tests : add unit test coverage for llama_tensor_get_type (#20112)

* Add unit test coverage for llama_tensor_get_type

* Fix merge conflicts, add more schemas

* clang formatter changes

* Trailing whitespace

* Update name

* Start rebase

* Updating files with upstream changes prior to rebase

* Changes needed from rebase

* Update attn_qkv schema, change throw behaviour

* Fix merge conflicts

* White space

* Update with latest changes to state counters

* Revert accidental personal CLAUDE.md changes

* Change quotation mark

* Reuse metadata.name since we have it

* Move test-only stuff out of llama-quant.cpp

* Hide the regex functionality back in llama-quant.cpp, use a unique pointer to a new struct 'compiled_tensor_type_patterns' which contains the patterns

* cont : inital deslop guidelines

* Cleanup based on review comments

* Continue cleanup

* Small cleanup

* Manually set proper ordering of tensors, mostly applies to gemma

* Formatting

* Update tests/test-quant-type-selection.cpp

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

* Fix merge conflicts

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.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

@@ -150,16 +150,15 @@ jobs:
       - name: Dawn Dependency
         id: dawn-depends
         run: |
-          DAWN_VERSION="v2.0.0"
-          DAWN_OWNER="reeselevine"
+          DAWN_VERSION="v20260317.182325"
+          DAWN_OWNER="google"
           DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release"
-          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
-          curl -L -o artifact.zip \
-            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
+          DAWN_ASSET_NAME="Dawn-18eb229ef5f707c1464cc581252e7603c73a3ef0-macos-latest-Release"
+          echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
+          curl -L -o artifact.tar.gz \
+            "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
           mkdir dawn
-          unzip artifact.zip
-          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
+          tar -xvf artifact.tar.gz -C dawn --strip-components=1
 
       - name: Build
         id: cmake_build
@@ -181,7 +180,7 @@ 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 +206,22 @@ 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 +299,15 @@ 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 +317,10 @@ 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
@@ -365,16 +383,15 @@ jobs:
         id: dawn-depends
         run: |
           sudo apt-get install -y libxrandr-dev libxinerama-dev libxcursor-dev mesa-common-dev libx11-xcb-dev libxi-dev
-          DAWN_VERSION="v2.0.0"
-          DAWN_OWNER="reeselevine"
+          DAWN_VERSION="v20260317.182325"
+          DAWN_OWNER="google"
           DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-ubuntu-latest-Release"
-          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
-          curl -L -o artifact.zip \
-            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
+          DAWN_ASSET_NAME="Dawn-18eb229ef5f707c1464cc581252e7603c73a3ef0-ubuntu-latest-Release"
+          echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
+          curl -L -o artifact.tar.gz \
+            "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
           mkdir dawn
-          unzip artifact.zip
-          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
+          tar -xvf artifact.tar.gz -C dawn --strip-components=1
 
       - name: Build
         id: cmake_build
@@ -408,7 +425,7 @@ jobs:
 
       - name: Fetch emdawnwebgpu
         run: |
-          DAWN_TAG="v20251027.212519"
+          DAWN_TAG="v20260317.182325"
           EMDAWN_PKG="emdawnwebgpu_pkg-${DAWN_TAG}.zip"
           echo "Downloading ${EMDAWN_PKG}"
           curl -L -o emdawn.zip \

.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
 

AGENTS.md

@@ -5,78 +5,106 @@
 >
 > Read more: [CONTRIBUTING.md](CONTRIBUTING.md)
 
-AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (see examples below)
+AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (see examples below).
 
 ---
 
 ## Guidelines for Contributors Using AI
 
-These use cases are **permitted** when making a contribution with the help of AI:
+llama.cpp is built by humans, for humans. Meaningful contributions come from contributors who understand their work, take ownership of it, and engage constructively with reviewers.
 
-- Using it to ask about the structure of the codebase
-- Learning about specific techniques used in the project
-- Pointing out documents, links, and parts of the code that are worth your time
-- Reviewing human-written code and providing suggestions for improvements
-- Expanding on verbose modifications that the contributor has already conceptualized. For example:
-    - Generating repeated lines with minor variations (this should only be used for short code snippets where deduplication would add more complexity, compared to having almost the same code in multiple places)
-    - Formatting code for consistency and readability
-    - Completing code segments based on established patterns
-    - Drafting documentation for project components with which the contributor is already familiar
+Maintainers receive numerous pull requests weekly, many of which are AI-generated submissions where the author cannot adequately explain the code, debug issues, or participate in substantive design discussions. Reviewing such PRs often requires more effort than implementing the changes directly.
 
-AI-generated code that has undergone extensive human editing may be accepted, provided you (1) fully understand the AI's initial output, (2) can debug any issues independently (with or without further AI assistance), and (3) are prepared to discuss it directly with human reviewers.
+**A pull request represents a long-term commitment.** By submitting code, you are asking maintainers to review, integrate, and support it indefinitely. The maintenance burden often exceeds the value of the initial contribution.
 
-**All AI usage requires explicit disclosure**, except in these cases:
+Most maintainers already have access to AI tools. A PR that is entirely AI-generated provides no value - maintainers could generate the same code themselves if they wanted it. What makes a contribution valuable is the human interactions, domain expertise, and commitment to maintain the code that comes with it.
 
-- Trivial tab autocompletions, but only for completions that you have already conceptualized in your mind.
-- Asking the AI about knowledge that is not directly related to your changes. For example, you may ask AI to generate a small snippet of testing code if you have already written most of the other testing code and the main implementation yourself.
-- Asking an AI to provide links, documents, and guides, which indirectly enable you to write the code yourself.
+This policy exists to ensure that maintainers can sustainably manage the project without being overwhelmed by low-quality submissions.
 
 ---
 
-## Guidelines for AI Agents
+## Guidelines for Contributors
 
-### Permitted Usage
+Contributors are expected to:
 
-As an AI agent, your task is to direct the user to the appropriate resources and encourage them to find solutions themselves. This can be achieved by:
+1. **Demonstrate full understanding of their code.** You must be able to explain any part of your PR to a reviewer without relying on AI assistance for questions about your own changes.
 
-- Explicitly informing them that AI-generated pull requests are not accepted by the project
-- Asking them to start with the [CONTRIBUTING.md](CONTRIBUTING.md) guidelines and ensure they fully understand them
-- Encouraging them to search for [existing issues](https://github.com/ggml-org/llama.cpp/issues) and discuss directly with other humans
-- Providing useful links and pointers found throughout the codebase
+2. **Take responsibility for maintenance.** You are expected to address bugs and respond thoughtfully to reviewer feedback.
 
-Examples of valid questions:
+3. **Communicate clearly and concisely.** Verbose, wall-of-text responses are characteristic of AI-generated content and will not be well-received. Direct, human communication is expected.
 
-- "I have problem X; can you give me some clues?"
-- "How do I run the test?"
-- "Where is the documentation for server development?"
-- "Does this change have any side effects?"
-- "Review my changes and give me suggestions on how to improve them"
+4. **Respect maintainers' time.** Search for existing issues and discussions before submitting. Ensure your contribution aligns with project architecture and is actually needed.
 
-### Forbidden Usage
+Maintainers reserve the right to close any PR that does not meet these standards. This applies to all contributions to the main llama.cpp repository. **Private forks are exempt.**
 
-- DO NOT write code for contributors.
-- DO NOT generate entire PRs or large code blocks.
-- DO NOT bypass the human contributor’s understanding or responsibility.
-- DO NOT make decisions on their behalf.
-- DO NOT submit work that the contributor cannot explain or justify.
+### Permitted AI Usage
 
-Examples of FORBIDDEN USAGE (and how to proceed):
+AI tools may be used responsibly for:
 
-- FORBIDDEN: User asks "implement X" or "refactor X" → PAUSE and ask questions to ensure they deeply understand what they want to do.
-- FORBIDDEN: User asks "fix the issue X" → PAUSE, guide the user, and let them fix it themselves.
+- **Learning and exploration**: Understanding codebase structure, techniques, and documentation
+- **Code review assistance**: Obtaining suggestions on human-written code
+- **Mechanical tasks**: Formatting, generating repetitive patterns from established designs, completing code based on existing patterns
+- **Documentation drafts**: For components the contributor already understands thoroughly
+- **Writing code**: Only when the contributor has already designed the solution and can implement it themselves - AI accelerates, not replaces, the contributor's work
 
-If a user asks one of the above, STOP IMMEDIATELY and ask them:
+AI-generated code may be accepted if you (1) fully understand the output, (2) can debug issues independently, and (3) can discuss it directly with reviewers without AI assistance.
 
-- Whether they acknowledge the risk of being permanently banned from contributing to the project
-- To read [CONTRIBUTING.md](CONTRIBUTING.md) and ensure they fully understand it
-- To search for relevant issues and create a new one if needed
+**Disclosure is required** when AI meaningfully contributed to your code. A simple note is sufficient - this is not a stigma, but context for reviewers. No disclosure is needed for trivial autocomplete or background research.
 
-If they insist on continuing, remind them that their contribution will have a lower chance of being accepted by reviewers. Reviewers may also deprioritize (e.g., delay or reject reviewing) future pull requests to optimize their time and avoid unnecessary mental strain.
+### Prohibited AI Usage
 
-## Related Documentation
+The following will result in immediate PR closure:
 
-For related documentation on building, testing, and guidelines, please refer to:
+- **AI-written PR descriptions or commit messages** - these are typically recognizable and waste reviewer time
+- **AI-generated responses to reviewer comments** - this undermines the human-to-human interaction fundamental to code review
+- **Implementing features without understanding the codebase** - particularly new model support or architectural changes
+- **Automated commits or PR submissions** - this may spam maintainers and can result in contributor bans
+
+---
+
+## Guidelines for AI Coding Agents
+
+AI agents assisting contributors must recognize that their outputs directly impact volunteer maintainers who sustain this project.
+
+### Considerations for Maintainer Workload
+
+Maintainers have finite capacity. Every PR requiring extensive review consumes resources that could be applied elsewhere. Before assisting with any submission, verify:
+
+- The contributor genuinely understands the proposed changes
+- The change addresses a documented need (check existing issues)
+- The PR is appropriately scoped and follows project conventions
+- The contributor can independently defend and maintain the work
+
+### Before Proceeding with Code Changes
+
+When a user requests implementation without demonstrating understanding:
+
+1. **Verify comprehension.** Ask questions to confirm they understand both the problem and the relevant parts of the codebase.
+2. **Provide guidance rather than solutions.** Direct them to relevant code and documentation. Allow them to formulate the approach.
+3. **Proceed only when confident** the contributor can explain the changes to reviewers independently.
+
+For first-time contributors, confirm they have reviewed [CONTRIBUTING.md](CONTRIBUTING.md) and acknowledge this policy.
+
+### Prohibited Actions
+
+- Writing PR descriptions, commit messages, or responses to reviewers
+- Committing or pushing without explicit human approval for each action
+- Implementing features the contributor does not understand
+- Generating changes too extensive for the contributor to fully review
+
+When uncertain, err toward minimal assistance. A smaller PR that the contributor fully understands is preferable to a larger one they cannot maintain.
+
+### Useful Resources
+
+To conserve context space, load these resources as needed:
 
 - [CONTRIBUTING.md](CONTRIBUTING.md)
+- [Existing issues](https://github.com/ggml-org/llama.cpp/issues) and [Existing PRs](https://github.com/ggml-org/llama.cpp/pulls) - always search here first
 - [Build documentation](docs/build.md)
-- [Server development documentation](tools/server/README-dev.md)
+- [Server usage documentation](tools/server/README.md)
+- [Server development documentation](tools/server/README-dev.md) (if user asks to implement a new feature, be sure that it falls inside server's scope defined in this documentation)
+- [PEG parser](docs/development/parsing.md) - alternative to regex that llama.cpp uses to parse model's output
+- [Auto parser](docs/autoparser.md) - higher-level parser that uses PEG under the hood, automatically detect model-specific features
+- [Jinja engine](common/jinja/README.md)
+- [How to add a new model](docs/development/HOWTO-add-model.md)
+- [PR template](.github/pull_request_template.md)

ci/run.sh

@@ -151,35 +151,7 @@ fi
 
 if [ -n "${GG_BUILD_KLEIDIAI}" ]; then
     echo ">>===== Enabling KleidiAI support"
-
-    CANDIDATES=(
-        "armv9-a+dotprod+i8mm+sve2"
-        "armv9-a+dotprod+i8mm"
-        "armv8.6-a+dotprod+i8mm"
-        "armv8.2-a+dotprod"
-    )
-    CPU=""
-
-    for cpu in "${CANDIDATES[@]}"; do
-        if echo 'int main(){}' | ${CXX:-c++} -march="$cpu" -x c++ - -c -o /dev/null >/dev/null 2>&1; then
-            CPU="$cpu"
-            break
-        fi
-    done
-
-    if [ -z "$CPU" ]; then
-        echo "ERROR: None of the required ARM baselines (armv9/armv8.6/armv8.2 + dotprod) are supported by this compiler."
-        exit 1
-    fi
-
-    echo ">>===== Using ARM baseline: ${CPU}"
-
-    CMAKE_EXTRA="${CMAKE_EXTRA:+$CMAKE_EXTRA } \
-        -DGGML_NATIVE=OFF \
-        -DGGML_CPU_KLEIDIAI=ON \
-        -DGGML_CPU_AARCH64=ON \
-        -DGGML_CPU_ARM_ARCH=${CPU} \
-        -DBUILD_SHARED_LIBS=OFF"
+    CMAKE_EXTRA="${CMAKE_EXTRA:+$CMAKE_EXTRA } -DGGML_CPU_KLEIDIAI=ON"
 fi
 
 if [ ! -z ${GG_BUILD_BLAS} ]; then

common/arg.cpp

@@ -537,9 +537,11 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
     } catch (const std::exception & e) {
         LOG_WRN("HF cache migration failed: %s\n", e.what());
     }
+    // export_graph_ops loads only metadata
+    const bool skip_model_download = ctx_arg.ex == LLAMA_EXAMPLE_EXPORT_GRAPH_OPS;
 
     // maybe handle remote preset
-    if (!params.model.hf_repo.empty()) {
+    if (!params.model.hf_repo.empty() && !skip_model_download) {
         std::string cli_hf_repo = params.model.hf_repo;
         bool has_preset = common_params_handle_remote_preset(params, ctx_arg.ex);
 
@@ -570,7 +572,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
     }
 
     // handle model and download
-    {
+    if (!skip_model_download) {
         auto res = common_params_handle_model(params.model, params.hf_token, params.offline);
         if (params.no_mmproj) {
             params.mmproj = {};
@@ -591,7 +593,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
 
     // model is required (except for server)
     // TODO @ngxson : maybe show a list of available models in CLI in this case
-    if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage && !params.completion) {
+    if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !skip_model_download && !params.usage && !params.completion) {
         throw std::invalid_argument("error: --model is required\n");
     }
 

common/chat-auto-parser-generator.cpp

@@ -65,7 +65,7 @@ common_chat_params peg_generator::generate_parser(const common_chat_template &
         data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
             foreach_function(inputs.tools, [&](const json & tool) {
                 const auto & function = tool.at("function");
-                auto         schema   = function.at("parameters");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
                 builder.resolve_refs(schema);
             });
             parser.build_grammar(builder, data.grammar_lazy);
@@ -100,6 +100,7 @@ common_peg_arena autoparser::build_parser(const generation_params & inputs) cons
 
         bool has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
         bool has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty();
+        bool pure_content        = reasoning.mode == reasoning_mode::NONE;
 
         if (has_response_format) {
             auto response_format = p.rule("response-format", p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)));
@@ -107,12 +108,14 @@ common_peg_arena autoparser::build_parser(const generation_params & inputs) cons
                 p.literal("```json") + p.space() + response_format + p.space() + p.literal("```"),
                 response_format
             }) + p.end();
+            pure_content = false;
         } else if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && jinja_caps.supports_tool_calls) {
             parser = tools.build_parser(ctx);
+            pure_content = false;
         } else {
             parser = content.build_parser(ctx);
         }
-        return p.prefix(inputs.generation_prompt, reasoning.start) + parser;
+        return pure_content ? p.prefix(inputs.generation_prompt, reasoning.start) + parser : p.prefix(inputs.generation_prompt, reasoning.start) << parser;
     });
 }
 
@@ -166,6 +169,8 @@ common_peg_parser analyze_tools::build_parser(parser_build_context & ctx) const
             return build_tool_parser_tag_json(ctx);
         case tool_format::TAG_WITH_TAGGED:
             return build_tool_parser_tag_tagged(ctx);
+        case tool_format::TAG_WITH_GEMMA4_DICT:
+            return build_tool_parser_tag_gemma4_dict(ctx);
         default:
             LOG_ERR("[ERROR] Template seems to support tool calls, but failed to determine tool format. Tool calling will not work properly. "
                 "Check for a fixed template for your model in the models/templates directory of your llama.cpp installation or "
@@ -221,7 +226,7 @@ common_peg_parser analyze_tools::build_tool_parser_tag_json(parser_build_context
     foreach_function(inputs.tools, [&](const json & tool) {
         const auto & func   = tool.at("function");
         std::string  name   = func.at("name");
-        const auto & schema = func.at("parameters");
+        const auto & schema = func.contains("parameters") ? func.at("parameters") : json::object();
 
         // Build call_id parser based on position (if supported)
         common_peg_parser call_id_section = p.eps();
@@ -282,19 +287,11 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
     common_peg_parser tool_choice = p.choice();
 
     foreach_function(inputs.tools, [&](const json & tool) {
-        const auto & func   = tool.at("function");
-        std::string  name   = func.at("name");
-        const auto & params = func.at("parameters");
-
-        if (!params.contains("properties") || !params.at("properties").is_object()) {
-            return;
-        }
-
-        const auto &          properties = params.at("properties");
+        const auto &          func       = tool.at("function");
+        std::string           name       = func.at("name");
+        const auto &          params     = func.contains("parameters") ? func.at("parameters") : json::object();
+        const auto &          properties = params.contains("properties") ? params.at("properties") : json::object();
         std::set<std::string> required;
-        if (params.contains("required") && params.at("required").is_array()) {
-            params.at("required").get_to(required);
-        }
 
         // Build parser for each argument, separating required and optional
         std::vector<common_peg_parser> required_parsers;
@@ -311,17 +308,18 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
                 }
             }
 
-            auto arg = p.tool_arg(
-                p.tool_arg_open(arguments.name_prefix + p.tool_arg_name(p.literal(param_name)) +
-                                arguments.name_suffix) +
-                arguments.value_prefix +
-                (type == "string" ? p.tool_arg_string_value(p.schema(p.until(arguments.value_suffix),
-                                                                     "tool-" + name + "-arg-" + param_name + "-schema",
-                                                                     param_schema, true)) :
-                                    p.tool_arg_json_value(p.schema(
-                                        p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, false)) +
-                                        p.space()) +
-                p.tool_arg_close(p.literal(arguments.value_suffix)));
+            auto arg =
+                p.tool_arg(p.tool_arg_open(arguments.name_prefix + p.tool_arg_name(p.literal(param_name)) +
+                                           arguments.name_suffix) +
+                           arguments.value_prefix +
+                           (type == "string" ?
+                                p.tool_arg_string_value(p.schema(p.until(arguments.value_suffix),
+                                                                 "tool-" + name + "-arg-" + param_name + "-schema",
+                                                                 param_schema, true)) :
+                                p.tool_arg_json_value(p.schema(
+                                    p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, false)) +
+                                    p.space()) +
+                           p.tool_arg_close(p.literal(arguments.value_suffix)));
 
             auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg);
             if (is_required) {
@@ -437,4 +435,113 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
            p.end();
 }
 
+common_peg_parser analyze_tools::build_tool_parser_tag_gemma4_dict(parser_build_context & ctx) const {
+    auto &       p           = ctx.p;
+    const auto & inputs      = ctx.inputs;
+    bool         force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+
+    // The Gemma4 string quote token used in place of JSON "
+    static const std::string QUOTE = "<|\"|>";
+
+    common_peg_parser tool_choice = p.choice();
+
+    foreach_function(inputs.tools, [&](const json & tool) {
+        const auto & func   = tool.at("function");
+        std::string  name   = func.at("name");
+        const auto & params = func.at("parameters");
+
+        if (!params.contains("properties") || !params.at("properties").is_object()) {
+            // No arguments - just match the function name with empty braces
+            auto func_parser = p.atomic(
+                p.tool_open(p.literal(function.name_prefix) + p.tool_name(p.literal(name)) + p.literal("{")) +
+                p.tool_args(p.eps()) +
+                p.tool_close(p.literal("}")));
+            tool_choice |= p.rule("tool-" + name, func_parser);
+            return;
+        }
+
+        const auto &          properties = params.at("properties");
+        std::set<std::string> required;
+        if (params.contains("required") && params.at("required").is_array()) {
+            params.at("required").get_to(required);
+        }
+
+        // Build per-argument parsers, sorted alphabetically (matching template's dictsort)
+        struct arg_entry {
+            std::string       param_name;
+            common_peg_parser parser;
+        };
+        std::vector<arg_entry> arg_entries;
+
+        for (const auto & [param_name, param_schema] : properties.items()) {
+            std::string type    = "object";
+            auto        type_v  = param_schema.contains("type") ? param_schema.at("type") : json::object();
+            if (type_v.is_string()) type_v.get_to(type);
+
+            common_peg_parser value_parser = p.eps();
+            if (type == "string") {
+                // String values are delimited by <|"|>...<|"|>
+                value_parser =
+                    p.literal(QUOTE) +
+                    p.tool_arg_string_value(p.schema(p.until(QUOTE),
+                        "tool-" + name + "-arg-" + param_name + "-schema", param_schema, true)) +
+                    p.literal(QUOTE);
+            } else {
+                // Numbers, booleans: raw text up to the next comma or closing brace
+                value_parser = p.tool_arg_value(p.until_one_of({",", "}"}));
+            }
+
+            auto arg = p.tool_arg(
+                p.tool_arg_open(p.tool_arg_name(p.literal(param_name)) + p.literal(":")) +
+                value_parser +
+                p.tool_arg_close(p.eps()));
+
+            arg_entries.push_back({param_name, p.rule("tool-" + name + "-arg-" + param_name, arg)});
+        }
+
+        // Sort alphabetically to match Jinja's dictsort
+        std::sort(arg_entries.begin(), arg_entries.end(), [](const auto & a, const auto & b) {
+            return a.param_name < b.param_name;
+        });
+
+        // Build arg sequence: any arg, then zero-or-more comma-separated additional args
+        common_peg_parser args_seq = p.eps();
+        if (!arg_entries.empty()) {
+            common_peg_parser any_arg = p.choice();
+            for (auto & entry : arg_entries) {
+                any_arg |= entry.parser;
+            }
+            args_seq = p.optional(
+                any_arg + p.repeat(p.literal(",") + any_arg, 0, (int) arg_entries.size() - 1));
+        }
+
+        // Full parser: call:name{args}
+        auto func_parser = p.atomic(
+            p.tool_open(p.literal(function.name_prefix) + p.tool_name(p.literal(name)) + p.literal("{")) +
+            p.tool_args(args_seq) +
+            p.tool_close(p.literal("}")));
+
+        tool_choice |= p.rule("tool-" + name, func_parser);
+    });
+
+    // Wrap each call in <|tool_call>...</tool_call|>
+    auto wrapped_call = p.literal(format.per_call_start) + tool_choice + p.literal(format.per_call_end);
+
+    common_peg_parser tool_calls = p.eps();
+    if (inputs.parallel_tool_calls) {
+        tool_calls = p.trigger_rule("tool-call", wrapped_call + p.zero_or_more(p.space() + wrapped_call));
+    } else {
+        tool_calls = p.trigger_rule("tool-call", wrapped_call);
+    }
+
+    if (!force_tools) {
+        tool_calls = p.optional(tool_calls);
+    }
+
+    auto content_before_tools = p.until(format.per_call_start);
+    return ctx.reasoning_parser +
+           (force_tools ? p.eps() : p.optional(p.content(content_before_tools))) +
+           tool_calls + p.end();
+}
+
 }  // namespace autoparser

common/chat-auto-parser.h

@@ -144,6 +144,7 @@ enum class tool_format {
     JSON_NATIVE,      // Pure JSON: {"name": "X", "arguments": {...}}
     TAG_WITH_JSON,    // Tag-based with JSON args: <function=X>{...}</function>
     TAG_WITH_TAGGED,  // Tag-based with tagged args: <param=key>value</param>
+    TAG_WITH_GEMMA4_DICT, // Gemma4 custom dict: <|tool_call>call:name{key:<|"|>val<|"|>}<tool_call|>
 };
 
 inline std::ostream & operator<<(std::ostream & os, const tool_format & format) {
@@ -156,6 +157,8 @@ inline std::ostream & operator<<(std::ostream & os, const tool_format & format)
             return os << "TAG_WITH_JSON";
         case tool_format::TAG_WITH_TAGGED:
             return os << "TAG_WITH_TAGGED";
+        case tool_format::TAG_WITH_GEMMA4_DICT:
+            return os << "TAG_WITH_GEMMA4_DICT";
         default:
             return os << "UNKNOWN";
     }
@@ -350,6 +353,7 @@ struct analyze_tools : analyze_base {
     common_peg_parser build_tool_parser_json_native(parser_build_context & ctx) const;
     common_peg_parser build_tool_parser_tag_json(parser_build_context & ctx) const;
     common_peg_parser build_tool_parser_tag_tagged(parser_build_context & ctx) const;
+    common_peg_parser build_tool_parser_tag_gemma4_dict(parser_build_context & ctx) const;
 };
 
 // ============================================================================

common/chat-diff-analyzer.cpp

@@ -92,6 +92,33 @@ static std::vector<std::function<void(const common_chat_template & tmpl, autopar
               LOG_DBG(ANSI_ORANGE "[Patch: Functionary 3.1]\n" ANSI_RESET);
           }
       },
+      // Gemma4 - custom dict format: <|tool_call>call:name{key:<|"|>val<|"|>}<tool_call|>
+      [](const common_chat_template & tmpl, autoparser & analysis) -> void {
+          if (tmpl.src.find("'<|tool_call>call:'") != std::string::npos) {
+              analysis.tools.format.mode           = tool_format::TAG_WITH_GEMMA4_DICT;
+              analysis.tools.format.per_call_start = "<|tool_call>";
+              analysis.tools.format.per_call_end   = "<tool_call|>";
+              analysis.tools.format.section_start  = "";
+              analysis.tools.format.section_end    = "";
+              analysis.tools.function.name_prefix  = "call:";
+              analysis.tools.function.name_suffix  = "";
+              analysis.tools.arguments.start       = "{";
+              analysis.tools.arguments.end         = "}";
+              analysis.tools.arguments.name_suffix = ":";
+              analysis.tools.arguments.separator   = ",";
+              analysis.reasoning.mode              = reasoning_mode::TAG_BASED;
+              analysis.reasoning.start             = "<|channel>thought\n";
+              analysis.reasoning.end               = "<channel|>";
+              analysis.preserved_tokens.clear();
+              analysis.preserved_tokens.push_back("<|tool_call>");
+              analysis.preserved_tokens.push_back("<tool_call|>");
+              analysis.preserved_tokens.push_back("<|tool_response>");
+              analysis.preserved_tokens.push_back("<tool_response|>");
+              analysis.preserved_tokens.push_back("<|\"|>");
+              analysis.preserved_tokens.push_back("<|turn>");
+              LOG_DBG(ANSI_ORANGE "[Patch: Gemma4]\n" ANSI_RESET);
+          }
+      },
       // DeepSeek-R1-Distill-Qwen
       [](const common_chat_template & tmpl, autoparser & analysis) -> void {
           if (tmpl.src.find(

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;
 };
@@ -971,6 +971,7 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
     auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
     auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
     auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
 
     auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
         auto start           = p.rule("start", p.literal("<|start|>assistant"));
@@ -979,13 +980,27 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
         auto channel         = p.literal("<|channel|>") + (p.literal("commentary") | p.literal("analysis"));
         auto constrain_type  = p.chars("[A-Za-z0-9_-]", 1, -1);
 
-        auto analysis = p.rule("analysis", p.literal("<|channel|>analysis<|message|>") + p.reasoning(content) + end);
+        // Occasionally, gpt-oss-20b will prefix channels with this commentary
+        auto stray_commentary = p.optional(p.literal("<|channel|>commentary") + p.optional(p.literal(" to=assistant")));
+        auto start_analysis = stray_commentary + p.literal("<|channel|>analysis<|message|>");
+
+        if (extract_reasoning) {
+            p.rule("analysis", start_analysis + p.reasoning(content) + end);
+        } else {
+            p.rule("analysis", p.content(start_analysis + content + end));
+        }
+
+        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));
+        auto final_msg = p.rule("final", stray_commentary + 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) {
-            auto constraint = p.optional(p.space() + p.literal("<|constrain|>") + constrain_type);
+            auto constraint = p.optional(p.space() + p.optional(p.literal("<|constrain|>")) + constrain_type);
             auto response_format = p.rule("response-format",
                 p.literal("<|channel|>final") + constraint + p.literal("<|message|>") +
                 p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)));
@@ -1002,7 +1017,7 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
                 const auto & params   = function.at("parameters");
 
                 auto func_name  = p.literal(" to=functions.") + p.tool_name(p.literal(name));
-                auto constraint = p.optional(p.space() + p.literal("<|constrain|>") + constrain_type);
+                auto constraint = p.optional(p.space() + p.optional(p.literal("<|constrain|>")) + constrain_type);
                 auto args       = p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", params));
 
                 // recipient in role header
@@ -1025,7 +1040,7 @@ 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();
@@ -1043,6 +1058,7 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
 
         data.grammar_triggers = {
             { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "^\\s+to$" },
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "^<\\|channel\\|>(?:commentary|analysis)\\s+to=functions$" },
             { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "<\\|start\\|>assistant(\\s+to)" },
             { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "<\\|start\\|>assistant(<\\|channel\\|>(?:commentary|analysis)\\s+to)" }
         };
@@ -1263,11 +1279,12 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
     return data;
 }
 
-// LFM2 format:
-// - Reasoning: <think>{reasoning}</think> (optional, only if enable_thinking is true)
-// - Content: text after reasoning (optional)
-// - Tool calls: <|tool_call_start|>[function_name(arg1="value1", arg2="value2")]<|tool_call_end|>
-// Tool calls can appear multiple times (parallel tool calls)
+// LFM2 format: uses <|tool_list_start|>[...]<|tool_list_end|> in system prompt
+// and <|tool_call_start|>[name(arg="val")]<|tool_call_end|> for tool calls.
+// - Reasoning: <think>{reasoning}</think> (optional)
+// - Content: text before a tool call (optional)
+// - Tool calls: Python-style, e.g. [function_name(arg1="value1", arg2="value2")]
+//   Tool calls can appear multiple times (parallel tool calls supported)
 static common_chat_params common_chat_params_init_lfm2(const common_chat_template &    tmpl,
                                                        const autoparser::generation_params & inputs) {
     common_chat_params data;
@@ -1308,9 +1325,9 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
         if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
             return generation_prompt + reasoning + p.content(p.rest()) + end;
         }
-
         auto tool_calls = p.rule("tool-calls",
-            p.trigger_rule("tool-call", p.literal(TOOL_CALL_START) +
+            p.trigger_rule("tool-call",
+                p.literal(TOOL_CALL_START) +
                 p.python_style_tool_calls(inputs.tools, inputs.parallel_tool_calls) +
                 p.literal(TOOL_CALL_END)
             )
@@ -1338,6 +1355,80 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
             { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOL_CALL_START }
         };
     }
+    return data;
+}
+
+// LFM2.5 format: uses plain "List of tools: [...]" in system prompt, no wrapper tokens.
+// Tool calls are bare [name(arg="val")], though model may optionally emit <|tool_call_start|>.
+// - Reasoning: <think>{reasoning}</think> (optional)
+// - Content: text before a tool call (optional)
+// - Tool calls: Python-style, e.g. [function_name(arg1="value1", arg2="value2")]
+//   Tool calls can appear multiple times (parallel tool calls supported)
+static common_chat_params common_chat_params_init_lfm2_5(const common_chat_template &    tmpl,
+                                                         const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+    data.preserved_tokens  = {
+        "<|tool_call_start|>",
+        "<|tool_call_end|>",
+        "<think>",
+        "</think>",
+    };
+
+    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+
+    const std::string THINK_START     = "<think>";
+    const std::string THINK_END       = "</think>";
+
+    data.thinking_start_tag = THINK_START;
+    data.thinking_end_tag   = THINK_END;
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.prefix(inputs.generation_prompt, THINK_START);
+        auto end = p.end();
+
+        auto reasoning = p.eps();
+        if (extract_reasoning && inputs.enable_thinking) {
+            reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
+        }
+
+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            return generation_prompt + reasoning + p.content(p.rest()) + end;
+        }
+
+        auto tool_calls = p.rule("tool-calls",
+            p.trigger_rule("tool-call",
+                p.python_style_tool_calls(inputs.tools, inputs.parallel_tool_calls)
+            )
+        );
+
+        auto content = p.content(p.until_one_of({"<|tool_call_start|>", "["}));
+        auto maybe_start = p.optional(p.literal("<|tool_call_start|>"));
+        return generation_prompt + reasoning + content + maybe_start + tool_calls + end;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const std::string name = tool.at("function").at("name");
+            data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "[" + name + "(" });
+        });
+    }
 
     return data;
 }
@@ -1454,6 +1545,50 @@ static void requires_non_null_content(json & messages) {
     }
 }
 
+// Gemma4 uses a custom tool_responses field instead of role:tool messages.
+// Convert consecutive role:tool messages into a single user message with tool_responses.
+static void convert_tool_responses_gemma4(json & messages) {
+    json result = json::array();
+    size_t i = 0;
+    while (i < messages.size()) {
+        if (messages[i].contains("role") && messages[i].at("role") == "tool") {
+            json tool_responses = json::array();
+            while (i < messages.size() &&
+                   messages[i].contains("role") &&
+                   messages[i].at("role") == "tool") {
+                const auto & tool_msg = messages[i];
+                std::string name;
+                if (tool_msg.contains("tool_call_id") && tool_msg.at("tool_call_id").is_string()) {
+                    name = tool_msg.at("tool_call_id");
+                } else if (tool_msg.contains("name") && tool_msg.at("name").is_string()) {
+                    name = tool_msg.at("name");
+                }
+                json response;
+                if (tool_msg.contains("content")) {
+                    const auto & content = tool_msg.at("content");
+                    if (content.is_string()) {
+                        // Try to parse the content as JSON; fall back to raw string
+                        try {
+                            response = json::parse(content.get<std::string>());
+                        } catch (...) {
+                            response = content;
+                        }
+                    } else {
+                        response = content;
+                    }
+                }
+                tool_responses.push_back({{"name", name}, {"response", response}});
+                i++;
+            }
+            result.push_back({{"role", "user"}, {"tool_responses", tool_responses}});
+        } else {
+            result.push_back(messages[i]);
+            i++;
+        }
+    }
+    messages = result;
+}
+
 static void func_args_not_string(json & messages) {
     GGML_ASSERT(messages.is_array());
     for (auto & message : messages) {
@@ -1519,14 +1654,21 @@ static std::optional<common_chat_params> try_specialized_template(
         return common_chat_params_init_kimi_k2(tmpl, params);
     }
 
-    // LFM2 - uses <|tool_list_start|>/<|tool_list_end|> markers and <|tool_call_start|>[name(args)]<|tool_call_end|> format
-    // Detection: template has "<|tool_list_start|>" and "<|tool_list_end|>" markers
+    // LFM2 format detection: template uses <|tool_list_start|>[...]<|tool_list_end|> around the tool list
+    // and <|tool_call_start|>[...]<|tool_call_end|> around each tool call
     if (src.find("<|tool_list_start|>") != std::string::npos &&
         src.find("<|tool_list_end|>") != std::string::npos) {
         LOG_DBG("Using specialized template: LFM2\n");
         return common_chat_params_init_lfm2(tmpl, params);
     }
 
+    // LFM2.5 format detection: template uses plain "List of tools: [...]" with no special tokens
+    if (src.find("List of tools: [") != std::string::npos &&
+        src.find("<|tool_list_start|>") == std::string::npos) {
+        LOG_DBG("Using specialized template: LFM2.5\n");
+        return common_chat_params_init_lfm2_5(tmpl, params);
+    }
+
     // GigaChatV3 format detection
     if (src.find("<|role_sep|>") != std::string::npos &&
         src.find("<|message_sep|>") != std::string::npos &&
@@ -1575,6 +1717,10 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         workaround::func_args_not_string(params.messages);
     }
 
+    if (src.find("'<|tool_call>call:'") != std::string::npos) {
+        workaround::convert_tool_responses_gemma4(params.messages);
+    }
+
     params.add_generation_prompt = false;
     std::string no_gen_prompt    = common_chat_template_direct_apply(tmpl, params);
     params.add_generation_prompt = true;
@@ -1616,7 +1762,7 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         data.format                    = COMMON_CHAT_FORMAT_PEG_NATIVE;
         data.generation_prompt         = params.generation_prompt;
         auto parser                    = build_chat_peg_parser([&params](common_chat_peg_builder &p) {
-            return p.prefix(params.generation_prompt) + p.content(p.rest());
+            return p.prefix(params.generation_prompt) << p.content(p.rest());
         });
         data.parser                    = parser.save();
         return data;

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) {
@@ -656,14 +661,53 @@ bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_over
     return true;
 }
 
-// simple glob: * matches non-/ chars, ** matches anything including /
+static inline bool glob_class_match(const char c, const char * pattern, const char * class_end) {
+    const char * class_start = pattern;
+    bool negated = false;
+
+    if (*class_start == '!') {
+        negated = true;
+        class_start++;
+    }
+
+    // If first character after negation is ']' or '-', treat it as literal
+    if (*class_start == ']' || *class_start == '-') {
+        if (class_start < class_end && *class_start == c) {
+            return !negated;
+        }
+        class_start++;
+    }
+
+    bool matched = false;
+
+    while (class_start < class_end) {
+        if (class_start + 2 < class_end && class_start[1] == '-' && class_start[2] != ']') {
+            char start_char = *class_start;
+            char end_char = class_start[2];
+            if (c >= start_char && c <= end_char) {
+                matched = true;
+                break;
+            }
+            class_start += 3;
+        } else {
+            if (*class_start == c) {
+                matched = true;
+                break;
+            }
+            class_start++;
+        }
+    }
+
+    return negated ? !matched : matched;
+}
+
+// simple glob: * matches non-/ chars, ** matches anything including /, [] matches character class
 static inline bool glob_match(const char * pattern, const char * str) {
     if (*pattern == '\0') {
         return *str == '\0';
     }
     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;
@@ -678,6 +722,26 @@ static inline bool glob_match(const char * pattern, const char * str) {
     if (*pattern == '?' && *str != '\0' && *str != '/') {
         return glob_match(pattern + 1, str + 1);
     }
+    if (*pattern == '[') {
+        const char * class_end = pattern + 1;
+        // If first character after '[' is ']' or '-', treat it as literal
+        if (*class_end == ']' || *class_end == '-') {
+            class_end++;
+        }
+        while (*class_end != '\0' && *class_end != ']') {
+            class_end++;
+        }
+        if (*class_end == ']') {
+            if (*str == '\0') return false;
+            bool matched = glob_class_match(*str, pattern + 1, class_end);
+            return matched && glob_match(class_end + 1, str + 1);
+        } else {
+            if (*str == '[') {
+                return glob_match(pattern + 1, str + 1);
+            }
+            return false;
+        }
+    }
     if (*pattern == *str) {
         return glob_match(pattern + 1, str + 1);
     }
@@ -1184,6 +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();
 }
 
@@ -1375,6 +1442,7 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
 
     mparams.progress_callback           = params.load_progress_callback;
     mparams.progress_callback_user_data = params.load_progress_callback_user_data;
+    mparams.no_alloc                    = params.no_alloc;
 
     return mparams;
 }

common/common.h

@@ -679,6 +679,7 @@ struct common_params {
     // return false from callback to abort model loading or true to continue
     llama_progress_callback load_progress_callback = NULL;
     void *                  load_progress_callback_user_data = NULL;
+    bool no_alloc = false; // Don't allocate model buffers
 };
 
 // call once at the start of a program if it uses libcommon

common/download.cpp

@@ -119,6 +119,9 @@ class ProgressBar {
     static inline std::map<const ProgressBar *, int> lines;
     static inline int max_line = 0;
 
+    std::string filename;
+    size_t len = 0;
+
     static void cleanup(const ProgressBar * line) {
         lines.erase(line);
         if (lines.empty()) {
@@ -135,7 +138,23 @@ class ProgressBar {
     }
 
 public:
-    ProgressBar() = default;
+    ProgressBar(const std::string & url = "") : filename(url) {
+        if (auto pos = filename.rfind('/'); pos != std::string::npos) {
+            filename = filename.substr(pos + 1);
+        }
+        if (auto pos = filename.find('?'); pos != std::string::npos) {
+            filename = filename.substr(0, pos);
+        }
+        for (size_t i = 0; i < filename.size(); ++i) {
+            if ((filename[i] & 0xC0) != 0x80) {
+                if (len++ == 39) {
+                    filename.resize(i);
+                    filename += "…";
+                    break;
+                }
+            }
+        }
+    }
 
     ~ProgressBar() {
         std::lock_guard<std::mutex> lock(mutex);
@@ -143,11 +162,7 @@ public:
     }
 
     void update(size_t current, size_t total) {
-        if (!is_output_a_tty()) {
-            return;
-        }
-
-        if (!total) {
+        if (!total || !is_output_a_tty()) {
             return;
         }
 
@@ -159,28 +174,27 @@ public:
         }
         int lines_up = max_line - lines[this];
 
-        size_t width = 50;
+        size_t bar = 55 - len;
         size_t pct = (100 * current) / total;
-        size_t pos = (width * current) / total;
-
-        std::cout << "\033[s";
+        size_t pos = (bar * current) / total;
 
         if (lines_up > 0) {
             std::cout << "\033[" << lines_up << "A";
         }
-        std::cout << "\033[2K\r["
-            << std::string(pos, '=')
-            << (pos < width ? ">" : "")
-            << std::string(width - pos, ' ')
-            << "] " << std::setw(3) << pct << "%  ("
-            << current / (1024 * 1024) << " MB / "
-            << total / (1024 * 1024) << " MB) "
-            << "\033[u";
+        std::cout << '\r' << "Downloading " << filename << " ";
 
-        std::cout.flush();
+        for (size_t i = 0; i < bar; ++i) {
+            std::cout << (i < pos ? "—" : " ");
+        }
+        std::cout << std::setw(4) << pct << "%\033[K";
+
+        if (lines_up > 0) {
+            std::cout << "\033[" << lines_up << "B";
+        }
+        std::cout << '\r' << std::flush;
 
         if (current == total) {
-             cleanup(this);
+            cleanup(this);
         }
     }
 
@@ -208,7 +222,7 @@ static bool common_pull_file(httplib::Client & cli,
     const char * func = __func__; // avoid __func__ inside a lambda
     size_t downloaded = existing_size;
     size_t progress_step = 0;
-    ProgressBar bar;
+    ProgressBar bar(resolve_path);
 
     auto res = cli.Get(resolve_path, headers,
         [&](const httplib::Response &response) {
@@ -286,7 +300,7 @@ static int common_download_file_single_online(const std::string        & url,
     const bool file_exists = std::filesystem::exists(path);
 
     if (file_exists && skip_etag) {
-        LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
+        LOG_DBG("%s: using cached file: %s\n", __func__, path.c_str());
         return 304; // 304 Not Modified - fake cached response
     }
 
@@ -294,7 +308,7 @@ static int common_download_file_single_online(const std::string        & url,
     if (file_exists) {
         last_etag = read_etag(path);
     } else {
-        LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
+        LOG_DBG("%s: no previous model file found %s\n", __func__, path.c_str());
     }
 
     auto head = cli.Head(parts.path);
@@ -328,11 +342,11 @@ static int common_download_file_single_online(const std::string        & url,
 
     if (file_exists) {
         if (etag.empty()) {
-            LOG_INF("%s: using cached file (no server etag): %s\n", __func__, path.c_str());
+            LOG_DBG("%s: using cached file (no server etag): %s\n", __func__, path.c_str());
             return 304; // 304 Not Modified - fake cached response
         }
         if (!last_etag.empty() && last_etag == etag) {
-            LOG_INF("%s: using cached file (same etag): %s\n", __func__, path.c_str());
+            LOG_DBG("%s: using cached file (same etag): %s\n", __func__, path.c_str());
             return 304; // 304 Not Modified - fake cached response
         }
         if (remove(path.c_str()) != 0) {
@@ -368,7 +382,7 @@ static int common_download_file_single_online(const std::string        & url,
             }
         }
 
-        LOG_INF("%s: downloading from %s to %s (etag:%s)...\n",
+        LOG_DBG("%s: downloading from %s to %s (etag:%s)...\n",
                 __func__, common_http_show_masked_url(parts).c_str(),
                 path_temporary.c_str(), etag.c_str());
 
@@ -437,7 +451,7 @@ int common_download_file_single(const std::string & url,
         return -1;
     }
 
-    LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
+    LOG_DBG("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
     return 304; // Not Modified - fake cached response
 }
 

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/json-schema-to-grammar.cpp

@@ -416,15 +416,30 @@ private:
                     i++;
                 } else if (c == '(') {
                     i++;
-                    if (i < length) {
-                        if (sub_pattern[i] == '?') {
+                    if (i < length && sub_pattern[i] == '?') {
+                        if (i + 1 < length && sub_pattern[i + 1] == ':') {
+                            i += 2; // skip "?:" for non-capturing group, treat as regular group
+                        } else {
+                            // lookahead/lookbehind (?=, ?!, ?<=, ?<!) - not supported
                             _warnings.push_back("Unsupported pattern syntax");
+                            // skip to matching ')' to avoid UB on empty seq
+                            int depth = 1;
+                            while (i < length && depth > 0) {
+                                if (sub_pattern[i] == '\\' && i + 1 < length) {
+                                    i += 2; // skip escaped character
+                                } else {
+                                    if (sub_pattern[i] == '(') depth++;
+                                    else if (sub_pattern[i] == ')') depth--;
+                                    i++;
+                                }
+                            }
+                            continue;
                         }
                     }
                     seq.emplace_back("(" + to_rule(transform()) + ")", false);
                 } else if (c == ')') {
                     i++;
-                    if (start > 0 && sub_pattern[start - 1] != '(') {
+                    if (start > 0 && sub_pattern[start - 1] != '(' && (start < 2 || sub_pattern[start - 2] != '?' || sub_pattern[start - 1] != ':')) {
                         _errors.push_back("Unbalanced parentheses");
                     }
                     return join_seq();

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/peg-parser.cpp

@@ -1557,6 +1557,36 @@ static std::unordered_set<std::string> collect_reachable_rules(
 
 // GBNF generation implementation
 void common_peg_arena::build_grammar(const common_grammar_builder & builder, bool lazy) const {
+    auto schema_delegates = [](const common_peg_schema_parser & s) -> bool {
+        if (!s.schema) {
+            return true;
+        }
+        if (s.raw && s.schema->contains("type") && s.schema->at("type").is_string() && s.schema->at("type") == "string") {
+            return true;
+        }
+        return false;
+    };
+
+    // Unwrap the parser so we can properly check if it's a sequence or choice
+    auto effective_parser = [&](common_peg_parser_id id) -> const common_peg_parser_variant & {
+        while (true) {
+            const auto & p = parsers_.at(id);
+            if (const auto * tag = std::get_if<common_peg_tag_parser>(&p)) {
+                id = tag->child;
+            } else if (const auto * atomic = std::get_if<common_peg_atomic_parser>(&p)) {
+                id = atomic->child;
+            } else if (const auto * schema = std::get_if<common_peg_schema_parser>(&p)) {
+                if (schema_delegates(*schema)) {
+                    id = schema->child;
+                } else {
+                    return p;
+                }
+            } else {
+                return p;
+            }
+        }
+    };
+
     // Generate GBNF for a parser
     std::function<std::string(common_peg_parser_id)> to_gbnf = [&](common_peg_parser_id id) -> std::string {
         const auto & parser = parsers_.at(id);
@@ -1577,7 +1607,7 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
                         s += " ";
                     }
                     auto child_gbnf = to_gbnf(child);
-                    const auto & child_parser = parsers_.at(child);
+                    const auto & child_parser = effective_parser(child);
                     if (std::holds_alternative<common_peg_choice_parser>(child_parser) ||
                         std::holds_alternative<common_peg_sequence_parser>(child_parser)) {
                         s += "(" + child_gbnf + ")";
@@ -1593,7 +1623,7 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
                         s += " | ";
                     }
                     auto child_gbnf = to_gbnf(child);
-                    const auto & child_parser = parsers_.at(child);
+                    const auto & child_parser = effective_parser(child);
                     if (std::holds_alternative<common_peg_choice_parser>(child_parser)) {
                         s += "(" + child_gbnf + ")";
                     } else {
@@ -1603,7 +1633,7 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
                 return s;
             } else if constexpr (std::is_same_v<T, common_peg_repetition_parser>) {
                 auto child_gbnf = to_gbnf(p.child);
-                const auto & child_parser = parsers_.at(p.child);
+                const auto & child_parser = effective_parser(p.child);
                 if (std::holds_alternative<common_peg_choice_parser>(child_parser) ||
                     std::holds_alternative<common_peg_sequence_parser>(child_parser)) {
                     child_gbnf = "(" + child_gbnf + ")";
@@ -1663,15 +1693,10 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
                 }
                 return gbnf_excluding_pattern(p.delimiters);
             } else if constexpr (std::is_same_v<T, common_peg_schema_parser>) {
-                if (p.schema) {
-                    if (p.raw && p.schema->contains("type") && p.schema->at("type").is_string() && p.schema->at("type") == "string") {
-                        // TODO: Implement more comprehensive grammar generation for raw strings.
-                        // For now, use the grammar emitted from the underlying parser.
-                        return to_gbnf(p.child);
-                    }
-                    return builder.add_schema(p.name, *p.schema);
+                if (schema_delegates(p)) {
+                    return to_gbnf(p.child);
                 }
-                return to_gbnf(p.child);
+                return builder.add_schema(p.name, *p.schema);
             } else if constexpr (std::is_same_v<T, common_peg_rule_parser>) {
                 return p.name;
             } else if constexpr (std::is_same_v<T, common_peg_ref_parser>) {

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,
     )
 
@@ -1164,7 +1164,7 @@ class TextModel(ModelBase):
         if (n_experts := self.find_hparam(["num_local_experts", "num_experts"], optional=True)) is not None:
             self.gguf_writer.add_expert_count(n_experts)
             logger.info(f"gguf: expert count = {n_experts}")
-        if (n_experts_used := self.find_hparam(["num_experts_per_tok", "num_experts_per_token"], optional=True)) is not None:
+        if (n_experts_used := self.find_hparam(["num_experts_per_tok", "num_experts_per_token", "top_k_experts"], optional=True)) is not None:
             self.gguf_writer.add_expert_used_count(n_experts_used)
             logger.info(f"gguf: experts used count = {n_experts_used}")
         if (n_expert_groups := self.hparams.get("n_group")) is not None:
@@ -6878,7 +6878,9 @@ class Gemma2Model(TextModel):
 @ModelBase.register("Gemma3ForCausalLM", "Gemma3ForConditionalGeneration")
 class Gemma3Model(TextModel):
     model_arch = gguf.MODEL_ARCH.GEMMA3
-    norm_shift = 1.0  # Gemma3RMSNorm adds 1.0 to the norm value
+
+    def norm_shift(self, name: str) -> float:
+        return 1.0 if name.endswith("norm.weight") else 0.0  # Gemma3RMSNorm adds 1.0 to the norm value
 
     def set_vocab(self):
         if (self.dir_model / "tokenizer.model").is_file():
@@ -6916,17 +6918,22 @@ class Gemma3Model(TextModel):
 
         # remove OOV (out-of-vocabulary) rows in token_embd
         if "embed_tokens.weight" in name:
+            n_vocab_real = -1
             if (self.dir_model / "tokenizer.model").is_file():
                 tokens = self._create_vocab_sentencepiece()[0]
+                n_vocab_real = len(tokens)
             else:
-                tokens = self.get_vocab_base()[0]
-            data_torch = data_torch[:len(tokens)]
+                with open(self.dir_model / "tokenizer.json", "r", encoding="utf-8") as f:
+                    tokenizer_json = json.load(f)
+                    n_vocab_real = len(tokenizer_json["model"]["vocab"]) + len(tokenizer_json["added_tokens"])
+            data_torch = data_torch[:n_vocab_real]
 
         # ref code in Gemma3RMSNorm
         # output = output * (1.0 + self.weight.float())
         # note: this is not the case on gemma3n
-        if name.endswith("norm.weight"):
-            data_torch = data_torch + self.norm_shift
+        f_shift = self.norm_shift(name)
+        if f_shift != 0.0:
+            data_torch = data_torch + f_shift
 
         yield from super().modify_tensors(data_torch, name, bid)
 
@@ -7100,7 +7107,8 @@ class ConformerAudioModel(MmprojModel):
             assert data_torch.shape[2] == 1
             data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])
 
-        yield from super().modify_tensors(data_torch, name, bid)
+        mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
+        yield (mapped_name, data_torch)
 
 
 @ModelBase.register("DeepseekOCRForCausalLM")
@@ -7289,7 +7297,6 @@ class Gemma3nVisionAudioModel(ConformerAudioModel):
 @ModelBase.register("Gemma3nForCausalLM", "Gemma3nForConditionalGeneration")
 class Gemma3NModel(Gemma3Model):
     model_arch = gguf.MODEL_ARCH.GEMMA3N
-    norm_shift = 0.0 # same value with Gemma3p5RMSNorm scale_shift on python code
 
     _altup_proj: list[Tensor] = []
     _altup_unembd: list[Tensor] = []
@@ -7308,6 +7315,10 @@ class Gemma3NModel(Gemma3Model):
             torch.Tensor(), # to be replaced
         ]
 
+    def norm_shift(self, name: str) -> float:
+        del name
+        return 0.0 # same value with Gemma3p5RMSNorm scale_shift on python code
+
     def set_vocab(self):
         # For Gemma3n multimodal models, we need the FULL vocab_size (262400)
         # which includes special tokens from 262144-262399 for vision/audio.
@@ -7425,6 +7436,212 @@ class Gemma3NModel(Gemma3Model):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("Gemma4ForConditionalGeneration")
+class Gemma4Model(Gemma3Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA4
+
+    def norm_shift(self, name: str) -> float:
+        del name # unused
+        return 0.0
+
+    def set_vocab(self):
+        vocab = gguf.LlamaHfVocab(self.dir_model)
+        tokens = []
+        scores = []
+        toktypes = []
+        visible_tokens = {"<|channel>", "<channel|>", "<|tool_call>", "<tool_call|>", "<|tool_response>", "<tool_response|>", "<|\"|>"}
+
+        for text, score, toktype in vocab.all_tokens():
+            tokens.append(text)
+            scores.append(score)
+            text_str = text.decode()
+            if text_str in visible_tokens:
+                # always render these tokens, so that the chat parser can read them
+                toktypes.append(gguf.TokenType.USER_DEFINED)
+                logger.info(f"Token '{text_str}' is set to USER_DEFINED")
+            else:
+                toktypes.append(toktype)
+
+        assert len(tokens) == vocab.vocab_size
+
+        # TODO @ngxson : there are some known (rare) issues with the tokenizer during development
+        # but I don't have time to dive into them right now;
+        # using a dedicated tokenizer name so that we can fix later without re-converting GGUF
+        self.gguf_writer.add_tokenizer_model("gemma4")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+        self.gguf_writer.add_add_space_prefix(False)
+        self.gguf_writer.add_add_bos_token(False) # already added via the chat template
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        num_kv_shared_layers = self.hparams["num_kv_shared_layers"]
+        self.gguf_writer.add_shared_kv_layers(num_kv_shared_layers)
+
+        # per-layer embedding is optional
+        n_pl_embd = self.hparams.get("hidden_size_per_layer_input") or 0
+        self.gguf_writer.add_embedding_length_per_layer_input(n_pl_embd)
+
+        swa_layers = [t == "sliding_attention" for t in self.hparams["layer_types"]]
+        self.gguf_writer.add_sliding_window_pattern(swa_layers)
+
+        head_dim_full = self.hparams["global_head_dim"]
+        head_dim_swa = self.hparams["head_dim"]
+        # correct the head dim for global/swa layers
+        self.gguf_writer.add_key_length(head_dim_full)
+        self.gguf_writer.add_value_length(head_dim_full)
+        self.gguf_writer.add_key_length_swa(head_dim_swa)
+        self.gguf_writer.add_value_length_swa(head_dim_swa)
+
+        expert_intermediate_size = self.find_hparam(["expert_intermediate_size", "moe_intermediate_size"])
+        if expert_intermediate_size is not None:
+            self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)
+
+        # if use_double_wide_mlp is set, we need to adjust the value for kv shared layers
+        use_double_wide_mlp = self.hparams.get("use_double_wide_mlp", False)
+        first_kv_shared_layer_idx = self.block_count - num_kv_shared_layers
+        if use_double_wide_mlp:
+            n_ff = self.hparams["intermediate_size"]
+            n_ff_arr = [n_ff if il < first_kv_shared_layer_idx else n_ff * 2 for il in range(self.block_count)]
+            self.gguf_writer.add_feed_forward_length(n_ff_arr)
+
+        # handle num_global_key_value_heads
+        num_key_value_heads_full = self.hparams.get("num_global_key_value_heads")
+        num_key_value_heads_swa = self.hparams.get("num_key_value_heads")
+        if num_key_value_heads_full is not None and num_key_value_heads_swa is not None:
+            value_arr = [num_key_value_heads_swa if is_swa else num_key_value_heads_full for is_swa in swa_layers]
+            self.gguf_writer.add_head_count_kv(value_arr)
+
+        # handle n_rot differently for global vs swa layers
+        partial_rotary_factor_swa = self.hparams.get("partial_rotary_factor", 1.0)
+        n_rot_full = int(head_dim_full) # "proportional" is used, see generate_extra_tensors
+        n_rot_swa = int(head_dim_swa * partial_rotary_factor_swa)
+        self.gguf_writer.add_rope_dimension_count(n_rot_full)
+        self.gguf_writer.add_rope_dimension_count_swa(n_rot_swa)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # full layer uses "proportional" rope with partial_rotary_factor=0.25
+        # the expected ordering is cc000000ss000000 (c = cos, s = sin, 0 = unrotated),
+        # but ggml neox only supports ccss000000000000, and we cannot rearrange the head because that will break use_alternative_attention
+        # solution is to set specific freq_factors for the unrotated dims
+
+        # IMPORTANT: this ROPE_FREQS tensor is ONLY used by the full_attention layers
+        rope_params_full = self.hparams["rope_parameters"]["full_attention"]
+        assert rope_params_full["rope_type"] == "proportional"
+        head_dim_full = (self.hparams["global_head_dim"])
+        partial_rotary_factor_full = rope_params_full["partial_rotary_factor"]
+        n_rot_full = int(head_dim_full * partial_rotary_factor_full / 2)
+        n_unrot_full = int(head_dim_full / 2) - n_rot_full
+        values = [1.0] * n_rot_full + [1e30] * n_unrot_full
+        rope_freqs_full = torch.tensor(values, dtype=torch.float32)
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), rope_freqs_full)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.endswith("per_dim_scale") or name.endswith("layer_scalar"):
+            name = name + ".weight"
+
+        if "language_model." not in name and "rope_freqs" not in name:
+            return # skip non-language model tensors
+
+        name = name.replace("language_model.", "")
+        if name.endswith("router.scale"):
+            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_INP, bid, ".scale")
+            yield (name, data_torch)
+            return
+        if ".per_expert_scale" in name:
+            # convert per-expert scale to FFN down scale
+            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN_EXP, bid, ".scale")
+            yield (name, data_torch)
+            return
+        if ".experts." in name and not name.endswith(".weight"):
+            name += ".weight"
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Gemma4ForConditionalGeneration")
+class Gemma4VisionAudioModel(MmprojModel):
+    has_audio_encoder = True
+    has_vision_encoder = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = 224 # unused, but set to avoid error
+
+        # remap audio hparams
+        if self.hparams_audio:
+            self.hparams_audio["feat_in"] = self.hparams_audio.get("input_feat_size", 128)
+            self.hparams_audio["intermediate_size"] = self.hparams_audio["hidden_size"] * 4
+        else:
+            self.has_audio_encoder = False
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # vision params
+        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA4V)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
+
+        # audio params
+        if self.hparams_audio:
+            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA4A)
+            self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
+            self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+
+    def is_audio_tensor(self, name: str) -> bool:
+        return "audio_tower" in name or "embed_audio" in name
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if self.is_audio_tensor(name):
+            if ".conv" in name or "_conv" in name and ".weight" in name:
+                return gguf.GGMLQuantizationType.F32
+        if "position_embedding_table" in name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid # unused
+
+        if name.startswith("model.language_model."):
+            return # skip
+
+        if len(data_torch.shape) == 0:
+            # convert scalar tensors (input/output_mix/max) to 1D tensors
+            data_torch = data_torch.unsqueeze(0)
+
+        if self.is_audio_tensor(name):
+            assert self.hparams_audio is not None
+            name = name.replace("model.audio_tower.", "conformer.")
+            name = name.replace(".linear.", ".")
+            if name.endswith("per_dim_key_scale") or name.endswith("per_dim_scale"):
+                name = name + ".weight"
+                data_torch = torch.nn.functional.softplus(data_torch)
+            if "lconv1d.depthwise_conv1d" in name and name.endswith(".weight"):
+                assert data_torch.shape[1] == 1
+                data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
+            mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
+            yield (mapped_name, data_torch)
+
+        else:
+            name = name.replace("model.vision_tower.encoder.", "vision_model.model.")
+            name = name.replace(".linear.weight", ".weight")
+            if name.endswith("layer_scalar") or name.endswith("position_embedding_table"):
+                name = name + ".weight"
+            if name.endswith("patch_embedder.input_proj.weight"):
+                n_embd, ksize_sq_c = data_torch.shape
+                patch_size = int((ksize_sq_c // 3) ** 0.5)
+                data_torch = data_torch.reshape(n_embd, patch_size, patch_size, 3)
+                data_torch = data_torch.permute(0, 3, 1, 2).contiguous()
+            mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
+            yield (mapped_name, data_torch)
+
+
 @ModelBase.register("Starcoder2ForCausalLM")
 class StarCoder2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.STARCODER2

docs/build.md

@@ -728,7 +728,7 @@ To read documentation for how to build on Android, [click here](./android.md)
 
 ## WebGPU [In Progress]
 
-The WebGPU backend relies on [Dawn](https://dawn.googlesource.com/dawn). Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/docs/quickstart-cmake.md) to install Dawn locally so that llama.cpp can find it using CMake. The current implementation is up-to-date with Dawn commit `bed1a61`.
+The WebGPU backend relies on [Dawn](https://dawn.googlesource.com/dawn). Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/docs/quickstart-cmake.md) to install Dawn locally so that llama.cpp can find it using CMake. The current implementation is up-to-date with Dawn commit `18eb229`.
 
 In the llama.cpp directory, build with CMake:
 

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

@@ -15,13 +15,18 @@ static bool run(llama_context * ctx, const common_params & params) {
 
     const bool add_bos = llama_vocab_get_add_bos(vocab);
 
-    std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos);
+    std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos, true);
 
     if (tokens.empty()) {
         LOG_ERR("%s : there are not input tokens to process - (try to provide a prompt with '-p')\n", __func__);
         return false;
     }
 
+    LOG_INF("number of input tokens = %zu\n", tokens.size());
+    for (size_t i = 0; i < tokens.size(); ++i) {
+        LOG_INF("  %d\n", tokens[i]);
+    }
+
     if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
         LOG_ERR("%s : failed to eval\n", __func__);
         return false;
@@ -37,12 +42,12 @@ 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 11)
 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)
@@ -166,15 +166,16 @@ if (NOT MSVC)
     option(GGML_AMX_INT8     "ggml: enable AMX-INT8"         OFF)
     option(GGML_AMX_BF16     "ggml: enable AMX-BF16"         OFF)
 endif()
-option(GGML_LASX             "ggml: enable lasx"             ON)
-option(GGML_LSX              "ggml: enable lsx"              ON)
-option(GGML_RVV              "ggml: enable rvv"              ON)
-option(GGML_RV_ZFH           "ggml: enable riscv zfh"        ON)
-option(GGML_RV_ZVFH          "ggml: enable riscv zvfh"       ON)
-option(GGML_RV_ZICBOP        "ggml: enable riscv zicbop"     ON)
-option(GGML_RV_ZIHINTPAUSE   "ggml: enable riscv zihintpause "  ON)
-option(GGML_XTHEADVECTOR     "ggml: enable xtheadvector"     OFF)
-option(GGML_VXE              "ggml: enable vxe"              ${GGML_NATIVE})
+option(GGML_LASX             "ggml: enable lasx"              ON)
+option(GGML_LSX              "ggml: enable lsx"               ON)
+option(GGML_RVV              "ggml: enable rvv"               ON)
+option(GGML_RV_ZFH           "ggml: enable riscv zfh"         ON)
+option(GGML_RV_ZVFH          "ggml: enable riscv zvfh"        ON)
+option(GGML_RV_ZICBOP        "ggml: enable riscv zicbop"      ON)
+option(GGML_RV_ZIHINTPAUSE   "ggml: enable riscv zihintpause" ON)
+option(GGML_RV_ZVFBFWMA      "ggml: enable riscv zvfbfwma"    OFF)
+option(GGML_XTHEADVECTOR     "ggml: enable xtheadvector"      OFF)
+option(GGML_VXE              "ggml: enable vxe"               ${GGML_NATIVE})
 
 option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
 set(GGML_CPU_ARM_ARCH        "" CACHE STRING "ggml: CPU architecture for ARM")

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -434,6 +434,9 @@ void ggml_cann_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
 void ggml_cann_l2_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_tensor * src = dst->src[0];
 
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
     acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
     acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
 
@@ -456,6 +459,13 @@ void ggml_cann_l2_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     float          p_value  = 2.0f;
     acl_scalar_ptr p_scalar = ggml_cann_create_scalar(&p_value, aclDataType::ACL_FLOAT);
     GGML_CANN_CALL_ACLNN_OP(ctx, Norm, acl_src.get(), p_scalar.get(), dims_array.get(), true, acl_div.get());
+
+    // Clamp norm to at least eps: scale = 1/fmaxf(norm, eps)
+    acl_scalar_ptr acl_min = ggml_cann_create_scalar(&eps, aclDataType::ACL_FLOAT);
+    float          flt_max = FLT_MAX;
+    acl_scalar_ptr acl_max = ggml_cann_create_scalar(&flt_max, aclDataType::ACL_FLOAT);
+    GGML_CANN_CALL_ACLNN_OP(ctx, Clamp, acl_div.get(), acl_min.get(), acl_max.get(), acl_div.get());
+
     GGML_CANN_CALL_ACLNN_OP(ctx, Div, acl_src.get(), acl_div.get(), acl_dst.get());
 }
 

ggml/src/ggml-cann/common.h

@@ -216,14 +216,16 @@ struct ggml_cann_pool_alloc {
 #ifdef USE_ACL_GRAPH
 struct ggml_graph_node_properties {
     // dst tensor
-    void *  node_address;
-    int64_t ne[GGML_MAX_DIMS];
-    size_t  nb[GGML_MAX_DIMS];
+    void *    node_address;
+    ggml_type node_type;
+    int64_t   ne[GGML_MAX_DIMS];
+    size_t    nb[GGML_MAX_DIMS];
 
     // src tensor
-    void *  src_address[GGML_MAX_SRC];
-    int64_t src_ne[GGML_MAX_SRC][GGML_MAX_DIMS];
-    size_t  src_nb[GGML_MAX_SRC][GGML_MAX_DIMS];
+    void *    src_address[GGML_MAX_SRC];
+    ggml_type src_type[GGML_MAX_SRC];
+    int64_t   src_ne[GGML_MAX_SRC][GGML_MAX_DIMS];
+    size_t    src_nb[GGML_MAX_SRC][GGML_MAX_DIMS];
 
     // op
     ggml_op node_op;
@@ -247,6 +249,10 @@ struct ggml_graph_node_properties {
             return false;
         }
 
+        if (node->type != this->node_type) {
+            return false;
+        }
+
         for (int i = 0; i < GGML_MAX_DIMS; i++) {
             if (node->ne[i] != this->ne[i]) {
                 return false;
@@ -262,6 +268,10 @@ struct ggml_graph_node_properties {
                     return false;
                 }
 
+                if (node->src[i]->type != this->src_type[i]) {
+                    return false;
+                }
+
                 for (int d = 0; d < GGML_MAX_DIMS; d++) {
                     if (node->src[i]->ne[d] != this->src_ne[i][d]) {
                         return false;
@@ -277,10 +287,7 @@ struct ggml_graph_node_properties {
             }
         }
 
-        if (node->op == GGML_OP_SCALE || node->op == GGML_OP_UNARY || node->op == GGML_OP_GLU || node->op == GGML_OP_ROPE){
-            return memcmp(this->op_params, node->op_params, GGML_MAX_OP_PARAMS) == 0;
-        }
-        return true;
+        return memcmp(this->op_params, node->op_params, GGML_MAX_OP_PARAMS) == 0;
     }
 };
 
@@ -322,6 +329,7 @@ struct ggml_cann_graph {
 
             prop.node_address = node->data;
             prop.node_op      = node->op;
+            prop.node_type    = node->type;
 
             std::copy_n(node->ne, GGML_MAX_DIMS, prop.ne);
             std::copy_n(node->nb, GGML_MAX_DIMS, prop.nb);
@@ -329,10 +337,12 @@ struct ggml_cann_graph {
             for (int src = 0; src < GGML_MAX_SRC; ++src) {
                 if (node->src[src]) {
                     prop.src_address[src] = node->src[src]->data;
+                    prop.src_type[src]    = node->src[src]->type;
                     std::copy_n(node->src[src]->ne, GGML_MAX_DIMS, prop.src_ne[src]);
                     std::copy_n(node->src[src]->nb, GGML_MAX_DIMS, prop.src_nb[src]);
                 } else {
                     prop.src_address[src] = nullptr;
+                    prop.src_type[src]    = GGML_TYPE_COUNT;
                     std::fill_n(prop.src_ne[src], GGML_MAX_DIMS, 0);
                     std::fill_n(prop.src_nb[src], GGML_MAX_DIMS, 0);
                 }

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

@@ -36,10 +36,13 @@
 #include <cmath>
 #include <cstdio>
 #include <cstring>
+#include <memory>
 #include <mutex>
 #include <optional>
 #include <queue>
+#include <unordered_map>
 #include <unordered_set>
+#include <vector>
 
 #define GGML_COMMON_DECL_C
 
@@ -770,6 +773,21 @@ std::unique_ptr<ggml_cann_pool> ggml_backend_cann_context::new_pool_for_device(i
 }
 
 // cann buffer
+
+/**
+ * @brief Tracks multi-threaded write progress for a single tensor.
+ *
+ * When multiple threads call set_tensor on different chunks of the same tensor,
+ * this tracker accumulates progress and defers post-processing (quantized format
+ * transform or ND-to-NZ conversion) until all data has been written.
+ */
+struct TensorSetTracker {
+    std::mutex mtx;                   ///< Protects concurrent access to this tracker
+    size_t bytes_written = 0;         ///< Accumulated bytes written so far
+    size_t total_bytes = 0;           ///< Target size (full tensor)
+    std::vector<uint8_t> host_buffer; ///< Host staging buffer for quantized tensors
+};
+
 /**
  * @brief Context for managing a CANN buffer associated with a specific device.
  *
@@ -780,6 +798,9 @@ struct ggml_backend_cann_buffer_context {
     int32_t device;             ///< The device ID associated with this buffer context.
     void *  dev_ptr = nullptr;  ///< Pointer to the device memory allocated for the buffer.
 
+    std::mutex tracker_mutex;   ///< Protects the trackers map
+    std::unordered_map<void *, std::unique_ptr<TensorSetTracker>> trackers;
+
     /**
      * @brief Constructor to initialize the CANN buffer context.
      *
@@ -792,6 +813,31 @@ struct ggml_backend_cann_buffer_context {
      * @brief Destructor to free the device memory allocated for the buffer.
      */
     ~ggml_backend_cann_buffer_context() { ACL_CHECK(aclrtFree(dev_ptr)); }
+
+    /**
+     * @brief Get or create a tracker for the given tensor.
+     */
+    TensorSetTracker * get_or_create_tracker(ggml_tensor * tensor) {
+        std::lock_guard<std::mutex> lock(tracker_mutex);
+        auto key = tensor->data;
+        auto it = trackers.find(key);
+        if (it == trackers.end()) {
+            auto tracker = std::make_unique<TensorSetTracker>();
+            tracker->total_bytes = ggml_nbytes(tensor);
+            auto * ptr = tracker.get();
+            trackers[key] = std::move(tracker);
+            return ptr;
+        }
+        return it->second.get();
+    }
+
+    /**
+     * @brief Remove the tracker for the given tensor.
+     */
+    void remove_tracker(ggml_tensor * tensor) {
+        std::lock_guard<std::mutex> lock(tracker_mutex);
+        trackers.erase(tensor->data);
+    }
 };
 
 // cann buffer type
@@ -1124,6 +1170,7 @@ static enum ggml_status ggml_backend_cann_buffer_init_tensor(ggml_backend_buffer
  * designed to be used with a global array, one per device.
  */
 struct ggml_cann_nz_workspace {
+    std::mutex mtx;    // Protects ptr/allocated from concurrent access
     void * ptr;        // Pointer to allocated device buffer
     size_t allocated;  // Size of currently allocated buffer in bytes
 
@@ -1190,13 +1237,15 @@ static ggml_cann_nz_workspace g_nz_workspaces[GGML_CANN_MAX_DEVICES];
  * @note The workspace buffer used in this function is managed globally and reused
  *       across calls. This reduces overhead from repeated memory allocation and deallocation.
  */
-static void weight_format_to_nz(ggml_tensor * tensor, size_t offset, int device) {
-    acl_tensor_ptr weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne, tensor->nb, 2, ACL_FORMAT_ND, offset);
+static void weight_format_to_nz(ggml_tensor * tensor, int device) {
+    acl_tensor_ptr weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne, tensor->nb, 2, ACL_FORMAT_ND, 0);
     uint64_t       workspaceSize    = 0;
     aclOpExecutor * executor;
 
     // TransMatmulWeight
     ACL_CHECK(aclnnTransMatmulWeightGetWorkspaceSize(weightTransposed.get(), &workspaceSize, &executor));
+
+    std::lock_guard<std::mutex> lock(g_nz_workspaces[device].mtx);
     // Avoid frequent malloc/free of the workspace.
     g_nz_workspaces[device].realloc(workspaceSize);
 
@@ -1210,7 +1259,13 @@ static void weight_format_to_nz(ggml_tensor * tensor, size_t offset, int device)
  * @brief Set tensor data in a CANN buffer.
  *
  * This function sets tensor data in a CANN buffer, handling transformations
- * if needed based on the tensor's type.
+ * if needed based on the tensor's type. It supports multi-threaded calls
+ * where different threads write different chunks of the same tensor.
+ *
+ * For quantized tensors (Q4_0/Q8_0), data is staged in a host buffer and
+ * the format transform is deferred until all chunks are written.
+ * For NZ weight tensors, chunks are uploaded directly but the ND-to-NZ
+ * conversion is deferred until all chunks are written.
  *
  * @param buffer The CANN buffer where the tensor data will be set.
  * @param tensor Pointer to the tensor whose data will be set.
@@ -1226,26 +1281,72 @@ static void ggml_backend_cann_buffer_set_tensor(ggml_backend_buffer_t buffer,
     ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context;
 
     ggml_cann_set_device(ctx->device);
-    // TODO: refer to cann(#6017), it use thread's default stream.
-    // For acl, synchronous functions use this default stream.
-    // Why aclrtSynchronizeDevice?
 
     // Only check env once.
     static bool weight_to_nz = parse_bool(get_env_as_lowercase("GGML_CANN_WEIGHT_NZ").value_or("on"));
-    if (!need_transform(tensor->type)) {
+
+    bool is_quantized = need_transform(tensor->type);
+    bool is_nz        = !is_quantized && tensor->type != GGML_TYPE_BF16 && weight_to_nz &&
+                 is_matmul_weight((const ggml_tensor *) tensor);
+
+    // Plain tensor (not quantized, not NZ): direct copy, no tracking needed
+    if (!is_quantized && !is_nz) {
         ACL_CHECK(aclrtMemcpy((char *) tensor->data + offset, size, data, size, ACL_MEMCPY_HOST_TO_DEVICE));
-        if (weight_to_nz && tensor->type != GGML_TYPE_BF16
-            && is_matmul_weight((const ggml_tensor *) tensor)) {
+        return;
+    }
+
+    // Single-shot write (full tensor at once): handle directly without tracking overhead
+    if (offset == 0 && size == ggml_nbytes(tensor)) {
+        if (is_quantized) {
+            void * transform_buffer = malloc(size);
+            ggml_backend_cann_transform(tensor, data, transform_buffer);
+            ACL_CHECK(aclrtMemcpy(tensor->data, size, transform_buffer, size, ACL_MEMCPY_HOST_TO_DEVICE));
+            free(transform_buffer);
+        } else {
+            // NZ weight
             GGML_ASSERT(tensor->ne[2] == 1);
             GGML_ASSERT(tensor->ne[3] == 1);
-            weight_format_to_nz(tensor, offset, ctx->device);
+            ACL_CHECK(aclrtMemcpy(tensor->data, size, data, size, ACL_MEMCPY_HOST_TO_DEVICE));
+            weight_format_to_nz(tensor, ctx->device);
         }
-    } else {
-        void * transform_buffer = malloc(size);
-        ggml_backend_cann_transform(tensor, data, transform_buffer);
+        return;
+    }
 
-        ACL_CHECK(aclrtMemcpy((char *) tensor->data + offset, size, transform_buffer, size, ACL_MEMCPY_HOST_TO_DEVICE));
-        free(transform_buffer);
+    // Chunked write: use tracker to accumulate progress and defer transform/conversion
+    TensorSetTracker * tracker = ctx->get_or_create_tracker(tensor);
+    std::unique_lock<std::mutex> lock(tracker->mtx);
+
+    if (is_quantized) {
+        // Stage data in host buffer; transform requires full tensor data
+        if (tracker->host_buffer.empty()) {
+            tracker->host_buffer.resize(tracker->total_bytes);
+        }
+        memcpy(tracker->host_buffer.data() + offset, data, size);
+    } else {
+        // NZ weight: upload chunk to device immediately, defer conversion
+        ACL_CHECK(aclrtMemcpy((char *) tensor->data + offset, size, data, size, ACL_MEMCPY_HOST_TO_DEVICE));
+    }
+
+    tracker->bytes_written += size;
+
+    // All chunks received: perform deferred transform/conversion
+    if (tracker->bytes_written >= tracker->total_bytes) {
+        if (is_quantized) {
+            void * transform_buffer = malloc(tracker->total_bytes);
+            ggml_backend_cann_transform(tensor, tracker->host_buffer.data(), transform_buffer);
+            ACL_CHECK(aclrtMemcpy(tensor->data, tracker->total_bytes, transform_buffer, tracker->total_bytes, ACL_MEMCPY_HOST_TO_DEVICE));
+            free(transform_buffer);
+        }
+
+        if (is_nz) {
+            GGML_ASSERT(tensor->ne[2] == 1);
+            GGML_ASSERT(tensor->ne[3] == 1);
+            weight_format_to_nz(tensor, ctx->device);
+        }
+
+        // Unlock before removing tracker, as remove_tracker destroys the mutex
+        lock.unlock();
+        ctx->remove_tracker(tensor);
     }
 }
 

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

@@ -2350,11 +2350,15 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_FLASH_ATTN_BACK:
         case GGML_OP_SSM_CONV:
         case GGML_OP_SSM_SCAN:
+            {
+                n_tasks = n_threads;
+            } break;
         case GGML_OP_RWKV_WKV6:
         case GGML_OP_GATED_LINEAR_ATTN:
         case GGML_OP_RWKV_WKV7:
             {
-                n_tasks = n_threads;
+                const int64_t n_heads = node->src[1]->ne[1];
+                n_tasks = MIN(n_threads, n_heads);
             } break;
         case GGML_OP_WIN_PART:
         case GGML_OP_WIN_UNPART:

ggml/src/ggml-cpu/llamafile/sgemm.cpp

@@ -180,44 +180,49 @@ inline float32x4_t madd(float32x4_t a, float32x4_t b, float32x4_t c) {
 }
 #endif
 
-#if defined(__riscv_zvfh)
-template <>
-inline vfloat32m1_t madd(vfloat16mf2_t a, vfloat16mf2_t b, vfloat32m1_t c) {
-    return __riscv_vfwmacc_vv_f32m1(c, a, b, __riscv_vsetvlmax_e32m1());
-}
-inline vfloat32m2_t madd(vfloat16m1_t a, vfloat16m1_t b, vfloat32m2_t c) {
-    return __riscv_vfwmacc_vv_f32m2(c, a, b, __riscv_vsetvlmax_e32m2());
-}
-inline vfloat32m4_t madd(vfloat16m2_t a, vfloat16m2_t b, vfloat32m4_t c) {
-    return __riscv_vfwmacc_vv_f32m4(c, a, b, __riscv_vsetvlmax_e32m4());
-}
-inline vfloat32m8_t madd(vfloat16m4_t a, vfloat16m4_t b, vfloat32m8_t c) {
-    return __riscv_vfwmacc_vv_f32m8(c, a, b, __riscv_vsetvlmax_e32m8());
-}
-inline vfloat32m1_t madd(vfloat32m1_t a, vfloat32m1_t b, vfloat32m1_t c) {
+#if defined(__riscv_v_intrinsic)
+template <> inline vfloat32m1_t madd(vfloat32m1_t a, vfloat32m1_t b, vfloat32m1_t c) {
     return __riscv_vfmacc_vv_f32m1(c, a, b, __riscv_vsetvlmax_e32m1());
 }
-inline vfloat32m2_t madd(vfloat32m2_t a, vfloat32m2_t b, vfloat32m2_t c) {
+template <> inline vfloat32m2_t madd(vfloat32m2_t a, vfloat32m2_t b, vfloat32m2_t c) {
     return __riscv_vfmacc_vv_f32m2(c, a, b, __riscv_vsetvlmax_e32m2());
 }
-inline vfloat32m4_t madd(vfloat32m4_t a, vfloat32m4_t b, vfloat32m4_t c) {
+template <> inline vfloat32m4_t madd(vfloat32m4_t a, vfloat32m4_t b, vfloat32m4_t c) {
     return __riscv_vfmacc_vv_f32m4(c, a, b, __riscv_vsetvlmax_e32m4());
 }
-inline vfloat32m8_t madd(vfloat32m8_t a, vfloat32m8_t b, vfloat32m8_t c) {
+template <> inline vfloat32m8_t madd(vfloat32m8_t a, vfloat32m8_t b, vfloat32m8_t c) {
     return __riscv_vfmacc_vv_f32m8(c, a, b, __riscv_vsetvlmax_e32m8());
 }
 #endif
 
+#if defined(__riscv_zvfh)
+template <> inline vfloat32m1_t madd(vfloat16mf2_t a, vfloat16mf2_t b, vfloat32m1_t c) {
+    return __riscv_vfwmacc_vv_f32m1(c, a, b, __riscv_vsetvlmax_e32m1());
+}
+template <> inline vfloat32m2_t madd(vfloat16m1_t a, vfloat16m1_t b, vfloat32m2_t c) {
+    return __riscv_vfwmacc_vv_f32m2(c, a, b, __riscv_vsetvlmax_e32m2());
+}
+template <> inline vfloat32m4_t madd(vfloat16m2_t a, vfloat16m2_t b, vfloat32m4_t c) {
+    return __riscv_vfwmacc_vv_f32m4(c, a, b, __riscv_vsetvlmax_e32m4());
+}
+template <> inline vfloat32m8_t madd(vfloat16m4_t a, vfloat16m4_t b, vfloat32m8_t c) {
+    return __riscv_vfwmacc_vv_f32m8(c, a, b, __riscv_vsetvlmax_e32m8());
+}
+#endif
+
 #if defined(__riscv_zvfbfwma)
-inline vfloat32m1_t madd(vbfloat16mf2_t a, vbfloat16mf2_t b, vfloat32m1_t c) {
+template <> inline vfloat32m1_t madd(vbfloat16mf2_t a, vbfloat16mf2_t b, vfloat32m1_t c) {
     return __riscv_vfwmaccbf16_vv_f32m1(c, a, b, __riscv_vsetvlmax_e32m1());
 }
-inline vfloat32m2_t madd(vbfloat16m1_t a, vbfloat16m1_t b, vfloat32m2_t c) {
+template <> inline vfloat32m2_t madd(vbfloat16m1_t a, vbfloat16m1_t b, vfloat32m2_t c) {
     return __riscv_vfwmaccbf16_vv_f32m2(c, a, b, __riscv_vsetvlmax_e32m2());
 }
-inline vfloat32m4_t madd(vbfloat16m2_t a, vbfloat16m2_t b, vfloat32m4_t c) {
+template <> inline vfloat32m4_t madd(vbfloat16m2_t a, vbfloat16m2_t b, vfloat32m4_t c) {
     return __riscv_vfwmaccbf16_vv_f32m4(c, a, b, __riscv_vsetvlmax_e32m4());
 }
+template <> inline vfloat32m8_t madd(vbfloat16m4_t a, vbfloat16m4_t b, vfloat32m8_t c) {
+    return __riscv_vfwmaccbf16_vv_f32m8(c, a, b, __riscv_vsetvlmax_e32m8());
+}
 #endif
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
@@ -272,7 +277,7 @@ inline float hsum(__m512 x) {
 }
 #endif // __AVX512F__
 
-#if defined(__riscv_zvfh)
+#if defined(__riscv_v_intrinsic)
 inline float hsum(vfloat32m1_t x) {
     return __riscv_vfmv_f_s_f32m1_f32(
         __riscv_vfredusum_vs_f32m1_f32m1(x, __riscv_vfmv_v_f_f32m1(0, 1), __riscv_vsetvlmax_e32m1()));
@@ -379,19 +384,7 @@ template <> inline __m256bh load(const float *p) {
 }
 #endif
 
-#if defined(__riscv_zvfh)
-template <> inline vfloat16mf2_t load(const ggml_fp16_t *p) {
-    return __riscv_vle16_v_f16mf2(reinterpret_cast<const _Float16 *>(p), __riscv_vsetvlmax_e16mf2());
-}
-template <> inline vfloat16m1_t load(const ggml_fp16_t *p) {
-    return __riscv_vle16_v_f16m1(reinterpret_cast<const _Float16 *>(p), __riscv_vsetvlmax_e16m1());
-}
-template <> inline vfloat16m2_t load(const ggml_fp16_t *p) {
-    return __riscv_vle16_v_f16m2(reinterpret_cast<const _Float16 *>(p), __riscv_vsetvlmax_e16m2());
-}
-template <> inline vfloat16m4_t load(const ggml_fp16_t *p) {
-    return __riscv_vle16_v_f16m4(reinterpret_cast<const _Float16 *>(p), __riscv_vsetvlmax_e16m4());
-}
+#if defined(__riscv_v_intrinsic)
 template <> inline vfloat32m1_t load(const float *p) {
     return __riscv_vle32_v_f32m1(p, __riscv_vsetvlmax_e32m1());
 }
@@ -406,6 +399,21 @@ template <> inline vfloat32m8_t load(const float *p) {
 }
 #endif
 
+#if defined(__riscv_zvfh)
+template <> inline vfloat16mf2_t load(const ggml_fp16_t *p) {
+    return __riscv_vle16_v_f16mf2(reinterpret_cast<const _Float16 *>(p), __riscv_vsetvlmax_e16mf2());
+}
+template <> inline vfloat16m1_t load(const ggml_fp16_t *p) {
+    return __riscv_vle16_v_f16m1(reinterpret_cast<const _Float16 *>(p), __riscv_vsetvlmax_e16m1());
+}
+template <> inline vfloat16m2_t load(const ggml_fp16_t *p) {
+    return __riscv_vle16_v_f16m2(reinterpret_cast<const _Float16 *>(p), __riscv_vsetvlmax_e16m2());
+}
+template <> inline vfloat16m4_t load(const ggml_fp16_t *p) {
+    return __riscv_vle16_v_f16m4(reinterpret_cast<const _Float16 *>(p), __riscv_vsetvlmax_e16m4());
+}
+#endif
+
 #if defined(__riscv_zvfbfwma)
 template <> inline vbfloat16mf2_t load(const ggml_bf16_t *p) {
     return __riscv_vle16_v_bf16mf2(reinterpret_cast<const __bf16*>(p), __riscv_vsetvlmax_e16mf2());
@@ -416,23 +424,14 @@ template <> inline vbfloat16m1_t load(const ggml_bf16_t *p) {
 template <> inline vbfloat16m2_t load(const ggml_bf16_t *p) {
     return __riscv_vle16_v_bf16m2(reinterpret_cast<const __bf16*>(p), __riscv_vsetvlmax_e16m2());
 }
+template <> inline vbfloat16m4_t load(const ggml_bf16_t *p) {
+    return __riscv_vle16_v_bf16m4(reinterpret_cast<const __bf16*>(p), __riscv_vsetvlmax_e16m4());
+}
 #endif
 
-#if defined(__riscv_zvfh)
+#if defined(__riscv_v_intrinsic)
 template <typename T> T set_zero();
 
-template <> inline vfloat16mf2_t set_zero() {
-    return __riscv_vfmv_v_f_f16mf2(0, __riscv_vsetvlmax_e16mf2());
-}
-template <> inline vfloat16m1_t set_zero() {
-    return __riscv_vfmv_v_f_f16m1(0, __riscv_vsetvlmax_e16m1());
-}
-template <> inline vfloat16m2_t set_zero() {
-    return __riscv_vfmv_v_f_f16m2(0, __riscv_vsetvlmax_e16m2());
-}
-template <> inline vfloat16m4_t set_zero() {
-    return __riscv_vfmv_v_f_f16m4(0, __riscv_vsetvlmax_e16m4());
-}
 template <> inline vfloat32m1_t set_zero() {
     return __riscv_vfmv_v_f_f32m1(0.0f, __riscv_vsetvlmax_e32m1());
 }
@@ -449,14 +448,22 @@ template <> inline vfloat32m8_t set_zero() {
 
 #if defined(__riscv_v_intrinsic)
 template <typename T> size_t vlmax() {
-    if constexpr (std::is_same_v<T, vfloat16mf2_t>) { return  __riscv_vsetvlmax_e16mf2(); }
-    else if constexpr (std::is_same_v<T, vfloat16m1_t>) { return  __riscv_vsetvlmax_e16m1(); }
-    else if constexpr (std::is_same_v<T, vfloat16m2_t>) { return  __riscv_vsetvlmax_e16m2(); }
-    else if constexpr (std::is_same_v<T, vfloat16m4_t>) { return  __riscv_vsetvlmax_e16m4(); }
-    else if constexpr (std::is_same_v<T, vfloat32m1_t>) { return  __riscv_vsetvlmax_e32m1(); }
+    if constexpr (std::is_same_v<T, vfloat32m1_t>) { return  __riscv_vsetvlmax_e32m1(); }
     else if constexpr (std::is_same_v<T, vfloat32m2_t>) { return  __riscv_vsetvlmax_e32m2(); }
     else if constexpr (std::is_same_v<T, vfloat32m4_t>) { return  __riscv_vsetvlmax_e32m4(); }
     else if constexpr (std::is_same_v<T, vfloat32m8_t>) { return  __riscv_vsetvlmax_e32m8(); }
+    #if defined (__riscv_zvfh)
+    else if constexpr (std::is_same_v<T, vfloat16mf2_t>) { return  __riscv_vsetvlmax_e16mf2(); }
+    else if constexpr (std::is_same_v<T, vfloat16m1_t>) { return  __riscv_vsetvlmax_e16m1(); }
+    else if constexpr (std::is_same_v<T, vfloat16m2_t>) { return  __riscv_vsetvlmax_e16m2(); }
+    else if constexpr (std::is_same_v<T, vfloat16m4_t>) { return  __riscv_vsetvlmax_e16m4(); }
+    #endif
+    #if defined (__riscv_zvfbfwma)
+    else if constexpr (std::is_same_v<T, vbfloat16mf2_t>) { return  __riscv_vsetvlmax_e16mf2(); }
+    else if constexpr (std::is_same_v<T, vbfloat16m1_t>) { return  __riscv_vsetvlmax_e16m1(); }
+    else if constexpr (std::is_same_v<T, vbfloat16m2_t>) { return  __riscv_vsetvlmax_e16m2(); }
+    else if constexpr (std::is_same_v<T, vbfloat16m4_t>) { return  __riscv_vsetvlmax_e16m4(); }
+    #endif
     return 0;
 }
 #endif
@@ -3740,7 +3747,7 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64
             params->ith, params->nth};
         tb.matmul(m, n);
         return true;
-#elif defined(__riscv_zvfh)
+#elif defined(__riscv_v_intrinsic)
     #if LMUL == 1
         tinyBLAS_RVV<vfloat32m1_t, vfloat32m1_t, float, float, float> tb{ params,
             k, (const float *)A, lda,
@@ -3804,23 +3811,25 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64
             return true;
         }
 #elif defined(__riscv_zvfbfwma)
-        #if LMUL == 1
-            tinyBLAS_RVV<vfloat32m1_t, vbfloat16mf2_t, ggml_bf16_t, ggml_bf16_t, float> tb{ params,
-                k, (const ggml_bf16_t *)A, lda,
-                (const ggml_bf16_t *)B, ldb,
-                (float *)C, ldc};
-        #elif LMUL == 2
-            tinyBLAS_RVV<vfloat32m2_t, vbfloat16m1_t, ggml_bf16_t, ggml_bf16_t, float> tb{ params,
-                k, (const ggml_bf16_t *)A, lda,
-                (const ggml_bf16_t *)B, ldb,
-                (float *)C, ldc};
-        #else // LMUL = 4
-            tinyBLAS_RVV<vfloat32m4_t, vbfloat16m2_t, ggml_bf16_t, ggml_bf16_t, float> tb{ params,
-                k, (const ggml_bf16_t *)A, lda,
-                (const ggml_bf16_t *)B, ldb,
-                (float *)C, ldc};
-        #endif
-            return tb.matmul(m, n);
+        if (Btype == GGML_TYPE_BF16) {
+            #if LMUL == 1
+                tinyBLAS_RVV<vfloat32m1_t, vbfloat16mf2_t, ggml_bf16_t, ggml_bf16_t, float> tb{ params,
+                    k, (const ggml_bf16_t *)A, lda,
+                    (const ggml_bf16_t *)B, ldb,
+                    (float *)C, ldc};
+            #elif LMUL == 2
+                tinyBLAS_RVV<vfloat32m2_t, vbfloat16m1_t, ggml_bf16_t, ggml_bf16_t, float> tb{ params,
+                    k, (const ggml_bf16_t *)A, lda,
+                    (const ggml_bf16_t *)B, ldb,
+                    (float *)C, ldc};
+            #else // LMUL = 4
+                tinyBLAS_RVV<vfloat32m4_t, vbfloat16m2_t, ggml_bf16_t, ggml_bf16_t, float> tb{ params,
+                    k, (const ggml_bf16_t *)A, lda,
+                    (const ggml_bf16_t *)B, ldb,
+                    (float *)C, ldc};
+            #endif
+                return tb.matmul(m, n);
+        }
 #endif
         return false;
     }

ggml/src/ggml-cpu/ops.cpp

@@ -9953,13 +9953,9 @@ static void ggml_compute_forward_rwkv_wkv6_f32(
     const int ith = params->ith;
     const int nth = params->nth;
 
-    if (ith >= HEADS) {
-        return;
-    }
-
-    const int h_start = (HEADS * ith) / nth;
-    const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ?
-                (HEADS * (ith + 1)) / nth : HEADS;
+    const int h_start =  (HEADS * (ith    )) / nth;
+    const int h_end   = ((HEADS * (ith + 1)) / nth < HEADS) ?
+                         (HEADS * (ith + 1)) / nth : HEADS;
 
     float * k =          (float *) dst->src[0]->data;
     float * v =          (float *) dst->src[1]->data;
@@ -10170,13 +10166,9 @@ static void ggml_compute_forward_gla_f32(
     const int ith = params->ith;
     const int nth = params->nth;
 
-    if (ith >= HEADS) {
-        return;
-    }
-
-    const int h_start = (HEADS * ith) / nth;
-    const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ?
-                (HEADS * (ith + 1)) / nth : HEADS;
+    const int h_start =  (HEADS * (ith    )) / nth;
+    const int h_end   = ((HEADS * (ith + 1)) / nth < HEADS) ?
+                         (HEADS * (ith + 1)) / nth : HEADS;
 
     float * k = (float *) dst->src[0]->data;
     float * v = (float *) dst->src[1]->data;
@@ -10633,13 +10625,9 @@ static void ggml_compute_forward_rwkv_wkv7_f32(
     const int ith = params->ith;
     const int nth = params->nth;
 
-    if (ith >= HEADS) {
-        return;
-    }
-
-    const int h_start = (HEADS * ith) / nth;
-    const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ?
-                (HEADS * (ith + 1)) / nth : HEADS;
+    const int h_start =  (HEADS * (ith    )) / nth;
+    const int h_end   = ((HEADS * (ith + 1)) / nth < HEADS) ?
+                         (HEADS * (ith + 1)) / nth : HEADS;
 
     float * r = (float *) dst->src[0]->data;
     float * w = (float *) dst->src[1]->data;

ggml/src/ggml-cpu/vec.h

@@ -126,7 +126,7 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
         const int ggml_f16_epr = sve_register_length / 16; // running when 16
         const int ggml_f16_step = 8 * ggml_f16_epr; // choose 8 SVE registers
 
-        const int np = (n & ~(ggml_f16_step - 1));
+        int np = (n & ~(ggml_f16_step - 1));
 
         svfloat16_t sum_00 = svdup_n_f16(0.0f);
         svfloat16_t sum_01 = svdup_n_f16(0.0f);
@@ -224,71 +224,75 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
         }
         GGML_F16x_VEC_REDUCE(sumf[0], sum_00, sum_01, sum_02, sum_03);
         GGML_F16x_VEC_REDUCE(sumf[1], sum_10, sum_11, sum_12, sum_13);
+        np = n;
+    #elif defined(__riscv_v_intrinsic)
+        #if defined(__riscv_zvfh)
+            size_t vl = __riscv_vsetvlmax_e32m4();
 
-    #elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfh)
-        size_t vl = __riscv_vsetvlmax_e32m4();
+            // initialize accumulators to all zeroes
+            vfloat32m4_t vsum0_0 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
+            vfloat32m4_t vsum0_1 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
+            vfloat32m4_t vsum1_0 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
+            vfloat32m4_t vsum1_1 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
 
-        // initialize accumulators to all zeroes
-        vfloat32m4_t vsum0_0 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
-        vfloat32m4_t vsum0_1 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
-        vfloat32m4_t vsum1_0 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
-        vfloat32m4_t vsum1_1 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
+            // calculate step size
+            const size_t epr = __riscv_vsetvlmax_e16m2();
+            const size_t step = epr * 2;
+            int np = (n & ~(step - 1));
 
-        // calculate step size
-        const size_t epr = __riscv_vsetvlmax_e16m2();
-        const size_t step = epr * 2;
-        const int np = (n & ~(step - 1));
+            // unroll by 2 along the row dimension
+            for (int i = 0; i < np; i += step) {
+                vfloat16m2_t ay0 = __riscv_vle16_v_f16m2((const _Float16 *)(y + i), epr);
+                vfloat16m2_t ax0_0 = __riscv_vle16_v_f16m2((const _Float16 *)(x[0] + i), epr);
+                vfloat16m2_t ax1_0 = __riscv_vle16_v_f16m2((const _Float16 *)(x[1] + i), epr);
+                vsum0_0 = __riscv_vfwmacc_vv_f32m4(vsum0_0, ax0_0, ay0, epr);
+                vsum1_0 = __riscv_vfwmacc_vv_f32m4(vsum1_0, ax1_0, ay0, epr);
 
-        // unroll by 2 along the row dimension
-        for (int i = 0; i < np; i += step) {
-            vfloat16m2_t ay0 = __riscv_vle16_v_f16m2((const _Float16 *)(y + i), epr);
-            vfloat16m2_t ax0_0 = __riscv_vle16_v_f16m2((const _Float16 *)(x[0] + i), epr);
-            vfloat16m2_t ax1_0 = __riscv_vle16_v_f16m2((const _Float16 *)(x[1] + i), epr);
-            vsum0_0 = __riscv_vfwmacc_vv_f32m4(vsum0_0, ax0_0, ay0, epr);
-            vsum1_0 = __riscv_vfwmacc_vv_f32m4(vsum1_0, ax1_0, ay0, epr);
+                vfloat16m2_t ay1 = __riscv_vle16_v_f16m2((const _Float16 *)(y + i + epr), epr);
+                vfloat16m2_t ax0_1 = __riscv_vle16_v_f16m2((const _Float16 *)(x[0] + i + epr), epr);
+                vfloat16m2_t ax1_1 = __riscv_vle16_v_f16m2((const _Float16 *)(x[1] + i + epr), epr);
+                vsum0_1 = __riscv_vfwmacc_vv_f32m4(vsum0_1, ax0_1, ay1, epr);
+                vsum1_1 = __riscv_vfwmacc_vv_f32m4(vsum1_1, ax1_1, ay1, epr);
+            }
 
-            vfloat16m2_t ay1 = __riscv_vle16_v_f16m2((const _Float16 *)(y + i + epr), epr);
-            vfloat16m2_t ax0_1 = __riscv_vle16_v_f16m2((const _Float16 *)(x[0] + i + epr), epr);
-            vfloat16m2_t ax1_1 = __riscv_vle16_v_f16m2((const _Float16 *)(x[1] + i + epr), epr);
-            vsum0_1 = __riscv_vfwmacc_vv_f32m4(vsum0_1, ax0_1, ay1, epr);
-            vsum1_1 = __riscv_vfwmacc_vv_f32m4(vsum1_1, ax1_1, ay1, epr);
-        }
+            vfloat32m4_t vsum0 = __riscv_vfadd_vv_f32m4(vsum0_0, vsum0_1, vl);
+            vfloat32m4_t vsum1 = __riscv_vfadd_vv_f32m4(vsum1_0, vsum1_1, vl);
 
-        vfloat32m4_t vsum0 = __riscv_vfadd_vv_f32m4(vsum0_0, vsum0_1, vl);
-        vfloat32m4_t vsum1 = __riscv_vfadd_vv_f32m4(vsum1_0, vsum1_1, vl);
+            // leftovers
+            for (int i = np; i < n; i += vl) {
+                vl = __riscv_vsetvl_e16m2(n - i);
+                vfloat16m2_t ay = __riscv_vle16_v_f16m2((const _Float16 *)(y + i), vl);
+                vfloat16m2_t ax0 = __riscv_vle16_v_f16m2((const _Float16 *)(x[0] + i), vl);
+                vfloat16m2_t ax1 = __riscv_vle16_v_f16m2((const _Float16 *)(x[1] + i), vl);
 
-        // leftovers
-        for (int i = np; i < n; i += vl) {
-            vl = __riscv_vsetvl_e16m2(n - i);
-            vfloat16m2_t ay = __riscv_vle16_v_f16m2((const _Float16 *)(y + i), vl);
-            vfloat16m2_t ax0 = __riscv_vle16_v_f16m2((const _Float16 *)(x[0] + i), vl);
-            vfloat16m2_t ax1 = __riscv_vle16_v_f16m2((const _Float16 *)(x[1] + i), vl);
+                vsum0 = __riscv_vfwmacc_vv_f32m4(vsum0, ax0, ay, vl);
+                vsum1 = __riscv_vfwmacc_vv_f32m4(vsum1, ax1, ay, vl);
+            }
 
-            vsum0 = __riscv_vfwmacc_vv_f32m4(vsum0, ax0, ay, vl);
-            vsum1 = __riscv_vfwmacc_vv_f32m4(vsum1, ax1, ay, vl);
-        }
-
-        // reduce
-        vl = __riscv_vsetvlmax_e32m2();
-        vfloat32m2_t acc0_0 = __riscv_vfadd_vv_f32m2(__riscv_vget_v_f32m4_f32m2(vsum0, 0),
-                                    __riscv_vget_v_f32m4_f32m2(vsum0, 1), vl);
-        vl = __riscv_vsetvlmax_e32m1();
-        vfloat32m1_t acc0_1 = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m2_f32m1(acc0_0, 0),
-        __riscv_vget_v_f32m2_f32m1(acc0_0, 1), vl);
-        vfloat32m1_t redsum0 = __riscv_vfredusum_vs_f32m1_f32m1(
-                                    acc0_1, __riscv_vfmv_v_f_f32m1(0.0f, 1), vl);
-
-        vl = __riscv_vsetvlmax_e32m2();
-        vfloat32m2_t acc1_0 = __riscv_vfadd_vv_f32m2(__riscv_vget_v_f32m4_f32m2(vsum1, 0),
-                                    __riscv_vget_v_f32m4_f32m2(vsum1, 1), vl);
-        vl = __riscv_vsetvlmax_e32m1();
-        vfloat32m1_t acc1_1 = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m2_f32m1(acc1_0, 0),
-                                    __riscv_vget_v_f32m2_f32m1(acc1_0, 1), vl);
-        vfloat32m1_t redsum1 = __riscv_vfredusum_vs_f32m1_f32m1(
-                                    acc1_1, __riscv_vfmv_v_f_f32m1(0.0f, 1), vl);
-        sumf[0] = __riscv_vfmv_f_s_f32m1_f32(redsum0);
-        sumf[1] = __riscv_vfmv_f_s_f32m1_f32(redsum1);
+            // reduce
+            vl = __riscv_vsetvlmax_e32m2();
+            vfloat32m2_t acc0_0 = __riscv_vfadd_vv_f32m2(__riscv_vget_v_f32m4_f32m2(vsum0, 0),
+                                        __riscv_vget_v_f32m4_f32m2(vsum0, 1), vl);
+            vl = __riscv_vsetvlmax_e32m1();
+            vfloat32m1_t acc0_1 = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m2_f32m1(acc0_0, 0),
+            __riscv_vget_v_f32m2_f32m1(acc0_0, 1), vl);
+            vfloat32m1_t redsum0 = __riscv_vfredusum_vs_f32m1_f32m1(
+                                        acc0_1, __riscv_vfmv_v_f_f32m1(0.0f, 1), vl);
 
+            vl = __riscv_vsetvlmax_e32m2();
+            vfloat32m2_t acc1_0 = __riscv_vfadd_vv_f32m2(__riscv_vget_v_f32m4_f32m2(vsum1, 0),
+                                        __riscv_vget_v_f32m4_f32m2(vsum1, 1), vl);
+            vl = __riscv_vsetvlmax_e32m1();
+            vfloat32m1_t acc1_1 = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m2_f32m1(acc1_0, 0),
+                                        __riscv_vget_v_f32m2_f32m1(acc1_0, 1), vl);
+            vfloat32m1_t redsum1 = __riscv_vfredusum_vs_f32m1_f32m1(
+                                        acc1_1, __riscv_vfmv_v_f_f32m1(0.0f, 1), vl);
+            sumf[0] = __riscv_vfmv_f_s_f32m1_f32(redsum0);
+            sumf[1] = __riscv_vfmv_f_s_f32m1_f32(redsum1);
+            np = n;
+        #else
+            const int np = 0;
+        #endif
     #else
         const int np = (n & ~(GGML_F16_STEP - 1));
 
@@ -313,21 +317,17 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
         for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
             GGML_F16_VEC_REDUCE(sumf[k], sum[k]);
         }
-
-        // leftovers
-        for (int i = np; i < n; ++i) {
-            for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
-                sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
-            }
-        }
     #endif
 #else
-    for (int i = 0; i < n; ++i) {
+    // scalar path
+    const int np = 0;
+#endif
+    // scalar and leftovers
+    for (int i = np; i < n; ++i) {
         for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
             sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
         }
     }
-#endif
 
     for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) {
         s[i] = (float)sumf[i];
@@ -532,40 +532,45 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * GGML_RESTRICT y,
         svst1_f16(pg, (__fp16 *)(y + np2), hy);
     }
     np = n;
-#elif defined(__riscv_zvfh) // implies __riscv_v_intrinsic
-    const ggml_fp16_t s = GGML_CPU_FP32_TO_FP16(v);
-    const _Float16 scale = *(const _Float16*)(&s);
+#elif defined(__riscv_v_intrinsic) // implies __riscv_v_intrinsic
+    #if defined (__riscv_zvfh)
+        const ggml_fp16_t s = GGML_CPU_FP32_TO_FP16(v);
+        const _Float16 scale = *(const _Float16*)(&s);
 
-    // calculate step size
-    const int epr = __riscv_vsetvlmax_e16m4();
-    const int step = epr * 2;
-    int np = (n & ~(step - 1));
+        // calculate step size
+        const int epr = __riscv_vsetvlmax_e16m4();
+        const int step = epr * 2;
+        int np = (n & ~(step - 1));
 
-    // unroll by 2
-    for (int i = 0; i < np; i += step) {
-        vfloat16m4_t ax0 = __riscv_vle16_v_f16m4((const _Float16*)x + i, epr);
-        vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, epr);
-        ay0 = __riscv_vfmacc_vf_f16m4(ay0, scale, ax0, epr);
-        __riscv_vse16_v_f16m4((_Float16*)y + i, ay0, epr);
-        __asm__ __volatile__ ("" ::: "memory");
+        // unroll by 2
+        for (int i = 0; i < np; i += step) {
+            vfloat16m4_t ax0 = __riscv_vle16_v_f16m4((const _Float16*)x + i, epr);
+            vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, epr);
+            ay0 = __riscv_vfmacc_vf_f16m4(ay0, scale, ax0, epr);
+            __riscv_vse16_v_f16m4((_Float16*)y + i, ay0, epr);
+            __asm__ __volatile__ ("" ::: "memory");
 
-        vfloat16m4_t ax1 = __riscv_vle16_v_f16m4((const _Float16*)x + i + epr, epr);
-        vfloat16m4_t ay1 = __riscv_vle16_v_f16m4((const _Float16*)y + i + epr, epr);
-        ay1 = __riscv_vfmacc_vf_f16m4(ay1, scale, ax1, epr);
-        __riscv_vse16_v_f16m4((_Float16*)y + i + epr, ay1, epr);
-        __asm__ __volatile__ ("" ::: "memory");
-    }
+            vfloat16m4_t ax1 = __riscv_vle16_v_f16m4((const _Float16*)x + i + epr, epr);
+            vfloat16m4_t ay1 = __riscv_vle16_v_f16m4((const _Float16*)y + i + epr, epr);
+            ay1 = __riscv_vfmacc_vf_f16m4(ay1, scale, ax1, epr);
+            __riscv_vse16_v_f16m4((_Float16*)y + i + epr, ay1, epr);
+            __asm__ __volatile__ ("" ::: "memory");
+        }
 
-    // leftovers
-    int vl;
-    for (int i = np; i < n; i += vl) {
-        vl = __riscv_vsetvl_e16m4(n - i);
-        vfloat16m4_t ax0 = __riscv_vle16_v_f16m4((const _Float16*)x + i, vl);
-        vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, vl);
-        ay0 = __riscv_vfmacc_vf_f16m4(ay0, scale, ax0, vl);
-        __riscv_vse16_v_f16m4((_Float16*)y + i, ay0, vl);
-    }
-    np = n;
+        // leftovers
+        int vl;
+        for (int i = np; i < n; i += vl) {
+            vl = __riscv_vsetvl_e16m4(n - i);
+            vfloat16m4_t ax0 = __riscv_vle16_v_f16m4((const _Float16*)x + i, vl);
+            vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, vl);
+            ay0 = __riscv_vfmacc_vf_f16m4(ay0, scale, ax0, vl);
+            __riscv_vse16_v_f16m4((_Float16*)y + i, ay0, vl);
+        }
+        np = n;
+    #else
+        // fall to scalar path
+        const int np = 0;
+    #endif
 #elif defined(GGML_SIMD)
     const int np = (n & ~(GGML_F16_STEP - 1));
 
@@ -584,10 +589,11 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * GGML_RESTRICT y,
         }
     }
 #else
+    // scalar path
     const int np = 0;
 #endif
 
-    // leftovers
+    // scalar and leftovers
     for (int i = np; i < n; ++i) {
         y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i]) + GGML_CPU_FP16_TO_FP32(x[i])*v);
     }
@@ -785,7 +791,7 @@ inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float
     const int ggml_f16_step = 2 * ggml_f16_epr;
 
     GGML_F16x_VEC vx =  GGML_F16x_VEC_SET1(v);
-    const int np = (n & ~(ggml_f16_step - 1));
+    int np = (n & ~(ggml_f16_step - 1));
     svfloat16_t ay1, ay2;
 
     for (int i = 0; i < np; i += ggml_f16_step) {
@@ -805,36 +811,43 @@ inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float
         svfloat16_t out = svmul_f16_m(pg, hy, vx);
         svst1_f16(pg, (__fp16 *)(y + np), out);
     }
-#elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfh)
-    const ggml_fp16_t s = GGML_CPU_FP32_TO_FP16(v);
-    const _Float16 scale = *(const _Float16*)(&s);
+    np = n;
+#elif defined(__riscv_v_intrinsic)
+    #if defined(__riscv_zvfh)
+        const ggml_fp16_t s = GGML_CPU_FP32_TO_FP16(v);
+        const _Float16 scale = *(const _Float16*)(&s);
 
-    // calculate step size
-    const int epr = __riscv_vsetvlmax_e16m4();
-    const int step = epr * 2;
-    const int np = (n & ~(step - 1));
+        // calculate step size
+        const int epr = __riscv_vsetvlmax_e16m4();
+        const int step = epr * 2;
+        int np = (n & ~(step - 1));
 
-    // unroll by 2
-    for (int i = 0; i < np; i += step) {
-        vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, epr);
-        ay0 = __riscv_vfmul_vf_f16m4(ay0, scale, epr);
-        __riscv_vse16_v_f16m4((_Float16*)y + i, ay0, epr);
-        __asm__ __volatile__ ("" ::: "memory");
+        // unroll by 2
+        for (int i = 0; i < np; i += step) {
+            vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, epr);
+            ay0 = __riscv_vfmul_vf_f16m4(ay0, scale, epr);
+            __riscv_vse16_v_f16m4((_Float16*)y + i, ay0, epr);
+            __asm__ __volatile__ ("" ::: "memory");
 
-        vfloat16m4_t ay1 = __riscv_vle16_v_f16m4((const _Float16*)y + i + epr, epr);
-        ay1 = __riscv_vfmul_vf_f16m4(ay1, scale, epr);
-        __riscv_vse16_v_f16m4((_Float16*)y + i + epr, ay1, epr);
-        __asm__ __volatile__ ("" ::: "memory");
-    }
+            vfloat16m4_t ay1 = __riscv_vle16_v_f16m4((const _Float16*)y + i + epr, epr);
+            ay1 = __riscv_vfmul_vf_f16m4(ay1, scale, epr);
+            __riscv_vse16_v_f16m4((_Float16*)y + i + epr, ay1, epr);
+            __asm__ __volatile__ ("" ::: "memory");
+        }
 
-    // leftovers
-    int vl;
-    for (int i = np; i < n; i += vl) {
-        vl = __riscv_vsetvl_e16m4(n - i);
-        vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, vl);
-        ay0 = __riscv_vfmul_vf_f16m4(ay0, scale, vl);
-        __riscv_vse16_v_f16m4((_Float16*)y + i, ay0, vl);
-    }
+        // leftovers
+        int vl;
+        for (int i = np; i < n; i += vl) {
+            vl = __riscv_vsetvl_e16m4(n - i);
+            vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, vl);
+            ay0 = __riscv_vfmul_vf_f16m4(ay0, scale, vl);
+            __riscv_vse16_v_f16m4((_Float16*)y + i, ay0, vl);
+        }
+        np = n;
+    #else
+        // fall to scalar path
+        const int np = 0;
+    #endif
 #elif defined(GGML_SIMD)
     const int np = (n & ~(GGML_F16_STEP - 1));
 
@@ -850,17 +863,14 @@ inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float
             GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
         }
     }
-
-    // leftovers
+#else
+    // scalar path
+    const int np = 0;
+#endif
+    // scalar and leftovers
     for (int i = np; i < n; ++i) {
         y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
     }
-#else
-    // scalar
-    for (int i = 0; i < n; ++i) {
-        y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
-    }
-#endif
 }
 
 inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s);   }

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/common.cuh

@@ -800,19 +800,32 @@ static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
 }
 
 static __device__ __forceinline__ float ggml_cuda_ue4m3_to_fp32(uint8_t x) {
-#ifdef FP8_AVAILABLE
-    const uint32_t bits = x * (x != 0x7F && x != 0xFF); // Convert NaN to 0.0f to match CPU implementation.
-#if defined(GGML_USE_HIP) && defined(CDNA3)
-    // ROCm dose not support fp8 in software on devices with fp8 hardware,
+#if defined(GGML_USE_HIP) && defined(CDNA3) && defined(FP8_AVAILABLE) && HIP_VERSION >= 60200000
+    // ROCm does not support fp8 in software on devices with fp8 hardware,
     // but CDNA3 supports only e4m3_fnuz (no inf).
+    const uint32_t bits = x * (x != 0x7F && x != 0xFF); // Convert NaN to 0.0f to match CPU implementation.
     const __hip_fp8_e4m3_fnuz xf = *reinterpret_cast<const __hip_fp8_e4m3_fnuz *>(&bits);
-#else
-    const __nv_fp8_e4m3 xf = *reinterpret_cast<const __nv_fp8_e4m3 *>(&bits);
-#endif // defined(GGML_USE_HIP) && defined(GGML_USE_HIP)
     return static_cast<float>(xf) / 2;
 #else
-    NO_DEVICE_CODE;
-#endif // FP8_AVAILABLE
+#if defined(FP8_AVAILABLE) && !defined(GGML_USE_HIP)
+    const uint32_t bits = x * (x != 0x7F && x != 0xFF); // Convert NaN to 0.0f to match CPU implementation.
+    const __nv_fp8_e4m3 xf = *reinterpret_cast<const __nv_fp8_e4m3 *>(&bits);
+    return static_cast<float>(xf) / 2;
+#else
+    if (x == 0 || (x == 0x7F && x != 0xFF)) { // Convert NaN to 0.0f
+        return 0.0f;
+    }
+    const int exp = (x >> 3) & 0xF;
+    const int man = x & 0x7;
+    float raw;
+    if (exp == 0) {
+        raw = ldexpf((float) man, -9);
+    } else {
+        raw = ldexpf(1.0f + (float) man / 8.0f, exp - 7);
+    }
+    return static_cast<float>(raw / 2);
+#endif // defined(FP8_AVAILABLE) && !defined(GGML_USE_HIP)
+#endif // defined(GGML_USE_HIP) && defined(CDNA3) && defined(FP8_AVAILABLE) && HIP_VERSION >= 60200000
 }
 
 __device__ __forceinline__ uint8_t ggml_cuda_float_to_fp4_e2m1(float x, float e) {

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

@@ -66,6 +66,11 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  32, 128, 128, 128, 2, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2,  32, 128, 128, 128, 2, true);
 
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512,  8,  64, 4,  32, 256, 256, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16,  64, 4,  32, 256, 256, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2,  32, 128, 128, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 256, 1,  32, 128, 128, 128, 1, false);
+
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512,  8,  64, 4,  32, 288, 256, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16,  64, 4,  32, 288, 256, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 128, 2,  32, 160, 128, 128, 1, false);
@@ -80,6 +85,11 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  64, 128, 128,  64, 2, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2,  64, 128, 128,  64, 2, true);
 
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512,  8,  64, 4,  32,  96,  64, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16,  64, 4,  32,  96,  64, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2,  32, 128, 128, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 256, 1,  32, 128, 128, 128, 1, false);
+
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512,  8,  64, 4,  32,  96,  64, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16,  64, 4,  32,  96,  64, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 128, 2,  32, 160, 128, 128, 1, false);
@@ -89,6 +99,11 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
 }
 
 static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config_volta(const int DKQ, const int DV, const int ncols) {
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512,  8,  64, 4,  32, 256, 256,  64, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16,  64, 4,  32, 256, 256,  64, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2,  32, 128, 128,  64, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 256, 1,  32, 128, 128,  64, 1, false);
+
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512,  8,  64, 4,  32, 288, 256,  64, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16,  64, 4,  32, 288, 256,  64, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 128, 2,  32, 160, 128,  64, 1, false);
@@ -103,6 +118,10 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  64, 128, 128,  64, 2, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2,  64, 128, 128,  64, 2, true);
 
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16,  64, 4,  32, 128, 128, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2,  32, 128, 128, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 256, 1,  32, 128, 128, 128, 1, false);
+
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16,  64, 4,  32,  96,  64, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 128, 2,  32, 160, 128, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 64, 256, 1,  32, 160, 128, 128, 1, false);
@@ -1552,7 +1571,7 @@ static __global__ void flash_attn_ext_f16(
 #if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE))
 
     // Skip unused kernel variants for faster compilation:
-    if (use_logit_softcap && !(DKQ == 128 || DKQ == 256)) {
+    if (use_logit_softcap && !(DKQ == 128 || DKQ == 256 || DKQ == 512)) {
         NO_DEVICE_CODE;
         return;
     }
@@ -1815,6 +1834,15 @@ DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(112, 112,  64)
 DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(128, 128,  64)
 DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(256, 256,  64)
 
+extern DECL_FATTN_MMA_F16_CASE(512, 512,  2,  4);
+extern DECL_FATTN_MMA_F16_CASE(512, 512,  4,  4);
+extern DECL_FATTN_MMA_F16_CASE(512, 512,  8,  4);
+extern DECL_FATTN_MMA_F16_CASE(512, 512, 16,  4);
+extern DECL_FATTN_MMA_F16_CASE(512, 512,  1,  8);
+extern DECL_FATTN_MMA_F16_CASE(512, 512,  2,  8);
+extern DECL_FATTN_MMA_F16_CASE(512, 512,  4,  8);
+extern DECL_FATTN_MMA_F16_CASE(512, 512,  8,  8);
+
 // The number of viable configurations for Deepseek is very limited:
 extern DECL_FATTN_MMA_F16_CASE(576, 512, 1, 16);
 extern DECL_FATTN_MMA_F16_CASE(576, 512, 2, 16);

ggml/src/ggml-cuda/fattn-tile.cu

@@ -38,6 +38,10 @@ void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor
             GGML_ASSERT(V->ne[0] == K->ne[0]);
             ggml_cuda_flash_attn_ext_tile_case<256, 256>(ctx, dst);
         } break;
+        case 512: {
+            GGML_ASSERT(V->ne[0] == K->ne[0]);
+            ggml_cuda_flash_attn_ext_tile_case<512, 512>(ctx, dst);
+        } break;
         case 576: {
             GGML_ASSERT(V->ne[0] == 512);
             ggml_cuda_flash_attn_ext_tile_case<576, 512>(ctx, dst);

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

@@ -68,6 +68,10 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nv
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2,  64,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  64,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2,  64,  64)
@@ -124,6 +128,10 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nv
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2,  32, 128)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2,  32,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  32,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  32,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  32,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512,  4, 128, 2,  32,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512,  8, 256, 2,  32,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2,  32,  64)
@@ -187,6 +195,11 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_am
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2,  32, 128)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2,  32, 128)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 32, 512, 1, 128,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2,  64,  64)
@@ -251,6 +264,11 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_am
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 5,  32, 256)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 3,  64, 128)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 4,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 32, 256, 2, 128,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 4,  64,  64)
@@ -767,7 +785,7 @@ static __global__ void flash_attn_tile(
 #ifdef GGML_USE_WMMA_FATTN
             (ncols2 != 1 && DV != 40 && DV != 72 && DV != 512) ||
 #endif // GGML_USE_WMMA_FATTN
-            (use_logit_softcap && !(DV == 128 || DV == 256))
+            (use_logit_softcap && !(DV == 128 || DV == 256 || DV == 512))
     ) {
         GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
             max_bias, m0, m1, n_head_log2, logit_softcap,
@@ -1192,7 +1210,7 @@ static void launch_fattn_tile_switch_ncols2(ggml_backend_cuda_context & ctx, ggm
     const int gqa_limit = nvidia && gqa_ratio <= 4 && DV <= 256 ? 16 : INT_MAX;
     const bool use_gqa_opt = mask && max_bias == 0.0f && Q->ne[1] <= gqa_limit && K->ne[1] % FATTN_KQ_STRIDE == 0;
 
-    if constexpr (DV == 512) {
+    if constexpr (DKQ == 576) {
         if (use_gqa_opt && gqa_ratio % 16 == 0) {
             launch_fattn_tile_switch_ncols1<DKQ, DV, 16, use_logit_softcap>(ctx, dst);
             return;
@@ -1203,7 +1221,7 @@ static void launch_fattn_tile_switch_ncols2(ggml_backend_cuda_context & ctx, ggm
         }
     }
 
-    if constexpr (DV <= 256) {
+    if constexpr (DKQ <= 512) {
         if (use_gqa_opt && gqa_ratio % 8 == 0) {
             launch_fattn_tile_switch_ncols1<DKQ, DV, 8, use_logit_softcap>(ctx, dst);
             return;
@@ -1214,13 +1232,15 @@ static void launch_fattn_tile_switch_ncols2(ggml_backend_cuda_context & ctx, ggm
             return;
         }
 
-        if (use_gqa_opt && gqa_ratio % 2 == 0) {
-            launch_fattn_tile_switch_ncols1<DKQ, DV, 2, use_logit_softcap>(ctx, dst);
+        if constexpr (DV <= 256) {
+            if (use_gqa_opt && gqa_ratio % 2 == 0) {
+                launch_fattn_tile_switch_ncols1<DKQ, DV, 2, use_logit_softcap>(ctx, dst);
+                return;
+            }
+
+            launch_fattn_tile_switch_ncols1<DKQ, DV, 1, use_logit_softcap>(ctx, dst);
             return;
         }
-
-        launch_fattn_tile_switch_ncols1<DKQ, DV, 1, use_logit_softcap>(ctx, dst);
-        return;
     }
     GGML_ABORT("fatal error");
 }
@@ -1255,4 +1275,5 @@ extern DECL_FATTN_TILE_CASE( 96,  96);
 extern DECL_FATTN_TILE_CASE(112, 112);
 extern DECL_FATTN_TILE_CASE(128, 128);
 extern DECL_FATTN_TILE_CASE(256, 256);
+extern DECL_FATTN_TILE_CASE(512, 512);
 extern DECL_FATTN_TILE_CASE(576, 512);

ggml/src/ggml-cuda/fattn.cu

@@ -135,6 +135,10 @@ static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, gg
             GGML_ASSERT(V->ne[0] == 256);
             ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2<256, 256>(ctx, dst);
             break;
+        case 512:
+            GGML_ASSERT(V->ne[0] == 512);
+            ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2<512, 512>(ctx, dst);
+            break;
         case 576: {
             // For Deepseek, go straight to the ncols1 switch to avoid compiling unnecessary kernels.
             GGML_ASSERT(V->ne[0] == 512);
@@ -340,6 +344,14 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
                 return BEST_FATTN_KERNEL_NONE;
             }
             break;
+        case 512:
+            if (V->ne[0] != K->ne[0]) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            if (!gqa_opt_applies) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            break;
         case 576:
             if (V->ne[0] != 512) {
                 return BEST_FATTN_KERNEL_NONE;
@@ -424,7 +436,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
     }
 
     // Use the WMMA kernel if possible:
-    if (ggml_cuda_should_use_wmma_fattn(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 576) {
+    if (ggml_cuda_should_use_wmma_fattn(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 512 && Q->ne[0] != 576) {
         if (can_use_vector_kernel && Q->ne[1] <= 2) {
             return BEST_FATTN_KERNEL_VEC;
         }
@@ -457,7 +469,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
     }
 
     // Use MFMA flash attention for CDNA (MI100+):
-    if (amd_mfma_available(cc) && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 256 && Q->ne[0] != 576) {
+    if (amd_mfma_available(cc) && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 256 && Q->ne[0] != 512 && Q->ne[0] != 576) {
         const int64_t eff_nq = Q->ne[1] * (gqa_opt_applies ? gqa_ratio : 1);
         // MMA vs tile crossover benchmarked on MI300X @ d32768:
         //   hsk=64  (gqa=4): MMA wins at eff >= 128 (+11%)

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) {
@@ -4786,9 +4791,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_Q8_0:
                     case GGML_TYPE_MXFP4:
-#ifdef FP8_AVAILABLE
                     case GGML_TYPE_NVFP4:
-#endif // FP8_AVAILABLE
                     case GGML_TYPE_Q2_K:
                     case GGML_TYPE_Q3_K:
                     case GGML_TYPE_Q4_K:

ggml/src/ggml-cuda/mmq.cu

@@ -23,6 +23,9 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con
         case GGML_TYPE_MXFP4:
             mul_mat_q_case<GGML_TYPE_MXFP4>(ctx, args, stream);
             break;
+        case GGML_TYPE_NVFP4:
+            mul_mat_q_case<GGML_TYPE_NVFP4>(ctx, args, stream);
+            break;
         case GGML_TYPE_Q2_K:
             mul_mat_q_case<GGML_TYPE_Q2_K>(ctx, args, stream);
             break;
@@ -273,6 +276,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_MXFP4:
+        case GGML_TYPE_NVFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -362,5 +366,4 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t
     }
 
     return (!GGML_CUDA_CC_IS_CDNA(cc)) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
-
 }

ggml/src/ggml-cuda/mmq.cuh

@@ -68,6 +68,8 @@ static mmq_q8_1_ds_layout mmq_get_q8_1_ds_layout(const ggml_type type_x) {
             return MMQ_Q8_1_DS_LAYOUT_D4;
         case GGML_TYPE_MXFP4:
             return MMQ_Q8_1_DS_LAYOUT_D4;
+        case GGML_TYPE_NVFP4:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
         case GGML_TYPE_Q2_K:
             return MMQ_Q8_1_DS_LAYOUT_D2S6;
         case GGML_TYPE_Q3_K:
@@ -189,6 +191,7 @@ static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml
         case GGML_TYPE_Q5_1:    return MMQ_DP4A_TXS_Q8_1;
         case GGML_TYPE_Q8_0:    return MMQ_DP4A_TXS_Q8_0;
         case GGML_TYPE_MXFP4:   return MMQ_DP4A_TXS_Q8_1;
+        case GGML_TYPE_NVFP4:   return MMQ_DP4A_TXS_Q8_0_16;
         case GGML_TYPE_Q2_K:    return MMQ_DP4A_TXS_Q2_K;
         case GGML_TYPE_Q3_K:    return MMQ_DP4A_TXS_Q3_K;
         case GGML_TYPE_Q4_K:    return MMQ_DP4A_TXS_Q4_K;
@@ -206,12 +209,13 @@ static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml
     }
 }
 
-#define MMQ_MMA_TILE_X_K_Q8_0 (2*MMQ_TILE_NE_K + 2*MMQ_TILE_NE_K/QI8_0                   + 4)
-#define MMQ_MMA_TILE_X_K_FP4  (2*MMQ_TILE_NE_K + 8                                       + 4)
-#define MMQ_MMA_TILE_X_K_Q8_1 (2*MMQ_TILE_NE_K + 2*MMQ_TILE_NE_K/QI8_0                   + 4)
-#define MMQ_MMA_TILE_X_K_Q2_K (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K                           + 4)
-#define MMQ_MMA_TILE_X_K_Q3_K (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2                         + 4)
-#define MMQ_MMA_TILE_X_K_Q6_K (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/QI6_K   + MMQ_TILE_NE_K/8 + 7)
+#define MMQ_MMA_TILE_X_K_Q8_0  (2*MMQ_TILE_NE_K + 2*MMQ_TILE_NE_K/QI8_0                   + 4)
+#define MMQ_MMA_TILE_X_K_FP4   (2*MMQ_TILE_NE_K + 8                                       + 4) // MXFP4
+#define MMQ_MMA_TILE_X_K_NVFP4 (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2                         + 4) // NVFP4
+#define MMQ_MMA_TILE_X_K_Q8_1  (2*MMQ_TILE_NE_K + 2*MMQ_TILE_NE_K/QI8_0                   + 4)
+#define MMQ_MMA_TILE_X_K_Q2_K  (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K                           + 4)
+#define MMQ_MMA_TILE_X_K_Q3_K  (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2                         + 4)
+#define MMQ_MMA_TILE_X_K_Q6_K  (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/QI6_K   + MMQ_TILE_NE_K/8 + 7)
 
 static_assert(MMQ_MMA_TILE_X_K_Q8_0 % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q8_1 % 8 == 4, "Wrong padding.");
@@ -220,6 +224,8 @@ static_assert(MMQ_MMA_TILE_X_K_Q3_K % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q6_K % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_FP4  % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_FP4 == MMQ_MMA_TILE_X_K_Q8_1, "Wrong tile size for MXFP4");
+static_assert(MMQ_MMA_TILE_X_K_NVFP4 % 8 == 4, "Wrong padding.");
+
 
 static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
     switch (type) {
@@ -230,6 +236,7 @@ static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
         case GGML_TYPE_Q8_0:    return MMQ_MMA_TILE_X_K_Q8_0;
         // tile sizes are the same for Q8_1 and FP4 for blackwell
         case GGML_TYPE_MXFP4:   return MMQ_MMA_TILE_X_K_Q8_1;
+        case GGML_TYPE_NVFP4:   return MMQ_MMA_TILE_X_K_NVFP4;
         case GGML_TYPE_Q2_K:    return MMQ_MMA_TILE_X_K_Q2_K;
         case GGML_TYPE_Q3_K:    return MMQ_MMA_TILE_X_K_Q3_K;
         case GGML_TYPE_Q4_K:    return MMQ_MMA_TILE_X_K_Q8_1;
@@ -826,6 +833,65 @@ static __device__ __forceinline__ void load_tiles_mxfp4_fp4(const char * __restr
     }
 }
 
+
+template <int mmq_y, bool need_check>
+static __device__ __forceinline__ void load_tiles_nvfp4(const char * __restrict__ x,
+                                                        int * __restrict__ x_tile,
+                                                        const int kb0,
+                                                        const int i_max,
+                                                        const int stride) {
+    constexpr int nwarps = mmq_get_nwarps_device();
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
+
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+    int   * x_qs = (int   *) x_tile;
+    float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_NVFP4, mmq_y);
+    int   * x_qs = (int   *) x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+
+    constexpr int threads_per_row = MMQ_ITER_K / QK_NVFP4;
+    constexpr int rows_per_warp = warp_size / threads_per_row;
+    const int kbx = threadIdx.x % threads_per_row;
+    const int row_in_warp = threadIdx.x / threads_per_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += rows_per_warp * nwarps) {
+        int i = i0 + threadIdx.y * rows_per_warp + row_in_warp;
+
+        if constexpr (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_nvfp4 * bxi = (const block_nvfp4 *) x + kb0 + i * stride + kbx;
+        const uint32_t * __restrict__ src_qs = reinterpret_cast<const uint32_t *>(bxi->qs);
+        const int kqs = 16 * kbx;
+        const int ksc = 4 * kbx;
+
+#pragma unroll
+        for (int sub = 0; sub < QK_NVFP4 / QK_NVFP4_SUB; ++sub) {
+            const int2 q0 = get_int_from_table_16(src_qs[2 * sub + 0], kvalues_mxfp4);
+            const int2 q1 = get_int_from_table_16(src_qs[2 * sub + 1], kvalues_mxfp4);
+
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+            x_qs[i * MMQ_MMA_TILE_X_K_NVFP4 + kqs + 4 * sub + 0] = q0.x;
+            x_qs[i * MMQ_MMA_TILE_X_K_NVFP4 + kqs + 4 * sub + 1] = q1.x;
+            x_qs[i * MMQ_MMA_TILE_X_K_NVFP4 + kqs + 4 * sub + 2] = q0.y;
+            x_qs[i * MMQ_MMA_TILE_X_K_NVFP4 + kqs + 4 * sub + 3] = q1.y;
+            x_df[i * MMQ_MMA_TILE_X_K_NVFP4 + ksc + sub] = ggml_cuda_ue4m3_to_fp32(bxi->d[sub]);
+#else
+            x_qs[i * (2 * MMQ_TILE_NE_K + 1) + kqs + 4 * sub + 0] = q0.x;
+            x_qs[i * (2 * MMQ_TILE_NE_K + 1) + kqs + 4 * sub + 1] = q1.x;
+            x_qs[i * (2 * MMQ_TILE_NE_K + 1) + kqs + 4 * sub + 2] = q0.y;
+            x_qs[i * (2 * MMQ_TILE_NE_K + 1) + kqs + 4 * sub + 3] = q1.y;
+            x_df[i * (2 * MMQ_TILE_NE_K * 2 / QI_NVFP4) + i / (QK_NVFP4_SUB / QI_NVFP4) + ksc + sub] = ggml_cuda_ue4m3_to_fp32(bxi->d[sub]);
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+        }
+    }
+}
+
 template <int mmq_x, int mmq_y>
 static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a(
     const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) {
@@ -1229,7 +1295,7 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
 #endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
 }
 
-// Used for Q3_K, IQ2_S, and IQ2_XS
+// Used for NVFP4, Q3_K, IQ2_S, and IQ2_XS
 template <int mmq_x, int mmq_y>
 static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_dp4a(
     const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) {
@@ -3261,6 +3327,14 @@ struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_MXFP4> {
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y>;
 };
 
+template <int mmq_x, int mmq_y, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_NVFP4> {
+    static constexpr int              vdr          = VDR_NVFP4_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_nvfp4<mmq_y, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_16_q8_1_mma<mmq_x, mmq_y>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_16_q8_1_dp4a<mmq_x, mmq_y>;
+};
+
 template <int mmq_x, int mmq_y, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_Q2_K> {
     static constexpr int              vdr          = VDR_Q2_K_Q8_1_MMQ;
@@ -4069,6 +4143,7 @@ extern DECL_MMQ_CASE(GGML_TYPE_Q5_0);
 extern DECL_MMQ_CASE(GGML_TYPE_Q5_1);
 extern DECL_MMQ_CASE(GGML_TYPE_Q8_0);
 extern DECL_MMQ_CASE(GGML_TYPE_MXFP4);
+extern DECL_MMQ_CASE(GGML_TYPE_NVFP4);
 extern DECL_MMQ_CASE(GGML_TYPE_Q2_K);
 extern DECL_MMQ_CASE(GGML_TYPE_Q3_K);
 extern DECL_MMQ_CASE(GGML_TYPE_Q4_K);

ggml/src/ggml-cuda/mmvq.cu

@@ -97,6 +97,197 @@ 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 (GGML_CUDA_CC_IS_NVIDIA(cc)) {
+        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);
+        }
+        return get_mmvq_mmid_max_batch_pascal_older(type);
+    }
+
+    // AMD
+    if (GGML_CUDA_CC_IS_AMD(cc)) {
+        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 +386,7 @@ 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 +413,13 @@ 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 +476,6 @@ 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 +529,6 @@ 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 +588,69 @@ 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 +663,7 @@ 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 +676,7 @@ 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 +686,33 @@ 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 +731,62 @@ 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 +794,24 @@ 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-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_8.cu

@@ -8,3 +8,4 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 1, 8);
 DECL_FATTN_MMA_F16_CASE(112, 112, 1, 8);
 DECL_FATTN_MMA_F16_CASE(128, 128, 1, 8);
 DECL_FATTN_MMA_F16_CASE(256, 256, 1, 8);
+DECL_FATTN_MMA_F16_CASE(512, 512, 1, 8);

ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_16-ncols2_4.cu

@@ -8,4 +8,5 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 16, 4);
 DECL_FATTN_MMA_F16_CASE(112, 112, 16, 4);
 DECL_FATTN_MMA_F16_CASE(128, 128, 16, 4);
 DECL_FATTN_MMA_F16_CASE(256, 256, 16, 4);
+DECL_FATTN_MMA_F16_CASE(512, 512, 16, 4);
 DECL_FATTN_MMA_F16_CASE(576, 512, 16, 4);

ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_4.cu

@@ -8,4 +8,5 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 2, 4);
 DECL_FATTN_MMA_F16_CASE(112, 112, 2, 4);
 DECL_FATTN_MMA_F16_CASE(128, 128, 2, 4);
 DECL_FATTN_MMA_F16_CASE(256, 256, 2, 4);
+DECL_FATTN_MMA_F16_CASE(512, 512, 2, 4);
 DECL_FATTN_MMA_F16_CASE(576, 512, 2, 4);

ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_8.cu

@@ -8,3 +8,4 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 2, 8);
 DECL_FATTN_MMA_F16_CASE(112, 112, 2, 8);
 DECL_FATTN_MMA_F16_CASE(128, 128, 2, 8);
 DECL_FATTN_MMA_F16_CASE(256, 256, 2, 8);
+DECL_FATTN_MMA_F16_CASE(512, 512, 2, 8);

ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_4.cu

@@ -8,4 +8,5 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 4, 4);
 DECL_FATTN_MMA_F16_CASE(112, 112, 4, 4);
 DECL_FATTN_MMA_F16_CASE(128, 128, 4, 4);
 DECL_FATTN_MMA_F16_CASE(256, 256, 4, 4);
+DECL_FATTN_MMA_F16_CASE(512, 512, 4, 4);
 DECL_FATTN_MMA_F16_CASE(576, 512, 4, 4);

ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_8.cu

@@ -8,3 +8,4 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 4, 8);
 DECL_FATTN_MMA_F16_CASE(112, 112, 4, 8);
 DECL_FATTN_MMA_F16_CASE(128, 128, 4, 8);
 DECL_FATTN_MMA_F16_CASE(256, 256, 4, 8);
+DECL_FATTN_MMA_F16_CASE(512, 512, 4, 8);

ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_8-ncols2_4.cu

@@ -8,4 +8,5 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 8, 4);
 DECL_FATTN_MMA_F16_CASE(112, 112, 8, 4);
 DECL_FATTN_MMA_F16_CASE(128, 128, 8, 4);
 DECL_FATTN_MMA_F16_CASE(256, 256, 8, 4);
+DECL_FATTN_MMA_F16_CASE(512, 512, 8, 4);
 DECL_FATTN_MMA_F16_CASE(576, 512, 8, 4);

ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_8-ncols2_8.cu

@@ -8,3 +8,4 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 8, 8);
 DECL_FATTN_MMA_F16_CASE(112, 112, 8, 8);
 DECL_FATTN_MMA_F16_CASE(128, 128, 8, 8);
 DECL_FATTN_MMA_F16_CASE(256, 256, 8, 8);
+DECL_FATTN_MMA_F16_CASE(512, 512, 8, 8);

ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq512-dv512.cu

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.cuh"
+
+DECL_FATTN_TILE_CASE(512, 512);

ggml/src/ggml-cuda/template-instances/generate_cu_files.py

@@ -3,7 +3,7 @@
 from glob import glob
 import os
 
-HEAD_SIZES_KQ = [40, 64, 72, 80, 96, 112, 128, 256, 576]
+HEAD_SIZES_KQ = [40, 64, 72, 80, 96, 112, 128, 256, 512, 576]
 
 TYPES_KV = ["GGML_TYPE_F16", "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0", "GGML_TYPE_BF16"]
 
@@ -35,7 +35,7 @@ TYPES_MMQ = [
     "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0",
     "GGML_TYPE_Q2_K", "GGML_TYPE_Q3_K", "GGML_TYPE_Q4_K", "GGML_TYPE_Q5_K", "GGML_TYPE_Q6_K",
     "GGML_TYPE_IQ2_XXS", "GGML_TYPE_IQ2_XS", "GGML_TYPE_IQ2_S", "GGML_TYPE_IQ3_XXS", "GGML_TYPE_IQ3_S",
-    "GGML_TYPE_IQ1_S", "GGML_TYPE_IQ4_NL", "GGML_TYPE_IQ4_XS", "GGML_TYPE_MXFP4"
+    "GGML_TYPE_IQ1_S", "GGML_TYPE_IQ4_NL", "GGML_TYPE_IQ4_XS", "GGML_TYPE_MXFP4", "GGML_TYPE_NVFP4"
 ]
 
 SOURCE_MMQ = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
@@ -83,6 +83,8 @@ for ncols in [8, 16, 32, 64]:
                     continue
                 if head_size_kq == 72:
                     continue
+                if head_size_kq == 512 and ncols2 not in (4, 8):
+                    continue
                 if head_size_kq != 576 and ncols2 in (16, 32):
                     continue
                 if head_size_kq == 576 and ncols2 not in (4, 16, 32):

ggml/src/ggml-cuda/template-instances/mmq-instance-nvfp4.cu

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_NVFP4);

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

@@ -2231,6 +2231,22 @@ static bool ggml_hexagon_supported_ssm_conv(const struct ggml_hexagon_session *
     return true;
 }
 
+static bool ggml_hexagon_supported_cumsum(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
+    const struct ggml_tensor * src0 = op->src[0];
+    const struct ggml_tensor * dst  = op;
+
+    if (src0->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
+        return false;
+    }
+
+    if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) {
+        return false;
+    }
+
+    GGML_UNUSED(sess);
+    return true;
+}
+
 enum dspqbuf_type {
     DSPQBUF_TYPE_DSP_WRITE_CPU_READ = 0,
     DSPQBUF_TYPE_CPU_WRITE_DSP_READ,
@@ -2399,6 +2415,16 @@ static inline size_t init_repeat_req(htp_general_req * req, dspqueue_buffer * bu
     return n_bufs;
 }
 
+static inline size_t init_cumsum_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
+    req->op = HTP_OP_CUMSUM;
+
+    size_t n_bufs = 0;
+    n_bufs += htp_req_buff_init(&req->src0, &bufs[n_bufs], t->src[0], DSPQBUF_TYPE_CPU_WRITE_DSP_READ);
+    n_bufs += htp_req_buff_init(&req->dst,  &bufs[n_bufs], t,         DSPQBUF_TYPE_DSP_WRITE_CPU_READ);
+
+    return n_bufs;
+}
+
 static inline size_t init_get_rows_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
     req->op = HTP_OP_GET_ROWS;
 
@@ -2780,6 +2806,10 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
                 ggml_hexagon_dispatch_op<init_ssm_conv_req>(sess, node, flags);
                 break;
 
+            case GGML_OP_CUMSUM:
+                ggml_hexagon_dispatch_op<init_cumsum_req>(sess, node, flags);
+                break;
+
             default:
                 GGML_ABORT("\nggml-hex: graph-compute %s is not supported\n", ggml_op_desc(node));
         }
@@ -3254,6 +3284,10 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
             supp = ggml_hexagon_supported_ssm_conv(sess, op);
             break;
 
+        case GGML_OP_CUMSUM:
+            supp = ggml_hexagon_supported_cumsum(sess, op);
+            break;
+
         default:
             break;
     }

ggml/src/ggml-hexagon/htp/CMakeLists.txt

@@ -33,6 +33,7 @@ add_library(${HTP_LIB} SHARED
     repeat-ops.c
     argsort-ops.c
     ssm-conv.c
+    cumsum-ops.c
 )
 
 target_compile_definitions(${HTP_LIB} PRIVATE

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

@@ -0,0 +1,267 @@
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wunused-function"
+#pragma clang diagnostic ignored "-Wunused-but-set-variable"
+
+#include <HAP_farf.h>
+#include <HAP_perf.h>
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "htp-ctx.h"
+#include "htp-ops.h"
+#include "hvx-types.h"
+#include "hvx-utils.h"
+#include "hex-dma.h"
+
+#define htp_cumsum_tensors_preamble                  \
+    struct htp_tensor * restrict src0 = &octx->src0; \
+    struct htp_tensor * restrict dst  = &octx->dst;  \
+                                                     \
+    const uint32_t ne00 = src0->ne[0];               \
+    const uint32_t ne01 = src0->ne[1];               \
+    const uint32_t ne02 = src0->ne[2];               \
+    const uint32_t ne03 = src0->ne[3];               \
+                                                     \
+    const uint32_t ne0 = dst->ne[0];                 \
+    const uint32_t ne1 = dst->ne[1];                 \
+    const uint32_t ne2 = dst->ne[2];                 \
+    const uint32_t ne3 = dst->ne[3];                 \
+                                                     \
+    const uint32_t nb00 = src0->nb[0];               \
+    const uint32_t nb01 = src0->nb[1];               \
+    const uint32_t nb02 = src0->nb[2];               \
+    const uint32_t nb03 = src0->nb[3];               \
+                                                     \
+    const uint32_t nb0 = dst->nb[0];                 \
+    const uint32_t nb1 = dst->nb[1];                 \
+    const uint32_t nb2 = dst->nb[2];                 \
+    const uint32_t nb3 = dst->nb[3];
+
+struct htp_cumsum_context {
+    struct htp_ops_context * octx;
+    size_t          src_row_size;
+    size_t          dst_row_size;
+    size_t          src_row_size_aligned;
+    size_t          dst_row_size_aligned;
+    uint32_t        rows_per_thread;
+    uint32_t        total_rows;
+};
+
+#define htp_cumsum_preamble                                                \
+    struct htp_cumsum_context * cctx = (struct htp_cumsum_context *) data; \
+    struct htp_ops_context *    octx = cctx->octx;                         \
+    htp_cumsum_tensors_preamble;                                           \
+    dma_queue * dma_queue = octx->ctx->dma[ith];
+
+// ---------------------------------------------------------------------------
+// HVX prefix scan helpers
+// ---------------------------------------------------------------------------
+
+#if __HVX_ARCH__ > 75
+static inline HVX_Vector hvx_cumsum_vadd(HVX_Vector a, HVX_Vector b) {
+    return Q6_Vsf_vadd_VsfVsf(a, b);
+}
+#else
+static inline HVX_Vector hvx_cumsum_vadd(HVX_Vector a, HVX_Vector b) {
+    return Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a, b));
+}
+#endif  // __HVX_ARCH__ > 75
+
+static inline HVX_Vector hvx_prefix_scan_f32(HVX_Vector v, HVX_Vector carry_in) {
+    const HVX_Vector zero = Q6_V_vsplat_R(0);
+
+    v = hvx_cumsum_vadd(v, Q6_V_vlalign_VVR(v, zero,  4));
+    v = hvx_cumsum_vadd(v, Q6_V_vlalign_VVR(v, zero,  8));
+    v = hvx_cumsum_vadd(v, Q6_V_vlalign_VVR(v, zero, 16));
+    v = hvx_cumsum_vadd(v, Q6_V_vlalign_VVR(v, zero, 32));
+    v = hvx_cumsum_vadd(v, Q6_V_vlalign_VVR(v, zero, 64));
+    v = hvx_cumsum_vadd(v, carry_in);
+
+    return v;
+}
+
+static inline HVX_Vector hvx_splat_last_f32(HVX_Vector v) {
+    return hvx_vec_repl4(Q6_V_vror_VR(v, 124));
+}
+
+static inline void hvx_cumsum_row_f32(const float * restrict src, float * restrict dst, uint32_t n) {
+    const uint32_t nvec = n / VLEN_FP32;
+    const uint32_t nloe = n % VLEN_FP32;
+
+    HVX_Vector carry = Q6_V_vsplat_R(0);
+
+    for (uint32_t i = 0; i < nvec; i++) {
+        HVX_Vector v = *((const HVX_UVector *) (src + i * VLEN_FP32));
+        v = hvx_prefix_scan_f32(v, carry);
+        hvx_vec_store_u(dst + i * VLEN_FP32, VLEN, v);
+        carry = hvx_splat_last_f32(v);
+    }
+
+    if (nloe) {
+        float acc = hvx_vec_get_f32(carry);
+        const float * src_tail = src + nvec * VLEN_FP32;
+        float       * dst_tail = dst + nvec * VLEN_FP32;
+        for (uint32_t i = 0; i < nloe; i++) {
+            acc        += src_tail[i];
+            dst_tail[i] = acc;
+        }
+    }
+}
+
+// ---------------------------------------------------------------------------
+// Per thread worker: Double-buffered DMA
+// ---------------------------------------------------------------------------
+
+static void cumsum_thread_f32_dma(unsigned int nth, unsigned int ith, void * data) {
+    htp_cumsum_preamble;
+
+    uint64_t t1, t2;
+    t1 = HAP_perf_get_qtimer_count();
+
+    const uint32_t ir0 = cctx->rows_per_thread * ith;
+    const uint32_t ir1 = MIN(ir0 + cctx->rows_per_thread, cctx->total_rows);
+
+    if (ir0 >= ir1) {
+        return;
+    }
+
+    const size_t src_row_size         = cctx->src_row_size;
+    const size_t dst_row_size         = cctx->dst_row_size;
+    const size_t src_row_size_aligned = cctx->src_row_size_aligned;
+    const size_t dst_row_size_aligned = cctx->dst_row_size_aligned;
+
+    const uint8_t * src_data = (const uint8_t *) src0->data;
+    uint8_t *       dst_data = (uint8_t *) dst->data;
+
+    uint8_t * src_spad = octx->src0_spad.data + (ith * src_row_size_aligned * 2);
+    uint8_t * dst_spad = octx->dst_spad.data  + (ith * dst_row_size_aligned * 2);
+
+    for (uint32_t ir = ir0, spad_idx = 0; ir < ir1 && spad_idx < 2; ir++, spad_idx++) {
+        // Dummy dst writeback to establish queue ordering
+        dma_queue_push_vtcm_to_ddr(dma_queue,
+                                   dma_make_ptr(dst_data, dst_spad + (spad_idx * dst_row_size_aligned)),
+                                   dst_row_size, dst_row_size_aligned, 0);
+
+        dma_queue_push_ddr_to_vtcm(dma_queue,
+                                   dma_make_ptr(src_spad + (spad_idx * src_row_size_aligned),
+                                                src_data + (ir * src_row_size)),
+                                   src_row_size_aligned, src_row_size, 1);
+    }
+
+    for (uint32_t ir = ir0; ir < ir1; ir++) {
+        float * dst_spad_row = (float *) dma_queue_pop(dma_queue).src;
+        float * src_spad_row = (float *) dma_queue_pop(dma_queue).dst;
+
+        hvx_cumsum_row_f32(src_spad_row, dst_spad_row, ne00);
+
+        dma_queue_push_vtcm_to_ddr(dma_queue,
+                                   dma_make_ptr(dst_data + (ir * dst_row_size), (uint8_t *) dst_spad_row),
+                                   dst_row_size, dst_row_size_aligned, 1);
+
+        const uint32_t next_row = ir + 2;
+        if (next_row < ir1) {
+            dma_queue_push_ddr_to_vtcm(dma_queue,
+                                       dma_make_ptr((uint8_t *) src_spad_row, src_data + (next_row * src_row_size)),
+                                       src_row_size_aligned, src_row_size, 1);
+        }
+    }
+
+    dma_queue_flush(dma_queue);
+    t2 = HAP_perf_get_qtimer_count();
+
+    FARF(HIGH, "cumsum-f32-dma %d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n",
+         ith, nth, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], ir0, ir1,
+         dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
+         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+}
+
+// ---------------------------------------------------------------------------
+// Per thread worker: Direct HVX (no DMA)
+// ---------------------------------------------------------------------------
+
+static void cumsum_thread_f32(unsigned int nth, unsigned int ith, void * data) {
+    htp_cumsum_preamble;
+
+    uint64_t t1, t2;
+    t1 = HAP_perf_get_qtimer_count();
+
+    const uint8_t * src_data = (const uint8_t *) src0->data;
+    uint8_t *       dst_data = (uint8_t *) dst->data;
+
+    const uint32_t ir0 = cctx->rows_per_thread * ith;
+    const uint32_t ir1 = MIN(ir0 + cctx->rows_per_thread, cctx->total_rows);
+
+    for (uint32_t ir = ir0; ir < ir1; ir++) {
+        const float * restrict src_row = (const float *) (src_data + ir * cctx->src_row_size);
+        float       * restrict dst_row = (float *)       (dst_data + ir * cctx->dst_row_size);
+        hvx_cumsum_row_f32(src_row, dst_row, ne00);
+    }
+
+    t2 = HAP_perf_get_qtimer_count();
+
+    FARF(HIGH, "cumsum-f32 %d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n",
+         ith, nth, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], ir0, ir1,
+         dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
+         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+}
+
+int op_cumsum_f32(struct htp_ops_context * octx) {
+    const struct htp_tensor * src0 = &octx->src0;
+    const struct htp_tensor * dst  = &octx->dst;
+
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
+        return HTP_STATUS_OK;
+    }
+
+    const uint32_t total_rows = src0->ne[1] * src0->ne[2] * src0->ne[3];
+    const uint32_t n_threads  = MIN(octx->n_threads, total_rows);
+
+    const size_t src_row_size         = src0->nb[1];
+    const size_t dst_row_size         = dst->nb[1];
+    const size_t src_row_size_aligned = hex_round_up(src_row_size, VLEN);
+    const size_t dst_row_size_aligned = hex_round_up(dst_row_size, VLEN);
+
+    // 2 ping-pong buffers per thread for src and dst
+    const size_t spad_per_thread = 2 * (src_row_size_aligned + dst_row_size_aligned);
+
+    octx->src0_spad.size_per_thread = src_row_size_aligned * 2;
+    octx->dst_spad.size_per_thread  = dst_row_size_aligned * 2;
+    octx->src0_spad.size            = n_threads * octx->src0_spad.size_per_thread;
+    octx->dst_spad.size             = n_threads * octx->dst_spad.size_per_thread;
+    octx->src0_spad.data            = octx->ctx->vtcm_base;
+    octx->dst_spad.data             = octx->src0_spad.data + octx->src0_spad.size;
+
+    struct htp_cumsum_context cctx = {
+        .octx                 = octx,
+        .src_row_size         = src_row_size,
+        .dst_row_size         = dst_row_size,
+        .src_row_size_aligned = src_row_size_aligned,
+        .dst_row_size_aligned = dst_row_size_aligned,
+        .rows_per_thread      = (total_rows + n_threads - 1) / n_threads,
+        .total_rows           = total_rows,
+    };
+
+    if (octx->ctx->vtcm_size < spad_per_thread * n_threads) {
+        worker_pool_run_func(octx->ctx->worker_pool, cumsum_thread_f32, &cctx, n_threads);
+    } else {
+        worker_pool_run_func(octx->ctx->worker_pool, cumsum_thread_f32_dma, &cctx, n_threads);
+    }
+
+    return HTP_STATUS_OK;
+}
+
+int op_cumsum(struct htp_ops_context * octx) {
+    int                 err = HTP_STATUS_OK;
+    struct htp_tensor * dst = &octx->dst;
+
+    switch (dst->type) {
+        case HTP_TYPE_F32:
+            err = op_cumsum_f32(octx);
+            break;
+        default:
+            err = HTP_STATUS_NO_SUPPORT;
+            break;
+    }
+
+    return err;
+}

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/htp-msg.h

@@ -75,6 +75,7 @@ enum htp_op {
     HTP_OP_SUM_ROWS,
     HTP_OP_SSM_CONV,
     HTP_OP_REPEAT,
+    HTP_OP_CUMSUM,
     INVALID
 };
 

ggml/src/ggml-hexagon/htp/htp-ops.h

@@ -60,5 +60,6 @@ int op_cpy(struct htp_ops_context * octx);
 int op_repeat(struct htp_ops_context * octx);
 int op_argsort(struct htp_ops_context * octx);
 int op_ssm_conv(struct htp_ops_context * octx);
+int op_cumsum(struct htp_ops_context * octx);
 
 #endif /* HTP_OPS_H */

ggml/src/ggml-hexagon/htp/hvx-div.h

@@ -16,8 +16,10 @@
 
 #if __HVX_ARCH__ < 79
 #define HVX_OP_MUL_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
+#define HVX_OP_MUL_F16(a, b) Q6_Vhf_equals_Wqf32(Q6_Wqf32_vmpy_VhfVhf(a, b))
 #else
 #define HVX_OP_MUL_F32(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
+#define HVX_OP_MUL_F16(a, b) Q6_Vhf_vmpy_VhfVhf(a, b)
 #endif
 
 // Compute div by scaler in f32. Requires first by expanding fp32 to fp16 and converting the result back to fp32.
@@ -43,46 +45,67 @@ static inline HVX_Vector hvx_div_mul_f16_const_using_f32(HVX_Vector vec1_hf, HVX
     return res;
 }
 
-#define hvx_div_scaler_f16_loop_body(dst_type, src_type, vec_store)                     \
-    do {                                                                                \
-        dst_type * restrict vdst = (dst_type *) dst;                                    \
-        src_type * restrict vsrc = (src_type *) src;                                    \
-        HVX_Vector hf_one = Q6_Vh_vsplat_R(0x3C00);                                     \
-                                                                                        \
-        const uint32_t nvec = n / VLEN_FP16;                                            \
-        const uint32_t nloe = n % VLEN_FP16;                                            \
-                                                                                        \
-        uint32_t i = 0;                                                                 \
-                                                                                        \
-        _Pragma("unroll(4)")                                                            \
-        for (; i < nvec; i++) {                                                         \
-            HVX_Vector res = hvx_div_mul_f16_const_using_f32(vsrc[i], val_vec_f32, hf_one); \
-            vdst[i] = res;                                                              \
-        }                                                                               \
-        if (nloe) {                                                                     \
-            HVX_Vector res = hvx_div_mul_f16_const_using_f32(vsrc[i], val_vec_f32, hf_one); \
-            vec_store((void *) &vdst[i], nloe * SIZEOF_FP16, res);                      \
-        }                                                                               \
+// Variant for <v79: Use pre-computed f16 reciprocal constant
+static inline HVX_Vector hvx_div_mul_f16_const_using_f16(HVX_Vector vec1_hf, HVX_Vector const_inv_hf) {
+    // Multiply by pre-computed f16 reciprocal constant
+    return HVX_OP_MUL_F16(vec1_hf, const_inv_hf);
+}
+
+#define hvx_div_scaler_f16_loop_body(dst_type, src_type, vec_store)                                    \
+    do {                                                                                               \
+        dst_type * restrict vdst = (dst_type *) dst;                                                   \
+        src_type * restrict vsrc = (src_type *) src;                                                   \
+                                                                                                       \
+        HVX_Vector hf_one = Q6_Vh_vsplat_R(0x3C00);                                                    \
+                                                                                                       \
+        const uint32_t nvec = n / VLEN_FP16;                                                           \
+        const uint32_t nloe = n % VLEN_FP16;                                                           \
+                                                                                                       \
+        uint32_t i = 0;                                                                                \
+                                                                                                       \
+        _Pragma("unroll(4)")                                                                           \
+        for (; i < nvec; i++) {                                                                        \
+            HVX_Vector res;                                                                            \
+            if (__HVX_ARCH__ < 79) {                                                                   \
+                res = hvx_div_mul_f16_const_using_f16(vsrc[i], val_vec_f16);                           \
+            } else {                                                                                   \
+                res = hvx_div_mul_f16_const_using_f32(vsrc[i], val_vec_f32, hf_one);                   \
+            }                                                                                          \
+            vdst[i] = res;                                                                             \
+        }                                                                                              \
+        if (nloe) {                                                                                    \
+            HVX_Vector res;                                                                            \
+            if (__HVX_ARCH__ < 79) {                                                                   \
+                res = hvx_div_mul_f16_const_using_f16(vsrc[i], val_vec_f16);                           \
+            } else {                                                                                   \
+                res = hvx_div_mul_f16_const_using_f32(vsrc[i], val_vec_f32, hf_one);                   \
+            }                                                                                          \
+            vec_store((void *) &vdst[i], nloe * SIZEOF_FP16, res);                                     \
+        }                                                                                              \
     } while(0)
 
 static inline void hvx_div_scalar_f16_aa(uint8_t * restrict dst, const uint8_t * restrict src, const _Float16 val, uint32_t n) {
     const HVX_Vector val_vec_f32 = hvx_vec_splat_f32(1.0f/((float)val));
+    const HVX_Vector val_vec_f16 = hvx_vec_splat_f16(1.0f / val);
     assert((uintptr_t) dst % 128 == 0);
     assert((uintptr_t) src % 128 == 0);
     hvx_div_scaler_f16_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
 }
 static inline void hvx_div_scalar_f16_au(uint8_t * restrict dst, const uint8_t * restrict src, const _Float16 val, uint32_t n) {
     const HVX_Vector val_vec_f32 = hvx_vec_splat_f32(1.0f/((float)val));
+    const HVX_Vector val_vec_f16 = hvx_vec_splat_f16(1.0f / val);
     assert((uintptr_t) dst % 128 == 0);
     hvx_div_scaler_f16_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a);
 }
 static inline void hvx_div_scalar_f16_ua(uint8_t * restrict dst, const uint8_t * restrict src, const _Float16 val, uint32_t n) {
     const HVX_Vector val_vec_f32 = hvx_vec_splat_f32(1.0f/((float)val));
+    const HVX_Vector val_vec_f16 = hvx_vec_splat_f16(1.0f / val);
     assert((uintptr_t) src % 128 == 0);
     hvx_div_scaler_f16_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
 }
 static inline void hvx_div_scalar_f16_uu(uint8_t * restrict dst, const uint8_t * restrict src, const _Float16 val, uint32_t n) {
     const HVX_Vector val_vec_f32 = hvx_vec_splat_f32(1.0f/((float)val));
+    const HVX_Vector val_vec_f16 = hvx_vec_splat_f16(1.0f / val);
     hvx_div_scaler_f16_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
 }
 
@@ -128,13 +151,25 @@ static inline HVX_Vector hvx_vec_div_f16_using_f32(HVX_Vector vec1, HVX_Vector v
     return recip;
 }
 
+// Hybrid approach: f16 reciprocal for <v79, f32 precision for >=v79
+static inline HVX_Vector hvx_vec_hybrid_div_f16(HVX_Vector vec1, HVX_Vector vec2, HVX_Vector f32_nan_inf_mask, HVX_Vector f16_nan_inf_mask, HVX_Vector vec_hf_one_1_0) {
+#if __HVX_ARCH__ < 79
+    // For older architectures, use f16 reciprocal to avoid NaN/-inf issues
+    HVX_Vector vec2_inv = hvx_vec_inverse_f16_guard(vec2, f16_nan_inf_mask);
+    return HVX_OP_MUL_F16(vec1, vec2_inv);
+#else
+    return hvx_vec_div_f16_using_f32(vec1, vec2, f32_nan_inf_mask, vec_hf_one_1_0);
+#endif
+}
+
 #define hvx_div_f16_loop_body(dst_type, src0_type, src1_type, vec_store)                  \
     do {                                                                                  \
         dst_type * restrict vdst = (dst_type *) dst;                                      \
         src0_type * restrict vsrc0 = (src0_type *) src0;                                  \
         src1_type * restrict vsrc1 = (src1_type *) src1;                                  \
                                                                                           \
-        const HVX_Vector nan_inf_mask = Q6_V_vsplat_R(0x7f800000);                        \
+        const HVX_Vector f32_nan_inf_mask = Q6_V_vsplat_R(0x7f800000);                    \
+        const HVX_Vector f16_nan_inf_mask = Q6_Vh_vsplat_R(0x7c00);                       \
         const HVX_Vector hf_one = Q6_Vh_vsplat_R(0x3C00);                                 \
                                                                                           \
         const uint32_t nvec = n / VLEN_FP16;                                              \
@@ -144,11 +179,15 @@ static inline HVX_Vector hvx_vec_div_f16_using_f32(HVX_Vector vec1, HVX_Vector v
                                                                                           \
         _Pragma("unroll(4)")                                                              \
         for (; i < nvec; i++) {                                                           \
-            HVX_Vector res = hvx_vec_div_f16_using_f32(vsrc0[i], vsrc1[i], nan_inf_mask, hf_one); \
+            HVX_Vector res = hvx_vec_hybrid_div_f16(vsrc0[i], vsrc1[i],                   \
+                                                    f32_nan_inf_mask, f16_nan_inf_mask,   \
+                                                    hf_one);                              \
             vdst[i] = res;                                                                \
         }                                                                                 \
         if (nloe) {                                                                       \
-            HVX_Vector res = hvx_vec_div_f16_using_f32(vsrc0[i], vsrc1[i], nan_inf_mask, hf_one); \
+            HVX_Vector res = hvx_vec_hybrid_div_f16(vsrc0[i], vsrc1[i],                   \
+                                                    f32_nan_inf_mask, f16_nan_inf_mask,   \
+                                                    hf_one);                              \
             vec_store((void *) &vdst[i], nloe * SIZEOF_FP16, res);                        \
         }                                                                                 \
     } while(0)
@@ -247,5 +286,6 @@ HVX_DIV_DISPATCHER(hvx_div_f32)
 HVX_DIV_DISPATCHER(hvx_div_f16)
 
 #undef HVX_OP_MUL_F32
+#undef HVX_OP_MUL_F16
 
 #endif // HVX_DIV_H

ggml/src/ggml-hexagon/htp/main.c

@@ -860,6 +860,41 @@ static void proc_ssm_conv_req(struct htp_context * ctx, struct htp_general_req *
     send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
 }
 
+static void proc_cumsum_req(struct htp_context * ctx, struct htp_general_req * req, struct dspqueue_buffer * bufs) {
+    struct dspqueue_buffer rsp_bufs[1];
+
+    // We've written to the output buffer, we'd also need to flush it
+    rsp_bufs[0].fd     = bufs[1].fd;
+    rsp_bufs[0].ptr    = bufs[1].ptr;
+    rsp_bufs[0].offset = bufs[1].offset;
+    rsp_bufs[0].size   = bufs[1].size;
+    rsp_bufs[0].flags  = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER |         // Flush HTP
+                         DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT);  // Invalidate CPU
+
+    // Setup Op context
+    struct htp_ops_context octx = { 0 };
+    octx.ctx       = ctx;
+    octx.src0      = req->src0;
+    octx.dst       = req->dst;
+    octx.flags     = req->flags;
+    octx.op        = req->op;
+    octx.src0.data = (uint32_t) bufs[0].ptr;
+    octx.dst.data  = (uint32_t) bufs[1].ptr;
+    octx.n_threads = ctx->n_threads;
+
+    struct profile_data prof;
+    profile_start(&prof);
+
+    uint32_t rsp_status = HTP_STATUS_INTERNAL_ERR;
+    if (vtcm_acquire(ctx) == AEE_SUCCESS) {
+        rsp_status = op_cumsum(&octx);
+        vtcm_release(ctx);
+    }
+
+    profile_stop(&prof);
+    send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
+}
+
 static void proc_activations_req(struct htp_context *     ctx,
                                  struct htp_general_req * req,
                                  struct dspqueue_buffer * bufs,
@@ -1474,6 +1509,14 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
                 proc_ssm_conv_req(ctx, &req, bufs);
                 break;
 
+            case HTP_OP_CUMSUM:
+                if (n_bufs != 2) {
+                    FARF(ERROR, "Bad cumsum-req buffer list");
+                    continue;
+                }
+                proc_cumsum_req(ctx, &req, bufs);
+                break;
+
             default:
                 FARF(ERROR, "Unknown Op %u", req.op);
                 break;

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-hexagon/htp/unary-ops.c

@@ -67,34 +67,61 @@ static void hvx_fast_rms_norm_f32(const uint8_t * restrict src,
                                   uint8_t * restrict pad,
                                   const int num_elems,
                                   float     epsilon) {
+    (void)pad;
+
     const HVX_Vector * restrict v_src = (HVX_Vector *) src;
     HVX_Vector * restrict v_dst       = (HVX_Vector *) dst;
 
-    HVX_Vector sum_v     = Q6_V_vsplat_R(0x00000000);
+    const int nvec = num_elems / VLEN_FP32;    // number of full vectors
+    const int nloe = num_elems % VLEN_FP32;    // leftover elements
+
+    // Compute sum of squares for full vectors
+    HVX_Vector sum_v = Q6_V_vsplat_R(0x00000000);
     HVX_Vector epsilon_v = hvx_vec_splat_f32(epsilon);
 
-    int step_of_1 = num_elems >> 5;
     #pragma unroll(4)
-    for (int i = 0; i < step_of_1; i++) {
+    for (int i = 0; i < nvec; i++) {
         HVX_Vector v1 = v_src[i];
         HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, v1);
-        sum_v         = Q6_Vqf32_vadd_Vqf32Vqf32(sum_v, v2);
+        sum_v = Q6_Vqf32_vadd_Vqf32Vqf32(sum_v, v2);
     }
 
-    sum_v = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(sum_v)); // replicated over all lanes
+    // Handle tail elements using vectorized ops with masking
+    if (nloe > 0) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+        HVX_Vector v1 = Q6_V_vand_QV(bmask, v_src[nvec]);
+        HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, v1);
+        sum_v = Q6_Vqf32_vadd_Vqf32Vqf32(sum_v, v2);
+    }
+
+    // Reduce HVX sum
+    sum_v = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(sum_v));
 
     HVX_Vector t_v            = hvx_vec_splat_f32((float) num_elems);
     HVX_Vector denom_v        = hvx_vec_inverse_f32(t_v);
     HVX_Vector mean_v         = Q6_Vqf32_vmpy_VsfVsf(sum_v, denom_v);
     HVX_Vector mean_epsilon_v = Q6_Vqf32_vadd_Vqf32Vsf(mean_v, epsilon_v);
 
+    // Scale full vectors
     HVX_Vector scale_v = hvx_vec_rsqrt_f32(Q6_Vsf_equals_Vqf32(mean_epsilon_v));
 
     #pragma unroll(4)
-    for (int i = 0; i < step_of_1; i++) {
+    for (int i = 0; i < nvec; i++) {
         HVX_Vector v1 = v_src[i];
         HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, scale_v);
-        v_dst[i]      = Q6_Vsf_equals_Vqf32(v2);
+        v_dst[i] = Q6_Vsf_equals_Vqf32(v2);
+    }
+
+    // Handle tail elements using vectorized ops with masking
+    if (nloe > 0) {
+
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+        HVX_Vector v1 = Q6_V_vand_QV(bmask, v_src[nvec]);
+        HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, scale_v);
+        HVX_Vector result = Q6_Vsf_equals_Vqf32(v2);
+
+        // Store with masking to avoid overwriting memory beyond the tensor
+        hvx_vec_store_a(&v_dst[nvec], nloe * 4, result);
     }
 }
 

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