server: (webui) no more gzip compression (#21073)

* webui: no more gzip

* try changing a small line

* Revert "try changing a small line"

This reverts commit 0d7a353159.

* fix lint

* fix test

* rebuild

* split into html/css/js

* lint

* chore: update webui build output

* chore: Update git hooks script

* server: update webui build output

* chore: Update pre-commit hook

* refactor: Cleanup

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>

.devops/cpu.Dockerfile

@@ -36,7 +36,7 @@ RUN mkdir -p /app/full \
 FROM ubuntu:$UBUNTU_VERSION AS base
 
 RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
+    && apt-get install -y libgomp1 curl \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

.devops/cuda-new.Dockerfile

@@ -1,6 +1,6 @@
 ARG UBUNTU_VERSION=24.04
 # This needs to generally match the container host's environment.
-ARG CUDA_VERSION=13.1.0
+ARG CUDA_VERSION=13.1.1
 # Target the CUDA build image
 ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
 
@@ -12,7 +12,9 @@ FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 ARG CUDA_DOCKER_ARCH=default
 
 RUN apt-get update && \
-    apt-get install -y build-essential cmake python3 python3-pip git libssl-dev libgomp1
+    apt-get install -y gcc-14 g++-14 build-essential cmake python3 python3-pip git libssl-dev libgomp1
+
+ENV CC=gcc-14 CXX=g++-14 CUDAHOSTCXX=g++-14
 
 WORKDIR /app
 
@@ -39,7 +41,7 @@ RUN mkdir -p /app/full \
 FROM ${BASE_CUDA_RUN_CONTAINER} AS base
 
 RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
+    && apt-get install -y libgomp1 curl \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

.devops/cuda.Dockerfile

@@ -1,6 +1,6 @@
-ARG UBUNTU_VERSION=22.04
+ARG UBUNTU_VERSION=24.04
 # This needs to generally match the container host's environment.
-ARG CUDA_VERSION=12.4.0
+ARG CUDA_VERSION=12.8.1
 # Target the CUDA build image
 ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
 
@@ -12,7 +12,9 @@ FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 ARG CUDA_DOCKER_ARCH=default
 
 RUN apt-get update && \
-    apt-get install -y build-essential cmake python3 python3-pip git libssl-dev libgomp1
+    apt-get install -y gcc-14 g++-14 build-essential cmake python3 python3-pip git libssl-dev libgomp1
+
+ENV CC=gcc-14 CXX=g++-14 CUDAHOSTCXX=g++-14
 
 WORKDIR /app
 
@@ -39,7 +41,7 @@ RUN mkdir -p /app/full \
 FROM ${BASE_CUDA_RUN_CONTAINER} AS base
 
 RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
+    && apt-get install -y libgomp1 curl \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \
@@ -60,7 +62,8 @@ RUN apt-get update \
     git \
     python3 \
     python3-pip \
-    && pip install --upgrade pip setuptools wheel \
+    python3-wheel \
+    && pip install --break-system-packages --upgrade setuptools \
     && pip install --break-system-packages -r requirements.txt \
     && apt autoremove -y \
     && apt clean -y \

.devops/intel.Dockerfile

@@ -33,8 +33,25 @@ RUN mkdir -p /app/full \
 
 FROM intel/deep-learning-essentials:$ONEAPI_VERSION AS base
 
+ARG IGC_VERSION=v2.30.1
+ARG IGC_VERSION_FULL=2_2.30.1+20950
+ARG COMPUTE_RUNTIME_VERSION=26.09.37435.1
+ARG COMPUTE_RUNTIME_VERSION_FULL=26.09.37435.1-0
+ARG IGDGMM_VERSION=22.9.0
+RUN mkdir /tmp/neo/ && cd /tmp/neo/ \
+  && wget https://github.com/intel/intel-graphics-compiler/releases/download/$IGC_VERSION/intel-igc-core-${IGC_VERSION_FULL}_amd64.deb \
+  && wget https://github.com/intel/intel-graphics-compiler/releases/download/$IGC_VERSION/intel-igc-opencl-${IGC_VERSION_FULL}_amd64.deb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/intel-ocloc-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/intel-ocloc_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/intel-opencl-icd-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/intel-opencl-icd_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/libigdgmm12_${IGDGMM_VERSION}_amd64.deb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/libze-intel-gpu1-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/libze-intel-gpu1_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
+  && dpkg --install *.deb
+
 RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
+    && apt-get install -y libgomp1 curl \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

.devops/musa.Dockerfile

@@ -46,7 +46,7 @@ RUN mkdir -p /app/full \
 FROM ${BASE_MUSA_RUN_CONTAINER} AS base
 
 RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
+    && apt-get install -y libgomp1 curl \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

.devops/openvino.Dockerfile

@@ -78,7 +78,7 @@ ARG http_proxy
 ARG https_proxy
 
 RUN apt-get update \
-    && apt-get install -y libgomp1 libtbb12 curl\
+    && apt-get install -y libgomp1 libtbb12 curl \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

.devops/rocm.Dockerfile

@@ -58,7 +58,7 @@ RUN mkdir -p /app/full \
 FROM ${BASE_ROCM_DEV_CONTAINER} AS base
 
 RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
+    && apt-get install -y libgomp1 curl \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \
@@ -79,7 +79,7 @@ RUN apt-get update \
     git \
     python3-pip \
     python3 \
-    python3-wheel\
+    python3-wheel \
     && pip install --break-system-packages --upgrade setuptools \
     && pip install --break-system-packages -r requirements.txt \
     && apt autoremove -y \

.devops/vulkan.Dockerfile

@@ -49,17 +49,20 @@ COPY --from=build /app/full /app
 
 WORKDIR /app
 
+ENV PATH="/root/.venv/bin:/root/.local/bin:${PATH}"
+
+# Flag for compatibility with pip
+ARG UV_INDEX_STRATEGY="unsafe-best-match"
 RUN apt-get update \
     && apt-get install -y \
     build-essential \
+    curl \
     git \
-    python3.13 \
-    python3.13-dev \
-    python3-pip \
-    python3-wheel \
-    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.13 100 \
-    && pip install --break-system-packages --upgrade setuptools \
-    && pip install --break-system-packages -r requirements.txt \
+    ca-certificates \
+    && curl -LsSf https://astral.sh/uv/install.sh | sh \
+    && uv python install 3.13 \
+    && uv venv --python 3.13 /root/.venv \
+    && uv pip install --python /root/.venv/bin/python -r requirements.txt \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

.editorconfig

@@ -21,14 +21,6 @@ indent_style = tab
 [prompts/*.txt]
 insert_final_newline = unset
 
-[tools/server/public/*]
-indent_size = 2
-
-[tools/server/public/deps_*]
-trim_trailing_whitespace = unset
-indent_style = unset
-indent_size = unset
-
 [tools/server/deps_*]
 trim_trailing_whitespace = unset
 indent_style = unset
@@ -61,6 +53,14 @@ charset = unset
 trim_trailing_whitespace = unset
 insert_final_newline = unset
 
+[tools/server/public/**]
+indent_style = unset
+indent_size = unset
+end_of_line = unset
+charset = unset
+trim_trailing_whitespace = unset
+insert_final_newline = unset
+
 [benches/**]
 indent_style = unset
 indent_size = unset

.gitattributes

@@ -0,0 +1,4 @@
+# Treat the generated single-file WebUI build as binary for diff purposes.
+# Git's pack-file delta compression still works (byte-level), but this prevents
+# git diff from printing the entire minified file on every change.
+tools/server/public/index.html -diff

.github/workflows/build.yml

@@ -181,7 +181,7 @@ jobs:
           - build: 'x64'
             os: ubuntu-22.04
           - build: 'arm64'
-            os: ubuntu-22.04-arm
+            os: ubuntu-24.04-arm
           - build: 's390x'
             os: ubuntu-24.04-s390x
           - build: 'ppc64le'
@@ -207,14 +207,22 @@ jobs:
         run: |
           sudo apt-get update
           sudo apt-get install -y --no-install-recommends \
-            python3 python3-pip python3-dev \
+            python3 python3-pip python3-dev python3-wheel \
             libjpeg-dev build-essential libssl-dev \
             git-lfs
 
+      - name: Toolchain workaround (GCC 14)
+        if: ${{ contains(matrix.os, 'ubuntu-24.04') }}
+        run: |
+          sudo apt-get install -y gcc-14 g++-14
+          echo "CC=gcc-14" >> "$GITHUB_ENV"
+          echo "CXX=g++-14" >> "$GITHUB_ENV"
+
       - name: Python Dependencies
         id: python_depends
         run: |
-          python3 -m pip install --upgrade pip
+          export PIP_BREAK_SYSTEM_PACKAGES="1"
+          python3 -m pip install --upgrade pip setuptools
           pip3 install ./gguf-py
 
       - name: Swap Endianness
@@ -292,7 +300,15 @@ jobs:
           ctest -L main --verbose
 
   ubuntu-24-vulkan:
-    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
+    strategy:
+      matrix:
+        include:
+          - build: 'x64'
+            os: ubuntu-24.04
+          - build: 'arm64'
+            os: ubuntu-24.04-arm
+
+    runs-on: ${{ matrix.os }}
 
     steps:
       - name: Clone
@@ -302,7 +318,10 @@ jobs:
       - name: Dependencies
         id: depends
         run: |
-          sudo apt-get install -y glslc libvulkan-dev libssl-dev ninja-build
+          sudo apt-get update
+          sudo apt-get install -y gcc-14 g++-14 build-essential glslc libvulkan-dev libssl-dev ninja-build
+          echo "CC=gcc-14" >> "$GITHUB_ENV"
+          echo "CXX=g++-14" >> "$GITHUB_ENV"
 
       - name: Configure
         id: cmake_configure

.github/workflows/docker.yml

@@ -25,184 +25,13 @@ permissions:
   packages: write
 
 jobs:
-  push_to_registry:
-    name: Push Docker image to Docker Hub
-
-    runs-on: ${{ matrix.config.runs_on }}
-    env:
-      COMMIT_SHA: ${{ github.sha }}
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          # Multi-stage build
-          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/arm64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
-          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
-          - { tag: "cuda cuda12", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-24.04", cuda_version: "12.4.0", ubuntu_version: "22.04" }
-          - { tag: "cuda13", dockerfile: ".devops/cuda-new.Dockerfile",  platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-24.04", cuda_version: "13.1.0", ubuntu_version: "24.04" }
-          - { tag: "musa",   dockerfile: ".devops/musa.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-24.04" }
-          - { tag: "intel",  dockerfile: ".devops/intel.Dockerfile",  platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-24.04" }
-          - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
-          - { tag: "s390x",  dockerfile: ".devops/s390x.Dockerfile",  platforms: "linux/s390x", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04-s390x" }
-          - { tag: "rocm",   dockerfile: ".devops/rocm.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-24.04" }
-          - { tag: "openvino", dockerfile: ".devops/openvino.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
-    steps:
-      - name: Check out the repo
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0 # preserve git history, so we can determine the build number
-
-      - name: Set up QEMU
-        if: ${{ matrix.config.tag != 's390x' }}
-        uses: docker/setup-qemu-action@c7c53464625b32c7a7e944ae62b3e17d2b600130 # v3
-        with:
-          image: tonistiigi/binfmt:qemu-v10.2.1
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9 # v3
-        with:
-          registry: ghcr.io
-          username: ${{ github.repository_owner }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Determine source tag name
-        id: srctag
-        uses: ./.github/actions/get-tag-name
-        env:
-          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
-
-      - name: Determine image tag name
-        id: tag
-        shell: bash
-        run: |
-          REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}"  # to lower case
-          REPO_NAME="${{ github.event.repository.name }}"
-          PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:"
-
-          # list all tags possible
-          tags="${{ matrix.config.tag }}"
-          for tag in $tags; do
-              if [[ "$tag" == "cpu" ]]; then
-                  TYPE=""
-              else
-                  TYPE="-$tag"
-              fi
-              CACHETAGS="${PREFIX}buildcache${TYPE}"
-              FULLTAGS="${FULLTAGS:+$FULLTAGS,}${PREFIX}full${TYPE},${PREFIX}full${TYPE}-${{ steps.srctag.outputs.name }}"
-              LIGHTTAGS="${LIGHTTAGS:+$LIGHTTAGS,}${PREFIX}light${TYPE},${PREFIX}light${TYPE}-${{ steps.srctag.outputs.name }}"
-              SERVERTAGS="${SERVERTAGS:+$SERVERTAGS,}${PREFIX}server${TYPE},${PREFIX}server${TYPE}-${{ steps.srctag.outputs.name }}"
-          done
-          echo "cache_output_tags=$CACHETAGS" >> $GITHUB_OUTPUT
-          echo "full_output_tags=$FULLTAGS" >> $GITHUB_OUTPUT
-          echo "light_output_tags=$LIGHTTAGS" >> $GITHUB_OUTPUT
-          echo "server_output_tags=$SERVERTAGS" >> $GITHUB_OUTPUT
-          echo "cache_output_tags=$CACHETAGS"  # print out for debugging
-          echo "full_output_tags=$FULLTAGS"  # print out for debugging
-          echo "light_output_tags=$LIGHTTAGS"  # print out for debugging
-          echo "server_output_tags=$SERVERTAGS"  # print out for debugging
-        env:
-          GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
-
-      - name: Free Disk Space (Ubuntu)
-        if: ${{ matrix.config.free_disk_space == true }}
-        uses: ggml-org/free-disk-space@v1.3.1
-        with:
-          # this might remove tools that are actually needed,
-          # if set to "true" but frees about 6 GB
-          tool-cache: false
-
-          # all of these default to true, but feel free to set to
-          # "false" if necessary for your workflow
-          android: true
-          dotnet: true
-          haskell: true
-          large-packages: true
-          docker-images: true
-          swap-storage: true
-
-      - name: Build and push Full Docker image (tagged + versioned)
-        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }}
-        uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
-        with:
-          context: .
-          push: true
-          platforms: ${{ matrix.config.platforms }}
-          # tag list is generated from step above
-          tags: ${{ steps.tag.outputs.full_output_tags }}
-          file: ${{ matrix.config.dockerfile }}
-          target: full
-          provenance: false
-          build-args: |
-            ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
-            ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
-          # using github experimental cache
-          #cache-from: type=gha
-          #cache-to: type=gha,mode=max
-          # return to this if the experimental github cache is having issues
-          #cache-to: type=local,dest=/tmp/.buildx-cache
-          #cache-from: type=local,src=/tmp/.buildx-cache
-          # using registry cache (no storage limit)
-          cache-from: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }}
-          cache-to: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }},mode=max
-
-      - name: Build and push Light Docker image (tagged + versioned)
-        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }}
-        uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
-        with:
-          context: .
-          push: true
-          platforms: ${{ matrix.config.platforms }}
-          # tag list is generated from step above
-          tags: ${{ steps.tag.outputs.light_output_tags }}
-          file: ${{ matrix.config.dockerfile }}
-          target: light
-          provenance: false
-          build-args: |
-            ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
-            ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
-          # using github experimental cache
-          #cache-from: type=gha
-          #cache-to: type=gha,mode=max
-          # return to this if the experimental github cache is having issues
-          #cache-to: type=local,dest=/tmp/.buildx-cache
-          #cache-from: type=local,src=/tmp/.buildx-cache
-          # using registry cache (no storage limit)
-          cache-from: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }}
-          cache-to: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }},mode=max
-
-      - name: Build and push Server Docker image (tagged + versioned)
-        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }}
-        uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
-        with:
-          context: .
-          push: true
-          platforms: ${{ matrix.config.platforms }}
-          # tag list is generated from step above
-          tags: ${{ steps.tag.outputs.server_output_tags }}
-          file: ${{ matrix.config.dockerfile }}
-          target: server
-          provenance: false
-          build-args: |
-            ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
-            ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
-          # using github experimental cache
-          #cache-from: type=gha
-          #cache-to: type=gha,mode=max
-          # return to this if the experimental github cache is having issues
-          #cache-to: type=local,dest=/tmp/.buildx-cache
-          #cache-from: type=local,src=/tmp/.buildx-cache
-          # using registry cache (no storage limit)
-          cache-from: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }}
-          cache-to: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }},mode=max
-
   create_tag:
     name: Create and push git tag
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-slim
     permissions:
       contents: write
+    outputs:
+      source_tag: ${{ steps.srctag.outputs.name }}
 
     steps:
       - name: Clone
@@ -223,3 +52,391 @@ jobs:
         run: |
           git tag ${{ steps.srctag.outputs.name }} || exit 0
           git push origin ${{ steps.srctag.outputs.name }} || exit 0
+
+  prepare_matrices:
+    name: Prepare Docker matrices
+    runs-on: ubuntu-24.04
+    outputs:
+      build_matrix: ${{ steps.matrices.outputs.build_matrix }}
+      merge_matrix: ${{ steps.matrices.outputs.merge_matrix }}
+
+    steps:
+      - name: Generate build and merge matrices
+        id: matrices
+        shell: bash
+        run: |
+          set -euo pipefail
+
+          # Keep all build targets in one place and derive merge targets from it.
+          cat > build-matrix.json <<'JSON'
+          [
+            { "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" },
+            { "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-arm" },
+            { "tag": "cpu", "dockerfile": ".devops/s390x.Dockerfile", "platforms": "linux/s390x", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-s390x" },
+            { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
+            { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
+            { "tag": "cuda13", "dockerfile": ".devops/cuda-new.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
+            { "tag": "cuda13", "dockerfile": ".devops/cuda-new.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
+            { "tag": "musa", "dockerfile": ".devops/musa.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
+            { "tag": "intel", "dockerfile": ".devops/intel.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
+            { "tag": "vulkan", "dockerfile": ".devops/vulkan.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" },
+            { "tag": "vulkan", "dockerfile": ".devops/vulkan.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-arm" },
+            { "tag": "rocm", "dockerfile": ".devops/rocm.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
+            { "tag": "openvino", "dockerfile": ".devops/openvino.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" }
+          ]
+          JSON
+
+          BUILD_MATRIX="$(jq -c . build-matrix.json)"
+          MERGE_MATRIX="$(jq -c '
+            reduce .[] as $entry ({}; .[$entry.tag] |= (
+              . // {
+                tag: $entry.tag,
+                arches: [],
+                full: false,
+                light: false,
+                server: false
+              }
+              | .full = (.full or ($entry.full // false))
+              | .light = (.light or ($entry.light // false))
+              | .server = (.server or ($entry.server // false))
+              | .arches += [($entry.platforms | sub("^linux/"; ""))]
+            ))
+            # Backward compatibility: s390x tags are aliases of cpu for the linux/s390x platform.
+            | if (has("cpu") and (((.cpu.arches // []) | index("s390x")) != null)) then
+                . + {
+                  s390x: {
+                    tag: "s390x",
+                    arches: ["s390x"],
+                    full: .cpu.full,
+                    light: .cpu.light,
+                    server: .cpu.server
+                  }
+                }
+              else
+                .
+              end
+            | [.[] | .arches = (.arches | unique | sort | join(" "))]
+          ' build-matrix.json)"
+
+          echo "build_matrix=$BUILD_MATRIX" >> "$GITHUB_OUTPUT"
+          echo "merge_matrix=$MERGE_MATRIX" >> "$GITHUB_OUTPUT"
+
+  push_to_registry:
+    name: Push Docker image to Docker Registry
+    needs: [prepare_matrices, create_tag]
+
+    runs-on: ${{ matrix.config.runs_on }}
+    strategy:
+      fail-fast: false
+      matrix:
+        config: ${{ fromJSON(needs.prepare_matrices.outputs.build_matrix) }}
+    steps:
+      - name: Check out the repo
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+          ref: ${{ needs.create_tag.outputs.source_tag }}
+
+      - name: Set up QEMU
+        if: ${{ contains(matrix.config.platforms, 'linux/amd64') }}
+        uses: docker/setup-qemu-action@ce360397dd3f832beb865e1373c09c0e9f86d70a # v4
+        with:
+          image: tonistiigi/binfmt:qemu-v10.2.1
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@4d04d5d9486b7bd6fa91e7baf45bbb4f8b9deedd # v4
+
+      - name: Log in to Docker Registry
+        uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Determine image metadata
+        id: meta
+        shell: bash
+        run: |
+          set -euo pipefail
+
+          REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}"  # to lower case
+          REPO_NAME="${{ github.event.repository.name }}"
+          IMAGE_REPO="ghcr.io/${REPO_OWNER}/${REPO_NAME}"
+          PREFIX="${IMAGE_REPO}:"
+          PLATFORM="${{ matrix.config.platforms }}"
+          ARCH_SUFFIX="${PLATFORM#linux/}"
+
+          # list all tags possible
+          tags="${{ matrix.config.tag }}"
+          for tag in $tags; do
+              if [[ "$tag" == "cpu" ]]; then
+                  TYPE=""
+              else
+                  TYPE="-$tag"
+              fi
+              CACHETAG="${PREFIX}buildcache${TYPE}-${ARCH_SUFFIX}"
+          done
+
+          SAFE_TAGS="$(echo "$tags" | tr ' ' '_')"
+
+          echo "image_repo=$IMAGE_REPO" >> $GITHUB_OUTPUT
+          echo "arch_suffix=$ARCH_SUFFIX" >> $GITHUB_OUTPUT
+          echo "cache_output_tag=$CACHETAG" >> $GITHUB_OUTPUT
+          echo "digest_artifact_suffix=${SAFE_TAGS}-${ARCH_SUFFIX}" >> $GITHUB_OUTPUT
+          echo "cache_output_tag=$CACHETAG"  # print out for debugging
+        env:
+          GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
+
+      - name: Free Disk Space (Ubuntu)
+        if: ${{ matrix.config.free_disk_space == true }}
+        uses: ggml-org/free-disk-space@v1.3.1
+        with:
+          # this might remove tools that are actually needed,
+          # if set to "true" but frees about 6 GB
+          tool-cache: false
+
+          # all of these default to true, but feel free to set to
+          # "false" if necessary for your workflow
+          android: true
+          dotnet: true
+          haskell: true
+          large-packages: true
+          docker-images: true
+          swap-storage: true
+
+      - name: Build and push Full Docker image by digest
+        id: build_full
+        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }}
+        uses: docker/build-push-action@d08e5c354a6adb9ed34480a06d141179aa583294 # v7
+        with:
+          context: .
+          platforms: ${{ matrix.config.platforms }}
+          outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true
+          file: ${{ matrix.config.dockerfile }}
+          target: full
+          provenance: false
+          build-args: |
+            ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
+            ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
+          # using github experimental cache
+          #cache-from: type=gha
+          #cache-to: type=gha,mode=max
+          # return to this if the experimental github cache is having issues
+          #cache-to: type=local,dest=/tmp/.buildx-cache
+          #cache-from: type=local,src=/tmp/.buildx-cache
+          # using registry cache (no storage limit)
+          cache-from: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }}
+          cache-to: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }},mode=max
+
+      - name: Build and push Light Docker image by digest
+        id: build_light
+        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }}
+        uses: docker/build-push-action@d08e5c354a6adb9ed34480a06d141179aa583294 # v7
+        with:
+          context: .
+          platforms: ${{ matrix.config.platforms }}
+          outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true
+          file: ${{ matrix.config.dockerfile }}
+          target: light
+          provenance: false
+          build-args: |
+            ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
+            ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
+          # using github experimental cache
+          #cache-from: type=gha
+          #cache-to: type=gha,mode=max
+          # return to this if the experimental github cache is having issues
+          #cache-to: type=local,dest=/tmp/.buildx-cache
+          #cache-from: type=local,src=/tmp/.buildx-cache
+          # using registry cache (no storage limit)
+          cache-from: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }}
+          cache-to: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }},mode=max
+
+      - name: Build and push Server Docker image by digest
+        id: build_server
+        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }}
+        uses: docker/build-push-action@d08e5c354a6adb9ed34480a06d141179aa583294 # v7
+        with:
+          context: .
+          platforms: ${{ matrix.config.platforms }}
+          outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true
+          file: ${{ matrix.config.dockerfile }}
+          target: server
+          provenance: false
+          build-args: |
+            ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
+            ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
+          # using github experimental cache
+          #cache-from: type=gha
+          #cache-to: type=gha,mode=max
+          # return to this if the experimental github cache is having issues
+          #cache-to: type=local,dest=/tmp/.buildx-cache
+          #cache-from: type=local,src=/tmp/.buildx-cache
+          # using registry cache (no storage limit)
+          cache-from: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }}
+          cache-to: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }},mode=max
+
+      - name: Export digest metadata
+        shell: bash
+        run: |
+            set -euo pipefail
+
+            TAGS="${{ matrix.config.tag }}"
+            ARCH_SUFFIX="${{ steps.meta.outputs.arch_suffix }}"
+            DIGEST_FILE="/tmp/digests/${{ steps.meta.outputs.digest_artifact_suffix }}.tsv"
+            mkdir -p /tmp/digests
+
+            add_digest_rows() {
+                local image_type="$1"
+                local digest="$2"
+
+                if [[ -z "$digest" ]]; then
+                  echo "Missing digest for image_type=${image_type}" >&2
+                  exit 1
+                fi
+
+                for tag in $TAGS; do
+                    printf '%s\t%s\t%s\t%s\n' "$tag" "$ARCH_SUFFIX" "$image_type" "$digest" >> "$DIGEST_FILE"
+                done
+            }
+
+            if [[ "${{ matrix.config.full }}" == "true" ]]; then
+                add_digest_rows "full" "${{ steps.build_full.outputs.digest }}"
+            fi
+
+            if [[ "${{ matrix.config.light }}" == "true" ]]; then
+                add_digest_rows "light" "${{ steps.build_light.outputs.digest }}"
+            fi
+
+            if [[ "${{ matrix.config.server }}" == "true" ]]; then
+                add_digest_rows "server" "${{ steps.build_server.outputs.digest }}"
+            fi
+
+      - name: Upload digest metadata
+        uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7
+        with:
+          name: digests-${{ steps.meta.outputs.digest_artifact_suffix }}
+          path: /tmp/digests/${{ steps.meta.outputs.digest_artifact_suffix }}.tsv
+          if-no-files-found: error
+
+  merge_arch_tags:
+    name: Create shared tags from digests
+    needs: [prepare_matrices, push_to_registry, create_tag]
+    runs-on: ubuntu-24.04
+    strategy:
+      fail-fast: false
+      matrix:
+        config: ${{ fromJSON(needs.prepare_matrices.outputs.merge_matrix) }}
+
+    steps:
+      - name: Check out the repo
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      - name: Download digest metadata
+        uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8
+        with:
+          pattern: digests-*
+          path: /tmp/digests
+          merge-multiple: true
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@4d04d5d9486b7bd6fa91e7baf45bbb4f8b9deedd # v4
+
+      - name: Log in to Docker Registry
+        uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Create tags from digests
+        shell: bash
+        run: |
+          set -euo pipefail
+
+          REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}"  # to lower case
+          REPO_NAME="${{ github.event.repository.name }}"
+          IMAGE_REPO="ghcr.io/${REPO_OWNER}/${REPO_NAME}"
+          PREFIX="${IMAGE_REPO}:"
+          SRC_TAG="${{ needs.create_tag.outputs.source_tag }}"
+          TAGS="${{ matrix.config.tag }}"
+          ARCHES="${{ matrix.config.arches }}"
+          DIGEST_GLOB="/tmp/digests/*.tsv"
+
+          if ! ls ${DIGEST_GLOB} >/dev/null 2>&1; then
+              echo "No digest metadata found in /tmp/digests" >&2
+              exit 1
+          fi
+
+          if [[ -z "$SRC_TAG" ]]; then
+              echo "Missing source tag from create_tag" >&2
+              exit 1
+          fi
+
+          find_digest() {
+              local tag_name="$1"
+              local arch="$2"
+              local image_type="$3"
+              local digest
+
+              digest="$(awk -F '\t' -v t="$tag_name" -v a="$arch" -v i="$image_type" '$1 == t && $2 == a && $3 == i { print $4; exit }' ${DIGEST_GLOB})"
+
+              # Backward compatibility: s390x tags are aliases of cpu for the linux/s390x platform.
+              if [[ -z "$digest" && "$tag_name" == "s390x" && "$arch" == "s390x" ]]; then
+                digest="$(awk -F '\t' -v t="cpu" -v a="$arch" -v i="$image_type" '$1 == t && $2 == a && $3 == i { print $4; exit }' ${DIGEST_GLOB})"
+              fi
+
+              if [[ -z "$digest" ]]; then
+                echo "Missing digest for tag=${tag_name} arch=${arch} image_type=${image_type}" >&2
+                exit 1
+              fi
+
+              echo "$digest"
+          }
+
+          create_manifest_tags() {
+              local image_type="$1"
+              local tag_name="$2"
+              local suffix="$3"
+
+              local merged_tag="${PREFIX}${image_type}${suffix}"
+              local merged_versioned_tag="${merged_tag}-${SRC_TAG}"
+
+              local refs=()
+
+              for arch in $ARCHES; do
+                  local digest
+                  digest="$(find_digest "$tag_name" "$arch" "$image_type")"
+                  refs+=("${IMAGE_REPO}@${digest}")
+              done
+
+              echo "Creating ${merged_tag} from ${refs[*]}"
+              docker buildx imagetools create --tag "${merged_tag}" "${refs[@]}"
+
+              echo "Creating ${merged_versioned_tag} from ${refs[*]}"
+              docker buildx imagetools create --tag "${merged_versioned_tag}" "${refs[@]}"
+          }
+
+          for tag in $TAGS; do
+              if [[ "$tag" == "cpu" ]]; then
+                  TYPE=""
+              else
+                  TYPE="-$tag"
+              fi
+
+              if [[ "${{ matrix.config.full }}" == "true" ]]; then
+                  create_manifest_tags "full" "$tag" "$TYPE"
+              fi
+
+              if [[ "${{ matrix.config.light }}" == "true" ]]; then
+                  create_manifest_tags "light" "$tag" "$TYPE"
+              fi
+
+              if [[ "${{ matrix.config.server }}" == "true" ]]; then
+                  create_manifest_tags "server" "$tag" "$TYPE"
+              fi
+          done
+        env:
+          GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'

.github/workflows/python-type-check.yml

@@ -31,7 +31,7 @@ jobs:
         uses: actions/setup-python@v6
         with:
           python-version: "3.11"
-          pip-install: -r requirements/requirements-all.txt ty==0.0.24
+          pip-install: -r requirements/requirements-all.txt ty==0.0.26
       # - name: Type-check with Pyright
       #   uses: jakebailey/pyright-action@v2
       #   with:

.github/workflows/release.yml

@@ -131,17 +131,16 @@ jobs:
           path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.tar.gz
           name: llama-bin-macos-x64.tar.gz
 
-  ubuntu-22-cpu:
+  ubuntu-cpu:
     strategy:
       matrix:
         include:
           - build: 'x64'
             os: ubuntu-22.04
+          - build: 'arm64'
+            os: ubuntu-24.04-arm
           - build: 's390x'
             os: ubuntu-24.04-s390x
-          # GGML_BACKEND_DL and GGML_CPU_ALL_VARIANTS are not currently supported on arm
-          # - build: 'arm64'
-          #   os: ubuntu-22.04-arm
 
     runs-on: ${{ matrix.os }}
 
@@ -165,6 +164,13 @@ jobs:
           sudo apt-get update
           sudo apt-get install build-essential libssl-dev
 
+      - name: Toolchain workaround (GCC 14)
+        if: ${{ contains(matrix.os, 'ubuntu-24.04') }}
+        run: |
+          sudo apt-get install -y gcc-14 g++-14
+          echo "CC=gcc-14" >> "$GITHUB_ENV"
+          echo "CXX=g++-14" >> "$GITHUB_ENV"
+
       - name: Build
         id: cmake_build
         run: |
@@ -194,8 +200,16 @@ jobs:
           path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz
           name: llama-bin-ubuntu-${{ matrix.build }}.tar.gz
 
-  ubuntu-22-vulkan:
-    runs-on: ubuntu-22.04
+  ubuntu-vulkan:
+    strategy:
+      matrix:
+        include:
+          - build: 'x64'
+            os: ubuntu-22.04
+          - build: 'arm64'
+            os: ubuntu-24.04-arm
+
+    runs-on: ${{ matrix.os }}
 
     steps:
       - name: Clone
@@ -207,16 +221,23 @@ jobs:
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
         with:
-          key: ubuntu-22-vulkan
+          key: ubuntu-vulkan-${{ matrix.build }}
           evict-old-files: 1d
 
       - name: Dependencies
         id: depends
         run: |
-          wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
-          sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
-          sudo apt-get update -y
-          sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libssl-dev
+          if [[ "${{ matrix.os }}" =~ "ubuntu-22.04" ]]; then
+            wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
+            sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
+            sudo apt-get update -y
+            sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libssl-dev
+          else
+            sudo apt-get update -y
+            sudo apt-get install -y gcc-14 g++-14 build-essential glslc libvulkan-dev libssl-dev ninja-build
+            echo "CC=gcc-14" >> "$GITHUB_ENV"
+            echo "CXX=g++-14" >> "$GITHUB_ENV"
+          fi
 
       - name: Build
         id: cmake_build
@@ -239,13 +260,13 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
         with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz
-          name: llama-bin-ubuntu-vulkan-x64.tar.gz
+          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz
+          name: llama-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz
 
   ubuntu-24-openvino:
     runs-on: ubuntu-24.04
@@ -977,8 +998,8 @@ jobs:
       - windows-sycl
       - windows-hip
       - ubuntu-22-rocm
-      - ubuntu-22-cpu
-      - ubuntu-22-vulkan
+      - ubuntu-cpu
+      - ubuntu-vulkan
       - ubuntu-24-openvino
       - macOS-arm64
       - macOS-x64
@@ -1061,9 +1082,11 @@ jobs:
 
             **Linux:**
             - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.tar.gz)
-            - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz)
-            - [Ubuntu 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 arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-arm64.tar.gz)
             - [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-s390x.tar.gz)
+            - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz)
+            - [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-arm64.tar.gz)
+            - [Ubuntu x64 (ROCm 7.2)](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)
 
             **Windows:**

.gitignore

@@ -95,6 +95,8 @@
 # Server Web UI temporary files
 /tools/server/webui/node_modules
 /tools/server/webui/dist
+# we no longer use gz for index.html
+/tools/server/public/index.html.gz
 
 # Python
 

common/chat.cpp

@@ -221,7 +221,7 @@ using chat_template_caps = jinja::caps;
 struct common_chat_templates {
     bool add_bos;
     bool add_eos;
-    bool has_explicit_template;  // Model had builtin template or template overridde was specified.
+    bool has_explicit_template;  // Model had builtin template or template overridden was specified.
     std::unique_ptr<common_chat_template> template_default;  // always set (defaults to chatml)
     std::unique_ptr<common_chat_template> template_tool_use;
 };
@@ -989,6 +989,10 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
         auto analysis = p.ref("analysis");
         auto preamble = p.rule("preamble", p.literal("<|channel|>commentary<|message|>") + p.content(content) + end);
         auto final_msg = p.rule("final", p.literal("<|channel|>final<|message|>") + p.content(content));
+
+        // Consume any unsolicited tool calls, e.g. builtin functions
+        auto unsolicited = p.rule("unsolicited", p.atomic(p.optional(channel) + p.literal(" to=") + content + end));
+
         auto any = p.rule("any", preamble | analysis);
 
         if (has_response_format) {
@@ -1032,7 +1036,7 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
             return p.zero_or_more(start + any) + start + (tool_call | final_msg);
         }
 
-        return p.zero_or_more(start + any) + start + final_msg;
+        return p.zero_or_more(start + any) + start + (final_msg | unsolicited);
     });
 
     data.parser = parser.save();

common/common.cpp

@@ -359,6 +359,11 @@ bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREAD
 }
 
 void common_init() {
+#if defined(_WIN32)
+    SetConsoleOutputCP(CP_UTF8);
+    SetConsoleCP(CP_UTF8);
+#endif
+
     llama_log_set(common_log_default_callback, NULL);
 
 #ifdef NDEBUG
@@ -367,7 +372,7 @@ void common_init() {
     const char * build_type = " (debug)";
 #endif
 
-    LOG_INF("build: %d (%s) with %s for %s%s\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT, LLAMA_COMPILER, LLAMA_BUILD_TARGET, build_type);
+    LOG_DBG("build: %d (%s) with %s for %s%s\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT, LLAMA_COMPILER, LLAMA_BUILD_TARGET, build_type);
 }
 
 std::string common_params_get_system_info(const common_params & params) {
@@ -703,7 +708,6 @@ static inline bool glob_match(const char * pattern, const char * str) {
     }
     if (pattern[0] == '*' && pattern[1] == '*') {
         const char * p = pattern + 2;
-        if (*p == '/') p++;
         if (glob_match(p, str)) return true;
         if (*str != '\0') return glob_match(pattern, str + 1);
         return false;
@@ -1244,6 +1248,9 @@ llama_context * common_init_result::context() {
 }
 
 common_sampler * common_init_result::sampler(llama_seq_id seq_id) {
+    if (seq_id < 0 || seq_id >= (int) pimpl->samplers.size()) {
+        return nullptr;
+    }
     return pimpl->samplers[seq_id].get();
 }
 

common/jinja/parser.cpp

@@ -539,6 +539,9 @@ private:
             statement_ptr step = slices.size() > 2 ? std::move(slices[2]) : nullptr;
             return mk_stmt<slice_expression>(start_pos, std::move(start), std::move(stop), std::move(step));
         }
+        if (slices.empty()) {
+            return mk_stmt<blank_expression>(start_pos);
+        }
         return std::move(slices[0]);
     }
 

common/jinja/runtime.cpp

@@ -771,10 +771,15 @@ value member_expression::execute_impl(context & ctx) {
     }
 
     JJ_DEBUG("Member expression on object type %s, property type %s", object->type().c_str(), property->type().c_str());
-    ensure_key_type_allowed(property);
-
     value val = mk_val<value_undefined>("object_property");
 
+    if (property->is_undefined()) {
+        JJ_DEBUG("%s", "Member expression property is undefined, returning undefined");
+        return val;
+    }
+
+    ensure_key_type_allowed(property);
+
     if (is_val<value_undefined>(object)) {
         JJ_DEBUG("%s", "Accessing property on undefined object, returning undefined");
         return val;

common/jinja/runtime.h

@@ -263,6 +263,14 @@ struct comment_statement : public statement {
 
 // Expressions
 
+// Represents an omitted expression in a computed member, e.g. `a[]`.
+struct blank_expression : public expression {
+    std::string type() const override { return "BlankExpression"; }
+    value execute_impl(context &) override {
+        return mk_val<value_undefined>();
+    }
+};
+
 struct member_expression : public expression {
     statement_ptr object;
     statement_ptr property;

common/ngram-map.h

@@ -51,7 +51,7 @@ struct common_ngram_map_value {
 // statistics of a n-gram
 struct common_ngram_map_key {
     size_t   key_idx;   // index of key n-gram in token-history
-    size_t   stat_idx;  // index of last token of stastistics computation (key_num, values)
+    size_t   stat_idx;  // index of last token of statistics computation (key_num, values)
 
     uint16_t key_num;   // number of occurrences of this key n-gram in token-history
     common_ngram_map_value values[COMMON_NGRAM_MAX_VALUES]; // some known values after the key

common/sampling.cpp

@@ -383,6 +383,12 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
         params.backend_sampling = false;
     }
 
+    if (rbudget && params.backend_sampling) {
+        LOG_WRN("%s: backend sampling is not compatible with reasoning budget, disabling\n", __func__);
+
+        params.backend_sampling = false;
+    }
+
     auto * result = new common_sampler {
         /* .params  = */ params,
         /* .grmr    = */ grmr,

convert_hf_to_gguf.py

@@ -31,10 +31,10 @@ import gguf
 from gguf.vocab import MistralTokenizerType, MistralVocab
 
 try:
-    from mistral_common.tokens.tokenizers.base import TokenizerVersion # type: ignore[import-not-found]
-    from mistral_common.tokens.tokenizers.multimodal import DATASET_MEAN as _MISTRAL_COMMON_DATASET_MEAN, DATASET_STD as _MISTRAL_COMMON_DATASET_STD # type: ignore[import-not-found]
-    from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found]
-    from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found]
+    from mistral_common.tokens.tokenizers.base import TokenizerVersion # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.multimodal import DATASET_MEAN as _MISTRAL_COMMON_DATASET_MEAN, DATASET_STD as _MISTRAL_COMMON_DATASET_STD # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found, ty:unresolved-import]
         SentencePieceTokenizer,
     )
 

docs/docker.md

@@ -13,24 +13,30 @@ We have three Docker images available for this project:
 
 Additionally, there the following images, similar to the above:
 
-- `ghcr.io/ggml-org/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`)
-- `ghcr.io/ggml-org/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`)
-- `ghcr.io/ggml-org/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`)
-- `ghcr.io/ggml-org/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
-- `ghcr.io/ggml-org/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
-- `ghcr.io/ggml-org/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggml-org/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA 12 support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggml-org/llama.cpp:full-cuda13`: Same as `full` but compiled with CUDA 13 support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggml-org/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA 12 support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggml-org/llama.cpp:light-cuda13`: Same as `light` but compiled with CUDA 13 support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggml-org/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA 12 support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggml-org/llama.cpp:server-cuda13`: Same as `server` but compiled with CUDA 13 support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggml-org/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:full-musa`: Same as `full` but compiled with MUSA support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:light-musa`: Same as `light` but compiled with MUSA support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:server-musa`: Same as `server` but compiled with MUSA support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:full-intel`: Same as `full` but compiled with SYCL support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:light-intel`: Same as `light` but compiled with SYCL support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:server-intel`: Same as `server` but compiled with SYCL support. (platforms: `linux/amd64`)
-- `ghcr.io/ggml-org/llama.cpp:full-vulkan`: Same as `full` but compiled with Vulkan support. (platforms: `linux/amd64`)
-- `ghcr.io/ggml-org/llama.cpp:light-vulkan`: Same as `light` but compiled with Vulkan support. (platforms: `linux/amd64`)
-- `ghcr.io/ggml-org/llama.cpp:server-vulkan`: Same as `server` but compiled with Vulkan support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:full-vulkan`: Same as `full` but compiled with Vulkan support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggml-org/llama.cpp:light-vulkan`: Same as `light` but compiled with Vulkan support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggml-org/llama.cpp:server-vulkan`: Same as `server` but compiled with Vulkan support. (platforms: `linux/amd64`, `linux/arm64`)
 - `ghcr.io/ggml-org/llama.cpp:full-openvino`: Same as `full` but compiled with OpenVino support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:light-openvino`: Same as `light` but compiled with OpenVino support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:server-openvino`: Same as `server` but compiled with OpenVino support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:full-s390x`: Identical to `full`, an alias for the `s390x` platform. (platforms: `linux/s390x`)
+- `ghcr.io/ggml-org/llama.cpp:light-s390x`: Identical to `light`, an alias for the `s390x` platform. (platforms: `linux/s390x`)
+- `ghcr.io/ggml-org/llama.cpp:server-s390x`: Identical to `server`, an alias for the `s390x` platform. (platforms: `linux/s390x`)
 
 The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA, ROCm or MUSA library, you'll need to build the images locally for now).
 
@@ -82,7 +88,7 @@ You may want to pass in some different `ARGS`, depending on the CUDA environment
 
 The defaults are:
 
-- `CUDA_VERSION` set to `12.4.0`
+- `CUDA_VERSION` set to `12.8.1`
 - `CUDA_DOCKER_ARCH` set to the cmake build default, which includes all the supported architectures
 
 The resulting images, are essentially the same as the non-CUDA images:

examples/batched/batched.cpp

@@ -24,12 +24,12 @@ int main(int argc, char ** argv) {
     params.prompt = "Hello my name is";
     params.n_predict = 32;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BATCHED, print_usage)) {
         return 1;
     }
 
-    common_init();
-
     // number of parallel batches
     int n_parallel = params.n_parallel;
 

examples/debug/debug.cpp

@@ -213,12 +213,12 @@ static bool run(llama_context * ctx, const common_params & params) {
 int main(int argc, char ** argv) {
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DEBUG, print_usage)) {
         return 1;
     }
 
-    common_init();
-
     llama_backend_init();
     llama_numa_init(params.numa);
 

examples/diffusion/diffusion-cli.cpp

@@ -545,11 +545,12 @@ int main(int argc, char ** argv) {
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DIFFUSION)) {
         return 1;
     }
 
-    common_init();
     llama_backend_init();
 
     llama_model_params model_params = llama_model_default_params();

examples/embedding/embedding.cpp

@@ -99,12 +99,12 @@ int main(int argc, char ** argv) {
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) {
         return 1;
     }
 
-    common_init();
-
     params.embedding = true;
 
     // get max number of sequences per batch

examples/eval-callback/eval-callback.cpp

@@ -37,12 +37,12 @@ int main(int argc, char ** argv) {
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 
-    common_init();
-
     llama_backend_init();
     llama_numa_init(params.numa);
 

examples/idle/idle.cpp

@@ -19,12 +19,12 @@ static void print_usage(int /*argc*/, char ** argv) {
 int main(int argc, char ** argv) {
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
         return 1;
     }
 
-    common_init();
-
     // init LLM
 
     llama_backend_init();

examples/lookahead/lookahead.cpp

@@ -43,12 +43,12 @@ int main(int argc, char ** argv) {
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 
-    common_init();
-
     const int W = 15; // lookahead window
     const int N = 5;  // n-gram size
     const int G = 15; // max verification n-grams

examples/lookup/lookup-create.cpp

@@ -12,6 +12,8 @@ int main(int argc, char ** argv){
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
         return 1;
     }

examples/lookup/lookup-stats.cpp

@@ -18,12 +18,12 @@ int main(int argc, char ** argv){
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
         return 1;
     }
 
-    common_init();
-
     const int n_draft = params.speculative.n_max;
 
     // init llama.cpp

examples/lookup/lookup.cpp

@@ -18,12 +18,12 @@ int main(int argc, char ** argv){
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
         return 1;
     }
 
-    common_init();
-
     // max. number of additional tokens to draft if match is found
     const int n_draft = params.speculative.n_max;
 

examples/model-conversion/scripts/causal/compare-logits.py

@@ -7,7 +7,7 @@ import os
 
 # Add utils directory to path for direct script execution
 sys.path.insert(0, str(Path(__file__).parent.parent / "utils"))
-from common import get_model_name_from_env_path, compare_tokens, exit_with_warning  # type: ignore[import-not-found]
+from common import get_model_name_from_env_path, compare_tokens, exit_with_warning  # type: ignore[import-not-found, ty:unresolved-import]
 
 def quick_logits_check(pytorch_file, llamacpp_file):
     """Lightweight sanity check before NMSE"""

examples/model-conversion/scripts/utils/check-nmse.py

@@ -5,7 +5,7 @@ import sys
 import os
 import argparse
 from pathlib import Path
-from common import get_model_name_from_env_path  # type: ignore[import-not-found]
+from common import get_model_name_from_env_path  # type: ignore[import-not-found, ty:unresolved-import]
 
 def calculate_nmse(reference, test):
     mse = np.mean((test - reference) ** 2)

examples/model-conversion/scripts/utils/compare_tokens.py

@@ -2,7 +2,7 @@
 
 import argparse
 import sys
-from common import compare_tokens  # type: ignore[import-not-found]
+from common import compare_tokens  # type: ignore[import-not-found, ty:unresolved-import]
 
 
 def parse_arguments():

examples/model-conversion/scripts/utils/semantic_check.py

@@ -7,7 +7,7 @@ import importlib
 from pathlib import Path
 
 from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, AutoModel
-from common import compare_tokens, exit_with_warning  # type: ignore[import-not-found]
+from common import compare_tokens, exit_with_warning  # type: ignore[import-not-found, ty:unresolved-import]
 
 unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
 

examples/parallel/parallel.cpp

@@ -163,12 +163,12 @@ int main(int argc, char ** argv) {
     params.n_predict = 128;
     params.n_junk = 1;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) {
         return 1;
     }
 
-    common_init();
-
     // number of simultaneous "clients" to simulate
     const int32_t n_clients = params.n_parallel;
 

examples/passkey/passkey.cpp

@@ -25,12 +25,12 @@ int main(int argc, char ** argv) {
     params.n_keep = 32;
     params.i_pos  = -1;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PASSKEY, print_usage)) {
         return 1;
     }
 
-    common_init();
-
     int n_junk = params.n_junk;
     int n_keep = params.n_keep;
     int n_grp  = params.grp_attn_n;

examples/retrieval/retrieval.cpp

@@ -117,12 +117,12 @@ int main(int argc, char ** argv) {
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_RETRIEVAL, print_usage)) {
         return 1;
     }
 
-    common_init();
-
     // For BERT models, batch size must be equal to ubatch size
     params.n_ubatch = params.n_batch;
     params.embedding = true;

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

@@ -17,6 +17,8 @@ int main(int argc, char ** argv) {
 
     const std::string_view state_file = "dump_state.bin";
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
@@ -27,8 +29,6 @@ int main(int argc, char ** argv) {
         params.kv_unified = true;
     }
 
-    common_init();
-
     if (params.n_predict < 0) {
         params.n_predict = 16;
     }

examples/speculative-simple/speculative-simple.cpp

@@ -16,6 +16,8 @@ int main(int argc, char ** argv) {
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
         return 1;
     }
@@ -25,8 +27,6 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    common_init();
-
     if (params.speculative.mparams_dft.path.empty()) {
         LOG_ERR("%s: --model-draft is required\n", __func__);
         return 1;

examples/speculative/speculative.cpp

@@ -38,6 +38,8 @@ int main(int argc, char ** argv) {
     // needed to get candidate probs even for temp <= 0.0
     params.sampling.n_probs = 128;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
         return 1;
     }
@@ -47,8 +49,6 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    common_init();
-
     if (params.speculative.mparams_dft.path.empty()) {
         LOG_ERR("%s: --model-draft is required\n", __func__);
         return 1;

examples/sycl/build.sh

@@ -20,4 +20,4 @@ cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA
 #cmake --build . --config Release --target llama-bench
 
 #build all binary
-cmake --build . --config Release -j -v
+cmake --build . --config Release -j$((($(nproc)+1)/2)) -v

examples/sycl/run-llama2.sh

@@ -23,9 +23,9 @@ if [ $# -gt 0 ]; then
     GGML_SYCL_DEVICE=$1
     echo "use $GGML_SYCL_DEVICE as main GPU"
     #use signle GPU only
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none ${LOAD_MODE}
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none ${LOAD_MODE}
 
 else
     #use multiple GPUs with same max compute units
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} ${LOAD_MODE}
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s 0 -c ${CONTEXT} ${LOAD_MODE}
 fi

examples/training/finetune.cpp

@@ -20,6 +20,8 @@ int main(int argc, char ** argv) {
     common_params params;
     params.escape = false;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_FINETUNE)) {
         return 1;
     }
@@ -38,7 +40,6 @@ int main(int argc, char ** argv) {
         params.cache_type_v = GGML_TYPE_F32;
     }
 
-    common_init();
     llama_backend_init();
     llama_numa_init(params.numa);
     // load the model and apply lora adapter, if any

ggml/CMakeLists.txt

@@ -4,7 +4,7 @@ project("ggml" C CXX ASM)
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
 set(GGML_VERSION_MINOR 9)
-set(GGML_VERSION_PATCH 8)
+set(GGML_VERSION_PATCH 9)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 
 find_program(GIT_EXE NAMES git git.exe NO_CMAKE_FIND_ROOT_PATH)

ggml/src/ggml-cuda/argsort.cu

@@ -47,9 +47,11 @@ void argsort_f32_i32_cuda_cub(ggml_cuda_pool & pool,
 #ifdef STRIDED_ITERATOR_AVAILABLE
     auto offset_iterator = cuda::make_strided_iterator(cuda::make_counting_iterator(0), ncols);
 #else
-    ggml_cuda_pool_alloc<int> offsets_alloc(pool, nrows + 1);
+    // offset_iterator needs to populate nrows + 1 elements, so we also have to ceildiv nrows + 1 by block_size
+    const int                 nrows_offset = nrows + 1;
+    ggml_cuda_pool_alloc<int> offsets_alloc(pool, nrows_offset);
     int *                     offset_iterator = offsets_alloc.get();
-    const dim3                offset_grid((nrows + block_size - 1) / block_size);
+    const dim3                offset_grid((nrows_offset + block_size - 1) / block_size);
     init_offsets<<<offset_grid, block_size, 0, stream>>>(offset_iterator, ncols, nrows);
 #endif
     CUDA_CHECK(cudaMemcpyAsync(temp_keys, x, ncols * nrows * sizeof(float), cudaMemcpyDeviceToDevice, stream));

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

@@ -2343,7 +2343,8 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
         static_assert(MMVQ_MAX_BATCH_SIZE == MMVF_MAX_BATCH_SIZE);
         if (ne2 <= MMVQ_MAX_BATCH_SIZE) {
             if (ggml_is_quantized(src0->type)) {
-                if (ne2 <= MMVQ_MMID_MAX_BATCH_SIZE) {
+                const int mmvq_mmid_max = get_mmvq_mmid_max_batch(src0->type, cc);
+                if (ne2 <= mmvq_mmid_max) {
                     ggml_cuda_mul_mat_vec_q(ctx, src0, src1, ids, dst);
                     return;
                 }
@@ -2946,14 +2947,18 @@ static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) {
         }
 
         // [TAG_MUL_MAT_ID_CUDA_GRAPHS]
-        if (node->op == GGML_OP_MUL_MAT_ID && (!ggml_is_quantized(node->src[0]->type) || node->ne[2] > MMVQ_MMID_MAX_BATCH_SIZE)) {
-            // under these conditions, the mul_mat_id operation will need to synchronize the stream, so we cannot use CUDA graphs
-            // TODO: figure out a way to enable for larger batch sizes, without hurting performance
-            // ref: https://github.com/ggml-org/llama.cpp/pull/18958
-            use_cuda_graph = false;
+        if (node->op == GGML_OP_MUL_MAT_ID) {
+            const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+            const int mmvq_mmid_max = get_mmvq_mmid_max_batch(node->src[0]->type, cc);
+            if (!ggml_is_quantized(node->src[0]->type) || node->ne[2] > mmvq_mmid_max) {
+                // under these conditions, the mul_mat_id operation will need to synchronize the stream, so we cannot use CUDA graphs
+                // TODO: figure out a way to enable for larger batch sizes, without hurting performance
+                // ref: https://github.com/ggml-org/llama.cpp/pull/18958
+                use_cuda_graph = false;
 #ifndef NDEBUG
-            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported node type\n", __func__);
+                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported node type\n", __func__);
 #endif
+            }
         }
 
         if (!use_cuda_graph) {

ggml/src/ggml-cuda/mmvq.cu

@@ -97,6 +97,194 @@ static __host__ mmvq_parameter_table_id get_device_table_id(int cc) {
     return MMVQ_PARAMETERS_GENERIC;
 }
 
+// Per-architecture maximum batch size for which MMVQ should be used for MUL_MAT_ID.
+// Returns a value <= MMVQ_MAX_BATCH_SIZE. Default is MMVQ_MAX_BATCH_SIZE.
+// Check https://github.com/ggml-org/llama.cpp/pull/20905#issuecomment-4145835627 for details
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_pascal_older(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ1_S:   return 6;
+        case GGML_TYPE_IQ1_M:   return 6;
+        case GGML_TYPE_IQ2_S:   return 4;
+        case GGML_TYPE_IQ2_XS:  return 5;
+        case GGML_TYPE_IQ2_XXS: return 5;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 4;
+        case GGML_TYPE_IQ4_NL:  return 6;
+        case GGML_TYPE_IQ4_XS:  return 5;
+        case GGML_TYPE_MXFP4:   return 4;
+        case GGML_TYPE_Q2_K:    return 4;
+        case GGML_TYPE_Q3_K:    return 4;
+        case GGML_TYPE_Q4_0:    return 6;
+        case GGML_TYPE_Q4_1:    return 6;
+        case GGML_TYPE_Q4_K:    return 5;
+        case GGML_TYPE_Q5_0:    return 6;
+        case GGML_TYPE_Q5_1:    return 6;
+        case GGML_TYPE_Q5_K:    return 5;
+        case GGML_TYPE_Q6_K:    return 4;
+        case GGML_TYPE_Q8_0:    return 4;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_turing_plus(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ2_S:   return 7;
+        case GGML_TYPE_IQ3_S:   return 6;
+        case GGML_TYPE_IQ3_XXS: return 7;
+        case GGML_TYPE_MXFP4:   return 7;
+        case GGML_TYPE_Q2_K:    return 7;
+        case GGML_TYPE_Q3_K:    return 5;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_gcn(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ1_S:   return 5;
+        case GGML_TYPE_IQ1_M:   return 5;
+        case GGML_TYPE_IQ2_S:   return 4;
+        case GGML_TYPE_IQ2_XS:  return 4;
+        case GGML_TYPE_IQ2_XXS: return 4;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 4;
+        case GGML_TYPE_IQ4_NL:  return 6;
+        case GGML_TYPE_IQ4_XS:  return 4;
+        case GGML_TYPE_Q2_K:    return 4;
+        case GGML_TYPE_Q3_K:    return 4;
+        case GGML_TYPE_Q4_0:    return 5;
+        case GGML_TYPE_Q4_1:    return 5;
+        case GGML_TYPE_Q4_K:    return 4;
+        case GGML_TYPE_Q5_K:    return 4;
+        case GGML_TYPE_Q6_K:    return 4;
+        case GGML_TYPE_Q8_0:    return 4;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_cdna(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ2_S:   return 5;
+        case GGML_TYPE_IQ2_XS:  return 5;
+        case GGML_TYPE_IQ2_XXS: return 5;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 5;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_rdna1_rdna2(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ2_S:   return 4;
+        case GGML_TYPE_IQ2_XS:  return 4;
+        case GGML_TYPE_IQ2_XXS: return 4;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 4;
+        case GGML_TYPE_Q2_K:    return 7;
+        case GGML_TYPE_Q3_K:    return 4;
+        case GGML_TYPE_Q4_K:    return 5;
+        case GGML_TYPE_Q5_K:    return 6;
+        case GGML_TYPE_Q6_K:    return 5;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_rdna3(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ1_S:   return 6;
+        case GGML_TYPE_IQ1_M:   return 6;
+        case GGML_TYPE_IQ2_S:   return 4;
+        case GGML_TYPE_IQ2_XS:  return 4;
+        case GGML_TYPE_IQ2_XXS: return 4;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 4;
+        case GGML_TYPE_IQ4_NL:  return 6;
+        case GGML_TYPE_IQ4_XS:  return 6;
+        case GGML_TYPE_Q4_K:    return 4;
+        case GGML_TYPE_Q5_K:    return 4;
+        case GGML_TYPE_Q6_K:    return 4;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_rdna4(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ1_S:   return 7;
+        case GGML_TYPE_IQ1_M:   return 7;
+        case GGML_TYPE_IQ2_S:   return 4;
+        case GGML_TYPE_IQ2_XS:  return 4;
+        case GGML_TYPE_IQ2_XXS: return 4;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 4;
+        case GGML_TYPE_IQ4_NL:  return 7;
+        case GGML_TYPE_IQ4_XS:  return 5;
+        case GGML_TYPE_MXFP4:   return 5;
+        case GGML_TYPE_Q3_K:    return 4;
+        case GGML_TYPE_Q4_0:    return 7;
+        case GGML_TYPE_Q4_1:    return 7;
+        case GGML_TYPE_Q4_K:    return 4;
+        case GGML_TYPE_Q5_0:    return 7;
+        case GGML_TYPE_Q5_1:    return 7;
+        case GGML_TYPE_Q5_K:    return 5;
+        case GGML_TYPE_Q6_K:    return 5;
+        case GGML_TYPE_Q8_0:    return 7;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+// Host function: returns the max batch size for the current arch+type at runtime.
+int get_mmvq_mmid_max_batch(ggml_type type, int cc) {
+    // NVIDIA: Volta, Ada Lovelace, and Blackwell always use MMVQ for MUL_MAT_ID.
+    if (cc == GGML_CUDA_CC_VOLTA || cc >= GGML_CUDA_CC_ADA_LOVELACE) {
+        return MMVQ_MAX_BATCH_SIZE;
+    }
+    if (cc >= GGML_CUDA_CC_TURING) {
+        return get_mmvq_mmid_max_batch_turing_plus(type);
+    }
+    if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
+        return get_mmvq_mmid_max_batch_pascal_older(type);
+    }
+    // AMD
+    if (GGML_CUDA_CC_IS_RDNA4(cc)) {
+        return get_mmvq_mmid_max_batch_rdna4(type);
+    }
+    if (GGML_CUDA_CC_IS_RDNA3(cc)) {
+        return get_mmvq_mmid_max_batch_rdna3(type);
+    }
+    if (GGML_CUDA_CC_IS_RDNA1(cc) || GGML_CUDA_CC_IS_RDNA2(cc)) {
+        return get_mmvq_mmid_max_batch_rdna1_rdna2(type);
+    }
+    if (GGML_CUDA_CC_IS_CDNA(cc)) {
+        return get_mmvq_mmid_max_batch_cdna(type);
+    }
+    if (GGML_CUDA_CC_IS_GCN(cc)) {
+        return get_mmvq_mmid_max_batch_gcn(type);
+    }
+    return MMVQ_MAX_BATCH_SIZE;
+}
+
+// Device constexpr: returns the max batch size for the current arch+type at compile time.
+template <ggml_type type>
+static constexpr __device__ int get_mmvq_mmid_max_batch_for_device() {
+#if defined(RDNA4)
+    return get_mmvq_mmid_max_batch_rdna4(type);
+#elif defined(RDNA3)
+    return get_mmvq_mmid_max_batch_rdna3(type);
+#elif defined(RDNA2) || defined(RDNA1)
+    return get_mmvq_mmid_max_batch_rdna1_rdna2(type);
+#elif defined(CDNA)
+    return get_mmvq_mmid_max_batch_cdna(type);
+#elif defined(GCN)
+    return get_mmvq_mmid_max_batch_gcn(type);
+#elif defined(__CUDA_ARCH__) && (__CUDA_ARCH__ == GGML_CUDA_CC_VOLTA || __CUDA_ARCH__ >= GGML_CUDA_CC_ADA_LOVELACE)
+    return MMVQ_MAX_BATCH_SIZE;
+#elif defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
+    return get_mmvq_mmid_max_batch_turing_plus(type);
+#else
+    return get_mmvq_mmid_max_batch_pascal_older(type);
+#endif
+}
+
 static constexpr __host__ __device__ int calc_nwarps(ggml_type type, int ncols_dst, mmvq_parameter_table_id table_id) {
     if (table_id == MMVQ_PARAMETERS_GENERIC) {
         switch (ncols_dst) {
@@ -195,7 +383,7 @@ static constexpr __host__ __device__ int calc_rows_per_block(int ncols_dst, int
     return 1;
 }
 
-template <ggml_type type, int ncols_dst, bool has_fusion, bool is_multi_token_id = false, bool small_k = false>
+template <ggml_type type, int ncols_dst, bool has_fusion, bool small_k = false>
 __launch_bounds__(calc_nwarps(type, ncols_dst, get_device_table_id())*ggml_cuda_get_physical_warp_size(), 1)
 static __global__ void mul_mat_vec_q(
         const void * __restrict__ vx, const void * __restrict__ vy, const int32_t * __restrict__ ids, const ggml_cuda_mm_fusion_args_device fusion, float * __restrict__ dst,
@@ -222,22 +410,13 @@ static __global__ void mul_mat_vec_q(
 
     const uint32_t channel_dst = blockIdx.y;
 
-    uint32_t token_idx = 0;
     uint32_t channel_x;
     uint32_t channel_y;
     uint32_t sample_dst;
 
-    if constexpr (is_multi_token_id) {
-        // Multi-token MUL_MAT_ID path, adding these in the normal path causes a perf regression for n_tokens=1 case
-        token_idx  = blockIdx.z;
-        channel_x  = ids[channel_dst + token_idx * ids_stride];
-        channel_y  = fastmodulo(channel_dst, nchannels_y);
-        sample_dst = 0;
-    } else {
-        channel_x  = ncols_dst == 1 && ids ? ids[channel_dst]                     : fastdiv(channel_dst, channel_ratio);
-        channel_y  = ncols_dst == 1 && ids ? fastmodulo(channel_dst, nchannels_y) : channel_dst;
-        sample_dst = blockIdx.z;
-    }
+    channel_x  = ncols_dst == 1 && ids ? ids[channel_dst]                     : fastdiv(channel_dst, channel_ratio);
+    channel_y  = ncols_dst == 1 && ids ? fastmodulo(channel_dst, nchannels_y) : channel_dst;
+    sample_dst = blockIdx.z;
 
     const uint32_t sample_x    = fastdiv(sample_dst, sample_ratio);
     const uint32_t sample_y    = sample_dst;
@@ -294,9 +473,6 @@ static __global__ void mul_mat_vec_q(
     float tmp_gate[ncols_dst][rows_per_cuda_block] = {{0.0f}};
 
     const block_q8_1 * y = ((const block_q8_1 *) vy) + sample_y*stride_sample_y + channel_y*stride_channel_y;
-    if constexpr (is_multi_token_id) {
-        y += token_idx*stride_col_y;
-    }
     const int kbx_offset = sample_x*stride_sample_x + channel_x*stride_channel_x + row0*stride_row_x;
 
     for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) {
@@ -350,10 +526,6 @@ static __global__ void mul_mat_vec_q(
 
     dst += sample_dst*stride_sample_dst + channel_dst*stride_channel_dst + row0;
 
-    if constexpr (is_multi_token_id) {
-        dst += token_idx*stride_col_dst;
-    }
-
     // sum up partial sums and write back result
 #pragma unroll
     for (int j = 0; j < ncols_dst; ++j) {
@@ -413,6 +585,69 @@ static __global__ void mul_mat_vec_q(
     }
 }
 
+// Dedicated MoE multi-token kernel.
+// Grid: (ceil(nrows_x / c_rows_per_block), nchannels_dst)
+// Block: (warp_size, ncols_dst) - each warp handles one token independently.
+// No shared memory reduction needed since each warp works alone.
+template <ggml_type type, int c_rows_per_block>
+__launch_bounds__(get_mmvq_mmid_max_batch_for_device<type>()*ggml_cuda_get_physical_warp_size(), 1)
+static __global__ void mul_mat_vec_q_moe(
+        const void * __restrict__ vx, const void * __restrict__ vy, const int32_t * __restrict__ ids,
+        float * __restrict__ dst,
+        const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t nrows_x,
+        const uint32_t stride_row_x, const uint32_t stride_col_y, const uint32_t stride_col_dst,
+        const uint32_t stride_channel_x, const uint32_t stride_channel_y, const uint32_t stride_channel_dst,
+        const uint32_t ncols_dst, const uint32_t ids_stride) {
+
+    constexpr int qk  = ggml_cuda_type_traits<type>::qk;
+    constexpr int qi  = ggml_cuda_type_traits<type>::qi;
+    constexpr int vdr = get_vdr_mmvq(type);
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
+
+    constexpr vec_dot_q_cuda_t vec_dot_q_cuda = get_vec_dot_q_cuda(type);
+
+    const uint32_t token_idx   = threadIdx.y;
+    const int      row0        = c_rows_per_block*blockIdx.x;
+    const int      blocks_per_row_x = ncols_x / qk;
+    constexpr int  blocks_per_iter  = vdr * warp_size / qi;
+
+    const uint32_t channel_dst = blockIdx.y;
+
+    if (token_idx >= ncols_dst) {
+        return;
+    }
+
+    const uint32_t channel_x = ids[channel_dst + token_idx * ids_stride];
+    const uint32_t channel_y = fastmodulo(channel_dst, nchannels_y);
+
+    const block_q8_1 * y = ((const block_q8_1 *) vy) + channel_y*stride_channel_y + token_idx*stride_col_y;
+    const int kbx_offset  = channel_x*stride_channel_x + row0*stride_row_x;
+
+    // partial sum for each thread
+    float tmp[c_rows_per_block] = {0.0f};
+
+    for (int kbx = threadIdx.x / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) {
+        const int kby = kbx * (qk/QK8_1);
+        const int kqs = vdr * (threadIdx.x % (qi/vdr));
+
+#pragma unroll
+        for (int i = 0; i < c_rows_per_block; ++i) {
+            tmp[i] += vec_dot_q_cuda(vx, &y[kby], kbx_offset + i*stride_row_x + kbx, kqs);
+        }
+    }
+
+    // Warp-level reduction only - no shared memory needed
+#pragma unroll
+    for (int i = 0; i < c_rows_per_block; ++i) {
+        tmp[i] = warp_reduce_sum<warp_size>(tmp[i]);
+    }
+
+    // Write results
+    if (threadIdx.x < c_rows_per_block && (c_rows_per_block == 1 || uint32_t(row0 + threadIdx.x) < nrows_x)) {
+        dst[channel_dst*stride_channel_dst + token_idx*stride_col_dst + row0 + threadIdx.x] = tmp[threadIdx.x];
+    }
+}
+
 template<ggml_type type>
 static std::pair<dim3, dim3> calc_launch_params(
         const int ncols_dst, const int nrows_x, const int nchannels_dst, const int nsamples_or_ntokens,
@@ -425,7 +660,7 @@ static std::pair<dim3, dim3> calc_launch_params(
     return {block_nums, block_dims};
 }
 
-template<ggml_type type, int c_ncols_dst, bool is_multi_token_id = false, bool small_k = false>
+template<ggml_type type, int c_ncols_dst, bool small_k = false>
 static void mul_mat_vec_q_switch_fusion(
         const void * vx, const void * vy, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
         const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t stride_row_x, const uint32_t stride_col_y,
@@ -438,7 +673,7 @@ static void mul_mat_vec_q_switch_fusion(
     const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
     if constexpr (c_ncols_dst == 1) {
         if (has_fusion) {
-            mul_mat_vec_q<type, c_ncols_dst, true, is_multi_token_id, small_k><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec_q<type, c_ncols_dst, true, small_k><<<block_nums, block_dims, nbytes_shared, stream>>>
                 (vx, vy, ids, fusion, dst, ncols_x, nchannels_y, stride_row_x, stride_col_y, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride);
@@ -448,12 +683,33 @@ static void mul_mat_vec_q_switch_fusion(
 
     GGML_ASSERT(!has_fusion && "fusion only supported for ncols_dst=1");
 
-    mul_mat_vec_q<type, c_ncols_dst, false, is_multi_token_id, small_k><<<block_nums, block_dims, nbytes_shared, stream>>>
+    mul_mat_vec_q<type, c_ncols_dst, false, small_k><<<block_nums, block_dims, nbytes_shared, stream>>>
         (vx, vy, ids, fusion, dst, ncols_x, nchannels_y, stride_row_x, stride_col_y, stride_col_dst,
         channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
         sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride);
 }
 
+template <ggml_type type>
+static void mul_mat_vec_q_moe_launch(
+        const void * vx, const void * vy, const int32_t * ids, float * dst,
+        const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t nrows_x,
+        const uint32_t stride_row_x, const uint32_t stride_col_y, const uint32_t stride_col_dst,
+        const uint32_t stride_channel_x, const uint32_t stride_channel_y, const uint32_t stride_channel_dst,
+        const uint32_t ncols_dst, const uint32_t ids_stride,
+        const int warp_size, const int nchannels_dst, cudaStream_t stream) {
+
+    constexpr int rows_per_block = 2; // 2 gives best perf based on tuning
+    const int64_t nblocks_rows = (nrows_x + rows_per_block - 1) / rows_per_block;
+    const dim3 block_nums(nblocks_rows, nchannels_dst);
+    const dim3 block_dims(warp_size, ncols_dst);
+
+    mul_mat_vec_q_moe<type, rows_per_block><<<block_nums, block_dims, 0, stream>>>(
+        vx, vy, ids, dst, ncols_x, nchannels_y, nrows_x,
+        stride_row_x, stride_col_y, stride_col_dst,
+        stride_channel_x, stride_channel_y, stride_channel_dst,
+        ncols_dst, ids_stride);
+}
+
 template <ggml_type type>
 static void mul_mat_vec_q_switch_ncols_dst(
         const void * vx, const void * vy, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
@@ -472,20 +728,62 @@ static void mul_mat_vec_q_switch_ncols_dst(
     const uint3 sample_ratio_fd  = init_fastdiv_values(nsamples_dst  / nsamples_x);
 
     const int device = ggml_cuda_get_device();
+    const int                     cc        = ggml_cuda_info().devices[device].cc;
     const int warp_size = ggml_cuda_info().devices[device].warp_size;
-    const mmvq_parameter_table_id table_id = get_device_table_id(ggml_cuda_info().devices[device].cc);
+    const mmvq_parameter_table_id table_id  = get_device_table_id(cc);
 
     const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
     const bool has_ids = ids != nullptr;
 
+    const auto should_use_small_k = [&](int c_ncols_dst) {
+        // When K is small, increase rows_per_block to match nwarps so each warp has more work to do
+        // Trigger when the full thread block covers all K blocks in a single loop iteration and few threads remain idle.
+        constexpr int qk                    = ggml_cuda_type_traits<type>::qk;
+        constexpr int qi                    = ggml_cuda_type_traits<type>::qi;
+        constexpr int vdr                   = get_vdr_mmvq(type);
+        const int     blocks_per_row_x      = ncols_x / qk;
+        const int     blocks_per_iter_1warp = vdr * warp_size / qi;
+        const int     nwarps                = calc_nwarps(type, c_ncols_dst, table_id);
+        bool          use                   = nwarps > 1 && blocks_per_row_x < nwarps * blocks_per_iter_1warp;
+
+        constexpr std::array<ggml_type, 2> iq_slow_turing = {
+            GGML_TYPE_IQ3_XXS,
+            GGML_TYPE_IQ3_S,
+        };
+        constexpr std::array<ggml_type, 8> iq_slow_other = {
+            GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,   GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS,
+            GGML_TYPE_IQ2_S, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S,   GGML_TYPE_IQ4_XS,
+        };
+        constexpr std::array<ggml_type, 3> slow_pascal = {
+            GGML_TYPE_IQ3_S,
+            GGML_TYPE_Q2_K,
+            GGML_TYPE_Q3_K,
+        };
+
+        const bool is_nvidia_turing_plus  = GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_TURING;
+        const bool is_nvidia_pascal_older = GGML_CUDA_CC_IS_NVIDIA(cc) && cc < GGML_CUDA_CC_VOLTA;
+
+        if (is_nvidia_turing_plus) {
+            if (ncols_dst == 1 &&
+                    std::find(iq_slow_turing.begin(), iq_slow_turing.end(), type) != iq_slow_turing.end()) {
+                use = false;
+            }
+        } else if ((ncols_dst == 1 && std::find(iq_slow_other.begin(), iq_slow_other.end(), type) != iq_slow_other.end()) ||
+                (is_nvidia_pascal_older && std::find(slow_pascal.begin(), slow_pascal.end(), type) != slow_pascal.end()) ||
+                GGML_CUDA_CC_IS_RDNA(cc)) {
+            use = false;
+        }
+
+        return use;
+    };
+
     if (has_ids && ncols_dst > 1) {
-        // Multi-token MUL_MAT_ID path only - single-token goes through regular path below
-        constexpr int c_ncols_dst = 1;
-        std::pair<dim3, dim3> dims = calc_launch_params<type>(c_ncols_dst, nrows_x, nchannels_dst, ncols_dst, warp_size, table_id);
-        mul_mat_vec_q_switch_fusion<type, c_ncols_dst, true>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
-             channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-             sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-             dims.first, dims.second, 0, ids_stride, stream);
+        // Multi-token MUL_MAT_ID path - dedicated MoE kernel
+        mul_mat_vec_q_moe_launch<type>(
+            vx, vy, ids, dst, ncols_x, nchannels_y_fd, nrows_x,
+            stride_row_x, stride_col_y, stride_col_dst,
+            stride_channel_x, stride_channel_y, stride_channel_dst,
+            ncols_dst, ids_stride, warp_size, nchannels_dst, stream);
         return;
     }
 
@@ -493,31 +791,24 @@ static void mul_mat_vec_q_switch_ncols_dst(
         case 1: {
             constexpr int c_ncols_dst = 1;
 
-            // When K is small, increase rows_per_block to match nwarps so each warp has more work to do
-            // Trigger when the full thread block covers all K blocks in a single loop iteration and few threads remain idle.
-            constexpr int qk  = ggml_cuda_type_traits<type>::qk;
-            constexpr int qi  = ggml_cuda_type_traits<type>::qi;
-            constexpr int vdr = get_vdr_mmvq(type);
-            const int blocks_per_row_x = ncols_x / qk;
-            const int blocks_per_iter_1warp = vdr * warp_size / qi;
-            const int nwarps = calc_nwarps(type, c_ncols_dst, table_id);
-            const bool use_small_k = nwarps > 1 && blocks_per_row_x < nwarps * blocks_per_iter_1warp;
+            bool use_small_k = should_use_small_k(c_ncols_dst);
+
             if (use_small_k) {
-                std::pair<dim3, dim3> dims = calc_launch_params<type>(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst,
-                                                                    warp_size, table_id, true);
-                mul_mat_vec_q_switch_fusion<type, c_ncols_dst, false, true>(
+                std::pair<dim3, dim3> dims = calc_launch_params<type>(c_ncols_dst, nrows_x, nchannels_dst,
+                                                                        nsamples_dst, warp_size, table_id, true);
+                mul_mat_vec_q_switch_fusion<type, c_ncols_dst, true>(
                     vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
-                    channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                    sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                    dims.first, dims.second, 0, ids_stride, stream);
+                    channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd,
+                    stride_sample_x, stride_sample_y, stride_sample_dst, dims.first, dims.second, 0, ids_stride,
+                    stream);
             } else {
-                std::pair<dim3, dim3> dims = calc_launch_params<type>(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst,
-                                                                    warp_size, table_id);
+                std::pair<dim3, dim3> dims = calc_launch_params<type>(c_ncols_dst, nrows_x, nchannels_dst,
+                                                                        nsamples_dst, warp_size, table_id);
                 mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(
                     vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
-                    channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                    sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                    dims.first, dims.second, 0, ids_stride, stream);
+                    channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd,
+                    stride_sample_x, stride_sample_y, stride_sample_dst, dims.first, dims.second, 0, ids_stride,
+                    stream);
             }
         } break;
         case 2: {

ggml/src/ggml-cuda/mmvq.cuh

@@ -1,7 +1,10 @@
 #include "common.cuh"
 
 #define MMVQ_MAX_BATCH_SIZE 8 // Max. batch size for which to use MMVQ kernels.
-#define MMVQ_MMID_MAX_BATCH_SIZE 4 // Max. batch size for which to use MMVQ kernels for MUL_MAT_ID
+
+// Returns the maximum batch size for which MMVQ should be used for MUL_MAT_ID,
+// based on the quantization type and GPU architecture (compute capability).
+int get_mmvq_mmid_max_batch(ggml_type type, int cc);
 
 void ggml_cuda_mul_mat_vec_q(ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst, const ggml_cuda_mm_fusion_args_host * fusion = nullptr);

ggml/src/ggml-hexagon/htp/flash-attn-ops.c

@@ -346,6 +346,9 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
 
     const HVX_Vector logit_cap = hvx_vec_splat_f32(factx->logit_softcap);
 
+    dma_cache m_cache;
+    dma_cache_init(&m_cache, spad_m, factx->size_m_block, DMA_CACHE_MAX_SIZE);
+
     for (uint32_t ir = ir0; ir < ir1; ++ir) {
         const uint32_t iq3 = fastdiv(ir, &factx->src0_div21);
         const uint32_t iq2 = fastdiv(ir - iq3*neq2*neq1, &factx->src0_div1);
@@ -389,9 +392,8 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
             // Mask
             if (mask) {
                 const uint8_t * m_src = (const uint8_t *) (mp_base + ic_start);
-                uint8_t * m_dst = spad_m + (ib % 2) * factx->size_m_block;
                 // Mask is 1D contiguous for this row
-                dma_queue_push(dma, dma_make_ptr(m_dst, m_src), current_block_size * 2, current_block_size * 2, current_block_size * 2, 1);
+                dma_cache_push(dma, &m_cache, m_src, current_block_size * 2, current_block_size * 2, current_block_size * 2, 1);
             }
 
             // FARF(HIGH, "fa %u: prefetch KVM: ir %u ib %u iq1 %u iq2 %u iq3 %u : size_k_row %u size_v_row %u bs %u: usec %u",
@@ -554,7 +556,7 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
                 // Mask
                 if (mask) {
                     const uint8_t * m_src = (const uint8_t *) (mp_base + next_ic_start);
-                    dma_queue_push(dma, dma_make_ptr(m_base, m_src), next_block_size * 2, next_block_size * 2, next_block_size * 2, 1);
+                    dma_cache_push(dma, &m_cache, m_src, next_block_size * 2, next_block_size * 2, next_block_size * 2, 1);
                 }
 
                 // FARF(HIGH, "fa %u: prefetch KVM: ir %u ib %u : iq1 %u iq2 %u iq3 %u : size_k_row %u size_v_row %u bs %u: usec %u",
@@ -684,7 +686,7 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
     octx->src0_spad.size_per_thread = size_q_block * 1;
     octx->src1_spad.size_per_thread = factx.size_k_block * 2;
     octx->src2_spad.size_per_thread = factx.size_v_block * 2;
-    octx->src3_spad.size_per_thread = mask ? factx.size_m_block * 2 : 0;
+    octx->src3_spad.size_per_thread = mask ? factx.size_m_block * DMA_CACHE_MAX_SIZE : 0;
     octx->dst_spad.size_per_thread  = size_vkq_acc;
 
     octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
@@ -705,6 +707,8 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
     octx->src3_spad.data = octx->src2_spad.data + octx->src2_spad.size;
     octx->dst_spad.data  = octx->src3_spad.data + octx->src3_spad.size;
 
+    // FARF(ERROR, "fa: qrows-per-thread %u", factx.qrows_per_thread);
+
     if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
         worker_pool_run_func(octx->ctx->worker_pool, flash_attn_ext_f16_thread, &factx, octx->n_threads);
     }

ggml/src/ggml-hexagon/htp/hex-dma.h

@@ -143,7 +143,7 @@ static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t
     desc->desc_size  = 0; // 1D mode
     desc->src_bypass = dma_src_l2_bypass_on;
     desc->dst_bypass = dma_dst_l2_bypass_on;
-    desc->order      = 1;
+    desc->order      = 0;
     desc->done       = 0;
     desc->src        = (void *) dptr.src;
     desc->dst        = (void *) dptr.dst;
@@ -151,8 +151,12 @@ static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t
 
     q->dptr[q->push_idx] = dptr;
 
-    dmlink(q->tail, desc);
-    q->tail = (dma_descriptor_2d *) desc;
+    if (size) {
+        dmlink(q->tail, desc);
+        q->tail = (dma_descriptor_2d *) desc;
+    } else {
+        desc->done = 1;
+    }
 
     // FARF(ERROR, "dma-push: i %u row-size %u nrows %d dst %p src %p\n", q->push_idx, row_size, nrows, dptr.dst, dptr.src);
     q->push_idx = (q->push_idx + 1) & q->idx_mask;
@@ -175,7 +179,7 @@ static inline bool dma_queue_push_single_2d(dma_queue * q, dma_ptr dptr, size_t
     desc->dst_bypass     = dma_dst_l2_bypass_on;
     desc->src_comp       = 0;
     desc->dst_comp       = 0;
-    desc->order          = 1;
+    desc->order          = 0;
     desc->done           = 0;
     desc->src_stride     = src_stride;
     desc->dst_stride     = dst_stride;
@@ -197,8 +201,12 @@ static inline bool dma_queue_push_single_2d(dma_queue * q, dma_ptr dptr, size_t
 
     q->dptr[q->push_idx] = dptr;
 
-    dmlink(q->tail, desc);
-    q->tail = desc;
+    if (nrows) {
+        dmlink(q->tail, desc);
+        q->tail = desc;
+    } else {
+        desc->done = 1;
+    }
 
     // FARF(ERROR, "dma-push: i %u row-size %u nrows %d dst %p src %p\n", q->push_idx, row_size, nrows, dptr.dst, dptr.src);
     q->push_idx = (q->push_idx + 1) & q->idx_mask;
@@ -215,12 +223,9 @@ static inline dma_ptr dma_queue_pop(dma_queue * q) {
     dma_descriptor_2d * desc = &q->desc[q->pop_idx];
 
     // Wait for desc to complete
-    while (1) {
-        dmpoll();
-        if (desc->done) {
-            break;
-        }
+    while (!desc->done) {
         // FARF(ERROR, "dma-pop: waiting for DMA : %u\n", q->pop_idx);
+        dmpoll();
     }
 
     dptr = q->dptr[q->pop_idx];
@@ -312,6 +317,54 @@ static inline bool dma_queue_push_vtcm_to_ddr(dma_queue * q, dma_ptr dptr, size_
     return dma_queue_push(q, dptr, dst_row_size, src_row_size, dst_row_size, nrows);
 }
 
+#define DMA_CACHE_MAX_SIZE 64U
+
+typedef struct {
+    uint8_t *base;
+    uint32_t line_size;
+    uint32_t capacity;
+    uint32_t src[DMA_CACHE_MAX_SIZE];
+    uint16_t age[DMA_CACHE_MAX_SIZE];
+} dma_cache;
+
+static inline void dma_cache_init(dma_cache *c, uint8_t *base, uint32_t line_size, uint32_t capacity)
+{
+    c->capacity  = (capacity > DMA_CACHE_MAX_SIZE) ? DMA_CACHE_MAX_SIZE : capacity;
+    c->base      = base;
+    c->line_size = line_size;
+
+    for (unsigned i=0; i < c->capacity; i++) {
+        c->src[i] = 0;
+        c->age[i] = 0;
+    }
+}
+
+static inline bool dma_cache_push(dma_queue *q, dma_cache *c, const uint8_t * src, uint32_t dst_stride, uint32_t src_stride, uint32_t row_size, uint32_t nrows)
+{
+    uint32_t o_idx = 0;
+    uint16_t o_age = 0;
+    uint8_t *  dst = 0;
+
+    for (unsigned i=0; i < c->capacity; i++) {
+        if (c->src[i] == (uint32_t) src) {
+            c->age[i] = 0;
+            dst = c->base + (i * c->line_size); nrows = 0; // dummy dma
+            // FARF(ERROR, "dma-cache: found %p", src);
+        } else {
+            c->age[i]++;
+            if (c->age[i] > o_age) { o_age = c->age[i]; o_idx = i; }
+        }
+    }
+    if (!dst) {
+        // FARF(ERROR, "dma-cache: replacing #%u : age %u %p -> %p", o_idx, c->age[o_idx], (void *) c->src[o_idx], src);
+        c->age[o_idx] = 0;
+        c->src[o_idx] = (uint32_t) src;
+        dst = c->base + o_idx * c->line_size; // normal nrows dma
+    }
+
+    return dma_queue_push(q, dma_make_ptr(dst, src), dst_stride, src_stride, row_size, nrows);
+}
+
 #ifdef __cplusplus
 }  // extern "C"
 #endif

ggml/src/ggml-hexagon/htp/rope-ops.c

@@ -333,8 +333,8 @@ static void rope_job_f32(unsigned int nth, unsigned int ith, void * data) {
                     //         (unsigned) HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - rctx->t_start));
                 }
 
-                // Skip DMA transactions from prev block (if any)
-                // No need to wait for these since the DMA is setup for in-order processing
+                // Skip output DMA transactions from prev block (if any)
+                // No need to wait for those here since we're explicitly waiting for the latest prefecthes below.
                 for (uint32_t d=0; d < dma_depth; d++) { dma_queue_pop_nowait(dma_queue); }
 
                 // Compute loop

ggml/src/ggml-opencl/CMakeLists.txt

@@ -114,6 +114,8 @@ set(GGML_OPENCL_KERNELS
     gemv_noshuffle_q4_1_f32
     gemm_noshuffle_q4_1_f32
     gemv_noshuffle_general_q8_0_f32
+    gemv_noshuffle_q4_k_f32
+    gemm_noshuffle_q4_k_f32
     gemv_noshuffle_q6_k_f32
     gemm_noshuffle_q6_k_f32
     mul

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

@@ -538,6 +538,8 @@ 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_q4_K_noshuffle;
+    cl_kernel kernel_restore_block_q4_K_noshuffle;
     cl_kernel kernel_convert_block_q4_K, kernel_restore_block_q4_K;
     cl_kernel kernel_convert_block_q6_K, kernel_restore_block_q6_K;
     cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
@@ -720,6 +722,8 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_gemm_noshuffle_q4_1_f32;
     cl_kernel kernel_mul_mm_q8_0_f32_8x4;
     cl_kernel CL_mul_mat_vec_q8_0_f32;
+    cl_kernel kernel_gemv_noshuffle_q4_k_f32;
+    cl_kernel kernel_gemm_noshuffle_q4_k_f32;
     cl_kernel kernel_gemv_noshuffle_q6_K_f32;
     cl_kernel kernel_gemm_noshuffle_q6_K_f32;
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
@@ -932,6 +936,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         CL_CHECK((backend_ctx->kernel_restore_block_q8_0_trans  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q8_0_trans", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q4_K  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_K", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q4_K  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_K", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_q4_K_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_K_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_q4_K_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_K_noshuffle", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q6_K  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q6_K", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q6_K  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q6_K", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q6_K_noshuffle  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q6_K_noshuffle", &err), err));
@@ -2619,6 +2625,45 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // gemm_noshuffle_q4_k_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_noshuffle_q4_k_f32.cl.h"
+       };
+#else
+        const std::string kernel_src = read_file("gemm_noshuffle_q4_k_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_q4_k_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemv_noshuffle_q4_k_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_q4_k_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemv_noshuffle_q4_k_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_q4_k_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_k_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     std::string CL_moe_compile_opts = std::string("-cl-std=") + opencl_c_std +
             " -cl-mad-enable "
             " -cl-fast-relaxed-math";
@@ -5060,12 +5105,25 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
             CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
         CL_CHECK(err);
 
+        #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
         cl_kernel kernel = backend_ctx->kernel_convert_block_q4_K;
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+            kernel = backend_ctx->kernel_convert_block_q4_K_noshuffle;
+        }
+        #else
+        cl_kernel kernel = backend_ctx->kernel_convert_block_q4_K;
+        #endif
+
+        cl_uchar mask_0F = 0x0F;
+        cl_uchar mask_F0 = 0xF0;
+
         CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
         CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
         CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->s));
         CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->d));
         CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra->dm));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask_0F));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_uchar), &mask_F0));
 
         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};
@@ -5076,6 +5134,20 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
         CL_CHECK(clReleaseMemObject(data_device));
 
         tensor->extra  = extra;
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+
+            int M = tensor->ne[1];
+            int K = tensor->ne[0];
+
+            GGML_ASSERT(K % 32 == 0);
+
+            // Transpose q, d, dm as ushort
+            transpose_2d_as_16b(backend_ctx, extra->q, extra->q, size_q, K/4, M);
+            transpose_2d_as_16b(backend_ctx, extra->d, extra->d, size_d, K/256, M);
+            transpose_2d_as_16b(backend_ctx, extra->dm, extra->dm, size_dm, K/256, M);
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
         return;
     }
     if (tensor->type == GGML_TYPE_Q6_K) {
@@ -5516,12 +5588,60 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
             ggml_nbytes(tensor), NULL, &err);
         CL_CHECK(err);
 
+        cl_uchar mask_0F = 0x0F;
+        cl_uchar mask_F0 = 0xF0;
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+            int M = tensor->ne[1];
+            int K = tensor->ne[0];
+
+            size_t size_q  = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
+            size_t size_d  = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+            size_t size_dm = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+
+            static ggml_cl_buffer buf_trans_q;
+            static ggml_cl_buffer buf_trans_d;
+            static ggml_cl_buffer buf_trans_dm;
+
+            buf_trans_q.allocate(backend_ctx->context, size_q);
+            buf_trans_d.allocate(backend_ctx->context, size_d);
+            buf_trans_dm.allocate(backend_ctx->context, size_dm);
+
+            // Transpose q, d, dm back
+            transpose_2d_as_16b(backend_ctx, extra->q,  buf_trans_q.buffer,  size_q,  M, K/4);
+            transpose_2d_as_16b(backend_ctx, extra->d,  buf_trans_d.buffer,  size_d,  M, K/256);
+            transpose_2d_as_16b(backend_ctx, extra->dm, buf_trans_dm.buffer, size_dm, M, K/256);
+
+            cl_kernel kernel = backend_ctx->kernel_restore_block_q4_K_noshuffle;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &buf_trans_q.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->s));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &buf_trans_d.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &buf_trans_dm.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &data_device));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask_0F));
+            CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_uchar), &mask_F0));
+
+            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_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
+                global_work_size, local_work_size, 0, NULL, NULL));
+            CL_CHECK(clEnqueueReadBuffer(queue, data_device, CL_TRUE, offset,
+                size, data, 0, NULL, NULL));
+            CL_CHECK(clReleaseMemObject(data_device));
+            return;
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
         cl_kernel kernel = backend_ctx->kernel_restore_block_q4_K;
         CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
         CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->s));
         CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
         CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->dm));
         CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask_0F));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_uchar), &mask_F0));
 
         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};
@@ -9688,6 +9808,192 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t
 #endif
 }
 
+static void ggml_cl_mul_mat_q4_k_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_q4_K * extra0_q4_k = (ggml_tensor_extra_cl_q4_K *)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;
+
+    cl_uchar mask_d6 = 0x3F;
+    cl_uchar mask_d4 = 0x0F;
+    cl_uchar mask_hi2 = 0xC0;
+
+    if (ne1 == 1) {
+        cl_mem q_img = nullptr;
+        cl_mem b_sub_buf = nullptr;
+        cl_mem b_img = nullptr;
+
+        // image for q
+        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_q4_k->q;
+        CL_CHECK((q_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_q4_k_f32;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &q_img));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_q4_k->d));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra0_q4_k->dm));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &extra0_q4_k->s));
+        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));
+        CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_uchar), &mask_d6));
+        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_uchar), &mask_d4));
+        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_uchar), &mask_hi2));
+
+        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(q_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;
+
+        // 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));
+
+        // 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_q4_k_f32;
+        int padded_N = N + padding;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0_q4_k->q));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_q4_k->s));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra0_q4_k->d));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &extra0_q4_k->dm));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &b_img_trans));
+        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),   &ne01));
+        CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int),   &padded_N));
+        CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_int),   &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_int),   &ne1));
+        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_uchar), &mask_d6));
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_uchar), &mask_d4));
+        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_uchar), &mask_hi2));
+
+        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));
+    }
+#else
+    GGML_UNUSED(backend);
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+#endif
+}
+
 static void ggml_cl_mul_mat_q6_K_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);
@@ -10014,6 +10320,12 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
             return;
     }
 
+    // q4_k x fp32
+    if (src0t == GGML_TYPE_Q4_K && src1t == GGML_TYPE_F32) {
+            ggml_cl_mul_mat_q4_k_f32_adreno(backend, src0, src1, dst);
+            return;
+    }
+
     // q6_K x fp32
     if (src0t == GGML_TYPE_Q6_K && src1t == GGML_TYPE_F32) {
         ggml_cl_mul_mat_q6_K_f32_adreno(backend, src0, src1, dst);

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

@@ -424,13 +424,17 @@ kernel void kernel_restore_block_q8_0_trans(
 // Convert the block_q4_K format to 4 separate arrays (AOS -> SOA).
 // This kernel does not deshuffle the bits.
 // Each thread processes a super block.
+// Mask args are just to keep the signature consistent with the no-shuffle
+// version and they are not used in this kernel.
 //------------------------------------------------------------------------------
 kernel void kernel_convert_block_q4_K(
     global struct block_q4_K * src0,
     global uchar * dst_q,
     global uchar * dst_s,
     global half  * dst_d,
-    global half  * dst_dm
+    global half  * dst_dm,
+    uchar mask_0F,
+    uchar mask_F0
 ) {
     global struct block_q4_K * b = (global struct block_q4_K *) src0 + get_global_id(0);
     global uchar * q  = (global uchar *) dst_q  + QK_K/2*get_global_id(0);
@@ -451,12 +455,15 @@ kernel void kernel_convert_block_q4_K(
 
 // Restore block_q4_K from flattened arrays.
 // Each thread processes a super block.
+// Mask args are just to keep the signature consistent with the no-shuffle ones.
 kernel void kernel_restore_block_q4_K(
     global uchar * src_q,
     global uchar * src_s,
     global half  * src_d,
     global half  * src_dm,
-    global struct block_q4_K * dst
+    global struct block_q4_K * dst,
+    uchar mask_0F,
+    uchar mask_F0
 ) {
     global struct block_q4_K * b = (global struct block_q4_K *) dst + get_global_id(0);
     global uchar * q  = (global uchar *) src_q  + QK_K/2*get_global_id(0);
@@ -475,6 +482,70 @@ kernel void kernel_restore_block_q4_K(
     }
 }
 
+kernel void kernel_convert_block_q4_K_noshuffle(
+    global struct block_q4_K * src0,
+    global uchar * dst_q,
+    global uchar * dst_s,
+    global half  * dst_d,
+    global half  * dst_dm,
+    uchar mask_0F,
+    uchar mask_F0
+) {
+    global struct block_q4_K * b = (global struct block_q4_K *) src0 + get_global_id(0);
+    global uchar * q  = (global uchar *) dst_q  + QK_K/2 * get_global_id(0);
+    global uchar * s  = (global uchar *) dst_s  + K_SCALE_SIZE * get_global_id(0);
+    global half  * d  = (global half  *) dst_d  + get_global_id(0);
+    global half  * dm = (global half  *) dst_dm + get_global_id(0);
+
+    *d  = b->d;
+    *dm = b->dm;
+
+    for (int i = 0; i < QK_K / 64; ++i) {
+        for (int j = 0; j < 16; ++j) {
+            uchar x0 = b->q[i*32 + 2*j];
+            uchar x1 = b->q[i*32 + 2*j + 1];
+            q[i*32 + j]      = convert_uchar(x0 & mask_0F) | convert_uchar((x1 & mask_0F) << 4);
+            q[i*32 + j + 16] = convert_uchar((x0 & mask_F0) >> 4)   | convert_uchar(x1 & mask_F0);
+        }
+    }
+
+    for (int i = 0; i < K_SCALE_SIZE; ++i) {
+        s[i] = b->s[i];
+    }
+}
+
+kernel void kernel_restore_block_q4_K_noshuffle(
+    global uchar * src_q,
+    global uchar * src_s,
+    global half  * src_d,
+    global half  * src_dm,
+    global struct block_q4_K * dst,
+    uchar mask_0F,
+    uchar mask_F0
+) {
+    global struct block_q4_K * b = (global struct block_q4_K *) dst + get_global_id(0);
+    global uchar * q  = (global uchar *) src_q  + QK_K/2 * get_global_id(0);
+    global uchar * s  = (global uchar *) src_s  + K_SCALE_SIZE * get_global_id(0);
+    global half  * d  = (global half  *) src_d  + get_global_id(0);
+    global half  * dm = (global half  *) src_dm + get_global_id(0);
+
+    b->d  = *d;
+    b->dm = *dm;
+
+    for (int i = 0; i < QK_K / 64; ++i) {
+        for (int j = 0; j < 16; ++j) {
+            uchar lo = q[i*32 + j];
+            uchar hi = q[i*32 + j + 16];
+            b->q[i*32 + 2*j]     = convert_uchar((lo & mask_0F) | ((hi & mask_0F) << 4));
+            b->q[i*32 + 2*j + 1] = convert_uchar(((lo & mask_F0) >> 4) | (hi & mask_F0));
+        }
+    }
+
+    for (int i = 0; i < K_SCALE_SIZE; ++i) {
+        b->s[i] = s[i];
+    }
+}
+
 //------------------------------------------------------------------------------
 // kernel_convert_block_q6_K
 // Convert the block_q6_K format to 3 separate arrays (AOS -> SOA).

ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_f32.cl

@@ -0,0 +1,172 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : 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
+#define QK_K         256
+#define K_SCALE_SIZE 12
+
+inline void get_scale_min_k4(
+    int j,
+    global const uchar * q,
+    uchar * d,
+    uchar * m,
+    uchar mask_d6,
+    uchar mask_d4,
+    uchar mask_hi2
+) {
+    if (j < 4) {
+        *d = q[j]   & mask_d6;
+        *m = q[j+4] & mask_d6;
+    } else {
+        *d = (q[j+4] & mask_d4) | ((q[j-4] & mask_hi2) >> 2);
+        *m = ((q[j+4] >> 4) & mask_d4) | ((q[j]   & mask_hi2) >> 2);
+    }
+}
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_128
+#endif
+kernel void kernel_gemm_noshuffle_q4_k_f32(
+    global const ushort * src0_q,
+    global const uchar  * src0_s,
+    global const half   * src0_d,
+    global const half   * src0_dm,
+    read_only image1d_buffer_t src1,
+    global float * dst,
+    ulong offsetd,
+    int m,
+    int n,
+    int k,
+    int n_no_padding,
+    uchar mask_d6,
+    uchar mask_d4,
+    uchar mask_hi2
+) {
+    dst = (global float *)((global char *)dst + offsetd);
+    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;
+
+    int num_blocks_K = k / QK_K;
+
+    global const ushort * weight_ptr = src0_q + gx_2;
+    global const half   * d_ptr      = src0_d  + gx_2;
+    global const half   * dm_ptr     = src0_dm + gx_2;
+
+    for (int i = 0; i < k; i += 32) {
+        int sb_idx  = i / QK_K;
+        int sub_idx = (i / 32) % 8;
+
+        half4 d  = vload4(0, d_ptr  + sb_idx * m);
+        half4 dm = vload4(0, dm_ptr + sb_idx * m);
+
+        global const uchar * sc0 = src0_s + (gx_2+0) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE;
+        global const uchar * sc1 = src0_s + (gx_2+1) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE;
+        global const uchar * sc2 = src0_s + (gx_2+2) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE;
+        global const uchar * sc3 = src0_s + (gx_2+3) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE;
+
+        uchar sv0, mn0, sv1, mn1, sv2, mn2, sv3, mn3;
+        get_scale_min_k4(sub_idx, sc0, &sv0, &mn0, mask_d6, mask_d4, mask_hi2);
+        get_scale_min_k4(sub_idx, sc1, &sv1, &mn1, mask_d6, mask_d4, mask_hi2);
+        get_scale_min_k4(sub_idx, sc2, &sv2, &mn2, mask_d6, mask_d4, mask_hi2);
+        get_scale_min_k4(sub_idx, sc3, &sv3, &mn3, mask_d6, mask_d4, mask_hi2);
+
+        half4 scale = convert_half4(convert_float4(d)  * convert_float4((uchar4)(sv0, sv1, sv2, sv3)));
+        half4 mval  = convert_half4(convert_float4(dm) * convert_float4((uchar4)(mn0, mn1, mn2, mn3)));
+
+        for (int l = 0; l < 32; l += 4) {
+            int ki = i + l;
+            ushort4 bits4 = vload4(0, weight_ptr + (ki/4) * m);
+
+            // j=0
+            B.s0123 = read_imageh(src1, gy*2   + (ki+0) * n_4);
+            B.s4567 = read_imageh(src1, gy*2+1 + (ki+0) * n_4);
+            dequantized_weights.s0 = (bits4.s0 & 0x000F) * scale.s0 - mval.s0;
+            dequantized_weights.s1 = (bits4.s1 & 0x000F) * scale.s1 - mval.s1;
+            dequantized_weights.s2 = (bits4.s2 & 0x000F) * scale.s2 - mval.s2;
+            dequantized_weights.s3 = (bits4.s3 & 0x000F) * scale.s3 - mval.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   + (ki+1) * n_4);
+            B.s4567 = read_imageh(src1, gy*2+1 + (ki+1) * n_4);
+            dequantized_weights.s0 = ((bits4.s0 & 0x00F0) >> 4) * scale.s0 - mval.s0;
+            dequantized_weights.s1 = ((bits4.s1 & 0x00F0) >> 4) * scale.s1 - mval.s1;
+            dequantized_weights.s2 = ((bits4.s2 & 0x00F0) >> 4) * scale.s2 - mval.s2;
+            dequantized_weights.s3 = ((bits4.s3 & 0x00F0) >> 4) * scale.s3 - mval.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   + (ki+2) * n_4);
+            B.s4567 = read_imageh(src1, gy*2+1 + (ki+2) * n_4);
+            dequantized_weights.s0 = ((bits4.s0 & 0x0F00) >> 8) * scale.s0 - mval.s0;
+            dequantized_weights.s1 = ((bits4.s1 & 0x0F00) >> 8) * scale.s1 - mval.s1;
+            dequantized_weights.s2 = ((bits4.s2 & 0x0F00) >> 8) * scale.s2 - mval.s2;
+            dequantized_weights.s3 = ((bits4.s3 & 0x0F00) >> 8) * scale.s3 - mval.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   + (ki+3) * n_4);
+            B.s4567 = read_imageh(src1, gy*2+1 + (ki+3) * n_4);
+            dequantized_weights.s0 = ((bits4.s0 & 0xF000) >> 12) * scale.s0 - mval.s0;
+            dequantized_weights.s1 = ((bits4.s1 & 0xF000) >> 12) * scale.s1 - mval.s1;
+            dequantized_weights.s2 = ((bits4.s2 & 0xF000) >> 12) * scale.s2 - mval.s2;
+            dequantized_weights.s3 = ((bits4.s3 & 0xF000) >> 12) * scale.s3 - mval.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_q4_k_f32.cl

@@ -0,0 +1,318 @@
+#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 QK_K  256
+#define NSUBGROUPS 4
+#define SUBGROUP_SIZE 64
+
+inline void get_scale_min_k4(
+    int j,
+    global const uchar * q,
+    uchar * d,
+    uchar * m,
+    uchar mask_d6,
+    uchar mask_d4,
+    uchar mask_hi2
+) {
+    if (j < 4) {
+        *d = q[j]   & mask_d6;
+        *m = q[j+4] & mask_d6;
+    } else {
+        *d = (q[j+4] & mask_d4) | ((q[j-4] & mask_hi2) >> 2);
+        *m = ((q[j+4] >> 4) & mask_d4) | ((q[j]   & mask_hi2) >> 2);
+    }
+}
+
+#define dequantizeBlockAccum_ns_sgbroadcast_1_hi(total_sums, bits4, scale, minv, y) \
+    float shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 0); \
+    total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 0); \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 0); \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 0); \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 0); \
+    total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 0); \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 0); \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 0); \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 1); \
+    total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 1); \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 1); \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 1); \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 1); \
+    total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 1); \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 1); \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 1); \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_1_lo(total_sums, bits4, scale, minv, y) \
+    shared_y = sub_group_broadcast(y.s0, 2); \
+    total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 2); \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 2); \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 2); \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 2); \
+    total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 2); \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 2); \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 2); \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 3); \
+    total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 3); \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 3); \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 3); \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 3); \
+    total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 3); \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 3); \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 3); \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_8_hi(total_sums, bits4, scale, minv, y) \
+    float8 shared_y; \
+    shared_y = sub_group_broadcast(y, 0); \
+    total_sums.s0 += ((bits4.s0 & 0x000F)         * scale.s0 - minv.s0) * shared_y.s0; \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4)  * scale.s0 - minv.s0) * shared_y.s1; \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8)  * scale.s0 - minv.s0) * shared_y.s2; \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s3; \
+    total_sums.s0 += ((bits4.s2 & 0x000F)         * scale.s0 - minv.s0) * shared_y.s4; \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4)  * scale.s0 - minv.s0) * shared_y.s5; \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8)  * scale.s0 - minv.s0) * shared_y.s6; \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s7; \
+    total_sums.s1 += ((bits4.s1 & 0x000F)         * scale.s1 - minv.s1) * shared_y.s0; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4)  * scale.s1 - minv.s1) * shared_y.s1; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8)  * scale.s1 - minv.s1) * shared_y.s2; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s3; \
+    total_sums.s1 += ((bits4.s3 & 0x000F)         * scale.s1 - minv.s1) * shared_y.s4; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4)  * scale.s1 - minv.s1) * shared_y.s5; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8)  * scale.s1 - minv.s1) * shared_y.s6; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 1); \
+    total_sums.s0 += ((bits4.s4 & 0x000F)         * scale.s0 - minv.s0) * shared_y.s0; \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4)  * scale.s0 - minv.s0) * shared_y.s1; \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8)  * scale.s0 - minv.s0) * shared_y.s2; \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s3; \
+    total_sums.s0 += ((bits4.s6 & 0x000F)         * scale.s0 - minv.s0) * shared_y.s4; \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4)  * scale.s0 - minv.s0) * shared_y.s5; \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8)  * scale.s0 - minv.s0) * shared_y.s6; \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s7; \
+    total_sums.s1 += ((bits4.s5 & 0x000F)         * scale.s1 - minv.s1) * shared_y.s0; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4)  * scale.s1 - minv.s1) * shared_y.s1; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8)  * scale.s1 - minv.s1) * shared_y.s2; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s3; \
+    total_sums.s1 += ((bits4.s7 & 0x000F)         * scale.s1 - minv.s1) * shared_y.s4; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4)  * scale.s1 - minv.s1) * shared_y.s5; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8)  * scale.s1 - minv.s1) * shared_y.s6; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s7; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_8_lo(total_sums, bits4, scale, minv, y) \
+    shared_y = sub_group_broadcast(y, 2); \
+    total_sums.s0 += ((bits4.s0 & 0x000F)         * scale.s0 - minv.s0) * shared_y.s0; \
+    total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4)  * scale.s0 - minv.s0) * shared_y.s1; \
+    total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8)  * scale.s0 - minv.s0) * shared_y.s2; \
+    total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s3; \
+    total_sums.s0 += ((bits4.s2 & 0x000F)         * scale.s0 - minv.s0) * shared_y.s4; \
+    total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4)  * scale.s0 - minv.s0) * shared_y.s5; \
+    total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8)  * scale.s0 - minv.s0) * shared_y.s6; \
+    total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s7; \
+    total_sums.s1 += ((bits4.s1 & 0x000F)         * scale.s1 - minv.s1) * shared_y.s0; \
+    total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4)  * scale.s1 - minv.s1) * shared_y.s1; \
+    total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8)  * scale.s1 - minv.s1) * shared_y.s2; \
+    total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s3; \
+    total_sums.s1 += ((bits4.s3 & 0x000F)         * scale.s1 - minv.s1) * shared_y.s4; \
+    total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4)  * scale.s1 - minv.s1) * shared_y.s5; \
+    total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8)  * scale.s1 - minv.s1) * shared_y.s6; \
+    total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 3); \
+    total_sums.s0 += ((bits4.s4 & 0x000F)         * scale.s0 - minv.s0) * shared_y.s0; \
+    total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4)  * scale.s0 - minv.s0) * shared_y.s1; \
+    total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8)  * scale.s0 - minv.s0) * shared_y.s2; \
+    total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s3; \
+    total_sums.s0 += ((bits4.s6 & 0x000F)         * scale.s0 - minv.s0) * shared_y.s4; \
+    total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4)  * scale.s0 - minv.s0) * shared_y.s5; \
+    total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8)  * scale.s0 - minv.s0) * shared_y.s6; \
+    total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s7; \
+    total_sums.s1 += ((bits4.s5 & 0x000F)         * scale.s1 - minv.s1) * shared_y.s0; \
+    total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4)  * scale.s1 - minv.s1) * shared_y.s1; \
+    total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8)  * scale.s1 - minv.s1) * shared_y.s2; \
+    total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s3; \
+    total_sums.s1 += ((bits4.s7 & 0x000F)         * scale.s1 - minv.s1) * shared_y.s4; \
+    total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4)  * scale.s1 - minv.s1) * shared_y.s5; \
+    total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8)  * scale.s1 - minv.s1) * shared_y.s6; \
+    total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s7; \
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_gemv_noshuffle_q4_k_f32(
+        read_only  image1d_buffer_t src0_q,
+        global half2  * src0_d,
+        global half2  * src0_m,
+        global uchar  * src0_s,
+        read_only  image1d_buffer_t src1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        uchar mask_d6,
+        uchar mask_d4,
+        uchar mask_hi2)
+{
+    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;
+    uint scales_per_row = (K / QK_K) * 12;
+
+    private uint4     regA;
+    private half2     regS;
+    private half2     regM;
+    private float8    regB;
+
+    private float2 totalSum = (float2)(0.0f);
+
+    for (uint k = groupId; k < (K / 32); k += NSUBGROUPS) {
+        uint sb = k / 8;
+        uint j  = k % 8;
+
+        half2 d   = src0_d[gid + sb * LINE_STRIDE_A];
+        half2 dm  = src0_m[gid + sb * LINE_STRIDE_A];
+
+        global const uchar * sc0 = src0_s + 2 * gid * scales_per_row + sb * 12;
+        global const uchar * sc1 = src0_s + (2 * gid + 1) * scales_per_row + sb * 12;
+
+        uchar sv0, mn0, sv1, mn1;
+        get_scale_min_k4(j, sc0, &sv0, &mn0, mask_d6, mask_d4, mask_hi2);
+        get_scale_min_k4(j, sc1, &sv1, &mn1, mask_d6, mask_d4, mask_hi2);
+
+        regS = convert_half2(convert_float2(d)  * convert_float2((uchar2)(sv0, sv1)));
+        regM = convert_half2(convert_float2(dm) * convert_float2((uchar2)(mn0, mn1)));
+
+        if (slid < 4) {
+            regB.s0123 = read_imagef(src1, (slid * 2 + k * 8));
+            regB.s4567 = read_imagef(src1, (1 + slid * 2 + k * 8));
+        }
+
+        // load half weights for two blocks in consecutive rows
+        regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 0)).x;
+        regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x;
+        regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x;
+        regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x;
+#ifdef VECTOR_SUB_GROUP_BROADCAST
+        dequantizeBlockAccum_ns_sgbroadcast_8_hi(totalSum, as_ushort8(regA), regS, regM, regB);
+#else
+        dequantizeBlockAccum_ns_sgbroadcast_1_hi(totalSum, as_ushort8(regA), regS, regM, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAST
+
+        regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x;
+        regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x;
+        regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
+        regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
+#ifdef VECTOR_SUB_GROUP_BROADCAST
+        dequantizeBlockAccum_ns_sgbroadcast_8_lo(totalSum, as_ushort8(regA), regS, regM, regB);
+#else
+        dequantizeBlockAccum_ns_sgbroadcast_1_lo(totalSum, as_ushort8(regA), 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-rpc/ggml-rpc.cpp

@@ -1340,7 +1340,9 @@ bool rpc_server::init_tensor(const rpc_msg_init_tensor_req & request) {
     if (buffer && buffer->iface.init_tensor) {
         buffer->iface.init_tensor(buffer, tensor);
     } else {
-        GGML_LOG_ERROR("Null buffer for tensor passed to init_tensor function\n");
+        if (!buffer) {
+            GGML_LOG_ERROR("Tensor with null buffer passed to init_tensor function\n");
+        }
     }
 
     if (tensor->extra != nullptr) {

ggml/src/ggml-sycl/fattn-tile.hpp

@@ -70,6 +70,7 @@ static constexpr uint32_t ggml_sycl_fattn_tile_get_config_fp16(const int DKQ, co
     GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512,  4, 128, 2,  64,  64)
     GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512,  8, 256, 2,  64,  64)
     GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2,  64,  64)
+    GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 32, 256, 2,  64,  64)
 
     return 0;
 }
@@ -310,11 +311,11 @@ static __dpct_inline__ void flash_attn_tile_load_tile(const sycl::half2 * const
                                                       sycl::half2 * const __restrict__ tile_KV,
                                                       const int stride_KV,
                                                       const int i_sup) {
+    auto      item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
     constexpr int cpy_nb = ggml_sycl_get_max_cpy_bytes();
     constexpr int cpy_ne = cpy_nb / 4;
 
     auto load = [&] (const int n) {
-        auto      item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
         const int stride_j = warp_size >> n;
 
         if (stride_j == 0) {
@@ -455,7 +456,7 @@ static __dpct_inline__ void flash_attn_tile_iter_KQ(T_vec_dot * const Q_tmp,
 
     flash_attn_tile_load_tile<warp_size, nwarps, nbatch_fa, nbatch_K, cpy_ne, oob_check>
         (K_h2 + int64_t(k_VKQ_0)*stride_K2 + k_KQ_0/2, KV_tmp, stride_K2, k_VKQ_sup);
-    item_ct1.barrier();
+    item_ct1.barrier(sycl::access::fence_space::local_space);
 
 #ifdef SYCL_FAST_FP16
     static_assert((nbatch_K/2) % cpy_ne == 0, "bad nbatch_K");
@@ -505,7 +506,7 @@ static __dpct_inline__ void flash_attn_tile_iter_KQ(T_vec_dot * const Q_tmp,
     }
 
     if (k_KQ_0 + nbatch_K < DKQ) {
-        item_ct1.barrier();  // Sync not needed on last iteration.
+        item_ct1.barrier(sycl::access::fence_space::local_space);  // Sync not needed on last iteration.
     }
 }
 
@@ -545,7 +546,7 @@ static __dpct_inline__ void flash_attn_tile_iter(T_vec_dot * const Q_tmp,
                                                  const int         k_VKQ_max,
                                                  const int         col_Q_0,
                                                  float *           KQ_max_new_shared) {
-    auto          item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
+    auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
     constexpr int cpy_nb   = ggml_sycl_get_max_cpy_bytes();
     constexpr int cpy_ne = cpy_nb / 4;
 
@@ -620,14 +621,14 @@ static __dpct_inline__ void flash_attn_tile_iter(T_vec_dot * const Q_tmp,
     }
 
     if constexpr (np == 1) {
-        item_ct1.barrier();
+        item_ct1.barrier(sycl::access::fence_space::local_space);
     } else {
         static_assert(cpw == 1, "bad cpw");
 
         if (item_ct1.get_local_id(2) == 0) {
             KQ_max_new_shared[item_ct1.get_local_id(1)] = KQ_max_new[0];
         }
-        item_ct1.barrier();
+        item_ct1.barrier(sycl::access::fence_space::local_space);
         KQ_max_new[0] = KQ_max_new_shared[(item_ct1.get_local_id(1) & ~(np - 1)) + item_ct1.get_local_id(2) % np];
         KQ_max_new[0] = warp_reduce_max<np>(KQ_max_new[0]);
     }
@@ -697,7 +698,7 @@ static __dpct_inline__ void flash_attn_tile_iter(T_vec_dot * const Q_tmp,
     for (int k0 = 0; k0 < nbatch_fa; k0 += nbatch_V) {
         flash_attn_tile_load_tile<warp_size, nwarps, nbatch_V, DV, 0, oob_check>
             (V_h2 + int64_t(k_VKQ_0 + k0)*stride_V2, KV_tmp, stride_V2, k_VKQ_sup - k0);
-        item_ct1.barrier();
+        item_ct1.barrier(sycl::access::fence_space::local_space);
 
 #ifdef SYCL_FAST_FP16
 #pragma unroll
@@ -765,7 +766,7 @@ static __dpct_inline__ void flash_attn_tile_iter(T_vec_dot * const Q_tmp,
             }
         }
 #endif // SYCL_FAST_FP16
-        item_ct1.barrier();
+        item_ct1.barrier(sycl::access::fence_space::local_space);
     }
 }
 
@@ -972,7 +973,7 @@ static void flash_attn_tile(const char *  Q,
         }
     }
 
-    item_ct1.barrier();
+    item_ct1.barrier(sycl::access::fence_space::local_space);
 
     // Main loop over KV cache:
     const int k_VKQ_max = KV_max ? KV_max[sequence * item_ct1.get_group_range(2) + item_ct1.get_group(2)] : ne11;
@@ -1051,7 +1052,7 @@ static void flash_attn_tile(const char *  Q,
             return;
         }
 
-        item_ct1.barrier();
+        item_ct1.barrier(sycl::access::fence_space::local_space);
 
 #pragma unroll
         for (int ip = 1; ip < np; ++ip) {
@@ -1193,37 +1194,39 @@ static void launch_fattn_tile_switch_ncols1(ggml_backend_sycl_context & ctx, ggm
 
     constexpr size_t nbytes_shared = 0;
 
-    if constexpr (DV <= 256) {
-        if (Q->ne[1] > 16/ncols2) {
-            constexpr int cols_per_block = 32;
-            const int nwarps    = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
-            const int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc);
-            launch_fattn<DV, cols_per_block/ncols2, ncols2,
-                flash_attn_tile<DKQ, DV, cols_per_block / ncols2, ncols2, use_logit_softcap, warp_size>, warp_size>
-                (ctx, dst, nwarps, nbytes_shared, nbatch_fa, true, true, false);
-            return;
+    if (DV < 512 && Q->ne[1] < 32) {
+        if constexpr (ncols2 <= 32) {
+            if (Q->ne[1] > 16/ncols2) {
+                constexpr int cols_per_block = 32;
+                const int nwarps    = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
+                const int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc);
+                launch_fattn<DV, cols_per_block/ncols2, ncols2,
+                    flash_attn_tile<DKQ, DV, cols_per_block / ncols2, ncols2, use_logit_softcap, warp_size>, warp_size>
+                    (ctx, dst, nwarps, nbytes_shared, nbatch_fa, true, true, false);
+                return;
+            }
         }
-    }
-
-    if (Q->ne[1] > 8/ncols2) {
-        constexpr int cols_per_block = 16;
-        const int nwarps    = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
-        const int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc);
-        launch_fattn<DV, cols_per_block/ncols2, ncols2,
-            flash_attn_tile<DKQ, DV, cols_per_block / ncols2, ncols2, use_logit_softcap, warp_size>, warp_size>
-            (ctx, dst, nwarps, nbytes_shared, nbatch_fa, true, true, false);
-        return;
-    }
-
-    if constexpr (ncols2 <= 8) {
-        if (Q->ne[1] > 4/ncols2) {
-            constexpr int cols_per_block = 8;
-            const int nwarps    = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
-            const int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc);
-            launch_fattn<DV, cols_per_block/ncols2, ncols2,
-                flash_attn_tile<DKQ, DV, cols_per_block / ncols2, ncols2, use_logit_softcap, warp_size>, warp_size>
-                (ctx, dst, nwarps, nbytes_shared, nbatch_fa, true, true, false);
-            return;
+        if constexpr (ncols2 <= 16) {
+            if (Q->ne[1] > 8/ncols2) {
+                constexpr int cols_per_block = 16;
+                const int nwarps    = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
+                const int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc);
+                launch_fattn<DV, cols_per_block/ncols2, ncols2,
+                    flash_attn_tile<DKQ, DV, cols_per_block / ncols2, ncols2, use_logit_softcap, warp_size>, warp_size>
+                    (ctx, dst, nwarps, nbytes_shared, nbatch_fa, true, true, false);
+                return;
+            }
+        }
+        if constexpr (ncols2 <= 8) {
+            if (Q->ne[1] > 4/ncols2) {
+                constexpr int cols_per_block = 8;
+                const int nwarps    = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
+                const int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc);
+                launch_fattn<DV, cols_per_block/ncols2, ncols2,
+                    flash_attn_tile<DKQ, DV, cols_per_block / ncols2, ncols2, use_logit_softcap, warp_size>, warp_size>
+                    (ctx, dst, nwarps, nbytes_shared, nbatch_fa, true, true, false);
+                return;
+            }
         }
     }
 

gguf-py/gguf/vocab.py

@@ -14,12 +14,12 @@ except ImportError:
     SentencePieceProcessor: Any = None
 
 try:
-    from mistral_common.tokens.tokenizers.mistral import MistralTokenizer # type: ignore[import-not-found]
-    from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found]
-    from mistral_common.tokens.tokenizers.utils import ( # type: ignore[import-not-found]
+    from mistral_common.tokens.tokenizers.mistral import MistralTokenizer # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.utils import ( # type: ignore[import-not-found, ty:unresolved-import]
         _filter_valid_tokenizer_files,
     )
-    from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found]
+    from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found, ty:unresolved-import]
         SentencePieceTokenizer,
     )
 except ImportError:
@@ -32,7 +32,7 @@ else:
     _mistral_common_installed = True
 
 try:
-    from mistral_common.tokens.tokenizers.utils import ( # type: ignore[import-not-found]
+    from mistral_common.tokens.tokenizers.utils import ( # type: ignore[import-not-found, ty:unresolved-import]
         get_one_valid_tokenizer_file,
     )
 except ImportError:

scripts/gen-unicode-data.py

@@ -147,7 +147,7 @@ ranges_nfd: list[tuple[int, int, int]] = [(0, 0, 0)]  # start, last, nfd
 for codepoint, norm in table_nfd:
     start = ranges_nfd[-1][0]
     if ranges_nfd[-1] != (start, codepoint - 1, norm):
-        ranges_nfd.append(None)  # type: ignore[arg-type]  # dummy, will be replaced below
+        ranges_nfd.append((0, 0, 0))  # dummy, will be replaced below
         start = codepoint
     ranges_nfd[-1] = (start, codepoint, norm)
 

scripts/sync-ggml.last

@@ -1 +1 @@
-c044a8eeae2591faa0950c8b5e514cbc4bbfc4ca
+a04eea0761a85d18f3f504d6ab970c5c9dce705f

src/llama-adapter.cpp

@@ -294,7 +294,7 @@ static void llama_adapter_lora_init_impl(llama_model & model, const char * path_
     }
 
     // get extra buffer types of the CPU
-    // TODO: a more general solution for non-CPU extra buft should be imlpemented in the future
+    // TODO: a more general solution for non-CPU extra buft should be implemented in the future
     //       ref: https://github.com/ggml-org/llama.cpp/pull/12593#pullrequestreview-2718659948
     std::vector<ggml_backend_buffer_type_t> buft_extra;
     {

src/llama-arch.cpp

@@ -557,6 +557,8 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_OUTPUT_NORM,
                 LLM_TENSOR_OUTPUT,
                 LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ROPE_FACTORS_LONG,
+                LLM_TENSOR_ROPE_FACTORS_SHORT,
                 LLM_TENSOR_ATTN_NORM,
                 LLM_TENSOR_ATTN_Q,
                 LLM_TENSOR_ATTN_K,

src/llama-batch.h

@@ -18,7 +18,7 @@ struct llama_ubatch {
     }
 
     // typical for M-RoPE cases:
-    //   0 - sequantial position of the tokens/embeddings in the sequence
+    //   0 - sequential position of the tokens/embeddings in the sequence
     //   1 - y position in the image
     //   2 - x position in the image
     //   3 - other

src/llama-context.cpp

@@ -586,7 +586,7 @@ void llama_context::sched_reserve() {
 
     // reserve again with pp graph to avoid ggml-alloc reallocations during inference
     {
-        // TODO: not sure if the following graph would be worster case for multi-stream KV caches:
+        // TODO: not sure if the following graph would be worst case for multi-stream KV caches:
         //
         // auto * gf = graph_reserve(n_tokens, 1, n_tokens, mctx.get());
         //

src/llama-graph.cpp

@@ -1665,7 +1665,7 @@ ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
 
 ggml_tensor * llm_graph_context::build_inp_out_ids() const {
     // note: when all tokens are output, we could skip this optimization to spare the ggml_get_rows() calls,
-    //       but this would make the graph topology depend on the number of output tokens, which can interere with
+    //       but this would make the graph topology depend on the number of output tokens, which can interfere with
     //       features that require constant topology such as pipeline parallelism
     //       ref: https://github.com/ggml-org/llama.cpp/pull/14275#issuecomment-2987424471
     //if (n_outputs < n_tokens) {

src/llama-kv-cache.h

@@ -333,7 +333,7 @@ public:
     ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
 
     // store k_cur and v_cur in the cache based on the provided head location
-    // note: the heads in k_cur and v_cur should be layed out contiguously in memory
+    // note: the heads in k_cur and v_cur should be laid out contiguously in memory
     //   - k_cur  [n_embd_head_k, n_head_k, n_tokens]
     //   - k_idxs [n_tokens]
     //   - v_cur  [n_embd_head_v, n_head_v, n_tokens]

src/llama-model-loader.cpp

@@ -1158,6 +1158,12 @@ struct ggml_tensor * llama_model_loader::create_tensor(
                     if (overrides->buft == ggml_backend_cpu_buffer_type()) {
                         // when overriding to a CPU buffer, consider the extra buffer types
                         buft = select_weight_buft(hparams, t_meta, op, buft_list_cpu);
+                        if (use_mmap) {
+                            static std::once_flag once;
+                            std::call_once(once, [] {
+                                LLAMA_LOG_WARN("llama_model_loader: tensor overrides to CPU are used with mmap enabled - consider using --no-mmap for better performance\n");
+                            });
+                        }
                     } else {
                         buft = overrides->buft;
                     }

src/models/gemma-embedding.cpp

@@ -9,7 +9,7 @@ llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model,
 
     inpL = build_inp_embd(model.tok_embd);
 
-    // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
+    // important: do not normalize weights for raw embeddings input (i.e. encoded image embeddings)
     inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
     cb(inpL, "inp_scaled", -1);
 

src/models/gemma3.cpp

@@ -9,7 +9,7 @@ llm_build_gemma3<iswa>::llm_build_gemma3(const llama_model & model, const llm_gr
 
     inpL = build_inp_embd(model.tok_embd);
 
-    // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
+    // important: do not normalize weights for raw embeddings input (i.e. encoded image embeddings)
     inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
     cb(inpL, "inp_scaled", -1);
 

src/models/gemma3n-iswa.cpp

@@ -12,7 +12,7 @@ llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const
 
     inpL = build_inp_embd(model.tok_embd);
 
-    // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
+    // important: do not normalize weights for raw embeddings input (i.e. encoded image embeddings)
     inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
     cb(inpL, "inp_scaled", -1);
 

tests/export-graph-ops.cpp

@@ -118,12 +118,12 @@ int main(int argc, char ** argv) {
     common_params params;
     params.out_file = "tests.txt";
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_GRAPH_OPS)) {
         return 1;
     }
 
-    common_init();
-
     // Load CPU-only
     ggml_backend_dev_t cpu_device = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
     params.devices = { cpu_device, nullptr };

tests/test-backend-ops.cpp

@@ -8424,6 +8424,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {1023, 2, 1, 3}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {1024, 2, 1, 3}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {1025, 2, 1, 3}, order));
+        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {1025, 256, 1, 1}, order)); // test ceildiv in CUDA's CUB's DeviceSegmentedSort
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {2047, 2, 1, 3}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {2048, 2, 1, 3}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {2049, 2, 1, 3}, order));

tests/test-chat.cpp

@@ -3077,6 +3077,27 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             .expect_reasoning("I need to output the invoice details in JSON")
             .expect_content(R"({"amount": 123.45, "date": "2025-12-03"})")
             .run();
+
+
+        // Unsolicited tool calls. There is no good way to handle these, so we return empty content.
+
+        // Builtin function - recipient in role
+        tst.test(
+               "<|channel|>analysis<|message|>I will execute python to say hello<|end|>"
+               "<|start|>assistant to=container.exec<|channel|>commentary<|message|>python3 -c 'print(\"hello\")'")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .expect_reasoning("I will execute python to say hello")
+            .expect_content("")
+            .run();
+
+        // Builtin function - recipient in channel
+        tst.test(
+               "<|channel|>analysis<|message|>I will execute python to say hello<|end|>"
+               "<|start|>assistant<|channel|>commentary to=python <|constrain|>code<|message|>print(\"hello\")")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .expect_reasoning("I will execute python to say hello")
+            .expect_content("")
+            .run();
     }
 
     {

tests/test-jinja.cpp

@@ -387,6 +387,24 @@ static void test_expressions(testing & t) {
         "Bob"
     );
 
+    test_template(t, "empty computed member defaults to undefined",
+        "{{ a[]|default('fallback') }}",
+        {{"a", {{"name", "Bob"}}}},
+        "fallback"
+    );
+
+    test_template(t, "empty computed member is undefined",
+        "{{ a[] is undefined }}",
+        {{"a", {{"name", "Bob"}}}},
+        "True"
+    );
+
+    test_template(t, "undefined computed member is undefined",
+        "{{ a[undefined] is undefined }}",
+        {{"a", {{"name", "Bob"}}}},
+        "True"
+    );
+
     test_template(t, "array access",
         "{{ items[1] }}",
         {{"items", json::array({"a", "b", "c"})}},

tests/test-state-restore-fragmented.cpp

@@ -22,12 +22,12 @@ int main(int argc, char ** argv) {
     params.n_parallel = 3;
     params.n_ctx = 256;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 
-    common_init();
-
     // init
     common_init_result_ptr llama_init = common_init_from_params(params);
 

tests/test-thread-safety.cpp

@@ -16,12 +16,12 @@
 int main(int argc, char ** argv) {
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 
-    common_init();
-
     llama_backend_init();
     llama_numa_init(params.numa);
 

tools/batched-bench/batched-bench.cpp

@@ -20,12 +20,12 @@ int main(int argc, char ** argv) {
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) {
         return 1;
     }
 
-    common_init();
-
     int is_pp_shared   = params.is_pp_shared;
     int is_tg_separate = params.is_tg_separate;
 

tools/cli/cli.cpp

@@ -347,6 +347,8 @@ int main(int argc, char ** argv) {
 
     params.verbosity = LOG_LEVEL_ERROR; // by default, less verbose logs
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_CLI)) {
         return 1;
     }
@@ -357,8 +359,6 @@ int main(int argc, char ** argv) {
         console::error("please use llama-completion instead\n");
     }
 
-    common_init();
-
     // struct that contains llama context and inference
     cli_context ctx_cli(params);
 

tools/completion/completion.cpp

@@ -90,12 +90,12 @@ int main(int argc, char ** argv) {
     common_params params;
     g_params = &params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMPLETION, print_usage)) {
         return 1;
     }
 
-    common_init();
-
     auto & sparams = params.sampling;
 
     // save choice to use color for later
@@ -146,19 +146,13 @@ int main(int argc, char ** argv) {
 
     ctx   = llama_init->context();
     model = llama_init->model();
+    smpl  = llama_init->sampler(0);
 
     if (ctx == NULL) {
         LOG_ERR("%s: error: unable to create context\n", __func__);
         return 1;
     }
 
-    if (model == NULL) {
-        LOG_ERR("%s: error: unable to load model\n", __func__);
-        return 1;
-    }
-
-    smpl = llama_init->sampler(0);
-
     llama_memory_t mem = llama_get_memory(ctx);
     const llama_vocab * vocab = llama_model_get_vocab(model);
 

tools/cvector-generator/cvector-generator.cpp

@@ -400,6 +400,8 @@ int main(int argc, char ** argv) {
 
     params.out_file = "control_vector.gguf";
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) {
         return 1;
     }

tools/export-lora/export-lora.cpp

@@ -418,6 +418,8 @@ int main(int argc, char ** argv) {
 
     params.out_file = "ggml-lora-merged-f16.gguf";
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) {
         return 1;
     }

tools/fit-params/fit-params.cpp

@@ -17,11 +17,12 @@ using namespace std::chrono_literals;
 int main(int argc, char ** argv) {
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 
-    common_init();
     llama_backend_init();
     llama_numa_init(params.numa);
     auto mparams = common_model_params_to_llama(params);

tools/imatrix/imatrix.cpp

@@ -1212,6 +1212,8 @@ int main(int argc, char ** argv) {
     params.n_ctx = 512;
     params.escape = false;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {
         return 1;
     }
@@ -1223,8 +1225,6 @@ int main(int argc, char ** argv) {
         return 0;
     }
 
-    common_init();
-
     const int32_t n_ctx = params.n_ctx;
 
     if (n_ctx <= 0) {

tools/mtmd/debug/mtmd-debug.cpp

@@ -54,11 +54,12 @@ int main(int argc, char ** argv) {
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MTMD, show_additional_info)) {
         return 1;
     }
 
-    common_init();
     mtmd_helper_log_set(common_log_default_callback, nullptr);
 
     if (params.mmproj.path.empty()) {

tools/mtmd/mtmd-cli.cpp

@@ -281,11 +281,12 @@ int main(int argc, char ** argv) {
 
     common_params params;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MTMD, show_additional_info)) {
         return 1;
     }
 
-    common_init();
     mtmd_helper_log_set(common_log_default_callback, nullptr);
 
     if (params.mmproj.path.empty()) {

tools/perplexity/perplexity.cpp

@@ -2012,12 +2012,12 @@ int main(int argc, char ** argv) {
     params.n_ctx = 512;
     params.escape = false;
 
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
         return 1;
     }
 
-    common_init();
-
     const int32_t n_ctx = params.n_ctx;
 
     if (n_ctx <= 0) {

tools/results/results.cpp

@@ -58,6 +58,9 @@ static std::vector<float> get_logits(
 int main(int argc, char ** argv) {
     common_params params;
     params.escape = false;
+
+    common_init();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_RESULTS)) {
         return 1;
     }
@@ -65,7 +68,6 @@ int main(int argc, char ** argv) {
         LOG_ERR("%s: an output file must be specified", __func__);
         return 1;
     }
-    common_init();
     llama_backend_init();
     llama_numa_init(params.numa);
     common_init_result_ptr llama_init = common_init_from_params(params);

tools/server/CMakeLists.txt

@@ -42,7 +42,9 @@ option(LLAMA_BUILD_WEBUI "Build the embedded Web UI" ON)
 
 if (LLAMA_BUILD_WEBUI)
     set(PUBLIC_ASSETS
-        index.html.gz
+        index.html
+        bundle.js
+        bundle.css
         loading.html
     )
 

tools/server/README-dev.md

@@ -259,6 +259,6 @@ npm run test
 npm run build
 ```
 
-After `public/index.html.gz` has been generated, rebuild `llama-server` as described in the [build](#build) section to include the updated UI.
+After `public/index.html` has been generated, rebuild `llama-server` as described in the [build](#build) section to include the updated UI.
 
 **Note:** The Vite dev server automatically proxies API requests to `http://localhost:8080`. Make sure `llama-server` is running on that port during development.

tools/server/server-cors-proxy.h

@@ -32,13 +32,22 @@ static server_http_res_ptr proxy_request(const server_http_req & req, std::strin
 
     SRV_INF("proxying %s request to %s://%s:%i%s\n", method.c_str(), parsed_url.scheme.c_str(), parsed_url.host.c_str(), parsed_url.port, parsed_url.path.c_str());
 
+    std::map<std::string, std::string> headers;
+    for (auto [key, value] : req.headers) {
+        auto new_key = key;
+        if (string_starts_with(new_key, "X-Proxy-Header-")) {
+            string_replace_all(new_key, "X-Proxy-Header-", "");
+        }
+        headers[new_key] = value;
+    }
+
     auto proxy = std::make_unique<server_http_proxy>(
             method,
             parsed_url.scheme,
             parsed_url.host,
             parsed_url.port,
             parsed_url.path,
-            req.headers,
+            headers,
             req.body,
             req.should_stop,
             600, // timeout_read (default to 10 minutes)

tools/server/server-http.cpp

@@ -10,7 +10,9 @@
 
 #ifdef LLAMA_BUILD_WEBUI
 // auto generated files (see README.md for details)
-#include "index.html.gz.hpp"
+#include "index.html.hpp"
+#include "bundle.js.hpp"
+#include "bundle.css.hpp"
 #include "loading.html.hpp"
 #endif
 
@@ -272,16 +274,19 @@ bool server_http_context::init(const common_params & params) {
         } else {
 #ifdef LLAMA_BUILD_WEBUI
             // using embedded static index.html
-            srv->Get(params.api_prefix + "/", [](const httplib::Request & req, httplib::Response & res) {
-                if (req.get_header_value("Accept-Encoding").find("gzip") == std::string::npos) {
-                    res.set_content("Error: gzip is not supported by this browser", "text/plain");
-                } else {
-                    res.set_header("Content-Encoding", "gzip");
-                    // COEP and COOP headers, required by pyodide (python interpreter)
-                    res.set_header("Cross-Origin-Embedder-Policy", "require-corp");
-                    res.set_header("Cross-Origin-Opener-Policy", "same-origin");
-                    res.set_content(reinterpret_cast<const char*>(index_html_gz), index_html_gz_len, "text/html; charset=utf-8");
-                }
+            srv->Get(params.api_prefix + "/", [](const httplib::Request & /*req*/, httplib::Response & res) {
+                // COEP and COOP headers, required by pyodide (python interpreter)
+                res.set_header("Cross-Origin-Embedder-Policy", "require-corp");
+                res.set_header("Cross-Origin-Opener-Policy", "same-origin");
+                res.set_content(reinterpret_cast<const char*>(index_html), index_html_len, "text/html; charset=utf-8");
+                return false;
+            });
+            srv->Get(params.api_prefix + "/bundle.js", [](const httplib::Request & /*req*/, httplib::Response & res) {
+                res.set_content(reinterpret_cast<const char*>(bundle_js), bundle_js_len, "application/javascript; charset=utf-8");
+                return false;
+            });
+            srv->Get(params.api_prefix + "/bundle.css", [](const httplib::Request & /*req*/, httplib::Response & res) {
+                res.set_content(reinterpret_cast<const char*>(bundle_css), bundle_css_len, "text/css; charset=utf-8");
                 return false;
             });
 #endif