NORMALIZER_STRIP_ACCENTS,
     LLM_KV_TOKENIZER_FIM_PRE_ID,
     LLM_KV_TOKENIZER_FIM_SUF_ID,
     LLM_KV_TOKENIZER_FIM_MID_ID,
@@ -336,6 +339,10 @@ enum llm_kv {
 
     LLM_KV_CLASSIFIER_OUTPUT_LABELS,
 
+    LLM_KV_TARGET_LAYERS,
+    LLM_KV_TARGET_HIDDEN_SIZE,
+    LLM_KV_NORM_BEFORE_RESIDUAL,
+
     LLM_KV_SHORTCONV_L_CACHE,
 
     LLM_KV_XIELU_ALPHA_N,
@@ -568,6 +575,8 @@ enum llm_tensor {
     LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
     LLM_TENSOR_MASKED_EMBD_CENTROIDS,
     LLM_TENSOR_MASKED_EMBD_ORDERING,
+    LLM_TENSOR_FC,
+    LLM_TENSOR_D2T,
 };
 
 

src/llama-context.cpp

@@ -71,6 +71,9 @@ llama_context::llama_context(
     cparams.no_perf                 = params.no_perf;
     cparams.warmup                  = false;
 
+    cparams.embeddings_layer_inp.resize(hparams.n_layer(), false);
+    embd_layer_inp.resize(hparams.n_layer());
+
     cparams.ctx_type     = params.ctx_type;
     cparams.pooling_type = params.pooling_type;
 
@@ -91,12 +94,21 @@ llama_context::llama_context(
     if (model.arch == LLM_ARCH_GEMMA4_ASSISTANT) {
         if (params.ctx_other == nullptr) {
             // TODO: change from runtime_error to llama_exception to avoid printing error message
-            throw std::runtime_error("Gemma4Assistant requires ctx_other to be set (this is normal during memory fitting)");
+            throw std::runtime_error("Gemma4Assistant requires ctx_other to be set (this warning is normal during memory fitting)");
         }
 
         cparams.ctx_other = params.ctx_other;
     }
 
+    if (model.arch == LLM_ARCH_EAGLE3) {
+        if (model.tok_embd == nullptr || model.output == nullptr) {
+            if (params.ctx_other == nullptr) {
+                throw std::runtime_error("EAGLE3 requires ctx_other to be set (this warning is normal during memory fitting)");
+            }
+            cparams.ctx_other = params.ctx_other;
+        }
+    }
+
     // Initialize backend samplers here so they are part of the sampling graph
     // before the reserve passes run later in this function. This avoids a later
     // re-reserve when graph nodes change.
@@ -194,7 +206,7 @@ llama_context::llama_context(
 
     cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
 
-    cparams.n_outputs_max = params.n_outputs_max == 0 ? cparams.n_batch : params.n_outputs_max;
+    cparams.n_outputs_max = params.n_outputs_max == 0 || llama_model_has_encoder(&model) ? cparams.n_batch : params.n_outputs_max;
 
     cparams.op_offload = params.op_offload;
     cparams.kv_unified = params.kv_unified;
@@ -938,6 +950,14 @@ float * llama_context::get_embeddings_nextn_ith(int32_t i) {
     }
 }
 
+float * llama_context::get_embeddings_layer_inp(uint32_t lid) {
+    output_reorder();
+
+    GGML_ASSERT(lid < embd_layer_inp.size() && embd_layer_inp[lid].has_data());
+
+    return embd_layer_inp[lid].data;
+}
+
 llama_token llama_context::get_sampled_token_ith(int32_t idx) {
     output_reorder();
 
@@ -1125,6 +1145,17 @@ void llama_context::set_embeddings_nextn(bool value, bool masked) {
     cparams.embeddings_nextn_masked = masked;
 }
 
+void llama_context::set_embeddings_layer_inp(uint32_t lid, bool enable) {
+    LLAMA_LOG_DEBUG("%s: lid = %d, enable = %d\n", __func__, lid, enable);
+
+    GGML_ASSERT(lid < model.hparams.n_layer());
+
+    cparams.embeddings_layer_inp[lid] = enable;
+
+    // note: without this reserve, the draft acceptance drops to zero. not sure why - this is unexpected
+    sched_need_reserve = true;
+}
+
 void llama_context::set_causal_attn(bool value) {
     LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
 
@@ -1350,7 +1381,8 @@ int llama_context::encode(const llama_batch & batch_inp) {
 
     const auto & hparams = model.hparams;
 
-    const int64_t n_embd  = hparams.n_embd_inp();
+    // eagle3/DFlash: features as encoder input, and non-draft paths fall back to model's input dim
+    const int64_t n_embd = hparams.n_embd_inp();
     const int64_t n_vocab = model.vocab.n_tokens();
 
     // note: during encode, we always pass the full sequence starting from pos = 0
@@ -1925,6 +1957,8 @@ int llama_context::decode(const llama_batch & batch_inp) {
             }
         }
 
+        extract_layer_inputs(res, n_tokens_prev, ubatch.n_tokens);
+
         // extract nextn embeddings before
         // only meaningful in LLAMA_POOLING_TYPE_NONE (per-token); other pooling modes are ignored.
         {
@@ -2029,6 +2063,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
     const auto n_batch    = cparams.n_batch;
     const auto n_vocab    = vocab.n_tokens();
+    const auto n_embd     = hparams.n_embd;
     const auto n_embd_out = hparams.n_embd_out();
 
     bool has_logits     = true;
@@ -2041,9 +2076,9 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
         has_embd   = true;
     }
 
-
     size_t backend_float_count = 0;
     size_t backend_token_count = 0;
+    size_t embd_layer_inp_float_count = 0;
 
     logits.size     = has_logits     ? n_vocab*n_outputs_max     : 0;
     embd.size       = has_embd       ? n_embd_out*n_outputs_max  : 0;
@@ -2055,6 +2090,12 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
         embd_nextn.size = (size_t) n_embd_out * n_batch;
     }
 
+    for (bool enabled : cparams.embeddings_layer_inp) {
+        if (enabled) {
+            embd_layer_inp_float_count += (size_t) n_embd * n_batch;
+        }
+    }
+
     // Allocate backend sampling output buffers if there are backend samplers configured.
     const bool has_sampling = !sampling.samplers.empty();
     if (has_sampling) {
@@ -2069,8 +2110,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
     const size_t prev_size = buf_output ? ggml_backend_buffer_get_size(buf_output.get()) : 0;
     const size_t new_size  =
-        (logits.size + embd.size + embd_nextn.size + backend_float_count) * sizeof(float) +
-        (                                               backend_token_count) * sizeof(llama_token);
+        (logits.size + embd.size + embd_nextn.size + embd_layer_inp_float_count + backend_float_count) * sizeof(float) +
+        (                                                                         backend_token_count) * sizeof(llama_token);
 
     // alloc only when more than the current capacity is required
     // TODO: also consider shrinking the buffer
@@ -2087,6 +2128,9 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
             logits.data = nullptr;
             embd.data = nullptr;
             embd_nextn.data = nullptr;
+            for (auto & layer_inp : embd_layer_inp) {
+                layer_inp = {nullptr, 0};
+            }
         }
 
         auto * buft = ggml_backend_cpu_buffer_type();
@@ -2118,6 +2162,15 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     embd_nextn = has_embd_nextn ? buffer_view<float>{(float *) (base + offset), embd_nextn.size} : buffer_view<float>{nullptr, 0};
     offset += embd_nextn.size * sizeof(float);
 
+    for (uint32_t il = 0; il < embd_layer_inp.size(); ++il) {
+        if (cparams.embeddings_layer_inp[il]) {
+            embd_layer_inp[il] = buffer_view<float>{(float *) (base + offset), (size_t) n_embd * n_batch};
+            offset += embd_layer_inp[il].size * sizeof(float);
+        } else {
+            embd_layer_inp[il] = buffer_view<float>{nullptr, 0};
+        }
+    }
+
     if (has_sampling) {
         sampling.logits = {(float *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
         offset += sampling.logits.size * sizeof(float);
@@ -2164,6 +2217,34 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     return n_outputs_max;
 }
 
+void llama_context::extract_layer_inputs(const llm_graph_result * res, size_t token_offset, size_t n_tokens) {
+    for (uint32_t il = 0; il < cparams.embeddings_layer_inp.size(); ++il) {
+        if (!cparams.embeddings_layer_inp[il]) {
+            continue;
+        }
+        if (!embd_layer_inp[il].has_data()) {
+            GGML_ABORT("output layer input buffer not allocated");
+        }
+        ggml_tensor * t = res->get_layer_inp((int) il);
+        if (!t) {
+            GGML_ABORT("layer input tensor not found");
+        }
+
+        const size_t nbytes = ggml_nbytes(t);
+        const size_t nfloats = nbytes / sizeof(float);
+        GGML_ASSERT(n_tokens > 0);
+        GGML_ASSERT(nfloats % n_tokens == 0);
+
+        const size_t row_floats = nfloats / n_tokens;
+        const size_t dst_offset = token_offset * row_floats;
+        GGML_ASSERT(dst_offset + nfloats <= embd_layer_inp[il].size);
+
+        ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t);
+        GGML_ASSERT(backend != nullptr);
+        ggml_backend_tensor_get_async(backend, t, embd_layer_inp[il].data + dst_offset, 0, nbytes);
+    }
+}
+
 void llama_context::output_reorder() {
     const uint64_t n_vocab = model.vocab.n_tokens();
     const uint64_t n_embd  = model.hparams.n_embd;
@@ -2190,6 +2271,16 @@ void llama_context::output_reorder() {
             }
         }
 
+        if (embd_layer_inp.size() > 0) {
+            for (int lid = 0; lid < (int) embd_layer_inp.size(); ++lid) {
+                if (embd_layer_inp[lid].size > 0) {
+                    for (uint64_t k = 0; k < n_embd; ++k) {
+                        std::swap(embd_layer_inp[lid].data[i0*n_embd + k], embd_layer_inp[lid].data[i1*n_embd + k]);
+                    }
+                }
+            }
+        }
+
         if (!sampling.samplers.empty()) {
             assert(sampling.logits.size > 0);
             assert(sampling.probs.size > 0);
@@ -3604,6 +3695,10 @@ void llama_set_embeddings_nextn(llama_context * ctx, bool value, bool masked) {
     ctx->set_embeddings_nextn(value, masked);
 }
 
+void llama_set_embeddings_layer_inp(llama_context * ctx, uint32_t lid, bool value) {
+    ctx->set_embeddings_layer_inp(lid, value);
+}
+
 llama_memory_t llama_get_memory(const struct llama_context * ctx) {
     if (!ctx) {
         return nullptr;
@@ -3624,6 +3719,12 @@ float * llama_get_embeddings_nextn_ith(llama_context * ctx, int32_t i) {
     return ctx->get_embeddings_nextn_ith(i);
 }
 
+float * llama_get_embeddings_layer_inp(llama_context * ctx, uint32_t lid) {
+    ctx->synchronize();
+
+    return ctx->get_embeddings_layer_inp(lid);
+}
+
 bool llama_set_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * smpl) {
     return ctx->set_sampler(seq_id, smpl);
 }

src/llama-context.h

@@ -88,6 +88,8 @@ struct llama_context {
     float * get_embeddings_nextn();
     float * get_embeddings_nextn_ith(int32_t i);
 
+    float * get_embeddings_layer_inp(uint32_t lid);
+
     llama_token * get_sampled_tokens() const;
     llama_token   get_sampled_token_ith(int32_t idx);
 
@@ -112,6 +114,7 @@ struct llama_context {
 
     void set_embeddings (bool value);
     void set_embeddings_nextn(bool value, bool masked);
+    void set_embeddings_layer_inp(uint32_t lid, bool enable);
     void set_causal_attn(bool value);
     void set_warmup(bool value);
 
@@ -226,6 +229,10 @@ private:
     // map the output row index `i` to batch index
     int64_t output_resolve_row(int32_t i) const;
 
+    // async-copy enabled layer-input tensors (per cparams.output_layer_inp)
+    // from backend into host-side embd_layer_inp buffers
+    void extract_layer_inputs(const llm_graph_result * res, size_t token_offset, size_t n_tokens);
+
     //
     // graph
     //
@@ -288,6 +295,10 @@ private:
     // sets llm_graph_result::t_h_nextn
     buffer_view<float> embd_nextn = {nullptr, 0};
 
+    // host buffers for output layer input embeddings, per layer
+    // populated when cparams.output_layer_inp[il] is true
+    std::vector<buffer_view<float>> embd_layer_inp;
+
     struct sampling_info {
         // !samplers.empty() to check if any samplers are active
         std::map<llama_seq_id, llama_sampler *> samplers;

src/llama-cparams.h

@@ -3,6 +3,7 @@
 #include "llama.h"
 
 #include <cstdint>
+#include <vector>
 
 #define LLAMA_MAX_SEQ 256
 
@@ -44,6 +45,8 @@ struct llama_cparams {
     bool kv_unified;
     bool pipeline_parallel;
 
+    std::vector<bool> embeddings_layer_inp; // [n_layer()] extract input embeddings for layer
+
     enum llama_context_type ctx_type;
     enum llama_pooling_type pooling_type;
 

src/llama-ext.h

@@ -2,6 +2,7 @@
 
 // this is a staging header for new llama.cpp API
 // breaking changes and C++ are allowed. everything here should be considered WIP
+// try as much as possible to not include this header in the rest of the codebase
 
 #include "llama.h"
 
@@ -101,4 +102,20 @@ LLAMA_API float * llama_get_embeddings_nextn(struct llama_context * ctx);
 // LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
 LLAMA_API float * llama_get_embeddings_nextn_ith(struct llama_context * ctx, int32_t i);
 
+// Set whether the context outputs the input embeddings of a specific layer
+LLAMA_API void llama_set_embeddings_layer_inp(struct llama_context * ctx, uint32_t lid, bool value);
+
+// mirrors:
+// LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
+LLAMA_API float * llama_get_embeddings_layer_inp(struct llama_context * ctx, uint32_t lid);
+
 LLAMA_API llama_context * llama_get_ctx_other(struct llama_context * ctx);
+
+//
+// model/context data extraction
+//
+
+// returns pointer to the target-model layer indices
+LLAMA_API const int32_t * llama_model_target_layer_ids  (const struct llama_model * model);
+// returns the number of extracted layers from target model
+LLAMA_API uint32_t        llama_model_target_layer_ids_n(const struct llama_model * model);