Merge branch 'master' into xsn/lora_keep_track

.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 openssl-devel python3 python3-pip && \
+RUN yum install -y gcc g++ cmake make git libcurl-devel python3 python3-pip && \
     yum clean all && \
     rm -rf /var/cache/yum
 
@@ -42,7 +42,6 @@ RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh --force \
         -DGGML_CANN=ON \
         -DCMAKE_BUILD_TYPE=Release \
         -DSOC_TYPE=ascend${CHIP_TYPE} \
-        -DUSE_ACL_GRAPH=ON \
         . && \
     cmake --build build --config Release -j$(nproc)
 

.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 libssl-dev
+    apt-get install -y build-essential git cmake libcurl4-openssl-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 libssl-dev libgomp1
+    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-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 libssl-dev libgomp1
+    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-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 libssl-dev
+    apt-get install -y git libcurl4-openssl-dev
 
 WORKDIR /app
 

.devops/llama-cli-cann.Dockerfile

@@ -6,7 +6,7 @@ WORKDIR /app
 
 COPY . .
 
-RUN yum install -y gcc g++ cmake make openssl-devel
+RUN yum install -y gcc g++ cmake make libcurl-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 \
-    libssl-dev \
+    libcurl4-openssl-dev \
     libgomp1
 
 WORKDIR /app

.devops/nix/package.nix

@@ -32,6 +32,7 @@
   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
@@ -159,13 +160,15 @@ effectiveStdenv.mkDerivation (finalAttrs: {
     ++ optionals useMpi [ mpi ]
     ++ optionals useRocm rocmBuildInputs
     ++ optionals useBlas [ blas ]
-    ++ optionals useVulkan vulkanBuildInputs;
+    ++ optionals useVulkan vulkanBuildInputs
+    ++ optionals enableCurl [ curl ];
 
   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 \
-    libssl-dev \
+    libcurl4-openssl-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 libssl-dev && \
+        libopenblas-dev libcurl4-openssl-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 SSL and Vulkan SDK dependencies
-RUN apt install -y libssl-dev curl \
+# Install cURL and Vulkan SDK dependencies
+RUN apt install -y libcurl4-openssl-dev curl \
     libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc
 
 # Build it

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

@@ -20,7 +20,7 @@ jobs:
         run: |
           PREFIX="$(pwd)"/inst
           cmake -S . -B build -DCMAKE_PREFIX_PATH="$PREFIX" \
-                -DLLAMA_OPENSSL=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=OFF \
+                -DLLAMA_CURL=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_OPENSSL=OFF \
+  #         cmake -B build -DLLAMA_CURL=OFF \
   #                        -DCMAKE_BUILD_TYPE=Release \
   #                        -DGGML_OPENMP=OFF \
   #                        -DLLAMA_BUILD_EXAMPLES=ON \
@@ -76,7 +76,7 @@ jobs:
 
   #     - name: Build
   #       run: |
-  #         cmake -B build -DLLAMA_OPENSSL=OFF \
+  #         cmake -B build -DLLAMA_CURL=OFF \
   #                        -DCMAKE_BUILD_TYPE=Release \
   #                        -DGGML_VULKAN=ON \
   #                        -DGGML_OPENMP=OFF \
@@ -122,7 +122,7 @@ jobs:
 
   #     - name: Build
   #       run: |
-  #         cmake -B build -DLLAMA_OPENSSL=OFF \
+  #         cmake -B build -DLLAMA_CURL=OFF \
   #                        -DCMAKE_BUILD_TYPE=Release \
   #                        -DGGML_VULKAN=ON \
   #                        -DGGML_OPENMP=OFF \
@@ -178,7 +178,7 @@ jobs:
 
       - name: Build
         run: |
-          cmake -B build -DLLAMA_OPENSSL=OFF \
+          cmake -B build -DLLAMA_CURL=OFF \
                          -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \
@@ -235,7 +235,7 @@ jobs:
 
       - name: Build
         run: |
-          cmake -B build -DLLAMA_OPENSSL=OFF \
+          cmake -B build -DLLAMA_CURL=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_OPENSSL=OFF \
+          cmake -B build -DLLAMA_CURL=OFF \
                          -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \

.github/workflows/build.yml

@@ -79,6 +79,7 @@ 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 \
@@ -91,7 +92,7 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest -L main --verbose --timeout 900
+          ctest -L 'main|curl' --verbose --timeout 900
 
   macOS-latest-cmake-x64:
     runs-on: macos-15-intel
@@ -117,6 +118,7 @@ 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 \
@@ -225,6 +227,8 @@ 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)
@@ -233,7 +237,7 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest -L main --verbose --timeout 900
+          ctest -L 'main|curl' --verbose --timeout 900
 
       - name: Test llama2c conversion
         id: llama2c_test
@@ -289,6 +293,8 @@ 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 }}
@@ -299,6 +305,8 @@ 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 }} \
@@ -328,10 +336,14 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          cmake -B build \
+          mkdir build
+          cd build
+          cmake .. \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_LLGUIDANCE=ON
-          cmake --build build --config Release -j $(nproc)
+          cmake --build . --config Release -j $(nproc)
 
       - name: Test
         id: cmake_test
@@ -365,6 +377,8 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DGGML_RPC=ON
           cmake --build build --config Release -j $(nproc)
 
@@ -398,6 +412,8 @@ 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 \
@@ -454,6 +470,8 @@ 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)
 
@@ -527,6 +545,8 @@ jobs:
         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)
 
@@ -573,7 +593,7 @@ jobs:
           source emsdk/emsdk_env.sh
           emcmake cmake -B build-wasm \
             -DGGML_WEBGPU=ON \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DEMDAWNWEBGPU_DIR=emdawnwebgpu_pkg
 
           cmake --build build-wasm --target test-backend-ops -j $(nproc)
@@ -604,6 +624,8 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build -S . \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
             -DGGML_HIP_ROCWMMA_FATTN=ON \
             -DGGML_HIP=ON
@@ -635,6 +657,8 @@ 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)
 
@@ -682,6 +706,8 @@ 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
@@ -731,6 +757,8 @@ 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 \
@@ -865,7 +893,7 @@ jobs:
           cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -1015,7 +1043,7 @@ jobs:
         id: cmake_build
         run: |
           cmake -S . -B build ${{ matrix.defines }} `
-            -DLLAMA_BUILD_BORINGSSL=ON
+            -DLLAMA_CURL=OFF -DLLAMA_BUILD_BORINGSSL=ON
           cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS}
 
       - name: Add libopenblas.dll
@@ -1073,6 +1101,8 @@ 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 \
@@ -1120,6 +1150,7 @@ 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 ^
@@ -1227,6 +1258,7 @@ 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="${env:HIP_PATH}" `
             -DGGML_HIP=ON `
@@ -1253,7 +1285,7 @@ jobs:
           cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -1320,7 +1352,7 @@ jobs:
       matrix:
         include:
           - build: 'arm64-cpu'
-            defines: '-D ANDROID_ABI=arm64-v8a -D ANDROID_PLATFORM=android-31 -D CMAKE_TOOLCHAIN_FILE=${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_OPENSSL=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_CURL=OFF -D GGML_OPENMP=OFF'
           - build: 'arm64-snapdragon'
             defines: '--preset arm64-android-snapdragon-release'
 
@@ -1394,11 +1426,6 @@ jobs:
         arch: [x86, aarch64]
         chip_type: ['910b', '310p']
         build: ['Release']
-        use_acl_graph: ['on', 'off']
-        exclude:
-          # 310P does not support USE_ACL_GRAPH=on
-          - chip_type: '310p'
-            use_acl_graph: 'on'
     runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
     steps:
       - name: Checkout
@@ -1424,7 +1451,6 @@ jobs:
         env:
           BUILD_TYPE: ${{ matrix.build }}
           SOC_TYPE: ascend${{ matrix.chip_type }}
-          USE_ACL_GRAPH: ${{ matrix.use_acl_graph }}
         run: |
           HOST_UID=$(id -u)
           HOST_GID=$(id -g)
@@ -1434,7 +1460,6 @@ jobs:
             -w /workspace \
             -e SOC_TYPE=${SOC_TYPE} \
             -e BUILD_TYPE=${BUILD_TYPE} \
-            -e USE_ACL_GRAPH=${USE_ACL_GRAPH} \
             "${{ steps.cann-image.outputs.image }}" \
             bash -lc '
               set -e
@@ -1444,9 +1469,10 @@ jobs:
               export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
               cmake -S . -B build \
                   -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
+                  -DLLAMA_CURL=OFF \
+                  -DLLAMA_OPENSSL=ON \
                   -DGGML_CANN=on \
-                  -DSOC_TYPE=${SOC_TYPE} \
-                  -DUSE_ACL_GRAPH=${USE_ACL_GRAPH}
+                  -DSOC_TYPE=${SOC_TYPE}
               cmake --build build -j $(nproc)
 
               chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build
@@ -1473,7 +1499,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Test
         id: ggml-ci
@@ -1499,7 +1525,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Test
         id: ggml-ci
@@ -1525,7 +1551,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Test
         id: ggml-ci
@@ -1551,7 +1577,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Test
         id: ggml-ci
@@ -1577,7 +1603,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Test
         id: ggml-ci
@@ -1741,7 +1767,7 @@ jobs:
          id: depends
          run: |
            sudo apt-get update
-           sudo apt-get install -y build-essential
+           sudo apt-get install -y build-essential libcurl4-openssl-dev
 
        - name: Test
          id: ggml-ci
@@ -1808,6 +1834,8 @@ 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 \
@@ -1825,7 +1853,7 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest -L main --verbose --timeout 900
+          ctest -L 'main|curl' --verbose --timeout 900
 
       - name: Test llama2c conversion
         id: llama2c_test
@@ -1900,7 +1928,7 @@ jobs:
         if: ${{ matrix.sanitizer != 'THREAD' }}
         run: |
           cmake -B build \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
             -DGGML_OPENMP=ON \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -1919,7 +1947,7 @@ jobs:
         if: ${{ matrix.sanitizer == 'THREAD' }}
         run: |
           cmake -B build \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
             -DGGML_OPENMP=OFF \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -1990,7 +2018,7 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DCMAKE_BUILD_TYPE=Release \
             -DGGML_OPENMP=OFF \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -2064,6 +2092,8 @@ 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 \
@@ -2099,6 +2129,7 @@ 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 libssl-dev
+          sudo apt-get install build-essential libcurl4-openssl-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

@@ -45,6 +45,7 @@ jobs:
             -DCMAKE_INSTALL_RPATH='@loader_path' \
             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
+            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_BORINGSSL=ON \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
@@ -94,6 +95,7 @@ jobs:
             -DCMAKE_INSTALL_RPATH='@loader_path' \
             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
+            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_BORINGSSL=ON \
             -DGGML_METAL=OFF \
             -DGGML_RPC=ON \
@@ -159,6 +161,8 @@ jobs:
             -DGGML_NATIVE=OFF \
             -DGGML_CPU_ALL_VARIANTS=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             ${{ env.CMAKE_ARGS }}
           cmake --build build --config Release -j $(nproc)
 
@@ -208,6 +212,8 @@ jobs:
           cmake -B build \
             -DCMAKE_INSTALL_RPATH='$ORIGIN' \
             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DGGML_BACKEND_DL=ON \
             -DGGML_NATIVE=OFF \
             -DGGML_CPU_ALL_VARIANTS=ON \
@@ -263,6 +269,7 @@ jobs:
           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_CURL=OFF ^
             -DLLAMA_BUILD_BORINGSSL=ON ^
             -DGGML_NATIVE=OFF ^
             -DGGML_BACKEND_DL=ON ^
@@ -351,7 +358,7 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          cmake -S . -B build ${{ matrix.defines }} -DGGML_NATIVE=OFF -DGGML_CPU=OFF -DGGML_BACKEND_DL=ON -DLLAMA_BUILD_BORINGSSL=ON
+          cmake -S . -B build ${{ matrix.defines }} -DGGML_NATIVE=OFF -DGGML_CPU=OFF -DGGML_BACKEND_DL=ON -DLLAMA_CURL=OFF
           cmake --build build --config Release --target ${{ matrix.target }}
 
       - name: Pack artifacts
@@ -405,7 +412,7 @@ jobs:
             -DGGML_NATIVE=OFF ^
             -DGGML_CPU=OFF ^
             -DGGML_CUDA=ON ^
-            -DLLAMA_BUILD_BORINGSSL=ON ^
+            -DLLAMA_CURL=OFF ^
             -DGGML_CUDA_CUB_3DOT2=ON
           set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
           cmake --build build --config Release -j %NINJA_JOBS% --target ggml-cuda
@@ -474,7 +481,7 @@ jobs:
             -DCMAKE_BUILD_TYPE=Release ^
             -DGGML_BACKEND_DL=ON -DBUILD_SHARED_LIBS=ON ^
             -DGGML_CPU=OFF -DGGML_SYCL=ON ^
-            -DLLAMA_BUILD_BORINGSSL=ON
+            -DLLAMA_CURL=OFF
           cmake --build build --target ggml-sycl -j
 
       - name: Build the release package
@@ -601,7 +608,7 @@ jobs:
             -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}" `
             -DGGML_HIP_ROCWMMA_FATTN=ON `
             -DGGML_HIP=ON `
-            -DLLAMA_BUILD_BORINGSSL=ON
+            -DLLAMA_CURL=OFF
           cmake --build build --target ggml-hip -j ${env:NUMBER_OF_PROCESSORS}
           md "build\bin\rocblas\library\"
           md "build\bin\hipblaslt\library"
@@ -642,7 +649,7 @@ jobs:
           cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -681,25 +688,9 @@ jobs:
   openEuler-cann:
     strategy:
       matrix:
-        include:
-          # 910b with aclgraph (both architectures)
-          - arch: x86
-            chip_type: '910b'
-            build: 'Release'
-            use_acl_graph: 'on'
-          - arch: aarch64
-            chip_type: '910b'
-            build: 'Release'
-            use_acl_graph: 'on'
-          # 310p without aclgraph (both architectures)
-          - arch: x86
-            chip_type: '310p'
-            build: 'Release'
-            use_acl_graph: 'off'
-          - arch: aarch64
-            chip_type: '310p'
-            build: 'Release'
-            use_acl_graph: 'off'
+        arch: [x86, aarch64]
+        chip_type: ['910b', '310p']
+        build: ['Release']
     runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
     steps:
       - name: Checkout
@@ -725,7 +716,6 @@ jobs:
         env:
           BUILD_TYPE: ${{ matrix.build }}
           SOC_TYPE: ascend${{ matrix.chip_type }}
-          USE_ACL_GRAPH: ${{ matrix.use_acl_graph }}
         run: |
           HOST_UID=$(id -u)
           HOST_GID=$(id -g)
@@ -735,7 +725,6 @@ jobs:
             -w /workspace \
             -e SOC_TYPE=${SOC_TYPE} \
             -e BUILD_TYPE=${BUILD_TYPE} \
-            -e USE_ACL_GRAPH=${USE_ACL_GRAPH} \
             "${{ steps.cann-image.outputs.image }}" \
             bash -lc '
               set -e
@@ -745,9 +734,10 @@ jobs:
               export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
               cmake -S . -B build \
                   -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
+                  -DLLAMA_CURL=OFF \
+                  -DLLAMA_OPENSSL=ON \
                   -DGGML_CANN=on \
-                  -DSOC_TYPE=${SOC_TYPE} \
-                  -DUSE_ACL_GRAPH=${USE_ACL_GRAPH}
+                  -DSOC_TYPE=${SOC_TYPE}
               cmake --build build -j $(nproc)
 
               chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build
@@ -760,13 +750,13 @@ jobs:
       - name: Pack artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v4
         with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz
-          name: llama-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz
+          path: llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz
+          name: llama-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz
 
   release:
     if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
@@ -881,9 +871,9 @@ jobs:
 
             **openEuler:**
             - [openEuler x86 (310p)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-310p-openEuler-x86.tar.gz)
-            - [openEuler x86 (910b, ACL Graph)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-x86-aclgraph.tar.gz)
+            - [openEuler x86 (910b)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-x86.tar.gz)
             - [openEuler aarch64 (310p)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-310p-openEuler-aarch64.tar.gz)
-            - [openEuler aarch64 (910b, ACL Graph)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-aarch64-aclgraph.tar.gz)
+            - [openEuler aarch64 (910b)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-aarch64.tar.gz)
 
       - name: Upload release
         id: upload_release

.github/workflows/server-webui.yml

@@ -168,6 +168,8 @@ 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 \
@@ -180,6 +182,8 @@ 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 ;
@@ -191,6 +195,8 @@ 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_BUILD_BORINGSSL=ON
+          cmake -B build -DLLAMA_CURL=OFF -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_BUILD_BORINGSSL=ON
+          cmake -B build -DLLAMA_CURL=OFF -DLLAMA_BUILD_BORINGSSL=ON
           cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
 
       - name: Python setup

CMakeLists.txt

@@ -111,16 +111,11 @@ 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_HTTPLIB    "llama: httplib for downloading functionality" ON)
-option(LLAMA_OPENSSL    "llama: use openssl to support HTTPS" ON)
+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_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)
@@ -217,6 +212,11 @@ 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)

README.md

@@ -586,5 +586,6 @@ $ 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
+- [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

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_OPENSSL=OFF \
+    -DLLAMA_CURL=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_OPENSSL=OFF \
+    -DLLAMA_CURL=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_OPENSSL=OFF \
+    -DLLAMA_CURL=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_OPENSSL=OFF \
+    -DLLAMA_CURL=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 ${COMMON_CXX_FLAGS}" \
-    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_CURL=OFF \
     -DLLAMA_HTTPLIB=OFF \
     -DLLAMA_BUILD_SERVER=OFF \
     -S .
@@ -487,7 +487,7 @@ cmake -B build-tvos-sim -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvsimulator \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_CURL=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_OPENSSL=OFF \
+    -DLLAMA_CURL=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=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_OPENSSL=OFF -DGGML_SCHED_NO_REALLOC=ON"
+CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_CURL=ON -DGGML_SCHED_NO_REALLOC=ON"
 
 if [ ! -z ${GG_BUILD_METAL} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"

cmake/download-models.cmake

@@ -1,21 +0,0 @@
-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()

common/CMakeLists.txt

@@ -60,8 +60,6 @@ add_library(${TARGET} STATIC
     common.h
     console.cpp
     console.h
-    debug.cpp
-    debug.h
     download.cpp
     download.h
     http.h
@@ -97,7 +95,17 @@ endif()
 # TODO: use list(APPEND LLAMA_COMMON_EXTRA_LIBS ...)
 set(LLAMA_COMMON_EXTRA_LIBS build_info)
 
-if (LLAMA_HTTPLIB)
+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} ${CURL_LIBRARIES})
+elseif (LLAMA_HTTPLIB)
+    # otherwise, use cpp-httplib
     target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_HTTPLIB)
     set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} cpp-httplib)
 endif()

common/arg.cpp

@@ -341,7 +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); // error message already printed
+                        exit(1); // built without CURL, error message already printed
                     }
                     model.name    = model.hf_repo;      // repo name with tag
                     model.hf_repo = auto_detected.repo; // repo name without tag
@@ -1729,26 +1729,6 @@ 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),

common/chat-parser.cpp

@@ -1403,118 +1403,6 @@ 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());
@@ -1602,9 +1490,6 @@ 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

@@ -670,7 +670,6 @@ 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";
@@ -2540,65 +2539,6 @@ 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);
@@ -2769,13 +2709,6 @@ 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

@@ -125,7 +125,6 @@ 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

@@ -1172,6 +1172,7 @@ 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();

common/common.h

@@ -119,7 +119,6 @@ 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
@@ -167,34 +166,32 @@ 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)
-    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
+    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
     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

common/debug.cpp

@@ -1,165 +0,0 @@
-#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

@@ -1,43 +0,0 @@
-#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

@@ -19,7 +19,10 @@
 #include <thread>
 #include <vector>
 
-#if defined(LLAMA_USE_HTTPLIB)
+#if defined(LLAMA_USE_CURL)
+#include <curl/curl.h>
+#include <curl/easy.h>
+#elif defined(LLAMA_USE_HTTPLIB)
 #include "http.h"
 #endif
 
@@ -168,7 +171,336 @@ std::pair<std::string, std::string> common_download_split_repo_tag(const std::st
     return {hf_repo, tag};
 }
 
-#if defined(LLAMA_USE_HTTPLIB)
+#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;
+}
+
+// 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;
+        }
+    }
+
+    return n_items;
+}
+
+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
+// 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 common_header_list & custom_headers) {
+    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;
+
+        for (const auto & h : custom_headers) {
+             std::string s = h.first + ": " + h.second;
+             http_headers.ptr = curl_slist_append(http_headers.ptr, s.c_str());
+        }
+        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 -1;
+                }
+            }
+
+            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 -1;
+                    }
+                }
+
+                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 -1;
+                    }
+                }
+            }
+            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);
+
+            int status = static_cast<int>(http_code);
+            if (!is_http_status_ok(http_code)) {
+                LOG_ERR("%s: invalid http status code received: %ld\n", __func__, http_code);
+                return status; // TODO: maybe only return on certain codes
+            }
+
+            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 -1;
+            }
+
+            return static_cast<int>(http_code);
+        } else {
+            LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
+
+            return 304; // Not Modified - fake cached response
+        }
+    }
+
+    return -1; // max attempts reached
+}
+
+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) {
+        std::string header_ = header.first + ": " + header.second;
+        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)
 
 class ProgressBar {
     static inline std::mutex mutex;
@@ -465,6 +797,10 @@ 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)
+
 int common_download_file_single(const std::string & url,
                                 const std::string & path,
                                 const std::string & bearer_token,
@@ -815,7 +1151,7 @@ int common_download_file_single(const std::string &,
     throw std::runtime_error("download functionality is not enabled in this build");
 }
 
-#endif // defined(LLAMA_USE_HTTPLIB)
+#endif // LLAMA_USE_CURL || LLAMA_USE_HTTPLIB
 
 std::vector<common_cached_model_info> common_list_cached_models() {
     std::vector<common_cached_model_info> models;

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, adaptive_target = %.3f, adaptive_decay = %.3f",
+            "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.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, adaptive_target, adaptive_decay);
+            mirostat, mirostat_eta, mirostat_tau);
 
     return std::string(result);
 }
@@ -255,9 +255,6 @@ 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:
@@ -267,54 +264,43 @@ 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));
-                    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;
+                    samplers.push_back(llama_sampler_init_penalties  (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
                     break;
                 default:
                     GGML_ASSERT(false && "unknown sampler type");
             }
         }
-        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));
-        }
+
+        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));
@@ -348,21 +334,15 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
 }
 
 void common_sampler_free(struct common_sampler * gsmpl) {
-    if (!gsmpl) {
-        return;
+    if (gsmpl) {
+        llama_sampler_free(gsmpl->grmr);
+        llama_sampler_free(gsmpl->chain);
+
+        delete gsmpl;
     }
-
-    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) {
@@ -375,10 +355,6 @@ 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();
 }
 
@@ -439,10 +415,6 @@ 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;
 }
 
@@ -639,7 +611,6 @@ 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 '?';
     }
 }
@@ -656,7 +627,6 @@ 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 "";
     }
 }
@@ -673,7 +643,6 @@ 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
@@ -689,7 +658,6 @@ 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;
@@ -726,7 +694,6 @@ 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

@@ -1252,9 +1252,6 @@ 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")
@@ -8751,102 +8748,6 @@ 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"""

convert_hf_to_gguf_update.py

@@ -147,7 +147,6 @@ 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_OPENSSL":    "OFF"
+            "LLAMA_CURL":       "OFF"
         }
     },
 
@@ -38,7 +38,7 @@
             "GGML_OPENCL":      "ON",
             "GGML_HEXAGON":     "ON",
             "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
-            "LLAMA_OPENSSL":    "OFF"
+            "LLAMA_CURL":       "OFF"
         }
     },
 

docs/backend/hexagon/README.md

@@ -210,10 +210,6 @@ 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_OPENSSL=OFF \
+    -DLLAMA_CURL=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
       ```
-- 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`
+- 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
 
 ## BLAS Build
 

examples/batched/batched.cpp

@@ -81,6 +81,7 @@ 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/debug.cpp

@@ -1,9 +1,11 @@
-#include "debug.h"
 #include "arg.h"
 #include "common.h"
 #include "log.h"
 #include "llama.h"
+#include "ggml.h"
 
+#include <cmath>
+#include <cstdint>
 #include <cstdlib>
 #include <string>
 #include <vector>
@@ -11,7 +13,7 @@
 #include <fstream>
 #include <regex>
 
-static void print_usage(int /*argc*/, char ** argv) {
+static void print_usage(int, char ** argv) {
     const std::string usage_template = R"(
         example usage:
 
@@ -33,6 +35,28 @@ static void print_usage(int /*argc*/, char ** argv) {
     LOG("%s\n", usage.c_str());
 }
 
+static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data);
+
+struct callback_data {
+    std::vector<uint8_t>    data;
+    std::vector<std::regex> tensor_filters;
+
+    callback_data() = default;
+
+    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           = ggml_debug;
+        params.cb_eval_user_data = this;
+    }
+};
+
 static bool has_pooling(llama_context * ctx) {
     switch (llama_pooling_type(ctx)) {
         case LLAMA_POOLING_TYPE_NONE:
@@ -96,6 +120,168 @@ struct output_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];
+    switch (type) {
+        case GGML_TYPE_F16:
+            return ggml_fp16_to_fp32(*(const ggml_fp16_t *) &data[i]);
+        case GGML_TYPE_F32:
+            return *(const float *) &data[i];
+        case GGML_TYPE_I64:
+            return (float) *(const int64_t *) &data[i];
+        case GGML_TYPE_I32:
+            return (float) *(const int32_t *) &data[i];
+        case GGML_TYPE_I16:
+            return (float) *(const int16_t *) &data[i];
+        case GGML_TYPE_I8:
+            return (float) *(const int8_t *) &data[i];
+        case GGML_TYPE_BF16:
+            return ggml_compute_bf16_to_fp32(*(const ggml_bf16_t *) &data[i]);
+        default:
+            GGML_ABORT("fatal error");
+    }
+}
+
+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;
+    float sum_sq = 0.0;
+    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;
+                    sum_sq += v * v;
+                }
+            }
+        }
+    }
+    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
+        LOG_DBG("                                     [\n");
+        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
+            if (i2 == n && ne[2] > 2*n) {
+                LOG_DBG("                                      ..., \n");
+                i2 = ne[2] - n;
+            }
+            LOG_DBG("                                      [\n");
+            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
+                if (i1 == n && ne[1] > 2*n) {
+                    LOG_DBG("                                       ..., \n");
+                    i1 = ne[1] - n;
+                }
+                LOG_DBG("                                       [");
+                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
+                    if (i0 == n && ne[0] > 2*n) {
+                        LOG_DBG("..., ");
+                        i0 = ne[0] - n;
+                    }
+                    const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
+                    LOG_DBG("%12.4f", v);
+                    if (i0 < ne[0] - 1) {
+                        LOG_DBG(", ");
+                    }
+                }
+                LOG_DBG("],\n");
+            }
+            LOG_DBG("                                      ],\n");
+        }
+        LOG_DBG("                                     ]\n");
+        LOG_DBG("                                     sum    = %f\n", sum);
+        LOG_DBG("                                     sum_sq = %f\n", sum_sq);
+    }
+
+    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
+    }
+
+    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, ggml_ne_string(src1).c_str());
+    }
+
+    if (matches_filter) {
+        LOG_DBG("%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());
+    }
+
+    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();
+        ggml_print_tensor(data, t->type, t->ne, t->nb, 3);
+    }
+
+    return true;
+}
+
+
 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);
@@ -222,7 +408,7 @@ int main(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    base_callback_data cb_data(params, params.tensor_filter);
+    callback_data cb_data(params, params.tensor_filter);
 
     auto llama_init = common_init_from_params(params);
 

examples/eval-callback/CMakeLists.txt

@@ -4,23 +4,12 @@ install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
 
-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)
+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)
 endif()
+set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl)

examples/eval-callback/eval-callback.cpp

@@ -1,12 +1,165 @@
 #include "arg.h"
 #include "common.h"
-#include "debug.h"
 #include "log.h"
 #include "llama.h"
-#include "llama-cpp.h"
+#include "ggml.h"
+
+#include <cmath>
+#include <cstdio>
 #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);
@@ -29,7 +182,7 @@ static bool run(llama_context * ctx, const common_params & params) {
 }
 
 int main(int argc, char ** argv) {
-    base_callback_data cb_data;
+    callback_data cb_data;
 
     common_params params;
 
@@ -44,7 +197,7 @@ 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 = common_debug_cb_eval<false>;
+    params.cb_eval = ggml_debug;
     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_OPENSSL=OFF"
+                arguments += "-DLLAMA_CURL=OFF"
 
                 arguments += "-DGGML_NATIVE=OFF"
                 arguments += "-DGGML_BACKEND_DL=ON"

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
+llama-server -hf {namespace}/{model_name}-GGUF -c 0
 ```
 
 Then, access http://localhost:8080

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_OPENSSL=OFF # faster for long-prompt inference
+#cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DLLAMA_CURL=OFF # faster for long-prompt inference
 
 #for FP32
-cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_OPENSSL=OFF
+cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=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_OPENSSL=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_CURL=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_OPENSSL=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release
+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
 if %errorlevel% neq 0 goto ERROR
 
 ::  build all binary

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -58,7 +58,6 @@
 #include <aclnnop/aclnn_mean.h>
 #include <aclnnop/aclnn_mm.h>
 #include <aclnnop/aclnn_mul.h>
-#include <aclnnop/aclnn_mv.h>
 #include <aclnnop/aclnn_permute.h>
 #include <aclnnop/aclnn_pow.h>
 #include <aclnnop/aclnn_pow_tensor_tensor.h>
@@ -2339,21 +2338,20 @@ static void aclnn_rope_cache_init(ggml_backend_cann_context & ctx,
 
     // Step1.2: prepare rope_yarn_ramp, if this part updated, should update theta_scale_tensor.
     // TODO: acl_yarn_ramp_tensor use rope cache.
-    bool           yarn_ramp_tensor_updated = false;
-    acl_tensor_ptr acl_yarn_ramp_tensor;
+    bool                 yarn_ramp_tensor_updated = false;
+    acl_tensor_ptr       acl_yarn_ramp_tensor;
     if (ext_factor != 0 && (theta_scale_updated || ctx.rope_cache.theta_scale_length != theta_scale_length ||
                             ctx.rope_cache.freq_scale != freq_scale)) {
         yarn_ramp_tensor_updated = true;
         if (ctx.rope_cache.yarn_ramp_cache != nullptr) {
             ACL_CHECK(aclrtFree(ctx.rope_cache.yarn_ramp_cache));
         }
-        ACL_CHECK(aclrtMalloc(&ctx.rope_cache.yarn_ramp_cache, theta_scale_length * sizeof(float),
-                              ACL_MEM_MALLOC_HUGE_FIRST));
+        ACL_CHECK(aclrtMalloc(&ctx.rope_cache.yarn_ramp_cache, theta_scale_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST));
         // -rope_yarn_ramp
         // const float y = (i0 / 2 - low) / MAX(0.001f, high - low);
         // return MIN(1, MAX(0, y)) - 1;
-        acl_yarn_ramp_tensor      = ggml_cann_create_tensor(ctx.rope_cache.yarn_ramp_cache, ACL_FLOAT, sizeof(float),
-                                                            theta_scale_ne, theta_scale_nb, 1);
+        acl_yarn_ramp_tensor =
+            ggml_cann_create_tensor(ctx.rope_cache.yarn_ramp_cache, ACL_FLOAT, sizeof(float), theta_scale_ne, theta_scale_nb, 1);
         float          zero_value = 0, one_value = 1;
         float          denom_safe_value = MAX(0.001f, corr_dims[1] - corr_dims[0]);
         acl_scalar_ptr low              = ggml_cann_create_scalar(&corr_dims[0], aclDataType::ACL_FLOAT);
@@ -2384,8 +2382,8 @@ static void aclnn_rope_cache_init(ggml_backend_cann_context & ctx,
         GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_yarn_ramp_tensor.get(), freq_scale_1_sc.get());
         GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdds, acl_yarn_ramp_tensor.get(), freq_scale_sc.get(), one.get());
     } else {
-        acl_yarn_ramp_tensor = ggml_cann_create_tensor(ctx.rope_cache.yarn_ramp_cache, ACL_FLOAT, sizeof(float),
-                                                       theta_scale_ne, theta_scale_nb, 1);
+        acl_yarn_ramp_tensor =
+            ggml_cann_create_tensor(ctx.rope_cache.yarn_ramp_cache, ACL_FLOAT, sizeof(float), theta_scale_ne, theta_scale_nb, 1);
     }
     // Step 1.3: update theta_scale_tensor according to ext_factor or freq_scale.
     if (ext_factor != 0) {
@@ -2993,20 +2991,20 @@ void ggml_cann_argmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     GGML_CANN_CALL_ACLNN_OP(ctx, ArgMax, acl_src.get(), 3, false, acl_dst.get());
 }
 
-void ggml_cann_conv_transpose_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst){
     ggml_tensor * src0 = dst->src[0];
     ggml_tensor * src1 = dst->src[1];
 
     // stride
-    int64_t s0 = ((const int32_t *) (dst->op_params))[0];
+    int64_t s0 = ((const int32_t*)(dst->op_params))[0];
 
-    acl_tensor_ptr acl_input  = ggml_cann_create_tensor(src1, src1->ne, src1->nb, 3, ACL_FORMAT_NCL);
+    acl_tensor_ptr acl_input = ggml_cann_create_tensor(src1, src1->ne, src1->nb, 3, ACL_FORMAT_NCL);
     acl_tensor_ptr acl_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3, ACL_FORMAT_NCL);
-    acl_tensor_ptr acl_dst    = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3, ACL_FORMAT_NCL);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3, ACL_FORMAT_NCL);
 
     // get base information of input and kernel
-    int64_t input_len   = *(src1->ne);
-    int64_t dst_len     = *(dst->ne);
+    int64_t input_len = *(src1->ne);
+    int64_t dst_len = *(dst->ne);
     int64_t kernel_size = *(src0->ne);
 
     // set the max kernel size for each conv
@@ -3014,55 +3012,56 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context & ctx, ggml_tensor *
 
     // compute the partition of kernel
     int64_t part_num = 1;
-    part_num         = (kernel_size + max_kernel_size - 1) / max_kernel_size;
+    part_num = (kernel_size + max_kernel_size - 1) / max_kernel_size;
 
     int64_t strideVal[1];
-    strideVal[0]                    = s0;
-    acl_int_array_ptr stride        = ggml_cann_create_int_array(strideVal, 1);
-    int64_t           paddingVal[]  = { 0 };
-    acl_int_array_ptr padding       = ggml_cann_create_int_array(paddingVal, 1);
-    int64_t           dilationVal[] = { 1 };
-    acl_int_array_ptr dilation      = ggml_cann_create_int_array(dilationVal, 1);
-    bool              transposed    = true;
-    int64_t           groups        = 1;
-    int8_t            cubeMathType  = 0;
+    strideVal[0] = s0;
+    acl_int_array_ptr stride = ggml_cann_create_int_array(strideVal, 1);
+    int64_t paddingVal[] = {0};
+    acl_int_array_ptr padding = ggml_cann_create_int_array(paddingVal, 1);
+    int64_t dilationVal[] = {1};
+    acl_int_array_ptr dilation = ggml_cann_create_int_array(dilationVal, 1);
+    bool transposed = true;
+    int64_t groups = 1;
+    int8_t cubeMathType = 0;
 
 #ifdef ASCEND_310P
     cubeMathType = 1;
 #endif
 
     auto weight_type = ggml_cann_type_mapping(src0->type);
-    auto dst_type    = ggml_cann_type_mapping(dst->type);
+    auto dst_type = ggml_cann_type_mapping(dst->type);
 
     // slice the kernel to make each conv available
-    int64_t slice_dim   = -1;
+    int64_t slice_dim = -1;
     int64_t slice_start = 0;
-    int64_t slice_end   = max_kernel_size;
-    int64_t slice_step  = 1;
-    int64_t interval    = max_kernel_size;
+    int64_t slice_end = max_kernel_size;
+    int64_t slice_step = 1;
+    int64_t interval = max_kernel_size;
 
-    int64_t left_pad_len  = dilationVal[0] * (max_kernel_size - 1) + 1 - 2 * paddingVal[0];
+    int64_t left_pad_len = dilationVal[0] * (max_kernel_size - 1) + 1 - 2 * paddingVal[0];
     int64_t right_pad_len = 0;
 
-    acl_scalar_ptr alpha      = nullptr;
-    float          alphaValue = 1.0;
-    alpha                     = ggml_cann_create_scalar(&alphaValue, aclDataType::ACL_FLOAT);
+    acl_scalar_ptr alpha = nullptr;
+    float alphaValue = 1.0;
+    alpha = ggml_cann_create_scalar(&alphaValue, aclDataType::ACL_FLOAT);
 
     // set zero to destination
     GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, acl_dst.get());
 
-    for (int k = 0; k < part_num; k++) {
+    for(int k = 0; k < part_num; k++){
+
         // create part kernel tensor and slice from big kernel
         slice_start = max_kernel_size * k;
-        if (k == part_num - 1) {
+        if(k == part_num - 1){
             slice_end = kernel_size;
-            interval  = kernel_size - max_kernel_size * k;
-        } else {
-            slice_end = max_kernel_size * (k + 1);
+            interval = kernel_size - max_kernel_size * k;
+        }else{
+            slice_end = max_kernel_size * (k+1);
         }
 
         int64_t part_ne[4];
-        for (int i = 0; i < 4; i++) {
+        for(int i = 0; i < 4; i++) {
             part_ne[i] = *(src0->ne + i);
         }
         part_ne[0] = interval;
@@ -3075,17 +3074,16 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context & ctx, ggml_tensor *
 
         ggml_cann_pool_alloc part_kernel_allocator;
         part_kernel_allocator.alloc(ctx.pool(), part_nb[3]);
-        void * part_kernel_buf = part_kernel_allocator.get();
+        void* part_kernel_buf = part_kernel_allocator.get();
 
-        acl_tensor_ptr part_kernel = ggml_cann_create_tensor(part_kernel_buf, weight_type, ggml_element_size(src0),
-                                                             part_ne, part_nb, 3, ACL_FORMAT_NCL);
+        acl_tensor_ptr part_kernel = ggml_cann_create_tensor(part_kernel_buf, weight_type,
+                                ggml_element_size(src0), part_ne, part_nb, 3, ACL_FORMAT_NCL);
 
-        GGML_CANN_CALL_ACLNN_OP(ctx, Slice, acl_weight.get(), slice_dim, slice_start, slice_end, slice_step,
-                                part_kernel.get());
+        GGML_CANN_CALL_ACLNN_OP(ctx, Slice, acl_weight.get(), slice_dim, slice_start, slice_end, slice_step, part_kernel.get());
 
         // create the part conv result tensor
         int64_t part_dst_ne[4];
-        for (int i = 0; i < 4; i++) {
+        for(int i = 0; i < 4; i++){
             part_dst_ne[i] = *(dst->ne + i);
         }
         part_dst_ne[0] = (input_len - 1) * strideVal[0] - 2 * paddingVal[0] + dilationVal[0] * (part_ne[0] - 1) + 1;
@@ -3097,33 +3095,32 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context & ctx, ggml_tensor *
         }
         ggml_cann_pool_alloc part_dst_allocator;
         part_dst_allocator.alloc(ctx.pool(), part_dst_nb[3]);
-        void * part_dst_buf = part_dst_allocator.get();
+        void* part_dst_buf = part_dst_allocator.get();
 
         acl_tensor_ptr acl_part_dst = ggml_cann_create_tensor(part_dst_buf, dst_type, ggml_element_size(dst),
-                                                              part_dst_ne, part_dst_nb, 3, ACL_FORMAT_NCL);
+                                    part_dst_ne, part_dst_nb, 3, ACL_FORMAT_NCL);
         GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, acl_part_dst.get());
 
         // compute part conv transpose 1d
         GGML_CANN_CALL_ACLNN_OP(ctx, Convolution, acl_input.get(), part_kernel.get(), nullptr, stride.get(),
-                                padding.get(), dilation.get(), transposed, padding.get(), groups, acl_part_dst.get(),
-                                cubeMathType);
+        padding.get(), dilation.get(), transposed, padding.get(), groups, acl_part_dst.get(), cubeMathType);
 
         // compute the position of part result in final result
         int64_t global_start = slice_start;
-        int64_t global_end   = std::min((input_len - 1) * strideVal[0] + slice_end, dst_len);
+        int64_t global_end = std::min((input_len - 1) * strideVal[0] + slice_end, dst_len);
 
-        left_pad_len  = global_start;
+        left_pad_len = global_start;
         right_pad_len = dst_len - global_end;
 
-        std::vector<int64_t> padDataVal = { left_pad_len, right_pad_len };
-        acl_int_array_ptr    padData    = ggml_cann_create_int_array(padDataVal.data(), 2);
+        std::vector<int64_t> padDataVal = {left_pad_len,right_pad_len};
+        acl_int_array_ptr padData = ggml_cann_create_int_array(padDataVal.data(), 2);
 
-        acl_scalar_ptr pad_value    = nullptr;
-        float          pad_valueVal = 0.0;
-        pad_value                   = ggml_cann_create_scalar(&pad_valueVal, aclDataType::ACL_FLOAT);
+        acl_scalar_ptr pad_value = nullptr;
+        float pad_valueVal = 0.0;
+        pad_value = ggml_cann_create_scalar(&pad_valueVal, aclDataType::ACL_FLOAT);
 
         int64_t conv_result_ne[4];
-        for (int i = 0; i < 4; i++) {
+        for(int i = 0; i < 4; i++){
             conv_result_ne[i] = *(dst->ne + i);
         }
 
@@ -3135,14 +3132,13 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context & ctx, ggml_tensor *
 
         ggml_cann_pool_alloc conv_result_allocator;
         conv_result_allocator.alloc(ctx.pool(), conv_result_nb[3]);
-        void * conv_result_buf = conv_result_allocator.get();
+        void* conv_result_buf = conv_result_allocator.get();
 
         acl_tensor_ptr conv_result = ggml_cann_create_tensor(conv_result_buf, dst_type, ggml_element_size(dst),
-                                                             conv_result_ne, conv_result_nb, 3, ACL_FORMAT_NCL);
+                                    conv_result_ne, conv_result_nb, 3, ACL_FORMAT_NCL);
 
         GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, conv_result.get());
-        GGML_CANN_CALL_ACLNN_OP(ctx, ConstantPadNd, acl_part_dst.get(), padData.get(), pad_value.get(),
-                                conv_result.get());
+        GGML_CANN_CALL_ACLNN_OP(ctx, ConstantPadNd, acl_part_dst.get(), padData.get(), pad_value.get(), conv_result.get());
         GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdd, acl_dst.get(), conv_result.get(), alpha.get());
     }
 }
@@ -3746,15 +3742,15 @@ void ggml_cann_ssm_conv(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     // we want a view:  ne_w = { nc, 1, nr }   // [K, 1, C]
     // so that reversed dims -> [C, 1, K] which matches
     //   [out_channels, in_channels/groups, kernel_size]
-    int64_t w_ne[GGML_MAX_DIMS] = { nc, 1, nr, 1 };  // [K, 1 input ch. per group, C groups]
+    int64_t w_ne[GGML_MAX_DIMS] = { nc, 1, nr, 1 }; // [K, 1 input ch. per group, C groups]
     // Layout: src1 data is [K, C] with
     //   offset(k, c) = k*nb0 + c*nb1
     // We want offset_w(k, 0, c) = k*nb0 + c*nb1,
     // so we can reuse nb0 and nb1, and set nb2 = nb1.
-    size_t  w_nb[GGML_MAX_DIMS] = { src1->nb[0], src1->nb[1], src1->nb[1], src1->nb[3] };  // same as src1
+    size_t  w_nb[GGML_MAX_DIMS] = { src1->nb[0], src1->nb[1], src1->nb[1], src1->nb[3] }; // same as src1
 
-    acl_tensor_ptr acl_w = ggml_cann_create_tensor(src1->data, ggml_cann_type_mapping(src1->type),
-                                                   ggml_type_size(src1->type), w_ne, w_nb, 3, ACL_FORMAT_NCL);
+    acl_tensor_ptr acl_w = ggml_cann_create_tensor(
+        src1->data, ggml_cann_type_mapping(src1->type), ggml_type_size(src1->type), w_ne, w_nb, 3, ACL_FORMAT_NCL);
 
     // 3) Output: dst is { d_inner, n_t, n_s } (CLN)
     //
@@ -3772,12 +3768,11 @@ void ggml_cann_ssm_conv(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     //   nb_y[0] = nr * sizeof(float);           // step in L
     //   nb_y[1] = sizeof(float);                // step in C
     //   nb_y[2] = nr * n_t * sizeof(float);     // step in N
-    int64_t y_ne[GGML_MAX_DIMS] = { n_t, nr, n_s, 1 };  // [L_out, C, N]
-    size_t  y_nb[GGML_MAX_DIMS] = { dst->ne[0] * sizeof(float), sizeof(float), dst->ne[0] * dst->ne[1] * sizeof(float),
-                                    dst->nb[3] };       // [nr, 1, nr * n_t]
+    int64_t y_ne[GGML_MAX_DIMS] = { n_t, nr, n_s, 1 }; // [L_out, C, N]
+    size_t  y_nb[GGML_MAX_DIMS] = { dst->ne[0] * sizeof(float), sizeof(float), dst->ne[0] * dst->ne[1] * sizeof(float), dst->nb[3] }; // [nr, 1, nr * n_t]
 
-    acl_tensor_ptr acl_y = ggml_cann_create_tensor(dst->data, ggml_cann_type_mapping(dst->type),
-                                                   ggml_type_size(dst->type), y_ne, y_nb, 3, ACL_FORMAT_NCL);
+    acl_tensor_ptr acl_y = ggml_cann_create_tensor(
+        dst->data, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), y_ne, y_nb, 3, ACL_FORMAT_NCL);
 
     // --- Conv1d parameters: depthwise, stride 1, no padding ("valid") ---
     int64_t strideVal[1]   = { 1 };
@@ -3796,15 +3791,22 @@ void ggml_cann_ssm_conv(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     cubeMathType = 1;
 #endif
 
-    GGML_CANN_CALL_ACLNN_OP(ctx, Convolution,
+    GGML_CANN_CALL_ACLNN_OP(ctx,
+                            Convolution,
                             acl_x.get(),    // input:  N, C, L_in = ncs
                             acl_w.get(),    // weight: [C, 1, K] with groups=nr
                             nullptr,        // bias
-                            stride.get(), padding.get(), dilation.get(), transposed,
-                            padding.get(),  // output padding (unused for non-transposed)
-                            groups, acl_y.get(), cubeMathType);
+                            stride.get(),
+                            padding.get(),
+                            dilation.get(),
+                            transposed,
+                            padding.get(),   // output padding (unused for non-transposed)
+                            groups,
+                            acl_y.get(),
+                            cubeMathType);
 }
 
+
 void ggml_cann_op_add_rms_norm_fused(ggml_backend_cann_context & ctx,
                                      ggml_tensor *               add_node,
                                      ggml_tensor *               rms_norm_node) {
@@ -3858,71 +3860,3 @@ void ggml_cann_op_add_rms_norm_fused(ggml_backend_cann_context & ctx,
                             eps,  // double type
                             acl_yout.get(), acl_rstd.get(), acl_xout.get());
 }
-
-void ggml_cann_gated_linear_attn(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
-    ggml_tensor * k = dst->src[0];
-    ggml_tensor * v = dst->src[1];
-    ggml_tensor * q = dst->src[2];
-    ggml_tensor * g = dst->src[3];
-    ggml_tensor * s = dst->src[4];
-
-    int64_t B = dst->src[4]->ne[1];
-    int64_t T = dst->src[0]->ne[2];
-    int64_t H = dst->src[0]->ne[1];
-    int64_t C = dst->ne[0];
-    int64_t D = C / H;
-    int64_t L = T / B;
-
-    int64_t ne_qkg[2] = { 1, D };
-    int64_t ne_s[2]   = { D, D };
-    int64_t ne_st[2]  = { ne_s[1], ne_s[0] };
-    int64_t ne_vo[2]  = { D, 1 };
-    int64_t ne_q[1]   = { D };
-    size_t  nb_base   = ggml_type_size(k->type);
-    size_t  nb_qkg[2] = { nb_base, nb_base };
-    size_t  nb_s[2]   = { nb_base, D * nb_base };
-    size_t  nb_st[2]  = { nb_s[1], nb_s[0] };
-    size_t  nb_vo[2]  = { nb_base, D * nb_base };
-    size_t  nb_q[1]   = { nb_base };
-
-    const float scale = ggml_get_op_params_f32(dst, 0);
-
-    acl_tensor_ptr acl_s     = ggml_cann_create_tensor(s, s->ne, s->nb, 2, ACL_FORMAT_ND);
-    acl_tensor_ptr new_state = ggml_cann_create_tensor(dst, s->ne, s->nb, 2, ACL_FORMAT_ND, (B * L * H * D) * nb_base);
-    cann_copy(ctx, acl_s.get(), new_state.get());
-
-    for (int64_t b = 0; b < B; b++) {
-        for (int64_t h = 0; h < H; h++) {
-            size_t         s_offset = (b * (H * D * D) + h * (D * D)) * nb_base;
-            // D * D
-            acl_tensor_ptr acl_s_new =
-                ggml_cann_create_tensor(dst, ne_s, nb_s, 2, ACL_FORMAT_ND, (B * L * H * D) * nb_base + s_offset);
-            acl_tensor_ptr acl_s_new_t =
-                ggml_cann_create_tensor(dst, ne_st, nb_st, 2, ACL_FORMAT_ND, (B * L * H * D) * nb_base + s_offset);
-            for (int64_t l = 0; l < L; l++) {
-                size_t               qkvgo_offset = (b * (L * H * D) + l * (H * D) + h * (D)) * nb_base;
-                // D * 1
-                acl_tensor_ptr       acl_k = ggml_cann_create_tensor(k, ne_qkg, nb_qkg, 2, ACL_FORMAT_ND, qkvgo_offset);
-                acl_tensor_ptr       acl_g = ggml_cann_create_tensor(g, ne_qkg, nb_qkg, 2, ACL_FORMAT_ND, qkvgo_offset);
-                // D
-                acl_tensor_ptr       acl_q = ggml_cann_create_tensor(q, ne_q, nb_q, 1, ACL_FORMAT_ND, qkvgo_offset);
-                // 1 * D
-                acl_tensor_ptr       acl_v = ggml_cann_create_tensor(v, ne_vo, nb_vo, 2, ACL_FORMAT_ND, qkvgo_offset);
-                // D
-                acl_tensor_ptr       acl_o = ggml_cann_create_tensor(dst, ne_q, nb_q, 1, ACL_FORMAT_ND, qkvgo_offset);
-                // k ⊗ v
-                size_t               buf_size = D * D * nb_base;
-                ggml_cann_pool_alloc buffer_allocator(ctx.pool(), buf_size);
-                acl_tensor_ptr       tmp_tensor = ggml_cann_create_tensor(
-                    buffer_allocator.get(), ggml_cann_type_mapping(k->type), nb_base, ne_s, nb_s, 2);
-                aclnn_mul(ctx, acl_k.get(), acl_v.get(), tmp_tensor.get());
-                //s_new = g ⊗ s_old + k ⊗ v
-                aclnn_mul(ctx, acl_s_new.get(), acl_g.get(), nullptr);
-                aclnn_add(ctx, acl_s_new.get(), tmp_tensor.get(), nullptr);
-                // compute output
-                GGML_CANN_CALL_ACLNN_OP(ctx, Mv, acl_s_new_t.get(), acl_q.get(), acl_o.get(), 1);
-                aclnn_muls(ctx, acl_o.get(), scale, nullptr, true);
-            }
-        }
-    }
-}

ggml/src/ggml-cann/aclnn_ops.h

@@ -814,20 +814,67 @@ void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst);
  */
 void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
-/**
- * @brief Forward Gated Linear Attention on the CANN backend.
- *
- * Expects dst->src[0..4] = {k, v, q, g, s} with shape conventions:
- *   k, v, q, g: [D] with outer dims T x H batched as ne[2]=T, ne[1]=H
- *   s: initial state [B, H, D, D], where B is batch and D=C/H
- * dst holds both outputs (o) and updated state; a scale factor is read from op params.
- *
- * The kernel updates per time step l: S_new = g ⊗ S_old + k ⊗ v, then computes o = (S_new^T q) * scale.
- *
- * @param ctx Backend context providing stream/allocator utilities.
- * @param dst Output tensor; src deps are k, v, q, g, s as above.
+/*
+ * @brief A generic wrapper for ACL resources with custom deleter support.
  */
-void ggml_cann_gated_linear_attn(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+using any_acl_resource = std::unique_ptr<void, std::function<void(void *)>>;
+
+/**
+ * @brief Trait structure used to define how to destroy a given ACL resource type.
+ *
+ * @tparam T ACL resource type.
+ */
+template <typename T> struct acl_resource_traits;
+
+/**
+ * @brief Specialization for aclTensor, defines how to destroy an aclTensor resource.
+ */
+template <> struct acl_resource_traits<aclTensor> {
+    static void destroy(void * p) { ACL_CHECK(aclDestroyTensor(static_cast<aclTensor *>(p))); }
+};
+
+/**
+ * @brief Specialization for aclIntArray, defines how to destroy an aclIntArray resource.
+ */
+template <> struct acl_resource_traits<aclIntArray> {
+    static void destroy(void * p) { ACL_CHECK(aclDestroyIntArray(static_cast<aclIntArray *>(p))); }
+};
+
+/**
+ * @brief Specialization for aclScalar, defines how to destroy an aclScalar resource.
+ */
+template <> struct acl_resource_traits<aclScalar> {
+    static void destroy(void * p) { ACL_CHECK(aclDestroyScalar(static_cast<aclScalar *>(p))); }
+};
+
+/**
+ * @brief Specialization for aclTensorList, defines how to destroy an aclTensorList resource.
+ */
+template <> struct acl_resource_traits<aclTensorList> {
+    static void destroy(void * p) { ACL_CHECK(aclDestroyTensorList(static_cast<aclTensorList *>(p))); }
+};
+
+/**
+ * @brief Creates a generic ACL resource wrapper with proper destruction logic.
+ *
+ * @tparam T ACL resource type.
+ * @param ptr Raw pointer to ACL resource.
+ * @return any_acl_resource Smart pointer that handles destruction.
+ */
+template <typename T> any_acl_resource make_acl_resource(T * ptr) {
+    return any_acl_resource(static_cast<void *>(ptr), [](void * p) { acl_resource_traits<T>::destroy(p); });
+}
+
+/**
+ * @brief Registers multiple ACL resources into a vector for lifetime management.
+ *
+ * @tparam Args Variadic list of ACL resource types.
+ * @param vec Target vector to hold ACL resources.
+ * @param args Raw pointers to ACL resources.
+ */
+template <typename... Args> void register_acl_resources(std::vector<any_acl_resource> & vec, Args *... args) {
+    (vec.emplace_back(make_acl_resource(args)), ...);
+}
 
 /**
  * @brief Launches an asynchronous task using the memory allocator.
@@ -847,19 +894,19 @@ void ggml_cann_gated_linear_attn(ggml_backend_cann_context & ctx, ggml_tensor *
  * same stream are executed in queue order.
  */
 
-#    define GGML_CANN_CALL_ACLNN_OP(CTX, OP_NAME, ...)                                           \
-        do {                                                                                     \
-            uint64_t        workspaceSize = 0;                                                   \
-            aclOpExecutor * executor;                                                            \
-            void *          workspaceAddr = nullptr;                                             \
-            ACL_CHECK(aclnn##OP_NAME##GetWorkspaceSize(__VA_ARGS__, &workspaceSize, &executor)); \
-            /* workspace should alloced in main thread to keep malloc order when using vmm. */   \
-            if (workspaceSize > 0) {                                                             \
-                ggml_cann_pool_alloc workspace_allocator(CTX.pool(), workspaceSize);             \
-                workspaceAddr = workspace_allocator.get();                                       \
-            }                                                                                    \
-            ACL_CHECK(aclnn##OP_NAME(workspaceAddr, workspaceSize, executor, CTX.stream()));     \
-        } while (0)
+#define GGML_CANN_CALL_ACLNN_OP(CTX, OP_NAME, ...)                                           \
+    do {                                                                                     \
+        uint64_t        workspaceSize = 0;                                                   \
+        aclOpExecutor * executor;                                                            \
+        void *          workspaceAddr = nullptr;                                             \
+        ACL_CHECK(aclnn##OP_NAME##GetWorkspaceSize(__VA_ARGS__, &workspaceSize, &executor)); \
+        /* workspace should alloced in main thread to keep malloc order when using vmm. */   \
+        if (workspaceSize > 0) {                                                             \
+            ggml_cann_pool_alloc workspace_allocator(CTX.pool(), workspaceSize);             \
+            workspaceAddr = workspace_allocator.get();                                       \
+        }                                                                                    \
+        ACL_CHECK(aclnn##OP_NAME(workspaceAddr, workspaceSize, executor, CTX.stream()));     \
+    } while (0)
 
 /**
  * @brief   Performs sparse expert-based matrix multiplication using the CANN backend.
@@ -900,9 +947,7 @@ void ggml_cann_mul_mat_id(ggml_backend_cann_context & ctx, ggml_tensor * dst);
  * @param rms_norm_tensor The RMS_NORM operation node, contains the gamma weights
  *                        and epsilon parameter.
  */
-void ggml_cann_op_add_rms_norm_fused(ggml_backend_cann_context & ctx,
-                                     ggml_tensor *               add_node,
-                                     ggml_tensor *               rms_norm_node);
+void ggml_cann_op_add_rms_norm_fused(ggml_backend_cann_context & ctx, ggml_tensor * add_node, ggml_tensor * rms_norm_node);
 
 /**
  * @brief   Check whether a tensor is a weight tensor for matrix multiplication.
@@ -1059,13 +1104,13 @@ void ggml_cann_op_unary_gated(std::function<void(ggml_backend_cann_context &, ac
  * @see ggml_cann_op_unary
  * @see GGML_CANN_CALL_ACLNN_OP
  */
-#    define GGML_CANN_CALL_OP_UNARY(OP_NAME)                                                              \
-        do {                                                                                              \
-            auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { \
-                GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst);                                  \
-            };                                                                                            \
-            ggml_cann_op_unary(lambda, ctx, dst);                                                         \
-        } while (0)
+#define GGML_CANN_CALL_OP_UNARY(OP_NAME)                                                              \
+    do {                                                                                              \
+        auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { \
+            GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst);                                  \
+        };                                                                                            \
+        ggml_cann_op_unary(lambda, ctx, dst);                                                         \
+    } while (0)
 
 /**
  * @brief Helper macro to call a gated unary ACL operator via ggml_cann_op_unary_gated.
@@ -1088,13 +1133,13 @@ void ggml_cann_op_unary_gated(std::function<void(ggml_backend_cann_context &, ac
  * @see ggml_cann_op_unary_gated
  * @see GGML_CANN_CALL_ACLNN_OP
  */
-#    define GGML_CANN_CALL_OP_UNARY_GATED(OP_NAME)                                                        \
-        do {                                                                                              \
-            auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { \
-                GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst);                                  \
-            };                                                                                            \
-            ggml_cann_op_unary_gated(lambda, ctx, dst);                                                   \
-        } while (0)
+#define GGML_CANN_CALL_OP_UNARY_GATED(OP_NAME)                                                        \
+    do {                                                                                              \
+        auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { \
+            GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst);                                  \
+        };                                                                                            \
+        ggml_cann_op_unary_gated(lambda, ctx, dst);                                                   \
+    } while (0)
 
 #endif  // CANN_ACLNN_OPS
 

ggml/src/ggml-cann/common.h

@@ -382,7 +382,7 @@ struct ggml_cann_graph_lru_cache {
 
     std::list<ggml_cann_graph *> cache_list; /**< List storing cached graphs as raw pointers. */
 
-    ggml_cann_graph_lru_cache() { capacity = parse_integer(get_env_as_lowercase("GGML_CANN_GRAPH_CACHE_CAPACITY").value_or("12")); }
+    ggml_cann_graph_lru_cache() { capacity = parse_integer(get_env("GGML_CANN_GRAPH_CACHE_CAPACITY").value_or("12")); }
 
     /**
      * @brief Push a new graph to the front of the cache.
@@ -574,7 +574,7 @@ struct ggml_backend_cann_context {
         description = aclrtGetSocName();
 
 #ifdef USE_ACL_GRAPH
-        acl_graph_mode = parse_bool(get_env_as_lowercase("GGML_CANN_ACL_GRAPH").value_or("on"));
+        acl_graph_mode = parse_bool(get_env("GGML_CANN_ACL_GRAPH").value_or("on"));
         GGML_LOG_INFO("%s: device %d execution mode is %s (%s)\n", __func__, device, acl_graph_mode ? "GRAPH" : "EAGER",
                       acl_graph_mode ? "acl graph enabled" : "acl graph disabled");
 #endif

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

@@ -1889,9 +1889,6 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
         case GGML_OP_OUT_PROD:
             ggml_cann_out_prod(ctx, dst);
             break;
-        case GGML_OP_GATED_LINEAR_ATTN:
-            ggml_cann_gated_linear_attn(ctx, dst);
-            break;
         case GGML_OP_SSM_CONV:
             ggml_cann_ssm_conv(ctx, dst);
             break;
@@ -2457,7 +2454,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
         case GGML_OP_MEAN:
         case GGML_OP_PAD_REFLECT_1D:
         case GGML_OP_COUNT_EQUAL:
-        case GGML_OP_GATED_LINEAR_ATTN:
             return true;
         case GGML_OP_OUT_PROD:
             {

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

@@ -654,14 +654,6 @@ 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
@@ -698,29 +690,6 @@ 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

@@ -237,24 +237,6 @@ 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/common.cuh

@@ -262,10 +262,6 @@ 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);
@@ -530,86 +526,6 @@ 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
 

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

@@ -59,7 +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) {
-        __align__(16) half2 tmp[cpy_ne];
+        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) {
@@ -309,7 +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");
-        __align__(16) half2 tmp[ne/2];
+        half2 tmp[ne/2];
         ggml_cuda_memcpy_1<ne*sizeof(half)>(tmp, (const half *) vx + i0);
         float2 * dst_f2 = (float2 *) dst;
 #pragma unroll
@@ -914,7 +914,7 @@ void launch_fattn(
 
         const int nblocks_stream_k = max_blocks;
 
-        const bool use_stream_k = cc >= GGML_CUDA_CC_ADA_LOVELACE || amd_wmma_available(cc) || tiles_efficiency_percent < 75;
+        const bool use_stream_k = cc >= GGML_CUDA_CC_ADA_LOVELACE || 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

@@ -98,19 +98,6 @@ 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);
@@ -118,9 +105,6 @@ 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);
 }
@@ -132,8 +116,6 @@ 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);
@@ -204,23 +186,6 @@ 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) {
@@ -428,10 +393,10 @@ 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) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     constexpr int  ncols           = ncols1 * ncols2;
     constexpr int  cols_per_warp   = T_B_KQ::I;
-    constexpr int  cols_per_thread = get_cols_per_thread();
+    constexpr int  cols_per_thread = 2; // This is specifically KQ columns, Volta only has a single VKQ column.
     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);
@@ -448,8 +413,6 @@ 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)
@@ -498,14 +461,8 @@ 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
-#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.
+                        // Wide version of KQ_C is column-major => swap A and B.
                         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)
                     }
                 }
             }
@@ -522,14 +479,8 @@ 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
-#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.
+                    // Wide version of KQ_C is column-major => swap A and B.
                     mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], Q_B[0], K_A);
-#endif // defined(AMD_WMMA_AVAILABLE)
                 }
             }
         }
@@ -581,13 +532,7 @@ 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) {
-#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);
+                    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);
                 }
             }
         }
@@ -607,14 +552,8 @@ 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) {
-#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];
+                    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];
                 } else {
                     KQ_C[k0/(np*T_C_KQ::I)].x[l] = 0.0f;
                 }
@@ -645,13 +584,8 @@ 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:
-                    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);
+                    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);
                 }
             }
         }
@@ -662,11 +596,7 @@ 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;
-#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
+#else
             // Values per KQ column are spread across 2 threads:
             constexpr int offset_first = 2;
             constexpr int offset_last  = 2;
@@ -682,15 +612,10 @@ 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) {
-#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];
+                    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];
                 } else {
                     KQ_C[(k0/(np*T_C_KQ::J))].x[l] = 0.0f;
                 }
@@ -714,7 +639,7 @@ 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[cols_per_thread - 1]);
+            const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[1]);
 #pragma unroll
             for (int i = 0; i < DV/T_C_VKQ::I; ++i) {
 #pragma unroll
@@ -735,16 +660,6 @@ 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]);
@@ -792,10 +707,6 @@ 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
@@ -816,7 +727,7 @@ 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) || defined(AMD_WMMA_AVAILABLE)
+#if defined(TURING_MMA_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) {
@@ -826,26 +737,12 @@ 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.
-#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.
+                    // Wide version of VKQ_C is column-major => swap A and B.
                     mma(VKQ_C[i_VKQ_0/i0_stride], B[k00/(np*T_A_VKQ::J)], A);
-#endif // defined(AMD_WMMA_AVAILABLE)
                 }
             }
         }
@@ -864,7 +761,7 @@ 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) || defined(AMD_WMMA_AVAILABLE)
+#endif // defined(TURING_MMA_AVAILABLE)
 
         if constexpr (nstages <= 1) {
             __syncthreads(); // Only needed if tile_K == tile_V.
@@ -877,7 +774,7 @@ 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) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 }
 
 #if defined(TURING_MMA_AVAILABLE)
@@ -897,15 +794,6 @@ 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
@@ -940,7 +828,7 @@ 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) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     constexpr int ncols = ncols1 * ncols2;
@@ -952,7 +840,7 @@ 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 = get_cols_per_thread();
+    constexpr int  cols_per_thread = 2; // This is specifically KQ columns, Volta only has a single VKQ column.
     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);
@@ -983,8 +871,6 @@ 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)
@@ -1124,10 +1010,6 @@ 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;
@@ -1165,7 +1047,7 @@ 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[cols_per_thread - 1]);
+            const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[1]);
 #pragma unroll
             for (int i = 0; i < DV/T_C_VKQ::I; ++i) {
 #pragma unroll
@@ -1186,15 +1068,6 @@ 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]);
@@ -1246,10 +1119,6 @@ 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]);
@@ -1450,7 +1319,7 @@ 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) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 }
 
 template<int DKQ, int DV, int ncols1, int ncols2, bool use_logit_softcap, bool mla>
@@ -1477,7 +1346,7 @@ 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) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)))
+#if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE))
 
     // Skip unused kernel variants for faster compilation:
     if (use_logit_softcap && !(DKQ == 128 || DKQ == 256)) {
@@ -1491,13 +1360,6 @@ 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;
@@ -1611,7 +1473,7 @@ 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) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)))
+#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE))
 }
 
 template <int DKQ, int DV, int ncols1, int ncols2>
@@ -1630,7 +1492,7 @@ 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, get_cols_per_warp(cc));
+    const int cols_per_warp = std::min(ncols, turing_mma_available(cc) ? 16 : 32);
     const int nwarps        = nthreads / WARP_SIZE;
 
     constexpr bool mla = DKQ == 576;
@@ -1650,34 +1512,29 @@ 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_MUSA)
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
         static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
         if (!shared_memory_limit_raised[id]) {
-            CUDA_CHECK(cudaFuncSetAttribute(reinterpret_cast<fattn_kernel_ptr_t>(fattn_kernel), cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
+            CUDA_CHECK(cudaFuncSetAttribute(fattn_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
             shared_memory_limit_raised[id] = true;
         }
-#endif // !defined(GGML_USE_MUSA)
+#endif // !defined(GGML_USE_HIP) && !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_MUSA)
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
         static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
         if (!shared_memory_limit_raised[id]) {
-            CUDA_CHECK(cudaFuncSetAttribute(reinterpret_cast<fattn_kernel_ptr_t>(fattn_kernel), cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
+            CUDA_CHECK(cudaFuncSetAttribute(fattn_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
             shared_memory_limit_raised[id] = true;
         }
-#endif // !defined(GGML_USE_MUSA)
+#endif // !defined(GGML_USE_HIP) && !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 __align__(16) half2 zero[cpy_ne] = {{0.0f, 0.0f}};
+                    const 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}};
-                    __align__(16) half2 tmp_h2[cpy_ne/2];
+                    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);
 
-                    __align__(16) float2 tmp_f2[cpy_ne/2];
+                    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) {
-        __align__(16) half2 K_k[nbatch_fa/(np*warp_size)][cpy_ne];
-        __align__(16) half2 Q_k[cpw][cpy_ne];
+        half2 K_k[nbatch_fa/(np*warp_size)][cpy_ne];
+        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) {
-        __align__(16) float K_k[nbatch_fa/(np*warp_size)][cpy_ne];
-        __align__(16) float Q_k[cpw][cpy_ne];
+        float K_k[nbatch_fa/(np*warp_size)][cpy_ne];
+        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
-        __align__(16) half  tmp[nbatch_fa/(np*warp_size)][KQ_cs];
+        half  tmp[nbatch_fa/(np*warp_size)][KQ_cs];
 #else
-        __align__(16) float tmp[nbatch_fa/(np*warp_size)][KQ_cs];
+        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) {
-            __align__(16) half2 V_k[(DVp/2)/warp_size];
-            __align__(16) half2 KQ_k[cpw];
+            half2 V_k[(DVp/2)/warp_size];
+            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);
 
-                __align__(16) half tmp[KQ_cs];
+                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) {
-            __align__(16) float2 V_k[(DVp/2)/warp_size];
-            __align__(16) float  KQ_k[cpw];
+            float2 V_k[(DVp/2)/warp_size];
+            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];
-    __align__(16) half2 VKQ[cpw * ((DVp/2)/warp_size)] = {{0.0f, 0.0f}};
+    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];
-    __align__(16) float2 VKQ[cpw * ((DVp/2)/warp_size)] = {{0.0f, 0.0f}};
+    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) {
-                __align__(16) float tmp_f[cpy_ne_D] = {0.0f};
+                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
-                __align__(16) half2 tmp_h2[cpy_ne_D/2];
+                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) {
-                __align__(16) half2 tmp[cpy_ne_D];
+                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) {
-                __align__(16) float tmp[cpy_ne_D];
+                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) {
-            __align__(16) float2 tmp[cpy_ne_D];
+            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 does not support unrolling loops
+// Currenlty llvm with the amdgcn target dose 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
-    __align__(16) float2 Q_reg[ncols][(D/2)/nthreads_KQ] = {{{0.0f, 0.0f}}}; // May be only partially initialized.
+    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;
 
-                __align__(16) float2 tmp[cpy_ne] = {{0.0f, 0.0f}};
+                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) || amd_wmma_available(cc)) && Q->ne[1] <= 16/ncols2) {
+    if (turing_mma_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 || amd_wmma_available(cc) || Q->ne[1] <= 32/ncols2) {
+    if (ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_TURING || Q->ne[1] <= 32/ncols2) {
         ggml_cuda_flash_attn_ext_mma_f16_case<DKQ, DV, 32/ncols2, ncols2>(ctx, dst);
         return;
     }
@@ -230,18 +230,7 @@ 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,
-    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 bool gqa_opt_applies = gqa_ratio % 2 == 0 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
 
     const int cc = ggml_cuda_info().devices[device].cc;
 
@@ -348,31 +337,6 @@ 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

@@ -3730,10 +3730,8 @@ static bool ggml_cuda_graph_set_enabled(ggml_backend_cuda_context * cuda_ctx) {
 
     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;
+            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
         }
     }
 
@@ -4553,7 +4551,7 @@ 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]);
+            return ggml_is_contiguous(op->src[0]) && op->ne[0] % WARP_SIZE == 0;
             break;
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:

ggml/src/ggml-cuda/mma.cuh

@@ -206,16 +206,10 @@ namespace ggml_cuda_mma {
 
         static __device__ __forceinline__ int get_j(const int l) {
             if constexpr (I == 16 && J == 16) {
+                // matrix C
 #if defined(RDNA3)
-                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;
-                }
+                return 2 * l + (threadIdx.x / 16);
 #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) {
@@ -627,21 +621,6 @@ 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) {
@@ -660,19 +639,6 @@ 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)
@@ -912,17 +878,6 @@ 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;

ggml/src/ggml-cuda/norm.cu

@@ -25,8 +25,19 @@ static __global__ void norm_f32(
     }
 
     // sum up partial sums
-    extern __shared__ float2 s_sum2[];
-    mean_var = block_reduce<block_reduce_method::SUM, block_size>(mean_var, s_sum2);
+    mean_var = warp_reduce_sum(mean_var);
+    if constexpr (block_size > WARP_SIZE) {
+        static_assert(block_size == 1024, "unexpected block_size");
+        __shared__ float2 s_sum[32];
+        const int warp_id = threadIdx.x / WARP_SIZE;
+        const int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = mean_var;
+        }
+        __syncthreads();
+        mean_var = s_sum[lane_id];
+        mean_var = warp_reduce_sum(mean_var);
+    }
 
     const float mean = mean_var.x / ncols;
     const float var = mean_var.y / ncols - mean * mean;
@@ -50,8 +61,19 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
         tmp += x[j];
     }
 
-    extern __shared__ float s_sum[];
-    tmp = block_reduce<block_reduce_method::SUM, block_size>(tmp, s_sum);
+    tmp = warp_reduce_sum(tmp);
+    if constexpr (block_size > WARP_SIZE) {
+        static_assert(block_size == 1024, "unexpected block_size");
+        __shared__ float s_sum[32];
+        const int warp_id = threadIdx.x / WARP_SIZE;
+        const int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
 
     const float mean = tmp / group_size;
     tmp = 0.0f;
@@ -62,7 +84,18 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
         tmp += xi * xi;
     }
 
-    tmp = block_reduce<block_reduce_method::SUM, block_size>(tmp, s_sum);
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __shared__ float s_sum[32];
+        const int warp_id = threadIdx.x / WARP_SIZE;
+        const int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
 
     const float variance = tmp / group_size;
     const float scale = rsqrtf(variance + eps);
@@ -130,8 +163,22 @@ static __global__ void rms_norm_f32(const float * x,
     }
 
     // sum up partial sums
-    extern __shared__ float s_sum[];
-    tmp = block_reduce<block_reduce_method::SUM, block_size>(tmp, s_sum);
+    tmp = warp_reduce_sum(tmp);
+    if constexpr (block_size > WARP_SIZE) {
+        static_assert((block_size <= 1024) && (block_size % 32 == 0), "unexpected block_size");
+        __shared__ float s_sum[32];
+        const int        warp_id = tid / WARP_SIZE;
+        const int        lane_id = tid % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = 0.0f;
+        if (lane_id < (block_size / WARP_SIZE)) {
+            tmp = s_sum[lane_id];
+        }
+        tmp = warp_reduce_sum(tmp);
+    }
 
     const float mean = tmp / ncols;
     const float scale = rsqrtf(mean + eps);
@@ -259,8 +306,19 @@ static __global__ void l2_norm_f32(
     }
 
     // sum up partial sums
-    extern __shared__ float s_sum[];
-    tmp = block_reduce<block_reduce_method::SUM, block_size>(tmp, s_sum);
+    tmp = warp_reduce_sum(tmp);
+    if constexpr (block_size > WARP_SIZE) {
+        static_assert(block_size == 1024, "unexpected block_size");
+        __shared__ float s_sum[32];
+        const int warp_id = threadIdx.x / WARP_SIZE;
+        const int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
 
     // from https://pytorch.org/docs/stable/generated/torch.nn.functional.normalize.html
     const float scale = rsqrtf(fmaxf(tmp, eps * eps));
@@ -279,7 +337,7 @@ static void norm_f32_cuda(
         norm_f32<WARP_SIZE><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     } else {
         const dim3 block_dims(1024, 1, 1);
-        norm_f32<1024><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float2): 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
+        norm_f32<1024><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     }
 }
 
@@ -290,7 +348,7 @@ static void group_norm_f32_cuda(
         group_norm_f32<WARP_SIZE><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
     } else {
         const dim3 block_dims(1024, 1, 1);
-        group_norm_f32<1024><<<num_groups, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(x, dst, group_size, ne_elements, eps);
+        group_norm_f32<1024><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
     }
 }
 
@@ -300,10 +358,10 @@ static void rms_norm_f32_cuda(
     const dim3 blocks_num(nrows, nchannels, nsamples);
     if (ncols < 1024) {
         const dim3 block_dims(256, 1, 1);
-        rms_norm_f32<256, false><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
+        rms_norm_f32<256, false><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     } else {
         const dim3 block_dims(1024, 1, 1);
-        rms_norm_f32<1024, false><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
+        rms_norm_f32<1024, false><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     }
 }
 
@@ -346,12 +404,12 @@ static void rms_norm_mul_f32_cuda(const float *  x,
         const uint3 mul_nsamples_packed  = init_fastdiv_values(mul_nsamples);
         if (ncols < 1024) {
             const dim3 block_dims(256, 1, 1);
-            rms_norm_f32<256, true><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(
+            rms_norm_f32<256, true><<<blocks_num, block_dims, 0, stream>>>(
                 x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
                 mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed);
         } else {
             const dim3 block_dims(1024, 1, 1);
-            rms_norm_f32<1024, true><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(
+            rms_norm_f32<1024, true><<<blocks_num, block_dims, 0, stream>>>(
                 x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
                 mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed);
         }
@@ -367,14 +425,14 @@ static void rms_norm_mul_f32_cuda(const float *  x,
         const uint3 add_nsamples_packed  = init_fastdiv_values(add_nsamples);
         if (ncols < 1024) {
             const dim3 block_dims(256, 1, 1);
-            rms_norm_f32<256, true, true><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(
+            rms_norm_f32<256, true, true><<<blocks_num, block_dims, 0, stream>>>(
                 x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
                 mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed, add,
                 add_stride_row, add_stride_channel, add_stride_sample, add_ncols_packed, add_nrows_packed,
                 add_nchannels_packed, add_nsamples_packed);
         } else {
             const dim3 block_dims(1024, 1, 1);
-            rms_norm_f32<1024, true, true><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(
+            rms_norm_f32<1024, true, true><<<blocks_num, block_dims, 0, stream>>>(
                 x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
                 mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed, add,
                 add_stride_row, add_stride_channel, add_stride_sample, add_ncols_packed, add_nrows_packed,
@@ -402,7 +460,7 @@ static void l2_norm_f32_cuda(
         l2_norm_f32<WARP_SIZE><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     } else {
         const dim3 block_dims(1024, 1, 1);
-        l2_norm_f32<1024><<<blocks_num, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
+        l2_norm_f32<1024><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     }
 }
 

ggml/src/ggml-cuda/reduce_rows.cuh

@@ -28,8 +28,22 @@ static __global__ void reduce_rows_f32(const float * __restrict__ x, float * __r
     }
 
     // sum up partial sums
-    __shared__ float shared_vals[32];
-    sum = block_reduce<block_reduce_method::SUM>(sum, shared_vals);
+    sum = warp_reduce_sum(sum);
+    if (blockDim.x > WARP_SIZE) {
+        assert((blockDim.x <= 1024) && (blockDim.x % WARP_SIZE) == 0);
+        __shared__ float s_sum[32];
+        const int        warp_id = threadIdx.x / WARP_SIZE;
+        const int        lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = sum;
+        }
+        __syncthreads();
+        sum = 0.0f;
+        if (lane_id < (static_cast<int>(blockDim.x) / WARP_SIZE)) {
+            sum = s_sum[lane_id];
+        }
+        sum = warp_reduce_sum(sum);
+    }
 
     if (col != 0) {
         return;

ggml/src/ggml-cuda/softmax.cu

@@ -75,6 +75,9 @@ static __global__ void soft_max_f32(
 
     const int block_size = block_size_template == 0 ? blockDim.x : block_size_template;
 
+    const int warp_id = threadIdx.x / WARP_SIZE;
+    const int lane_id = threadIdx.x % WARP_SIZE;
+
     const float slope = get_alibi_slope(p.max_bias, i02, p.n_head_log2, p.m0, p.m1);
 
     extern __shared__ float data_soft_max_f32[];
@@ -99,7 +102,21 @@ static __global__ void soft_max_f32(
     }
 
     // find the max value in the block
-    max_val = block_reduce<block_reduce_method::MAX, block_size_template>(max_val, buf_iw);
+    max_val = warp_reduce_max(max_val);
+    if (block_size > WARP_SIZE) {
+        if (warp_id == 0) {
+            buf_iw[lane_id] = -INFINITY;
+        }
+        __syncthreads();
+
+        if (lane_id == 0) {
+            buf_iw[warp_id] = max_val;
+        }
+        __syncthreads();
+
+        max_val = buf_iw[lane_id];
+        max_val = warp_reduce_max(max_val);
+    }
 
     float tmp = 0.0f; // partial sum
 
@@ -117,7 +134,22 @@ static __global__ void soft_max_f32(
     }
 
     // find the sum of exps in the block
-    tmp = block_reduce<block_reduce_method::SUM, block_size_template>(tmp, buf_iw);
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __syncthreads();
+        if (warp_id == 0) {
+            buf_iw[lane_id] = 0.0f;
+        }
+        __syncthreads();
+
+        if (lane_id == 0) {
+            buf_iw[warp_id] = tmp;
+        }
+        __syncthreads();
+
+        tmp = buf_iw[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
 
     if (sinks) {
         tmp += expf(sinks[i02] - max_val);
@@ -137,6 +169,50 @@ static __global__ void soft_max_f32(
     }
 }
 
+
+// TODO: This is a common pattern used across kernels that could be moved to common.cuh + templated
+static __device__ float two_stage_warp_reduce_max(float val) {
+    val = warp_reduce_max(val);
+    if (blockDim.x > WARP_SIZE) {
+        assert((blockDim.x <= 1024) && (blockDim.x % WARP_SIZE) == 0);
+        __shared__ float local_vals[32];
+        const int        warp_id = threadIdx.x / WARP_SIZE;
+        const int        lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            local_vals[warp_id] = val;
+        }
+        __syncthreads();
+        val = -INFINITY;
+        if (lane_id < (static_cast<int>(blockDim.x) / WARP_SIZE)) {
+            val = local_vals[lane_id];
+        }
+        return warp_reduce_max(val);
+    } else {
+        return val;
+    }
+}
+
+static __device__ float two_stage_warp_reduce_sum(float val) {
+    val = warp_reduce_sum(val);
+    if (blockDim.x > WARP_SIZE) {
+        assert((blockDim.x <= 1024) && (blockDim.x % WARP_SIZE) == 0);
+        __shared__ float local_vals[32];
+        const int        warp_id = threadIdx.x / WARP_SIZE;
+        const int        lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            local_vals[warp_id] = val;
+        }
+        __syncthreads();
+        val = 0.0f;
+        if (lane_id < (static_cast<int>(blockDim.x) / WARP_SIZE)) {
+            val = local_vals[lane_id];
+        }
+        return warp_reduce_sum(val);
+    } else {
+        return val;
+    }
+}
+
 // TODO: Template to allow keeping ncols in registers if they fit
 static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __restrict__ x,
                                                                 float * __restrict__ dst,
@@ -154,7 +230,6 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __
     float     local_vals[n_elem_per_thread] = { -INFINITY, -INFINITY, -INFINITY, -INFINITY };
     float     local_max                     = -INFINITY;
     const int step_size                     = gridDim.x * blockDim.x;
-    __shared__ float shared_vals[32];
 
     // Compute thread-local max
     for (int col = col_start; col < p.ncols;) {
@@ -171,7 +246,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __
     }
 
     // Compute CTA-level max
-    local_max = block_reduce<block_reduce_method::MAX>(local_max, shared_vals);
+    local_max = two_stage_warp_reduce_max(local_max);
 
     // Store CTA-level max to GMEM
     if (tid == 0) {
@@ -186,7 +261,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __
     } else {
         local_max = -INFINITY;
     }
-    local_max = block_reduce<block_reduce_method::MAX>(local_max, shared_vals);
+    local_max = two_stage_warp_reduce_max(local_max);
 
     // Compute softmax dividends, accumulate divisor
     float tmp_expf = 0.0f;
@@ -209,7 +284,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __
     }
 
     // Reduce divisor within CTA
-    tmp_expf = block_reduce<block_reduce_method::SUM>(tmp_expf, shared_vals);
+    tmp_expf = two_stage_warp_reduce_sum(tmp_expf);
 
     // Store CTA-level sum to GMEM
     if (tid == 0) {
@@ -223,7 +298,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __
     } else {
         tmp_expf = 0.0f;
     }
-    tmp_expf = block_reduce<block_reduce_method::SUM>(tmp_expf, shared_vals);
+    tmp_expf = two_stage_warp_reduce_sum(tmp_expf);
 
     // Divide dividend by global sum + store data
     for (int col = col_start; col < p.ncols;) {

ggml/src/ggml-cuda/vendors/hip.h

@@ -138,8 +138,6 @@
 #define cudaStream_t hipStream_t
 #define cudaSuccess hipSuccess
 #define cudaOccupancyMaxActiveBlocksPerMultiprocessor hipOccupancyMaxActiveBlocksPerMultiprocessor
-#define cudaFuncSetAttribute hipFuncSetAttribute
-#define cudaFuncAttributeMaxDynamicSharedMemorySize hipFuncAttributeMaxDynamicSharedMemorySize
 #define __trap() do { abort(); __builtin_unreachable(); } while(0)
 #define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
 #define CUBLAS_STATUS_NOT_INITIALIZED HIPBLAS_STATUS_NOT_INITIALIZED

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

@@ -42,12 +42,12 @@
 #include "htp_iface.h"
 
 static size_t opt_ndev         = 1;
-static size_t opt_nhvx         = 0; // use all
-static int    opt_arch         = 0; // autodetect
+static size_t opt_nhvx         = 0;  // use all
+static int    opt_arch         = 0;  // autodetect
 static int    opt_etm          = 0;
 static int    opt_verbose      = 0;
 static int    opt_profile      = 0;
-static int    opt_hostbuf      = 1; // hostbuf ON by default
+static int    opt_hostbuf      = 1;
 static int    opt_experimental = 0;
 
 // Enable all stages by default
@@ -1753,9 +1753,6 @@ static bool ggml_backend_buffer_is_hexagon(const struct ggml_backend_buffer * b)
 }
 
 static inline bool ggml_backend_buffer_is_hexagon_repack(const struct ggml_backend_buffer * b) {
-    if (!opt_hostbuf) {
-        return ggml_backend_buffer_is_hexagon(b);
-    }
     return b->buft->iface.alloc_buffer == ggml_backend_hexagon_repack_buffer_type_alloc_buffer;
 }
 
@@ -2305,16 +2302,6 @@ static inline size_t init_binary_req(htp_general_req * req, dspqueue_buffer * bu
     return n_bufs;
 }
 
-static inline size_t init_cpy_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
-    req->op = HTP_OP_CPY;
-
-    size_t n_bufs = 0;
-    n_bufs += htp_req_buff_init(&req->src0, &bufs[n_bufs], t->src[0], DSPQBUF_TYPE_CPU_WRITE_DSP_READ);
-    n_bufs += htp_req_buff_init(&req->dst,  &bufs[n_bufs], t,         DSPQBUF_TYPE_DSP_WRITE_CPU_READ);
-
-    return n_bufs;
-}
-
 static inline size_t init_get_rows_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
     req->op = HTP_OP_GET_ROWS;
 
@@ -2570,10 +2557,6 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
                 ggml_hexagon_dispatch_op<init_get_rows_req>(sess, node, flags);
                 break;
 
-            case GGML_OP_CPY:
-                ggml_hexagon_dispatch_op<init_cpy_req>(sess, node, flags);
-                break;
-
             default:
                 GGML_ABORT("\nggml-hex: graph-compute %s is not supported\n", ggml_op_desc(node));
         }
@@ -2875,27 +2858,6 @@ static bool ggml_hexagon_supported_buffers(ggml_hexagon_session *sess, const str
     return true;
 }
 
-static bool ggml_hexagon_supported_cpy(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
-    const struct ggml_tensor * src0 = op->src[0];
-    const struct ggml_tensor * dst  = op;
-
-    // for now we can do f32 -> f16 and f16 -> f32 (without reshaping)
-    if (src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) return false;
-    if ( dst->type != GGML_TYPE_F32 &&  dst->type != GGML_TYPE_F16) return false;
-
-    const bool sametype   = (src0->type == dst->type);
-    const bool transposed = ggml_is_transposed(src0) || ggml_is_transposed(dst);
-    const bool sameshape  = !transposed && ggml_are_same_shape(src0, dst);
-
-    // can handle any shape and any same-type (pretty slow if reshaping is required)
-    if (sametype) return true;
-
-    // cannot handle re-shaping and type conversion at the same time
-    if (!sameshape) return false;
-
-    return true;
-}
-
 static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
     auto sess = static_cast<ggml_hexagon_session *>(dev->context);
 
@@ -2974,10 +2936,6 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
             supp = ggml_hexagon_supported_get_rows(sess, op);
             break;
 
-        case GGML_OP_CPY:
-            supp = ggml_hexagon_supported_cpy(sess, op);
-            break;
-
         default:
             break;
     }
@@ -3103,7 +3061,7 @@ static ggml_backend_dev_t ggml_backend_hexagon_reg_get_device(ggml_backend_reg_t
 }
 
 static void * ggml_backend_hexagon_get_proc_address(ggml_backend_reg_t reg, const char * name) {
-    if (strcmp(name, "ggml_backend_dev_get_extra_bufts") == 0 && opt_hostbuf) {
+    if (strcmp(name, "ggml_backend_dev_get_extra_bufts") == 0) {
         ggml_backend_dev_get_extra_bufts_t fct = ggml_backend_hexagon_device_get_extra_buffers_type;
         return (void *) fct;
     }
@@ -3120,31 +3078,34 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
     static_assert((unsigned int) HTP_TYPE_MXFP4 == (unsigned int) GGML_TYPE_MXFP4,
                   "please update hexagon_type to match ggml_type");
 
-    const char * str_experimental = getenv("GGML_HEXAGON_EXPERIMENTAL");
     const char * str_verbose = getenv("GGML_HEXAGON_VERBOSE");
     const char * str_hostbuf = getenv("GGML_HEXAGON_HOSTBUF");
-    const char * str_opmask  = getenv("GGML_HEXAGON_OPMASK");
-    const char * str_opsync  = getenv("GGML_HEXAGON_OPSYNC");
-    const char * str_profile = getenv("GGML_HEXAGON_PROFILE");
-    const char * str_etm     = getenv("GGML_HEXAGON_ETM");
-    const char * str_nhvx    = getenv("GGML_HEXAGON_NHVX");
-    const char * str_ndev    = getenv("GGML_HEXAGON_NDEV");
-    const char * str_arch    = getenv("GGML_HEXAGON_ARCH");
 
-    opt_experimental = str_experimental ? atoi(str_experimental) : 0;
     opt_verbose      = str_verbose ? atoi(str_verbose) : 0;
-    opt_hostbuf      = str_hostbuf ? atoi(str_hostbuf) : opt_hostbuf;
-    opt_opmask       = str_opmask  ? strtoul(str_opmask, NULL, 0) : opt_opmask;
-    opt_opsync       = str_opsync  ? atoi(str_opsync)  : 0;
-    opt_profile      = str_profile ? atoi(str_profile) : 0;
-    opt_etm          = str_etm     ? atoi(str_etm) : 0;
-    opt_nhvx         = str_nhvx    ? strtoul(str_nhvx, NULL, 0) : opt_nhvx;
-    opt_ndev         = str_ndev    ? strtoul(str_ndev, NULL, 0) : opt_ndev;
+    opt_profile      = getenv("GGML_HEXAGON_PROFILE") != nullptr;
+    opt_etm          = getenv("GGML_HEXAGON_ETM") != nullptr;
+    opt_experimental = getenv("GGML_HEXAGON_EXPERIMENTAL") != nullptr;
 
-    if (opt_ndev > GGML_HEXAGON_MAX_SESSIONS) {
-        opt_ndev = GGML_HEXAGON_MAX_SESSIONS;
+    const char * str_opmask = getenv("GGML_HEXAGON_OPMASK");
+    if (str_opmask != nullptr) {
+        opt_opmask = strtoul(str_opmask, NULL, 0);
+    }
+    opt_opsync = getenv("GGML_HEXAGON_OPSYNC") != nullptr;
+
+    const char * str_ndev = getenv("GGML_HEXAGON_NDEV");
+    if (str_ndev) {
+        opt_ndev = strtoul(str_ndev, NULL, 0);
+        if (opt_ndev > GGML_HEXAGON_MAX_SESSIONS) {
+            opt_ndev = GGML_HEXAGON_MAX_SESSIONS;
+        }
     }
 
+    const char * str_nhvx = getenv("GGML_HEXAGON_NHVX");
+    if (str_nhvx) {
+        opt_nhvx = strtoul(str_nhvx, NULL, 0);
+    }
+
+    const char * str_arch = getenv("GGML_HEXAGON_ARCH");
     if (str_arch) {
         if (str_arch[0] == 'v') {
             str_arch++;
@@ -3152,6 +3113,8 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
         opt_arch = strtoul(str_arch, NULL, 0);
     }
 
+    opt_hostbuf = str_hostbuf ? atoi(str_hostbuf) : 1;
+
     reg->context = new ggml_hexagon_registry(reg);
 
     HEX_VERBOSE("ggml-hex: size-of-general-req %zu size-of-general-rsp %zu\n", sizeof(struct htp_general_req),

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

@@ -17,7 +17,11 @@ add_library(${HTP_LIB} SHARED
     main.c
     htp_iface_skel.c
     worker-pool.c
-    hex-dma.c
+    htp-dma.c
+    hvx-sigmoid.c
+    hvx-inverse.c
+    hvx-exp.c
+    hvx-utils.c
     matmul-ops.c
     binary-ops.c
     unary-ops.c
@@ -27,12 +31,10 @@ add_library(${HTP_LIB} SHARED
     flash-attn-ops.c
     set-rows-ops.c
     get-rows-ops.c
-    cpy-ops.c
 )
 
 target_compile_definitions(${HTP_LIB} PRIVATE
     $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,HTP_DEBUG=1,NDEBUG=1>
-    $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,FARF_HIGH=1,>
     FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE})
 
 build_idl(htp_iface.idl ${HTP_LIB})

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

@@ -2,20 +2,27 @@
 #pragma clang diagnostic ignored "-Wunused-function"
 #pragma clang diagnostic ignored "-Wunused-but-set-variable"
 
+#ifdef HTP_DEBUG
+#    define FARF_HIGH 1
+#endif
 #include <HAP_farf.h>
+#include <HAP_mem.h>
 #include <HAP_perf.h>
-
+#include <HAP_ps.h>
+#include <hexagon_protos.h>
+#include <hexagon_types.h>
 #include <math.h>
+#include <qurt_thread.h>
 #include <string.h>
 
-#include "hex-dma.h"
-#include "hvx-utils.h"
-
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
+#include "htp-dma.h"
 #include "htp-msg.h"
 #include "htp-ops.h"
+#include "hvx-utils.h"
+#include "ops-utils.h"
 
 #define htp_act_preamble3              \
     const uint32_t ne00 = src0->ne[0]; \
@@ -69,7 +76,7 @@
     const uint32_t nb2 = dst->nb[2];   \
     const uint32_t nb3 = dst->nb[3];
 
-static void glu_swiglu_f32_per_thread(const struct htp_tensor * src0,
+static void glu_swiglu_fp32_per_thread(const struct htp_tensor * src0,
                                        const struct htp_tensor * src1,
                                        struct htp_tensor *       dst,
                                        const int32_t *           op_params,
@@ -117,9 +124,9 @@ static void glu_swiglu_f32_per_thread(const struct htp_tensor * src0,
         data_src1 += swapped ? 0 : nc_in_bytes;
     }
 
-    const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
-    const size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN);
-    const size_t dst_row_size_aligned  = hex_round_up(dst_row_size, VLEN);
+    const size_t src0_row_size_aligned = htp_round_up(src0_row_size, VLEN);
+    const size_t src1_row_size_aligned = htp_round_up(src1_row_size, VLEN);
+    const size_t dst_row_size_aligned  = htp_round_up(dst_row_size, VLEN);
 
     uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread);
     uint8_t * restrict src1_spad_data = src1_spad->data + (ith * src1_spad->size_per_thread);
@@ -168,9 +175,9 @@ static void glu_swiglu_f32_per_thread(const struct htp_tensor * src0,
             float *       dst_spad_ptr  = dst_spad + ib * (dst_row_size_aligned / sizeof(float));
 
             //swiglu(x) = x1 * sigmoid(x0)
-            hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, nc);
-            hvx_mul_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr,
-                                (const uint8_t *) src1_spad_ptr, nc);
+            hvx_fast_sigmoid_f32((const uint8_t *) src0_spad_ptr, (uint8_t *) dst_spad_ptr, nc);
+            hvx_mul_mul_f32_opt((const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr,
+                                (const uint8_t *) src1_spad_ptr, (uint8_t *) dst_spad_ptr, nc);
         }
 
         dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size,
@@ -196,7 +203,7 @@ static void glu_swiglu_f32_per_thread(const struct htp_tensor * src0,
          (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
-static void glu_swiglu_oai_f32_per_thread(const struct htp_tensor * src0,
+static void glu_swiglu_oai_fp32_per_thread(const struct htp_tensor * src0,
                                            const struct htp_tensor * src1,
                                            struct htp_tensor *       dst,
                                            const int32_t *           op_params,
@@ -242,9 +249,9 @@ static void glu_swiglu_oai_f32_per_thread(const struct htp_tensor * src0,
         data_src1 += swapped ? 0 : nc_in_bytes;
     }
 
-    const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
-    const size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN);
-    const size_t dst_row_size_aligned  = hex_round_up(dst_row_size, VLEN);
+    const size_t src0_row_size_aligned = htp_round_up(src0_row_size, VLEN);
+    const size_t src1_row_size_aligned = htp_round_up(src1_row_size, VLEN);
+    const size_t dst_row_size_aligned  = htp_round_up(dst_row_size, VLEN);
 
     uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread);
     uint8_t * restrict src1_spad_data = src1_spad->data + (ith * src1_spad->size_per_thread);
@@ -297,18 +304,18 @@ static void glu_swiglu_oai_f32_per_thread(const struct htp_tensor * src0,
             float *       dst_spad_ptr  = dst_spad + ib * (dst_row_size_aligned / sizeof(float));
 
             // x (src0_spad_data) = std::min(src0_p[k], limit);
-            hvx_min_scalar_f32((uint8_t *) src0_spad_ptr, (const uint8_t *) src0_spad_ptr, limit, nc);
+            hvx_min_scalar_f32((const uint8_t *) src0_spad_ptr, limit, (uint8_t *) src0_spad_ptr, nc);
             // y1 (src1_spad_data) = std::clamp(src1_p[k], -limit, limit);
-            hvx_clamp_scalar_f32((uint8_t *) src1_spad_ptr, (const uint8_t *) src1_spad_ptr, -limit, limit, nc);
+            hvx_clamp_scalar_f32((const uint8_t *) src1_spad_ptr, -limit, limit, (uint8_t *) src1_spad_ptr, nc);
             // y (src1_spad_data)  = y1 + 1.f
-            hvx_add_scalar_f32((uint8_t *) src1_spad_ptr, (const uint8_t *) src1_spad_ptr, 1.0, nc);
+            hvx_add_scalar_f32((const uint8_t *) src1_spad_ptr, 1.0, (uint8_t *) src1_spad_ptr, nc);
             // x1 (dst_spad_data) = alpha * (x)
-            hvx_mul_scalar_f32((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, alpha, nc);
+            hvx_mul_scalar_f32((const uint8_t *) src0_spad_ptr, alpha, (uint8_t *) dst_spad_ptr, nc);
             // x2 (dst_spad_data) = sigmoid(x1) = 1/(1+exp(-x1))
-            hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, nc);
+            hvx_fast_sigmoid_f32((const uint8_t *) dst_spad_ptr, (uint8_t *) dst_spad_ptr, nc);
             // out = x * sigmoid(alpha * x) * (y + 1.f)
-            hvx_mul_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr,
-                                (const uint8_t *) src1_spad_ptr, nc);
+            hvx_mul_mul_f32_opt((const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr,
+                                (const uint8_t *) src1_spad_ptr, (uint8_t *) dst_spad_ptr, nc);
         }
 
         dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size,
@@ -335,7 +342,7 @@ static void glu_swiglu_oai_f32_per_thread(const struct htp_tensor * src0,
 }
 
 
-static void unary_gelu_f32_per_thread(const struct htp_tensor * src0,
+static void unary_gelu_fp32_per_thread(const struct htp_tensor * src0,
                                        struct htp_tensor *       dst,
                                        const int32_t *           op_params,
                                        struct htp_spad *         src0_spad,
@@ -351,8 +358,8 @@ static void unary_gelu_f32_per_thread(const struct htp_tensor * src0,
 
     const size_t src0_row_size = nb01;
     const size_t dst_row_size  = nb1;
-    const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
-    const size_t dst_row_size_aligned  = hex_round_up(dst_row_size, VLEN);
+    const size_t src0_row_size_aligned = htp_round_up(src0_row_size, VLEN);
+    const size_t dst_row_size_aligned  = htp_round_up(dst_row_size, VLEN);
 
     const uint32_t src0_nrows = ne01 * ne02 * ne03;
 
@@ -408,9 +415,9 @@ static void unary_gelu_f32_per_thread(const struct htp_tensor * src0,
             float* dst_spad_ptr        = dst_spad  + ib * (dst_row_size_aligned  / sizeof(float));
 
             // gelu = x * sigmoid(1.702 * x) // current implementation
-            hvx_mul_scalar_f32((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (float) 1.702, ne0);
-            hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
-            hvx_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
+            hvx_mul_scalar_f32((const uint8_t *) src0_spad_ptr, (float) 1.702, (uint8_t *) dst_spad_ptr, ne0);
+            hvx_fast_sigmoid_f32((const uint8_t *) dst_spad_ptr, (uint8_t *) dst_spad_ptr, ne0);
+            hvx_mul_f32_opt((const uint8_t *) src0_spad_ptr, (uint8_t *) dst_spad_ptr, (uint8_t *) dst_spad_ptr, ne0);
         }
 
         dma_queue_push_vtcm_to_ddr(dma_queue,
@@ -435,15 +442,15 @@ static void unary_gelu_f32_per_thread(const struct htp_tensor * src0,
          ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
-static void unary_gelu_f32(unsigned int n, unsigned int i, void * data) {
+static void unary_gelu_fp32(unsigned int n, unsigned int i, void * data) {
     struct htp_ops_context * octx = (struct htp_ops_context *) data;
-    unary_gelu_f32_per_thread(&octx->src0, &octx->dst, octx->op_params, &octx->src0_spad, &octx->dst_spad, n, i,
+    unary_gelu_fp32_per_thread(&octx->src0, &octx->dst, octx->op_params, &octx->src0_spad, &octx->dst_spad, n, i,
                                octx->src0_nrows_per_thread, octx->ctx->dma[i]);
 }
 
 
 
-static void unary_silu_f32_per_thread(const struct htp_tensor * src0,
+static void unary_silu_fp32_per_thread(const struct htp_tensor * src0,
                                        struct htp_tensor *       dst,
                                        const int32_t *           op_params,
                                        struct htp_spad *         src0_spad,
@@ -459,8 +466,8 @@ static void unary_silu_f32_per_thread(const struct htp_tensor * src0,
 
     const size_t src0_row_size = nb01;
     const size_t dst_row_size  = nb1;
-    const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
-    const size_t dst_row_size_aligned  = hex_round_up(dst_row_size, VLEN);
+    const size_t src0_row_size_aligned = htp_round_up(src0_row_size, VLEN);
+    const size_t dst_row_size_aligned  = htp_round_up(dst_row_size, VLEN);
 
     const uint32_t src0_nrows = ne01 * ne02 * ne03;
 
@@ -515,8 +522,8 @@ static void unary_silu_f32_per_thread(const struct htp_tensor * src0,
             float* dst_spad_ptr        = dst_spad  + ib * (dst_row_size_aligned  / sizeof(float));
 
             // silu = x * sigmoid(x)
-            hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, ne0);
-            hvx_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
+            hvx_fast_sigmoid_f32((const uint8_t *) src0_spad_ptr, (uint8_t *) dst_spad_ptr, ne0);
+            hvx_mul_f32_opt((const uint8_t *) src0_spad_ptr, (uint8_t *) dst_spad_ptr, (uint8_t *) dst_spad_ptr, ne0);
         }
 
         dma_queue_push_vtcm_to_ddr(dma_queue,
@@ -541,25 +548,25 @@ static void unary_silu_f32_per_thread(const struct htp_tensor * src0,
          ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
-static void unary_silu_f32(unsigned int n, unsigned int i, void * data) {
+static void unary_silu_fp32(unsigned int n, unsigned int i, void * data) {
     struct htp_ops_context * octx = (struct htp_ops_context *) data;
-    unary_silu_f32_per_thread(&octx->src0, &octx->dst, octx->op_params, &octx->src0_spad, &octx->dst_spad, n, i,
+    unary_silu_fp32_per_thread(&octx->src0, &octx->dst, octx->op_params, &octx->src0_spad, &octx->dst_spad, n, i,
                                octx->src0_nrows_per_thread, octx->ctx->dma[i]);
 }
 
-static void glu_swiglu_f32(unsigned int n, unsigned int i, void * data) {
+static void glu_swiglu_fp32(unsigned int n, unsigned int i, void * data) {
     struct htp_ops_context * octx = (struct htp_ops_context *) data;
-    glu_swiglu_f32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->op_params, &octx->src0_spad,
+    glu_swiglu_fp32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->op_params, &octx->src0_spad,
                                &octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread, octx->ctx->dma[i]);
 }
 
-static void glu_swiglu_oai_f32(unsigned int n, unsigned int i, void * data) {
+static void glu_swiglu_oai_fp32(unsigned int n, unsigned int i, void * data) {
     struct htp_ops_context * octx = (struct htp_ops_context *) data;
-    glu_swiglu_oai_f32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->op_params, &octx->src0_spad,
+    glu_swiglu_oai_fp32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->op_params, &octx->src0_spad,
                                    &octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread, octx->ctx->dma[i]);
 }
 
-static int execute_op_activations_f32(struct htp_ops_context * octx) {
+static int execute_op_activations_fp32(struct htp_ops_context * octx) {
     int err = HTP_STATUS_OK;
 
     const struct htp_tensor * src0 = &octx->src0;
@@ -576,21 +583,21 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) {
 
     switch (octx->op) {
         case HTP_OP_UNARY_SILU:
-            act_op_func = unary_silu_f32;
+            act_op_func = unary_silu_fp32;
             op_type     = "silu-f32";
             break;
 
         case HTP_OP_GLU_SWIGLU:
-            act_op_func = glu_swiglu_f32;
+            act_op_func = glu_swiglu_fp32;
             op_type     = "swiglu-f32";
             break;
 
         case HTP_OP_GLU_SWIGLU_OAI:
-            act_op_func = glu_swiglu_oai_f32;
+            act_op_func = glu_swiglu_oai_fp32;
             op_type     = "swiglu-oai-f32";
             break;
         case HTP_OP_UNARY_GELU:
-            act_op_func = unary_gelu_f32;
+            act_op_func = unary_gelu_fp32;
             op_type     = "gelu-f32";
             break;
         default:
@@ -610,9 +617,9 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) {
         src1_row_size = src0_row_size;
     }
 
-    const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
-    const size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN);
-    const size_t dst_row_size_aligned  = hex_round_up(dst_row_size, VLEN);
+    const size_t src0_row_size_aligned = htp_round_up(src0_row_size, VLEN);
+    const size_t src1_row_size_aligned = htp_round_up(src1_row_size, VLEN);
+    const size_t dst_row_size_aligned  = htp_round_up(dst_row_size, VLEN);
     // VTCM scratchpads for all tensors
     // N rows per thread, padded to HVX vector size
 
@@ -663,7 +670,7 @@ int op_activations(struct htp_ops_context * octx) {
 
     switch (octx->src0.type) {
         case HTP_TYPE_F32:
-            err = execute_op_activations_f32(octx);
+            err = execute_op_activations_fp32(octx);
             break;
 
         default:

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

@@ -2,25 +2,36 @@
 #pragma clang diagnostic ignored "-Wunused-function"
 #pragma clang diagnostic ignored "-Wunused-but-set-variable"
 
+#ifdef HTP_DEBUG
+#    define FARF_HIGH 1
+#endif
+
 #include <HAP_farf.h>
+#include <HAP_mem.h>
 #include <HAP_perf.h>
-
+#include <HAP_ps.h>
+#include <hexagon_protos.h>
+#include <hexagon_types.h>
 #include <math.h>
+#include <qurt_thread.h>
 #include <string.h>
 
-#include "hex-dma.h"
-#include "hvx-utils.h"
-
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
+#include "htp-dma.h"
 #include "htp-msg.h"
 #include "htp-ops.h"
+#include "hvx-utils.h"
+#include "ops-utils.h"
 
-typedef void (*hvx_elemwise_f32_func)(uint8_t * data_dst, const uint8_t * src0, const uint8_t * src1, const uint32_t num_elems);
+typedef void (*hvx_elemwise_f32_func)(const uint8_t * src0,
+                                      const uint8_t * src1,
+                                      uint8_t *       data_dst,
+                                      const int       num_elems);
 
 static hvx_elemwise_f32_func func_table_HVX[]     = { hvx_mul_f32, hvx_add_f32, hvx_sub_f32 };
-static hvx_elemwise_f32_func func_table_HVX_opt[] = { hvx_mul_f32_aa, hvx_add_f32_aa, hvx_sub_f32_aa };
+static hvx_elemwise_f32_func func_table_HVX_opt[] = { hvx_mul_f32_opt, hvx_add_f32_opt, hvx_sub_f32_opt };
 
 #define htp_binary_preamble            \
     const struct htp_tensor * src0 = &octx->src0; \
@@ -87,8 +98,9 @@ static void binary_job_f32_per_thread(struct htp_ops_context * octx,
 
     int is_aligned = 1;
     int opt_path   = 0;
-    if ((0 == hex_is_aligned((void *) src0->data, VLEN)) || (0 == hex_is_aligned((void *) src1->data, VLEN)) ||
-        (0 == hex_is_aligned((void *) dst->data, VLEN))) {
+    if ((0 == htp_is_aligned((void *) src0->data, VLEN)) || (0 == htp_is_aligned((void *) src1->data, VLEN)) ||
+        (0 == htp_is_aligned((void *) dst->data, VLEN))) {
+        FARF(HIGH, "binary-f32: unaligned addresses in elementwise op, possibly slower execution\n");
         is_aligned = 0;
     }
     if ((1 == is_aligned) && !(nb01 & (VLEN - 1))) {
@@ -118,24 +130,24 @@ static void binary_job_f32_per_thread(struct htp_ops_context * octx,
         const uint8_t * restrict src1_ptr = data_src1 + i13 * nb13 + i12 * nb12 + i11 * src1_row_size;
 
         if (ir + 1 < src0_end_row) {
-            hex_l2fetch(src0_ptr + ne00, src0_row_size, src0_row_size, 1);
+            htp_l2fetch(src0_ptr + ne00, 1, src0_row_size, src0_row_size);
             if (src1_row_size == src0_row_size) {
-                hex_l2fetch(src1_ptr, src1_row_size, src1_row_size, 1);
+                htp_l2fetch(src1_ptr, 1, src1_row_size, src1_row_size);
             }
         }
 
         const uint32_t nr0 = ne00 / ne10;
         if (nr0 > 1) {
             if ((1 == is_aligned) && (nr0 == ne00)) {
-                hvx_splat_f32_a(spad_data_th, *(float *) src1_ptr, nr0);
+                hvx_bcast_fp32_a(spad_data_th, *(float *) src1_ptr, nr0);
             } else {
                 for (uint32_t r = 0; r < nr0; r++) {
                     memcpy(spad_data_th + r * nb11, (const uint8_t *) src1_ptr, nb11);
                 }
             }
-            func_HVX((uint8_t *) dst_ptr, (const uint8_t *) src0_ptr, (const uint8_t *) spad_data_th, ne00);
+            func_HVX((const uint8_t *) src0_ptr, (const uint8_t *) spad_data_th, (uint8_t *) dst_ptr, ne00);
         } else {
-            func_HVX((uint8_t *) dst_ptr, (const uint8_t *) src0_ptr, (const uint8_t *) src1_ptr, ne00);
+            func_HVX((const uint8_t *) src0_ptr, (const uint8_t *) src1_ptr, (uint8_t *) dst_ptr, ne00);
         }
 
         src0_ptr += src0_row_size;
@@ -173,6 +185,11 @@ static void binary_add_id_job_f32_per_thread(struct htp_ops_context * octx,
     uint64_t t1, t2;
     t1 = HAP_perf_get_qtimer_count();
 
+    if ((0 == htp_is_aligned((void *) src0->data, VLEN)) || (0 == htp_is_aligned((void *) src1->data, VLEN)) ||
+        (0 == htp_is_aligned((void *) dst->data, VLEN))) {
+        FARF(HIGH, "add-id-f32: unaligned addresses, possibly slower execution\n");
+    }
+
     const uint8_t * restrict data_src0 = (const uint8_t *) src0->data;
     const uint8_t * restrict data_src1 = (const uint8_t *) src1->data;
     uint8_t * restrict data_dst        = (uint8_t *) dst->data;
@@ -193,9 +210,9 @@ static void binary_add_id_job_f32_per_thread(struct htp_ops_context * octx,
         const float * restrict src1_ptr = (const float *) (data_src1 + 0 + 0 + i11 * nb11);
 
         if (ir + 1 < src0_end_row) {
-            hex_l2fetch(src0_ptr + ne00, src0_row_size, src0_row_size, 1);
+            htp_l2fetch(src0_ptr + ne00, 1, src0_row_size, src0_row_size);
             if (src1_row_size == src0_row_size) {
-                hex_l2fetch(src1_ptr + ne10, src1_row_size, src1_row_size, 1);
+                htp_l2fetch(src1_ptr + ne10, 1, src1_row_size, src1_row_size);
             }
         }
 
@@ -204,9 +221,9 @@ static void binary_add_id_job_f32_per_thread(struct htp_ops_context * octx,
             for (uint32_t r = 0; r < nr0; r++) {
                 memcpy(spad_data + r * nb10, (const uint8_t *) src1_ptr, nb10);
             }
-            func_HVX((uint8_t *) dst_ptr, (const uint8_t *) src0_ptr, (const uint8_t *) spad_data, ne00);
+            func_HVX((const uint8_t *) src0_ptr, (const uint8_t *) spad_data, (uint8_t *) dst_ptr, ne00);
         } else {
-            func_HVX((uint8_t *) dst_ptr, (const uint8_t *) src0_ptr, (const uint8_t *) src1_ptr, ne00);
+            func_HVX((const uint8_t *) src0_ptr, (const uint8_t *) src1_ptr, (uint8_t *) dst_ptr, ne00);
         }
     }
 
@@ -282,9 +299,9 @@ static int execute_op_binary_f32(struct htp_ops_context * octx) {
     const size_t dst_row_size  = dst->nb[1];
 
     // VTCM scratchpads for all tensors
-    octx->dst_spad.size  = hex_round_up(dst_row_size, 128) * n_threads;
-    octx->src0_spad.size = hex_round_up(src0_row_size, 128) * n_threads;
-    octx->src1_spad.size = hex_round_up(src1_row_size, 128) * n_threads;
+    octx->dst_spad.size  = htp_round_up(dst_row_size, 128) * n_threads;
+    octx->src0_spad.size = htp_round_up(src0_row_size, 128) * n_threads;
+    octx->src1_spad.size = htp_round_up(src1_row_size, 128) * n_threads;
 
     size_t spad_size = octx->src0_spad.size + octx->src1_spad.size + octx->dst_spad.size;
 

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

@@ -1,251 +0,0 @@
-#pragma clang diagnostic ignored "-Wunused-variable"
-#pragma clang diagnostic ignored "-Wunused-function"
-#pragma clang diagnostic ignored "-Wunused-but-set-variable"
-
-#include <HAP_farf.h>
-#include <HAP_perf.h>
-
-#include <math.h>
-#include <string.h>
-
-#define GGML_COMMON_DECL_C
-#include "ggml-common.h"
-#include "htp-ctx.h"
-#include "htp-msg.h"
-#include "htp-ops.h"
-#include "hvx-utils.h"
-
-struct htp_copy_context {
-    struct htp_ops_context * octx;
-
-    uint32_t          src0_type_size;
-    uint32_t          src0_block_size;
-
-    uint32_t          dst_type_size;
-    uint32_t          dst_block_size;
-
-    uint32_t          src0_blocks_per_row;
-    uint32_t          dst_blocks_per_row;
-
-    uint32_t          src0_nrows_per_thread;
-
-    void (*copy)(struct htp_copy_context * ct, struct htp_ops_context * octx, int nth, int ith);
-};
-
-#define cpy_preamble                       \
-    struct htp_tensor *src0 = &octx->src0; \
-    struct htp_tensor *dst  = &octx->dst;  \
-                                           \
-    const uint32_t ne00 = src0->ne[0];     \
-    const uint32_t ne01 = src0->ne[1];     \
-    const uint32_t ne02 = src0->ne[2];     \
-    const uint32_t ne03 = src0->ne[3];     \
-                                           \
-    const uint32_t nb00 = src0->nb[0];     \
-    const uint32_t nb01 = src0->nb[1];     \
-    const uint32_t nb02 = src0->nb[2];     \
-    const uint32_t nb03 = src0->nb[3];     \
-                                           \
-    const uint32_t  ne0 = dst->ne[0];      \
-    const uint32_t  ne1 = dst->ne[1];      \
-    const uint32_t  ne2 = dst->ne[2];      \
-    const uint32_t  ne3 = dst->ne[3];      \
-                                           \
-    const uint32_t  nb0 = dst->nb[0];      \
-    const uint32_t  nb1 = dst->nb[1];      \
-    const uint32_t  nb2 = dst->nb[2];      \
-    const uint32_t  nb3 = dst->nb[3];      \
-                                           \
-    const uint32_t   nr = ne01;
-
-static void cpy_thread_sametype_sameshape(struct htp_copy_context * ct, struct htp_ops_context * octx, const int nth, const int ith) {
-    cpy_preamble;
-
-    // parallelize by src0 rows
-    const uint32_t dr  = ct->src0_nrows_per_thread;
-    const uint32_t ir0 = dr * ith;
-    const uint32_t ir1 = (ir0 + dr) < nr ? (ir0 + dr) : nr;
-
-    // copy by rows
-    for (uint32_t i03 = 0; i03 < ne03; i03++) {
-        for (uint32_t i02 = 0; i02 < ne02; i02++) {
-            #pragma unroll(2)
-            for (uint32_t i01 = ir0; i01 < ir1; i01++) {
-                uint8_t* dst_ptr  = (uint8_t*) dst->data  + i01*nb1  + i02*nb2  + i03*nb3;
-                uint8_t* src0_ptr = (uint8_t*) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
-                hex_l2fetch(src0_ptr, ne00 * ct->src0_type_size, nb01, 2);
-                hvx_copy_uu(dst_ptr, src0_ptr, ne00, ct->src0_type_size);
-            }
-        }
-    }
-}
-
-static void cpy_thread_sametype_reshape(struct htp_copy_context * ct, struct htp_ops_context * octx, int nth, int ith) {
-    cpy_preamble;
-
-    // parallelize by src0 rows
-    const uint32_t dr  = ct->src0_nrows_per_thread;
-    const uint32_t ir0 = dr * ith;
-    const uint32_t ir1 = (ir0 + dr) < nr ? (ir0 + dr) : nr;
-
-    // dst counters
-    int64_t k10 = 0;
-    int64_t i11 = 0;
-    int64_t i12 = 0;
-    int64_t i13 = 0;
-
-    // number of blocks in a row
-    const int64_t nk00 = ct->src0_blocks_per_row;
-    const int64_t nk0  = ct->dst_blocks_per_row;
-
-    for (int64_t i03 = 0; i03 < ne03; i03++) {
-        for (int64_t i02 = 0; i02 < ne02; i02++) {
-            k10 += nk00 * ir0;
-            while (k10 >= nk0) {
-                k10 -= nk0;
-                if (++i11 == ne1) {
-                    i11 = 0;
-                    if (++i12 == ne2) {
-                        i12 = 0;
-                        if (++i13 == ne3) {
-                            i13 = 0;
-                        }
-                    }
-                }
-            }
-            for (int64_t i01 = ir0; i01 < ir1; i01++) {
-                for (int64_t k00 = 0; k00 < nk00; k00++) {
-                    const char * src0_ptr = ((char *) src0->data + k00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
-                          char * dst_ptr  = ((char *)  dst->data + k10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
-                    memcpy(dst_ptr, src0_ptr, ct->dst_type_size);
-
-                    if (++k10 == nk0) {
-                        k10 = 0;
-                        if (++i11 == ne1) {
-                            i11 = 0;
-                            if (++i12 == ne2) {
-                                i12 = 0;
-                                if (++i13 == ne3) {
-                                    i13 = 0;
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            k10 += nk00 * (ne01 - ir1);
-            while (k10 >= nk0) {
-                k10 -= nk0;
-                if (++i11 == ne1) {
-                    i11 = 0;
-                    if (++i12 == ne2) {
-                        i12 = 0;
-                        if (++i13 == ne3) {
-                            i13 = 0;
-                        }
-                    }
-                }
-            }
-        }
-    }
-}
-
-static void cpy_thread_f16_f32_sameshape(struct htp_copy_context * ct, struct htp_ops_context * octx, const int nth, const int ith) {
-    cpy_preamble;
-
-    // parallelize by src0 rows
-    const uint32_t dr  = ct->src0_nrows_per_thread;
-    const uint32_t ir0 = dr * ith;
-    const uint32_t ir1 = (ir0 + dr) < nr ? (ir0 + dr) : nr;
-
-    // copy by rows
-    for (uint32_t i03 = 0; i03 < ne03; i03++) {
-        for (uint32_t i02 = 0; i02 < ne02; i02++) {
-            #pragma unroll(2)
-            for (uint32_t i01 = ir0; i01 < ir1; i01++) {
-                uint8_t* dst_ptr  = (uint8_t*) dst->data  + i01*nb1  + i02*nb2  + i03*nb3;
-                uint8_t* src0_ptr = (uint8_t*) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
-                hex_l2fetch(src0_ptr, ne00 * sizeof(float), nb01, 2);
-                hvx_copy_f16_f32_uu(dst_ptr, src0_ptr, ne00);
-            }
-        }
-    }
-}
-
-static void cpy_thread_f32_f16_sameshape(struct htp_copy_context * ct, struct htp_ops_context * octx, const int nth, const int ith) {
-    cpy_preamble;
-
-    // parallelize by src0 rows
-    const uint32_t dr  = ct->src0_nrows_per_thread;
-    const uint32_t ir0 = dr * ith;
-    const uint32_t ir1 = (ir0 + dr) < nr ? (ir0 + dr) : nr;
-
-    // copy by rows
-    for (uint32_t i03 = 0; i03 < ne03; i03++) {
-        for (uint32_t i02 = 0; i02 < ne02; i02++) {
-            #pragma unroll(2)
-            for (uint32_t i01 = ir0; i01 < ir1; i01++) {
-                uint8_t* dst_ptr  = (uint8_t*) dst->data  + i01*nb1  + i02*nb2  + i03*nb3;
-                uint8_t* src0_ptr = (uint8_t*) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
-                hex_l2fetch(src0_ptr, ne00 * sizeof(__fp16), nb01, 2);
-                hvx_copy_f32_f16_uu(dst_ptr, src0_ptr, ne00);
-            }
-        }
-    }
-}
-
-static void cpy_work_func(unsigned int n, unsigned int i, void *data) {
-    struct htp_copy_context *ct = (struct htp_copy_context *) data;
-    ct->copy(ct, ct->octx, n, i);
-}
-
-int op_cpy(struct htp_ops_context * octx) {
-    cpy_preamble;
-
-    struct htp_copy_context ct;
-    ct.octx = octx;
-
-    switch (src0->type) {
-    case HTP_TYPE_F32: ct.src0_type_size = 4; ct.src0_block_size = 1; ct.src0_blocks_per_row = ne00 / 1; break;
-    case HTP_TYPE_F16: ct.src0_type_size = 2; ct.src0_block_size = 1; ct.src0_blocks_per_row = ne00 / 1; break;
-    default:
-        return HTP_STATUS_NO_SUPPORT;
-    }
-
-    switch (dst->type) {
-    case HTP_TYPE_F32: ct.dst_type_size = 4; ct.dst_block_size = 1; ct.dst_blocks_per_row = ne0 / 1; break;
-    case HTP_TYPE_F16: ct.dst_type_size = 2; ct.dst_block_size = 1; ct.dst_blocks_per_row = ne0 / 1; break;
-    default:
-        return HTP_STATUS_NO_SUPPORT;
-    }
-
-    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
-        return HTP_STATUS_OK;
-    }
-
-    const bool sametype   = (src0->type == dst->type);
-    const bool transposed = (nb00 > nb01) || (nb0 > nb1);
-    const bool sameshape  = !transposed && (ne00 == ne0 && ne01 == ne1 && ne02 == ne2 && ne03 == ne3);
-
-    const uint32_t n_jobs = MIN(nr, octx->n_threads);
-    ct.src0_nrows_per_thread = (nr + n_jobs - 1) / n_jobs;
-
-    if (sametype && sameshape) {
-        ct.copy = cpy_thread_sametype_sameshape;
-    } else if (sameshape) {
-        /**/ if (dst->type == HTP_TYPE_F16 && src0->type == HTP_TYPE_F32)
-            ct.copy = cpy_thread_f16_f32_sameshape;
-        else if (dst->type == HTP_TYPE_F32 && src0->type == HTP_TYPE_F16)
-            ct.copy = cpy_thread_f32_f16_sameshape;
-        else
-            return HTP_STATUS_NO_SUPPORT;
-    } else if (sametype) {
-        ct.copy = cpy_thread_sametype_reshape;
-    } else {
-        return HTP_STATUS_NO_SUPPORT;
-    }
-
-    worker_pool_run_func(octx->ctx->worker_pool, cpy_work_func, &ct, n_jobs);
-
-    return HTP_STATUS_OK;
-}

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

@@ -2,20 +2,25 @@
 #pragma clang diagnostic ignored "-Wunused-function"
 #pragma clang diagnostic ignored "-Wunused-but-set-variable"
 
+#ifdef HTP_DEBUG
+#    define FARF_HIGH 1
+#endif
 #include <HAP_farf.h>
+#include <HAP_mem.h>
 #include <HAP_perf.h>
-
+#include <hexagon_protos.h>
+#include <hexagon_types.h>
 #include <math.h>
 #include <string.h>
 
-#include "hex-dma.h"
-#include "hvx-utils.h"
-
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
+#include "htp-dma.h"
 #include "htp-msg.h"
 #include "htp-ops.h"
+#include "hvx-utils.h"
+#include "ops-utils.h"
 
 // Dot product of FP32 and FP16 vectors, accumulating to float
 static inline void hvx_dot_f32_f16_aa(float * restrict r, const void * restrict y, const void * restrict x, unsigned int n, float s) {
@@ -65,8 +70,8 @@ static inline void hvx_dot_f32_f16_aa(float * restrict r, const void * restrict
         rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf)));
     }
 
-    rsum = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(rsum), hvx_vec_splat_f32(s));
-    rsum = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum));
+    rsum = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(rsum), hvx_vec_splat_fp32(s));
+    rsum = Q6_Vsf_equals_Vqf32(hvx_vec_qf32_reduce_sum(rsum));
 
     hvx_vec_store_u(r, 4, rsum);
 }
@@ -106,8 +111,8 @@ static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict
         rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
     }
 
-    rsum = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(rsum), hvx_vec_splat_f32(s));
-    rsum = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum));
+    rsum = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(rsum), hvx_vec_splat_fp32(s));
+    rsum = Q6_Vsf_equals_Vqf32(hvx_vec_qf32_reduce_sum(rsum));
     hvx_vec_store_u(r, 4, rsum);
 }
 
@@ -119,7 +124,7 @@ static inline void hvx_mad_f32_f16_aa(float * restrict y, const void * restrict
     uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
     uint32_t nloe = n % VLEN_FP16; // leftover elements
 
-    HVX_Vector S = hvx_vec_splat_f16(s);
+    HVX_Vector S = hvx_vec_splat_fp16(s);
 
     uint32_t i = 0;
     #pragma unroll(4)
@@ -143,7 +148,7 @@ static inline void hvx_mad_f32_f16_aa(float * restrict y, const void * restrict
 
         if (nloe) {
             HVX_Vector xy = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(xs, ptr_y[i]));
-            hvx_vec_store_a(&ptr_y[i], nloe * 4, xy);
+            hvx_vec_store_u(&ptr_y[i], nloe * 4, xy);
         }
     }
 }
@@ -220,18 +225,18 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
     const uint32_t DV = nev0;
 
     const size_t size_q_row = DK * ((q->type == HTP_TYPE_F32) ? 4 : 2);
-    const size_t size_q_row_padded = hex_round_up(size_q_row, 128);
+    const size_t size_q_row_padded = htp_round_up(size_q_row, 128);
 
     const size_t size_k_row = DK * sizeof(__fp16);
     const size_t size_v_row = DV * sizeof(__fp16);
     const size_t size_m_row = FLASH_ATTN_BLOCK_SIZE * sizeof(__fp16); // Treat block as one row for mask
 
-    const size_t size_k_row_padded = hex_round_up(size_k_row, 128);
-    const size_t size_v_row_padded = hex_round_up(size_v_row, 128);
+    const size_t size_k_row_padded = htp_round_up(size_k_row, 128);
+    const size_t size_v_row_padded = htp_round_up(size_v_row, 128);
 
     const size_t size_k_block = size_k_row_padded * FLASH_ATTN_BLOCK_SIZE;
     const size_t size_v_block = size_v_row_padded * FLASH_ATTN_BLOCK_SIZE;
-    const size_t size_m_block = hex_round_up(FLASH_ATTN_BLOCK_SIZE * sizeof(__fp16), 128);
+    const size_t size_m_block = htp_round_up(FLASH_ATTN_BLOCK_SIZE * sizeof(__fp16), 128);
 
     // Scratchpad buffers for Q, K, V, Mask, and VKQ32 accumulator
     uint8_t * spad_q = octx->src0_spad.data + octx->src0_spad.size_per_thread * ith;
@@ -267,8 +272,8 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
         float M = -INFINITY; // maximum KQ value
 
         // Clear accumulator
-        hvx_splat_f32_a(spad_a, 0, DV);
         float * VKQ32 = (float *) spad_a;
+        memset(VKQ32, 0, DV * sizeof(float));
 
         const __fp16 * mp_base = NULL;
         if (mask) {
@@ -335,30 +340,30 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
 
                 // 2. Softcap
                 if (logit_softcap != 0.0f) {
-                    scores = hvx_vec_tanh_f32(scores);
-                    scores = Q6_Vqf32_vmpy_VsfVsf(scores, hvx_vec_splat_f32(logit_softcap));
+                    scores = hvx_vec_tanh_fp32(scores);
+                    scores = Q6_Vqf32_vmpy_VsfVsf(scores, hvx_vec_splat_fp32(logit_softcap));
                     scores = Q6_Vsf_equals_Vqf32(scores);
                 }
 
                 // 3. Mask
                 if (mask) {
                     const __fp16 * mp = m_base + ic;
-                    HVX_Vector m_vals_f16 = *(const HVX_UVector *) mp;
+                    HVX_Vector m_vals_fp16 = *(const HVX_UVector *) mp;
 
-                    HVX_Vector one_f16 = Q6_Vh_vsplat_R(0x3c00);
-                    HVX_VectorPair m_vals_f32_pair = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(m_vals_f16), one_f16);
+                    HVX_Vector one_fp16 = Q6_Vh_vsplat_R(0x3c00);
+                    HVX_VectorPair m_vals_fp32_pair = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(m_vals_fp16), one_fp16);
 
-                    HVX_Vector m_vals_f32 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(m_vals_f32_pair));
+                    HVX_Vector m_vals_fp32 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(m_vals_fp32_pair));
 
-                    HVX_Vector slope_vec = hvx_vec_splat_f32(slope);
-                    HVX_Vector add_val = Q6_Vqf32_vmpy_VsfVsf(m_vals_f32, slope_vec);
+                    HVX_Vector slope_vec = hvx_vec_splat_fp32(slope);
+                    HVX_Vector add_val = Q6_Vqf32_vmpy_VsfVsf(m_vals_fp32, slope_vec);
                     scores = Q6_Vqf32_vadd_VsfVsf(scores, Q6_Vsf_equals_Vqf32(add_val));
                     scores = Q6_Vsf_equals_Vqf32(scores);
                 }
 
                 // 4. Online Softmax Update
-                HVX_Vector v_max = hvx_vec_reduce_max_f32(scores);
-                float m_block = hvx_vec_get_f32(v_max);
+                HVX_Vector v_max = hvx_vec_reduce_max_fp32(scores);
+                float m_block = hvx_vec_get_fp32(v_max);
 
                 float M_old = M;
                 float M_new = (m_block > M) ? m_block : M;
@@ -369,12 +374,12 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
                 hvx_scale_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms);
                 S = S * ms;
 
-                HVX_Vector M_new_vec = hvx_vec_splat_f32(M_new);
+                HVX_Vector M_new_vec = hvx_vec_splat_fp32(M_new);
                 HVX_Vector scores_shifted = Q6_Vqf32_vsub_VsfVsf(scores, M_new_vec);
-                HVX_Vector P = hvx_vec_exp_f32(Q6_Vsf_equals_Vqf32(scores_shifted));
+                HVX_Vector P = hvx_vec_exp_fp32(Q6_Vsf_equals_Vqf32(scores_shifted));
 
-                HVX_Vector p_sum_vec = hvx_vec_reduce_sum_f32(P);
-                float p_sum = hvx_vec_get_f32(p_sum_vec);
+                HVX_Vector p_sum_vec = hvx_vec_fp32_reduce_sum(P);
+                float p_sum = hvx_vec_get_fp32(p_sum_vec);
                 S += p_sum;
 
                 // 5. Accumulate V
@@ -479,9 +484,9 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
         uint8_t * dst_ptr = (uint8_t *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1) * nb1;
 
         if (dst->type == HTP_TYPE_F32) {
-            hvx_copy_f32_ua(dst_ptr, (uint8_t *) VKQ32, DV);
+            hvx_copy_fp32_ua(dst_ptr, (uint8_t *) VKQ32, DV);
         } else if (dst->type == HTP_TYPE_F16) {
-            hvx_copy_f16_f32_ua(dst_ptr, (uint8_t *) VKQ32, DV);
+            hvx_copy_fp16_fp32_ua(dst_ptr, (uint8_t *) VKQ32, DV);
         }
     }
 }
@@ -518,16 +523,16 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
         octx->src3_div3 = init_fastdiv_values(mask->ne[3]);
     }
 
-    size_t size_q_row_padded = hex_round_up(q->ne[0] * (q->type == HTP_TYPE_F32 ? 4 : 2), 128);
-    size_t size_k_row_padded = hex_round_up(k->ne[0] * sizeof(__fp16), 128);
-    size_t size_v_row_padded = hex_round_up(v->ne[0] * sizeof(__fp16), 128);
+    size_t size_q_row_padded = htp_round_up(q->ne[0] * (q->type == HTP_TYPE_F32 ? 4 : 2), 128);
+    size_t size_k_row_padded = htp_round_up(k->ne[0] * sizeof(__fp16), 128);
+    size_t size_v_row_padded = htp_round_up(v->ne[0] * sizeof(__fp16), 128);
 
     size_t size_q_block = size_q_row_padded * 1; // single row for now
     size_t size_k_block = size_k_row_padded * FLASH_ATTN_BLOCK_SIZE;
     size_t size_v_block = size_v_row_padded * FLASH_ATTN_BLOCK_SIZE;
-    size_t size_m_block = hex_round_up(FLASH_ATTN_BLOCK_SIZE * sizeof(__fp16), 128);
+    size_t size_m_block = htp_round_up(FLASH_ATTN_BLOCK_SIZE * sizeof(__fp16), 128);
 
-    size_t size_vkq_acc = hex_round_up(v->ne[0] * sizeof(float), 128); // VKQ32
+    size_t size_vkq_acc = htp_round_up(v->ne[0] * sizeof(float), 128); // VKQ32
 
     octx->src0_spad.size_per_thread = size_q_block * 1;
     octx->src1_spad.size_per_thread = size_k_block * 2;

ggml/src/ggml-hexagon/htp/get-rows-ops.c

@@ -2,9 +2,14 @@
 #pragma clang diagnostic ignored "-Wunused-function"
 #pragma clang diagnostic ignored "-Wunused-but-set-variable"
 
+#ifdef HTP_DEBUG
+#    define FARF_HIGH 1
+#endif
 #include <HAP_farf.h>
+#include <HAP_mem.h>
 #include <HAP_perf.h>
-
+#include <hexagon_protos.h>
+#include <hexagon_types.h>
 #include <math.h>
 #include <string.h>
 
@@ -14,6 +19,7 @@
 #include "htp-msg.h"
 #include "htp-ops.h"
 #include "hvx-utils.h"
+#include "ops-utils.h"
 
 #define get_rows_preamble \
     const uint32_t ne00 = octx->src0.ne[0]; \
@@ -66,7 +72,7 @@ static int get_rows_thread_f32_f32(struct htp_ops_context * octx, const int nth,
 
         const uintptr_t src0_ptr = octx->src0.data + i01*nb01 + i11*nb02 + i12*nb03;
         const uintptr_t dst_ptr  = octx->dst.data  + i10*nb1  + i11*nb2  + i12*nb3;
-        hvx_copy_f32_uu((uint8_t *)dst_ptr, (const uint8_t *)src0_ptr, ne00);
+        hvx_copy_fp32_uu((uint8_t *)dst_ptr, (const uint8_t *)src0_ptr, ne00);
     }
 
     return HTP_STATUS_OK;

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

@@ -1,77 +0,0 @@
-#ifndef HEX_DUMP_H
-#define HEX_DUMP_H
-
-#include <HAP_farf.h>
-
-static inline void hex_dump_int8_line(char * pref, const int8_t * x, int n) {
-    char str[1024], *p = str, *p_end = str + sizeof(str);
-    p += snprintf(p, p_end - p, "%s: ", pref);
-    for (int i = 0; i < n && p < p_end; i++) {
-        p += snprintf(p, p_end - p, "%d, ", x[i]);
-    }
-    FARF(HIGH, "%s\n", str);
-}
-
-static inline void hex_dump_uint8_line(char * pref, const uint8_t * x, uint32_t n) {
-    char str[1024], *p = str, *p_end = str + sizeof(str);
-    p += snprintf(p, p_end - p, "%s: ", pref);
-    for (int i = 0; i < n && p < p_end; i++) {
-        p += snprintf(p, p_end - p, "%d, ", x[i]);
-    }
-    FARF(HIGH, "%s\n", str);
-}
-
-static inline void hex_dump_int32_line(char * pref, const int32_t * x, uint32_t n) {
-    char str[1024], *p = str, *p_end = str + sizeof(str);
-    p += snprintf(p, p_end - p, "%s: ", pref);
-    for (int i = 0; i < n; i++) {
-        p += snprintf(p, p_end - p, "%d, ", (int) x[i]);
-    }
-    FARF(HIGH, "%s\n", str);
-}
-
-static inline void hex_dump_f16_line(char * pref, const __fp16 * x, uint32_t n) {
-    char str[1024], *p = str, *p_end = str + sizeof(str);
-    p += snprintf(p, p_end - p, "%s: ", pref);
-    for (int i = 0; i < n; i++) {
-        p += snprintf(p, p_end - p, "%.6f, ", (float) x[i]);
-    }
-    FARF(HIGH, "%s\n", str);
-}
-
-static inline void hex_dump_f32_line(char * pref, const float * x, uint32_t n) {
-    char str[1024], *p = str, *p_end = str + sizeof(str);
-    p += snprintf(p, p_end - p, "%s: ", pref);
-    for (int i = 0; i < n; i++) {
-        p += snprintf(p, p_end - p, "%.6f, ", x[i]);
-    }
-    FARF(HIGH, "%s\n", str);
-}
-
-static inline void hex_dump_f32(char * pref, const float * x, uint32_t n) {
-    uint32_t n0 = n / 16;
-    uint32_t n1 = n % 16;
-
-    uint32_t i = 0;
-    for (; i < n0; i++) {
-        hex_dump_f32_line(pref, x + (16 * i), 16);
-    }
-    if (n1) {
-        hex_dump_f32_line(pref, x + (16 * i), n1);
-    }
-}
-
-static inline void hex_dump_f16(char * pref, const __fp16 * x, uint32_t n) {
-    uint32_t n0 = n / 16;
-    uint32_t n1 = n % 16;
-
-    uint32_t i = 0;
-    for (; i < n0; i++) {
-        hex_dump_f16_line(pref, x + (16 * i), 16);
-    }
-    if (n1) {
-        hex_dump_f16_line(pref, x + (16 * i), n1);
-    }
-}
-
-#endif /* HEX_DUMP_H */

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

@@ -1,37 +0,0 @@
-#ifndef HEX_FASTDIV_H
-#define HEX_FASTDIV_H
-
-// See https://gmplib.org/~tege/divcnst-pldi94.pdf figure 4.1.
-// Precompute mp (m' in the paper) and L such that division
-// can be computed using a multiply (high 32b of 64b result)
-// and a shift:
-//
-// n/d = (mulhi(n, mp) + n) >> L;
-struct fastdiv_values {
-    uint32_t mp;
-    uint32_t l;
-};
-
-static inline struct fastdiv_values init_fastdiv_values(uint32_t d) {
-    struct fastdiv_values result = { 0, 0 };
-    // compute L = ceil(log2(d));
-    while (result.l < 32 && ((uint32_t) 1 << result.l) < d) {
-        ++(result.l);
-    }
-
-    result.mp = (uint32_t) (((uint64_t) 1 << 32) * (((uint64_t) 1 << result.l) - d) / d + 1);
-    return result;
-}
-
-static inline uint32_t fastdiv(uint32_t n, const struct fastdiv_values * vals) {
-    // Compute high 32 bits of n * mp
-    const uint32_t hi = (uint32_t) (((uint64_t) n * vals->mp) >> 32);  // mulhi(n, mp)
-    // add n, apply bit shift
-    return (hi + n) >> vals->l;
-}
-
-static inline uint32_t fastmodulo(uint32_t n, uint32_t d, const struct fastdiv_values * vals) {
-    return n - fastdiv(n, vals) * d;
-}
-
-#endif /* HEX_FASTDIV_H */

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

@@ -1,51 +0,0 @@
-#ifndef HEX_UTILS_H
-#define HEX_UTILS_H
-
-#include <stdbool.h>
-#include <stdint.h>
-
-#include "hexagon_types.h"
-
-#include "hex-fastdiv.h"
-#include "hex-dump.h"
-
-#ifndef MAX
-#define MAX(a, b) ((a) > (b) ? (a) : (b))
-#endif
-
-#ifndef MIN
-#define MIN(a, b) ((a) < (b) ? (a) : (b))
-#endif
-
-static inline uint64_t hex_get_cycles() {
-    uint64_t cycles = 0;
-    asm volatile(" %0 = c15:14\n" : "=r"(cycles));
-    return cycles;
-}
-
-static inline uint64_t hex_get_pktcnt() {
-    uint64_t pktcnt;
-    asm volatile(" %0 = c19:18\n" : "=r"(pktcnt));
-    return pktcnt;
-}
-
-static inline int32_t hex_is_aligned(void * addr, uint32_t align) {
-    return ((size_t) addr & (align - 1)) == 0;
-}
-
-static inline int32_t hex_is_one_chunk(void * addr, uint32_t n, uint32_t chunk_size) {
-    uint32_t left_off  = (size_t) addr & (chunk_size - 1);
-    uint32_t right_off = left_off + n;
-    return right_off <= chunk_size;
-}
-
-static inline uint32_t hex_round_up(uint32_t n, uint32_t m) {
-    return m * ((n + m - 1) / m);
-}
-
-static inline void hex_l2fetch(const void * p, uint32_t width, uint32_t stride, uint32_t height) {
-    const uint64_t control = Q6_P_combine_RR(stride, Q6_R_combine_RlRl(width, height));
-    Q6_l2fetch_AP((void *) p, control);
-}
-
-#endif /* HEX_UTILS_H */

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

@@ -1,7 +1,7 @@
 #ifndef HTP_CTX_H
 #define HTP_CTX_H
 
-#include "hex-dma.h"
+#include "htp-dma.h"
 #include "worker-pool.h"
 
 #include <assert.h>

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

@@ -1,4 +1,4 @@
-#include "hex-dma.h"
+#include "htp-dma.h"
 
 #include <stdbool.h>
 #include <stdlib.h>

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

@@ -2,6 +2,7 @@
 #define HTP_DMA_H
 
 #include <HAP_farf.h>
+#include <hexagon_protos.h>
 #include <hexagon_types.h>
 #include <stdbool.h>
 #include <stdint.h>

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

@@ -63,7 +63,6 @@ enum htp_op {
     HTP_OP_SET_ROWS       = 15,
     HTP_OP_SCALE          = 16,
     HTP_OP_GET_ROWS       = 17,
-    HTP_OP_CPY            = 18,
     INVALID
 };
 

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

@@ -4,12 +4,11 @@
 #include "htp-ctx.h"
 #include "htp-msg.h"
 #include "worker-pool.h"
+#include "ops-utils.h"
 
 #include <assert.h>
 #include <stdint.h>
 
-#include <hex-fastdiv.h>
-
 // ggml-common.h must be included prior to this header
 
 struct htp_spad {
@@ -75,14 +74,6 @@ struct htp_ops_context {
     struct fastdiv_values get_rows_div_ne10;      // fastdiv values for ne10
     struct fastdiv_values get_rows_div_ne10_ne11; // fastdiv values for ne10 * ne11
 
-    struct fastdiv_values cpy_div_ne01; // fastdiv values for ne01
-    struct fastdiv_values cpy_div_ne02; // fastdiv values for ne02
-    struct fastdiv_values cpy_div_ne03; // fastdiv values for ne03
-
-    struct fastdiv_values cpy_rshp_div_n0;       // fastdiv values for ne00
-    struct fastdiv_values cpy_rshp_div_n1n0;     // fastdiv values for ne00*ne01
-    struct fastdiv_values cpy_rshp_div_n2n1n0;   // fastdiv values for ne00*ne01*ne02
-
     uint32_t flags;
 };
 
@@ -97,6 +88,5 @@ int op_rope(struct htp_ops_context * octx);
 int op_flash_attn_ext(struct htp_ops_context * octx);
 int op_set_rows(struct htp_ops_context * octx);
 int op_get_rows(struct htp_ops_context * octx);
-int op_cpy(struct htp_ops_context * octx);
 
 #endif /* HTP_OPS_H */

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

@@ -1,457 +0,0 @@
-#ifndef HVX_ARITH_H
-#define HVX_ARITH_H
-
-#include <assert.h>
-#include <stddef.h>
-#include <stdint.h>
-#include <math.h>
-
-#include "hvx-base.h"
-#include "hex-utils.h"
-
-//
-// Binary operations (add, mul, sub)
-//
-
-#define hvx_arith_loop_body(dst_type, src0_type, src1_type, vec_store, vec_op) \
-    do {                                                                       \
-        dst_type * restrict vdst  = (dst_type *) dst;                          \
-        src0_type * restrict vsrc0 = (src0_type *) src0;                       \
-        src1_type * restrict vsrc1 = (src1_type *) src1;                       \
-                                                                               \
-        const uint32_t elem_size = sizeof(float);                              \
-        const uint32_t epv  = 128 / elem_size;                                 \
-        const uint32_t nvec = n / epv;                                         \
-        const uint32_t nloe = n % epv;                                         \
-                                                                               \
-        uint32_t i = 0;                                                        \
-                                                                               \
-        _Pragma("unroll(4)")                                                   \
-        for (; i < nvec; i++) {                                                \
-            vdst[i] = vec_op(vsrc0[i], vsrc1[i]);                              \
-        }                                                                      \
-        if (nloe) {                                                            \
-            HVX_Vector v = vec_op(vsrc0[i], vsrc1[i]);                         \
-            vec_store((void *) &vdst[i], nloe * elem_size, v);                 \
-        }                                                                      \
-    } while(0)
-
-#if __HVX_ARCH__ < 79
-#define HVX_OP_ADD(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a, b))
-#define HVX_OP_SUB(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(a, b))
-#define HVX_OP_MUL(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
-#else
-#define HVX_OP_ADD(a, b) Q6_Vsf_vadd_VsfVsf(a, b)
-#define HVX_OP_SUB(a, b) Q6_Vsf_vsub_VsfVsf(a, b)
-#define HVX_OP_MUL(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
-#endif
-
-// ADD variants
-
-static inline void hvx_add_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_ADD);
-}
-
-static inline void hvx_add_f32_au(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_ADD);
-}
-
-static inline void hvx_add_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, HVX_OP_ADD);
-}
-
-static inline void hvx_add_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_ADD);
-}
-
-// SUB variants
-
-static inline void hvx_sub_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_SUB);
-}
-
-static inline void hvx_sub_f32_au(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_SUB);
-}
-
-static inline void hvx_sub_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, HVX_OP_SUB);
-}
-
-static inline void hvx_sub_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_SUB);
-}
-
-// MUL variants
-
-static inline void hvx_mul_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_MUL);
-}
-
-static inline void hvx_mul_f32_au(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_MUL);
-}
-
-static inline void hvx_mul_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, HVX_OP_MUL);
-}
-
-static inline void hvx_mul_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_MUL);
-}
-
-// Dispatchers
-
-static inline void hvx_add_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src0, 128)) {
-        if (hex_is_aligned((void *) src1, 128)) {
-            hvx_add_f32_aa(dst, src0, src1, num_elems);
-        } else {
-            hvx_add_f32_au(dst, src0, src1, num_elems);
-        }
-    } else if (hex_is_aligned((void *) src0, 128) && hex_is_aligned((void *) src1, 128)) {
-        hvx_add_f32_ua(dst, src0, src1, num_elems);
-    } else {
-        hvx_add_f32_uu(dst, src0, src1, num_elems);
-    }
-}
-
-static inline void hvx_sub_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src0, 128)) {
-        if (hex_is_aligned((void *) src1, 128)) {
-            hvx_sub_f32_aa(dst, src0, src1, num_elems);
-        } else {
-            hvx_sub_f32_au(dst, src0, src1, num_elems);
-        }
-    } else if (hex_is_aligned((void *) src0, 128) && hex_is_aligned((void *) src1, 128)) {
-        hvx_sub_f32_ua(dst, src0, src1, num_elems);
-    } else {
-        hvx_sub_f32_uu(dst, src0, src1, num_elems);
-    }
-}
-
-static inline void hvx_mul_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src0, 128)) {
-        if (hex_is_aligned((void *) src1, 128)) {
-            hvx_mul_f32_aa(dst, src0, src1, num_elems);
-        } else {
-            hvx_mul_f32_au(dst, src0, src1, num_elems);
-        }
-    } else if (hex_is_aligned((void *) src0, 128) && hex_is_aligned((void *) src1, 128)) {
-        hvx_mul_f32_ua(dst, src0, src1, num_elems);
-    } else {
-        hvx_mul_f32_uu(dst, src0, src1, num_elems);
-    }
-}
-
-// Mul-Mul Optimized
-
-static inline void hvx_mul_mul_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint8_t * restrict src2, const uint32_t num_elems) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    assert((unsigned long) src2 % 128 == 0);
-
-    HVX_Vector * restrict vdst  = (HVX_Vector *) dst;
-    HVX_Vector * restrict vsrc0 = (HVX_Vector *) src0;
-    HVX_Vector * restrict vsrc1 = (HVX_Vector *) src1;
-    HVX_Vector * restrict vsrc2 = (HVX_Vector *) src2;
-
-    const uint32_t elem_size = sizeof(float);
-    const uint32_t epv  = 128 / elem_size;
-    const uint32_t nvec = num_elems / epv;
-    const uint32_t nloe = num_elems % epv;
-
-    uint32_t i = 0;
-
-    _Pragma("unroll(4)")
-    for (; i < nvec; i++) {
-        HVX_Vector v1 = HVX_OP_MUL(vsrc0[i], vsrc1[i]);
-        vdst[i] = HVX_OP_MUL(v1, vsrc2[i]);
-    }
-
-    if (nloe) {
-        HVX_Vector v1 = HVX_OP_MUL(vsrc0[i], vsrc1[i]);
-        HVX_Vector v2 = HVX_OP_MUL(v1, vsrc2[i]);
-        hvx_vec_store_a((void *) &vdst[i], nloe * elem_size, v2);
-    }
-}
-
-// Scalar Operations
-
-#define hvx_scalar_loop_body(dst_type, src_type, vec_store, scalar_op_macro)   \
-    do {                                                                       \
-        dst_type * restrict vdst = (dst_type *) dst;                           \
-        src_type * restrict vsrc = (src_type *) src;                           \
-                                                                               \
-        const uint32_t elem_size = sizeof(float);                              \
-        const uint32_t epv  = 128 / elem_size;                                 \
-        const uint32_t nvec = n / epv;                                         \
-        const uint32_t nloe = n % epv;                                         \
-                                                                               \
-        uint32_t i = 0;                                                        \
-                                                                               \
-        _Pragma("unroll(4)")                                                   \
-        for (; i < nvec; i++) {                                                \
-            HVX_Vector v = vsrc[i];                                            \
-            vdst[i] = scalar_op_macro(v);                                      \
-        }                                                                      \
-        if (nloe) {                                                            \
-            HVX_Vector v = vsrc[i];                                            \
-            v = scalar_op_macro(v);                                            \
-            vec_store((void *) &vdst[i], nloe * elem_size, v);                 \
-        }                                                                      \
-    } while(0)
-
-#define HVX_OP_ADD_SCALAR(v) \
-    ({ \
-        const HVX_VectorPred pred_inf = Q6_Q_vcmp_eq_VwVw(inf, v); \
-        HVX_Vector out = HVX_OP_ADD(v, val_vec); \
-        Q6_V_vmux_QVV(pred_inf, inf, out); \
-    })
-
-#define HVX_OP_MUL_SCALAR(v) HVX_OP_MUL(v, val_vec)
-#define HVX_OP_SUB_SCALAR(v) HVX_OP_SUB(v, val_vec)
-
-// Add Scalar Variants
-
-static inline void hvx_add_scalar_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    const HVX_Vector inf = hvx_vec_splat_f32(INFINITY);
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src % 128 == 0);
-    hvx_scalar_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_ADD_SCALAR);
-}
-
-static inline void hvx_add_scalar_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    const HVX_Vector inf = hvx_vec_splat_f32(INFINITY);
-    assert((unsigned long) dst % 128 == 0);
-    hvx_scalar_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_ADD_SCALAR);
-}
-
-static inline void hvx_add_scalar_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    const HVX_Vector inf = hvx_vec_splat_f32(INFINITY);
-    assert((unsigned long) src % 128 == 0);
-    hvx_scalar_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u, HVX_OP_ADD_SCALAR);
-}
-
-static inline void hvx_add_scalar_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    static const float kInf = INFINITY;
-    const HVX_Vector inf = hvx_vec_splat_f32(kInf);
-    hvx_scalar_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_ADD_SCALAR);
-}
-
-// Sub Scalar Variants
-
-static inline void hvx_sub_scalar_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src % 128 == 0);
-    hvx_scalar_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_SUB_SCALAR);
-}
-
-static inline void hvx_sub_scalar_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    assert((unsigned long) dst % 128 == 0);
-    hvx_scalar_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_SUB_SCALAR);
-}
-
-static inline void hvx_sub_scalar_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    assert((unsigned long) src % 128 == 0);
-    hvx_scalar_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u, HVX_OP_SUB_SCALAR);
-}
-
-static inline void hvx_sub_scalar_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    hvx_scalar_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_SUB_SCALAR);
-}
-
-// Mul Scalar Variants
-
-static inline void hvx_mul_scalar_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src % 128 == 0);
-    hvx_scalar_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_MUL_SCALAR);
-}
-
-static inline void hvx_mul_scalar_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    assert((unsigned long) dst % 128 == 0);
-    hvx_scalar_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_MUL_SCALAR);
-}
-
-static inline void hvx_mul_scalar_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    assert((unsigned long) src % 128 == 0);
-    hvx_scalar_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u, HVX_OP_MUL_SCALAR);
-}
-
-static inline void hvx_mul_scalar_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    hvx_scalar_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_MUL_SCALAR);
-}
-
-static inline void hvx_add_scalar_f32(uint8_t * restrict dst, const uint8_t * restrict src, const float val, const int num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src, 128)) {
-        hvx_add_scalar_f32_aa(dst, src, val, num_elems);
-    } else if (hex_is_aligned((void *) dst, 128)) {
-        hvx_add_scalar_f32_au(dst, src, val, num_elems);
-    } else if (hex_is_aligned((void *) src, 128)) {
-        hvx_add_scalar_f32_ua(dst, src, val, num_elems);
-    } else {
-        hvx_add_scalar_f32_uu(dst, src, val, num_elems);
-    }
-}
-
-static inline void hvx_mul_scalar_f32(uint8_t * restrict dst, const uint8_t * restrict src, const float val, const int num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src, 128)) {
-        hvx_mul_scalar_f32_aa(dst, src, val, num_elems);
-    } else if (hex_is_aligned((void *) dst, 128)) {
-        hvx_mul_scalar_f32_au(dst, src, val, num_elems);
-    } else if (hex_is_aligned((void *) src, 128)) {
-        hvx_mul_scalar_f32_ua(dst, src, val, num_elems);
-    } else {
-        hvx_mul_scalar_f32_uu(dst, src, val, num_elems);
-    }
-}
-
-static inline void hvx_sub_scalar_f32(uint8_t * restrict dst, const uint8_t * restrict src, const float val, const int num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src, 128)) {
-        hvx_sub_scalar_f32_aa(dst, src, val, num_elems);
-    } else if (hex_is_aligned((void *) dst, 128)) {
-        hvx_sub_scalar_f32_au(dst, src, val, num_elems);
-    } else if (hex_is_aligned((void *) src, 128)) {
-        hvx_sub_scalar_f32_ua(dst, src, val, num_elems);
-    } else {
-        hvx_sub_scalar_f32_uu(dst, src, val, num_elems);
-    }
-}
-
-// MIN Scalar variants
-
-#define HVX_OP_MIN_SCALAR(v) Q6_Vsf_vmin_VsfVsf(val_vec, v)
-
-static inline void hvx_min_scalar_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src % 128 == 0);
-    hvx_scalar_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_MIN_SCALAR);
-}
-
-static inline void hvx_min_scalar_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    assert((unsigned long) dst % 128 == 0);
-    hvx_scalar_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_MIN_SCALAR);
-}
-
-static inline void hvx_min_scalar_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    assert((unsigned long) src % 128 == 0);
-    hvx_scalar_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u, HVX_OP_MIN_SCALAR);
-}
-
-static inline void hvx_min_scalar_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) {
-    const HVX_Vector val_vec = hvx_vec_splat_f32(val);
-    hvx_scalar_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_MIN_SCALAR);
-}
-
-static inline void hvx_min_scalar_f32(uint8_t * restrict dst, const uint8_t * restrict src, const float val, const int num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src, 128)) {
-        hvx_min_scalar_f32_aa(dst, src, val, num_elems);
-    } else if (hex_is_aligned((void *) dst, 128)) {
-        hvx_min_scalar_f32_au(dst, src, val, num_elems);
-    } else if (hex_is_aligned((void *) src, 128)) {
-        hvx_min_scalar_f32_ua(dst, src, val, num_elems);
-    } else {
-        hvx_min_scalar_f32_uu(dst, src, val, num_elems);
-    }
-}
-
-// CLAMP Scalar variants
-
-#define HVX_OP_CLAMP_SCALAR(v) \
-    ({ \
-        HVX_VectorPred pred_cap_right = Q6_Q_vcmp_gt_VsfVsf(v, max_vec); \
-        HVX_VectorPred pred_cap_left  = Q6_Q_vcmp_gt_VsfVsf(min_vec, v); \
-        HVX_Vector tmp = Q6_V_vmux_QVV(pred_cap_right, max_vec, v); \
-        Q6_V_vmux_QVV(pred_cap_left, min_vec, tmp); \
-    })
-
-static inline void hvx_clamp_scalar_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const float min, const float max, uint32_t n) {
-    const HVX_Vector min_vec = hvx_vec_splat_f32(min);
-    const HVX_Vector max_vec = hvx_vec_splat_f32(max);
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src % 128 == 0);
-    hvx_scalar_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_CLAMP_SCALAR);
-}
-
-static inline void hvx_clamp_scalar_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const float min, const float max, uint32_t n) {
-    const HVX_Vector min_vec = hvx_vec_splat_f32(min);
-    const HVX_Vector max_vec = hvx_vec_splat_f32(max);
-    assert((unsigned long) dst % 128 == 0);
-    hvx_scalar_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_CLAMP_SCALAR);
-}
-
-static inline void hvx_clamp_scalar_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const float min, const float max, uint32_t n) {
-    const HVX_Vector min_vec = hvx_vec_splat_f32(min);
-    const HVX_Vector max_vec = hvx_vec_splat_f32(max);
-    assert((unsigned long) src % 128 == 0);
-    hvx_scalar_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u, HVX_OP_CLAMP_SCALAR);
-}
-
-static inline void hvx_clamp_scalar_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const float min, const float max, uint32_t n) {
-    const HVX_Vector min_vec = hvx_vec_splat_f32(min);
-    const HVX_Vector max_vec = hvx_vec_splat_f32(max);
-    hvx_scalar_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_CLAMP_SCALAR);
-}
-
-static inline void hvx_clamp_scalar_f32(uint8_t * restrict dst, const uint8_t * restrict src, const float min, const float max, const int num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src, 128)) {
-        hvx_clamp_scalar_f32_aa(dst, src, min, max, num_elems);
-    } else if (hex_is_aligned((void *) dst, 128)) {
-        hvx_clamp_scalar_f32_au(dst, src, min, max, num_elems);
-    } else if (hex_is_aligned((void *) src, 128)) {
-        hvx_clamp_scalar_f32_ua(dst, src, min, max, num_elems);
-    } else {
-        hvx_clamp_scalar_f32_uu(dst, src, min, max, num_elems);
-    }
-}
-
-#undef HVX_OP_ADD
-#undef HVX_OP_SUB
-#undef HVX_OP_MUL
-#undef hvx_arith_loop_body
-#undef HVX_OP_ADD_SCALAR
-#undef HVX_OP_SUB_SCALAR
-#undef HVX_OP_MUL_SCALAR
-#undef hvx_scalar_loop_body
-#undef HVX_OP_MIN_SCALAR
-#undef HVX_OP_CLAMP_SCALAR
-
-#endif // HVX_ARITH_H

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

@@ -1,167 +0,0 @@
-#ifndef HVX_BASE_H
-#define HVX_BASE_H
-
-#include <stdbool.h>
-#include <stdint.h>
-
-#include "hex-utils.h"
-#include "hvx-types.h"
-
-static inline void hvx_vec_store_u(void * restrict dst, uint32_t n, HVX_Vector v) {
-    // Rotate as needed.
-    v = Q6_V_vlalign_VVR(v, v, (size_t) dst);
-
-    uint32_t left_off  = (size_t) dst & 127;
-    uint32_t right_off = left_off + n;
-
-    HVX_VectorPred ql_not = Q6_Q_vsetq_R((size_t) dst);
-    HVX_VectorPred qr     = Q6_Q_vsetq2_R(right_off);
-
-    if (right_off > 128) {
-        Q6_vmem_QRIV(qr, (HVX_Vector *) dst + 1, v);
-        // all 1's
-        qr = Q6_Q_vcmp_eq_VbVb(v, v);
-    }
-
-    ql_not = Q6_Q_or_QQn(ql_not, qr);
-    Q6_vmem_QnRIV(ql_not, (HVX_Vector *) dst, v);
-}
-
-static inline void hvx_vec_store_a(void * restrict dst, uint32_t n, HVX_Vector v) {
-    assert((unsigned long) dst % 128 == 0);
-    HVX_VectorPred m = Q6_Q_or_QQn(Q6_Q_vsetq_R((unsigned long) dst), Q6_Q_vsetq2_R(n));
-    Q6_vmem_QnRIV(m, (HVX_Vector *) dst, v);
-}
-
-static inline HVX_Vector hvx_vec_splat_f32(float v) {
-    union { float  f; uint32_t i; } u = { .f = v };
-    return Q6_V_vsplat_R(u.i);
-}
-
-static inline HVX_Vector hvx_vec_splat_f16(float v) {
-    union { __fp16 f; uint16_t i; } u = { .f = v };
-    return Q6_Vh_vsplat_R(u.i);
-}
-
-static inline HVX_Vector hvx_vec_repl4(HVX_Vector v) {
-    // vdelta control to replicate first 4 bytes across all elements
-    static const uint8_t __attribute__((aligned(128))) repl[128] = {
-        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-    };
-
-    HVX_Vector ctrl = *(HVX_Vector *) repl;
-    return Q6_V_vdelta_VV(v, ctrl);
-}
-
-static inline float hvx_vec_get_f32(HVX_Vector v) {
-    float __attribute__((aligned(128))) x;
-    hvx_vec_store_a(&x, 4, v);
-    return x;
-}
-
-static inline HVX_Vector hvx_vec_abs_f16(HVX_Vector v) {
-    // abs by clearing the fp16 sign bit
-    HVX_Vector mask = Q6_Vh_vsplat_R(0x7fff);
-    return Q6_V_vand_VV(v, mask);
-}
-
-static inline HVX_Vector hvx_vec_neg_f16(HVX_Vector v) {
-    // neg by setting the fp16 sign bit
-    HVX_Vector mask = Q6_Vh_vsplat_R(0x8000);
-    return Q6_V_vxor_VV(v, mask);
-}
-
-static inline HVX_Vector hvx_vec_abs_f32(HVX_Vector v) {
-    // abs by clearing the fp32 sign bit
-    HVX_Vector mask = Q6_V_vsplat_R(0x7fffffff);
-    return Q6_V_vand_VV(v, mask);
-}
-
-static inline HVX_Vector hvx_vec_neg_f32(HVX_Vector v) {
-#if __HVX_ARCH__ > 75
-    return Q6_Vsf_vfneg_Vsf(v);
-#else
-    // neg by setting the fp32 sign bit
-    HVX_Vector mask = Q6_V_vsplat_R(0x80000000);
-    return Q6_V_vxor_VV(v, mask);
-#endif  // __HVX_ARCH__ > 75
-}
-
-static inline HVX_VectorPred hvx_vec_is_nan_f16(HVX_Vector v) {
-    const HVX_Vector vnan_exp  = Q6_Vh_vsplat_R(0x7C00);
-    const HVX_Vector vnan_frac = Q6_Vh_vsplat_R(0x7FFF);
-
-    // get pred of which are NaN, i.e., exponent bits all 1s and fraction bits non 0s
-    HVX_VectorPred p_exp  = Q6_Q_vcmp_eq_VhVh(Q6_V_vand_VV(v, vnan_exp), vnan_exp);
-    HVX_VectorPred p_frac = Q6_Q_not_Q(Q6_Q_vcmp_eq_VhVh(Q6_V_vand_VV(v, vnan_frac), vnan_exp));
-    return Q6_Q_and_QQ(p_exp, p_frac);
-}
-
-static inline HVX_Vector hvx_vec_f32_to_f16(HVX_Vector v0, HVX_Vector v1) {
-    const HVX_Vector zero = Q6_V_vsplat_R(0);
-    HVX_Vector q0 = Q6_Vqf32_vadd_VsfVsf(v0, zero);
-    HVX_Vector q1 = Q6_Vqf32_vadd_VsfVsf(v1, zero);
-    HVX_Vector  v = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(q1, q0)));
-
-#if __HVX_ARCH__ < 79
-    // replace NaNs with -INF, older arches produce NaNs for (-INF + 0.0)
-    const HVX_Vector neg_inf = hvx_vec_splat_f16(-INFINITY);
-    HVX_VectorPred nan = hvx_vec_is_nan_f16(v);
-    v = Q6_V_vmux_QVV(nan, neg_inf, v);
-#endif
-
-    return v;
-}
-
-/* Q6_Vsf_equals_Vw is only available on v73+.*/
-#if __HVX_ARCH__ < 73
-static inline HVX_Vector hvx_vec_i32_to_qf32(HVX_Vector const in)
-{
-    HVX_Vector const vzero = Q6_V_vzero();
-    HVX_VectorPred is_zero = Q6_Q_vcmp_eq_VwVw(in, vzero);
-    HVX_Vector lshift = Q6_Vw_vnormamt_Vw(in);
-    HVX_Vector normalized = Q6_Vw_vasl_VwVw(in, lshift);
-    HVX_Vector vexp = Q6_Vw_vsub_VwVw(Q6_V_vsplat_R(0x7f + 30), lshift);
-    HVX_Vector mant = Q6_V_vand_VV(Q6_V_vsplat_R(0xFFFFFF00), normalized);
-    HVX_Vector ret = Q6_V_vmux_QVV(is_zero, vzero, Q6_Vw_vadd_VwVw(mant, vexp));
-    return ret;
-}
-
-static inline HVX_Vector Q6_Vsf_equals_Vw(HVX_Vector const in)
-{
-    return Q6_Vsf_equals_Vqf32(hvx_vec_i32_to_qf32(in));
-}
-#endif
-
-static inline HVX_Vector hvx_vec_i16_from_hf_rnd_sat(HVX_Vector vin) {
-    // This looks complicated.
-    // Ideally should just be Q6_Vh_equals_Vhf(vin)
-    // but that instruction does not do proper rounding.
-
-    // convert to qf32, multiplying by 1.0 in the process.
-    HVX_VectorPair v32 = Q6_Wqf32_vmpy_VhfVhf(vin, Q6_Vh_vsplat_R(0x3C00));
-
-    // 'in-range' values are +/32752.
-    // add 192K to it, convert to sf
-    HVX_Vector v192K = Q6_V_vsplat_R(0x48400000);
-    HVX_Vector vsf_0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_V_lo_W(v32), v192K));
-    HVX_Vector vsf_1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_V_hi_W(v32), v192K));
-
-    // for in-range cases, result is {163858... 229360} so the exponent is always 144.
-    // if we extract bits 21..0 as a signed quantity, and round 6 bits off, that will be the answer.
-    // Start by <<10 to get the final 'sign' bit in bit 15...
-    vsf_0 = Q6_Vw_vasl_VwR(vsf_0, 10);
-    vsf_1 = Q6_Vw_vasl_VwR(vsf_1, 10);
-
-    // now round down to 16
-    return Q6_Vh_vround_VwVw_sat(vsf_1, vsf_0);
-}
-
-#endif /* HVX_BASE_H */

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

@@ -1,247 +0,0 @@
-#ifndef HVX_COPY_H
-#define HVX_COPY_H
-
-#include <assert.h>
-#include <stddef.h>
-#include <stdint.h>
-
-#include "hvx-base.h"
-
-#define hvx_splat_loop_body(dst_type, vec_store)                 \
-    do {                                                         \
-        dst_type * restrict vdst = (dst_type *) dst;             \
-                                                                 \
-        uint32_t nvec = n / (128 / elem_size);                   \
-        uint32_t nloe = n % (128 / elem_size);                   \
-                                                                 \
-        uint32_t i = 0;                                          \
-                                                                 \
-        _Pragma("unroll(4)")                                     \
-        for (; i < nvec; i++) {                                  \
-            vdst[i] = src;                                       \
-        }                                                        \
-        if (nloe) {                                              \
-            vec_store((void *) &vdst[i], nloe * elem_size, src); \
-        }                                                        \
-    } while(0)
-
-static inline void hvx_splat_a(uint8_t * restrict dst, HVX_Vector src, uint32_t n, uint32_t elem_size) {
-    assert((unsigned long) dst % 128 == 0);
-    hvx_splat_loop_body(HVX_Vector, hvx_vec_store_a);
-}
-
-static inline void hvx_splat_u(uint8_t * restrict dst, HVX_Vector src, uint32_t n, uint32_t elem_size) {
-    hvx_splat_loop_body(HVX_UVector, hvx_vec_store_u);
-}
-
-static inline void hvx_splat_f32_a(uint8_t * restrict dst, float v, uint32_t n) {
-    hvx_splat_a(dst,  hvx_vec_splat_f32(v), n, sizeof(float));
-}
-
-static inline void hvx_splat_f32_u(uint8_t * restrict dst, float v, uint32_t n) {
-    hvx_splat_u(dst,  hvx_vec_splat_f32(v), n, sizeof(float));
-}
-
-static inline void hvx_splat_f16_a(uint8_t * restrict dst, float v, uint32_t n) {
-    hvx_splat_u(dst,  hvx_vec_splat_f16(v), n, sizeof(__fp16));
-}
-
-static inline void hvx_splat_f16_u(uint8_t * restrict dst, float v, uint32_t n) {
-    hvx_splat_u(dst,  hvx_vec_splat_f16(v), n, sizeof(__fp16));
-}
-
-#define hvx_copy_loop_body(dst_type, src_type, vec_store)            \
-    do {                                                             \
-        dst_type * restrict vdst = (dst_type *) dst;                 \
-        src_type * restrict vsrc = (src_type *) src;                 \
-                                                                     \
-        const uint32_t epv  = 128 / elem_size;                       \
-        const uint32_t nvec = n / epv;                               \
-        const uint32_t nloe = n % epv;                               \
-                                                                     \
-        uint32_t i = 0;                                              \
-                                                                     \
-        _Pragma("unroll(4)")                                         \
-        for (; i < nvec; i++) { vdst[i] = vsrc[i]; }                 \
-        if (nloe) {                                                  \
-            vec_store((void *) &vdst[i], nloe * elem_size, vsrc[i]); \
-        }                                                            \
-    } while(0)
-
-// Generic copy routines
-static inline void hvx_copy_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n, uint32_t elem_size) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src % 128 == 0);
-    hvx_copy_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
-}
-
-static inline void hvx_copy_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n, uint32_t elem_size) {
-    assert((unsigned long) dst % 128 == 0);
-    hvx_copy_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a);
-}
-
-static inline void hvx_copy_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n, uint32_t elem_size) {
-    assert((unsigned long) src % 128 == 0);
-    hvx_copy_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
-}
-
-static inline void hvx_copy_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n, uint32_t elem_size) {
-    hvx_copy_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
-}
-
-// copy n fp16 elements : source and destination are aligned to HVX Vector (128)
-static inline void hvx_copy_f16_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_copy_aa(dst, src, n, sizeof(__fp16));
-}
-
-// copy n fp16 elements : source is aligned, destination is potentially unaligned
-static inline void hvx_copy_f16_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_copy_au(dst, src, n, sizeof(__fp16));
-}
-
-// copy n fp16 elements : source is aligned, destination is potentially unaligned
-static inline void hvx_copy_f16_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_copy_ua(dst, src, n, sizeof(__fp16));
-}
-
-// copy n fp16 elements : source is aligned, destination is potentially unaligned
-static inline void hvx_copy_f16_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_copy_uu(dst, src, n, sizeof(__fp16));
-}
-
-// copy n fp32 elements : source and destination are aligned to HVX Vector (128)
-static inline void hvx_copy_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_copy_aa(dst, src, n, sizeof(float));
-}
-
-// copy n fp32 elements : source is aligned, destination is unaligned
-static inline void hvx_copy_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_copy_ua(dst, src, n, sizeof(float));
-}
-
-// copy n fp32 elements : source is unaligned, destination is aligned
-static inline void hvx_copy_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_copy_au(dst, src, n, sizeof(float));
-}
-
-// copy n fp32 elements : source is unaligned, destination unaligned
-static inline void hvx_copy_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_copy_uu(dst, src, n, sizeof(float));
-}
-
-//// fp32 -> fp16
-
-#define hvx_copy_f16_f32_loop_body(dst_type, src_type, vec_store)                   \
-    do {                                                                            \
-        dst_type * restrict vdst = (dst_type *) dst;                                \
-        src_type * restrict vsrc = (src_type *) src;                                \
-                                                                                    \
-        const HVX_Vector zero = Q6_V_vsplat_R(0);                                   \
-                                                                                    \
-        const uint32_t elem_size = sizeof(__fp16);                                  \
-        const uint32_t epv  = 128 / elem_size;                                      \
-        const uint32_t nvec = n / epv;                                              \
-        const uint32_t nloe = n % epv;                                              \
-                                                                                    \
-        uint32_t i = 0;                                                             \
-                                                                                    \
-        _Pragma("unroll(4)")                                                        \
-        for (; i < nvec; i++) {                                                     \
-            vdst[i] = hvx_vec_f32_to_f16(vsrc[i*2+0], vsrc[i*2+1]);                 \
-        }                                                                           \
-        if (nloe) {                                                                 \
-            HVX_Vector v = hvx_vec_f32_to_f16(vsrc[i*2+0], vsrc[i*2+1]);            \
-            vec_store((void *) &vdst[i], nloe * elem_size, v);                      \
-        }                                                                           \
-    } while(0)
-
-// copy/convert n fp32 elements into n fp16 elements : source is aligned, destination is aligned
-static inline void hvx_copy_f16_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src % 128 == 0);
-    hvx_copy_f16_f32_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
-}
-
-// copy/convert n fp32 elements into n fp16 elements : source is unaligned, destination is aligned
-static inline void hvx_copy_f16_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    hvx_copy_f16_f32_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a);
-}
-
-// copy/convert n fp32 elements into n fp16 elements : source is aligned, destination is unaligned
-static inline void hvx_copy_f16_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) src % 128 == 0);
-    hvx_copy_f16_f32_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
-}
-
-// copy/convert n fp32 elements into n fp16 elements : source is unaligned, destination is unaligned
-static inline void hvx_copy_f16_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_copy_f16_f32_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
-}
-
-//// fp16 -> fp32
-
-#define hvx_copy_f32_f16_loop_body(dst_type, src_type, vec_store)                   \
-    do {                                                                            \
-        dst_type * restrict vdst = (dst_type *) dst;                                \
-        src_type * restrict vsrc = (src_type *) src;                                \
-                                                                                    \
-        const HVX_Vector one = hvx_vec_splat_f16(1.0);                              \
-                                                                                    \
-        const uint32_t elem_size = sizeof(__fp16);                                  \
-        const uint32_t epv  = 128 / elem_size;                                      \
-        const uint32_t nvec = n / epv;                                              \
-              uint32_t nloe = n % epv;                                              \
-                                                                                    \
-        uint32_t i = 0;                                                             \
-                                                                                    \
-        _Pragma("unroll(4)")                                                        \
-        for (i = 0; i < nvec; ++i) {                                                \
-            HVX_VectorPair p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vsrc[i]), one); \
-            vdst[i*2]   = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(p));                        \
-            vdst[i*2+1] = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(p));                        \
-        }                                                                           \
-                                                                                    \
-        if (nloe) {                                                                 \
-            HVX_VectorPair p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vsrc[i]), one); \
-                                                                                    \
-            HVX_Vector vd = Q6_V_lo_W(p);                                           \
-            i = 2 * i;                                                              \
-                                                                                    \
-            if (nloe >= 32) {                                                       \
-                vdst[i] = Q6_Vsf_equals_Vqf32(vd);                                  \
-                nloe -= 32; ++i; vd = Q6_V_hi_W(p);                                 \
-            }                                                                       \
-                                                                                    \
-            if (nloe) {                                                             \
-                vd = Q6_Vsf_equals_Vqf32(vd);                                       \
-                hvx_vec_store_u(&vdst[i], nloe * sizeof(float), vd);                \
-            }                                                                       \
-        }                                                                           \
-    } while(0)
-
-// copy/convert n fp16 elements into n fp32 elements : source is aligned, destination is aligned
-static inline void hvx_copy_f32_f16_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src % 128 == 0);
-    hvx_copy_f32_f16_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
-}
-
-// copy/convert n fp16 elements into n fp32 elements : source is unaligned, destination is aligned
-static inline void hvx_copy_f32_f16_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    hvx_copy_f32_f16_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a);
-}
-
-// copy/convert n fp16 elements into n fp32 elements : source is aligned, destination is unaligned
-static inline void hvx_copy_f32_f16_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) src % 128 == 0);
-    hvx_copy_f32_f16_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
-}
-
-// copy/convert n fp16 elements into n fp32 elements : source is unaligned, destination is unaligned
-static inline void hvx_copy_f32_f16_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_copy_f32_f16_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
-}
-
-#endif // HVX_COPY_H

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

@@ -1,132 +0,0 @@
-#ifndef HVX_DUMP_H
-#define HVX_DUMP_H
-
-#include <HAP_farf.h>
-
-#include <stdbool.h>
-#include <stdint.h>
-
-#include "hex-utils.h"
-#include "hvx-types.h"
-
-static void hvx_vec_dump_f16_n(char * pref, HVX_Vector v, uint32_t n) {
-    HVX_VectorAlias u = { .v = v };
-
-    const uint32_t n0 = n / 16;
-    const uint32_t n1 = n % 16;
-    int            i  = 0;
-    for (; i < n0; i++) {
-        hex_dump_f16_line(pref, u.fp16 + (16 * i), 16);
-    }
-    if (n1) {
-        hex_dump_f16_line(pref, u.fp16 + (16 * i), n1);
-    }
-}
-
-static void hvx_vec_dump_f16(char * pref, HVX_Vector v) {
-    hvx_vec_dump_f16_n(pref, v, 64);
-}
-
-static void hvx_vec_dump_f32_n(char * pref, HVX_Vector v, uint32_t n) {
-    union {
-        HVX_Vector v;
-        float      d[32];
-    } u = { .v = v };
-
-    const uint32_t n0 = n / 16;
-    const uint32_t n1 = n % 16;
-    int            i  = 0;
-    for (; i < n0; i++) {
-        hex_dump_f32_line(pref, u.d + (16 * i), 16);
-    }
-    if (n1) {
-        hex_dump_f32_line(pref, u.d + (16 * i), n1);
-    }
-}
-
-static void hvx_vec_dump_f32_hmt(char * pref, HVX_Vector v) {
-    union {
-        HVX_Vector v;
-        float      d[32];
-    } u = { .v = v };
-
-    FARF(HIGH, "%s: %.6f %.6f %.6f %.6f ...  %.6f %.6f %.6f %.6f ... %.6f %.6f %.6f %.6f\n", pref, u.d[0], u.d[1],
-         u.d[2], u.d[3], u.d[12], u.d[13], u.d[14], u.d[15], u.d[28], u.d[29], u.d[30], u.d[31]);
-}
-
-static void hvx_vec_dump_f32(char * pref, HVX_Vector v) {
-    hvx_vec_dump_f32_n(pref, v, 32);
-}
-
-static void hvx_vec_dump_int32(char * pref, HVX_Vector v) {
-    union {
-        HVX_Vector v;
-        int32_t    d[32];
-    } u = { .v = v };
-
-    for (int i = 0; i < 32 / 16; i++) {
-        hex_dump_int32_line(pref, u.d + (16 * i), 16);
-    }
-}
-
-static void hvx_vec_dump_int32_hmt(char * pref, HVX_Vector v) {
-    union {
-        HVX_Vector v;
-        int32_t    d[32];
-    } u = { .v = v };
-
-    FARF(HIGH, "%s: %d %d %d %d ... %d %d %d %d ... %d %d %d %d\n", pref, u.d[0], u.d[1], u.d[2], u.d[3], u.d[12],
-         u.d[13], u.d[14], u.d[15], u.d[28], u.d[29], u.d[30], u.d[31]);
-}
-
-static void hvx_vec_dump_int8_hmt(char * pref, HVX_Vector v) {
-    union {
-        HVX_Vector v;
-        int8_t     d[128];
-    } u = { .v = v };
-
-    FARF(HIGH, "%s: %d %d %d %d ... %d %d %d %d ... %d %d %d %d\n", pref, u.d[0], u.d[1], u.d[2], u.d[3], u.d[60],
-         u.d[61], u.d[62], u.d[63], u.d[124], u.d[125], u.d[126], u.d[127]);
-}
-
-static void hvx_vec_dump_int8(char * pref, HVX_Vector v) {
-    union {
-        HVX_Vector v;
-        int8_t     d[128];
-    } u = { .v = v };
-
-    for (int i = 0; i < 128 / 16; i++) {
-        hex_dump_int8_line(pref, u.d + (16 * i), 16);
-    }
-}
-
-static void hvx_vec_dump_uint8(char * pref, HVX_Vector v) {
-    union {
-        HVX_Vector v;
-        uint8_t    d[128];
-    } u = { .v = v };
-
-    for (int i = 0; i < 128 / 16; i++) {
-        hex_dump_uint8_line(pref, u.d + (16 * i), 16);
-    }
-}
-
-static bool hvx_vec_eq(HVX_Vector v0, HVX_Vector v1, size_t n) {
-    typedef union {
-        HVX_Vector v;
-        int8_t     d[128];
-    } U;
-
-    U u0 = { .v = v0 };
-    U u1 = { .v = v1 };
-
-    for (int i = 0; i < n; i++) {
-        if (u0.d[i] != u1.d[i]) {
-            return false;
-        }
-    }
-
-    return true;
-}
-
-#endif /* HVX_DUMP_H */

ggml/src/ggml-hexagon/htp/hvx-exp.c

@@ -0,0 +1,94 @@
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wunused-function"
+#pragma clang diagnostic ignored "-Wunused-but-set-variable"
+
+#include <hexagon_protos.h>
+#include <hexagon_types.h>
+#include <math.h>
+#include <string.h>
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "htp-ctx.h"
+#include "htp-dma.h"
+#include "htp-msg.h"
+#include "htp-ops.h"
+#include "hvx-utils.h"
+#include "ops-utils.h"
+
+static inline HVX_Vector hvx_vec_exp_fp32_guard(HVX_Vector in_vec, HVX_Vector max_exp, HVX_Vector inf) {
+    const HVX_VectorPred pred0 = Q6_Q_vcmp_gt_VsfVsf(in_vec, max_exp);
+
+    HVX_Vector out = hvx_vec_exp_fp32(in_vec);
+
+    return Q6_V_vmux_QVV(pred0, inf, out);
+}
+
+void hvx_exp_f32(const uint8_t * restrict src, uint8_t * restrict dst, const int num_elems, bool negate) {
+    int left_over       = num_elems & (VLEN_FP32 - 1);
+    int num_elems_whole = num_elems - left_over;
+
+    int unaligned_addr = 0;
+    int unaligned_loop = 0;
+    if ((0 == htp_is_aligned((void *) src, VLEN)) || (0 == htp_is_aligned((void *) dst, VLEN))) {
+        FARF(HIGH, "hvx_exp_f32: unaligned address in hvx op, possibly slower execution\n");
+        unaligned_addr = 1;
+    }
+    // assert((0 == unaligned_addr) || (0 == num_elems_whole));
+    if ((1 == unaligned_addr) && (num_elems_whole != 0)) {
+        unaligned_loop = 1;
+        FARF(HIGH, "hvx_exp_f32: unaligned loop in hvx op, possibly slower execution\n");
+    }
+
+    HVX_Vector vec_out = Q6_V_vzero();
+
+    static const float kInf    = INFINITY;
+    static const float kMaxExp = 88.02f;  // log(INF)
+
+    const HVX_Vector max_exp = hvx_vec_splat_fp32(kMaxExp);
+    const HVX_Vector inf     = hvx_vec_splat_fp32(kInf);
+
+    if (0 == unaligned_loop) {
+        HVX_Vector * p_vec_in1 = (HVX_Vector *) src;
+        HVX_Vector * p_vec_out = (HVX_Vector *) dst;
+
+        #pragma unroll(4)
+        for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
+            if (true == negate) {
+                HVX_Vector neg_vec_in = hvx_vec_neg_fp32(*p_vec_in1++);
+                *p_vec_out++          = hvx_vec_exp_fp32_guard(neg_vec_in, max_exp, inf);
+            } else {
+                *p_vec_out++ = hvx_vec_exp_fp32_guard(*p_vec_in1++, max_exp, inf);
+            }
+        }
+    } else {
+        #pragma unroll(4)
+        for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
+            HVX_Vector in = *(HVX_UVector *) (src + i * SIZEOF_FP32);
+
+            if (true == negate) {
+                HVX_Vector neg_vec_in                    = hvx_vec_neg_fp32(in);
+                *(HVX_UVector *) (dst + i * SIZEOF_FP32) = hvx_vec_exp_fp32_guard(neg_vec_in, max_exp, inf);
+            } else {
+                *(HVX_UVector *) (dst + i * SIZEOF_FP32) = hvx_vec_exp_fp32_guard(in, max_exp, inf);
+            }
+        }
+    }
+
+    if (left_over > 0) {
+        const float * srcf = (float *) src + num_elems_whole;
+        float *       dstf = (float *) dst + num_elems_whole;
+
+        HVX_Vector in = *(HVX_UVector *) srcf;
+
+        if (true == negate) {
+            HVX_Vector neg_vec_in = hvx_vec_neg_fp32(in);
+
+            vec_out = hvx_vec_exp_fp32_guard(neg_vec_in, max_exp, inf);
+        } else {
+            vec_out = hvx_vec_exp_fp32_guard(in, max_exp, inf);
+        }
+
+        hvx_vec_store_u((void *) dstf, left_over * SIZEOF_FP32, vec_out);
+    }
+}

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

@@ -1,215 +0,0 @@
-#ifndef HVX_EXP_H
-#define HVX_EXP_H
-
-#include <stdbool.h>
-#include <stdint.h>
-
-#include "hvx-base.h"
-#include "hvx-floor.h"
-
-#define EXP_COEFF_5 (0x39506967)  // 0.000198757 = 1/(7!)
-#define EXP_COEFF_4 (0x3AB743CE)  // 0.0013982   = 1/(6!)
-#define EXP_COEFF_3 (0x3C088908)  // 0.00833345  = 1/(5!)
-#define EXP_COEFF_2 (0x3D2AA9C1)  // 0.416658    = 1/(4!)
-#define EXP_COEFF_1 (0x3E2AAAAA)  // 0.16666667  = 1/(3!)
-#define EXP_COEFF_0 (0x3F000000)  // 0.5         = 1/(2!)
-#define EXP_LOGN2   (0x3F317218)  // ln(2)   = 0.6931471805
-#define EXP_LOG2E   (0x3FB8AA3B)  // log2(e) = 1/ln(2) = 1.4426950408
-#define EXP_ONE     (0x3f800000)  // 1.0
-#define EXP_RANGE_R (0x41a00000)  // 20.0
-#define EXP_RANGE_L (0xc1a00000)  // -20.0
-
-static inline HVX_Vector hvx_vec_exp_f32(HVX_Vector in_vec) {
-    HVX_Vector z_qf32_v;
-    HVX_Vector x_v;
-    HVX_Vector x_qf32_v;
-    HVX_Vector y_v;
-    HVX_Vector k_v;
-    HVX_Vector f_v;
-    HVX_Vector epsilon_v;
-    HVX_Vector log2e = Q6_V_vsplat_R(EXP_LOG2E);
-    HVX_Vector logn2 = Q6_V_vsplat_R(EXP_LOGN2);
-    HVX_Vector E_const;
-    HVX_Vector zero_v = Q6_V_vzero();
-
-    // exp(x) is approximated as follows:
-    //   f = floor(x/ln(2)) = floor(x*log2(e))
-    //   epsilon = x - f*ln(2)
-    //   exp(x) = exp(epsilon+f*ln(2))
-    //          = exp(epsilon)*exp(f*ln(2))
-    //          = exp(epsilon)*2^f
-    //
-    //   Since epsilon is close to zero, it can be approximated with its Taylor series:
-    //            exp(x) ~= 1+x+x^2/2!+x^3/3!+...+x^n/n!+...
-    //   Preserving the first eight elements, we get:
-    //            exp(x) ~= 1+x+e0*x^2+e1*x^3+e2*x^4+e3*x^5+e4*x^6+e5*x^7
-    //                   =  1+x+(E0+(E1+(E2+(E3+(E4+E5*x)*x)*x)*x)*x)*x^2
-
-    HVX_Vector temp_v = in_vec;
-
-    // Clamp inputs to (-20.0, 20.0)
-    HVX_VectorPred pred_cap_right = Q6_Q_vcmp_gt_VsfVsf(in_vec, Q6_V_vsplat_R(EXP_RANGE_R));
-    HVX_VectorPred pred_cap_left  = Q6_Q_vcmp_gt_VsfVsf(Q6_V_vsplat_R(EXP_RANGE_L), in_vec);
-
-    in_vec = Q6_V_vmux_QVV(pred_cap_right, Q6_V_vsplat_R(EXP_RANGE_R), temp_v);
-    in_vec = Q6_V_vmux_QVV(pred_cap_left, Q6_V_vsplat_R(EXP_RANGE_L), temp_v);
-
-    epsilon_v = Q6_Vqf32_vmpy_VsfVsf(log2e, in_vec);
-    epsilon_v = Q6_Vsf_equals_Vqf32(epsilon_v);
-
-    //    f_v is the floating point result and k_v is the integer result
-    f_v = hvx_vec_floor_f32(epsilon_v);
-    k_v = hvx_vec_truncate_f32(f_v);
-
-    x_qf32_v = Q6_Vqf32_vadd_VsfVsf(in_vec, zero_v);
-
-    //  x = x - f_v * logn2;
-    epsilon_v = Q6_Vqf32_vmpy_VsfVsf(f_v, logn2);
-    x_qf32_v  = Q6_Vqf32_vsub_Vqf32Vqf32(x_qf32_v, epsilon_v);
-    // normalize before every QFloat's vmpy
-    x_qf32_v  = Q6_Vqf32_vadd_Vqf32Vsf(x_qf32_v, zero_v);
-
-    // z = x * x;
-    z_qf32_v = Q6_Vqf32_vmpy_Vqf32Vqf32(x_qf32_v, x_qf32_v);
-    z_qf32_v = Q6_Vqf32_vadd_Vqf32Vsf(z_qf32_v, zero_v);
-
-    x_v = Q6_Vsf_equals_Vqf32(x_qf32_v);
-
-    // y = E4 + E5 * x;
-    E_const = Q6_V_vsplat_R(EXP_COEFF_5);
-    y_v     = Q6_Vqf32_vmpy_VsfVsf(E_const, x_v);
-    E_const = Q6_V_vsplat_R(EXP_COEFF_4);
-    y_v     = Q6_Vqf32_vadd_Vqf32Vsf(y_v, E_const);
-    y_v     = Q6_Vqf32_vadd_Vqf32Vsf(y_v, zero_v);
-
-    // y = E3 + y * x;
-    E_const = Q6_V_vsplat_R(EXP_COEFF_3);
-    y_v     = Q6_Vqf32_vmpy_Vqf32Vqf32(y_v, x_qf32_v);
-    y_v     = Q6_Vqf32_vadd_Vqf32Vsf(y_v, E_const);
-    y_v     = Q6_Vqf32_vadd_Vqf32Vsf(y_v, zero_v);
-
-    // y = E2 + y * x;
-    E_const = Q6_V_vsplat_R(EXP_COEFF_2);
-    y_v     = Q6_Vqf32_vmpy_Vqf32Vqf32(y_v, x_qf32_v);
-    y_v     = Q6_Vqf32_vadd_Vqf32Vsf(y_v, E_const);
-    y_v     = Q6_Vqf32_vadd_Vqf32Vsf(y_v, zero_v);
-
-    // y = E1 + y * x;
-    E_const = Q6_V_vsplat_R(EXP_COEFF_1);
-    y_v     = Q6_Vqf32_vmpy_Vqf32Vqf32(y_v, x_qf32_v);
-    y_v     = Q6_Vqf32_vadd_Vqf32Vsf(y_v, E_const);
-    y_v     = Q6_Vqf32_vadd_Vqf32Vsf(y_v, zero_v);
-
-    // y = E0 + y * x;
-    E_const = Q6_V_vsplat_R(EXP_COEFF_0);
-    y_v     = Q6_Vqf32_vmpy_Vqf32Vqf32(y_v, x_qf32_v);
-    y_v     = Q6_Vqf32_vadd_Vqf32Vsf(y_v, E_const);
-    y_v     = Q6_Vqf32_vadd_Vqf32Vsf(y_v, zero_v);
-
-    // y = x + y * z;
-    y_v = Q6_Vqf32_vmpy_Vqf32Vqf32(y_v, z_qf32_v);
-    y_v = Q6_Vqf32_vadd_Vqf32Vqf32(y_v, x_qf32_v);
-    y_v = Q6_Vqf32_vadd_Vqf32Vsf(y_v, zero_v);
-
-    // y = y + 1.0;
-    y_v = Q6_Vqf32_vadd_Vqf32Vsf(y_v, Q6_V_vsplat_R(EXP_ONE));
-
-    // insert exponents
-    //        y = ldexpf(y, k);
-    //    y_v += k_v; // qf32
-    // modify exponent
-
-    y_v = Q6_Vsf_equals_Vqf32(y_v);
-
-    // add k_v to the exponent of y_v
-    HVX_Vector y_v_exponent = Q6_Vw_vasl_VwR(y_v, 1);
-
-    y_v_exponent = Q6_Vuw_vlsr_VuwR(y_v_exponent, IEEE_VSF_MANTLEN + 1);
-    y_v_exponent = Q6_Vw_vadd_VwVw(k_v, y_v_exponent);
-
-    // exponent cannot be negative; if overflow is detected, result is set to zero
-    HVX_VectorPred qy_v_negative_exponent = Q6_Q_vcmp_gt_VwVw(zero_v, y_v_exponent);
-
-    y_v = Q6_Vw_vaslacc_VwVwR(y_v, k_v, IEEE_VSF_MANTLEN);
-
-    y_v = Q6_V_vmux_QVV(qy_v_negative_exponent, zero_v, y_v);
-
-    return y_v;
-}
-
-static inline HVX_Vector hvx_vec_exp_f32_guard(HVX_Vector in_vec, HVX_Vector max_exp, HVX_Vector inf) {
-    const HVX_VectorPred pred0 = Q6_Q_vcmp_gt_VsfVsf(in_vec, max_exp);
-
-    HVX_Vector out = hvx_vec_exp_f32(in_vec);
-
-    return Q6_V_vmux_QVV(pred0, inf, out);
-}
-
-static inline void hvx_exp_f32(const uint8_t * restrict src, uint8_t * restrict dst, const int num_elems, bool negate) {
-    int left_over       = num_elems & (VLEN_FP32 - 1);
-    int num_elems_whole = num_elems - left_over;
-
-    int unaligned_addr = 0;
-    int unaligned_loop = 0;
-    if ((0 == hex_is_aligned((void *) src, VLEN)) || (0 == hex_is_aligned((void *) dst, VLEN))) {
-        unaligned_addr = 1;
-    }
-    // assert((0 == unaligned_addr) || (0 == num_elems_whole));
-    if ((1 == unaligned_addr) && (num_elems_whole != 0)) {
-        unaligned_loop = 1;
-    }
-
-    HVX_Vector vec_out = Q6_V_vzero();
-
-    static const float kInf    = INFINITY;
-    static const float kMaxExp = 88.02f;  // log(INF)
-
-    const HVX_Vector max_exp = hvx_vec_splat_f32(kMaxExp);
-    const HVX_Vector inf     = hvx_vec_splat_f32(kInf);
-
-    if (0 == unaligned_loop) {
-        HVX_Vector * p_vec_in1 = (HVX_Vector *) src;
-        HVX_Vector * p_vec_out = (HVX_Vector *) dst;
-
-        #pragma unroll(4)
-        for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
-            if (true == negate) {
-                HVX_Vector neg_vec_in = hvx_vec_neg_f32(*p_vec_in1++);
-                *p_vec_out++          = hvx_vec_exp_f32_guard(neg_vec_in, max_exp, inf);
-            } else {
-                *p_vec_out++ = hvx_vec_exp_f32_guard(*p_vec_in1++, max_exp, inf);
-            }
-        }
-    } else {
-        #pragma unroll(4)
-        for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
-            HVX_Vector in = *(HVX_UVector *) (src + i * SIZEOF_FP32);
-
-            if (true == negate) {
-                HVX_Vector neg_vec_in                    = hvx_vec_neg_f32(in);
-                *(HVX_UVector *) (dst + i * SIZEOF_FP32) = hvx_vec_exp_f32_guard(neg_vec_in, max_exp, inf);
-            } else {
-                *(HVX_UVector *) (dst + i * SIZEOF_FP32) = hvx_vec_exp_f32_guard(in, max_exp, inf);
-            }
-        }
-    }
-
-    if (left_over > 0) {
-        const float * srcf = (float *) src + num_elems_whole;
-        float *       dstf = (float *) dst + num_elems_whole;
-
-        HVX_Vector in = *(HVX_UVector *) srcf;
-
-        if (true == negate) {
-            HVX_Vector neg_vec_in = hvx_vec_neg_f32(in);
-
-            vec_out = hvx_vec_exp_f32_guard(neg_vec_in, max_exp, inf);
-        } else {
-            vec_out = hvx_vec_exp_f32_guard(in, max_exp, inf);
-        }
-
-        hvx_vec_store_u((void *) dstf, left_over * SIZEOF_FP32, vec_out);
-    }
-}
-
-#endif /* HVX_EXP_H */

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

@@ -1,100 +0,0 @@
-#ifndef HVX_FLOOR_H
-#define HVX_FLOOR_H
-
-#include <stdbool.h>
-#include <stdint.h>
-
-#include "hvx-base.h"
-
-#define IEEE_VSF_EXPLEN   (8)
-#define IEEE_VSF_EXPBIAS  (127)
-#define IEEE_VSF_EXPMASK  (0xFF)
-#define IEEE_VSF_MANTLEN  (23)
-#define IEEE_VSF_MANTMASK (0x7FFFFF)
-#define IEEE_VSF_MIMPMASK (0x800000)
-
-static inline HVX_Vector hvx_vec_truncate_f32(HVX_Vector in_vec) {
-    HVX_Vector mask_mant_v  = Q6_V_vsplat_R(IEEE_VSF_MANTMASK);
-    HVX_Vector mask_impl_v  = Q6_V_vsplat_R(IEEE_VSF_MIMPMASK);
-    HVX_Vector const_zero_v = Q6_V_vzero();
-
-    HVX_VectorPred q_negative = Q6_Q_vcmp_gt_VwVw(const_zero_v, in_vec);
-
-    HVX_Vector expval_v = in_vec >> IEEE_VSF_MANTLEN;
-    expval_v &= IEEE_VSF_EXPMASK;
-    expval_v -= IEEE_VSF_EXPBIAS;
-
-    // negative exp == fractional value
-    HVX_VectorPred q_negexp = Q6_Q_vcmp_gt_VwVw(const_zero_v, expval_v);
-
-    HVX_Vector rshift_v = IEEE_VSF_MANTLEN - expval_v;         // fractional bits - exp shift
-
-    HVX_Vector mant_v = in_vec & mask_mant_v;                  // obtain mantissa
-    HVX_Vector vout   = Q6_Vw_vadd_VwVw(mant_v, mask_impl_v);  // add implicit 1.0
-
-    vout = Q6_Vw_vasr_VwVw(vout, rshift_v);                    // shift to obtain truncated integer
-    vout = Q6_V_vmux_QVV(q_negexp, const_zero_v, vout);        // expval<0 -> 0
-
-    HVX_Vector neg_vout = -vout;
-
-    vout = Q6_V_vmux_QVV(q_negative, neg_vout, vout);  // handle negatives
-
-    return (vout);
-}
-
-static inline HVX_Vector hvx_vec_floor_f32(HVX_Vector in_vec) {
-    HVX_Vector mask_mant_v    = Q6_V_vsplat_R(IEEE_VSF_MANTMASK);
-    HVX_Vector mask_impl_v    = Q6_V_vsplat_R(IEEE_VSF_MIMPMASK);
-    HVX_Vector const_mnlen_v  = Q6_V_vsplat_R(IEEE_VSF_MANTLEN);
-    HVX_Vector const_zero_v   = Q6_V_vzero();
-    HVX_Vector const_negone_v = Q6_V_vsplat_R(0xbf800000);  // -1 IEEE vsf
-
-    HVX_VectorPred q_negative = Q6_Q_vcmp_gt_VwVw(const_zero_v, in_vec);
-
-    HVX_Vector expval_v = in_vec >> IEEE_VSF_MANTLEN;
-    expval_v &= IEEE_VSF_EXPMASK;
-    expval_v -= IEEE_VSF_EXPBIAS;
-
-    HVX_VectorPred q_negexp     = Q6_Q_vcmp_gt_VwVw(const_zero_v, expval_v);
-    HVX_VectorPred q_expltmn    = Q6_Q_vcmp_gt_VwVw(const_mnlen_v, expval_v);
-    HVX_VectorPred q_negexp_pos = Q6_Q_vcmp_gtand_QVwVw(q_negexp, in_vec, const_zero_v);
-    HVX_VectorPred q_negexp_neg = Q6_Q_vcmp_gtand_QVwVw(q_negexp, const_zero_v, in_vec);
-
-    // if expval < 0 (q_negexp)         // <0, floor is 0
-    //    if vin > 0
-    //       floor = 0
-    //    if vin < 0
-    //       floor = -1
-    // if expval < mant_len (q_expltmn) // >0, but fraction may exist
-    //    get sign (q_negative)
-    //    mask >> expval                // fraction bits to mask off
-    //    vout = ~(mask)                // apply mask to remove fraction
-    //    if (qneg)                     // negative floor is one less (more, sign bit for neg)
-    //      vout += ((impl_mask) >> expval)
-    //    if (mask && vin)
-    //      vout = vin
-    // else                             // already an integer
-    //    ;                             // no change
-
-    // compute floor
-    mask_mant_v >>= expval_v;
-    HVX_Vector neg_addin_v    = mask_impl_v >> expval_v;
-    HVX_Vector vout_neg_addin = Q6_Vw_vadd_VwVw(in_vec, neg_addin_v);
-    HVX_Vector vout           = Q6_V_vmux_QVV(q_negative, vout_neg_addin, in_vec);
-
-    HVX_Vector     mask_chk_v = Q6_V_vand_VV(in_vec, mask_mant_v);  // chk if bits set
-    HVX_VectorPred q_integral = Q6_Q_vcmp_eq_VwVw(const_zero_v, mask_chk_v);
-
-    HVX_Vector not_mask_v = Q6_V_vnot_V(mask_mant_v);        // frac bits to clear
-    HVX_Vector vfrfloor_v = Q6_V_vand_VV(vout, not_mask_v);  // clear frac bits
-
-    vout = in_vec;
-    vout = Q6_V_vmux_QVV(q_expltmn, vfrfloor_v, vout);         // expval<mant
-    vout = Q6_V_vmux_QVV(q_integral, in_vec, vout);            // integral values
-    vout = Q6_V_vmux_QVV(q_negexp_pos, const_zero_v, vout);    // expval<0 x>0 -> 0
-    vout = Q6_V_vmux_QVV(q_negexp_neg, const_negone_v, vout);  // expval<0 x<0 -> -1
-
-    return vout;
-}
-
-#endif /* HVX_FLOOR_H */

ggml/src/ggml-hexagon/htp/hvx-inverse.c

@@ -0,0 +1,72 @@
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wunused-function"
+#pragma clang diagnostic ignored "-Wunused-but-set-variable"
+
+#include <hexagon_protos.h>
+#include <hexagon_types.h>
+#include <math.h>
+#include <string.h>
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "htp-ctx.h"
+#include "htp-dma.h"
+#include "htp-msg.h"
+#include "htp-ops.h"
+#include "hvx-utils.h"
+#include "ops-utils.h"
+
+static inline HVX_Vector hvx_vec_inverse_fp32_guard(HVX_Vector v_sf, HVX_Vector nan_inf_mask) {
+    HVX_Vector out = hvx_vec_inverse_fp32(v_sf);
+
+    HVX_Vector           masked_out = Q6_V_vand_VV(out, nan_inf_mask);
+    const HVX_VectorPred pred       = Q6_Q_vcmp_eq_VwVw(nan_inf_mask, masked_out);
+
+    return Q6_V_vmux_QVV(pred, Q6_V_vzero(), out);
+}
+
+void hvx_inverse_f32(const uint8_t * restrict src, uint8_t * restrict dst, const int num_elems) {
+    int left_over       = num_elems & (VLEN_FP32 - 1);
+    int num_elems_whole = num_elems - left_over;
+
+    int unaligned_addr = 0;
+    int unaligned_loop = 0;
+    if ((0 == htp_is_aligned((void *) src, VLEN)) || (0 == htp_is_aligned((void *) dst, VLEN))) {
+        FARF(HIGH, "hvx_inverse_f32: unaligned address in hvx op, possibly slower execution\n");
+        unaligned_addr = 1;
+    }
+    // assert((0 == unaligned_addr) || (0 == num_elems_whole));
+    if ((1 == unaligned_addr) && (num_elems_whole != 0)) {
+        unaligned_loop = 1;
+        FARF(HIGH, "hvx_inverse_f32: unaligned loop in hvx op, possibly slower execution\n");
+    }
+
+    static const uint32_t kNanInfMask  = 0x7f800000;
+    const HVX_Vector      nan_inf_mask = Q6_V_vsplat_R(kNanInfMask);
+
+    if (0 == unaligned_loop) {
+        HVX_Vector * p_vec_in  = (HVX_Vector *) src;
+        HVX_Vector * p_vec_out = (HVX_Vector *) dst;
+
+        #pragma unroll(4)
+        for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
+            *p_vec_out++ = hvx_vec_inverse_fp32_guard(*p_vec_in++, nan_inf_mask);
+        }
+    } else {
+        #pragma unroll(4)
+        for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
+            HVX_Vector in                            = *(HVX_UVector *) (src + i * SIZEOF_FP32);
+            *(HVX_UVector *) (dst + i * SIZEOF_FP32) = hvx_vec_inverse_fp32_guard(in, nan_inf_mask);
+        }
+    }
+
+    if (left_over > 0) {
+        const float * srcf = (float *) src + num_elems_whole;
+        float *       dstf = (float *) dst + num_elems_whole;
+
+        HVX_Vector in  = *(HVX_UVector *) srcf;
+        HVX_Vector out = hvx_vec_inverse_fp32_guard(in, nan_inf_mask);
+
+        hvx_vec_store_u((void *) dstf, left_over * SIZEOF_FP32, out);
+    }
+}

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

@@ -1,176 +0,0 @@
-#ifndef HVX_INVERSE_H
-#define HVX_INVERSE_H
-
-#include <HAP_farf.h>
-
-#include <math.h>
-#include <string.h>
-#include <assert.h>
-#include <stddef.h>
-#include <stdint.h>
-
-#include "hvx-base.h"
-
-// ====================================================
-// FUNCTION: 1/(x+1)     y(0) = 1,  y(0.5) = 0.6667, y(1) = 0.5
-// Order:3; continuity: True; Ends forced: True
-// Mode: unsigned;   Result fractional bits: 14
-// Peak Error: 1.1295e-04  Rms Error: 2.8410e-05   Mean Error: 1.1370e-05
-//      32769  -32706   31252  -10589
-//      32590  -30635   22793   -4493
-//      32066  -27505   16481   -2348
-//      31205  -24054   11849   -1306
-
-static inline HVX_Vector hvx_vec_recip_xp1_O3_unsigned(HVX_Vector vx) {
-    // input is 0..0xffff representing 0.0  .. 1.0
-    HVX_Vector p;
-    p = Q6_Vh_vlut4_VuhPh(vx, 0xFAE6F6D4EE73D6A3ull);
-    p = Q6_Vh_vmpa_VhVhVuhPuh_sat(p, vx, 0x2E49406159097A14ull);
-    p = Q6_Vh_vmps_VhVhVuhPuh_sat(p, vx, 0x5DF66B7177AB7FC2ull);
-    p = Q6_Vh_vmpa_VhVhVuhPuh_sat(p, vx, 0x79E57D427F4E8001ull);
-    return p;  // signed result, 14 fractional bits
-}
-
-// Find reciprocal of fp16.
-// (1) first, convert to fp32, multiplying by 1.0; this is done to
-//    handle denormals. Ignoring sign and zero, result should be at
-//    least 5.9604645e-08 (32-bit code 0x33800000) and at most 131008 (0x47ffe000)
-//    (exponent in range [103,143])
-// (2) extract the mantissa into 16-bit unsigned; find reciprocal using a fitted poly
-// (3) put this, along with '253-exp' (exp from (1)) together to make an qf32
-// (4) convert that to fp16
-// (5) put sign back in. Also, if the original value (w/o sign) was <0x81, replace
-//     the result with the max value.
-static inline HVX_Vector hvx_vec_inverse_f16(HVX_Vector vals) {
-    HVX_Vector     em_mask  = Q6_Vh_vsplat_R(0x7FFF);
-    HVX_Vector     avals    = Q6_V_vand_VV(vals, em_mask);
-    HVX_VectorPred is_neg   = Q6_Q_vcmp_gt_VhVh(avals, vals);
-    // is too small to 1/x ? for 'standard' fp16, this would be 0x101
-    HVX_VectorPred is_small = Q6_Q_vcmp_gt_VhVh(Q6_Vh_vsplat_R(0x101), avals);
-
-    HVX_VectorPair to_qf32  = Q6_Wqf32_vmpy_VhfVhf(avals, Q6_Vh_vsplat_R(0x3C00));  // *1.0
-    HVX_Vector     to_f32_0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(to_qf32));
-    HVX_Vector     to_f32_1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(to_qf32));
-
-    // bits 22..13 contain the mantissa now (w/o hidden bit); move to bit 14..5 of a 16-bit vector
-    HVX_Vector mant_u16 = Q6_Vh_vshuffo_VhVh(Q6_Vw_vasl_VwR(to_f32_1, 9), Q6_Vw_vasl_VwR(to_f32_0, 9));
-    // likewise extract the upper 16 from each, containing the exponents in range 103..142
-    HVX_Vector exp_u16  = Q6_Vh_vshuffo_VhVh(to_f32_1, to_f32_0);
-    //Get exponent in IEEE 32-bit representation
-    exp_u16             = Q6_Vuh_vlsr_VuhR(exp_u16, 7);
-
-    // so, mant_u16 contains an unbiased mantissa in upper 10 bits of each u16 lane
-    // We can consider it to be x-1.0, with 16 fractional bits, where 'x' is in range [1.0,2.0)
-    // Use poly to transform to 1/x, with 14 fractional bits
-    //
-    HVX_Vector rm = hvx_vec_recip_xp1_O3_unsigned(mant_u16);
-
-    HVX_Vector vcl0 = Q6_Vuh_vcl0_Vuh(rm);  //count leading zeros
-
-    // Get mantissa for 16-bit represenation
-    HVX_Vector mant_recip = Q6_V_vand_VV(Q6_Vh_vasr_VhR(Q6_Vh_vasl_VhVh(rm, vcl0), 5), Q6_Vh_vsplat_R(0x03FF));
-
-    //Compute Reciprocal Exponent
-    HVX_Vector exp_recip =
-        Q6_Vh_vsub_VhVh(Q6_Vh_vsub_VhVh(Q6_Vh_vsplat_R(254), exp_u16), Q6_Vh_vsub_VhVh(vcl0, Q6_Vh_vsplat_R(1)));
-    //Convert it for 16-bit representation
-    exp_recip = Q6_Vh_vadd_VhVh_sat(Q6_Vh_vsub_VhVh(exp_recip, Q6_Vh_vsplat_R(127)), Q6_Vh_vsplat_R(15));
-    exp_recip = Q6_Vh_vasl_VhR(exp_recip, 10);
-
-    //Merge exponent and mantissa for reciprocal
-    HVX_Vector recip = Q6_V_vor_VV(exp_recip, mant_recip);
-    // map 'small' inputs to standard largest value 0x7bff
-    recip            = Q6_V_vmux_QVV(is_small, Q6_Vh_vsplat_R(0x7bff), recip);
-    // add sign back
-    recip            = Q6_V_vandor_VQR(recip, is_neg, 0x80008000);
-    return recip;
-}
-
-static inline HVX_Vector hvx_vec_inverse_f32(HVX_Vector v_sf) {
-    HVX_Vector inv_aprox_sf = Q6_V_vsplat_R(0x7EEEEBB3);
-    HVX_Vector two_sf       = hvx_vec_splat_f32(2.0);
-
-    // First approximation
-    HVX_Vector i_sf = Q6_Vw_vsub_VwVw(inv_aprox_sf, v_sf);
-
-    HVX_Vector r_qf;
-
-    // Refine
-    r_qf = Q6_Vqf32_vmpy_VsfVsf(
-        i_sf, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(two_sf, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(i_sf, v_sf)))));
-    r_qf = Q6_Vqf32_vmpy_Vqf32Vqf32(
-        r_qf, Q6_Vqf32_vsub_VsfVsf(two_sf, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(r_qf), v_sf))));
-    r_qf = Q6_Vqf32_vmpy_Vqf32Vqf32(
-        r_qf, Q6_Vqf32_vsub_VsfVsf(two_sf, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(r_qf), v_sf))));
-
-    return Q6_Vsf_equals_Vqf32(r_qf);
-}
-
-static inline HVX_Vector hvx_vec_inverse_f32_guard(HVX_Vector v_sf, HVX_Vector nan_inf_mask) {
-    HVX_Vector out = hvx_vec_inverse_f32(v_sf);
-
-    HVX_Vector     masked_out = Q6_V_vand_VV(out, nan_inf_mask);
-    const HVX_VectorPred pred = Q6_Q_vcmp_eq_VwVw(nan_inf_mask, masked_out);
-
-    return Q6_V_vmux_QVV(pred, Q6_V_vzero(), out);
-}
-
-#define hvx_inverse_f32_loop_body(dst_type, src_type, vec_store)             \
-    do {                                                                     \
-        dst_type * restrict vdst = (dst_type *) dst;                         \
-        src_type * restrict vsrc = (src_type *) src;                         \
-                                                                             \
-        const HVX_Vector nan_inf_mask = Q6_V_vsplat_R(0x7f800000);           \
-                                                                             \
-        const uint32_t nvec = n / VLEN_FP32;                                 \
-        const uint32_t nloe = n % VLEN_FP32;                                 \
-                                                                             \
-        uint32_t i = 0;                                                      \
-                                                                             \
-        _Pragma("unroll(4)")                                                 \
-        for (; i < nvec; i++) {                                              \
-             vdst[i] = hvx_vec_inverse_f32_guard(vsrc[i], nan_inf_mask);     \
-        }                                                                    \
-        if (nloe) {                                                          \
-            HVX_Vector v = hvx_vec_inverse_f32_guard(vsrc[i], nan_inf_mask); \
-            vec_store((void *) &vdst[i], nloe * SIZEOF_FP32, v);             \
-        }                                                                    \
-    } while(0)
-
-static inline void hvx_inverse_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src % 128 == 0);
-    hvx_inverse_f32_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
-}
-
-static inline void hvx_inverse_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    hvx_inverse_f32_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a);
-}
-
-static inline void hvx_inverse_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) src % 128 == 0);
-    hvx_inverse_f32_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
-}
-
-static inline void hvx_inverse_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_inverse_f32_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
-}
-
-static inline void hvx_inverse_f32(uint8_t * restrict dst, uint8_t * restrict src, const int num_elems) {
-    if ((unsigned long) dst % 128 == 0) {
-        if ((unsigned long) src % 128 == 0) {
-            hvx_inverse_f32_aa(dst, src, num_elems);
-        } else {
-            hvx_inverse_f32_au(dst, src, num_elems);
-        }
-    } else {
-        if ((unsigned long) src % 128 == 0) {
-            hvx_inverse_f32_ua(dst, src, num_elems);
-        } else {
-            hvx_inverse_f32_uu(dst, src, num_elems);
-        }
-    }
-}
-
-#endif // HVX_INVERSE_H

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

@@ -1,225 +0,0 @@
-#ifndef HVX_REDUCE_H
-#define HVX_REDUCE_H
-
-#include <math.h>
-#include <stdbool.h>
-#include <stdint.h>
-#include <assert.h>
-
-#include "hex-utils.h"
-#include "hvx-base.h"
-#include "hvx-types.h"
-
-static inline HVX_Vector hvx_vec_reduce_sum_n_i32(HVX_Vector in, unsigned int n) {
-    unsigned int total = n * 4;  // total vec nbytes
-    unsigned int width = 4;      // int32
-
-    HVX_Vector sum = in, sum_t;
-    while (width < total) {
-        sum_t = Q6_V_vror_VR(sum, width);     // rotate right
-        sum   = Q6_Vw_vadd_VwVw(sum_t, sum);  // elementwise sum
-        width = width << 1;
-    }
-    return sum;
-}
-
-static inline HVX_Vector hvx_vec_reduce_sum_i32(HVX_Vector in) {
-    return hvx_vec_reduce_sum_n_i32(in, 32);
-}
-
-static inline HVX_Vector hvx_vec_reduce_sum_n_qf32(HVX_Vector in, unsigned int n) {
-    unsigned int total = n * 4;  // total vec nbytes
-    unsigned int width = 4;      // fp32 nbytes
-
-    HVX_Vector sum = in, sum_t;
-    while (width < total) {
-        sum_t = Q6_V_vror_VR(Q6_Vsf_equals_Vqf32(sum), width);  // rotate right
-        sum   = Q6_Vqf32_vadd_Vqf32Vsf(sum, sum_t);             // elementwise sum
-        width = width << 1;
-    }
-    return sum;
-}
-
-static inline HVX_Vector hvx_vec_reduce_sum_qf32(HVX_Vector in) {
-    return hvx_vec_reduce_sum_n_qf32(in, 32);
-}
-
-static inline HVX_Vector hvx_vec_reduce_sum_n_f32(HVX_Vector in, unsigned int n) {
-    unsigned int total = n * 4;  // total vec nbytes
-    unsigned int width = 4;      // fp32 nbytes
-
-    HVX_Vector sum = in, sum_t;
-    while (width < total) {
-        sum_t = Q6_V_vror_VR(sum, width);                               // rotate right
-        sum   = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(sum, sum_t));  // elementwise sum
-        width = width << 1;
-    }
-    return sum;
-}
-
-static inline HVX_Vector hvx_vec_reduce_sum_f32(HVX_Vector in) {
-    return hvx_vec_reduce_sum_n_f32(in, 32);
-}
-
-static inline HVX_Vector hvx_vec_reduce_max_f16(HVX_Vector in) {
-    unsigned total = 128;  // total vec nbytes
-    unsigned width = 2;    // fp16 nbytes
-
-    HVX_Vector _max = in, _max_t;
-    while (width < total) {
-        _max_t = Q6_V_vror_VR(_max, width);         // rotate right
-        _max   = Q6_Vhf_vmax_VhfVhf(_max_t, _max);  // elementwise max
-        width  = width << 1;
-    }
-
-    return _max;
-}
-
-static inline HVX_Vector hvx_vec_reduce_max2_f16(HVX_Vector in, HVX_Vector _max) {
-    unsigned total = 128;  // total vec nbytes
-    unsigned width = 2;    // fp32 nbytes
-
-    HVX_Vector _max_t;
-
-    _max = Q6_Vhf_vmax_VhfVhf(in, _max);
-    while (width < total) {
-        _max_t = Q6_V_vror_VR(_max, width);         // rotate right
-        _max   = Q6_Vhf_vmax_VhfVhf(_max_t, _max);  // elementwise max
-        width  = width << 1;
-    }
-
-    return _max;
-}
-
-static inline HVX_Vector hvx_vec_reduce_max_f32(HVX_Vector in) {
-    unsigned total = 128;  // total vec nbytes
-    unsigned width = 4;    // fp32 nbytes
-
-    HVX_Vector _max = in, _max_t;
-    while (width < total) {
-        _max_t = Q6_V_vror_VR(_max, width);         // rotate right
-        _max   = Q6_Vsf_vmax_VsfVsf(_max_t, _max);  // elementwise max
-        width  = width << 1;
-    }
-
-    return _max;
-}
-
-static inline HVX_Vector hvx_vec_reduce_max2_f32(HVX_Vector in, HVX_Vector _max) {
-    unsigned total = 128;  // total vec nbytes
-    unsigned width = 4;    // fp32 nbytes
-
-    HVX_Vector _max_t;
-
-    _max = Q6_Vsf_vmax_VsfVsf(in, _max);
-    while (width < total) {
-        _max_t = Q6_V_vror_VR(_max, width);         // rotate right
-        _max   = Q6_Vsf_vmax_VsfVsf(_max_t, _max);  // elementwise max
-        width  = width << 1;
-    }
-
-    return _max;
-}
-
-#define hvx_reduce_loop_body(src_type, init_vec, pad_vec, vec_op, reduce_op, scalar_reduce) \
-    do {                                                                                    \
-        src_type * restrict vsrc = (src_type *) src;                                        \
-        HVX_Vector acc = init_vec;                                                          \
-                                                                                            \
-        const uint32_t elem_size = sizeof(float);                                           \
-        const uint32_t epv  = 128 / elem_size;                                              \
-        const uint32_t nvec = num_elems / epv;                                              \
-        const uint32_t nloe = num_elems % epv;                                              \
-                                                                                            \
-        uint32_t i = 0;                                                                     \
-        _Pragma("unroll(4)")                                                                \
-        for (; i < nvec; i++) {                                                             \
-            acc = vec_op(acc, vsrc[i]);                                                     \
-        }                                                                                   \
-        if (nloe) {                                                                         \
-            const float * srcf = (const float *) src + i * epv;                             \
-            HVX_Vector in = *(HVX_UVector *) srcf;                                          \
-            HVX_Vector temp = Q6_V_valign_VVR(in, pad_vec, nloe * elem_size);               \
-            acc = vec_op(acc, temp);                                                        \
-        }                                                                                   \
-        HVX_Vector v = reduce_op(acc);                                                      \
-        return scalar_reduce(v);                                                            \
-    } while(0)
-
-#define HVX_REDUCE_MAX_OP(acc, val) Q6_Vsf_vmax_VsfVsf(acc, val)
-#define HVX_REDUCE_SUM_OP(acc, val) Q6_Vqf32_vadd_VsfVsf(Q6_Vsf_equals_Vqf32(acc), val)
-#define HVX_SUM_SQ_OP(acc, val) Q6_Vqf32_vadd_Vqf32Vqf32(acc, Q6_Vqf32_vmpy_VsfVsf(val, val))
-#define HVX_REDUCE_MAX_SCALAR(v) hvx_vec_get_f32(v)
-#define HVX_REDUCE_SUM_SCALAR(v) hvx_vec_get_f32(Q6_Vsf_equals_Vqf32(v))
-
-// Max variants
-
-static inline float hvx_reduce_max_f32_a(const uint8_t * restrict src, const int num_elems) {
-    HVX_Vector init_vec = hvx_vec_splat_f32(((const float *) src)[0]);
-    assert((unsigned long) src % 128 == 0);
-    hvx_reduce_loop_body(HVX_Vector, init_vec, init_vec, HVX_REDUCE_MAX_OP, hvx_vec_reduce_max_f32, HVX_REDUCE_MAX_SCALAR);
-}
-
-static inline float hvx_reduce_max_f32_u(const uint8_t * restrict src, const int num_elems) {
-    HVX_Vector init_vec = hvx_vec_splat_f32(((const float *) src)[0]);
-    hvx_reduce_loop_body(HVX_UVector, init_vec, init_vec, HVX_REDUCE_MAX_OP, hvx_vec_reduce_max_f32, HVX_REDUCE_MAX_SCALAR);
-}
-
-static inline float hvx_reduce_max_f32(const uint8_t * restrict src, const int num_elems) {
-    if (hex_is_aligned((void *) src, 128)) {
-        return hvx_reduce_max_f32_a(src, num_elems);
-    } else {
-        return hvx_reduce_max_f32_u(src, num_elems);
-    }
-}
-
-// Sum variants
-
-static inline float hvx_reduce_sum_f32_a(const uint8_t * restrict src, const int num_elems) {
-    HVX_Vector init_vec = Q6_V_vsplat_R(0);
-    assert((unsigned long) src % 128 == 0);
-    hvx_reduce_loop_body(HVX_Vector, init_vec, init_vec, HVX_REDUCE_SUM_OP, hvx_vec_reduce_sum_qf32, HVX_REDUCE_SUM_SCALAR);
-}
-
-static inline float hvx_reduce_sum_f32_u(const uint8_t * restrict src, const int num_elems) {
-    HVX_Vector init_vec = Q6_V_vsplat_R(0);
-    hvx_reduce_loop_body(HVX_UVector, init_vec, init_vec, HVX_REDUCE_SUM_OP, hvx_vec_reduce_sum_qf32, HVX_REDUCE_SUM_SCALAR);
-}
-
-static inline float hvx_reduce_sum_f32(const uint8_t * restrict src, const int num_elems) {
-    if (hex_is_aligned((void *) src, 128)) {
-        return hvx_reduce_sum_f32_a(src, num_elems);
-    } else {
-        return hvx_reduce_sum_f32_u(src, num_elems);
-    }
-}
-
-// Sum of squares variants
-
-static inline float hvx_sum_of_squares_f32_a(const uint8_t * restrict src, const int num_elems) {
-    HVX_Vector init_vec = Q6_V_vsplat_R(0);
-    assert((uintptr_t) src % 128 == 0);
-    hvx_reduce_loop_body(HVX_Vector, init_vec, init_vec, HVX_SUM_SQ_OP, hvx_vec_reduce_sum_qf32, HVX_REDUCE_SUM_SCALAR);
-}
-
-static inline float hvx_sum_of_squares_f32_u(const uint8_t * restrict src, const int num_elems) {
-    HVX_Vector init_vec = Q6_V_vsplat_R(0);
-    hvx_reduce_loop_body(HVX_UVector, init_vec, init_vec, HVX_SUM_SQ_OP, hvx_vec_reduce_sum_qf32, HVX_REDUCE_SUM_SCALAR);
-}
-
-static inline float hvx_sum_of_squares_f32(const uint8_t * restrict src, const int num_elems) {
-    if (hex_is_aligned((void *) src, 128)) {
-        return hvx_sum_of_squares_f32_a(src, num_elems);
-    } else {
-        return hvx_sum_of_squares_f32_u(src, num_elems);
-    }
-}
-
-#undef hvx_reduce_loop_body
-#undef HVX_REDUCE_MAX_OP
-#undef HVX_REDUCE_SUM_OP
-#undef HVX_REDUCE_MAX_SCALAR
-#undef HVX_REDUCE_SUM_SCALAR
-#undef HVX_SUM_SQ_OP
-
-#endif /* HVX_REDUCE_H */

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

@@ -1,133 +0,0 @@
-#ifndef HVX_SCALE_H
-#define HVX_SCALE_H
-
-#include <assert.h>
-#include <stddef.h>
-#include <stdint.h>
-
-#include "hvx-base.h"
-
-#define hvx_scale_f32_loop_body(dst_type, src_type, vec_store)                       \
-    do {                                                                             \
-        dst_type * restrict vdst = (dst_type *) dst;                                 \
-        src_type * restrict vsrc = (src_type *) src;                                 \
-                                                                                     \
-        HVX_Vector vs = hvx_vec_splat_f32(scale);                                    \
-                                                                                     \
-        const uint32_t elem_size = sizeof(float);                                    \
-        const uint32_t epv = 128 / elem_size;                                        \
-        const uint32_t nvec = n / epv;                                               \
-        const uint32_t nloe = n % epv;                                               \
-                                                                                     \
-        uint32_t i = 0;                                                              \
-                                                                                     \
-        _Pragma("unroll(4)")                                                         \
-        for (; i < nvec; ++i) {                                                      \
-            HVX_Vector v = Q6_Vqf32_vmpy_VsfVsf(vsrc[i], vs);                        \
-            vdst[i]      = Q6_Vsf_equals_Vqf32(v);                                   \
-        }                                                                            \
-        if (nloe) {                                                                  \
-            HVX_Vector v = Q6_Vqf32_vmpy_VsfVsf(vsrc[i], vs);                        \
-            vec_store((void *) &vdst[i], nloe * elem_size, Q6_Vsf_equals_Vqf32(v));  \
-        }                                                                            \
-    } while(0)
-
-static inline void hvx_scale_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const int n, const float scale) {
-    assert((size_t) dst % 128 == 0);
-    assert((size_t) src % 128 == 0);
-    hvx_scale_f32_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
-}
-
-static inline void hvx_scale_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const int n, const float scale) {
-    assert((size_t) dst % 128 == 0);
-    hvx_scale_f32_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a);
-}
-
-static inline void hvx_scale_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const int n, const float scale) {
-    assert((size_t) src % 128 == 0);
-    hvx_scale_f32_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
-}
-
-static inline void hvx_scale_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const int n, const float scale) {
-    hvx_scale_f32_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
-}
-
-static inline void hvx_scale_f32(uint8_t * restrict dst, const uint8_t * restrict src, const int n, const float scale) {
-    if (((size_t) dst & 127) == 0) {
-        if (((size_t) src & 127) == 0) {
-            hvx_scale_f32_aa(dst, src, n, scale);
-        } else {
-            hvx_scale_f32_au(dst, src, n, scale);
-        }
-    } else {
-        if (((size_t) src & 127) == 0) {
-            hvx_scale_f32_ua(dst, src, n, scale);
-        } else {
-            hvx_scale_f32_uu(dst, src, n, scale);
-        }
-    }
-}
-
-#define hvx_scale_offset_f32_loop_body(dst_type, src_type, vec_store)                \
-    do {                                                                             \
-        dst_type * restrict vdst = (dst_type *) dst;                                 \
-        src_type * restrict vsrc = (src_type *) src;                                 \
-                                                                                     \
-        HVX_Vector vs = hvx_vec_splat_f32(scale);                                    \
-        HVX_Vector vo = hvx_vec_splat_f32(offset);                                   \
-                                                                                     \
-        const uint32_t elem_size = sizeof(float);                                    \
-        const uint32_t epv = 128 / elem_size;                                        \
-        const uint32_t nvec = n / epv;                                               \
-        const uint32_t nloe = n % epv;                                               \
-                                                                                     \
-        uint32_t i = 0;                                                              \
-                                                                                     \
-        _Pragma("unroll(4)")                                                         \
-        for (; i < nvec; ++i) {                                                      \
-            HVX_Vector v = Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vmpy_VsfVsf(vsrc[i], vs), vo); \
-            vdst[i] = Q6_Vsf_equals_Vqf32(v);                                        \
-        }                                                                            \
-        if (nloe) {                                                                  \
-            HVX_Vector v = Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vmpy_VsfVsf(vsrc[i], vs), vo); \
-            vec_store((void *) &vdst[i], nloe * elem_size, Q6_Vsf_equals_Vqf32(v));  \
-        }                                                                            \
-    } while(0)
-
-static inline void hvx_scale_offset_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const int n, const float scale, const float offset) {
-    assert((size_t) dst % 128 == 0);
-    assert((size_t) src % 128 == 0);
-    hvx_scale_offset_f32_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
-}
-
-static inline void hvx_scale_offset_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const int n, const float scale, const float offset) {
-    assert((size_t) dst % 128 == 0);
-    hvx_scale_offset_f32_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a);
-}
-
-static inline void hvx_scale_offset_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const int n, const float scale, const float offset) {
-    assert((size_t) src % 128 == 0);
-    hvx_scale_offset_f32_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
-}
-
-static inline void hvx_scale_offset_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const int n, const float scale, const float offset) {
-    hvx_scale_offset_f32_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
-}
-
-static inline void hvx_scale_offset_f32(uint8_t * restrict dst, const uint8_t * restrict src, const int n, const float scale, const float offset) {
-    if (((size_t) dst & 127) == 0) {
-        if (((size_t) src & 127) == 0) {
-            hvx_scale_offset_f32_aa(dst, src, n, scale, offset);
-        } else {
-            hvx_scale_offset_f32_au(dst, src, n, scale, offset);
-        }
-    } else {
-        if (((size_t) src & 127) == 0) {
-            hvx_scale_offset_f32_ua(dst, src, n, scale, offset);
-        } else {
-            hvx_scale_offset_f32_uu(dst, src, n, scale, offset);
-        }
-    }
-}
-
-#endif // HVX_SCALE_H

ggml/src/ggml-hexagon/htp/hvx-sigmoid.c

@@ -0,0 +1,49 @@
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wunused-function"
+#pragma clang diagnostic ignored "-Wunused-but-set-variable"
+
+#include <hexagon_protos.h>
+#include <hexagon_types.h>
+#include <math.h>
+#include <string.h>
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "htp-ctx.h"
+#include "htp-dma.h"
+#include "htp-msg.h"
+#include "htp-ops.h"
+#include "hvx-utils.h"
+#include "ops-utils.h"
+
+#if 0
+// Reference algo used in hvx-utils
+static void fast_sigmoid_f32(const float*  restrict src, float* restrict dst, const int num_elems)
+{
+    const float c1 = 0.03138777;
+    const float c2 = 0.276281267;
+    const float c_log2f = 1.442695022;
+
+    int32_t store_ints[32];
+    float store_floats[3][32];
+
+    for (int i = 0; i < num_elems; i++)
+    {
+        float v = src0[i];
+
+        v *= c_log2f*0.5;
+        int intPart = (int)v;
+        float x = (v - intPart);
+        float xx = x * x;
+        float v1 = c_log2f + c2 * xx;
+        float v2 = x + xx * c1 * x;
+        float v3 = (v2 + v1);
+        *((int*)&v3) += intPart << 24;
+        float v4 = v2 - v1;
+        float v5 = v3 - v4;
+        float res = v3 / v5;
+
+        dst[i] = res;
+    }
+}
+#endif

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

@@ -1,114 +0,0 @@
-#ifndef HVX_SIGMOID_H
-#define HVX_SIGMOID_H
-
-#include "hvx-base.h"
-
-#define FAST_SIGMOID_LOG2F (0x3fb8aa3b)  // 1.442695022
-#define FAST_SIGMOID_C1    (0x3d009076)  // 0.03138777
-#define FAST_SIGMOID_C2    (0x3e8d74bd)  // 0.276281267
-#define FAST_SIGMOID_C3    (0x3f000000)  // 0.5
-
-static inline HVX_Vector hvx_vec_fast_sigmoid_f32(HVX_Vector v) {
-    v = Q6_Vqf32_vmpy_VsfVsf(v, Q6_V_vsplat_R(FAST_SIGMOID_LOG2F));
-    v = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(v), Q6_V_vsplat_R(FAST_SIGMOID_C3));
-
-    HVX_Vector in_int = hvx_vec_truncate_f32(Q6_Vsf_equals_Vqf32(v));
-    HVX_Vector x      = Q6_Vqf32_vsub_Vqf32Vsf(v, Q6_Vsf_equals_Vw(in_int));
-    HVX_Vector xx     = Q6_Vqf32_vmpy_Vqf32Vqf32(x, x);
-
-    HVX_Vector v1 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(xx), Q6_V_vsplat_R(FAST_SIGMOID_C2));
-    v1            = Q6_Vqf32_vadd_Vqf32Vsf(v1, Q6_V_vsplat_R(FAST_SIGMOID_LOG2F));
-
-    HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(x), Q6_V_vsplat_R(FAST_SIGMOID_C1));
-    v2            = Q6_Vqf32_vmpy_Vqf32Vqf32(v2, xx);
-    v2            = Q6_Vqf32_vadd_Vqf32Vqf32(v2, x);
-
-    HVX_Vector v3          = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vqf32(v2, v1));
-    HVX_Vector v3_exponent = Q6_Vw_vasl_VwR(v3, 1);
-    v3_exponent            = Q6_Vuw_vlsr_VuwR(v3_exponent, 24);
-    v3_exponent            = Q6_Vw_vadd_VwVw(in_int, v3_exponent);
-    v3                     = Q6_Vw_vaslacc_VwVwR(v3, in_int, 24);
-
-    HVX_Vector v4 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_Vqf32Vqf32(v2, v1));
-    HVX_Vector v5 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(v3, v4));
-
-    HVX_Vector res = hvx_vec_inverse_f32(v5);
-    res            = Q6_Vqf32_vmpy_VsfVsf(v3, res);
-
-    return Q6_Vsf_equals_Vqf32(res);
-}
-
-static inline HVX_Vector hvx_vec_fast_sigmoid_f32_guard(HVX_Vector v,
-                                                         HVX_Vector one,
-                                                         HVX_Vector max_exp,
-                                                         HVX_Vector min_exp) {
-    const HVX_VectorPred pred_max = Q6_Q_vcmp_gt_VsfVsf(max_exp, v);
-    const HVX_VectorPred pred_min = Q6_Q_vcmp_gt_VsfVsf(v, min_exp);
-
-    HVX_Vector out = hvx_vec_fast_sigmoid_f32(v);
-    out            = Q6_V_vmux_QVV(pred_max, out, one);
-    return Q6_V_vmux_QVV(pred_min, out, Q6_V_vzero());
-}
-
-static inline HVX_Vector hvx_vec_tanh_f32(HVX_Vector x) {
-    // tanh(x) = 2 * sigmoid(2x) - 1
-    HVX_Vector two = hvx_vec_splat_f32(2.0f);
-    HVX_Vector one = hvx_vec_splat_f32(1.0f);
-    HVX_Vector x2  = Q6_Vqf32_vmpy_VsfVsf(x, two);
-
-    HVX_Vector max_exp = hvx_vec_splat_f32(87.f);
-    HVX_Vector min_exp = hvx_vec_splat_f32(-87.f);
-
-    HVX_Vector sig2x = hvx_vec_fast_sigmoid_f32_guard(Q6_Vsf_equals_Vqf32(x2), one, max_exp, min_exp);
-
-    HVX_Vector res = Q6_Vqf32_vmpy_VsfVsf(sig2x, two);
-    res = Q6_Vqf32_vsub_Vqf32Vsf(res, one);
-    return Q6_Vsf_equals_Vqf32(res);
-}
-
-#define hvx_sigmoid_loop_body(dst_type, src_type, vec_store)    \
-    do {                                                        \
-        dst_type * restrict vdst = (dst_type *) dst;            \
-        src_type * restrict vsrc = (src_type *) src;            \
-                                                                \
-        const HVX_Vector one     = hvx_vec_splat_f32(1.f);      \
-        const HVX_Vector max_exp = hvx_vec_splat_f32(87.f);     \
-        const HVX_Vector min_exp = hvx_vec_splat_f32(-87.f);    \
-                                                                \
-        const uint32_t epv  = 128 / sizeof(float);              \
-        const uint32_t nvec = n / epv;                          \
-        const uint32_t nloe = n % epv;                          \
-                                                                \
-        uint32_t i = 0;                                         \
-                                                                \
-        _Pragma("unroll(4)")                                    \
-        for (; i < nvec; i++) {                                 \
-             vdst[i] = hvx_vec_fast_sigmoid_f32_guard(vsrc[i], one, max_exp, min_exp); \
-        }                                                       \
-        if (nloe) {                                             \
-             HVX_Vector tmp = hvx_vec_fast_sigmoid_f32_guard(vsrc[i], one, max_exp, min_exp); \
-             vec_store((void *) &vdst[i], nloe * sizeof(float), tmp); \
-        }                                                       \
-    } while(0)
-
-static inline void hvx_sigmoid_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src % 128 == 0);
-    hvx_sigmoid_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
-}
-
-static inline void hvx_sigmoid_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    hvx_sigmoid_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a);
-}
-
-static inline void hvx_sigmoid_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    assert((unsigned long) src % 128 == 0);
-    hvx_sigmoid_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
-}
-
-static inline void hvx_sigmoid_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
-    hvx_sigmoid_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
-}
-
-#endif /* HVX_SIGMOID_H */

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

@@ -1,60 +0,0 @@
-#ifndef HVX_SQRT_H
-#define HVX_SQRT_H
-
-#include <stdbool.h>
-#include <stdint.h>
-
-#include "hex-utils.h"
-
-#include "hvx-base.h"
-
-#define RSQRT_CONST        0x5f3759df  // Constant for fast inverse square root calculation
-#define RSQRT_ONE_HALF     0x3f000000  // 0.5
-#define RSQRT_THREE_HALVES 0x3fc00000  // 1.5
-
-static inline HVX_Vector hvx_vec_rsqrt_f32(HVX_Vector in_vec) {
-    //Algorithm :
-    //  x2 = input*0.5
-    //  y  = * (long *) &input
-    //  y  = 0x5f3759df - (y>>2)
-    //  y  = y*(threehalfs - x2*y*y)
-
-    HVX_Vector rsqrtconst = Q6_V_vsplat_R(RSQRT_CONST);
-    HVX_Vector onehalf    = Q6_V_vsplat_R(RSQRT_ONE_HALF);
-    HVX_Vector threehalfs = Q6_V_vsplat_R(RSQRT_THREE_HALVES);
-
-    HVX_Vector x2, y, ypower2, temp;
-
-    x2 = Q6_Vqf32_vmpy_VsfVsf(in_vec, onehalf);
-    x2 = Q6_Vqf32_vadd_Vqf32Vsf(x2, Q6_V_vzero());
-
-    y = Q6_Vw_vasr_VwR(in_vec, 1);
-    y = Q6_Vw_vsub_VwVw(rsqrtconst, y);
-
-    // 1st iteration
-    ypower2 = Q6_Vqf32_vmpy_VsfVsf(y, y);
-    ypower2 = Q6_Vqf32_vadd_Vqf32Vsf(ypower2, Q6_V_vzero());
-    temp    = Q6_Vqf32_vmpy_Vqf32Vqf32(x2, ypower2);
-    temp    = Q6_Vqf32_vsub_VsfVsf(threehalfs, Q6_Vsf_equals_Vqf32(temp));
-    temp    = Q6_Vqf32_vmpy_VsfVsf(y, Q6_Vsf_equals_Vqf32(temp));
-
-    // 2nd iteration
-    y       = Q6_Vqf32_vadd_Vqf32Vsf(temp, Q6_V_vzero());
-    ypower2 = Q6_Vqf32_vmpy_Vqf32Vqf32(y, y);
-    ypower2 = Q6_Vqf32_vadd_Vqf32Vsf(ypower2, Q6_V_vzero());
-    temp    = Q6_Vqf32_vmpy_Vqf32Vqf32(x2, ypower2);
-    temp    = Q6_Vqf32_vsub_VsfVsf(threehalfs, Q6_Vsf_equals_Vqf32(temp));
-    temp    = Q6_Vqf32_vmpy_Vqf32Vqf32(y, temp);
-
-    // 3rd iteration
-    y       = Q6_Vqf32_vadd_Vqf32Vsf(temp, Q6_V_vzero());
-    ypower2 = Q6_Vqf32_vmpy_Vqf32Vqf32(y, y);
-    ypower2 = Q6_Vqf32_vadd_Vqf32Vsf(ypower2, Q6_V_vzero());
-    temp    = Q6_Vqf32_vmpy_Vqf32Vqf32(x2, ypower2);
-    temp    = Q6_Vqf32_vsub_VsfVsf(threehalfs, Q6_Vsf_equals_Vqf32(temp));
-    temp    = Q6_Vqf32_vmpy_Vqf32Vqf32(y, temp);
-
-    return Q6_Vsf_equals_Vqf32(temp);
-}
-
-#endif /* HVX_SQRT_H */

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

@@ -1,36 +0,0 @@
-#ifndef HVX_TYPES_H
-#define HVX_TYPES_H
-
-#include <stdbool.h>
-#include <stdint.h>
-
-#include <hexagon_types.h>
-
-#define SIZEOF_FP32 (4)
-#define SIZEOF_FP16 (2)
-#define VLEN        (128)
-#define VLEN_FP32   (VLEN / SIZEOF_FP32)
-#define VLEN_FP16   (VLEN / SIZEOF_FP16)
-
-typedef union {
-    HVX_Vector v;
-    uint8_t    b[VLEN];
-    uint16_t   h[VLEN_FP16];
-    uint32_t   w[VLEN_FP32];
-    __fp16     fp16[VLEN_FP16];
-    float      fp32[VLEN_FP32];
-} __attribute__((aligned(VLEN), packed)) HVX_VectorAlias;
-
-typedef struct {
-    HVX_Vector v[2];
-} HVX_Vector_x2;
-
-typedef struct {
-    HVX_Vector v[4];
-} HVX_Vector_x4;
-
-typedef struct {
-    HVX_Vector v[8];
-} HVX_Vector_x8;
-
-#endif /* HVX_TYPES_H */

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

@@ -1,13 +1,17 @@
 #pragma clang diagnostic ignored "-Wgnu-zero-variadic-macro-arguments"
 #pragma clang diagnostic ignored "-Wunused-function"
 
-#include <HAP_farf.h>
-#include <HAP_perf.h>
+#define FARF_ERROR  1
+#define FARF_HIGH   1
+#define FARF_MEDIUM 0
+#define FARF_LOW    0
 #include <AEEStdErr.h>
 #include <dspqueue.h>
 #include <HAP_compute_res.h>
 #include <HAP_etm_config.h>
+#include <HAP_farf.h>
 #include <HAP_mem.h>
+#include <HAP_perf.h>
 #include <HAP_power.h>
 #include <HAP_ps.h>
 #include <qurt.h>
@@ -15,14 +19,13 @@
 #include <remote.h>
 #include <string.h>
 
-#include "hex-dma.h"
-#include "hex-utils.h"
-
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
+#include "htp-dma.h"
 #include "htp-msg.h"
 #include "htp-ops.h"
+#include "ops-utils.h"
 #include "worker-pool.h"
 
 AEEResult htp_iface_open(const char * uri, remote_handle64 * handle) {
@@ -359,14 +362,14 @@ struct profile_data {
 
 static inline void profile_start(struct profile_data * d) {
     d->usecs  = HAP_perf_get_qtimer_count();
-    d->cycles = hex_get_cycles();
-    d->pkts   = hex_get_pktcnt();
+    d->cycles = htp_get_cycles();
+    d->pkts   = htp_get_pktcnt();
 }
 
 static inline void profile_stop(struct profile_data * d) {
     d->usecs  = HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - d->usecs);
-    d->cycles = hex_get_cycles() - d->cycles;
-    d->pkts   = hex_get_pktcnt() - d->pkts;
+    d->cycles = htp_get_cycles() - d->cycles;
+    d->pkts   = htp_get_pktcnt() - d->pkts;
 }
 
 static int send_htp_rsp(struct htp_context *     c,
@@ -440,43 +443,6 @@ static void proc_matmul_req(struct htp_context *     ctx,
     send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
 }
 
-static void proc_cpy_req(struct htp_context * ctx, struct htp_general_req * req, struct dspqueue_buffer * bufs) {
-    struct dspqueue_buffer rsp_bufs[1];
-
-    // We had written to the output buffer, we'd also need to flush it
-    rsp_bufs[0].fd     = bufs[1].fd;
-    rsp_bufs[0].ptr    = bufs[1].ptr;
-    rsp_bufs[0].offset = bufs[1].offset;
-    rsp_bufs[0].size   = bufs[1].size;
-    rsp_bufs[0].flags  = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER |         // Flush HTP
-                         DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT);  // Invalidate CPU
-
-    // Setup Op context
-    struct htp_ops_context octx = { 0 };
-    octx.ctx                    = ctx;
-    octx.src0                   = req->src0;
-    octx.dst                    = req->dst;
-    octx.flags                  = req->flags;
-    octx.op                     = req->op;
-
-    // Update data pointers
-    octx.src0.data = (uint32_t) bufs[0].ptr;
-    octx.dst.data  = (uint32_t) bufs[1].ptr;
-    octx.n_threads = ctx->n_threads;
-
-    struct profile_data prof;
-    profile_start(&prof);
-
-    uint32_t rsp_status = HTP_STATUS_INTERNAL_ERR;
-    if (vtcm_acquire(ctx) == AEE_SUCCESS) {
-        rsp_status = op_cpy(&octx);
-        vtcm_release(ctx);
-    }
-
-    profile_stop(&prof);
-    send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
-}
-
 static void proc_get_rows_req(struct htp_context * ctx, struct htp_general_req * req, struct dspqueue_buffer * bufs) {
     struct dspqueue_buffer rsp_bufs[1];
 
@@ -1027,14 +993,6 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
                 proc_get_rows_req(ctx, &req, bufs);
                 break;
 
-            case HTP_OP_CPY:
-                if (n_bufs != 2) {
-                    FARF(ERROR, "Bad cpy-req buffer list");
-                    continue;
-                }
-                proc_cpy_req(ctx, &req, bufs);
-                break;
-
             default:
                 FARF(ERROR, "Unknown Op %u", req.op);
                 break;

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

@@ -3,20 +3,28 @@
 #pragma clang diagnostic ignored "-Wunused-variable"
 #pragma clang diagnostic ignored "-Wunused-but-set-variable"
 
+#ifdef HTP_DEBUG
+#    define FARF_HIGH 1
+#endif
+
 #include <HAP_farf.h>
+#include <HAP_mem.h>
 #include <HAP_perf.h>
-
+#include <HAP_ps.h>
+#include <hexagon_protos.h>
+#include <hexagon_types.h>
 #include <math.h>
+#include <qurt_thread.h>
 #include <string.h>
 
-#include "hex-dma.h"
-#include "hvx-utils.h"
-
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
+#include "htp-dma.h"
 #include "htp-msg.h"
 #include "htp-ops.h"
+#include "hvx-utils.h"
+#include "ops-utils.h"
 
 #define MM_SPAD_SRC0_NROWS 16
 #define MM_SPAD_SRC1_NROWS 16
@@ -28,8 +36,20 @@ struct htp_matmul_type {
     void (*vec_dot_rx2)(const int n, float * restrict s, const void * restrict vx, uint32_t vx_row_size, const void * restrict vy);
 };
 
+typedef struct {
+    HVX_Vector v[2];
+} HVX_Vector_x2;
+
+typedef struct {
+    HVX_Vector v[4];
+} HVX_Vector_x4;
+
+typedef struct {
+    HVX_Vector v[8];
+} HVX_Vector_x8;
+
 // vdelta control to replicate first 4x fp32 values across lanes
-static const uint8_t __attribute__((aligned(128))) repl_4x_f32[128] = {
+static const uint8_t __attribute__((aligned(128))) repl_4x_fp32[128] = {
     0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, 0x10,
     0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x20, 0x20,
     0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, 0x10, 0x10, 0x04,
@@ -40,7 +60,7 @@ static const uint8_t __attribute__((aligned(128))) repl_4x_f32[128] = {
 };
 
 // vdelta control to replicate and interleave first 8x fp32 values across lanes
-static const uint8_t __attribute__((aligned(128))) repl_interleave_8x_f32[128] = {
+static const uint8_t __attribute__((aligned(128))) repl_interleave_8x_fp32[128] = {
     0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x00, 0x00, 0x00,
     0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x20, 0x20,
     0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x20, 0x20, 0x20, 0x20, 0x04,
@@ -51,7 +71,7 @@ static const uint8_t __attribute__((aligned(128))) repl_interleave_8x_f32[128] =
 };
 
 // vdelta control to replicate first fp32 value across all elements
-static const uint8_t __attribute__((aligned(128))) repl_1x_f32[128] = {
+static const uint8_t __attribute__((aligned(128))) repl_1x_fp32[128] = {
     0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, 0x10,
     0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x20, 0x20, 0x20, 0x20, 0x04, 0x04,
     0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08,
@@ -62,7 +82,7 @@ static const uint8_t __attribute__((aligned(128))) repl_1x_f32[128] = {
 };
 
 // vdelta control to replicate first fp16 value across all elements
-static const uint8_t __attribute__((aligned(128))) repl_1x_f16[128] = {
+static const uint8_t __attribute__((aligned(128))) repl_1x_fp16[128] = {
     0x00, 0x00, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x10, 0x10, 0x02,
     0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x20, 0x20, 0x02, 0x02, 0x04, 0x04,
     0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08,
@@ -73,7 +93,7 @@ static const uint8_t __attribute__((aligned(128))) repl_1x_f16[128] = {
 };
 
 // vdelta control to replicate first fp16 value across all elements
-static const uint8_t __attribute__((aligned(128))) repl_2x_f16[128] = {
+static const uint8_t __attribute__((aligned(128))) repl_2x_fp16[128] = {
     0x00, 0x00, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
     0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
     0x20, 0x20, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
@@ -109,7 +129,7 @@ static inline size_t q8x4x2_row_size(uint32_t ne) {
     // ensures perfect alignment of quants and full row
     const uint32_t qk = QK_Q8_0x4x2;
     const uint32_t nb = (ne + qk - 1) / qk;
-    return hex_round_up(ne + nb * 8 * sizeof(__fp16), 128);
+    return htp_round_up(ne + nb * 8 * sizeof(__fp16), 128);
 }
 
 static inline HVX_Vector_x8 hvx_vec_load_q4x4x8(const uint8_t * restrict ptr) {
@@ -369,7 +389,7 @@ static void vec_dot_q4x4x2_q8x4x2(const int n, float * restrict s, const void *
     }
 
     // Reduce and convert into fp32
-    r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum));
+    r0_sum = hvx_vec_fp32_reduce_sum(Q6_Vsf_equals_Vqf32(r0_sum));
 
     hvx_vec_store_u(&s[0], 4, r0_sum);
 }
@@ -465,8 +485,8 @@ static void vec_dot_q4x4x2_q8x4x2_rx2(const int n,
     }
 
     // Convert into fp32 and reduce
-    r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum));
-    r1_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r1_sum));
+    r0_sum = hvx_vec_fp32_reduce_sum(Q6_Vsf_equals_Vqf32(r0_sum));
+    r1_sum = hvx_vec_fp32_reduce_sum(Q6_Vsf_equals_Vqf32(r1_sum));
     HVX_VectorPair p0 = Q6_W_vshuff_VVR(r1_sum, r0_sum, 4);
 
     hvx_vec_store_u(&s[0], 8, Q6_V_lo_W(p0));
@@ -542,7 +562,7 @@ static void vec_dot_q8x4x2_q8x4x2(const int n, float * restrict s, const void *
     }
 
     // Reduce and convert into fp32
-    r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum));
+    r0_sum = hvx_vec_fp32_reduce_sum(Q6_Vsf_equals_Vqf32(r0_sum));
 
     hvx_vec_store_u(&s[0], 4, r0_sum);
 }
@@ -638,8 +658,8 @@ static void vec_dot_q8x4x2_q8x4x2_rx2(const int n,
     }
 
     // Convert into fp32 and reduce
-    r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum));
-    r1_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r1_sum));
+    r0_sum = hvx_vec_fp32_reduce_sum(Q6_Vsf_equals_Vqf32(r0_sum));
+    r1_sum = hvx_vec_fp32_reduce_sum(Q6_Vsf_equals_Vqf32(r1_sum));
     HVX_VectorPair p0 = Q6_W_vshuff_VVR(r1_sum, r0_sum, 4);
 
     hvx_vec_store_u(&s[0], 8, Q6_V_lo_W(p0));
@@ -748,7 +768,7 @@ static void vec_dot_mxfp4x4x2_q8x4x2(const int n,
     }
 
     // Reduce and convert into fp32
-    r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum));
+    r0_sum = hvx_vec_fp32_reduce_sum(Q6_Vsf_equals_Vqf32(r0_sum));
 
     hvx_vec_store_u(&s[0], 4, r0_sum);
 }
@@ -880,8 +900,8 @@ static void vec_dot_mxfp4x4x2_q8x4x2_rx2(const int n,
     }
 
     // Convert into fp32 and reduce
-    r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum));
-    r1_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r1_sum));
+    r0_sum = hvx_vec_fp32_reduce_sum(Q6_Vsf_equals_Vqf32(r0_sum));
+    r1_sum = hvx_vec_fp32_reduce_sum(Q6_Vsf_equals_Vqf32(r1_sum));
     HVX_VectorPair p0 = Q6_W_vshuff_VVR(r1_sum, r0_sum, 4);
 
     hvx_vec_store_u(&s[0], 8, Q6_V_lo_W(p0));
@@ -913,7 +933,7 @@ static void vec_dot_f16_f16_aa(const int n, float * restrict s, const void * res
         rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
     }
 
-    rsum = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum));
+    rsum = Q6_Vsf_equals_Vqf32(hvx_vec_qf32_reduce_sum(rsum));
     hvx_vec_store_u(&s[0], 4, rsum);
 }
 
@@ -957,8 +977,8 @@ static void vec_dot_f16_f16_aa_rx2(const int n,
         rsum1 = Q6_Vqf32_vadd_Vqf32Vqf32(rsum1, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf)));
     }
 
-    rsum0 = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum0));
-    rsum1 = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum1));
+    rsum0 = Q6_Vsf_equals_Vqf32(hvx_vec_qf32_reduce_sum(rsum0));
+    rsum1 = Q6_Vsf_equals_Vqf32(hvx_vec_qf32_reduce_sum(rsum1));
     HVX_VectorPair p0 = Q6_W_vshuff_VVR(rsum1, rsum0, 4);
 
     hvx_vec_store_u(&s[0], 8, Q6_V_lo_W(p0));
@@ -990,7 +1010,7 @@ static void vec_dot_f16_f16_uu(const int n, float * restrict s, const void * res
         rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
     }
 
-    rsum = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum));
+    rsum = Q6_Vsf_equals_Vqf32(hvx_vec_qf32_reduce_sum(rsum));
     hvx_vec_store_u(&s[0], 4, rsum);
 }
 
@@ -1042,7 +1062,7 @@ static void vec_dot_f16_f32_uu(const int n, float * restrict s, const void * res
         rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
     }
 
-    rsum = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum));
+    rsum = Q6_Vsf_equals_Vqf32(hvx_vec_qf32_reduce_sum(rsum));
     hvx_vec_store_u(&s[0], 4, rsum);
 }
 
@@ -1339,7 +1359,7 @@ static void matvec_2d(struct htp_matmul_type * mt, struct htp_ops_context * octx
         mt->vec_dot(ne00, &tmp[ir0 - src0_start_row], ss0, src1_col);
     }
 
-    hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, src0_end_row - src0_start_row);
+    hvx_copy_fp32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, src0_end_row - src0_start_row);
 
     t2 = HAP_perf_get_qtimer_count();
 
@@ -1391,7 +1411,7 @@ static void matmul_id(struct htp_matmul_type * mt, struct htp_ops_context * octx
     const size_t src0_row_size = nb01;
     const size_t src1_row_size = q8x4x2_row_size(ne10);
 
-    const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
+    const size_t src0_row_size_padded = htp_round_up(src0_row_size, 128);
 
     // Per-thread VTCM scratchpads for all tensors
     // Note that the entire src1 tensor is already in VTCM
@@ -1504,7 +1524,7 @@ static void matvec_id(struct htp_matmul_type * mt, struct htp_ops_context * octx
     const size_t src0_row_size = nb01;
     const size_t src1_row_size = q8x4x2_row_size(ne10);
 
-    const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
+    const size_t src0_row_size_padded = htp_round_up(src0_row_size, 128);
 
     const uint32_t n_aids = src2->ne[0];  // num activated experts
     const uint32_t n_ids  = ne02;         // num experts
@@ -1570,7 +1590,7 @@ static void matvec_id(struct htp_matmul_type * mt, struct htp_ops_context * octx
 
 // *** dynamic quant
 
-static inline void quantize_block_f32_q8x1(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
+static inline void quantize_block_fp32_q8x1(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
     assert((unsigned long) x % 128 == 0);
     assert((unsigned long) y_q % 128 == 0);
 
@@ -1578,10 +1598,10 @@ static inline void quantize_block_f32_q8x1(float * restrict x, uint8_t * restric
     HVX_Vector zero   = Q6_V_vsplat_R(0);
 
     // Use reduce max fp32 to find max(abs(e)) first
-    HVX_Vector vmax0_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[0]));
-    HVX_Vector vmax1_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[1]));
-    HVX_Vector vmax2_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[2]));
-    HVX_Vector vmax3_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[3]));
+    HVX_Vector vmax0_sf = hvx_vec_reduce_max_fp32(hvx_vec_abs_fp32(vx[0]));
+    HVX_Vector vmax1_sf = hvx_vec_reduce_max_fp32(hvx_vec_abs_fp32(vx[1]));
+    HVX_Vector vmax2_sf = hvx_vec_reduce_max_fp32(hvx_vec_abs_fp32(vx[2]));
+    HVX_Vector vmax3_sf = hvx_vec_reduce_max_fp32(hvx_vec_abs_fp32(vx[3]));
     // Load and convert into QF32
     HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero);  // 32 elements
     HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero);  // 32 elements
@@ -1603,7 +1623,7 @@ static inline void quantize_block_f32_q8x1(float * restrict x, uint8_t * restric
     HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
 
     // Replicate first fp16 scale across all lanes
-    HVX_Vector ctrl = *(const HVX_Vector *) repl_2x_f16;
+    HVX_Vector ctrl = *(const HVX_Vector *) repl_2x_fp16;
     vmax01_hf         = Q6_V_vdelta_VV(vmax01_hf, ctrl);
     vmax23_hf         = Q6_V_vdelta_VV(vmax23_hf, ctrl);
 
@@ -1621,8 +1641,8 @@ static inline void quantize_block_f32_q8x1(float * restrict x, uint8_t * restric
     hvx_vec_store_u(y_d + 6, 2, rotated_vd_hf);
 
     // Divide input by the scale
-    HVX_Vector vd01_inv_hf = hvx_vec_inverse_f16(vd01_hf);
-    HVX_Vector vd23_inv_hf = hvx_vec_inverse_f16(vd23_hf);
+    HVX_Vector vd01_inv_hf = hvx_vec_inverse_fp16(vd01_hf);
+    HVX_Vector vd23_inv_hf = hvx_vec_inverse_fp16(vd23_hf);
     vx01_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd01_inv_hf));
     vx23_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd23_inv_hf));
 
@@ -1634,7 +1654,7 @@ static inline void quantize_block_f32_q8x1(float * restrict x, uint8_t * restric
     *(HVX_Vector *) y_q = vx_i8;
 }
 
-static inline void quantize_block_f32_q8x2(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
+static inline void quantize_block_fp32_q8x2(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
     assert((unsigned long) x % 128 == 0);
     assert((unsigned long) y_q % 128 == 0);
 
@@ -1652,11 +1672,11 @@ static inline void quantize_block_f32_q8x2(float * restrict x, uint8_t * restric
     HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
 
     // Compute max and scale
-    HVX_Vector vmax01_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx01_hf));
-    HVX_Vector vmax23_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx23_hf));
+    HVX_Vector vmax01_hf = hvx_vec_reduce_max_fp16(hvx_vec_abs_fp16(vx01_hf));
+    HVX_Vector vmax23_hf = hvx_vec_reduce_max_fp16(hvx_vec_abs_fp16(vx23_hf));
 
     // Replicate first fp16 scale across all lanes
-    HVX_Vector ctrl = *(const HVX_Vector *) repl_1x_f16;
+    HVX_Vector ctrl = *(const HVX_Vector *) repl_1x_fp16;
     vmax01_hf         = Q6_V_vdelta_VV(vmax01_hf, ctrl);
     vmax23_hf         = Q6_V_vdelta_VV(vmax23_hf, ctrl);
 
@@ -1669,8 +1689,8 @@ static inline void quantize_block_f32_q8x2(float * restrict x, uint8_t * restric
     hvx_vec_store_u(y_d + 4, 4, vd23_hf);
 
     // Divide input by the scale
-    HVX_Vector vd01_inv_hf = hvx_vec_inverse_f16(vd01_hf);
-    HVX_Vector vd23_inv_hf = hvx_vec_inverse_f16(vd23_hf);
+    HVX_Vector vd01_inv_hf = hvx_vec_inverse_fp16(vd01_hf);
+    HVX_Vector vd23_inv_hf = hvx_vec_inverse_fp16(vd23_hf);
     vx01_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd01_inv_hf));
     vx23_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd23_inv_hf));
 
@@ -1682,7 +1702,7 @@ static inline void quantize_block_f32_q8x2(float * restrict x, uint8_t * restric
     *(HVX_Vector *) y_q = vx_i8;
 }
 
-static inline void quantize_block_f32_q8x4(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
+static inline void quantize_block_fp32_q8x4(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
     assert((unsigned long) x % 128 == 0);
     assert((unsigned long) y_q % 128 == 0);
 
@@ -1700,11 +1720,11 @@ static inline void quantize_block_f32_q8x4(float * restrict x, uint8_t * restric
     HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
 
     // Compute max and scale
-    HVX_Vector vmax_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx01_hf));
-    vmax_hf            = hvx_vec_reduce_max2_f16(hvx_vec_abs_f16(vx23_hf), vmax_hf);
+    HVX_Vector vmax_hf = hvx_vec_reduce_max_fp16(hvx_vec_abs_fp16(vx01_hf));
+    vmax_hf            = hvx_vec_reduce_max2_fp16(hvx_vec_abs_fp16(vx23_hf), vmax_hf);
 
     // Replicate first fp16 scale across all lanes
-    HVX_Vector ctrl = *(const HVX_Vector *) repl_1x_f16;
+    HVX_Vector ctrl = *(const HVX_Vector *) repl_1x_fp16;
     vmax_hf         = Q6_V_vdelta_VV(vmax_hf, ctrl);
 
     HVX_Vector vd_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
@@ -1713,7 +1733,7 @@ static inline void quantize_block_f32_q8x4(float * restrict x, uint8_t * restric
     *(HVX_UVector *) y_d = vd_hf;
 
     // Divide input by the scale
-    HVX_Vector vd_inv_hf = hvx_vec_inverse_f16(vd_hf);
+    HVX_Vector vd_inv_hf = hvx_vec_inverse_fp16(vd_hf);
     vx01_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd_inv_hf));
     vx23_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd_inv_hf));
 
@@ -1726,7 +1746,7 @@ static inline void quantize_block_f32_q8x4(float * restrict x, uint8_t * restric
 }
 
 // Overrides input x
-static void quantize_row_f32_q8x4x2(float * restrict x, uint8_t * restrict y, uint32_t k) {
+static void quantize_row_fp32_q8x4x2(float * restrict x, uint8_t * restrict y, uint32_t k) {
     assert(k % 32 == 0);
     const uint32_t qk = QK_Q8_0x4x2;
     const uint32_t nb = (k + qk - 1) / qk;
@@ -1744,24 +1764,24 @@ static void quantize_row_f32_q8x4x2(float * restrict x, uint8_t * restrict y, ui
 
     for (uint32_t i = 0; i < nb; i++) {
 #if FP32_QUANTIZE_GROUP_SIZE == 32
-        quantize_block_f32_q8x1(x + (i*2 + 0) * qk/2, y_q + (i*2 + 0) * qblk_size/2, t_d + (i*2 + 0) * dblk_size/2);
-        quantize_block_f32_q8x1(x + (i*2 + 1) * qk/2, y_q + (i*2 + 1) * qblk_size/2, t_d + (i*2 + 1) * dblk_size/2);
+        quantize_block_fp32_q8x1(x + (i*2 + 0) * qk/2, y_q + (i*2 + 0) * qblk_size/2, t_d + (i*2 + 0) * dblk_size/2);
+        quantize_block_fp32_q8x1(x + (i*2 + 1) * qk/2, y_q + (i*2 + 1) * qblk_size/2, t_d + (i*2 + 1) * dblk_size/2);
 #elif FP32_QUANTIZE_GROUP_SIZE == 64
-        quantize_block_f32_q8x2(x + (i*2 + 0) * qk/2, y_q + (i*2 + 0) * qblk_size/2, t_d + (i*2 + 0) * dblk_size/2);
-        quantize_block_f32_q8x2(x + (i*2 + 1) * qk/2, y_q + (i*2 + 1) * qblk_size/2, t_d + (i*2 + 1) * dblk_size/2);
+        quantize_block_fp32_q8x2(x + (i*2 + 0) * qk/2, y_q + (i*2 + 0) * qblk_size/2, t_d + (i*2 + 0) * dblk_size/2);
+        quantize_block_fp32_q8x2(x + (i*2 + 1) * qk/2, y_q + (i*2 + 1) * qblk_size/2, t_d + (i*2 + 1) * dblk_size/2);
 #elif FP32_QUANTIZE_GROUP_SIZE == 128
-        quantize_block_f32_q8x4(x + (i*2 + 0) * qk/2, y_q + (i*2 + 0) * qblk_size/2, t_d + (i*2 + 0) * dblk_size/2);
-        quantize_block_f32_q8x4(x + (i*2 + 1) * qk/2, y_q + (i*2 + 1) * qblk_size/2, t_d + (i*2 + 1) * dblk_size/2);
+        quantize_block_fp32_q8x4(x + (i*2 + 0) * qk/2, y_q + (i*2 + 0) * qblk_size/2, t_d + (i*2 + 0) * dblk_size/2);
+        quantize_block_fp32_q8x4(x + (i*2 + 1) * qk/2, y_q + (i*2 + 1) * qblk_size/2, t_d + (i*2 + 1) * dblk_size/2);
 #else
 #error "FP32_QUANTIZE_GROUP_SIZE must be 32, 64, or 128"
 #endif
     }
 
     // now copy the scales into final location
-    hvx_copy_f16_ua(y_d, t_d, nb * 8);
+    hvx_copy_fp16_ua(y_d, t_d, nb * 8);
 }
 
-static void quantize_f32_q8x4x2(const struct htp_tensor * src,
+static void quantize_fp32_q8x4x2(const struct htp_tensor * src,
                                  uint8_t * restrict dst,
                                  struct htp_spad * spad,
                                  uint32_t          nth,
@@ -1787,26 +1807,26 @@ static void quantize_f32_q8x4x2(const struct htp_tensor * src,
     uint8_t * restrict dst_data = (uint8_t *) dst + (dst_row_size * ir_first);
     uint8_t * restrict tmp_data = (uint8_t *) spad->data + (spad->size_per_thread * ith);
 
-    const size_t src_row_size_padded = hex_round_up(src_row_size, QK_Q8_0x4x2 * sizeof(float));
+    const size_t src_row_size_padded = htp_round_up(src_row_size, QK_Q8_0x4x2 * sizeof(float));
     memset(tmp_data, 0, src_row_size_padded);  // zero-out temp row data for padding
 
     for (uint32_t i = ir_first; i < ir_last; ++i) {
-        hex_l2fetch(src_data, src_row_size, src_row_size, 2);
-        hvx_copy_f32_aa(tmp_data, src_data, ne0);
+        htp_l2fetch(src_data, 2, src_row_size, src_row_size);
+        hvx_copy_fp32_aa(tmp_data, src_data, ne0);
 
         // FARF(HIGH, "quantize-q8x4-row: %u\n", i);
-        quantize_row_f32_q8x4x2((float *) tmp_data, dst_data, ne0);
+        quantize_row_fp32_q8x4x2((float *) tmp_data, dst_data, ne0);
         dst_data += dst_row_size;
         src_data += src_row_size;
     }
 
     uint64_t t2 = HAP_perf_get_qtimer_count();
 
-    FARF(HIGH, "quantize-f32-q8x4: %u/%u : n-rows %u (%u:%u) row-size %u -> %u usec %u\n", ith, nth, nrows, ir_first,
+    FARF(HIGH, "quantize-fp32-q8x4: %u/%u : n-rows %u (%u:%u) row-size %u -> %u usec %u\n", ith, nth, nrows, ir_first,
          ir_last, src_row_size, dst_row_size, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
-static void quantize_f32_f16(const struct htp_tensor * src, uint8_t * restrict dst, uint32_t nth, uint32_t ith,
+static void quantize_fp32_fp16(const struct htp_tensor * src, uint8_t * restrict dst, uint32_t nth, uint32_t ith,
                               uint32_t nrows_per_thread, uint32_t dst_stride) {
 
     uint64_t t1 = HAP_perf_get_qtimer_count();
@@ -1828,8 +1848,8 @@ static void quantize_f32_f16(const struct htp_tensor * src, uint8_t * restrict d
     uint8_t * restrict dst_data = (uint8_t *) dst       + (dst_stride * ir_first);
 
     for (uint32_t i = ir_first; i < ir_last; ++i) {
-        hex_l2fetch(src_data, src_row_size, src_stride, 2);
-        hvx_copy_f16_f32_au(dst_data, src_data, ne0);
+        htp_l2fetch(src_data, 2, src_row_size, src_stride);
+        hvx_copy_fp16_fp32_au(dst_data, src_data, ne0);
 
         dst_data += dst_stride;
         src_data += src_stride;
@@ -1837,12 +1857,12 @@ static void quantize_f32_f16(const struct htp_tensor * src, uint8_t * restrict d
 
     uint64_t t2 = HAP_perf_get_qtimer_count();
 
-    FARF(HIGH, "quantize-f32-f16: %u/%u : n-rows %u (%u:%u) row-size %u (%u) -> %u usec %u\n", ith, nth, nrows, ir_first,
+    FARF(HIGH, "quantize-fp32-fp16: %u/%u : n-rows %u (%u:%u) row-size %u (%u) -> %u usec %u\n", ith, nth, nrows, ir_first,
         ir_last, src_row_size, src_stride, dst_stride, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
 // TODO just a plain copy that should be done via the DMA during the Op setup
-static void quantize_f16_f16(const struct htp_tensor * src, uint8_t * restrict dst, uint32_t nth, uint32_t ith,
+static void quantize_fp16_fp16(const struct htp_tensor * src, uint8_t * restrict dst, uint32_t nth, uint32_t ith,
                               uint32_t nrows_per_thread, uint32_t dst_stride) {
 
     uint64_t t1 = HAP_perf_get_qtimer_count();
@@ -1864,8 +1884,8 @@ static void quantize_f16_f16(const struct htp_tensor * src, uint8_t * restrict d
     uint8_t * restrict dst_data = (uint8_t *) dst       + (dst_stride * ir_first);
 
     for (uint32_t i = ir_first; i < ir_last; ++i) {
-        hex_l2fetch(src_data, src_row_size, src_stride, 2);
-        hvx_copy_f16_au(dst_data, src_data, ne0);
+        htp_l2fetch(src_data, 2, src_row_size, src_stride);
+        hvx_copy_fp16_au(dst_data, src_data, ne0);
 
         dst_data += dst_stride;
         src_data += src_stride;
@@ -1873,23 +1893,23 @@ static void quantize_f16_f16(const struct htp_tensor * src, uint8_t * restrict d
 
     uint64_t t2 = HAP_perf_get_qtimer_count();
 
-    FARF(HIGH, "quantize-f16-f16: %u/%u : n-rows %u (%u:%u) row-size %u (%u) -> %u usec %u\n", ith, nth, nrows, ir_first,
+    FARF(HIGH, "quantize-fp16-fp16: %u/%u : n-rows %u (%u:%u) row-size %u (%u) -> %u usec %u\n", ith, nth, nrows, ir_first,
         ir_last, src_row_size, src_stride, dst_stride, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
-static void htp_quantize_f32_q8x4x2(unsigned int n, unsigned int i, void * data) {
+static void htp_quantize_fp32_q8x4x2(unsigned int n, unsigned int i, void * data) {
     struct htp_ops_context * octx = data;
-    quantize_f32_q8x4x2(&octx->src1, octx->src1_spad.data, &octx->src0_spad, n, i, octx->src1_nrows_per_thread);
+    quantize_fp32_q8x4x2(&octx->src1, octx->src1_spad.data, &octx->src0_spad, n, i, octx->src1_nrows_per_thread);
 }
 
-static void htp_quantize_f32_f16(unsigned int n, unsigned int i, void * data) {
+static void htp_quantize_fp32_fp16(unsigned int n, unsigned int i, void * data) {
     struct htp_ops_context * octx = data;
-    quantize_f32_f16(&octx->src1, octx->src1_spad.data, n, i, octx->src1_nrows_per_thread, octx->src1_spad.stride);
+    quantize_fp32_fp16(&octx->src1, octx->src1_spad.data, n, i, octx->src1_nrows_per_thread, octx->src1_spad.stride);
 }
 
-static void htp_quantize_f16_f16(unsigned int n, unsigned int i, void * data) {
+static void htp_quantize_fp16_fp16(unsigned int n, unsigned int i, void * data) {
     struct htp_ops_context * octx = data;
-    quantize_f16_f16(&octx->src1, octx->src1_spad.data, n, i, octx->src1_nrows_per_thread, octx->src1_spad.stride);
+    quantize_fp16_fp16(&octx->src1, octx->src1_spad.data, n, i, octx->src1_nrows_per_thread, octx->src1_spad.stride);
 }
 
 // ** matmul/matvec callbacks for worker_pool
@@ -2088,7 +2108,7 @@ int op_matmul(struct htp_ops_context * octx) {
     const size_t dst_row_size  = nb1;
     size_t       src1_row_size = nb11;
 
-    const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
+    const size_t src0_row_size_padded = htp_round_up(src0_row_size, 128);
     size_t       src1_row_size_padded;
 
     worker_callback_t quant_job_func;
@@ -2098,8 +2118,8 @@ int op_matmul(struct htp_ops_context * octx) {
 
     switch (src0->type) {
         case HTP_TYPE_Q4_0:
-            op_type        = "q4x4x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
+            op_type        = "q4x4x2-fp32";
+            quant_job_func = htp_quantize_fp32_q8x4x2;
             if (src1_nrows > 1) {
                 matmul_job_func = htp_matmul_2d_q4x4x2_q8x4x2;
             } else {
@@ -2111,12 +2131,12 @@ int op_matmul(struct htp_ops_context * octx) {
             // Entire src1 tensor is placed into the VTCM
             // For other tensors we allocate N rows per thread, padded to HVX vector size
 
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
+            octx->dst_spad.size_per_thread  = htp_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
+            octx->src0_spad.size_per_thread = htp_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
+            octx->src1_spad.size_per_thread = htp_round_up(src1_row_size * src1_nrows, 256);
 
             // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
+            src1_row_size_padded = htp_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
             if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
                 octx->src0_spad.size_per_thread = src1_row_size_padded;
             }
@@ -2127,8 +2147,8 @@ int op_matmul(struct htp_ops_context * octx) {
             break;
 
         case HTP_TYPE_Q8_0:
-            op_type        = "q8x4x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
+            op_type        = "q8x4x2-fp32";
+            quant_job_func = htp_quantize_fp32_q8x4x2;
             if (src1_nrows > 1) {
                 matmul_job_func = htp_matmul_2d_q8x4x2_q8x4x2;
             } else {
@@ -2140,12 +2160,12 @@ int op_matmul(struct htp_ops_context * octx) {
             // Entire src1 tensor is placed into the VTCM
             // For other tensors we allocate N rows per thread, padded to HVX vector size
 
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
+            octx->dst_spad.size_per_thread  = htp_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
+            octx->src0_spad.size_per_thread = htp_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
+            octx->src1_spad.size_per_thread = htp_round_up(src1_row_size * src1_nrows, 256);
 
             // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
+            src1_row_size_padded = htp_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
             if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
                 octx->src0_spad.size_per_thread = src1_row_size_padded;
             }
@@ -2157,7 +2177,7 @@ int op_matmul(struct htp_ops_context * octx) {
 
         case HTP_TYPE_MXFP4:
             op_type        = "mxfp4x4x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
+            quant_job_func = htp_quantize_fp32_q8x4x2;
             if (src1_nrows > 1) {
                 matmul_job_func = htp_matmul_2d_mxfp4x4x2_q8x4x2;
             } else {
@@ -2169,12 +2189,12 @@ int op_matmul(struct htp_ops_context * octx) {
             // Entire src1 tensor is placed into the VTCM
             // For other tensors we allocate N rows per thread, padded to HVX vector size
 
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
+            octx->dst_spad.size_per_thread  = htp_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
+            octx->src0_spad.size_per_thread = htp_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
+            octx->src1_spad.size_per_thread = htp_round_up(src1_row_size * src1_nrows, 256);
 
             // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
+            src1_row_size_padded = htp_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
             if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
                 octx->src0_spad.size_per_thread = src1_row_size_padded;
             }
@@ -2187,10 +2207,10 @@ int op_matmul(struct htp_ops_context * octx) {
         case HTP_TYPE_F16:
             {
                 // Try optimized f16-f16 path first (src1 in VTCM)
-                const size_t f16_src1_row_size  = hex_round_up(ne10 * 2, 128);
-                const size_t f16_src1_spad_size = hex_round_up(f16_src1_row_size * src1_nrows, 256);
-                const size_t f16_src0_spad_size = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256) * octx->n_threads;
-                const size_t f16_dst_spad_size  = hex_round_up(MM_SPAD_DST_NROWS  * dst_row_size, 256) * octx->n_threads;
+                const size_t f16_src1_row_size  = htp_round_up(ne10 * 2, 128);
+                const size_t f16_src1_spad_size = htp_round_up(f16_src1_row_size * src1_nrows, 256);
+                const size_t f16_src0_spad_size = htp_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256) * octx->n_threads;
+                const size_t f16_dst_spad_size  = htp_round_up(MM_SPAD_DST_NROWS  * dst_row_size, 256) * octx->n_threads;
 
                 const size_t f16_total_size = f16_src1_spad_size + f16_src0_spad_size + f16_dst_spad_size;
 
@@ -2202,7 +2222,7 @@ int op_matmul(struct htp_ops_context * octx) {
                 if (!is_batched && !is_permuted && f16_total_size <= octx->ctx->vtcm_size) {
                     // Optimized path
                     op_type        = "f16-f16";
-                    quant_job_func = (src1->type == HTP_TYPE_F32) ? htp_quantize_f32_f16 : htp_quantize_f16_f16;
+                    quant_job_func = (src1->type == HTP_TYPE_F32) ? htp_quantize_fp32_fp16 : htp_quantize_fp16_fp16;
                     if (src1_nrows > 1) {
                         matmul_job_func = htp_matmul_2d_f16_f16;
                     } else {
@@ -2211,9 +2231,9 @@ int op_matmul(struct htp_ops_context * octx) {
 
                     src1_row_size = f16_src1_row_size; // row size post quantization
 
-                    octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-                    octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-                    octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
+                    octx->dst_spad.size_per_thread  = htp_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
+                    octx->src0_spad.size_per_thread = htp_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
+                    octx->src1_spad.size_per_thread = htp_round_up(src1_row_size * src1_nrows, 256);
 
                     octx->src1_spad.size = octx->src1_spad.size_per_thread;
                     octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
@@ -2231,9 +2251,9 @@ int op_matmul(struct htp_ops_context * octx) {
 
                     src1_row_size = nb11; // original row size in DDR
 
-                    octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-                    octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size, 256);
-                    octx->src1_spad.size_per_thread = hex_round_up(MM_SPAD_SRC1_NROWS * src1_row_size, 256);
+                    octx->dst_spad.size_per_thread  = htp_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
+                    octx->src0_spad.size_per_thread = htp_round_up(MM_SPAD_SRC0_NROWS * src0_row_size, 256);
+                    octx->src1_spad.size_per_thread = htp_round_up(MM_SPAD_SRC1_NROWS * src1_row_size, 256);
 
                     octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
                     octx->src1_spad.size = octx->src1_spad.size_per_thread * octx->n_threads;
@@ -2312,7 +2332,7 @@ int op_matmul_id(struct htp_ops_context * octx) {
     const size_t src0_row_size = nb01;
     const size_t dst_row_size  = nb1;
 
-    const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
+    const size_t src0_row_size_padded = htp_round_up(src0_row_size, 128);
 
     const uint32_t src0_nrows = ne01;  // per expert
     const uint32_t src1_nrows = ne11 * ne12 * ne13;
@@ -2330,7 +2350,7 @@ int op_matmul_id(struct htp_ops_context * octx) {
     switch (src0->type) {
         case HTP_TYPE_Q4_0:
             op_type        = "q4x2x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
+            quant_job_func = htp_quantize_fp32_q8x4x2;
             src1_row_size  = q8x4x2_row_size(ne10);  // row size post quantization
             if (src1_nrows > 1) {
                 matmul_id_job_func = htp_matmul_id_q4x4x2_q8x4x2;
@@ -2340,13 +2360,13 @@ int op_matmul_id(struct htp_ops_context * octx) {
 
             // Entire src1 tensor is placed into the VTCM
             // For other tensors we allocate N rows per thread, padded to HVX vector size
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
-            octx->src2_spad.size_per_thread = hex_round_up(matrix_row_counts_size + matrix_row_map_size, 256);
+            octx->dst_spad.size_per_thread  = htp_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
+            octx->src0_spad.size_per_thread = htp_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
+            octx->src1_spad.size_per_thread = htp_round_up(src1_row_size * src1_nrows, 256);
+            octx->src2_spad.size_per_thread = htp_round_up(matrix_row_counts_size + matrix_row_map_size, 256);
 
             // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
+            src1_row_size_padded = htp_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
             if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
                 octx->src0_spad.size_per_thread = src1_row_size_padded;
             }
@@ -2359,7 +2379,7 @@ int op_matmul_id(struct htp_ops_context * octx) {
 
         case HTP_TYPE_Q8_0:
             op_type        = "q8x2x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
+            quant_job_func = htp_quantize_fp32_q8x4x2;
             src1_row_size  = q8x4x2_row_size(ne10);  // row size post quantization
             if (src1_nrows > 1) {
                 matmul_id_job_func = htp_matmul_id_q8x4x2_q8x4x2;
@@ -2369,13 +2389,13 @@ int op_matmul_id(struct htp_ops_context * octx) {
 
             // Entire src1 tensor is placed into the VTCM
             // For other tensors we allocate N rows per thread, padded to HVX vector size
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
-            octx->src2_spad.size_per_thread = hex_round_up(matrix_row_counts_size + matrix_row_map_size, 256);
+            octx->dst_spad.size_per_thread  = htp_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
+            octx->src0_spad.size_per_thread = htp_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
+            octx->src1_spad.size_per_thread = htp_round_up(src1_row_size * src1_nrows, 256);
+            octx->src2_spad.size_per_thread = htp_round_up(matrix_row_counts_size + matrix_row_map_size, 256);
 
             // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
+            src1_row_size_padded = htp_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
             if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
                 octx->src0_spad.size_per_thread = src1_row_size_padded;
             }
@@ -2388,7 +2408,7 @@ int op_matmul_id(struct htp_ops_context * octx) {
 
         case HTP_TYPE_MXFP4:
             op_type        = "mxfp4x2x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
+            quant_job_func = htp_quantize_fp32_q8x4x2;
             src1_row_size  = q8x4x2_row_size(ne10);  // row size post quantization
             if (src1_nrows > 1) {
                 matmul_id_job_func = htp_matmul_id_mxfp4x4x2_q8x4x2;
@@ -2398,13 +2418,13 @@ int op_matmul_id(struct htp_ops_context * octx) {
 
             // Entire src1 tensor is placed into the VTCM
             // For other tensors we allocate N rows per thread, padded to HVX vector size
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
-            octx->src2_spad.size_per_thread = hex_round_up(matrix_row_counts_size + matrix_row_map_size, 256);
+            octx->dst_spad.size_per_thread  = htp_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
+            octx->src0_spad.size_per_thread = htp_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
+            octx->src1_spad.size_per_thread = htp_round_up(src1_row_size * src1_nrows, 256);
+            octx->src2_spad.size_per_thread = htp_round_up(matrix_row_counts_size + matrix_row_map_size, 256);
 
             // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
+            src1_row_size_padded = htp_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
             if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
                 octx->src0_spad.size_per_thread = src1_row_size_padded;
             }