ggml-zendnn : add Q8_0 quantization support (#23414)

* ggml-zendnn : add Q8_0 quantization support

* ggml-zendnn : sync with latest ZenDNN

* ggml-zendnn : address review comments for Q8_0

.devops/cann.Dockerfile

@@ -5,6 +5,9 @@
 # Define the CANN base image for easier version updates later
 ARG CHIP_TYPE=910b
 ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.5.0-${CHIP_TYPE}-openeuler24.03-py3.11
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
 
 # ==============================================================================
 # BUILD STAGE
@@ -55,6 +58,7 @@ RUN mkdir -p /app/lib && \
 RUN mkdir -p /app/full && \
     cp build/bin/* /app/full/ && \
     cp *.py /app/full/ && \
+    cp -r conversion /app/full/ && \
     cp -r gguf-py /app/full/ && \
     cp -r requirements /app/full/ && \
     cp requirements.txt /app/full/
@@ -67,6 +71,19 @@ RUN mkdir -p /app/full && \
 # ==============================================================================
 FROM ${CANN_BASE_IMAGE} AS base
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
+ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama.cpp" \
+      org.opencontainers.image.description="LLM inference in C/C++" \
+      org.opencontainers.image.url=$IMAGE_URL \
+      org.opencontainers.image.source=$IMAGE_SOURCE
+
 # -- Install runtime dependencies --
 RUN yum install -y libgomp curl && \
     yum clean all && \

.devops/cpu.Dockerfile

@@ -1,4 +1,7 @@
 ARG UBUNTU_VERSION=24.04
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
 
 FROM ubuntu:$UBUNTU_VERSION AS build
 
@@ -27,6 +30,7 @@ RUN mkdir -p /app/lib && \
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \
     && cp *.py /app/full \
+    && cp -r conversion /app/full \
     && cp -r gguf-py /app/full \
     && cp -r requirements /app/full \
     && cp requirements.txt /app/full \
@@ -35,6 +39,19 @@ RUN mkdir -p /app/full \
 ## Base image
 FROM ubuntu:$UBUNTU_VERSION AS base
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
+ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama.cpp" \
+      org.opencontainers.image.description="LLM inference in C/C++" \
+      org.opencontainers.image.url=$IMAGE_URL \
+      org.opencontainers.image.source=$IMAGE_SOURCE
+
 RUN apt-get update \
     && apt-get install -y libgomp1 curl \
     && apt autoremove -y \

.devops/cuda.Dockerfile

@@ -6,6 +6,10 @@ ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VER
 
 ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+
 FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 
 # CUDA architecture to build for (defaults to all supported archs)
@@ -32,6 +36,7 @@ RUN mkdir -p /app/lib && \
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \
     && cp *.py /app/full \
+    && cp -r conversion /app/full \
     && cp -r gguf-py /app/full \
     && cp -r requirements /app/full \
     && cp requirements.txt /app/full \
@@ -40,6 +45,19 @@ RUN mkdir -p /app/full \
 ## Base image
 FROM ${BASE_CUDA_RUN_CONTAINER} AS base
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
+ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama.cpp" \
+      org.opencontainers.image.description="LLM inference in C/C++" \
+      org.opencontainers.image.url=$IMAGE_URL \
+      org.opencontainers.image.source=$IMAGE_SOURCE
+
 RUN apt-get update \
     && apt-get install -y libgomp1 curl \
     && apt autoremove -y \

.devops/intel.Dockerfile

@@ -1,4 +1,7 @@
 ARG ONEAPI_VERSION=2025.3.3-0-devel-ubuntu24.04
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
 
 ## Build Image
 
@@ -33,6 +36,7 @@ RUN mkdir -p /app/lib && \
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \
     && cp *.py /app/full \
+    && cp -r conversion /app/full \
     && cp -r gguf-py /app/full \
     && cp -r requirements /app/full \
     && cp requirements.txt /app/full \
@@ -40,6 +44,19 @@ RUN mkdir -p /app/full \
 
 FROM intel/deep-learning-essentials:$ONEAPI_VERSION AS base
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
+ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama.cpp" \
+      org.opencontainers.image.description="LLM inference in C/C++" \
+      org.opencontainers.image.url=$IMAGE_URL \
+      org.opencontainers.image.source=$IMAGE_SOURCE
+
 ARG IGC_VERSION=v2.20.5
 ARG IGC_VERSION_FULL=2_2.20.5+19972
 ARG COMPUTE_RUNTIME_VERSION=25.40.35563.10

.devops/llama-cli-cann.Dockerfile

@@ -1,4 +1,7 @@
 ARG ASCEND_VERSION=8.5.0-910b-openeuler22.03-py3.10
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
 
 FROM ascendai/cann:$ASCEND_VERSION AS build
 
@@ -28,6 +31,20 @@ RUN echo "Building with static libs" && \
 
 # TODO: use image with NNRT
 FROM ascendai/cann:$ASCEND_VERSION AS runtime
+
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
+ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama.cpp" \
+      org.opencontainers.image.description="LLM inference in C/C++" \
+      org.opencontainers.image.url=$IMAGE_URL \
+      org.opencontainers.image.source=$IMAGE_SOURCE
+
 COPY --from=build /app/build/bin/llama-cli /app/build/bin/llama-completion /
 
 ENV LC_ALL=C.utf8

.devops/musa.Dockerfile

@@ -6,6 +6,10 @@ ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_V
 
 ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+
 FROM ${BASE_MUSA_DEV_CONTAINER} AS build
 
 # MUSA architecture to build for (defaults to all supported archs)
@@ -37,6 +41,7 @@ RUN mkdir -p /app/lib && \
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \
     && cp *.py /app/full \
+    && cp -r conversion /app/full \
     && cp -r gguf-py /app/full \
     && cp -r requirements /app/full \
     && cp requirements.txt /app/full \
@@ -45,6 +50,19 @@ RUN mkdir -p /app/full \
 ## Base image
 FROM ${BASE_MUSA_RUN_CONTAINER} AS base
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
+ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama.cpp" \
+      org.opencontainers.image.description="LLM inference in C/C++" \
+      org.opencontainers.image.url=$IMAGE_URL \
+      org.opencontainers.image.source=$IMAGE_SOURCE
+
 RUN apt-get update \
     && apt-get install -y libgomp1 curl \
     && apt autoremove -y \

.devops/openvino.Dockerfile

@@ -18,6 +18,10 @@ ARG LIBZE1_VERSION=1.27.0-1~24.04~ppa2
 ARG http_proxy=
 ARG https_proxy=
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+
 ## Build Image
 FROM ubuntu:${UBUNTU_VERSION} AS build
 
@@ -77,6 +81,7 @@ RUN mkdir -p /app/lib && \
 RUN mkdir -p /app/full \
     && cp build/ReleaseOV/bin/* /app/full/ \
     && cp *.py /app/full \
+    && cp -r conversion /app/full \
     && cp -r gguf-py /app/full \
     && cp -r requirements /app/full \
     && cp requirements.txt /app/full \
@@ -88,6 +93,18 @@ FROM ubuntu:${UBUNTU_VERSION} AS base
 # Pass proxy args to runtime stage
 ARG http_proxy
 ARG https_proxy
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
+ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama.cpp" \
+      org.opencontainers.image.description="LLM inference in C/C++" \
+      org.opencontainers.image.url=$IMAGE_URL \
+      org.opencontainers.image.source=$IMAGE_SOURCE
 
 RUN apt-get update \
     && apt-get install -y libgomp1 libtbb12 curl wget ocl-icd-libopencl1 \

.devops/rocm.Dockerfile

@@ -7,6 +7,10 @@ ARG AMDGPU_VERSION=7.2.1
 # Target the ROCm build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+
 ### Build image
 FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 
@@ -49,6 +53,7 @@ RUN mkdir -p /app/lib \
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \
     && cp *.py /app/full \
+    && cp -r conversion /app/full \
     && cp -r gguf-py /app/full \
     && cp -r requirements /app/full \
     && cp requirements.txt /app/full \
@@ -57,6 +62,19 @@ RUN mkdir -p /app/full \
 ## Base image
 FROM ${BASE_ROCM_DEV_CONTAINER} AS base
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
+ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama.cpp" \
+      org.opencontainers.image.description="LLM inference in C/C++" \
+      org.opencontainers.image.url=$IMAGE_URL \
+      org.opencontainers.image.source=$IMAGE_SOURCE
+
 RUN apt-get update \
     && apt-get install -y libgomp1 curl \
     && apt autoremove -y \

.devops/s390x.Dockerfile

@@ -1,5 +1,8 @@
 ARG GCC_VERSION=15.2.0
 ARG UBUNTU_VERSION=24.04
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
 
 ### Build Llama.cpp stage
 FROM gcc:${GCC_VERSION} AS build
@@ -34,6 +37,7 @@ RUN --mount=type=cache,target=/root/.ccache \
 
 COPY *.py             /opt/llama.cpp/bin
 COPY .devops/tools.sh /opt/llama.cpp/bin
+COPY conversion       /opt/llama.cpp/conversion
 
 COPY gguf-py          /opt/llama.cpp/gguf-py
 COPY requirements.txt /opt/llama.cpp/gguf-py
@@ -44,14 +48,28 @@ COPY requirements     /opt/llama.cpp/gguf-py/requirements
 FROM scratch AS collector
 
 # Copy llama.cpp binaries and libraries
-COPY --from=build /opt/llama.cpp/bin     /llama.cpp/bin
-COPY --from=build /opt/llama.cpp/lib     /llama.cpp/lib
-COPY --from=build /opt/llama.cpp/gguf-py /llama.cpp/gguf-py
+COPY --from=build /opt/llama.cpp/bin        /llama.cpp/bin
+COPY --from=build /opt/llama.cpp/lib        /llama.cpp/lib
+COPY --from=build /opt/llama.cpp/gguf-py    /llama.cpp/gguf-py
+COPY --from=build /opt/llama.cpp/conversion /llama.cpp/conversion
 
 
 ### Base image
 FROM ubuntu:${UBUNTU_VERSION} AS base
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
+ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama.cpp" \
+      org.opencontainers.image.description="LLM inference in C/C++" \
+      org.opencontainers.image.url=$IMAGE_URL \
+      org.opencontainers.image.source=$IMAGE_SOURCE
+
 RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
     --mount=type=cache,target=/var/lib/apt/lists,sharing=locked \
     apt update -y && \
@@ -91,6 +109,7 @@ RUN curl https://sh.rustup.rs -sSf | bash -s -- -y
 
 COPY --from=collector /llama.cpp/bin /app
 COPY --from=collector /llama.cpp/gguf-py /app/gguf-py
+COPY --from=collector /llama.cpp/conversion /app/conversion
 
 RUN pip install --no-cache-dir --break-system-packages \
         -r /app/gguf-py/requirements.txt

.devops/vulkan.Dockerfile

@@ -1,4 +1,7 @@
 ARG UBUNTU_VERSION=26.04
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
 
 FROM ubuntu:$UBUNTU_VERSION AS build
 
@@ -23,6 +26,7 @@ RUN mkdir -p /app/lib && \
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \
     && cp *.py /app/full \
+    && cp -r conversion /app/full \
     && cp -r gguf-py /app/full \
     && cp -r requirements /app/full \
     && cp requirements.txt /app/full \
@@ -31,6 +35,19 @@ RUN mkdir -p /app/full \
 ## Base image
 FROM ubuntu:$UBUNTU_VERSION AS base
 
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
+ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama.cpp" \
+      org.opencontainers.image.description="LLM inference in C/C++" \
+      org.opencontainers.image.url=$IMAGE_URL \
+      org.opencontainers.image.source=$IMAGE_SOURCE
+
 RUN apt-get update \
     && apt-get install -y libgomp1 curl libvulkan1 mesa-vulkan-drivers \
     libglvnd0 libgl1 libglx0 libegl1 libgles2 \

.editorconfig

@@ -45,7 +45,7 @@ insert_final_newline = unset
 trim_trailing_whitespace = unset
 insert_final_newline = unset
 
-[tools/server/webui/**]
+[tools/ui/**]
 indent_style = unset
 indent_size = unset
 end_of_line = unset

.github/ISSUE_TEMPLATE/011-bug-results.yml

@@ -100,8 +100,8 @@ body:
       label: Relevant log output
       description: >
           Please copy and paste any relevant log output, including the command that you entered and any generated text.
-          For very long logs (thousands of lines), preferably upload them as files instead.
-          On Linux you can redirect console output into a file by appending ` > llama.log 2>&1` to your command.
+          For very long logs (thousands of lines), please upload them as files instead; the `--log-file` CLI argument can be used for this purpose.
+          On Linux you can alternatively redirect the console output of any command into a file by appending ` > llama.log 2>&1` to your command.
       value: |
         <details>
         <summary>Logs</summary>

.github/ISSUE_TEMPLATE/019-bug-misc.yml

@@ -88,8 +88,8 @@ body:
       description: >
           If applicable, please copy and paste any relevant log output, including any generated text.
           If you are encountering problems specifically with the `llama_params_fit` module, always upload `--verbose` logs as well.
-          For very long logs (thousands of lines), please upload them as files instead.
-          On Linux you can redirect console output into a file by appending ` > llama.log 2>&1` to your command.
+          For very long logs (thousands of lines), please upload them as files instead; the `--log-file` CLI argument can be used for this purpose.
+          On Linux you can alternatively redirect the console output of any command into a file by appending ` > llama.log 2>&1` to your command.
       value: |
         <details>
         <summary>Logs</summary>

.github/labeler.yml

@@ -73,10 +73,10 @@ android:
     - changed-files:
         - any-glob-to-any-file:
             - examples/llama.android/**
-server/webui:
+server/ui:
     - changed-files:
         - any-glob-to-any-file:
-            - tools/server/webui/**
+            - tools/ui/**
 server:
     - changed-files:
         - any-glob-to-any-file:

.github/workflows/build-and-test-snapdragon.yml

@@ -31,7 +31,7 @@ jobs:
   android-ndk-snapdragon:
     runs-on: ubuntu-latest
     container:
-      image: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.3'
+      image: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.6'
     defaults:
       run:
         shell: bash
@@ -61,7 +61,7 @@ jobs:
   linux-iot-snapdragon:
     runs-on: ubuntu-latest
     container:
-      image: 'ghcr.io/snapdragon-toolchain/arm64-linux:v0.1'
+      image: 'ghcr.io/snapdragon-toolchain/arm64-linux:v0.6'
     defaults:
       run:
         shell: bash

.github/workflows/build-apple.yml

@@ -59,6 +59,7 @@ jobs:
           cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DLLAMA_BUILD_APP=OFF \
             -DLLAMA_BUILD_COMMON=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
@@ -89,6 +90,7 @@ jobs:
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_BUILD_APP=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -138,6 +140,7 @@ jobs:
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_COMMON=OFF \
+            -DLLAMA_BUILD_APP=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -163,6 +166,7 @@ jobs:
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_COMMON=OFF \
+            -DLLAMA_BUILD_APP=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -206,6 +210,7 @@ jobs:
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_BUILD_APP=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \

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

@@ -19,9 +19,14 @@ jobs:
       - name: Build
         run: |
           PREFIX="$(pwd)"/inst
-          cmake -S . -B build -DCMAKE_PREFIX_PATH="$PREFIX" \
-                -DLLAMA_OPENSSL=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=OFF \
-                -DLLAMA_BUILD_EXAMPLES=OFF -DCMAKE_BUILD_TYPE=Release
+          cmake -S . -B build \
+                -DCMAKE_PREFIX_PATH="$PREFIX" \
+                -DLLAMA_OPENSSL=OFF \
+                -DLLAMA_BUILD_TESTS=OFF \
+                -DLLAMA_BUILD_TOOLS=OFF \
+                -DLLAMA_BUILD_EXAMPLES=OFF \
+                -DLLAMA_BUILD_APP=OFF \
+                -DCMAKE_BUILD_TYPE=Release
           cmake --build build --config Release
           cmake --install build --prefix "$PREFIX" --config Release
 

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

@@ -68,6 +68,8 @@ jobs:
       - name: Determine tag name
         id: tag
         uses: ./.github/actions/get-tag-name
+        env:
+          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
 
   ggml-ci-nvidia-cuda:
     needs: determine-tag
@@ -81,7 +83,7 @@ jobs:
       - name: Test
         id: ggml-ci
         env:
-          HF_WEBUI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           nvidia-smi
           GG_BUILD_CUDA=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
@@ -98,7 +100,7 @@ jobs:
       - name: Test
         id: ggml-ci
         env:
-          HF_WEBUI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           vulkaninfo --summary
           GG_BUILD_VULKAN=1 GGML_VK_DISABLE_COOPMAT2=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
@@ -115,7 +117,7 @@ jobs:
       - name: Test
         id: ggml-ci
         env:
-          HF_WEBUI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           vulkaninfo --summary
           GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
@@ -205,7 +207,7 @@ jobs:
       - name: Test
         id: ggml-ci
         env:
-          HF_WEBUI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
 
@@ -234,7 +236,7 @@ jobs:
       - name: Test
         id: ggml-ci
         env:
-          HF_WEBUI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
             bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
@@ -251,7 +253,7 @@ jobs:
       - name: Test
         id: ggml-ci
         env:
-          HF_WEBUI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           vulkaninfo --summary
           GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
@@ -270,7 +272,7 @@ jobs:
       - name: Test
         id: ggml-ci
         env:
-          HF_WEBUI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           vulkaninfo --summary
           GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
@@ -291,7 +293,7 @@ jobs:
           MSYSTEM: UCRT64
           CHERE_INVOKING: 1
           PATH: C:\msys64\ucrt64\bin;C:\msys64\usr\bin;C:\Windows\System32;${{ env.PATH }}
-          HF_WEBUI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           vulkaninfo --summary
           # Skip python related tests with GG_BUILD_LOW_PERF=1 since Windows MSYS2 UCRT64 currently fails to create
@@ -332,7 +334,7 @@ jobs:
       - name: Test
         id: ggml-ci
         env:
-          HF_WEBUI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           source ./openvino_toolkit/setupvars.sh
           GG_BUILD_OPENVINO=1 GGML_OPENVINO_DEVICE=GPU GG_BUILD_LOW_PERF=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

.github/workflows/docker.yml

@@ -11,6 +11,11 @@ name: Publish Docker image
 
 on:
   workflow_dispatch: # allows manual triggering
+    inputs:
+      skip_s390x:
+        description: "Skip the s390x build target (useful for fast test runs that do not need the IBM Z runner)"
+        type: boolean
+        default: false
   schedule:
     # Rebuild daily rather than on every push because it is expensive
     - cron: '12 4 * * *'
@@ -64,6 +69,8 @@ jobs:
       - name: Generate build and merge matrices
         id: matrices
         shell: bash
+        env:
+          SKIP_S390X: ${{ inputs.skip_s390x || 'false' }}
         run: |
           set -euo pipefail
 
@@ -86,6 +93,11 @@ jobs:
           ]
           JSON
 
+          if [ "${SKIP_S390X}" = "true" ]; then
+            jq 'map(select(.platforms != "linux/s390x"))' build-matrix.json > build-matrix.json.tmp
+            mv build-matrix.json.tmp build-matrix.json
+          fi
+
           BUILD_MATRIX="$(jq -c . build-matrix.json)"
           MERGE_MATRIX="$(jq -c '
             reduce .[] as $entry ({}; .[$entry.tag] |= (
@@ -132,6 +144,7 @@ jobs:
         config: ${{ fromJSON(needs.prepare_matrices.outputs.build_matrix) }}
     steps:
       - name: Check out the repo
+        id: checkout
         uses: actions/checkout@v6
         with:
           fetch-depth: 0
@@ -187,6 +200,10 @@ jobs:
         env:
           GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
 
+      - name: Get build date
+        id: build_date
+        run: echo "date=$(date -u +"%Y-%m-%dT%H:%M:%SZ")" >> $GITHUB_OUTPUT
+
       - name: Free Disk Space (Ubuntu)
         if: ${{ matrix.config.free_disk_space == true }}
         uses: ggml-org/free-disk-space@v1.3.1
@@ -211,13 +228,26 @@ jobs:
         with:
           context: .
           platforms: ${{ matrix.config.platforms }}
-          outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true
+          outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true,oci-mediatypes=true
           file: ${{ matrix.config.dockerfile }}
           target: full
           provenance: false
           build-args: |
+            BUILD_DATE=${{ steps.build_date.outputs.date }}
+            APP_VERSION=${{ needs.create_tag.outputs.source_tag }}
+            APP_REVISION=${{ steps.checkout.outputs.commit }}
+            IMAGE_URL=${{ github.server_url }}/${{ github.repository }}
+            IMAGE_SOURCE=${{ github.server_url }}/${{ github.repository }}
             ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
             ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
+          annotations: |
+            manifest:org.opencontainers.image.created=${{ steps.build_date.outputs.date }}
+            manifest:org.opencontainers.image.version=${{ needs.create_tag.outputs.source_tag }}
+            manifest:org.opencontainers.image.revision=${{ steps.checkout.outputs.commit }}
+            manifest:org.opencontainers.image.title=llama.cpp
+            manifest:org.opencontainers.image.description=LLM inference in C/C++
+            manifest:org.opencontainers.image.url=${{ github.server_url }}/${{ github.repository }}
+            manifest:org.opencontainers.image.source=${{ github.server_url }}/${{ github.repository }}
           # using github experimental cache
           #cache-from: type=gha
           #cache-to: type=gha,mode=max
@@ -235,13 +265,26 @@ jobs:
         with:
           context: .
           platforms: ${{ matrix.config.platforms }}
-          outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true
+          outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true,oci-mediatypes=true
           file: ${{ matrix.config.dockerfile }}
           target: light
           provenance: false
           build-args: |
+            BUILD_DATE=${{ steps.build_date.outputs.date }}
+            APP_VERSION=${{ needs.create_tag.outputs.source_tag }}
+            APP_REVISION=${{ steps.checkout.outputs.commit }}
+            IMAGE_URL=${{ github.server_url }}/${{ github.repository }}
+            IMAGE_SOURCE=${{ github.server_url }}/${{ github.repository }}
             ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
             ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
+          annotations: |
+            manifest:org.opencontainers.image.created=${{ steps.build_date.outputs.date }}
+            manifest:org.opencontainers.image.version=${{ needs.create_tag.outputs.source_tag }}
+            manifest:org.opencontainers.image.revision=${{ steps.checkout.outputs.commit }}
+            manifest:org.opencontainers.image.title=llama.cpp
+            manifest:org.opencontainers.image.description=LLM inference in C/C++
+            manifest:org.opencontainers.image.url=${{ github.server_url }}/${{ github.repository }}
+            manifest:org.opencontainers.image.source=${{ github.server_url }}/${{ github.repository }}
           # using github experimental cache
           #cache-from: type=gha
           #cache-to: type=gha,mode=max
@@ -259,13 +302,26 @@ jobs:
         with:
           context: .
           platforms: ${{ matrix.config.platforms }}
-          outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true
+          outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true,oci-mediatypes=true
           file: ${{ matrix.config.dockerfile }}
           target: server
           provenance: false
           build-args: |
+            BUILD_DATE=${{ steps.build_date.outputs.date }}
+            APP_VERSION=${{ needs.create_tag.outputs.source_tag }}
+            APP_REVISION=${{ steps.checkout.outputs.commit }}
+            IMAGE_URL=${{ github.server_url }}/${{ github.repository }}
+            IMAGE_SOURCE=${{ github.server_url }}/${{ github.repository }}
             ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
             ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
+          annotations: |
+            manifest:org.opencontainers.image.created=${{ steps.build_date.outputs.date }}
+            manifest:org.opencontainers.image.version=${{ needs.create_tag.outputs.source_tag }}
+            manifest:org.opencontainers.image.revision=${{ steps.checkout.outputs.commit }}
+            manifest:org.opencontainers.image.title=llama.cpp
+            manifest:org.opencontainers.image.description=LLM inference in C/C++
+            manifest:org.opencontainers.image.url=${{ github.server_url }}/${{ github.repository }}
+            manifest:org.opencontainers.image.source=${{ github.server_url }}/${{ github.repository }}
           # using github experimental cache
           #cache-from: type=gha
           #cache-to: type=gha,mode=max
@@ -330,10 +386,15 @@ jobs:
 
     steps:
       - name: Check out the repo
+        id: checkout
         uses: actions/checkout@v6
         with:
           fetch-depth: 0
 
+      - name: Get build date
+        id: build_date
+        run: echo "date=$(date -u +"%Y-%m-%dT%H:%M:%SZ")" >> $GITHUB_OUTPUT
+
       - name: Download digest metadata
         uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8
         with:
@@ -361,6 +422,8 @@ jobs:
           IMAGE_REPO="ghcr.io/${REPO_OWNER}/${REPO_NAME}"
           PREFIX="${IMAGE_REPO}:"
           SRC_TAG="${{ needs.create_tag.outputs.source_tag }}"
+          BUILD_DATE="${{ steps.build_date.outputs.date }}"
+          COMMIT_SHA="${{ steps.checkout.outputs.commit }}"
           TAGS="${{ matrix.config.tag }}"
           ARCHES="${{ matrix.config.arches }}"
           DIGEST_GLOB="/tmp/digests/*.tsv"
@@ -412,11 +475,21 @@ jobs:
                   refs+=("${IMAGE_REPO}@${digest}")
               done
 
+              local annotations=(
+                  --annotation "index:org.opencontainers.image.created=${BUILD_DATE}"
+                  --annotation "index:org.opencontainers.image.version=${SRC_TAG}"
+                  --annotation "index:org.opencontainers.image.revision=${COMMIT_SHA}"
+                  --annotation "index:org.opencontainers.image.title=llama.cpp"
+                  --annotation "index:org.opencontainers.image.description=LLM inference in C/C++"
+                  --annotation "index:org.opencontainers.image.url=${{ github.server_url }}/${{ github.repository }}"
+                  --annotation "index:org.opencontainers.image.source=${{ github.server_url }}/${{ github.repository }}"
+              )
+
               echo "Creating ${merged_tag} from ${refs[*]}"
-              docker buildx imagetools create --tag "${merged_tag}" "${refs[@]}"
+              docker buildx imagetools create "${annotations[@]}" --tag "${merged_tag}" "${refs[@]}"
 
               echo "Creating ${merged_versioned_tag} from ${refs[*]}"
-              docker buildx imagetools create --tag "${merged_versioned_tag}" "${refs[@]}"
+              docker buildx imagetools create "${annotations[@]}" --tag "${merged_versioned_tag}" "${refs[@]}"
           }
 
           for tag in $TAGS; do

.github/workflows/release.yml

@@ -36,13 +36,8 @@ env:
   CMAKE_ARGS: "-DLLAMA_BUILD_EXAMPLES=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=ON -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON"
 
 jobs:
-  webui-build:
-    name: Build WebUI
-    uses: ./.github/workflows/webui-build.yml
 
   macOS-cpu:
-    needs:
-      - webui-build
 
     strategy:
       matrix:
@@ -71,11 +66,12 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
@@ -104,7 +100,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-macos-${{ matrix.build }}.tar.gz -s ",^.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-macos-${{ matrix.build }}.tar.gz -s ",^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -113,8 +109,6 @@ jobs:
           name: llama-bin-macos-${{ matrix.build }}.tar.gz
 
   ubuntu-cpu:
-    needs:
-      - webui-build
 
     strategy:
       matrix:
@@ -135,11 +129,12 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: ccache
         if: ${{ matrix.build != 's390x' }}
@@ -182,7 +177,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz --transform "s,^.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -191,8 +186,6 @@ jobs:
           name: llama-bin-ubuntu-${{ matrix.build }}.tar.gz
 
   ubuntu-vulkan:
-    needs:
-      - webui-build
 
     strategy:
       matrix:
@@ -211,11 +204,12 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
@@ -259,7 +253,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz --transform "s,^.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
+          tar -czvf llama-${{ 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
@@ -268,8 +262,6 @@ jobs:
           name: llama-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz
 
   android-arm64:
-    needs:
-      - webui-build
 
     runs-on: ubuntu-latest
 
@@ -283,11 +275,12 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
@@ -337,7 +330,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-android-arm64.tar.gz --transform "s,^.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-android-arm64.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -346,8 +339,6 @@ jobs:
           name: llama-bin-android-arm64.tar.gz
 
   ubuntu-24-openvino:
-    needs:
-      - webui-build
 
     runs-on: ubuntu-24.04
 
@@ -370,11 +361,12 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
@@ -426,7 +418,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/ReleaseOV/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz --transform "s,^.,llama-${{ steps.tag.outputs.name }}," -C ./build/ReleaseOV/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/ReleaseOV/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -435,8 +427,6 @@ jobs:
           name: llama-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz
 
   windows-cpu:
-    needs:
-      - webui-build
 
     runs-on: windows-2025
 
@@ -452,11 +442,12 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
@@ -496,8 +487,6 @@ jobs:
           name: llama-bin-win-cpu-${{ matrix.arch }}.zip
 
   windows:
-    needs:
-      - webui-build
 
     runs-on: windows-2025
 
@@ -522,11 +511,12 @@ jobs:
         id: checkout
         uses: actions/checkout@v6
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
@@ -587,8 +577,6 @@ jobs:
           name: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip
 
   windows-cuda:
-    needs:
-      - webui-build
 
     runs-on: windows-2022
 
@@ -601,11 +589,12 @@ jobs:
         id: checkout
         uses: actions/checkout@v6
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: Install ccache
         uses: ggml-org/ccache-action@v1.2.21
@@ -667,8 +656,6 @@ jobs:
           name: cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip
 
   windows-sycl:
-    needs:
-      - webui-build
 
     runs-on: windows-2022
 
@@ -708,11 +695,12 @@ jobs:
           Expand-Archive -Path "level-zero-win-sdk.zip" -DestinationPath "C:/level-zero-sdk" -Force
           "LEVEL_ZERO_V1_SDK_PATH=C:/level-zero-sdk" | Out-File -FilePath $env:GITHUB_ENV -Append
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
@@ -781,8 +769,6 @@ jobs:
           name: llama-bin-win-sycl-x64.zip
 
   ubuntu-24-sycl:
-    needs:
-      - webui-build
 
     strategy:
       matrix:
@@ -831,11 +817,12 @@ jobs:
           wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero-devel_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero-devel.deb
           sudo apt-get install -y ./level-zero.deb ./level-zero-devel.deb
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
@@ -867,7 +854,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz --transform "s,^.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -876,8 +863,6 @@ jobs:
           name: llama-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz
 
   ubuntu-22-rocm:
-    needs:
-      - webui-build
 
     runs-on: ubuntu-22.04
 
@@ -895,11 +880,12 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: Free up disk space
         uses: ggml-org/free-disk-space@v1.3.1
@@ -979,7 +965,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz --transform "s,^.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -988,8 +974,6 @@ jobs:
           name: llama-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
 
   windows-hip:
-    needs:
-      - webui-build
 
     runs-on: windows-2022
 
@@ -1007,11 +991,12 @@ jobs:
         id: checkout
         uses: actions/checkout@v6
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: Grab rocWMMA package
         id: grab_rocwmma
@@ -1123,6 +1108,7 @@ jobs:
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_BUILD_APP=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -1240,7 +1226,7 @@ jobs:
       - name: Pack artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz --transform "s,^.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -1259,7 +1245,6 @@ jobs:
     runs-on: ubuntu-slim
 
     needs:
-      - webui-build
       - windows
       - windows-cpu
       - windows-cuda
@@ -1404,14 +1389,14 @@ jobs:
               }
             }
 
-  webui-publish:
+  ui-publish:
     if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
 
     needs:
       - release
 
-    uses: ./.github/workflows/webui-publish.yml
+    uses: ./.github/workflows/ui-publish.yml
     with:
       version_tag: ${{ needs.release.outputs.tag_name }}
     secrets:
-      hf_token: ${{ secrets.HF_TOKEN_WEBUI_STATIC_OUTPUT }}
+      hf_token: ${{ secrets.HF_TOKEN_UI_STATIC_OUTPUT }}

.github/workflows/server-sanitize.yml

@@ -67,6 +67,13 @@ jobs:
           fetch-depth: 0
           ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: Build
         id: cmake_build
         run: |

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

@@ -39,12 +39,7 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  webui-build:
-    name: Build WebUI
-    uses: ./.github/workflows/webui-build.yml
-
   server-metal:
-    needs: webui-build
     runs-on: [self-hosted, llama-server, macOS, ARM64]
 
     name: server-metal (${{ matrix.wf_name }})
@@ -72,11 +67,12 @@ jobs:
           fetch-depth: 0
           ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: Build
         id: cmake_build
@@ -134,3 +130,68 @@ jobs:
   #          pip install -r requirements.txt
   #          export ${{ matrix.extra_args }}
   #          pytest -v -x -m "not slow"
+
+  server-kleidiai:
+    runs-on: ah-ubuntu_22_04-c8g_8x
+
+    name: server-kleidiai (${{ matrix.wf_name }})
+    strategy:
+      matrix:
+        include:
+          - build_type: Release
+            extra_build_flags: "-DGGML_CPU_KLEIDIAI=ON"
+            extra_args: ""
+            wf_name:    "CPUx1, kleidiai"
+      fail-fast: false
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Dependencies
+        id: depends
+        run: |
+          set -euxo pipefail
+          sudo apt-get update
+          sudo DEBIAN_FRONTEND=noninteractive NEEDRESTART_MODE=a \
+          apt-get install -y \
+           build-essential \
+           libssl-dev \
+           python3-venv \
+           gpg \
+           wget \
+           time \
+           git-lfs
+
+          git lfs install
+
+          # install the latest cmake
+          sudo install -d /usr/share/keyrings
+          wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc \
+           | gpg --dearmor \
+           | sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null
+          echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ jammy main' \
+           | sudo tee /etc/apt/sources.list.d/kitware.list
+          sudo apt-get update
+          sudo apt-get install -y cmake
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cmake -B build -DGGML_SCHED_NO_REALLOC=ON ${{ matrix.extra_build_flags }}
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
+
+      - name: Tests
+        id: server_integration_tests
+        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
+        run: |
+          cd tools/server/tests
+          python3 -m venv venv
+          source venv/bin/activate
+          pip install -r requirements.txt
+          export ${{ matrix.extra_args }}
+          pytest -v -x -m "not slow"

.github/workflows/server.yml

@@ -54,12 +54,7 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  webui-build:
-    name: Build WebUI
-    uses: ./.github/workflows/webui-build.yml
-
   server:
-    needs: webui-build
     runs-on: ubuntu-latest
 
     name: server (${{ matrix.wf_name }})
@@ -98,11 +93,12 @@ jobs:
           fetch-depth: 0
           ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: Build
         id: cmake_build
@@ -136,7 +132,6 @@ jobs:
           SLOW_TESTS=1 pytest -v -x
 
   server-windows:
-    needs: webui-build
     runs-on: windows-2022
 
     steps:
@@ -147,11 +142,10 @@ jobs:
           fetch-depth: 0
           ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
-      - name: Download WebUI build artifact
-        uses: actions/download-artifact@v7
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          node-version: "24"
 
       - name: Build
         id: cmake_build

.github/workflows/ui-build.yml

@@ -1,11 +1,11 @@
-name: Build WebUI
+name: UI Build
 
 on:
   workflow_call:
 
 jobs:
   build:
-    name: Build WebUI
+    name: Build static output
     runs-on: ubuntu-slim
     env:
       BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
@@ -19,26 +19,26 @@ jobs:
         with:
           node-version: "24"
           cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: Install dependencies
         run: npm ci
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Build application
         run: npm run build
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Generate checksums
         run: |
-          cd tools/server/public
+          cd build/tools/ui/dist
           for f in *; do
             sha256sum "$f" | awk '{print $1, $2}' >> checksums.txt
           done
 
-      - name: Upload built webui
+      - name: Upload built UI
         uses: actions/upload-artifact@v6
         with:
-          name: webui-build
-          path: tools/server/public/
+          name: ui-build
+          path: build/tools/ui/dist/
           retention-days: 1

.github/workflows/ui-ci.yml

@@ -1,4 +1,4 @@
-name: Server WebUI
+name: CI (UI)
 
 on:
   workflow_dispatch:
@@ -11,15 +11,15 @@ on:
     branches:
       - master
     paths: [
-      '.github/workflows/server-webui.yml',
-      'tools/server/webui/**.*',
+      '.github/workflows/ui-ci.yml',
+      'tools/ui/**.*',
       'tools/server/tests/**.*'
     ]
   pull_request:
     types: [opened, synchronize, reopened]
     paths: [
-      '.github/workflows/server-webui.yml',
-      'tools/server/webui/**.*',
+      '.github/workflows/ui-ci.yml',
+      'tools/ui/**.*',
       'tools/server/tests/**.*'
     ]
 
@@ -34,14 +34,14 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  webui-build:
-    name: Build WebUI
-    uses: ./.github/workflows/webui-build.yml
+  ui-build:
+    name: Build static output
+    uses: ./.github/workflows/ui-build.yml
 
-  webui-checks:
-    name: WebUI Checks
-    needs: webui-build
-    runs-on: ubuntu-24.04-arm
+  ui-checks:
+    name: UI Checks
+    needs: ui-build
+    runs-on: ubuntu-latest
     continue-on-error: true
     steps:
       - name: Checkout code
@@ -56,44 +56,44 @@ jobs:
         with:
           node-version: "24"
           cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: Install dependencies
         id: setup
         if: ${{ steps.node.conclusion == 'success' }}
         run: npm ci
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Run type checking
         if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npm run check
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Run linting
         if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npm run lint
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Install Playwright browsers
         id: playwright
         if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npx playwright install --with-deps
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Run Client tests
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:client
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Run Unit tests
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:unit
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
   e2e-tests:
     name: E2E Tests
-    needs: webui-build
-    runs-on: ubuntu-24.04-arm
+    needs: ui-build
+    runs-on: ubuntu-latest
     steps:
       - name: Checkout code
         uses: actions/checkout@v6
@@ -107,36 +107,36 @@ jobs:
         with:
           node-version: "24"
           cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: Install dependencies
         id: setup
         if: ${{ steps.node.conclusion == 'success' }}
         run: npm ci
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Build application
         if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npm run build
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Install Playwright browsers
         id: playwright
         if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npx playwright install --with-deps
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Build Storybook
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run build-storybook
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Run UI tests
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:ui -- --testTimeout=60000
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Run E2E tests
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:e2e
-        working-directory: tools/server/webui
+        working-directory: tools/ui

.github/workflows/ui-publish.yml

@@ -1,4 +1,4 @@
-name: WebUI Publish
+name: UI Publish
 
 on:
   workflow_call:
@@ -13,15 +13,20 @@ on:
         required: true
 
 jobs:
+  build:
+    name: Build static output
+    uses: ./.github/workflows/ui-build.yml
+
   publish:
-    name: Publish WebUI Static Output
+    name: Publish UI Static Output
+    needs: build
     runs-on: ubuntu-24.04-arm
 
     permissions:
       contents: read
 
     env:
-      HF_BUCKET_NAME: ${{ vars.HF_BUCKET_WEBUI_STATIC_OUTPUT }}
+      HF_BUCKET_NAME: ${{ vars.HF_BUCKET_UI_STATIC_OUTPUT }}
 
     steps:
       - name: Checkout code
@@ -29,11 +34,11 @@ jobs:
         with:
           fetch-depth: 1
 
-      - name: Download WebUI build artifact
+      - name: Download UI build artifact
         uses: actions/download-artifact@v7
         with:
-          name: webui-build
-          path: tools/server/public/
+          name: ui-build
+          path: build/tools/ui/dist/
 
       - name: Install Hugging Face Hub CLI
         run: pip install -U huggingface_hub
@@ -44,12 +49,12 @@ jobs:
       - name: Sync built files to Hugging Face bucket (version tag)
         run: |
           # Upload the built files to the Hugging Face bucket under the release version
-          hf buckets sync tools/server/public hf://buckets/ggml-org/${{ env.HF_BUCKET_NAME }}/${{ inputs.version_tag }} --delete --quiet
+          hf buckets sync build/tools/ui/dist hf://buckets/ggml-org/${{ env.HF_BUCKET_NAME }}/${{ inputs.version_tag }} --delete --quiet
 
       - name: Sync built files to Hugging Face bucket (latest)
         run: |
           # Also upload to the 'latest' directory for fallback downloads
-          hf buckets sync tools/server/public hf://buckets/ggml-org/${{ env.HF_BUCKET_NAME }}/latest --delete --quiet
+          hf buckets sync build/tools/ui/dist hf://buckets/ggml-org/${{ env.HF_BUCKET_NAME }}/latest --delete --quiet
 
       - name: Verify upload
         run: |

.gitignore

@@ -54,7 +54,6 @@
 /tmp/
 /autogen-*.md
 /common/build-info.cpp
-/tools/server/public
 
 # Deprecated
 
@@ -93,10 +92,12 @@
 !/examples/sycl/*.bat
 !/examples/sycl/*.sh
 
-# Server Web UI temporary files
+# Server Web UI temporary files (+ legacy directory)
 
 /tools/server/webui/node_modules
 /tools/server/webui/dist
+/tools/ui/node_modules
+/tools/ui/dist
 
 # Python
 

.pi/gg/SYSTEM.md

@@ -1,7 +1,7 @@
 You are a coding agent. Here are some very important rules that you must follow:
 
 General:
-- By very precise and concise when writing code, comments, explanations, etc.
+- Be very precise and concise when writing code, comments, explanations, etc.
 - PR and commit titles format: `<module> : <title>`. Lookup recents for examples
 - Don't try to build or run the code unless you are explicitly asked to do so
 - Use the `gh` CLI tool when querying PRs, issues, or other GitHub resources
@@ -16,12 +16,15 @@ Pull requests (PRs):
 - New branch names are prefixed with "gg/"
 - Before opening a pull request, ask the user to confirm the description
 - When creating a pull request, look for the repository's PR template and follow it
-- For the AI usage disclosure section, write "YES. llama.cpp + pi"
+- For the AI usage disclosure section, write "YES. llama.cpp + pi + [MODEL]"
+- Ask the user to tell you what model was used and write it in place of [MODEL]
 - Always create the pull requests in draft mode
 
 Commits:
 - On every commit that you make, include a "Assisted-by: llama.cpp:local pi" tag
 - Do not explicitly set the git author in commits - rely on the default git config
+- Always use `--no-gpg-sign` when committing
+- Never `git push` without explicit confirmation from the user
 
 Resources (read on demand):
 - [CONTRIBUTING.md](CONTRIBUTING.md)

CMakeLists.txt

@@ -104,14 +104,26 @@ option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
 option(LLAMA_BUILD_COMMON "llama: build common utils library" ${LLAMA_STANDALONE})
 
 # extra artifacts
-option(LLAMA_BUILD_TESTS            "llama: build tests"                                                                            ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_TOOLS            "llama: build tools"                                                                            ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_EXAMPLES         "llama: build examples"                                                                         ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_SERVER           "llama: build server example"                                                                   ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_WEBUI            "llama: build the embedded Web UI for server"                                                   ON)
-option(LLAMA_USE_PREBUILT_WEBUI     "llama: use prebuilt WebUI from HF Bucket when available (requires LLAMA_BUILD_WEBUI=ON)"       ON)
-option(LLAMA_TOOLS_INSTALL          "llama: install tools"                                                                          ${LLAMA_TOOLS_INSTALL_DEFAULT})
-option(LLAMA_TESTS_INSTALL          "llama: install tests"                                                                          ON)
+option(LLAMA_BUILD_TESTS     "llama: build tests"                                                                ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_TOOLS     "llama: build tools"                                                                ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_EXAMPLES  "llama: build examples"                                                             ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_SERVER    "llama: build server example"                                                       ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_APP       "llama: build the unified binary"                                                   ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_UI        "llama: build the embedded Web UI for server"                                       ON)
+option(LLAMA_USE_PREBUILT_UI "llama: use prebuilt UI from HF Bucket when available (requires LLAMA_BUILD_UI=ON)" ON)
+
+# Backward compat: when old var is set but new one isn't, forward the value
+if(DEFINED LLAMA_BUILD_WEBUI)
+    set(LLAMA_BUILD_UI ${LLAMA_BUILD_WEBUI})
+    message(DEPRECATION "LLAMA_BUILD_WEBUI is deprecated, use LLAMA_BUILD_UI instead")
+endif()
+if(DEFINED LLAMA_USE_PREBUILT_WEBUI)
+    set(LLAMA_USE_PREBUILT_UI ${LLAMA_USE_PREBUILT_WEBUI})
+    message(DEPRECATION "LLAMA_USE_PREBUILT_WEBUI is deprecated, use LLAMA_USE_PREBUILT_UI instead")
+endif()
+
+option(LLAMA_TOOLS_INSTALL "llama: install tools" ${LLAMA_TOOLS_INSTALL_DEFAULT})
+option(LLAMA_TESTS_INSTALL "llama: install tests" ON)
 
 # 3rd party libs
 option(LLAMA_OPENSSL    "llama: use openssl to support HTTPS" ON)
@@ -216,6 +228,10 @@ if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TOOLS)
     add_subdirectory(tools)
 endif()
 
+if (LLAMA_BUILD_APP)
+    add_subdirectory(app)
+endif()
+
 # Automatically add all files from the 'licenses' directory
 file(GLOB EXTRA_LICENSES "${CMAKE_SOURCE_DIR}/licenses/LICENSE-*")
 
@@ -271,18 +287,6 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake
               ${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake
         DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama)
 
-install(
-    FILES convert_hf_to_gguf.py
-    PERMISSIONS
-        OWNER_READ
-        OWNER_WRITE
-        OWNER_EXECUTE
-        GROUP_READ
-        GROUP_EXECUTE
-        WORLD_READ
-        WORLD_EXECUTE
-    DESTINATION ${CMAKE_INSTALL_BINDIR})
-
 configure_file(cmake/llama.pc.in
         "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
         @ONLY)

CODEOWNERS

@@ -15,7 +15,7 @@
 # ggml-org/llama-common     : ggerganov, aldehir, angt, danbev, ngxson, pwilkin
 # ggml-org/llama-mtmd       : ngxson
 # ggml-org/llama-server     : ggerganov, ngxson, allozaur, angt, ServeurpersoCom
-# ggml-org/llama-webui      : allozaur
+# ggml-org/llama-ui           : allozaur
 
 /.devops/*.Dockerfile                   @ngxson
 /.github/actions/                       @ggml-org/ci
@@ -26,6 +26,7 @@
 /common/fit.*                           @JohannesGaessler
 /common/jinja/                          @CISC
 /common/ngram-map.*                     @srogmann
+/conversion/                            @CISC
 /convert_*.py                           @CISC
 /docs/backend/snapdragon/               @ggml-org/ggml-hexagon
 /examples/batched.swift/                @ggerganov
@@ -48,7 +49,6 @@
 /examples/parallel/                     @ggerganov
 /examples/passkey/                      @ggerganov
 /examples/retrieval/                    @ggerganov
-/examples/save-load-state/              @ggerganov
 /examples/speculative-simple/           @ggerganov
 /examples/speculative/                  @ggerganov
 /ggml/cmake/                            @ggerganov
@@ -107,7 +107,7 @@
 /tools/rpc/                             @ggml-org/ggml-rpc
 /tools/server/*                         @ggml-org/llama-server # no subdir
 /tools/server/tests/                    @ggml-org/llama-server
-/tools/server/webui/                    @ggml-org/llama-webui
+/tools/ui/                              @ggml-org/llama-ui
 /tools/tokenize/                        @ggerganov
 /tools/tts/                             @ggerganov
 /vendor/                                @ggerganov

README.md

@@ -27,6 +27,7 @@ LLM inference in C/C++
 - Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
 - Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggml-org/llama.cpp/discussions/9669
 - Hugging Face GGUF editor: [discussion](https://github.com/ggml-org/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor)
+- WebGPU support is now available in the browser, see a blog/demo introducing it [here](https://reeselevine.github.io/llamas-on-the-web/).
 
 ----
 
@@ -280,7 +281,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [Metal](docs/build.md#metal-build) | Apple Silicon |
 | [BLAS](docs/build.md#blas-build) | All |
 | [BLIS](docs/backend/BLIS.md) | All |
-| [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU |
+| [SYCL](docs/backend/SYCL.md) | Intel GPU |
 | [OpenVINO [In Progress]](docs/backend/OPENVINO.md) | Intel CPUs, GPUs, and NPUs |
 | [MUSA](docs/build.md#musa) | Moore Threads GPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
@@ -290,7 +291,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [CANN](docs/build.md#cann) | Ascend NPU |
 | [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
 | [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE |
-| [WebGPU [In Progress]](docs/build.md#webgpu) | All |
+| [WebGPU](docs/build.md#webgpu) | All |
 | [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
 | [Hexagon [In Progress]](docs/backend/snapdragon/README.md) | Snapdragon |
 | [VirtGPU](docs/backend/VirtGPU.md) | VirtGPU APIR |

app/CMakeLists.txt

@@ -0,0 +1,20 @@
+set(TARGET llama-app)
+
+add_executable(${TARGET} llama.cpp)
+set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama)
+
+target_link_libraries(${TARGET} PRIVATE
+    llama-server-impl
+    llama-cli-impl
+    llama-completion-impl
+    llama-bench-impl
+    llama-batched-bench-impl
+    llama-fit-params-impl
+    llama-quantize-impl
+    llama-perplexity-impl
+)
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
+
+if(LLAMA_TOOLS_INSTALL)
+    install(TARGETS ${TARGET} RUNTIME)
+endif()

app/llama.cpp

@@ -0,0 +1,95 @@
+#include "build-info.h"
+
+#include <cstdio>
+#include <cstdlib>
+#include <string>
+#include <vector>
+
+// visible
+int llama_server(int argc, char ** argv);
+int llama_cli(int argc, char ** argv);
+
+// hidden
+int llama_completion(int argc, char ** argv);
+int llama_bench(int argc, char ** argv);
+int llama_batched_bench(int argc, char ** argv);
+int llama_fit_params(int argc, char ** argv);
+int llama_quantize(int argc, char ** argv);
+int llama_perplexity(int argc, char ** argv);
+
+static int help(int argc, char ** argv);
+static int version(int argc, char ** argv);
+
+struct command {
+    const char * name;
+    const char * desc;
+    std::vector<std::string> aliases;
+    bool hidden;
+    int (*func)(int, char **);
+};
+
+static const command cmds[] = {
+    {"serve",         "HTTP API server",                                    {"server"},   false, llama_server       },
+    {"cli",           "Command-line interactive interface",                 {"client"},   false, llama_cli          },
+    {"completion",    "Text completion",                                    {"complete"}, true,  llama_completion   },
+    {"bench",         "Benchmark prompt processing and text generation",    {},           true,  llama_bench        },
+    {"batched-bench", "Benchmark batched decoding performance",             {},           true,  llama_batched_bench},
+    {"fit-params",    "Compute parameters to fit a model in device memory", {},           true,  llama_fit_params   },
+    {"quantize",      "Quantize a model",                                   {},           true,  llama_quantize     },
+    {"perplexity",    "Compute model perplexity and KL divergence",         {},           true,  llama_perplexity   },
+    {"version",       "Show version",                                       {},           true,  version            },
+    {"help",          "Show available commands",                            {},           true,  help               },
+};
+
+static int version(int argc, char ** argv) {
+    printf("%s\n", llama_build_info());
+    return 0;
+}
+
+static int help(int argc, char ** argv) {
+    const bool show_all = argc >= 2 && std::string(argv[1]) == "all";
+
+    printf("Usage: llama <command> [options]\n\nAvailable commands:\n");
+
+    for (const auto & cmd : cmds) {
+        if (show_all || !cmd.hidden) {
+            printf("  %-15s %s\n", cmd.name, cmd.desc);
+        }
+    }
+    printf("\nRun 'llama <command> --help' for command-specific usage.\n");
+
+    return 0;
+}
+
+static bool matches(const std::string & arg, const command & cmd) {
+    if (arg == cmd.name) {
+        return true;
+    }
+    for (const auto & alias : cmd.aliases) {
+        if (arg == alias) {
+            return true;
+        }
+    }
+    return false;
+}
+
+int main(int argc, char ** argv) {
+    const std::string arg = argc >= 2 ? argv[1] : "help";
+
+    for (const auto & cmd : cmds) {
+        if (matches(arg, cmd)) {
+
+            // router spawns children through this same binary, it needs the
+            // subcommand to relaunch as 'llama serve' and not bare options
+#ifdef _WIN32
+            _putenv_s("LLAMA_APP_CMD", cmd.name);
+#else
+            setenv("LLAMA_APP_CMD", cmd.name, 1);
+#endif
+            return cmd.func(argc - 1, argv + 1);
+        }
+    }
+
+    fprintf(stderr, "error: unknown command '%s'\n", arg.c_str());
+    return 1;
+}

build-xcframework.sh

@@ -7,6 +7,7 @@ VISIONOS_MIN_OS_VERSION=1.0
 TVOS_MIN_OS_VERSION=16.4
 
 BUILD_SHARED_LIBS=OFF
+LLAMA_BUILD_APP=OFF
 LLAMA_BUILD_EXAMPLES=OFF
 LLAMA_BUILD_TOOLS=OFF
 LLAMA_BUILD_TESTS=OFF
@@ -31,6 +32,7 @@ COMMON_CMAKE_ARGS=(
     -DCMAKE_XCODE_ATTRIBUTE_STRIP_INSTALLED_PRODUCT=NO
     -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
     -DBUILD_SHARED_LIBS=${BUILD_SHARED_LIBS}
+    -DLLAMA_BUILD_APP=${LLAMA_BUILD_APP}
     -DLLAMA_BUILD_EXAMPLES=${LLAMA_BUILD_EXAMPLES}
     -DLLAMA_BUILD_TOOLS=${LLAMA_BUILD_TOOLS}
     -DLLAMA_BUILD_TESTS=${LLAMA_BUILD_TESTS}

ci/run.sh

@@ -117,6 +117,12 @@ if [ ! -z ${GG_BUILD_VULKAN} ]; then
     # if on Mac, disable METAL
     if [[ "$OSTYPE" == "darwin"* ]]; then
         CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=OFF -DGGML_BLAS=OFF"
+
+        MACOS_RUNNER_CUSTOM_VULKAN_CMAKE_LOCATION="/usr/local/lib/cmake/vulkan"
+        MACOS_RUNNER_CUSTOM_SPIRV_HEADERS_LOCATION="${MACOS_RUNNER_CUSTOM_VULKAN_CMAKE_LOCATION}/SPIRV-Headers/SPIRV-HeadersConfig.cmake"
+        if [[ -f "${MACOS_RUNNER_CUSTOM_SPIRV_HEADERS_LOCATION}" || -h "${MACOS_RUNNER_CUSTOM_SPIRV_HEADERS_LOCATION}" ]]; then
+            CMAKE_EXTRA="${CMAKE_EXTRA} -DSPIRV-Headers_DIR=${MACOS_RUNNER_CUSTOM_VULKAN_CMAKE_LOCATION}/SPIRV-Headers"
+        fi
     fi
 
     # Build shared libs on Windows
@@ -455,10 +461,10 @@ function gg_run_qwen3_0_6b {
 
     (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa off --no-op-offload) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa on  --no-op-offload) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa off                ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa on                 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/test-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa off --no-op-offload) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/test-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa on  --no-op-offload) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/test-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa off                ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/test-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa on                 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"

cmake/llama-config.cmake.in

@@ -7,7 +7,7 @@ set(LLAMA_SHARED_LIB   @BUILD_SHARED_LIBS@)
 
 set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@")
 set_and_check(LLAMA_LIB_DIR     "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
-set_and_check(LLAMA_BIN_DIR     "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
+set(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
 
 find_package(ggml REQUIRED HINTS ${LLAMA_LIB_DIR}/cmake)
 

common/arg.cpp

@@ -4,7 +4,6 @@
 #include "chat.h"
 #include "common.h"
 #include "download.h"
-#include "hf-cache.h"
 #include "json-schema-to-grammar.h"
 #include "log.h"
 #include "sampling.h"
@@ -337,11 +336,15 @@ static bool common_params_handle_remote_preset(common_params & params, llama_exa
 struct handle_model_result {
     bool found_mmproj = false;
     common_params_model mmproj;
+
+    bool found_mtp = false;
+    common_params_model mtp;
 };
 
 static handle_model_result common_params_handle_model(struct common_params_model & model,
                                                       const std::string          & bearer_token,
-                                                      bool                         offline) {
+                                                      bool                         offline,
+                                                      bool                         search_mtp = false) {
     handle_model_result result;
 
     if (!model.docker_repo.empty()) {
@@ -356,7 +359,7 @@ static handle_model_result common_params_handle_model(struct common_params_model
         common_download_opts opts;
         opts.bearer_token = bearer_token;
         opts.offline = offline;
-        auto download_result = common_download_model(model, opts, true);
+        auto download_result = common_download_model(model, opts, true, search_mtp);
 
         if (download_result.model_path.empty()) {
             throw std::runtime_error("failed to download model from Hugging Face");
@@ -369,6 +372,11 @@ static handle_model_result common_params_handle_model(struct common_params_model
             result.found_mmproj = true;
             result.mmproj.path  = download_result.mmproj_path;
         }
+
+        if (!download_result.mtp_path.empty()) {
+            result.found_mtp = true;
+            result.mtp.path  = download_result.mtp_path;
+        }
     } else if (!model.url.empty()) {
         if (model.path.empty()) {
             auto f = string_split<std::string>(model.url, '#').front();
@@ -436,7 +444,11 @@ static bool parse_bool_value(const std::string & value) {
 //
 
 void common_params_handle_models(common_params & params, llama_example curr_ex) {
-    auto res = common_params_handle_model(params.model, params.hf_token, params.offline);
+    const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
+                                         params.speculative.types.end(),
+                                         COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
+
+    auto res = common_params_handle_model(params.model, params.hf_token, params.offline, spec_type_draft_mtp);
     if (params.no_mmproj) {
         params.mmproj = {};
     } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
@@ -450,6 +462,14 @@ void common_params_handle_models(common_params & params, llama_example curr_ex)
             break;
         }
     }
+    // when --spec-type mtp is set and no draft model was provided explicitly,
+    // fall back to the MTP head discovered alongside the -hf model
+    if (spec_type_draft_mtp && res.found_mtp &&
+        params.speculative.draft.mparams.path.empty() &&
+        params.speculative.draft.mparams.hf_repo.empty() &&
+        params.speculative.draft.mparams.url.empty()) {
+        params.speculative.draft.mparams.path = res.mtp.path;
+    }
     common_params_handle_model(params.speculative.draft.mparams, params.hf_token, params.offline);
     common_params_handle_model(params.vocoder.model,             params.hf_token, params.offline);
 }
@@ -516,7 +536,11 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
                 throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
             }
             if (!seen_args.insert(arg).second) {
-                LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
+                const bool skip = (arg == "--spec-type");
+
+                if (!skip) {
+                    LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
+                }
             }
             auto & tmp = arg_to_options[arg];
             auto opt = *tmp.first;
@@ -565,12 +589,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
     // parse the first time to get -hf option (used for remote preset)
     parse_cli_args();
 
-    // TODO: Remove later
-    try {
-        hf_cache::migrate_old_cache_to_hf_cache(params.hf_token, params.offline);
-    } 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;
 
@@ -879,7 +897,11 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
             throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
         }
         if (!seen_args.insert(arg).second) {
-            LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
+            const bool skip = (arg == "--spec-type");
+
+            if (!skip) {
+                LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
+            }
         }
         auto opt = *arg_to_options[arg];
         std::string val;
@@ -2787,7 +2809,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.embd_normalize = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_DEBUG}));
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_DEBUG}));
     add_opt(common_arg(
         {"--embd-output-format"}, "FORMAT",
         "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix, \"raw\" = plain whitespace-delimited output (one embedding per line)",
@@ -2844,28 +2866,64 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.api_prefix = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX"));
+    // Deprecated: use --ui-config instead (kept for backward compat)
     add_opt(common_arg(
         {"--webui-config"}, "JSON",
-        "JSON that provides default WebUI settings (overrides WebUI defaults)",
+        "[DEPRECATED: use --ui-config] JSON that provides default WebUI settings (overrides WebUI defaults)",
         [](common_params & params, const std::string & value) {
+            params.ui_config_json = value;
             params.webui_config_json = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG"));
+
+    add_opt(common_arg(
+        {"--ui-config"}, "JSON",
+        "JSON that provides default UI settings (overrides UI defaults)",
+        [](common_params & params, const std::string & value) {
+            params.ui_config_json = value;
+            params.webui_config_json = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_CONFIG"));
+
+    // Deprecated: use --ui-config-file instead (kept for backward compat)
     add_opt(common_arg(
         {"--webui-config-file"}, "PATH",
-        "JSON file that provides default WebUI settings (overrides WebUI defaults)",
+        "[DEPRECATED: use --ui-config-file] JSON file that provides default WebUI settings (overrides WebUI defaults)",
         [](common_params & params, const std::string & value) {
-            params.webui_config_json = read_file(value);
+            params.ui_config_json = read_file(value);
+            params.webui_config_json = params.ui_config_json;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG_FILE"));
+
+    add_opt(common_arg(
+        {"--ui-config-file"}, "PATH",
+        "JSON file that provides default UI settings (overrides UI defaults)",
+        [](common_params & params, const std::string & value) {
+            params.ui_config_json = read_file(value);
+            params.webui_config_json = params.ui_config_json;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_CONFIG_FILE"));
+
+    // Deprecated: use --ui-mcp-proxy instead (kept for backward compat)
     add_opt(common_arg(
         {"--webui-mcp-proxy"},
         {"--no-webui-mcp-proxy"},
-        string_format("experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: %s)", params.webui_mcp_proxy ? "enabled" : "disabled"),
+        "[DEPRECATED: use --ui-mcp-proxy/--no-ui-mcp-proxy] experimental: whether to enable MCP CORS proxy",
         [](common_params & params, bool value) {
+            params.ui_mcp_proxy = value;
             params.webui_mcp_proxy = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_MCP_PROXY"));
+
+    add_opt(common_arg(
+        {"--ui-mcp-proxy"},
+        {"--no-ui-mcp-proxy"},
+        "experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)",
+        [](common_params & params, bool value) {
+            params.ui_mcp_proxy = value;
+            params.webui_mcp_proxy = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_MCP_PROXY"));
     add_opt(common_arg(
         {"--tools"}, "TOOL1,TOOL2,...",
         "experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)\n"
@@ -2875,14 +2933,26 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.server_tools = parse_csv_row(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS"));
+    // Deprecated: use --ui/--no-ui instead (kept for backward compat)
     add_opt(common_arg(
         {"--webui"},
         {"--no-webui"},
-        string_format("whether to enable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"),
+        "[DEPRECATED: use --ui/--no-ui] whether to enable the Web UI",
         [](common_params & params, bool value) {
+            params.ui = value;
             params.webui = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI"));
+
+    add_opt(common_arg(
+        {"--ui"},
+        {"--no-ui"},
+        string_format("whether to enable the Web UI (default: %s)", params.ui ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.ui = value;
+            params.webui = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI"));
     add_opt(common_arg(
         {"--embedding", "--embeddings"},
         string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
@@ -3294,7 +3364,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             " - 1: error\n"
             " - 2: warning\n"
             " - 3: info\n"
-            " - 4: debug\n"
+            " - 4: trace (more info)\n"
+            " - 5: debug\n"
             "(default: %d)\n", params.verbosity),
         [](common_params & params, int value) {
             params.verbosity = value;
@@ -3520,6 +3591,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.speculative.draft.p_min = std::stof(value);
         }
     ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_P_MIN"));
+    add_opt(common_arg(
+        {"--spec-draft-backend-sampling"},
+        {"--no-spec-draft-backend-sampling"},
+        string_format("offload draft sampling to the backend (default: %s)",
+                      params.speculative.draft.backend_sampling ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.speculative.draft.backend_sampling = value;
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_BACKEND_SAMPLING"));
     add_opt(common_arg(
         {"--spec-draft-device", "-devd", "--device-draft"}, "<dev1,dev2,..>",
         "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n"
@@ -3560,8 +3640,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         string_format("comma-separated list of types of speculative decoding to use (default: %s)\n",
             common_speculative_type_name_str(params.speculative.types).c_str()),
         [](common_params & params, const std::string & value) {
-            const auto enabled_types = string_split<std::string>(value, ',');
-            params.speculative.types = common_speculative_types_from_names(enabled_types);
+            const auto types_str = string_split<std::string>(value, ',');
+            auto types = common_speculative_types_from_names(types_str);
+            params.speculative.types.insert(params.speculative.types.end(), types.begin(), types.end());
         }
     ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_TYPE"));
     add_opt(common_arg(
@@ -4050,10 +4131,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--spec-default"},
         string_format("enable default speculative decoding config"),
         [](common_params & params) {
-            params.speculative.types = { COMMON_SPECULATIVE_TYPE_NGRAM_MOD };
+            params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_NGRAM_MOD);
             params.speculative.ngram_mod.n_match = 24;
             params.speculative.ngram_mod.n_min = 48;
             params.speculative.ngram_mod.n_max = 64;
+
+            // TODO: not sure if this is a good config - explore more settings and potentially enable it
+            //params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V);
+            //params.speculative.ngram_map_k4v.size_n = 8;
+            //params.speculative.ngram_map_k4v.size_m = 24;
+            //params.speculative.ngram_map_k4v.min_hits = 2;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
 

common/chat-auto-parser-generator.cpp

@@ -43,11 +43,33 @@ common_chat_params peg_generator::generate_parser(const common_chat_template &
                                                   const autoparser &              autoparser) {
     // Create the result structure
     common_chat_params data;
-    data.prompt           = common_chat_template_direct_apply(tmpl, inputs);
-    data.format           = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.preserved_tokens = autoparser.preserved_tokens;
+    data.prompt            = common_chat_template_direct_apply(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.preserved_tokens  = autoparser.preserved_tokens;
 
-    auto parser = autoparser.build_parser(inputs);
+    std::string parser_generation_prompt = data.generation_prompt;
+
+    if (inputs.continue_final_message != COMMON_CHAT_CONTINUATION_NONE && !inputs.continue_msg.empty()) {
+        // Build up generation prompt manually
+        const auto & msg = inputs.continue_msg;
+
+        if (!autoparser.reasoning.start.empty()) {
+            data.generation_prompt = data.generation_prompt.substr(0, data.generation_prompt.find(autoparser.reasoning.start));
+            data.generation_prompt += autoparser.reasoning.start + msg.reasoning_content;
+            if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+                data.generation_prompt += autoparser.reasoning.end;
+            }
+        }
+
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto parser = autoparser.build_parser(inputs, parser_generation_prompt);
     data.parser = parser.save();
 
     // Build grammar if tools are present
@@ -87,7 +109,7 @@ common_chat_params peg_generator::generate_parser(const common_chat_template &
     return data;
 }
 
-common_peg_arena autoparser::build_parser(const generation_params & inputs) const {
+common_peg_arena autoparser::build_parser(const generation_params & inputs, const std::string & generation_prompt) const {
     if (!analysis_complete) {
         throw std::invalid_argument("Cannot call build_parser on autoparser without performing analysis first, call analyze_template(...)");
     }
@@ -121,7 +143,7 @@ common_peg_arena autoparser::build_parser(const generation_params & inputs) cons
         } else {
             parser = content.build_parser(ctx);
         }
-        return pure_content ? p.prefix(inputs.generation_prompt, reasoning.start) + parser : p.prefix(inputs.generation_prompt, reasoning.start) << parser;
+        return pure_content ? p.prefix(generation_prompt, reasoning.start) + parser : p.prefix(generation_prompt, reasoning.start) << parser;
     });
 }
 

common/chat-auto-parser.h

@@ -60,16 +60,21 @@ struct generation_params {
     common_reasoning_format               reasoning_format    = COMMON_REASONING_FORMAT_AUTO;
     bool                                  stream              = true;
     std::string                           grammar;
-    bool                                  add_generation_prompt = false;
-    bool                                  enable_thinking       = true;
-    std::chrono::system_clock::time_point now                   = std::chrono::system_clock::now();
-    std::string                           generation_prompt;
+    bool                                  add_generation_prompt  = false;
+    common_chat_continuation              continue_final_message = COMMON_CHAT_CONTINUATION_NONE;
+    common_chat_msg                       continue_msg;
+    bool                                  enable_thinking        = true;
+    std::chrono::system_clock::time_point now                    = std::chrono::system_clock::now();
     json                                  extra_context;
     bool                                  add_bos       = false;
     bool                                  add_eos       = false;
     bool                                  is_inference  = true;
     bool                                  add_inference = false;
     bool                                  mark_input    = true;  // whether to mark input strings in the jinja context
+
+    bool has_continuation() const {
+        return continue_final_message != COMMON_CHAT_CONTINUATION_NONE && !continue_msg.empty();
+    }
 };
 
 // ============================================================================
@@ -386,7 +391,7 @@ struct autoparser {
     void analyze_template(const common_chat_template & tmpl);
 
     // Build the PEG parser for this template
-    common_peg_arena build_parser(const generation_params & inputs) const;
+    common_peg_arena build_parser(const generation_params & inputs, const std::string & generation_prompt) const;
 
   private:
     // Collect tokens from entire analysis to preserve

common/chat-peg-parser.cpp

@@ -358,35 +358,7 @@ void common_chat_peg_mapper::map(const common_peg_ast_node & node) {
             if (is_potential_container) {
                 value_content = normalize_container_value(value_content);
             }
-
-            // Try to parse as JSON value (number, bool, null, object, array)
-            try {
-                ordered_json parsed = ordered_json::parse(value_content);
-                if (parsed.is_string()) {
-                    // Don't add closing quote yet (added by arg_close) for monotonic streaming
-                    std::string escaped = parsed.dump();
-                    if (!escaped.empty() && escaped.back() == '"') {
-                        escaped.pop_back();
-                    }
-                    value_to_add          = escaped;
-                    closing_quote_pending = true;
-                } else {
-                    // Non-string values: use raw content to preserve whitespace for monotonicity
-                    value_to_add = value_content;
-                }
-            } catch (...) {
-                if (node.is_partial && is_potential_container) {
-                    // Partial container: pass through the already-normalized content
-                    value_to_add = value_content;
-                } else {
-                    // Not valid JSON - treat as string value
-                    if (!closing_quote_pending) {
-                        value_to_add          = "\"";
-                        closing_quote_pending = true;
-                    }
-                    value_to_add += escape_json_string_inner(value_content);
-                }
-            }
+            value_to_add += value_content;
         }
 
         args_target() += value_to_add;
@@ -813,7 +785,7 @@ common_peg_parser common_chat_peg_builder::prefix(const std::string & s, const s
     if (delimiter.empty()) {
         return literal(s);
     }
-    return literal(s.substr(0, s.rfind(delimiter)));
+    return literal(s.substr(0, s.find(delimiter)));
 }
 
 common_peg_parser common_chat_peg_builder::optspace(const std::string & tag) {

common/chat-peg-parser.h

@@ -90,7 +90,7 @@ class common_chat_peg_builder : public common_peg_parser_builder {
 
     // Use for schema-declared string types - won't be treated as potential JSON container
     common_peg_parser tool_arg_string_value(const common_peg_parser & p) { return tag(TOOL_ARG_STRING_VALUE, p); }
-    common_peg_parser tool_arg_json_value(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_VALUE, p)); }
+    common_peg_parser tool_arg_json_value(const common_peg_parser & p) { return tag(TOOL_ARG_VALUE, p); }
 
 
     // Return a parser that parses the prefix of a string, up to a given delimiter.

common/chat.cpp

@@ -70,6 +70,26 @@ static bool has_content_or_tool_calls(const common_chat_msg & msg) {
     return !msg.content.empty() || !msg.tool_calls.empty();
 }
 
+std::string common_chat_msg::render_content(const std::string & delimiter) const {
+    if (!content.empty() && !content_parts.empty()) {
+        throw std::runtime_error("Cannot specify both content and content_parts");
+    }
+    if (!content.empty()) {
+        return content;
+    }
+
+    std::string text;
+    for (const auto & part : content_parts) {
+        if (part.type == "text") {
+            if (!text.empty()) {
+                text += delimiter;
+            }
+            text += part.text;
+        }
+    }
+    return text;
+}
+
 json common_chat_msg::to_json_oaicompat(bool concat_typed_text) const {
     if (!content.empty() && !content_parts.empty()) {
         throw std::runtime_error("Cannot specify both content and content_parts");
@@ -451,6 +471,22 @@ std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const json & too
     return result;
 }
 
+common_chat_continuation common_chat_continuation_parse(const nlohmann::ordered_json & value) {
+    if (value.is_boolean() && value.get<bool>()) {
+        return COMMON_CHAT_CONTINUATION_AUTO;
+    }
+    if (value.is_string()) {
+        auto value_str = value.get<std::string>();
+        if (value_str == "reasoning_content") {
+            return COMMON_CHAT_CONTINUATION_REASONING;
+        }
+        if (value_str == "content") {
+            return COMMON_CHAT_CONTINUATION_CONTENT;
+        }
+    }
+    return COMMON_CHAT_CONTINUATION_NONE;
+}
+
 bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) {
     if (use_jinja) {
         try {
@@ -811,6 +847,36 @@ std::string common_chat_template_direct_apply(
     return common_chat_template_direct_apply_impl(tmpl, inputs, std::nullopt, std::nullopt, std::nullopt);
 }
 
+static std::string common_chat_template_generation_prompt_impl(
+    const common_chat_template & tmpl,
+    const autoparser::generation_params & inputs,
+    const std::optional<json> & messages_override = std::nullopt,
+    const std::optional<json> & tools_override = std::nullopt,
+    const std::optional<json> & additional_context = std::nullopt) {
+
+    auto adjusted_messages = messages_override ? *messages_override : inputs.messages;
+
+    autoparser::generation_params params = inputs;
+    params.add_generation_prompt = false;
+    params.continue_final_message = COMMON_CHAT_CONTINUATION_NONE;
+    std::string no_gen_prompt    = common_chat_template_direct_apply_impl(tmpl, params, adjusted_messages, tools_override, additional_context);
+    params.add_generation_prompt = true;
+    std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params, adjusted_messages, tools_override, additional_context);
+
+    size_t prefix_len = 0;
+    size_t min_size = std::min(no_gen_prompt.size(), gen_prompt.size());
+    while (prefix_len < min_size && no_gen_prompt[prefix_len] == gen_prompt[prefix_len]) {
+        prefix_len++;
+    }
+    return gen_prompt.substr(prefix_len);
+}
+
+std::string common_chat_template_generation_prompt(
+    const common_chat_template & tmpl,
+    const autoparser::generation_params & inputs) {
+    return common_chat_template_generation_prompt_impl(tmpl, inputs, std::nullopt, std::nullopt, std::nullopt);
+}
+
 static common_chat_params common_chat_params_init_ministral_3(const common_chat_template &    tmpl,
                                                               const autoparser::generation_params & inputs) {
     common_chat_params data;
@@ -863,6 +929,7 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
     data.thinking_start_tag = "[THINK]";
     data.thinking_end_tag   = "[/THINK]";
     data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs, /* messages_override = */ adjusted_messages);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, /* messages_override = */ adjusted_messages);
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.preserved_tokens  = {
         "[THINK]",
@@ -871,8 +938,19 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
         "[ARGS]",
     };
 
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = "[THINK]" + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += "[/THINK]" + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
     auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto generation_prompt = p.prefix(inputs.generation_prompt, "[THINK]");
+        auto generation_prompt = p.eps();
         auto reasoning =
             extract_reasoning ? p.optional("[THINK]" + p.reasoning(p.until("[/THINK]")) + "[/THINK]") : p.eps();
 
@@ -963,6 +1041,7 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
     }
 
     data.prompt            = prompt;
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, /* messages_override= */ adjusted_messages);
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking = true;
 
@@ -972,6 +1051,18 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
         "<|channel|>", "<|constrain|>", "<|message|>", "<|start|>", "<|end|>",
     };
 
+    // Adjust prompt for continuation
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = "<|start|>assistant<|channel|>analysis<|message|>" + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += "<|end|><|start|>assistant<|channel|>final<|message|>" + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
     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);
@@ -1080,12 +1171,14 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ
     common_chat_params data;
 
     data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
 
     if (inputs.add_generation_prompt && string_ends_with(data.prompt, "<turn|>\n")) {
         // This may happen if the model generates content + tool_call, the
         // template does not add the model's next turn and confuses the model
         // from emitting its proper reasoning token sequence.
-        data.prompt += "<|turn>model\n";
+        data.generation_prompt = "<|turn>model\n";
+        data.prompt += data.generation_prompt;
     }
 
     data.format            = COMMON_CHAT_FORMAT_PEG_GEMMA4;
@@ -1101,13 +1194,25 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ
         "<|turn>",
     };
 
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = string_ends_with(data.prompt, "<turn|>\n") ? "<|turn>model\n" : "";
+        data.generation_prompt += "<|channel>thought\n" + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += "<channel|>" + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
     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.prefix(inputs.generation_prompt, "<|channel>"));
+        auto start = p.rule("start", p.optional(p.literal("<|turn>model\n")));
 
         if (extract_reasoning) {
             p.rule("thought", p.literal("<|channel>thought") + p.space() + p.reasoning(p.until("<channel|>")) + p.literal("<channel|>"));
@@ -1224,15 +1329,22 @@ static common_chat_params common_chat_params_init_functionary_v3_2(const common_
                                                                    const autoparser::generation_params & inputs) {
     common_chat_params data;
 
-    data.prompt           = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.format           = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.preserved_tokens = {
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.preserved_tokens  = {
         ">>>all",
     };
 
     auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
     auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
 
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+        data.generation_prompt = "<|start_header_id|>assistant<|end_header_id|>\n\n>>>all\n" + msg.render_content();
+        data.prompt += data.generation_prompt;
+    }
+
     auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
         // Functionary v3.2 format:
         // - Normal content: >>>all\n{content}
@@ -1244,7 +1356,7 @@ static common_chat_params common_chat_params_init_functionary_v3_2(const common_
         // When no tools, content goes until end
         auto content_until_tool = p.literal("all\n") + p.content(p.until(">>>"));
         auto content_until_end  = p.literal("all\n") + p.content(p.rest());
-        auto generation_prompt  = p.literal(inputs.generation_prompt);
+        auto generation_prompt  = p.literal("<|start_header_id|>assistant<|end_header_id|>\n\n>>>");
 
         // If no tools or tool_choice is NONE, just parse content
         if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
@@ -1318,9 +1430,10 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
                                                           const autoparser::generation_params & inputs) {
     common_chat_params data;
 
-    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking  = true;
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
     data.preserved_tokens  = {
         "<|tool_calls_section_begin|>",
         "<|tool_calls_section_end|>",
@@ -1343,10 +1456,22 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
 
     const std::string THINK_START = "<think>";
     const std::string THINK_END   = "</think>";
+    const std::string GEN_PROMPT  = "<|im_assistant|>assistant<|im_middle|>";
 
     data.thinking_start_tag = THINK_START;
     data.thinking_end_tag   = THINK_END;
 
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += THINK_END + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
     auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
         // Kimi K2 Thinking format:
         // - Reasoning: <think>{reasoning}</think>
@@ -1366,7 +1491,7 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
         auto reasoning = extract_reasoning ? p.optional(THINK_START + p.reasoning(
             p.until_one_of({ THINK_END, "<|tool_calls_section_begin|>", "<|tool_call_begin|>" })) +
             p.optional(p.literal(THINK_END))) : p.eps();
-        auto generation_prompt = p.prefix(inputs.generation_prompt, THINK_START);
+        auto generation_prompt = p.literal(GEN_PROMPT);
 
 
         // Content only parser (no tools)
@@ -1442,6 +1567,7 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
     common_chat_params data;
 
     data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking = true;
     data.preserved_tokens  = {
@@ -1461,12 +1587,24 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
     const std::string TOOL_CALL_END   = "<|tool_call_end|>";
     const std::string THINK_START     = "<think>";
     const std::string THINK_END       = "</think>";
+    const std::string GEN_PROMPT      = "<|im_start|>assistant\n";
 
     data.thinking_start_tag = THINK_START;
     data.thinking_end_tag   = THINK_END;
 
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += THINK_END + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
     auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto generation_prompt = p.prefix(inputs.generation_prompt, THINK_START);
+        auto generation_prompt = p.literal(GEN_PROMPT);
         auto end = p.end();
 
         auto reasoning = p.eps();
@@ -1521,6 +1659,7 @@ static common_chat_params common_chat_params_init_lfm2_5(const common_chat_templ
     common_chat_params data;
 
     data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking = true;
     data.preserved_tokens  = {
@@ -1536,12 +1675,24 @@ static common_chat_params common_chat_params_init_lfm2_5(const common_chat_templ
 
     const std::string THINK_START     = "<think>";
     const std::string THINK_END       = "</think>";
+    const std::string GEN_PROMPT      = "<|im_start|>assistant\n";
 
     data.thinking_start_tag = THINK_START;
     data.thinking_end_tag   = THINK_END;
 
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += THINK_END + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
     auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto generation_prompt = p.prefix(inputs.generation_prompt, THINK_START);
+        auto generation_prompt = p.literal(GEN_PROMPT);
         auto end = p.end();
 
         auto reasoning = p.eps();
@@ -1592,6 +1743,7 @@ static common_chat_params common_chat_params_init_gigachat_v3(
     common_chat_params data;
 
     data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking = false;
     data.preserved_tokens  = {
@@ -1599,6 +1751,12 @@ static common_chat_params common_chat_params_init_gigachat_v3(
         "<|role_sep|>\n",
     };
 
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+        data.generation_prompt = "assistant<|role_sep|>\n" + msg.render_content();
+        data.prompt += data.generation_prompt;
+    }
+
     auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
     auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
     const auto *tool_call_start_prefix = "<|message_sep|>\n\nfunction call<|role_sep|>\n";
@@ -1634,7 +1792,7 @@ static common_chat_params common_chat_params_init_gigachat_v3(
             ret = p.content(p.rest());
         }
 
-        return p.literal(inputs.generation_prompt) + ret;
+        return p.literal("assistant<|role_sep|>\n") + ret;
     });
 
     data.parser = parser.save();
@@ -1662,12 +1820,13 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha
                                                                  const autoparser::generation_params & inputs) {
     common_chat_params data;
 
-    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking = true;
+    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt  = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking  = true;
     data.thinking_start_tag = "<think>";
     data.thinking_end_tag   = "</think>";
-    data.preserved_tokens  = {
+    data.preserved_tokens   = {
         "｜DSML｜",
         "<think>",
         "</think>",
@@ -1687,9 +1846,21 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha
     const std::string INVOKE_END   = "</" + DSML + "invoke>";
     const std::string PARAM_START  = "<" + DSML + "parameter";
     const std::string PARAM_END    = "</" + DSML + "parameter>";
+    const std::string GEN_PROMPT   = "<｜Assistant｜>";
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += THINK_END + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
 
     auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto generation_prompt = p.prefix(inputs.generation_prompt, THINK_START);
+        auto generation_prompt = p.literal(GEN_PROMPT);
         auto end = p.end();
 
         auto reasoning = p.eps();
@@ -2116,21 +2287,6 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
     return std::nullopt;
 }
 
-static std::string common_chat_templates_generation_prompt(const common_chat_template & tmpl, const autoparser::generation_params & inputs) {
-    autoparser::generation_params params = inputs;
-    params.add_generation_prompt = false;
-    std::string no_gen_prompt    = common_chat_template_direct_apply_impl(tmpl, params);
-    params.add_generation_prompt = true;
-    std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params);
-
-    size_t prefix_len = 0;
-    size_t min_size = std::min(no_gen_prompt.size(), gen_prompt.size());
-    while (prefix_len < min_size && no_gen_prompt[prefix_len] == gen_prompt[prefix_len]) {
-        prefix_len++;
-    }
-    return gen_prompt.substr(prefix_len);
-}
-
 static common_chat_params common_chat_templates_apply_jinja(const struct common_chat_templates *        tmpls,
                                                             const struct common_chat_templates_inputs & inputs) {
     autoparser::generation_params params;
@@ -2149,6 +2305,27 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
     params.add_bos               = tmpls->add_bos;
     params.add_eos               = tmpls->add_eos;
 
+    params.continue_final_message = inputs.continue_final_message;
+    if (params.continue_final_message != COMMON_CHAT_CONTINUATION_NONE) {
+        params.add_generation_prompt = false;
+
+        if (!inputs.messages.empty()) {
+            // Render messages[:-1] and store continuation message separately
+            params.continue_msg = inputs.messages.back();
+            params.messages.erase(params.messages.size() - 1);
+        }
+
+        if (params.continue_final_message == COMMON_CHAT_CONTINUATION_AUTO && !inputs.messages.empty()) {
+            // Resolve based on message content
+            params.continue_final_message = COMMON_CHAT_CONTINUATION_CONTENT;
+            if (!params.continue_msg.reasoning_content.empty() &&
+                params.continue_msg.content.empty() &&
+                params.continue_msg.content_parts.empty()) {
+                params.continue_final_message = COMMON_CHAT_CONTINUATION_REASONING;
+            }
+        }
+    }
+
     if (src.find("<|channel|>") == std::string::npos) {
         // map developer to system for all models except for GPT-OSS
         workaround::map_developer_role_to_system(params.messages);
@@ -2169,8 +2346,6 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         workaround::func_args_not_string(params.messages);
     }
 
-    params.generation_prompt = common_chat_templates_generation_prompt(tmpl, params);
-
     params.extra_context = common_chat_extra_context();
     for (auto el : inputs.chat_template_kwargs) {
         params.extra_context[el.first] = json::parse(el.second);
@@ -2200,17 +2375,16 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         auto params_copy               = params;
         params_copy.reasoning_format   = COMMON_REASONING_FORMAT_NONE;
         data.prompt                    = common_chat_template_direct_apply_impl(tmpl, params_copy);
+        data.generation_prompt         = common_chat_template_generation_prompt_impl(tmpl, params);
         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());
+        auto parser                    = build_chat_peg_parser([&data](common_chat_peg_builder &p) {
+            return p.literal(data.generation_prompt) << p.content(p.rest());
         });
         data.parser                    = parser.save();
         return data;
     }
 
     if (auto result = common_chat_try_specialized_template(tmpl, src, params)) {
-        result->generation_prompt = params.generation_prompt;
         return *result;
     }
 
@@ -2224,7 +2398,6 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
             auto_params.thinking_start_tag = trim_whitespace(autoparser.reasoning.start);
             auto_params.thinking_end_tag   = trim_whitespace(autoparser.reasoning.end);
         }
-        auto_params.generation_prompt = params.generation_prompt;
         common_peg_arena arena;
         arena.load(auto_params.parser);
         LOG_DBG("%s: generated parser:\n%s\n\nparser generation prompt: %s\n", __func__, arena.dump(arena.root()).c_str(), auto_params.generation_prompt.c_str());

common/chat.h

@@ -89,6 +89,8 @@ struct common_chat_msg {
 
     nlohmann::ordered_json to_json_oaicompat(bool concat_typed_text = false) const;
 
+    std::string render_content(const std::string & delimiter = "\n\n") const;
+
     bool empty() const {
         return content.empty() && content_parts.empty() && tool_calls.empty() && reasoning_content.empty() &&
                tool_name.empty() && tool_call_id.empty();
@@ -164,12 +166,22 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_COUNT,  // Not a format, just the # formats
 };
 
+
+// Continuation method provided via `continue_final_message`
+enum common_chat_continuation {
+    COMMON_CHAT_CONTINUATION_NONE,
+    COMMON_CHAT_CONTINUATION_AUTO,
+    COMMON_CHAT_CONTINUATION_REASONING,
+    COMMON_CHAT_CONTINUATION_CONTENT,
+};
+
 struct common_chat_templates_inputs {
     std::vector<common_chat_msg>          messages;
     std::string                           grammar;
     std::string                           json_schema;
-    bool                                  add_generation_prompt = true;
-    bool                                  use_jinja             = true;
+    bool                                  add_generation_prompt  = true;
+    common_chat_continuation              continue_final_message = COMMON_CHAT_CONTINUATION_NONE;
+    bool                                  use_jinja              = true;
     // Parameters below only supported when use_jinja is true
     std::vector<common_chat_tool>         tools;
     common_chat_tool_choice               tool_choice         = COMMON_CHAT_TOOL_CHOICE_AUTO;
@@ -207,6 +219,7 @@ struct common_chat_parser_params {
     bool                    reasoning_in_content = false;
     std::string             generation_prompt;
     bool                    parse_tool_calls     = true;
+    bool                    echo                 = false;  // Include assistant prefilled msg in output
     bool                    debug                = false;  // Enable debug output for PEG parser
     common_peg_arena        parser               = {};
     common_chat_parser_params() = default;
@@ -267,6 +280,8 @@ std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const nlohmann::or
 
 std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const nlohmann::ordered_json & tools);
 
+common_chat_continuation common_chat_continuation_parse(const nlohmann::ordered_json & value);
+
 // DEPRECATED: only used in tests
 nlohmann::ordered_json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text = false);
 
@@ -279,6 +294,10 @@ std::string common_chat_template_direct_apply(
     const common_chat_template & tmpl,
     const autoparser::generation_params & inputs);
 
+std::string common_chat_template_generation_prompt(
+    const common_chat_template &          tmpl,
+    const autoparser::generation_params & inputs);
+
 std::optional<common_chat_params> common_chat_try_specialized_template(
         const common_chat_template &          tmpl,
         const std::string &                   src,

common/common.cpp

@@ -7,6 +7,7 @@
 #include "log.h"
 #include "llama.h"
 #include "sampling.h"
+#include "speculative.h"
 #include "unicode.h"
 
 #include <algorithm>
@@ -372,7 +373,7 @@ void common_init() {
     llama_log_set(common_log_default_callback, NULL);
 }
 
-void common_params_print_info(const common_params & params) {
+void common_params_print_info(const common_params & params, bool print_devices) {
 #ifdef NDEBUG
     const char * build_type = "";
 #else
@@ -381,12 +382,16 @@ void common_params_print_info(const common_params & params) {
     LOG_TRC("%s: build %d (%s) with %s for %s%s\n", __func__, llama_build_number(), llama_commit(), llama_compiler(), llama_build_target(), build_type);
 
     LOG_INF("log_info: verbosity = %d (adjust with the `-lv N` CLI arg)\n", common_log_get_verbosity_thold());
-    LOG_INF("device_info:\n");
-    for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
-        auto * dev = ggml_backend_dev_get(i);
-        size_t free, total;
-        ggml_backend_dev_memory(dev, &free, &total);
-        LOG_INF("  - %-8s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
+
+    // device enumeration creates a primary context on CUDA backends, skip it when the caller does not own any device
+    if (print_devices) {
+        LOG_INF("device_info:\n");
+        for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
+            auto * dev = ggml_backend_dev_get(i);
+            size_t free, total;
+            ggml_backend_dev_memory(dev, &free, &total);
+            LOG_INF("  - %-8s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
+        }
     }
     LOG_INF("%s\n", common_params_get_system_info(params).c_str());
 }
@@ -1155,7 +1160,7 @@ struct common_init_result::impl {
     std::vector<llama_sampler_seq_config> samplers_seq_config;
 };
 
-common_init_result::common_init_result(common_params & params) :
+common_init_result::common_init_result(common_params & params, bool model_only) :
     pimpl(new impl{}) {
     auto mparams = common_model_params_to_llama(params);
     auto cparams = common_context_params_to_llama(params);
@@ -1168,7 +1173,7 @@ common_init_result::common_init_result(common_params & params) :
             params.tensor_buft_overrides.data(),
             params.fit_params_target.data(),
             params.fit_params_min_ctx,
-            params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
+            params.verbosity >= LOG_LEVEL_DEBUG ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
     }
 
     llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
@@ -1178,6 +1183,10 @@ common_init_result::common_init_result(common_params & params) :
 
     pimpl->model.reset(model);
 
+    if (model_only) {
+        return;
+    }
+
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
     // load and optionally apply lora adapters
@@ -1281,8 +1290,8 @@ std::vector<llama_adapter_lora_ptr> & common_init_result::lora() {
     return pimpl->lora;
 }
 
-common_init_result_ptr common_init_from_params(common_params & params) {
-    common_init_result_ptr res(new common_init_result(params));
+common_init_result_ptr common_init_from_params(common_params & params, bool model_only) {
+    common_init_result_ptr res(new common_init_result(params, model_only));
 
     llama_model * model = res->model();
     if (model == NULL) {
@@ -1290,6 +1299,10 @@ common_init_result_ptr common_init_from_params(common_params & params) {
         return res;
     }
 
+    if (model_only) {
+        return res;
+    }
+
     llama_context * lctx = res->context();
     if (lctx == NULL) {
         LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
@@ -1353,7 +1366,7 @@ common_init_result_ptr common_init_from_params(common_params & params) {
     }
 
     if (params.warmup) {
-        LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
+        LOG_INF("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
 
         llama_set_warmup(lctx, true);
 
@@ -1435,6 +1448,12 @@ common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) {
         goto done;
     }
 
+    if (llama_n_rs_seq(ctx) > 0) {
+        LOG_INF("%s: the context supports bounded partial sequence removal\n", __func__);
+        res = COMMON_CONTEXT_SEQ_RM_TYPE_RS;
+        goto done;
+    }
+
     // try to remove the last tokens
     if (!llama_memory_seq_rm(mem, 0, 1, -1)) {
         LOG_TRC("%s: the context does not support partial sequence removal\n", __func__);
@@ -1449,6 +1468,23 @@ done:
     return res;
 }
 
+void common_context_seq_rm(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+    auto * mem = llama_get_memory(ctx);
+    if (!llama_memory_seq_rm(mem, seq_id, p0, p1)) {
+        GGML_ABORT("%s", string_format("failed to remove sequence %d with p0=%d, p1=%d\n", seq_id, p0, p1).c_str());
+    }
+}
+
+void common_context_seq_cp(llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+    auto * mem = llama_get_memory(ctx);
+    llama_memory_seq_cp(mem, seq_id_src, seq_id_dst, p0, p1);
+}
+
+void common_context_seq_add(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
+    auto * mem = llama_get_memory(ctx);
+    llama_memory_seq_add(mem, seq_id, p0, p1, delta);
+}
+
 void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora) {
     std::vector<llama_adapter_lora *> loras;
     std::vector<float> scales;
@@ -1505,6 +1541,7 @@ struct llama_context_params common_context_params_to_llama(const common_params &
 
     cparams.n_ctx             = params.n_ctx;
     cparams.n_seq_max         = params.n_parallel;
+    cparams.n_rs_seq          = params.speculative.need_n_rs_seq();
     cparams.n_batch           = params.n_batch;
     cparams.n_ubatch          = params.n_ubatch;
     cparams.n_threads         = params.cpuparams.n_threads;
@@ -2074,3 +2111,11 @@ void common_prompt_checkpoint::load_dft(
         GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", data_dft.size(), n);
     }
 }
+
+void common_prompt_checkpoint::clear_tgt() {
+    data_tgt.clear();
+}
+
+void common_prompt_checkpoint::clear_dft() {
+    data_dft.clear();
+}

common/common.h

@@ -13,6 +13,7 @@
 #include <string_view>
 #include <vector>
 #include <map>
+#include <algorithm>
 
 #if defined(_WIN32) && !defined(_WIN32_WINNT)
 #define _WIN32_WINNT 0x0A00
@@ -159,6 +160,7 @@ enum common_speculative_type {
     COMMON_SPECULATIVE_TYPE_NONE,          // no speculative decoding
     COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE,  // standalone draft model speculative decoding
     COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3,  // Eagle3 speculative decoding
+    COMMON_SPECULATIVE_TYPE_DRAFT_MTP,     // Multi-token prediction
     COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE,  // simple self-speculative decoding based on n-grams
     COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K,   // self-speculative decoding with n-gram keys only
     COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, // self-speculative decoding with n-gram keys and 4 m-gram values
@@ -297,11 +299,13 @@ struct common_params_model {
 
 // draft-model-based speculative decoding parameters
 struct common_params_speculative_draft {
-    int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding
-    int32_t n_min = 0;  // minimum number of draft tokens to use for speculative decoding
+    int32_t n_max = 3; // maximum number of tokens to draft during speculative decoding
+    int32_t n_min = 0; // minimum number of draft tokens to use for speculative decoding
 
-    float p_split = 0.1f;  // speculative decoding split probability
-    float p_min   = 0.75f; // minimum speculative decoding probability (greedy)
+    float p_split = 0.1f; // speculative decoding split probability
+    float p_min   = 0.0f; // minimum speculative decoding probability (greedy)
+
+    bool backend_sampling = true; // offload draft sampling to the backend (default: on)
 
     common_params_model mparams;
 
@@ -355,6 +359,14 @@ struct common_params_speculative {
     bool has_dft() const {
         return !draft.mparams.path.empty() || !draft.mparams.hf_repo.empty();
     }
+
+    uint32_t need_n_rs_seq() const {
+        bool needs_rs_seq = std::any_of(types.begin(), types.end(), [&](auto t) {
+            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP;
+        });
+
+        return needs_rs_seq ? draft.n_max : 0u;
+    }
 };
 
 struct common_params_vocoder {
@@ -604,15 +616,21 @@ struct common_params {
 
     std::map<std::string, std::string> default_template_kwargs;
 
-    // webui configs
-#ifdef LLAMA_WEBUI_DEFAULT_ENABLED
-    bool webui = LLAMA_WEBUI_DEFAULT_ENABLED != 0;
+    // UI configs
+#ifdef LLAMA_UI_DEFAULT_ENABLED
+    bool ui = LLAMA_UI_DEFAULT_ENABLED != 0;
 #else
-    bool webui = true; // default to enabled when not set
+    bool ui = true; // default to enabled when not set
 #endif
+
+    // Deprecated: use ui, ui_mcp_proxy, ui_config_json instead
+    bool webui = ui;
     bool webui_mcp_proxy = false;
     std::string webui_config_json;
 
+    bool ui_mcp_proxy = false;
+    std::string ui_config_json;
+
     // "advanced" endpoints are disabled by default for better security
     bool endpoint_slots   = true;
     bool endpoint_props   = false; // only control POST requests, not GET
@@ -690,7 +708,7 @@ struct common_params {
 // initializes the logging system and prints info about the build
 void common_init();
 
-void common_params_print_info(const common_params & params);
+void common_params_print_info(const common_params & params, bool print_devices = true);
 std::string common_params_get_system_info(const common_params & params);
 
 bool parse_cpu_range(const std::string & range, bool(&boolmask)[GGML_MAX_N_THREADS]);
@@ -841,7 +859,7 @@ struct common_sampler;
 
 // note: defines the model, context, samplers, ets. lifetimes
 struct common_init_result {
-    common_init_result(common_params & params);
+    common_init_result(common_params & params, bool model_only = false);
     ~common_init_result();
 
     llama_model * model();
@@ -859,7 +877,7 @@ private:
 
 using common_init_result_ptr = std::unique_ptr<common_init_result>;
 
-common_init_result_ptr common_init_from_params(common_params & params);
+common_init_result_ptr common_init_from_params(common_params & params, bool model_only = false);
 
 struct llama_model_params     common_model_params_to_llama  (      common_params & params);
 struct llama_context_params   common_context_params_to_llama(const common_params & params);
@@ -876,15 +894,20 @@ std::string common_get_model_endpoint();
 //
 
 enum common_context_seq_rm_type {
-    COMMON_CONTEXT_SEQ_RM_TYPE_NO   = 0, // seq_rm not supported (e.g. no memory module)
-    COMMON_CONTEXT_SEQ_RM_TYPE_PART = 1, // can seq_rm partial sequences
-    COMMON_CONTEXT_SEQ_RM_TYPE_FULL = 2, // can seq_rm full sequences only
+    COMMON_CONTEXT_SEQ_RM_TYPE_NO           = 0, // seq_rm not supported (e.g. no memory module)
+    COMMON_CONTEXT_SEQ_RM_TYPE_PART         = 1, // can seq_rm partial sequences
+    COMMON_CONTEXT_SEQ_RM_TYPE_FULL         = 2, // can seq_rm full sequences only
+    COMMON_CONTEXT_SEQ_RM_TYPE_RS = 3, // can seq_rm partial sequences, bounded by n_rs_seq
 };
 
 // check if the llama_context can remove sequences
 // note: clears the memory of the context
 common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx);
 
+// aborts execution on failure
+void common_context_seq_rm (llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1);
+void common_context_seq_add(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta);
+void common_context_seq_cp (llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1);
 
 //
 // Batch utils
@@ -1066,4 +1089,7 @@ struct common_prompt_checkpoint {
             llama_context * ctx,
             llama_seq_id seq_id,
             llama_state_seq_flags flags) const;
+
+    void clear_tgt();
+    void clear_dft();
 };

common/download.cpp

@@ -566,8 +566,11 @@ static hf_cache::hf_files get_split_files(const hf_cache::hf_files & files,
     return result;
 }
 
-static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files,
-                                          const std::string        & model) {
+// pick the best sibling GGUF whose filename contains `keyword` (e.g. "mmproj" / "mtp"),
+// preferring deeper shared directory prefix with the model, then closest quantization
+static hf_cache::hf_file find_best_sibling(const hf_cache::hf_files & files,
+                                           const std::string        & model,
+                                           const std::string        & keyword) {
     hf_cache::hf_file best;
     size_t best_depth = 0;
     int best_diff = 0;
@@ -579,20 +582,20 @@ static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files,
 
     for (const auto & f : files) {
         if (!string_ends_with(f.path, ".gguf") ||
-            f.path.find("mmproj") == std::string::npos) {
+            f.path.find(keyword) == std::string::npos) {
             continue;
         }
 
-        auto mmproj_parts = string_split<std::string>(f.path, '/');
-        auto mmproj_dir = mmproj_parts.end() - 1;
+        auto sib_parts = string_split<std::string>(f.path, '/');
+        auto sib_dir = sib_parts.end() - 1;
 
         auto [_, dir] = std::mismatch(model_parts.begin(), model_dir,
-                                      mmproj_parts.begin(), mmproj_dir);
-        if (dir != mmproj_dir) {
+                                      sib_parts.begin(), sib_dir);
+        if (dir != sib_dir) {
             continue;
         }
 
-        size_t depth = dir - mmproj_parts.begin();
+        size_t depth = dir - sib_parts.begin();
         auto bits = extract_quant_bits(f.path);
         auto diff = std::abs(bits - model_bits);
 
@@ -606,6 +609,16 @@ static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files,
     return best;
 }
 
+static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files,
+                                          const std::string        & model) {
+    return find_best_sibling(files, model, "mmproj");
+}
+
+static hf_cache::hf_file find_best_mtp(const hf_cache::hf_files & files,
+                                       const std::string        & model) {
+    return find_best_sibling(files, model, "mtp-");
+}
+
 static bool gguf_filename_is_model(const std::string & filepath) {
     if (!string_ends_with(filepath, ".gguf")) {
         return false;
@@ -617,7 +630,8 @@ static bool gguf_filename_is_model(const std::string & filepath) {
     }
 
     return filename.find("mmproj")  == std::string::npos &&
-           filename.find("imatrix") == std::string::npos;
+           filename.find("imatrix") == std::string::npos &&
+           filename.find("mtp-")    == std::string::npos;
 }
 
 static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
@@ -673,11 +687,13 @@ struct hf_plan {
     hf_cache::hf_file primary;
     hf_cache::hf_files model_files;
     hf_cache::hf_file mmproj;
+    hf_cache::hf_file mtp;
 };
 
 static hf_plan get_hf_plan(const common_params_model  & model,
                            const common_download_opts & opts,
-                           bool download_mmproj) {
+                           bool download_mmproj,
+                           bool download_mtp) {
     hf_plan plan;
     hf_cache::hf_files all;
 
@@ -723,6 +739,10 @@ static hf_plan get_hf_plan(const common_params_model  & model,
         plan.mmproj = find_best_mmproj(all, primary.path);
     }
 
+    if (download_mtp) {
+        plan.mtp = find_best_mtp(all, primary.path);
+    }
+
     return plan;
 }
 
@@ -756,7 +776,8 @@ static std::vector<download_task> get_url_tasks(const common_params_model & mode
 
 common_download_model_result common_download_model(const common_params_model  & model,
                                                    const common_download_opts & opts,
-                                                   bool download_mmproj) {
+                                                   bool download_mmproj,
+                                                   bool download_mtp) {
     common_download_model_result result;
     std::vector<download_task> tasks;
     hf_plan hf;
@@ -764,13 +785,16 @@ common_download_model_result common_download_model(const common_params_model  &
     bool is_hf = !model.hf_repo.empty();
 
     if (is_hf) {
-        hf = get_hf_plan(model, opts, download_mmproj);
+        hf = get_hf_plan(model, opts, download_mmproj, download_mtp);
         for (const auto & f : hf.model_files) {
             tasks.push_back({f.url, f.local_path});
         }
         if (!hf.mmproj.path.empty()) {
             tasks.push_back({hf.mmproj.url, hf.mmproj.local_path});
         }
+        if (!hf.mtp.path.empty()) {
+            tasks.push_back({hf.mtp.url, hf.mtp.local_path});
+        }
     } else if (!model.url.empty()) {
         tasks = get_url_tasks(model);
     } else {
@@ -807,6 +831,10 @@ common_download_model_result common_download_model(const common_params_model  &
         if (!hf.mmproj.path.empty()) {
             result.mmproj_path = hf_cache::finalize_file(hf.mmproj);
         }
+
+        if (!hf.mtp.path.empty()) {
+            result.mtp_path = hf_cache::finalize_file(hf.mtp);
+        }
     } else {
         result.model_path = model.path;
     }
@@ -946,7 +974,8 @@ std::vector<common_cached_model_info> common_list_cached_models() {
     for (const auto & f : files) {
         auto split = get_gguf_split_info(f.path);
         if (split.index != 1 || split.tag.empty() ||
-            split.prefix.find("mmproj") != std::string::npos) {
+            split.prefix.find("mmproj") != std::string::npos ||
+            split.prefix.find("mtp-")   != std::string::npos) {
             continue;
         }
         if (seen.insert(f.repo_id + ":" + split.tag).second) {

common/download.h

@@ -59,6 +59,7 @@ struct common_download_opts {
 struct common_download_model_result {
     std::string model_path;
     std::string mmproj_path;
+    std::string mtp_path;
 };
 
 // Download model from HuggingFace repo or URL
@@ -83,12 +84,14 @@ struct common_download_model_result {
 // when opts.offline=true, no network requests are made
 // when download_mmproj=true, searches for mmproj in same directory as model or any parent directory
 // then with the closest quantization bits
+// when download_mtp=true, applies the same sibling search for an MTP-head GGUF
 //
-// returns result with model_path and mmproj_path (empty on failure)
+// returns result with model_path, mmproj_path and mtp_path (empty when not found / on failure)
 common_download_model_result common_download_model(
     const common_params_model & model,
     const common_download_opts & opts = {},
-    bool download_mmproj = false
+    bool download_mmproj = false,
+    bool download_mtp    = false
 );
 
 // returns list of cached models

common/hf-cache.cpp

@@ -11,7 +11,6 @@
 #include <filesystem>
 #include <fstream>
 #include <atomic>
-#include <regex> // migration only
 #include <string>
 #include <string_view>
 #include <stdexcept>
@@ -336,15 +335,9 @@ hf_files get_repo_files(const std::string & repo_id,
                 if (item["lfs"].contains("oid") && item["lfs"]["oid"].is_string()) {
                     file.oid = item["lfs"]["oid"].get<std::string>();
                 }
-                if (item["lfs"].contains("size") && item["lfs"]["size"].is_number()) {
-                    file.size = item["lfs"]["size"].get<size_t>();
-                }
             } else if (item.contains("oid") && item["oid"].is_string()) {
                 file.oid = item["oid"].get<std::string>();
             }
-            if (file.size == 0 && item.contains("size") && item["size"].is_number()) {
-                file.size = item["size"].get<size_t>();
-            }
 
             if (!file.oid.empty() && !is_valid_oid(file.oid)) {
                 LOG_WRN("%s: skip invalid oid: %s\n", __func__, file.oid.c_str());
@@ -502,271 +495,4 @@ std::string finalize_file(const hf_file & file) {
     return file.final_path;
 }
 
-// delete everything after this line, one day
-
-// copied from download.cpp without the tag part
-struct gguf_split_info {
-    std::string prefix; // tag included
-    int index;
-    int count;
-};
-
-static gguf_split_info get_gguf_split_info(const std::string & path) {
-    static const std::regex re_split("^(.+)-([0-9]{5})-of-([0-9]{5})$", std::regex::icase);
-    std::smatch m;
-
-    std::string prefix = path;
-    if (!string_remove_suffix(prefix, ".gguf")) {
-        return {};
-    }
-
-    int index = 1;
-    int count = 1;
-
-    if (std::regex_match(prefix, m, re_split)) {
-        index = std::stoi(m[2].str());
-        count = std::stoi(m[3].str());
-        prefix = m[1].str();
-    }
-
-    return {std::move(prefix), index, count};
-}
-
-static std::pair<std::string, std::string> parse_manifest_name(std::string & filename) {
-    static const std::regex re(R"(^manifest=([^=]+)=([^=]+)=.*\.json$)");
-    std::smatch match;
-    if (std::regex_match(filename, match, re)) {
-        return {match[1].str(), match[2].str()};
-    }
-    return {};
-}
-
-static std::string make_old_cache_filename(const std::string & owner,
-                                           const std::string & repo,
-                                           const std::string & filename) {
-    auto result = owner + "_" + repo + "_" + filename;
-    string_replace_all(result, "/", "_");
-    return result;
-}
-
-struct migrate_file {
-    std::string path;
-    std::string sha256;
-    size_t size;
-    fs::path old_path;
-    fs::path etag_path;
-    const hf_file * file;
-};
-
-using migrate_files = std::vector<migrate_file>;
-
-static bool collect_file(const fs::path    & old_cache,
-                         const std::string & owner,
-                         const std::string & repo,
-                         const std::string & path,
-                         const std::string & sha256,
-                         const hf_files    & files,
-                         migrate_files     & to_migrate) {
-
-    const hf_file * file = nullptr;
-
-    for (const auto & f : files) {
-        if (f.path == path) {
-            file = &f;
-            break;
-        }
-    }
-
-    std::string old_filename = make_old_cache_filename(owner, repo, path);
-    fs::path old_path = old_cache / old_filename;
-    fs::path etag_path = old_path.string() + ".etag";
-
-    if (!fs::exists(old_path)) {
-        if (file && fs::exists(file->final_path)) {
-            return true;
-        }
-        LOG_WRN("%s: %s not found in old cache or HF cache\n", __func__, old_filename.c_str());
-        return false;
-    }
-
-    if (!file) {
-        LOG_WRN("%s: %s not found in current repo\n", __func__, old_filename.c_str());
-        return false;
-    }
-
-    if (!sha256.empty() && !file->oid.empty() && sha256 != file->oid) {
-        LOG_WRN("%s: %s is not up to date (sha256 mismatch)\n", __func__, old_filename.c_str());
-        return false;
-    }
-
-    if (file->size > 0) {
-        size_t size = fs::file_size(old_path);
-        if (size != file->size) {
-            LOG_WRN("%s: %s has wrong size %zu (expected %zu)\n", __func__, old_filename.c_str(), size, file->size);
-            return false;
-        }
-    }
-
-    to_migrate.push_back({path, sha256, file->size, old_path, etag_path, file});
-    return true;
-}
-
-static bool collect_files(const fs::path    & old_cache,
-                          const std::string & owner,
-                          const std::string & repo,
-                          const nl::json    & node,
-                          const hf_files    & files,
-                          migrate_files     & to_migrate) {
-
-    if (!node.contains("rfilename") ||
-        !node.contains("lfs")       ||
-        !node["lfs"].contains("sha256")) {
-        return true;
-    }
-
-    std::string path = node["rfilename"];
-    std::string sha256 = node["lfs"]["sha256"];
-
-    auto split = get_gguf_split_info(path);
-
-    if (split.count <= 1) {
-        return collect_file(old_cache, owner, repo, path, sha256, files, to_migrate);
-    }
-
-    std::vector<std::pair<std::string, std::string>> splits;
-
-    for (const auto & f : files) {
-        auto split_f = get_gguf_split_info(f.path);
-        if (split_f.count == split.count && split_f.prefix == split.prefix) {
-            // sadly the manifest only provides the sha256 of the first file (index == 1)
-            // the rest will be verified using the size...
-            std::string f_sha256 = (split_f.index == 1) ? sha256 : "";
-            splits.emplace_back(f.path, f_sha256);
-        }
-    }
-
-    if ((int)splits.size() != split.count) {
-        LOG_WRN("%s: expected %d split files but found %d in repo\n", __func__, split.count, (int)splits.size());
-        return false;
-    }
-
-    for (const auto & [f_path, f_sha256] : splits) {
-        if (!collect_file(old_cache, owner, repo, f_path, f_sha256, files, to_migrate)) {
-            return false;
-        }
-    }
-
-    return true;
-}
-
-static bool migrate_file(const migrate_file & file) {
-    std::error_code ec;
-
-    fs::path new_path(file.file->local_path);
-    fs::create_directories(new_path.parent_path(), ec);
-
-    if (!fs::exists(new_path, ec)) {
-        fs::rename(file.old_path, new_path, ec);
-        if (ec) {
-            fs::copy_file(file.old_path, new_path, ec);
-            if (ec) {
-                LOG_ERR("%s: failed to move/copy %s: %s\n", __func__, file.old_path.string().c_str(), ec.message().c_str());
-                return false;
-            }
-        }
-        fs::remove(file.old_path, ec);
-    }
-    fs::remove(file.etag_path, ec);
-
-    std::string filename = finalize_file(*file.file);
-    LOG_INF("%s: migrated %s -> %s\n", __func__, file.old_path.filename().string().c_str(), filename.c_str());
-    return true;
-}
-
-void migrate_old_cache_to_hf_cache(const std::string & token, bool offline) {
-    fs::path old_cache = fs_get_cache_directory();
-    if (!fs::exists(old_cache)) {
-        return;
-    }
-
-    if (offline) {
-        LOG_WRN("%s: skipping migration in offline mode (will run when online)\n", __func__);
-        return; // -hf is not going to work
-    }
-
-    bool warned = false;
-
-    for (const auto & entry : fs::directory_iterator(old_cache)) {
-        if (!entry.is_regular_file()) {
-            continue;
-        }
-        auto filename = entry.path().filename().string();
-        auto [owner, repo] = parse_manifest_name(filename);
-
-        if (owner.empty() || repo.empty()) {
-            continue;
-        }
-
-        if (!warned) {
-            warned = true;
-            LOG_WRN("================================================================================\n"
-                    "WARNING: Migrating cache to HuggingFace cache directory\n"
-                    "  Old cache: %s\n"
-                    "  New cache: %s\n"
-                    "This one-time migration moves models previously downloaded with -hf\n"
-                    "from the legacy llama.cpp cache to the standard HuggingFace cache.\n"
-                    "Models downloaded with --model-url are not affected.\n"
-                    "================================================================================\n",
-                    old_cache.string().c_str(), get_cache_directory().string().c_str());
-        }
-
-        auto repo_id = owner + "/" + repo;
-        auto files = get_repo_files(repo_id, token);
-
-        if (files.empty()) {
-            LOG_WRN("%s: could not get repo files for %s, skipping\n", __func__, repo_id.c_str());
-            continue;
-        }
-
-        migrate_files to_migrate;
-        bool ok = true;
-
-        try {
-            std::ifstream manifest(entry.path());
-            auto json = nl::json::parse(manifest);
-            for (const char * key : {"ggufFile", "mmprojFile"}) {
-                if (json.contains(key)) {
-                    if (!collect_files(old_cache, owner, repo, json[key], files, to_migrate)) {
-                        ok = false;
-                        break;
-                    }
-                }
-            }
-        } catch (const std::exception & e) {
-            LOG_WRN("%s: failed to parse manifest %s: %s\n", __func__, filename.c_str(), e.what());
-            continue;
-        }
-
-        if (!ok) {
-            LOG_WRN("%s: migration skipped: one or more files failed validation\n", __func__);
-            continue;
-        }
-
-        for (const auto & file : to_migrate) {
-            if (!migrate_file(file)) {
-                ok = false;
-                break;
-            }
-        }
-
-        if (!ok) {
-            LOG_WRN("%s: migration failed: could not migrate all files\n", __func__);
-            continue;
-        }
-
-        LOG_INF("%s: migration complete, deleting manifest: %s\n", __func__, entry.path().string().c_str());
-        fs::remove(entry.path());
-    }
-}
-
 } // namespace hf_cache

common/hf-cache.h

@@ -14,7 +14,6 @@ struct hf_file {
     std::string final_path;
     std::string oid;
     std::string repo_id;
-    size_t size = 0; // only for the migration
 };
 
 using hf_files = std::vector<hf_file>;
@@ -30,7 +29,4 @@ hf_files get_cached_files(const std::string & repo_id = {});
 // Create snapshot path (link or move/copy) and return it
 std::string finalize_file(const hf_file & file);
 
-// TODO: Remove later
-void migrate_old_cache_to_hf_cache(const std::string & token, bool offline = false);
-
 } // namespace hf_cache

common/ngram-map.cpp

@@ -471,7 +471,7 @@ void common_ngram_map_draft(common_ngram_map & map,
         sum_occur += curr_occur;
     }
 
-    LOG_INF("%s: key_offset = %zu, max_occur = %d, sum_occur = %d, slot_max = %d [%zu/%d, %zu/%d, %zu/%d, %zu/%d]\n", __func__,
+    LOG_DBG("%s: key_offset = %zu, max_occur = %d, sum_occur = %d, slot_max = %d [%zu/%d, %zu/%d, %zu/%d, %zu/%d]\n", __func__,
             key_offset,
             max_occur, sum_occur, slot_max,
             curr_key.values[0].value_idx, curr_key.values[0].value_num,
@@ -482,7 +482,7 @@ void common_ngram_map_draft(common_ngram_map & map,
     // Print the tokens of the four values (if idx != 0), use LOG_INF
     for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) {
         if (curr_key.values[v].value_idx != 0) {
-            LOG_INF("%s: value[%d] = %s\n", __func__, v, common_tokens_to_str(inp, curr_key.values[v].value_idx, m).c_str());
+            LOG_DBG("%s: value[%d] = %s\n", __func__, v, common_tokens_to_str(inp, curr_key.values[v].value_idx, m).c_str());
         }
     }
 
@@ -500,7 +500,7 @@ void common_ngram_map_draft(common_ngram_map & map,
         draft.push_back(inp[match_pos + n + i]);
     }
 
-    LOG_INF("%s: key_offset = %zu, slot_max = %d, key_num = %d, draft.size = %zu\n", __func__,
+    LOG_DBG("%s: key_offset = %zu, slot_max = %d, key_num = %d, draft.size = %zu\n", __func__,
             key_offset, slot_max,
             curr_key.key_num, draft.size());
 

common/speculative.cpp

@@ -3,6 +3,7 @@
 #include "common.h"
 #include "ggml.h"
 #include "llama.h"
+#include "../src/llama-ext.h" // staging API: llama_set_embeddings_pre_norm / llama_get_embeddings_pre_norm_ith (used by MTP)
 #include "log.h"
 #include "ngram-cache.h"
 #include "ngram-map.h"
@@ -23,6 +24,7 @@ const std::map<std::string, common_speculative_type> common_speculative_type_fro
     {"none",          COMMON_SPECULATIVE_TYPE_NONE},
     {"draft-simple",  COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE},
     {"draft-eagle3",  COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3},
+    {"draft-mtp",     COMMON_SPECULATIVE_TYPE_DRAFT_MTP},
     {"ngram-simple",  COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE},
     {"ngram-map-k",   COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K},
     {"ngram-map-k4v", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V},
@@ -30,6 +32,18 @@ const std::map<std::string, common_speculative_type> common_speculative_type_fro
     {"ngram-cache",   COMMON_SPECULATIVE_TYPE_NGRAM_CACHE}
 };
 
+static std::string common_speculative_get_devices_str(const std::vector<ggml_backend_dev_t> & devices) {
+    std::string result;
+    for (size_t i = 0; i < devices.size(); i++) {
+        if (devices[i] == nullptr) {
+            continue;
+        }
+        if (!result.empty()) result += ", ";
+        result += ggml_backend_dev_name(devices[i]);
+    }
+    return result.empty() ? "default" : result;
+}
+
 struct common_speculative_config {
     common_speculative_type type;
     common_params_speculative params;
@@ -142,7 +156,13 @@ struct common_speculative_impl {
 
     virtual void draft(common_speculative_draft_params_vec & dparams) = 0;
 
-    virtual void accept(llama_seq_id seq_id, uint16_t n_accepted) = 0;
+    virtual void accept(llama_seq_id seq_id, uint16_t n_accepted, bool is_other) = 0;
+
+    // true if this implementation requires the target context to extract post-norm embeddings
+    virtual bool need_embd() const = 0;
+
+    // true if this implementation requires the target context to extract pre-norm embeddings
+    virtual bool need_embd_pre_norm() const { return false; }
 };
 
 struct common_speculative_impl_draft_simple : public common_speculative_impl {
@@ -159,6 +179,16 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
         auto * ctx_dft = this->params.ctx_dft;
         auto * ctx_tgt = this->params.ctx_tgt;
 
+        LOG_INF("%s: adding speculative implementation 'draft-simple'\n", __func__);
+        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min);
+        LOG_INF("%s: - gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n", __func__,
+                this->params.n_gpu_layers,
+                ggml_type_name(this->params.cache_type_k),
+                ggml_type_name(this->params.cache_type_v),
+                ctx_tgt ? "yes" : "no",
+                ctx_dft ? "yes" : "no",
+                common_speculative_get_devices_str(this->params.devices).c_str());
+
         batch = llama_batch_init(llama_n_batch(ctx_dft), 0, 1);
 
         // TODO: optimize or pass from outside?
@@ -335,16 +365,24 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
         }
     }
 
-    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
+
+    bool need_embd() const override {
+        return false;
+    }
 };
 
 struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
     //common_params_speculative_eagle3 params;
 
-    common_speculative_impl_draft_eagle3(const common_params_speculative & /*params*/, uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq) {}
+    common_speculative_impl_draft_eagle3(const common_params_speculative & params, uint32_t n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq)
+    {
+        LOG_INF("%s: adding speculative implementation 'draft-eagle3'\n", __func__);
+        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%f\n", __func__, params.draft.n_max, params.draft.n_min, params.draft.p_min);
+    }
 
     void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override {
         // noop
@@ -359,9 +397,384 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
         // TODO: implement
     }
 
-    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
+
+    bool need_embd() const override {
+        return false;
+    }
+};
+
+struct common_speculative_impl_draft_mtp : public common_speculative_impl {
+    common_params_speculative_draft params; // reuses the draft-model params slot (ctx_tgt/ctx_dft)
+
+    llama_batch batch;
+
+    std::vector<common_sampler_ptr> smpls;
+
+    // backend sampler chain per seq, attached to ctx_dft
+    std::vector<llama_sampler *> backend_chains;
+
+    int32_t n_embd = 0;
+
+    // Per-sequence cross-batch carryover: pair (h_p, x_{p+1}) at MTP pos p+1.
+    // The last h-row of one process() call needs the first token of the NEXT
+    // call to pair with, so it's stashed here until that next call fires.
+    std::vector<std::vector<float>> pending_h;   // [n_seq][n_embd]
+
+    std::vector<int32_t> i_batch_beg;
+    std::vector<int32_t> i_batch_end;
+
+    // Hidden rows from the most recent target verification batch, grouped by seq.
+    // Row 0 corresponds to the sampled token, row N to the Nth accepted draft token.
+    std::vector<std::vector<float>> verify_h;
+    std::vector<int32_t> verify_h_rows;
+
+    // Per-seq draft length from the last draft() call, used in accept() to
+    // roll back ctx_dft's recurrent state past the AR draft's redundant
+    // pre-advancement before process() mirrored the verify batch.
+    std::vector<uint16_t> last_n_drafted;
+
+    common_speculative_impl_draft_mtp(const common_params_speculative & params, uint32_t n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, n_seq)
+        , params(params.draft)
+    {
+        auto * ctx_tgt = this->params.ctx_tgt;
+        auto * ctx_dft = this->params.ctx_dft;
+        GGML_ASSERT(ctx_tgt && ctx_dft && "MTP requires ctx_tgt and ctx_dft to be set");
+
+        n_embd = llama_model_n_embd(llama_get_model(ctx_dft));
+
+        LOG_INF("%s: adding speculative implementation 'draft-mtp'\n", __func__);
+        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f, n_embd=%d, backend_sampling=%d\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min, n_embd, (int) this->params.backend_sampling);
+        LOG_INF("%s: - gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n", __func__,
+                this->params.n_gpu_layers,
+                ggml_type_name(this->params.cache_type_k),
+                ggml_type_name(this->params.cache_type_v),
+                ctx_tgt ? "yes" : "no",
+                ctx_dft ? "yes" : "no",
+                common_speculative_get_devices_str(this->params.devices).c_str());
+
+        const int32_t n_b = (int32_t) llama_n_batch(ctx_dft);
+        batch = llama_batch_init(/*n_tokens=*/ n_b, /*embd=*/ n_embd, /*n_seq_max=*/ 1);
+        // llama_batch_init allocates only one of token/embd; MTP needs both.
+        // TODO: fix, how to call without malloc
+        batch.token = (llama_token *) malloc(sizeof(llama_token) * n_b);
+
+        smpls.resize(n_seq);
+        for (auto & s : smpls) {
+            common_params_sampling sparams;
+            sparams.no_perf  = false;
+            sparams.top_k    = 10;
+            sparams.samplers = { COMMON_SAMPLER_TYPE_TOP_K };
+            s.reset(common_sampler_init(llama_get_model(ctx_dft), sparams));
+        }
+
+        // offload draft sampling to the backend
+        backend_chains.assign(n_seq, nullptr);
+        if (this->params.backend_sampling) {
+            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                llama_sampler * chain = llama_sampler_chain_init(llama_sampler_chain_default_params());
+                llama_sampler_chain_add(chain, llama_sampler_init_top_k(10));
+
+                if (!llama_set_sampler(ctx_dft, seq_id, chain)) {
+                    LOG_WRN("%s: backend offload failed for seq_id=%d; using CPU sampler\n", __func__, (int) seq_id);
+                    llama_sampler_free(chain);
+                    chain = nullptr;
+                }
+                backend_chains[seq_id] = chain;
+            }
+        }
+
+        llama_set_embeddings_pre_norm(ctx_tgt, true, /*masked*/ false);
+        llama_set_embeddings_pre_norm(ctx_dft, true, /*masked*/ true);
+
+        pending_h.assign(n_seq, std::vector<float>(n_embd, 0.0f));
+
+        i_batch_beg.assign(n_seq, -1);
+        i_batch_end.assign(n_seq, -1);
+
+        verify_h.assign(n_seq, {});
+        verify_h_rows.assign(n_seq, 0);
+
+        last_n_drafted.assign(n_seq, 0);
+    }
+
+    ~common_speculative_impl_draft_mtp() override {
+        auto * ctx_dft = this->params.ctx_dft;
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) backend_chains.size(); ++seq_id) {
+            if (backend_chains[seq_id] == nullptr) {
+                continue;
+            }
+            if (ctx_dft) {
+                llama_set_sampler(ctx_dft, seq_id, nullptr);
+            }
+            llama_sampler_free(backend_chains[seq_id]);
+        }
+        backend_chains.clear();
+
+        if (batch.token != nullptr) {
+            free(batch.token);
+            batch.token = nullptr;
+        }
+        llama_batch_free(batch);
+    }
+
+    void begin(llama_seq_id seq_id, const llama_tokens & prompt) override {
+        const int32_t N = (int32_t) prompt.size();
+        if (N <= 0) {
+            return;
+        }
+        auto * ctx_dft = this->params.ctx_dft;
+        const llama_pos pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
+        if (pos_max < N - 1) {
+            LOG_WRN("%s: ctx_dft pos_max=%d < N-1=%d - "
+                    "process() hook may not have run on every prefill ubatch "
+                    "(need_embd / logits=1 on every prompt position?). "
+                    "Drafts may degrade.\n",
+                    __func__, (int) pos_max, N - 1);
+        }
+    }
+
+    bool process(const llama_batch & batch_in) override {
+        if (batch_in.n_tokens <= 0) {
+            return true;
+        }
+
+        // TODO: how to make it work with vision tokens?
+        if (batch_in.token == nullptr || batch_in.embd != nullptr) {
+            return true;
+        }
+
+        const int32_t n_tokens = batch_in.n_tokens;
+
+        // remember the frist and last batch index for each sequence
+        std::fill(i_batch_beg.begin(), i_batch_beg.end(), -1);
+        std::fill(i_batch_end.begin(), i_batch_end.end(), -1);
+
+        for (int k = 0; k < n_tokens; ++k) {
+            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                GGML_ASSERT(batch_in.n_seq_id[k] == 1);
+
+                if (batch_in.seq_id[k][0] == seq_id) {
+                    i_batch_end[seq_id] = k;
+                    if (i_batch_beg[seq_id] < 0) {
+                        i_batch_beg[seq_id] = k;
+                    }
+                }
+            }
+        }
+
+        auto * ctx_tgt = this->params.ctx_tgt;
+        auto * ctx_dft = this->params.ctx_dft;
+
+        const size_t row_bytes = (size_t) n_embd * sizeof(float);
+
+        common_batch_clear(batch);
+
+        for (int k = 0; k < n_tokens; ++k) {
+            common_batch_add(batch, batch_in.token[k], batch_in.pos[k], { batch_in.seq_id[k][0] }, 0);
+        }
+
+        // shift the tgt embeddings to the right by one position
+        // assumes that the tokens in the batch are sequential for each sequence
+        // i.e. we cannot have seq_id like this: [0, 0, 0, 1, 1, 0, 1, 1]
+        //                                                       ^--- this is a problem
+        // TODO:this is generally true, but would be nice to assert it
+        {
+            const float * h_tgt = llama_get_embeddings_pre_norm(ctx_tgt);
+            std::memcpy(batch.embd + (size_t) 1 * n_embd, h_tgt, row_bytes * (n_tokens-1));
+
+            //{
+            //    // string with seq_ids in the batch
+            //    std::stringstream ss;
+            //    for (int i = 0; i < n_tokens; ++i) {
+            //        ss << batch_in.seq_id[i][0] << ",";
+            //    }
+            //    LOG_WRN("%s: batch_in.seq_id = %s\n", __func__, ss.str().c_str());
+            //}
+        }
+
+        // fill the pending embeddings from a previous run
+        auto set_h = [&](int idx, const float * h_row) {
+            std::memcpy(batch.embd + (size_t) idx * n_embd, h_row, row_bytes);
+        };
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            if (i_batch_beg[seq_id] < 0) {
+                continue;
+            }
+
+            set_h(i_batch_beg[seq_id], pending_h[seq_id].data());
+        }
+
+        const int32_t rc = llama_decode(ctx_dft, batch);
+        if (rc != 0) {
+            LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (pos=%d)\n", __func__, (int) rc, (int) batch_in.pos[0]);
+            return false;
+        }
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            if (i_batch_end[seq_id] < 0) {
+                continue;
+            }
+
+            const int32_t n_rows = i_batch_end[seq_id] - i_batch_beg[seq_id] + 1;
+            verify_h_rows[seq_id] = n_rows;
+            verify_h[seq_id].resize((size_t) n_rows * n_embd);
+
+            for (int32_t i = 0; i < n_rows; ++i) {
+                const float * h = llama_get_embeddings_pre_norm_ith(ctx_tgt, i_batch_beg[seq_id] + i);
+                std::memcpy(verify_h[seq_id].data() + (size_t) i * n_embd, h, row_bytes);
+            }
+
+            std::memcpy(pending_h[seq_id].data(),
+                    verify_h[seq_id].data() + (size_t) (n_rows - 1) * n_embd, row_bytes);
+        }
+
+        return true;
+    }
+
+    void draft(common_speculative_draft_params_vec & dparams) override {
+        auto & ctx_dft = params.ctx_dft;
+
+        common_batch_clear(batch);
+
+        // keep track of which sequences are still drafting
+        int n_drafting = 0;
+        std::vector<bool> drafting(n_seq);
+
+        const float * h_row = nullptr;
+        const size_t row_bytes = (size_t) n_embd * sizeof(float);
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+
+            if (!dp.drafting) {
+                continue;
+            }
+
+            n_drafting++;
+            drafting[seq_id] = true;
+            common_sampler_reset(smpls[seq_id].get());
+
+            common_batch_add(batch, dp.id_last, dp.n_past, { seq_id }, true);
+
+            h_row = pending_h[seq_id].data();
+            std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
+        }
+
+        int ret = llama_decode(ctx_dft, batch);
+        if (ret != 0) {
+            LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
+            return;
+        }
+
+        int i = 0;
+
+        while (n_drafting > 0) {
+            int i_batch = 0;
+
+            common_batch_clear(batch);
+
+            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                if (!drafting[seq_id]) {
+                    continue;
+                }
+
+                auto * smpl = smpls[seq_id].get();
+
+                common_sampler_sample(smpl, ctx_dft, i_batch, true);
+                h_row = llama_get_embeddings_pre_norm_ith(ctx_dft, i_batch);
+                ++i_batch;
+
+                const auto * cur_p = common_sampler_get_candidates(smpl, true);
+
+                for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
+                    LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                            seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
+                            common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
+                }
+
+                // add drafted token for each sequence
+                const llama_token id = cur_p->data[0].id;
+
+                // only collect very high-confidence draft tokens
+                if (cur_p->data[0].p < params.p_min) {
+                    drafting[seq_id] = false;
+                    n_drafting--;
+
+                    continue;
+                }
+
+                common_sampler_accept(smpl, id, true);
+
+                auto & dp = dparams.at(seq_id);
+                auto & result = *dp.result;
+
+                result.push_back(id);
+
+                if (params.n_max <= (int) result.size()) {
+                    drafting[seq_id] = false;
+                    n_drafting--;
+                    continue;
+                }
+
+                common_batch_add(batch, id, dp.n_past + i + 1, { seq_id }, true);
+                std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
+            }
+
+            if (batch.n_tokens == 0) {
+                break;
+            }
+
+            // evaluate the drafted tokens on the draft model
+            ret = llama_decode(ctx_dft, batch);
+            if (ret != 0) {
+                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+                break;
+            }
+
+            ++i;
+        }
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+            if (!dp.drafting) {
+                continue;
+            }
+
+            if (dp.result->size() < (size_t) params.n_min) {
+                dp.result->clear();
+            }
+
+            last_n_drafted[seq_id] = (uint16_t) dp.result->size();
+        }
+    }
+
+    void accept(llama_seq_id seq_id, uint16_t n_accepted, bool /*is_other*/) override {
+        if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+            return;
+        }
+
+        const int32_t n_rows = verify_h_rows[seq_id];
+        if (n_rows <= 0) {
+            return;
+        }
+
+        const int32_t i_h = std::min<int32_t>(n_accepted, n_rows - 1);
+        const size_t row_bytes = (size_t) n_embd * sizeof(float);
+        std::memcpy(pending_h[seq_id].data(), verify_h[seq_id].data() + (size_t) i_h * n_embd, row_bytes);
+    }
+
+    bool need_embd() const override {
+        return false;
+    }
+
+    bool need_embd_pre_norm() const override {
+        return true;
+    }
 };
 
 // state of self-speculation (simple implementation, not ngram-map)
@@ -376,7 +789,12 @@ struct common_speculative_impl_ngram_simple : public common_speculative_impl {
             common_ngram_simple_config config)
         : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, n_seq)
         , params(params.ngram_simple)
-        , config(config) {}
+        , config(config)
+    {
+        LOG_INF("%s: adding speculative implementation 'ngram-simple'\n", __func__);
+        LOG_INF("%s: - size_n=%d, size_m=%d, min_hits=%d\n", __func__,
+                this->params.size_n, this->params.size_m, this->params.min_hits);
+    }
 
     void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override {
         // noop
@@ -400,26 +818,31 @@ struct common_speculative_impl_ngram_simple : public common_speculative_impl {
         }
     }
 
-    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
+
+    bool need_embd() const override {
+        return false;
+    }
 };
 
 struct common_speculative_impl_ngram_map_k : public common_speculative_impl {
-    common_params_speculative_ngram_map params;
-
     // n_seq configs
     std::vector<common_ngram_map> config;
 
     common_speculative_impl_ngram_map_k(
-            const common_params_speculative & params,
             const common_ngram_map & config,
             uint32_t n_seq)
         : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, n_seq)
-        , params(params.ngram_map_k) {
+    {
         for (uint32_t i = 0; i < n_seq; i++) {
             this->config.push_back(config);
         }
+
+        LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(this->type).c_str());
+        LOG_INF("%s: - size_key=%d, size_value=%d, key_only=%d, min_hits=%d\n", __func__,
+                config.size_key, config.size_value, config.key_only, config.min_hits);
     }
 
     void begin(llama_seq_id seq_id, const llama_tokens & prompt) override {
@@ -446,11 +869,19 @@ struct common_speculative_impl_ngram_map_k : public common_speculative_impl {
         }
     }
 
-    void accept(llama_seq_id seq_id, uint16_t n_accepted) override {
+    void accept(llama_seq_id seq_id, uint16_t n_accepted, bool is_other) override {
         GGML_ASSERT((seq_id < (llama_seq_id) config.size()));
 
+        if (is_other) {
+            return;
+        }
+
         common_ngram_map_accept(config[seq_id], n_accepted);
     }
+
+    bool need_embd() const override {
+        return false;
+    }
 };
 
 struct common_speculative_impl_ngram_mod : public common_speculative_impl {
@@ -466,7 +897,7 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
         // the last position in the prompt that was added to the ngram container
         size_t i_last = 0;
 
-        // length of the last drafted n‑gram (number of tokens returned by draft)
+        // length of the last drafted n-gram (number of tokens returned by draft)
         size_t n_draft_last = 0;
 
         // consecutive accept rounds with low acceptance fraction (< 0.5)
@@ -484,8 +915,11 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
         , verbose(std::getenv("LLAMA_TRACE") != nullptr) {
         static_assert(sizeof(llama_token) == sizeof(common_ngram_mod::entry_t));
 
-        LOG_INF("%s: initialized ngram_mod with n_match=%d, size=%zu (%.3f MB)\n", __func__,
-                this->params.n_match, mod.size(), (float)(mod.size_bytes())/1024/1024);
+        LOG_INF("%s: adding speculative implementation 'ngram-mod'\n", __func__);
+        LOG_INF("%s: - n_match=%d, n_max=%d, n_min=%d\n", __func__,
+                this->params.n_match, this->params.n_max, this->params.n_min);
+        LOG_INF("%s: - mod size=%zu (%.3f MB)\n", __func__,
+                mod.size(), (float)(mod.size_bytes())/1024/1024);
 
         if (this->params.n_match < 16) {
             LOG_WRN("%s: ngram_mod n_match=%d is too small - poor quality is possible, "
@@ -575,7 +1009,7 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
         }
         result.resize(result.size() - n);
 
-        // store length of drafted n‑gram for later acceptance analysis
+        // store length of drafted n-gram for later acceptance analysis
         sinfo.n_draft_last = result.size();
     }
 
@@ -597,17 +1031,21 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
         }
     }
 
-    void accept(llama_seq_id seq_id, uint16_t n_accepted) override {
+    void accept(llama_seq_id seq_id, uint16_t n_accepted, bool is_other) override {
+        if (is_other) {
+            return;
+        }
+
         auto & sinfo = sinfos[seq_id];
 
         // compute acceptance fraction if we have a recorded draft length
         if (sinfo.n_draft_last > 0) {
             const double f_acc = (double)n_accepted / (double)sinfo.n_draft_last;
-            if (f_acc < 0.5) {
+            if (f_acc < 0.25) {
                 sinfo.n_low++;
-                if (sinfo.n_low >= 3) {
+                if (sinfo.n_low >= 5) {
                     if (verbose) {
-                        LOG_WRN("%s: low acceptance streak (%d) – resetting ngram_mod\n", __func__, sinfo.n_low);
+                        LOG_WRN("%s: low acceptance streak (%d) - resetting ngram_mod\n", __func__, sinfo.n_low);
                     }
 
                     mod.reset();
@@ -619,6 +1057,10 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
             }
         }
     }
+
+    bool need_embd() const override {
+        return false;
+    }
 };
 
 struct common_speculative_impl_ngram_cache : public common_speculative_impl {
@@ -653,6 +1095,12 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl {
         , save_dynamic(save_dynamic)
         , save_static(save_static)
     {
+        LOG_INF("%s: adding speculative implementation 'ngram-cache'\n", __func__);
+        LOG_INF("%s: - n_draft=%d, cache_static=%s, cache_dynamic=%s\n", __func__,
+                n_draft,
+                path_static.empty() ? "none" : path_static.c_str(),
+                path_dynamic.empty() ? "none" : path_dynamic.c_str());
+
         sinfos.resize(n_seq);
 
         if (!path_static.empty()) {
@@ -749,9 +1197,13 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl {
         }
     }
 
-    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
+
+    bool need_embd() const override {
+        return false;
+    }
 };
 
 struct common_speculative {
@@ -820,6 +1272,7 @@ std::string common_speculative_type_to_str(common_speculative_type type) {
         case COMMON_SPECULATIVE_TYPE_NONE:          return "none";
         case COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE:  return "draft-simple";
         case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3:  return "draft-eagle3";
+        case COMMON_SPECULATIVE_TYPE_DRAFT_MTP:     return "draft-mtp";
         case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE:  return "ngram-simple";
         case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K:   return "ngram-map-k";
         case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: return "ngram-map-k4v";
@@ -875,8 +1328,8 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         bool has_draft_model_path = !params.draft.mparams.path.empty();
 
         bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
-        // bool has_mtp = false; // TODO: add MTP here
         bool has_draft_eagle3 = false; // TODO PR-18039: if params.speculative.eagle3
+        bool has_mtp = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP)) && params.draft.ctx_dft != nullptr;
 
         bool has_ngram_cache   = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_CACHE));
         bool has_ngram_simple  = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE));
@@ -885,7 +1338,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         bool has_ngram_mod     = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MOD));
 
         // when adding a new type - update here the logic above
-        static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 8);
+        static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 9);
 
         // this list here defines the priority of the speculators
         // the one with highest priority are listed first
@@ -911,7 +1364,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
                 LOG_WRN("%s: draft model is not specified - cannot use 'draft' type\n", __func__);
                 has_draft_simple = false;
             }
-        } else if (has_draft_model_path) {
+        } else if (has_draft_model_path && !has_mtp && !has_draft_eagle3) {
             LOG_WRN("%s: draft model is specified but 'draft' speculative type is not explicitly enabled - enabling it\n", __func__);
             has_draft_simple = true;
         }
@@ -919,16 +1372,17 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         if (has_draft_simple) {
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE, params));
         }
-        // TODO: add MTP here
         if (has_draft_eagle3) {
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, params));
         }
+        if (has_mtp) {
+            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, params));
+        }
     }
 
     std::vector<std::unique_ptr<common_speculative_impl>> impls = {};
 
     for (const common_speculative_config & config : configs) {
-        LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(config.type).c_str());
         switch (config.type) {
             case COMMON_SPECULATIVE_TYPE_NONE:
                 break;
@@ -940,6 +1394,10 @@ common_speculative * common_speculative_init(common_params_speculative & params,
                 impls.push_back(std::make_unique<common_speculative_impl_draft_eagle3>(config.params, n_seq));
                 break;
             }
+            case COMMON_SPECULATIVE_TYPE_DRAFT_MTP: {
+                impls.push_back(std::make_unique<common_speculative_impl_draft_mtp>(config.params, n_seq));
+                break;
+            }
             case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: {
                 common_ngram_map ngram_map = get_common_ngram_map(config.type, config.params.ngram_simple);
 
@@ -958,11 +1416,16 @@ common_speculative * common_speculative_init(common_params_speculative & params,
                 impls.push_back(std::move(state));
                 break;
             }
-            case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K:
+            case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K: {
+                impls.push_back(
+                        std::make_unique<common_speculative_impl_ngram_map_k>(
+                            get_common_ngram_map(config.type, config.params.ngram_map_k), n_seq));
+                break;
+            }
             case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: {
                 impls.push_back(
                         std::make_unique<common_speculative_impl_ngram_map_k>(
-                            config.params, get_common_ngram_map(config.type, config.params.ngram_map_k), n_seq));
+                            get_common_ngram_map(config.type, config.params.ngram_map_k4v), n_seq));
                 break;
             }
             case COMMON_SPECULATIVE_TYPE_NGRAM_MOD: {
@@ -1040,6 +1503,34 @@ bool common_speculative_process(common_speculative * spec, const llama_batch & b
     return result;
 }
 
+bool common_speculative_need_embd(common_speculative * spec) {
+    if (spec == nullptr) {
+        return false;
+    }
+
+    for (auto & impl : spec->impls) {
+        if (impl->need_embd()) {
+            return true;
+        }
+    }
+
+    return false;
+}
+
+bool common_speculative_need_embd_pre_norm(common_speculative * spec) {
+    if (spec == nullptr) {
+        return false;
+    }
+
+    for (auto & impl : spec->impls) {
+        if (impl->need_embd_pre_norm()) {
+            return true;
+        }
+    }
+
+    return false;
+}
+
 void common_speculative_draft(common_speculative * spec) {
     if (spec == nullptr) {
         return;
@@ -1122,10 +1613,6 @@ void common_speculative_draft(common_speculative * spec) {
 }
 
 void common_speculative_accept(common_speculative * spec, llama_seq_id seq_id, uint16_t n_accepted) {
-    if (n_accepted == 0) {
-        return;
-    }
-
     common_speculative_impl * impl = spec->impl_last[seq_id];
 
     GGML_ASSERT(impl);
@@ -1137,9 +1624,16 @@ void common_speculative_accept(common_speculative * spec, llama_seq_id seq_id, u
             impl->n_acc_tokens += n_accepted;
         }
 
-        impl->accept(seq_id, n_accepted);
+        impl->accept(seq_id, n_accepted, false);
         impl->n_call_accept++;
     }
+
+    // accept with the rest of the implementations, using is_other == true
+    for (auto & impl_other : spec->impls) {
+        if (impl_other.get() != impl) {
+            impl_other->accept(seq_id, n_accepted, true);
+        }
+    }
 }
 
 void common_speculative_print_stats(const common_speculative * spec) {
@@ -1159,7 +1653,7 @@ void common_speculative_print_stats(const common_speculative * spec) {
             str_perf = "";
         }
 
-        LOG_INF("statistics %s: #calls(b,g,a) = %zu %zu %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n",
+        LOG_INF("statistics %16s: #calls(b,g,a) = %4zu %6zu %6zu, #gen drafts = %6zu, #acc drafts = %5zu, #gen tokens = %6zu, #acc tokens = %5zu%s\n",
                 common_speculative_type_to_str(impl->type).c_str(),
                 impl->n_call_begin, impl->n_call_draft, impl->n_call_accept,
                 impl->n_gen_drafts,

common/speculative.h

@@ -53,6 +53,12 @@ void common_speculative_begin(common_speculative * spec, llama_seq_id seq_id, co
 // process the batch and update the internal state of the speculative context
 bool common_speculative_process(common_speculative * spec, const llama_batch & batch);
 
+// true if any implementation requires target post-norm embeddings to be extracted
+bool common_speculative_need_embd(common_speculative * spec);
+
+// true if any implementation requires target pre-norm embeddings to be extracted
+bool common_speculative_need_embd_pre_norm(common_speculative * spec);
+
 // generate drafts for the sequences specified with `common_speculative_get_draft_params`
 void common_speculative_draft(common_speculative * spec);
 

conversion/base.py

@@ -91,6 +91,7 @@ class ModelBase:
     gguf_writer: gguf.GGUFWriter
     model_name: str | None
     metadata_override: Path | None
+    metadata: gguf.Metadata
     dir_model_card: Path
     remote_hf_model_id: str | None
 
@@ -106,6 +107,11 @@ class ModelBase:
     disable_mistral_community_chat_template: bool = False
     sentence_transformers_dense_modules: bool = False
 
+    # MTP (multi-token prediction) export modes; set by main() before instantiation.
+    # Architectures opt in by overriding the handling (see _Qwen35MtpMixin).
+    mtp_only: bool = False
+    no_mtp: bool = False
+
     def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, *, is_big_endian: bool = False,
                  use_temp_file: bool = False, eager: bool = False,
                  metadata_override: Path | None = None, model_name: str | None = None,
@@ -1604,6 +1610,47 @@ class TextModel(ModelBase):
         special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
         special_vocab.add_to_gguf(self.gguf_writer)
 
+    def _set_vocab_hybriddna(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))  # ty: ignore[unresolved-attribute]
+        assert max(tokenizer.vocab.values()) < vocab_size  # ty: ignore[unresolved-attribute]
+
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
+        # k-mers can share text with a base-vocab BPE token (e.g. CCCCCC) and get
+        # dropped by get_vocab(); a reserved marker suffix (U+E000) keeps each
+        # k-mer's own id (llama.cpp strips it on detokenization)
+        for kmer in tokenizer.kmers:  # ty: ignore[unresolved-attribute]
+            reverse_vocab[tokenizer.dna_token_to_id[kmer]] = kmer + "\ue000"  # ty: ignore[unresolved-attribute]
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
+        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]
+
+        tokens: list[str] = []
+        toktypes: list[int] = []
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            else:
+                token: str = reverse_vocab[i]
+                if token in added_vocab:
+                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                else:
+                    toktypes.append(gguf.TokenType.NORMAL)
+                tokens.append(token)
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+        self.gguf_writer.add_tokenizer_model("hybriddna")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
     def _set_vocab_qwen(self):
         from .qwen import QwenModel
 

conversion/hunyuan.py

@@ -189,7 +189,8 @@ class HunYuanModel(TextModel):
             self.gguf_writer.add_token_list(tokens)
             self.gguf_writer.add_token_types(toktypes)
 
-            # HunyuanOCR has pad_token_id=-1 in config.json; exclude pad from SpecialVocab
+            # Some HunYuanVL variants (e.g. OCR-style configs) have pad_token_id=-1;
+            # guard SpecialVocab so it doesn't try to emit an invalid pad id.
             token_types = None
             if (self.hparams.get("pad_token_id") or 0) < 0:
                 token_types = ('bos', 'eos', 'unk', 'sep', 'cls', 'mask')
@@ -250,7 +251,8 @@ class HunYuanModel(TextModel):
             self._fix_special_tokens()
 
     def set_gguf_parameters(self):
-        # HunyuanOCR has num_experts=1 which is not MoE, prevent parent from writing it
+        # Some HunYuanVL variants set num_experts=1 (not real MoE);
+        # prevent the parent class from emitting expert_count metadata in that case.
         saved_num_experts = self.hparams.pop("num_experts", None)
         super().set_gguf_parameters()
         if saved_num_experts is not None and saved_num_experts > 1:
@@ -288,51 +290,21 @@ class HunYuanModel(TextModel):
 
 @ModelBase.register("HunYuanVLForConditionalGeneration")
 class HunyuanVLVisionModel(MmprojModel):
-    # Handles both HunyuanOCR and HunyuanVL, which share the HF architecture name
-    # "HunYuanVLForConditionalGeneration" and the `vit.perceive.*` vision layout.
-    # Each variant maps to a different projector type in clip.cpp so image
-    # preprocessing follows the correct code path.
-
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         assert self.hparams_vision is not None
-        # HunyuanOCR / HunyuanVL uses max_image_size instead of image_size
+        # HunyuanVL uses max_image_size instead of image_size
         if "image_size" not in self.hparams_vision:
             self.hparams_vision["image_size"] = self.hparams_vision.get("max_image_size", 2048)
 
-    @staticmethod
-    def is_ocr_variant(hparams: dict) -> bool:
-        """Return True for HunyuanOCR, False for HunyuanVL.
-
-        The projector's output dim must equal the text model's hidden_size by
-        construction (that's what "projector" means). HunyuanOCR pairs a 1B text
-        backbone (hidden=1024); HunyuanVL pairs a 4B one (hidden=3072). So the
-        ViT -> LLM projection dim is a hard architectural signature, not a
-        magic number.
-        """
-        vision_out = int((hparams.get("vision_config") or {}).get("out_hidden_size", 0))
-        return vision_out == 1024
-
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         assert self.hparams_vision is not None
         vcfg = self.hparams_vision
-
-        if self.is_ocr_variant(self.global_config):
-            # --- HunyuanOCR ---
-            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.HUNYUANOCR)
-            self.gguf_writer.add_vision_use_gelu(True)
-            self.gguf_writer.add_vision_attention_layernorm_eps(vcfg.get("rms_norm_eps", 1e-5))
-            self.gguf_writer.add_vision_spatial_merge_size(vcfg.get("spatial_merge_size", 2))
-            self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
-            self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
-            return
-
-        # --- HunyuanVL ---
         self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.HUNYUANVL)
-        self.gguf_writer.add_vision_use_gelu(str(vcfg["hidden_act"]).lower() == "gelu")
-        self.gguf_writer.add_vision_attention_layernorm_eps(float(vcfg["rms_norm_eps"]))
-        self.gguf_writer.add_vision_spatial_merge_size(int(vcfg["spatial_merge_size"]))
+        self.gguf_writer.add_vision_use_gelu(True)
+        self.gguf_writer.add_vision_attention_layernorm_eps(vcfg.get("rms_norm_eps", 1e-5))
+        self.gguf_writer.add_vision_spatial_merge_size(vcfg.get("spatial_merge_size", 2))
         self.gguf_writer.add_vision_min_pixels(int(self.preprocessor_config["min_pixels"]))
         self.gguf_writer.add_vision_max_pixels(int(self.preprocessor_config["max_pixels"]))
 
@@ -353,7 +325,7 @@ class HunyuanVLVisionModel(MmprojModel):
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
         # force conv weights to F32 or F16 to avoid BF16 IM2COL issues on Metal
-        # Both HunyuanOCR and HunyuanVL emit the ViT -> LLM projection as mm.0/mm.2.
+        # HunyuanVL emit the ViT -> LLM projection as mm.0/mm.2.
         if ("mm.0." in new_name or "mm.2." in new_name) and new_name.endswith(".weight"):
             return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
         return super().tensor_force_quant(name, new_name, bid, n_dims)
@@ -361,40 +333,18 @@ class HunyuanVLVisionModel(MmprojModel):
 
 @ModelBase.register("HunYuanVLForConditionalGeneration")
 class HunyuanVLTextModel(HunYuanModel):
-    # The "HunYuanVLForConditionalGeneration" HF architecture covers both HunyuanOCR
-    # and HunyuanVL. HunyuanOCR reuses the HunYuan-Dense text backbone (standard RoPE),
-    # while HunyuanVL introduces a new LLM arch with XD-RoPE. Detect the variant from
-    # the config and pick the matching GGUF architecture.
     model_arch = gguf.MODEL_ARCH.HUNYUAN_VL
 
-    @staticmethod
-    def _is_ocr_config(hparams: dict) -> bool:
-        # OCR pairs a 1B text backbone (hidden=1024) with a ViT projector that
-        # outputs 1024-d; HunyuanVL uses 3072-d. Keep in sync with
-        # HunyuanVLVisionModel.is_ocr_variant.
-        return int((hparams.get("vision_config") or {}).get("out_hidden_size", 0)) == 1024
-
     def __init__(self, dir_model: Path, *args, **kwargs):
-        raw_hparams = kwargs.get("hparams") or ModelBase.load_hparams(dir_model, is_mistral_format=False)
-        if self._is_ocr_config(raw_hparams):
-            self.model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE
-        else:
-            self.model_arch = gguf.MODEL_ARCH.HUNYUAN_VL
         super().__init__(dir_model, *args, **kwargs)
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
-        # Only emit XD-RoPE metadata for the HunyuanVL backbone; HunyuanOCR uses
-        # the HunYuan-Dense arch which already handles standard rope in super().
-        if self.model_arch != gguf.MODEL_ARCH.HUNYUAN_VL:
-            return
-
+        # XD-RoPE metadata for the HunyuanVL;
         if self.rope_parameters.get("rope_type") != "xdrope":
             return
 
-        # defaults for HunyuanVL. The C++ side later computes:
-        #   freq_base = rope_theta * alpha ** (head_dim / (head_dim - 2))
         self.gguf_writer.add_rope_freq_base(float(self.rope_parameters["rope_theta"]))
         self.gguf_writer.add_rope_scaling_alpha(float(self.rope_parameters["alpha"]))
         self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)

conversion/llama.py

@@ -51,6 +51,15 @@ class LlamaModel(TextModel):
         if path_tekken_json.is_file() and not path_tokenizer_json.is_file():
             self._set_vocab_mistral()
 
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                if (add_prefix_space := tokenizer_config_json.get("add_prefix_space")) is not None:
+                    self.gguf_writer.add_add_space_prefix(add_prefix_space)
+                if tokenizer_config_json.get("tokenizer_class") == "HybridDNATokenizer":
+                    return self._set_vocab_hybriddna()
+
         try:
             self._set_vocab_sentencepiece()
         except FileNotFoundError:
@@ -72,13 +81,6 @@ class LlamaModel(TextModel):
             special_vocab._set_special_token("eot",    32010)
             special_vocab.add_to_gguf(self.gguf_writer)
 
-        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
-        if tokenizer_config_file.is_file():
-            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-                tokenizer_config_json = json.load(f)
-                if "add_prefix_space" in tokenizer_config_json:
-                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
-
         # Apply to granite small models only
         if self.hparams.get("vocab_size", 32000) == 49152:
             self.gguf_writer.add_add_bos_token(False)

conversion/qwen.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
-from typing import Callable, Iterable, TYPE_CHECKING
+from pathlib import Path
+from typing import Any, Callable, Iterable, TYPE_CHECKING
 
 import torch
 
@@ -534,11 +535,94 @@ class _Qwen35MRopeMixin:
             self.gguf_writer.add_rope_dimension_sections(self._QWEN35_DEFAULT_MROPE_SECTION)
 
 
+class _Qwen35MtpMixin:
+    """Shared MTP wiring for Qwen3.5/3.6 text variants. The HF config carries
+    the MTP block under `mtp_num_hidden_layers` and the tensors under
+    `mtp.*`; we extend block_count, emit the nextn metadata key, and remap
+    `mtp.*` to the standard layer-indexed nextn naming so the existing
+    tensor_map handles them."""
+
+    hparams: dict[str, Any]
+    model_arch: gguf.MODEL_ARCH
+    gguf_writer: gguf.GGUFWriter
+    block_count: int
+    tensor_map: gguf.TensorNameMap
+    no_mtp: bool
+    mtp_only: bool
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.block_count = self.hparams["num_hidden_layers"]
+        if not self.no_mtp:
+            self.block_count += self.hparams.get("mtp_num_hidden_layers", 0)
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    @classmethod
+    def filter_tensors(cls, item):
+        name, _ = item
+        if name.startswith("mtp."):
+            if cls.no_mtp:
+                return None
+            return item
+        if cls.mtp_only:
+            canonical = name.replace("language_model.", "")
+            keep = canonical in (
+                "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+                "embed_tokens.weight", "norm.weight",
+            )
+            if not keep:
+                return None
+        return super().filter_tensors(item)  # ty: ignore[unresolved-attribute]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()  # ty: ignore[unresolved-attribute]
+        if self.no_mtp:
+            return
+        if (n := self.hparams.get("mtp_num_hidden_layers", 0)) > 0:
+            self.gguf_writer.add_nextn_predict_layers(n)
+
+    def prepare_metadata(self, vocab_only: bool):
+        from_dir = self.fname_out.is_dir()
+        super().prepare_metadata(vocab_only=vocab_only)  # ty: ignore[unresolved-attribute]
+
+        if not self.mtp_only or not from_dir:
+            return
+
+        output_type: str = self.ftype.name.partition("_")[2]  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,                  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)    # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("mtp."):
+            n_layer = self.hparams["num_hidden_layers"]
+            if name.find("layers.") != -1:
+                assert bid is not None
+                name = name.replace(f"mtp.layers.{bid}", f"model.layers.{bid + n_layer}")
+                bid = bid + n_layer
+            else:
+                remapper = {
+                    "mtp.fc":                    "model.layers.{bid}.eh_proj",
+                    "mtp.pre_fc_norm_embedding": "model.layers.{bid}.enorm",
+                    "mtp.pre_fc_norm_hidden":    "model.layers.{bid}.hnorm",
+                    "mtp.norm":                  "model.layers.{bid}.shared_head.norm",
+                }
+                stem   = Path(name).stem
+                suffix = Path(name).suffix
+                tmpl   = remapper[stem] + suffix
+                for b in range(n_layer, self.block_count):
+                    yield from super().modify_tensors(data_torch, tmpl.format(bid=b), b)  # ty: ignore[unresolved-attribute]
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)  # ty: ignore[unresolved-attribute]
+
+
 @ModelBase.register("Qwen3_5ForConditionalGeneration", "Qwen3_5ForCausalLM")
-class Qwen3_5TextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase):
+class Qwen3_5TextModel(_Qwen35MtpMixin, _Qwen35MRopeMixin, _LinearAttentionVReorderBase):
     model_arch = gguf.MODEL_ARCH.QWEN35
 
 
 @ModelBase.register("Qwen3_5MoeForConditionalGeneration", "Qwen3_5MoeForCausalLM")
-class Qwen3_5MoeTextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase):
+class Qwen3_5MoeTextModel(_Qwen35MtpMixin, _Qwen35MRopeMixin, _LinearAttentionVReorderBase):
     model_arch = gguf.MODEL_ARCH.QWEN35MOE

convert_hf_to_gguf.py

@@ -115,7 +115,15 @@ def parse_args() -> argparse.Namespace:
     )
     parser.add_argument(
         "--mmproj", action="store_true",
-        help="(Experimental) Export multimodal projector (mmproj) for vision models. This will only work on some vision models. A prefix 'mmproj-' will be added to the output file name.",
+        help="Export multimodal projector (mmproj) for vision models. This will only work on some vision models. An 'mmproj-' prefix will be added to the output file name.",
+    )
+    parser.add_argument(
+        "--mtp", action="store_true",
+        help="Export only the multi-token prediction (MTP) head as a separate GGUF, suitable for use as a speculative draft. An 'mtp-' prefix will be added to the output file name.",
+    )
+    parser.add_argument(
+        "--no-mtp", action="store_true",
+        help="Exclude the multi-token prediction (MTP) head from the converted GGUF. Pair with --mtp on a second run to publish trunk and MTP as two files. Note: the split form duplicates embeddings, but even though the bundled default is more space-efficient overall, this allows differing quantization which may be more performant.",
     )
     parser.add_argument(
         "--mistral-format", action="store_true",
@@ -233,6 +241,20 @@ def main() -> None:
             from conversion.mistral import MistralModel
             model_class = MistralModel
 
+        if args.mtp and args.no_mtp:
+            logger.error("--mtp and --no-mtp are mutually exclusive")
+            sys.exit(1)
+
+        if args.mtp or args.no_mtp:
+            from conversion.qwen import _Qwen35MtpMixin
+            if not issubclass(model_class, _Qwen35MtpMixin):
+                logger.error("--mtp / --no-mtp are only supported for Qwen3.5/3.6 text variants today")
+                sys.exit(1)
+            if args.no_mtp:
+                model_class.no_mtp = True
+            if args.mtp:
+                model_class.mtp_only = True
+
         model_instance = model_class(dir_model, output_type, fname_out,
                                      is_big_endian=args.bigendian, use_temp_file=args.use_temp_file,
                                      eager=args.no_lazy,

convert_lora_to_gguf.py

@@ -445,6 +445,11 @@ if __name__ == '__main__':
                     if self.lazy:
                         tensor = LazyTorchTensor.from_eager(tensor)
                     base_name = get_base_tensor_name(name)
+                    # filter base name, ignore tensor transformations for now
+                    data_gen = lambda g=tensor: g  # noqa: E731
+                    if (titem := self.filter_tensors((base_name, data_gen))) is None:
+                        continue
+                    base_name, _ = titem
                     # note: mergekit-extract-lora also adds token embeddings to the adapter
                     is_lora_a = ".lora_A.weight" in name or ".lora_embedding_A" in name
                     is_lora_b = ".lora_B.weight" in name or ".lora_embedding_B" in name

docs/backend/SYCL.md

@@ -5,6 +5,7 @@
 - [News](#news)
 - [OS](#os)
 - [Hardware](#hardware)
+- [Performance Reference](#performance-reference)
 - [Docker](#docker)
 - [Linux](#linux)
 - [Windows](#windows)
@@ -51,9 +52,8 @@ The packages for FP32 and FP16 would have different accuracy and performance on
 
 ## News
 
-- 2026.04
-
-  - Optimize mul_mat by reorder feature for data type: Q4_K, Q5_K, Q_K, Q8_0.
+- 2026.04-05
+  - Optimize mul_mat by reorder feature for data type: Q4_K, Q5_K, Q6_K, Q8_0.
   - Fused MoE.
   - Upgrate CI and built package for oneAPI 2025.3.3, support Ubuntu 24.04 built package.
 
@@ -150,6 +150,13 @@ On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the
 
 NA
 
+## Performance Reference
+
+
+To get the supported LLMs, GPUs, and performance reference, please check [Performance of llama.cpp on Intel GPU with SYCL backend](https://github.com/ggml-org/llama.cpp/discussions/23313).
+
+You could update your test result in it directly.
+
 ## Docker
 
 The docker build option is currently limited to *Intel GPU* targets.

docs/backend/snapdragon/CMakeUserPresets.json

@@ -10,8 +10,8 @@
             "ANDROID_ABI":      "arm64-v8a",
             "ANDROID_PLATFORM": "android-31",
             "CMAKE_TOOLCHAIN_FILE": "$env{ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake",
-            "CMAKE_C_FLAGS":   "-march=armv8.7a+fp16 -fvectorize -ffp-model=fast -fno-finite-math-only -flto -D_GNU_SOURCE",
-            "CMAKE_CXX_FLAGS": "-march=armv8.7a+fp16 -fvectorize -ffp-model=fast -fno-finite-math-only -flto -D_GNU_SOURCE",
+            "CMAKE_C_FLAGS":   "-march=armv8.7a+fp16+dotprod+i8mm -fvectorize -ffp-model=fast -fno-finite-math-only -flto -D_GNU_SOURCE",
+            "CMAKE_CXX_FLAGS": "-march=armv8.7a+fp16+dotprod+i8mm -fvectorize -ffp-model=fast -fno-finite-math-only -flto -D_GNU_SOURCE",
             "CMAKE_C_FLAGS_RELEASE":          "-O3 -DNDEBUG",
             "CMAKE_CXX_FLAGS_RELEASE":        "-O3 -DNDEBUG",
             "CMAKE_C_FLAGS_RELWITHDEBINFO":   "-O3 -DNDEBUG -g",
@@ -59,8 +59,8 @@
         "toolset":      { "value": "host=x86_64", "strategy": "external" },
         "cacheVariables": {
             "CMAKE_TOOLCHAIN_FILE": "cmake/arm64-linux-clang.cmake",
-            "CMAKE_C_FLAGS":   "-march=armv8 -fno-finite-math-only -flto -D_GNU_SOURCE",
-            "CMAKE_CXX_FLAGS": "-march=armv8 -fno-finite-math-only -flto -D_GNU_SOURCE",
+            "CMAKE_C_FLAGS":   "-march=armv8.2a+fp16+dotprod -fvectorize -fno-finite-math-only -flto -D_GNU_SOURCE",
+            "CMAKE_CXX_FLAGS": "-march=armv8.2a+fp16+dotprod -fvectorize -fno-finite-math-only -flto -D_GNU_SOURCE",
             "CMAKE_C_FLAGS_RELEASE":          "-O3 -DNDEBUG",
             "CMAKE_CXX_FLAGS_RELEASE":        "-O3 -DNDEBUG",
             "CMAKE_C_FLAGS_RELWITHDEBINFO":   "-O3 -DNDEBUG -g",

docs/backend/snapdragon/README.md

@@ -10,7 +10,7 @@ This image includes Android NDK, OpenCL SDK, Hexagon SDK, CMake, etc.
 This method works on Linux, macOS, and Windows. macOS and Windows users should install Docker Desktop.
 
 ```
-~/src/llama.cpp$ docker run -it -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-android:v0.3
+~/src/llama.cpp$ docker run -it -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-android:v0.6
 [d]/> cd /workspace
 ```
 

docs/build.md

@@ -735,7 +735,7 @@ ninja
 
 To read documentation for how to build on Android, [click here](./android.md)
 
-## WebGPU [In Progress]
+## WebGPU
 
 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`.
 

docs/speculative.md

@@ -108,11 +108,12 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
 ### General Speculative Parameters
 
 ```
---spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
-                                        type of speculative decoding to use when no draft model is provided
+--spec-type [none|draft-simple|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
+                                        comma-separated list of types of speculative decoding to use
                                         (default: none)
                                         (env: LLAMA_ARG_SPEC_TYPE)
---spec-default                          use default speculative decoding
+--spec-default                          use default speculative decoding config
+                                        (enables ngram-mod)
 ```
 
 ### Draft Model Parameters
@@ -123,8 +124,9 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
                                         (env: LLAMA_ARG_SPEC_DRAFT_MODEL)
 --spec-draft-hf, -hfd, -hfrd, --hf-repo-draft  <user>/<model>[:quant]
                                         HuggingFace repository for the draft model
+                                        (env: LLAMA_ARG_SPEC_DRAFT_HF_REPO)
 --spec-draft-n-max                      N
-                                        number of tokens to draft for speculative decoding (default: 16)
+                                        number of tokens to draft for speculative decoding (default: 3)
                                         (env: LLAMA_ARG_SPEC_DRAFT_N_MAX)
 --spec-draft-n-min                      N
                                         minimum number of draft tokens to use for speculative decoding (default: 0)
@@ -133,18 +135,64 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
                                         speculative decoding split probability (default: 0.10)
                                         (env: LLAMA_ARG_SPEC_DRAFT_P_SPLIT)
 --spec-draft-p-min, --draft-p-min       P
-                                        minimum speculative decoding probability (greedy) (default: 0.75)
+                                        minimum speculative decoding probability (greedy) (default: 0.00)
                                         (env: LLAMA_ARG_SPEC_DRAFT_P_MIN)
---spec-draft-ctx-size, -cd, --ctx-size-draft  N
-                                        size of the prompt context for the draft model (default: 0, 0 = loaded from model)
-                                        (env: LLAMA_ARG_SPEC_DRAFT_CTX_SIZE)
 --spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft  N
                                         max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)
                                         (env: LLAMA_ARG_N_GPU_LAYERS_DRAFT)
 --spec-draft-device, -devd, --device-draft  <dev1,dev2,..>
                                         comma-separated list of devices to use for offloading the draft model
---spec-draft-replace, --spec-replace    TARGET  DRAFT
-                                        translate the string in TARGET into DRAFT if the draft model and main model are not compatible
+                                        (use --list-devices to see available devices)
+```
+
+### Draft Model CPU Scheduling Parameters
+
+```
+--spec-draft-threads, -td, --threads-draft  N
+                                        number of CPU threads to use during generation
+--spec-draft-threads-batch, -tbd, --threads-batch-draft  N
+                                        number of threads to use during batch and prompt processing (default: same as --threads-draft)
+--spec-draft-cpu-mask, -Cd, --cpu-mask-draft  M
+                                        Draft model CPU affinity mask. Complements cpu-range-draft
+--spec-draft-cpu-range, -Crd, --cpu-range-draft  lo-hi
+                                        Ranges of CPUs for affinity. Complements --cpu-mask-draft
+--spec-draft-cpu-strict, --cpu-strict-draft  <0|1>
+                                        Use strict CPU placement for draft model (default: same as --cpu-strict)
+--spec-draft-prio, --prio-draft  N
+                                        set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime
+--spec-draft-poll, --poll-draft  <0|1>
+                                        Use polling to wait for draft model work (default: same as --poll)
+--spec-draft-cpu-mask-batch, -Cbd, --cpu-mask-batch-draft  M
+                                        Draft model CPU affinity mask for batch. Complements cpu-range-batch-draft
+--spec-draft-cpu-range-batch, -Crbd, --cpu-range-batch-draft  lo-hi
+                                        Ranges of CPUs for affinity for batch. Complements --cpu-mask-batch-draft
+--spec-draft-cpu-strict-batch, --cpu-strict-batch-draft  <0|1>
+                                        Use strict CPU placement for draft model batch (default: --cpu-strict-draft)
+--spec-draft-prio-batch, --prio-batch-draft  N
+                                        set draft process/thread priority for batch : 0-normal, 1-medium, 2-high, 3-realtime
+--spec-draft-poll-batch, --poll-batch-draft  <0|1>
+                                        Use polling to wait for draft model work for batch (default: --poll-draft)
+```
+
+### Draft Model KV Cache and Tensor Override Parameters
+
+```
+--spec-draft-type-k, -ctkd, --cache-type-k-draft  TYPE
+                                        KV cache data type for K for the draft model
+                                        allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
+                                        (env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_K)
+--spec-draft-type-v, -ctvd, --cache-type-v-draft  TYPE
+                                        KV cache data type for V for the draft model
+                                        allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
+                                        (env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_V)
+--spec-draft-override-tensor, -otd, --override-tensor-draft  <tensor name pattern>=<buffer type>,...
+                                        override tensor buffer type for draft model
+--spec-draft-cpu-moe, -cmoed, --cpu-moe-draft
+                                        keep all Mixture of Experts (MoE) weights in the CPU for the draft model
+                                        (env: LLAMA_ARG_SPEC_DRAFT_CPU_MOE)
+--spec-draft-n-cpu-moe, --spec-draft-ncmoe, -ncmoed, --n-cpu-moe-draft  N
+                                        keep the MoE weights of the first N layers in the CPU for the draft model
+                                        (env: LLAMA_ARG_SPEC_DRAFT_N_CPU_MOE)
 ```
 
 ### n-gram Mod Parameters
@@ -193,11 +241,13 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
 
 ### `--spec-type TYPE`
 
-Specifies a type of speculative decoding without draft model.
+Specifies a comma-separated list of speculative decoding types to use.
 
 | Type | Description |
 |------|-------------|
 | `none` | No speculative decoding (default) |
+| `draft-simple` | Use a simple draft model for speculation |
+| `draft-mtp` | Use Multi Token Prediction (MTP) heads from the main model |
 | `ngram-cache` | Use n-gram cache lookup |
 | `ngram-simple` | Use simple n-gram pattern matching |
 | `ngram-map-k` | Use n-gram pattern matching with n-gram-keys |
@@ -209,6 +259,11 @@ Specifies a type of speculative decoding without draft model.
 ./llama-server [...] --spec-type ngram-simple
 ```
 
+**Example:** Multiple speculative implementations.
+```bash
+./llama-server [...] --spec-type ngram-mod,ngram-map-k4v
+```
+
 ### `--spec-ngram-*-size-n N`
 
 Sets the size N of the lookup n-gram for n-gram map based speculative decoding.

examples/CMakeLists.txt

@@ -27,7 +27,6 @@ else()
     add_subdirectory(parallel)
     add_subdirectory(passkey)
     add_subdirectory(retrieval)
-    add_subdirectory(save-load-state)
     add_subdirectory(simple)
     add_subdirectory(simple-chat)
     add_subdirectory(speculative)

examples/llama-eval/llama-eval.py

@@ -149,6 +149,8 @@ class TaskState:
     t_gen_ms: Optional[float] = None
     reasoning_content: Optional[str] = None
     server_name: Optional[str] = None
+    chunk_idx: int = 0
+    problem_idx: int = 0
 
 
 class EvalState:
@@ -233,7 +235,9 @@ class EvalState:
         tps_gen: Optional[float] = None,
         t_gen_ms: Optional[float] = None,
         reasoning_content: Optional[str] = None,
-        server_name: Optional[str] = None
+        server_name: Optional[str] = None,
+        chunk_idx: int = 0,
+        problem_idx: int = 0,
     ):
         with self._lock:
             if "cases" not in self.task_states:
@@ -252,7 +256,9 @@ class EvalState:
                 "tps_gen": tps_gen,
                 "t_gen_ms": t_gen_ms,
                 "reasoning_content": reasoning_content,
-                "server_name": server_name
+                "server_name": server_name,
+                "chunk_idx": chunk_idx,
+                "problem_idx": problem_idx,
             }
 
             self.correct = sum(1 for c in self.task_states.get("cases", {}).values() if c.get("correct", False))
@@ -289,6 +295,9 @@ class EvalState:
             all_cases = {}
             for i, task_id in tasks_to_save:
                 question_text, prompt, expected = self.get_case(i)
+                # Extract chunk_idx from task_id for pending cases
+                _parts = task_id.rsplit("_", 2)
+                _chunk_idx = int(_parts[-2]) if len(_parts) >= 3 else 0
                 if task_id in self.task_states.get("cases", {}):
                     all_cases[task_id] = self.task_states["cases"][task_id]
                 else:
@@ -306,7 +315,9 @@ class EvalState:
                         "tps_gen": None,
                         "t_gen_ms": None,
                         "reasoning_content": None,
-                        "server_name": None
+                        "server_name": None,
+                        "chunk_idx": _chunk_idx,
+                        "problem_idx": i,
                     }
 
             ci_lower, ci_upper = self.accuracy_ci()
@@ -382,11 +393,12 @@ class EvalState:
             grader_log_str = self._escape_html(json.dumps(grader_log, indent=2))
             escaped_server = self._escape_html(server_name)
 
+            answer_class = status_class if status == "ok" else ""
             rows.append(f"""<tr class="task-row" onclick="toggleDetails('{task_id}')">
                 <td>{task_id}</td>
                 <td class="{status_class}">{status_text}</td>
                 <td>{self._escape_html(expected)}</td>
-                <td>{self._escape_html(answer)}</td>
+                <td class="{answer_class}">{self._escape_html(answer)}</td>
                 <td>{tokens_str}</td>
                 <td>{tps_str}</td>
                 <td>{t_gen_str}</td>
@@ -405,6 +417,53 @@ class EvalState:
 
         rows_html = "\n".join(rows)
 
+        # ---- per-problem summary table ----
+        problem_groups: Dict[int, List[Dict[str, Any]]] = {}
+        for _tid, _case in cases.items():
+            if _case.get("status") != "ok":
+                continue
+            _pidx = _case.get("problem_idx")
+            if _pidx is None:
+                _p_parts = _tid.rsplit("_", 2)
+                _pidx = int(_p_parts[-1]) if len(_p_parts) >= 3 else 0
+            problem_groups.setdefault(_pidx, []).append(_case)
+
+        summary_rows_html = ""
+        if problem_groups:
+            def _stat(v, fmt=".1f", avg_fmt=None):
+                if not v:
+                    return ("–", "–", "–")
+                af = fmt if avg_fmt is None else avg_fmt
+                return (f"{min(v):{fmt}}", f"{sum(v)/len(v):{af}}", f"{max(v):{fmt}}")
+
+            summary_data = []
+            for pidx, g in problem_groups.items():
+                runs = len(g)
+                n_ok = sum(1 for c in g if c.get("correct", False))
+                toks = [c["tokens"] for c in g if c.get("tokens") is not None]
+                tps = [c["tps_gen"] for c in g if c.get("tps_gen") is not None]
+                tg = [c["t_gen_ms"] / 1000 for c in g if c.get("t_gen_ms") is not None]
+                summary_data.append((
+                    pidx, runs, n_ok,
+                    _stat(toks, "d", ".0f"),
+                    _stat(tps),
+                    _stat(tg),
+                ))
+
+            summary_data.sort(key=lambda r: r[0])  # sort by problem index ascending
+
+            summary_rows_html = "\n".join(
+                f"""<tr class="summary-row">
+                    <td>{p:03d}</td>
+                    <td>{r}</td>
+                    <td>{n}/{r}</td>
+                    <td>{tk[0]}</td><td>{tk[1]}</td><td>{tk[2]}</td>
+                    <td>{tp[0]}</td><td>{tp[1]}</td><td>{tp[2]}</td>
+                    <td>{tg[0]}</td><td>{tg[1]}</td><td>{tg[2]}</td>
+                </tr>"""
+                for p, r, n, tk, tp, tg in summary_data
+            )
+
         html_content = f"""<!DOCTYPE html>
 <html>
 <head>
@@ -412,10 +471,10 @@ class EvalState:
 <title>{self.dataset_type.upper()} Eval</title>
 <style>
         body {{ font-family: system-ui, sans-serif; margin: 0; padding: 16px; background: #fff; color: #222; }}
-        .bar {{ padding: 8px 0; font-size: 14px; color: #555; }}
-        .bar span {{ margin-right: 20px; }}
-        .bar b {{ color: #222; }}
-        table {{ width: 100%; border-collapse: collapse; font-size: 13px; }}
+        .bar {{ padding: 8px 0; font-size: 13px; color: #555; font-family: 'SF Mono', 'Menlo', 'Consolas', monospace; display: grid; grid-template-columns: auto 1fr auto 1fr; gap: 2px 12px; align-items: baseline; }}
+        .bar .label {{ color: #888; }}
+        .bar .value {{ color: #222; }}
+        table {{ width: 100%; border-collapse: collapse; font-size: 13px; font-family: 'SF Mono', 'Menlo', 'Consolas', monospace; }}
         th {{ text-align: left; padding: 6px 8px; border-bottom: 2px solid #ccc; font-weight: 600; }}
         td {{ padding: 4px 8px; border-bottom: 1px solid #eee; vertical-align: top; }}
         .task-row {{ cursor: pointer; }}
@@ -429,37 +488,88 @@ class EvalState:
         .details-content {{ padding: 8px 16px; background: #f6f8fa; font-size: 12px; }}
         .details-content b {{ color: #555; }}
         .details-content pre {{ background: #fff; border: 1px solid #e1e4e8; padding: 8px; overflow-x: auto; white-space: pre-wrap; word-wrap: break-word; margin: 4px 0 8px; }}
+        .summary-table {{ margin-bottom: 16px; font-size: 13px; width: 100%; }}
+        .summary-row {{ background: #fafbfc; }}
+        .summary-row:hover {{ background: #f5f5f5; }}
+        .summary-table th {{ text-align: right; font-weight: 600; }}
+        .summary-table th:first-child {{ text-align: left; }}
+        .summary-table th[colspan] {{ text-align: center; }}
+        .summary-table td {{ text-align: right; }}
+        .summary-table td:first-child {{ text-align: left; }}
+        .tabs {{ display: flex; border-bottom: 2px solid #ddd; margin: 12px 0 0; }}
+        .tab-btn {{ padding: 6px 16px; border: none; background: none; font-size: 13px; cursor: pointer; color: #555; border-bottom: 2px solid transparent; margin-bottom: -2px; font-weight: 500; }}
+        .tab-btn:hover {{ color: #222; }}
+        .tab-btn.active {{ color: #222; border-bottom-color: #222; font-weight: 600; }}
+        .tab-content {{ display: none; }}
+        .tab-content.active {{ display: block; }}
 </style>
 </head>
 <body>
     <div class="bar">
-        <span><b>{self.dataset_type.upper()}</b></span>
-        <span>Model: {self.model_name or 'N/A'}</span>
-        <span>Accuracy: <b>{accuracy:.1f}%</b> [{ci_lower*100:.1f}%, {ci_upper*100:.1f}%]</span>
-        <span>Correct: <span class="correct">{n_correct}</span> / {len(completed)}</span>
-        <span>Pending: {n_pending}</span>
-        <span>Time: {self.total_time:.1f}s</span>
-        <span>Sampling: {sampling_str}</span>
+        <div class="label">Dataset</div><div class="value"><b>{self.dataset_type.upper()}</b></div>
+        <div class="label">Model</div><div class="value"><b>{self.model_name or 'N/A'}</b></div>
+        <div class="label">Accuracy</div><div class="value"><b>{accuracy:.1f}%</b> [{ci_lower*100:.1f}%, {ci_upper*100:.1f}%]</div>
+        <div class="label">Correct</div><div class="value"><span class="correct">{n_correct}</span> / {len(completed)}</div>
+        <div class="label">Pending</div><div class="value">{n_pending}</div>
+        <div class="label">Time</div><div class="value">{self.total_time:.1f}s</div>
+        <div class="label">Sampling</div><div class="value">{sampling_str}</div>
+    </div>
+    <div class="tabs">
+        <button class="tab-btn active" data-tab="detailed" onclick="switchTab(this)">Detailed</button>
+        <button class="tab-btn" data-tab="summary" onclick="switchTab(this)">Summary</button>
+    </div>
+    <div id="tab-detailed" class="tab-content active">
+        <table>
+            <thead>
+                <tr>
+                    <th>ID</th>
+                    <th></th>
+                    <th>Gold</th>
+                    <th>Answer</th>
+                    <th>Tokens</th>
+                    <th>T/s</th>
+                    <th>Gen s</th>
+                    <th>Server</th>
+                </tr>
+            </thead>
+            <tbody>
+                {rows_html}
+            </tbody>
+        </table>
+    </div>
+    <div id="tab-summary" class="tab-content">
+        <table class="summary-table">
+            <thead>
+                <tr>
+                    <th>Problem</th>
+                    <th>Runs</th>
+                    <th>Correct</th>
+                    <th colspan="3">Tokens</th>
+                    <th colspan="3">T/s</th>
+                    <th colspan="3">Gen s</th>
+                </tr>
+                <tr>
+                    <th></th>
+                    <th></th>
+                    <th></th>
+                    <th>min</th><th>avg</th><th>max</th>
+                    <th>min</th><th>avg</th><th>max</th>
+                    <th>min</th><th>avg</th><th>max</th>
+                </tr>
+            </thead>
+            <tbody>
+                {summary_rows_html}
+            </tbody>
+        </table>
     </div>
-    <table>
-        <thead>
-            <tr>
-                <th>ID</th>
-                <th></th>
-                <th>Gold</th>
-                <th>Answer</th>
-                <th>Tokens</th>
-                <th>T/s</th>
-                <th>Gen s</th>
-                <th>Server</th>
-            </tr>
-        </thead>
-        <tbody>
-            {rows_html}
-        </tbody>
-    </table>
     <script>
         function toggleDetails(id) {{ document.getElementById('details-'+id).classList.toggle('open'); }}
+        function switchTab(btn) {{
+            document.querySelectorAll('.tab-btn').forEach(b => b.classList.remove('active'));
+            document.querySelectorAll('.tab-content').forEach(c => c.classList.remove('active'));
+            btn.classList.add('active');
+            document.getElementById('tab-'+btn.dataset.tab).classList.add('active');
+        }}
     </script>
 </body>
 </html>"""
@@ -1062,12 +1172,19 @@ class Processor:
     ) -> TaskState:
         question_text, prompt, expected = eval_state.get_case(i)
 
+        # Extract chunk_idx from task_id: "{dataset_type}_{chunk_idx:03d}_{index:03d}"
+        _parts = task_id.rsplit("_", 2)
+        chunk_idx = int(_parts[-2]) if len(_parts) >= 3 else 0
+        problem_idx = i
+
         task_state = TaskState(
             task_id=task_id,
             prompt=prompt,
             expected=expected,
             question_text=question_text,
-            server_name=server_config.name
+            server_name=server_config.name,
+            chunk_idx=chunk_idx,
+            problem_idx=problem_idx,
         )
 
         try:
@@ -1085,7 +1202,8 @@ class Processor:
                 eval_state.add_result(
                     task_id, prompt, expected, result, None,
                     {"finish_reason": finish_reason}, False, task_state.status,
-                    tokens, tps_gen, t_gen_ms, reasoning_content, server_config.name
+                    tokens, tps_gen, t_gen_ms, reasoning_content, server_config.name,
+                    chunk_idx, problem_idx,
                 )
                 eval_state.dump()
                 return task_state
@@ -1108,7 +1226,8 @@ class Processor:
             eval_state.add_result(
                 task_id, prompt, expected, result, answer,
                 grader_log, is_correct, "ok",
-                tokens, tps_gen, t_gen_ms, reasoning_content, server_config.name
+                tokens, tps_gen, t_gen_ms, reasoning_content, server_config.name,
+                chunk_idx, problem_idx,
             )
 
             eval_state.dump()

examples/llama-eval/llama-server-simulator.py

@@ -65,34 +65,70 @@ def normalize_number(s: str) -> Optional[int]:
     return int(match.group(0))
 
 class AimeDataset:
-    def __init__(self, split: str = "train"):
+    def __init__(self, split: str = "train", dataset_type: str = "aime"):
         self.split = split
+        self.dataset_type = dataset_type
         self.questions: List[Dict] = []
         self._load_dataset()
 
-    def _load_dataset(self):
-        print(f"Loading AIME dataset (split: {self.split})...")
+    def _get_question_text(self, question: Dict) -> str:
+        """Get question text, handling different dataset field names."""
+        return question.get("problem", question.get("question", ""))
 
-        cache_path = Path.home() / ".cache" / "huggingface" / "datasets" / "AI-MO___aimo-validation-aime" / "default" / "0.0.0"
-        if cache_path.exists():
-            print(f"Using cached dataset from {cache_path}")
-            ds = datasets.load_dataset("AI-MO/aimo-validation-aime", split=self.split, cache_dir=str(cache_path))
+    def _load_dataset(self):
+        if self.dataset_type == "aime":
+            print(f"Loading AIME dataset (split: {self.split})...")
+            cache_path = Path.home() / ".cache" / "huggingface" / "datasets" / "AI-MO___aimo-validation-aime" / "default" / "0.0.0"
+            if cache_path.exists():
+                print(f"Using cached dataset from {cache_path}")
+                ds = datasets.load_dataset("AI-MO/aimo-validation-aime", split=self.split, cache_dir=str(cache_path))
+            else:
+                ds = datasets.load_dataset("AI-MO/aimo-validation-aime", split=self.split)
+        elif self.dataset_type == "aime2025":
+            print(f"Loading AIME2025 dataset...")
+            ds_list = []
+            for config_name in ["AIME2025-I", "AIME2025-II"]:
+                cache_path = Path.home() / ".cache" / "huggingface" / "datasets" / "opencompass___AIME2025" / "default" / "0.0.0"
+                if cache_path.exists():
+                    print(f"Using cached dataset from {cache_path}")
+                    ds = datasets.load_dataset("opencompass/AIME2025", config_name, split="test", cache_dir=str(cache_path))
+                else:
+                    ds = datasets.load_dataset("opencompass/AIME2025", config_name, split="test")
+                ds_list.extend(ds)
+            ds = ds_list
         else:
-            ds = datasets.load_dataset("AI-MO/aimo-validation-aime", split=self.split)
+            raise ValueError(f"Unknown dataset type: {self.dataset_type}")
 
         self.questions = list(ds)
-        print(f"AIME dataset loaded: {len(self.questions)} questions")
+        print(f"{self.dataset_type} dataset loaded: {len(self.questions)} questions")
 
     def find_question(self, request_text: str) -> Optional[Dict]:
+        # Strip common template prefixes to get the actual question text
+        # Templates include things like "Solve the following math problem step by step..."
+        # The actual question usually follows a blank line or after the template instruction
+        cleaned = request_text
+        # Split on double newline and take the part that looks like the problem
+        parts = cleaned.split('\n\n')
+        if len(parts) > 1:
+            # Find the part that's longest (likely the actual problem text)
+            problem_parts = [p for p in parts if len(p.strip()) > 100]
+            if problem_parts:
+                cleaned = max(problem_parts, key=lambda x: len(x))
+
         best_match = None
         best_distance = -1
         best_index = -1
 
         for i, question in enumerate(self.questions):
-            question_text = question["problem"]
-            request_lower = request_text.lower()
+            question_text = self._get_question_text(question)
+            request_lower = cleaned.lower()
             question_lower = question_text.lower()
 
+            # Check if question text is contained in the cleaned request
+            if question_lower in request_lower or request_lower in question_lower:
+                debug_log(f"DEBUG: Found substring match at index {i}")
+                return question
+
             # Exact match
             if question_lower == request_lower:
                 debug_log(f"DEBUG: Found exact match at index {i}")
@@ -118,7 +154,7 @@ class AimeDataset:
             debug_log(f"DEBUG: Found best partial match at index {best_index} with distance {best_distance:.3f}")
             return best_match
 
-        debug_log(f"DEBUG: No matching question found for: {request_text[:100]}...")
+        debug_log(f"DEBUG: No matching question found for cleaned: {cleaned[:100]}...")
         return None
 
     def get_answer(self, question: Dict) -> str:
@@ -134,15 +170,16 @@ class Simulator:
         port: int = 8033,
         host: str = "localhost",
         success_rate: float = 0.8,
-        dataset_split: str = "train"
+        dataset_split: str = "train",
+        dataset_type: str = "aime"
     ):
         self.port = port
         self.host = host
         self.success_rate = success_rate
-        self.dataset = AimeDataset(dataset_split)
+        self.dataset = AimeDataset(dataset_split, dataset_type)
         self.eval_state = EvalState(
-            id="aime-2025",
-            tasks=["aime"],
+            id=dataset_type,
+            tasks=[dataset_type],
             task_states={},
             sampling_config={"temperature": 0, "max_tokens": 2048}
         )
@@ -159,6 +196,10 @@ class Simulator:
         else:
             response_text = self._generate_wrong_answer(question)
 
+        comp_tokens = random.randint(10000, 60000)
+        tps_gen = random.uniform(90.0, 110.0)
+        t_gen_ms = comp_tokens / tps_gen * 1000
+
         return {
             "id": f"chatcmpl-{int(time.time())}",
             "object": "chat.completion",
@@ -176,8 +217,12 @@ class Simulator:
             ],
             "usage": {
                 "prompt_tokens": 100,
-                "completion_tokens": 50,
-                "total_tokens": 150
+                "completion_tokens": comp_tokens,
+                "total_tokens": 100 + comp_tokens
+            },
+            "timings": {
+                "predicted_ms": t_gen_ms,
+                "predicted_per_second": tps_gen
             }
         }
 
@@ -218,6 +263,12 @@ class Simulator:
         return response
 
 class RequestHandler(BaseHTTPRequestHandler):
+    def do_GET(self):
+        if self.path == "/v1/models":
+            self._send_json({"data": [{"id": "llama", "object": "model"}]}, 200)
+            return
+        self._send_json({"error": "Not found"}, 404)
+
     def do_POST(self):
         if self.path != "/v1/chat/completions":
             self._send_json({"error": "Not found"}, 404)
@@ -280,6 +331,13 @@ def main():
         default=0.8,
         help="Success rate 0-1 (default: 0.8)"
     )
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="aime",
+        choices=["aime", "aime2025"],
+        help="Dataset type (default: aime)"
+    )
     parser.add_argument(
         "--dataset-split",
         type=str,
@@ -294,7 +352,8 @@ def main():
         port=args.port,
         host=args.host,
         success_rate=args.success_rate,
-        dataset_split=args.dataset_split
+        dataset_split=args.dataset_split,
+        dataset_type=args.dataset
     )
 
     server = HTTPServer((args.host, args.port), RequestHandler)
@@ -304,7 +363,7 @@ def main():
     print("\n=== llama-server-simulator ===")
     print(f"Server running on http://{args.host}:{args.port}")
     print(f"Success rate: {args.success_rate}")
-    print(f"AIME dataset loaded: {len(simulator.dataset.questions)} questions")
+    print(f"{args.dataset} dataset loaded: {len(simulator.dataset.questions)} questions")
     print("\nPress Ctrl+C to stop\n")
 
     try:

examples/llama.android/lib/build.gradle.kts

@@ -25,6 +25,7 @@ android {
                 arguments += "-DCMAKE_VERBOSE_MAKEFILE=ON"
 
                 arguments += "-DBUILD_SHARED_LIBS=ON"
+                arguments += "-DLLAMA_BUILD_APP=OFF"
                 arguments += "-DLLAMA_BUILD_COMMON=ON"
                 arguments += "-DLLAMA_OPENSSL=OFF"
 

examples/save-load-state/CMakeLists.txt

@@ -1,5 +0,0 @@
-set(TARGET llama-save-load-state)
-add_executable(${TARGET} save-load-state.cpp)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama-common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)

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

@@ -1,320 +0,0 @@
-#include "arg.h"
-#include "common.h"
-#include "llama.h"
-
-#include <clocale>
-#include <vector>
-#include <cstdio>
-
-
-int main(int argc, char ** argv) {
-    std::setlocale(LC_NUMERIC, "C");
-
-    common_params params;
-
-    params.prompt = "The quick brown fox";
-    params.sampling.seed = 1234;
-
-    const std::string_view state_file = "dump_state.bin";
-
-    common_init();
-
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
-        return 1;
-    }
-
-    if (params.n_parallel == 1) {
-        // the example uses 2 sequences, so when n_parallel == 1, we need to enable unified kv cache
-        printf("%s: n_parallel == 1, enabling unified kv cache\n", __func__);
-        params.kv_unified = true;
-    }
-
-    if (params.n_predict < 0) {
-        params.n_predict = 16;
-    }
-
-    auto n_past = 0;
-
-    std::string result0;
-    std::string result1;
-    std::string result2;
-    std::string result3;
-
-    // init
-
-    ggml_backend_load_all();
-
-    auto llama_init = common_init_from_params(params);
-
-    auto * model = llama_init->model();
-    auto * ctx   = llama_init->context();
-
-    if (model == nullptr || ctx == nullptr) {
-        fprintf(stderr, "%s : failed to init\n", __func__);
-        return 1;
-    }
-
-    auto sparams = llama_sampler_chain_default_params();
-
-    llama_sampler * smpl = llama_sampler_chain_init(sparams);
-
-    llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sampling.seed));
-
-    // tokenize prompt
-    auto tokens = common_tokenize(ctx, params.prompt, true);
-
-    const bool save_state = true;
-    if (!common_prompt_batch_decode(ctx, tokens, n_past, params.n_batch, state_file, save_state)) {
-        return 1;
-    }
-
-    // first run
-    printf("\nfirst run: %s", params.prompt.c_str());
-
-    llama_batch batch = llama_batch_init(1, 0, 1);
-
-    for (auto i = 0; i < params.n_predict; i++) {
-        auto next_token     = llama_sampler_sample(smpl, ctx, -1);
-        auto next_token_str = common_token_to_piece(ctx, next_token);
-
-        printf("%s", next_token_str.c_str());
-        result0 += next_token_str;
-
-        common_batch_clear(batch);
-        common_batch_add(batch, next_token, n_past, {0}, true);
-
-        if (llama_decode(ctx, batch)) {
-            fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
-            llama_batch_free(batch);
-            return 1;
-        }
-        n_past += 1;
-    }
-
-    printf("\n\n");
-
-    // make new context
-    llama_context * ctx2 = llama_init_from_model(model, common_context_params_to_llama(params));
-
-    llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
-
-    llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sampling.seed));
-
-    printf("\nsecond run: %s", params.prompt.c_str());
-
-    // load state from file
-    std::vector<llama_token> unused_sts(tokens.size()); // unused session tokens.
-    size_t n_token_count_out = 0;
-
-    if (!llama_state_load_file(ctx2, state_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
-        fprintf(stderr, "\n%s : failed to load state\n", __func__);
-        return 1;
-    }
-
-    fprintf(stderr, "%s : loaded state with %zu tokens\n", __func__, n_token_count_out);
-
-    // restore state (last tokens)
-    n_past = n_token_count_out;
-    if (!common_replay_last_token(ctx2, tokens.back(), n_past)) {
-        return 1;
-    }
-    ++n_past;
-
-    // second run
-    for (auto i = 0; i < params.n_predict; i++) {
-        auto next_token     = llama_sampler_sample(smpl2, ctx2, -1);
-        auto next_token_str = common_token_to_piece(ctx2, next_token);
-
-        printf("%s", next_token_str.c_str());
-        result1 += next_token_str;
-
-        common_batch_clear(batch);
-        common_batch_add(batch, next_token, n_past, {0}, true);
-
-        if (llama_decode(ctx2, batch)) {
-            fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
-            llama_batch_free(batch);
-            return 1;
-        }
-        n_past += 1;
-    }
-
-    printf("\n\n");
-
-    if (result0 != result1) {
-        fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__);
-        return 1;
-    }
-
-    // make new context
-    auto params_ctx3 = common_context_params_to_llama(params);
-    params_ctx3.n_seq_max = 2;
-    llama_context * ctx3 = llama_init_from_model(model, params_ctx3);
-
-    llama_sampler * smpl3 = llama_sampler_chain_init(sparams);
-
-    llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sampling.seed));
-
-    printf("\nsingle seq run: %s", params.prompt.c_str());
-
-    // load state (rng, logits, embedding and kv_cache) from file
-    n_token_count_out = 0;
-
-    if (!llama_state_load_file(ctx3, state_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
-        fprintf(stderr, "\n%s : failed to load state\n", __func__);
-        return 1;
-    }
-
-    fprintf(stderr, "%s : loaded state with %zu tokens\n", __func__, n_token_count_out);
-
-    // restore state (last tokens)
-    n_past = n_token_count_out;
-    if (!common_replay_last_token(ctx3, tokens.back(), n_past)) {
-        return 1;
-    }
-    ++n_past;
-
-    // save seq 0 and load into seq 1
-    {
-        // save kv of seq 0
-        std::vector<uint8_t> seq_store(llama_state_seq_get_size(ctx3, 0));
-        const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), seq_store.size(), 0);
-        if (ncopy != seq_store.size()) {
-            fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
-            return 1;
-        }
-        fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
-
-        // erase whole kv
-        llama_memory_clear(llama_get_memory(ctx3), true);
-        fprintf(stderr, "%s : kv cache cleared\n", __func__);
-
-        // restore kv into seq 1
-        const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), seq_store.size(), 1);
-        if (nset != seq_store.size()) {
-            fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
-            return 1;
-        }
-        fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset);
-    }
-
-    // third run with seq 1 instead of 0
-    for (auto i = 0; i < params.n_predict; i++) {
-        auto next_token     = llama_sampler_sample(smpl3, ctx3, -1);
-        auto next_token_str = common_token_to_piece(ctx3, next_token);
-
-        printf("%s", next_token_str.c_str());
-        result2 += next_token_str;
-
-        common_batch_clear(batch);
-        common_batch_add(batch, next_token, n_past, {1}, true);
-
-        if (llama_decode(ctx3, batch)) {
-            fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
-            llama_batch_free(batch);
-            return 1;
-        }
-        n_past += 1;
-    }
-
-    // test on-device state save/load
-    auto params_ctx4 = common_context_params_to_llama(params);
-    params_ctx4.n_seq_max = 2;
-    llama_context * ctx4 = llama_init_from_model(model, params_ctx4);
-
-    llama_sampler * smpl4 = llama_sampler_chain_init(sparams);
-
-    llama_sampler_chain_add(smpl4, llama_sampler_init_dist(params.sampling.seed));
-
-    printf("\nsingle seq run: %s", params.prompt.c_str());
-
-    // load state (rng, logits, embedding and kv_cache) from file
-    n_token_count_out = 0;
-
-    if (!llama_state_load_file(ctx4, state_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
-        fprintf(stderr, "\n%s : failed to load state\n", __func__);
-        return 1;
-    }
-
-    fprintf(stderr, "%s : loaded state with %zu tokens\n", __func__, n_token_count_out);
-
-    // restore state (last tokens)
-    n_past = n_token_count_out;
-    if (!common_replay_last_token(ctx4, tokens.back(), n_past)) {
-        return 1;
-    }
-    ++n_past;
-
-    // save seq 0 and load into seq 1
-    {
-        // save kv of seq 0
-        std::vector<uint8_t> seq_store(llama_state_seq_get_size_ext(ctx4, 0, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE));
-        const size_t ncopy = llama_state_seq_get_data_ext(ctx4, seq_store.data(), seq_store.size(), 0, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
-        if (ncopy != seq_store.size()) {
-            fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
-            return 1;
-        }
-        fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
-
-        // erase whole kv
-        llama_memory_clear(llama_get_memory(ctx4), true);
-        fprintf(stderr, "%s : kv cache cleared\n", __func__);
-
-        // restore kv into seq 0
-        const size_t nset = llama_state_seq_set_data_ext(ctx4, seq_store.data(), seq_store.size(), 1, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
-        if (nset != seq_store.size()) {
-            fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
-            return 1;
-        }
-        fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset);
-    }
-
-    // forth run
-    for (auto i = 0; i < params.n_predict; i++) {
-        auto next_token     = llama_sampler_sample(smpl4, ctx4, -1);
-        auto next_token_str = common_token_to_piece(ctx4, next_token);
-
-        printf("%s", next_token_str.c_str());
-        result3 += next_token_str;
-
-        common_batch_clear(batch);
-        common_batch_add(batch, next_token, n_past, {1}, true);
-
-        if (llama_decode(ctx4, batch)) {
-            fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
-            llama_batch_free(batch);
-            return 1;
-        }
-        n_past += 1;
-    }
-
-    printf("\n");
-
-    llama_sampler_free(smpl);
-    llama_sampler_free(smpl2);
-    llama_sampler_free(smpl3);
-    llama_sampler_free(smpl4);
-
-    llama_batch_free(batch);
-
-    // this one is managed by common_init_result
-    //llama_free(ctx);
-
-    llama_free(ctx2);
-    llama_free(ctx3);
-    llama_free(ctx4);
-
-    if (result0 != result2) {
-        fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
-        return 1;
-    }
-
-    if (result0 != result3) {
-        fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
-        return 1;
-    }
-
-    fprintf(stderr, "\n%s : success\n", __func__);
-
-    return 0;
-}

examples/sycl/start-svr.sh

@@ -111,7 +111,6 @@ if [ $GGML_SYCL_DEVICE -ne -1 ]; then
     echo "Use $GGML_SYCL_DEVICE as main GPU"
     #use signle GPU only
     GPUS_SETTING="-mg $GGML_SYCL_DEVICE -sm ${SPLIT_MODE}"
-    export ONEAPI_DEVICE_SELECTOR="level_zero:${GGML_SYCL_DEVICE}"
     echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}"
 else
     echo "Use all Intel GPUs, including iGPU & dGPU"

examples/sycl/test.sh

@@ -119,7 +119,6 @@ if [ $GGML_SYCL_DEVICE -ne -1 ]; then
     echo "Use $GGML_SYCL_DEVICE as main GPU"
     #use signle GPU only
     GPUS_SETTING="-mg $GGML_SYCL_DEVICE -sm ${SPLIT_MODE}"
-    export ONEAPI_DEVICE_SELECTOR="level_zero:${GGML_SYCL_DEVICE}"
     echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}"
 else
     echo "Use all Intel GPUs, including iGPU & dGPU"

examples/sycl/win-start-svr.bat

@@ -164,7 +164,6 @@ if not "%GGML_SYCL_DEVICE%"=="-1" (
   echo Use %GGML_SYCL_DEVICE% as main GPU
   REM Use single GPU only.
   set "GPUS_SETTING=-mg %GGML_SYCL_DEVICE% -sm %SPLIT_MODE%"
-  set "ONEAPI_DEVICE_SELECTOR=level_zero:%GGML_SYCL_DEVICE%"
   echo ONEAPI_DEVICE_SELECTOR=%ONEAPI_DEVICE_SELECTOR%
 ) else (
   echo Use all Intel GPUs, including iGPU ^& dGPU

examples/sycl/win-test.bat

@@ -186,7 +186,6 @@ if not "%GGML_SYCL_DEVICE%"=="-1" (
   echo Use %GGML_SYCL_DEVICE% as main GPU
   REM Use single GPU only.
   set "GPUS_SETTING=-mg %GGML_SYCL_DEVICE% -sm %SPLIT_MODE%"
-  set "ONEAPI_DEVICE_SELECTOR=level_zero:%GGML_SYCL_DEVICE%"
   echo ONEAPI_DEVICE_SELECTOR=%ONEAPI_DEVICE_SELECTOR%
 ) else (
   echo Use all Intel GPUs, including iGPU ^& dGPU

ggml/CMakeLists.txt

@@ -4,8 +4,8 @@ project("ggml" C CXX ASM)
 
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
-set(GGML_VERSION_MINOR 11)
-set(GGML_VERSION_PATCH 1)
+set(GGML_VERSION_MINOR 12)
+set(GGML_VERSION_PATCH 0)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
@@ -353,7 +353,7 @@ if (GGML_STANDALONE)
         @ONLY)
 
     install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ggml.pc
-        DESTINATION share/pkgconfig)
+        DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig)
 endif()
 
 #

ggml/include/ggml.h

@@ -2541,6 +2541,11 @@ extern "C" {
 
     // TODO: add ggml_gated_delta_net_set_bcast() to be able to configure Q, K broadcast type: tiled vs interleaved [TAG_GGML_GDN_BCAST]
     // ref: https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306
+    //
+    // state is a 3D tensor of shape (S_v*S_v*H, K, n_seqs):
+    //   K == 1: output carries the final state only.
+    //   K  > 1: output carries K snapshot slots; the kernel writes the last min(n_tokens, K)
+    //   per-token snapshots into the trailing slots
     GGML_API struct ggml_tensor * ggml_gated_delta_net(
             struct ggml_context * ctx,
             struct ggml_tensor  * q,

ggml/src/ggml-backend-meta.cpp

@@ -753,7 +753,9 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(co
         GGML_ASSERT(src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_1);
         GGML_ASSERT(src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_1);
         GGML_ASSERT(src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_1);
-        GGML_ASSERT(src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_2);
+        // state shape is (S_v*S_v*H, K, n_seqs); the heads dim is nested inside axis 0,
+        // so a head-aligned split on the input cache reshapes to axis 0 here (not axis 2).
+        GGML_ASSERT(src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_2 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_1 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_0);
         return {GGML_BACKEND_SPLIT_AXIS_0, {0}, 1};
     };
 
@@ -2140,4 +2142,3 @@ ggml_backend_t ggml_backend_meta_simple_backend(ggml_backend_t meta_backend, siz
     const ggml_backend_meta_context * backend_ctx = (const ggml_backend_meta_context *) meta_backend->context;
     return backend_ctx->backend_configs[index].backend;
 }
-

ggml/src/ggml-backend.cpp

@@ -379,7 +379,7 @@ void ggml_backend_tensor_get_2d(const struct ggml_tensor * tensor, void * data,
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
     GGML_ASSERT(buf != NULL && "tensor buffer not set");
 
-    if (n_copies <= 1 || buf->iface.set_tensor_2d == NULL) {
+    if (n_copies <= 1 || buf->iface.get_tensor_2d == NULL) {
         for (size_t i = 0; i < n_copies; i++) {
             ggml_backend_tensor_get(tensor, (char *) data + i*stride_data, offset + i*stride_tensor, size);
         }

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

@@ -2943,7 +2943,9 @@ struct ggml_cplan ggml_graph_plan(
                 case GGML_OP_GATED_DELTA_NET:
                     {
                         const int64_t S_v = node->src[2]->ne[0];
-                        cur = S_v * sizeof(float) * n_tasks;
+                        const int64_t K   = node->src[5]->ne[1];  // state is (D, K, n_seqs)
+                        const int64_t per_thread = S_v + (K > 1 ? S_v * S_v : 0);
+                        cur = per_thread * sizeof(float) * n_tasks;
                     } break;
                 case GGML_OP_COUNT:
                     {

ggml/src/ggml-cpu/ops.cpp

@@ -10513,19 +10513,30 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
 
     const bool kda = (neg0 == S_v);
 
-    // scratch layout per thread: [delta(S_v)]
-    const int64_t scratch_per_thread = S_v;
+    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
+    const int64_t K = src_state->ne[1];
+    GGML_ASSERT(K >= 1);
+    // per-seq stride in floats (slot 0 of seq s lives at state + s * seq_stride)
+    const int64_t state_seq_stride = src_state->nb[2] / sizeof(float);
+
+    const int64_t per_thread = S_v + (K > 1 ? S_v * S_v : 0);
     const int ith = params->ith;
 
-    float * delta = (float *)params->wdata + ith * scratch_per_thread + CACHE_LINE_SIZE_F32;
+    float * delta       = (float *)params->wdata + ith * per_thread + CACHE_LINE_SIZE_F32;
+    float * state_work  = K > 1 ? (delta + S_v) : nullptr;
 
     // output layout: [attn_scores | new_states]
-    // attn_scores: S_v * H * n_tokens * n_seqs floats
-    // new_states:  S_v * S_v * H * n_seqs floats
-    const int64_t attn_score_elems = S_v * H * n_tokens * n_seqs;
+    // attn_scores: S_v * H * n_tokens * n_seqs    floats
+    // new_states:  S_v * S_v * H * n_seqs * K     floats  (K snapshot slots; last min(n_tokens, K))
+    const int64_t attn_score_elems    = S_v * H * n_tokens * n_seqs;
+    const int64_t state_size_per_snap = S_v * S_v * H * n_seqs;
     float * attn_out_base  = (float *)dst->data;
     float * state_out_base = (float *)dst->data + attn_score_elems;
 
+    // snapshot slot mapping: target_slot = t - shift. When n_tokens < K only the last
+    // n_tokens slots are written; earlier slots are left untouched (caller-owned).
+    const int64_t shift = n_tokens - K;
+
     const float * state_in_base = (const float *)src_state->data;
 
   //const int64_t rq1 = nev1 / neq1;
@@ -10545,10 +10556,15 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
         const int64_t iq3 = iv3 / rq3;
         const int64_t ik3 = iv3 / rk3;
 
-        float * s_out = state_out_base + (iv3 * H + iv1) * S_v * S_v;
+        // For K=1, write directly to the single output slot to avoid an extra memcpy at the end.
+        // For K>1, work in scratch and copy out per-token when the slot is in range.
+        float * s_out = (K > 1)
+            ? state_work
+            : state_out_base + (iv3 * H + iv1) * S_v * S_v;
 
-        // copy input state into output buffer and operate in-place
-        const float * s_in = state_in_base + (iv3 * H + iv1) * S_v * S_v;
+        // copy input state into the working buffer and operate in-place
+        // state layout (D, K, n_seqs): slot 0 of seq iv3 starts at iv3 * state_seq_stride.
+        const float * s_in = state_in_base + iv3 * state_seq_stride + iv1 * S_v * S_v;
         memcpy(s_out, s_in, S_v * S_v * sizeof(float));
 
         // attn output pointer for first token of this (head, seq)
@@ -10598,6 +10614,15 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
             }
 
             attn_data += S_v * H; // advance to next token
+
+            if (K > 1) {
+                const int64_t target_slot = t - shift;
+                if (target_slot >= 0 && target_slot < K) {
+                    float * curr_state_o = state_out_base + target_slot * state_size_per_snap +
+                                     (iv3 * H + iv1) * S_v * S_v;
+                    memcpy(curr_state_o, s_out, S_v * S_v * sizeof(float));
+                }
+            }
         }
     }
 }

ggml/src/ggml-cuda/CMakeLists.txt

@@ -15,6 +15,7 @@ if (CUDAToolkit_FOUND)
         # 80     == Ampere, asynchronous data loading, faster tensor core instructions
         # 86     == RTX 3000, needs CUDA v11.1
         # 89     == RTX 4000, needs CUDA v11.8
+        # 90     == Hopper H100/200, needs CUDA v11.8
         # 120    == Blackwell, needs CUDA v12.8, FP4 tensor cores
         #
         # XX-virtual == compile CUDA code as PTX, do JIT compilation to binary code on first run
@@ -33,7 +34,7 @@ if (CUDAToolkit_FOUND)
             list(APPEND CMAKE_CUDA_ARCHITECTURES 75-virtual 80-virtual 86-real)
 
             if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.8")
-                list(APPEND CMAKE_CUDA_ARCHITECTURES 89-real)
+                list(APPEND CMAKE_CUDA_ARCHITECTURES 89-real 90-virtual)
             endif()
 
             if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "12.8")

ggml/src/ggml-cuda/binbcast.cu

@@ -2,6 +2,9 @@
 #include <cstdint>
 #include <utility>
 
+template<typename T, size_t>
+using type_for_index = T;
+
 static __device__ __forceinline__ float op_repeat(const float a, const float b) {
     return b;
     GGML_UNUSED(a);
@@ -52,6 +55,7 @@ static __global__ void k_bin_bcast(const src0_t *         src0,
                                    const int              s12,
                                    const int              s13,
                                    src1_ptrs... src1s) {
+    ggml_cuda_pdl_lc();
     const uint32_t i0s = blockDim.x * blockIdx.x + threadIdx.x;
     const uint32_t i1  = (blockDim.y * blockIdx.y + threadIdx.y);
     const uint32_t i2  = fastdiv((blockDim.z * blockIdx.z + threadIdx.z), ne3);
@@ -72,6 +76,7 @@ static __global__ void k_bin_bcast(const src0_t *         src0,
     const src0_t * src0_row = src0 ? (src0 + i_src0) : nullptr;
     dst_t * dst_row = dst + i_dst;
 
+    ggml_cuda_pdl_sync();
     for (int i0 = i0s; i0 < ne0; i0 += blockDim.x * gridDim.x) {
         const uint32_t i10 = fastmodulo(i0, ne10);
 
@@ -141,6 +146,7 @@ static __global__ void k_bin_bcast_unravel(const src0_t *         src0,
 
     const int i10 = fastmodulo(i0, ne10);
 
+    ggml_cuda_pdl_sync();
     float result = src0_row ? (float) src0_row[i0*s00] : 0.0f;
     if constexpr (sizeof...(src1_ptrs) > 0) {
         result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10*s10])));
@@ -282,35 +288,24 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
             const uint3 ne1_fastdiv = init_fastdiv_values((uint32_t) ne1);
             const uint3 ne2_fastdiv = init_fastdiv_values((uint32_t) ne2);
 
-            if constexpr (sizeof...(I) > 0) {
-                k_bin_bcast_unravel<bin_op, src0_t, src1_t, dst_t><<<block_num, block_size, 0, stream>>>(
+            {
+                const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params((dim3)block_num, block_size, 0, stream);
+                ggml_cuda_kernel_launch(k_bin_bcast_unravel<bin_op, src0_t, src1_t, dst_t, type_for_index<const src1_t *, I>...>, launch_params,
                     src0_dd, src1_dd, dst_dd, ne0_fastdiv, ne1_fastdiv, ne2_fastdiv, ne3, prod_012, prod_01, ne10, ne11,
                     ne12, ne13,
                   /*s0,*/ s1,  s2,  s3,
                     s00, s01, s02, s03,
                     s10, s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
-            } else {
-                k_bin_bcast_unravel<bin_op, src0_t, src1_t, dst_t>
-                    <<<block_num, block_size, 0, stream>>>(src0_dd, src1_dd, dst_dd, ne0_fastdiv, ne1_fastdiv,
-                                                           ne2_fastdiv, ne3, prod_012, prod_01, ne10, ne11, ne12, ne13,
-                                                         /*s0,*/ s1,  s2,  s3,
-                                                           s00, s01, s02, s03,
-                                                           s10, s11, s12, s13);
             }
         } else {
             const uint3 ne3_fastdiv = init_fastdiv_values((uint32_t) ne3);
-            if constexpr (sizeof...(I) > 0) {
-                k_bin_bcast<bin_op, src0_t, src1_t, dst_t><<<block_nums, block_dims, 0, stream>>>(
+            {
+                const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);
+                ggml_cuda_kernel_launch(k_bin_bcast<bin_op, src0_t, src1_t, dst_t, type_for_index<const src1_t *, I>...>, launch_params,
                     src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3_fastdiv, ne10, ne11, ne12, ne13,
                   /*s0,*/ s1, s2,  s3,
-                    s00 ,s01, s02, s03,
-                    s10, s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
-            } else {
-                k_bin_bcast<bin_op, src0_t, src1_t, dst_t><<<block_nums, block_dims, 0, stream>>>(
-                    src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3_fastdiv, ne10, ne11, ne12, ne13,
-                  /*s0,*/ s1,  s2,  s3,
                     s00, s01, s02, s03,
-                    s10, s11, s12, s13);
+                    s10, s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
             }
         }
     }
@@ -333,6 +328,7 @@ static __global__ void k_repeat_back(
     }
 
     T sum = 0;
+    ggml_cuda_pdl_sync();
     for (int64_t i3 = tid3; i3 < ne03; i3 += ne3) {
         for (int64_t i2 = tid2; i2 < ne02; i2 += ne2) {
             for (int64_t i1 = tid1; i1 < ne01; i1 += ne1) {

ggml/src/ggml-cuda/common.cuh

@@ -5,6 +5,7 @@
 #include "ggml-cuda.h"
 
 #include <cstdint>
+#include <cstdlib>
 #include <memory>
 
 #if defined(GGML_USE_HIP)
@@ -27,6 +28,7 @@
 #include <cstdio>
 #include <string>
 #include <unordered_map>
+#include <utility>
 #include <vector>
 
 #if defined(GGML_USE_HIP)
@@ -50,6 +52,7 @@
 #define GGML_CUDA_CC_TURING          750
 #define GGML_CUDA_CC_AMPERE          800
 #define GGML_CUDA_CC_ADA_LOVELACE    890
+#define GGML_CUDA_CC_HOPPER          900
 // While BW spans CC 1000, 1100 & 1200, we are integrating Tensor Core instructions available to 1200 family, see
 // https://docs.nvidia.com/cutlass/media/docs/cpp/blackwell_functionality.html#blackwell-sm120-gemms
 #define GGML_CUDA_CC_BLACKWELL       1200
@@ -107,6 +110,24 @@
 #    define GGML_CUDA_USE_CUB
 #endif  // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11070
 
+// PDL host-side support (cudaLaunchKernelEx) requires CUDART >= 11.8 and excludes HIP/MUSA.
+// __CUDA_ARCH__  is undefined in host passes; GPU arch check happens in device-side code.
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11080
+#    define GGML_CUDA_USE_PDL
+#endif  // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11080
+
+static __device__ __forceinline__ void ggml_cuda_pdl_sync() {
+#if defined(GGML_CUDA_USE_PDL) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= GGML_CUDA_CC_HOPPER
+    cudaGridDependencySynchronize();
+#endif // defined(GGML_CUDA_USE_PDL) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= GGML_CUDA_CC_HOPPER
+}
+
+static __device__ __forceinline__ void ggml_cuda_pdl_lc() {
+#if defined(GGML_CUDA_USE_PDL) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= GGML_CUDA_CC_HOPPER
+    cudaTriggerProgrammaticLaunchCompletion();
+#endif // defined(GGML_CUDA_USE_PDL) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= GGML_CUDA_CC_HOPPER
+}
+
 #ifdef __CUDA_ARCH_LIST__
 constexpr bool ggml_cuda_has_arch_impl(int) {
     return false;
@@ -165,6 +186,7 @@ void ggml_cuda_error(const char * stmt, const char * func, const char * file, in
 
 #define CUDA_CHECK(err) CUDA_CHECK_GEN(err, cudaSuccess, cudaGetErrorString)
 
+
 #if CUDART_VERSION >= 12000 || defined(GGML_USE_MUSA)
     static const char * cublas_get_error_str(const cublasStatus_t err) {
         return cublasGetStatusString(err);
@@ -1487,3 +1509,68 @@ struct ggml_cuda_mm_fusion_args_device {
     const void * gate_bias = nullptr;
     ggml_glu_op glu_op;
 };
+
+struct ggml_cuda_kernel_launch_params {
+    dim3 block_nums;
+    dim3 block_dims;
+    size_t shmem;
+    cudaStream_t stream;
+
+    // size_t shmem
+    ggml_cuda_kernel_launch_params(const dim3& block_nums_, const dim3& block_dims_, const size_t shmem_, const cudaStream_t stream_)
+        : block_nums(block_nums_), block_dims(block_dims_), shmem(shmem_), stream(stream_) {}
+
+    // Some call sites pass ints instead of the required size_t. This 2nd constructor casts int->size_t to avoid these -Wnarrowing warnings.
+    ggml_cuda_kernel_launch_params(const dim3& block_nums_, const dim3& block_dims_, const int shmem_, const cudaStream_t stream_)
+        : block_nums(block_nums_), block_dims(block_dims_), shmem((size_t)shmem_), stream(stream_) {}
+};
+
+#if defined(GGML_CUDA_USE_PDL)
+struct ggml_cuda_pdl_config {
+    cudaLaunchAttribute attr;
+    cudaLaunchConfig_t  cfg;
+
+    ggml_cuda_pdl_config(const ggml_cuda_kernel_launch_params & params) {
+        attr.id = cudaLaunchAttributeProgrammaticStreamSerialization;
+        attr.val.programmaticStreamSerializationAllowed = 1;
+
+        cfg = {};
+        cfg.gridDim          = params.block_nums;
+        cfg.blockDim         = params.block_dims;
+        cfg.dynamicSmemBytes = params.shmem;
+        cfg.stream           = params.stream;
+        cfg.attrs            = &attr;
+        cfg.numAttrs         = 1;
+    }
+
+    // Delete due to &attr
+    ggml_cuda_pdl_config(const ggml_cuda_pdl_config&) = delete;
+    ggml_cuda_pdl_config& operator=(const ggml_cuda_pdl_config&) = delete;
+    ggml_cuda_pdl_config& operator=(ggml_cuda_pdl_config&&) = delete;
+
+};
+#endif //defined(GGML_CUDA_USE_PDL)
+
+
+template<typename Kernel, typename... Args>
+static __inline__ void ggml_cuda_kernel_launch(Kernel kernel, const ggml_cuda_kernel_launch_params & launch_params, Args&&... args) {
+#if defined(GGML_CUDA_USE_PDL)
+
+    static const bool env_pdl_enabled = []() {
+        const char * env = getenv("GGML_CUDA_PDL");
+        return env == nullptr || std::atoi(env) != 0;
+    }();
+
+    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+    if (env_pdl_enabled && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_HOPPER) {
+        auto pdl_cfg = ggml_cuda_pdl_config(launch_params);
+
+        CUDA_CHECK(cudaLaunchKernelEx(&pdl_cfg.cfg, kernel, std::forward<Args>(args)... ));
+        return;
+    }
+#endif //defined(GGML_CUDA_USE_PDL)
+
+    kernel<<<launch_params.block_nums, launch_params.block_dims, launch_params.shmem, launch_params.stream>>>(std::forward<Args>(args)... );
+    CUDA_CHECK(cudaGetLastError());
+}
+

ggml/src/ggml-cuda/concat.cu

@@ -15,6 +15,7 @@ static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE) concat_f32_cont
 
     const int64_t n = ne0 * ne1 * ne2;
 
+    ggml_cuda_pdl_sync();
     for (int64_t i = (int64_t) blockIdx.x * blockDim.x + threadIdx.x; i < n; i += (int64_t) blockDim.x * gridDim.x) {
         if constexpr (dim == 0) {
             const int64_t row = i / ne0;
@@ -64,8 +65,8 @@ static void concat_f32_cuda(const float * x,
     const int     num_blocks = (n + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
 
     if (dim == 0) {
-        concat_f32_cont<0>
-            <<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream);
+        ggml_cuda_kernel_launch(concat_f32_cont<0>, launch_params,x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
         return;
     }
     if (dim == 1) {

ggml/src/ggml-cuda/cpy.cu

@@ -16,6 +16,7 @@ static __global__ void cpy_scalar(const char * cx, char * cdst, const int64_t ne
                                   const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
                                   const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11,
                                   const int64_t nb12, const int64_t nb13) {
+    ggml_cuda_pdl_lc();
     const int64_t i = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
 
     if (i >= ne) {
@@ -36,6 +37,7 @@ static __global__ void cpy_scalar(const char * cx, char * cdst, const int64_t ne
     const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
     const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
 
+    ggml_cuda_pdl_sync();
     cpy_1(cx + x_offset, cdst + dst_offset);
 }
 
@@ -59,6 +61,7 @@ static __global__ void cpy_scalar_transpose(const char * cx, char * cdst, const
     __shared__ float tile[2][CUDA_CPY_TILE_DIM_2D][CUDA_CPY_TILE_DIM_2D+1];
     int cur_tile_buf = 0;
 
+    ggml_cuda_pdl_sync();
 #pragma unroll
     for (int i = 0; i < CUDA_CPY_BLOCK_NM; ++i) {
 
@@ -142,6 +145,7 @@ static __global__ void cpy_f32_q(const char * cx, char * cdst, const int64_t ne,
     const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
     const int64_t dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
 
+    ggml_cuda_pdl_sync();
     cpy_blck(cx + x_offset, cdst + dst_offset);
 }
 
@@ -168,6 +172,7 @@ static __global__ void cpy_q_f32(const char * cx, char * cdst, const int64_t ne,
     const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
     const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
 
+    ggml_cuda_pdl_sync();
     cpy_blck(cx + x_offset, cdst + dst_offset);
 }
 
@@ -182,6 +187,7 @@ static __global__ void cpy_scalar_contiguous(const char * cx, char * cdst, const
     const src_t * x = (const src_t *) cx;
     dst_t *     dst = (dst_t *) cdst;
 
+    ggml_cuda_pdl_sync();
     dst[i] = ggml_cuda_cast<dst_t>(x[i]);
 }
 
@@ -192,8 +198,8 @@ cudaStream_t stream) {
 
     const int64_t num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks < UINT_MAX);
-    cpy_scalar_contiguous<src_t, dst_t><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne);
+    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params((dim3)num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream);
+    ggml_cuda_kernel_launch(cpy_scalar_contiguous<src_t, dst_t>, launch_params, cx, cdst, ne);
 }
 
 template<typename src_t, typename dst_t, bool transposed = false>
@@ -223,13 +229,15 @@ static void ggml_cpy_scalar_cuda(
         GGML_ASSERT(grid_z < USHRT_MAX);
         dim3 dimGrid(grid_x, grid_y, grid_z);
         dim3 dimBlock(CUDA_CPY_TILE_DIM_2D, CUDA_CPY_BLOCK_ROWS, 1);
-        cpy_scalar_transpose<dst_t><<<dimGrid, dimBlock, 0, stream>>>
-            (cx, cdst, ne, ne00n, ne01n, ne02n, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(dimGrid, dimBlock, 0, stream);
+        ggml_cuda_kernel_launch(cpy_scalar_transpose<dst_t>, launch_params,
+            cx, cdst, ne, ne00n, ne01n, ne02n, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
     } else {
         const int64_t num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
         GGML_ASSERT(num_blocks < UINT_MAX);
-        cpy_scalar<cpy_1_scalar<src_t, dst_t>><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-            (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params((dim3)num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream);
+        ggml_cuda_kernel_launch(cpy_scalar<cpy_1_scalar<src_t, dst_t>>, launch_params,
+            cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
     }
 }
 

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

@@ -636,6 +636,7 @@ static __global__ void flash_attn_mask_to_KV_max(
     if (tid < WARP_SIZE) {
         buf_iw[tid] = 1;
     }
+    ggml_cuda_pdl_sync();
     __syncthreads();
 
     int KV_max_sj = (ne30 - 1) * FATTN_KQ_STRIDE;
@@ -687,6 +688,7 @@ static __global__ void flash_attn_stream_k_fixup_uniform(
         const uint3 fd_iter_j_z,
         const uint3 fd_iter_j) {
     constexpr int ncols = ncols1*ncols2;
+    ggml_cuda_pdl_lc();
 
     const int tile_idx = blockIdx.x; // One block per output tile.
     const int j        = blockIdx.y;
@@ -718,6 +720,7 @@ static __global__ void flash_attn_stream_k_fixup_uniform(
 
     dst += sequence*ne02*ne01*D + jt*ne02*(ncols1*D) + zt_Q*D + (j*ne02 + c)*D + tid;
 
+    ggml_cuda_pdl_sync();
     // Load the partial result that needs a fixup
     float dst_val = *dst;
     float max_val;
@@ -809,6 +812,7 @@ static __global__ void flash_attn_stream_k_fixup_general(
     float dst_val = 0.0f;
     float max_val = 0.0f;
     float rowsum  = 0.0f;
+    ggml_cuda_pdl_sync();
     {
         dst_val = *dst;
 
@@ -867,6 +871,7 @@ static __global__ void flash_attn_combine_results(
         const float2 * __restrict__ VKQ_meta,
         float * __restrict__ dst,
         const int parallel_blocks) {
+    ggml_cuda_pdl_lc();
     // Dimension 0: threadIdx.x
     // Dimension 1: blockIdx.x
     // Dimension 2: blockIdx.y
@@ -890,6 +895,7 @@ static __global__ void flash_attn_combine_results(
     __builtin_assume(tid < D);
 
     extern __shared__ float2 meta[];
+    ggml_cuda_pdl_sync();
     for (int i = tid; i < 2*parallel_blocks; i += D) {
         ((float *) meta)[i] = ((const float *)VKQ_meta) [i];
     }
@@ -1146,7 +1152,9 @@ void launch_fattn(
     const uint3 ne01 = init_fastdiv_values(Q->ne[1]);
 
     GGML_ASSERT(block_dim.x % warp_size == 0);
-    fattn_kernel<<<blocks_num, block_dim, nbytes_shared, main_stream>>>(
+
+    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(blocks_num, block_dim, nbytes_shared, main_stream);
+    ggml_cuda_kernel_launch(fattn_kernel, launch_params,
         (const char *) Q->data,
         K_data,
         V_data,
@@ -1176,9 +1184,9 @@ void launch_fattn(
             const dim3 block_dim_combine(DV, 1, 1);
             const dim3 blocks_num_combine = {(unsigned)ntiles_dst, ncols1, ncols2};
 
-            flash_attn_stream_k_fixup_uniform<DV, ncols1, ncols2>
-                <<<blocks_num_combine, block_dim_combine, 0, main_stream>>>
-                ((float *) KQV->data, dst_tmp_meta.ptr,
+            const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(blocks_num_combine, block_dim_combine, 0, main_stream);
+            ggml_cuda_kernel_launch(flash_attn_stream_k_fixup_uniform<DV, ncols1, ncols2>, launch_params,
+                (float *) KQV->data, dst_tmp_meta.ptr,
                  Q->ne[1], Q->ne[2], K->ne[2], nblocks_sk,
                  gqa_ratio, bpt, fd0, fd1, fd2);
         } else if (ntiles_dst % blocks_num.x != 0) {
@@ -1193,9 +1201,9 @@ void launch_fattn(
             const dim3 block_dim_combine(DV, 1, 1);
             const dim3 blocks_num_combine = {blocks_num.x, ncols1, ncols2};
 
-            flash_attn_stream_k_fixup_general<DV, ncols1, ncols2>
-                <<<blocks_num_combine, block_dim_combine, 0, main_stream>>>
-                ((float *) KQV->data, dst_tmp_meta.ptr,
+            const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(blocks_num_combine, block_dim_combine, 0, main_stream);
+            ggml_cuda_kernel_launch(flash_attn_stream_k_fixup_general<DV, ncols1, ncols2>, launch_params,
+                (float *) KQV->data, dst_tmp_meta.ptr,
                  Q->ne[1], Q->ne[2], gqa_ratio, total_work,
                  fd_k_j_z_ne12, fd_k_j_z, fd_k_j, fd_k);
         }
@@ -1204,9 +1212,9 @@ void launch_fattn(
         const dim3 blocks_num_combine(Q->ne[1], Q->ne[2], Q->ne[3]);
         const size_t nbytes_shared_combine = parallel_blocks*sizeof(float2);
 
-        flash_attn_combine_results<DV>
-            <<<blocks_num_combine, block_dim_combine, nbytes_shared_combine, main_stream>>>
-            (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data, parallel_blocks);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(blocks_num_combine, block_dim_combine, nbytes_shared_combine, main_stream);
+        ggml_cuda_kernel_launch(flash_attn_combine_results<DV>, launch_params,
+            dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data, parallel_blocks);
     }
     CUDA_CHECK(cudaGetLastError());
 }

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

@@ -1724,6 +1724,7 @@ static __global__ void flash_attn_ext_f16(
                             const int32_t nb21, const int32_t nb22, const int64_t nb23,
                             const int32_t ne31, const int32_t ne32, const int32_t ne33,
                             const int32_t nb31, const int32_t nb32, const int64_t nb33) {
+    ggml_cuda_pdl_sync(); // TODO optimize placement
 #if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE))
 
     // Skip unused kernel variants for faster compilation:

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

@@ -894,6 +894,8 @@ static __global__ void flash_attn_tile(
     }
     float KQ_sum[cpw] = {0.0f};
 
+    ggml_cuda_pdl_sync();
+
     // Load Q data, convert to FP16 if fast:
 #pragma unroll
     for (int jc0 = 0; jc0 < cpw; ++jc0) {

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

@@ -40,6 +40,7 @@ static __global__ void flash_attn_ext_vec(
                             const int32_t nb21, const int32_t nb22, const int64_t nb23,
                             const int32_t ne31, const int32_t ne32, const int32_t ne33,
                             const int32_t nb31, const int32_t nb32, const int64_t nb33) {
+    ggml_cuda_pdl_lc();
 #ifdef FLASH_ATTN_AVAILABLE
 
     // Skip unused kernel variants for faster compilation:
@@ -136,6 +137,8 @@ static __global__ void flash_attn_ext_vec(
 #endif // V_DOT2_F32_F16_AVAILABLE
     int    Q_i32[ncols][1 > D/(sizeof(int)*nthreads_KQ) ? 1 : D/(sizeof(int)*nthreads_KQ)];
     float2  Q_ds[ncols][1 > D/(sizeof(int)*nthreads_KQ) ? 1 : D/(sizeof(int)*nthreads_KQ)];
+
+    ggml_cuda_pdl_sync();
     if constexpr (Q_q8_1) {
 #pragma unroll
         for (int j0 = 0; j0 < ncols; j0 += nwarps) {

ggml/src/ggml-cuda/fattn-wmma-f16.cu

@@ -86,6 +86,7 @@ static __global__ void flash_attn_ext_f16(
     constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
     constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
 
+    ggml_cuda_pdl_sync();
     const int sequence = blockIdx.z / ne02;
     const int head = blockIdx.z - sequence*ne02;
     const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.

ggml/src/ggml-cuda/gated_delta_net.cu

@@ -1,6 +1,7 @@
 #include "gated_delta_net.cuh"
+#include "ggml-cuda/common.cuh"
 
-template <int S_v, bool KDA>
+template <int S_v, bool KDA, bool keep_rs_t>
 __global__ void __launch_bounds__((ggml_cuda_get_physical_warp_size() < S_v ? ggml_cuda_get_physical_warp_size() : S_v) * 4, 2)
 gated_delta_net_cuda(const float * q,
                                      const float * k,
@@ -23,7 +24,8 @@ gated_delta_net_cuda(const float * q,
                                      int64_t       sb3,
                                      const uint3   neqk1_magic,
                                      const uint3   rq3_magic,
-                                     float         scale) {
+                                     float         scale,
+                                     int           K) {
     const uint32_t h_idx    = blockIdx.x;
     const uint32_t sequence = blockIdx.y;
     // each warp owns one column, using warp-level primitives to reduce across rows
@@ -37,9 +39,13 @@ gated_delta_net_cuda(const float * q,
     float *       attn_data        = dst;
     float *       state            = dst + attn_score_elems;
 
-    const int64_t state_offset = (sequence * H + h_idx) * S_v * S_v;
-    state += state_offset;
-    curr_state += state_offset + col * S_v;
+    // input state layout (D, K, n_seqs) — seq stride is K * D = K * H * S_v * S_v.
+    // output state layout (per-slot D * n_seqs) — same per-(seq,head) offset as before.
+    const int64_t state_in_offset      = sequence * K * H * S_v * S_v + h_idx * S_v * S_v;
+    const int64_t state_out_offset     = (sequence * H + h_idx) * S_v * S_v;
+    const int64_t state_size_per_token = S_v * S_v * H * n_seqs; // per-slot stride in output
+    state += state_out_offset;
+    curr_state += state_in_offset + col * S_v;
     attn_data += (sequence * n_tokens * H + h_idx) * S_v;
 
     constexpr int warp_size = ggml_cuda_get_physical_warp_size() < S_v ? ggml_cuda_get_physical_warp_size() : S_v;
@@ -48,12 +54,17 @@ gated_delta_net_cuda(const float * q,
     float         s_shard[rows_per_lane];
     // state is stored transposed: M[col][i] = S[i][col], row col is contiguous
 
+    ggml_cuda_pdl_sync();
 #pragma unroll
     for (int r = 0; r < rows_per_lane; r++) {
         const int i = r * warp_size + lane;
         s_shard[r]  = curr_state[i];
     }
 
+    // slot mapping: target_slot = t - shift. When n_tokens < K only the last n_tokens slots
+    // are written; earlier slots are left untouched (caller-owned).
+    const int shift = (int) n_tokens - K;
+
     for (int t = 0; t < n_tokens; t++) {
         const float * q_t = q + iq3 * sq3 + t * sq2 + iq1 * sq1;
         const float * k_t = k + iq3 * sq3 + t * sq2 + iq1 * sq1;
@@ -135,17 +146,30 @@ gated_delta_net_cuda(const float * q,
         }
 
         attn_data += S_v * H;
+
+        if constexpr (keep_rs_t) {
+            const int target_slot = t - shift;
+            if (target_slot >= 0 && target_slot < K) {
+                float * curr_state = (dst + attn_score_elems) + target_slot * state_size_per_token + state_out_offset;
+#pragma unroll
+                for (int r = 0; r < rows_per_lane; r++) {
+                    const int i = r * warp_size + lane;
+                    curr_state[col * S_v + i] = s_shard[r];
+                }
+            }
+        }
     }
 
-    // Write state back to global memory (transposed layout)
+    if constexpr (!keep_rs_t) {
 #pragma unroll
-    for (int r = 0; r < rows_per_lane; r++) {
-        const int i          = r * warp_size + lane;
-        state[col * S_v + i] = s_shard[r];
+        for (int r = 0; r < rows_per_lane; r++) {
+            const int i          = r * warp_size + lane;
+            state[col * S_v + i] = s_shard[r];
+        }
     }
 }
 
-template <bool KDA>
+template <bool KDA, bool keep_rs_t>
 static void launch_gated_delta_net(
         const float * q_d, const float * k_d, const float * v_d,
         const float * g_d, const float * b_d, const float * s_d,
@@ -155,7 +179,7 @@ static void launch_gated_delta_net(
         int64_t sv1,   int64_t sv2, int64_t sv3,
         int64_t sb1,   int64_t sb2, int64_t sb3,
         int64_t neqk1, int64_t rq3,
-        float scale, cudaStream_t stream) {
+        float scale, int K, cudaStream_t stream) {
     //TODO: Add chunked kernel for even faster pre-fill
     const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size;
     const int num_warps = 4;
@@ -167,31 +191,32 @@ static void launch_gated_delta_net(
 
     int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
 
+    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, stream);
     switch (S_v) {
         case 16:
-            gated_delta_net_cuda<16, KDA><<<grid_dims, block_dims, 0, stream>>>(
+            ggml_cuda_kernel_launch(gated_delta_net_cuda<16, KDA, keep_rs_t>, launch_params,
                 q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
             break;
         case 32:
-            gated_delta_net_cuda<32, KDA><<<grid_dims, block_dims, 0, stream>>>(
+            ggml_cuda_kernel_launch(gated_delta_net_cuda<32, KDA, keep_rs_t>, launch_params,
                 q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
             break;
         case 64: {
-            gated_delta_net_cuda<64, KDA><<<grid_dims, block_dims, 0, stream>>>(
+            ggml_cuda_kernel_launch(gated_delta_net_cuda<64, KDA, keep_rs_t>, launch_params,
                 q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
             break;
         }
         case 128: {
-            gated_delta_net_cuda<128, KDA><<<grid_dims, block_dims, 0, stream>>>(
+            ggml_cuda_kernel_launch(gated_delta_net_cuda<128, KDA, keep_rs_t>, launch_params,
                 q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
             break;
         }
         default:
@@ -261,13 +286,29 @@ void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor *
 
     cudaStream_t stream = ctx.stream();
 
+    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
+    const int K = (int) src_state->ne[1];
+    const bool keep_rs = K > 1;
+
     if (kda) {
-        launch_gated_delta_net<true>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
-            S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-            sb1, sb2, sb3, neqk1, rq3, scale, stream);
+        if (keep_rs) {
+            launch_gated_delta_net<true, true>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+                S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+        } else {
+            launch_gated_delta_net<true, false>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+                S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+        }
     } else {
-        launch_gated_delta_net<false>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
-            S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-            sb1, sb2, sb3, neqk1, rq3, scale, stream);
+        if (keep_rs) {
+            launch_gated_delta_net<false, true>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+                S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+        } else {
+            launch_gated_delta_net<false, false>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+                S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+        }
     }
 }

ggml/src/ggml-cuda/getrows.cu

@@ -11,6 +11,7 @@ static __global__ void k_get_rows(
         /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03,
         const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) {
 
+    ggml_cuda_pdl_sync();
     for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) {
         for (int64_t i00 = 2*(blockIdx.y*blockDim.x + threadIdx.x); i00 < ne00; i00 += gridDim.y*blockDim.x) {
             // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher.
@@ -48,6 +49,8 @@ static __global__ void k_get_rows_float(
         /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03,
         const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) {
 
+    ggml_cuda_pdl_lc();
+    ggml_cuda_pdl_sync();
     for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) {
         for (int64_t i00 = blockIdx.y*blockDim.x + threadIdx.x; i00 < ne00; i00 += gridDim.y*blockDim.x) {
             // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher.
@@ -83,6 +86,7 @@ static __global__ void k_get_rows_back_float(
 
     float sum = 0.0f;
 
+    ggml_cuda_pdl_sync();
     for (int64_t i = 0; i < nrows_grad; ++i) {
         if (rows[i] != dst_row) {
             continue;
@@ -156,7 +160,8 @@ static void get_rows_cuda_float(
     GGML_ASSERT(ne11 <= std::numeric_limits<uint32_t>::max() / ne12);
     const uint3 ne12_fdv = init_fastdiv_values(ne12);
 
-    k_get_rows_float<<<block_nums, block_dims, 0, stream>>>(
+    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{block_nums, block_dims, 0, stream};
+    ggml_cuda_kernel_launch(k_get_rows_float<src0_t, dst_t>, launch_params,
         src0_d, src1_d, dst_d,
         ne00, /*ne01, ne02, ne03,*/
         /*ne10,*/ ne11, ne12_fdv, /*ne13,*/

ggml/src/ggml-cuda/mean.cu

@@ -67,9 +67,11 @@ void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     // See discussion in: https://github.com/ggml-org/llama.cpp/pull/15132
     if ((nrows / nsm) < 2) {
         const dim3 block_dims(512, 1, 1);
-        reduce_rows_f32</*norm=*/true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);
+        ggml_cuda_kernel_launch(reduce_rows_f32</*norm=*/true>, launch_params, src0_d, dst_d, ncols);
     } else {
         const dim3 block_dims(ncols < 1024 ? 32 : 128, 1, 1);
-        reduce_rows_f32</*norm=*/true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);
+        ggml_cuda_kernel_launch(reduce_rows_f32</*norm=*/true>, launch_params, src0_d, dst_d, ncols);
     }
 }

ggml/src/ggml-cuda/mmvf.cu

@@ -21,6 +21,7 @@ static __global__ void mul_mat_vec_f(
     int channel_y;
     int sample_dst;
 
+    ggml_cuda_pdl_sync();
     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;
@@ -298,6 +299,7 @@ static __global__ void mul_mat_vec_f(
         static_assert(std::is_same_v<T, void>, "unsupported type");
     }
 
+    ggml_cuda_pdl_lc();
 #pragma unroll
     for (int j = 0; j < ncols_dst; ++j) {
         sumf[j] = warp_reduce_sum<warp_size>(sumf[j]);
@@ -382,11 +384,13 @@ static void mul_mat_vec_f_switch_fusion(
         const uint3 sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
         const dim3 & block_dims, const dim3 & block_nums, const int nbytes_shared, const int ids_stride, const cudaStream_t stream) {
 
+    const ggml_cuda_kernel_launch_params launch_params = {block_nums, block_dims, nbytes_shared, stream};
+
     const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
     if constexpr (ncols_dst == 1) {
         if (has_fusion) {
-            mul_mat_vec_f<T, type_acc, ncols_dst, block_size, true, is_multi_token_id><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, fusion, dst, ncols, nchannels_y, stride_row, stride_col_y, stride_col_dst,
+            ggml_cuda_kernel_launch(mul_mat_vec_f<T, type_acc, ncols_dst, block_size, true, is_multi_token_id>, launch_params,
+                x, y, ids, fusion, dst, ncols, nchannels_y, stride_row, 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);
             return;
@@ -395,8 +399,8 @@ static void mul_mat_vec_f_switch_fusion(
 
     GGML_ASSERT(!has_fusion && "fusion only supported for ncols_dst=1");
 
-    mul_mat_vec_f<T, type_acc, ncols_dst, block_size, false, is_multi_token_id><<<block_nums, block_dims, nbytes_shared, stream>>>
-        (x, y, ids, fusion, dst, ncols, nchannels_y, stride_row, stride_col_y, stride_col_dst,
+    ggml_cuda_kernel_launch(mul_mat_vec_f<T, type_acc, ncols_dst, block_size, false, is_multi_token_id>, launch_params,
+        x, y, ids, fusion, dst, ncols, nchannels_y, stride_row, 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);
 

ggml/src/ggml-cuda/mmvq.cu

@@ -359,7 +359,9 @@ static constexpr __host__ __device__ int calc_nwarps(ggml_type type, int ncols_d
                 case GGML_TYPE_Q5_1:
                 case GGML_TYPE_Q8_0:
                 case GGML_TYPE_Q4_K:
+                    return 8;
                 case GGML_TYPE_Q6_K:
+                    return 2;
                 case GGML_TYPE_IQ4_NL:
                     return 8;
                 default:
@@ -422,6 +424,7 @@ static __global__ void mul_mat_vec_q(
     uint32_t channel_y;
     uint32_t sample_dst;
 
+    ggml_cuda_pdl_sync();
     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;
@@ -681,8 +684,9 @@ 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, 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,
+            const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, nbytes_shared, stream);
+            ggml_cuda_kernel_launch(mul_mat_vec_q<type, c_ncols_dst, true, small_k>, launch_params,
+                 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);
             return;
@@ -691,8 +695,9 @@ 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, 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,
+    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, nbytes_shared, stream);
+    ggml_cuda_kernel_launch(mul_mat_vec_q<type, c_ncols_dst, false, small_k>, launch_params,
+        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);
 }

ggml/src/ggml-cuda/norm.cu

@@ -18,6 +18,7 @@ static __global__ void norm_f32(
 
     float2 mean_var = make_float2(0.0f, 0.0f);
 
+    ggml_cuda_pdl_sync();
     for (int col = tid; col < ncols; col += block_size) {
         const float xi = x[col];
         mean_var.x += xi;
@@ -46,6 +47,7 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
 
     float tmp = 0.0f; // partial sum for thread in warp
 
+    ggml_cuda_pdl_sync();
     for (int j = start; j < end; j += block_size) {
         tmp += x[j];
     }
@@ -95,6 +97,7 @@ static __global__ void rms_norm_f32(const float * x,
                                     const uint3   add_nrows_packed     = make_uint3(0, 0, 0),
                                     const uint3   add_nchannels_packed = make_uint3(0, 0, 0),
                                     const uint3   add_nsamples_packed  = make_uint3(0, 0, 0)) {
+    ggml_cuda_pdl_lc();
     const int nrows     = gridDim.x;
     const int nchannels = gridDim.y;
 
@@ -124,6 +127,7 @@ static __global__ void rms_norm_f32(const float * x,
 
     float tmp = 0.0f; // partial sum for thread in warp
 
+    ggml_cuda_pdl_sync();
     for (int col = tid; col < ncols; col += block_size) {
         const float xi = x[col];
         tmp += xi * xi;
@@ -163,6 +167,7 @@ static __global__ void rms_norm_back_f32(
     float sum_xx = 0.0f; // sum for squares of x, equivalent to forward pass
     float sum_xg = 0.0f; // sum for x * gradient, needed because RMS norm mixes inputs
 
+    ggml_cuda_pdl_sync();
     for (int col = tid; col < ncols; col += block_size) {
         const float xfi = xf[col];
         sum_xx += xfi * xfi;
@@ -253,6 +258,7 @@ static __global__ void l2_norm_f32(
 
     float tmp = 0.0f; // partial sum for thread in warp
 
+    ggml_cuda_pdl_sync();
     for (int col = tid; col < ncols; col += block_size) {
         const float xi = x[col];
         tmp += xi * xi;
@@ -261,6 +267,7 @@ static __global__ void l2_norm_f32(
     // sum up partial sums
     extern __shared__ float s_sum[];
     tmp = block_reduce<block_reduce_method::SUM, block_size>(tmp, s_sum);
+    ggml_cuda_pdl_lc();
 
     // from https://pytorch.org/docs/stable/generated/torch.nn.functional.normalize.html
     const float scale = rsqrtf(fmaxf(tmp, eps * eps));
@@ -300,10 +307,19 @@ static void rms_norm_f32_cuda(
     const dim3 blocks_num(nrows, nchannels, nsamples);
     if (ncols < 1024) {
         const dim3 block_dims(256, 1, 1);
-        rms_norm_f32<256, false><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
+        const ggml_cuda_kernel_launch_params launch_params = {blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream};
+        ggml_cuda_kernel_launch(rms_norm_f32<256, false>, launch_params,
+            x, dst, ncols, stride_row, stride_channel, stride_sample, eps,
+        // underlying cudaLaunchKernelEx does not support default params
+        nullptr, 0, 0, 0, make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0),
+        nullptr, 0, 0, 0, make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0));
     } else {
         const dim3 block_dims(1024, 1, 1);
-        rms_norm_f32<1024, false><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream};
+        ggml_cuda_kernel_launch(rms_norm_f32<1024, false>, launch_params, x, dst, ncols, stride_row, stride_channel, stride_sample, eps,
+        // underlying cudaLaunchKernelEx does not support default params
+        nullptr, 0, 0, 0, make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0),
+        nullptr, 0, 0, 0, make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0));
     }
 }
 
@@ -346,14 +362,20 @@ static void rms_norm_mul_f32_cuda(const float *  x,
         const uint3 mul_nsamples_packed  = init_fastdiv_values(mul_nsamples);
         if (ncols < 1024) {
             const dim3 block_dims(256, 1, 1);
-            rms_norm_f32<256, true><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(
+            const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream};
+            ggml_cuda_kernel_launch(rms_norm_f32<256, true>, launch_params,
                 x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
-                mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed);
+                mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed,
+                // underlying cudaLaunchKernelEx does not support default params
+            nullptr, 0, 0, 0, make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0));
         } else {
             const dim3 block_dims(1024, 1, 1);
-            rms_norm_f32<1024, true><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(
+            const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream};
+            ggml_cuda_kernel_launch(rms_norm_f32<1024, true>, launch_params,
                 x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
-                mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed);
+                mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed,
+                // underlying cudaLaunchKernelEx does not support default params
+            nullptr, 0, 0, 0, make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0), make_uint3(0, 0, 0));
         }
     } else {
         const uint3 mul_ncols_packed     = init_fastdiv_values(mul_ncols);
@@ -367,14 +389,16 @@ static void rms_norm_mul_f32_cuda(const float *  x,
         const uint3 add_nsamples_packed  = init_fastdiv_values(add_nsamples);
         if (ncols < 1024) {
             const dim3 block_dims(256, 1, 1);
-            rms_norm_f32<256, true, true><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(
+            const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{blocks_num, block_dims,block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream};
+            ggml_cuda_kernel_launch(rms_norm_f32<256, true, true>, launch_params,
                 x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
                 mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed, add,
                 add_stride_row, add_stride_channel, add_stride_sample, add_ncols_packed, add_nrows_packed,
                 add_nchannels_packed, add_nsamples_packed);
         } else {
             const dim3 block_dims(1024, 1, 1);
-            rms_norm_f32<1024, true, true><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(
+            const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream};
+            ggml_cuda_kernel_launch(rms_norm_f32<1024, true, true>, launch_params,
                 x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
                 mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed, add,
                 add_stride_row, add_stride_channel, add_stride_sample, add_ncols_packed, add_nrows_packed,
@@ -399,10 +423,12 @@ static void l2_norm_f32_cuda(
     const dim3 blocks_num(nrows, nchannels, nsamples);
     if (ncols < 1024) {
         const dim3 block_dims(WARP_SIZE, 1, 1);
-        l2_norm_f32<WARP_SIZE><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{blocks_num, block_dims, 0, stream};
+        ggml_cuda_kernel_launch(l2_norm_f32<WARP_SIZE>, launch_params, x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     } else {
         const dim3 block_dims(1024, 1, 1);
-        l2_norm_f32<1024><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream};
+        ggml_cuda_kernel_launch(l2_norm_f32<1024>, launch_params, x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     }
 }
 

ggml/src/ggml-cuda/quantize.cu

@@ -6,6 +6,7 @@ static __global__ void quantize_q8_1(
         const float * __restrict__ x, void * __restrict__ vy,
         const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
         const int64_t ne0, const uint32_t ne1, const uint3 ne2) {
+    ggml_cuda_pdl_lc();
     const int64_t i0 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
 
     if (i0 >= ne0) {
@@ -28,6 +29,7 @@ static __global__ void quantize_q8_1(
     const int64_t ib  = i_cont / QK8_1; // block index
     const int64_t iqs = i_cont % QK8_1; // quant index
 
+    ggml_cuda_pdl_sync();
     const float xi = i0 < ne00 ? x[i03*s03 + i02*s02 + i01*s01 + i00] : 0.0f;
     float amax = fabsf(xi);
     float sum = xi;
@@ -196,6 +198,7 @@ static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x,
     const int64_t i2 = blockIdx.z % ne2;
     const int64_t i3 = blockIdx.z / ne2;
 
+    ggml_cuda_pdl_sync();
     const int64_t i01 = ids ? ids[i1] : i1;
     const int64_t i02 = i2;
     const int64_t i03 = i3;
@@ -288,6 +291,7 @@ static __global__ void quantize_mmq_q8_1(
     const int64_t i3 = blockIdx.z / ne2;
 
     const int64_t i00 = i0;
+    ggml_cuda_pdl_sync();
     const int64_t i01 = ids ? ids[i1] : i1;
     const int64_t i02 = i2;
     const int64_t i03 = i3;
@@ -378,7 +382,8 @@ void quantize_row_q8_1_cuda(
     const int64_t block_num_x = (ne0 + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
     const dim3 num_blocks(block_num_x, ne1, ne2*ne3);
     const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
-    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, ne00, s01, s02, s03, ne0, ne1, ne2_fastdiv);
+    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(num_blocks, block_size, 0, stream);
+    ggml_cuda_kernel_launch(quantize_q8_1, launch_params, x, vy, ne00, s01, s02, s03, ne0, ne1, ne2_fastdiv);
     GGML_UNUSED(type_src0);
 }
 

ggml/src/ggml-cuda/reduce_rows.cuh

@@ -10,6 +10,8 @@ static __global__ void reduce_rows_f32(const float * __restrict__ x, float * __r
     const int num_unroll = 8;
     float     temp[num_unroll];
     float     sum_temp[num_unroll] = { 0.0f };
+
+    ggml_cuda_pdl_sync();
     for (int i = col; i < ncols;) {
         for (int j = 0; j < num_unroll; ++j) {
             if (i < ncols) {

ggml/src/ggml-cuda/rope.cu

@@ -134,6 +134,7 @@ static __global__ void rope_neox(const T *            x,
                                  const float *        freq_factors,
                                  const int64_t *      row_indices,
                                  const int            set_rows_stride) {
+    ggml_cuda_pdl_lc();
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (i0 >= ne00) {
@@ -148,6 +149,7 @@ static __global__ void rope_neox(const T *            x,
 
     int       idst = i0 / 2 + i1 * s1  + i2 * s2  + i3 * s3;
     const int ix   = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
+    ggml_cuda_pdl_sync();
 
     // Fusion optimization: ROPE + VIEW + SET_ROWS.
     // The rope output is viewed as a 1D tensor and offset based on a row index in row_indices.
@@ -216,6 +218,7 @@ static __global__ void rope_multi(const T *            x,
     int       idst = i0 / 2 + i1 * s1  + i2 * s2  + i3 * s3;
     const int ix   = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
 
+    ggml_cuda_pdl_sync();
     if (i0 >= n_dims) {
         dst[idst + i0/2 + 0] = x[ix + i0/2 + 0];
         dst[idst + i0/2 + 1] = x[ix + i0/2 + 1];
@@ -300,6 +303,7 @@ static __global__ void rope_vision(const T *            x,
     int       idst = i0 / 2 + i1 * s1  + i2 * s2  + i3 * s3;
     const int ix   = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
 
+    ggml_cuda_pdl_sync();
     const int sect_dims = sections.v[0] + sections.v[1];
     const int sec_w     = sections.v[1] + sections.v[0];
     const int sector    = (i0 / 2) % sect_dims;
@@ -399,13 +403,14 @@ static void rope_neox_cuda(const T *            x,
     const dim3 block_nums(nr, n_blocks_x, 1);
 
     const float theta_scale = powf(freq_base, -2.0f / n_dims);
+    const ggml_cuda_kernel_launch_params launch_params = {block_nums, block_dims, 0, stream};
 
     if (freq_factors == nullptr) {
-        rope_neox<forward, false><<<block_nums, block_dims, 0, stream>>>(
+        ggml_cuda_kernel_launch(rope_neox<forward, false, T, D>, launch_params,
             x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
     } else {
-        rope_neox<forward, true><<<block_nums, block_dims, 0, stream>>>(
+        ggml_cuda_kernel_launch(rope_neox<forward, true, T, D>, launch_params,
             x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
     }
@@ -443,11 +448,13 @@ static void rope_multi_cuda(const T *            x,
     const float theta_scale = powf(freq_base, -2.0f / n_dims);
 
     if (freq_factors == nullptr) {
-        rope_multi<forward, false, T><<<block_nums, block_dims, 0, stream>>>(
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);
+        ggml_cuda_kernel_launch(rope_multi<forward, false, T>, launch_params,
             x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, sections, is_imrope);
     } else {
-        rope_multi<forward, true, T><<<block_nums, block_dims, 0, stream>>>(
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);
+        ggml_cuda_kernel_launch(rope_multi<forward, true, T>, launch_params,
             x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, sections, is_imrope);
     }

ggml/src/ggml-cuda/scale.cu

@@ -3,9 +3,11 @@
 #define MAX_GRIDDIM_X 0x7FFFFFFF
 
 static __global__ void scale_f32(const float * x, float * dst, const float scale, const float bias, const int64_t nelements) {
+    ggml_cuda_pdl_lc();
     int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
     int64_t stride = (int64_t)blockDim.x * (int64_t)gridDim.x;
 
+    ggml_cuda_pdl_sync();
     for (int64_t i = tid; i < nelements; i += stride) {
         dst[i] = scale * x[i] + bias;
     }
@@ -13,7 +15,8 @@ static __global__ void scale_f32(const float * x, float * dst, const float scale
 
 static void scale_f32_cuda(const float * x, float * dst, const float scale, const float bias, const int64_t nelements, cudaStream_t stream) {
     const int64_t num_blocks = (nelements + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
-    scale_f32<<<MIN(MAX_GRIDDIM_X, num_blocks), CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, bias, nelements);
+    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(MIN(MAX_GRIDDIM_X, num_blocks), CUDA_SCALE_BLOCK_SIZE, 0, stream);
+    ggml_cuda_kernel_launch(scale_f32, launch_params, x, dst, scale, bias, nelements);
 }
 
 void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {

ggml/src/ggml-cuda/set-rows.cu

@@ -53,6 +53,7 @@ static __global__ void k_set_rows_quant(const float * __restrict__ src0,
     const int64_t i11 = fastmodulo((uint32_t) i02, ne11_fd);
     const int64_t i10 = i01;
 
+    ggml_cuda_pdl_sync();
     const int64_t dst_row = *(src1 + i10*s10 + i11*s11 + i12*s12);
 
     const float * src0_row = src0 + i01*s01 + i02*s02 + i03*s03;
@@ -157,7 +158,9 @@ static __global__ void k_set_rows(const src_t * __restrict__ src0,
     const int64_t i11 = fastmodulo((uint32_t) i02, ne11_fd);
     const int64_t i10 = i01;
 
+    ggml_cuda_pdl_sync();
     const int64_t dst_row = *(src1 + i10*s10 + i11*s11 + i12*s12);
+    ggml_cuda_pdl_lc();
 
     const src_t * src0_row = src0 + i01*s01 + i02*s02 + i03*s03;
     dst_t * dst_row_ptr    = dst + dst_row*s1 + i02*s2 + i03*s3;
@@ -203,9 +206,11 @@ static void set_rows_cuda(
         const uint3 ne11_fd = init_fastdiv_values((uint32_t) ne11);
         const uint3 ne12_fd = init_fastdiv_values((uint32_t) ne12);
 
-        k_set_rows<<<grid_size, block_size, 0, stream>>>(src0_d, src1_d, dst_d, ne_total, ne10, ne11, ne12, ne13, s01,
-                                                         s02, s03, s10, s11, s12, s1, s2, s3, ne00_fd, ne01_fd, ne02_fd,
-                                                         ne11_fd, ne12_fd);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_size, block_size, 0, stream);
+        ggml_cuda_kernel_launch(k_set_rows<src_t, idx_t, dst_t>, launch_params,
+            src0_d, src1_d, dst_d, ne_total, ne10, ne11, ne12, ne13, s01,
+            s02, s03, s10, s11, s12, s1, s2, s3, ne00_fd, ne01_fd, ne02_fd,
+            ne11_fd, ne12_fd);
     }
 }