OpenCL: add SOLVE_TRI op support (#18846)

* initial commit for branch

* simplify constants

* add params to `struct common_params_sampling`, add reference to PR

* explicitly clamp `min_target` and `max_target` to `[0.0, 1.0]`

* add args, rename `queue_size` -> `window_size`

* improved comments

* minor

* remove old unused code from algorithm

* minor

* add power law case to `common_sampler_init`, add sampler name mappings

* clarify behaviour when `window_size = 0`

* add missing enums

* remove `target_range` param, make `target == 1` no-op, cleanup code

* oops, straggler

* add missing parameters in `server-task.cpp`

* copy from author

ref:
https://gist.github.com/MrJackSpade/9be99c7efbba7b95a41377e123b7b069

* remove old debug log, style nit

* fix compiler warning, add commented-out logging per token

* re-write + change parameters + simplify

* oops forgot args.cpp

* fix leftover `window_size`

* add missing values to `common_params_sampling::print()`

* with logging

* does this fix it?

* no, but does this?

* update default decay

* optimize

* fix bad merge

my git skills are lacking

* silence `missing initializer for member`

* update default decay to 0.9

* fix logging

* format (double)

* add power law to the new `samplers` vector

* log sampler init values

* improve logging messages in llama_sampler_power_law

* remove extraneous logging

* simplify target computation

last commit with debug logging!

* remove debug logging, explicitly clamp params at init

* add `use_power_law` flag + logic, minor cleanup

* update `power-law` -> `adaptive-p`

* fix cold start EMA

- `ctx->weighted_sum` is now initialized and reset to `target / (1.0f -
clamped_decay)`
- `ctx->total_weight` is now initialized and reset to `1.0f / (1.0f -
clamped_decay)`

this fixes a "cold start" problem with the moving average

* update `SHARPNESS` constant to `10.0f`

* minor style fixes

no functional changes

* minor style fixes cont.

* update `llama_sampler_adaptive_p_i` for backend sampling (ref: #17004)

* separate into `apply` + `accept` functions

* `pending_token_idx`: switch from `llama_token` to `int32`

functionally identical (`llama.h` has `typedef int32_t llama_token;`),
but its more correct now

* don't transform logits <= -1e9f

* fix masking in backend top-p, min-p

* address review comments

* typo in comments `RND` -> `RNG`

* add docs

* add recommended values in completion docs

* address PR feedback

* remove trailing whitespace (for CI `editorconfig`)

* add to adaptive-p to `common_sampler_types_from_chars`

.devops/cann.Dockerfile

@@ -13,7 +13,7 @@ ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc2-${CHIP_TYPE}-openeuler24.03-py3.
 FROM ${CANN_BASE_IMAGE} AS build
 
 # -- Install build dependencies --
-RUN yum install -y gcc g++ cmake make git libcurl-devel python3 python3-pip && \
+RUN yum install -y gcc g++ cmake make git openssl-devel python3 python3-pip && \
     yum clean all && \
     rm -rf /var/cache/yum
 

.devops/cpu.Dockerfile

@@ -5,7 +5,7 @@ FROM ubuntu:$UBUNTU_VERSION AS build
 ARG TARGETARCH
 
 RUN apt-get update && \
-    apt-get install -y build-essential git cmake libcurl4-openssl-dev
+    apt-get install -y build-essential git cmake libssl-dev
 
 WORKDIR /app
 

.devops/cuda-new.Dockerfile

@@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 ARG CUDA_DOCKER_ARCH=default
 
 RUN apt-get update && \
-    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
+    apt-get install -y build-essential cmake python3 python3-pip git libssl-dev libgomp1
 
 WORKDIR /app
 

.devops/cuda.Dockerfile

@@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 ARG CUDA_DOCKER_ARCH=default
 
 RUN apt-get update && \
-    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
+    apt-get install -y build-essential cmake python3 python3-pip git libssl-dev libgomp1
 
 WORKDIR /app
 

.devops/intel.Dockerfile

@@ -6,7 +6,7 @@ FROM intel/deep-learning-essentials:$ONEAPI_VERSION AS build
 
 ARG GGML_SYCL_F16=OFF
 RUN apt-get update && \
-    apt-get install -y git libcurl4-openssl-dev
+    apt-get install -y git libssl-dev
 
 WORKDIR /app
 

.devops/llama-cli-cann.Dockerfile

@@ -6,7 +6,7 @@ WORKDIR /app
 
 COPY . .
 
-RUN yum install -y gcc g++ cmake make libcurl-devel
+RUN yum install -y gcc g++ cmake make openssl-devel
 ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
 ENV LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:$LIBRARY_PATH
 ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/lib64/plugin/opskernel:${ASCEND_TOOLKIT_HOME}/lib64/plugin/nnengine:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH}

.devops/musa.Dockerfile

@@ -18,7 +18,7 @@ RUN apt-get update && \
     python3 \
     python3-pip \
     git \
-    libcurl4-openssl-dev \
+    libssl-dev \
     libgomp1
 
 WORKDIR /app

.devops/nix/package.nix

@@ -32,7 +32,6 @@
   useMpi ? false,
   useRocm ? config.rocmSupport,
   rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets,
-  enableCurl ? true,
   useVulkan ? false,
   useRpc ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
@@ -160,15 +159,13 @@ effectiveStdenv.mkDerivation (finalAttrs: {
     ++ optionals useMpi [ mpi ]
     ++ optionals useRocm rocmBuildInputs
     ++ optionals useBlas [ blas ]
-    ++ optionals useVulkan vulkanBuildInputs
-    ++ optionals enableCurl [ curl ];
+    ++ optionals useVulkan vulkanBuildInputs;
 
   cmakeFlags =
     [
       (cmakeBool "LLAMA_BUILD_SERVER" true)
       (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
       (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
-      (cmakeBool "LLAMA_CURL" enableCurl)
       (cmakeBool "GGML_NATIVE" false)
       (cmakeBool "GGML_BLAS" useBlas)
       (cmakeBool "GGML_CUDA" useCuda)

.devops/rocm.Dockerfile

@@ -27,7 +27,7 @@ RUN apt-get update \
     build-essential \
     cmake \
     git \
-    libcurl4-openssl-dev \
+    libssl-dev \
     curl \
     libgomp1
 

.devops/s390x.Dockerfile

@@ -11,7 +11,7 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
     apt install -y --no-install-recommends \
         git cmake ccache ninja-build \
         # WARNING: Do not use libopenblas-openmp-dev. libopenblas-dev is faster.
-        libopenblas-dev libcurl4-openssl-dev && \
+        libopenblas-dev libssl-dev && \
     rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app

.devops/vulkan.Dockerfile

@@ -5,8 +5,8 @@ FROM ubuntu:$UBUNTU_VERSION AS build
 # Install build tools
 RUN apt update && apt install -y git build-essential cmake wget xz-utils
 
-# Install cURL and Vulkan SDK dependencies
-RUN apt install -y libcurl4-openssl-dev curl \
+# Install SSL and Vulkan SDK dependencies
+RUN apt install -y libssl-dev curl \
     libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc
 
 # Build it
@@ -33,6 +33,7 @@ FROM ubuntu:$UBUNTU_VERSION AS base
 
 RUN apt-get update \
     && apt-get install -y libgomp1 curl libvulkan1 mesa-vulkan-drivers \
+    libglvnd0 libgl1 libglx0 libegl1 libgles2 \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

.github/actions/windows-setup-curl/action.yml

@@ -1,30 +0,0 @@
-name: 'Windows - Setup CURL'
-description: 'Composite action, to be reused in other workflow'
-inputs:
-  curl_version:
-    description: 'CURL version'
-    required: false
-    default: '8.6.0_6'
-  architecture:
-    description: 'Architecture of the libcurl to download'
-    required: false
-    default: 'win64'
-outputs:
-  curl_path:
-    description: "Path to the downloaded libcurl"
-    value: ${{ steps.get_libcurl.outputs.curl_path }}
-
-runs:
-  using: "composite"
-  steps:
-    - name: libCURL
-      id: get_libcurl
-      shell: powershell
-      env:
-        CURL_VERSION: ${{ inputs.curl_version }}
-        ARCHITECTURE: ${{ inputs.architecture }}
-      run: |
-        curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-${env:ARCHITECTURE}-mingw.zip"
-        mkdir $env:RUNNER_TEMP/libcurl
-        tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl
-        echo "curl_path=$env:RUNNER_TEMP/libcurl" >> $env:GITHUB_OUTPUT

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

@@ -20,7 +20,7 @@ jobs:
         run: |
           PREFIX="$(pwd)"/inst
           cmake -S . -B build -DCMAKE_PREFIX_PATH="$PREFIX" \
-                -DLLAMA_CURL=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=OFF \
+                -DLLAMA_OPENSSL=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=OFF \
                 -DLLAMA_BUILD_EXAMPLES=OFF -DCMAKE_BUILD_TYPE=Release
           cmake --build build --config Release
           cmake --install build --prefix "$PREFIX" --config Release

.github/workflows/build-linux-cross.yml

@@ -30,7 +30,7 @@ jobs:
 
   #     - name: Build
   #       run: |
-  #         cmake -B build -DLLAMA_CURL=OFF \
+  #         cmake -B build -DLLAMA_OPENSSL=OFF \
   #                        -DCMAKE_BUILD_TYPE=Release \
   #                        -DGGML_OPENMP=OFF \
   #                        -DLLAMA_BUILD_EXAMPLES=ON \
@@ -76,7 +76,7 @@ jobs:
 
   #     - name: Build
   #       run: |
-  #         cmake -B build -DLLAMA_CURL=OFF \
+  #         cmake -B build -DLLAMA_OPENSSL=OFF \
   #                        -DCMAKE_BUILD_TYPE=Release \
   #                        -DGGML_VULKAN=ON \
   #                        -DGGML_OPENMP=OFF \
@@ -122,7 +122,7 @@ jobs:
 
   #     - name: Build
   #       run: |
-  #         cmake -B build -DLLAMA_CURL=OFF \
+  #         cmake -B build -DLLAMA_OPENSSL=OFF \
   #                        -DCMAKE_BUILD_TYPE=Release \
   #                        -DGGML_VULKAN=ON \
   #                        -DGGML_OPENMP=OFF \
@@ -178,7 +178,7 @@ jobs:
 
       - name: Build
         run: |
-          cmake -B build -DLLAMA_CURL=OFF \
+          cmake -B build -DLLAMA_OPENSSL=OFF \
                          -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \
@@ -235,7 +235,7 @@ jobs:
 
       - name: Build
         run: |
-          cmake -B build -DLLAMA_CURL=OFF \
+          cmake -B build -DLLAMA_OPENSSL=OFF \
                          -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_VULKAN=ON \
                          -DGGML_OPENMP=OFF \
@@ -281,7 +281,7 @@ jobs:
       - name: Build
         run: |
           export RISCV_ROOT_PATH=${PWD}/spacemit_toolchain
-          cmake -B build -DLLAMA_CURL=OFF \
+          cmake -B build -DLLAMA_OPENSSL=OFF \
                          -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \

.github/workflows/build.yml

@@ -79,7 +79,6 @@ jobs:
           cmake -B build \
             -DCMAKE_BUILD_RPATH="@loader_path" \
             -DLLAMA_FATAL_WARNINGS=ON \
-            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_BORINGSSL=ON \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=OFF \
@@ -92,7 +91,7 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest -L 'main|curl' --verbose --timeout 900
+          ctest -L main --verbose --timeout 900
 
   macOS-latest-cmake-x64:
     runs-on: macos-15-intel
@@ -118,7 +117,6 @@ jobs:
           cmake -B build \
             -DCMAKE_BUILD_RPATH="@loader_path" \
             -DLLAMA_FATAL_WARNINGS=ON \
-            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_BORINGSSL=ON \
             -DGGML_METAL=OFF \
             -DGGML_RPC=ON \
@@ -152,13 +150,13 @@ jobs:
           DAWN_VERSION="v2.0.0"
           DAWN_OWNER="reeselevine"
           DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release.zip"
-          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release"
+          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
           curl -L -o artifact.zip \
-            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
           mkdir dawn
           unzip artifact.zip
-          tar -xvf Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release.tar.gz -C dawn --strip-components=1
+          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
 
       - name: Build
         id: cmake_build
@@ -227,8 +225,6 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DGGML_RPC=ON
           cmake --build build --config Release -j $(nproc)
@@ -237,7 +233,7 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest -L 'main|curl' --verbose --timeout 900
+          ctest -L main --verbose --timeout 900
 
       - name: Test llama2c conversion
         id: llama2c_test
@@ -293,8 +289,6 @@ jobs:
         if: ${{ matrix.sanitizer != 'THREAD' }}
         run: |
           cmake -B build \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
@@ -305,8 +299,6 @@ jobs:
         if: ${{ matrix.sanitizer == 'THREAD' }}
         run: |
           cmake -B build \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
@@ -336,14 +328,10 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          mkdir build
-          cd build
-          cmake .. \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
+          cmake -B build \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_LLGUIDANCE=ON
-          cmake --build . --config Release -j $(nproc)
+          cmake --build build --config Release -j $(nproc)
 
       - name: Test
         id: cmake_test
@@ -377,8 +365,6 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DGGML_RPC=ON
           cmake --build build --config Release -j $(nproc)
 
@@ -412,8 +398,6 @@ jobs:
         id: cmake_configure
         run: |
           cmake -B build \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DCMAKE_BUILD_TYPE=RelWithDebInfo \
             -DGGML_BACKEND_DL=ON \
             -DGGML_CPU_ALL_VARIANTS=ON \
@@ -470,8 +454,6 @@ jobs:
         run: |
           source ./vulkan_sdk/setup-env.sh
           cmake -B build \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DGGML_VULKAN=ON
           cmake --build build --config Release -j $(nproc)
 
@@ -532,21 +514,19 @@ jobs:
           DAWN_VERSION="v2.0.0"
           DAWN_OWNER="reeselevine"
           DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-ubuntu-latest-Release.zip"
-          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-ubuntu-latest-Release"
+          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
           curl -L -o artifact.zip \
-            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
           mkdir dawn
           unzip artifact.zip
-          tar -xvf Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-ubuntu-latest-Release.tar.gz -C dawn --strip-components=1
+          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
 
       - name: Build
         id: cmake_build
         run: |
           export Dawn_DIR=dawn/lib64/cmake/Dawn
           cmake -B build \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DGGML_WEBGPU=ON
           cmake --build build --config Release -j $(nproc)
 
@@ -593,7 +573,7 @@ jobs:
           source emsdk/emsdk_env.sh
           emcmake cmake -B build-wasm \
             -DGGML_WEBGPU=ON \
-            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=OFF \
             -DEMDAWNWEBGPU_DIR=emdawnwebgpu_pkg
 
           cmake --build build-wasm --target test-backend-ops -j $(nproc)
@@ -624,8 +604,6 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build -S . \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
             -DGGML_HIP_ROCWMMA_FATTN=ON \
             -DGGML_HIP=ON
@@ -657,8 +635,6 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build -S . \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DGGML_MUSA=ON
           cmake --build build --config Release -j $(nproc)
 
@@ -706,8 +682,6 @@ jobs:
         run: |
           source /opt/intel/oneapi/setvars.sh
           cmake -B build \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DGGML_SYCL=ON \
             -DCMAKE_C_COMPILER=icx \
             -DCMAKE_CXX_COMPILER=icpx
@@ -757,8 +731,6 @@ jobs:
         run: |
           source /opt/intel/oneapi/setvars.sh
           cmake -B build \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DGGML_SYCL=ON \
             -DCMAKE_C_COMPILER=icx \
             -DCMAKE_CXX_COMPILER=icpx \
@@ -893,7 +865,7 @@ jobs:
           cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -1043,7 +1015,7 @@ jobs:
         id: cmake_build
         run: |
           cmake -S . -B build ${{ matrix.defines }} `
-            -DLLAMA_CURL=OFF -DLLAMA_BUILD_BORINGSSL=ON
+            -DLLAMA_BUILD_BORINGSSL=ON
           cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS}
 
       - name: Add libopenblas.dll
@@ -1101,8 +1073,6 @@ jobs:
           # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
           run: |
             cmake -S . -B build -G Ninja \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
               -DLLAMA_FATAL_WARNINGS=ON \
               -DCMAKE_BUILD_TYPE=Release \
               -DCMAKE_CUDA_ARCHITECTURES=89-real \
@@ -1150,7 +1120,6 @@ jobs:
           call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
           cmake -S . -B build -G "Ninja Multi-Config" ^
             -DLLAMA_BUILD_SERVER=ON ^
-            -DLLAMA_CURL=OFF ^
             -DLLAMA_BUILD_BORINGSSL=ON ^
             -DGGML_NATIVE=OFF ^
             -DGGML_BACKEND_DL=ON ^
@@ -1258,7 +1227,6 @@ jobs:
             -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
             -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-${{ env.ROCM_VERSION }}/include/" `
             -DCMAKE_BUILD_TYPE=Release `
-            -DLLAMA_CURL=OFF `
             -DLLAMA_BUILD_BORINGSSL=ON `
             -DROCM_DIR="${env:HIP_PATH}" `
             -DGGML_HIP=ON `
@@ -1285,7 +1253,7 @@ jobs:
           cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -1352,7 +1320,7 @@ jobs:
       matrix:
         include:
           - build: 'arm64-cpu'
-            defines: '-D ANDROID_ABI=arm64-v8a -D ANDROID_PLATFORM=android-31 -D CMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -D GGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm -G Ninja -D LLAMA_CURL=OFF -D GGML_OPENMP=OFF'
+            defines: '-D ANDROID_ABI=arm64-v8a -D ANDROID_PLATFORM=android-31 -D CMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -D GGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm -G Ninja -D LLAMA_OPENSSL=OFF -D GGML_OPENMP=OFF'
           - build: 'arm64-snapdragon'
             defines: '--preset arm64-android-snapdragon-release'
 
@@ -1418,7 +1386,6 @@ jobs:
           echo "FIXME: test on devices"
 
   openEuler-latest-cmake-cann:
-    if: ${{ github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'Ascend NPU') }}
     defaults:
       run:
         shell: bash -el {0}
@@ -1464,7 +1431,7 @@ jobs:
             "${{ steps.cann-image.outputs.image }}" \
             bash -lc '
               set -e
-              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel
+              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake openssl-devel
               yum clean all && rm -rf /var/cache/yum
               git config --global --add safe.directory "/workspace"
               export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
@@ -1498,7 +1465,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential libcurl4-openssl-dev
+          sudo apt-get install build-essential
 
       - name: Test
         id: ggml-ci
@@ -1524,7 +1491,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential libcurl4-openssl-dev
+          sudo apt-get install build-essential
 
       - name: Test
         id: ggml-ci
@@ -1550,7 +1517,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential libcurl4-openssl-dev
+          sudo apt-get install build-essential
 
       - name: Test
         id: ggml-ci
@@ -1576,7 +1543,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential libcurl4-openssl-dev
+          sudo apt-get install build-essential
 
       - name: Test
         id: ggml-ci
@@ -1602,7 +1569,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential libcurl4-openssl-dev
+          sudo apt-get install build-essential
 
       - name: Test
         id: ggml-ci
@@ -1705,6 +1672,34 @@ jobs:
         run: |
           GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
 
+  ggml-ci-mac-webgpu:
+    runs-on: [self-hosted, macOS, ARM64]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: Dawn Dependency
+        id: dawn-depends
+        run: |
+          DAWN_VERSION="v2.0.0"
+          DAWN_OWNER="reeselevine"
+          DAWN_REPO="dawn"
+          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release"
+          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
+          curl -L -o artifact.zip \
+            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
+          mkdir dawn
+          unzip artifact.zip
+          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
+            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+
   ggml-ci-mac-vulkan:
     runs-on: [self-hosted, macOS, ARM64]
 
@@ -1738,7 +1733,7 @@ jobs:
          id: depends
          run: |
            sudo apt-get update
-           sudo apt-get install -y build-essential libcurl4-openssl-dev
+           sudo apt-get install -y build-essential
 
        - name: Test
          id: ggml-ci
@@ -1805,8 +1800,6 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DCMAKE_BUILD_TYPE=Release \
             -DGGML_OPENMP=OFF \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -1824,7 +1817,7 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest -L 'main|curl' --verbose --timeout 900
+          ctest -L main --verbose --timeout 900
 
       - name: Test llama2c conversion
         id: llama2c_test
@@ -1899,7 +1892,7 @@ jobs:
         if: ${{ matrix.sanitizer != 'THREAD' }}
         run: |
           cmake -B build \
-            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=OFF \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
             -DGGML_OPENMP=ON \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -1918,7 +1911,7 @@ jobs:
         if: ${{ matrix.sanitizer == 'THREAD' }}
         run: |
           cmake -B build \
-            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=OFF \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
             -DGGML_OPENMP=OFF \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -1989,7 +1982,7 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
-            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=OFF \
             -DCMAKE_BUILD_TYPE=Release \
             -DGGML_OPENMP=OFF \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -2063,8 +2056,6 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
-            -DLLAMA_CURL=OFF \
-            -DLLAMA_OPENSSL=ON \
             -DCMAKE_BUILD_TYPE=Release \
             -DGGML_OPENMP=OFF \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -2100,7 +2091,6 @@ jobs:
            sudo DEBIAN_FRONTEND=noninteractive NEEDRESTART_MODE=a \
            apt-get install -y \
             build-essential \
-            libcurl4-openssl-dev \
             python3-venv \
             gpg \
             wget \

.github/workflows/copilot-setup-steps.yml

@@ -38,7 +38,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential libcurl4-openssl-dev
+          sudo apt-get install build-essential libssl-dev
           # Install git-clang-format script for formatting only changed code
           wget -O /tmp/git-clang-format https://raw.githubusercontent.com/llvm/llvm-project/release/18.x/clang/tools/clang-format/git-clang-format
           sudo cp /tmp/git-clang-format /usr/local/bin/git-clang-format

.github/workflows/release.yml

@@ -37,13 +37,6 @@ jobs:
           key: macOS-latest-cmake-arm64
           evict-old-files: 1d
 
-      - name: Dependencies
-        id: depends
-        continue-on-error: true
-        run: |
-          brew update
-          brew install curl
-
       - name: Build
         id: cmake_build
         run: |
@@ -52,6 +45,7 @@ jobs:
             -DCMAKE_INSTALL_RPATH='@loader_path' \
             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
+            -DLLAMA_BUILD_BORINGSSL=ON \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DGGML_RPC=ON \
@@ -90,13 +84,6 @@ jobs:
           key: macOS-latest-cmake-x64
           evict-old-files: 1d
 
-      - name: Dependencies
-        id: depends
-        continue-on-error: true
-        run: |
-          brew update
-          brew install curl
-
       - name: Build
         id: cmake_build
         run: |
@@ -107,6 +94,7 @@ jobs:
             -DCMAKE_INSTALL_RPATH='@loader_path' \
             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
+            -DLLAMA_BUILD_BORINGSSL=ON \
             -DGGML_METAL=OFF \
             -DGGML_RPC=ON \
             -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
@@ -159,7 +147,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential libcurl4-openssl-dev
+          sudo apt-get install build-essential libssl-dev
 
       - name: Build
         id: cmake_build
@@ -212,7 +200,7 @@ jobs:
           wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
           sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
           sudo apt-get update -y
-          sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libcurl4-openssl-dev
+          sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libssl-dev
 
       - name: Build
         id: cmake_build
@@ -269,34 +257,23 @@ jobs:
         run: |
           choco install ninja
 
-      - name: libCURL
-        id: get_libcurl
-        uses: ./.github/actions/windows-setup-curl
-        with:
-          architecture: ${{ matrix.arch == 'x64' && 'win64' || 'win64a' }}
-
       - name: Build
         shell: cmd
-        env:
-          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
         run: |
           call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" ${{ matrix.arch == 'x64' && 'x64' || 'amd64_arm64' }}
           cmake -S . -B build -G "Ninja Multi-Config" ^
             -D CMAKE_TOOLCHAIN_FILE=cmake/${{ matrix.arch }}-windows-llvm.cmake ^
+            -DLLAMA_BUILD_BORINGSSL=ON ^
             -DGGML_NATIVE=OFF ^
             -DGGML_BACKEND_DL=ON ^
             -DGGML_CPU_ALL_VARIANTS=${{ matrix.arch == 'x64' && 'ON' || 'OFF' }} ^
             -DGGML_OPENMP=ON ^
-            -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include" ^
             ${{ env.CMAKE_ARGS }}
           cmake --build build --config Release
 
       - name: Pack artifacts
         id: pack_artifacts
-        env:
-          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
         run: |
-          Copy-Item $env:CURL_PATH\bin\libcurl-${{ matrix.arch }}.dll .\build\bin\Release\
           Copy-Item "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Redist\MSVC\14.44.35112\debug_nonredist\${{ matrix.arch }}\Microsoft.VC143.OpenMP.LLVM\libomp140.${{ matrix.arch == 'x64' && 'x86_64' || 'aarch64' }}.dll" .\build\bin\Release\
           7z a -snl llama-bin-win-cpu-${{ matrix.arch }}.zip .\build\bin\Release\*
 
@@ -374,7 +351,7 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          cmake -S . -B build ${{ matrix.defines }} -DGGML_NATIVE=OFF -DGGML_CPU=OFF -DGGML_BACKEND_DL=ON -DLLAMA_CURL=OFF
+          cmake -S . -B build ${{ matrix.defines }} -DGGML_NATIVE=OFF -DGGML_CPU=OFF -DGGML_BACKEND_DL=ON -DLLAMA_BUILD_BORINGSSL=ON
           cmake --build build --config Release --target ${{ matrix.target }}
 
       - name: Pack artifacts
@@ -428,7 +405,7 @@ jobs:
             -DGGML_NATIVE=OFF ^
             -DGGML_CPU=OFF ^
             -DGGML_CUDA=ON ^
-            -DLLAMA_CURL=OFF ^
+            -DLLAMA_BUILD_BORINGSSL=ON ^
             -DGGML_CUDA_CUB_3DOT2=ON
           set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
           cmake --build build --config Release -j %NINJA_JOBS% --target ggml-cuda
@@ -497,7 +474,7 @@ jobs:
             -DCMAKE_BUILD_TYPE=Release ^
             -DGGML_BACKEND_DL=ON -DBUILD_SHARED_LIBS=ON ^
             -DGGML_CPU=OFF -DGGML_SYCL=ON ^
-            -DLLAMA_CURL=OFF
+            -DLLAMA_BUILD_BORINGSSL=ON
           cmake --build build --target ggml-sycl -j
 
       - name: Build the release package
@@ -624,7 +601,7 @@ jobs:
             -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}" `
             -DGGML_HIP_ROCWMMA_FATTN=ON `
             -DGGML_HIP=ON `
-            -DLLAMA_CURL=OFF
+            -DLLAMA_BUILD_BORINGSSL=ON
           cmake --build build --target ggml-hip -j ${env:NUMBER_OF_PROCESSORS}
           md "build\bin\rocblas\library\"
           md "build\bin\hipblaslt\library"
@@ -665,7 +642,7 @@ jobs:
           cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -744,7 +721,7 @@ jobs:
             "${{ steps.cann-image.outputs.image }}" \
             bash -lc '
               set -e
-              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel
+              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake openssl-devel
               yum clean all && rm -rf /var/cache/yum
               git config --global --add safe.directory "/workspace"
               export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}

.github/workflows/server-webui.yml

@@ -168,8 +168,6 @@ jobs:
         run: |
           cmake -B build \
               -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
               -DLLAMA_BUILD_SERVER=ON \
               -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
               -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
@@ -182,8 +180,6 @@ jobs:
         run: |
           cmake -B build \
               -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
               -DLLAMA_BUILD_SERVER=ON \
               -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
               -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
@@ -195,8 +191,6 @@ jobs:
         run: |
           cmake -B build \
               -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
               -DLLAMA_BUILD_SERVER=ON \
               -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server

.github/workflows/server.yml

@@ -72,7 +72,7 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          cmake -B build -DLLAMA_CURL=OFF -DLLAMA_BUILD_BORINGSSL=ON
+          cmake -B build -DLLAMA_BUILD_BORINGSSL=ON
           cmake --build build --config ${{ matrix.build_type }} -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
 
       - name: Python setup
@@ -108,7 +108,7 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          cmake -B build -DLLAMA_CURL=OFF -DLLAMA_BUILD_BORINGSSL=ON
+          cmake -B build -DLLAMA_BUILD_BORINGSSL=ON
           cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
 
       - name: Python setup

.gitignore

@@ -130,6 +130,7 @@ poetry.toml
 # Local scripts
 /run-vim.sh
 /run-chat.sh
+/run-spec.sh
 /.ccache/
 
 # IDE

CMakeLists.txt

@@ -111,11 +111,16 @@ option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
 option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_DEFAULT})
 
 # 3rd party libs
-option(LLAMA_CURL       "llama: use libcurl to download model from an URL" ON)
-option(LLAMA_HTTPLIB    "llama: if libcurl is disabled, use httplib to download model from an URL" ON)
-option(LLAMA_OPENSSL    "llama: use openssl to support HTTPS" OFF)
+option(LLAMA_HTTPLIB    "llama: httplib for downloading functionality" ON)
+option(LLAMA_OPENSSL    "llama: use openssl to support HTTPS" ON)
 option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF)
 
+# deprecated
+option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
+if (LLAMA_CURL)
+    message(WARNING "LLAMA_CURL option is deprecated and will be ignored")
+endif()
+
 # Required for relocatable CMake package
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake)
@@ -182,6 +187,9 @@ if (NOT MSVC)
     endif()
 endif()
 
+include("cmake/license.cmake")
+license_add_file("llama.cpp" "LICENSE")
+
 #
 # 3rd-party
 #
@@ -209,11 +217,6 @@ add_subdirectory(src)
 # utils, programs, examples and tests
 #
 
-if (NOT LLAMA_BUILD_COMMON)
-    message(STATUS "LLAMA_BUILD_COMMON is OFF, disabling LLAMA_CURL")
-    set(LLAMA_CURL OFF)
-endif()
-
 if (LLAMA_BUILD_COMMON)
     add_subdirectory(common)
     if (LLAMA_HTTPLIB)
@@ -235,6 +238,19 @@ if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TOOLS)
     add_subdirectory(tools)
 endif()
 
+# Automatically add all files from the 'licenses' directory
+file(GLOB EXTRA_LICENSES "${CMAKE_SOURCE_DIR}/licenses/LICENSE-*")
+
+foreach(FILE_PATH ${EXTRA_LICENSES})
+    get_filename_component(FILE_NAME "${FILE_PATH}" NAME)
+    string(REGEX REPLACE "^LICENSE-" "" NAME "${FILE_NAME}")
+    license_add_file("${NAME}" "${FILE_PATH}")
+endforeach()
+
+if (LLAMA_BUILD_COMMON)
+    license_generate(common)
+endif()
+
 #
 # install
 #

CONTRIBUTING.md

@@ -20,7 +20,7 @@ If AI is used to generate any portion of the code, contributors must adhere to t
 1. Explicitly disclose the manner in which AI was employed.
 2. Perform a comprehensive manual review prior to submitting the pull request.
 3. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
-4. Using AI to respond to human reviewers is strictly prohibited.
+4. Using AI to write pull request descriptions or to respond to human reviewers is strictly prohibited.
 
 For more info, please refer to the [AGENTS.md](AGENTS.md) file.
 

README.md

@@ -200,6 +200,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
 
 - [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
+- [BonzAI App](https://apps.apple.com/us/app/bonzai-your-local-ai-agent/id6752847988) (proprietary)
 - [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
 - [Dot](https://github.com/alexpinel/Dot) (GPL)
 - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
@@ -482,21 +483,6 @@ To learn more about model quantization, [read this documentation](tools/quantize
 
     </details>
 
-## [`llama-run`](tools/run)
-
-#### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3].
-
-- <details>
-    <summary>Run a model with a specific prompt (by default it's pulled from Ollama registry)</summary>
-
-    ```bash
-    llama-run granite-code
-    ```
-
-    </details>
-
-[^3]: [RamaLama](https://github.com/containers/ramalama)
-
 ## [`llama-simple`](examples/simple)
 
 #### A minimal example for implementing apps with `llama.cpp`. Useful for developers.
@@ -600,7 +586,5 @@ $ echo "source ~/.llama-completion.bash" >> ~/.bashrc
 - [stb-image](https://github.com/nothings/stb) - Single-header image format decoder, used by multimodal subsystem - Public domain
 - [nlohmann/json](https://github.com/nlohmann/json) - Single-header JSON library, used by various tools/examples - MIT License
 - [minja](https://github.com/google/minja) - Minimal Jinja parser in C++, used by various tools/examples - MIT License
-- [linenoise.cpp](./tools/run/linenoise.cpp/linenoise.cpp) - C++ library that provides readline-like line editing capabilities, used by `llama-run` - BSD 2-Clause License
-- [curl](https://curl.se/) - Client-side URL transfer library, used by various tools/examples - [CURL License](https://curl.se/docs/copyright.html)
 - [miniaudio.h](https://github.com/mackron/miniaudio) - Single-header audio format decoder, used by multimodal subsystem - Public domain
 - [subprocess.h](https://github.com/sheredom/subprocess.h) - Single-header process launching solution for C and C++ - Public domain

SECURITY.md

@@ -1,12 +1,52 @@
 # Security Policy
 
+ - [**Reporting a vulnerability**](#reporting-a-vulnerability)
+ - [**Requirements**](#requirements)
+ - [**Covered Topics**](#covered-topics)
  - [**Using llama.cpp securely**](#using-llamacpp-securely)
    - [Untrusted models](#untrusted-models)
    - [Untrusted inputs](#untrusted-inputs)
    - [Data privacy](#data-privacy)
    - [Untrusted environments or networks](#untrusted-environments-or-networks)
    - [Multi-Tenant environments](#multi-tenant-environments)
- - [**Reporting a vulnerability**](#reporting-a-vulnerability)
+
+## Reporting a vulnerability
+
+If you have discovered a security vulnerability in this project that falls inside the [covered topics](#covered-topics), please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
+
+Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new).
+
+A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
+
+> [!IMPORTANT]
+> For collaborators: if you are interested in helping out with reviewing privting security disclosures, please see: https://github.com/ggml-org/llama.cpp/discussions/18080
+
+## Requirements
+
+Before submitting your report, ensure you meet the following requirements:
+
+- You have read this policy and fully understand it.
+- AI is only permitted in an assistive capacity as stated in [AGENTS.md](AGENTS.md). We do not accept reports that are written exclusively by AI.
+- Your report must include a working Proof-of-Concept in the form of a script and/or attached files.
+
+Maintainers reserve the right to close the report if these requirements are not fulfilled.
+
+## Covered Topics
+
+Only vulnerabilities that fall within these parts of the project are considered valid. For problems falling outside of this list, please report them as issues.
+
+- `src/**/*`
+- `ggml/**/*`
+- `gguf-py/**/*`
+- `tools/server/*`, **excluding** the following topics:
+    - Web UI
+    - Features marked as experimental
+    - Features not recommended for use in untrusted environments (e.g., router, MCP)
+    - Bugs that can lead to Denial-of-Service attack
+
+Note that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities in LLaMA C++.
+
+For vulnerabilities that fall within the `vendor` directory, please report them directly to the third-party project.
 
 ## Using llama.cpp securely
 
@@ -55,19 +95,3 @@ If you intend to run multiple models in parallel with shared memory, it is your
 3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk.
 
 4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time.
-
-## Reporting a vulnerability
-
-Beware that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities of LLaMA C++.
-
-<!-- normal version -->
-However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
-
-Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new).
-
-Please note that using AI to identify vulnerabilities and generate reports is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before submitting the report.
-
-A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
-
-> [!IMPORTANT]
-> For collaborators: if you are interested in helping out with reviewing privting security disclosures, please see: https://github.com/ggml-org/llama.cpp/discussions/18080

build-xcframework.sh

@@ -414,7 +414,7 @@ cmake -B build-ios-sim -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphonesimulator \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-    -DLLAMA_CURL=OFF \
+    -DLLAMA_OPENSSL=OFF \
     -S .
 cmake --build build-ios-sim --config Release -- -quiet
 
@@ -428,7 +428,7 @@ cmake -B build-ios-device -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphoneos \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-    -DLLAMA_CURL=OFF \
+    -DLLAMA_OPENSSL=OFF \
     -S .
 cmake --build build-ios-device --config Release -- -quiet
 
@@ -439,7 +439,7 @@ cmake -B build-macos -G Xcode \
     -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-    -DLLAMA_CURL=OFF \
+    -DLLAMA_OPENSSL=OFF \
     -S .
 cmake --build build-macos --config Release -- -quiet
 
@@ -453,7 +453,7 @@ cmake -B build-visionos -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xros \
     -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \
-    -DLLAMA_CURL=OFF \
+    -DLLAMA_OPENSSL=OFF \
     -DLLAMA_HTTPLIB=OFF \
     -DLLAMA_BUILD_SERVER=OFF \
     -S .
@@ -469,7 +469,7 @@ cmake -B build-visionos-sim -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xrsimulator \
     -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \
-    -DLLAMA_CURL=OFF \
+    -DLLAMA_OPENSSL=OFF \
     -DLLAMA_HTTPLIB=OFF \
     -DLLAMA_BUILD_SERVER=OFF \
     -S .
@@ -487,7 +487,7 @@ cmake -B build-tvos-sim -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvsimulator \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-    -DLLAMA_CURL=OFF \
+    -DLLAMA_OPENSSL=OFF \
     -S .
 cmake --build build-tvos-sim --config Release -- -quiet
 
@@ -502,7 +502,7 @@ cmake -B build-tvos-device -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvos \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-    -DLLAMA_CURL=OFF \
+    -DLLAMA_OPENSSL=OFF \
     -S .
 cmake --build build-tvos-device --config Release -- -quiet
 

ci/run.sh

@@ -45,7 +45,7 @@ sd=`dirname $0`
 cd $sd/../
 SRC=`pwd`
 
-CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_CURL=ON -DGGML_SCHED_NO_REALLOC=ON"
+CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_OPENSSL=OFF -DGGML_SCHED_NO_REALLOC=ON"
 
 if [ ! -z ${GG_BUILD_METAL} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
@@ -105,7 +105,20 @@ if [ ! -z ${GG_BUILD_VULKAN} ]; then
 fi
 
 if [ ! -z ${GG_BUILD_WEBGPU} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_WEBGPU=1"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_WEBGPU=1 -DGGML_METAL=OFF -DGGML_BLAS=OFF"
+
+    if [ ! -z "${GG_BUILD_WEBGPU_DAWN_PREFIX}" ]; then
+        if [ -z "${CMAKE_PREFIX_PATH}" ]; then
+            export CMAKE_PREFIX_PATH="${GG_BUILD_WEBGPU_DAWN_PREFIX}"
+        else
+            export CMAKE_PREFIX_PATH="${GG_BUILD_WEBGPU_DAWN_PREFIX}:${CMAKE_PREFIX_PATH}"
+        fi
+    fi
+
+    # For some systems, Dawn_DIR needs to be set explicitly, e.g., the lib64 path
+    if [ ! -z "${GG_BUILD_WEBGPU_DAWN_DIR}" ]; then
+        CMAKE_EXTRA="${CMAKE_EXTRA} -DDawn_DIR=${GG_BUILD_WEBGPU_DAWN_DIR}"
+    fi
 fi
 
 if [ ! -z ${GG_BUILD_MUSA} ]; then
@@ -284,7 +297,8 @@ function gg_sum_test_scripts {
 }
 
 function gg_get_model {
-    local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-f16.gguf"
+    #local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-f16.gguf"
+    local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-q4_0.gguf"
     if [[ -s $gguf_0 ]]; then
         echo -n "$gguf_0"
     else

cmake/download-models.cmake

@@ -0,0 +1,21 @@
+get_filename_component(DEST_DIR "${DEST}" DIRECTORY)
+file(MAKE_DIRECTORY "${DEST_DIR}")
+
+if(NOT EXISTS "${DEST}")
+    message(STATUS "Downloading ${NAME} from ggml-org/models...")
+endif()
+
+file(DOWNLOAD
+    "https://huggingface.co/ggml-org/models/resolve/main/${NAME}?download=true"
+    "${DEST}"
+    TLS_VERIFY ON
+    EXPECTED_HASH ${HASH}
+    STATUS status
+)
+
+list(GET status 0 code)
+
+if(NOT code EQUAL 0)
+    list(GET status 1 msg)
+    message(FATAL_ERROR "Failed to download ${NAME}: ${msg}")
+endif()

cmake/license.cmake

@@ -0,0 +1,40 @@
+define_property(GLOBAL PROPERTY LICENSE_TEXT
+    BRIEF_DOCS "Embedded licenses"
+    FULL_DOCS  "Global string containing all aggregated licenses"
+)
+
+function(license_add_file NAME FILE)
+    if(NOT IS_ABSOLUTE "${FILE}")
+        set(FILE "${CMAKE_CURRENT_SOURCE_DIR}/${FILE}")
+    endif()
+    if(EXISTS "${FILE}")
+        set(TITLE "License for ${NAME}")
+        string(REGEX REPLACE "." "=" UNDERLINE "${TITLE}")
+        file(READ "${FILE}" TEXT)
+        get_property(TMP GLOBAL PROPERTY LICENSE_TEXT)
+        string(APPEND TMP "R\"=L=(${TITLE}\n${UNDERLINE}\n\n${TEXT})=L=\",\n")
+        set_property(GLOBAL PROPERTY LICENSE_TEXT "${TMP}")
+    else()
+        message(WARNING "License file '${FILE}' not found")
+    endif()
+endfunction()
+
+function(license_generate TARGET_NAME)
+    message(STATUS "Generating embedded license file for target: ${TARGET_NAME}")
+    get_property(TEXT GLOBAL PROPERTY LICENSE_TEXT)
+
+    set(CPP_CONTENT "// Generated by CMake\n\n")
+    string(APPEND CPP_CONTENT "const char* LICENSES[] = {\n")
+    string(APPEND CPP_CONTENT "${TEXT}")
+    string(APPEND CPP_CONTENT "nullptr\n")
+    string(APPEND CPP_CONTENT "};\n")
+
+    set(CPP_FILE "${CMAKE_BINARY_DIR}/license.cpp")
+    file(WRITE "${CPP_FILE}" "${CPP_CONTENT}")
+
+    if(TARGET ${TARGET_NAME})
+        target_sources(${TARGET_NAME} PRIVATE "${CPP_FILE}")
+    else()
+        message(FATAL_ERROR "Target '${TARGET_NAME}' does not exist")
+    endif()
+endfunction()

common/CMakeLists.txt

@@ -60,6 +60,8 @@ add_library(${TARGET} STATIC
     common.h
     console.cpp
     console.h
+    debug.cpp
+    debug.h
     download.cpp
     download.h
     http.h
@@ -95,17 +97,7 @@ endif()
 # TODO: use list(APPEND LLAMA_COMMON_EXTRA_LIBS ...)
 set(LLAMA_COMMON_EXTRA_LIBS build_info)
 
-if (LLAMA_CURL)
-    # Use curl to download model url
-    find_package(CURL)
-    if (NOT CURL_FOUND)
-        message(FATAL_ERROR "Could NOT find CURL. Hint: to disable this feature, set -DLLAMA_CURL=OFF")
-    endif()
-    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL)
-    include_directories(${CURL_INCLUDE_DIRS})
-    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARIES})
-elseif (LLAMA_HTTPLIB)
-    # otherwise, use cpp-httplib
+if (LLAMA_HTTPLIB)
     target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_HTTPLIB)
     set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} cpp-httplib)
 endif()
@@ -155,27 +147,3 @@ if (LLAMA_LLGUIDANCE)
 endif ()
 
 target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
-
-
-#
-# copy the license files
-#
-
-# Check if running in GitHub Actions
-if (DEFINED ENV{GITHUB_ACTIONS} AND "$ENV{GITHUB_ACTIONS}" STREQUAL "true")
-    message(STATUS "Running inside GitHub Actions - copying license files")
-
-    # Copy all files from licenses/ to build/bin/
-    file(GLOB LICENSE_FILES "${CMAKE_SOURCE_DIR}/licenses/*")
-    foreach(LICENSE_FILE ${LICENSE_FILES})
-        get_filename_component(FILENAME ${LICENSE_FILE} NAME)
-        add_custom_command(
-            POST_BUILD
-            TARGET ${TARGET}
-            COMMAND ${CMAKE_COMMAND} -E copy_if_different
-                "${LICENSE_FILE}"
-                "$<TARGET_FILE_DIR:llama>/${FILENAME}"
-            COMMENT "Copying ${FILENAME} to ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}")
-        message(STATUS "Copying ${LICENSE_FILE} to ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${FILENAME}")
-    endforeach()
-endif()

common/arg.cpp

@@ -2,10 +2,11 @@
 
 #include "chat.h"
 #include "common.h"
+#include "download.h"
 #include "json-schema-to-grammar.h"
 #include "log.h"
 #include "sampling.h"
-#include "download.h"
+#include "preset.h"
 
 // fix problem with std::min and std::max
 #if defined(_WIN32)
@@ -47,6 +48,8 @@
 
 #define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
 
+extern const char * LICENSES[];
+
 using json = nlohmann::ordered_json;
 using namespace common_arg_utils;
 
@@ -268,6 +271,55 @@ static void parse_tensor_buffer_overrides(const std::string & value, std::vector
     }
 }
 
+static std::string clean_file_name(const std::string & fname) {
+    std::string clean_fname = fname;
+    string_replace_all(clean_fname, "\\", "_");
+    string_replace_all(clean_fname, "/", "_");
+    return clean_fname;
+}
+
+static bool common_params_handle_remote_preset(common_params & params, llama_example ex) {
+    GGML_ASSERT(!params.model.hf_repo.empty());
+
+    // the returned hf_repo is without tag
+    auto [hf_repo, hf_tag] = common_download_split_repo_tag(params.model.hf_repo);
+
+    // "latest" tag (default if not specified) is translated to "default" preset
+    if (hf_tag == "latest") {
+        hf_tag = "default";
+    }
+
+    const bool offline = params.offline;
+    std::string model_endpoint = get_model_endpoint();
+    auto preset_url = model_endpoint + hf_repo + "/resolve/main/preset.ini";
+
+    // prepare local path for caching
+    auto preset_fname = clean_file_name(hf_repo + "_preset.ini");
+    auto preset_path = fs_get_cache_file(preset_fname);
+    const int status = common_download_file_single(preset_url, preset_path, params.hf_token, offline);
+    const bool has_preset = status >= 200 && status < 400;
+
+    // remote preset is optional, so we don't error out if not found
+    if (has_preset) {
+        LOG_INF("applying remote preset from %s\n", preset_url.c_str());
+        common_preset_context ctx(ex, /* only_remote_allowed */ true);
+        common_preset global;
+        auto remote_presets = ctx.load_from_ini(preset_path, global);
+        remote_presets = ctx.cascade(global, remote_presets);
+        if (remote_presets.find(hf_tag) != remote_presets.end()) {
+            common_preset preset = remote_presets.at(hf_tag);
+            LOG_INF("\n%s", preset.to_ini().c_str()); // to_ini already added trailing newline
+            preset.apply_to_params(params);
+        } else {
+            throw std::runtime_error("Remote preset.ini does not contain [" + std::string(hf_tag) + "] section");
+        }
+    } else {
+        LOG_INF("%s", "no remote preset found, skipping\n");
+    }
+
+    return has_preset;
+}
+
 struct handle_model_result {
     bool found_mmproj = false;
     common_params_model mmproj;
@@ -289,7 +341,7 @@ static handle_model_result common_params_handle_model(
                 if (model.path.empty()) {
                     auto auto_detected = common_get_hf_file(model.hf_repo, bearer_token, offline);
                     if (auto_detected.repo.empty() || auto_detected.ggufFile.empty()) {
-                        exit(1); // built without CURL, error message already printed
+                        exit(1); // error message already printed
                     }
                     model.name    = model.hf_repo;      // repo name with tag
                     model.hf_repo = auto_detected.repo; // repo name without tag
@@ -309,9 +361,7 @@ static handle_model_result common_params_handle_model(
             // make sure model path is present (for caching purposes)
             if (model.path.empty()) {
                 // this is to avoid different repo having same file name, or same file name in different subdirs
-                std::string filename = model.hf_repo + "_" + model.hf_file;
-                // to make sure we don't have any slashes in the filename
-                string_replace_all(filename, "/", "_");
+                std::string filename = clean_file_name(model.hf_repo + "_" + model.hf_file);
                 model.path = fs_get_cache_file(filename);
             }
 
@@ -425,61 +475,87 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         }
     };
 
-    std::set<std::string> seen_args;
+    auto parse_cli_args = [&]() {
+        std::set<std::string> seen_args;
 
-    for (int i = 1; i < argc; i++) {
-        const std::string arg_prefix = "--";
+        for (int i = 1; i < argc; i++) {
+            const std::string arg_prefix = "--";
 
-        std::string arg = argv[i];
-        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
-            std::replace(arg.begin(), arg.end(), '_', '-');
-        }
-        if (arg_to_options.find(arg) == arg_to_options.end()) {
-            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());
-        }
-        auto & tmp = arg_to_options[arg];
-        auto opt = *tmp.first;
-        bool is_positive = tmp.second;
-        if (opt.has_value_from_env()) {
-            fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
-        }
-        try {
-            if (opt.handler_void) {
-                opt.handler_void(params);
-                continue;
+            std::string arg = argv[i];
+            if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+                std::replace(arg.begin(), arg.end(), '_', '-');
             }
-            if (opt.handler_bool) {
-                opt.handler_bool(params, is_positive);
-                continue;
+            if (arg_to_options.find(arg) == arg_to_options.end()) {
+                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());
+            }
+            auto & tmp = arg_to_options[arg];
+            auto opt = *tmp.first;
+            bool is_positive = tmp.second;
+            if (opt.has_value_from_env()) {
+                fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
+            }
+            try {
+                if (opt.handler_void) {
+                    opt.handler_void(params);
+                    continue;
+                }
+                if (opt.handler_bool) {
+                    opt.handler_bool(params, is_positive);
+                    continue;
+                }
 
-            // arg with single value
-            check_arg(i);
-            std::string val = argv[++i];
-            if (opt.handler_int) {
-                opt.handler_int(params, std::stoi(val));
-                continue;
-            }
-            if (opt.handler_string) {
-                opt.handler_string(params, val);
-                continue;
-            }
+                // arg with single value
+                check_arg(i);
+                std::string val = argv[++i];
+                if (opt.handler_int) {
+                    opt.handler_int(params, std::stoi(val));
+                    continue;
+                }
+                if (opt.handler_string) {
+                    opt.handler_string(params, val);
+                    continue;
+                }
 
-            // arg with 2 values
-            check_arg(i);
-            std::string val2 = argv[++i];
-            if (opt.handler_str_str) {
-                opt.handler_str_str(params, val, val2);
-                continue;
+                // arg with 2 values
+                check_arg(i);
+                std::string val2 = argv[++i];
+                if (opt.handler_str_str) {
+                    opt.handler_str_str(params, val, val2);
+                    continue;
+                }
+            } catch (std::exception & e) {
+                throw std::invalid_argument(string_format(
+                    "error while handling argument \"%s\": %s\n\n"
+                    "usage:\n%s\n\nto show complete usage, run with -h",
+                    arg.c_str(), e.what(), opt.to_string().c_str()));
             }
-        } catch (std::exception & e) {
-            throw std::invalid_argument(string_format(
-                "error while handling argument \"%s\": %s\n\n"
-                "usage:\n%s\n\nto show complete usage, run with -h",
-                arg.c_str(), e.what(), opt.to_string().c_str()));
+        }
+    };
+
+    // parse the first time to get -hf option (used for remote preset)
+    parse_cli_args();
+
+    // maybe handle remote preset
+    if (!params.model.hf_repo.empty()) {
+        std::string cli_hf_repo = params.model.hf_repo;
+        bool has_preset = common_params_handle_remote_preset(params, ctx_arg.ex);
+
+        // special case: if hf_repo explicitly set by preset, we need to preserve it (ignore CLI value)
+        // this is useful when we have one HF repo pointing to other HF repos (one model - multiple GGUFs)
+        std::string preset_hf_repo = params.model.hf_repo;
+        bool preset_has_hf_repo = preset_hf_repo != cli_hf_repo;
+
+        if (has_preset) {
+            // re-parse CLI args to override preset values
+            parse_cli_args();
+        }
+
+        // preserve hf_repo from preset if needed
+        if (preset_has_hf_repo) {
+            params.model.hf_repo = preset_hf_repo;
         }
     }
 
@@ -679,7 +755,6 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
         "llama-quantize",
         "llama-qwen2vl-cli",
         "llama-retrieval",
-        "llama-run",
         "llama-save-load-state",
         "llama-server",
         "llama-simple",
@@ -854,6 +929,54 @@ bool common_arg_utils::is_autoy(const std::string & value) {
     return value == "auto" || value == "-1";
 }
 
+// Simple CSV parser that handles quoted fields and escaped quotes
+// example:
+//    input:  value1,"value, with, commas","value with ""escaped"" quotes",value4
+//    output: [value1] [value, with, commas] [value with "escaped" quotes] [value4]
+static std::vector<std::string> parse_csv_row(const std::string& input) {
+    std::vector<std::string> fields;
+    std::string field;
+    bool in_quotes = false;
+
+    for (size_t i = 0; i < input.length(); ++i) {
+        char ch = input[i];
+
+        if (ch == '"') {
+            if (!in_quotes) {
+                // start of quoted field (only valid if at beginning of field)
+                if (!field.empty()) {
+                    // quote appeared in middle of unquoted field, treat as literal
+                    field += '"';
+                } else {
+                    in_quotes = true; // start
+                }
+            } else {
+                if (i + 1 < input.length() && input[i + 1] == '"') {
+                    // escaped quote: ""
+                    field += '"';
+                    ++i; // skip the next quote
+                } else {
+                    in_quotes = false; // end
+                }
+            }
+        } else if (ch == ',') {
+            if (in_quotes) {
+                field += ',';
+            } else {
+                fields.push_back(std::move(field));
+                field.clear();
+            }
+        } else {
+            field += ch;
+        }
+    }
+
+    // Add the last field
+    fields.push_back(std::move(field));
+
+    return fields;
+}
+
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
     // per-example default params
     // we define here to make sure it's included in llama-gen-docs
@@ -918,6 +1041,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             exit(0);
         }
     ));
+    add_opt(common_arg(
+        {"--license"},
+        "show source code license and dependencies",
+        [](common_params &) {
+            for (int i = 0; LICENSES[i]; ++i) {
+                printf("%s\n", LICENSES[i]);
+            }
+            exit(0);
+        }
+    ));
     add_opt(common_arg(
         {"-cl", "--cache-list"},
         "show list of models in cache",
@@ -1162,7 +1295,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.kv_unified = true;
         }
-    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY}));
+    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED}));
     add_opt(common_arg(
         {"--context-shift"},
         {"--no-context-shift"},
@@ -1250,7 +1383,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--in-file"}, "FNAME",
         "an input file (use comma-separated values to specify multiple files)",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : string_split<std::string>(value, ',')) {
+            for (const auto & item : parse_csv_row(value)) {
                 std::ifstream file(item);
                 if (!file) {
                     throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str()));
@@ -1397,7 +1530,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, bool value) {
             params.warmup = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_DEBUG}));
     add_opt(common_arg(
         {"--spm-infill"},
         string_format(
@@ -1596,6 +1729,26 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_sparam());
+    add_opt(common_arg(
+        {"--adaptive-target"}, "N",
+        string_format("adaptive-p: select tokens near this probability (valid range 0.0 "
+                      "to 1.0; negative = disabled) (default: %.2f)\n"
+                      "[(more info)](https://github.com/ggml-org/llama.cpp/pull/17927)",
+                      (double)params.sampling.adaptive_target),
+        [](common_params & params, const std::string & value) {
+            params.sampling.adaptive_target = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(common_arg(
+        {"--adaptive-decay"}, "N",
+        string_format("adaptive-p: decay rate for target adaptation over time. lower values "
+                      "are more reactive, higher values are more stable.\n"
+                      "(valid range 0.0 to 0.99) (default: %.2f)",
+                      (double)params.sampling.adaptive_decay),
+        [](common_params & params, const std::string & value) {
+            params.sampling.adaptive_decay = std::stof(value);
+        }
+    ).set_sparam());
     add_opt(common_arg(
         {"--dynatemp-range"}, "N",
         string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sampling.dynatemp_range),
@@ -1713,7 +1866,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             else if (value == "rank") { params.pooling_type = LLAMA_POOLING_TYPE_RANK; }
             else { throw std::invalid_argument("invalid value"); }
         }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_POOLING"));
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_DEBUG}).set_env("LLAMA_ARG_POOLING"));
     add_opt(common_arg(
         {"--attention"}, "{causal,non-causal}",
         "attention type for embeddings, use model default if unspecified",
@@ -2002,7 +2155,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--image", "--audio"}, "FILE",
         "path to an image or audio file. use with multimodal models, use comma-separated values for multiple files\n",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : string_split<std::string>(value, ',')) {
+            for (const auto & item : parse_csv_row(value)) {
                 params.image.emplace_back(item);
             }
         }
@@ -2041,11 +2194,22 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     add_opt(common_arg(
         {"--mmap"},
         {"--no-mmap"},
-        string_format("whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"),
+        string_format("whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"),
         [](common_params & params, bool value) {
             params.use_mmap = value;
+            if (value) {
+                params.use_direct_io = false;  // disable direct io when mmap is explicitly enabled
+            }
         }
     ).set_env("LLAMA_ARG_MMAP"));
+    add_opt(common_arg(
+        {"-dio", "--direct-io"},
+        {"-ndio", "--no-direct-io"},
+        string_format("use DirectIO if available. Takes precedence over --mmap (default: %s)", params.use_direct_io ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.use_direct_io = value;
+        }
+    ).set_env("LLAMA_ARG_DIO"));
     add_opt(common_arg(
         {"--numa"}, "TYPE",
         "attempt optimizations that help on some NUMA systems\n"
@@ -2197,7 +2361,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             std::vector<std::string> split_arg{ it, {} };
             if (split_arg.size() >= llama_max_devices()) {
                 throw std::invalid_argument(
-                    string_format("got %d input configs, but system only has %d devices", (int)split_arg.size(), (int)llama_max_devices())
+                    string_format("got %zu input configs, but system only has %zu devices", split_arg.size(), llama_max_devices())
                 );
             }
             for (size_t i = 0; i < llama_max_devices(); ++i) {
@@ -2237,10 +2401,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_FIT"));
     add_opt(common_arg(
-        { "-fitt", "--fit-target" }, "MiB",
-        string_format("target margin per device for --fit option, default: %zu", params.fit_params_target/(1024*1024)),
-        [](common_params & params, int value) {
-            params.fit_params_target = value * size_t(1024*1024);
+        { "-fitt", "--fit-target" }, "MiB0,MiB1,MiB2,...",
+        string_format("target margin per device for --fit, comma-separated list of values, "
+            "single value is broadcast across all devices, default: %zu", params.fit_params_target[0]/(1024*1024)),
+        [](common_params & params, const std::string & value) {
+            std::string arg_next = value;
+
+            // split string by , and /
+            const std::regex regex{ R"([,/]+)" };
+            std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
+            std::vector<std::string> split_arg{ it, {} };
+            if (split_arg.size() >= llama_max_devices()) {
+                throw std::invalid_argument(
+                    string_format("got %zu input configs, but system only has %zu devices", split_arg.size(), llama_max_devices())
+                );
+            }
+            if (split_arg.size() == 1) {
+                std::fill(params.fit_params_target.begin(), params.fit_params_target.end(), std::stoul(split_arg[0]) * 1024*1024);
+                return;
+            }
+            for (size_t i = 0; i < split_arg.size(); i++) {
+                params.fit_params_target[i] = std::stoul(split_arg[i]) * 1024*1024;
+            }
         }
     ).set_env("LLAMA_ARG_FIT_TARGET"));
     add_opt(common_arg(
@@ -2259,37 +2441,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"--override-kv"}, "KEY=TYPE:VALUE,...",
-        "advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated or repeat this argument.\n"
+        "advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated values.\n"
         "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false",
         [](common_params & params, const std::string & value) {
-            std::vector<std::string> kv_overrides;
-
-            std::string current;
-            bool escaping = false;
-
-            for (const char c : value) {
-                if (escaping) {
-                    current.push_back(c);
-                    escaping = false;
-                } else if (c == '\\') {
-                    escaping = true;
-                } else if (c == ',') {
-                    kv_overrides.push_back(current);
-                    current.clear();
-                } else {
-                    current.push_back(c);
-                }
-            }
-
-            if (escaping) {
-                current.push_back('\\');
-            }
-
-            kv_overrides.push_back(current);
-
-            for (const auto & kv_override : kv_overrides) {
-                if (!string_parse_kv_override(kv_override.c_str(), params.kv_overrides)) {
-                    throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", kv_override.c_str()));
+            for (const auto & item : parse_csv_row(value)) {
+                if (!string_parse_kv_override(item.c_str(), params.kv_overrides)) {
+                    throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", item.c_str()));
                 }
             }
         }
@@ -2306,7 +2463,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--lora"}, "FNAME",
         "path to LoRA adapter (use comma-separated values to load multiple adapters)",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : string_split<std::string>(value, ',')) {
+            for (const auto & item : parse_csv_row(value)) {
                 params.lora_adapters.push_back({ item, 1.0, "", "", nullptr });
             }
         }
@@ -2317,7 +2474,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)\n"
         "note: use comma-separated values",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : string_split<std::string>(value, ',')) {
+            for (const auto & item : parse_csv_row(value)) {
                 auto parts = string_split<std::string>(item, ':');
                 if (parts.size() != 2) {
                     throw std::invalid_argument("lora-scaled format: FNAME:SCALE");
@@ -2331,7 +2488,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--control-vector"}, "FNAME",
         "add a control vector\nnote: use comma-separated values to add multiple control vectors",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : string_split<std::string>(value, ',')) {
+            for (const auto & item : parse_csv_row(value)) {
                 params.control_vectors.push_back({ 1.0f, item, });
             }
         }
@@ -2341,7 +2498,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "add a control vector with user defined scaling SCALE\n"
         "note: use comma-separated values (format: FNAME:SCALE,...)",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : string_split<std::string>(value, ',')) {
+            for (const auto & item : parse_csv_row(value)) {
                 auto parts = string_split<std::string>(item, ':');
                 if (parts.size() != 2) {
                     throw std::invalid_argument("control-vector-scaled format: FNAME:SCALE");
@@ -2439,7 +2596,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--context-file"}, "FNAME",
         "file to load context from (use comma-separated values to specify multiple files)",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : string_split<std::string>(value, ',')) {
+            for (const auto & item : parse_csv_row(value)) {
                 std::ifstream file(item, std::ios::binary);
                 if (!file) {
                     throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str()));
@@ -2586,7 +2743,7 @@ 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}));
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, 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)",
@@ -2664,7 +2821,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.embedding = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_DEBUG}).set_env("LLAMA_ARG_EMBEDDINGS"));
     add_opt(common_arg(
         {"--rerank", "--reranking"},
         string_format("enable reranking endpoint on server (default: %s)", "disabled"),
@@ -2675,9 +2832,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_RERANKING"));
     add_opt(common_arg(
         {"--api-key"}, "KEY",
-        "API key to use for authentication (default: none)",
+        "API key to use for authentication, multiple keys can be provided as a comma-separated list (default: none)",
         [](common_params & params, const std::string & value) {
-            params.api_keys.push_back(value);
+            for (const auto & key : parse_csv_row(value)) {
+                if (!key.empty()) {
+                    params.api_keys.push_back(key);
+                }
+            }
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY"));
     add_opt(common_arg(
@@ -2691,7 +2852,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             std::string key;
             while (std::getline(key_file, key)) {
                 if (!key.empty()) {
-                        params.api_keys.push_back(key);
+                    params.api_keys.push_back(key);
                 }
             }
             key_file.close();
@@ -2713,7 +2874,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_CERT_FILE"));
     add_opt(common_arg(
         {"--chat-template-kwargs"}, "STRING",
-        string_format("sets additional params for the json template parser"),
+        "sets additional params for the json template parser, must be a valid json object string, e.g. '{\"key1\":\"value1\",\"key2\":\"value2\"}'",
         [](common_params & params, const std::string & value) {
             auto parsed = json::parse(value);
             for (const auto & item : parsed.items()) {
@@ -2736,10 +2897,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.n_threads_http = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP"));
+    add_opt(common_arg(
+        {"--cache-prompt"},
+        {"--no-cache-prompt"},
+        string_format("whether to enable prompt caching (default: %s)", params.cache_prompt ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.cache_prompt = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CACHE_PROMPT"));
     add_opt(common_arg(
         {"--cache-reuse"}, "N",
         string_format(
-            "min chunk size to attempt reusing from the cache via KV shifting (default: %d)\n"
+            "min chunk size to attempt reusing from the cache via KV shifting, requires prompt caching to be enabled (default: %d)\n"
             "[(card)](https://ggml.ai/f0.png)", params.n_cache_reuse
         ),
         [](common_params & params, int value) {
@@ -3351,6 +3520,27 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_examples({ LLAMA_EXAMPLE_FINETUNE }));
+    add_opt(common_arg(
+        {"--save-logits"},
+        string_format("save final logits to files for verification (default: %s)", params.save_logits ? "true" : "false"),
+        [](common_params & params) {
+            params.save_logits = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_DEBUG}));
+    add_opt(common_arg(
+        {"--logits-output-dir"}, "PATH",
+        string_format("directory for saving logits output files (default: %s)", params.logits_output_dir.c_str()),
+        [](common_params & params, const std::string & value) {
+            params.logits_output_dir = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_DEBUG}));
+    add_opt(common_arg(
+        {"--tensor-filter"}, "REGEX",
+        "filter tensor names for debug output (regex pattern, can be specified multiple times)",
+        [](common_params & params, const std::string & value) {
+            params.tensor_filter.push_back(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_DEBUG}));
 
     // presets
     add_opt(common_arg(

common/arg.h

@@ -129,11 +129,3 @@ void common_params_add_preset_options(std::vector<common_arg> & args);
 
 // initialize argument parser context - used by test-arg-parser and preset
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
-
-struct common_remote_params {
-    std::vector<std::string> headers;
-    long timeout = 0; // CURLOPT_TIMEOUT, in seconds ; 0 means no timeout
-    long max_size = 0; // max size of the response ; unlimited if 0 ; max is 2GB
-};
-// get remote file content, returns <http_code, raw_response_body>
-std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params);

common/chat-parser.cpp

@@ -1403,6 +1403,118 @@ static void common_chat_parse_solar_open(common_chat_msg_parser & builder) {
     builder.add_content(builder.consume_rest());
 }
 
+static void common_chat_parse_exaone_moe_content(common_chat_msg_parser & builder) {
+    // 1) <tool_call>{ "name": "...", "arguments": {...} }</tool_call>
+    // 2) <tool_call>{ "id": "...", "type": "function", "function": { "name": "...", "arguments": {...} } }</tool_call>
+    static const common_regex tool_call_open(R"(<tool_call[^>]*>)");
+
+    if (!builder.syntax().parse_tool_calls) {
+        LOG_DBG("%s: not parse_tool_calls\n", __func__);
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    LOG_DBG("%s: parse_tool_calls\n", __func__);
+
+    // Find all <tool_call></tool_call> blocks
+    while (auto first = builder.try_find_regex(tool_call_open, std::string::npos, /* add_prelude_to_content= */ true)) {
+        builder.move_to(first->groups[0].end);
+        builder.consume_spaces();
+
+        builder.try_consume_literal("```json");
+        builder.try_consume_literal("```");
+        builder.consume_spaces();
+
+        // Consume JSON object
+        auto data = builder.consume_json();
+
+        builder.consume_spaces();
+        builder.try_consume_literal("```");
+        builder.consume_spaces();
+
+        if (!builder.try_consume_literal("</tool_call>")) {
+            throw common_chat_msg_partial_exception("incomplete tool call");
+        }
+        builder.consume_spaces();
+
+        // Extract name and arguments
+        std::string name;
+        std::string id;
+        nlohmann::ordered_json arguments;
+
+        const auto extract_args = [&](const nlohmann::ordered_json & obj) -> bool {
+            if (!obj.contains("name") || !obj.contains("arguments")) {
+                return false;
+            }
+            name = obj.at("name").get<std::string>();
+            arguments = obj.at("arguments");
+            if (obj.contains("id") && obj.at("id").is_string()) {
+                id = obj.at("id").get<std::string>();
+            }
+            return true;
+        };
+
+        if (!extract_args(data.json)) {
+            if (data.json.contains("function") && data.json.at("function").is_object()) {
+                auto fn = data.json.at("function");
+                extract_args(fn);
+                if (id.empty() && data.json.contains("id") && data.json.at("id").is_string()) {
+                    id = data.json.at("id").get<std::string>();
+                }
+            }
+        }
+
+        // If name is empty, treat the JSON object as content
+        if (name.empty()) {
+            LOG_DBG("%s: tool call missing name, treating as content\n", __func__);
+            builder.add_content(data.json.dump());
+            continue;
+        }
+
+        std::string args_str = arguments.dump();
+        if (!builder.add_tool_call(name, id, args_str)) {
+            throw common_chat_msg_partial_exception("incomplete tool call");
+        }
+    }
+
+    builder.add_content(builder.consume_rest());
+}
+
+static void common_chat_parse_exaone_moe(common_chat_msg_parser & builder) {
+    LOG_DBG("%s: parsing exaone_moe\n", __func__);
+    // EXAONE MoE outputs reasoning content between "<think>" and "</think>" tags, followed by regular content
+    // First try to parse using the standard reasoning parsing method
+    LOG_DBG("%s: thinking_forced_open: %s\n", __func__, std::to_string(builder.syntax().thinking_forced_open).c_str());
+
+    auto start_pos = builder.pos();
+    auto found_end_think = builder.try_find_literal("</think>");
+    builder.move_to(start_pos);
+
+    if (builder.syntax().thinking_forced_open && !builder.is_partial() && !found_end_think) {
+        LOG_DBG("%s: no end_think, not partial, adding content\n", __func__);
+        common_chat_parse_exaone_moe_content(builder);
+    } else if (builder.try_parse_reasoning("<think>", "</think>")) {
+        // If reasoning was parsed successfully, the remaining content is regular content
+        LOG_DBG("%s: parsed reasoning, adding content\n", __func__);
+        common_chat_parse_exaone_moe_content(builder);
+    } else {
+        if (builder.syntax().reasoning_format == COMMON_REASONING_FORMAT_NONE) {
+          LOG_DBG("%s: reasoning_format none, adding content\n", __func__);
+          common_chat_parse_exaone_moe_content(builder);
+          return;
+        }
+        // If no reasoning tags found, check if we should treat everything as reasoning
+        if (builder.syntax().thinking_forced_open) {
+            // If thinking is forced open but no tags found, treat everything as reasoning
+            LOG_DBG("%s: thinking_forced_open, adding reasoning content\n", __func__);
+            builder.add_reasoning_content(builder.consume_rest());
+        } else {
+            LOG_DBG("%s: no thinking_forced_open, adding content\n", __func__);
+            common_chat_parse_exaone_moe_content(builder);
+        }
+    }
+}
+
 static void common_chat_parse_content_only(common_chat_msg_parser & builder) {
     builder.try_parse_reasoning("<think>", "</think>");
     builder.add_content(builder.consume_rest());
@@ -1490,6 +1602,9 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
         case COMMON_CHAT_FORMAT_SOLAR_OPEN:
             common_chat_parse_solar_open(builder);
             break;
+        case COMMON_CHAT_FORMAT_EXAONE_MOE:
+            common_chat_parse_exaone_moe(builder);
+            break;
         default:
             throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
     }

common/chat.cpp

@@ -670,6 +670,7 @@ const char * common_chat_format_name(common_chat_format format) {
         case COMMON_CHAT_FORMAT_APRIEL_1_5: return "Apriel 1.5";
         case COMMON_CHAT_FORMAT_XIAOMI_MIMO: return "Xiaomi MiMo";
         case COMMON_CHAT_FORMAT_SOLAR_OPEN: return "Solar Open";
+        case COMMON_CHAT_FORMAT_EXAONE_MOE: return "EXAONE MoE";
         case COMMON_CHAT_FORMAT_PEG_SIMPLE: return "peg-simple";
         case COMMON_CHAT_FORMAT_PEG_NATIVE: return "peg-native";
         case COMMON_CHAT_FORMAT_PEG_CONSTRUCTED: return "peg-constructed";
@@ -2539,6 +2540,65 @@ static common_chat_params common_chat_params_init_solar_open(const common_chat_t
     return data;
 }
 
+static common_chat_params common_chat_params_init_exaone_moe(const common_chat_template & tmpl, const struct templates_params & inputs) {
+    common_chat_params data;
+
+    data.prompt = apply(tmpl, inputs);
+    data.format = COMMON_CHAT_FORMAT_EXAONE_MOE;
+    if (string_ends_with(data.prompt, "<think>\n")) {
+        if (!inputs.enable_thinking) {
+            data.prompt += "</think>\n\n";
+        } else {
+            data.thinking_forced_open = true;
+        }
+    }
+
+    if (inputs.tools.is_array() && !inputs.tools.empty()) {
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED && inputs.json_schema.is_null();
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            std::vector<std::string> tool_rules;
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                auto parameters = function.at("parameters");
+                builder.resolve_refs(parameters);
+                // Expect: <tool_call>{"name": "<name>", "arguments": {...}}</tool_call>
+                tool_rules.push_back(builder.add_rule(
+                    name + "-call",
+                    "\"<tool_call>\" space " +
+                        builder.add_schema(name + "-obj", json{
+                            {"type", "object"},
+                            {"properties", {
+                                {"name",      json{{"const", name}}},
+                                {"arguments", parameters},
+                            }},
+                            {"required", json::array({"name", "arguments"})},
+                        }) +
+                    " space \"</tool_call>\" space"));
+            });
+
+            auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | "));
+            builder.add_rule("root",
+                std::string(data.thinking_forced_open ? "( \"</think>\" space )? " : "") +
+                (inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call));
+
+            data.grammar_triggers.push_back({
+                COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
+                std::string(data.thinking_forced_open ? "[\\s\\S]*?(</think>\\s*)?" : "") +
+                    "(<tool_call>)[\\s\\S]*"
+            });
+            data.preserved_tokens = {
+                "<think>",
+                "</think>",
+                "<tool_call>",
+                "</tool_call>",
+            };
+        });
+    }
+
+    return data;
+}
+
 static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) {
     common_chat_params data;
     data.prompt = apply(tmpl, inputs);
@@ -2709,6 +2769,13 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_xiaomi_mimo(tmpl, params);
     }
 
+    // EXAONE MoE format detection
+    if (src.find("<tool_call>") != std::string::npos &&
+        src.find("<tool_result>") != std::string::npos &&
+        src.find("<|tool_declare|>") != std::string::npos) {
+        return common_chat_params_init_exaone_moe(tmpl, params);
+    }
+
     // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools)
     if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null()) {
         return common_chat_params_init_hermes_2_pro(tmpl, params);

common/chat.h

@@ -125,6 +125,7 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_APRIEL_1_5,
     COMMON_CHAT_FORMAT_XIAOMI_MIMO,
     COMMON_CHAT_FORMAT_SOLAR_OPEN,
+    COMMON_CHAT_FORMAT_EXAONE_MOE,
 
     // These are intended to be parsed by the PEG parser
     COMMON_CHAT_FORMAT_PEG_SIMPLE,

common/common.cpp

@@ -1097,7 +1097,7 @@ common_init_result::common_init_result(common_params & params) :
     if (params.fit_params) {
         LOG_INF("%s: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on\n", __func__);
         llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
-            params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx,
+            params.tensor_split, 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);
     }
 
@@ -1172,7 +1172,6 @@ common_init_result::common_init_result(common_params & params) :
         pimpl->samplers_seq_config[i] = { i, common_sampler_get(pimpl->samplers[i].get()) };
     }
 
-    // TODO: temporarily gated behind a flag
     if (params.sampling.backend_sampling) {
         cparams.samplers   = pimpl->samplers_seq_config.data();
         cparams.n_samplers = pimpl->samplers_seq_config.size();
@@ -1366,6 +1365,7 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
     mparams.split_mode      = params.split_mode;
     mparams.tensor_split    = params.tensor_split;
     mparams.use_mmap        = params.use_mmap;
+    mparams.use_direct_io   = params.use_direct_io;
     mparams.use_mlock       = params.use_mlock;
     mparams.check_tensors   = params.check_tensors;
     mparams.use_extra_bufts = !params.no_extra_bufts;

common/common.h

@@ -80,6 +80,8 @@ int32_t cpu_get_num_math();
 //
 
 enum llama_example {
+    LLAMA_EXAMPLE_BATCHED,
+    LLAMA_EXAMPLE_DEBUG,
     LLAMA_EXAMPLE_COMMON,
     LLAMA_EXAMPLE_SPECULATIVE,
     LLAMA_EXAMPLE_COMPLETION,
@@ -117,6 +119,7 @@ enum common_sampler_type {
     COMMON_SAMPLER_TYPE_INFILL      = 9,
     COMMON_SAMPLER_TYPE_PENALTIES   = 10,
     COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11,
+    COMMON_SAMPLER_TYPE_ADAPTIVE_P  = 12,
 };
 
 // dimensionality reduction methods, used by cvector-generator
@@ -164,32 +167,34 @@ enum common_params_sampling_config : uint64_t {
 struct common_params_sampling {
     uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
 
-    int32_t n_prev             = 64;    // number of previous tokens to remember
-    int32_t n_probs            = 0;     // if greater than 0, output the probabilities of top n_probs tokens.
-    int32_t min_keep           = 0;     // 0 = disabled, otherwise samplers should return at least min_keep tokens
-    int32_t top_k              = 40;    // <= 0 to use vocab size
-    float   top_p              = 0.95f; // 1.0 = disabled
-    float   min_p              = 0.05f; // 0.0 = disabled
-    float   xtc_probability    = 0.00f; // 0.0 = disabled
-    float   xtc_threshold      = 0.10f; // > 0.5 disables XTC
-    float   typ_p              = 1.00f; // typical_p, 1.0 = disabled
-    float   temp               = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
-    float   dynatemp_range     = 0.00f; // 0.0 = disabled
-    float   dynatemp_exponent  = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t penalty_last_n     = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float   penalty_repeat     = 1.00f; // 1.0 = disabled
-    float   penalty_freq       = 0.00f; // 0.0 = disabled
-    float   penalty_present    = 0.00f; // 0.0 = disabled
-    float   dry_multiplier     = 0.0f;  // 0.0 = disabled;      DRY repetition penalty for tokens extending repetition:
-    float   dry_base           = 1.75f; // 0.0 = disabled;      multiplier * base ^ (length of sequence before token - allowed length)
-    int32_t dry_allowed_length = 2;     // tokens extending repetitions beyond this receive penalty
-    int32_t dry_penalty_last_n = -1;    // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
-    int32_t mirostat           = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float   top_n_sigma        = -1.00f;// -1.0 = disabled
-    float   mirostat_tau       = 5.00f; // target entropy
-    float   mirostat_eta       = 0.10f; // learning rate
+    int32_t n_prev             = 64;     // number of previous tokens to remember
+    int32_t n_probs            = 0;      // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t min_keep           = 0;      // 0 = disabled, otherwise samplers should return at least min_keep tokens
+    int32_t top_k              = 40;     // <= 0 to use vocab size
+    float   top_p              = 0.95f;  // 1.0 = disabled
+    float   min_p              = 0.05f;  // 0.0 = disabled
+    float   xtc_probability    = 0.00f;  // 0.0 = disabled
+    float   xtc_threshold      = 0.10f;  // > 0.5 disables XTC
+    float   typ_p              = 1.00f;  // typical_p, 1.0 = disabled
+    float   temp               = 0.80f;  // <= 0.0 to sample greedily, 0.0 to not output probabilities
+    float   dynatemp_range     = 0.00f;  // 0.0 = disabled
+    float   dynatemp_exponent  = 1.00f;  // controls how entropy maps to temperature in dynamic temperature sampler
+    int32_t penalty_last_n     = 64;     // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    float   penalty_repeat     = 1.00f;  // 1.0 = disabled
+    float   penalty_freq       = 0.00f;  // 0.0 = disabled
+    float   penalty_present    = 0.00f;  // 0.0 = disabled
+    float   dry_multiplier     = 0.0f;   // 0.0 = disabled;      DRY repetition penalty for tokens extending repetition:
+    float   dry_base           = 1.75f;  // 0.0 = disabled;      multiplier * base ^ (length of sequence before token - allowed length)
+    int32_t dry_allowed_length = 2;      // tokens extending repetitions beyond this receive penalty
+    int32_t dry_penalty_last_n = -1;     // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
+    float   adaptive_target    = -1.0f;  // select tokens near this probability (valid range 0.0 to 1.0; negative = disabled)
+    float   adaptive_decay     = 0.90f;  // EMA decay for adaptation; history ≈ 1/(1-decay) tokens (0.0 - 0.99)
+    int32_t mirostat           = 0;      // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+    float   top_n_sigma        = -1.00f; // -1.0 = disabled
+    float   mirostat_tau       = 5.00f;  // target entropy
+    float   mirostat_eta       = 0.10f;  // learning rate
     bool    ignore_eos         = false;
-    bool    no_perf            = false; // disable performance metrics
+    bool    no_perf            = false;  // disable performance metrics
     bool    timing_per_token   = false;
 
     uint64_t user_sampling_config = 0; // bitfield to track user-specified samplers
@@ -331,12 +336,14 @@ struct common_params {
     // offload params
     std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
 
-    int32_t n_gpu_layers       = -1;               // number of layers to store in VRAM, -1 is auto, <= -2 is all
-    int32_t main_gpu           = 0;                // the GPU that is used for scratch and small tensors
-    float   tensor_split[128]  = {0};              // how split tensors should be distributed across GPUs
-    bool    fit_params         = true;             // whether to fit unset model/context parameters to free device memory
-    size_t  fit_params_target  = 1024 * 1024*1024; // margin per device in bytes for fitting parameters to free memory
-    int32_t fit_params_min_ctx = 4096;             // minimum context size to set when trying to reduce memory use
+    int32_t n_gpu_layers       = -1;   // number of layers to store in VRAM, -1 is auto, <= -2 is all
+    int32_t main_gpu           = 0;    // the GPU that is used for scratch and small tensors
+    float   tensor_split[128]  = {0};  // how split tensors should be distributed across GPUs
+    bool    fit_params         = true; // whether to fit unset model/context parameters to free device memory
+    int32_t fit_params_min_ctx = 4096; // minimum context size to set when trying to reduce memory use
+
+    // margin per device in bytes for fitting parameters to free memory:
+    std::vector<size_t> fit_params_target = std::vector<size_t>(llama_max_devices(), 1024 * 1024*1024);
 
     enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
 
@@ -372,6 +379,11 @@ struct common_params {
     std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding          // NOLINT
     std::string logits_file          = ""; // file for saving *all* logits                                  // NOLINT
 
+    // llama-debug specific options
+    std::string logits_output_dir = "data"; // directory for saving logits output files                     // NOLINT
+    bool        save_logits       = false;  // whether to save logits to files                              // NOLINT
+    std::vector<std::string> tensor_filter; // filter tensor names for debug output (regex)                 // NOLINT
+
     std::vector<std::string> in_files;   // all input files
     std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
     std::vector<llama_model_kv_override> kv_overrides;
@@ -422,7 +434,8 @@ struct common_params {
     bool kv_unified        = false; // enable unified KV cache
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
-    bool use_mmap          = true;  // use mmap for faster loads
+    bool use_mmap          = true;  // enable mmap to use filesystem cache
+    bool use_direct_io     = true;  // read from disk without buffering for faster model loading
     bool use_mlock         = false; // use mlock to keep model in memory
     bool verbose_prompt    = false; // print prompt tokens before generation
     bool display_prompt    = true;  // print prompt before generation
@@ -466,6 +479,7 @@ struct common_params {
     int32_t timeout_write     = timeout_read; // http write timeout in seconds
     int32_t n_threads_http    = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
     int32_t n_cache_reuse     = 0;            // min chunk size to reuse from the cache via KV shifting
+    bool    cache_prompt      = true;         // whether to enable prompt caching
     int32_t n_ctx_checkpoints = 8;            // max number of context checkpoints per slot
     int32_t cache_ram_mib     = 8192;         // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
 

common/debug.cpp

@@ -0,0 +1,165 @@
+#include "debug.h"
+
+#include "log.h"
+
+#include <cmath>
+#include <string>
+
+static std::string common_ggml_ne_string(const ggml_tensor * t) {
+    std::string str;
+    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+        str += std::to_string(t->ne[i]);
+        if (i + 1 < GGML_MAX_DIMS) {
+            str += ", ";
+        }
+    }
+    return str;
+}
+
+static float common_ggml_get_float_value(const uint8_t * data,
+                           ggml_type       type,
+                           const size_t *  nb,
+                           size_t          i0,
+                           size_t          i1,
+                           size_t          i2,
+                           size_t          i3) {
+    size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
+    float  v;
+    if (type == GGML_TYPE_F16) {
+        v = ggml_fp16_to_fp32(*(const ggml_fp16_t *) &data[i]);
+    } else if (type == GGML_TYPE_F32) {
+        v = *(const float *) &data[i];
+    } else if (type == GGML_TYPE_I64) {
+        v = (float) *(const int64_t *) &data[i];
+    } else if (type == GGML_TYPE_I32) {
+        v = (float) *(const int32_t *) &data[i];
+    } else if (type == GGML_TYPE_I16) {
+        v = (float) *(const int16_t *) &data[i];
+    } else if (type == GGML_TYPE_I8) {
+        v = (float) *(const int8_t *) &data[i];
+    } else if (type == GGML_TYPE_BF16) {
+        v = ggml_bf16_to_fp32(*(const ggml_bf16_t *) &data[i]);
+    } else {
+        GGML_ABORT("fatal error");
+    }
+    return v;
+}
+
+template <bool abort>
+void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
+    GGML_ASSERT(n > 0);
+    float sum = 0;
+    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
+        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
+            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
+                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
+                    const float v = common_ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
+                    sum += v;
+                }
+            }
+        }
+    }
+    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
+        LOG_ERR("                                     [\n");
+        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
+            if (i2 == n && ne[2] > 2 * n) {
+                LOG_ERR("                                      ..., \n");
+                i2 = ne[2] - n;
+            }
+            LOG_ERR("                                      [\n");
+            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
+                if (i1 == n && ne[1] > 2 * n) {
+                    LOG_ERR("                                       ..., \n");
+                    i1 = ne[1] - n;
+                }
+                LOG_ERR("                                       [");
+                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
+                    if (i0 == n && ne[0] > 2 * n) {
+                        LOG_ERR("..., ");
+                        i0 = ne[0] - n;
+                    }
+                    const float v = common_ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
+                    LOG_ERR("%12.4f", v);
+                    if (i0 < ne[0] - 1) {
+                        LOG_ERR(", ");
+                    }
+                }
+                LOG_ERR("],\n");
+            }
+            LOG_ERR("                                      ],\n");
+        }
+        LOG_ERR("                                     ]\n");
+        LOG_ERR("                                     sum = %f\n", sum);
+    }
+
+    if constexpr (abort) {
+        if (std::isnan(sum)) {
+            LOG_ERR("encountered NaN - aborting\n");
+            exit(0);
+        }
+    }
+}
+
+/**
+ * GGML operations callback during the graph execution.
+ *
+ * @param t current tensor
+ * @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor
+ *            if we return true, a follow-up call will be made with ask=false in which we can do the actual collection.
+ *            see ggml_backend_sched_eval_callback
+ * @param user_data user data to pass at each call back
+ * @return true to receive data or continue the graph, false otherwise
+ */
+template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
+    auto * cb_data = (base_callback_data *) user_data;
+
+    const struct ggml_tensor * src0 = t->src[0];
+    const struct ggml_tensor * src1 = t->src[1];
+
+    if (ask) {
+        return true;  // Always retrieve data
+    }
+
+    bool matches_filter = cb_data->tensor_filters.empty();
+
+    if (!matches_filter) {
+        for (const auto & filter : cb_data->tensor_filters) {
+            if (std::regex_search(t->name, filter)) {
+                matches_filter = true;
+                break;
+            }
+        }
+    }
+
+    char src1_str[128] = { 0 };
+    if (src1) {
+        snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, common_ggml_ne_string(src1).c_str());
+    }
+
+    if (matches_filter) {
+        LOG_ERR("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, t->name, ggml_type_name(t->type),
+                ggml_op_desc(t), src0->name, common_ggml_ne_string(src0).c_str(), src1 ? src1_str : "",
+                common_ggml_ne_string(t).c_str());
+    }
+
+    const bool is_host = ggml_backend_buffer_is_host(t->buffer);
+
+    if (!is_host) {
+        auto n_bytes = ggml_nbytes(t);
+        cb_data->data.resize(n_bytes);
+        ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
+    }
+
+    if (!ggml_is_quantized(t->type) && matches_filter) {
+        uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
+        common_debug_print_tensor<abort_on_nan>(data, t->type, t->ne, t->nb, 3);
+    }
+
+    return true;
+}
+
+// Explicit template instantiations
+template bool common_debug_cb_eval<false>(ggml_tensor *, bool, void *);
+template bool common_debug_cb_eval<true>(ggml_tensor *, bool, void *);
+template void common_debug_print_tensor<false>(uint8_t *, ggml_type, const int64_t *, const size_t *, int64_t);
+template void common_debug_print_tensor<true>(uint8_t *, ggml_type, const int64_t *, const size_t *, int64_t);

common/debug.h

@@ -0,0 +1,43 @@
+#pragma once
+#include "common.h"
+#include <string>
+#include <vector>
+#include <regex>
+
+// common debug functions and structs
+
+// Print a tensor's detailed data
+// data - the tensor's data in byte format
+// type - the tensor's quantization type
+// ne   - the tensor dimensions array
+// nb   - the tensor strides array
+// n    - the number of rows/columns to fully print
+template <bool abort_on_nan> void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n);
+
+// Intended to use as callback for ggml_backend_sched_eval_callback
+// prints tensors that are processed in the computation graph
+// by default prints all tensors, but can be configured by creating a `base_callback_data` instance with
+// non-empty filter_patterns. See examples/debug.ccp for possible usage patterns
+// The template parameter determins whether an error should be thrown whenever a NaN is encountered
+// in a tensor (useful for stopping debug sessions on first erroneous tensor)
+// The callback data will be passed as the third parameter (user_data)
+template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data);
+struct base_callback_data {
+    std::vector<uint8_t>    data;
+    std::vector<std::regex> tensor_filters;
+
+    base_callback_data() = default;
+
+    base_callback_data(common_params & params, const std::vector<std::string> & filter_patterns) {
+        for (const auto & pattern : filter_patterns) {
+            try {
+                std::string anchored_pattern = "^" + pattern;
+                tensor_filters.emplace_back(anchored_pattern, std::regex::optimize);
+            } catch (const std::regex_error & e) {
+                throw std::runtime_error("Invalid regex pattern '" + pattern + "': " + e.what());
+            }
+        }
+        params.cb_eval           = common_debug_cb_eval<false>;
+        params.cb_eval_user_data = this;
+    }
+};

common/download.cpp

@@ -19,10 +19,7 @@
 #include <thread>
 #include <vector>
 
-#if defined(LLAMA_USE_CURL)
-#include <curl/curl.h>
-#include <curl/easy.h>
-#elif defined(LLAMA_USE_HTTPLIB)
+#if defined(LLAMA_USE_HTTPLIB)
 #include "http.h"
 #endif
 
@@ -157,322 +154,21 @@ static std::string read_etag(const std::string & path) {
     return none;
 }
 
-#ifdef LLAMA_USE_CURL
-
-//
-// CURL utils
-//
-
-using curl_ptr = std::unique_ptr<CURL, decltype(&curl_easy_cleanup)>;
-
-// cannot use unique_ptr for curl_slist, because we cannot update without destroying the old one
-struct curl_slist_ptr {
-    struct curl_slist * ptr = nullptr;
-    ~curl_slist_ptr() {
-        if (ptr) {
-            curl_slist_free_all(ptr);
-        }
-    }
-};
-
-static CURLcode common_curl_perf(CURL * curl) {
-    CURLcode res = curl_easy_perform(curl);
-    if (res != CURLE_OK) {
-        LOG_ERR("%s: curl_easy_perform() failed\n", __func__);
-    }
-
-    return res;
+static bool is_http_status_ok(int status) {
+    return status >= 200 && status < 400;
 }
 
-// Send a HEAD request to retrieve the etag and last-modified headers
-struct common_load_model_from_url_headers {
-    std::string etag;
-    std::string last_modified;
-    std::string accept_ranges;
-};
-
-struct FILE_deleter {
-    void operator()(FILE * f) const { fclose(f); }
-};
-
-static size_t common_header_callback(char * buffer, size_t, size_t n_items, void * userdata) {
-    common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;
-    static std::regex                    header_regex("([^:]+): (.*)\r\n");
-    static std::regex                    etag_regex("ETag", std::regex_constants::icase);
-    static std::regex                    last_modified_regex("Last-Modified", std::regex_constants::icase);
-    static std::regex                    accept_ranges_regex("Accept-Ranges", std::regex_constants::icase);
-    std::string                          header(buffer, n_items);
-    std::smatch                          match;
-    if (std::regex_match(header, match, header_regex)) {
-        const std::string & key   = match[1];
-        const std::string & value = match[2];
-        if (std::regex_match(key, match, etag_regex)) {
-            headers->etag = value;
-        } else if (std::regex_match(key, match, last_modified_regex)) {
-            headers->last_modified = value;
-        } else if (std::regex_match(key, match, accept_ranges_regex)) {
-            headers->accept_ranges = value;
-        }
+std::pair<std::string, std::string> common_download_split_repo_tag(const std::string & hf_repo_with_tag) {
+    auto parts = string_split<std::string>(hf_repo_with_tag, ':');
+    std::string tag = parts.size() > 1 ? parts.back() : "latest";
+    std::string hf_repo = parts[0];
+    if (string_split<std::string>(hf_repo, '/').size() != 2) {
+        throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
     }
-
-    return n_items;
+    return {hf_repo, tag};
 }
 
-static size_t common_write_callback(void * data, size_t size, size_t nmemb, void * fd) {
-    return std::fwrite(data, size, nmemb, static_cast<FILE *>(fd));
-}
-
-// helper function to hide password in URL
-static std::string llama_download_hide_password_in_url(const std::string & url) {
-    // Use regex to match and replace the user[:password]@ pattern in URLs
-    // Pattern: scheme://[user[:password]@]host[...]
-    static const std::regex url_regex(R"(^(?:[A-Za-z][A-Za-z0-9+.-]://)(?:[^/@]+@)?.$)");
-    std::smatch             match;
-
-    if (std::regex_match(url, match, url_regex)) {
-        // match[1] = scheme (e.g., "https://")
-        // match[2] = user[:password]@ part
-        // match[3] = rest of URL (host and path)
-        return match[1].str() + "********@" + match[3].str();
-    }
-
-    return url;  // No credentials found or malformed URL
-}
-
-static void common_curl_easy_setopt_head(CURL * curl, const std::string & url) {
-    // Set the URL, allow to follow http redirection
-    curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
-    curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
-
-#    if defined(_WIN32)
-    // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
-    //   operating system. Currently implemented under MS-Windows.
-    curl_easy_setopt(curl, CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
-#    endif
-
-    curl_easy_setopt(curl, CURLOPT_NOBODY, 1L);      // will trigger the HEAD verb
-    curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 1L);  // hide head request progress
-    curl_easy_setopt(curl, CURLOPT_HEADERFUNCTION, common_header_callback);
-}
-
-static void common_curl_easy_setopt_get(CURL * curl) {
-    curl_easy_setopt(curl, CURLOPT_NOBODY, 0L);
-    curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, common_write_callback);
-
-    //  display download progress
-    curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L);
-}
-
-static bool common_pull_file(CURL * curl, const std::string & path_temporary) {
-    if (std::filesystem::exists(path_temporary)) {
-        const std::string partial_size = std::to_string(std::filesystem::file_size(path_temporary));
-        LOG_INF("%s: server supports range requests, resuming download from byte %s\n", __func__, partial_size.c_str());
-        const std::string range_str = partial_size + "-";
-        curl_easy_setopt(curl, CURLOPT_RANGE, range_str.c_str());
-    }
-
-    // Always open file in append mode could be resuming
-    std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "ab"));
-    if (!outfile) {
-        LOG_ERR("%s: error opening local file for writing: %s\n", __func__, path_temporary.c_str());
-        return false;
-    }
-
-    common_curl_easy_setopt_get(curl);
-    curl_easy_setopt(curl, CURLOPT_WRITEDATA, outfile.get());
-
-    return common_curl_perf(curl) == CURLE_OK;
-}
-
-static bool common_download_head(CURL *              curl,
-                                 curl_slist_ptr &    http_headers,
-                                 const std::string & url,
-                                 const std::string & bearer_token) {
-    if (!curl) {
-        LOG_ERR("%s: error initializing libcurl\n", __func__);
-        return false;
-    }
-
-    http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
-    // Check if hf-token or bearer-token was specified
-    if (!bearer_token.empty()) {
-        std::string auth_header = "Authorization: Bearer " + bearer_token;
-        http_headers.ptr        = curl_slist_append(http_headers.ptr, auth_header.c_str());
-    }
-
-    curl_easy_setopt(curl, CURLOPT_HTTPHEADER, http_headers.ptr);
-    common_curl_easy_setopt_head(curl, url);
-    return common_curl_perf(curl) == CURLE_OK;
-}
-
-// download one single file from remote URL to local path
-static bool common_download_file_single_online(const std::string & url,
-                                               const std::string & path,
-                                               const std::string & bearer_token) {
-    static const int max_attempts        = 3;
-    static const int retry_delay_seconds = 2;
-    for (int i = 0; i < max_attempts; ++i) {
-        std::string etag;
-
-        // Check if the file already exists locally
-        const auto file_exists = std::filesystem::exists(path);
-        if (file_exists) {
-            etag = read_etag(path);
-        } else {
-            LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
-        }
-
-        bool head_request_ok = false;
-        bool should_download = !file_exists;  // by default, we should download if the file does not exist
-
-        // Initialize libcurl
-        curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
-        common_load_model_from_url_headers headers;
-        curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
-        curl_slist_ptr http_headers;
-        const bool     was_perform_successful = common_download_head(curl.get(), http_headers, url, bearer_token);
-        if (!was_perform_successful) {
-            head_request_ok = false;
-        }
-
-        long http_code = 0;
-        curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
-        if (http_code == 200) {
-            head_request_ok = true;
-        } else {
-            LOG_WRN("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
-            head_request_ok = false;
-        }
-
-        // if head_request_ok is false, we don't have the etag or last-modified headers
-        // we leave should_download as-is, which is true if the file does not exist
-        bool should_download_from_scratch = false;
-        if (head_request_ok) {
-            // check if ETag or Last-Modified headers are different
-            // if it is, we need to download the file again
-            if (!etag.empty() && etag != headers.etag) {
-                LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(),
-                        headers.etag.c_str());
-                should_download              = true;
-                should_download_from_scratch = true;
-            }
-        }
-
-        const bool accept_ranges_supported = !headers.accept_ranges.empty() && headers.accept_ranges != "none";
-        if (should_download) {
-            if (file_exists &&
-                !accept_ranges_supported) {  // Resumable downloads not supported, delete and start again.
-                LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
-                if (remove(path.c_str()) != 0) {
-                    LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
-                    return false;
-                }
-            }
-
-            const std::string path_temporary = path + ".downloadInProgress";
-            if (should_download_from_scratch) {
-                if (std::filesystem::exists(path_temporary)) {
-                    if (remove(path_temporary.c_str()) != 0) {
-                        LOG_ERR("%s: unable to delete file: %s\n", __func__, path_temporary.c_str());
-                        return false;
-                    }
-                }
-
-                if (std::filesystem::exists(path)) {
-                    if (remove(path.c_str()) != 0) {
-                        LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
-                        return false;
-                    }
-                }
-            }
-            if (head_request_ok) {
-                write_etag(path, headers.etag);
-            }
-
-            // start the download
-            LOG_INF("%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n",
-                    __func__, llama_download_hide_password_in_url(url).c_str(), path_temporary.c_str(),
-                    headers.etag.c_str(), headers.last_modified.c_str());
-            const bool was_pull_successful = common_pull_file(curl.get(), path_temporary);
-            if (!was_pull_successful) {
-                if (i + 1 < max_attempts) {
-                    const int exponential_backoff_delay = std::pow(retry_delay_seconds, i) * 1000;
-                    LOG_WRN("%s: retrying after %d milliseconds...\n", __func__, exponential_backoff_delay);
-                    std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
-                } else {
-                    LOG_ERR("%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
-                }
-
-                continue;
-            }
-
-            long http_code = 0;
-            curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
-            if (http_code < 200 || http_code >= 400) {
-                LOG_ERR("%s: invalid http status code received: %ld\n", __func__, http_code);
-                return false;
-            }
-
-            if (rename(path_temporary.c_str(), path.c_str()) != 0) {
-                LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
-                return false;
-            }
-        } else {
-            LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
-        }
-
-        break;
-    }
-
-    return true;
-}
-
-std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params) {
-    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
-    curl_slist_ptr http_headers;
-    std::vector<char> res_buffer;
-
-    curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
-    curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
-    curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
-    curl_easy_setopt(curl.get(), CURLOPT_VERBOSE, 0L);
-    typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
-    auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t {
-        auto data_vec = static_cast<std::vector<char> *>(data);
-        data_vec->insert(data_vec->end(), (char *)ptr, (char *)ptr + size * nmemb);
-        return size * nmemb;
-    };
-    curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
-    curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_buffer);
-#if defined(_WIN32)
-    curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
-#endif
-    if (params.timeout > 0) {
-        curl_easy_setopt(curl.get(), CURLOPT_TIMEOUT, params.timeout);
-    }
-    if (params.max_size > 0) {
-        curl_easy_setopt(curl.get(), CURLOPT_MAXFILESIZE, params.max_size);
-    }
-    http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
-    for (const auto & header : params.headers) {
-        http_headers.ptr = curl_slist_append(http_headers.ptr, header.c_str());
-    }
-    curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
-
-    CURLcode res = curl_easy_perform(curl.get());
-
-    if (res != CURLE_OK) {
-        std::string error_msg = curl_easy_strerror(res);
-        throw std::runtime_error("error: cannot make GET request: " + error_msg);
-    }
-
-    long res_code;
-    curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code);
-
-    return { res_code, std::move(res_buffer) };
-}
-
-#elif defined(LLAMA_USE_HTTPLIB)
+#if defined(LLAMA_USE_HTTPLIB)
 
 class ProgressBar {
     static inline std::mutex mutex;
@@ -617,9 +313,11 @@ static bool common_pull_file(httplib::Client & cli,
 }
 
 // download one single file from remote URL to local path
-static bool common_download_file_single_online(const std::string & url,
+// returns status code or -1 on error
+static int common_download_file_single_online(const std::string & url,
                                                const std::string & path,
-                                               const std::string & bearer_token) {
+                                               const std::string & bearer_token,
+                                               const common_header_list & custom_headers) {
     static const int max_attempts        = 3;
     static const int retry_delay_seconds = 2;
 
@@ -629,6 +327,9 @@ static bool common_download_file_single_online(const std::string & url,
     if (!bearer_token.empty()) {
         default_headers.insert({"Authorization", "Bearer " + bearer_token});
     }
+    for (const auto & h : custom_headers) {
+        default_headers.emplace(h.first, h.second);
+    }
     cli.set_default_headers(default_headers);
 
     const bool file_exists = std::filesystem::exists(path);
@@ -647,8 +348,10 @@ static bool common_download_file_single_online(const std::string & url,
             LOG_WRN("%s: HEAD invalid http status code received: %d\n", __func__, head ? head->status : -1);
             if (file_exists) {
                 LOG_INF("%s: Using cached file (HEAD failed): %s\n", __func__, path.c_str());
-                return true;
+                return 304; // 304 Not Modified - fake cached response
             }
+            return head->status; // cannot use cached file, return raw status code
+            // TODO: maybe retry only on certain codes
         }
 
         std::string etag;
@@ -680,12 +383,12 @@ static bool common_download_file_single_online(const std::string & url,
         if (file_exists) {
             if (!should_download_from_scratch) {
                 LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
-                return true;
+                return 304; // 304 Not Modified - fake cached response
             }
             LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
             if (remove(path.c_str()) != 0) {
                 LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
-                return false;
+                return -1;
             }
         }
 
@@ -697,7 +400,7 @@ static bool common_download_file_single_online(const std::string & url,
                 existing_size = std::filesystem::file_size(path_temporary);
             } else if (remove(path_temporary.c_str()) != 0) {
                 LOG_ERR("%s: unable to delete file: %s\n", __func__, path_temporary.c_str());
-                return false;
+                return -1;
             }
         }
 
@@ -718,15 +421,16 @@ static bool common_download_file_single_online(const std::string & url,
 
         if (std::rename(path_temporary.c_str(), path.c_str()) != 0) {
             LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
-            return false;
+            return -1;
         }
         if (!etag.empty()) {
             write_etag(path, etag);
         }
-        break;
+
+        return head->status; // TODO: use actual GET status?
     }
 
-    return true;
+    return -1; // max attempts reached
 }
 
 std::pair<long, std::vector<char>> common_remote_get_content(const std::string          & url,
@@ -734,13 +438,9 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string
     auto [cli, parts] = common_http_client(url);
 
     httplib::Headers headers = {{"User-Agent", "llama-cpp"}};
+
     for (const auto & header : params.headers) {
-        size_t pos = header.find(':');
-        if (pos != std::string::npos) {
-            headers.emplace(header.substr(0, pos), header.substr(pos + 1));
-        } else {
-            headers.emplace(header, "");
-        }
+        headers.emplace(header.first, header.second);
     }
 
     if (params.timeout > 0) {
@@ -765,36 +465,45 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string
     return { res->status, std::move(buf) };
 }
 
-#endif // LLAMA_USE_CURL
-
-#if defined(LLAMA_USE_CURL) || defined(LLAMA_USE_HTTPLIB)
-
-static bool common_download_file_single(const std::string & url,
-                                        const std::string & path,
-                                        const std::string & bearer_token,
-                                        bool                offline) {
+int common_download_file_single(const std::string & url,
+                                const std::string & path,
+                                const std::string & bearer_token,
+                                bool offline,
+                                const common_header_list & headers) {
     if (!offline) {
-        return common_download_file_single_online(url, path, bearer_token);
+        return common_download_file_single_online(url, path, bearer_token, headers);
     }
 
     if (!std::filesystem::exists(path)) {
         LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str());
-        return false;
+        return -1;
     }
 
     LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
-    return true;
+    return 304; // Not Modified - fake cached response
 }
 
 // download multiple files from remote URLs to local paths
 // the input is a vector of pairs <url, path>
-static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls, const std::string & bearer_token, bool offline) {
+static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls,
+                                          const std::string & bearer_token,
+                                          bool offline,
+                                          const common_header_list & headers) {
     // Prepare download in parallel
     std::vector<std::future<bool>> futures_download;
+    futures_download.reserve(urls.size());
+
     for (auto const & item : urls) {
-        futures_download.push_back(std::async(std::launch::async, [bearer_token, offline](const std::pair<std::string, std::string> & it) -> bool {
-            return common_download_file_single(it.first, it.second, bearer_token, offline);
-        }, item));
+        futures_download.push_back(
+            std::async(
+                std::launch::async,
+                [&bearer_token, offline, &headers](const std::pair<std::string, std::string> & it) -> bool {
+                    const int http_status = common_download_file_single(it.first, it.second, bearer_token, offline, headers);
+                    return is_http_status_ok(http_status);
+                },
+                item
+            )
+        );
     }
 
     // Wait for all downloads to complete
@@ -807,17 +516,18 @@ static bool common_download_file_multiple(const std::vector<std::pair<std::strin
     return true;
 }
 
-bool common_download_model(
-        const common_params_model & model,
-        const std::string & bearer_token,
-        bool offline) {
+bool common_download_model(const common_params_model & model,
+                           const std::string & bearer_token,
+                           bool offline,
+                           const common_header_list & headers) {
     // Basic validation of the model.url
     if (model.url.empty()) {
         LOG_ERR("%s: invalid model url\n", __func__);
         return false;
     }
 
-    if (!common_download_file_single(model.url, model.path, bearer_token, offline)) {
+    const int http_status = common_download_file_single(model.url, model.path, bearer_token, offline, headers);
+    if (!is_http_status_ok(http_status)) {
         return false;
     }
 
@@ -876,27 +586,26 @@ bool common_download_model(
         }
 
         // Download in parallel
-        common_download_file_multiple(urls, bearer_token, offline);
+        common_download_file_multiple(urls, bearer_token, offline, headers);
     }
 
     return true;
 }
 
-common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & bearer_token, bool offline) {
-    auto parts = string_split<std::string>(hf_repo_with_tag, ':');
-    std::string tag = parts.size() > 1 ? parts.back() : "latest";
-    std::string hf_repo = parts[0];
-    if (string_split<std::string>(hf_repo, '/').size() != 2) {
-        throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
-    }
+common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag,
+                                      const std::string & bearer_token,
+                                      bool offline,
+                                      const common_header_list & custom_headers) {
+    // the returned hf_repo is without tag
+    auto [hf_repo, tag] = common_download_split_repo_tag(hf_repo_with_tag);
 
     std::string url = get_model_endpoint() + "v2/" + hf_repo + "/manifests/" + tag;
 
     // headers
-    std::vector<std::string> headers;
-    headers.push_back("Accept: application/json");
+    common_header_list headers = custom_headers;
+    headers.push_back({"Accept", "application/json"});
     if (!bearer_token.empty()) {
-        headers.push_back("Authorization: Bearer " + bearer_token);
+        headers.push_back({"Authorization", "Bearer " + bearer_token});
     }
     // Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response
     // User-Agent header is already set in common_remote_get_content, no need to set it here
@@ -952,7 +661,7 @@ common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, cons
     } else if (res_code == 401) {
         throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token");
     } else {
-        throw std::runtime_error(string_format("error from HF API, response code: %ld, data: %s", res_code, res_str.c_str()));
+        throw std::runtime_error(string_format("error from HF API (%s), response code: %ld, data: %s", url.c_str(), res_code, res_str.c_str()));
     }
 
     // check response
@@ -1031,9 +740,10 @@ std::string common_docker_resolve_model(const std::string & docker) {
         const std::string    url_prefix = "https://registry-1.docker.io/v2/" + repo;
         std::string          manifest_url = url_prefix + "/manifests/" + tag;
         common_remote_params manifest_params;
-        manifest_params.headers.push_back("Authorization: Bearer " + token);
-        manifest_params.headers.push_back(
-            "Accept: application/vnd.docker.distribution.manifest.v2+json,application/vnd.oci.image.manifest.v1+json");
+        manifest_params.headers.push_back({"Authorization", "Bearer " + token});
+        manifest_params.headers.push_back({"Accept",
+            "application/vnd.docker.distribution.manifest.v2+json,application/vnd.oci.image.manifest.v1+json"
+        });
         auto manifest_res = common_remote_get_content(manifest_url, manifest_params);
         if (manifest_res.first != 200) {
             throw std::runtime_error("Failed to get Docker manifest, HTTP code: " + std::to_string(manifest_res.first));
@@ -1070,7 +780,8 @@ std::string common_docker_resolve_model(const std::string & docker) {
         std::string local_path = fs_get_cache_file(model_filename);
 
         const std::string blob_url = url_prefix + "/blobs/" + gguf_digest;
-        if (!common_download_file_single(blob_url, local_path, token, false)) {
+        const int http_status = common_download_file_single(blob_url, local_path, token, false, {});
+        if (!is_http_status_ok(http_status)) {
             throw std::runtime_error("Failed to download Docker Model");
         }
 
@@ -1084,11 +795,11 @@ std::string common_docker_resolve_model(const std::string & docker) {
 
 #else
 
-common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool) {
+common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool, const common_header_list &) {
     throw std::runtime_error("download functionality is not enabled in this build");
 }
 
-bool common_download_model(const common_params_model &, const std::string &, bool) {
+bool common_download_model(const common_params_model &, const std::string &, bool, const common_header_list &) {
     throw std::runtime_error("download functionality is not enabled in this build");
 }
 
@@ -1096,7 +807,15 @@ std::string common_docker_resolve_model(const std::string &) {
     throw std::runtime_error("download functionality is not enabled in this build");
 }
 
-#endif // LLAMA_USE_CURL || LLAMA_USE_HTTPLIB
+int common_download_file_single(const std::string &,
+                                const std::string &,
+                                const std::string &,
+                                bool,
+                                const common_header_list &) {
+    throw std::runtime_error("download functionality is not enabled in this build");
+}
+
+#endif // defined(LLAMA_USE_HTTPLIB)
 
 std::vector<common_cached_model_info> common_list_cached_models() {
     std::vector<common_cached_model_info> models;

common/download.h

@@ -1,12 +1,27 @@
 #pragma once
 
 #include <string>
+#include <vector>
 
 struct common_params_model;
 
-//
-// download functionalities
-//
+using common_header      = std::pair<std::string, std::string>;
+using common_header_list = std::vector<common_header>;
+
+struct common_remote_params {
+    common_header_list headers;
+    long timeout  = 0;           // in seconds, 0 means no timeout
+    long max_size = 0;           // unlimited if 0
+};
+
+// get remote file content, returns <http_code, raw_response_body>
+std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params);
+
+// split HF repo with tag into <repo, tag>
+// for example: "user/model:tag" -> <"user/model", "tag">
+// if tag is not present, default to "latest"
+// example: "user/model" -> <"user/model", "latest">
+std::pair<std::string, std::string> common_download_split_repo_tag(const std::string & hf_repo_with_tag);
 
 struct common_cached_model_info {
     std::string manifest_path;
@@ -41,17 +56,29 @@ struct common_hf_file_res {
 common_hf_file_res common_get_hf_file(
     const std::string & hf_repo_with_tag,
     const std::string & bearer_token,
-    bool offline);
+    bool offline,
+    const common_header_list & headers = {}
+);
 
 // returns true if download succeeded
 bool common_download_model(
     const common_params_model & model,
     const std::string & bearer_token,
-    bool offline);
+    bool offline,
+    const common_header_list & headers = {}
+);
 
 // returns list of cached models
 std::vector<common_cached_model_info> common_list_cached_models();
 
+// download single file from url to local path
+// returns status code or -1 on error
+int common_download_file_single(const std::string & url,
+                                const std::string & path,
+                                const std::string & bearer_token,
+                                bool offline,
+                                const common_header_list & headers = {});
+
 // resolve and download model from Docker registry
 // return local path to downloaded model file
 std::string common_docker_resolve_model(const std::string & docker);

common/preset.cpp

@@ -16,6 +16,48 @@ static std::string rm_leading_dashes(const std::string & str) {
     return str.substr(pos);
 }
 
+// only allow a subset of args for remote presets for security reasons
+// do not add more args unless absolutely necessary
+// args that output to files are strictly prohibited
+static std::set<std::string> get_remote_preset_whitelist(const std::map<std::string, common_arg> & key_to_opt) {
+    static const std::set<std::string> allowed_options = {
+        "model-url",
+        "hf-repo",
+        "hf-repo-draft",
+        "hf-repo-v", // vocoder
+        "hf-file-v", // vocoder
+        "mmproj-url",
+        "pooling",
+        "jinja",
+        "batch-size",
+        "ubatch-size",
+        "cache-reuse",
+        "chat-template-kwargs",
+        "mmap",
+        // note: sampling params are automatically allowed by default
+        // negated args will be added automatically if the positive arg is specified above
+    };
+
+    std::set<std::string> allowed_keys;
+
+    for (const auto & it : key_to_opt) {
+        const std::string & key = it.first;
+        const common_arg & opt = it.second;
+        if (allowed_options.find(key) != allowed_options.end() || opt.is_sparam) {
+            allowed_keys.insert(key);
+            // also add variant keys (args without leading dashes and env vars)
+            for (const auto & arg : opt.get_args()) {
+                allowed_keys.insert(rm_leading_dashes(arg));
+            }
+            for (const auto & env : opt.get_env()) {
+                allowed_keys.insert(env);
+            }
+        }
+    }
+
+    return allowed_keys;
+}
+
 std::vector<std::string> common_preset::to_args(const std::string & bin_path) const {
     std::vector<std::string> args;
 
@@ -121,6 +163,29 @@ void common_preset::merge(const common_preset & other) {
     }
 }
 
+void common_preset::apply_to_params(common_params & params) const {
+    for (const auto & [opt, val] : options) {
+        // apply each option to params
+        if (opt.handler_string) {
+            opt.handler_string(params, val);
+        } else if (opt.handler_int) {
+            opt.handler_int(params, std::stoi(val));
+        } else if (opt.handler_bool) {
+            opt.handler_bool(params, common_arg_utils::is_truthy(val));
+        } else if (opt.handler_str_str) {
+            // not supported yet
+            throw std::runtime_error(string_format(
+                "%s: option with two values is not supported yet",
+                __func__
+            ));
+        } else if (opt.handler_void) {
+            opt.handler_void(params);
+        } else {
+            GGML_ABORT("unknown handler type");
+        }
+    }
+}
+
 static std::map<std::string, std::map<std::string, std::string>> parse_ini_from_file(const std::string & path) {
     std::map<std::string, std::map<std::string, std::string>> parsed;
 
@@ -230,10 +295,16 @@ static std::string parse_bool_arg(const common_arg & arg, const std::string & ke
     return value;
 }
 
-common_preset_context::common_preset_context(llama_example ex)
+common_preset_context::common_preset_context(llama_example ex, bool only_remote_allowed)
         : ctx_params(common_params_parser_init(default_params, ex)) {
     common_params_add_preset_options(ctx_params.options);
     key_to_opt = get_map_key_opt(ctx_params);
+
+    // setup allowed keys if only_remote_allowed is true
+    if (only_remote_allowed) {
+        filter_allowed_keys = true;
+        allowed_keys = get_remote_preset_whitelist(key_to_opt);
+    }
 }
 
 common_presets common_preset_context::load_from_ini(const std::string & path, common_preset & global) const {
@@ -249,7 +320,18 @@ common_presets common_preset_context::load_from_ini(const std::string & path, co
         }
         LOG_DBG("loading preset: %s\n", preset.name.c_str());
         for (const auto & [key, value] : section.second) {
+            if (key == "version") {
+                // skip version key (reserved for future use)
+                continue;
+            }
+
             LOG_DBG("option: %s = %s\n", key.c_str(), value.c_str());
+            if (filter_allowed_keys && allowed_keys.find(key) == allowed_keys.end()) {
+                throw std::runtime_error(string_format(
+                    "option '%s' is not allowed in remote presets",
+                    key.c_str()
+                ));
+            }
             if (key_to_opt.find(key) != key_to_opt.end()) {
                 const auto & opt = key_to_opt.at(key);
                 if (is_bool_arg(opt)) {
@@ -259,7 +341,10 @@ common_presets common_preset_context::load_from_ini(const std::string & path, co
                 }
                 LOG_DBG("accepted option: %s = %s\n", key.c_str(), preset.options[opt].c_str());
             } else {
-                // TODO: maybe warn about unknown key?
+                throw std::runtime_error(string_format(
+                    "option '%s' not recognized in preset '%s'",
+                    key.c_str(), preset.name.c_str()
+                ));
             }
         }
 

common/preset.h

@@ -6,6 +6,7 @@
 #include <string>
 #include <vector>
 #include <map>
+#include <set>
 
 //
 // INI preset parser and writer
@@ -40,6 +41,9 @@ struct common_preset {
 
     // merge another preset into this one, overwriting existing options
     void merge(const common_preset & other);
+
+    // apply preset options to common_params
+    void apply_to_params(common_params & params) const;
 };
 
 // interface for multiple presets in one file
@@ -50,7 +54,12 @@ struct common_preset_context {
     common_params default_params; // unused for now
     common_params_context ctx_params;
     std::map<std::string, common_arg> key_to_opt;
-    common_preset_context(llama_example ex);
+
+    bool filter_allowed_keys = false;
+    std::set<std::string> allowed_keys;
+
+    // if only_remote_allowed is true, only accept whitelisted keys
+    common_preset_context(llama_example ex, bool only_remote_allowed = false);
 
     // load presets from INI file
     common_presets load_from_ini(const std::string & path, common_preset & global) const;

common/sampling.cpp

@@ -167,11 +167,11 @@ std::string common_params_sampling::print() const {
             "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
             "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n"
             "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, top_n_sigma = %.3f, temp = %.3f\n"
-            "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
+            "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f, adaptive_target = %.3f, adaptive_decay = %.3f",
             penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
             dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n,
             top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, top_n_sigma, temp,
-            mirostat, mirostat_eta, mirostat_tau);
+            mirostat, mirostat_eta, mirostat_tau, adaptive_target, adaptive_decay);
 
     return std::string(result);
 }
@@ -255,6 +255,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
     }
 
     if (params.mirostat == 0) {
+
+        bool use_adaptive_p = false; // see below
+
         for (const auto & cnstr : params.samplers) {
             switch (cnstr) {
                 case COMMON_SAMPLER_TYPE_DRY:
@@ -264,43 +267,54 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
                         for (const auto & str : params.dry_sequence_breakers) {
                             c_breakers.push_back(str.c_str());
                         }
-
-                        samplers.push_back(llama_sampler_init_dry    (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                        samplers.push_back(llama_sampler_init_dry(vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
-                    samplers.push_back(llama_sampler_init_top_k      (params.top_k));
+                    samplers.push_back(llama_sampler_init_top_k(params.top_k));
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_P:
-                    samplers.push_back(llama_sampler_init_top_p      (params.top_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_top_p(params.top_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
                     samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma));
                     break;
                 case COMMON_SAMPLER_TYPE_MIN_P:
-                    samplers.push_back(llama_sampler_init_min_p      (params.min_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_min_p(params.min_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_XTC:
-                    samplers.push_back(llama_sampler_init_xtc        (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
+                    samplers.push_back(llama_sampler_init_xtc(params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
                     break;
                 case COMMON_SAMPLER_TYPE_TYPICAL_P:
-                    samplers.push_back(llama_sampler_init_typical    (params.typ_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_typical(params.typ_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                    samplers.push_back(llama_sampler_init_temp_ext   (params.temp, params.dynatemp_range, params.dynatemp_exponent));
+                    samplers.push_back(llama_sampler_init_temp_ext(params.temp, params.dynatemp_range, params.dynatemp_exponent));
                     break;
                 case COMMON_SAMPLER_TYPE_INFILL:
-                    samplers.push_back(llama_sampler_init_infill     (vocab));
+                    samplers.push_back(llama_sampler_init_infill(vocab));
                     break;
                 case COMMON_SAMPLER_TYPE_PENALTIES:
-                    samplers.push_back(llama_sampler_init_penalties  (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    samplers.push_back(llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    break;
+                case COMMON_SAMPLER_TYPE_ADAPTIVE_P:
+                    // the `adaptive-p` sampler is like `dist` and `mirostat` in that it selects
+                    // a single token, so we will add `dist` at the end of the chain by default,
+                    // unless the user specifically included `adaptive-p`. we set this flag here
+                    // so we know to add the sampler at the very end.
+                    use_adaptive_p = true;
                     break;
                 default:
                     GGML_ASSERT(false && "unknown sampler type");
             }
         }
-
-        samplers.push_back(llama_sampler_init_dist(params.seed));
+        if (use_adaptive_p) {
+            // only if user explicitly included adaptive-p sampler
+            samplers.push_back(llama_sampler_init_adaptive_p(params.adaptive_target, params.adaptive_decay, params.seed));
+        } else {
+            // default: sample from distribution
+            samplers.push_back(llama_sampler_init_dist(params.seed));
+        }
     } else if (params.mirostat == 1) {
         samplers.push_back(llama_sampler_init_temp(params.temp));
         samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
@@ -334,15 +348,21 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
 }
 
 void common_sampler_free(struct common_sampler * gsmpl) {
-    if (gsmpl) {
-        llama_sampler_free(gsmpl->grmr);
-        llama_sampler_free(gsmpl->chain);
-
-        delete gsmpl;
+    if (!gsmpl) {
+        return;
     }
+
+    llama_sampler_free(gsmpl->grmr);
+    llama_sampler_free(gsmpl->chain);
+
+    delete gsmpl;
 }
 
 void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
+    if (!gsmpl) {
+        return;
+    }
+
     const auto tm = gsmpl->tm();
 
     if (gsmpl->grmr && accept_grammar) {
@@ -355,6 +375,10 @@ void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, boo
 }
 
 void common_sampler_reset(struct common_sampler * gsmpl) {
+    if (!gsmpl) {
+        return;
+    }
+
     gsmpl->reset();
 }
 
@@ -415,6 +439,10 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
 }
 
 struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl) {
+    if (!gsmpl) {
+        return nullptr;
+    }
+
     return gsmpl->chain;
 }
 
@@ -611,6 +639,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_XTC:         return 'x';
         case COMMON_SAMPLER_TYPE_INFILL:      return 'i';
         case COMMON_SAMPLER_TYPE_PENALTIES:   return 'e';
+        case COMMON_SAMPLER_TYPE_ADAPTIVE_P:  return 'a';
         default : return '?';
     }
 }
@@ -627,6 +656,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
         case COMMON_SAMPLER_TYPE_INFILL:      return "infill";
         case COMMON_SAMPLER_TYPE_PENALTIES:   return "penalties";
+        case COMMON_SAMPLER_TYPE_ADAPTIVE_P:  return "adaptive_p";
         default : return "";
     }
 }
@@ -643,6 +673,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "xtc",         COMMON_SAMPLER_TYPE_XTC },
         { "infill",      COMMON_SAMPLER_TYPE_INFILL },
         { "penalties",   COMMON_SAMPLER_TYPE_PENALTIES },
+        { "adaptive_p",  COMMON_SAMPLER_TYPE_ADAPTIVE_P },
     };
 
     // since samplers names are written multiple ways
@@ -658,6 +689,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "typ",         COMMON_SAMPLER_TYPE_TYPICAL_P },
         { "min-p",       COMMON_SAMPLER_TYPE_MIN_P },
         { "temp",        COMMON_SAMPLER_TYPE_TEMPERATURE },
+        { "adaptive-p",  COMMON_SAMPLER_TYPE_ADAPTIVE_P },
     };
 
     std::vector<common_sampler_type> samplers;
@@ -694,6 +726,7 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC),         COMMON_SAMPLER_TYPE_XTC },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL),      COMMON_SAMPLER_TYPE_INFILL },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES),   COMMON_SAMPLER_TYPE_PENALTIES },
+        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_ADAPTIVE_P),  COMMON_SAMPLER_TYPE_ADAPTIVE_P },
     };
 
     std::vector<common_sampler_type> samplers;

convert_hf_to_gguf.py

@@ -528,7 +528,11 @@ class ModelBase:
         return ()
 
     def prepare_tensors(self):
-        max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
+        # Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
+        if self.tensor_map.mapping:
+            max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
+        else:
+            max_name_len = len("vision_encoder.weight,")  # Default reasonable length
 
         for name, data_torch in chain(self.generate_extra_tensors(), self.get_tensors()):
             # we don't need these
@@ -771,8 +775,8 @@ class TextModel(ModelBase):
 
         self.rope_parameters = self.hparams.get("rope_parameters", self.hparams.get("rope_scaling")) or {}
 
-        rope_theta = self.find_hparam(["rope_theta", "global_rope_theta", "rotary_emb_base"], optional=True)
-        local_rope_theta = self.find_hparam(["local_rope_theta", "rope_local_theta", "swa_rope_theta", "rope_local_base_freq"], optional=True)
+        rope_theta = self.find_hparam(["global_rope_theta", "rope_global_theta", "rope_theta_global", "rope_theta", "rotary_emb_base"], optional=True)
+        local_rope_theta = self.find_hparam(["local_rope_theta", "rope_local_theta", "rope_theta_local", "swa_rope_theta", "rope_local_base_freq"], optional=True)
 
         # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters
         if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters:
@@ -1248,6 +1252,9 @@ class TextModel(ModelBase):
         if chkhsh == "16389f0a1f51ee53e562ffd51c371dc508639ab0e4261502071836e50e223e91":
             # ref: https://huggingface.co/upstage/Solar-Open-100B
             res = "solar-open"
+        if chkhsh == "6c81ce329e0802883b22eabab0d3fa48357337ef1ecb45443828bf1f6254833f":
+            # ref: https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B
+            res = "exaone-moe"
 
         if res is None:
             logger.warning("\n")
@@ -4363,7 +4370,37 @@ class Qwen3NextModel(Qwen2MoeModel):
         elif name.endswith("norm.weight") and not name.endswith("linear_attn.norm.weight"):
             data_torch = data_torch + 1
 
-        yield from super().modify_tensors(data_torch, name, bid)
+        if "in_proj_qkvz.weight" in name:
+            # original order:  [q, k, v, z] * head_count
+            # corrected order: [q * head_count, k * head_count, v * head_count, z * head_count]
+            head_k_dim = self.hparams["linear_key_head_dim"]
+            head_v_dim = self.hparams["linear_value_head_dim"]
+            num_v_heads = self.hparams["linear_num_value_heads"]
+            num_k_heads = self.hparams["linear_num_key_heads"]
+            hidden_size = self.hparams["hidden_size"]
+            split_arg_list_qkvz = [
+                head_k_dim, # q partition
+                head_k_dim, # k partition
+                (num_v_heads // num_k_heads * head_v_dim), # v partition
+                (num_v_heads // num_k_heads * head_v_dim), # z partition
+            ]
+            # view as (n_embd, head_count, [q+k+v+z])
+            data_torch = data_torch.permute(1, 0).contiguous()
+            data_torch = data_torch.view(-1, num_k_heads, sum(split_arg_list_qkvz))
+            # split into q, k, v, z
+            q, k, v, z = torch.split(data_torch, split_arg_list_qkvz, dim=-1)
+            # flatten dim + head_count
+            q = q.contiguous().view(hidden_size, -1)
+            k = k.contiguous().view(hidden_size, -1)
+            v = v.contiguous().view(hidden_size, -1)
+            z = z.contiguous().view(hidden_size, -1)
+            # stack back
+            qkv = torch.cat([q, k, v], dim=-1).permute(1, 0).contiguous()
+            z = z.permute(1, 0).contiguous()
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV,  bid, ".weight"), qkv)
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_GATE, bid, ".weight"), z)
+        else:
+            yield from super().modify_tensors(data_torch, name, bid)
 
 
 @ModelBase.register("RND1")
@@ -6038,7 +6075,175 @@ class Gemma3VisionModel(MmprojModel):
         return [] # skip other tensors
 
 
+class ConformerAudioModel(MmprojModel):
+    _batch_norm_tensors: list[dict[str, Tensor]] | None = None
+
+    @staticmethod
+    def is_audio_tensor(name: str):
+        return any(p in name for p in ["audio", "codebook", "conformer", "depth_embedding", "depthformer", "depth_linear"])
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ConformerAudioModel.is_audio_tensor(name):
+            if ".conv" in name or "_conv" in name and ".weight" in name:
+                return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # fold running_mean, running_var and eps into weight and bias for batch_norm
+        if "batch_norm" in name:
+            if self._batch_norm_tensors is None:
+                self._batch_norm_tensors = [{} for _ in range(self.block_count)]
+            assert bid is not None
+            self._batch_norm_tensors[bid][name] = data_torch
+
+            if len(self._batch_norm_tensors[bid]) < 5:
+                return []
+
+            weight = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.weight"]
+            bias = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.bias"]
+            running_mean = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_mean"]
+            running_var = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_var"]
+            eps = 1e-5 # default value
+
+            a = weight / torch.sqrt(running_var + eps)
+            b = bias - running_mean * a
+            return [
+                (self.map_tensor_name(f"conformer.layers.{bid}.conv.batch_norm.weight"), a),
+                (self.map_tensor_name(f"conformer.layers.{bid}.conv.batch_norm.bias"), b),
+            ]
+
+        # reshape conv weights
+        if name.startswith("conformer.pre_encode.conv.") and name.endswith(".bias"):
+            data_torch = data_torch[:, None, None]
+        if "conv.depthwise_conv" in name and name.endswith(".weight"):
+            assert data_torch.shape[1] == 1
+            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
+        if "conv.pointwise_conv" in name and name.endswith(".weight"):
+            assert data_torch.shape[2] == 1
+            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
 @ModelBase.register("Gemma3nForConditionalGeneration")
+class Gemma3nVisionAudioModel(ConformerAudioModel):
+    has_audio_encoder = True
+    has_vision_encoder = True
+
+    # Double indexed mapping for MobileNetV5 blocks (not supported by tensor_mapping.py)
+    # This is the only known model having this, so we prefer implementing it outside of tensor_mapping.py
+    block_tensor_mapping = {
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_exp.weight":             "v.blk.{bid}.{sid}.conv_exp.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn1.weight":                  "v.blk.{bid}.{sid}.bn1.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_pwl.weight":             "v.blk.{bid}.{sid}.conv_pwl.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn2.weight":                  "v.blk.{bid}.{sid}.bn2.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.conv.weight":        "v.blk.{bid}.{sid}.dw_start.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.bn.weight":          "v.blk.{bid}.{sid}.dw_start.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.conv.weight":          "v.blk.{bid}.{sid}.dw_mid.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.bn.weight":            "v.blk.{bid}.{sid}.dw_mid.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.conv.weight":          "v.blk.{bid}.{sid}.pw_exp.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.bn.weight":            "v.blk.{bid}.{sid}.pw_exp.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.conv.weight":         "v.blk.{bid}.{sid}.pw_proj.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.bn.weight":           "v.blk.{bid}.{sid}.pw_proj.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.layer_scale.gamma":           "v.blk.{bid}.{sid}.layer_scale.gamma",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.query.proj.weight":      "v.blk.{bid}.{sid}.attn.query.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.proj.weight":        "v.blk.{bid}.{sid}.attn.key.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.proj.weight":      "v.blk.{bid}.{sid}.attn.value.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.output.proj.weight":     "v.blk.{bid}.{sid}.attn.output.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.down_conv.weight":   "v.blk.{bid}.{sid}.attn.key.down_conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.norm.weight":        "v.blk.{bid}.{sid}.attn.key.norm.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.down_conv.weight": "v.blk.{bid}.{sid}.attn.value.down_conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.norm.weight":      "v.blk.{bid}.{sid}.attn.value.norm.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.norm.weight":                 "v.blk.{bid}.{sid}.norm.weight",
+    }
+
+    def __init__(self, *args, **kwargs):
+        # Parent init will call find_hparam which now returns 0 for empty keys
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["n_layers"] = 128 # fake value for audio encoder, vision encoder doesn't use it
+        self.hparams_vision["intermediate_size"] = self.hparams_vision.get("intermediate_size", 2048) * 4
+        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_attention_heads", 8)
+
+        # MobileNetV5 does not use image_mean/std
+        self.preprocessor_config["image_mean"] = [0.0 ,0.0 , 0.0]
+        self.preprocessor_config["image_std"] = [1.0 ,1.0 ,1.0]
+        self.hparams_vision["image_size"] = self.preprocessor_config.get(
+            "size", {"height": 768, "width": 768}
+        )["height"]
+
+        # Image sequence length (256 tokens = 16x16 for Gemma3n)
+        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
+        image_size = self.hparams_vision["image_size"]
+        self.hparams_vision["patch_size"] = image_size // image_seq_length
+
+        # remap audio hparams
+        assert self.hparams_audio is not None
+        self.hparams_audio["n_layers"] = self.hparams_audio["conf_num_hidden_layers"]
+        self.hparams_audio["num_attention_heads"] = self.hparams_audio["conf_num_attention_heads"]
+        self.hparams_audio["feat_in"] = self.hparams_audio["input_feat_size"]
+        self.hparams_audio["intermediate_size"] = self.hparams_audio.get("intermediate_size", 6144)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # vision params
+        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA3NV)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
+
+        # audio params
+        assert self.hparams_audio is not None
+        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA3NA)
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # Force quantization settings for specific tensor types
+        if "input_projection" in name or "input_proj" in name:
+            return gguf.GGMLQuantizationType.F16
+        if ".embeddings." in name or "stem" in name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def custom_map(self, name: str) -> str:
+        """Parses names like model.vision_tower.timm_model.blocks.1.2.suffix and applies template mapping."""
+        parts = name.split(".")
+        # MobileNet blocks have at least 7 parts: model, vision_tower, timm_model, blocks, bid, sid, and suffix
+        if len(parts) >= 7:
+            bid, sid = parts[4], parts[5]
+            suffix = ".".join(parts[6:])
+            template = f"model.vision_tower.timm_model.blocks.{{bid}}.{{sid}}.{suffix}"
+            if template in self.block_tensor_mapping:
+                return self.block_tensor_mapping[template].format(bid=bid, sid=sid)
+
+        raise ValueError(f"Unknown name: {name}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if (ConformerAudioModel.is_audio_tensor(name)):
+            name = name.replace("model.audio_tower.conformer.", "conformer.layers.")
+            return super().modify_tensors(data_torch, name, bid)
+
+        # Gemma3n uses
+        # - model.embed_vision.* for projection layers
+        # - model.vision_tower.* for vision encoder
+        # Skip non-vision tensors
+        if not (name.startswith("model.embed_vision.") or name.startswith("model.vision_tower.")):
+            return []
+
+        if name.startswith("model.vision_tower.timm_model.blocks."):
+            # Double-indexed block tensors through custom logic
+            new_name = self.custom_map(name)
+        else:
+            # Route non-repeating (conv_stem, msfa, embedding, etc.) and un-catched through tensor_mapping.py
+            new_name = self.map_tensor_name(name)
+
+        if new_name.endswith("conv_stem.conv.bias") or new_name.endswith("layer_scale.gamma"):
+            data_torch = data_torch.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) # [1, C, 1, 1]
+
+        return [(new_name, data_torch)]
+
+
+@ModelBase.register("Gemma3nForCausalLM", "Gemma3nForConditionalGeneration")
 class Gemma3NModel(Gemma3Model):
     model_arch = gguf.MODEL_ARCH.GEMMA3N
     norm_shift = 0.0 # same value with Gemma3p5RMSNorm scale_shift on python code
@@ -6061,8 +6266,25 @@ class Gemma3NModel(Gemma3Model):
         ]
 
     def set_vocab(self):
+        # For Gemma3n multimodal models, we need the FULL vocab_size (262400)
+        # which includes special tokens from 262144-262399 for vision/audio.
+        # The vocab_size_per_layer_input (262144) is only the embedding size per layer.
+        # Temporarily override the hparams lookup order to prioritize vocab_size.
+
+        # Store original vocab_size_per_layer_input if it exists
+        vocab_size_per_layer_input = self.hparams.get("vocab_size_per_layer_input")
+
+        # Temporarily remove vocab_size_per_layer_input to force using vocab_size
+        if vocab_size_per_layer_input is not None:
+            del self.hparams["vocab_size_per_layer_input"]
+
+        # Call parent set_vocab which will now use vocab_size (262400)
         super().set_vocab()
 
+        # Restore vocab_size_per_layer_input for later use
+        if vocab_size_per_layer_input is not None:
+            self.hparams["vocab_size_per_layer_input"] = vocab_size_per_layer_input
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         self.gguf_writer.add_altup_active_idx(self.hparams["altup_active_idx"])
@@ -6098,8 +6320,32 @@ class Gemma3NModel(Gemma3Model):
         if "language_model." not in name:
             return [] # skip non-language model tensors
 
+        # Pad token embeddings for vision/audio special tokens (262144-262399)
+        if "embed_tokens.weight" in name or "embed_tokens_per_layer" in name:
+            # Move to CPU to avoid meta device issues during padding
+            data_torch = data_torch.to(device="cpu")
+
+            vocab_size = self.hparams.get("vocab_size", 262400)
+            current_size = data_torch.shape[0]  # First dimension is vocab_size
+
+            if current_size < vocab_size:
+                # Pad with zeros for vision/audio tokens (they get embeddings from vision tower)
+                padding_size = vocab_size - current_size
+                tensor_type = "per-layer embeddings" if "per_layer" in name else "token embeddings"
+                logger.info(f"Padding {tensor_type} shape {list(data_torch.shape)} from {current_size} to {vocab_size} (adding {padding_size} vision/audio token slots)")
+
+                # Create padding with zeros (vision tokens won't use these embeddings)
+                padding = torch.zeros((padding_size, data_torch.shape[1]), dtype=data_torch.dtype, device=data_torch.device)
+                data_torch = torch.cat([data_torch, padding], dim=0)
+
+            # Continue with normal processing
+            name = name.replace("language_model.", "")
+            return [(self.map_tensor_name(name), data_torch)]
+
         if "altup_unembed_projections" in name:
             data_torch = data_torch.to(device="cpu")
+            # altup_unembed matrices are [hidden_size, hidden_size], NOT vocab-based
+            # They should NOT be padded
             if ".0." in name:
                 self._altup_unembd[0] = data_torch
             elif ".1." in name:
@@ -7212,6 +7458,7 @@ class DeepseekModel(TextModel):
     "DeepseekV3ForCausalLM",
     "KimiVLForConditionalGeneration",
     "YoutuForCausalLM",
+    "YoutuVLForConditionalGeneration"
 )
 class DeepseekV2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2
@@ -8504,6 +8751,102 @@ class Exaone4Model(TextModel):
                 yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
 
 
+@ModelBase.register("ExaoneMoEForCausalLM")
+class ExaoneMoEModel(Exaone4Model):
+    model_arch = gguf.MODEL_ARCH.EXAONE_MOE
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_expert_count(self.hparams["num_experts"])
+        moe_intermediate_size = self.hparams["moe_intermediate_size"]
+        num_shared_experts = self.hparams["num_shared_experts"]
+        self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        self.gguf_writer.add_expert_shared_count(num_shared_experts)
+        self.gguf_writer.add_expert_shared_feed_forward_length(moe_intermediate_size * num_shared_experts)
+        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
+        self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
+        n_dense_layer = self.hparams.get("first_k_dense_replace", self.hparams.get("first_last_k_dense_replace", 0))
+        self.gguf_writer.add_leading_dense_block_count(n_dense_layer)
+        self.gguf_writer.add_nextn_predict_layers(self.hparams.get("num_nextn_predict_layers", 0))
+
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("mtp."):
+            if name.find("layers.") != -1:
+                # `mtp.layers.0.[module_name]` format
+                name = name.replace(f"mtp.layers.{bid}", f"model.layers.{bid + self.hparams['num_hidden_layers']}")
+            else:
+                # mtp fc/norm weights
+                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",
+                }
+                _n = Path(name)
+                new_name = remapper[_n.stem] + _n.suffix
+
+                # set shared weights for all NextN/MTP layers
+                tensors = []
+                for bid in range(self.hparams['num_hidden_layers'], self.block_count):
+                    new_name = new_name.format(bid=bid)
+                    tensors.append((self.map_tensor_name(new_name), data_torch))
+                return tensors
+
+        if name.endswith("e_score_correction_bias"):
+            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["num_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
 @ModelBase.register("GraniteForCausalLM")
 class GraniteModel(LlamaModel):
     """Conversion for IBM's GraniteForCausalLM"""
@@ -9935,7 +10278,7 @@ class LFM2Model(TextModel):
         self._add_feed_forward_length()
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if self._is_vision_tensor(name) or self._is_audio_tensor(name):
+        if self._is_vision_tensor(name) or ConformerAudioModel.is_audio_tensor(name):
             # skip multimodal tensors
             return []
 
@@ -9951,8 +10294,26 @@ class LFM2Model(TextModel):
     def _is_vision_tensor(self, name: str) -> bool:
         return "vision_tower" in name or "multi_modal_projector" in name
 
-    def _is_audio_tensor(self, name: str):
-        return any(p in name for p in ["audio", "codebook", "conformer", "depth_embedding", "depthformer", "depth_linear"])
+
+@ModelBase.register("Lfm2Model")
+class LFM2ColBertModel(LFM2Model):
+    model_arch = gguf.MODEL_ARCH.LFM2
+    dense_tensor_name = "dense_2"
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if not name.startswith(self.dense_tensor_name):
+            name = "model." + name
+
+        return super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # dense tensor is stored in a separate safetensors file
+        from safetensors.torch import load_file
+        tensors_file = self.dir_model / "1_Dense" / "model.safetensors"
+        assert tensors_file.is_file()
+        tensor = load_file(tensors_file)["linear.weight"]
+        self.gguf_writer.add_embedding_length_out(tensor.shape[0])
+        yield f"{self.dense_tensor_name}.weight", tensor.clone()
 
 
 @ModelBase.register("Lfm2MoeForCausalLM")
@@ -10060,13 +10421,11 @@ class LFM2VLModel(MmprojModel):
 
 
 @ModelBase.register("Lfm2AudioForConditionalGeneration")
-class LFM2AudioModel(MmprojModel):
+class LFM2AudioModel(ConformerAudioModel):
     has_vision_encoder = False
     has_audio_encoder = True
     model_name = "Lfm2AudioEncoder"
 
-    _batch_norm_tensors: list[dict[str, Tensor]] | None = None
-
     def get_audio_config(self) -> dict[str, Any] | None:
         return self.global_config.get("encoder")
 
@@ -10080,12 +10439,7 @@ class LFM2AudioModel(MmprojModel):
         self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
         self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
 
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".conv" in name and ".weight" in name:
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+    def modify_tensors(self, data_torch, name, bid):
         # skip language model tensors
         if name.startswith("lfm."):
             return []
@@ -10098,40 +10452,7 @@ class LFM2AudioModel(MmprojModel):
         if any(p in name for p in ["codebook_offsets", "depth_embeddings", "depth_linear", "depthformer"]):
             return []
 
-        # fold running_mean, running_var and eps into weight and bias for batch_norm
-        if "batch_norm" in name:
-            if self._batch_norm_tensors is None:
-                self._batch_norm_tensors = [{} for _ in range(self.block_count)]
-            assert bid is not None
-            self._batch_norm_tensors[bid][name] = data_torch
-
-            if len(self._batch_norm_tensors[bid]) < 5:
-                return []
-
-            weight = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.weight"]
-            bias = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.bias"]
-            running_mean = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_mean"]
-            running_var = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_var"]
-            eps = 1e-5 # default value
-
-            a = weight / torch.sqrt(running_var + eps)
-            b = bias - running_mean * a
-            return [
-                (self.map_tensor_name(f"conformer.layers.{bid}.conv.batch_norm.weight"), a),
-                (self.map_tensor_name(f"conformer.layers.{bid}.conv.batch_norm.bias"), b),
-            ]
-
-        # reshape conv weights
-        if name.startswith("conformer.pre_encode.conv.") and name.endswith(".bias"):
-            data_torch = data_torch[:, None, None]
-        if "conv.depthwise_conv" in name and name.endswith(".weight"):
-            assert data_torch.shape[1] == 1
-            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
-        if "conv.pointwise_conv" in name and name.endswith(".weight"):
-            assert data_torch.shape[2] == 1
-            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])
-
-        return [(self.map_tensor_name(name), data_torch)]
+        return super().modify_tensors(data_torch, name, bid)
 
 
 @ModelBase.register("SmallThinkerForCausalLM")
@@ -10674,8 +10995,8 @@ class JanusProVisionModel(MmprojModel):
         return []
 
 
-@ModelBase.register("YOUTUVLForConditionalGeneration", "YOUTUVLForCausalLM")
-class YOUTUVLVisionModel(MmprojModel):
+@ModelBase.register("YoutuVLForConditionalGeneration")
+class YoutuVLVisionModel(MmprojModel):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         assert self.hparams_vision is not None
@@ -10952,8 +11273,8 @@ def parse_args() -> argparse.Namespace:
 
     parser.add_argument(
         "--sentence-transformers-dense-modules", action="store_true",
-        help=("Whether to include sentence-transformers dense modules."
-              "It can be used for sentence-transformers models, like google/embeddinggemma-300m"
+        help=("Whether to include sentence-transformers dense modules. "
+              "It can be used for sentence-transformers models, like google/embeddinggemma-300m. "
               "Default these modules are not included.")
     )
 

convert_hf_to_gguf_update.py

@@ -147,6 +147,7 @@ models = [
     {"name": "kormo",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/KORMo-Team/KORMo-tokenizer", },
     {"name": "youtu",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Youtu-LLM-2B", },
     {"name": "solar-open",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/upstage/Solar-Open-100B", },
+    {"name": "exaone-moe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions

docs/backend/hexagon/CMakeUserPresets.json

@@ -1,4 +1,4 @@
-{
+{
   "version": 4,
   "configurePresets": [
     {
@@ -23,7 +23,7 @@
             "GGML_OPENCL":      "ON",
             "GGML_HEXAGON":     "ON",
             "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
-            "LLAMA_CURL":       "OFF"
+            "LLAMA_OPENSSL":    "OFF"
         }
     },
 
@@ -38,7 +38,7 @@
             "GGML_OPENCL":      "ON",
             "GGML_HEXAGON":     "ON",
             "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
-            "LLAMA_CURL":       "OFF"
+            "LLAMA_OPENSSL":    "OFF"
         }
     },
 

docs/backend/hexagon/README.md

@@ -210,6 +210,10 @@ build: 6a8cf8914 (6733)
   Controls whether the Hexagon backend allocates host buffers. By default, all buffers except for REPACK are host buffers.
   This option is required for testing Ops that require REPACK buffers (MUL_MAT and MUL_MAT_ID).
 
+- `GGML_HEXAGON_EXPERIMENTAL=1`
+  Controls whether the Hexagon backend enables experimental features.
+  This option is required for enabling/testing experimental Ops (FLASH_ATTN_EXT).
+
 - `GGML_HEXAGON_VERBOSE=1`
   Enables verbose logging of Ops from the backend. Example output:
 

docs/build-riscv64-spacemit.md

@@ -15,7 +15,7 @@ Below is the build script: it requires utilizing RISC-V vector instructions for
 cmake -B build \
     -DCMAKE_BUILD_TYPE=Release \
     -DGGML_CPU_RISCV64_SPACEMIT=ON \
-    -DLLAMA_CURL=OFF \
+    -DLLAMA_OPENSSL=OFF \
     -DGGML_RVV=ON \
     -DGGML_RV_ZFH=ON \
     -DGGML_RV_ZICBOP=ON \

docs/build.md

@@ -65,10 +65,10 @@ cmake --build build --config Release
       cmake --preset x64-windows-llvm-release
       cmake --build build-x64-windows-llvm-release
       ```
-- Curl usage is enabled by default and can be turned off with `-DLLAMA_CURL=OFF`. Otherwise you need to install development libraries for libcurl.
-  - **Debian / Ubuntu:** `sudo apt-get install libcurl4-openssl-dev`  # (or `libcurl4-gnutls-dev` if you prefer GnuTLS)
-  - **Fedora / RHEL / Rocky / Alma:** `sudo dnf install libcurl-devel`
-  - **Arch / Manjaro:** `sudo pacman -S curl`  # includes libcurl headers
+- If you want HTTPS/TLS features, you may install OpenSSL development libraries. If not installed, the project will build and run without SSL support.
+  - **Debian / Ubuntu:** `sudo apt-get install libssl-dev`
+  - **Fedora / RHEL / Rocky / Alma:** `sudo dnf install openssl-devel`
+  - **Arch / Manjaro:** `sudo pacman -S openssl`
 
 ## BLAS Build
 

docs/ops.md

@@ -22,7 +22,7 @@ Legend:
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
@@ -57,7 +57,6 @@ Legend:
 |                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|               GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -71,10 +70,9 @@ Legend:
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                     NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ |
+|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 |
 |                              PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -99,7 +97,6 @@ Legend:
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                          SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |

docs/ops/BLAS.csv

@@ -965,6 +965,7 @@
 "BLAS","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,1,2560],ne_kernel=[3,3,1,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
 "BLAS","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,2,2560],ne_kernel=[3,3,2,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
 "BLAS","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[5,5,1,32],ne_kernel=[3,4,1,32],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
+"BLAS","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[2,2,1536,729],ne_kernel=[2,2,1536,4096],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
 "BLAS","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","BLAS"
 "BLAS","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","BLAS"
 "BLAS","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","BLAS"
@@ -4964,6 +4965,7 @@
 "BLAS","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","0","no","BLAS"
 "BLAS","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","BLAS"
 "BLAS","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","BLAS"
+"BLAS","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","BLAS"
 "BLAS","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","0","no","BLAS"
 "BLAS","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","BLAS"
 "BLAS","ARGMAX","type=f32,ne=[32,1,1,1]","support","0","no","BLAS"
@@ -5715,15 +5717,15 @@
 "BLAS","L2_NORM","type=f32,ne=[64,5,4,3]","support","0","no","BLAS"
 "BLAS","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","0","no","BLAS"
 "BLAS","L2_NORM","type=f32,ne=[64,5,4,3]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,1024,4,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[6,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,1536,4,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[6,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,2048,4,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","0","no","BLAS"
@@ -5733,6 +5735,15 @@
 "BLAS","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,1024,1,1],ne_b=[9,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[18,1024,1,1],ne_b=[9,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,1024,4,1],ne_b=[9,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,1536,1,1],ne_b=[9,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[18,1536,1,1],ne_b=[9,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,1536,4,1],ne_b=[9,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,2048,1,1],ne_b=[9,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[18,2048,1,1],ne_b=[9,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,2048,4,1],ne_b=[9,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","0","no","BLAS"
 "BLAS","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","BLAS"
 "BLAS","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","BLAS"
@@ -6592,6 +6603,30 @@
 "BLAS","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=67,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=f32,type_b=f32,m=64,n=77,k=77,bs=[12,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","BLAS"
 "BLAS","MUL_MAT","type_a=q4_0,type_b=f32,m=576,n=512,k=576,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","BLAS"
+"BLAS","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=2048,k=8192,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=f32,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=f16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=bf16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q4_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q5_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q5_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q8_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=mxfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q2_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q3_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q4_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q5_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q6_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq2_xs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq2_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq1_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq1_m,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq4_nl,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq3_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq4_xs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","BLAS"
@@ -8916,6 +8951,11 @@
 "BLAS","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
 "BLAS","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
 "BLAS","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
+"BLAS","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
+"BLAS","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
+"BLAS","SOFT_MAX","type=f32,ne=[200000,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
+"BLAS","SOFT_MAX","type=f32,ne=[200000,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
+"BLAS","SOFT_MAX","type=f32,ne=[643251,3,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
 "BLAS","SOFT_MAX_BACK","type=f32,ne=[16,16,1,1],scale=1.000000,max_bias=0.000000","support","0","no","BLAS"
 "BLAS","SOFT_MAX_BACK","type=f32,ne=[15,15,1,1],scale=1.000000,max_bias=0.000000","support","0","no","BLAS"
 "BLAS","SOFT_MAX_BACK","type=f32,ne=[16,16,2,3],scale=1.000000,max_bias=0.000000","support","0","no","BLAS"
@@ -8968,6 +9008,7 @@
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -8977,6 +9018,7 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -8987,11 +9029,13 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9001,6 +9045,7 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9011,11 +9056,13 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9025,6 +9072,7 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9035,11 +9083,13 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9049,6 +9099,7 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9059,6 +9110,7 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9184,6 +9236,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9193,6 +9246,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9203,11 +9257,13 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9217,6 +9273,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9227,11 +9284,13 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9241,6 +9300,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9251,11 +9311,13 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9265,6 +9327,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9275,6 +9338,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9542,333 +9606,333 @@
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 "BLAS","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=0","support","0","no","BLAS"
 "BLAS","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=1","support","0","no","BLAS"
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-"BLAS","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=align_corners","support","0","no","BLAS"
-"BLAS","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear,flags=align_corners","support","0","no","BLAS"
-"BLAS","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear,flags=align_corners","support","0","no","BLAS"
-"BLAS","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic,flags=align_corners","support","0","no","BLAS"
-"BLAS","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bicubic,flags=align_corners","support","0","no","BLAS"
-"BLAS","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bicubic,flags=align_corners","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear|antialias,transpose=0","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear|antialias,transpose=1","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear|antialias","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear|antialias","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear|align_corners","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear|align_corners","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear|align_corners","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic|align_corners","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bicubic|align_corners","support","0","no","BLAS"
+"BLAS","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bicubic|align_corners","support","0","no","BLAS"
 "BLAS","SUM","type=f32,ne=[10,5,4,3]","support","0","no","BLAS"
 "BLAS","SUM_ROWS","type=f32,ne=[10,5,4,3],permute=0,slice=0","support","0","no","BLAS"
 "BLAS","SUM","type=f32,ne=[11,5,6,3],permute=[0,2,1,3]","support","0","no","BLAS"
@@ -9891,8 +9955,9 @@
 "BLAS","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","0","no","BLAS"
 "BLAS","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","0","no","BLAS"
 "BLAS","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=0","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[33,17,2,1],pad_0=4,pad_1=3,circular=1","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0,circular=0","support","0","no","BLAS"
 "BLAS","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","0","no","BLAS"
 "BLAS","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","0","no","BLAS"
 "BLAS","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","0","no","BLAS"
@@ -9914,6 +9979,7 @@
 "BLAS","CUMSUM","type=f32,ne=[2048,5,4,3]","support","0","no","BLAS"
 "BLAS","CUMSUM","type=f32,ne=[242004,1,1,1]","support","0","no","BLAS"
 "BLAS","CUMSUM","type=f32,ne=[375960,1,1,1]","support","0","no","BLAS"
+"BLAS","CUMSUM","type=f32,ne=[20481,4,1,1]","support","0","no","BLAS"
 "BLAS","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","BLAS"
 "BLAS","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","0","no","BLAS"
 "BLAS","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","0","no","BLAS"
@@ -9923,17 +9989,41 @@
 "BLAS","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","BLAS"
 "BLAS","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","BLAS"
 "BLAS","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","0","no","BLAS"
+"BLAS","DIAG","type=f32,ne=[10,1,4,3]","support","0","no","BLAS"
+"BLAS","DIAG","type=f32,ne=[79,1,19,13]","support","0","no","BLAS"
+"BLAS","DIAG","type=f32,ne=[256,1,8,16]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[64,64,2,2]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[79,79,5,3],ne_rhs=[417,79,5,3]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,2],ne_rhs=[32,128,4,2]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[80,80,2,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[79,80,2,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[81,80,2,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[80,80,8,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[79,80,8,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[81,80,8,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[84,84,4,4],ne_rhs=[32,84,4,4]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[95,95,8,8],ne_rhs=[40,95,8,8]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","0","no","BLAS"
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+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","0","no","BLAS"
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+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","0","no","BLAS"
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+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","0","no","BLAS"
 "BLAS","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","BLAS"
 "BLAS","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","BLAS"
 "BLAS","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","BLAS"

docs/preset.md

@@ -0,0 +1,97 @@
+# llama.cpp INI Presets
+
+## Introduction
+
+The INI preset feature, introduced in [PR#17859](https://github.com/ggml-org/llama.cpp/pull/17859), allows users to create reusable and shareable parameter configurations for llama.cpp.
+
+### Using Presets with the Server
+
+When running multiple models on the server (router mode), INI preset files can be used to configure model-specific parameters. Please refer to the [server documentation](../tools/server/README.md) for more details.
+
+### Using a Remote Preset
+
+> [!NOTE]
+>
+> This feature is currently only supported via the `-hf` option.
+
+For GGUF models hosted on Hugging Face, you can include a `preset.ini` file in the root directory of the repository to define specific configurations for that model.
+
+Example:
+
+```ini
+hf-repo-draft = username/my-draft-model-GGUF
+temp = 0.5
+top-k = 20
+top-p = 0.95
+```
+
+For security reasons, only certain options are allowed. Please refer to [preset.cpp](../common/preset.cpp) for the complete list of permitted options.
+
+Example usage:
+
+Assuming your repository `username/my-model-with-preset` contains a `preset.ini` with the configuration above:
+
+```sh
+llama-cli -hf username/my-model-with-preset
+
+# This is equivalent to:
+llama-cli -hf username/my-model-with-preset \
+  --hf-repo-draft username/my-draft-model-GGUF \
+  --temp 0.5 \
+  --top-k 20 \
+  --top-p 0.95
+```
+
+You can also override preset arguments by specifying them on the command line:
+
+```sh
+# Force temp = 0.1, overriding the preset value
+llama-cli -hf username/my-model-with-preset --temp 0.1
+```
+
+If you want to define multiple preset configurations for one or more GGUF models, you can create a blank HF repo for each preset. Each HF repo should contain a `preset.ini` file that references the actual model(s):
+
+```ini
+hf-repo = user/my-model-main
+hf-repo-draft = user/my-model-draft
+temp = 0.8
+ctx-size = 1024
+; (and other configurations)
+```
+
+### Named presets
+
+If you want to define multiple preset configurations for one or more GGUF models, you can create a blank HF repo containing a single `preset.ini` file that references the actual model(s):
+
+```ini
+[*]
+mmap = 1
+
+[gpt-oss-20b-hf]
+hf          = ggml-org/gpt-oss-20b-GGUF
+batch-size  = 2048
+ubatch-size = 2048
+top-p       = 1.0
+top-k       = 0
+min-p       = 0.01
+temp        = 1.0
+chat-template-kwargs = {"reasoning_effort": "high"}
+
+[gpt-oss-120b-hf]
+hf          = ggml-org/gpt-oss-120b-GGUF
+batch-size  = 2048
+ubatch-size = 2048
+top-p       = 1.0
+top-k       = 0
+min-p       = 0.01
+temp        = 1.0
+chat-template-kwargs = {"reasoning_effort": "high"}
+```
+
+You can then use it via `llama-cli` or `llama-server`, example:
+
+```sh
+llama-server -hf user/repo:gpt-oss-120b-hf
+```
+
+Please make sure to provide the correct `hf-repo` for each child preset. Otherwise, you may get error: `The specified tag is not a valid quantization scheme.`

examples/CMakeLists.txt

@@ -15,6 +15,7 @@ llama_add_compile_flags()
 if (EMSCRIPTEN)
 else()
     add_subdirectory(batched)
+    add_subdirectory(debug)
     add_subdirectory(embedding)
     add_subdirectory(eval-callback)
 
@@ -34,7 +35,6 @@ else()
     add_subdirectory(gen-docs)
     add_subdirectory(training)
     add_subdirectory(diffusion)
-    add_subdirectory(model-conversion)
     if (NOT GGML_BACKEND_DL)
         add_subdirectory(convert-llama2c-to-ggml)
         # these examples use the backends directly and cannot be built with dynamic loading

examples/batched/batched.cpp

@@ -21,7 +21,7 @@ int main(int argc, char ** argv) {
     params.prompt = "Hello my name is";
     params.n_predict = 32;
 
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BATCHED, print_usage)) {
         return 1;
     }
 
@@ -81,7 +81,6 @@ int main(int argc, char ** argv) {
         sampler_configs.push_back({ i, smpl });
     }
 
-    // TODO: temporarily gated behind a flag
     if (params.sampling.backend_sampling) {
         ctx_params.samplers   = sampler_configs.data();
         ctx_params.n_samplers = sampler_configs.size();

examples/debug/CMakeLists.txt

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

examples/debug/README.md

@@ -0,0 +1,54 @@
+# llama.cpp/examples/debug
+
+This is a utility intended to help debug a model by registering a callback that
+logs GGML operations and tensor data. It can also store the generated logits or
+embeddings as well as the prompt and token ids for comparision with the original
+model.
+
+### Usage
+
+```shell
+llama-debug \
+  --hf-repo ggml-org/models \
+  --hf-file phi-2/ggml-model-q4_0.gguf \
+  --model phi-2-q4_0.gguf \
+  --prompt hello \
+  --save-logits \
+  --verbose
+```
+The tensor data is logged as debug and required the --verbose flag. The reason
+for this is that while useful for a model with many layers there can be a lot of
+output. You can filter the tensor names using the `--tensor-filter` option.
+
+A recommended approach is to first run without `--verbose` and see if the
+generated logits/embeddings are close to the original model. If they are not,
+then it might be required to inspect tensor by tensor and in that case it is
+useful to enable the `--verbose` flag along with `--tensor-filter` to focus on
+specific tensors.
+
+### Options
+This example supports all standard `llama.cpp` options and also accepts the
+following options:
+```console
+$ llama-debug --help
+...
+
+----- example-specific params -----
+
+--save-logits                           save final logits to files for verification (default: false)
+--logits-output-dir PATH                directory for saving logits output files (default: data)
+--tensor-filter REGEX                   filter tensor names for debug output (regex pattern, can be specified multiple times)
+```
+
+### Output Files
+
+When `--save-logits` is enabled, the following files are created in the output
+directory:
+
+* `llamacpp-<model>[-embeddings].bin`        - Binary output (logits or embeddings)
+* `llamacpp-<model>[-embeddings].txt`        - Text output (logits or embeddings, one per line)
+* `llamacpp-<model>[-embeddings]-prompt.txt` - Prompt text and token IDs
+* `llamacpp-<model>[-embeddings]-tokens.bin` - Binary token IDs for programmatic comparison
+
+These files can be compared against the original model's output to verify the
+converted model.

examples/debug/debug.cpp

@@ -0,0 +1,253 @@
+#include "debug.h"
+#include "arg.h"
+#include "common.h"
+#include "log.h"
+#include "llama.h"
+
+#include <cstdlib>
+#include <string>
+#include <vector>
+#include <filesystem>
+#include <fstream>
+#include <regex>
+
+static void print_usage(int /*argc*/, char ** argv) {
+    const std::string usage_template = R"(
+        example usage:
+
+          Print tensors:
+
+          {prog} -m model.gguf -p "Hello my name is" --verbose
+
+          The tensors to be printed can be filtered with --tensor-filter option.
+
+          Save logits/embeddings:
+
+          {prog} -m model.gguf -p "Hello my name is" --save-logits
+
+          Add --embedding to save embeddings)" "\n";
+
+    // Fix the source code indentation above that is introduced by the raw string literal.
+    std::string usage = std::regex_replace(usage_template, std::regex("\\n {8}"), "\n");
+    usage = std::regex_replace(usage, std::regex("\\{prog\\}"), argv[0]);
+    LOG("%s\n", usage.c_str());
+}
+
+static bool has_pooling(llama_context * ctx) {
+    switch (llama_pooling_type(ctx)) {
+        case LLAMA_POOLING_TYPE_NONE:
+        case LLAMA_POOLING_TYPE_UNSPECIFIED:
+            return false;
+        default:
+            return true;
+    }
+}
+
+struct output_data {
+    float *                  data_ptr    = nullptr;
+    int                      data_size   = 0;
+    std::string              type_suffix;
+    std::vector<float>       embd_norm;
+    std::string              prompt;
+    std::vector<llama_token> tokens;
+
+    output_data(llama_context * ctx, const llama_model * model, const common_params & params) {
+        const llama_vocab * vocab = llama_model_get_vocab(model);
+        const bool add_bos = llama_vocab_get_add_bos(vocab);
+
+        tokens = common_tokenize(ctx, params.prompt, add_bos);
+        prompt = params.prompt;
+
+        if (params.embedding) {
+            const int n_embd       = llama_model_n_embd_out(model);
+            const bool pooling     = has_pooling(ctx);
+            const int n_embd_count = pooling ? 1 : tokens.size();
+            const int n_floats     = n_embd * n_embd_count;
+
+            float * embd_raw = pooling ? llama_get_embeddings_seq(ctx, 0) : llama_get_embeddings(ctx);
+            if (embd_raw == nullptr) {
+                throw std::runtime_error("failed to get embeddings from the model");
+            }
+
+            LOG_DBG("pooling_enabled: %s\n", pooling ? "true" : "false");
+            LOG_DBG("n_embd: %d\n", n_embd);
+            LOG_DBG("n_floats: %d\n", n_floats);
+            LOG_DBG("n_embd_count: %d\n", n_embd_count);
+
+            data_ptr    = embd_raw;
+            data_size   = n_floats;
+            type_suffix = "-embeddings";
+
+            if (params.embd_normalize >= 0) {
+                embd_norm.resize(n_floats);
+                for (int i = 0; i < n_embd_count; i++) {
+                    common_embd_normalize(embd_raw+i*n_embd, embd_norm.data()+i*n_embd, n_embd, params.embd_normalize);
+                }
+                data_ptr = embd_norm.data();
+            }
+        } else {
+            const float * logits = llama_get_logits_ith(ctx, tokens.size() - 1);
+            const int n_logits = llama_vocab_n_tokens(vocab);
+
+            data_ptr = const_cast<float*>(logits);
+            data_size = n_logits;
+            type_suffix = "";
+        }
+    }
+};
+
+static void save_output_data(const output_data & output, const std::string & model_name, const std::string & output_dir) {
+    std::filesystem::create_directory(output_dir);
+    auto base_path = std::filesystem::path{output_dir} / ("llamacpp-" + model_name + output.type_suffix);
+
+    // Save logits/embeddings to binary file.
+    {
+        std::filesystem::path filepath{base_path.string() + ".bin"};
+        std::ofstream file{filepath, std::ios::binary};
+        if (!file) {
+            throw std::runtime_error("failed to open binary output file: " + filepath.string());
+        }
+        file.write(reinterpret_cast<const char*>(output.data_ptr), output.data_size * sizeof(float));
+        LOG("Data saved to %s\n", filepath.c_str());
+    }
+
+    // Save logits/embeddings to text file.
+    {
+        std::filesystem::path filepath{base_path.string() + ".txt"};
+        std::ofstream file{filepath};
+        if (!file) {
+            throw std::runtime_error("failed to open text output file: " + filepath.string());
+        }
+        for (int i = 0; i < output.data_size; i++) {
+            file << i << ": " << output.data_ptr[i] << '\n';
+        }
+        LOG("Data saved to %s\n", filepath.c_str());
+    }
+
+    // Save prompt and tokens to text file.
+    {
+        std::filesystem::path filepath{base_path.string() + "-prompt.txt"};
+        std::ofstream file{filepath};
+        if (!file) {
+            throw std::runtime_error("failed to open prompt output file: " + filepath.string());
+        }
+
+        file << "prompt: " << output.prompt << '\n';
+        file << "n_tokens: " << output.tokens.size() << '\n';
+
+        file << "token ids: ";
+        for (size_t i = 0; i < output.tokens.size(); i++) {
+            file << output.tokens[i];
+            if (i + 1 < output.tokens.size()) {
+                file << ", ";
+            }
+        }
+        file << '\n';
+        LOG("Prompt saved to %s\n", filepath.c_str());
+    }
+
+    // Save token ids to binary file.
+    {
+        std::filesystem::path filepath{base_path.string() + "-tokens.bin"};
+        std::ofstream file{filepath, std::ios::binary};
+        if (!file) {
+            throw std::runtime_error("failed to open tokens binary file: " + filepath.string());
+        }
+        file.write(reinterpret_cast<const char*>(output.tokens.data()), output.tokens.size() * sizeof(llama_token));
+        LOG("Tokens saved to %s\n", filepath.c_str());
+    }
+
+}
+
+static void print_tokenized_prompt(llama_context * ctx, const std::vector<llama_token> & tokens, const std::string & prompt) {
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    LOG("Model add_bos: %s\n", llama_vocab_get_add_bos(vocab) ? "true" : "false");
+    LOG("Input prompt: \"%s\"\n", prompt.c_str());
+    LOG("Token ids (%zu):\n", tokens.size());
+
+    for (auto id : tokens) {
+        std::string piece(128, '\0');
+        int n = llama_token_to_piece(vocab, id, piece.data(), piece.size(), 0, true);
+        if (n < 0) {
+            LOG_ERR("failed to convert token %d to piece\n", id);
+            continue;
+        }
+        piece.resize(n);
+        LOG("%s(%d) ", piece.c_str(), id);
+    }
+    LOG("\n");
+}
+
+static bool run(llama_context * ctx, const common_params & params) {
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    const bool add_bos = llama_vocab_get_add_bos(vocab);
+
+    std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos);
+
+    if (tokens.empty()) {
+        LOG_ERR("%s : there are not input tokens to process - (try to provide a prompt with '-p')\n", __func__);
+        return false;
+    }
+
+    if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
+        LOG_ERR("%s : failed to eval\n", __func__);
+        return false;
+    }
+
+    print_tokenized_prompt(ctx, tokens, params.prompt);
+
+    if (params.save_logits) {
+        output_data output {ctx, model, params};
+        std::filesystem::path model_path{params.model.path};
+        std::string model_name{model_path.stem().string()};
+        save_output_data(output, model_name, params.logits_output_dir);
+    }
+
+    return true;
+}
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DEBUG, print_usage)) {
+        return 1;
+    }
+
+    common_init();
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    base_callback_data cb_data(params, params.tensor_filter);
+
+    auto llama_init = common_init_from_params(params);
+
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
+
+    if (model == nullptr || ctx == nullptr) {
+        LOG_ERR("%s : failed to init\n", __func__);
+        return 1;
+    }
+
+    {
+        LOG_INF("\n");
+        LOG_INF("%s\n", common_params_get_system_info(params).c_str());
+        LOG_INF("\n");
+    }
+
+    if (!run(ctx, params)) {
+        return 1;
+    }
+
+    LOG("\n");
+    llama_perf_context_print(ctx);
+
+    llama_backend_free();
+
+    return 0;
+}

examples/diffusion/diffusion-cli.cpp

@@ -553,6 +553,7 @@ int main(int argc, char ** argv) {
     model_params.n_gpu_layers       = params.n_gpu_layers;
     model_params.devices            = params.devices.data();
     model_params.use_mmap           = params.use_mmap;
+    model_params.use_direct_io      = params.use_direct_io;
     model_params.use_mlock          = params.use_mlock;
     model_params.check_tensors      = params.check_tensors;
 

examples/embedding/embedding.cpp

@@ -33,7 +33,7 @@ static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & toke
     }
 }
 
-static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) {
+static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd_out, int embd_norm) {
     const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
 
     // clear previous kv_cache values (irrelevant for embeddings)
@@ -65,8 +65,8 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
             GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");
         }
 
-        float * out = output + embd_pos * n_embd;
-        common_embd_normalize(embd, out, n_embd, embd_norm);
+        float * out = output + embd_pos * n_embd_out;
+        common_embd_normalize(embd, out, n_embd_out, embd_norm);
     }
 }
 
@@ -252,8 +252,8 @@ int main(int argc, char ** argv) {
     }
 
     // allocate output
-    const int n_embd = llama_model_n_embd(model);
-    std::vector<float> embeddings(n_embd_count * n_embd, 0);
+    const int n_embd_out = llama_model_n_embd_out(model);
+    std::vector<float> embeddings(n_embd_count * n_embd_out, 0);
     float * emb = embeddings.data();
 
     // break into batches
@@ -267,8 +267,8 @@ int main(int argc, char ** argv) {
 
         // encode if at capacity
         if (batch.n_tokens + n_toks > n_batch || s >= n_seq_max) {
-            float * out = emb + e * n_embd;
-            batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
+            float * out = emb + e * n_embd_out;
+            batch_decode(ctx, batch, out, s, n_embd_out, params.embd_normalize);
             e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;
             s = 0;
             common_batch_clear(batch);
@@ -280,8 +280,8 @@ int main(int argc, char ** argv) {
     }
 
     // final batch
-    float * out = emb + e * n_embd;
-    batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
+    float * out = emb + e * n_embd_out;
+    batch_decode(ctx, batch, out, s, n_embd_out, params.embd_normalize);
 
     if (params.embd_out.empty()) {
         LOG("\n");
@@ -289,19 +289,19 @@ int main(int argc, char ** argv) {
         if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
             for (int j = 0; j < n_embd_count; j++) {
                 LOG("embedding %d: ", j);
-                for (int i = 0; i < std::min(3, n_embd); i++) {
+                for (int i = 0; i < std::min(3, n_embd_out); i++) {
                     if (params.embd_normalize == 0) {
-                        LOG("%6.0f ", emb[j * n_embd + i]);
+                        LOG("%6.0f ", emb[j * n_embd_out + i]);
                     } else {
-                        LOG("%9.6f ", emb[j * n_embd + i]);
+                        LOG("%9.6f ", emb[j * n_embd_out + i]);
                     }
                 }
                 LOG(" ... ");
-                for (int i = n_embd - 3; i < n_embd; i++) {
+                for (int i = n_embd_out - 3; i < n_embd_out; i++) {
                     if (params.embd_normalize == 0) {
-                        LOG("%6.0f ", emb[j * n_embd + i]);
+                        LOG("%6.0f ", emb[j * n_embd_out + i]);
                     } else {
-                        LOG("%9.6f ", emb[j * n_embd + i]);
+                        LOG("%9.6f ", emb[j * n_embd_out + i]);
                     }
                 }
                 LOG("\n");
@@ -320,9 +320,9 @@ int main(int argc, char ** argv) {
                 for (uint32_t i = 0; i < n_cls_out; i++) {
                     // NOTE: if you change this log - update the tests in ci/run.sh
                     if (n_cls_out == 1) {
-                        LOG("rerank score %d: %8.3f\n", j, emb[j * n_embd]);
+                        LOG("rerank score %d: %8.3f\n", j, emb[j * n_embd_out]);
                     } else {
-                        LOG("rerank score %d: %8.3f [%s]\n", j, emb[j * n_embd + i], cls_out_labels[i].c_str());
+                        LOG("rerank score %d: %8.3f [%s]\n", j, emb[j * n_embd_out + i], cls_out_labels[i].c_str());
                     }
                 }
             }
@@ -330,11 +330,11 @@ int main(int argc, char ** argv) {
             // print the first part of the embeddings or for a single prompt, the full embedding
             for (int j = 0; j < n_prompts; j++) {
                 LOG("embedding %d: ", j);
-                for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
+                for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd_out) : n_embd_out); i++) {
                     if (params.embd_normalize == 0) {
-                        LOG("%6.0f ", emb[j * n_embd + i]);
+                        LOG("%6.0f ", emb[j * n_embd_out + i]);
                     } else {
-                        LOG("%9.6f ", emb[j * n_embd + i]);
+                        LOG("%9.6f ", emb[j * n_embd_out + i]);
                     }
                 }
                 LOG("\n");
@@ -350,7 +350,7 @@ int main(int argc, char ** argv) {
                 LOG("\n");
                 for (int i = 0; i < n_prompts; i++) {
                     for (int j = 0; j < n_prompts; j++) {
-                        float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
+                        float sim = common_embd_similarity_cos(emb + i * n_embd_out, emb + j * n_embd_out, n_embd_out);
                         LOG("%6.2f ", sim);
                     }
                     LOG("%1.10s", prompts[i].c_str());
@@ -368,9 +368,9 @@ int main(int argc, char ** argv) {
             if (notArray) LOG("    {\n      \"object\": \"embedding\",\n      \"index\": %d,\n      \"embedding\": ",j);
             LOG("[");
             for (int i = 0;;) { // at least one iteration (n_embd > 0)
-                LOG(params.embd_normalize == 0 ? "%1.0f" : "%1.7f", emb[j * n_embd + i]);
+                LOG(params.embd_normalize == 0 ? "%1.0f" : "%1.7f", emb[j * n_embd_out + i]);
                 i++;
-                if (i < n_embd) LOG(","); else break;
+                if (i < n_embd_out) LOG(","); else break;
             }
             LOG(notArray ? "]\n    }" : "]");
             j++;
@@ -383,7 +383,7 @@ int main(int argc, char ** argv) {
             for (int i = 0;;) { // at least two iteration (n_embd_count > 1)
                 LOG("    [");
                 for (int j = 0;;) { // at least two iteration (n_embd_count > 1)
-                    float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
+                    float sim = common_embd_similarity_cos(emb + i * n_embd_out, emb + j * n_embd_out, n_embd_out);
                     LOG("%6.2f", sim);
                     j++;
                     if (j < n_embd_count) LOG(", "); else break;
@@ -397,7 +397,7 @@ int main(int argc, char ** argv) {
 
         if (notArray) LOG("\n}\n");
     } else if (params.embd_out == "raw") {
-        print_raw_embeddings(emb, n_embd_count, n_embd, model, pooling_type, params.embd_normalize);
+        print_raw_embeddings(emb, n_embd_count, n_embd_out, model, pooling_type, params.embd_normalize);
     }
 
     LOG("\n");

examples/eval-callback/CMakeLists.txt

@@ -4,12 +4,23 @@ install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
 
-set(TEST_TARGET test-eval-callback)
-if(NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x")
-        add_test(NAME ${TEST_TARGET}
-                        COMMAND llama-eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0)
-else()
-        add_test(NAME ${TEST_TARGET}
-                        COMMAND llama-eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K-be.gguf --model stories260K-be.gguf --prompt hello --seed 42 -ngl 0)
+if(LLAMA_BUILD_TESTS)
+    if(NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x")
+        set(MODEL_NAME "tinyllamas/stories15M-q4_0.gguf")
+        set(MODEL_HASH "SHA256=66967fbece6dbe97886593fdbb73589584927e29119ec31f08090732d1861739")
+    else()
+        set(MODEL_NAME "tinyllamas/stories15M-be.Q4_0.gguf")
+        set(MODEL_HASH "SHA256=9aec857937849d976f30397e97eb1cabb53eb9dcb1ce4611ba8247fb5f44c65d")
+    endif()
+    set(MODEL_DEST "${CMAKE_BINARY_DIR}/${MODEL_NAME}")
+    set(TEST_TARGET test-eval-callback)
+    add_test(NAME ${TEST_TARGET}-download-model COMMAND ${CMAKE_COMMAND}
+        -DDEST=${MODEL_DEST}
+        -DNAME=${MODEL_NAME}
+        -DHASH=${MODEL_HASH}
+        -P ${CMAKE_SOURCE_DIR}/cmake/download-models.cmake
+    )
+    set_tests_properties(${TEST_TARGET}-download-model PROPERTIES FIXTURES_SETUP ${TEST_TARGET}-download-model)
+    add_test(NAME ${TEST_TARGET} COMMAND llama-eval-callback -m "${MODEL_DEST}" --prompt hello --seed 42 -ngl 0)
+    set_tests_properties(${TEST_TARGET} PROPERTIES FIXTURES_REQUIRED ${TEST_TARGET}-download-model)
 endif()
-set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl)

examples/eval-callback/eval-callback.cpp

@@ -1,165 +1,12 @@
 #include "arg.h"
 #include "common.h"
+#include "debug.h"
 #include "log.h"
 #include "llama.h"
-#include "ggml.h"
-
-#include <cmath>
-#include <cstdio>
+#include "llama-cpp.h"
 #include <string>
 #include <vector>
 
-/**
- * This the arbitrary data which will be passed to each callback.
- * Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor.
- */
-struct callback_data {
-    std::vector<uint8_t> data;
-};
-
-static std::string ggml_ne_string(const ggml_tensor * t) {
-    std::string str;
-    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
-        str += std::to_string(t->ne[i]);
-        if (i + 1 < GGML_MAX_DIMS) {
-            str += ", ";
-        }
-    }
-    return str;
-}
-
-static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) {
-    union {
-        float f;
-        uint32_t i;
-    } u;
-    u.i = (uint32_t)h.bits << 16;
-    return u.f;
-}
-
-static float ggml_get_float_value(const uint8_t * data, ggml_type type, const size_t * nb, size_t i0, size_t i1, size_t i2, size_t i3) {
-    size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
-    float v;
-    if (type == GGML_TYPE_F16) {
-        v = ggml_fp16_to_fp32(*(const ggml_fp16_t *) &data[i]);
-    } else if (type == GGML_TYPE_F32) {
-        v = *(const float *) &data[i];
-    } else if (type == GGML_TYPE_I64) {
-        v = (float) *(const int64_t *) &data[i];
-    } else if (type == GGML_TYPE_I32) {
-        v = (float) *(const int32_t *) &data[i];
-    } else if (type == GGML_TYPE_I16) {
-        v = (float) *(const int16_t *) &data[i];
-    } else if (type == GGML_TYPE_I8) {
-        v = (float) *(const int8_t *) &data[i];
-    } else if (type == GGML_TYPE_BF16) {
-        v = ggml_compute_bf16_to_fp32(*(const ggml_bf16_t *) &data[i]);
-    } else {
-        GGML_ABORT("fatal error");
-    }
-    return v;
-}
-
-static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
-    GGML_ASSERT(n > 0);
-    float sum = 0;
-    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
-        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
-            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
-                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
-                    const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
-                    sum += v;
-                }
-            }
-        }
-    }
-    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
-        LOG("                                     [\n");
-        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
-            if (i2 == n && ne[2] > 2*n) {
-                LOG("                                      ..., \n");
-                i2 = ne[2] - n;
-            }
-            LOG("                                      [\n");
-            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
-                if (i1 == n && ne[1] > 2*n) {
-                    LOG("                                       ..., \n");
-                    i1 = ne[1] - n;
-                }
-                LOG("                                       [");
-                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
-                    if (i0 == n && ne[0] > 2*n) {
-                        LOG("..., ");
-                        i0 = ne[0] - n;
-                    }
-                    const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
-                    LOG("%12.4f", v);
-                    if (i0 < ne[0] - 1) LOG(", ");
-                }
-                LOG("],\n");
-            }
-            LOG("                                      ],\n");
-        }
-        LOG("                                     ]\n");
-        LOG("                                     sum = %f\n", sum);
-    }
-
-    // TODO: make this abort configurable/optional?
-    if (std::isnan(sum)) {
-        LOG_ERR("encountered NaN - aborting\n");
-        exit(0);
-    }
-}
-
-/**
- * GGML operations callback during the graph execution.
- *
- * @param t current tensor
- * @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor
- *            if we return true, a follow-up call will be made with ask=false in which we can do the actual collection.
- *            see ggml_backend_sched_eval_callback
- * @param user_data user data to pass at each call back
- * @return true to receive data or continue the graph, false otherwise
- */
-static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
-    auto * cb_data = (callback_data *) user_data;
-
-    const struct ggml_tensor * src0 = t->src[0];
-    const struct ggml_tensor * src1 = t->src[1];
-
-    if (ask) {
-        return true; // Always retrieve data
-    }
-
-    char src1_str[128] = {0};
-    if (src1) {
-        snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
-    }
-
-    LOG("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__,
-         t->name, ggml_type_name(t->type), ggml_op_desc(t),
-         src0->name, ggml_ne_string(src0).c_str(),
-         src1 ? src1_str : "",
-         ggml_ne_string(t).c_str());
-
-
-    // copy the data from the GPU memory if needed
-    const bool is_host = ggml_backend_buffer_is_host(t->buffer);
-
-    if (!is_host) {
-        auto n_bytes = ggml_nbytes(t);
-        cb_data->data.resize(n_bytes);
-        ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
-    }
-
-    if (!ggml_is_quantized(t->type)) {
-        uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
-        ggml_print_tensor(data, t->type, t->ne, t->nb, 3);
-    }
-
-    return true;
-}
-
 static bool run(llama_context * ctx, const common_params & params) {
     const llama_model * model = llama_get_model(ctx);
     const llama_vocab * vocab = llama_model_get_vocab(model);
@@ -182,7 +29,7 @@ static bool run(llama_context * ctx, const common_params & params) {
 }
 
 int main(int argc, char ** argv) {
-    callback_data cb_data;
+    base_callback_data cb_data;
 
     common_params params;
 
@@ -197,7 +44,7 @@ int main(int argc, char ** argv) {
 
     // pass the callback to the backend scheduler
     // it will be executed for each node during the graph computation
-    params.cb_eval = ggml_debug;
+    params.cb_eval = common_debug_cb_eval<false>;
     params.cb_eval_user_data = &cb_data;
     params.warmup = false;
 

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

@@ -26,7 +26,7 @@ android {
 
                 arguments += "-DBUILD_SHARED_LIBS=ON"
                 arguments += "-DLLAMA_BUILD_COMMON=ON"
-                arguments += "-DLLAMA_CURL=OFF"
+                arguments += "-DLLAMA_OPENSSL=OFF"
 
                 arguments += "-DGGML_NATIVE=OFF"
                 arguments += "-DGGML_BACKEND_DL=ON"

examples/model-conversion/Makefile

@@ -61,7 +61,7 @@ causal-run-converted-model:
 	@CONVERTED_MODEL="$(CONVERTED_MODEL)" ./scripts/causal/run-converted-model.sh
 
 causal-verify-logits: causal-run-original-model causal-run-converted-model
-	@./scripts/causal/compare-logits.py
+	@MODEL_PATH="$(MODEL_PATH)" ./scripts/causal/compare-logits.py
 	@MODEL_PATH="$(MODEL_PATH)" ./scripts/utils/check-nmse.py -m ${MODEL_PATH}
 
 causal-run-original-embeddings:
@@ -138,16 +138,13 @@ embedding-run-original-model-st: embedding-run-original-model
 embedding-run-converted-model:
 	@./scripts/embedding/run-converted-model.sh $(CONVERTED_EMBEDDING_MODEL) \
 	$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") \
-	$(if $(USE_POOLING),--pooling)
-
-embedding-run-converted-model-st: USE_POOLING=1
-embedding-run-converted-model-st: embedding-run-converted-model
+	$(if $(EMBD_NORMALIZE),--embd-normalize "$(EMBD_NORMALIZE)")
 
 embedding-verify-logits: embedding-run-original-model embedding-run-converted-model
 	@./scripts/embedding/compare-embeddings-logits.sh \
 	$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
 
-embedding-verify-logits-st: embedding-run-original-model-st embedding-run-converted-model-st
+embedding-verify-logits-st: embedding-run-original-model-st embedding-run-converted-model
 	@./scripts/embedding/compare-embeddings-logits.sh \
 	$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
 

examples/model-conversion/README.md

@@ -198,14 +198,13 @@ model, and the other is a text file which allows for manual visual inspection.
 
 #### Using SentenceTransformer with numbered layers
 For models that have numbered SentenceTransformer layers (01_Pooling, 02_Dense,
-03_Dense, 04_Normalize), use the `-st` targets to apply all these layers:
+03_Dense, 04_Normalize), these will be applied automatically when running the
+converted model but currently there is a separate target to run the original
+version:
 
 ```console
 # Run original model with SentenceTransformer (applies all numbered layers)
 (venv) $ make embedding-run-original-model-st
-
-# Run converted model with pooling enabled
-(venv) $ make embedding-run-converted-model-st
 ```
 
 This will use the SentenceTransformer library to load and run the model, which
@@ -213,6 +212,17 @@ automatically applies all the numbered layers in the correct order. This is
 particularly useful when comparing with models that should include these
 additional transformation layers beyond just the base model output.
 
+The type of normalization can be specified for the converted model but is not
+strictly necessary as the verification uses cosine similarity and the magnitude
+of the output vectors does not affect this. But the normalization type can be
+specified as an argument to the target which might be useful for manual
+inspection:
+```console
+(venv) $ make embedding-verify-logits-st EMBD_NORMALIZE=1
+```
+The original model will apply the normalization according to the normalization
+layer specified in the modules.json configuration file.
+
 ### Model conversion
 After updates have been made to [gguf-py](../../gguf-py) to add support for the
 new model the model can be converted to GGUF format using the following command:

examples/model-conversion/logits.cpp

@@ -1,268 +0,0 @@
-#include "llama.h"
-#include "common.h"
-
-
-#include <cstdio>
-#include <cstring>
-#include <string>
-#include <vector>
-#include <ctype.h>
-#include <filesystem>
-
-static void print_usage(int, char ** argv) {
-    printf("\nexample usage:\n");
-    printf("\n    %s -m model.gguf [-ngl n_gpu_layers] -embd-mode [-pooling] [-embd-norm <norm>] [prompt]\n", argv[0]);
-    printf("\n");
-    printf("  -embd-norm: normalization type for pooled embeddings (default: 2)\n");
-    printf("              -1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm\n");
-    printf("\n");
-}
-
-int main(int argc, char ** argv) {
-    std::string model_path;
-    std::string prompt = "Hello, my name is";
-    int ngl = 0;
-    bool embedding_mode = false;
-    bool pooling_enabled = false;
-    int32_t embd_norm = 2;  // (-1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm)
-
-    {
-        int i = 1;
-        for (; i < argc; i++) {
-            if (strcmp(argv[i], "-m") == 0) {
-                if (i + 1 < argc) {
-                    model_path = argv[++i];
-                } else {
-                    print_usage(argc, argv);
-                    return 1;
-                }
-            } else if (strcmp(argv[i], "-ngl") == 0) {
-                if (i + 1 < argc) {
-                    try {
-                        ngl = std::stoi(argv[++i]);
-                    } catch (...) {
-                        print_usage(argc, argv);
-                        return 1;
-                    }
-                } else {
-                    print_usage(argc, argv);
-                    return 1;
-                }
-            } else if (strcmp(argv[i], "-embd-mode") == 0) {
-                embedding_mode = true;
-            } else if (strcmp(argv[i], "-pooling") == 0) {
-                pooling_enabled = true;
-            } else if (strcmp(argv[i], "-embd-norm") == 0) {
-                if (i + 1 < argc) {
-                    try {
-                        embd_norm = std::stoi(argv[++i]);
-                    } catch (...) {
-                        print_usage(argc, argv);
-                        return 1;
-                    }
-                } else {
-                    print_usage(argc, argv);
-                    return 1;
-                }
-            } else {
-                // prompt starts here
-                break;
-            }
-        }
-
-        if (model_path.empty()) {
-            print_usage(argc, argv);
-            return 1;
-        }
-
-        if (i < argc) {
-            prompt = argv[i++];
-            for (; i < argc; i++) {
-                prompt += " ";
-                prompt += argv[i];
-            }
-        }
-    }
-
-    ggml_backend_load_all();
-    llama_model_params model_params = llama_model_default_params();
-    model_params.n_gpu_layers = ngl;
-
-    llama_model * model = llama_model_load_from_file(model_path.c_str(), model_params);
-
-    if (model == NULL) {
-        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
-        return 1;
-    }
-
-    // Extract basename from model_path
-    const char * basename = strrchr(model_path.c_str(), '/');
-    basename = (basename == NULL) ? model_path.c_str() : basename + 1;
-
-    char model_name[256];
-    strncpy(model_name, basename, 255);
-    model_name[255] = '\0';
-
-    char * dot = strrchr(model_name, '.');
-    if (dot != NULL && strcmp(dot, ".gguf") == 0) {
-        *dot = '\0';
-    }
-    printf("Model name: %s\n", model_name);
-
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-    const int n_prompt = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, true, true);
-
-    std::vector<llama_token> prompt_tokens(n_prompt);
-    if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true, true) < 0) {
-        fprintf(stderr, "%s: error: failed to tokenize the prompt\n", __func__);
-        return 1;
-    }
-
-    llama_context_params ctx_params = llama_context_default_params();
-    ctx_params.n_ctx = n_prompt;
-    ctx_params.n_batch = n_prompt;
-    ctx_params.no_perf = false;
-    if (embedding_mode) {
-        ctx_params.embeddings = true;
-        ctx_params.pooling_type = pooling_enabled ? LLAMA_POOLING_TYPE_MEAN : LLAMA_POOLING_TYPE_NONE;
-        ctx_params.n_ubatch = ctx_params.n_batch;
-    }
-
-    llama_context * ctx = llama_init_from_model(model, ctx_params);
-    if (ctx == NULL) {
-        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
-        return 1;
-    }
-
-    printf("Input prompt: \"%s\"\n", prompt.c_str());
-    printf("Tokenized prompt (%d tokens): ", n_prompt);
-    for (auto id : prompt_tokens) {
-        char buf[128];
-        int n = llama_token_to_piece(vocab, id, buf, sizeof(buf), 0, true);
-        if (n < 0) {
-            fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__);
-            return 1;
-        }
-        std::string s(buf, n);
-        printf("%s (%d)", s.c_str(), id);
-    }
-    printf("\n");
-
-    llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
-
-    if (llama_decode(ctx, batch)) {
-        fprintf(stderr, "%s : failed to eval\n", __func__);
-        return 1;
-    }
-
-    float * data_ptr;
-    int data_size;
-    const char * type;
-    std::vector<float> embd_out;
-
-    if (embedding_mode) {
-        const int n_embd = llama_model_n_embd(model);
-        const int n_embd_count = pooling_enabled ? 1 : batch.n_tokens;
-        const int n_embeddings = n_embd * n_embd_count;
-        float * embeddings;
-        type = "-embeddings";
-
-        if (llama_pooling_type(ctx) != LLAMA_POOLING_TYPE_NONE) {
-            embeddings = llama_get_embeddings_seq(ctx, 0);
-            embd_out.resize(n_embeddings);
-            printf("Normalizing embeddings using norm: %d\n", embd_norm);
-            common_embd_normalize(embeddings, embd_out.data(), n_embeddings, embd_norm);
-            embeddings = embd_out.data();
-        } else {
-            embeddings = llama_get_embeddings(ctx);
-        }
-
-        printf("Embedding dimension: %d\n", n_embd);
-        printf("\n");
-
-        // Print embeddings in the specified format
-        for (int j = 0; j < n_embd_count; j++) {
-            printf("embedding %d: ", j);
-
-            // Print first 3 values
-            for (int i = 0; i < 3 && i < n_embd; i++) {
-                printf("%9.6f ", embeddings[j * n_embd + i]);
-            }
-
-            printf(" ... ");
-
-            // Print last 3 values
-            for (int i = n_embd - 3; i < n_embd; i++) {
-                if (i >= 0) {
-                    printf("%9.6f ", embeddings[j * n_embd + i]);
-                }
-            }
-
-            printf("\n");
-        }
-        printf("\n");
-
-        printf("Embeddings size: %d\n", n_embeddings);
-
-        data_ptr = embeddings;
-        data_size = n_embeddings;
-    } else {
-        float * logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
-        const int n_logits = llama_vocab_n_tokens(vocab);
-        type = "";
-        printf("Vocab size: %d\n", n_logits);
-
-        data_ptr = logits;
-        data_size = n_logits;
-    }
-
-    std::filesystem::create_directory("data");
-
-    // Save data to binary file
-    char bin_filename[512];
-    snprintf(bin_filename, sizeof(bin_filename), "data/llamacpp-%s%s.bin", model_name, type);
-    printf("Saving data to %s\n", bin_filename);
-
-    FILE * f = fopen(bin_filename, "wb");
-    if (f == NULL) {
-        fprintf(stderr, "%s: error: failed to open binary output file\n", __func__);
-        return 1;
-    }
-    fwrite(data_ptr, sizeof(float), data_size, f);
-    fclose(f);
-
-    // Also save as text for debugging
-    char txt_filename[512];
-    snprintf(txt_filename, sizeof(txt_filename), "data/llamacpp-%s%s.txt", model_name, type);
-    f = fopen(txt_filename, "w");
-    if (f == NULL) {
-        fprintf(stderr, "%s: error: failed to open text output file\n", __func__);
-        return 1;
-    }
-    for (int i = 0; i < data_size; i++) {
-        fprintf(f, "%d: %.6f\n", i, data_ptr[i]);
-    }
-    fclose(f);
-
-    if (!embedding_mode) {
-        printf("First 10 logits: ");
-        for (int i = 0; i < 10 && i < data_size; i++) {
-            printf("%.6f ", data_ptr[i]);
-        }
-        printf("\n");
-
-        printf("Last 10 logits: ");
-        for (int i = data_size - 10; i < data_size; i++) {
-            if (i >= 0) printf("%.6f ", data_ptr[i]);
-        }
-        printf("\n\n");
-    }
-
-    printf("Data saved to %s\n", bin_filename);
-    printf("Data saved to %s\n", txt_filename);
-
-    llama_free(ctx);
-    llama_model_free(model);
-
-    return 0;
-}

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

@@ -3,10 +3,11 @@
 import sys
 import numpy as np
 from pathlib import Path
+import os
 
 # Add utils directory to path for direct script execution
 sys.path.insert(0, str(Path(__file__).parent.parent / "utils"))
-from common import get_model_name_from_env_path  # type: ignore[import-not-found]
+from common import get_model_name_from_env_path, compare_tokens, exit_with_warning  # type: ignore[import-not-found]
 
 def quick_logits_check(pytorch_file, llamacpp_file):
     """Lightweight sanity check before NMSE"""
@@ -38,6 +39,7 @@ def quick_logits_check(pytorch_file, llamacpp_file):
     return True
 
 def main():
+    model_path = os.environ.get('MODEL_PATH')
     model_name = get_model_name_from_env_path('MODEL_PATH')
     data_dir = Path("data")
     pytorch_file = data_dir / f"pytorch-{model_name}.bin"
@@ -58,6 +60,12 @@ def main():
 
     print("Checked all required files were found. Proceeding...\n")
 
+    # Verify tokens as they are a prerequisite for logits comparison.
+    print("🔍 Token Comparison Check")
+    print("=" * 40)
+    if not compare_tokens(f"pytorch-{model_name}", f"llamacpp-{llamacpp_model_name}"):
+        exit_with_warning("\n❌ Token mismatch detected", model_path)
+    print()
 
     print("🔍 GGML Model Validation for model ", model_name)
     print("=" * 40)
@@ -73,8 +81,7 @@ def main():
         print("       Ok to proceed with NMSE check...")
         sys.exit(0)
     else:
-        print(f"❌ NOK: Top 10 predictions don't match - generation will differ")
-        sys.exit(1)
+        exit_with_warning(f"❌ NOK: Top 10 predictions don't match - generation will differ", model_path)
 
 if __name__ == "__main__":
     main()

examples/model-conversion/scripts/causal/modelcard.template

@@ -7,7 +7,7 @@ base_model:
 Recommended way to run this model:
 
 ```sh
-llama-server -hf {namespace}/{model_name}-GGUF -c 0
+llama-server -hf {namespace}/{model_name}-GGUF
 ```
 
 Then, access http://localhost:8080

examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py

@@ -67,7 +67,7 @@ with torch.no_grad():
     last_hidden_states = outputs.hidden_states[-1]
 
     # Get embeddings for all tokens
-    token_embeddings = last_hidden_states[0].cpu().numpy()  # Remove batch dimension
+    token_embeddings = last_hidden_states[0].float().cpu().numpy()  # Remove batch dimension
 
     print(f"Hidden states shape: {last_hidden_states.shape}")
     print(f"Token embeddings shape: {token_embeddings.shape}")

examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh

@@ -13,6 +13,6 @@ if [ -z "$CONVERTED_MODEL" ]; then
     exit 1
 fi
 
-cmake --build ../../build --target llama-logits -j8
+cmake --build ../../build --target llama-debug -j8
 
-../../build/bin/llama-logits -m $CONVERTED_MODEL -embd-mode "Hello world today"
+../../build/bin/llama-debug -m $CONVERTED_MODEL --embedding -p "Hello world today" --save-logits

examples/model-conversion/scripts/causal/run-converted-model.sh

@@ -21,6 +21,6 @@ fi
 echo $CONVERTED_MODEL
 echo $MODEL_TESTING_PROMPT
 
-cmake --build ../../build --target llama-logits -j8
+cmake --build ../../build --target llama-debug -j8
 
-../../build/bin/llama-logits -m "$CONVERTED_MODEL" "$MODEL_TESTING_PROMPT"
+../../build/bin/llama-debug -m "$CONVERTED_MODEL" -p "$MODEL_TESTING_PROMPT" --save-logits

examples/model-conversion/scripts/causal/run-org-model.py

@@ -7,12 +7,11 @@ import importlib
 import torch
 import numpy as np
 
-from pathlib import Path
 from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForImageTextToText, AutoConfig
 
 # Add parent directory to path for imports
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
-from utils.common import debug_hook
+from utils.common import debug_hook, save_output_data
 
 def parse_arguments():
     parser = argparse.ArgumentParser(description="Process model with specified path")
@@ -126,6 +125,7 @@ def main():
     device = next(model.parameters()).device
     prompt = get_prompt(args)
     input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+    token_ids = input_ids[0].cpu().tolist()
 
     print(f"Input tokens: {input_ids}")
     print(f"Input text: {repr(prompt)}")
@@ -151,19 +151,6 @@ def main():
         print(f"Last token logits shape: {last_logits.shape}")
         print(f"Vocab size: {len(last_logits)}")
 
-        data_dir = Path("data")
-        data_dir.mkdir(exist_ok=True)
-        bin_filename = data_dir / f"pytorch-{model_name}.bin"
-        txt_filename = data_dir / f"pytorch-{model_name}.txt"
-
-        # Save to file for comparison
-        last_logits.astype(np.float32).tofile(bin_filename)
-
-        # Also save as text file for easy inspection
-        with open(txt_filename, "w") as f:
-            for i, logit in enumerate(last_logits):
-                f.write(f"{i}: {logit:.6f}\n")
-
         # Print some sample logits for quick verification
         print(f"First 10 logits: {last_logits[:10]}")
         print(f"Last 10 logits: {last_logits[-10:]}")
@@ -175,8 +162,7 @@ def main():
             token = tokenizer.decode([idx])
             print(f"  Token {idx} ({repr(token)}): {last_logits[idx]:.6f}")
 
-        print(f"Saved bin logits to: {bin_filename}")
-        print(f"Saved txt logist to: {txt_filename}")
+        save_output_data(last_logits, token_ids, prompt, model_name)
 
 if __name__ == "__main__":
     main()

examples/model-conversion/scripts/embedding/run-converted-model.sh

@@ -5,7 +5,7 @@ set -e
 # Parse command line arguments
 CONVERTED_MODEL=""
 PROMPTS_FILE=""
-USE_POOLING=""
+EMBD_NORMALIZE="2"
 
 while [[ $# -gt 0 ]]; do
     case $1 in
@@ -13,9 +13,9 @@ while [[ $# -gt 0 ]]; do
             PROMPTS_FILE="$2"
             shift 2
             ;;
-        --pooling)
-            USE_POOLING="1"
-            shift
+        --embd-normalize)
+            EMBD_NORMALIZE="$2"
+            shift 2
             ;;
         *)
             if [ -z "$CONVERTED_MODEL" ]; then
@@ -50,10 +50,5 @@ fi
 
 echo $CONVERTED_MODEL
 
-cmake --build ../../build --target llama-logits -j8
-# TODO: update logits.cpp to accept a --file/-f option for the prompt
-if [ -n "$USE_POOLING" ]; then
-    ../../build/bin/llama-logits -m "$CONVERTED_MODEL" -embd-mode -pooling "$PROMPT"
-else
-    ../../build/bin/llama-logits -m "$CONVERTED_MODEL" -embd-mode "$PROMPT"
-fi
+cmake --build ../../build --target llama-debug -j8
+../../build/bin/llama-debug -m "$CONVERTED_MODEL" --embedding -p "$PROMPT" --save-logits --embd-normalize $EMBD_NORMALIZE

examples/model-conversion/scripts/embedding/run-original-model.py

@@ -3,13 +3,15 @@
 import argparse
 import os
 import sys
-import numpy as np
 import importlib
-from pathlib import Path
 
 from transformers import AutoTokenizer, AutoConfig, AutoModel
 import torch
 
+# Add parent directory to path for imports
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+from utils.common import save_output_data
+
 
 def parse_arguments():
     parser = argparse.ArgumentParser(description='Run original embedding model')
@@ -169,6 +171,7 @@ def main():
                 return_tensors="pt"
             )
             tokens = encoded['input_ids'][0]
+            token_ids = tokens.cpu().tolist()
             token_strings = tokenizer.convert_ids_to_tokens(tokens)
             for i, (token_id, token_str) in enumerate(zip(tokens, token_strings)):
                 print(f"{token_id:6d} -> '{token_str}'")
@@ -185,6 +188,7 @@ def main():
             )
 
             tokens = encoded['input_ids'][0]
+            token_ids = tokens.cpu().tolist()
             token_strings = tokenizer.convert_ids_to_tokens(tokens)
             for i, (token_id, token_str) in enumerate(zip(tokens, token_strings)):
                 print(f"{token_id:6d} -> '{token_str}'")
@@ -228,24 +232,11 @@ def main():
 
         print()
 
-        data_dir = Path("data")
-        data_dir.mkdir(exist_ok=True)
-        bin_filename = data_dir / f"pytorch-{model_name}-embeddings.bin"
-        txt_filename = data_dir / f"pytorch-{model_name}-embeddings.txt"
-
         flattened_embeddings = all_embeddings.flatten()
-        flattened_embeddings.astype(np.float32).tofile(bin_filename)
-
-        with open(txt_filename, "w") as f:
-            idx = 0
-            for j in range(n_embd_count):
-                for value in all_embeddings[j]:
-                    f.write(f"{idx}: {value:.6f}\n")
-                    idx += 1
         print(f"Total values: {len(flattened_embeddings)} ({n_embd_count} embeddings × {n_embd} dimensions)")
         print("")
-        print(f"Saved bin embeddings to: {bin_filename}")
-        print(f"Saved txt embeddings to: {txt_filename}")
+
+        save_output_data(flattened_embeddings, token_ids, prompt_text, model_name, type_suffix="-embeddings")
 
 
 if __name__ == "__main__":

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

@@ -3,6 +3,11 @@
 import os
 import sys
 import torch
+import transformers
+import json
+import textwrap
+import numpy as np
+from pathlib import Path
 
 
 def get_model_name_from_env_path(env_path_name):
@@ -148,3 +153,147 @@ def setup_rope_debug(model_module_path: str, function_name: str = "apply_rotary_
     # Patch it
     setattr(module, function_name, debug_rope)
     print(f"RoPE debug patching applied to {model_module_path}.{function_name}")
+
+
+def save_output_data(data, tokens, prompt, model_name, type_suffix="", output_dir="data"):
+    """
+    Save output data (logits/embeddings), tokens, and prompt to files.
+
+    Args:
+        data:        numpy array of floats (logits or embeddings)
+        tokens:      list or array of token IDs
+        prompt:      string containing the input prompt
+        model_name:  name of the model
+        type_suffix: optional suffix like "-embeddings" (default: "")
+        output_dir:  directory to save files (default: "data")
+
+    Creates the following files in output_dir:
+        - pytorch-{model_name}{type_suffix}.bin
+        - pytorch-{model_name}{type_suffix}.txt
+        - pytorch-{model_name}{type_suffix}-prompt.txt
+        - pytorch-{model_name}{type_suffix}-tokens.bin
+    """
+    data_dir = Path(output_dir)
+    data_dir.mkdir(exist_ok=True)
+    base_path = data_dir / f"pytorch-{model_name}{type_suffix}"
+
+    # Convert and flatten logits/embeddings
+    data = data.cpu().numpy() if isinstance(data, torch.Tensor) else np.asarray(data)
+    data = data.flatten() if data.ndim > 1 else data
+
+    # Save logits/embedding files
+    data.astype(np.float32).tofile(f"{base_path}.bin")
+    print(f"Data saved to {base_path}.bin")
+
+    with open(f"{base_path}.txt", "w") as f:
+        f.writelines(f"{i}: {value:.6f}\n" for i, value in enumerate(data))
+    print(f"Data saved to {base_path}.txt")
+
+    # Convert and flatten tokens
+    tokens = tokens.cpu().numpy() if isinstance(tokens, torch.Tensor) else np.asarray(tokens)
+    tokens = tokens.flatten() if tokens.ndim > 1 else tokens
+
+    # Save token binary file
+    tokens.astype(np.int32).tofile(f"{base_path}-tokens.bin")
+    print(f"Tokens saved to {base_path}-tokens.bin")
+
+    # Save prompt file
+    with open(f"{base_path}-prompt.txt", "w") as f:
+        f.write(f"prompt: {prompt}\n")
+        f.write(f"n_tokens: {len(tokens)}\n")
+        f.write(f"token ids: {', '.join(str(int(tid)) for tid in tokens)}\n")
+    print(f"Prompt saved to {base_path}-prompt.txt")
+
+
+def compare_tokens(original, converted, type_suffix="", output_dir="data"):
+    data_dir = Path(output_dir)
+
+    # Read tokens from both models
+    tokens1_file = data_dir / f"{original}{type_suffix}-tokens.bin"
+    tokens2_file = data_dir / f"{converted}{type_suffix}-tokens.bin"
+
+    if not tokens1_file.exists():
+        print(f"Error: Token file not found: {tokens1_file}")
+        return False
+
+    if not tokens2_file.exists():
+        print(f"Error: Token file not found: {tokens2_file}")
+        return False
+
+    tokens1 = np.fromfile(tokens1_file, dtype=np.int32)
+    tokens2 = np.fromfile(tokens2_file, dtype=np.int32)
+
+    print(f"\nComparing tokens between:")
+    print(f"  Original : {original} ({len(tokens1)} tokens)")
+    print(f"  Converted: {converted} ({len(tokens2)} tokens)")
+
+    if len(tokens1) != len(tokens2):
+        print(f"\n❌ Token count mismatch: {len(tokens1)} vs {len(tokens2)}")
+        return False
+
+    if np.array_equal(tokens1, tokens2):
+        print(f"\n✅ All {len(tokens1)} tokens match!")
+        return True
+
+    mismatches = np.where(tokens1 != tokens2)[0]
+    print(f"\n❌ Found {len(mismatches)} mismatched tokens:")
+
+    num_to_show = min(len(mismatches), 10)
+    for idx in mismatches[:num_to_show]:
+        print(f"  Position {idx}: {tokens1[idx]} vs {tokens2[idx]}")
+
+    if len(mismatches) > num_to_show:
+        print(f"  ... and {len(mismatches) - num_to_show} more mismatches")
+
+    return False
+
+
+def show_version_warning(current_version, model_version):
+    if not model_version:
+        return False
+
+    try:
+        from packaging.version import parse, InvalidVersion
+        try:
+            return parse(current_version) < parse(model_version)
+        except InvalidVersion:
+            return current_version != model_version
+    except ImportError:
+        return current_version != model_version
+
+def get_model_transformers_version(model_path):
+    if not model_path:
+        return None
+
+    config_path = Path(model_path) / "config.json"
+    if not config_path.is_file():
+        return None
+
+    try:
+        with open(config_path, "r", encoding="utf-8") as f:
+            config = json.load(f)
+        return config.get("transformers_version")
+    except (IOError, json.JSONDecodeError) as e:
+        print(f"Warning: Could not read or parse {config_path}: {e}", file=sys.stderr)
+        return None
+
+def exit_with_warning(message, model_path):
+    print(message)
+
+    if model_path and transformers is not None:
+        model_transformers_version = get_model_transformers_version(model_path)
+        transformers_version       = transformers.__version__
+        if show_version_warning(transformers_version, model_transformers_version):
+            warning_message = f"""
+                =====================================================================
+                Verification failure might be due to a transformers version mismatch:
+
+                Current transformers version: {transformers_version}
+                Model's required version    : {model_transformers_version}
+
+                Consider installing the version specified by the model's config:
+                pip install transformers=={model_transformers_version}
+                =====================================================================
+            """
+            print(textwrap.dedent(warning_message))
+    sys.exit(1)

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

@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+
+import argparse
+import sys
+from common import compare_tokens  # type: ignore
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(
+        description='Compare tokens between two models',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  %(prog)s pytorch-gemma-3-270m-it llamacpp-gemma-3-270m-it-bf16
+        """
+    )
+    parser.add_argument(
+        'original',
+        help='Original model name'
+    )
+    parser.add_argument(
+        'converted',
+        help='Converted model name'
+    )
+    parser.add_argument(
+        '-s', '--suffix',
+        default='',
+        help='Type suffix (e.g., "-embeddings")'
+    )
+    parser.add_argument(
+        '-d', '--data-dir',
+        default='data',
+        help='Directory containing token files (default: data)'
+    )
+    parser.add_argument(
+        '-v', '--verbose',
+        action='store_true',
+        help='Print prompts from both models'
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = parse_arguments()
+
+    if args.verbose:
+        from pathlib import Path
+        data_dir = Path(args.data_dir)
+
+        prompt1_file = data_dir / f"{args.original}{args.suffix}-prompt.txt"
+        prompt2_file = data_dir / f"{args.converted}{args.suffix}-prompt.txt"
+
+        if prompt1_file.exists():
+            print(f"\nOriginal model prompt ({args.original}):")
+            print(f"  {prompt1_file.read_text().strip()}")
+
+        if prompt2_file.exists():
+            print(f"\nConverted model prompt ({args.converted}):")
+            print(f"  {prompt2_file.read_text().strip()}")
+
+        print()
+
+    result = compare_tokens(
+        args.original,
+        args.converted,
+        type_suffix=args.suffix,
+        output_dir=args.data_dir
+    )
+
+    # Enable the script to be used in shell scripts so that they can check
+    # the exit code for success/failure.
+    sys.exit(0 if result else 1)
+
+
+if __name__ == "__main__":
+    main()

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

@@ -4,8 +4,10 @@ import numpy as np
 import argparse
 import os
 import importlib
+from pathlib import Path
 
 from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, AutoModel
+from common import compare_tokens, exit_with_warning  # type: ignore[import-not-found]
 
 unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
 
@@ -157,9 +159,24 @@ def main():
     else:
         prompt = args.prompt
 
+    python_emb_path = Path(args.python_embeddings)
+    cpp_emb_path = Path(args.cpp_embeddings)
+
+    # Extract base names (e.g., "pytorch-model-name-embeddings.bin" -> "pytorch-model-name")
+    python_model_name = python_emb_path.stem.replace("-embeddings", "")
+    cpp_model_name = cpp_emb_path.stem.replace("-embeddings", "")
+
     print("Semantic Similarity Test Between Python and llama.cpp Embedding Models")
     print("=" * 70)
 
+    # First verify tokens match before comparing embeddings
+    print("\n🔍 Token Comparison Check")
+    print("=" * 70)
+    data_dir = python_emb_path.parent
+    if not compare_tokens(python_model_name, cpp_model_name, type_suffix="-embeddings", output_dir=str(data_dir)):
+        exit_with_warning("\n❌ Token mismatch detected", args.model_path)
+    print()
+
     # Single prompt detailed comparison
     print(f"\nTesting with prompt: '{prompt}'")
 
@@ -219,7 +236,7 @@ def main():
     elif avg_cross_sim > 0.70:
         print("⚠️  FAIR: Models have some differences")
     else:
-        print("❌ POOR: Models are significantly different")
+        exit_with_warning("❌ POOR: Models are significantly different", args.model_path)
 
 if __name__ == "__main__":
     main()

examples/retrieval/retrieval.cpp

@@ -217,8 +217,8 @@ int main(int argc, char ** argv) {
     struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
 
     // allocate output
-    const int n_embd = llama_model_n_embd(model);
-    std::vector<float> embeddings(n_chunks * n_embd, 0);
+    const int n_embd_out = llama_model_n_embd_out(model);
+    std::vector<float> embeddings(n_chunks * n_embd_out, 0);
     float * emb = embeddings.data();
 
     // break into batches
@@ -232,8 +232,8 @@ int main(int argc, char ** argv) {
 
         // encode if at capacity
         if (batch.n_tokens + n_toks > n_batch || s >= llama_n_seq_max(ctx)) {
-            float * out = emb + p * n_embd;
-            batch_process(ctx, batch, out, s, n_embd);
+            float * out = emb + p * n_embd_out;
+            batch_process(ctx, batch, out, s, n_embd_out);
             common_batch_clear(batch);
             p += s;
             s = 0;
@@ -245,12 +245,12 @@ int main(int argc, char ** argv) {
     }
 
     // final batch
-    float * out = emb + p * n_embd;
-    batch_process(ctx, batch, out, s, n_embd);
+    float * out = emb + p * n_embd_out;
+    batch_process(ctx, batch, out, s, n_embd_out);
 
     // save embeddings to chunks
     for (int i = 0; i < n_chunks; i++) {
-        chunks[i].embedding = std::vector<float>(emb + i * n_embd, emb + (i + 1) * n_embd);
+        chunks[i].embedding = std::vector<float>(emb + i * n_embd_out, emb + (i + 1) * n_embd_out);
         // clear tokens as they are no longer needed
         chunks[i].tokens.clear();
     }
@@ -266,8 +266,8 @@ int main(int argc, char ** argv) {
 
         batch_add_seq(query_batch, query_tokens, 0);
 
-        std::vector<float> query_emb(n_embd, 0);
-        batch_process(ctx, query_batch, query_emb.data(), 1, n_embd);
+        std::vector<float> query_emb(n_embd_out, 0);
+        batch_process(ctx, query_batch, query_emb.data(), 1, n_embd_out);
 
         common_batch_clear(query_batch);
 
@@ -275,7 +275,7 @@ int main(int argc, char ** argv) {
         {
             std::vector<std::pair<int, float>> similarities;
             for (int i = 0; i < n_chunks; i++) {
-                float sim = common_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd);
+                float sim = common_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd_out);
                 similarities.push_back(std::make_pair(i, sim));
             }
 

examples/sycl/build.sh

@@ -8,10 +8,10 @@ cd build
 source /opt/intel/oneapi/setvars.sh
 
 #for FP16
-#cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DLLAMA_CURL=OFF # faster for long-prompt inference
+#cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DLLAMA_OPENSSL=OFF # faster for long-prompt inference
 
 #for FP32
-cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=OFF
+cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_OPENSSL=OFF
 
 #build example/main
 #cmake --build . --config Release --target main

examples/sycl/win-build-sycl.bat

@@ -13,10 +13,10 @@ if %errorlevel% neq 0 goto ERROR
 
 ::  for FP16
 ::  faster for long-prompt inference
-::  cmake -G "MinGW Makefiles" .. -DLLAMA_CURL=OFF -DGGML_SYCL=ON -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON
+::  cmake -G "MinGW Makefiles" .. -DLLAMA_OPENSSL=OFF -DGGML_SYCL=ON -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON
 
 ::  for FP32
-cmake -G "Ninja" .. -DLLAMA_CURL=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release
+cmake -G "Ninja" .. -DLLAMA_OPENSSL=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release
 if %errorlevel% neq 0 goto ERROR
 
 ::  build all binary

ggml/include/ggml.h

@@ -234,6 +234,11 @@
 
 #if UINTPTR_MAX == 0xFFFFFFFF
     #define GGML_MEM_ALIGN 4
+#elif defined(__EMSCRIPTEN__)
+// emscripten uses max_align_t == 8, so we need GGML_MEM_ALIGN == 8 for 64-bit wasm.
+// (for 32-bit wasm, the first conditional is true and GGML_MEM_ALIGN stays 4.)
+// ref: https://github.com/ggml-org/llama.cpp/pull/18628
+    #define GGML_MEM_ALIGN 8
 #else
     #define GGML_MEM_ALIGN 16
 #endif

ggml/src/ggml-backend-impl.h

@@ -144,7 +144,7 @@ extern "C" {
         // device description: short informative description of the device, could be the model name
         const char * (*get_description)(ggml_backend_dev_t dev);
 
-        // device memory in bytes
+        // device memory in bytes: 0 bytes to indicate no memory to report
         void         (*get_memory)(ggml_backend_dev_t dev, size_t * free, size_t * total);
 
         // device type

ggml/src/ggml-blas/CMakeLists.txt

@@ -32,14 +32,12 @@ if (BLAS_FOUND)
                 pkg_check_modules(DepBLAS openblas)
             endif()
         elseif (${GGML_BLAS_VENDOR} MATCHES "FLAME")
-            add_compile_definitions(GGML_BLAS_USE_BLIS)
             pkg_check_modules(DepBLAS blis)
         elseif (${GGML_BLAS_VENDOR} MATCHES "ATLAS")
             pkg_check_modules(DepBLAS blas-atlas)
         elseif (${GGML_BLAS_VENDOR} MATCHES "FlexiBLAS")
             pkg_check_modules(DepBLAS flexiblas_api)
         elseif (${GGML_BLAS_VENDOR} MATCHES "Intel")
-            add_compile_definitions(GGML_BLAS_USE_MKL)
             # all Intel* libraries share the same include path
             pkg_check_modules(DepBLAS mkl-sdl)
         elseif (${GGML_BLAS_VENDOR} MATCHES "NVHPC")
@@ -74,10 +72,26 @@ if (BLAS_FOUND)
 
     target_compile_options(ggml-blas PRIVATE ${BLAS_LINKER_FLAGS})
 
-    if ("${BLAS_INCLUDE_DIRS}" MATCHES "mkl" AND (${GGML_BLAS_VENDOR} MATCHES "Generic" OR ${GGML_BLAS_VENDOR} MATCHES "Intel"))
+    if ("${GGML_BLAS_VENDOR}" STREQUAL "")
+        message(WARNING "GGML_BLAS_VENDOR is not set; some methods may not link properly.")
+    endif()
+
+    if ("${GGML_BLAS_VENDOR}" MATCHES "Intel" OR ("${BLAS_INCLUDE_DIRS}" MATCHES "mkl" AND "${GGML_BLAS_VENDOR}" MATCHES "Generic"))
         add_compile_definitions(GGML_BLAS_USE_MKL)
     endif()
 
+    if ("${GGML_BLAS_VENDOR}" MATCHES "OpenBLAS")
+        add_compile_definitions(GGML_BLAS_USE_OPENBLAS)
+    endif()
+
+    if ("${GGML_BLAS_VENDOR}" MATCHES "FLAME" OR "${GGML_BLAS_VENDOR}" MATCHES "AOCL" OR "${GGML_BLAS_VENDOR}" MATCHES "AOCL_mt")
+        add_compile_definitions(GGML_BLAS_USE_BLIS)
+    endif()
+
+    if ("${GGML_BLAS_VENDOR}" MATCHES "NVPL")
+        add_compile_definitions(GGML_BLAS_USE_NVPL)
+    endif()
+
     target_link_libraries     (ggml-blas PRIVATE ${BLAS_LIBRARIES})
     target_include_directories(ggml-blas PRIVATE ${BLAS_INCLUDE_DIRS})
 else()

ggml/src/ggml-blas/ggml-blas.cpp

@@ -115,15 +115,11 @@ static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct gg
 #endif
     }
 
-#if defined(OPENBLAS_VERSION)
+#if defined(GGML_BLAS_USE_OPENBLAS)
     openblas_set_num_threads(ctx->n_threads);
-#endif
-
-#if defined(GGML_BLAS_USE_BLIS)
+#elif defined(GGML_BLAS_USE_BLIS)
     bli_thread_set_num_threads(ctx->n_threads);
-#endif
-
-#if defined(GGML_BLAS_USE_NVPL)
+#elif defined(GGML_BLAS_USE_NVPL)
     nvpl_blas_set_num_threads(ctx->n_threads);
 #endif
 
@@ -288,7 +284,7 @@ ggml_backend_t ggml_backend_blas_init(void) {
         /* .context = */ ctx,
     };
 
-#if defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP)
+#if defined(GGML_BLAS_USE_OPENBLAS) && defined(GGML_USE_OPENMP)
     if (openblas_get_parallel() != OPENBLAS_OPENMP) {
         GGML_LOG_DEBUG("%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__);
     }
@@ -329,7 +325,7 @@ static const char * ggml_backend_blas_device_get_description(ggml_backend_dev_t
         return "BLIS";
     #elif defined(GGML_BLAS_USE_NVPL)
         return "NVPL";
-    #elif defined(OPENBLAS_VERSION)
+    #elif defined(GGML_BLAS_USE_OPENBLAS)
         return "OpenBLAS";
     #else
         return "BLAS";

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -1963,7 +1963,7 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context & ctx, ggml_tensor *
     acl_tensor_ptr acl_weight_tensor;
 
     // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on"));
+    static bool weight_to_nz = parse_bool(get_env_as_lowercase("GGML_CANN_WEIGHT_NZ").value_or("on"));
     if (weight_to_nz && is_matmul_weight(weight)) {
         acl_weight_tensor = ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_FRACTAL_NZ);
     } else {

ggml/src/ggml-cann/common.h

@@ -103,7 +103,7 @@ const ggml_cann_device_info & ggml_cann_info();
 void    ggml_cann_set_device(int32_t device);
 int32_t ggml_cann_get_device();
 
-std::optional<std::string> get_env(const std::string & name);
+std::optional<std::string> get_env_as_lowercase(const std::string & name);
 bool                       parse_bool(const std::string & value);
 int                        parse_integer(const std::string & value);
 

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

@@ -105,10 +105,10 @@ int32_t ggml_cann_get_device() {
 }
 
 /**
- * @brief Get the value of the specified environment variable (name).
+ * @brief Get the value of the specified environment variable (name) as lowercase.
  *        if not empty, return a std::string object
  */
-std::optional<std::string> get_env(const std::string & name) {
+std::optional<std::string> get_env_as_lowercase(const std::string & name) {
     const char * val = std::getenv(name.c_str());
     if (!val) {
         return std::nullopt;
@@ -122,7 +122,7 @@ std::optional<std::string> get_env(const std::string & name) {
  * @brief Verify whether the environment variable is a valid value.
  */
 bool parse_bool(const std::string & value) {
-    std::unordered_set<std::string> valid_values = { "on", "1", "yes", "y", "enable", "true" };
+    static const std::unordered_set<std::string> valid_values = { "on", "1", "yes", "y", "enable", "true" };
     return valid_values.find(value) != valid_values.end();
 }
 
@@ -259,7 +259,7 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool {
      * @param device The device ID to associate with this buffer pool.
      */
     explicit ggml_cann_pool_buf_prio(int device) : device(device) {
-        disable_clean = parse_bool(get_env("GGML_CANN_DISABLE_BUF_POOL_CLEAN").value_or(""));
+        disable_clean = parse_bool(get_env_as_lowercase("GGML_CANN_DISABLE_BUF_POOL_CLEAN").value_or(""));
     }
 
     /**
@@ -452,7 +452,7 @@ struct ggml_cann_pool_buf : public ggml_cann_pool {
      * @param device The device ID to associate with this buffer pool.
      */
     explicit ggml_cann_pool_buf(int device) : device(device) {
-        disable_clean = parse_bool(get_env("GGML_CANN_DISABLE_BUF_POOL_CLEAN").value_or(""));
+        disable_clean = parse_bool(get_env_as_lowercase("GGML_CANN_DISABLE_BUF_POOL_CLEAN").value_or(""));
     }
 
     /**
@@ -764,7 +764,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool {
  * @return A unique pointer to the created CANN pool.
  */
 std::unique_ptr<ggml_cann_pool> ggml_backend_cann_context::new_pool_for_device(int device) {
-    std::string mem_pool_type = get_env("GGML_CANN_MEM_POOL").value_or("");
+    std::string mem_pool_type = get_env_as_lowercase("GGML_CANN_MEM_POOL").value_or("");
 
     if (mem_pool_type == "prio") {
         GGML_LOG_INFO("%s: device %d use buffer pool with priority queue\n", __func__, device);
@@ -1217,7 +1217,7 @@ static void ggml_backend_cann_buffer_set_tensor(ggml_backend_buffer_t buffer,
     // Why aclrtSynchronizeDevice?
 
     // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on"));
+    static bool weight_to_nz = parse_bool(get_env_as_lowercase("GGML_CANN_WEIGHT_NZ").value_or("on"));
     if (!need_transform(tensor->type)) {
         ACL_CHECK(aclrtMemcpy((char *) tensor->data + offset, size, data, size, ACL_MEMCPY_HOST_TO_DEVICE));
         if (weight_to_nz && is_matmul_weight((const ggml_tensor *) tensor)) {
@@ -1442,7 +1442,7 @@ static size_t ggml_backend_cann_buffer_type_get_alloc_size(ggml_backend_buffer_t
     int64_t ne0  = tensor->ne[0];
 
     // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on"));
+    static bool weight_to_nz = parse_bool(get_env_as_lowercase("GGML_CANN_WEIGHT_NZ").value_or("on"));
 
     // last line must bigger than 32, because every single op deal at
     // least 32 bytes.
@@ -2136,7 +2136,7 @@ static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx
 #endif  // USE_ACL_GRAPH
     // Only perform the graph execution if CANN graphs are not enabled, or we are capturing the graph.
     // With the use of CANN graphs, the execution will be performed by the graph launch.
-    static bool opt_fusion = parse_bool(get_env("GGML_CANN_OPERATOR_FUSION").value_or(""));
+    static bool opt_fusion = parse_bool(get_env_as_lowercase("GGML_CANN_OPERATOR_FUSION").value_or(""));
 
     if (!use_cann_graph || cann_graph_capture_required) {
         for (int i = 0; i < cgraph->n_nodes; i++) {
@@ -2201,7 +2201,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend,
 #ifdef USE_ACL_GRAPH
     bool use_cann_graph = true;
 
-    static bool prefill_use_graph = parse_bool(get_env("GGML_CANN_PREFILL_USE_GRAPH").value_or(""));
+    static bool prefill_use_graph = parse_bool(get_env_as_lowercase("GGML_CANN_PREFILL_USE_GRAPH").value_or(""));
     if (!prefill_use_graph) {
         // Do not use acl_graph for prefill.
         for (int i = 0; i < cgraph->n_nodes; i++) {
@@ -2541,27 +2541,6 @@ static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
     return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
 }
 
-/**
- * @brief Determines if a tensor operation should be offloaded to the CANN
- * backend.
- *
- * This function checks if a given tensor operation should be offloaded to the
- * CANN backend based on the operation type and the size of the tensor. It
- * returns true if the second dimension (ne[1]) of the tensor is greater than or
- * equal to the minimum batch size and the operation is not GGML_OP_GET_ROWS.
- *
- * @param backend Pointer to the CANN backend.
- * @param op Pointer to the tensor operation to check.
- * @return bool Returns true if the operation should be offloaded, otherwise
- * false.
- */
-static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
-    const int min_batch_size = 32;
-    GGML_UNUSED(dev);
-
-    return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS;
-}
-
 /**
  * @brief Records an event on the CANN backend stream.
  *
@@ -2637,6 +2616,7 @@ struct ggml_backend_cann_device_context {
     int         device;
     std::string name;
     std::string description;
+    int op_offload_min_batch_size;
 };
 
 static const char * ggml_backend_cann_device_get_name(ggml_backend_dev_t dev) {
@@ -2713,6 +2693,26 @@ static ggml_backend_buffer_type_t ggml_backend_cann_device_get_host_buffer_type(
     return ggml_backend_cann_host_buffer_type();
 }
 
+/**
+ * @brief Determines if a tensor operation should be offloaded to the CANN
+ * backend.
+ *
+ * This function checks if a given tensor operation should be offloaded to the
+ * CANN backend based on the operation type and the size of the tensor. It
+ * returns true if the second dimension (ne[1]) of the tensor is greater than or
+ * equal to the minimum batch size and the operation is not GGML_OP_GET_ROWS.
+ *
+ * @param backend Pointer to the CANN backend.
+ * @param op Pointer to the tensor operation to check.
+ * @return bool Returns true if the operation should be offloaded, otherwise
+ * false.
+ */
+static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
+    ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context;
+
+    return op->ne[1] >= dev_ctx->op_offload_min_batch_size && op->op != GGML_OP_GET_ROWS;
+}
+
 /**
  * @brief Creates a new event for the CANN backend device.
  *
@@ -2829,12 +2829,14 @@ ggml_backend_reg_t ggml_backend_cann_reg() {
         if (!initialized) {
             aclInit(nullptr);
             ggml_backend_cann_reg_context * ctx = new ggml_backend_cann_reg_context;
+            const int min_batch_size = getenv("GGML_OP_OFFLOAD_MIN_BATCH") ? atoi(getenv("GGML_OP_OFFLOAD_MIN_BATCH")) : 32;
 
             for (int i = 0; i < ggml_cann_info().device_count; i++) {
                 ggml_backend_cann_device_context * dev_ctx = new ggml_backend_cann_device_context();
                 dev_ctx->description                       = aclrtGetSocName();
                 dev_ctx->device                            = i;
                 dev_ctx->name                              = GGML_CANN_NAME + std::to_string(i);
+                dev_ctx->op_offload_min_batch_size         = min_batch_size;
                 ggml_cann_set_device(i);
                 ggml_backend_dev_t dev = new ggml_backend_device{ /* .iface   = */ ggml_backend_cann_device_interface,
                                                                   /* .reg     = */ &reg,

ggml/src/ggml-cpu/simd-mappings.h

@@ -654,6 +654,14 @@ static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) {
           vec_extract(x[0], 2) +               \
           vec_extract(x[0], 3);                \
 }
+#define GGML_F32x4_REDUCE_4(res, s0, s1, s2, s3)        \
+{                                                       \
+    vector float v = vec_add(vec_add(s0, s1),           \
+                             vec_add(s2, s3));          \
+    v = vec_add(v, vec_sld(v, v, 8));                   \
+    v = vec_add(v, vec_sld(v, v, 4));                   \
+    res += (ggml_float) vec_extract(v, 0);              \
+}
 
 #define GGML_F32_VEC        GGML_F32x4
 #define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
@@ -690,6 +698,29 @@ static inline unsigned char ggml_endian_byte(int i) {
                                    r[i - GGML_ENDIAN_BYTE(0)]), \
             0, p - GGML_F16_EPR)
 
+//BF16 POWER9
+#define GGML_BF16_STEP 16
+#define GGML_BF16_EPR  8
+
+#define GGML_BF16x8         vector unsigned short
+#define GGML_BF16x8_ZERO    vec_splats((unsigned short)0)
+#define GGML_BF16x8_LOAD(p) vec_xl(0, (const unsigned short *)(p))
+
+#define GGML_BF16_VEC          GGML_BF16x8
+#define GGML_BF16_VEC_ZERO     GGML_BF16x8_ZERO
+#define GGML_BF16_VEC_LOAD     GGML_BF16x8_LOAD
+#if defined(__LITTLE_ENDIAN__)
+#define GGML_BF16_TO_F32_LO(v) ((vector float) vec_mergel(GGML_BF16_VEC_ZERO, (v)))
+#define GGML_BF16_TO_F32_HI(v) ((vector float) vec_mergeh(GGML_BF16_VEC_ZERO, (v)))
+#else
+#define GGML_BF16_TO_F32_LO(v) ((vector float) vec_mergel((v), GGML_BF16_VEC_ZERO))
+#define GGML_BF16_TO_F32_HI(v) ((vector float) vec_mergeh((v), GGML_BF16_VEC_ZERO))
+#endif
+#define GGML_BF16_FMA_LO(acc, x, y) \
+    (acc) = GGML_F32x4_FMA((acc), GGML_BF16_TO_F32_LO(x), GGML_BF16_TO_F32_LO(y))
+#define GGML_BF16_FMA_HI(acc, x, y) \
+    (acc) = GGML_F32x4_FMA((acc), GGML_BF16_TO_F32_HI(x), GGML_BF16_TO_F32_HI(y))
+
 #elif defined(__wasm_simd128__)
 
 #define GGML_SIMD

ggml/src/ggml-cpu/vec.cpp

@@ -237,6 +237,24 @@ void ggml_vec_dot_bf16(int n, float * GGML_RESTRICT s, size_t bs, ggml_bf16_t *
     sumf += __riscv_vfmv_f_s_f32m1_f32(redsum);
 
 #endif
+#if defined(__POWER9_VECTOR__)
+    const int np = (n & ~(GGML_BF16_STEP - 1));
+    if (np > 0) {
+        GGML_F32_VEC sum[4] = {GGML_F32_VEC_ZERO};
+        for (; i < np; i += GGML_BF16_STEP) {
+            GGML_BF16_VEC vx0 = GGML_BF16_VEC_LOAD(x + i);
+            GGML_BF16_VEC vx1 = GGML_BF16_VEC_LOAD(x + i + 8);
+            GGML_BF16_VEC vy0 = GGML_BF16_VEC_LOAD(y + i);
+            GGML_BF16_VEC vy1 = GGML_BF16_VEC_LOAD(y + i + 8);
+            GGML_BF16_FMA_LO(sum[0], vx0, vy0);
+            GGML_BF16_FMA_HI(sum[1], vx0, vy0);
+            GGML_BF16_FMA_LO(sum[2], vx1, vy1);
+            GGML_BF16_FMA_HI(sum[3], vx1, vy1);
+        }
+        GGML_F32x4_REDUCE_4(sumf, sum[0], sum[1], sum[2], sum[3]);
+    }
+#endif
+
     for (; i < n; ++i) {
         sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) *
                              GGML_BF16_TO_FP32(y[i]));

ggml/src/ggml-cuda/CMakeLists.txt

@@ -47,7 +47,10 @@ if (CUDAToolkit_FOUND)
                 #     check Modules/Internal/CMakeCUDAArchitecturesValidate.cmake in the CMake git repository instead.
                 # However, the architectures 120a-real and 121a-real should work with basically any CMake version and
                 #     until the release of e.g. Rubin there is no benefit to shipping virtual architectures for Blackwell.
-                list(APPEND CMAKE_CUDA_ARCHITECTURES 120a-real 121a-real)
+                list(APPEND CMAKE_CUDA_ARCHITECTURES 120a-real)
+            endif()
+            if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "12.9")
+                list(APPEND CMAKE_CUDA_ARCHITECTURES 121a-real)
             endif()
         endif()
     endif()

ggml/src/ggml-cuda/common.cuh

@@ -262,6 +262,10 @@ static const char * cu_get_error_str(CUresult err) {
 #define FLASH_ATTN_AVAILABLE
 #endif // !defined(GGML_CUDA_NO_FA) && !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ < 220)
 
+#if defined(TURING_MMA_AVAILABLE)
+#define LDMATRIX_TRANS_AVAILABLE
+#endif // defined(TURING_MMA_AVAILABLE)
+
 static bool fp16_available(const int cc) {
     return ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_PASCAL ||
         (GGML_CUDA_CC_IS_MTHREADS(cc) && cc >= GGML_CUDA_CC_PH1);
@@ -526,6 +530,86 @@ static __device__ __forceinline__ half2 warp_prefix_inclusive_sum(half2 a) {
 #endif // FP16_AVAILABLE
 }
 
+enum class block_reduce_method {
+    MAX,
+    SUM,
+};
+
+template<block_reduce_method method_t, typename T>
+struct block_reduce_policy;
+
+template <typename T, typename... Ts>
+inline constexpr bool is_any = (std::is_same_v<T, Ts> || ...);
+
+template<typename...>
+inline constexpr bool ggml_cuda_dependent_false_v = false;
+
+template <typename T> struct block_reduce_policy<block_reduce_method::SUM, T> {
+    static __device__ T reduce(T val) {
+        if constexpr(is_any<T, float, float2, half2, int>) {
+            return warp_reduce_sum(val);
+        } else {
+            static_assert(ggml_cuda_dependent_false_v<T>, "Unsupported type for block reduce sum");
+        }
+    }
+
+    static __device__ T sentinel() {
+        if constexpr (std::is_same_v<T, float>) {
+            return 0.0f;
+        } else if constexpr (std::is_same_v<T, float2>) {
+            return make_float2(0.0f, 0.0f);
+        } else if constexpr (std::is_same_v<T, half2>) {
+            return make_half2(0.0f, 0.0f);
+        } else if constexpr (std::is_same_v<T, int>) {
+            return 0;
+        } else {
+            static_assert(ggml_cuda_dependent_false_v<T>, "Unsupported type for block reduce sum");
+        }
+    }
+};
+
+template <typename T> struct block_reduce_policy<block_reduce_method::MAX, T> {
+    static __device__ T reduce(T val) {
+        if constexpr (is_any<T, float, half2>) {
+            return warp_reduce_max(val);
+        } else {
+            static_assert(ggml_cuda_dependent_false_v<T>, "Unsupported type for block reduce max");
+        }
+    }
+
+    static __device__ T sentinel() {
+        if constexpr (std::is_same_v<T, float>) {
+            return -INFINITY;
+        } else if constexpr (std::is_same_v<T, half2>) {
+            return make_half2(-INFINITY, -INFINITY);
+        } else {
+            static_assert(ggml_cuda_dependent_false_v<T>, "Unsupported type for block reduce max");
+        }
+    }
+};
+
+template <block_reduce_method reduce_method_t, const unsigned int block_size_template = 0, typename T>
+static __device__ T block_reduce(T val, T * shared_vals) {
+    val                           = block_reduce_policy<reduce_method_t, T>::reduce(val);
+    const unsigned int block_size = block_size_template == 0 ? blockDim.x : block_size_template;
+    if (block_size > WARP_SIZE) {
+        assert((block_size <= 1024) && (block_size % WARP_SIZE) == 0);
+        const int warp_id = threadIdx.x / WARP_SIZE;
+        const int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            shared_vals[warp_id] = val;
+        }
+        __syncthreads();
+        val = block_reduce_policy<reduce_method_t, T>::sentinel();
+        if (lane_id < (static_cast<int>(block_size) / WARP_SIZE)) {
+            val = shared_vals[lane_id];
+        }
+        return block_reduce_policy<reduce_method_t, T>::reduce(val);
+    }
+
+    return val;
+}
+
 static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) {
 #ifdef FP16_AVAILABLE
 
@@ -1036,7 +1120,7 @@ struct ggml_tensor_extra_gpu {
 #define USE_CUDA_GRAPH
 #endif
 
-struct ggml_graph_node_properties {
+struct ggml_cuda_graph_node_properties {
     void * node_address;
     ggml_op node_op;
     int64_t ne[GGML_MAX_DIMS];
@@ -1061,10 +1145,25 @@ struct ggml_cuda_graph {
     std::vector<cudaGraphNode_t> nodes;
     bool disable_due_to_gpu_arch = false;
     bool disable_due_to_too_many_updates = false;
-    bool disable_due_to_failed_graph_capture = false;
     int number_consecutive_updates = 0;
-    bool cuda_graphs_enabled = false;
-    std::vector<ggml_graph_node_properties> ggml_graph_properties;
+    std::vector<ggml_cuda_graph_node_properties> props;
+
+    void record_update(bool use_graph, bool update_required) {
+        if (use_graph && update_required) {
+            number_consecutive_updates++;
+        } else {
+            number_consecutive_updates = 0;
+        }
+        if (number_consecutive_updates >= 4) {
+            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
+            disable_due_to_too_many_updates = true;
+        }
+    }
+
+    bool is_enabled() const {
+        static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr);
+        return !(disable_due_to_gpu_arch || disable_cuda_graphs_due_to_env || disable_due_to_too_many_updates);
+    }
 #endif
 };
 

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

@@ -11,10 +11,12 @@
 #define SOFTMAX_FTZ_THRESHOLD -20.0f                   // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs.
 
 // log(2) = 0.6931, by adding this to the KQ maximum used for the softmax the numerical range representable
-//     by the VKQ accumulators is effectively being shifted up by a factor of 8.
+//     by the VKQ accumulators is effectively being shifted up by a factor of 2.
 // This reduces issues with numerical overflow but also causes larger values to be flushed to zero.
 // However, as the output from FlashAttention will usually be used as an input for a matrix multiplication this should be negligible.
-#define FATTN_KQ_MAX_OFFSET 0.6931f
+// Still, the value range should be shifted as much as necessary but as little as possible.
+// The macro on the following line shifts it by a factor of 2**3=8, as was needed to fix https://github.com/ggml-org/llama.cpp/issues/18606 .
+#define FATTN_KQ_MAX_OFFSET (3.0f*0.6931f)
 
 typedef void (* fattn_kernel_t)(
         const char * __restrict__ Q,
@@ -57,7 +59,7 @@ static __device__ __forceinline__ float vec_dot_fattn_vec_KQ_f16(
 
 #pragma unroll
     for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += nthreads*cpy_ne) {
-        half2 tmp[cpy_ne];
+        __align__(16) half2 tmp[cpy_ne];
         ggml_cuda_memcpy_1<sizeof(tmp)>(tmp, K_h2 + k_KQ_0 + (threadIdx.x % nthreads)*cpy_ne);
 #pragma unroll
         for (int k_KQ_1 = 0; k_KQ_1 < cpy_ne; ++k_KQ_1) {
@@ -307,7 +309,7 @@ static __device__ __forceinline__ void dequantize_V_f16(const void * __restrict_
         ggml_cuda_memcpy_1<ne*sizeof(half)>(dst, (const half *) vx + i0);
     } else if constexpr (std::is_same_v<T, float>) {
         static_assert(ne % 2 == 0, "bad ne");
-        half2 tmp[ne/2];
+        __align__(16) half2 tmp[ne/2];
         ggml_cuda_memcpy_1<ne*sizeof(half)>(tmp, (const half *) vx + i0);
         float2 * dst_f2 = (float2 *) dst;
 #pragma unroll
@@ -912,7 +914,7 @@ void launch_fattn(
 
         const int nblocks_stream_k = max_blocks;
 
-        const bool use_stream_k = cc >= GGML_CUDA_CC_ADA_LOVELACE || tiles_efficiency_percent < 75;
+        const bool use_stream_k = cc >= GGML_CUDA_CC_ADA_LOVELACE || amd_wmma_available(cc) || tiles_efficiency_percent < 75;
 
         blocks_num.x = use_stream_k ? nblocks_stream_k : ntiles_total;
         blocks_num.y = 1;

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

@@ -98,6 +98,19 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
     return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
 }
 
+static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config_rdna(const int DKQ, const int DV, const int ncols) {
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16, 128, 2,  64, 128, 128, 128, 2, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  64, 128, 128,  64, 2, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2,  64, 128, 128,  64, 2, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16,  64, 4,  32,  96,  64, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 128, 2,  32, 160, 128, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 64, 256, 1,  32, 160, 128, 128, 1, false);
+
+    // TODO tune specifically for RDNA
+    return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
+}
+
 static __host__ fattn_mma_config ggml_cuda_fattn_mma_get_config(const int DKQ, const int DV, const int ncols, const int cc) {
     if (ampere_mma_available(cc)) {
         return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
@@ -105,6 +118,9 @@ static __host__ fattn_mma_config ggml_cuda_fattn_mma_get_config(const int DKQ, c
     if (turing_mma_available(cc)) {
         return ggml_cuda_fattn_mma_get_config_turing(DKQ, DV, ncols);
     }
+    if (amd_wmma_available(cc)) {
+        return ggml_cuda_fattn_mma_get_config_rdna(DKQ, DV, ncols);
+    }
     GGML_ASSERT(volta_mma_available(cc));
     return ggml_cuda_fattn_mma_get_config_volta(DKQ, DV, ncols);
 }
@@ -116,6 +132,8 @@ static constexpr __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config(cons
     return ggml_cuda_fattn_mma_get_config_turing(DKQ, DV, ncols);
 #elif defined(VOLTA_MMA_AVAILABLE)
     return ggml_cuda_fattn_mma_get_config_volta(DKQ, DV, ncols);
+#elif defined(AMD_WMMA_AVAILABLE)
+    return ggml_cuda_fattn_mma_get_config_rdna(DKQ, DV, ncols);
 #else
     GGML_UNUSED_VARS(DKQ, DV, ncols);
     return fattn_mma_config(32, 1, 0, 0, 0, 0, 0, false);
@@ -186,6 +204,23 @@ static constexpr __device__ bool ggml_cuda_fattn_mma_get_Q_in_reg(const int DKQ,
     return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols).Q_in_reg;
 }
 
+static constexpr __device__ int get_cols_per_thread() {
+#if defined(AMD_WMMA_AVAILABLE)
+    return 1; // RDNA has a single column.
+#else
+    return 2; // This is specifically KQ columns, Volta only has a single VKQ column.
+#endif // defined(AMD_WMMA_AVAILABLE)
+}
+
+static __host__ int get_cols_per_warp(const int cc) {
+    if (turing_mma_available(cc) || amd_wmma_available(cc)) {
+        return 16;
+    } else {
+        // Volta
+        return 32;
+    }
+}
+
 // ------------------------------------------------------------------------------------------------------------------
 
 static __host__ int ggml_cuda_fattn_mma_get_nstages(const int DKQ, const int DV, const int ncols1, const int ncols2, const int cc) {
@@ -393,10 +428,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         const int jt,
         const int kb0,
         const int k_VKQ_sup) {
-#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
+#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
     constexpr int  ncols           = ncols1 * ncols2;
     constexpr int  cols_per_warp   = T_B_KQ::I;
-    constexpr int  cols_per_thread = 2; // This is specifically KQ columns, Volta only has a single VKQ column.
+    constexpr int  cols_per_thread = get_cols_per_thread();
     constexpr int  np              = nwarps * (cols_per_warp/ncols2) / ncols1; // Number of parallel CUDA warps per Q column.
     constexpr int  nbatch_fa       = ggml_cuda_fattn_mma_get_nbatch_fa(DKQ, DV, ncols);
     constexpr int  nbatch_K2       = ggml_cuda_fattn_mma_get_nbatch_K2(DKQ, DV, ncols);
@@ -413,6 +448,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
     const int k_VKQ_0 = kb0 * nbatch_fa;
 #if defined(TURING_MMA_AVAILABLE)
     T_C_KQ KQ_C[nbatch_fa/(np*(cols_per_warp == 8 ? T_C_KQ::I : T_C_KQ::J))];
+#elif defined(AMD_WMMA_AVAILABLE)
+    T_C_KQ KQ_C[nbatch_fa/(np*T_C_KQ::J)];
 #else // Volta
     T_C_KQ KQ_C[nbatch_fa/(np*T_C_KQ::J)];
 #endif // defined(TURING_MMA_AVAILABLE)
@@ -461,8 +498,14 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                     if constexpr (cols_per_warp == 8) {
                         mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[k_KQ_0/T_A_KQ::J]);
                     } else {
-                        // Wide version of KQ_C is column-major => swap A and B.
+                        // Wide version of KQ_C is column-major
+#if defined(AMD_WMMA_AVAILABLE)
+                        // RDNA matrix C is column-major.
+                        mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[k_KQ_0/T_A_KQ::J]);
+#else
+                        // swap A and B for CUDA.
                         mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], Q_B[k_KQ_0/T_A_KQ::J], K_A);
+#endif // defined(AMD_WMMA_AVAILABLE)
                     }
                 }
             }
@@ -479,8 +522,14 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                     T_A_KQ K_A;
                     load_ldmatrix(K_A, tile_K + i_KQ_0*stride_tile_K + (k_KQ_0 - k0_start), stride_tile_K);
 
-                    // Wide version of KQ_C is column-major => swap A and B.
+                    // Wide version of KQ_C is column-major
+#if defined(AMD_WMMA_AVAILABLE)
+                    // RDNA matrix C is column-major.
+                    mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[0]);
+#else
+                    // swap A and B for CUDA.
                     mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], Q_B[0], K_A);
+#endif // defined(AMD_WMMA_AVAILABLE)
                 }
             }
         }
@@ -532,7 +581,13 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
 #pragma unroll
             for (int l = 0; l < T_C_KQ::ne; ++l) {
                 if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::I + T_C_KQ::get_i(l) < k_VKQ_sup) {
-                    KQ_max_new[l % 2] = fmaxf(KQ_max_new[l % 2], KQ_C[k0/(np*T_C_KQ::I)].x[l] + FATTN_KQ_MAX_OFFSET);
+#if defined(AMD_WMMA_AVAILABLE)
+                    constexpr int KQ_idx = 0;
+#else
+                    // Turing + Volta:
+                    const int KQ_idx = l % 2;
+#endif // defined(AMD_WMMA_AVAILABLE)
+                    KQ_max_new[KQ_idx] = fmaxf(KQ_max_new[KQ_idx], KQ_C[k0/(np*T_C_KQ::I)].x[l] + FATTN_KQ_MAX_OFFSET);
                 }
             }
         }
@@ -552,8 +607,14 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
 #pragma unroll
             for (int l = 0; l < T_C_KQ::ne; ++l) {
                 if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::I + T_C_KQ::get_i(l) < k_VKQ_sup) {
-                    KQ_C[k0/(np*T_C_KQ::I)].x[l] = expf(KQ_C[k0/(np*T_C_KQ::I)].x[l] - KQ_max_new[l % 2]);
-                    KQ_rowsum_add[l % 2] += KQ_C[k0/(np*T_C_KQ::I)].x[l];
+#if defined(AMD_WMMA_AVAILABLE)
+                    constexpr int KQ_idx = 0;
+#else
+                    // Turing + Volta:
+                    const int KQ_idx = l % 2;
+#endif // defined(AMD_WMMA_AVAILABLE)
+                    KQ_C[k0/(np*T_C_KQ::I)].x[l] = expf(KQ_C[k0/(np*T_C_KQ::I)].x[l] - KQ_max_new[KQ_idx]);
+                    KQ_rowsum_add[KQ_idx] += KQ_C[k0/(np*T_C_KQ::I)].x[l];
                 } else {
                     KQ_C[k0/(np*T_C_KQ::I)].x[l] = 0.0f;
                 }
@@ -584,8 +645,13 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
 #pragma unroll
             for (int l = 0; l < T_C_KQ::ne; ++l) {
                 if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::J + T_C_KQ::get_j(l) < k_VKQ_sup) {
+#if defined(AMD_WMMA_AVAILABLE)
+                    constexpr int KQ_idx = 0;
+#else
                     // Turing + Volta:
-                    KQ_max_new[(l/2) % 2] = fmaxf(KQ_max_new[(l/2) % 2], KQ_C[(k0/(np*T_C_KQ::J))].x[l] + FATTN_KQ_MAX_OFFSET);
+                    const int KQ_idx = (l/2) % 2;
+#endif // defined(AMD_WMMA_AVAILABLE)
+                    KQ_max_new[KQ_idx] = fmaxf(KQ_max_new[KQ_idx], KQ_C[(k0/(np*T_C_KQ::J))].x[l] + FATTN_KQ_MAX_OFFSET);
                 }
             }
         }
@@ -596,7 +662,11 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
             // Values per KQ column are spread across 4 threads:
             constexpr int offset_first = 2;
             constexpr int offset_last  = 1;
-#else
+#elif defined(AMD_WMMA_AVAILABLE)
+            // Values per KQ column are spread across 2 threads:
+            constexpr int offset_first = 16;
+            constexpr int offset_last  = 16;
+#else // Volta
             // Values per KQ column are spread across 2 threads:
             constexpr int offset_first = 2;
             constexpr int offset_last  = 2;
@@ -612,10 +682,15 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         for (int k0 = 0; k0 < nbatch_fa; k0 += np*T_C_KQ::J) {
 #pragma unroll
             for (int l = 0; l < T_C_KQ::ne; ++l) {
-                // Turing + Volta:
                 if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::J + T_C_KQ::get_j(l) < k_VKQ_sup) {
-                    KQ_C[(k0/(np*T_C_KQ::J))].x[l] = expf(KQ_C[(k0/(np*T_C_KQ::J))].x[l] - KQ_max_new[(l/2) % 2]);
-                    KQ_rowsum_add[(l/2) % 2] += KQ_C[(k0/(np*T_C_KQ::J))].x[l];
+#if defined(AMD_WMMA_AVAILABLE)
+                    constexpr int KQ_idx = 0;
+#else
+                    // Turing + Volta:
+                    const int KQ_idx = (l/2) % 2;
+#endif // defined(AMD_WMMA_AVAILABLE)
+                    KQ_C[(k0/(np*T_C_KQ::J))].x[l] = expf(KQ_C[(k0/(np*T_C_KQ::J))].x[l] - KQ_max_new[KQ_idx]);
+                    KQ_rowsum_add[KQ_idx] += KQ_C[(k0/(np*T_C_KQ::J))].x[l];
                 } else {
                     KQ_C[(k0/(np*T_C_KQ::J))].x[l] = 0.0f;
                 }
@@ -639,7 +714,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
 
 #if defined(TURING_MMA_AVAILABLE)
         if constexpr (cols_per_warp == 8) {
-            const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[1]);
+            const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[cols_per_thread - 1]);
 #pragma unroll
             for (int i = 0; i < DV/T_C_VKQ::I; ++i) {
 #pragma unroll
@@ -660,6 +735,16 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                 }
             }
         }
+#elif defined(AMD_WMMA_AVAILABLE)
+        const half2 KQ_max_scale_h2 = make_half2(
+            KQ_max_scale[0], KQ_max_scale[0]);
+#pragma unroll
+        for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
+#pragma unroll
+            for (int l = 0; l < T_C_VKQ::ne; ++l) {
+                VKQ_C[i].x[l] *= KQ_max_scale_h2;
+            }
+        }
 #else // Volta
         const half2 KQ_max_scale_h2 = make_half2(
             KQ_max_scale[(threadIdx.x / 2) % 2], KQ_max_scale[(threadIdx.x / 2) % 2]);
@@ -707,6 +792,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
     // Therefore, iterate over V in reverse and re-use the data if possible.
     static_assert(!mla || nstages <= 1, "combination of MLA and multi-stage loading not implemented");
     constexpr int reusable_cutoff = mla ? (DKQ - 1) - (DKQ - 1) % (2*nbatch_K2) - (DKQ - DV) : DV;
+#if defined(AMD_WMMA_AVAILABLE) && !defined(LDMATRIX_TRANS_AVAILABLE)
+    T_A_VKQ A_identity;
+    make_identity_mat(A_identity);
+#endif // defined(AMD_WMMA_AVAILABLE) && !defined(LDMATRIX_TRANS_AVAILABLE)
 
     // Calculate VKQ tile, need to use logical rather than physical elements for i0 due to transposition of V:
 #pragma unroll
@@ -727,7 +816,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         }
         const half2 * tile_V_i = i0_start < reusable_cutoff ? tile_V : tile_V + (i0_start - reusable_cutoff)/2;
 
-#if defined(TURING_MMA_AVAILABLE)
+#if defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
         constexpr int i0_stride = cols_per_warp == 8 ? T_C_VKQ::I : 2*T_C_VKQ::J;
 #pragma unroll
         for (int i_VKQ_0 = i0_start; i_VKQ_0 < i0_stop; i_VKQ_0 += i0_stride) {
@@ -737,12 +826,26 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                 const int k0 = k00 + (threadIdx.y % np)*T_A_VKQ::J;
 
                 T_A_VKQ A; // Transposed in SRAM but not in registers, gets transposed on load.
+#if defined(LDMATRIX_TRANS_AVAILABLE)
                 load_ldmatrix_trans(A, tile_V_i + 2*k0*stride_tile_V + (i_VKQ_0 - i0_start)/2, stride_tile_V);
+#else
+                // TODO: Try to transpose tile_V when loading gmem to smem.
+                // Use mma to transpose T_A_VKQ for RDNA.
+                T_A_VKQ A_trans;
+                load_ldmatrix(A_trans, tile_V_i + 2*k0*stride_tile_V + (i_VKQ_0 - i0_start)/2, stride_tile_V);
+                mma(A, A_trans, A_identity);
+#endif // defined(TURING_MMA_AVAILABLE)
                 if constexpr (T_B_KQ::I == 8) {
                     mma(VKQ_C[i_VKQ_0/i0_stride], A, B[k00/(np*T_A_VKQ::J)]);
                 } else {
-                    // Wide version of VKQ_C is column-major => swap A and B.
+                    // Wide version of VKQ_C is column-major.
+#if defined(AMD_WMMA_AVAILABLE)
+                    // RDNA matrix C is column-major.
+                    mma(VKQ_C[i_VKQ_0/i0_stride], A, B[k00/(np*T_A_VKQ::J)]);
+#else
+                    // swap A and B for CUDA.
                     mma(VKQ_C[i_VKQ_0/i0_stride], B[k00/(np*T_A_VKQ::J)], A);
+#endif // defined(AMD_WMMA_AVAILABLE)
                 }
             }
         }
@@ -761,7 +864,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                 mma(VKQ_C[i_VKQ_0/i0_stride], B[k00/(np*T_A_VKQ::I)], A);
             }
         }
-#endif // defined(TURING_MMA_AVAILABLE)
+#endif // defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
 
         if constexpr (nstages <= 1) {
             __syncthreads(); // Only needed if tile_K == tile_V.
@@ -774,7 +877,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         tile_Q, tile_K, tile_V, tile_mask,
         Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0);
     NO_DEVICE_CODE;
-#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
+#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
 }
 
 #if defined(TURING_MMA_AVAILABLE)
@@ -794,6 +897,15 @@ template<> struct mma_tile_sizes<8> {
     using T_B_VKQ = tile< 8,  8, half2>; // column-major
     using T_C_VKQ = tile<16,  4, half2>; // row-major
 };
+#elif defined(AMD_WMMA_AVAILABLE)
+template<int ncols> struct mma_tile_sizes {
+    using T_A_KQ  = tile<16,  8, half2>; // row-major
+    using T_B_KQ  = tile<16,  8, half2>; // column-major
+    using T_C_KQ  = tile<16, 16, float>; // column-major
+    using T_A_VKQ = tile<16,  8, half2>; // row-major
+    using T_B_VKQ = tile<16,  8, half2>; // column-major
+    using T_C_VKQ = tile<16,  8, half2>; // column-major
+};
 #else // Volta
 template<int ncols> struct mma_tile_sizes {
     using T_A_KQ  = tile< 8,  4, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // row-major
@@ -828,7 +940,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         const int jt,
         const int kb0_start,
         const int kb0_stop) {
-#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
+#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     constexpr int ncols = ncols1 * ncols2;
@@ -840,7 +952,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
     using     T_C_VKQ   = typename mma_tile_sizes<ncols>::T_C_VKQ;
 
     constexpr int  cols_per_warp   = T_B_KQ::I;
-    constexpr int  cols_per_thread = 2; // This is specifically KQ columns, Volta only has a single VKQ column.
+    constexpr int  cols_per_thread = get_cols_per_thread();
     constexpr int  np              = nwarps * (cols_per_warp/ncols2) / ncols1; // Number of parallel CUDA warps per Q column.
     constexpr int  nbatch_fa       = ggml_cuda_fattn_mma_get_nbatch_fa     (DKQ, DV, ncols);
     constexpr int  nbatch_K2       = ggml_cuda_fattn_mma_get_nbatch_K2     (DKQ, DV, ncols);
@@ -871,6 +983,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
     T_B_KQ    Q_B[(Q_in_reg ? DKQ/(2*T_B_KQ::J) : 1)];
 #if defined(TURING_MMA_AVAILABLE)
     T_C_VKQ VKQ_C[cols_per_warp == 8 ? DV/T_C_VKQ::I : DV/(2*T_C_VKQ::J)];
+#elif defined(AMD_WMMA_AVAILABLE)
+    T_C_VKQ VKQ_C[                                     DV/(2*T_C_VKQ::J)];
 #else // Volta
     T_C_VKQ VKQ_C[                                     DV/(2*T_C_VKQ::J)];
 #endif // defined(TURING_MMA_AVAILABLE)
@@ -1010,6 +1124,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         // The partial sums are spread across 8/4 threads.
         constexpr int offset_first = cols_per_warp == 8 ? 16 : 2;
         constexpr int offset_last  = cols_per_warp == 8 ?  4 : 1;
+#elif defined(AMD_WMMA_AVAILABLE)
+        // The partial sums are spread across 2 threads.
+        constexpr int offset_first = 16;
+        constexpr int offset_last  = 16;
 #else // Volta
         // The partial sums are spread across 2 threads.
         constexpr int offset_first = 2;
@@ -1047,7 +1165,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
 
 #if defined(TURING_MMA_AVAILABLE)
         if constexpr (cols_per_warp == 8) {
-            const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[1]);
+            const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[cols_per_thread - 1]);
 #pragma unroll
             for (int i = 0; i < DV/T_C_VKQ::I; ++i) {
 #pragma unroll
@@ -1068,6 +1186,15 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
                 }
             }
         }
+#elif defined(AMD_WMMA_AVAILABLE)
+        const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[0]);
+#pragma unroll
+        for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
+#pragma unroll
+            for (int l = 0; l < T_C_VKQ::ne; ++l) {
+                VKQ_C[i].x[l] *= KQ_max_scale_h2;
+            }
+        }
 #else // Volta
         const int col = (threadIdx.x / 2) % 2;
         const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[col], KQ_max_scale[col]);
@@ -1119,6 +1246,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         const int jc_cwm = threadIdx.y*cols_per_warp + T_C_VKQ::get_i(threadIdx.x % 4);
         const float2 KQ_cmr = make_float2(KQ_max[threadIdx.x % cols_per_thread], KQ_rowsum[threadIdx.x % cols_per_thread]);
         const bool thread_should_write = threadIdx.x % 4 < cols_per_thread;
+#elif defined(AMD_WMMA_AVAILABLE)
+        const int jc_cwm = threadIdx.y*cols_per_warp + T_C_VKQ::get_i(0);
+        const float2 KQ_cmr = make_float2(KQ_max[0], KQ_rowsum[0]);
+        const bool thread_should_write = threadIdx.x / 16 < cols_per_thread;
 #else // Volta
         const int jc_cwm = threadIdx.y*cols_per_warp + T_C_KQ::get_i(threadIdx.x & 2);
         const float2 KQ_cmr = make_float2(KQ_max[(threadIdx.x & 2) / 2], KQ_rowsum[(threadIdx.x & 2) / 2]);
@@ -1319,7 +1450,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         stride_Q1, stride_Q2, stride_K, stride_V, stride_mask,
         jt, kb0_start, kb0_stop);
     NO_DEVICE_CODE;
-#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
+#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
 }
 
 template<int DKQ, int DV, int ncols1, int ncols2, bool use_logit_softcap, bool mla>
@@ -1346,7 +1477,7 @@ 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) {
-#if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE))
+#if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)))
 
     // Skip unused kernel variants for faster compilation:
     if (use_logit_softcap && !(DKQ == 128 || DKQ == 256)) {
@@ -1360,6 +1491,13 @@ static __global__ void flash_attn_ext_f16(
     }
 #endif // __CUDA_ARCH__ == GGML_CUDA_CC_TURING
 
+#if defined(AMD_WMMA_AVAILABLE)
+    if (ncols1*ncols2 > 32 || ncols1*ncols2 < 16 || DKQ > 128 || ncols2 == 1) {
+        NO_DEVICE_CODE;
+        return;
+    }
+#endif // defined(AMD_WMMA_AVAILABLE)
+
     static_assert(!mla || DKQ >= DV, "MLA needs DKQ >= DV");
 
     constexpr int ncols     = ncols1 * ncols2;
@@ -1473,7 +1611,7 @@ static __global__ void flash_attn_ext_f16(
               ne31, ne32, ne33,
               nb31, nb32, nb33);
     NO_DEVICE_CODE;
-#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE))
+#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)))
 }
 
 template <int DKQ, int DV, int ncols1, int ncols2>
@@ -1492,7 +1630,7 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml
     const bool Q_in_reg       = ggml_cuda_fattn_mma_get_Q_in_reg      (DKQ, DV, ncols, cc);
     const int  nstages        = ggml_cuda_fattn_mma_get_nstages       (DKQ, DV, ncols1, ncols2, cc);
 
-    const int cols_per_warp = std::min(ncols, turing_mma_available(cc) ? 16 : 32);
+    const int cols_per_warp = std::min(ncols, get_cols_per_warp(cc));
     const int nwarps        = nthreads / WARP_SIZE;
 
     constexpr bool mla = DKQ == 576;
@@ -1512,29 +1650,34 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml
     float logit_softcap;
     memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
 
+#if defined(GGML_USE_HIP)
+    using fattn_kernel_ptr_t = const void*;
+#else
+    using fattn_kernel_ptr_t = fattn_kernel_t;
+#endif // defined(GGML_USE_HIP)
     fattn_kernel_t fattn_kernel;
     if (logit_softcap == 0.0f) {
         constexpr bool use_logit_softcap = false;
         fattn_kernel = flash_attn_ext_f16<DKQ, DV, ncols1, ncols2, use_logit_softcap, mla>;
 
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#if !defined(GGML_USE_MUSA)
         static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
         if (!shared_memory_limit_raised[id]) {
-            CUDA_CHECK(cudaFuncSetAttribute(fattn_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
+            CUDA_CHECK(cudaFuncSetAttribute(reinterpret_cast<fattn_kernel_ptr_t>(fattn_kernel), cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
             shared_memory_limit_raised[id] = true;
         }
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#endif // !defined(GGML_USE_MUSA)
     } else {
         constexpr bool use_logit_softcap = true;
         fattn_kernel = flash_attn_ext_f16<DKQ, DV, ncols1, ncols2, use_logit_softcap, mla>;
 
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#if !defined(GGML_USE_MUSA)
         static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
         if (!shared_memory_limit_raised[id]) {
-            CUDA_CHECK(cudaFuncSetAttribute(fattn_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
+            CUDA_CHECK(cudaFuncSetAttribute(reinterpret_cast<fattn_kernel_ptr_t>(fattn_kernel), cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
             shared_memory_limit_raised[id] = true;
         }
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#endif // !defined(GGML_USE_MUSA)
     }
 
     launch_fattn<DV, ncols1, ncols2>

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

@@ -343,7 +343,7 @@ static __device__ __forceinline__ void flash_attn_tile_load_tile(
                 for (int j0 = j0_start; j0 < j0_stop; j0 += stride_j) {
                     const int j = j0*cpy_ne + (stride_j == warp_size ? threadIdx.x : threadIdx.x % stride_j)*cpy_ne;
 
-                    const half2 zero[cpy_ne] = {{0.0f, 0.0f}};
+                    const __align__(16) half2 zero[cpy_ne] = {{0.0f, 0.0f}};
                     ggml_cuda_memcpy_1<cpy_nb>(
                         tile_KV + i*(J/2 + J_padding) + j,
                         !oob_check || i < i_sup ? KV + i*stride_KV + j : zero);
@@ -394,11 +394,11 @@ static __device__ __forceinline__ void flash_attn_tile_load_tile(
                     const int j = j0*(cpy_ne/2) + (stride_j == warp_size ? threadIdx.x : threadIdx.x % stride_j)*(cpy_ne/2);
 
                     const half2 zero[cpy_ne/2] = {{0.0f, 0.0f}};
-                    half2 tmp_h2[cpy_ne/2];
+                    __align__(16) half2 tmp_h2[cpy_ne/2];
                     ggml_cuda_memcpy_1<sizeof(tmp_h2)>(
                         tmp_h2, !oob_check || i < i_sup ? KV + i*stride_KV + j : zero);
 
-                    float2 tmp_f2[cpy_ne/2];
+                    __align__(16) float2 tmp_f2[cpy_ne/2];
 #pragma unroll
                     for (int l = 0; l < cpy_ne/2; ++l) {
                         tmp_f2[l] = __half22float2(tmp_h2[l]);
@@ -445,14 +445,14 @@ static __device__ __forceinline__ void flash_attn_tile_iter_KQ(
     static_assert((nbatch_K/2) % cpy_ne == 0, "bad nbatch_K");
 #pragma unroll
     for (int k_KQ_1 = 0; k_KQ_1 < nbatch_K/2; k_KQ_1 += cpy_ne) {
-        half2 K_k[nbatch_fa/(np*warp_size)][cpy_ne];
-        half2 Q_k[cpw][cpy_ne];
+        __align__(16) half2 K_k[nbatch_fa/(np*warp_size)][cpy_ne];
+        __align__(16) half2 Q_k[cpw][cpy_ne];
 #else
     static_assert(nbatch_K % cpy_ne == 0, "bad nbatch_K");
 #pragma unroll
     for (int k_KQ_1 = 0; k_KQ_1 < nbatch_K; k_KQ_1 += cpy_ne) {
-        float K_k[nbatch_fa/(np*warp_size)][cpy_ne];
-        float Q_k[cpw][cpy_ne];
+        __align__(16) float K_k[nbatch_fa/(np*warp_size)][cpy_ne];
+        __align__(16) float Q_k[cpw][cpy_ne];
 #endif // FAST_FP16_AVAILABLE
 
 #pragma unroll
@@ -602,9 +602,9 @@ static __device__ __forceinline__ void flash_attn_tile_iter(
 #pragma unroll
     for (int jc0 = 0; jc0 < cpw; jc0 += KQ_cs) {
 #ifdef FAST_FP16_AVAILABLE
-        half  tmp[nbatch_fa/(np*warp_size)][KQ_cs];
+        __align__(16) half  tmp[nbatch_fa/(np*warp_size)][KQ_cs];
 #else
-        float tmp[nbatch_fa/(np*warp_size)][KQ_cs];
+        __align__(16) float tmp[nbatch_fa/(np*warp_size)][KQ_cs];
 #endif // FAST_FP16_AVAILABLE
 
 #pragma unroll
@@ -664,8 +664,8 @@ static __device__ __forceinline__ void flash_attn_tile_iter(
 #ifdef FAST_FP16_AVAILABLE
 #pragma unroll
         for (int k1 = 0; k1 < nbatch_V; k1 += np) {
-            half2 V_k[(DVp/2)/warp_size];
-            half2 KQ_k[cpw];
+            __align__(16) half2 V_k[(DVp/2)/warp_size];
+            __align__(16) half2 KQ_k[cpw];
 
             constexpr int cpy_ne_D = cpy_ne/2 < (DVp/2)/warp_size ? cpy_ne/2 : (DVp/2)/warp_size;
 #pragma unroll
@@ -676,7 +676,7 @@ static __device__ __forceinline__ void flash_attn_tile_iter(
             for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; jc_VKQ_0 += KQ_cs) {
                 const int jc_KQ = jc_VKQ_0/KQ_cs + (threadIdx.y / np)*(cpw/KQ_cs);
 
-                half tmp[KQ_cs];
+                __align__(16) half tmp[KQ_cs];
                 ggml_cuda_memcpy_1<KQ_cs*sizeof(half)>(
                     &tmp, KQ + jc_KQ*(nbatch_fa*KQ_cs) + (k0 + k1 + threadIdx.y % np)*KQ_cs);
 #pragma unroll
@@ -696,8 +696,8 @@ static __device__ __forceinline__ void flash_attn_tile_iter(
 #else
 #pragma unroll
         for (int k1 = 0; k1 < nbatch_V; k1 += np) {
-            float2 V_k[(DVp/2)/warp_size];
-            float  KQ_k[cpw];
+            __align__(16) float2 V_k[(DVp/2)/warp_size];
+            __align__(16) float  KQ_k[cpw];
 
             constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size;
 #pragma unroll
@@ -821,12 +821,12 @@ static __global__ void flash_attn_tile(
     __shared__ half2 Q_tmp[ncols * DKQ/2];
     __shared__ half2 KV_tmp[nbatch_fa * (nbatch_K/2 + cpy_ne) + DVp-DV];
     __shared__ half  KQ[ncols * nbatch_fa];
-    half2 VKQ[cpw * ((DVp/2)/warp_size)] = {{0.0f, 0.0f}};
+    __align__(16) half2 VKQ[cpw * ((DVp/2)/warp_size)] = {{0.0f, 0.0f}};
 #else
     __shared__ float Q_tmp[ncols * DKQ];
     __shared__ float KV_tmp[nbatch_fa * (nbatch_K + cpy_ne) + DVp-DV];
     __shared__ float KQ[ncols * nbatch_fa];
-    float2 VKQ[cpw * ((DVp/2)/warp_size)] = {{0.0f, 0.0f}};
+    __align__(16) float2 VKQ[cpw * ((DVp/2)/warp_size)] = {{0.0f, 0.0f}};
 #endif // FAST_FP16_AVAILABLE
 
     float KQ_max[cpw];
@@ -849,7 +849,7 @@ static __global__ void flash_attn_tile(
 #pragma unroll
         for (int i0 = 0; i0 < DKQp; i0 += np*warp_size*cpy_ne_D) {
             if (i0 + np*warp_size*cpy_ne_D <= DKQ || i0 + (threadIdx.y % np)*(warp_size*cpy_ne_D) + threadIdx.x*cpy_ne_D < DKQ) {
-                float tmp_f[cpy_ne_D] = {0.0f};
+                __align__(16) float tmp_f[cpy_ne_D] = {0.0f};
                 ggml_cuda_memcpy_1<sizeof(tmp_f)>
                     (tmp_f, &Q_f[c*(nb02/sizeof(float)) + fastmodulo(col_Q_0 + j, ne01)*(nb01/sizeof(float))
                                  + i0 + (threadIdx.y % np)*(warp_size*cpy_ne_D) + threadIdx.x*cpy_ne_D]);
@@ -860,7 +860,7 @@ static __global__ void flash_attn_tile(
                 }
 
 #ifdef FAST_FP16_AVAILABLE
-                half2 tmp_h2[cpy_ne_D/2];
+                __align__(16) half2 tmp_h2[cpy_ne_D/2];
 #pragma unroll
                 for (int i1 = 0; i1 < cpy_ne_D; i1 += 2) {
                     tmp_h2[i1/2] = make_half2(tmp_f[i1 + 0], tmp_f[i1 + 1]);
@@ -959,7 +959,7 @@ static __global__ void flash_attn_tile(
             constexpr int cpy_ne_D = cpy_ne < (DVp/2)/warp_size ? cpy_ne : (DVp/2)/warp_size;
 #pragma unroll
             for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) {
-                half2 tmp[cpy_ne_D];
+                __align__(16) half2 tmp[cpy_ne_D];
                 ggml_cuda_memcpy_1<cpy_ne_D*4>(tmp, &VKQ_combine[(threadIdx.y + ip)*(DVp/2) + i0 + threadIdx.x*cpy_ne_D]);
 #pragma unroll
                 for (int i1 = 0; i1 < cpy_ne_D; ++i1) {
@@ -970,7 +970,7 @@ static __global__ void flash_attn_tile(
             constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size;
 #pragma unroll
             for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) {
-                float tmp[cpy_ne_D];
+                __align__(16) float tmp[cpy_ne_D];
                 ggml_cuda_memcpy_1<cpy_ne_D*4>(tmp, &VKQ_combine[(threadIdx.y + ip)*DVp + i0 + threadIdx.x*cpy_ne_D]);
 #pragma unroll
                 for (int i1 = 0; i1 < cpy_ne_D; ++i1) {
@@ -1033,7 +1033,7 @@ static __global__ void flash_attn_tile(
         constexpr int cpy_ne_D = cpy_ne/2 < (DVp/2)/warp_size ? cpy_ne/2 : (DVp/2)/warp_size;
 #pragma unroll
         for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) {
-            float2 tmp[cpy_ne_D];
+            __align__(16) float2 tmp[cpy_ne_D];
 #pragma unroll
             for (int i1 = 0; i1 < cpy_ne_D; ++i1) {
                 tmp[i1] = __half22float2(VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size + i1]);

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

@@ -10,7 +10,7 @@ static constexpr __device__ int ggml_cuda_fattn_vec_get_nthreads_device() {
     return 128;
 }
 
-// Currenlty llvm with the amdgcn target dose not support unrolling loops
+// Currenlty llvm with the amdgcn target does not support unrolling loops
 // that contain a break that can not be resolved at compile time.
 #ifdef __clang__
 #pragma clang diagnostic push
@@ -132,7 +132,7 @@ static __global__ void flash_attn_ext_vec(
 #ifdef V_DOT2_F32_F16_AVAILABLE
     half2  Q_reg[ncols][(D/2)/nthreads_KQ]; // Will be initialized completely.
 #else
-    float2 Q_reg[ncols][(D/2)/nthreads_KQ] = {{{0.0f, 0.0f}}}; // May be only partially initialized.
+    __align__(16) float2 Q_reg[ncols][(D/2)/nthreads_KQ] = {{{0.0f, 0.0f}}}; // May be only partially initialized.
 #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)];
@@ -200,7 +200,7 @@ static __global__ void flash_attn_ext_vec(
             for (int i0 = 0; i0 < D/2; i0 += nthreads_KQ*cpy_ne) {
                 const int i = i0 + (nthreads_KQ == WARP_SIZE ? threadIdx.x : threadIdx.x % nthreads_KQ)*cpy_ne;
 
-                float2 tmp[cpy_ne] = {{0.0f, 0.0f}};
+                __align__(16) float2 tmp[cpy_ne] = {{0.0f, 0.0f}};
                 if (ncols == 1 || ic0 + j < int(ne01.z)) {
                     ggml_cuda_memcpy_1<cpy_nb>(tmp,            &Q_j[i]);
                     ggml_cuda_memcpy_1<cpy_nb>(tmp + cpy_ne/2, &Q_j[i + cpy_ne/2]);

ggml/src/ggml-cuda/fattn.cu

@@ -18,12 +18,12 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ggml_backend_cuda_con
         }
     }
 
-    if (turing_mma_available(cc) && Q->ne[1] <= 16/ncols2) {
+    if ((turing_mma_available(cc) || amd_wmma_available(cc)) && Q->ne[1] <= 16/ncols2) {
         ggml_cuda_flash_attn_ext_mma_f16_case<DKQ, DV, 16/ncols2, ncols2>(ctx, dst);
         return;
     }
 
-    if (ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_TURING || Q->ne[1] <= 32/ncols2) {
+    if (ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_TURING || amd_wmma_available(cc) || Q->ne[1] <= 32/ncols2) {
         ggml_cuda_flash_attn_ext_mma_f16_case<DKQ, DV, 32/ncols2, ncols2>(ctx, dst);
         return;
     }
@@ -230,7 +230,18 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
 
     // The effective batch size for the kernel can be increased by gqa_ratio.
     // The kernel versions without this optimization are also used for ALiBi, if there is no mask, or if the KV cache is not padded,
-    const bool gqa_opt_applies = gqa_ratio % 2 == 0 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
+    bool gqa_opt_applies = gqa_ratio % 2 == 0 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
+    for (const ggml_tensor * t : {Q, K, V, mask}) {
+        if (t == nullptr) {
+            continue;
+        }
+        for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
+            if (t->nb[i] % 16 != 0) {
+                gqa_opt_applies = false;
+                break;
+            }
+        }
+    }
 
     const int cc = ggml_cuda_info().devices[device].cc;
 
@@ -337,6 +348,31 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
         return BEST_FATTN_KERNEL_WMMA_F16;
     }
 
+    if (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc) && gqa_opt_applies && Q->ne[0] <= 128 && Q->ne[0] != 40 && Q->ne[0] != 72) {
+        if (can_use_vector_kernel) {
+            if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) {
+                if (Q->ne[1] == 1) {
+                    if (!gqa_opt_applies) {
+                        return BEST_FATTN_KERNEL_VEC;
+                    }
+                }
+            } else {
+                if (Q->ne[1] <= 2) {
+                    return BEST_FATTN_KERNEL_VEC;
+                }
+            }
+        }
+        int gqa_ratio_eff = 1;
+        const int ncols2_max = Q->ne[0] == 576 ? 16 : 8;
+        while (gqa_ratio % (2*gqa_ratio_eff) == 0 && gqa_ratio_eff < ncols2_max) {
+            gqa_ratio_eff *= 2;
+        }
+        if (Q->ne[1] * gqa_ratio_eff <= 8) {
+            return BEST_FATTN_KERNEL_TILE; // AMD WMMA is only faster if the full tile width of 16 can be utilized.
+        }
+        return BEST_FATTN_KERNEL_MMA_F16;
+    }
+
     // If there are no tensor cores available, use the generic tile kernel:
     if (can_use_vector_kernel) {
         if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) {

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

@@ -2853,9 +2853,9 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
 }
 
 #ifdef USE_CUDA_GRAPH
-static bool check_node_graph_compatibility(ggml_cgraph * cgraph,
-    bool use_cuda_graph) {
+static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) {
 
+    bool use_cuda_graph = true;
     // Loop over nodes in GGML graph to obtain info needed for CUDA graph
 
     const std::string gemma3n_per_layer_proj_src0_name = "inp_per_layer_selected";
@@ -2915,41 +2915,41 @@ static bool check_node_graph_compatibility(ggml_cgraph * cgraph,
     return use_cuda_graph;
 }
 
-static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
-    graph_node_properties->node_address = node->data;
-    graph_node_properties->node_op = node->op;
+static void ggml_cuda_graph_node_set_properties(ggml_cuda_graph_node_properties * props, ggml_tensor * node) {
+    props->node_address = node->data;
+    props->node_op = node->op;
     for (int i = 0; i < GGML_MAX_DIMS; i++) {
-        graph_node_properties->ne[i] = node->ne[i];
-        graph_node_properties->nb[i] = node->nb[i];
+        props->ne[i] = node->ne[i];
+        props->nb[i] = node->nb[i];
     }
     for (int i = 0; i < GGML_MAX_SRC; i++) {
-        graph_node_properties->src_address[i] = node->src[i] ? node->src[i]->data : nullptr;
+        props->src_address[i] = node->src[i] ? node->src[i]->data : nullptr;
     }
-    memcpy(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS);
+    memcpy(props->op_params, node->op_params, GGML_MAX_OP_PARAMS);
 }
 
-static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
-    if (node->data != graph_node_properties->node_address &&
+static bool ggml_cuda_graph_node_properties_match(ggml_tensor * node, ggml_cuda_graph_node_properties * props) {
+    if (node->data != props->node_address &&
           node->op != GGML_OP_VIEW) {
         return false;
     }
 
-    if (node->op != graph_node_properties->node_op) {
+    if (node->op != props->node_op) {
         return false;
     }
 
     for (int i = 0; i < GGML_MAX_DIMS; i++) {
-        if (node->ne[i] != graph_node_properties->ne[i]) {
+        if (node->ne[i] != props->ne[i]) {
             return false;
         }
-        if (node->nb[i] != graph_node_properties->nb[i]) {
+        if (node->nb[i] != props->nb[i]) {
             return false;
         }
     }
 
     for (int i = 0; i < GGML_MAX_SRC; i++) {
         if (node->src[i] &&
-            node->src[i]->data != graph_node_properties->src_address[i] &&
+            node->src[i]->data != props->src_address[i] &&
             node->op != GGML_OP_VIEW
         ) {
             return false;
@@ -2957,44 +2957,55 @@ static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_gra
     }
 
     if ((node->op == GGML_OP_SCALE || node->op == GGML_OP_GLU) &&
-        memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
+        memcmp(props->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
         return false;
     }
 
     return true;
 }
 
-static bool is_cuda_graph_update_required(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph) {
+static bool ggml_cuda_graph_update_required(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph) {
 
-    bool cuda_graph_update_required = false;
+    bool res = false;
 
     if (cuda_ctx->cuda_graph->instance == nullptr) {
-        cuda_graph_update_required = true;
+        res = true;
     }
 
     // Check if the graph size has changed
-    if (cuda_ctx->cuda_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
-        cuda_graph_update_required = true;
-        cuda_ctx->cuda_graph->ggml_graph_properties.resize(cgraph->n_nodes);
+    if (cuda_ctx->cuda_graph->props.size() != (size_t)cgraph->n_nodes + cgraph->n_leafs) {
+        res = true;
+        cuda_ctx->cuda_graph->props.resize(cgraph->n_nodes + cgraph->n_leafs);
     }
 
     // Loop over nodes in GGML graph to determine if CUDA graph update is required
     // and store properties to allow this comparison for the next token
     for (int i = 0; i < cgraph->n_nodes; i++) {
-        bool has_matching_properties = true;
-        if (!cuda_graph_update_required) {
-            has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
+        bool props_match = true;
+        if (!res) {
+            props_match = ggml_cuda_graph_node_properties_match(cgraph->nodes[i], &cuda_ctx->cuda_graph->props[i]);
         }
-        if (!has_matching_properties) {
-            cuda_graph_update_required = true;
+        if (!props_match) {
+            res = true;
         }
-        set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
+        ggml_cuda_graph_node_set_properties(&cuda_ctx->cuda_graph->props[i], cgraph->nodes[i]);
     }
 
-    return cuda_graph_update_required;
+    for (int i = 0; i < cgraph->n_leafs; i++) {
+        bool props_match= true;
+        if (!res) {
+            props_match = ggml_cuda_graph_node_properties_match(cgraph->leafs[i], &cuda_ctx->cuda_graph->props[cgraph->n_nodes + i]);
+        }
+        if (!props_match) {
+            res = true;
+        }
+        ggml_cuda_graph_node_set_properties(&cuda_ctx->cuda_graph->props[cgraph->n_nodes + i], cgraph->leafs[i]);
+    }
+
+    return res;
 }
 
-static void update_cuda_graph_executable(ggml_backend_cuda_context * cuda_ctx) {
+static void ggml_cuda_graph_update_executable(ggml_backend_cuda_context * cuda_ctx) {
 
 #if CUDART_VERSION >= 12000
     cudaGraphExecUpdateResultInfo result_info;
@@ -3225,10 +3236,11 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
     return false;
 }
 
-static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph,
-    bool & graph_evaluated_or_captured, bool & use_cuda_graph, bool & cuda_graph_update_required) {
+static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph, const bool use_cuda_graph, const bool cuda_graph_update_required) {
+    bool graph_evaluated_or_captured = false;
+
     // flag used to determine whether it is an integrated_gpu
-    const bool integrated = ggml_cuda_info().devices[cuda_ctx->device].integrated;
+    const bool integrated            = ggml_cuda_info().devices[cuda_ctx->device].integrated;
 
     ggml_cuda_stream_context & stream_ctx = cuda_ctx->stream_context();
     bool                         is_concurrent_event_active = false;
@@ -3698,7 +3710,7 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
             CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
         }
         if (cuda_graph_update_required) { // Update graph executable
-            update_cuda_graph_executable(cuda_ctx);
+            ggml_cuda_graph_update_executable(cuda_ctx);
         }
         // Launch graph
         CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream()));
@@ -3708,43 +3720,28 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
     }
 }
 
-static bool ggml_cuda_set_cuda_graph_enabled(ggml_backend_cuda_context * cuda_ctx) {
+static bool ggml_cuda_graph_set_enabled(ggml_backend_cuda_context * cuda_ctx) {
 
 #ifdef USE_CUDA_GRAPH
-    static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr);
 
-    // Objects required for CUDA Graph
     if (cuda_ctx->cuda_graph == nullptr) {
         cuda_ctx->cuda_graph.reset(new ggml_cuda_graph());
     }
 
-    bool use_cuda_graph = true;
-
     if (cuda_ctx->cuda_graph->graph == nullptr) {
         if (ggml_cuda_info().devices[cuda_ctx->device].cc < GGML_CUDA_CC_AMPERE) {
+            if (!cuda_ctx->cuda_graph->disable_due_to_gpu_arch) {
+                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
+            }
             cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
-#ifndef NDEBUG
-            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
-#endif
         }
     }
 
-    // Disable CUDA graphs in presence of env var, old GPU, use-case which is changing too rapidly,
-    // or previous graph capture failure.
-    // Also disable for multi-gpu for now. TO DO investigate
-    if (disable_cuda_graphs_due_to_env
-        || cuda_ctx->cuda_graph->disable_due_to_gpu_arch
-        || cuda_ctx->cuda_graph->disable_due_to_too_many_updates
-        || cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture) {
-        use_cuda_graph = false;
-    }
-
-    cuda_ctx->cuda_graph->cuda_graphs_enabled = use_cuda_graph;
+    return cuda_ctx->cuda_graph->is_enabled();
 #else
-    bool use_cuda_graph = false;
+    GGML_UNUSED(cuda_ctx);
+    return false;
 #endif // USE_CUDA_GRAPH
-
-    return use_cuda_graph;
 }
 
 static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
@@ -3755,30 +3752,14 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
     bool use_cuda_graph             = false;
     bool cuda_graph_update_required = false;
 
-    // graph_optimize calls set_cuda_graph_enabled, in-case it not called (i.e. graph_compute is directly called)
-    // we call it here instead.
 #ifdef USE_CUDA_GRAPH
-    use_cuda_graph = ggml_cuda_set_cuda_graph_enabled(cuda_ctx);
+    use_cuda_graph = ggml_cuda_graph_set_enabled(cuda_ctx);
 
-    if (use_cuda_graph) {
-        cuda_graph_update_required = is_cuda_graph_update_required(cuda_ctx, cgraph);
+    if (cuda_ctx->cuda_graph->is_enabled()) {
+        cuda_graph_update_required = ggml_cuda_graph_update_required(cuda_ctx, cgraph);
+        use_cuda_graph             = ggml_cuda_graph_check_compability(cgraph);
 
-        use_cuda_graph = check_node_graph_compatibility(cgraph, use_cuda_graph);
-
-        // Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates.
-        if (use_cuda_graph && cuda_graph_update_required) {
-            cuda_ctx->cuda_graph->number_consecutive_updates++;
-        } else {
-            cuda_ctx->cuda_graph->number_consecutive_updates = 0;
-        }
-
-        if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
-            cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
-            cuda_ctx->cuda_graph->cuda_graphs_enabled = false;
-#ifndef NDEBUG
-            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
-#endif
-        }
+        cuda_ctx->cuda_graph->record_update(use_cuda_graph, cuda_graph_update_required);
     }
 #endif // USE_CUDA_GRAPH
 
@@ -3792,9 +3773,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
         CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
     }
 
-    bool graph_evaluated_or_captured = false;
-
-    evaluate_and_capture_cuda_graph(cuda_ctx, cgraph, graph_evaluated_or_captured, use_cuda_graph, cuda_graph_update_required);
+    ggml_cuda_graph_evaluate_and_capture(cuda_ctx, cgraph, use_cuda_graph, cuda_graph_update_required);
 
     return GGML_STATUS_SUCCESS;
 }
@@ -3827,7 +3806,7 @@ static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_ev
 static void ggml_backend_cuda_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
 
-    const bool use_cuda_graph = ggml_cuda_set_cuda_graph_enabled(cuda_ctx);
+    const bool use_cuda_graph = ggml_cuda_graph_set_enabled(cuda_ctx);
 
     static bool enable_graph_optimization = [] {
         const char * env     = getenv("GGML_CUDA_GRAPH_OPT");
@@ -4146,6 +4125,7 @@ struct ggml_backend_cuda_device_context {
     std::string name;
     std::string description;
     std::string pci_bus_id;
+    int op_offload_min_batch_size;
 };
 
 static const char * ggml_backend_cuda_device_get_name(ggml_backend_dev_t dev) {
@@ -4573,7 +4553,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_L2_NORM:
             return true;
         case GGML_OP_RMS_NORM_BACK:
-            return ggml_is_contiguous(op->src[0]) && op->ne[0] % WARP_SIZE == 0;
+            return ggml_is_contiguous(op->src[0]);
             break;
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
@@ -4700,11 +4680,9 @@ static int64_t get_op_batch_size(const ggml_tensor * op) {
 }
 
 static bool ggml_backend_cuda_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
-    const int min_batch_size = 32;
+    ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) dev->context;
 
-    return get_op_batch_size(op) >= min_batch_size;
-
-    GGML_UNUSED(dev);
+    return get_op_batch_size(op) >= dev_ctx->op_offload_min_batch_size;
 }
 
 static ggml_backend_event_t ggml_backend_cuda_device_event_new(ggml_backend_dev_t dev) {
@@ -4872,6 +4850,7 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
         std::lock_guard<std::mutex> lock(mutex);
         if (!initialized) {
             ggml_backend_cuda_reg_context * ctx = new ggml_backend_cuda_reg_context;
+            const int min_batch_size = getenv("GGML_OP_OFFLOAD_MIN_BATCH") ? atoi(getenv("GGML_OP_OFFLOAD_MIN_BATCH")) : 32;
 
             for (int i = 0; i < ggml_cuda_info().device_count; i++) {
                 ggml_backend_cuda_device_context * dev_ctx = new ggml_backend_cuda_device_context;
@@ -4885,6 +4864,7 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
                 char pci_bus_id[16] = {};
                 snprintf(pci_bus_id, sizeof(pci_bus_id), "%04x:%02x:%02x.0", prop.pciDomainID, prop.pciBusID, prop.pciDeviceID);
                 dev_ctx->pci_bus_id = pci_bus_id;
+                dev_ctx->op_offload_min_batch_size = min_batch_size;
 
                 ggml_backend_dev_t dev = new ggml_backend_device {
                     /* .iface   = */ ggml_backend_cuda_device_interface,

ggml/src/ggml-cuda/mean.cu

@@ -34,13 +34,11 @@ void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
             // CUDA_GRAPHS_DISABLED
             ((ncols > 65536) &&
              ((ctx.cuda_graph->instance == nullptr) && (iscapturing == cudaStreamCaptureStatusNone) ||
-              ctx.cuda_graph->disable_due_to_gpu_arch || ctx.cuda_graph->disable_due_to_too_many_updates ||
-              ctx.cuda_graph->disable_due_to_failed_graph_capture)) ||
+              ctx.cuda_graph->is_enabled())) ||
         // CUDA_GRAPHS ENABLED
         ((ncols > 32768) &&
          !((ctx.cuda_graph->instance == nullptr) && (iscapturing == cudaStreamCaptureStatusNone) ||
-           ctx.cuda_graph->disable_due_to_gpu_arch || ctx.cuda_graph->disable_due_to_too_many_updates ||
-           ctx.cuda_graph->disable_due_to_failed_graph_capture))) {
+            ctx.cuda_graph->is_enabled()))) {
 #else
         (ncols > 65536)) {
 #endif // USE_CUDA_GRAPH

ggml/src/ggml-cuda/mma.cuh

@@ -206,10 +206,16 @@ namespace ggml_cuda_mma {
 
         static __device__ __forceinline__ int get_j(const int l) {
             if constexpr (I == 16 && J == 16) {
-                // matrix C
 #if defined(RDNA3)
-                return 2 * l + (threadIdx.x / 16);
+                if constexpr (std::is_same_v<T, float> || std::is_same_v<T, int>) {
+                    // matrix C
+                    return 2 * l + (threadIdx.x / 16);
+                } else {
+                    // matrix A&B
+                    return l;
+                }
 #else
+                // matrix C is the transposed matrix A&B on RDNA4
                 return ne * (threadIdx.x / 16) + l;
 #endif // defined(RDNA3)
             } else if constexpr (I == 16 && J == 8) {
@@ -621,6 +627,21 @@ namespace ggml_cuda_mma {
 
         return ret;
     }
+#elif defined(AMD_WMMA_AVAILABLE)
+    template <int I, int J>
+    static __device__ __forceinline__ tile<I, J/2, half2> get_half2(const tile<I, J, float> & tile_float) {
+        tile<I, J/2, half2> ret;
+#pragma unroll
+        for (int l0 = 0; l0 < tile_float.ne; l0 += 2) {
+            ret.x[l0/2] = make_half2(tile_float.x[l0 + 0], tile_float.x[l0 + 1]);
+        }
+        return ret;
+    }
+
+    static __device__ __forceinline__ tile<8, 8, half2> get_transposed(const tile<16, 4, half2> & t) {
+        NO_DEVICE_CODE;
+        return tile<8, 8, half2>{};
+    }
 #else // Volta
     template <int I, int J>
     static __device__ __forceinline__ tile<I, J/2, half2> get_half2(const tile<I, J, float> & tile_float) {
@@ -639,6 +660,19 @@ namespace ggml_cuda_mma {
     }
 #endif // defined(TURING_MMA_AVAILABLE)
 
+    static __device__ __forceinline__ void make_identity_mat(tile<16, 8, half2> & t) {
+#if defined(RDNA4)
+        const int row = t.get_i(0);
+        const int left_right = t.get_j(0) / 4;
+        const int up_down = row / 8;
+        const int idx = row % 8;
+        reinterpret_cast<half*>(t.x)[idx] = left_right == up_down ? 1.0f : 0.0f;
+#else
+        GGML_UNUSED_VARS(t);
+        NO_DEVICE_CODE;
+#endif // defined(RDNA4)
+    }
+
     template <int I, int J, typename T, data_layout dl>
     static __device__ __forceinline__ void load_generic(tile<I, J, T, dl> & t, const T * __restrict__ xs0, const int stride) {
 #if defined(AMD_MFMA_AVAILABLE)
@@ -878,6 +912,17 @@ namespace ggml_cuda_mma {
             : "+r"(Dxi[2]), "+r"(Dxi[3])
             : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[3]));
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#elif defined(AMD_WMMA_AVAILABLE)
+#if defined(RDNA4)
+        using halfx8_t = __attribute__((ext_vector_type(8))) _Float16;
+        halfx8_t& acc_frag = reinterpret_cast<halfx8_t&>(D.x[0]);
+        const halfx8_t& a_frag = reinterpret_cast<const halfx8_t&>(A.x[0]);
+        const halfx8_t& b_frag = reinterpret_cast<const halfx8_t&>(B.x[0]);
+        acc_frag = __builtin_amdgcn_wmma_f16_16x16x16_f16_w32_gfx12(a_frag, b_frag, acc_frag);
+#else
+        GGML_UNUSED_VARS(D, A, B);
+        NO_DEVICE_CODE;
+#endif // defined(RDNA4)
 #else
         GGML_UNUSED_VARS(D, A, B);
         NO_DEVICE_CODE;